ecruhue Goto Github PK

body-pix-react-render

deep_sort_realtime

A really more real-time adaptation of deep sort

ecruhue.github.io

ion-sfu

Pure Go WebRTC SFU

javascript-voronoi

A Javascript implementation of Fortune's algorithm to compute Voronoi cells

jekyll-now

Build a Jekyll blog in minutes, without touching the command line.

llmrisks.github.io

Website for UVA Seminar on Risks (and Benefits) of Generative AI and Large Language Models

networked-aframe

A web framework for building multi-user virtual reality experiences.

online_shopper_intention

Predicting and clustering online shoppers intention with KMeans Clustering and classification methods

pedometer-step-counting-algorithm

collected real time walking data(patterns) with gyroscope, use Fast Fourier Transformation to extract the clustering features, and build the step counting algorithm for pedometer

phyphox_activity-detection

using mobile sensor by phyphox to collect data and build a zoom-in adaptation for better posture

pixellibinstancesegmentation

Real Time instance segmentation using PixelLib and Mask-RCNN

stock-data

Stock price data is diverse and situational, and it is unlikely that any single model will be uniformly best across all industries or contexts. Based on our results, ARIMA GARCH methods are better for Consumer Discretionary and Financial industries, and LSTM models are better for Healthcare and Industrials. Specifically, we found Cumulative Year (CumYr) ARIMA GARCH performs best for the Consumer Discretionary industry, year by year (YrByYr) ARIMA GARCH performs best for Financials, YrByYr multivariate LSTM performs best for Healthcare, and YrByYr univariate LSTM performs best for Industrials. Overall, the LSTM models with YrByYr under multivariate condition perform better than LSTM models under other conditions.

type-ii-diabetes-detection

classify early biomarkers for type 2 diabetes

visualblocks

Visual Blocks for ML is a Google visual programming framework that lets you create ML pipelines in a no-code graph editor. You – and your users – can quickly prototype workflows by connecting drag-and-drop ML components, including models, user inputs, processors, and visualizations.

website

zed-examples

ZED SDK Example projects

zed-pytorch

3D Object detection using the ZED and Pytorch