Design and Implementation of Intelligent Home Automatic Control and Monitoring System

With the development of social economy and science and technology in China, the performance and application range of microprocessor chips are constantly improving. In the current era, the development of home towards intelligence has become the main trend. Therefore, it is an urgent problem to explore the home control system with stable state, strong practicability and lower power consumption cost. This paper analyzes the current situation of smart home and discusses its automatic control system

Embedded Edge Intelligent Processing for End-To-End Predictive Maintenance in Industrial Applications

This article advances innovative approaches to the design and implementation of an embedded intelligent system for predictive maintenance (PdM) in industrial applications. It is based on the integration of advanced artificial intelligence (AI) techniques into micro-edge Industrial Internet of Things (IIoT) devices running on Armr Cortexr microcontrollers (MCUs) and addresses the impact of a) adapting to the constraints of MCUs, b) analysing sensor patterns in the time and frequency domain and c) optimising the AI model architecture and hyperparameter tuning, stressing that hardware–software co-exploration is the key ingredient to converting micro-edge IIoT devices into intelligent PdM systems. Moreover, this article highlights the importance of end-to-end AI development solutions by employing existing frameworks and inference engines that permit the integration of complex AI mechanisms within MCUs, such as NanoEdgeTM AI Studio, Edge Impulse and STM32 Cube.AI. Both quantitative and qualitative insights are presented in complementary workflows with different design and learning components, as well as in the backend flow for deployment onto IIoT devices with a common inference platform based on Armr Cortexr-M-based MCUs. The use case is an n-class classification based on the vibration of generic motor rotating equipment. The results have been used to lay down the foundation of the PdM strategy, which will be included in future work insights derived from anomaly detection, regression and forecasting applications.publishedVersio

Design and Research of the monitoring system of civil aviation airport navigation lights based on WSN

民用航空机场助航灯属于民用航空机场目视引导助航信号的设施，是保障民用飞机在漆黑的夜间和复杂气候能见度低的条件下能顺利着陆、滑行和起飞的目视助航灯，主要由目视进近坡度指示灯、进近指示灯、滑行道指示灯和跑道指示灯等助航指示灯组成。随着我国民用航空工业技术及其机场监控技术的迅速发展，对民用航空机场助航灯监控系统的自动化、远程化、智能化发展起到重要作用。传统的工业控制软硬件难以满足现代航空工业发展需求。 随着物联网技术、嵌入式技术和移动互联技术的快速发展，无线传感器网络(WirelessSensorNetworks，简称“WSN”)技术得到了有效提升，在监控区域采用各种方式部署大量集成计算、感知和无...Civil aviation airport navigation lights belong to the civil aviation airport visual guidance to help the aircraft signal facilities, is to protect the aircraft at night and complex weather conditions can be a smooth landing, sliding and take-off of the visual aids to navigation lights. It is mainly composed of the visual into the near slope indicator, into the near lights, taxi track lights and r...学位：工程硕士院系专业：信息科学与技术学院_工程硕士(电子与通信工程)学号：X201122200

ECG-Based Arrhythmia Classification using Recurrent Neural Networks in Embedded Systems

Cardiac arrhythmia is one of the most important cardiovascular diseases (CVDs), causing million deaths every year. Moreover it is difficult to diagnose because it occurs intermittently and as such requires the analysis of large amount of data, collected during the daily life of patients. An important tool for CVD diagnosis is the analysis of electrocardiogram (ECG), because of its non-invasive nature and simplicity of acquisition. In this work we propose a classification algorithm for arrhythmia based on recurrent neural networks (RNNs) that operate directly on ECG data, exploring the effectiveness and efficiency of several variations of the general RNN, in particular using different types of layers implementing the network memory. We use the MIT-BIH arrhythmia database and the evaluation protocol recommended by the Association for the Advancement of Medical Instrumentation (AAMI). After designing and testing the effectiveness of the different networks, we then test its porting to an embedded platform, namely the STM32 microcontroller architecture from ST, using a specific framework to port a pre-built RNN to the embedded hardware, convert it to optimized code for the platform and evaluate its performance in terms of resource usage. Both in binary and multiclass classification, the basic RNN model outperforms the other architectures in terms of memory storage (∼117 KB), number of parameters (∼5 k) and inference time (∼150 ms), while the RNN LSTM-based achieved the best accuracy (∼90%)

OpenIPMC: a free and open source Intelligent Platform Management Controller

OpenIPMC is a free and open source firmware designed to operate as an Intelligent Platform Management Controller (IPMC). An IPMC is a fundamental component of electronic boards conformant to the Advanced Telecommunications Computing Architecture (ATCA) standard, currently being adopted by a number of high energy physics experiments, and is responsible for monitoring the health parameters of the board, managing its power states, and providing board control, debug and recovery functions to remote clients. OpenIPMC is based on the FreeRTOS real-time operating system and is designed to be architecture-independent, allowing it to be built for a variety of different Microcontrollers. Having a fully free and open source code is an innovative aspect for this kind of firmware, allowing full customization by the user. In this work we present the features and structure of OpenIPMC and its example implementations on Xilinx Zynq UltraScale+ (ZynqUS+), Espressif ESP32, and ST Microelectronics STM32 architectures.Comment: 8 pages, double-column, 9 figures, 2 tables. Paper submitted as proceeding for the IEEE Real-Time 2020 conferenc

Real Time Bearing Fault Diagnosis Based on Convolutional Neural Network and STM32 Microcontroller

With the rapid development of big data and edge computing, many researchers focus on improving the accuracy of bearing fault classification using deep learning models, and implementing the deep learning classification model on limited resource platforms such as STM32. To this end, this paper realizes the identification of bearing fault vibration signal based on convolutional neural network, the fault identification accuracy of the optimised model can reach 98.9%. In addition, this paper successfully applies the convolutional neural network model to STM32H743VI microcontroller, the running time of each diagnosis is 19ms. Finally, a complete real-time communication framework between the host computer and the STM32 is designed, which can perfectly complete the data transmission through the serial port and display the diagnosis results on the TFT-LCD screen.Comment: 6 pages, 9 figure

Analysis of Single Board Architectures Integrating Sensors Technologies

Development boards, Single-Board Computers (SBCs) and Single-Board Microcontrollers (SBMs) integrating sensors and communication technologies have become a very popular and interesting solution in the last decade. They are of interest for their simplicity, versatility, adaptability, ease of use and prototyping, which allow them to serve as a starting point for projects and as reference for all kinds of designs. In this sense, there are innumerable applications integrating sensors and communication technologies where they are increasingly used, including robotics, domotics, testing and measurement, Do-It-Yourself (DIY) projects, Internet of Things (IoT) devices in the home or workplace and science, technology, engineering, educational and also academic world for STEAM (Science, Technology, Engineering and Mathematics) skills. The interest in single-board architectures and their applications have caused that all electronics manufacturers currently develop low-cost single board platform solutions. In this paper we realized an analysis of the most important topics related with single-board architectures integrating sensors. We analyze the most popular platforms based on characteristics as: cost, processing capacity, integrated processing technology and opensource license, as well as power consumption (mA@V), reliability (%), programming flexibility, support availability and electronics utilities. For evaluation, an experimental framework has been designed and implemented with six sensors (temperature, humidity, CO2/TVOC, pressure, ambient light and CO) and different data storage and monitoring options: locally on a µSD (Micro Secure Digital), on a Cloud Server, on a Web Server or on a Mobile ApplicationThis research was partially supported by the Centro Científico Tecnológico de Huelva (CCTH), University of Huelv

A Intelligent Campus Based on the Internet of Things------Campus Intelligent Drainage System

Nowadays, the Internet of Things is being used in various fields. Drainage is placed at the top of the priority in campus life. However, the current campus drainage system cannot meet people’s yearning for a better life, so the system needs to be optimized. The optimized drainage system is divided into four parts. Sensor part, communication part, cloud control part, water storage/drainage part, identification part. RFID and AI technology are used for identification, temperature, humidity and pressure sensors are used for perception, cloud computing technology is used for cloud control, WIFI technology is used for communication and water storage and drainage control has three modes: remote control, automatic closed-loop control and manual control. Drainage network with circular pipe network drainage system, to achieve intelligent, accurate goals

Design and implementation of comprehensive sign monitor based on Internet +

Along with our country has accelerated into the aging society, the majority of the elderly medical and health problems, the shortage of medical resources, diffi cult to see a doctor, long cycle and other problems become increasingly prominent. In order to provide the elderly with better and more convenient health status monitoring, this paper proposes a comprehensive sign monitor based on Internet +. In this paper, the overall design framework of the comprehensive signs monitor is studied, STM32 processor is used as the CPU, and the physiological signifi cance and measurement methods of each physical sign parameter (blood oxygen, blood pressure, body temperature, etc.) are introduced. It allows doctors to obtain patients’ health data remotely, analyze patients’ health status, and achieve timely prevention and diagnosis and treatment, which provides great convenience for doctors and patients