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2015_traffic_guide_analysis

A modern system guide to public transport users to track and know the arrivals and the estimated time of their travel.

2016_mobile_games_ranking_and_prediciton

To develop a Mobile Game Prediction System for top mobile games prediction and providing the range of downloads and estimated Ranking for the games along with the trends and traits of the top games, thereby enabling the developers to design a good game with ease.

2020_hashtag-counter-using-fibonacci-heap

Implementation of the hashtag counter model efficiently using a Fibonacci heap data structure to increase/decrease the frequency of keys in O(logN) complexity.

covid19-pooling-strategy

Due to the current increase in demand for the covid19 reagents, this project aims at using the test reagents and resources efficiently by pooling multiple test samples into a single sample.

dbi_midpoints

distributed-multimedia-systems_term-paper

fsharp-akka-actor-model-remote-deploy

The problem finding perfect squares that are sums of consecutive squares. A more interesting example is Lucas‘ Square Pyramid. The goal of this first project is to use F# and the actor model to build a good solution to this problem that runs well on multi-core machines.

fsharp-akka-lucas-square-pyramid-multi-machines

The problem finding perfect squares that are sums of consecutive squares. A more interesting example is Lucas‘ Square Pyramid. The goal of this first project is to use F# and the actor model to build a good solution to this problem that runs well on multi-core machines.

fsharp-akka-single-machine-lucas-square-pyramid

The problem finding perfect squares that are sums of consecutive squares. A more interesting example is Lucas‘ Square Pyramid. The goal of this first project is to use F# and the actor model to build a good solution to this problem that runs well on multi-core machines.

fsharp-samples

F# Akka actor samples - initial learning

gossip-simulator

Gossip type algorithms can be used both for group communication and for aggregate computation. The goal of this project is to determine the convergence of such algorithms through a simulator based on actors written in F#. Since actors in F# are fully asynchronous, the particular type of Gossip implemented is the so called Asynchronous Gossip.

pastry_protocol

Implemented a Pastry algorithm by handling network join and routing apis. When a pastry node is given a message and key, using the pastry algorithm it routes to the live node which is numerically closest to the key. Reference: ​Pastry: Scalable, decentralized object location and routing for large-scale peer-to-peer systems by Antony Rowstron and Peter Druschel

peertopeer-filesharing

In this project, you are asked to write a P2P file sharing software similar to BitTorrent. You can complete the project in Java or C/C++. There will be no extra credit for C/C++. BitTorrent is a popular P2P protocol for file distribution. Among its interesting features, you are asked to implement the choking-unchoking mechanism which is one of the most important features of BitTorrent. In the following Protocol Description section, you can read the protocol description, which has been modified a little bit from the original BitTorrent protocol. After reading the protocol description carefully, you must follow the implementation specifics shown in the Implementation Specifics section.

twitter-clone

Implemented a Twitter server engine using suave framework which provides support for both Websockets and Http and performed REST operations like GET and POST. The server engine uses an actor to push the live feeds to the online users through websockets. Implemented a simple client - UI in the browser, using HTML and JavaScript which invokes connections with the server using websockets and http.

twitter-simulator

Implement a Twitter like engine using the Akka actors model and support the following functionalities: Register, Tweet, Subscribe, Re-Tweet and Query specific to hashtags and mentions. Also have to develop a client simulator for generating the requests and subscribers are distributed based on Zipf distribution model.