Embedded Linux System Research Articles

Non-Intrusive Load Identification Based on Retrainable Siamese Network.

Non-intrusive load monitoring (NILM) can identify each electrical load and its operating state in a household by using the voltage and current data measured at a single point on the bus, thereby behaving as a key technology for smart grid construction and effective energy consumption. The existing NILM methods mainly focus on the identification of pre-trained loads, which can achieve high identification accuracy and satisfying outcomes. However, unknown load identification is rarely involved among those methods and the scalability of NILM is still a crucial problem at the current stage. In light of this, we have proposed a non-intrusive load identification method based on a Siamese network, which can be retrained after the detection of an unknown load to increase the identification accuracy for unknown loads. The proposed Siamese network comprises a fixed convolutional neural network (CNN) and two retrainable back propagation (BP) networks. When an unknown load is detected, the low-dimensional features of its voltage-current (V-I) trajectory are extracted by using the fixed CNN model, and the BP networks are retrained online. The finetuning of BP network parameters through retraining can improve the representation ability of the network model; thus, a high accuracy of unknown load identification can be achieved by updating the Siamese network in real time. The public WHITED and PLAID datasets are used for the validation of the proposed method. Finally, the practicality and scalability of the method are demonstrated using a real-house environment test to prove the ability of online retraining on an embedded Linux system with STM32MP1 as the core.

Receiving ADS-B Signals on Embedded Linux using RTL-SDR: A Practical Guide

This paper presents a solution for ADS-B signal reception with RTL-SDR based on embedded Linux. This system utilizes an inexpensive RTL-SDR radio receiver for real-time reading of aeronautical messages, and it is experimentally demonstrated that this combination provides a significantly cost-effective way to track aircraft. By using an embedded Linux system, this paper demonstrates the high degree of portability and automation that can be achieved with RTL-SDR and its built-in ADS-B signal reception capability. The solution can be used in a wide range of applications and has high practical value.

Design and study of a hydrogen concentration online monitoring system

This paper designs a hydrogen concentration online monitoring system, mainly completes the construction of the software and hardware platform, select the I.MX6ULL embedded processor as the core hardware platform and embedded Linux system transplantation, root file system construction, hardware peripherals driver design, complete the human-computer interface based on QT application framework and save the collected data to the memory of the embedded motherboard and send to the cloud in real time through the network port.

System design and research of multi-channel electroacupuncture instrument

This paper introduces the development history, working principle, curative effect under different output waveforms and the hardware and software design of the system. The proportional constant current output circuit is analyzed in detail. A system structure of electroacupuncture instrument based on modular design is proposed. The system structure mainly includes multi-channel electroacupuncture control module and display control module, which are connected through CAN bus. Multi-channel electroneedle control module adopts embedded processor and real-time operating system design, display control module is application level processor, running embedded Linux system design. The system has reasonable structure design and high expansibility, which is convenient for further research.

Adaptive Non-Intrusive Load Monitoring Based on Feature Fusion

Intelligent power management system is an important part of the smart grid, in which the non-intrusive load monitoring technology is one of the key technologies. However, most of the load monitoring methods regard this task as a multi-classification problem, thus it is not effective to identify the unknown loads that did not participate in training. In this paper, an adaptive non-intrusive load monitoring method based on feature fusion has been proposed which utilizes both information of harmonic current feature and voltage-current (V-I) trajectory feature. The harmonic current feature is obtained from the high-frequency load sampling data through Fast Fourier Transform (FFT). At the same time, the V-I trajectory feature is obtained by using the pre-trained convolutional autoencoder neural network that was trained for feature extraction on a public dataset. After the feature extraction, the similarity between these two feature vectors and the available feature vectors in the database is calculated by TOPSIS algorithm. Then the load monitoring can be carried out according to the similarity. When the maximum similarity is greater or equal than the set threshold, the load is considered to be one of the existing loads in the database, otherwise, it will be considered as a new type of load, and the load feature database will be updated. The autoencoder model is trained by using the V-I trajectory from the BLUED dataset and the PLAID dataset is used to verify the identification accuracy of the proposed algorithm by comparison with different algorithms. On the PLAID dataset, the identification accuracy can reach above 97%. Finally, on the embedded Linux system with STM32MP1 as the core, some household electrical appliances are used for validation in real house environment. The results show that the proposed method has improved the capability of load identification by using the complementarity of different features. It can be carried out in real time on embedded system and is able to identify the unknown load at the same time.

Community Environmental Detection and Community Outdoor Space Health Promotion Based on Cloud Resource Scheduling Mechanism.

In the context of rapid economic growth, people's living standards are improving day by day, and China's urbanization process is also gradually accelerating. Urban renewal is a very complex project. The renewal and expansion of most cities in the country usually involve demolishing and rebuilding. This way of urban renewal only pays attention to the renewal efficiency of cities and towns, and ignores the health needs of residents, which will make community residents lack a sense of security, pleasure, comfort, and belonging to the community, and greatly reduce the quality of life and physical fitness of community residents. Building a healthy city is an important goal of today's society, and community is the basic unit of a city, so it is urgent to build a health-promoting community. We should pay attention to and study the social environment, the external space environment of residential areas, and the interaction between them, so as to make develop them in a healthy direction and create a healthy and positive community. Therefore, it is an urgent task to improve the community residents' attention to the above problems and build community outdoor space, which has very important practical significance. In this paper, we use the cloud resource scheduling mechanism and the Internet of things to study the healthy role of community outdoor space planning. Through the transplantation of embedded Linux system, the development of communication interface and the external acquisition sensing device, we collect the information of working status and running status, and transmit it to the database built in the cloud through the 4G module on the motherboard. Then, HTML + java script + CSS + JSON + Ajax technology is used to write the display interface and logical relationship, so that the data can be displayed on the mobile terminal and PC, which can provide a useful technical exploration for outdoor space planning. The community Internet of Things technology realizes the intelligent community environment detection, and the community cloud resource scheduling mechanism combined with the community Internet of Things can obtain the residents' help information in time and allocate the community outdoor resources in real time, which can not only meet the health needs of community members but also be conducive to the sustainable development of a health-promoting community. In addition, the cloud resource scheduling mechanism and the Internet of things also provide new ideas for the study of the health promotion research of community outdoor space planning.

Vehicle Anti-Theft Detection and Protection with Air Quality IoT Notification

The crime related to vehicle theft has been a tremendous rise with burglar’s becoming smarter every day. This generates an crucial need for an effective vehicle theft diagnosis system. In this paper, a compact, cheap and efficient system is studied, designed and explored using Raspberry Pi as the core processing unit of the whole system. Sensors data of Passive Infra-Red (PIR) motion sensor, pressure sensor, gas sensor, including Global Positioning System (GPS), Pi camera, buzzer and a Liquid Crystal Display (LCD) display are collected by embedded Linux system. The device functions in two modes: User mode and Theft mode. The gadget has ability of detecting intrusion and on detection will ring an alarm and send coordinates to the user’s assigned E-mail. The device will send the latitude and longitudinal details to the user’s e-mail along with the captured image using remote sensing whenever there is an intrusion or if the device is displaced after a certain threshold as in the case of bike theft wherein the complete vehicle can be effortlessly displaced without starting the engine or deactivating physical locks. It is easy to locate the vehicle and get accurate position on Google Maps using this design. This paper explores the possibility of a compact, versatile, viable, cheap and efficient vehicle theft detection system.

Research and Implementation of Digital Microscope Image Acquisition System Based on Embedded Linux

<p style="text-align: justify;"><span style="font-family: "times new roman";">With the rapid development of multimedia and Internet in today's society, embedded Linux system also involves many places, and the application of digital microscope is also reused in many industrial manufacturing areas. The main purpose of this paper is to complete the research and implementation of digital microscope image acquisition system based on Embedded Linux, including image acquisition card, logic function, hardware circuit design and image acquisition system. In this paper, the CMOS digital camera is used to connect the CMOS digital camera with the computer through the transmission network, so as to collect the data. In the image preprocessing, the adaptive method based on mean and variance is implemented to correct the gray level of the collected image to avoid high error. In the image registration, the registration algorithm is used to smooth the mosaic place. Then, Sobel algorithm is used to carry out the color transition step by step to eliminate the gap in the image and realize the microscopic image mosaic. The results are as follows: the overall design scheme of the system is designed; the circuit board of digital microscope image acquisition and processing unit based on Embedded Linux is realized; image mosaic is realized by using image preprocessing, registration algorithm and fusion technology, In order to improve the quality of digital microscope image acquisition; through the feasibility test of the system, it is concluded that the performance of the embedded Linux digital microscope image acquisition system proposed in this paper is good, which is worthy of wide application.</span>

A Novel Intelligent Garbage Classification System Based on Deep Learning and an Embedded Linux System

The dramatic increase in the amount of garbage and complex diversity of the materials in the garbage bring serious environmental pollution problems and wastes resources. Recycling reduces waste but manual pipeline waste sorting involves a harsh working environment at high labor intensity with low sorting efficiency. In our paper, a novel intelligent garbage classification system based on deep learning and an embedded Linux system is proposed. The system is divided into three parts. First, a Raspberry Pi 4B is utilized as the master board for the hardware system. The peripherals of the system consist of a touch panel, sensors, a 2-DOF (degree of freedom) servo, and a camera. Second, a new GNet model for garbage classification based on transfer learning and the improved MobileNetV3 model is proposed. Third, a GUI based on Python and QT is employed to build a human-computer interaction system to facilitate system manipulation and observation. A series of garbage classification experiments on the Huawei Garbage Classification Challenge Cup dataset were conducted. The proposed classification system’s prediction accuracy was 92.62% at 0.63 s efficiency. The experimental results in this paper demonstrate that the proposed intelligent garbage classification system delivers high performance both in terms of accuracy and efficiency.

Design and development of rotary machinery vibration monitoring system

This paper designs and develops a monitoring system for rotary mechanical vibration. The system uses ARM+FPGA dual processor combination, FPGA control signal acquisition and data transmission, ARM transplantation embedded Linux system with touch screen to complete signal analysis and time domain, frequency domain curve display. The vibration signals of multiple positions on the mechanical device are synchronously sampled, and the FFT analysis of the signals is completed on the terminal, and the vibration waveform diagram and the spectrum diagram of each channel are displayed. At the same time, the system combines current IoT technology and cloud diagnostic technology to provide wired and wireless network communication solutions. The monitoring system can connect to the cloud server through the Ethernet interface and the WiFi interface to perform cloud diagnosis on the device.

Embedded EPICS based beam loss monitor system of HLS-II storage ring

A new beam loss monitor system based on embedded EPICS is developed for the top-off upgrade of HLS-Ⅱ, to monitor the position and value of beam loss of storage ring. The electron shower signals are obtained by the double PIN photodiode detectors around the storage ring, then acquired by the new BLM processors, where the beam loss signals are analysed and distributed to the accelerator control network, by embedded EPICS on embedded Linux system of the BLM processors, and the beam loss data could be obtained in the central control room. The new BLM system could actuate the self-checking function of the double PIN photodiode detectors, eliminate hidden malfunction of the detectors, and improve stability and reliability of the whole system. Preliminary operation shows that the new BLM system can meet the needs of the top-off upgrade of HLS-Ⅱ.

Development and delay analysis of data acquisition gateway for train monitoring and control system

To solve data processing for the train multiple monitoring and control devices, a multi-protocol data acquisition gateway based on the embedded Linux system was designed. The principle of the gateway was proposed. The gateway’s communication abilities of RS485 network and Ethernet as well as RS485 network based on HDLC protocol and Ethernet were demonstrated by delay analysis for data propagation. The implementation of data acquisition and retransmission for the LKJ and TCMS provides support for achieving the aims of remote monitoring and fault diagnosis for rail transit train.

ED-255EK Embeded Education Platform Sebagai Modul Praktikum Embeded System Dengan Robot Arm Module Dan Voice Module

Abstract— Literally embedded systems are defined as equipment that computes a particular job. Embedded Linux System was also developed as a means of education, one of which is ED-255EK Embedded Education Platform, designed to be very practical and consume very low power. ED-255EK is also equipped with a PXA255 processor that is compatible with ARM version 5TE ISA, so it can be used to carry out various kinds of lab work or experiments on embedded systems. The working principle of this tool begins with the configuration of data communication between Fedora Linux and ED-255EK. When the communication goes well, it is downloaded by the ED-255EK system to a laptop or computer that has been installed with the Fedora 9.0 Linux operating system. To take advantage of the ED-255FPGA module, driver module installation is required first, after which the application program is installed. Laptops or computers that have installed the Fedora 9.0 Linux operating system will act as input and output programs making it easier to monitor each module that is being run. The module used is the ARM 1923 Robot Module and Voice Module ED4321.

Embedded GPU 3D Panoramic Viewing System Based on Virtual Camera Roaming 3D Environment

The traditional 2D panoramic view has the problem of small imaging range, low utilization of original image and serious stretching of the foreground. This paper designs a 3D panoramic viewing system. The real-time performance of the panoramic system requires a large number of parallel image transformations. The general-purpose processor CPU is incapable. This paper designs an embedded GPU solution. The core computing module is based on NXP's i.MX6Q application processor and builds an embedded Linux system. Using V4L2 to capture images to reduce memory and CPU usage, and use OpenGL ES shaders to process image transformations in parallel at high speed. Because it is in a 3D environment, it is impossible to cover all images through a still viewpoint like a 2D look-around. In order to roam the 3D scene, this paper proposes to design a virtual camera system. Using the virtual camera to roam the 3D environment, realize the virtual viewpoint in the 3D scene, traverse each scene in the grid, eliminate the dead angle of the field of view, and finally realize the 3D panoramic view, and verified on the embedded hardware platform, which can clearly roam every image on the surface. And recorded the video of 3-D looping. Experiments show that the 3D panoramic viewing system proposed in this paper has excellent performance and good effects. It has a good real-time performance on the image display, and the response speed is increased by 5%. The main operation is undertaken by the GPU and only takes a small number of CPU resources. The proposed virtual camera system can better realize the 3D panoramic view.

Embedded Linux System for Digital Image Recognition using Internet of Things

The present paper describes the use of Digital Image Processing and Internet of Things for gestures recognition using depth sensors available on Kinect device. Using the open source libraries OpenCV and libfreenect, image data are translated and used for communicating Raspberry Pi embedded Linux system with PIC microcontroller board for peripheral devices controlling. A LED is triggered according to the hand gesture representing the corresponding number. Data are stored in a PHP Apache server running locally on Raspberry Pi. The proposed system can be used as a multifunctional tool in areas such as learning process, post-traumatic rehabilitation and visual and motor cognition time. Using image binarization and Naive-Bayes classifier, the achieved results show error lower than 5%.

Internet of Things Data Acquisition System Applied to Photovoltaic Water Pumping

This paper presents the design and implementation of an IoT (Internet of Things) data acquisition and monitoring system applied to a photovoltaic (PV) water pumping plant. The system allows control and monitoring analog and digital sensors configured in decentralized generation plants, using an Analog/Digital Converter Embedded System (ADCES) developed in this work. The ADCES is based on a microcontroller system of the PIC18Fxx5x family with USB interface known as SanUSB. A Raspberry Pi (Rpi) board was used to develop an Embedded Linux System (ELS) to ensure communication between the monitored plant and cloud services. The main contribution of this work is the possibility to remotely monitor and control any microgeneration plant through the Internet, without the need of a dedicated computer. Meteorological variables such as solar radiation, relative humidity, ambient temperature and temperature of the PV module, electrical parameters such as voltage and current of the PV module and hydraulic parameters such as pressure and water flow of the pumping plant are monitored. An innovative contribution of this research is the possibility to apply IoT projects using video monitoring in Cloud, a concept not addressed in the literature review presented.

Multi-interface Gateway Protocol Conversion Method for Nearspace-air-vehicle-ground Dedicated Network

There are many kinds of monitoring and detecting load equipments in the Nearspace-air-vehicle-ground network, including visible light, infrared pods and various sensors, and there are various bus interfaces for communication and transmission of different types of loads, including network port, RS422, CAN, LVDS and so on. This paper proposes the use of FPGA and embedded Linux system software and hardware cooperation method to achieve interoperability between multi-bus interface and Ethernet, making full use of software flexibility and hardware fast forwarding capabilities. The test results show that this method realizes the data transmission between the multi-interface bus and Ethernet, and plays an important role in the transmission of information between the landing platform and ground in the Nearspace-air-vehicle-ground Dedicated Network

A High Performance SURF Image Feature Detecting System Based on ZYNQ

In this paper, a power-efficient and real-time image feature detecting system is implemented, which is based on the Speeded-Up Robust Feature (SURF) algorithm. We optimized the SURF algorithm, and implemented on the FPGA fabric of Xilinx ZYNQ-7020 device. Our design of SURF algorithm circuit can work up to 100Mhz clock frequency, and its processing speed up to 270 fps for standard VGA (640 * 480) resolution gray image. We implemented the system on the ZYNQ platform with the hardware and software co-design approach. The image feature detecting system based on SURF algorithm circuit runs embedded Linux system. There is a GUI application for Linux system designed with QT and open-cv, which can capture video, process and display image or video. The system meets the real-time and low-power requirements of embedded devices, with great practical value.

IoT embedded linux system based on Raspberry Pi applied to real-time cloud monitoring of a decentralized photovoltaic plant

In this project we propose, describe, implement and test the Renewable Energy Monitoring System (REMS), a new concept on data acquisition and transmission systems (DATS) applied to real-time cloud monitoring of a decentralized photovoltaic (PV) plant. To achieve this latest design, we went through various systems projects alongside the evolution of technology. From this practical experience and in agreement with Brazil’s policy of diversifying the electricity generation matrix, our proposal focuses on a multi-user remote system using Raspberry Pi and Internet of Things (IoT) concept. REMS is capable of sensing and modifying monitoring process management via remote firmware update through the developed Analog/Digital Converter Embedded System (ADCES) as well as communicating with a personal developed cloud server profile via RPi Embedded Linux System (ELS), thus not requiring a dedicated PC. The measured variables are PV voltage and current, ambient and PV module temperature, solar irradiance, and relative humidity.

Underwater radio frequency image sensor using progressive image compression and region of interest

The increasing demand for underwater robotic intervention systems around the world in several application domains requires more versatile and inexpensive systems. By using a wireless communication system, supervised semi-autonomous robots have freedom of movement; however, the limited and varying bandwidth of underwater radio frequency (RF) channels is a major obstacle for the operator to get camera feedback and supervise the intervention. This paper proposes the use of progressive (embedded) image compression and region of interest (ROI) for the design of an underwater image sensor to be installed in an autonomous underwater vehicle, specially when there are constraints on the available bandwidth, allowing a more agile data exchange between the vehicle and a human operator supervising the underwater intervention. The operator can dynamically decide the size, quality, frame rate, or resolution of the received images so that the available bandwidth is utilized to its fullest potential and with the required minimum latency. The paper focuses first on the description of the system, which uses a camera, an embedded Linux system, and an RF emitter installed in an OpenROV housing cylinder. The RF receiver is connected to a computer on the user side, which controls the camera monitoring parameters, including the compression inputs, such as region of interest (ROI), size of the image, and frame rate. The paper focuses on the compression subsystem and does not attempt to improve the communications physical media for better underwater RF links. Instead, it proposes a unified system that uses well-integrated modules (compression and transmission) to provide the scientific community with a higher-level protocol for image compression and transmission in sub-sea robotic interventions.