Displaying 1 to 11 from 11 results

Johnny-Five - JavaScript Robotics and IoT programming framework

  •    Javascript

Johnny-Five is an Open Source, Firmata Protocol based, IoT and Robotics programming framework, developed at Bocoup. Johnny-Five programs can be written for Arduino (all models), Electric Imp, Beagle Bone, Intel Galileo & Edison, Linino One, Pinoccio, pcDuino3, Raspberry Pi, Particle/Spark Core & Photon, Tessel 2, TI Launchpad and more!

stm32f429 - Keil projects and libraries for STM32F4xx devices

  •    C

When you download repository and open project, you have to set correct your target, for device you use. If you use other device in your project, then set your project and copy settings there from target options.

RIOT - RIOT - The friendly OS for IoT

  •    C

RIOT is a real-time multi-threading operating system that supports a range of devices that are typically found in the Internet of Things (IoT): 8-bit, 16-bit and 32-bit microcontrollers. RIOT is based on the following design principles: energy-efficiency, real-time capabilities, small memory footprint, modularity, and uniform API access, independent of the underlying hardware (this API offers partial POSIX compliance).

mbed - Tool for Rapid Prototyping with Microcontrollers

  •    C

The mbed Software Development Kit (SDK) is a C/C++ microcontroller software to write code for ARM microcontrollers. It provides enough hardware abstraction to be intuitive and concise, but powerful enough to build complex projects. It includes all the base level startup code, C runtime and library pre-integrated and tested for the target microcontrollers, and high-level MCU peripheral APIs that allow you to drive the peripherals of the microcontrollers without going near a datasheet.




distortos - object-oriented C++ RTOS for microcontrollers

  •    C++

distortos tries to follow typical CMake cross-compiling workflow, which means that you always have to use a so-called toolchain file. Toolchain files in distortos also serve another purpose - they select the board which is going to be used by your application. You can obviously replace step 1 with git clone https://github.com/DISTORTEC/distortos.

ML-KWS-for-MCU - Keyword spotting on Arm Cortex-M Microcontrollers

  •    C

This repository consists of the tensorflow models and training scripts used in the paper: Hello Edge: Keyword spotting on Microcontrollers. The scripts are adapted from Tensorflow examples and some are repeated here for the sake of making these scripts self-contained. The command line argument --model_size_info is used to pass the neural network layer dimensions such as number of layers, convolution filter size/stride as a list to models.py, which builds the tensorflow graph based on the provided model architecture and layer dimensions. For more info on model_size_info for each network architecture see models.py. The training commands with all the hyperparameters to reproduce the models shown in the paper are given here.


sefr_multiclass_classifier - Code for the multiclass classifier version of SEFR

  •    Python

This is based on SEFR: A Fast Linear-Time Classifier for Ultra-Low Power Devices and its implementation sefr-classifier/sefr, which was originally a binary classifier. I use one-vs-rest (ovr) strategy to expand it into a working multi-class (multi-label) version. The idea is to quickly calculate weighted averages and find hyperplanes between classes, so it needs far less computing resources/time and can actually run on-board training on low-end microcontrollers, including 16 MHz AVR processors that have as much as 2 KB ram. It would also be possible to directly re-train models on the device whenever there are new data avaliable.






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