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android_device_oneplus_bacon

android_device_oneplus_bacon-1

OnePlus One device configuration (bacon)

android_device_oppo_msm8974-common

android_kernel_oneplus_msm8974

android_vendor_nameless

car-accident-severity-analysis

Reducing traffic accidents is an essential public safety challenge all over the world; therefore, accident analysis has been a subject of much research in recent decades. The objective of the project is to analyze the US accident data from 50 states to inform the US government agencies and the public on trends and possible causes of traffic accidents and what could be done to reduce them. We established a multilevel interactive map to demonstrate over a few million-accident data inside the United States for 49 states. Some of the visuals include the number of accidents by year, number of accidents by state, the best time to travel by month, day and hour, an accident-prone area in each state, factors responsible for the accidents like weather, wind flow, temperature, location, etc. The dashboard plots will be built using D3.js and Plotly, while the front-end will be developed in HTML/CSS and will be deployed through Flask.

data-structures

device_aosip2

device_du

device_oneplus_bacon

device_oneplus_bacon-1

device_oneplus_bacon-2

device_oneplus_bacon-aosip

device_screwd

device_screwd-new

device_xos

device_zos

frameworks_av_screwd

gan-control

This projects aims to analyze interpretable controls for GAN models. Specifically, we study the GANSpace paper and explore improvements to their method while addressing the drawbacks of using PCA in GANSpace. Furthermore, we study the reasons for improvement in performance of the GAN on using Wasserstein metric instead of JS divergence or MLE.

ganspace

Discovering Interpretable GAN Controls [NeurIPS 2020]

inboxnews

An intuitive Android application for delivering personalized news and facts from any category of choice

java

Some rudimentary java programs

kv9801.github.io

A Jekyll version of the "Forty" theme by HTML5 UP.

kv9801.github.io-old

manifest-1

object-detector

A YOLOv5 based object detection model. Supported image formats: JPG, JPEG

patcher

Script that applies necessary patches for building.

platform_packages_apps_settings

platform_vendor_aosip

proprietary_vendor_oneplus