[{ "id": "/notes/pubs/10-years/", "title": "10 Years of CV Debugging Lessons", "url": "/notes/pubs/10-years/", "tags": ["computer-vision","debugging","lessons-learned","opencv"], "hashtags": "#cv #debugging #lessonslearned #opencv", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n 10 Years of CV Debugging Lessons — Lessons learned from a decade of debugging computer vision systems in production. — https://www.pirahansiah.com/notes/pubs/10-years/\nLessons learned from a decade of debugging computer vision systems in production.\n\n\nLast updated: 2026-08-08. \n\nI engage directly with investors, customers, and companies to present our solutions and understand their concerns and product needs. By organizing and analyzing these requirements from a professional, technical perspective, I design and architect robust software systems. I translate these user needs into actionable user stories, project milestones, and tasks to deliver reliable solutions, while planning future optimizations and feature roadmaps.\nupdate Aug. 2026\n\nfor color detection HSV can use multiple colors so better and more robust\nusing multi color HSV during ROI selection for detect the multi objects\n\nMetis PCIe 4-AIPU card axelera \ndifferent position of camera \nshadow rejection \ni tested many models for Hermes for 4060 with 8gb gpu ram only need 2gb model + 6gb contecsx 64k so \nHermes: need 5gb for context , so the qwen3:4b is only 3gb and can run with it on 4060 laptop 8gb ram, so connect browser is working fine and fast so the ram is really important\n\nthe speed of 4060 vs 5060 gap gddr 6 vs gddr7\nthe apple use lines to increase bus ram for lpddr so achieve same with low ram\n\n#ComputerVision_Edge_AI\nHigh-Accuracy Real-Time Medical Image Processing on Embedded Systems\n#RealTimeEmbeddedMedicalImageProcessing\nC++:\ncv::setNumThreads(cv::getNumberOfCPUs(\nPython..." },{ "id": "/notes/pubs/papers/2d-3d-mapping/", "title": "2D vs 3D Mapping for Moving Objects", "url": "/notes/pubs/papers/2d-3d-mapping/", "tags": [], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n 2D vs 3D Mapping for Moving Objects — Comparison of 2D and 3D mapping approaches for environment movement detection. — https://www.pirahansiah.com/notes/pubs/papers/2d-3d-mapping/\nComparison of 2D and 3D mapping approaches for environment movement detection.\n\n\nLast updated: 2026-08-08. \n\ntags: 3d-mapping, object-detection, computer-vision, robotics]\nhashtags: “#3dmapping #objectdetection #cv #robotics”\n\nMy_Conference_Paper_2D_versus_3D_Map_for_Environment_Movement_Objects\n\nhttps://www.pirahansiah.com/notes/pubs/papers/conference-paper_2D_versus_3D_Map_for_Environment_Movement_Objects\n\nspotify\n\nPDF Download My Conference Paper\n\n\n\nPaper: 2D versus 3D Map for Environment Movement Objects\n\n1. Introduction\n\n Main Focus: The use of 2D vs. 3D mapping in tracking environmental movement objects.\n Key Applications:\n \n Autonomous systems\n Robotics\n \n \n Challenges:\n \n Object rotation issues in 2D mapping\n Scaling problems in 2D environments\n \n \n\n\n2. Materials and Methods\n\n 3D Mapping Algorithms:\n \n Introduces algorithms that manage:\n \n Scale\n Position\n Orientation\n \n \n \n \n Data Capture:\n \n Details the camera and sensor systems used to capture movement data.\n \n \n\n\n3. Experiments and Discussions\n\n System Performance Comparison:\n \n Evaluates 2D vs. 3D mapping systems based on:\n \n Accuracy\n Adaptability to different conditions\n \n \n Results indicate significant improvement in accuracy with the 3D s..." },{ "id": "/notes/docs/cv/3d/", "title": "3D Vision & Real-Time Multi-Camera Systems", "url": "/notes/docs/cv/3d/", "tags": ["computer-vision","3d-vision","depth-sensing","multi-camera"], "hashtags": "#cv #3dvision #depthsensing #multicamera", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n 3D Vision & Real-Time Multi-Camera Systems — Real-time 3D point cloud generation, depth sensing, and multi-camera synchronization for computer vision applications. — https://www.pirahansiah.com/notes/docs/cv/3d/\nReal-time 3D point cloud generation, depth sensing, and multi-camera synchronization for computer vision applications.\n\n\nLast updated: 2026-08-08. \n\n\n MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors\n \n\n\nReal-Time 3D Point Cloud Generation and Visualization from Depth Data\n\nThe fusion of depth sensing and 3D visualization opens remarkable possibilities for interactive applications. By converting 2D depth maps into 3D point clouds, we can build systems that bridge physical and digital realms in real-time.\n\nDepth to 3D Conversion\n\nThe foundation of this approach lies in the deprojection process - transforming pixel coordinates and their associated depth values into 3D space. This requires camera intrinsic parameters (focal length, principal point to perform the perspective transformation:\n\ndef deproject_point(u, v, depth, camera_matrix:\n fx = camera_matrix0, 0] # Focal length X\n fy = camera_matrix1, 1] # Focal length Y\n cx = camera_matrix0, 2] # Principal point X\n cy = camera_matrix1, 2] # Principal point Y\n \n # Convert to 3D coordinates\n x = (u - cx * depth / fx\n y = (v - cy * depth / fy\n z = depth\n \n return np.array(x, y, z]\n\n\nReal-Time Visualization Strategies\n\nVisualizing 3D data interactively requires threading to prevent blocking the main application loop. A separate th..." },{ "id": "/404.html", "title": "Page Not Found", "url": "/404.html", "tags": [], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-06-14\n\n404 — the page you were looking for on pirahansiah.com doesn’t exist or has been moved.\n\nLast updated: 2026-06-14. \n\n 404\n This page doesn't exist or has been moved.\n ← Back to Home\n\n" },{ "id": "/notes/pkm/AboutMe/", "title": "Dr. Farshid Pirahansiah — AI & Computer Vision Engineer", "url": "/notes/pkm/AboutMe/", "tags": [], "hashtags": "", "category": "pkm", "body": "\n\n\n\n\n\n\n \n // About Me\n Dr. FarshidPirahansiah\n \n Senior AI Engineer · Computer Vision ResearcherEdge AI Specialist · PhD · Author & Speaker\n \n \n 11+Years Experience\n 21Publications\n 3Patents\n 38%Inference Cost Cut\n \n \n \n // Professional Identity\n \n I build intelligent systems that see, understand, and act — from real-time edge AI on embedded hardware to large-scale MLOps pipelines in the cloud.\n \n With a PhD in Computer Vision and 12+ years bridging research and production, I specialise in taking AI from prototype to deployment — whether that's running embedded deep learning models on a Nvidia, Raspberry Pi, Axelera, Hailo AI chip or scaling medical imaging analysis to tens of thousands of patients daily.\n \n \n Computer Vision\n Edge AI\n Medical Imaging\n MLOps\n Embedded Systems\n 3D Reconstruction\n IoT\n Cloud ML\n \n \n\n\n\n\n \n \n 01\n My Story\n \"Innovation thrives where precision meets possibility.\"\n \n \n My journey into AI began with a fascination for how machines could be taught to perceive the physical world. During my PhD, I developed fuzzy logic systems for camera calibration and image segmentation — work that led to patents and set the foundation for a career at the intersection of theory and hardware.\n Over the years I have worked across academia, enterprise, and deep-tech — from MIMOS (Malaysia's national ICT research centre, where I built patented Intelligent Video Analytics systems, to medical device companies improving d..." },{ "id": "/notes/README/", "title": "PKM — Knowledge Base", "url": "/notes/README/", "tags": [], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-16\n\nThe personal knowledge management vault behind pirahansiah.com, maintained with Obsidian and published with Jekyll on GitHub Pages.\n\nLast updated: 2026-08-16. \nPKM\n\nPersonal Knowledge Management vault for pirahansiah.com.\n\nMaintained with Obsidian, published with Jekyll on GitHub Pages. Last updated July 2026.\n" },{ "id": "/notes/pkm/TOC/", "title": "PKM Methods & Website Structure", "url": "/notes/pkm/TOC/", "tags": ["pkm","knowledge-management","productivity"], "hashtags": "#pkm #knowledgemanagement #productivity", "category": "pkm", "body": "last_modified_at: 2026-08-08\n\n PKM Methods & Website Structure — Personal knowledge management approaches from top practitioners and site architecture — https://www.pirahansiah.com/notes/pkm/TOC/\nPersonal knowledge management approaches from top practitioners and site architecture\n\n\nLast updated: 2026-08-08. \n\nPARA, Zettelkasten, Second Brain, Link Your Thinking, LYT, Maps of Content, MOC, Johnny.Decimal, Atomic Notes, GTD, Getting Things Done, CODE, Pillars Pipelines Vaults, Inbox Method, Action Method, Luhmann System, Slipbox, Commonplace Book, Laboratory Method, Evergreen Notes, Digital Garden, The Archive, Bullet Journal, Accessory System, Folderless System, Atomic Thinking, Knowledge Graph, Semantic Linkage, Personal Information Management, PIM, Personal Research Management, PRM, Personal Knowledge Infrastructure, PKI, Information Scaffolding, Transactive Memory, Knowledge Crystallization, Cognitive Offloading, Distributed Cognition, Cybernetic PKM, Rhizomatic Learning, Memex, Project Xanadu, Sensemaking, Information Foraging Theory, Personal Digital Archiving, Curatorial Learning, Epistemic Foraging, Holigarchy, BASB, ACCESS, ACE, The Pillar Method, PAIN, REAP, QEC, SQ3R, PQ4R, Cornell Method, Feynman Technique, DiSSS, SQRRR, Morphological Analysis, Knowledge Graphing, Triple-Entry Accounting of Thought, Heuristic Systematic Model, PASK, Conversation Theory, Cynefin Framework, Double-Loop Learning, Metacognitive Scaffolding, Personal Learning Networks, PLN, Knowledge Work, Cognitive Mapping, Concept Mapping, Spatial Hypertext, Non-Linear Documentation, Multi-..." },{ "id": "/notes/pubs/papers/adaptive-segmentation/", "title": "PSNR-Based Segmentation for LPR", "url": "/notes/pubs/papers/adaptive-segmentation/", "tags": ["image-segmentation","psnr","thresholding","license-plate-recognition"], "hashtags": "#imagesegmentation #psnr #thresholding #lpr", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n PSNR-Based Segmentation for LPR — Adaptive image segmentation using PSNR for license plate recognition systems. — https://www.pirahansiah.com/notes/pubs/papers/adaptive-segmentation/\nAdaptive image segmentation using PSNR for license plate recognition systems.\n\n\nLast updated: 2026-08-08. \n\nAdaptive_Image_Segmentation_Based_on_PSNR_for_License_Plate_Recognition\n\nhttps://www.pirahansiah.com/notes/pubs/papers/Adaptive_Image_Segmentation_Based_on_PSNR_for_License_Plate_Recognition\n\nspotify\n\nPDF Download My Conference Paper\n\n\n\nAdaptive Image Segmentation Based on PSNR for License Plate Recognition\n\n1. Introduction\n\n Objective: Propose an adaptive threshold method using Peak Signal-to-Noise Ratio (PSNR.\n Applications of Image Segmentation:\n \n License Plate Recognition (LPR\n Preprocessing in image analysis\n Object detection and classification\n \n \n\n\n2. Pattern Recognition\n\n Applications:\n \n Optical Character Recognition (OCR\n Biometrics (e.g., face, fingerprint recognition\n Medical diagnostics (e.g., X-ray analysis\n Military applications (e.g., Automated Target Recognition, Image Segmentation\n \n \n\n\n3. Motivation\n\n Thresholding:\n \n Single and multi-level thresholding methods\n PSNR-based threshold for improved accuracy in segmentation\n \n \n Challenges:\n \n Differing environmental conditions (e.g., contrast, lighting\n Object complexity (e.g., fonts, colors, sizes\n \n \n\n\n4. Proposed Method\n\n PSNR as a Metric:\n \n Used to determine optimal threshold value\n ..." },{ "id": "/notes/pubs/journals/adaptive-thresholding/", "title": "Adaptive Thresholding Based on PSNR", "url": "/notes/pubs/journals/adaptive-thresholding/", "tags": ["thresholding","psnr","adaptive-thresholding","image-segmentation"], "hashtags": "#thresholding #psnr #adaptivethresholding #imagesegmentation", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Adaptive Thresholding Based on PSNR — PSNR-based adaptive thresholding for image segmentation in OCR and license plate recognition. — https://www.pirahansiah.com/notes/pubs/journals/adaptive-thresholding/\nPSNR-based adaptive thresholding for image segmentation in OCR and license plate recognition.\n\n\nLast updated: 2026-08-08. \n\nAdaptive_Image_Thresholding_Based_on_the_Peak_Signal-to-noise_Ratio\n\nspotify\n\nPDF Download\nPDF Download\n\n\n\nMind Map: Adaptive Image Thresholding Based on PSNR\n\n1. Introduction\n\n Research Focus: Enhancing image thresholding techniques using Peak Signal-to-noise Ratio (PSNR.\n Key Concept: PSNR-based thresholding improves object-background separation in images.\n Applications: License Plate Recognition (LPR, Optical Character Recognition (OCR, Standard and Handwritten Image Processing.\n\n\n2. Key Components\n\n2.1 Thresholding\n\n Definition: Segmentation technique used to differentiate objects from the background in images.\n Types:\n \n Single Thresholding: Produces binary images; faster computation.\n Multilevel Thresholding: Produces gray-scale images; more complex but can capture finer details.\n \n \n Importance: Reduces data size and improves computational efficiency in image processing.\n\n\n2.2 Peak Signal-to-noise Ratio (PSNR\n\n Role: Used as an indicator to segment images by measuring image quality.\n Purpose: Helps in selecting optimal threshold values for image segmentation.\n Formula: PSNR measures similarity between the original image and segmented image.\n\n\n3. Image Thresholding Methods\n\n3.1 Single..." },{ "id": "/notes/docs/llm/agents/", "title": "Mind Map: Orchestrating AI Agents", "url": "/notes/docs/llm/agents/", "tags": ["ai","llm","agents","multi-agent"], "hashtags": "#ai #llm #agents #multiagent", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Mind Map: Orchestrating AI Agents — Mind map and guide to orchestrating multiple AI agents for complex task completion using LLMs. — https://www.pirahansiah.com/notes/docs/llm/agents/\nMind map and guide to orchestrating multiple AI agents for complex task completion using LLMs.\n\n\nLast updated: 2026-08-08. \n\n\n\nMind Map: Orchestrating Agents\n\n1. Introduction\n\n Concept: Coordination of multiple AI agents to perform complex tasks\n Goal: To achieve tasks that are too complicated for a single agent to handle\n Example Tasks: Research, planning, multi-step processes\n\n\n2. Key Components\n2.1 Agents\n\n Definition: Autonomous units designed to carry out specific tasks\n Types:\n \n Single-purpose agents\n General-purpose agents\n \n \n Capabilities:\n \n Interact with environments\n Process inputs and produce outputs\n Self-contained decision-making\n \n \n\n\n2.2 Orchestrator\n\n Role: Coordinates and manages multiple agents\n Tasks:\n \n Delegates tasks among agents\n Monitors agent progress\n Handles communication between agents\n Combines results from various agents to complete the overall task\n \n \n\n\n2.3 Communication\n\n Importance: Enables agents to work together\n Methods:\n \n Message passing between agents\n API calls between different AI models or functions\n Shared memory or database for information exchange\n \n \n\n\n3. Orchestrating Multiple Agents\n3.1 Task Decomposition\n\n Purpose: Breaking down complex tasks into manageable subtasks\n Method:\n \n Assign subtasks to sp..." },{ "id": "/notes/docs/projects/ai-cost-calculator/", "title": "AI Model Cost Calculator", "url": "/notes/docs/projects/ai-cost-calculator/", "tags": ["ai","llm","cost","calculator","gpt4","claude","gemini"], "hashtags": "#ai #llm #costcalculator #gpt4 #claude #gemini", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n AI Model Cost Calculator — Estimate text and image processing costs for GPT-4, Claude, and Gemini — token-aware cost planning for computer vision and multimodal AI. — https://www.pirahansiah.com/notes/docs/projects/ai-cost-calculator/\nEstimate text and image processing costs for GPT-4, Claude, and Gemini — token-aware cost planning for computer vision and multimodal AI.\n\n\nLast updated: 2026-08-08. \n\nAI Model Cost Calculator\n\nEstimate token and processing costs for multimodal AI models — GPT-4 Turbo with Vision, Claude, and Google Gemini. Use this guide to budget computer-vision and LLM workloads before you build.\n\nWhy Track Token Costs?\n\nVision and language models charge per token. Images can be expensive: a 1080×1080 image in GPT-4 Vision high mode is split into 512-pixel tiles and billed as base cost plus per-tile cost. Understanding the math prevents surprise bills in production.\n\nCost Factors\n\n\n Input tokens — text prompt plus image tokens.\n Output tokens — generated text (usually pricier per token.\n Image mode — low mode (fixed 85 tokens/image vs high mode (base 85 + 170 tokens per 512px tile.\n Model tier — GPT-4 class, Claude Opus/Sonnet, Gemini Pro — each has different rates.\n Caching & batching — prompt caching and batched inference reduce effective cost.\n\n\nHow to Estimate\n\n\n Count prompt tokens (a rough rule: ~1.3 tokens per word, plus image tokens.\n Multiply by the model’s input price per million tokens.\n Add estimated output tokens at the output price.\n Multiply by expected request volume per month.\n\n\nLinks\n\n\n Ope..." },{ "id": "/notes/courses/ai-hardware/", "title": "AI Hardware Accelerators", "url": "/notes/courses/ai-hardware/", "tags": ["AI-hardware","accelerators","NPU","FarshidPirahansiah"], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n AI Hardware Accelerators — Workshop covering custom accelerators, NPUs, and edge AI chips. — https://www.pirahansiah.com/notes/courses/ai-hardware/\nWorkshop covering custom accelerators, NPUs, and edge AI chips.\n\n\nLast updated: 2026-08-08. \n\nAI Hardware Accelerators\n\nWorkshop on custom AI accelerators, NPUs, and edge computing chips.\n\n#AIHardware #Accelerators #FarshidPirahansiah\n" },{ "id": "/ai-toolkit/", "title": "AI Prompt & Agent Toolkit", "url": "/ai-toolkit/", "tags": ["prompts","claude","gpt","mcp","skills","agents","ai-toolkit","etsy"], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-16\n\n AI Toolkit — 27 skills · 3 agents · MCP tools · coding rules — https://pirahansiah.com/ai-toolkit/\n27 battle-tested Claude Code skills, code-review agents, MCP tools, and coding rules — the AI engineering stack behind pirahansiah.com, packaged as prompt packs for modern LLMs.\n\n\nLast updated: 2026-08-16. \n\n\n\n\n AI Engineering Stack\n AI Prompt & Agent Toolkit\n Twenty-seven battle-tested Claude Code skills, three code-review agents, MCP tooling, and coding rules — the exact AI engineering stack behind pirahansiah.com, organized into ready-to-use prompt packs for modern LLMs.\n\n\n\n\nPrompt Packs\nEach pack is a themed collection of skills and prompts. They ship as copy-paste prompt files that drop straight into Claude Code, Cursor, or any instruction-following agent.\n\n\n\n\n Pack 01 — Computer Vision & Edge AI\n CV Engineering\n \n cv-pipeline — detection, tracking, segmentation, annotation, video analysis.\n edge-deploy — Hailo, Axelera, Qualcomm, Apple Neural Engine, Jetson, Movidius.\n quantize — INT8/INT4 via NNCF, TensorRT, ONNX Runtime.\n \n\n\n\n Pack 02 — Code Review & Debugging\n Quality & Security\n \n code-reviewer — quality, security, maintainability reviews.\n debugger — errors, test failures, unexpected behavior.\n security-auditor — auth, data handling, security-sensitive code.\n diagnosing-bugs — structured loop for hard bugs and regressions.\n \n\n\n\n Pack 03 — System Design\n Architecture & Modeling\n \n codebase-design — shared vocabulary for deep modules.\n domain-modeling — pin down domain ter..." },{ "id": "/notes/pubs/journals/ant-colony/", "title": "Ant Colony Optimization for Image Processing", "url": "/notes/pubs/journals/ant-colony/", "tags": ["ant-colony-optimization","image-processing","thresholding","optimization"], "hashtags": "#aco #imageprocessing #thresholding #optimization", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Ant Colony Optimization for Image Processing — ACO-based optimization for image thresholding and edge detection in OCR systems. — https://www.pirahansiah.com/notes/pubs/journals/ant-colony/\nACO-based optimization for image thresholding and edge detection in OCR systems.\n\n\nLast updated: 2026-08-08. \n\nUSING_AN_ANT_COLONY_OPTIMIZATION_ALGORITHM\n\n\n \n https://www.pirahansiah.com/notes/pubs/journals/USING_AN_ANT_COLONY_OPTIMIZATION_ALGORITHM/\n \n \n spotify\n \n \n PDF Download\n\n \n \n\n\nAnt Colony Optimization for Image Edge Detection\n\n1. Introduction\n\n Thresholding: Used in various computer vision applications like OCR, image segmentation, and object tracking.\n Ant Colony Optimization (ACO: Population-based metaheuristic for optimization.\n Objective: Combining ACO, edge detection, and thresholding for Optical Character Recognition (OCR systems.\n\n\n2. State of the Art\n2.1 Thresholding Methods\n\n Categories: Single, Multilevel, Multi-thresholding\n Single Thresholding: Converts the image into binary (black and white.\n \n Pirahansiah’s Single Threshold Method: A custom single threshold method using PSNR.\n \n \n Multilevel Thresholding: Separates objects based on gray values using multiple thresholds.\n Multi-threshold: Uses multiple threshold values to identify objects in images.\n\n\n2.2 Ant Colony Optimization (ACO\n\n Introduction: Initially proposed by Marco Dorigo in 1992 for combinatorial optimization problems.\n Application: Used for image edge detection in this paper.\n Process:\n \n Initialize ants randomly.\n ..." },{ "id": "/notes/docs/cuda/apple-ml/", "title": "MLX, CoreML, and Metal for Apple Silicon", "url": "/notes/docs/cuda/apple-ml/", "tags": ["apple-silicon","coreml","metal","mlx"], "hashtags": "#applesilicon #coreml #metal #mlx", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n MLX, CoreML, and Metal for Apple Silicon — Guide to Numba JIT, PyCUDA-to-Metal transition, CoreML conversion, and MLX framework on Apple Silicon. — https://www.pirahansiah.com/notes/docs/cuda/apple-ml/\nGuide to Numba JIT, PyCUDA-to-Metal transition, CoreML conversion, and MLX framework on Apple Silicon.\n\n\nLast updated: 2026-08-08. \n\nNumba JIT Tutorial and PyCUDA with Apple Silicon Adaptation\n\nNumba JIT on Apple Silicon\nThis tutorial explores using Numba’s @jit(nopython=True decorator to optimize Python code for faster execution. The @jit(nopython=True decorator from Numba compiles Python functions into machine code for improved performance, especially for numerical tasks.\n\nBasic Example: Sum of Squares\nfrom numba import jit\n\n@jit(nopython=True\ndef sum_of_squares(n:\n total = 0\n for i in range(n:\n total += i * i\n return total\n\nprint(sum_of_squares(10 # Output: 285\n\n\nOn Apple Silicon, Numba can be used to optimize CPU-bound tasks. Although it doesn’t directly support GPU via Metal or NPU, you can use it to significantly speed up CPU computations, which Apple’s M1/M2 chips handle efficiently with multiple cores.\n\nTransition to Metal for GPU\nTo offload heavy parallel tasks to the GPU, Apple uses Metal, an API for high-performance graphics and computation on macOS. Metal’s Metal Shading Language (MSL provides a way to run GPU tasks that would otherwise be written for CUDA in environments like PyCUDA.\n\nPyCUDA to Metal for Apple Silicon\nPyCUDA is typically used for running GPU tasks on NVIDIA hardware using CUDA. However, on Appl..." },{ "id": "/notes/docs/llm/avatar/", "title": "Local Video Avatar Generator with Ollama", "url": "/notes/docs/llm/avatar/", "tags": ["ai","llm","video-generation","ollama"], "hashtags": "#ai #llm #videogeneration #ollama", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Local Video Avatar Generator with Ollama — Build a fully local video avatar generator using Ollama LLM, Piper TTS, and Wav2Lip without cloud services. — https://www.pirahansiah.com/notes/docs/llm/avatar/\nBuild a fully local video avatar generator using Ollama LLM, Piper TTS, and Wav2Lip without cloud services.\n\n\nLast updated: 2026-08-08. \n\nBuilding a Local Video Avatar Generator Using Ollama and Open-Source Tools\n\nCreating a video avatar generator completely locally without cloud services or API keys is challenging but possible. Here’s a step-by-step guide to build a system that generates talking video avatars using locally-run models.\n\nPrerequisites\n\n\n A computer with decent GPU (at least 8GB VRAM recommended\n 16GB+ RAM\n 50GB+ free storage space\n Linux or macOS (Windows with WSL also works\n Basic familiarity with command line\n\n\nStep 1: Set Up Ollama for Local LLM\n\nOllama allows you to run large language models locally for text generation.\n\n\n Install Ollama:\n # For macOS/Linux\ncurl -fsSL https://ollama.com/install.sh sh\n \n# For Windows (via WSL\n# First install WSL, then run the Linux command above\n \n \n Pull a suitable model (Llama3 recommended for better performance:\n ollama pull llama3\n \n \n Test your Ollama installation:\n ollama run llama3 \"Write a short 30-second script about climate change\"\n \n \n\n\nStep 2: Install Local Text-to-Speech Engine\n\nWe’ll use Piper, a fast local TTS system:\n\n\n Install dependencies:\n sudo apt-get update\nsudo apt-get install -y build-essential python3-pip python3-venv\n \n \n..." },{ "id": "/notes/docs/llm/blog/", "title": "Blog: AI, LLMs, and Computer Vision", "url": "/notes/docs/llm/blog/", "tags": ["ai","llm","rag","multi-agent"], "hashtags": "#ai #llm #rag #multiagent", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Blog: AI, LLMs, and Computer Vision — Blog posts on RAG vs CAG, multi-agent architectures, AI’s impact on jobs, and the future of software development. — https://www.pirahansiah.com/notes/docs/llm/blog/\nBlog posts on RAG vs CAG, multi-agent architectures, AI’s impact on jobs, and the future of software development.\n\n\nLast updated: 2026-08-08. \n\n2025\n\nMay\n\nRAG vs. CAG: Choosing the Right Approach for Your AI Projects\n\n AI Text Generation Methods\n / \\\n / \\\n Generation Approaches Emerging LLM Methods\n / \\ / \\\n / \\ / \\\n RAG CAG Transformer² MML Mosaic\n / \\ / \\ \nAccess to Higher Fast Simple Self- Modular Composite\nup-to-date complex- response architec-adaptive components pruning\n info ity times ture weights \n Better Faster\n reasoning inference\n\n\nUltra-Brief Summary: Compare RAG (retrieval-based, updated info, complex with CAG (cache-based, faster, simpler approaches, plus three new LLM methods: self-adaptive Transformer², modular MML, and efficient Mosaic pruning.\n\nRetrieval-Augmented Generation (RAG\nRAG joins a language model with a retrieval system that gets relevant documents from ..." },{ "id": "/notes/courses/book-summary/", "title": "Book Summaries", "url": "/notes/courses/book-summary/", "tags": ["books","summaries","PKM","FarshidPirahansiah"], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Book Summaries — Book summaries and knowledge management resources. — https://www.pirahansiah.com/notes/courses/book-summary/\nBook summaries and knowledge management resources.\n\n\nLast updated: 2026-08-08. \n\nBook Summaries\n\n\n Topics & Projects — All topics and project index\n Machine Learning Specialization — ML fundamentals and course notes\n Full Stack Deep Learning — End-to-end DL deployment\n MLOps — ML pipeline operations and monitoring\n Portfolio & Use Cases — Personal knowledge management and project portfolio\n\n\n#Books #PKM #KnowledgeManagement\n" },{ "id": "/book/", "title": "Book — Computer Vision & AI: An Engineer's Guide", "url": "/book/", "tags": ["book","computer vision","ai","gpu","cuda","llm","pdf"], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-16\n\n The Book — Computer Vision & AI: An Engineer’s Guide — 406 pages · 21 chapters · 7 parts — https://pirahansiah.com/book/\nComputer Vision & AI: An Engineer’s Guide by Dr. Farshid Pirahansiah — a 406-page practical reference covering 3D vision, GPU & CUDA programming, LLMs, optimization, and startup strategy.\n\n\nLast updated: 2026-08-16. \n\n\n\n\n Computer Vision & AI — An Engineer's Guide\n A practical reference distilling more than a decade of hands-on work in computer vision, artificial intelligence, and GPU computing into a single book.\n \n 406 pages21 chapters7 partsPDF\n \n Download the PDF ↓\n\n\n\n\n## About the Book\n\nWritten for practicing engineers, researchers, and technical founders, this book brings together the notes, tutorials, and engineering playbooks published across pirahansiah.com — organized from first principles to production systems. Every chapter is self-contained and paired with a mind map that summarizes the key ideas at a glance.\n\n## What's Inside\n\nPart I — Computer Vision: 3D Vision & Multi-Camera Systems, Optical Flow, Real-Time Multi-Camera Systems, and a Computer Vision Coaching Roadmap.\n\nPart II — GPU & CUDA Programming: Numba JIT, PyCUDA kernels, CUDA in VS Code, and Apple Silicon ML (MLX, CoreML & Metal.\n\nPart III — AI & Large Language Models: advanced LLM concepts, orchestrating AI agents, and a local video avatar generator.\n\nPart IV — Optimization & Prompting: CV/DL/ML model optimization and prompt-engineering templates.\n\nPart V — Programming & Tools: a C++ reference..." },{ "id": "/notes/pubs/books/camera-calibration/", "title": "Camera Calibration & Video Stabilization", "url": "/notes/pubs/books/camera-calibration/", "tags": ["camera-calibration","video-stabilization","robotics","slam"], "hashtags": "#cameracalibration #videostabilization #robotics #slam", "category": "hub", "body": "last_modified_at: 2026-08-14\n\nSpringer book chapter on fuzzy camera calibration and video stabilization for robot localization.\n\nLast updated: 2026-08-14. \nMy_Book_chapter_Camera_Calibration_and_Video_Stabilization_Framework_for_Robot_Localization in the Book entitled “Control Engineering in Robotics and Industrial Automation” published in Springer\n\nhttps://www.pirahansiah.com/notes/pubs/books/My_Book_chapter_Camera_Calibration_and_Video_Stabilization_Framework_for_Robot_Localization/\n\nspotify\n\nPDF Download book chapter titled “book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation” published in Springer\n\n<img src=”/notes/pubs/books/camera-calibration-video-stabilization.png” alt=”book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation” published in Springer” style=”max-width: 100%; height: auto;”>\n\nCamera Calibration and Video Stabilization Framework for Robot Localization\n1. Introduction\n\n Key Issues in Localization: Camera calibration (CC and video stabilization (VS.\n Problems in Camera Calibration:\n \n Current methods use fixed thresholds, neglecting slope information, leading to blurring.\n Gaussian pyramid parameters in optical flow require manual tuning.\n \n \n Challenges in Robot Vision:\n \n Large motion, motion blur, and defocus blur.\n Landmark recognition and probabilistic models fail ..." },{ "id": "/notes/pubs/journals/character-recognition/", "title": "Character & Object Recognition via Features", "url": "/notes/pubs/journals/character-recognition/", "tags": ["character-recognition","object-recognition","feature-extraction","glcm"], "hashtags": "#characterrecognition #objectrecognition #featureextraction #glcm", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Character & Object Recognition via Features — Global feature extraction methods for character and object recognition in computer vision. — https://www.pirahansiah.com/notes/pubs/journals/character-recognition/\nGlobal feature extraction methods for character and object recognition in computer vision.\n\n\nLast updated: 2026-08-08. \n\nCHARACTER_AND_OBJECT_RECOGNITION_BASED_ON_GLOBAL_FEATURE_EXTRACTION\n\nspotify\n\nPDF Download\n\n\n\nMind Map: Character and Object Recognition Based on Global Feature Extraction\n\n1. Introduction\n\n Optical Character Recognition (OCR: Recognizes handwritten, irregular, and machine-printed characters.\n Key Tasks in OCR:\n \n Pre-processing\n Segmentation\n Feature Extraction\n Classification\n Recognition\n \n \n\n\n2. Feature Extraction Methods\n2.1 Global Feature Extraction\n\n Definition: Uses entire image characteristics to extract features.\n Methods:\n \n Gray Level Co-occurrence Matrix (GLCM: Uses spatial distribution of gray-level values.\n Edge Direction Matrix (EDMS: Captures edge directions but produces a limited number of features.\n \n \n Challenges:\n \n Less discriminative features.\n Higher dimensionality leads to longer processing times.\n \n \n\n\n2.2 Spatial Feature Extraction\n\n Definition: Focuses on local image characteristics.\n Techniques:\n \n Robinson Compass Mask: Uses gradient filters in eight directions.\n \n \n Strengths: Better for character recognition.\n Limitations: Time-consuming due to high-dimensional data.\n\n\n3. Proposed Method\n\n Co..." },{ "id": "/notes/courses/cloud-native/", "title": "Cloud-Native with Kubernetes", "url": "/notes/courses/cloud-native/", "tags": ["Docker","Kubernetes","cloud-native","FarshidPirahansiah"], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Cloud-Native with Kubernetes — Docker and Kubernetes fundamentals for cloud-native infrastructure. — https://www.pirahansiah.com/notes/courses/cloud-native/\nDocker and Kubernetes fundamentals for cloud-native infrastructure.\n\n\nLast updated: 2026-08-08. \n\nCloud-Native Infrastructure with Kubernetes\n\nDocker Basics\n\ndocker run -ti ubuntu:latest bash\ndocker ps -format $FORMAT\ndocker ps -l\ndocker commit ID\ndocker tag imageID my-image\ndocker run --rm -ti ubuntu sleep 5\ndocker run -d -ti ubuntu bash # detached\ndocker attach name # Ctrl+P /Q to detach\ndocker logs container_name -p\ndocker images\n\n\n#Docker #Kubernetes #CloudNative\n" },{ "id": "/notes/docs/coaching/", "title": "CV Coaching & Teaching Roadmap", "url": "/notes/docs/coaching/", "tags": ["computer-vision","coaching","teaching","curriculum"], "hashtags": "#cv #coaching #teaching #curriculum", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n CV Coaching & Teaching Roadmap — Personalized computer vision coaching with a structured curriculum from fundamentals to edge AI. — https://www.pirahansiah.com/notes/docs/coaching/\nPersonalized computer vision coaching with a structured curriculum from fundamentals to edge AI.\n\n\nLast updated: 2026-08-08. \n\nComputer Vision Expertise: Teaching & Coaching Services\n\nWelcome to my professional computer vision teaching service. I offer personalized coaching, tutoring, and online sessions to help you master image processing and computer vision.\n\nAbout Me\n\nI am an experienced computer vision educator with deep expertise across the entire computer vision pipeline. My teaching approach emphasizes building strong foundations while connecting theory to practical applications. I help students develop both theoretical understanding and hands-on implementation skills.\n\nTeaching Philosophy\n\nMy teaching is built around:\n\n Connecting theoretical concepts to real-world applications\n Progressive skill building from fundamentals to advanced topics\n Hands-on projects that reinforce learning\n Personalized guidance based on your background and goals\n\n\nServices Offered\n\n\n One-on-One Tutoring: Personalized sessions tailored to your learning pace and specific interests\n Group Workshops: Collaborative learning environments focused on specific topics\n Project-Based Coaching: Guidance on implementing computer vision in your specific applications\n Code Reviews: Analysis of your implementations with suggestions for improvement\n Career Guidance: Mentorsh..." },{ "id": "/notes/pubs/books/computational-intelligence/", "title": "Augmented Optical Flow for Video Stabilization", "url": "/notes/pubs/books/computational-intelligence/", "tags": ["optical-flow","video-stabilization","fuzzy-logic","computer-vision"], "hashtags": "#opticalflow #videostabilization #fuzzylogic #cv", "category": "hub", "body": "last_modified_at: 2026-08-14\n\nBook chapter on Type-2 Fuzzy Gaussian Pyramid optical flow for video stabilization.\n\nLast updated: 2026-08-14. \nBook_Computational_Intelligence_From_Theory_to_Application_explores_augmented_optical_flow_methods_for_video_stabilization\n\nhttps://www.pirahansiah.com/notes/pubs/books/Book_Computational_Intelligence_From_Theory_to_Application_explores_augmented_optical_flow_methods_for_video_stabilization/\n\nspotify\n\nPDF Download book chapter titled “Augmented Optical Flow Methods for Video Stabilization”, In Computational Intelligence: from theory to application. (2017 (p18.\n\n\n\nComputational Intelligence: From Theory to Application\nChapter 4: Augmented Optical Flow Methods for Video Stabilization\n1. Introduction\n\n Purpose of Video Stabilization: Eliminates undesired motion from camera frames.\n \n Important for mobile robot vision systems.\n Motion analysis is a major task in computer vision.\n Interpretation of motion is crucial in dynamic scenes.\n \n \n Applications: Computer vision tasks in diverse fields (e.g., mobile robots.\n \n Reduces blurring due to camera motion.\n Key qualitative feature for vision systems.\n Importance of motion analysis for camera and scene interactions.\n \n \n\n\n2. Related Work\n\n Video Stabilization (VS Techniques:\n \n Applied to reduce jitter and motion blur.\n Optical flow methods: Incorporate motion features for stabilization.\n \n \n Dense 2D Motion Field:\n \n Optical flow-based video stabilization model.\n Combines feature point selection and delta opti..." },{ "id": "/notes/pubs/papers/conference-paper/", "title": "Publications — Conference Papers", "url": "/notes/pubs/papers/conference-paper/", "tags": [], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Publications — Conference Papers — All peer-reviewed conference papers by Dr. Farshid Pirahansiah on image processing, camera calibration, pattern recognition, and grid computing. — https://www.pirahansiah.com/notes/pubs/papers/conference-paper/\nAll peer-reviewed conference papers by Dr. Farshid Pirahansiah on image processing, camera calibration, pattern recognition, and grid computing.\n\n\nLast updated: 2026-08-08. \n\ntags: research, papers, image-processing, computer-vision, camera-calibration, pattern-recognition]\nhashtags: “#research #papers #computervision #imageprocessing”\n\nConference Papers\n\nAll peer-reviewed conference papers by Dr. Farshid Pirahansiah.\n\n\n\nImage Processing & Thresholding\n\nAdaptive Image Segmentation Based on PSNR for License Plate Recognition\nAdaptive thresholding using PSNR for license plate recognition systems.\n\nLicense Plate Recognition with Multi-Threshold Based on Entropy\nEntropy-based multi-threshold approach for license plate detection.\n\nMulti-threshold Approach for License Plate Recognition System\nMulti-level thresholding with adaptive MLP for license plate recognition.\n\nComparison of Single Thresholding Method for Handwritten Images Segmentation\nComparison of Otsu and PSNR-based thresholding for handwritten image segmentation.\n\n\n\nCamera Calibration & 3D Vision\n\nCamera Calibration for Multi-Modal Robot Vision\nAutomatic calibration framework for multi-modal robot vision systems.\n\nPattern Image Significance for Camera Calibration\nEvaluation of calibration pattern significance for robot vision.\n\n2D vers..." },{ "id": "/notes/docs/cpp/", "title": "C++ Quick Reference", "url": "/notes/docs/cpp/", "tags": ["cpp","programming","debugging","memory-management"], "hashtags": "#cpp #programming #debugging #memorymanagement", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n C++ Quick Reference — Quick reference for C++ memory management, data structures, debugging tools, and shell basics. — https://www.pirahansiah.com/notes/docs/cpp/\nQuick reference for C++ memory management, data structures, debugging tools, and shell basics.\n\n\nLast updated: 2026-08-08. \n\nC++\n\nhash\nstd::map<string,int> prices;\nprices‘aa’]=310;\nprices={‘aa’:310, ‘bb’:410}\n\nstack\nstd::stack stk;\nstk.push(5;\nstk.pop(; //5\n\nqueue\nstd::queue q;\nq.push(5; ... 89\nq.pop(; //5\n\nfrom collections import deque\nstk=deque(\nstk.append(‘asadfas’\nstk.pop(\n\n o(n\nstk=]\nstk.pop(.rstk-1]\n\n\ncollections import lifo\ndeque(\nappendLeft(5\npop\n\nstack\n\n automatically managed\n LIFO\n at compile time\n short term\n 8 MB : 8192 kb\n fast\n ulimit_a : show memory\n push / pop\n\n\nheap\n\n need managed by yourself\n if we need to use more than 8 MB we need to use heap\n run time;\n dynamic\n long time\n new/delete\n slower\n \n pointer\n \n \n \n \n \n pmap ‘pidof _____’\n tail _n1\n grep_o ‘0-9]*’\n awk ‘{print $ 0/(1024*1024” GiB]”}’\n \n \n \n \n tools\n \n valgrind ./my_program\n fsanitize = address\n \n \n echo %errorlevel%\n gflags /i print+Greeting.ext +sls\n \n cdb printGreeting.ext\n \n start with “/” is absolute path\n start with “folder/file…” is relative path\n ”/” is root\n ”~” is home folder\n ”.” is current folder\n \n ”..” is parent folder\n \n a-c] is abc\n grep & ls.txt search inside file\n ”;” calls all command one after\n && same but if error strop next\n \n \n ..." },{ "id": "/crypto/", "title": "Crypto Payments — Support", "url": "/crypto/", "tags": ["crypto","bitcoin","ethereum","solana","base","bnb","donations","support"], "hashtags": "#crypto #bitcoin #ethereum #solana #base #bnb #donate #web3", "category": "hub", "body": "last_modified_at: 2026-08-16\n\n Crypto Payments — Support open-source computer vision and edge AI work with cryptocurrency. — https://pirahansiah.com/crypto/\nSupport Dr. Farshid Pirahansiah’s open-source computer vision and edge AI work with cryptocurrency. Bitcoin, Ethereum, Solana, Base and BNB Smart Chain wallet addresses.\n\n\nLast updated: 2026-08-16. \n\n\n\n\n Support via Crypto\n If my open-source computer vision and edge AI work has helped you, you can support it directly with cryptocurrency. Select the token below to reveal the matching wallet address.\n\n\n\n \n ⚠️\n Warning — transfers are irreversible\n Crypto transactions cannot be reversed, refunded, or recovered once confirmed. There is no chargeback and no support line that can undo a transfer. Double-check everything before you send.\n \n \n ⚠️\n Caution — send on the correct network\n Ethereum, Base and BNB Smart Chain share the same address (0xFcE7…53B0C. Sending on the wrong network — for example Base instead of Ethereum — can permanently lose your funds. Always verify the network in your wallet before confirming.\n \n \n ℹ️\n Note — voluntary, no invoice\n This page is a voluntary support option, not a purchase or invoice. Crypto payments are final with no automatic receipt or confirmation. If you are unsure, send a small test amount first.\n \n \n 💡\n When to use it\n Use crypto to support the work anonymously or internationally with low fees. For contracts, invoices, consulting, or anything that needs a receipt, email info@piraha..." },{ "id": "/notes/docs/cuda/cuda-vscode/", "title": "CUDA Development Environment in VS Code on Windows", "url": "/notes/docs/cuda/cuda-vscode/", "tags": ["cuda","gpu","development","visual-studio-code"], "hashtags": "#cuda #gpu #devtools #vscode", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n CUDA Development Environment in VS Code on Windows — Step-by-step guide to configure VS Code for CUDA C++ development on Windows with nvcc, IntelliSense, and debugging. — https://www.pirahansiah.com/notes/docs/cuda/cuda-vscode/\nStep-by-step guide to configure VS Code for CUDA C++ development on Windows with nvcc, IntelliSense, and debugging.\n\n\nLast updated: 2026-08-08. \n\nSimple Setting Up a CUDA Development Environment in VS Code in Windows\n\nhttps://www.pirahansiah.com/notes/docs/cuda/cuda-vscode/ \n\n\n🚀 Setting Up a CUDA Development Environment in VS Code (Windows\n\nIf you’re working with CUDA C++ and want a clean and efficient workflow inside Visual Studio Code, this guide shows how to configure tasks and launch settings to build and debug .cu files using nvcc and the MSVC toolchain.\n\nBelow is a breakdown of how the key configuration files come together.\n\n⸻\n\n🛠️ tasks.json – Automating the Build Process\n{\n \"version\": \"2.0.0\",\n \"tasks\": \n {\n \"label\": \"Build CUDA Project\",\n \"type\": \"shell\",\n \"command\": \"nvcc\",\n \"args\": \n \"-I\",\n \"C:\\\\Program Files\\\\Microsoft Visual Studio\\\\2022\\\\Enterprise\\\\VC\\\\Tools\\\\MSVC\\\\14.42.34433\\\\include\",\n \"-L\",\n \"C:\\\\Program Files\\\\Microsoft Visual Studio\\\\2022\\\\Enterprise\\\\VC\\\\Tools\\\\MSVC\\\\14.42.34433\\\\lib\\\\x64\",\n \"${workspaceFolder}/main.cu\",\n \"-o\",\n \"${workspaceFolder}/main.exe\"\n ],\n \"group\": {\n \"kind\": \"build\",\n \"isDefault\": true\n },\n \"problemMatcher\": ]\n }\n ]\n}\n\n\nThis task uses nvcc to compile a CUDA fi..." },{ "id": "/notes/pubs/books/ai/cv-meets-llm/", "title": "Computer Vision Meets LLM", "url": "/notes/pubs/books/ai/cv-meets-llm/", "tags": [], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Computer Vision Meets LLM — Book exploring multimodal AI combining computer vision with large language models. — https://www.pirahansiah.com/notes/pubs/books/ai/cv-meets-llm/\nBook exploring multimodal AI combining computer vision with large language models.\n\n\nLast updated: 2026-08-08. \n\ntags: computer-vision, llm, multimodal, rag]\nhashtags: “#cv #llm #multimodal #rag”\n\nFarshid Pirahansiah\n\nComputer Vision Meets LLM\n\nAI computer vision locally LLMs on device\n\nComputer Vision Meets LLM: Multi-Agent Swarm with RAG for Images and Videos\n\nIntroduction\n\nThe convergence of Computer Vision (CV and Large Language Models (LLMs marks a significant advancement in artificial intelligence, enabling more comprehensive and intelligent systems capable of understanding and interacting with the world in multimodal ways. By integrating multi-agent swarms with Retrieval-Augmented Generation (RAG, developers can create sophisticated applications that process and analyze images and videos alongside textual data. This synergy enhances capabilities in areas such as image recognition, video analysis, document processing, and interactive user experiences.\n\n1. Integration of Computer Vision and Large Language Models\n\nCombining CV and LLMs leverages the strengths of both modalities:\n\n Computer Vision excels in interpreting and analyzing visual data, identifying patterns, objects, and actions within images and videos.\n Large Language Models (e.g., GPT-4 are proficient in understanding and generating human-like text, enabling nuanced interactions and contextual under..." },{ "id": "/notes/docs/cv-overview/", "title": "Dr. Farshid Pirahansiah — CV & AI", "url": "/notes/docs/cv-overview/", "tags": ["computer-vision","ai","portfolio","about"], "hashtags": "#cv #ai #portfolio #about", "category": "hub", "body": "last_modified_at: 2026-08-15\n\n Dr. Farshid Pirahansiah — CV & AI — Dr. Farshid Pirahansiah — CV & AI — https://www.pirahansiah.com/notes/docs/cv-overview/\nOverview of Dr. Farshid Pirahansiah’s computer vision and AI expertise: 3D vision, multi-camera systems, optical flow, model optimization, and edge AI deployment.\n\n\nLast updated: 2026-08-15. \n\nDr. Farshid Pirahansiah\n\n\n \n \n Computer Vision Research Engineer & Technical Lead\n Berlin, Germany\n \n \n\n\nAbout Me\n\nI am an accomplished Research Engineer with 12+ years of experience, including a PhD in Computer Science. My career has been dedicated to Computer Vision, Machine Learning, and ML Operations, with a proven track record of transforming complex algorithms into production-ready applications.\n\nExperience\n\n\n 12+ years: Computer Vision, C++, R&D\n 10+ years: Machine Learning, Deep Learning, Python, Embedded Systems, Multi-Camera Systems\n 7+ years: IoT, Model Optimization, Robotics, Medical Imaging, Cloud (AWS\n 5+ years: Technical Lead, Global Collaboration\n 2+ years: LLMs, Multimodal AI, RAG, Agentic Workflows\n\n\nCore Skills\n\n\n Computer Vision & AI: Image processing, deep learning, real-time systems\n Languages: Python, C++, MATLAB\n Frameworks: PyTorch, TensorFlow, ONNX Runtime, TensorRT, OpenVINO\n Tools: Docker, Kubernetes, AWS, Git, MLflow, CI/CD\n Edge AI: Jetson, Coral TPU, model quantization (INT8/FP16\n\n\nPublished Research\n\n\n 3 Patents (Face Image Augmentation, Vehicle Detection, Facial Analysis Advertising\n 2 Book Chapters (Springer\n \n \n \n \n 6 J..." },{ "id": "/notes/pubs/cv/", "title": "CV — Dr. Farshid Pirahansiah", "url": "/notes/pubs/cv/", "tags": ["computer-vision","resume","publications","patents"], "hashtags": "#cv #resume #publications #patents", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n CV — Dr. Farshid Pirahansiah — Curriculum vitae covering 12+ years of computer vision R&D, 21 publications, and 3 patents. — https://www.pirahansiah.com/notes/pubs/cv/\nCurriculum vitae covering 12+ years of computer vision R&D, 21 publications, and 3 patents.\n\n\nLast updated: 2026-08-08. \n\nAs an AI and computer vision expert with over a decade of experience collaborating with esteemed global organizations, my expertise encompasses AI research and development, machine learning, deep learning, Internet of Things (IoT, and model optimization for edge and cloud-based solutions. With a portfolio of 21 publications, three patents, and extensive practical experience in real-time computer vision applications, I have spearheaded groundbreaking projects in generative AI, video analytics, and intelligent systems. Proficient in C++, Python, OpenCV, and advanced GPU optimization, I am recognized for bridging the gap between cutting-edge research and commercially viable products.\n\nMe\n\n I have a total of 21 publications, including 3 patents, 2 book chapters, 6 journal articles, and 10 conference papers. My Google Scholar profile indicates 137 citations, an h-index of 7, and an i10-index of 5.\n Dr. Farshid Pirahansiah CV\n My podcast\n\n\nMy Academic Contributions and Publications\n\n \n My Google Scholar Profile\n\n \n \n \n Metric\n Value\n \n \n \n \n Citations\n 142\n \n \n h-index\n 8\n \n \n i10-index\n 5\n \n \n ..." },{ "id": "/notes/docs/dev-tools/", "title": "Developer Tools & Setup Guide", "url": "/notes/docs/dev-tools/", "tags": ["developer-tools","docker","github","productivity"], "hashtags": "#devtools #docker #github #productivity", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Developer Tools & Setup Guide — Curated developer tools, shell essentials, Docker tips, and GitHub tricks. — https://www.pirahansiah.com/notes/docs/dev-tools/\nCurated developer tools, shell essentials, Docker tips, and GitHub tricks.\n\n\nLast updated: 2026-08-08. \n\nRecommended Tools\n\n\n NeoHtop — Modern htop alternative\n Cap — Open source Loom alternative for screen recordings\n RustDesk — Open source remote desktop\n Helix Editor — Post-modern modal editor\n Blender MCP — Blender + MCP integration\n RX Resume — Resume builder\n Practical Computer Vision — CV learning resources\n CUDA Codes — CUDA flash attention algorithms\n Intel RealSense HDR — HDR depth cameras\n Apple ML-GBC — Apple ML framework\n Python CLI — Python CLI tool\n Data Structures for Image Processing — Newsletter\n\n\n\n\nShell Essentials\n\nEcho & Variables\n\n echo \"value is $foo\" → value is bar (variable expanded\n echo 'value is $foo' → value is $foo (literal\n foo=bar — no space around =\n $PATH — environment variable\n cd - — go to previous directory\n\n\nNavigation\n\n / — root directory\n ~ — home folder\n . — current folder\n .. — parent folder\n Absolute path starts with /, relative path starts with folder/file\n\n\nShortcuts\n\n Ctrl+L — clear terminal\n Ctrl+R — reverse search history\n !! — repeat last command\n\n\nPipes & Redirection\n\n ; — run commands sequentially\n && — run next only if previous succeeded\n — pipe output to next command\n >> — append to file\n # — root/sudo prompt\n\n\nUseful Commands\n\n xdg-open file — open with default app\n ulim..." },{ "id": "/notes/courses/edge-ai-summit/", "title": "Edge AI Summit 2020", "url": "/notes/courses/edge-ai-summit/", "tags": ["edge-AI","summit","tinyML","FarshidPirahansiah"], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Edge AI Summit 2020 — Notes from Edge AI Summit covering tinyML, neuromorphic computing, and edge inference. — https://www.pirahansiah.com/notes/courses/edge-ai-summit/\nNotes from Edge AI Summit covering tinyML, neuromorphic computing, and edge inference.\n\n\nLast updated: 2026-08-08. \n\nEdge AI Summit 2020 (Nov 18-20\n\nWednesday Nov 18\n\n Predictive Maintenance at Sensor Level (SensiML\n Fish Farming with Deep Learning (Aquabyte\n TinyMLPerf: Benchmarking Ultra-low Power ML\n Neuromorphic Intelligence for Sensor Edge (Innatera\n Breaking Barriers to Deploy DNNs on Low-Power Hardware (Deeplite\n Optimizing ML Models at Edge (OctoML/Apache TVM\n\n\nThursday Nov 19\n\n Edge AI for Post-Pandemic Society (FogHorn\n Evolving Landscape of Edge AI (Google Coral\n InferX X1 Edge Inference Accelerator (Flex Logix\n Edge in Retail: Walmart Case Study (NVIDIA\n Analog AI Compute (Mythic\n Edge AI for Repetitive Motion (Bosch Sensortec\n\n\nFriday Nov 20\n\n Spatial Computing: Edge + Cloud (Magic Leap\n Autonomous IoT Network (Rakuten Mobile\n Practical Edge Inferencing with Sparsity (GrAI Matter Labs\n Large Scale DL on Edge\n AI Hardware Accelerators 2020-21 (Kisaco Research\n\n\n#EdgeAI #TinyML #Summit #FarshidPirahansiah\n" },{ "id": "/notes/pubs/patents/face-augmentation/", "title": "Face Image Augmentation WO2021060971A1", "url": "/notes/pubs/patents/face-augmentation/", "tags": ["patent","face-augmentation","gan","data-augmentation"], "hashtags": "#patent #faceaugmentation #gan #dataaugmentation", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Face Image Augmentation WO2021060971A1 — GAN-based method for augmenting face images from surveillance with fuzzy logic quality filtering. — https://www.pirahansiah.com/notes/pubs/patents/face-augmentation/\nGAN-based method for augmenting face images from surveillance with fuzzy logic quality filtering.\n\n\nLast updated: 2026-08-08. \n\nspotify\n\n\n \n Your browser does not support the audio element.\n\n\nPDF Download\n\n\n\nThe patent WO2021060971A1 describes a method for augmenting face images, particularly for use in video surveillance systems. The invention addresses the limitations of traditional surveillance, where cameras capture face images from limited angles, leading to incomplete or substandard images. The method involves acquiring face images from both cameras and the internet, applying data augmentation techniques to increase the number of images, and using a Generative Adversarial Network (GAN to create additional face images. This process helps generate better-quality face images that can improve facial recognition systems.\n\nThe system includes several components, such as an image acquisition module connected to a camera, data input and augmentation modules, and a GAN module. The data augmentation modules apply transformations like rotations and flips to increase the variety of face images, while the GAN module generates new images based on trained models. A fuzzy logic module is also employed to evaluate the quality of the generated images, ensuring that only the best images are selected and stored for training a deep learning module...." },{ "id": "/notes/pubs/patents/facial-analysis/", "title": "Facial Analysis Advertisement WO2020141969A2", "url": "/notes/pubs/patents/facial-analysis/", "tags": [], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Facial Analysis Advertisement WO2020141969A2 — System for targeted digital advertising using real-time facial analysis and demographic detection. — https://www.pirahansiah.com/notes/pubs/patents/facial-analysis/\nSystem for targeted digital advertising using real-time facial analysis and demographic detection.\n\n\nLast updated: 2026-08-08. \n\ndate_modified: 2024-10-12\ntags: patent, facial-recognition, advertising, deep-learning]\nhashtags: “#patent #facialrecognition #advertising #deeplearning”\n—\n\nspotify\n\n\n \n Your browser does not support the audio element.\n\n\nPDF Download\n\n\n\n🎧 Listen and Explore 🗺️ the engaging audio discussion about the innovative patent “SYSTEM AND METHOD FOR PROVIDING ADVERTISEMENT CONTENTS BASED ON FACIAL ANALYSIS WO2020141969A2”!\n\nThis system leverages facial recognition technology to deliver personalized, dynamic ads tailored to users based on age, gender, emotions, and more—while prioritizing privacy! 🔒✨ Discover how AI is revolutionizing digital advertising by matching ads to user profiles without the need for direct personal data collection.\n\n🔗 Mindmap visualization available too! Uncover how this innovation is shaping the future of advertising. #AI #FacialRecognition #DigitalMarketing #Innovation #LLM\n\nSYSTEM AND METHOD FOR PROVIDING ADVERTISEMENT CONTENTS BASED ON FACIAL ANALYSIS WO2020141969A2\n\nIn this text delves into his patented innovation that transforms digital advertising by utilizing facial analysis to deliver targeted content. The system captures images of users through an embedded camera in digital sign..." },{ "id": "/notes/courses/fsdl-2022/", "title": "FSDL 2022 Notes", "url": "/notes/courses/fsdl-2022/", "tags": ["FSDL","deep-learning","MLOps","FarshidPirahansiah"], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n FSDL 2022 Notes — Notes from Full Stack Deep Learning 2022 covering ML lifecycle, data management, and continual learning. — https://www.pirahansiah.com/notes/courses/fsdl-2022/\nNotes from Full Stack Deep Learning 2022 covering ML lifecycle, data management, and continual learning.\n\n\nLast updated: 2026-08-08. \n\nFull Stack Deep Learning 2022\n\nLecture 01: When to Use ML and Course Vision\n\n Formulating problems and estimating project cost\n Sourcing, cleaning, processing, labeling data\n Picking the right framework and compute\n Troubleshooting training and ensuring reproducibility\n Deploying the model at scale\n Monitoring and continually improving\n\n\nLecture 02: Development Infrastructure & Tooling\n\nLecture 03: Testing\n\nLecture 04: Data Management\n\n Data sources: filesystem, object storage, database, data warehouse, data lake\n SQL and DataFrames (DASK, RAPIDS\n Airflow, Prefect, Dagster\n Feature stores: Tecton, Feast, Featureform\n Labeling: Label Studio, Diffgram, Snorkel.ai\n Data versioning: DVC\n\n\nLecture 06: Continual Learning\n\n Monitoring metrics: data quality, distribution drift\n System monitoring: Datadog, Honeycomb, NewRelic\n Data curation: random, stratified, active learning\n Retraining triggers and dataset formation\n Online testing: shadow mode, A/B testing\n\n\n#FSDL #DeepLearning #MLOps #FarshidPirahansiah\n" },{ "id": "/notes/courses/fsdl/", "title": "Full Stack Deep Learning", "url": "/notes/courses/fsdl/", "tags": ["FSDL","full-stack-deep-learning","FarshidPirahansiah"], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Full Stack Deep Learning — Full Stack Deep Learning course notes covering ML production from data to deployment. — https://www.pirahansiah.com/notes/courses/fsdl/\nFull Stack Deep Learning course notes covering ML production from data to deployment.\n\n\nLast updated: 2026-08-08. \n\nFull Stack Deep Learning\n\nFull Stack Deep Learning (fullstackdeeplearning.com — Notes for week 1 to week 12 (2021\n\nReference: https://fullstackdeeplearning.com/spring2021\n\nUnderfitting vs Overfitting\n\nUnderfitting (reducing bias:\n\n Bigger model\n Reduce regularization\n Error analysis\n Different model architecture\n Tune hyper-parameters\n Add features\n\n\nOverfitting (reducing variance:\n\n Add more training data\n Add normalization (batch norm, layer norm\n Add data augmentation\n Increase regularization (dropout, L2, weight decay\n Error analysis\n Choose different model architecture\n Tune hyper-parameters\n Early stopping\n Remove features\n Reduce model size\n\n\n#StackDeepLearning #computervision #AI #deeplearning\n" },{ "id": "/notes/pubs/journals/fuzzy-thresholding/", "title": "GSFT-PSNR: Fuzzy Threshold Method", "url": "/notes/pubs/journals/fuzzy-thresholding/", "tags": ["fuzzy-logic","thresholding","psnr","image-segmentation"], "hashtags": "#fuzzylogic #thresholding #psnr #imagesegmentation", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n GSFT-PSNR: Fuzzy Threshold Method — Fuzzy logic approach to global single thresholding using PSNR for image segmentation. — https://www.pirahansiah.com/notes/pubs/journals/fuzzy-thresholding/\nFuzzy logic approach to global single thresholding using PSNR for image segmentation.\n\n\nLast updated: 2026-08-08. \n\nGSFT-PSNR_Global_Single_Fuzzy_Threshold\n\nspotify\n\nPDF Download\n\n\n\nMind Map: GSFT-PSNR Global Single Fuzzy Threshold\n\n1. Introduction\n\n Thresholding Importance: Critical in OCR, image analysis, and camera calibration.\n Challenges: Handling varying lighting and environments.\n\n\n2. Key Contributions\n\n GSFT-PSNR Method: Combines fuzzy logic and PSNR for adaptive thresholding.\n \n Applications: OCR, license plate recognition, handwritten document processing.\n Strengths: Works well in varied lighting and complex environments.\n \n \n\n\n3. Materials and Methods\n3.1 One Level Thresholding\n\n Single Thresholding: Faster, effective for binary image conversion.\n Equation: Conversion to binary based on a threshold value.\n\n\n3.2 Multilevel Thresholding\n\n More Detailed: Captures more pixel values, but higher computational cost.\n Application: Best for grayscale images with complex scenes.\n\n\n3.3 Proposed Method (GSFT-PSNR\n\n Key Features:\n \n Uses PSNR as an indicator to determine the optimal threshold.\n Refined using fuzzy logic to adapt to lighting changes.\n \n \n Process:\n \n Calculate PSNR for a range of thresholds.\n Apply fuzzy logic to adjust based on the average intensity value (AIV.\n \n \n\n\n4. PSNR E..." },{ "id": "/game/", "title": "Games", "url": "/game/", "tags": [], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-16\n\n Games — Play classic arcade games — Snake, Flappy Bird & 2048 — right in your browser or as a Telegram Mini App — https://pirahansiah.com/game/\nPlay Snake, Flappy Bird and 2048 — classic arcade games in your browser or as a Telegram Mini App. High scores, haptics and a share button.\n\n\nLast updated: 2026-08-16. \n\n\n \n 🎮 Arcade\n Snake · Flappy Bird · 2048 — play right here, or open it inside Telegram as a Mini App.\n 🕐 Checking…\n \n\n \n 🐍 Snake\n 🐦 Flappy\n 🔢 2048\n \n\n \n Score 0\n Best 0\n ↻ Restart\n 🔗 Share\n \n\n \n \n \n \n \n Snake\n Steer with arrows, WASD, swipe or the pad below. Eat 🍎 to grow.\n Start\n \n \n\n \n \n \n \n Flappy Bird\n Tap, click or press Space / ↑ to flap. Fly through the gaps.\n Start\n \n \n\n \n \n \n \n \n 2048\n Swipe or use arrows to merge tiles. Reach the 2048 tile.\n Start\n \n \n \n \n\n \n \n ↑\n \n ←\n ●\n →\n \n ↓\n \n \n Arrows / WASD / swipe to steer.\n Tap or Space to flap.\n Swipe or arrows to slide & merge.\n \n \n\n\n\n\n\n\n" },{ "id": "/notes/pubs/papers/geometrical-topological/", "title": "Classification with Geometrical Features", "url": "/notes/pubs/papers/geometrical-topological/", "tags": ["classification","geometrical-features","pattern-recognition","machine-learning"], "hashtags": "#classification #geometricalfeatures #patternrecognition #ml", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Classification with Geometrical Features — Evaluation of classification techniques using enhanced geometrical and topological features. — https://www.pirahansiah.com/notes/pubs/papers/geometrical-topological/\nEvaluation of classification techniques using enhanced geometrical and topological features.\n\n\nLast updated: 2026-08-08. \n\nAn_evaluation_of_classification_techniques_using_enhanced_Geometrical_Topological_Feature_Analysis\n\nhttps://www.pirahansiah.com/notes/pubs/papers/An_evaluation_of_classification_techniques_using_enhanced_Geometrical_Topological_Feature_Analysis\n\nspotify\n\nPDF Download My Conference Paper\n\n\n\nAn Evaluation of Classification Techniques Using Enhanced Geometrical Topological Feature Analysis\n\n1. Introduction\n\n Objective: Evaluation of classification techniques for the Malaysian License Plate Recognition (LPR system.\n Applications of LPR:\n \n Law enforcement\n Border protection\n Vehicle theft detection\n Automatic toll collection\n Traffic control\n \n \n\n\n2. Image Classification Techniques\n\n Artificial Immune Recognition System (AIRS:\n \n Mimics biological immune systems for pattern recognition.\n \n \n Neural Networks (NN:\n \n Machine learning technique that models the human brain to classify images.\n \n \n Bayesian Networks (BN:\n \n Probabilistic graphical models to classify data based on probability distributions.\n \n \n Support Vector Machine (SVM:\n \n Uses geometric representations to classify data.\n \n \n\n\n3. Enhanced Geometrical Topological Featu..." },{ "id": "/notes/pubs/papers/global-feature-recognition/", "title": "Character Recognition via Global Features", "url": "/notes/pubs/papers/global-feature-recognition/", "tags": ["character-recognition","feature-extraction","glcm","ocr"], "hashtags": "#characterrecognition #featureextraction #glcm #ocr", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Character Recognition via Global Features — Hybrid GLCM and EDMS method for character recognition in OCR applications. — https://www.pirahansiah.com/notes/pubs/papers/global-feature-recognition/\nHybrid GLCM and EDMS method for character recognition in OCR applications.\n\n\nLast updated: 2026-08-08. \n\nCharacter_Recognition_Based_on_Global_Feature\n\nhttps://www.pirahansiah.com/notes/pubs/papers/Character_Recognition_Based_on_Global_Feature\n\nspotify\n\nPDF Download My Conference Paper\n\n\n\nCharacter Recognition Based on Global Feature Extraction\n\n1. Introduction\n\n Objective: Propose a combination of two feature extraction techniques for character recognition.\n Key Techniques:\n \n Gray Level Co-occurrence Matrix (GLCM\n Edge Direction Matrix (EDMS\n \n \n Challenges:\n \n Selecting the best feature extraction technique for various character recognition tasks.\n \n \n\n\n2. Feature Extraction Techniques\n\n Gray Level Co-occurrence Matrix (GLCM:\n \n Focuses on texture and pixel relationships in the image.\n Extracts texture features for character recognition.\n \n \n Edge Direction Matrix (EDMS:\n \n Emphasizes edges and directions in character images.\n Extracts shape features.\n \n \n\n\n3. Proposed Method\n\n Combination of GLCM and EDMS:\n \n A hybrid approach that leverages both texture and edge features.\n Aims to improve accuracy over using either GLCM or EDMS alone.\n \n \n\n\n4. Classification Techniques\n\n Classifiers Used:\n \n Neural Networks (NN\n Bayesian Networks (BN\n Deci..." },{ "id": "/go/", "title": "Share — Invite & Referral Links", "url": "/go/", "tags": ["share","referral","invite","links"], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-09-04\n\n Share — invite & referral links to pass along to friends — https://pirahansiah.com/go/\nShare these invite and referral links with your friends — OpenCode, Trade Republic, Scalable Capital, Wise, eToro and Kraken.\n\n\nLast updated: 2026-09-04. \n\n\n\n\n \n Share with Friends\n A few things worth passing along. Open any link, download the QR, or copy it and send it to a friend.\n \n\n \n\n \n OpenCode\n Low cost coding models for everyone.\n \n opencode.ai/go?ref=3KMNQG0CS4\n \n Open\n Download\n Copy\n \n \n\n \n Trade Republic\n The smartest way to invest, spend and bank. Create an account via the link to secure a welcome bonus.\n \n refnocode.trade.re/z28xmjp6\n \n Open\n Download\n Copy\n \n \n\n \n Scalable Capital\n Join me at Scalable, the best place to invest in your financial future. Open an account in a few minutes using my link and secure a start bonus of €25.\n \n de.scalable.capital/en/invitation/bj2bkn\n \n Open\n Download\n Copy\n \n \n\n \n Wise\n Send and receive money internationally with low fees. Join with my invite link.\n \n wise.com/invite/ilpn/farshidp1\n \n Open\n Download\n Copy\n \n \n\n \n eToro\n Hey! I'm trading with eToro — 1,000s of assets and awesome tools. Want to join me? Use my link to sign up.\n \n etoro.tw/3XGZzNz\n \n Open\n Download\n Copy\n \n \n\n \n Kraken\n Buy, sel..." },{ "id": "/notes/pubs/papers/grid-computing/", "title": "TafreshGrid: Grid Computing", "url": "/notes/pubs/papers/grid-computing/", "tags": ["grid-computing","distributed-systems","high-performance-computing"], "hashtags": "#gridcomputing #distributedsystems #hpc", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n TafreshGrid: Grid Computing — Grid computing infrastructure implementation and evaluation at Tafresh University. — https://www.pirahansiah.com/notes/pubs/papers/grid-computing/\nGrid computing infrastructure implementation and evaluation at Tafresh University.\n\n\nLast updated: 2026-08-08. \n\nTafreshGrid_Grid_computing_in_Tafresh_university\n\nhttps://www.pirahansiah.com/notes/pubs/papers/TafreshGrid_Grid_computing_in_Tafresh_university\n\nspotify\n\nPDF Download My Conference Paper\n\n\n\nTafreshGrid: Grid Computing in Tafresh University\n\n1. Introduction\n\n Objective: Introduce TafreshGrid as a grid computing project aimed at solving computational problems in multiple institutions.\n Grid Computing:\n \n Coordinates distributed computing resources.\n Makes a virtual single image of heterogeneous resources to solve complex problems.\n \n \n\n\n2. Project Overview\n\n Phases of TafreshGrid Project:\n \n Implementation: Setting up the grid infrastructure at Azad University, Tafresh branch.\n Development: Extending the project to all Azad University branches.\n Expansion: Connecting TafreshGrid to the national grid computing infrastructure.\n \n \n\n\n3. Pre-Requisites for Grid Computing\n\n Required Infrastructure:\n \n Networking infrastructure (wired/wireless.\n Distributed computational resources.\n Continuous resource-sharing requirements.\n \n \n Software Tools:\n \n Globus Toolkit for managing grid computing tasks.\n Additional tools like J2SDK, APACHE ANT, and Perl for system configuration.\n \n \n\n\n4..." },{ "id": "/notes/pkm/guide/", "title": "Guide to This Knowledge Base", "url": "/notes/pkm/guide/", "tags": [], "hashtags": "", "category": "pkm", "body": "last_modified_at: 2026-08-14\n\nA practical guide to navigating this personal knowledge base: how notes are organized, how to search, and how to use the knowledge graph and LLM search.\n\nLast updated: 2026-08-14. \nThis site is a personal knowledge base and engineering notebook maintained by Dr. Farshid Pirahansiah. It collects technical deep dives, course notes, publications, and practical guides on computer vision, deep learning, edge AI, and LLM engineering.\n\nHow the knowledge base is organized\n\n\n Docs — technical notes on computer vision, CUDA/GPU programming, LLMs, optimization, and developer tools.\n Courses — structured notes from courses and workshops on ML, MLOps, ROS, embedded AI, and more.\n Publications — papers, patents, journals, books, and keynotes authored or co-authored by the site owner.\n PKM — personal knowledge management pages, including an about page and curated link collections.\n\n\nHow to find things\n\n\n Use the Search page (magnifier icon in the navigation to find notes by keyword.\n Use the Graph page to explore how notes are linked.\n Use the WebGPU LLM page to ask questions about the content of this site in your browser.\n\n\nContributing\n\nThis knowledge base is a living document. Content is added and refined continuously, and corrections are welcome via the contact link in the footer.\n" },{ "id": "/notes/pubs/papers/handwritten-thresholding/", "title": "Thresholding for Handwritten Segmentation", "url": "/notes/pubs/papers/handwritten-thresholding/", "tags": ["thresholding","handwritten-recognition","image-segmentation","psnr"], "hashtags": "#thresholding #handwritten #imagesegmentation #psnr", "category": "hub", "body": "last_modified_at: 2026-08-14\n\nComparison of PSNR and Otsu thresholding for handwritten image segmentation.\n\nLast updated: 2026-08-14. \nComparison_single_thresholding_method_for_handwritten_images_segmentation\n\nhttps://www.pirahansiah.com/notes/pubs/papers/Comparison_single_thresholding_method_for_handwritten_images_segmentation\n\nspotify\n\nPDF Download My Conference Paper\n\n\n\nComparison Single Thresholding Method for Handwritten Images Segmentation\n\n1. Introduction\n\n Objective: Propose and compare single thresholding methods for handwritten image segmentation.\n Key Concepts:\n \n Thresholding separates objects from the background in images.\n Peak Signal-to-Noise Ratio (PSNR is used to measure image quality.\n \n \n Importance:\n \n Handwritten recognition has various applications in mobile devices and OCR systems.\n \n \n\n\n2. State of the Art\n\n Otsu’s Method:\n \n An unsupervised, nonparametric method for automatic threshold selection.\n Uses bounding boxes of fragments and calculates global thresholds by maximizing class variance.\n \n \n Thresholding Techniques:\n \n Single Thresholding: Applies a single threshold value to the entire image.\n Multi-Level Thresholding: Uses multiple threshold values for segmenting complex images.\n \n \n\n\n3. Proposed Method\n\n PSNR-Based Thresholding:\n \n Uses PSNR to determine the quality of image segmentation.\n Measures the effectiveness of separating objects (characters from the background in handwritten images.\n \n \n Advantages:\n \n Optimized for average-quality hand..." },{ "id": "/notes/tools/image-in-md/", "title": "test mindmap", "url": "/notes/tools/image-in-md/", "tags": [], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n test mindmap — test mindmap — https://www.pirahansiah.com/notes/tools/image-in-md/\nQuick mind-map test page for the pirahansiah.com markmap renderer.\n\n\nLast updated: 2026-08-08. \n\nreference\ntopic\nimage\n\n\n\n# inside mindmap test\n# level1\n- in a\n - in b\n - in c\n - in d\n - test 4\n\n\n\n\n\n\n\n# level a1\n## level a2\n### level a3\n\n" },{ "id": "/notes/docs/prompts/", "title": "Prompt Engineering Templates", "url": "/notes/docs/prompts/", "tags": ["prompt-engineering","llm","ai","nlp"], "hashtags": "#promptengineering #llm #ai #nlp", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Prompt Engineering Templates — Reusable prompt templates for text simplification, mind maps, and structured content processing. — https://www.pirahansiah.com/notes/docs/prompts/\nReusable prompt templates for text simplification, mind maps, and structured content processing.\n\n\nLast updated: 2026-08-08. \n\n\n \n a\nConsider yourself an expert software developer with extended knowledge about C++\n \n \n b\nFor any text I provide, please process it according to these specific guidelines:\n \n\n\nLanguage Requirements\n\n Convert all content to simple English at B1 level (suitable for intermediate English learners\n Use short, clear sentences with basic vocabulary\n Break complex ideas into digestible points\n Aim for maximum clarity and readability\n\n\nRequired Outputs (in this order\n\n Mind Map Visualization: Create a markdown-based mind map showing the key concepts and their relationships\n Ultra-Brief Summary: Provide a concise overview in under 200 characters\n Reorganized Full Text: Present the complete content in a better structured format while preserving all original information\n\n\nSpecific Modifications\n\n Use formatting (bold, italics, headings to enhance readability\n Add bullet points for lists and sequential information\n Insert subheadings to organize longer sections\n\n\nAdditional Guidelines\n\n Maintain academic integrity while simplifying language\n Keep the original meaning intact despite simplification\n Add clarifying notes for culturally-specific concepts\n\n" },{ "id": "/notes/docs/optimization/", "title": "CV, DL & ML Optimization Guide", "url": "/notes/docs/optimization/", "tags": ["optimization","deep-learning","machine-learning","computer-vision"], "hashtags": "#optimization #deeplearning #machinelearning #cv", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n CV, DL & ML Optimization Guide — Comprehensive guide to optimizing deep learning models, CV systems, and data pipelines. — https://www.pirahansiah.com/notes/docs/optimization/\nComprehensive guide to optimizing deep learning models, CV systems, and data pipelines.\n\n\nLast updated: 2026-08-08. \n\nOptimization\n\nDL\n\n1. Model Optimization\n\n Quantization\n \n Convert to lower precision (INT8, FP16\n \n \n Pruning\n \n Remove unnecessary weights or layers\n \n \n Knowledge Distillation\n \n Use smaller “student” models for efficiency\n \n \n\n\n2. Hardware Utilization\n\n GPU/TPU Acceleration\n \n Fully utilize GPUs or TPUs\n Parallelize across multiple devices\n \n \n CUDA and cuDNN\n \n Optimize using CUDA and cuDNN libraries\n \n \n\n\n3. Efficient Data Loading\n\n Multi-threaded Data Loading\n \n Use PyTorch’s DataLoader\n \n \n Real-time Data Augmentation\n \n Perform on-the-fly augmentations\n \n \n\n\n4. Batch Size Tuning\n\n Increase Batch Size\n \n Improves throughput, balancing memory usage\n \n \n\n\n5. Algorithmic Improvements\n\n Early Stopping\n \n Stop training early when performance stabilizes\n \n \n Gradient Checkpointing\n \n Recompute intermediate activations to save memory\n \n \n\n\n6. Efficient Architectures\n\n MobileNet, EfficientNet, ResNet\n \n Architectures optimized for speed and performance\n \n \n\n\n7. Parallelization & Distributed Training\n\n Distributed Training\n \n Spread training across multiple machines\n \n \n\n\n8. Inference O..." },{ "id": "/notes/docs/cv/", "title": "Computer Vision", "url": "/notes/docs/cv/", "tags": ["computer-vision","3d","optical-flow"], "hashtags": "#cv #3d #opticalflow", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Computer Vision — Computer vision topics: 3D vision, optical flow, multi-camera systems. — https://www.pirahansiah.com/notes/docs/cv/\nComputer vision topics: 3D vision, optical flow, multi-camera systems.\n\n\nLast updated: 2026-08-08. \n\nComputer Vision\n\nGuides and tutorials on computer vision topics.\n\nTopics\n\n\n 3D Vision & Multi-Camera — Stereo vision, depth estimation, synchronized multi-camera pipelines.\n Optical Flow — Dense and sparse optical flow algorithms for motion analysis.\n Multi-Camera Systems — Scaling from 2 to 100+ cameras with GStreamer and GPU acceleration.\n\n" },{ "id": "/notes/docs/projects/solutions/", "title": "Solutions", "url": "/notes/docs/projects/solutions/", "tags": ["portfolio","computer-vision","ai","projects"], "hashtags": "#portfolio #cv #ai #projects", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Solutions — Showcase of industry applications, portfolio projects, and technical skills in computer vision and AI. — https://www.pirahansiah.com/notes/docs/projects/solutions/\nShowcase of industry applications, portfolio projects, and technical skills in computer vision and AI.\n\n\nLast updated: 2026-08-08. \n\nImpact Portfolio\n" },{ "id": "/notes/docs/llm/", "title": "AI & LLMs", "url": "/notes/docs/llm/", "tags": ["ai","llm","rag","agents"], "hashtags": "#ai #llm #rag #agents", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n AI & LLMs — Articles on LLMs, RAG, multi-agent systems, and local AI integration. — https://www.pirahansiah.com/notes/docs/llm/\nArticles on LLMs, RAG, multi-agent systems, and local AI integration.\n\n\nLast updated: 2026-08-08. \n\nAI & LLMs\n\nArticles on large language models, retrieval-augmented generation, and AI agents for computer vision workflows.\n\nArticles\n\n\n Advanced LLM Concepts — Transformer architecture, attention mechanisms, scaling laws.\n Orchestrating AI Agents — Multi-agent workflows, tool use, RAG pipelines.\n Blog: AI & LLMs — RAG vs CAG, multi-agent architectures, AI’s impact.\n Avatar Generator — Local video avatar with Ollama + Wav2Lip.\n\n" },{ "id": "/notes/docs/seo/", "title": "SEO for LLM-Powered Search", "url": "/notes/docs/seo/", "tags": ["seo","llm","ai","web-optimization"], "hashtags": "#seo #llm #ai #weboptimization", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n SEO for LLM-Powered Search — How to optimize websites for LLM-powered search engines using structured data and AI-friendly content. — https://www.pirahansiah.com/notes/docs/seo/\nHow to optimize websites for LLM-powered search engines using structured data and AI-friendly content.\n\n\nLast updated: 2026-08-08. \n\nThe New Era of SEO: Optimizing Websites for LLMs\n\nIn today’s rapidly evolving digital landscape, search engine optimization (SEO is undergoing a fundamental transformation with the rise of Large Language Models (LLMs like ChatGPT, Claude, and Gemini. These sophisticated AI systems are now integrated into search experiences, creating a new frontier for website visibility and discoverability.\n\nHow LLMs Are Changing SEO\n\nLLMs don’t just understand keywords—they comprehend context, semantics, and user intent at unprecedented levels. This shift requires website owners to adapt their SEO strategies beyond traditional keyword stuffing and backlink building.\n\nKey Strategies for LLM-Optimized SEO\n\n1. Structured Data & Machine-Readable Content\n\nLLMs thrive on well-organized, structured information. Implementing JSON-LD schema markup (FAQ, HowTo, Article, Product helps these models understand and extract information from your content efficiently. For example:\n\n{\n \"@context\": \"https://schema.org\",\n \"@type\": \"FAQPage\",\n \"mainEntity\": \n {\n \"@type\": \"Question\",\n \"name\": \"How do I optimize my website for AI search?\",\n \"acceptedAnswer\": {\n \"@type\": \"Answer\",\n \"text\": \"To optimize for AI search, use structured da..." },{ "id": "/notes/docs/python/", "title": "Python Configuration & C++ Integration", "url": "/notes/docs/python/", "tags": ["python","cpp","configuration","development"], "hashtags": "#python #cpp #configuration #devtools", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Python Configuration & C++ Integration — Python configuration methods comparison plus pybind11 and Cython tips for C++ integration. — https://www.pirahansiah.com/notes/docs/python/\nPython configuration methods comparison plus pybind11 and Cython tips for C++ integration.\n\n\nLast updated: 2026-08-08. \n\nPython\nPython\nA comparison of built-in and third-party configuration options for Python projects\nThis guide compares Python’s native configuration methods and popular third-party libraries, helping developers choose the best fit for their project’s needs.\n#Python #DevTools #Configuration #ConfigFiles #PythonTips #OpenSource #MachineLearning #WebDev #FastAP\n\n\n \n Python Configuration Management\n\n \n\n Python Configuration Management\n \n \n Tips and tricks python scale up projects\n\n Ttips\n \n\n\nPython Configuration Management\n\nA comparison of built-in and third-party configuration options for Python projects\n\n📌 Summary\n\nThis guide compares Python’s native configuration methods and popular third-party libraries, helping developers choose the best fit for their project’s needs.\n\n⸻\n\n🔧 Built-in Configuration Tools\n\nConfigParser (INI Files\n\t•\t📄 Simple structured text files\n\t•\t✅ Built-in\n\t•\t❗ Limitations: String-only, no nesting\n\nargparse (Command Line Arguments\n\t•\t🧰 Used in CLI tools\n\t•\t✅ Built-in\n\t•\t📝 Supports help text, types, defaults\n\nEnvironment Variables (os.environ\n\t•\t🔒 Ideal for secrets and deployment\n\t•\t✅ Built-in\n\t•\t❗ Flat and string-only\n\nPython Module as Config\n\t•\t🐍 Python file for configuration\n\t•\t✅ Built-in\n\t•\t🚀 Full fl..." },{ "id": "/notes/docs/", "title": "Content Hub", "url": "/notes/docs/", "tags": ["computer-vision","ai","llm","edge-ai"], "hashtags": "#cv #ai #llm #edgeai", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Content Hub — Technical articles and tutorials on computer vision, AI, edge deployment, and software engineering. — https://www.pirahansiah.com/notes/docs/\nTechnical articles and tutorials on computer vision, AI, edge deployment, and software engineering.\n\n\nLast updated: 2026-08-08. \n\nContent Hub\n\nTechnical articles, tutorials, and deep dives on computer vision, AI, edge deployment, and software engineering.\n\nComputer Vision\n\n 3D Vision & Multi-Camera Systems — Stereo vision, depth estimation, and synchronized multi-camera pipelines.\n Optical Flow — Dense and sparse optical flow algorithms for motion analysis.\n Multi-Camera Systems — Scaling from 2 to 100+ cameras with GStreamer and GPU acceleration.\n CV Coaching Roadmap — Structured learning path from fundamentals to production systems.\n Computer Vision Overview — CV overview and research.\n\n\nCUDA & GPU Computing\n\n Numba JIT Compilation — Accelerate Python loops with just-in-time compilation.\n PyCUDA Kernels — Write custom CUDA kernels from Python.\n CUDA in VS Code — Dev environment setup on Windows.\n MLX, CoreML & Metal — Apple Silicon ML frameworks.\n\n\nAI & LLMs\n\n Advanced LLM Concepts — Transformer architecture, attention mechanisms, and scaling laws.\n Orchestrating AI Agents — Multi-agent workflows, tool use, and RAG pipelines.\n Blog: AI & LLMs — RAG vs CAG, multi-agent architectures, AI’s impact.\n Avatar Generator — Local video avatar with Ollama + Wav2Lip.\n\n\nOptimization\n\n CV/DL/ML Optimization — Model quantization, pruning, and edge deployment strat..." },{ "id": "/notes/pubs/keynotes/", "title": "Keynotes", "url": "/notes/pubs/keynotes/", "tags": ["keynote","ai","computer-vision","multimodal"], "hashtags": "#keynote #ai #computervision #multimodal", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Keynotes — Technical keynotes on computer vision, LLMs, and multimodal AI. — https://www.pirahansiah.com/notes/pubs/keynotes/\nTechnical keynotes on computer vision, LLMs, and multimodal AI.\n\n\nLast updated: 2026-08-08. \n\nKeynotes\n\nTechnical keynotes and presentations by Dr. Farshid Pirahansiah.\n\n\n\nLLMs Meet Computer Vision\n\nExploring the convergence of Large Language Models and Computer Vision. Covers token economics, multimodal AI, RAG systems, and practical applications in document analysis, video understanding, and IoT/robotics.\n" },{ "id": "/notes/pubs/books/", "title": "Books & Book Chapters", "url": "/notes/pubs/books/", "tags": ["book","opencv","computer-vision","ai"], "hashtags": "#book #opencv #computervision #ai", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Books & Book Chapters — Books and book chapters on computer vision, camera calibration, and AI. — https://www.pirahansiah.com/notes/pubs/books/\nBooks and book chapters on computer vision, camera calibration, and AI.\n\n\nLast updated: 2026-08-08. \n\nBooks & Book Chapters\n\nBooks and book chapters authored or co-authored by Dr. Farshid Pirahansiah.\n\n\n\nSpringer Book Chapters\n\nCamera Calibration and Video Stabilization Framework for Robot Localization\nCamera calibration framework for robot localization in “Control Engineering in Robotics and Industrial Automation” (Springer.\n!camera-calibration-video-stabilization.png]]\nComputational Intelligence: Augmented Optical Flow for Video Stabilization\nOptical flow methods for video stabilization in “Computational Intelligence: From Theory to Application”.\n\n\ndrafts\n\nOpenCV 5 Ebook\n\nComputer Vision Meets LLM\nExploring the intersection of computer vision and large language models.\n\nOpenCV 5 — Chapter 1: Image Basics\nImage processing fundamentals: pixel operations, color spaces, histograms.\n\nOpenCV 5 — Chapter 2: Feature Detection\nFeature detection methods: SIFT, SURF, ORB, keypoint matching.\n\nOpenCV 5 — Chapter 3: Advanced Topics\nAdvanced computer vision techniques using OpenCV 5.\n\nLIST \nFROM #]]\n\n\n\n" },{ "id": "/notes/pubs/papers/", "title": "Conference Papers", "url": "/notes/pubs/papers/", "tags": ["research","paper","image-processing","computer-vision"], "hashtags": "#research #cv #papers #imageprocessing", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Conference Papers — Peer-reviewed conference papers on image processing, camera calibration, and pattern recognition. — https://www.pirahansiah.com/notes/pubs/papers/\nPeer-reviewed conference papers on image processing, camera calibration, and pattern recognition.\n\n\nLast updated: 2026-08-08. \n\nConference Papers\n\nAll peer-reviewed conference papers by Dr. Farshid Pirahansiah.\n\n\n\nImage Processing & Thresholding\n\nAdaptive Image Segmentation Based on PSNR for License Plate Recognition\nAdaptive thresholding using PSNR for license plate recognition systems.\n\nLicense Plate Recognition with Multi-Threshold Based on Entropy\nEntropy-based multi-threshold approach for license plate detection.\n\nMulti-threshold Approach for License Plate Recognition System\nMulti-level thresholding with adaptive MLP for license plate recognition.\n\nComparison of Single Thresholding Method for Handwritten Images Segmentation\nComparison of Otsu and PSNR-based thresholding for handwritten image segmentation.\n\n\n\nCamera Calibration & 3D Vision\n\nCamera Calibration for Multi-Modal Robot Vision\nAutomatic calibration framework for multi-modal robot vision systems.\n\nPattern Image Significance for Camera Calibration\nEvaluation of calibration pattern significance for robot vision.\n\n2D versus 3D Map for Environment Movement Objects\nComparison of 2D and 3D mapping for environment movement detection.\n\n\n\nPattern Recognition & Classification\n\nCharacter Recognition Based on Global Feature\nGLCM and EDMS-based hybrid method for character recognition.\n\nClassification Techniques ..." },{ "id": "/notes/pubs/journals/", "title": "Journal Publications", "url": "/notes/pubs/journals/", "tags": ["journal","image-processing","thresholding","pattern-recognition"], "hashtags": "#research #cv #journals #thresholding", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Journal Publications — Peer-reviewed journal articles on image processing, pattern recognition, and robotics. — https://www.pirahansiah.com/notes/pubs/journals/\nPeer-reviewed journal articles on image processing, pattern recognition, and robotics.\n\n\nLast updated: 2026-08-08. \n\nJournal Publications\n\nAll peer-reviewed journal articles by Dr. Farshid Pirahansiah.\n\n\n\nImage Processing & Thresholding\n\nAdaptive Image Thresholding Based on the Peak Signal-to-Noise Ratio\nPSNR-based adaptive thresholding for image segmentation.\n\nGSFT-PSNR: Global Single Fuzzy Threshold\nFuzzy logic approach to global single thresholding using PSNR.\n\nPeak Signal-to-Noise Ratio Based on Threshold Method for Image Segmentation\nPSNR thresholding method for image segmentation applications.\n\n\n\nPattern Recognition & Feature Extraction\n\nCharacter and Object Recognition Based on Global Feature Extraction\nGlobal feature extraction methods for character and object recognition.\n\n\n\nRobotics & 3D Vision\n\n3D SLAM: Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages\nSurvey of 3D SLAM methods and humanoid robot linkage systems.\n\n\n\nOptimization Algorithms\n\nUsing an Ant Colony Optimization Algorithm for Image Processing\nACO-based optimization for image processing tasks.\n" },{ "id": "/notes/pubs/patents/", "title": "Patents", "url": "/notes/pubs/patents/", "tags": ["patent","face-detection","vehicle-detection","augmented-reality"], "hashtags": "#patent #facedetection #vehicledetection #augmentedreality", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Patents — AI and computer vision patents covering face augmentation, vehicle detection, and facial analysis. — https://www.pirahansiah.com/notes/pubs/patents/\nAI and computer vision patents covering face augmentation, vehicle detection, and facial analysis.\n\n\nLast updated: 2026-08-08. \n\nPatents\n\nAI and Computer Vision patents by Dr. Farshid Pirahansiah.\n\n\n\nFace Image Augmentation\n\nA Method for Augmenting a Plurality of Face Images — WO 2021/060971 A1\nGAN-based generation of realistic face images from surveillance video with fuzzy logic quality filtering.\n\n\n\nVehicle Detection\n\nA Method for Detecting a Moving Vehicle — WO 2021/107761 A1\nAdvanced image processing for vehicle detection using illumination enhancement, Sobel edge detection, and geometric noise filtering.\n\n\n\nFacial Analysis & Advertising\n\nSystem and Method for Providing Advertisement Contents Based on Facial Analysis — WO 2020/141969 A2\nFacial recognition system that adjusts digital advertisements based on user demographics and emotions without collecting personal data.\n" },{ "id": "/notes/courses/", "title": "Topics & Projects", "url": "/notes/courses/", "tags": ["AI","OpenCV","camera-calibration","DRL","FarshidPirahansiah"], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Topics & Projects — Topics and projects covering AI, OpenCV, camera calibration, deep reinforcement learning, and more. — https://www.pirahansiah.com/notes/courses/\nTopics and projects covering AI, OpenCV, camera calibration, deep reinforcement learning, and more.\n\n\nLast updated: 2026-08-08. \n\nTopics & Projects\n\nAI & Machine Learning\n\n Advanced LLM Concepts — RAG, embeddings, multimodal\n Orchestrating AI Agents — Multi-agent systems\n AI Hardware Accelerators — NPU, edge AI chips\n OpenVINO — Intel inference optimization\n Machine Learning Specialization — ML fundamentals\n Full Stack Deep Learning — End-to-end DL\n MLOps — ML engineering for production\n\n\nComputer Vision\n\n 3D Vision & Multi-Camera — Point clouds, depth sensing\n Optical Flow — Motion estimation\n Multi-Camera Systems — Scaling to 100+ cameras\n CV Coaching Roadmap — Learning path\n\n\nGetting Started\n\n Machine Learning Specialization — ML fundamentals\n Full Stack Deep Learning — End-to-end DL\n Developer Tools & Setup — Docker, GitHub, CLI tools\n C++ Quick Reference — Memory, STL, debugging\n\n\nCUDA & GPU\n\n Numba JIT Tutorial — Python performance\n PyCUDA Kernels — CUDA from Python\n CUDA in VS Code — Dev environment\n MLX, CoreML & Metal — Apple Silicon ML\n\n\nCourses & Workshops\n\n FSDL 2022 — Continual learning, data management\n MLOps — ML engineering for production\n ROS — Robot Operating System\n Parallel Programming — Python parallel CV\n Modern C++ — C++29 for image processing\n Cloud-Native — Docker & Kubernetes\n TF Deployment..." },{ "id": "/notes/", "title": "Knowledge Base — Notes, Docs & Publications", "url": "/notes/", "tags": [], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-14\n\nThe knowledge base behind pirahansiah.com: technical notes on computer vision, edge AI, CUDA, LLMs, courses, and publications by Dr. Farshid Pirahansiah.\n\nLast updated: 2026-08-14. \nPKM\n\nThis is the knowledge base of Dr. Farshid Pirahansiah: a living collection of technical notes on computer vision, embedded and edge AI, GPU programming with CUDA, LLM engineering, and the courses and publications behind his research and consulting work.\n\nWhat you will find here\n\n\n Technical docs — practical notes on optical flow, multi-camera systems, model optimization and quantization, CUDA/Numba/PyCUDA, and LLM orchestration.\n Courses — structured notes from ML, MLOps, ROS, parallel programming, cloud-native, and embedded AI courses.\n Publications — papers, patents, journal articles, books, and keynotes, each with implementation details and links.\n PKM & guides — an about page, curated links, and a guide to navigating this knowledge base.\n\n\nWays to explore\n\n\n Search — use the search page to find notes by keyword.\n Graph — explore the knowledge graph of linked notes.\n WebGPU LLM — ask questions about the site content directly in your browser.\n\n\nMaintained with Obsidian, published with Jekyll on GitHub Pages. Last updated July 2026.\n" },{ "id": "/", "title": "Dr. Farshid Pirahansiah ; Embedded Computer Vision & Edge AI", "url": "/", "tags": [], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-16\n\nDr. Farshid Pirahansiah — Embedded Computer Vision & Edge AI Engineer with 12+ years in real-time image processing, robotics and edge AI deployment. Computer vision (C++, deep learning (Python, model optimization, LLM integration.\n\nLast updated: 2026-08-16. \n\n\n\n \n \n Dr. Farshid Pirahansiah\n \n Embedded Computer Vision & Edge AI at Scale — I help organizations move computer vision from research into production-ready edge applications.\n From model optimization and real-time inference to multi-camera deployment on NVIDIA Jetson, Raspberry Pi, Hailo, and Axelera platforms.\n \n \n I offer AI mentoring in Germany — book a complimentary first session.\n \n \n \n Let's Talk on LinkedIn\n \n\n\n\n 12+Years of Experience\n 3Patents\n 17+Publications\n 142Citations\n\n\n\n\n \n 👁\n Embedded Computer Vision\n Real-time object detection, tracking, and classification on edge devices. From prototype to production deployment on Jetson, Coral, Hailo, and Axelera accelerators.\n \n OpenCV\n YOLO\n TensorRT\n ONNX\n \n \n\n \n 📈\n Model Optimization & Quantization\n INT8/FP16 quantization, pruning, and knowledge distillation. Reduce model size by 4–10× while maintaining accuracy for real-time inference on resource-constrained hardware.\n \n TensorRT\n OpenVINO\n QDQ\n Pruning\n \n \n\n \n 🎥\n Multi-Camera Systems\n Synchronized multi-camera setups for 3D reconstruction, stereo vision, and surveillance. scaling from 2 to 100+ cameras wit..." },{ "id": "/notes/courses/iot-scholarship/", "title": "IoT Scholarship: Edge AI with OpenVINO", "url": "/notes/courses/iot-scholarship/", "tags": ["IoT","OpenVINO","edge-AI","FarshidPirahansiah"], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n IoT Scholarship: Edge AI with OpenVINO — IoT and Edge AI with OpenVINO: face recognition, object detection, pose estimation, and semantic segmentation. — https://www.pirahansiah.com/notes/courses/iot-scholarship/\nIoT and Edge AI with OpenVINO: face recognition, object detection, pose estimation, and semantic segmentation.\n\n\nLast updated: 2026-08-08. \n\nIoT Scholarship Foundation\n\nEdge AI with Intel OpenVINO\n\nKey Models\n\n Face recognition: OpenCV 4.1.1\n Image classification: Deep learning with high accuracy\n Object detection: MobileNet SSD (~5 FPS\n Pose estimation: Very fast and accurate\n Coral TPU: Good performance with modifications\n Intel Movidius Stick 2: Good with OpenCV and Python\n\n\nOpenVINO Pipeline\n\n Pre-trained models from Open Model Zoo\n Model Optimizer: TF/PyTorch/Caffe → IR format\n Inference Engine: Run optimized IR models\n Edge deployment: Input streams, MQTT, web serving\n\n\nModel Types\n\n Classification (yes/no, 1000 classes, 20K ImageNet\n Detection (bounding boxes + classification\n Segmentation (semantic: all same class; instance: separate objects\n Pose estimation\n Text recognition\n GANs\n\n\n#IoT #OpenVINO #EdgeAI #FarshidPirahansiah\n" },{ "id": "/notes/courses/iot/", "title": "Embedded IoT", "url": "/notes/courses/iot/", "tags": ["IoT","embedded","edge-AI","FarshidPirahansiah"], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Embedded IoT — Workshop on embedded IoT systems and edge computing applications. — https://www.pirahansiah.com/notes/courses/iot/\nWorkshop on embedded IoT systems and edge computing applications.\n\n\nLast updated: 2026-08-08. \n\nEmbedded IoT\n\nWorkshop on embedded IoT systems and edge computing applications.\n\n#IoT #Embedded #FarshidPirahansiah\n" },{ "id": "/notes/pubs/papers/license-plate/", "title": "Entropy-Based Multi-Threshold LPR", "url": "/notes/pubs/papers/license-plate/", "tags": ["license-plate-recognition","entropy","thresholding","image-segmentation"], "hashtags": "#lpr #entropy #thresholding #imagesegmentation", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Entropy-Based Multi-Threshold LPR — Multi-threshold license plate recognition using entropy-based image segmentation. — https://www.pirahansiah.com/notes/pubs/papers/license-plate/\nMulti-threshold license plate recognition using entropy-based image segmentation.\n\n\nLast updated: 2026-08-08. \n\nLicense_Plate_Recognition_with_Multi-Threshold_Based_on_Entropy\n\nhttps://www.pirahansiah.com/notes/pubs/papers/License_Plate_Recognition_with_Multi-Threshold_Based_on_Entropy\n\nspotify\n\nPDF Download My Conference Paper\n\n\n\nLicense Plate Recognition with Multi-threshold based on Entropy\n\n1. Introduction\n\n Objective: Propose a multi-thresholding method for license plate recognition.\n Importance of Thresholding:\n \n Simplifies image segmentation\n Ensures robustness and accuracy in recognizing license plate characters.\n \n \n Challenges:\n \n Selecting the correct threshold values for better segmentation results.\n \n \n\n\n2. Entropy-based Thresholding\n\n Method:\n \n Based on maximizing the cross entropy between the original image and the segmented image.\n Entropy is treated as a probability distribution of the image histogram.\n \n \n Historical Background:\n \n Originally proposed by Pun and later improved by Kapur for image segmentation.\n Entropy-based thresholding is widely used for bi-level and multi-level thresholding.\n \n \n\n\n3. Proposed Method\n\n Multi-thresholding Based on Maximum Entropy:\n \n Selects several threshold values by maximizing the entropy.\n Integrates partial ranges of the i..." },{ "id": "/notes/docs/linkedin/", "title": "Top LinkedIn Posts 2024", "url": "/notes/docs/linkedin/", "tags": ["linkedin","camera-calibration","cpp","robotics"], "hashtags": "#linkedin #cameracalibration #cpp #robotics", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Top LinkedIn Posts 2024 — Highlights from 2024 LinkedIn posts covering camera calibration, C++, NVIDIA optimization, and robotics. — https://www.pirahansiah.com/notes/docs/linkedin/\nHighlights from 2024 LinkedIn posts covering camera calibration, C++, NVIDIA optimization, and robotics.\n\n\nLast updated: 2026-08-08. \n\nOver the past year, my standout posts have featured camera calibration, Python, C++, OpenCV, NVIDIA optimizations, advanced C++ techniques, AI integration in robotics with LLMs, computer vision, and machine learning. Explore more: \nLink\n\nMy Top LinkedIn Posts from the Past Year\n\n\n Camera Calibration\n Camera Calibration\n Optimization Methods Computer Vision\n multi-GPU \n NVIDIA Jetson\n On-Device LLM\n On-Device LLM\n C++ \n C++ \n C++\n My experience Robotics 3D SLAM LLM Vision Multimodal\n\n\n1. Camera Calibration and Subpixel Precision\n\nCamera Calibration Post\n\nIn this post, I delved into advanced methods of camera calibration, focusing on geometric analysis and calibration patterns using tools like MATLAB, Python, and OpenCV. I highlighted a C++ algorithm implemented for high-speed, high-accuracy corner detection within calibration patterns, emphasizing rotation and orientation. The process was further refined using subpixel accuracy and noise reduction techniques, enhancing precision in computer vision applications.\n\n\n\n2. Optimization Methods in Computer Vision\n\nOptimization Methods Post\n\nOptimizing NVIDIA GPUs for deep learning has been a crucial topic, especially in multi-GPU setups. In this post, I explored how leveragi..." },{ "id": "/notes/pkm/links/", "title": "Curated Links & Resources", "url": "/notes/pkm/links/", "tags": ["curated-links","ai-tools","resources"], "hashtags": "#curatedlinks #aitools #resources", "category": "pkm", "body": "last_modified_at: 2026-08-08\n\n Curated Links & Resources — Collection of AI, CV, ML, and software development tools and tutorials — https://www.pirahansiah.com/notes/pkm/links/\nCollection of AI, CV, ML, and software development tools and tutorials\n\n\nLast updated: 2026-08-08. \n\nLink Collection\n\n\n \n \n Category\n Title\n Link\n Short Summary\n \n \n \n \n AI Agents\n Android Agent\n GitHub\n Open source Android AI agent.\n \n \n Audio\n SendBySound\n Website\n Data transfer through sound.\n \n \n GitHub\n bstnxbt\n GitHub\n Developer repositories.\n \n \n Python\n pyptx Docs\n Docs\n Installation guide for pyptx.\n \n \n AI Research\n Yann Dubs Repositories\n GitHub\n AI and ML projects.\n \n \n Computer Vision\n Ultralytics Hand Keypoints\n Docs\n Hand pose dataset documentation.\n \n \n AI Agents\n Tavily + OpenClaw\n Article\n AI agent tooling article.\n \n \n macOS\n CornerTasks\n GitHub\n Productivity utility for macOS.\n \n \n 3D\n MakeHuman Unity Export\n Website\n Exporting 3D models to Unity.\n \n \n Software Architecture\n Vertical Slice Architecture\n Article\n Software architecture pattern.\n \n \n AI Learning\n Andrej Karpathy Skills\n GitHub\n AI learning material collection.\n \n \n AI Agents\n Devin DeepWiki\n Docs\n Devin documentation.\n \n \n AI Agents\n Context Engineering Skills\n..." },{ "id": "/notes/docs/links/", "title": "Curated Links & Resources", "url": "/notes/docs/links/", "tags": ["resources","tools","tutorials","references"], "hashtags": "#resources #tools #tutorials #references", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Curated Links & Resources — Hand-picked tools, tutorials, and references for CV, AI, and software engineering. — https://www.pirahansiah.com/notes/docs/links/\nHand-picked tools, tutorials, and references for CV, AI, and software engineering.\n\n\nLast updated: 2026-08-08. \n\nCurated Links & Resources\n\nHand-picked tools, tutorials, and references for computer vision, AI, and software engineering.\n\nComputer Vision\n\n Vulkan C++ Qt Image Viewer — Image viewer built with Qt6, C++, and Vulkan for real-time rendering.\n\n\nCUDA & GPU\n\n CUDA Python Introduction — Getting started with CUDA from Python.\n CUDA Programming Guide (PDF — Official NVIDIA documentation.\n GPU Architecture Overview — Cornell’s GPU computing resource.\n CUDA Flash Attention Algorithms — Implementation of flash attention in CUDA.\n LeetGPU — GPU programming practice platform.\n CUDA YouTube Playlist — Video tutorials on CUDA programming.\n\n\nPython\n\n Pydoll — Browser automation without WebDriver.\n Hyperdiv — Python GUI framework.\n Python Multithreading Tutorial — Concurrent programming in Python.\n Python Data Structures — Essential data structures explained.\n\n\nAI & LLMs\n\n Multimodal RAG with Granite — IBM’s notebook on building RAG systems.\n MCP Server & Client Webinar — Building Model Context Protocol servers.\n AdaptiveCpp GPU Programming — 68x faster C++ GPU programming.\n\n\nFPGA\n\n Introduction to FPGA Part 11 — RISC-V Softcore Processor — Digi-Key’s FPGA tutorial series.\n\n\nStartup & Business\n\n How to Start a Startup (21 videos — Y Combinator’s st..." },{ "id": "/notes/docs/llm/llm-concepts/", "title": "Advanced LLM Concepts", "url": "/notes/docs/llm/llm-concepts/", "tags": ["ai","llm","rag","multimodal"], "hashtags": "#ai #llm #rag #multimodal", "category": "hub", "body": "last_modified_at: 2026-08-08\n\n Advanced LLM Concepts — Comprehensive mind map covering advanced LLM concepts including RAG, embeddings, multimodal models, and production deployment. — https://www.pirahansiah.com/notes/docs/llm/llm-concepts/\nComprehensive mind map covering advanced LLM concepts including RAG, embeddings, multimodal models, and production deployment.\n\n\nLast updated: 2026-08-08. \n\n\n\nMind Map: Orchestrating Agents & Advanced LLM Concepts\n\n1. Introduction\n\n Main Concept: Coordination of multiple AI agents for complex tasks, enhanced by Large Language Models (LLMs and multimodal technologies.\n Goals: Solve tasks beyond a single agent’s capability, using agents and LLMs in harmony.\n Key Technologies: LLMs, multimodal models (vision + text, task orchestration.\n\n\n2. Core Components of Agent Systems\n\n2.1 Agents\n\n Definition: Autonomous entities carrying out specific functions.\n Types:\n \n Single-purpose: Designed for specific tasks.\n General-purpose: Flexible agents capable of performing various tasks.\n \n \n Capabilities:\n \n Interaction with environments.\n Processing inputs and generating outputs.\n Self-contained decision-making.\n \n \n\n\n2.2 Orchestrator\n\n Definition: Central controller managing multiple agents and interacting with LLMs.\n Roles:\n \n Delegates tasks to agents.\n Monitors progress.\n Facilitates communication between agents and LLMs.\n Combines results for task completion.\n \n \n\n\n2.3 Communication Between Agents and LLMs\n\n Importance: Efficient communication enables effective ag..." },{ "id": "/notes/pubs/keynotes/llm-cv/", "title": "LLMs Meet Computer Vision", "url": "/notes/pubs/keynotes/llm-cv/", "tags": ["llm","computer-vision","multimodal","ai"], "hashtags": "#llm #cv #multimodal #ai", "category": "hub", "body": "last_modified_at: 2026-08-14\n\nKeynote on token economics, multimodal RAG, and practical CV+LLM applications.\n\nLast updated: 2026-08-14. \nFarshid Pirahansiah\n\ncoursera video RAG\n\nOllama + Vision\n\nWhere LLMs Meet Computer Vision: Unlocking Multimodal AI\n\nAI models like GPT-4 Turbo, Google Gemini 1.5 Pro, and Claude 3 Opus offer advanced multimodal processing, each excelling in specific tasks from text generation to image analysis. Their pricing varies based on token usage, making it essential to choose the model that best fits your project’s needs.\n\nAI Model Cost Calculator: Optimizing Costs for Computer Vision and Multimodal AI Solutions\n\nMy AI Model Cost Calculator with the latest pricing for GPT-4 Turbo, Google Gemini 1.5 Pro, and Claude 3 Opus! 💡 Calculate text and image processing costs easily with real-time estimates. Check it out! #AI #MachineLearning #CostCalculator #GPT4 #Claude #GoogleGemini\n\n AI Model Cost Calculator: Optimizing Costs for Computer Vision and Multimodal AI Solutions\n\n\nHow image to token\n\n\n https://community.openai.com/t/how-do-i-calculate-image-tokens-in-gpt4-vision/492318\n https://platform.openai.com/docs/guides/vision\n\n\nfrom math import ceil\n\ndef resize(width, height:\n if width > 1024 or height > 1024:\n if width > height:\n height = int(height * 1024 / width\n width = 1024\n else:\n width = int(width * 1024 / height\n height = 1024\n return width, height\n\ndef count_image_tokens(width: int, height: int:\n width, height = resize(width, height\n h = ceil(height / 512\n w ..." },{ "id": "/metamask/", "title": "MetaMask — Connect & Receive", "url": "/metamask/", "tags": [], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-16\n> **MetaMask Connect** — Connect your wallet, receive, or send crypto automatically. — https://pirahansiah.com/metamask/\nConnect MetaMask to auto-fill your EVM address, send crypto, or reveal a receive address. Private test page.\n\n*Last updated: 2026-08-16.* \n\n\n\n\n\n MetaMask — Connect & Receive\n Connect your wallet, then receive or send crypto automatically. Select a token to switch networks.\n\n\n\n Connect MetaMask\n \n \n \n Disconnect\n \n \n\n\n\n \n ⚠️\n Warning — transfers are irreversible\n Crypto transactions cannot be reversed, refunded, or recovered once confirmed. There is no chargeback and no support line that can undo a transfer. Double-check everything before you send.\n \n \n ⚠️\n Caution — send on the correct network\n Ethereum, Base and BNB Smart Chain share the same address (0xFcE7…53B0C. Sending on the wrong network — for example Base instead of Ethereum — can permanently lose your funds. The page auto-switches to the right network before sending.\n \n \n ℹ️\n Note — voluntary, no invoice\n This page is a voluntary support option, not a purchase or invoice. Crypto payments are final with no automatic receipt or confirmation. If you are unsure, send a small test amount first.\n \n \n 💡\n When to use it\n Use crypto to support the work anonymously or internationally with low fees. For contracts, invoices, consulting, or anything that needs a receipt, email info@pirahansiah.com instead.\n \n\n\n\n BTC\n ETH\n SOL\n BASE\n BNB\n\n\n\n \n ⚒ ET..." },{ "id": "/notes/courses/metaverse/", "title": "Metaverse & XR", "url": "/notes/courses/metaverse/", "tags": ["metaverse","XR","spatial-computing","FarshidPirahansiah"], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **Metaverse & XR** — Workshop on extended reality, spatial computing, and immersive technologies. — https://www.pirahansiah.com/notes/courses/metaverse/\nWorkshop on extended reality, spatial computing, and immersive technologies.\n\n*Last updated: 2026-08-08.* \n\n\n# Metaverse & XR\n\nWorkshop on extended reality, spatial computing, and immersive technologies.\n\n#Metaverse #XR #SpatialComputing #FarshidPirahansiah\n" },{ "id": "/notes/courses/ml-spec/", "title": "Machine Learning Specialization", "url": "/notes/courses/ml-spec/", "tags": ["ML","Coursera","machine-learning-specialization","FarshidPirahansiah"], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **Machine Learning Specialization** — Coursera ML Specialization notes covering regression, classification, clustering, and neural networks. — https://www.pirahansiah.com/notes/courses/ml-spec/\nCoursera ML Specialization notes covering regression, classification, clustering, and neural networks.\n\n*Last updated: 2026-08-08.* \n\n\n# Machine Learning Specialization 2022\n\nCoursera: Machine Learning Specialization (2022\n\nDownload source code and full text of Mind map: GitHub https://github.com/tiziran/tiziran.github.io/tree/main/Machine_Learning_Specialization\n\n## Course 1: Supervised Machine Learning: Regression and Classification\n\n- **Week 1:** Introduction to Machine Learning\n- **Week 2:** Regression with multiple input variables\n- **Week 3:** Classification\n\n## Course 2: Advanced Learning Algorithms\n\n- **Week 1:** Neural Networks\n- **Week 2:** Neural network training\n\n## Course 3: Unsupervised Learning, Recommenders, Reinforcement Learning\n\n- **Week 1-4:** Clustering, anomaly detection, recommender systems, reinforcement learning\n\n## Course 4: ML Clustering & Retrieval\n\n- Case Studies: Finding Similar Documents\n- K-nearest neighbors, KD-trees, locality sensitive hashing\n- K-means, expectation maximization (EM, latent Dirichlet allocation (LDA\n\n#Machine_Learning_Specialization #tiziran #FarshidPirahanSiah\n" },{ "id": "/notes/courses/mlops/", "title": "MLOps", "url": "/notes/courses/mlops/", "tags": ["MLOps","machine-learning-engineering","Coursera","FarshidPirahansiah"], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **MLOps** — Coursera MLOps Specialization covering ML production, data lifecycle, modeling pipelines, and deployment. — https://www.pirahansiah.com/notes/courses/mlops/\nCoursera MLOps Specialization covering ML production, data lifecycle, modeling pipelines, and deployment.\n\n*Last updated: 2026-08-08.* \n\n\n# MLOps — Machine Learning Engineering for Production\n\nCoursera: Machine Learning Engineering for Production (MLOps Specialization\n\n## Course 1: Introduction to Machine Learning in Production\n- Week 1: Overview of the ML Lifecycle and Deployment\n- Week 2: Selecting and Training a Model\n- Week 3: Data Definition and Baseline\n\n## Course 2: Machine Learning Data Lifecycle in Production\n- Week 1: Collecting, Labeling, and Validating data\n- Week 2: Feature Engineering, Transformation, and Selection\n- Week 3: Data Journey and Data Storage\n- Week 4: Advanced Data Labeling Methods, Data Augmentation\n\n## Course 3: Machine Learning Modeling Pipelines in Production\n- Week 1: Neural Architecture Search (NAS, AutoML\n- Week 2: Model Resource Management (PCA, SVD, quantization, pruning\n- Week 3: High-Performance Modeling (distributed training, knowledge distillation\n- Week 4: Model Analysis (TFMA, TFX\n- Week 5: Interpretability (SHAP, LIME, PDP\n\n## Course 4: Deploying Machine Learning Models in Production\n- Week 1-2: Model Serving (TensorFlow Serving, TorchServe, Triton\n- Week 3: Model Management and Delivery\n- Week 4: Model Monitoring and Logging\n\n#MLOps #ComputerVision #Tiziran\n" },{ "id": "/notes/courses/modern-cpp/", "title": "Modern C++ for Image Processing", "url": "/notes/courses/modern-cpp/", "tags": ["C++","C++23","image-processing","design-patterns","FarshidPirahansiah"], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **Modern C++ for Image Processing** — Modern C++23 for image processing: memory management, design patterns, CUDA, and OpenCV. — https://www.pirahansiah.com/notes/courses/modern-cpp/\nModern C++23 for image processing: memory management, design patterns, CUDA, and OpenCV.\n\n*Last updated: 2026-08-08.* \n\n\n# Advanced Programming with Modern C++ 23 for Image Processing\n\n## Key Topics\n\n### Memory Management\n- Call by value (stack vs call by reference (heap\n- Stack: LIFO, compile-time, 8MB limit, fast\n- Heap: manual management, runtime, dynamic, slower\n- Tools: valgrind, fsanitize\n\n### Design Patterns (Gang of Four\n**Creational (5:** Factory method, Abstract factory, Builder, Prototype, Singleton\n**Structural (7:** Adapter, Bridge, Composite, Decorator, Facade, Flyweight, Proxy\n**Behavioral (12:** Chain of responsibility, Command, Mediator, Observer, Interpreter, State, Strategy, Template method, Visitor, Iterator, Memento, Null-object\n\n### Smart Pointers\n- std::unique_ptr — exclusive ownership\n- std::shared_ptr — shared ownership\n- std::weak_ptr — non-owning reference\n- Rule of five for custom memory management\n\n### Compilation\n- CUDA Jetson: nvcc -std=c++14 -arch=sm_62 -o main.run main.cu\n- C++20 modules: clang++ -std=c++2a -c file.cpp -Xclang -emit-module-interface -o file.pcm\n\n## OpenCV C++ Tips\n- cv::Mat is a smart pointer — use .clone( for deep copy in vectors\n- imagesVector.push_back(imageMat.clone(;\n\n#OpenCV #C++ #tiziran #CPlusPlus\n" },{ "id": "/notes/docs/cv/multi-camera/", "title": "Real-Time Multi-Camera Vision Systems", "url": "/notes/docs/cv/multi-camera/", "tags": ["computer-vision","multi-camera","real-time","edge-ai"], "hashtags": "#cv #multicamera #realtime #edgeai", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **Real-Time Multi-Camera Vision Systems** — Deep technical guide to building real-time multi-camera vision systems with heterogeneous processing and scaling to 100+ cameras. — https://www.pirahansiah.com/notes/docs/cv/multi-camera/\nDeep technical guide to building real-time multi-camera vision systems with heterogeneous processing and scaling to 100+ cameras.\n\n*Last updated: 2026-08-08.* \n\n\n# Real-Time Multi-Camera Vision Systems\n\nBuilding real-time multi-camera AI requires synchronizing 10–100 cameras while processing with CPU, GPU, NPU, and direct I/O in parallel. This guide covers frameworks (OpenCV, GStreamer, DeepStream, OpenVINO, low-level optimizations (USB DMA, UVC driver tweaks, and scaling strategies.\n\n---\n\n## 1. System Architecture\n\n### Pipeline\n\n1. **Capture** – RTSP, USB, MIPI-CSI input\n2. **Decode** – CPU, GPU hardware decoder, or FPGA\n3. **Preprocess** – resize, color convert, normalization\n4. **Inference** – DNNs on GPU/NPU/CPU\n5. **Post-process** – tracking, feature extraction\n6. **Output** – GUI, storage, or network stream\n\n### Framework Examples\n\n- **NVIDIA DeepStream**: GPU-accelerated multi-camera inference with batching (nvstreammux, inference (nvinfer, trackers, and OSD\n- **Intel OpenVINO**: Multi-Camera Multi-Target demo with detector + re-ID + tracker\n- **GStreamer**: Flexible pipelines with hardware decoders, multithreaded elements, and timestamp handling\n\n---\n\n## 2. Heterogeneous Processing (CPU/GPU/NPU\n\n Processor Role \n ----------- ------ \n CPU I/O, buffering, lightweight pre/post-processing \n GPU Hi..." },{ "id": "/notes/pubs/papers/multi-threshold-plate/", "title": "Multi-Threshold for License Plate Recognition", "url": "/notes/pubs/papers/multi-threshold-plate/", "tags": ["license-plate-recognition","thresholding","image-segmentation"], "hashtags": "#lpr #thresholding #imagesegmentation", "category": "hub", "body": "last_modified_at: 2026-08-14\n\nAdaptive multi-threshold approach using MLP for license plate recognition systems.\n\n*Last updated: 2026-08-14.* \nMulti-threshold_Approach_for_License_Plate_Recognition_System\n\n\nhttps://www.pirahansiah.com/notes/pubs/papers/Multi-threshold_Approach_for_License_Plate_Recognition_System\n\n\nspotify https://podcasters.spotify.com/pod/show/pirahansiah/episodes/My-Conference-Paper-Multi-threshold-Approach-for-License-Plate-Recognition-System-e2ps2fe\n\nPDF Download My Conference Paper http://waset.org/publications/3636 \n\n\n{% if page.extname == \"multi-threshold-license-plate.md\" %}\n !My Conference Paper Multi-threshold Approach for License Plate Recognition System /notes/pubs/papers/multi-threshold-license-plate.png\n{% else %}\n \n{% endif %}\n\n\n# Multi-threshold Approach for License Plate Recognition System\n\n## 1. Introduction\n - **Objective**: Propose an adaptive multi-threshold approach for image segmentation, specifically in object detection.\n - **Application**: Malaysian License Plate Recognition (LPR system.\n - **Challenge**: Different types of license plates require varied detection techniques depending on the country.\n\n## 2. Adaptive Multi-threshold Approach\n - **Key Method**:\n - Multi Layer Perceptron (MLP trained by backpropagation to optimize threshold values.\n - Finds optimum threshold values by analyzing the peak value from a graph of object count versus threshold ranges.\n - **Advantages**:\n - Adaptive to different types of license plates, including single-line and double-line plates with varying fonts.\n\n## 3. Com..." },{ "id": "/notes/pubs/papers/multimodal-calibration/", "title": "Camera Calibration for Multi-Modal Vision", "url": "/notes/pubs/papers/multimodal-calibration/", "tags": ["camera-calibration","multi-modal","robotics","image-quality"], "hashtags": "#cameracalibration #multimodal #robotics #iqa", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **Camera Calibration for Multi-Modal Vision** — Automatic calibration framework for multi-modal robot vision using IQA metrics. — https://www.pirahansiah.com/notes/pubs/papers/multimodal-calibration/\nAutomatic calibration framework for multi-modal robot vision using IQA metrics.\n\n*Last updated: 2026-08-08.* \n\n\nCamera_Calibration_for_Multi-Modal_Robot_Vision\n\n\n\nhttps://www.pirahansiah.com/notes/pubs/papers/Camera_Calibration_for_Multi-Modal_Robot_Vision\n\n\nspotify https://podcasters.spotify.com/pod/show/pirahansiah/episodes/My-Conference-Paper-Camera-Calibration-for-Multi-Modal-Robot-Vision-e2ps1fn \n\nPDF Download My Conference Paper https://ieeexplore.ieee.org/document/7360336 \n\n\n{% if page.extname == \"camera-calibration-multi-modal.md\" %}\n !My Conference Paper Camera Calibration for Multi-Modal Robot Vision /notes/pubs/papers/camera-calibration-multi-modal.png\n{% else %}\n \n{% endif %}\n\n\n# Camera Calibration for Multi-Modal Robot Vision\n\n## 1. Introduction\n - **Objective**: Propose an automatic calibration method for multimodal robot vision.\n - **Challenges in Robot Vision**:\n - Image quality degradation\n - Difficulty in adjusting to different environments\n - **Key Issues**:\n - False negative data points due to poor calibration\n - Need for continuous recalibration in dynamic environments\n\n## 2. Image Quality and Calibration\n - **Image Quality Assessment (IQA**:\n - Impact of poor image quality on robot vision\n - Key metrics:\n - Peak Signal-to-Noise Ratio (PSNR\n - Structural Similarity Index (SSI..." },{ "id": "/notes/pubs/books/ai/new-developer-era/", "title": "OpenCV 5 — Chapter 0: Introduction", "url": "/notes/pubs/books/ai/new-developer-era/", "tags": [], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **OpenCV 5 — Chapter 0: Introduction** — Introduction to the new developer era, AI agents, and multi-agent systems. — https://www.pirahansiah.com/notes/pubs/books/ai/new-developer-era/\nIntroduction to the new developer era, AI agents, and multi-agent systems.\n\n*Last updated: 2026-08-08.* \n\n\nreference index: index]]\ntags: ai-agents, multi-agent-systems, llm, deep-learning]\nhashtags: \"#aiagents #multiagent #llm #deeplearning\"\n\n{{ site.author }}\n\n# The New Developer Era: Transforming Your Career and Building Production Ready AI Agents in 2027\n\n- **Title Page** \n - *The New Developer Era: Transforming Your Career and Building Production Ready AI Agents in 2027; Agents will replace all software* \n - **Author Name**\n **Dr. Farshid Pirahansiah** \n Lead LLM/AI/Computer Vision Engineer & Researcher \n LinkedIn: linkedin.com/in/pirahansiah https://linkedin.com/in/pirahansiah\n\n - **Publication Details** \n - **Date of Publication**: 2027\n - **Edition**: Draft Edition \n\n - **Disclaimers**\n - **Informational Purposes**: The content of this book is based on the author’s professional experiences and research. It should not be taken as legal, financial, or specific career advice. Always perform due diligence before making major career or technical decisions. \n - **No Guarantees**: The author and publisher assume no responsibility for errors, omissions, or contrary interpretations of the subject matter. The views expressed are solely those of the author. \n - **Trademarks**: All product..." },{ "id": "/notes/docs/cuda/numba/", "title": "Accelerate Python with Numba's @jit(nopython=True)", "url": "/notes/docs/cuda/numba/", "tags": ["python","numba","jit","performance"], "hashtags": "#python #numba #jit #performance", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **Accelerate Python with Numba's @jit(nopython=True** — Tutorial on using Numba's @jit decorator to compile Python functions into optimized machine code for faster numerical computing. — https://www.pirahansiah.com/notes/docs/cuda/numba/\nTutorial on using Numba's @jit decorator to compile Python functions into optimized machine code for faster numerical computing.\n\n*Last updated: 2026-08-08.* \n\n\nAccelerate Python with Numba’s @jit(nopython=True \n\n\n\n# 🚀 Accelerate Python with Numba’s @jit(nopython=True 🚀\n\nAre you looking to **optimize your Python code** for better performance? If you work with **large datasets** or run complex numerical computations, the **Numba** library can be a game-changer!\n\nWith **@jit(nopython=True**, Numba translates Python functions into **machine code** using Just-In-Time (JIT compilation. This drastically reduces execution time, especially for **loops** and **numerical operations**.\n\nLet me show you how it works! 👇\n\n## 🚀 What is @jit(nopython=True?\n\n@jit(nopython=True is a decorator from the Numba library. It compiles the entire function into machine code at runtime. Here’s why it’s special:\n\n- **nopython=True**: Forces Numba to fully compile the function to machine code, skipping the Python interpreter. This ensures maximum performance.\n- It’s great for **numerical computing** or operations involving large arrays, matrices, or loops.\n\n💡 If Numba detects a dynamic type (like Python objects, it will throw an error with nopython=True, ensuring you stay in the compiled mode.\n\n---\n\n## 🛠️ Example: Summing Squares Witho..." },{ "id": "/notes/pubs/books/ai/opencv5-ch1/", "title": "OpenCV 5 — Chapter 1: Image Basics", "url": "/notes/pubs/books/ai/opencv5-ch1/", "tags": [], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **OpenCV 5 — Chapter 1: Image Basics** — Glossary of AI/ML terms, references, and resources for computer vision practitioners. — https://www.pirahansiah.com/notes/pubs/books/ai/opencv5-ch1/\nGlossary of AI/ML terms, references, and resources for computer vision practitioners.\n\n*Last updated: 2026-08-08.* \n\n\ntags: opencv, image-processing, computer-vision, python]\nhashtags: \"#opencv #imageprocessing #cv #python\"\n\n{{ site.author }}\n# **Mastering AI and Machine Learning: Essential Glossary, Resources, and References**\n\nIn the rapidly evolving fields of Artificial Intelligence (AI and Machine Learning (ML, staying updated with key terms, resources, and the latest research is crucial for both novices and seasoned professionals. To support your journey, I’ve compiled a comprehensive guide encompassing a **Glossary of Key Terms**, **References and Further Reading**, and an **Index** of essential concepts and tools. Whether you're looking to deepen your understanding or seeking valuable resources to enhance your projects, this guide serves as a valuable roadmap.\n\n---\n\n## **Glossary of Key Terms**\n\nUnderstanding the foundational terminology is essential for navigating the complexities of AI and ML. Here are some pivotal terms:\n\n### **A**\n\n- **AI (Artificial Intelligence**: The simulation of human intelligence processes by machines, including learning, reasoning, and self-correction.\n- **Algorithm**: A step-by-step procedure or formula for solving a problem or accomplishing a task.\n- **API (Application Programming Interface**: A set of rules and prot..." },{ "id": "/notes/pubs/books/ai/opencv5-ch2/", "title": "OpenCV 5 — Chapter 2: Feature Detection", "url": "/notes/pubs/books/ai/opencv5-ch2/", "tags": [], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **OpenCV 5 — Chapter 2: Feature Detection** — Comprehensive guide to AI/ML concepts, tools, best practices, and ethical considerations. — https://www.pirahansiah.com/notes/pubs/books/ai/opencv5-ch2/\nComprehensive guide to AI/ML concepts, tools, best practices, and ethical considerations.\n\n*Last updated: 2026-08-08.* \n\n\ntags: opencv, feature-detection, computer-vision, ai]\nhashtags: \"#opencv #featuredetection #cv #ai\"\n\n{{ site.author }}\n\n# **Mastering AI and Machine Learning: A Comprehensive Summary**\n\nArtificial Intelligence (AI and Machine Learning (ML have transformed the technological landscape, driving innovations across various industries. This summary encapsulates the essential concepts, tools, resources, and ethical considerations outlined in \"Mastering AI and Machine Learning,\" providing a foundational understanding for both novices and seasoned professionals.\n\n---\n\n## **1. Introduction to AI and Machine Learning**\n\n### **1.1 Understanding AI**\nAI refers to the simulation of human intelligence processes by machines, particularly computer systems. These processes include learning (the acquisition of information and rules for using the information, reasoning (using rules to reach approximate or definite conclusions, and self-correction.\n\n### **1.2 Machine Learning Fundamentals**\nML is a subset of AI focused on building systems that learn from data to improve their accuracy over time without being explicitly programmed. It encompasses various techniques, including supervised learning, unsupervised learning, and reinforcement learning...." },{ "id": "/notes/pubs/books/ai/opencv5-ch3/", "title": "OpenCV 5 — Chapter 3: Advanced Topics", "url": "/notes/pubs/books/ai/opencv5-ch3/", "tags": ["opencv","computer-vision","image-processing","filtering"], "hashtags": "#opencv #cv #imageprocessing #filtering", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **OpenCV 5 — Chapter 3: Advanced Topics** — Advanced computer vision techniques including filtering, morphology, and G-API integration. — https://www.pirahansiah.com/notes/pubs/books/ai/opencv5-ch3/\nAdvanced computer vision techniques including filtering, morphology, and G-API integration.\n\n*Last updated: 2026-08-08.* \n\n\n" },{ "id": "/notes/courses/openvino/", "title": "OpenVINO Deep Learning", "url": "/notes/courses/openvino/", "tags": ["OpenVINO","Intel","inference","FarshidPirahansiah"], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **OpenVINO Deep Learning** — Intel OpenVINO toolkit for optimizing and deploying deep learning models. — https://www.pirahansiah.com/notes/courses/openvino/\nIntel OpenVINO toolkit for optimizing and deploying deep learning models.\n\n*Last updated: 2026-08-08.* \n\n\n# OpenVINO Deep Learning\n\nIntel OpenVINO toolkit for optimizing and deploying deep learning models on Intel hardware.\n\n#OpenVINO #Intel #Inference #FarshidPirahansiah\n" },{ "id": "/notes/docs/cv/optical-flow/", "title": "Optical Flow: Challenges and Solutions", "url": "/notes/docs/cv/optical-flow/", "tags": ["computer-vision","optical-flow","motion-analysis","opencv"], "hashtags": "#cv #opticalflow #motionanalysis #opencv", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **Optical Flow: Challenges and Solutions** — Comprehensive guide to optical flow challenges including illumination changes, occlusions, fast motion, and their solutions. — https://www.pirahansiah.com/notes/docs/cv/optical-flow/\nComprehensive guide to optical flow challenges including illumination changes, occlusions, fast motion, and their solutions.\n\n*Last updated: 2026-08-08.* \n\n\n1. Illumination Variations\n\n\t•\tProblem: Changes in lighting conditions can distort motion estimation.\n\t•\tSolution: Use robust optical flow algorithms like Lucas-Kanade with pyramids or deep learning models trained on diverse lighting conditions.\n \n\n\t•\tChallenge: Changes in lighting distort motion estimation.\n\t•\tSolution:\n\t•\tUse algorithms robust to illumination changes, such as:\n\t•\tHorn-Schunck with brightness constancy assumption modifications.\n\t•\tAdvanced methods like FlowNet2 or RAFT trained on diverse lighting conditions.\n\t•\tNormalize pixel intensities (e.g., histogram equalization or use illumination-invariant feature descriptors.\n\t•\tFunction/Algorithm Examples:\n\t•\tNormalized Cross-Correlation (NCC for robust feature matching under varying lighting.\n\t•\tIllumination-Invariant Optical Flow in the Horn-Schunck model (custom implementations exist.\n\t•\tDeep learning models like RAFT and PWC-Net, trained on diverse lighting.\n\n\t•\tOpenCV functions:\n\t•\tcv2.calcOpticalFlowFarneback(: Dense optical flow with Gaussian filtering (robust under moderate illumination changes.\n\t•\tcv2.createCLAHE(: Apply Contrast Limited Adaptive Histogram Equalization for preprocessing to no..." },{ "id": "/notes/courses/parallel/", "title": "Parallel Programming for Computer Vision", "url": "/notes/courses/parallel/", "tags": ["parallel-programming","Python","computer-vision","FarshidPirahansiah"], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **Parallel Programming for Computer Vision** — Python parallel programming techniques for computer vision applications. — https://www.pirahansiah.com/notes/courses/parallel/\nPython parallel programming techniques for computer vision applications.\n\n*Last updated: 2026-08-08.* \n\n\n# Parallel Programming\n\nPython Parallel Programming for computer vision applications.\n\nTopics covered:\n- Multi-threading and multi-processing\n- GPU acceleration with CUDA\n- Distributed computing\n- Performance optimization for CV pipelines\n\n#computervision #parallelprogramming #FarshidPirahansiah\n" },{ "id": "/notes/pubs/papers/pattern-calibration/", "title": "Pattern Image for Camera Calibration", "url": "/notes/pubs/papers/pattern-calibration/", "tags": ["camera-calibration","pattern-recognition","robotics"], "hashtags": "#cameracalibration #patternrecognition #robotics", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **Pattern Image for Camera Calibration** — Analysis of how pattern image design impacts camera calibration accuracy and reliability. — https://www.pirahansiah.com/notes/pubs/papers/pattern-calibration/\nAnalysis of how pattern image design impacts camera calibration accuracy and reliability.\n\n*Last updated: 2026-08-08.* \n\n\nPattern_Image_Significance_for_Camera_Calibration\n\n\nhttps://www.pirahansiah.com/notes/pubs/papers/Pattern_Image_Significance_for_Camera_Calibration\n\n\nspotify https://podcasters.spotify.com/pod/show/pirahansiah/episodes/Pattern-Image-Significance-for-Camera-Calibration-e2ps2mt \n\nPDF Download My Conference Paper http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342 \n\n\n{% if page.extname == \"pattern-image-calibration.md\" %}\n !My Conference Paper Pattern Image Significance for Camera Calibration /notes/pubs/papers/pattern-image-calibration.png\n{% else %}\n \n{% endif %}\n\n\n# Pattern Image Significance for Camera Calibration\n\n## 1. Introduction\n - **Objective**: Discuss the significance of pattern images in camera calibration.\n - **Camera Calibration**:\n - A method to estimate the parameters of a pinhole camera model.\n - Used to correct lens distortion and for 3D reconstruction in applications like machine vision, robotics, and navigation systems.\n\n## 2. Categories of Camera Calibration Methods\n - **Self-Calibration**:\n - Does not rely on a known calibration object.\n - Nonlinear, sensitive to noise, requires more computational power.\n - **Active Vision Calibration**:\n - ..." },{ "id": "/notes/slides/presentation/", "title": "Reducing Token Usage in AI-Assisted Development", "url": "/notes/slides/presentation/", "tags": ["presentation","ai","llm","token-optimization"], "hashtags": "#presentation #ai #llm #token-optimization", "category": "hub", "body": "last_modified_at: 2026-08-16\n> **Reducing Token Usage in AI-Assisted Development** — Reducing Token Usage in AI-Assisted Development — https://www.pirahansiah.com/notes/slides/presentation/\nPresentation: practical techniques to reduce token usage and cost when using AI assistants for software development.\n\n*Last updated: 2026-08-16.* \n\n\n\n\n\n\n\n\n ← tap back tap next →\n \n \n\n \n Reducing Token Usage\n in AI-Assisted Development\n Dr. Farshid Pirahansiah\n \n\n \n The Problem\n \n 162 KBFull .claude folder\n 100%Token cost\n SlowResponse time\n $$$API cost\n \n 1 token ≈ 4 chars • Every file = tokens\n \n\n \n Token Cost by Component\n \n ~10 KBCore files✅ Always needed\n ~90 KBSkills (32 filesSelect per project\n ~14 KBAgents (7 filesSelect per domain\n 6-10%Minimal configBest savings\n \n \n\n \n Strategy 1: .cursorignore\n \n 5-10%MinimalCore only\n 20-30%WebPython/backend\n 35-45%CV/MLYOLO, SAM2\n 40-50%Edge/C++Inference\n \n Copy template → project/.cursorignore\n \n\n \n Strategy 2: Selective Loading\n \n \n ✅ INCLUDE\n ! .claude/skills/cv-pipeline/\n ! .claude/agents/debugger.md\n ! .claude/CLAUDE.md\n \n \n ❌ EXCLUDE\n .claude/skills/portfolio/\n .claude/workflows/\n .claude/agent-memory/\n \n \n 38-45% instead of 100%..." },{ "id": "/privacy/", "title": "Privacy Policy", "url": "/privacy/", "tags": [], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-14\n\nPrivacy policy for pirahansiah.com: what data is collected, how Google AdSense and Google Analytics are used, cookie policy, GDPR rights, and contact details.\n\n*Last updated: 2026-08-14.* \n# Privacy Policy\n\n**Effective date:** 2025-11-30\n\n## 1. Who We Are\n\nThis website is operated by Dr. Farshid Pirahansiah.\n\n## 2. What Data We Collect\n\n- IP address and browser info (via analytics\n- Device and usage data (via cookies and Google Ads\n- Any data you enter in forms (e.g., contact forms, sign-ups\n\n## 3. Use of Google Services\n\nWe use Google services to deliver and measure content and advertising on this site. Specifically:\n\n- **Google AdSense / Ads:** We display ads using Google AdSense. Our AdSense Publisher ID is pub-7548808026663917. Google may use cookies and other identifiers to show personalized ads and to measure ad performance. For details see Google's advertising technologies https://policies.google.com/technologies/ads.\n- **Google Analytics:** We use Google Analytics to collect aggregated information about how visitors use the site. This helps us improve the website and content.\n\n## 4. Cookie Policy\n\nWe use cookies to:\n\n- Remember your preferences\n- Analyze site traffic\n- Serve personalized advertisements through Google AdSense\n\nYou can control cookies through your browser settings.\n\n## 5. Your Rights (GDPR\n\nIf you are located in the European Economic Area (EEA, you have the following rights under GDPR:\n- **Right of access** — Request a copy of the data we hold about you.\n- **Right to rectification** — Request correction of inaccurate ..." },{ "id": "/notes/docs/product/", "title": "Product — Embedded CV & Edge AI", "url": "/notes/docs/product/", "tags": ["computer-vision","edge-ai","embedded-systems","product"], "hashtags": "#cv #edgeai #embeddedsystems #product", "category": "hub", "body": "last_modified_at: 2026-08-16\n> **Product — Embedded CV & Edge AI** — Embedded computer vision and edge AI systems portfolio. — https://www.pirahansiah.com/notes/docs/product/\nEmbedded computer vision and edge AI systems portfolio.\n\n*Last updated: 2026-08-16.* \n\n\n\n\n\n Product\n Embedded computer vision & edge AI systems\n\n\n\n ← Home\n Research\n Solutions\n\n\n\n\n \n 👁\n Computer Vision Core\n \n → 3D Vision & Multi-Camera\n → Optical Flow\n → Multi-Camera Systems\n → CV Coaching Roadmap\n \n \n\n \n 🤖\n AI & LLMs New\n \n → Advanced LLM Concepts\n → Orchestrating AI Agents\n → AI Blog\n → Avatar Generator\n \n \n\n \n ⚡\n CUDA & GPU Dev\n \n → Numba JIT Tutorial\n → PyCUDA Kernels\n → CUDA in VS Code\n → MLX, CoreML & Metal\n \n \n\n \n ⚙\n Optimization ML\n \n → CV/DL/ML Optimization\n → Prompt Engineering\n \n \n\n \n 💻\n Programming\n \n → C++ Quick Reference\n → Python Configuration\n → Developer Tools\n → Shell & Vim\n \n \n\n\n" },{ "id": "/notes/pkm/proof/", "title": "Site Link Index", "url": "/notes/pkm/proof/", "tags": ["site-index","links","documentation"], "hashtags": "#siteindex #links #documentation", "category": "pkm", "body": "last_modified_at: 2026-08-08\n> **Site Link Index** — Complete index of all pages, publications, and resources on pirahansiah.com — https://www.pirahansiah.com/notes/pkm/proof/\nComplete index of all pages, publications, and resources on pirahansiah.com\n\n*Last updated: 2026-08-08.* \n\n\n# Pirahansiah.com — Complete Link Index\n\nAll content files are under notes/. Links below reference local files only.\n\n---\n\n## Landing & Profile\n\n File Path Description \n ------ ------ ------------- \n Home /notes/docs/ /notes/docs/ Main landing page \n Coaching /notes/docs/coaching/ /notes/docs/coaching/ CV coaching and teaching roadmap \n Resources /notes/docs/resources/ /notes/docs/resources/ Patents, books, papers, projects \n Links /notes/docs/links/ /notes/docs/links/ Curated links and tools \n\n---\n\n## Computer Vision & 3D\n\n File Path Description \n ------ ------ ------------- \n 3D Vision /notes/docs/cv/3d/ /notes/docs/cv/3d/ Point clouds, depth sensing, multi-camera sync \n Optical Flow /notes/docs/cv/optical-flow/ /notes/docs/cv/optical-flow/ Motion estimation challenges and solutions \n Multi-Camera /notes/docs/cv/multi-camera/ /notes/docs/cv/multi-camera/ Scaling to 100+ cameras \n\n---\n\n## AI & LLMs\n\n File Path Description \n ------ ------ ------------- \n Advanced LLM Concepts /notes/docs/llm/llm-concepts/ /notes/docs/llm/llm-concepts/ RAG, embeddings, multimodal \n Orchestrating Agents /notes/docs/llm/agents/ /notes/docs/llm/agents/ Multi-agent systems \n Blog /notes/docs/llm/blog/ /notes/docs/llm/blog/ RAG vs CAG, mu..." },{ "id": "/notes/pubs/journals/psnr-segmentation/", "title": "PSNR Threshold for Image Segmentation", "url": "/notes/pubs/journals/psnr-segmentation/", "tags": ["psnr","thresholding","image-segmentation","image-processing"], "hashtags": "#psnr #thresholding #imagesegmentation #imageprocessing", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **PSNR Threshold for Image Segmentation** — PSNR-based thresholding method for image segmentation in automated visual inspection. — https://www.pirahansiah.com/notes/pubs/journals/psnr-segmentation/\nPSNR-based thresholding method for image segmentation in automated visual inspection.\n\n*Last updated: 2026-08-08.* \n\n\nPEAK_SIGNAL-TO-NOISE_RATIO_BASED_ON_THRESHOLD_METHOD_FOR_IMAGE_SEGMENTATION\n\nhttps://www.pirahansiah.com/notes/pubs/journals/PEAK_SIGNAL-TO-NOISE_RATIO_BASED_ON_THRESHOLD_METHOD_FOR_IMAGE_SEGMENTATION/\n\nspotify https://podcasters.spotify.com/pod/show/pirahansiah/episodes/PEAK-SIGNAL-TO-NOISE-RATIO-BASED-ON-THRESHOLD-METHOD-FOR-IMAGE-SEGMENTATION-e2prep9\n\nPDF Download http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf \n\n\n{% if page.extname == \"psnr-threshold-segmentation.md\" %}\n !PEAK SIGNAL-TO-NOISE RATIO BASED ON THRESHOLD METHOD FOR IMAGE SEGMENTATION /notes/pubs/journals/psnr-threshold-segmentation.png\n{% else %}\n \n{% endif %}\n\n\n# Mind Map: Peak Signal-to-Noise Ratio Based on Threshold Method for Image Segmentation\n\n## 1. Introduction\n- **Importance of Thresholding**: Separates objects from the background, crucial in image processing and pattern recognition.\n- **Types of Thresholding**:\n - **Single Thresholding**: Produces binary images (0 and 1.\n - **Multilevel Thresholding**: Produces images with pixel values between 0 and 255.\n- **Objective**: Develop a new algorithm using Peak Signal-to-Noise Ratio (PSNR for image segmentation.\n\n## 2. Thresholding Techniques\n### 2.1 Single Thresholding\n- **Definition**: Uses a singl..." },{ "id": "/notes/docs/cuda/pycuda/", "title": "How PyCUDA Reads and Runs C Kernels", "url": "/notes/docs/cuda/pycuda/", "tags": ["cuda","gpu","python","parallel-computing"], "hashtags": "#cuda #gpu #python #parallelcomputing", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **How PyCUDA Reads and Runs C Kernels** — Explanation of how PyCUDA compiles and executes CUDA kernels written in C from Python, with step-by-step examples. — https://www.pirahansiah.com/notes/docs/cuda/pycuda/\nExplanation of how PyCUDA compiles and executes CUDA kernels written in C from Python, with step-by-step examples.\n\n*Last updated: 2026-08-08.* \n\n\nHow PyCUDA Reads and Runs C Kernels\n\n\n\n# How PyCUDA Reads and Runs C Kernels\n\nIn **PyCUDA**, you can run **CUDA kernels** (which are typically written in C or C++ directly from Python. PyCUDA provides a way to write CUDA code as a string, compile it at runtime, and execute it on the GPU. Let's walk through the process step-by-step, explaining how PyCUDA interacts with a kernel written in C and runs it.\n\n## 1. Importing PyCUDA Modules\nThe following lines import PyCUDA’s functionalities:\n\npython\nimport pycuda.curandom as curandom\nimport pycuda.driver as cuda\nimport pycuda.autoinit\n\n\n- **pycuda.driver as cuda**: This module provides the basic interface to communicate with the CUDA driver, which manages GPU resources and executes code.\n- **pycuda.autoinit**: This module automatically initializes CUDA when you import it, setting up the GPU and its context (the memory space for the program to run.\n- **pycuda.curandom**: This module is used to generate random numbers on the GPU using CUDA’s random number generation capabilities.\n\n## 2. Importing the CUDA Kernels\n\npython\nimport GraphTsetlinMachine.kernels as kernels\n\n\n- This imports a module named **kernels** from GraphTsetlinMachine. Presumably,..." },{ "id": "/qr/", "title": "QR Codes — Scan to Join & Refer", "url": "/qr/", "tags": ["qr","referral","invite","links","share"], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-09-04\n> **QR Codes** — Scan or tap to open referral & join links — OpenCode, Trade Republic, Scalable Capital, Wise, eToro — https://pirahansiah.com/qr/\nScan or tap QR codes to open Dr. Farshid Pirahansiah's referral and join links — OpenCode, Trade Republic, Scalable Capital, Wise and eToro — and share them with friends.\n\n*Last updated: 2026-09-04.* \n\n\n\n\n\n \n Scan & Share\n Point your phone camera at any code to open the link, or tap to open and copy it. Know someone who'd benefit? Share these with your friends.\n \n\n \n\n \n 💻\n OpenCode\n Low cost coding models for everyone.\n \n opencode.ai/go?ref=3KMNQG0CS4\n \n Open\n Download\n Copy\n \n \n\n \n 💸\n Trade Republic\n The smartest way to invest, spend and bank. Create an account via the link to secure a welcome bonus.\n \n refnocode.trade.re/z28xmjp6\n \n Open\n Download\n Copy\n \n \n\n \n 📊\n Scalable Capital\n Join me at Scalable — the best place to invest in your financial future. Open an account in minutes and secure a start bonus of €25.\n \n de.scalable.capital/en/invitation/bj2bkn\n \n Open\n Download\n Copy\n \n \n\n \n 💰\n Wise\n Send and receive money internationally with low fees. Join with my invite link.\n \n wise.com/invite/ilpn/farshidp1\n \n Open\n Download\n Copy\n \n \n\n \n 📈\n eToro\n Hey! I'm trading with eToro — 1,00..." },{ "id": "/notes/docs/research/", "title": "Research — Publications & Patents", "url": "/notes/docs/research/", "tags": ["research","publications","patents","computer-vision"], "hashtags": "#research #publications #patents #cv", "category": "hub", "body": "last_modified_at: 2026-08-16\n> **Research — Publications & Patents** — Complete list of patents, journal articles, conference papers, books, and keynotes. — https://www.pirahansiah.com/notes/docs/research/\nComplete list of patents, journal articles, conference papers, books, and keynotes.\n\n*Last updated: 2026-08-16.* \n\n\n\n\n\n Research\n 3 patents, 2 books, 6 journals, 11 conference papers, 1 keynote\n\n\n\n ← Home\n Product\n Solutions\n\n\n\n\n \n 📜\n Patents 3\n \n → Face Image Augmentation — WO 2021/060971\n → Vehicle Detection — WO 2021/107761\n → Facial Analysis Advertising — WO 2020/141969\n \n \n\n \n 📅\n Conference Papers 11\n \n → Adaptive Segmentation PSNR\n → License Plate — Entropy\n → Multi-threshold License Plate\n → Thresholding Handwritten\n → Camera Calibration Multi-Modal\n → Pattern Image Calibration\n → 2D vs 3D Map Movement\n → Character Recognition\n → Classification Geometrical\n → TafreshGrid\n → All Papers →\n \n \n\n \n 📄\n Journal Articles 6\n \n → Adaptive Thresholding PSNR\n → GSFT-PSNR Fuzzy Threshold\n → PSNR Threshold Segmentation\n → Character & Object Recognition\n → 3D SLAM & Humanoid Robots\n → Ant Colony Optimization\n \n \n\n \n 📖\n Books & Chapters 7\n \n → Computer Vision Meets LLM\n → OpenCV 5 — Ch.1 Image Basics\n → OpenCV 5 — Ch.2 Fea..." },{ "id": "/notes/docs/resources/", "title": "Portfolio & Publications", "url": "/notes/docs/resources/", "tags": ["portfolio","publications","patents","research"], "hashtags": "#portfolio #publications #patents #research", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **Portfolio & Publications** — Complete portfolio of patents, publications, books, and project highlights. — https://www.pirahansiah.com/notes/docs/resources/\nComplete portfolio of patents, publications, books, and project highlights.\n\n*Last updated: 2026-08-08.* \n\n\n# My Portfolio\n\n- Impact Portfolio /notes/docs/projects/solutions/\n\n# Resume\n\n- Dr. Farshid Pirahansiah CV /notes/pubs/cv/\n\n## My Publications\n\n### My Patents (3\n\n- A METHOD FOR AUGMENTING A PLURALITY OF FACE IMAGES WO2021060971A1 /notes/pubs/patents/face-augmentation/\n- SYSTEM AND METHOD FOR PROVIDING ADVERTISEMENT CONTENTS BASED ON FACIAL ANALYSIS WO2020141969A2 /notes/pubs/patents/facial-analysis/\n- A METHOD FOR DETECTING A MOVING VEHICLE WO2021107761A1 /notes/pubs/patents/vehicle-detection/\n\n### My Books (2\n\n- Computational Intelligence: From Theory to Application /notes/pubs/books/computational-intelligence/\n- Camera Calibration and Video Stabilization Framework for Robot Localization — Springer /notes/pubs/books/camera-calibration/\n\n### My Journals (6\n\n- Adaptive Image Thresholding Based on PSNR /notes/pubs/journals/adaptive-thresholding/\n- CHARACTER AND OBJECT RECOGNITION BASED ON GLOBAL FEATURE EXTRACTION /notes/pubs/journals/character-recognition/\n- GSFT-PSNR Global Single Fuzzy Threshold /notes/pubs/journals/fuzzy-thresholding/\n- PEAK SIGNAL-TO-NOISE RATIO BASED ON THRESHOLD METHOD FOR IMAGE SEGMENTATION /notes/pubs/journals/psnr-segmentation/\n- 3D SLAM Simultaneous Localization And Mapping /notes/pubs/journals/slam-humanoid/\n- USING AN ANT COLONY OPTIMIZATION ALGORIT..." },{ "id": "/notes/courses/risc-v/", "title": "RISC-V for AI", "url": "/notes/courses/risc-v/", "tags": ["RISC-V","edge-AI","processor","FarshidPirahansiah"], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **RISC-V for AI** — RISC-V open-source processor architecture for AI at the edge. — https://www.pirahansiah.com/notes/courses/risc-v/\nRISC-V open-source processor architecture for AI at the edge.\n\n*Last updated: 2026-08-08.* \n\n\n# RISC-V for AI\n\n## Key References\n- RISC-V Magazine (December 2020\n- RISC-V Summit 2020\n\n## Resources\n- riscv.org https://riscv.org/\n- HiFive1 Rev B https://www.amazon.de/-/en/HiFive1-Rev-B/dp/B086RGFS5N/\n- RISC-V 15 minute sample course https://www.eenewseurope.com/news/risc-v-boom-edge-ai-says-facebooks-chief-ai-scientist\n\n#RISC-V #EdgeAI #FarshidPirahansiah\n" },{ "id": "/notes/courses/ros/", "title": "ROS: Robot Operating System", "url": "/notes/courses/ros/", "tags": ["ROS","robotics","ETH-Zurich","FarshidPirahansiah"], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **ROS: Robot Operating System** — ETH Zurich lecture on ROS architecture, navigation, simulation, and robot models. — https://www.pirahansiah.com/notes/courses/ros/\nETH Zurich lecture on ROS architecture, navigation, simulation, and robot models.\n\n*Last updated: 2026-08-08.* \n\n\n# ROS — Robot Operating System\n\nSource: ETH Zurich Lecture https://www.youtube.com/watch?v=0BxVPCInS3M\n\nLecturers: Péter Fankhauser, Dominic Jud, Martin Wermelinger\n\n## Topics\n- ROS Architecture & Navigation\n- ROS System & Packages\n- Simulating ROS with Gazebo\n- Robot Models (URDF, SDF\n- Simulation Environments\n- Visualization (RViz, rqt\n\n#ROS #Robotics #Tiziran\n" },{ "id": "/share/", "title": "Share", "url": "/share/", "tags": [], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-16\n\n\n> **Share** — Invite friends to join — trading, investing, and AI tools — https://pirahansiah.com/share/\nInvite friends to join — trading, investing, and AI tools.\n\n*Last updated: 2026-08-16.* \n\n\n\n Share with Friends\n Invite friends to platforms I use — get bonuses on both sides.\n\n\n\n\n \n 🤖 OpenCode AI\n Low cost coding models for everyone. Free AI code generation with OpenCode Go.\n Join OpenCode\n \n\n \n 📈 Trade Republic\n The smartest way to invest, spend and bank. Create an account via the link to secure a welcome bonus.\n Join Trade Republic\n \n\n \n 📈 Scalable Capital\n The best place to invest in your financial future. Open an account in just a few minutes and secure a start bonus of 25 EUR.\n Join Scalable\n \n\n \n 💰 Wise\n Send money internationally with low fees. Get a free transfer when you sign up with my link.\n Join Wise\n \n\n \n 📈 eToro\n Trade 1000s of assets with awesome tools. Social trading, crypto, stocks, and more.\n Join eToro\n \n\n\n" },{ "id": "/notes/docs/shell-vim/", "title": "Shell & Vim Quick Reference", "url": "/notes/docs/shell-vim/", "tags": ["shell","vim","cli","developer-tools"], "hashtags": "#shell #vim #cli #devtools", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **Shell & Vim Quick Reference** — Quick reference for shell commands, vim basics, and essential CLI tools. — https://www.pirahansiah.com/notes/docs/shell-vim/\nQuick reference for shell commands, vim basics, and essential CLI tools.\n\n*Last updated: 2026-08-08.* \n\n\n- NeoHtop https://github.com/Abdenasser/neohtop\n- \n\n\n- The Shell\n - echo\n - \"\" \\ \n - $PATH\n - cd -\n \n - ctrl+L to clean \n - ctrl+r\n\n - >> append\n - output into input\n - # into root or sudo su\n - xdg-open opent the file with reletive app\n - foo=bar ; must be without space \n - echo \"value is $foo\" -> value is bar\n - echo 'value is $foo' -> value is $foo\n - mcd ( {\n mkdir -p \"$1\"\n cv \"$1\"\n }\n source mcd.sh\n !!\n\ncommand + /\noption+shif+a\n\n\n\n\n#!/usr/bin/env python\nimport sys\nfor arg in reversed(sys.argv1:]:\n print(arg\n\n# tools\ntldr\nlocate \ngrep \nripgrep = rg\nfzf\nbroot\nnnn\n\n\n# vim\n i\n esc\n r\n k\n s-v\n c-v\n :\n" },{ "id": "/shop/", "title": "Digital Downloads & AI Prompt Packs", "url": "/shop/", "tags": [], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-16\n> **Digital Downloads** — Technical guides, cheat sheets, and tutorials by Dr. Farshid Pirahansiah — https://pirahansiah.com/shop/\nTechnical guides, cheat sheets, AI prompt packs, and tutorials by Dr. Farshid Pirahansiah — computer vision, AI, CUDA, and software engineering.\n\n*Last updated: 2026-08-16.* \n\n\n\n\n\n Digital Downloads\n Technical guides, cheat sheets, AI prompt packs, and tutorials on computer vision, AI, CUDA, and software engineering — by Dr. Farshid Pirahansiah.\n\nEvery item below is a self-contained PDF. Click Download PDF to get the file. These are the same products sold as digital downloads on Etsy.\n\nAI Prompt Packs — Etsy\n\n\n Prompt Pack\n CV & Edge AI Engineering\n CV pipelines, edge deployment, and INT8/INT4 quantization skills — the full computer-vision agent stack.\n\n\n Prompt Pack\n Code Review & Debugging\n Code-reviewer, debugger, and security-auditor agents plus a structured bug-diagnosis loop.\n\n\n Prompt Pack\n System Design & Architecture\n Deep-module design vocabulary, domain modeling, and relentless design interviews.\n\n\n Prompt Pack\n Agent Workflows & Autonomy\n Autonomy ladders, evaluator-optimizer loops, handoffs, and PRD-to-issue pipelines.\n\n\n Prompt Pack\n Engineering Discipline\n TDD, project modernization, throwaway prototypes, and learning from failures.\n\n\n Prompt Pack\n Writing & Communication\n Humanize AI text, force terse output, and teach concepts clearly.\n\n\n Prompt Pack\n Knowledge Graph & Memory\n Turn anything into a knowledge graph and query it with the codebase-memory..." },{ "id": "/notes/sitemap/", "title": "Sitemap — All Pages", "url": "/notes/sitemap/", "tags": [], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-16\n\nFull sitemap of pirahansiah.com — every computer-vision, AI, LLM, CUDA and edge-AI page listed in one place.\n\n*Last updated: 2026-08-16.* \n\n\n\n Sitemap\n Navigate through all pages on pirahansiah.com\n\n\n\n 🔍\n \n\n\n\n {{ site.pages size }}Pages\n 3Patents\n 6Journals\n 11Papers\n\n\n\n\n \n \n 🏠\n Home Landing\n \n → Dr. Farshid Pirahansiah\n \n \n\n \n \n 👁\n Computer Vision AI\n \n → 3D Vision & Multi-Camera\n → Optical Flow\n → Multi-Camera Systems\n → CV Coaching Roadmap\n \n \n\n \n \n 🤖\n AI & LLMs New\n \n → Advanced LLM Concepts\n → Orchestrating AI Agents\n → Blog: AI & LLMs\n → Avatar Generator\n \n \n\n \n \n ⚡\n CUDA & GPU Dev\n \n → Numba JIT Tutorial\n → PyCUDA Kernels\n → CUDA in VS Code\n → MLX, CoreML & Metal\n \n \n\n \n \n 💻\n Programming Tools\n \n → C++ Quick Reference\n → Python Configuration\n → Developer Tools & Setup\n → Shell & Vim Reference\n \n \n\n \n \n ⚡\n Optimization ML\n \n → CV, DL & ML Optimization\n → Prompt Engineering\n \n \n\n \n \n 🚀\n Business & Career\n \n → Startup Guide\n → SEO for LLMs\n → Top LinkedIn Posts 2024\n \n \n\n \n \n 📚\n Resources\n \n → Portfolio & Publications\n → Curated Links\n → Content Hub\n \n \n\n \n \n 📜..." },{ "id": "/notes/pubs/journals/slam-humanoid/", "title": "3D SLAM and Humanoid Robot Linkages", "url": "/notes/pubs/journals/slam-humanoid/", "tags": ["slam","humanoid-robots","3d-mapping","robotics"], "hashtags": "#slam #humanoidrobots #3dmapping #robotics", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **3D SLAM and Humanoid Robot Linkages** — Survey of 3D SLAM techniques for localization, mapping, and humanoid robot applications. — https://www.pirahansiah.com/notes/pubs/journals/slam-humanoid/\nSurvey of 3D SLAM techniques for localization, mapping, and humanoid robot applications.\n\n*Last updated: 2026-08-08.* \n\n\n3D_SLAM_Simultaneous_Localization_And_Mapping_Trends_And_Humanoid_Robot_Linkages\n\nhttps://www.pirahansiah.com/notes/pubs/journals/3D_SLAM_Simultaneous_Localization_And_Mapping_Trends_And_Humanoid_Robot_Linkages/\n\nspotify https://podcasters.spotify.com/pod/show/pirahansiah/episodes/3D-SLAM-Simultaneous-Localization-And-Mapping-Trends-And-Humanoid-Robot-Linkages-e2prg07\n\nPDF Download http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf \n\n\n{% if page.extname == \"3d-slam-humanoid-robots.md\" %}\n !3D SLAM Simultaneous Localization And Mapping Trends And Humanoid Robot Linkages /notes/pubs/journals/3d-slam-humanoid-robots.png\n{% else %}\n \n{% endif %}\n\n\n# Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages\n\n## 1. Introduction\n- SLAM: Simultaneous Localization and Mapping\n - Real-time map creation and localization\n - Robotics application: goal determination, motion planning\n - Usage in rescue missions, medical field, pipeline inspection, and more\n - Challenges: sensor uncertainty, correspondence, loop closing, time complexity\n\n## 2. SLAM Methods\n- Kalman Filter (KF\n - Bayesian filter handling uncertainty\n - Extended KF (EKF, Unscented KF (UKF, SEIF improvements\n - Challenges: computational resources, lan..." },{ "id": "/solana-games/", "title": "Solana Arcade — Snake & Tetris", "url": "/solana-games/", "tags": ["solana","games","snake","tetris","phantom","web3","arcade"], "hashtags": "#solana #games #snake #tetris #phantom #web3 #crypto", "category": "hub", "body": "last_modified_at: 2026-08-16\n> **Solana Arcade** — Play Snake & Tetris, connect Phantom, and sign your high score as a cryptographic proof. — https://pirahansiah.com/solana-games/\nPlay Snake and Tetris in your browser, connect a Phantom wallet, and sign your high score as a cryptographic proof on Solana. No install, no account, runs locally.\n\n*Last updated: 2026-08-16.* \n\n\n\n\n\n \n Solana Arcade\n Play Snake and Tetris right in your browser, connect a Phantom wallet, and sign your high score as a cryptographic proof on Solana. No install, no account — everything runs locally.\n \n\n \n Connect Phantom wallet\n \n \n \n Disconnect\n \n \n 📱 On mobile or inside Telegram? Solana wallets can't connect inside Telegram's browser — the games still work everywhere, and you can send a tip to the SOL address below.\n \n\n \n 🐍 Snake\n 🧱 Tetris\n \n\n \n \n ℹ️\n Runs entirely in your browser\n No install, no account, no server. High scores are saved locally on your device (localStorage and can only be seen by you.\n \n \n 🛡️\n Connecting never sends a transaction\n The “Sign score” button uses signMessage only — a free message signature. It can never move, send, or spend any SOL.\n \n \n ⚠️\n No on-chain leaderboard yet\n Scores are local, not stored on-chain. Signing produces a proof you can verify against your public key, but scores are not written to the Solana network.\n \n \n\n \n \n Score0\n Best0\n \n \n..." },{ "id": "/notes/docs/solutions/", "title": "Solutions — AI Courses & Workshops", "url": "/notes/docs/solutions/", "tags": ["ai","courses","workshops","deep-learning"], "hashtags": "#ai #courses #workshops #deeplearning", "category": "hub", "body": "last_modified_at: 2026-08-16\n> **Solutions — AI Courses & Workshops** — AI 2026 courses, workshops, and business solutions for production-ready products. — https://www.pirahansiah.com/notes/docs/solutions/\nAI 2026 courses, workshops, and business solutions for production-ready products.\n\n*Last updated: 2026-08-16.* \n\n\n\n\n\n Solutions\n Turn AI research into production-ready products\n\n\n\n ← Home\n Product\n Research\n\n\n\n\n \n 🚀\n AI 2026 — Courses 9\n \n → ML Specialization\n → Full Stack Deep Learning\n → FSDL 2022\n → MLOps\n → ROS\n → Parallel Programming\n → Modern C++\n → Cloud-Native\n → TensorFlow Deployment\n \n \n\n \n 📚\n Workshops 7\n \n → RISC-V\n → Edge AI Summit\n → Embedded IoT\n → Tesla AI\n → AI Hardware\n → OpenVINO\n → Metaverse\n \n \n\n \n 💡\n Business Growth\n \n → Startup Guide\n → SEO for LLMs\n → Prompt Engineering\n \n \n\n \n 📚\n Resources All\n \n → Curated Links\n → Top LinkedIn Posts\n → Book Summaries\n → IoT Scholarship\n → All Topics\n \n \n\n \n 📝\n Connect\n \n → LinkedIn\n → GitHub (43 repos\n → Full Sitemap\n \n \n\n\n" },{ "id": "/notes/docs/startup/", "title": "Startup Guide: Edge AI Business", "url": "/notes/docs/startup/", "tags": ["startup","business","fundraising","ai"], "hashtags": "#startup #business #fundraising #ai", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **Startup Guide: Edge AI Business** — Edge AI business plan, fundraising in Germany, pitch decks, and growth strategies. — https://www.pirahansiah.com/notes/docs/startup/\nEdge AI business plan, fundraising in Germany, pitch decks, and growth strategies.\n\n*Last updated: 2026-08-08.* \n\n\n# Edge AI Solutions: Local LLM Implementation Business Plan\n\n## Executive Summary\n\nEdge AI Solutions will provide fully local, on-premises large language model (LLM deployments for businesses concerned with privacy, data security, compliance, and cost efficiency. By deploying powerful AI models directly on customer hardware, we eliminate cloud dependency, API costs, and data privacy concerns while delivering high-performance AI capabilities.\n\nKey value propositions:\n- Complete data privacy with no external connections\n- No recurring token costs or API fees\n- Hardware solutions ranging from $5,000 to $500,000\n- Customizable AI capabilities for specific industry needs\n- Compliance with strict data protection regulations\n\nOur target market includes industries with sensitive data requirements (healthcare, legal, finance, government and businesses seeking cost-effective AI implementation without ongoing usage fees.\n\n## Business Model Overview\n\n### Product Offerings\n\n1. **Edge AI Hardware Packages**:\n - Entry-level solutions: $5,000-20,000\n - Mid-tier enterprise solutions: $20,000-100,000\n - High-performance data center solutions: $100,000-500,000\n\n2. **Setup and Deployment Services**:\n - Professional installation: $1,000 per system\n - Custom model fine-t..." },{ "id": "/terms/", "title": "Terms of Service", "url": "/terms/", "tags": [], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-14\n> **Terms of Service** — Terms of Service — https://www.pirahansiah.com/terms/\nTerms of service for pirahansiah.com: acceptable use of the content, intellectual property notice, disclaimers, and contact information.\n\n*Last updated: 2026-08-14.* \n\n\n# Terms of Service\n\n**Effective date:** 2025-11-30\n\n## 1. Acceptance of Terms\n\nBy accessing this website (pirahansiah.com, you agree to be bound by these Terms of Service. If you do not agree with any part of these terms, please do not use this website.\n\n## 2. Content\n\nAll content on this site — including articles, research summaries, code examples, course notes, and publications — is provided for informational and educational purposes only. Dr. Farshid Pirahansiah makes reasonable efforts to ensure accuracy but provides no warranties regarding the completeness, reliability, or suitability of the content.\n\n## 3. Intellectual Property\n\nAll content, code, designs, and materials on this site are protected by copyright law. You may:\n- Read and share links to pages\n- Quote brief excerpts with attribution\n\nYou may not:\n- Reproduce, distribute, or republish full articles without written permission\n- Use content for commercial purposes without authorization\n- Remove copyright notices from any materials\n\n## 4. Third-Party Services\n\nThis website uses third-party services including:\n- **Google AdSense** for displaying advertisements\n- **Google Analytics** for traffic analysis\n- **GitHub Pages** for hosting\n\nThese services may collect data according to their own privacy policies. See our Privacy Policy /privac..." },{ "id": "/notes/courses/tesla/", "title": "Tesla AI", "url": "/notes/courses/tesla/", "tags": ["Tesla","autonomous-driving","AI","FarshidPirahansiah"], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **Tesla AI** — Workshop on Tesla's autonomous driving technology and AI systems. — https://www.pirahansiah.com/notes/courses/tesla/\nWorkshop on Tesla's autonomous driving technology and AI systems.\n\n*Last updated: 2026-08-08.* \n\n\n# Tesla AI\n\nWorkshop on Tesla's autonomous driving technology and AI systems.\n\n#Tesla #AutonomousDriving #FarshidPirahansiah\n" },{ "id": "/notes/courses/tf-deploy/", "title": "TensorFlow Deployment Specialization", "url": "/notes/courses/tf-deploy/", "tags": ["TensorFlow","TF.js","TF-Lite","mobile-ML","FarshidPirahansiah"], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **TensorFlow Deployment Specialization** — TensorFlow deployment: browser-based TF.js, device-based TF Lite, data pipelines, and optimization. — https://www.pirahansiah.com/notes/courses/tf-deploy/\nTensorFlow deployment: browser-based TF.js, device-based TF Lite, data pipelines, and optimization.\n\n*Last updated: 2026-08-08.* \n\n\n# TensorFlow: Data and Deployment Specialization\n\n## 4 Courses\n\n1. **Browser-based Models with TensorFlow.js**\n2. **Device-based Models with TensorFlow Lite**\n3. **Data Pipelines with TensorFlow Data Services**\n4. **Advanced Deployment Scenarios with TensorFlow**\n\n## TF Lite Benefits\n- Lightweight + low-latency + privacy\n- Improved power consumption\n- Efficient model ready to use\n\n## Optimization Techniques\n- Quantization (all CPU platforms, reduced latency, low memory\n- Weight pruning\n- Model topology transforms\n- Tensor decomposition\n- Distillation\n\n#TensorFlow #EdgeAI #MobileML #FarshidPirahansiah\n" },{ "id": "/notes/pkm/use-cases/", "title": "Complete Portfolio", "url": "/notes/pkm/use-cases/", "tags": ["portfolio","computer-vision","edge-ai","github-projects"], "hashtags": "#portfolio #computervision #edgeai #github", "category": "pkm", "body": "last_modified_at: 2026-08-08\n> **Complete Portfolio** — Publications, patents, expertise, tools, and GitHub projects across CV and AI — https://www.pirahansiah.com/notes/pkm/use-cases/\nPublications, patents, expertise, tools, and GitHub projects across CV and AI\n\n*Last updated: 2026-08-08.* \n\n\n# Table of Contents\n\n- About #about\n- Publications #publications\n - Patents #patents\n - Books #books\n - Journals #journals\n - Conference Papers #conference-papers\n - Keynotes #keynotes\n- Camera Calibration Expertise #camera-calibration-expertise\n- Hardware & Platforms #hardware--platforms\n- Products & Tools #products--tools\n- Open Source #open-source\n- Technical Content #technical-content\n - CUDA & GPU Programming #cuda--gpu-programming\n - Optical Flow #optical-flow\n - 3D Vision & Multi-Camera #3d-vision--multi-camera\n - Optimization #optimization\n - AI & LLM #ai--llm\n- CV Coaching Roadmap #cv-coaching-roadmap\n- Courses #courses\n- Workshops & Events #workshops--events\n- GitHub Projects Portfolio #github-projects-portfolio\n\n---\n\n# About\n\nDr. Farshid Pirahansiah — Computer Vision expert with 12+ years R&D. 3 AI patents, 141+ citations (h-index 7, Springer book chapter author. Specializes in real-time image processing, edge AI across Jetson, Raspberry Pi, Hailo, Axelera, ARM. Full-stack CV/DL: model training → fine-tuning → deployment → API integration.\n\n**Metrics:** 21 publications (3 patents, 2 books, 6 journals, 11 conferences · h-index 7 · i10-index 5 · LinkedIn 55K+ · Facebook 15K+\n\n---\n\n# Publications\n\n## Patents\n\n### 1. Face Image Augmentation — WO 2021/060971 A1\n\nGe..." },{ "id": "/notes/pubs/patents/vehicle-detection/", "title": "Moving Vehicle Detection WO2021107761A1", "url": "/notes/pubs/patents/vehicle-detection/", "tags": ["patent","vehicle-detection","image-processing","traffic-surveillance"], "hashtags": "#patent #vehicledetection #imageprocessing #trafficsurveillance", "category": "hub", "body": "last_modified_at: 2026-08-08\n> **Moving Vehicle Detection WO2021107761A1** — Image processing method for detecting moving vehicles using CLAHE, Sobel, and geometric filtering. — https://www.pirahansiah.com/notes/pubs/patents/vehicle-detection/\nImage processing method for detecting moving vehicles using CLAHE, Sobel, and geometric filtering.\n\n*Last updated: 2026-08-08.* \n\n\nspotify https://podcasters.spotify.com/pod/show/pirahansiah/episodes/A-METHOD-FOR-DETECTING-A-MOVING-VEHICLE-WO2021107761A1-e2pk5mj\n\n\n\n\n \n Your browser does not support the audio element.\n\n\nPDF Download https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf \n\n\n{% if page.extname == \"vehicle-detection.md\" %}\n !A METHOD FOR DETECTING A MOVING VEHICLE WO2021107761A1 /notes/pubs/patents/vehicle-detection.png\n{% else %}\n \n{% endif %}\n\n\n\nPatent Summary:\n\n\nField of Invention:\n\nThis patent relates to a method for detecting moving vehicles, specifically in the field of traffic surveillance and law enforcement. It aims to improve vehicle detection accuracy under poor lighting conditions.\n\nBackground:\n\nExisting methods for detecting moving vehicles, such as edge processing and comparison between target and reference images, struggle under low light or bad weather conditions. This patent seeks to overcome these challenges.\n\nSummary of Invention:\n\nThe patented method enhances the detection of moving vehicles by processing video streams, focusing on improving the illumination and edge detection of the vehicle in the captured image. It involves:\n\n\t1.\tGrabbing an initial image from..." },{ "id": "/notes/wiki/", "title": "All Pages — pirahansiah.com", "url": "/notes/wiki/", "tags": [], "hashtags": "", "category": "hub", "body": "last_modified_at: 2026-08-16\n\nA complete alphabetical index of every page on pirahansiah.com — computer vision, AI, LLM, CUDA and edge-deployment knowledge base.\n\n*Last updated: 2026-08-16.* \n\n\n\n\n All Pages\n From pirahansiah.com, the free knowledge base\n\n \n \n \n\n \n {{ site.pages size }} pages\n 3 patents\n 6 journals\n 11 papers\n 7 books\n 19 courses\n \n\n \n Contents\n \n Home\n Computer Vision\n AI & LLMs\n CUDA & GPU\n Programming\n Optimization\n Business & Career\n PKM & Knowledge\n Patents\n Journals\n Conference Papers\n Books\n Keynotes\n Courses\n Presentations\n \n \n\n \n Home\n \n Dr. Farshid Pirahansiah — Main landing page\n Computer Vision — CV overview and research\n \n \n\n \n Computer Vision\n \n 3D Vision & Multi-Camera Systems\n Optical Flow AI\n Multi-Camera Systems\n CV Coaching Roadmap\n Product — Embedded CV & Edge AI\n Computer Vision Overview\n \n \n\n \n AI & LLMs\n \n Advanced LLM Concepts New\n Orchestrating AI Agents\n Blog: AI & LLMs\n Avatar Generator\n \n \n\n \n CUDA & GPU\n \n Numba JIT Tutorial\n PyCUDA Kernels\n CUDA in VS Code\n MLX, CoreML & Metal\n \n \n\n \n Programming\n \n C++ Quick Reference\n Python Configuration\n Developer Tools & Setup\n Shell & Vim Reference\n \n \n\n \n Optimization\n \n CV, DL & ML Optimization\n Prompt Engineering\n \n \n\n \n Business & Career..." }]