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drl-robot-navigation

Deep Reinforcement Learning for mobile robot navigation in ROS Gazebo simulator. Using Twin Delayed Deep Deterministic Policy Gradient (TD3) neural network, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.

drwn

Darwin: A Framework for Machine Learning Research and Development

dsv_planner

Dual-Stage Viewpoint Planner for Autonomous Exploration

dw1000server

DW1000 Server

dynamic-memory-networks-in-theano

Implementation of Dynamic memory networks by Kumar et al. http://arxiv.org/abs/1506.07285

dynamic_display_astar

dynamic_robot_localization

Dynamic Robot Localization

eagleye

Precise localization based on GNSS and IMU.

easyeye

Iris image segmentation, encoding, and matching software

easyplayer

An elegant, simple, fast RTSP/RTMP/HLS/HTTP Player.EasyPlayer support RTSP(RTP over TCP/UDP)version& RTMP version & Pro version，cover all kinds of streaming media！EasyPlayer是一款精炼、高效、稳定的流媒体播放器，分为RTSP版、RTMP版和Pro版本，支持各种各样的流媒体音视频播放！

easyplayerpro-win

EasyPlayerPro是一款全功能的流媒体播放器，支持RTSP、RTMP、HTTP、HLS、UDP、RTP、File等多种流媒体协议播放、支持本地文件播放，支持本地抓拍、本地录像、播放旋转、多屏播放、倍数播放等多种功能特性，核心基于ffmpeg，稳定、高效、可靠、可控，支持Windows、Android、iOS三个平台，目前在多家教育、安防、行业型公司，都得到的应用，广受好评！

ebook-1

A collection of classic computer science books from Internet

ecg

A repository for work on the ECG project

ecg-1

A simple ECG mobile software against Android platform

ecg-baseline

ECG signal baseline filtering using Savitzky-Golay, Zero-phase Butterworth and Wavelets filters

ecg-classification

ECG signal classification using Machine Learning

ecg_cusum_analysis

Myocardial Infarction is one of the fatal heart diseases. It is essential that a patient is monitored for the early detection of MI. Owing to the newer technology such as wearable sensors which are capable of transmitting wirelessly, this can be done easily. However, there is a need for real-time applications that are able to accurately detect MI non-invasively. This project studies a prospective method by which we can detect MI. Our approach analyses the ECG (electrocardiogram) of a patient in real-time and extracts the ST elevation from each cycle. The ST elevation plays an important part in MI detection. We then use the sequential change point detection algorithm; CUmulative SUM (CUSUM), to detect any deviation in the ST elevation spectrum and to raise an alarm if we find any.

ecgandroid

Android平台下的Ecg绘图

ecgtoolbox

ECG signal processing---including basic wave detection, signal denoising, signal reconstruction metrics

ecgtoolkit

C# ECG Toolkit. Support for: SCP-ECG, DICOM, HL7 aECG, ISHNE & MUSE-XML. (Migrating from SourceForge ecgtoolkit-cs)

ekf_imu_gps

Extended Kalman Filter predicts the GNSS measurement based on IMU measurement

ekfoa

Monocular camera based real-time on-board obstacle avoidance for unmanned aerial vehicles.

electrocardiogram

Chart Egnine 心电图

elevation_mapping

Robot-centric elevation mapping for rough terrain navigation

emodetect

A project that enables computers to identify human emotion

emorec

Facial Emotion Recognition

emotime

Recognizing emotional states in faces

emotion-conv-net

Real-time emotion recognition using convolutional neural nets.

emotion-detection

Automatically exported from code.google.com/p/emotion-detection

emotion-detection-in-videos

The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.