rahulbhadani Goto Github PK

qcom

quantum-guaquamole

Curated Definitions from Quantum Information Processing

quantumcomputingexamples

Quantum computing examples with QISKit.

quantumspectral-python

Spectral Solver for Schrodinger-Poisson Quantum System

rahulbhadani.github.io

Personal Webpage

rca

Residual Component Analysis

re_trajectory-generator

KUKA LWR Tools

recipes

rendezvous

Rendezvous is a simple javascript GUI that runs in your browser and allows to track the position of your device/robot on a global map.

research

rnn

Recurrent Neural Network Implementation based on WildML's tutorial

rnn-tutorial-rnnlm

Recurrent Neural Network Tutorial, Part 2 - Implementing a RNN in Python and Theano

roboticssoftwareengineer

Begin your exploration into the world of robotics software engineering with a practical, system-focused approach to programming robots using the ROS framework and C++. In addition, learn and apply robotics software engineering algorithms such as localization, mapping, and navigation.

rodeo

ros-sharp

ROS# is a set of open source software libraries and tools in C# for communicating with ROS from .NET applications, in particular Unity3D

ros2-enigma

Memoirs of a ROS2 learner

ros_exploring

《ROS机器人开发实践》源码

rosbag.js

ROS bag file reader for JavaScript 👜

rosbook

Example code to accompany the book Programming Robots with ROS

rosjava_core

An implementation of ROS in pure Java with Android support.

rviz

ROS 3D Robot Visualizer

scda

segment coronagraph design & analysis

scgraphautoencoder

scgraphnet

scientific-python-training-2012

STSCI's Scientific Python Course 2012

scomix

Single-Cell Data Science

scomix_notebook

Notebook with use-cases from scomix

scvelo

RNA Velocity generalized through dynamical modeling

self-driving-car-specialization

This repository provides the a comprehensive understanding of state-of-the-art engineering practices used in the self-driving car industry. You'll get to interact with real data sets from an autonomous vehicle (AV) ― all through hands-on projects using the open source simulator CARLA.

sensorfusionnanodegree

Learn to detect obstacles in lidar point clouds through clustering and segmentation, apply thresholds and filters to radar data in order to accurately track objects, and augment your perception by projecting camera images into three dimensions and fusing these projections with other sensor data. Combine this sensor data with Kalman filters to perceive the world around a vehicle and track objects over time.