Hey, Welcome to my profile!

I'm
An Embedded Engineer A PCB Designer An Electronics Maker

I am working at IIT Madras as a Project Associate in Embedded System Design. I am always open for sharing and earning knowledge about new techs.

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Experiences

Present

Project Associate

IC & SR IIT Madras, Chennai[ Dec 2021 - Present ]

I am working as a researcher in domains like Microwave Photonics, Signal Processing, Modern FPGAs and specially assisting in the development and implementation of Next Generation Photonic Analog-to-digital converter (PADC).

2021

Embedded Engineer

Semicon Media Pvt. Ltd.[ Sept 2021 – Nov 2021 ]

My role was to develop and build the projects with proper documentations forCIRCUIT DIGEST

2021

Electronics Engineering Intern

Semicon Media Pvt. Ltd.[ June 2021 – Sept 2021 ]

My role was to learn about the project managements with proper documentations. And prepare detailed electronics projects forCIRCUIT DIGEST

Educations

2021

Electronics and Communication Engineering[ B.Tech ]

Rajasthan Institute of Engineering and Technology
Jaipur, Rajasthan.

2017

West Bengal Higher Secondary Examination[ 12th ]

Purulia Zilla School
Purulia, West Bengal

2015

West Bengal Madhyamik Examination[ 10th ]

Chittaranjan High School
Purulia, West Bengal

Academic Projects

RESEARCH WORKS

PAPER
An Arbitrary Biased EOM-based Pulse-Picker with Programmable Repetition Rate using FPGA

Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC), 2023

An Arbitrary Biased EOM-based Pulse-Picker with Programmable Repetition Rate using FPGA

Abstract: We experimentally demonstrate an arbitrarily biased electro-optic Mach-Zehnder modulator-based pulse picker with variable repetition rate (from 50 MHz to 500kHz) by using a fast synchronized FPGA. We report a maximum pulse extinction ratio of 34 dB.

Published in: IEEE Xplore, 2023

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PAPER
Device dependent distortion correction in time-stretch photonic analog to digital converters using deep neural networks

Optical Fiber Communication Conference, 2024

Device dependent distortion correction in time-stretch photonic analog to digital converters using deep neural networks

Abstract: We experimentally demonstrate a novel deep learning-aided time-stretch photonic front end architecture to overcome device-dependent distortions to improve the signal-to-noise and distortion ratio by more than 24 dB, and reduce the bandwidth requirements of the back-end electronic ADC by three times.

Published in: Technical Digest Series (Optica Publishing Group, 2024)

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Contact

joydip8764896142dutta@gmail.com