Ramkrishna Paira
Staff Engineer at Infineon Technologies
Bengaluru, Karnataka, India
Summary
👤
Senior🎓
Top SchoolRamkrishna Paira is a Staff Engineer based in Bengaluru with 8 years of experience building SoC design enabling software at Infineon Technologies, specializing in Python, C++, OOP and the Metagen framework. He approaches problems by deeply understanding requirements, decomposing complexity, and iterating on practical, well-engineered solutions that bridge hardware design needs and software tooling. A fast learner with an M.Tech in Computer Science from IIT (ISM) Dhanbad, he has progressed through multiple software roles at Infineon, now focusing on enabling productivity for chip and system teams. Colleagues describe him as a pragmatic problem-solver who pairs strong technical breadth with disciplined, component-driven design — often surfacing subtle integration issues early in the development cycle.
8 years of coding experience
2 years of employment as a software developer
Master of Technology (M.Tech), Computer Science, 1st Division, Master of Technology (M.Tech), Computer Science, 1st Division at Indian Institute of Technology (Indian School of Mines), DhanbadBachelor of Technology (B.Tech), Computer Science, 1st Division, Bachelor of Technology (B.Tech), Computer Science, 1st Division at Ch. Devi Lal Memorial Government Engineering College
Github Skills (16)
final-form5
dwt4
ycbcr4
content-based-image-retrieval4
texture3
rgb3
measurement3
scheme3
retrieval3
cbir2
image-processing2
image-retrieval2
database2
similarity2
vector2
Github contributions (5)
whdcumt/Texture-Features-Based-Image-Retrieval-in-DWT-Domain
Jan 2018 - Jan 2018
we have presented a Content Based Image Retrieval (CBIR) scheme using color, texture and shape feature information. Firstly an input RGB image is converted into YCbCr image and each part of the i.e. Y, Cb and Cr are extracted from it. Afterword, each components are uniformly quantized. Then BDIP and BVLC are computed over quantized Y-component on block size of 2×2 and receive respective BDIP and BVLC image. Then on these two received image 3-level dwt is implemented and on each sub band some statistical parameters are evaluated to form a part of a feature vector. Now, on extracted quantized Cb and Cr components, 2-level dwt is performed and on each sub band some statistical parameters are calculated and this form second part of the feature vector. Now both parts are concatenated to form final feature vector. To proof that our system is adequate to retrieve good results, we have tested our scheme on benchmark database Coral-1000 . Same processed has been taken place for all the images present in the database and on the basis of the similarity measurement Top-20 results are retrieved and stored and the results are quite satisfying.
Contributions:3 commits in 1 day
storeddwtcoralbandsub-band
rkpaira/HackerRank-HackerEarth
Mar 2019 - Jun 2019
Contributions:4 pushes, 1 branch in 3 months
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