Naren Akash R J (narenakash)

narenakash

Geek Repo

Company:Microsoft Research Lab

Location:Bengaluru, India

Home Page:https://researchweb.iiit.ac.in/~naren.akash/

Twitter:@NarenAkash

Github PK Tool:Github PK Tool


Organizations
cbs-iiith
Clubs-Council-IIITH
Computer-Vision-IIITH-2021
Digital-Image-Processing-IIITH

Naren Akash R J's repositories

Computer-Vision-and-Medical-Imaging-Conferences-and-Journals-2021

📢 A curated list of upcoming conferences, events, journal, and other resources in the field of computer vision, and medical image analysis.

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IoT-and-Smart-Laundry-using-OneM2M

ESP32 based Embedded System that monitors the environmental conditions and energy consumption of washing machines in hostels using DHT, water flow and energy meter. The data is transferred using OneM2M and stored in the central database which is then used for data analytics.

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awesome-computer-vision

A curated list of awesome computer vision resources

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FalconDB-Optimized-Inmemory-Key-Value-Storage-Software

FalconDB. An optimized In-Memory Key-Value Storage Software built in C++.

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JavaScript-Simulations-of-Circular-Dichroism-Spectroscopy-and-Molecular-Visualizations

Codebase of Circular Dichroism Spectroscopy web experiment using JavaScript for animations and molecular visualizations as the final project of Design and Analysis of Software Systems (DASS) course at IIIT Hyderabad. Developed web application for Virtual Labs, a Government of India initiative.

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conferences

List of awesome conferences

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JASH

Just Another SHell: A simple UNIX shell based on POSIX architecture built from scratch!

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BakulHonestyBox-SQL-Database-Application

Database application from scratch. The project starts by selecting the application, then designing the database, obtaining and loading data into a real DBMS (MySQL), and writing SQL queries and Python client program with embedded SQL queries.

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MICCAI-Geographical-Diversity-Dataset

MICCAI 2020 Hackathon Team Project: Visualization of geographic diversity in MICCAI from 2011 - 2020 and publicly available dataset of publications in MICCAI in the last ten years.

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Non-linear-Aggregation-of-Filters-to-Improve-Image-Denoising

Code for training and parameter tuning of a machine learning model for non-linear aggregation of image denoising estimators using COBRA combined regression strategy. The face images used for training and testing are taken from the Labelled Faces in the Wild (LFW) dataset.

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What-Objects-Are-Where

Two-stage object detection using rich hierarchical features in PyTorch from scratch. Trained and tested on PASCAL-VOC 2007 dataset.

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CSrankings

A web app for ranking computer science departments according to their research output in selective venues, and for finding active faculty across a wide range of areas.

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In-Memory-Key-Value-Storage

An optimized In-Memory Key-Value Storage Software built in C++

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Machine-Data-and-Learning

TLDR: Generic Algorithms, Decision Trees, Value Iteration, POMDPs, Bias-Variance. Data preprocessing using statistical techniques and visualization is crucial to understand and analyze the data before utilizing them to train a machine learning model. Several fundamental techniques for preprocessing are presented here.

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miccai-sb.github.io

MICCAI student board website. https://miccai-sb.github.io

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nerfherder

scripts for automating grading with GitHub Classroom and Moodle

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pytorch-3dunet

3D U-Net model for volumetric semantic segmentation written in pytorch

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ragc

RAGC: Residual Attention Graph Convolutional Network for Geometric 3D Scene Classification (ICCVW 2019)

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robotic-vision

Implementations and explorations of several pipelines and algorithms in Robotic Vision, as part of the Mobile Robotics (Monsoon 2020) class.

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Scene-Graph-Benchmark.pytorch

A new codebase for popular Scene Graph Generation methods (2020). Visualization & Scene Graph Extraction on custom images/datasets are provided. It's also a PyTorch implementation of paper “Unbiased Scene Graph Generation from Biased Training CVPR 2020”

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Software-Programming-For-Performance

Hands-on projects in C language based on performance analysis, algorithmic techniques for high performance, instruction-level optimizations, and building scalable software systems. Based on MIT 6.172 (EECS)

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summer-sessions-2020

Series of lectures and hands-on tutorials organized to familiarize new lab entrants with the fundamental areas of robotics research.

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