mambo225's Stars
vpeterson/MI-OpenBCI
You will find here the raw EEG and EMG data acquired by using the OpenBCI kit, as well as the OpenViBE scenarios and the Matlab Codes used for the protocol display and post-processing, respectively.
michidk/myo-dataset
This repository contains sEMG Data of 13 subjects recorded with the Myo Armband.
aljazfrancic/myo-readings-dataset
Myo armband electromyographic readings dataset for various wrist gestures.
Gabrock94/Pysiology
A Python package for physyological's signals processing
SebastianRestrepoA/EMG-pattern-recognition
timothybrooks/instruct-pix2pix
Valkyrja3607/tuning_playbook_ja
ディープラーニングモデルの性能を体系的に最大化するためのプレイブック
google-research/tuning_playbook
A playbook for systematically maximizing the performance of deep learning models.
kakitamedia/drone_dataset
xyanchen/WiFi-CSI-Sensing-Benchmark
justimyhxu/awesome-3D-generation
A curated list of awesome 3d generation papers
chenhsuanlin/3D-point-cloud-generation
Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction :grapes: (AAAI 2018)
AgaMiko/waste-datasets-review
List of image datasets with any kind of litter, garbage, waste and trash
Sanster/IOPaint
Image inpainting tool powered by SOTA AI Model. Remove any unwanted object, defect, people from your pictures or erase and replace(powered by stable diffusion) any thing on your pictures.
NiklasRosenstein/myo-python
Python bindings for the Myo SDK
balandinodidonato/MyoToolkit
A list of all third party code written for the Myo armband
Deephome/Awesome-LiDAR-Camera-Calibration
A Collection of LiDAR-Camera-Calibration Papers, Toolboxes and Notes
lucidrains/DALLE2-pytorch
Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
lucidrains/stylegan2-pytorch
Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement
lucidrains/imagen-pytorch
Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch
open-dynamic-robot-initiative/open_robot_actuator_hardware
miyamotok0105/opencv_sample
Sample for study myself.
Insta360Develop/CameraSDK-Cpp
CameraSDK-Cpp is a C++ library to control Insta360 cameras.
mangdangroboticsclub/QuadrupedRobot
Open-Source,ROS Robot Dog Kit
stanfordroboticsclub/Pupper
Build instructions and code documentation for the Stanford Pupper project.
Open-Bionics/Ada_3D_model_files
The STL & blender files for the Ada hand
open-dynamic-robot-initiative/robot_properties_solo
Developer-Y/cs-video-courses
List of Computer Science courses with video lectures.
tsyoshihara/Alzheimer-s-Classification-EEG
Alzheimer’s Disease (AD) is the most common neurodegenerative disease. It is typically late onset and can develop substantially before diagnosable symptoms appear. Electroencephalogram (EEG) could potentially serve as a noninvasive diagnostic tool for AD. Machine learning can be helpful in making inferences about changes in frequency bands in EEG data and how these changes relate to neural function. The EEG data was sourced from 2014 paper titled Alzheimer’s disease patients classification through EEG signals processing by Fiscon et al. There were patients with AD, mild cognitive impairment (MCI), and healthy controls. The data was already preprocessed using a fast fourier transform (FFT) to take the data from the time domain to the frequency domain. There were differing levels of effectiveness in terms of classification but generally, Fisher’s discriminant analysis (FDA), relevance vector machine, and random forest approaches were most successful. Due to inconsistent feature importances in different models, conclusions about important frequency bands for classification were not able to be made at this time. Similarly, different frequencies were not able to be localized to different regions of the brain. Further research is necessary to develop more interpretable models for classification.
DengpanFu/LUPerson
Unsupervised Pre-training for Person Re-identification (LUPerson)