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bilibili346623

Examples shown in Bilibili Live 346623

caffe

Caffe: a fast open framework for deep learning.

cnn

This is a matlab-code implementation of convolutional neural network

db1-ninapro-semg-classification-

deep-learning-for-image-classification

This project has a two-deep learning methods.1) Deep learning using by CNN. 2) Deep learning using Transfer learning using Alexnet. The second method uses two types of image dataset one is of flowers and the other is of roundworms.

deeplearntoolbox

Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.

dncnn

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017)

emg-feature-extraction-toolbox

This toolbox offers 40 feature extraction methods (EMAV, EWL, MAV, WL, SSC, ZC, and etc.) for Electromyography (EMG) signals applications.

github-slideshow

A robot powered training repository :robot:

hello--world

another reposlitory

hello-world

another learning

markdown-portfolio

mayloveorange.github.io

robotic-exoskeleton-for-arm-rehabilitation-rexar-

Rehabilitation of people afflicted with elbow joint ailments is quite challenging. Studies reveal that rehabilitation through robotic devices exhibits promising results, in particular exoskeleton robots. In this work, 1 degree of freedom active upper-limb exoskeleton robot with artificial intelligence aided myoelectric control system has been developed for elbow joint rehabilitation. The raw surface electromyogram (sEMG) signals from seventeen different subjects for five different elbow joint angles were acquired using the Myo armband. Time-domain statistical features such as waveform length, root mean square, variance, and a number of zero crossings were extracted and the most advantageous feature was investigated for Artificial Neural Network (ANN) – a backpropagation neural network with Levenberg-Marquardt training algorithm and Support Vector Machine (SVM) – with Gaussian kernel. The results show that waveform length consumes the least amount of computation time. With waveform length as an input feature, ANN and SVM exhibited an average overall classification accuracy of 91.33% and 91.03% respectively. Moreover, SVM consumed 36% more time than ANN or classification.

semg

semg_deeplearning

sEMG-based gesture recognition using deep learnig

semg_sdk

SDK of sEMG function of matlab

surface-emg

sEMG function in MATLAB

tracking

some visual tracking methods and source codes

transfer-learning-using-matlab

Transfer Learning for CNN based Image Classification Networks

trepnet

TRepNet: Ensemble Denoising Autoencoder for Time Series Representation Learning

xdupgthesis

西安电子科技大学研究生学位论文XeLaTeX模板