Knowledge on PYTHON and C

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CURRICULAM VITAE

Preetha M Present Address

Email:*************@*****.*** 34, Sundaram Mudhaliyar Street, Mobile: 887******* Arcot, Ranipet District

Pin Code - 632503

To pursue challenging and rewarding career in the esteemed organization. This will be mutually helpful for my continuous learning, research and contribution to organizational growth ACADEMIC PROFILE:

B.E., ECE from Kingston Engineering College, Anna University (8.4%) 2018 – 2022 HSC - Sri Ramakrishna Matric. Hr. Sec. School, Arcot (8.7%) 2017 - 2018 SSLC - Sri Shanthinikethan Matric. School, Arcot (9.1%) 2015 – 2016

Languages : Python, C (Intermediate)

Typing : English

ERP Packages : MS office

ACHIEVEMENTS

Participated in paper presentation on Robotics organized by Kingston Engineering College

Done a Technical Seminar on Image Processing in Electronics Participated in E-Quiz on Fundamentals of Electronics Winner in National level E-Quiz on Embedded Systems Won the Code Vita problem solving competition conducted by TCS Completed Internship at Codebind Technologies on Embedded Systems

PROJECT NAME: A DEEP LEARNING TO PERDICT FADING CHANNEL IN MIMO SYSTEMS USING DENSENET ALGORITHM

-Duration: 6 months

Technologies used: MATLAB,PYTHON

Career Objective:

Technical Skills:

Project Details:

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PROJECT DESCRIPTION

• Channel State Information (CSI),which enables wireless systems to adapt their transmission parameters to instantaneous channel conditions and consequently achieve greater performance, place an increasingly vital role in mobile communications.

• However, getting accurate CSI is challenging due to rapid channel variation caused by multi-path fading. The inaccuracy imposes impact on performance of wide range of adaptive wireless systems.

• Hence we propose a novel predictor, leveraging the strong time-series capability of deep learning.

• A deep learning method is used to predict channel fading in Channel State Information(CSI).

• The main objective of this project was to improve the signal to noise ratio in multi antenna systems. To improve this result two algorithms namely RNN and CNN are combined ie., the hybrid of RNN and CNN was performed. To implement the hybrid version of the algorithm, two models are used. They are DenseNet and ResNet model. These were combined using the DenseNet model by adding dense layer to the ResNet model. Thus by using the above stated algorithms and models the vanishing gradient can be decreased. Hence the SNR can be improved.

Date of birth : 15 Dec 2000

Father's Name : Murugan K

Languages known : English, Tamil, Telugu

Marital Status : Unmarried

Nationality : India

Pan Card No : ENZPP7124Q

AREA OF INTEREST:

Networking

Communication

DECLARATION

I hereby declare that the above furnished details are true to the best of my knowledge. Place: Chennai

Date: Preetha M

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