Securade.ai HUB - A generative AI based edge platform for computer vision that connects to existing CCTV cameras and makes them smart.

Signed MQTT deployments for edge fleets: control plane + agent, mTLS, Ed25519 command verification, Docker/K8s.

A lightweight, edge-first web framework built on Cloudflare Workers with authentication, D1 database, and a clean dark UI. Deploy a blog or small site globally in under 5 minutes. Version 1.0

Ingredient to Sugar Level Estimation (from training in Python to edge deployment in JS/TS)

Proyek ini menggunakan kerangka deteksi objek berbasis YOLO (You Only Look Once) untuk memantau ternak ayam secara real-time lewat kamera CCTV IP. Sistem kemudian mengintegrasikan hasil deteksi dengan komponen IoT (seperti kamera, pengiriman data via MQTT/HTTP, dan perangkat edge)

This project is an AI-powered mobile application capable of recognizing age, gender, and facial expressions from images.

Real-time SAM2 segmentation on edge devices - 40x faster C++ inference with ONNX Runtime for iOS/Android deployment

Light-weight 6D pose estimation for Edge devices

Production-ready responsive web interface built with semantic HTML5, CSS3, and vanilla JavaScript. Focused on layout fluidity, static rendering performance, and edge delivery optimization.

Example app using React Create App & Digital Optimization Group's ADN & CMS

This repository contains the complete pipeline for an edge-deployable computer vision model designed to analyze images and detect insulator defects. The model is trained to be lightweight and optimized, ensuring it runs efficiently on edge devices like the Nvidia Jetson Nano.

Code of the paper "Emotion Recognition on Edge Devices: Training and Deployment " by Pandelea et al.

YOLOv3-YOLO12 unified pipeline for edge deployment - Detection, segmentation, pose estimation with PyTorch to ONNX/TFLite/CoreML export

Industrial computer vision workflow for welding defect inspection using YOLO, OpenCV preprocessing, dataset QA, threshold governance, and edge-readiness analysis.

Toolset for creating and publishing OS images with automated TPM attestation process for Azure IoT Edge.

Optimized CNN achieving ~89% accuracy with 38.6% parameter reduction for production-ready digit recognition

Task-adaptive pruning framework for deploying Vision Transformers on heterogeneous edge devices without accessing private data (arXiv 2601.02437)

POSIX-compliant configuration parser for systematic build coordination with deterministic pass-mode resolution. Phase 1 implementation establishing foundational architecture for modular component discovery, threading infrastructure, and systematic validation within the NexusLink ecosystem. Waterfall methodology with comprehensive quality assurance.

UVA DS 6050 final project. This aims to build smaller models that are easier to use on edge devices

YOLOE-Unified is a novel framework that integrates YOLOE with distilled CLIP, runtime SAM refinement, and TensorRT optimization for efficient open-vocabulary object detection and instance segmentation on edge devices (Jetson Orin, etc.).