shashwat-kumar-phd

nns3_handover

NS3 program for comparision of handover algorithims.

go_kafka

Project on how to use Sarama producer and consumer with Kafka.

2020-DQM-UV

DASH metrics capture software

pointcloud_registration

This module provides functions for point cloud registration using Open3D. It includes functions for preprocessing point clouds, executing global registration, refining registration using ICP, and performing fast global registration.

2019-360dataset

A Taxonomy and Dataset for 360-degree videos

2020-5Gdataset

In this work, we present a 5G trace dataset collected from a major Irish mobile operator. The dataset is generated from two mobility patterns (static and car), and across two application patterns(video streaming and file download). The dataset is composed of client-side cellular key performance indicators (KPIs) comprised of channel-related metrics, context-related metrics, cell-related metrics and throughput information. These metrics are generated from a well-known non-rooted Android network monitoring application, G-NetTrack Pro. To the best of our knowledge, this is the first publicly available dataset that contains throughput, channel and context information for 5G networks. To supplement our real-time 5G production network dataset, we also provide a 5G large scale multi-cell ns-3 simulation framework. The availability of the 5G/mmwave module for the ns-3 mmwave network simulator provides an opportunity to improve our understanding of the dynamic reasoning for adaptive clients in 5G multi-cell wireless scenarios. The purpose of our framework is to provide additional information (such as competing metrics for users connected to the same cell), thus providing otherwise unavailable information about the basestation (eNodeB or eNB) environment and scheduling principle, to end user. Our framework permits other researchers to investigate this interaction through the generation of their own synthetic datasets.

2020-BiQPS

AMuSt-ndnSIM

Adaptive Multimedia Streaming with ndnSIM

apreshill

Personal website of Alison Presmanes Hill

baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

shashwat-kumar-phd/Shashwat-Kumar-PhD

shashwat-kumar-phd/go_kafka

Project on how to use Sarama producer and consumer with Kafka.

shashwat-kumar-phd/Shashwat-Kumar-PhD.github.io

My Website

shashwat-kumar-phd/nns3_handover

NS3 program for comparision of handover algorithims.

shashwat-kumar-phd/streamlit-example

Example Streamlit app that you can fork to test out share.streamlit.io

shashwat-kumar-phd/pointcloud_registration

This module provides functions for point cloud registration using Open3D. It includes functions for preprocessing point clouds, executing global registration, refining registration using ICP, and performing fast global registration.

shashwat-kumar-phd/CourseraSupervisedML

Code base of all the assignments in "Supervised Machine Learning: Regression and Classification" course

shashwat-kumar-phd/GoTesting

shashwat-kumar-phd/go-practice

Code dump to learn Go

shashwat-kumar-phd/mojo

The Mojo Programming Language

shashwat-kumar-phd/tapas360

TAPAS-360°: a Tool for the Design and Experimental Evaluation of 360° Video Streaming Systems

shashwat-kumar-phd/myblog

shashwat-kumar-phd/2020-BiQPS

shashwat-kumar-phd/2020-5Gdataset

In this work, we present a 5G trace dataset collected from a major Irish mobile operator. The dataset is generated from two mobility patterns (static and car), and across two application patterns(video streaming and file download). The dataset is composed of client-side cellular key performance indicators (KPIs) comprised of channel-related metrics, context-related metrics, cell-related metrics and throughput information. These metrics are generated from a well-known non-rooted Android network monitoring application, G-NetTrack Pro. To the best of our knowledge, this is the first publicly available dataset that contains throughput, channel and context information for 5G networks. To supplement our real-time 5G production network dataset, we also provide a 5G large scale multi-cell ns-3 simulation framework. The availability of the 5G/mmwave module for the ns-3 mmwave network simulator provides an opportunity to improve our understanding of the dynamic reasoning for adaptive clients in 5G multi-cell wireless scenarios. The purpose of our framework is to provide additional information (such as competing metrics for users connected to the same cell), thus providing otherwise unavailable information about the basestation (eNodeB or eNB) environment and scheduling principle, to end user. Our framework permits other researchers to investigate this interaction through the generation of their own synthetic datasets.

shashwat-kumar-phd/2020-DQM-UV

DASH metrics capture software

shashwat-kumar-phd/apreshill

Personal website of Alison Presmanes Hill

shashwat-kumar-phd/DHP

Predicting Head Movement in Panoramic Video: A Deep Reinforcement Learning Approach. TPAMI 2018.

shashwat-kumar-phd/mpath-simulator-percom

shashwat-kumar-phd/baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

shashwat-kumar-phd/spinningup

An educational resource to help anyone learn deep reinforcement learning.

shashwat-kumar-phd/prophet

Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

shashwat-kumar-phd/reinforcement-learning-an-introduction

Python Implementation of Reinforcement Learning: An Introduction

shashwat-kumar-phd/2019-360dataset

A Taxonomy and Dataset for 360-degree videos

shashwat-kumar-phd/MarTile

This is a simulation tool for generating Tile Distribution of 360-degree video streaming

shashwat-kumar-phd/EdgeCloudSim

EdgeCloudSim: An Environment for Performance Evaluation of Edge Computing Systems

shashwat-kumar-phd/dopamine

Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.

shashwat-kumar-phd/Paradrop

An Edge-computing platform based on Wi-Fi routers

shashwat-kumar-phd/rsportal

shashwat-kumar-phd/dash.js

A reference client implementation for the playback of MPEG DASH via Javascript and compliant browsers.

shashwat-kumar-phd/ndnSIM

ndnSIM: NS-3 based NDN simulator