Nuvoton.AI

Keyword Spotting

DNN/DS-CNN

This application focuses on Keyword Spotting (KWS) technology, exploring how to train Depthwise Separable Convolutional Neural Networks (DSCNN) or traditional Deep Neural Networks (DNN) by collecting specific sound data. The application will demonstrate the entire process, from data collection, preprocessing, and feature extraction to model training. The goal is to develop a model capable of accurately detecting and responding to specific voice commands suitable for various voice interaction applications such as smart assistants and voice control systems to enhance user interaction experiences.

ML_KWS
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Gesture Recognition

CNN

A Convolutional Neural Network (CNN) model is trained to recognize different gestures using a three-dimensional accelerometer to collect gesture motion data. This model is then deployed onto a development board for real-time gesture recognition.

ML_G-Sensor
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Image Classification

MobileNet

This application delves into the methods and practices of using the MobileNet model for image classification. MobileNet, as a lightweight deep learning architecture, is particularly suitable for devices with limited computational resources. The article starts with the model's basic structure, explaining how MobileNet reduces computational load and model size through depthwise separable convolutions while maintaining efficient classification performance.

Subsequently, the article will introduce how to train MobileNet using public datasets, including steps for data preprocessing, model training, and performance optimization. Additionally, it will explore how to deploy the trained model on different platforms, including cloud services and edge computing devices.

Finally, the application of MobileNet in image classification will be demonstrated through practical cases, such as object recognition and scene understanding, emphasizing its importance in real-time and high-efficiency image processing. This article will provide practical guidance and inspiration for readers who want to practice deep learning in image classification.

ML_Image_
Classification
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Object Detection

SSD_MobileNet_
fpnlite v2/v3

In this application, we will explore how to use the TensorFlow Object Detection API for image detection. This powerful API enables developers to easily train and deploy object recognition models suitable for various applications, from security surveillance to autonomous vehicles. The application will introduce the API basics, including setting up the development environment, preparing and annotating datasets, and training and evaluating models. Additionally, it will discuss how to apply trained models for real-time image detection and deploy them on various platforms and devices. This will provide a comprehensive guide and practical insights for developers who aspire to delve deeper into this field.

ML_Object_
Detection
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Object Detection

Yolo-fastest v1

This application delves deeply into how to use the DarkNet framework to train the YOLO (You Only Look Once) model, which is suitable for edge computing. It focuses on converting the trained model into TensorFlow Lite format and enhancing its performance with the Vela optimization tool. The application will cover key steps in model training, the conversion process, and optimization techniques to help developers effectively detect various types of objects, especially suitable for resource-limited edge computing devices.

ML_YOLO
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Anomaly Detection

DNN/
Autoencoder

This application details how to implement anomaly detection techniques to train Tiny ML (Micro Machine Learning) models and convert them into TensorFlow Lite (TFLite) format. The article will cover everything from data preprocessing and feature extraction to the application of anomaly detection algorithms, as well as how to optimize and convert the trained models into TFLite format for operation on resource-constrained devices. This approach suits applications requiring efficient and real-time anomaly detection, such as industrial monitoring or IoT (Internet of Things) devices.

ML_Gearbox_
Fault_Diagnosis
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Visual Wake Words

Small MobileNet RGB/gray

This application explores the implementation of Visual Wake Words (VWW) models on microcontrollers, focusing on identifying the presence of people or other specific objects in images. This approach utilizes lightweight deep-learning models designed to run on resource-constrained microcontrollers. The application will introduce how to effectively train and optimize such models and cover the process of deploying these models on microcontrollers. This provides a practical and efficient solution for developing low-power, real-time, responsive embedded visual applications.

ML_VWW
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