aco_path_planning

Python implementation of the ACO algorithm for solving path planning problems

alarm-raspberrypi

Alarm Clock

awesome-github-profile-readme-templates

This repository contains best profile readme's for your reference.

bmp180_attiny85

The BMP180 is the next-generation of sensors from Bosch and replaces the BMP085. This precision sensor from Bosch is the best low-cost sensing solution for measuring barometric pressure and temperature.

cv-course

Assigments from computer vision course conducted at University of Washington by Prof. Joseph Redmon

depth_using_pointcloud

homeautomation

monaco-react

Monaco Editor for React - use the monaco-editor in any React application without needing to use webpack (or rollup/parcel/etc) configuration files / plugins

office_automation

portfolio

Portfolio Website

pyleargist

My version of pyleargist, adds support for BW images and lets you pass in Numpy images directly.

r-hex

Negotiating difficult terrains has always been a challenging task for wheeled robots. To solve this issue, man has been trying to design various legged robots, taking inspiration from nature. Out of all these legged robots, six legged robots are the most common ones, since 6 legs have an edge over other legged robots considering their stability. In hexapods, generally a tripod gait is in used in which 2 sets of legs(3 each ) are alternatively on the ground. This hexapod is our attempt to tackle the same issue of all terrain driving capabilities. Instead of normal legs we have used C-shaped legs which have an added advantage of climbing obstacles, pretty taller than itself.

radhikatekade

robotsystems

ROB 599 - Introduction To Robotics II

shubham-p-patil

shubham-p-patil.github.io

portfolio

stock_trend_prediction_using_setiment_analysis_of_news_data.

I scrape the news data using webhose.io and stock price data using nsepy. then I labelled the data using information about rising or fall in prices corresponding to news scraped for that day. For the word embeddings, I used GloVe provided by Stanford University. I used stop words from NLTK to remove sentence fillers that do not change the context. Then I used TF-IDF to remove the words that provide the least information. then I used LSTM to map any time-dependent relations in the data set and predicted if a user should "buy" or "sell" the share and with what confidence based on today's news events.

superresolutionandrestoration

watch-schematic