Rashmi N

Sports Analytics Platform: (In-house research project):

• Video Streaming service: Built a streaming service that converts images processed by the Deep Learning model into an almost live-stream video using python and ffmpeg.
• Deep Learning: Researched and worked on neural network architectures for classification through have gained a subject overview and hands-on experience in using the ML and Deep Learning libraries.
• API Service: Worked on exposing the platform services as APIs using python-flask-mongoDb.

YOKOGAWA (A japanese electrical and software company) : A Web Application to build industry specific application to monitor and manage IOT sensors.

• API Builder : ( Java, SpringBoot and Activiti) Built a web application that can use drag and drop blocks to build a workflow that has the logic of the API to be built.
• Simulation service : (Java, SpringBoot) To mock the behavior of an IOT device ,built a service that pushes device-generated type data to the IOT hub.
• Listeners : (Microsoft Azure Event Hub) Built an event Hub using Microsoft Azure and a listener to listen to the events and trigger actions based on the events. Built a device hub listener that listens to Microsoft Azure's device Hub to get the changes in device values of an IOT sensor.
• DevOps : (Kubernetes) Build CI/CD pipelines and manage manual deployments. Manage databases and its backups by using Azure's volume mounts.

Analysis of classification techniques on RADAR images : (Python and OpenCV) : Assisted in the researched of preprocessing RADAR images for detecting land mines- implemented mean trace and least mean square algorithms from scratch to filter the noise from the images.

Analyzed the classification efficiency of SVM,ANN and RVM classifiers on the two different preprocessed images