Machine Learning

Faster And More Accurate Data Computation With Next-Gen DSP

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Faster And More Accurate Data Computation With Next-Gen DSP

The architecture of traditional Digital Signal Processors (DSP) has become inadequate for specific signal processing computational applications. However, the combination of Very Long Instruction Word (VLIW) and Single Instruction/Multi Data (SIMD) architectures can provide the parallel throughput required for high computational performance, with data typically being 16, 24, and 32 bits wide. This is particularly … Read more

CPO-SVMD Decomposition | Matlab Implementation of CPO-SVMD Hedgehog Algorithm Optimization

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CPO-SVMD Decomposition | Matlab Implementation of CPO-SVMD Hedgehog Algorithm Optimization

Reading time required 6 minutes Speed reading only takes 2 minutes Please respect the original work Please indicate the link to this article and the author: Machine Learning Heart Abstract: CPO-SVMD Decomposition | Matlab Implementation of CPO-SVMD Hedgehog Algorithm Optimization 1 Basic Introduction CPO-SVMD Decomposition | Matlab Implementation of CPO-SVMD Hedgehog Algorithm Optimization Includes 15 … Read more

Master MATLAB: Summer Advanced Course for Researchers

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Master MATLAB: Summer Advanced Course for Researchers

It is said that nearly 90% of researchers in the United States can use MATLAB!!! Researcher A: How do I draw a dual Y-axis graph? Research Expert: MATLAB Researcher A: What about a 3D dynamic graph? Research Expert: MATLAB Researcher A: What about principal component analysis? Research Expert: MATLAB Researcher A: What about pattern recognition? … Read more

How NOR Flash Overcomes Design Challenges in Wearables

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How NOR Flash Overcomes Design Challenges in Wearables

To continuously improve various features in next-generation devices, wearable and hearable devices rely on memory. Memory is a key design factor for implementing advanced devices… Although wearable and hearable technologies may seem like extensions of the previous generation of handheld devices, the innovative features required to enhance their value, user experience, and functionality significantly increase … Read more

ICLR 2020 | Simplifying Cryptographic Algorithms: SJTU’s Approach to Privacy in Mid-Level Features Using Complex Neural Networks

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ICLR 2020 | Simplifying Cryptographic Algorithms: SJTU's Approach to Privacy in Mid-Level Features Using Complex Neural Networks

Machine Heart Report Machine Heart Editorial Team The top AI conference ICLR 2020 will be held on April 26 in Addis Ababa, Ethiopia. Among the final 2594 submitted papers, 687 were accepted, with an acceptance rate of 26.5%. This article introduces a paper accepted by the team of Zhang Quanshi from Shanghai Jiao Tong University … Read more

The Secrets to Successfully Entering the ADAS Market

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The Secrets to Successfully Entering the ADAS Market

Editor’s note: In August this year, Lei Feng Network will hold the “Global Artificial Intelligence and Robotics Innovation Conference” (GAIR) in Shenzhen, where we will announce the “Top 25 Innovative Companies in Artificial Intelligence and Robotics”. Minieye is one of the companies we are focusing on. Today, we invite Minieye’s founder and CEO, Liu Guoqing, … Read more

Deep Learning on Microcontrollers: Using TinyML for Embedded AI

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Deep Learning on Microcontrollers: Using TinyML for Embedded AI

TinyML, or tiny machine learning, is used to implement machine learning on resource-constrained devices such as microcontrollers and embedded systems. If you want to leverage these low-cost, low-power yet remarkably powerful devices, then this book is for you. This book aims to increase the accessibility of TinyML applications, especially for professionals who lack the resources … Read more

Getting Started with TinyML: A Comprehensive Guide

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Getting Started with TinyML: A Comprehensive Guide

If you are interested in running machine learning on embedded devices but are unsure how to get started, Google’s TensorFlow Lite team member Pete Warden will introduce how to build and run your own TinyML applications. This will include an overview of the different boards, software frameworks, and tutorials available to help you get started … Read more

Hands-On Tutorial for STM32 Embedded Machine Learning (TinyML) – Part 1

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Hands-On Tutorial for STM32 Embedded Machine Learning (TinyML) - Part 1

Part 1: Overview In this tutorial, we will develop a machine vision application using machine learning technology on the STM32H747I Discovery development board. This tutorial is officially published by STMicroelectronics. Special thanks to the Edge Intelligence Lab for the original Chinese subtitles. The documents referenced in the video (also available for download from ST’s official … Read more

Implementing Voice Recognition with Syntiant TinyML and Edge Impulse

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Implementing Voice Recognition with Syntiant TinyML and Edge Impulse

Project Description This project uses the microphone built into the Syntiant TinyML development board to build a machine learning model through Edge Impulse, allowing the LED light on the board to display different effects based on Chinese voice commands. A total of four labels of sound data were trained, as detailed below. Label Keyword Action … Read more