A performance evaluation of QUIC server migration
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Updated
Oct 31, 2022 - C++
A performance evaluation of QUIC server migration
A Realistic, Versatile, and Easily Customizable Edge Computing Simulator.
An experimental implementation of QUIC server migration on top of mvfst
Machine Learning and Computer Vision at the Edge for pills counting using ESP32.
Deploy deep neural network models on esp32 SOC.
Building a custom digital voice assistant without security worries or wireless Internet is possible thanks to machine learning algorithms and powerful embedded hardware.
Application Data Distribution in Edge Computing
Complete project is now available at https://github.com/sedgecloud
TinyML stuff done on my Arduino Nano 33 BLE Sense
TensorFlow Lite for Microcontrollers brings deep neural network algorithms to embedded platforms such as NXP’s i.MX RT1050 microcontroller.
Toolkit to bring computing to the edge and create virtual overlay networks fast.
Xilinx DPU(Vitis AI)を用いたエッジAI実現に向けたサンプルプログラム
ECAS is a library for edge AI computing acceleration.
FleXR: A System Enabling Flexibly Distributed Extended Reality
Multiplexed-topics on nodelets
Edge analytics is an approach to data collection and analysis in which an automated analytical computation is performed on data at a sensor, network switch or other device instead of waiting for the data to be sent back to a centralised data store.
Offload object detection and ORBSLAM2 simultaneously from android client to servers via multipath.
A Realistic, Versatile, and Easily Customizable Edge Computing Simulator.
Edge+Autodriving demo system.
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