Samayam Renuka Satya Surya Sai
@22530015
M.Tech. System and Control
PG (I Year I Semester)

Student at IITR

Achievements

Achieved 10/10 CGPA in SSC(10th class)(2016).
Secured 202 State Rank In The APRJC Entrance Exam After 10th Class(2016).
Achieved All India Gate Rank Of 1380 In ECE Paper (2022) , Achieved All India Gate Rank Of 427 In Instrumentation Paper(2022).
Received Jnana Bhoomi Post-Metric Scholarship After 10th Class Onwards While Studying At RGUKT , Nuzvid(2016-2022).
Got First Prize In The Event 'Google Master' Which Was Conducted As Part Of The Techfest(Teckzite) Of RGUKT , Nuzvid.
Got Second Position As A Team In The Event Of Quiz Conducted As Part Of The Techfest(Teckzite) Of RGUKT , Nuzvid.
Selected For State Level Chekumuki Talent Test By getting 1st Place In Both Mandal Level And District Level As A Team Of 3 Members(2016).

Previous Education

Systems And Control - Graduate (UG) in Electronics And Communication Engineering
Rajiv Gandhi University Of Knowledge And Technologies , Nuzvid, 2022
CGPA: 8.970
Pre University Course - Intermediate (Class XII) in MPC
Rajiv Gandhi University Of Knowledge And Technologies , Nuzvid, 2018
CGPA: 9.480
SSC - Matriculate (Class X) in
SJNZP HIGH SCHOOL , PEDANINDRAKOLANU, 2016
CGPA: 10.000

Projects

Summary Generator in Rajiv Gandhi University Of Knowledge And Technologies , Nuzvid
Mar 2021 to Apr 2021

1.Internet is flooded with Textual data. 2.The internet age has brought unfathomably massive amounts of information to the fingertips of billons - if only we had time to read it. 3.Text summarization helps to direct people's attention to the most important contents and saves tremendous human labor for digging through the documents.

Bird Species Detection Through Sound Using Deep Learning in Rajiv Gandhi University Of Knowledge Technologies, Nuzvid
Aug 2021 to Apr 2022

We present a robust classification approach for avian vocalization in complex and diverse soundscapes. We illustrate how to make full use of pre-trained convolutional neural networks, by using an efficient modeling and training routine supplemented by novel augmentation methods. Thereby, we improve the generalization of weakly labeled crowd-sourced data to productive data collected by autonomous recording units. As such, we illustrate how to progress towards an accurate automated assessment of avian population which would enable global biodiversity monitoring at scale, impossible by manual annotation.

Last Published on: 13 August 2022, 13:16:10