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arl-eegmodels

This is the Army Research Laboratory (ARL) EEGModels Project: A Collection of Convolutional Neural Network (CNN) models for EEG signal classification, using Keras and Tensorflow

arm-track

asagao-classification

blockly-angular

blockly-react

bodypose3d

Real time 3D body pose estimation with Mediapipe

capilab-hompage

cmsis_5

CMSIS Version 5 Development Repository

cnmp

cpe-312-1-61

deep-modality-blending-networks

Code Repository of the Paper "Imitation and Mirror Systems in Robots through Deep Modality Blending Networks"

dqn_chsel

eeg_ch_optims

esdt-assignment2

evolutionaryalgorithm

TAIST Thesis

hivemind

Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.

jetson_nano

This repository is a collection of scripts/programs I use to set up the software development environment on my Jetson Nano, TX2, and Xavier NX.

kagerou

ACT-FF14 OverlayPlugin Skin - Modern, easily configurable, always up-to-date.

mi-eeg-1d-cnn

A new approach based on a 10-layer one-dimensional convolution neural network (1D-CNN) to classify five brain states (four MI classes plus a 'baseline' class) using a data augmentation algorithm and a limited number of EEG channels. Paper: https://doi.org/10.1088/1741-2552/ac4430

nooobot

fricking simple and stupid robot simulation

openbci_lsl

An application for establishing an OpenBCI stream through the Lab Streaming Layer (LSL)

openvslam

OpenVSLAM: A Versatile Visual SLAM Framework

poe-vae

A modular implementation of product of experts VAEs for multimodal data

progressive-neural-architecture-search

Implementation of Progressive Neural Architecture Search in Keras and Tensorflow

proj_lists

Disclosure of past experience project

qteegsampling

rl_channel_select

rose2023_tutorial

sdk

sdk-1

temi is an unparalleled robotic platform introducing a new dimension of development - movement. Using temi’s SDK, developers can create new functionalities and introduce new use cases via temi’s Android tablet. temi’s movement and navigation capabilities run off of it's Linux computer and uses a set of 16 sensors including a Lidar, depth cameras, driving cameras, and microphones. We encourage our developer community to suggest and request expanded functionality within the SDK and we will adhere to your needs. We want to enable you to create skills like never seen before! We will be constantly improving the SDK and its documentation. Please feel free to reach out to us with any questions or thoughts at [email protected]