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arjun-k-r's Projects

friendlypix-web icon friendlypix-web

FriendlyPix is a cross-platform Firebase example app - This is the web version

get-me-through icon get-me-through

A Free, Offline, Real-Time, Open-source web-app to assist organisers of any event in allowing only authorised/invited people using Face-Recognition Technology or QR Code.

guacamole-client icon guacamole-client

The HTML5/JavaScript Guacamole client, its containing web application, and related components.

html5-boilerplate icon html5-boilerplate

A professional front-end template for building fast, robust, and adaptable web apps or sites.

humhub icon humhub

HumHub - Open Source Social Network

ionic-firestarter icon ionic-firestarter

Ionic Firestarter is a open source project showcasing different Firebase features implemented in Typescript via the Ionic framework.

js-to-do icon js-to-do

A Simple To Do Web App using just JavaScript.

kite-trader icon kite-trader

An algorithmic trading platform for Zerodha's Kite-Connect

kiteconnectjs icon kiteconnectjs

The official NodeJs client library for the Kite Connect trading APIs

markets icon markets

Automated options trading for Android.

mir icon mir

web app powering mirai.audio

mobileapp_ionic icon mobileapp_ionic

This is the frontend of the mobile app ofthe farmer produce trading platform.It will be built using ionic and cordova .

mocapnet icon mocapnet

We present MocapNET, a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose NSRM representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the Human 3.6 Million (H3.6M) dataset compared to the baseline method (MocapNET) while maintaining real-time performance

nsetools icon nsetools

Realtime Data From National Stock Exchange (India)

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