LinMIxiaogu
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Final Project of Columbia COMS 4995 Applied Deep Learning
Uses Tensorflow Object Detection API to detect and track hands as well as estimate their distance from the camera
This project performs sensor fusion to track a mobile device's orientation. The data utilised are from three sensors: a) Accelerometer, b) Magnetic Field, d) Gyroscope. The sensor fusion is execute…
Orientation estimation using Smartphone Sensors
Hand-tracking model using Python, OpenCV, Pyautogui, and MediaPipe. It detects hand landmarks and calculates distances, ideal for VR, sign language, and gesture interfaces. A fusion of computer vis…
Detects and tracks hand(s) and calculates the distance of the hand from the screen using OpenCV and Python
这是一个 Flutter Packge 以实现摄像头精确追踪并识别十指的运动路径/轨迹和手势动作, 且输出22个手部关键点以支持更多手势自定义. 基于这个包可以编写业务逻辑将手势信息实时转化为指令信息: 一二三四五, rock, spiderman...还可以对不同手势编写不同特效. 可用于短视频直播特效, 智能硬件等领域, 为人机互动带来更自然丰富的体验
a motion tracking by combining the imu and camera(using KLT method) in android mobile
Deep learning library for node.js. (Includes Logistic-Regression, MLP, RBM, DBN, CRBM, CDBN)
Deep Learning with PyTorch Quick Start Guide, published by Packt
Power allocation in a dense Millimeter Wave network using Q-learning
A deep reinforcement learning based approach is used to allocate downlink power for multi-cell wireless system.
Reinforcement learning environment for MIMO communications.
Reinforcement learning for channel assignment in wireless mesh network
RL-Wireless: Reinforcement learning-based resource allocation in wireless networks
Resource allocation for Device-to-Device (D2D) communications using deep reinforcement learning.
Project regarding Resource allocation using RL
Optimizing resource allocation with deep reinforcement learning
Resource allocation for underlay DSA Cognitive Radio networks using reinforcement learning (Q-Learning))
Reproduce results of the research article "Deep Reinforcement Learning Based Resource Allocation for V2V Communications"