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Xiaozhong(Jim) Hua's Projects

actioncable-example-videos icon actioncable-example-videos

A demo project, based at actioncable-examples developed in Ruby-on-Rails, which you can share videos at chat-rooms

complete-procedure-to-train-and-recognize-you-by-raspberry-pi-3-and-tensorflow-lite icon complete-procedure-to-train-and-recognize-you-by-raspberry-pi-3-and-tensorflow-lite

A complete procedure making every step done with Tensorflow Lite, which starts with taking your own photos on Raspberry Pi 3 board and ends with you recognized by the board. Developers, interested in offline-machine-learing, can try this way to fast figure out what you will run exactly on the areas like Embedded intelligence, smart object (AI IoT) and etc.

cube-sessions icon cube-sessions

A mobile app to run client/server session on a surface of cube, which is developed in JavaScript, AJAX, Java serverlet and HTML5.

cube-videos icon cube-videos

Play videos on every surface of cube via HTML5, CSS3 and JavaScript

driving-map-without-limitation-on-place-number icon driving-map-without-limitation-on-place-number

There is limitation on place number when you create driving map based a map tool. Usually the number is 10, 15 or 20. I have just created a driving map as uploaded. It is a way to git rid of that limitation. The procedure was based on Baidu map, Natron and ffmpeg on Linux. The readme will come here soon when I get time.

full-log-4-new-issue-190313 icon full-log-4-new-issue-190313

prepare full log for "new issue' submitted on 190313 for https://github.com/tensorflow/models/tree/master/research/slim

gstreamer-studying icon gstreamer-studying

A presentation to analyze GStreamer, which is the de-facto core and framework for all media players.

tensorflow icon tensorflow

An Open Source Machine Learning Framework for Everyone

tensorflow-lite-apps-on-raspberry-pi-3 icon tensorflow-lite-apps-on-raspberry-pi-3

An "Off-line machine learning" (Offline AI, artificial intelligence) project, in which 2 tensorflow-lite apps are developed to run on Raspberry Pi 3. It hopes to propose a generic method on cross-developing, so that developer could fast follow to get your own apps started on Embedded intelligence (AI embedded system), smart object (AI IoT) and etc.

tensorflow-sparkfunedge-fulllifecycel-for-sequencemodel icon tensorflow-sparkfunedge-fulllifecycel-for-sequencemodel

An "AI on-device" project for sequence model. Based at Tensorflow Lite for micro-controller, the model is created/trained/converted/flashed. At the end, an app is able to run, at SparkFun Edge Dev board, to recongnize speech although just words.

watching-sensors-with-esp32--expressjs-twilio-sync-for-iot icon watching-sensors-with-esp32--expressjs-twilio-sync-for-iot

A project to demo cloud-iot. The major technical components and platforms required: Twilio-Sync, Audiuno, dht11 temperature/humidity sensor, ESP32 (reference board: esp-wrover-kit), Node.js, Express and Chart.js. As the result of project, you could get any information updated by an iot device (temperature/humidity sensor at your home, for example) from a browser client running at cell-phone, laptop... anywhere.

yourownmodel-tflite-raspberrypi icon yourownmodel-tflite-raspberrypi

An "AI-on-device" project walks with you through all necessary steps, from collecting your own data, creating and training your own Tensorflow model, generating your own Tensorflow-lite model, developing both Python and C++ programs to recognize images on Raspberry Pi 3.

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