Design Of Real-time Systems Research Articles

Design of Real-Time Monitoring System for Performance Parameter and Alarm of Main Engine RV. Baruna Jaya III

With the increasing age of RV. Baruna Jaya III (30 years), the ships often face various problems. The continuous operation of the engine causes a decrease in its performance which eventually results in the failure of the main engine. For instance, damage to the main engine can stop operating of the ship at sea which eventually have various impacts on ship and life safety as well as operational cost. In this context, efforts to manage ships especially for the operation of main engine and its prevention from errors through real time monitoring and control system have become a major issue. This study aims to design a monitoring and alarm system for the main engine performance on the RV. Baruna Jaya III as the preliminary step for the development of the overall monitoring system. As a result, a design for data acquisition of the main engine performance parameters in real time and the design of a ship’s main engine failure alarm system have been developed using PLC-based design technology and a SCADA-based monitoring system. This system can be thus further built for the performance of the ship’s main engine, as part of management ship operations and maintenance policies.

Design of a Real-Time Movement Decomposition-Based Rodent Tracker and Behavioral Analyzer Based on FPGA

In this work, a real-time movement decomposition-based automatic behavior analysis system is proposed. The proposed method is evaluated on a Morris water maze experiment with rat. The system integrated a high-speed tracker, a feature extraction module, and a classifier on a Xilinx Ultra-96 single computer board with a field-programmable gate array (FPGA). The high-speed tracker includes a movement predictor based on motion features and pose features, and a fast-checking algorithm to evaluate whether the tracking result is correct or not. A faster region-based convolutional neural network (Faster-RCNN)-based detector is used for initializing the tracker. The feature extraction module integrated a CNN-based feature point recognition network and a motion feature computing module. Motion and pose feature are calculated with the result of the feature points. The proposed system achieves a 66.2-frames/s processing speed, and the average point detection error is 4.01 pixels. For each video, the classification and regression tree (CART)-based classifier gives an explorative stage in five categories, which represents the rat’s strategy of exploration in Morris water maze task. This work focuses on a real-time local computing system design on small-scale datasets.

An analytical and experimental study on adaptive active vibration control of sandwich beam

A novel composite sandwich beam with adaptive active control system is proposed for low-frequency vibration reduction, and the experimental investigation on adaptive closed-loop vibration control is carried out. The macro fiber composite (MFC) piezoelectric patches and filtered-x least mean square (Fx-LMS) algorithm are employed to construct the adaptive closed-loop control system. An improved analytical model considering the effects of MFC piezoelectric patches is proposed to calculate the vibration characteristics of the composite sandwich beam. It provides a theoretical basis for the design of adaptive control scheme. The accuracy of present analytical model is verified by the results of experiment and numerical simulation. The adaptive vibration control is implemented analytically and experimentally, and the results demonstrate that the low-frequency vibration of the composite sandwich beam can be greatly and quickly suppressed by the proposed adaptive closed-loop control system. Moreover, vibrations induced by complex multi-frequency excitation with different amplitudes can be effectively reduced by using the proposed adaptive control system. The novelty of this work lies in the proposed adaptive closed-loop vibration control system and improved analytical method for piezoelectric composite sandwich beam. The study provides a novel design of real-time control system for the low-frequency vibration of composite sandwich structures.

A Perspective on Time Toward Wireless 6G

With the advent of 5G technology, the notion of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">latency</i> got a prominent role in wireless connectivity, serving as a proxy term for addressing the requirements for real-time communication. As wireless systems evolve toward 6G, the ambition to immerse the digital into physical reality will increase. Besides making the real-time requirements more stringent, this immersion will bring the notions of time, simultaneity, presence, and causality to a new level of complexity. A growing body of research points out that latency is insufficient to parameterize all real-time requirements. Notably, one such requirement that received significant attention is information freshness, defined through the Age of Information (AoI) and its derivatives. In general, the metrics derived from a conventional black-box approach to communication network design are not representative of new distributed paradigms, such as sensing, learning, or distributed consensus. The objective of this article is to investigate the general notion of timing in wireless communication systems and networks, and its relation to effective information generation, processing, transmission, and reconstruction at the senders and receivers. We establish a general statistical framework of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">timing</i> requirements in wireless communication systems, which subsumes both latency and AoI. The framework is made by associating a timing component with the two basic statistical operations: decision and estimation. We first use the framework to present a representative sample of the existing works that deal with timing in wireless communication. Next, it is shown how the framework can be used with different communication models of increasing complexity, starting from the basic Shannon one-way communication model and arriving at communication models for consensus, distributed learning, and inference. Overall, this article fills an important gap in the literature by providing a systematic treatment of various timing measures in wireless communication and sets the basis for design and optimization for the next-generation real-time systems.

Design of Remote Real-Time Monitoring and Control Management System for Smart Home Equipment Based on Wireless Multihop Sensor Network

With the intensification of the pace of modern urban life and the increase of work pressure, people have higher requirements for the safety and comfort of home. However, the rapid development of Internet of Things technology, wireless communication technology, embedded technology, and data fusion technology has made this desire possible, thus making intelligent and safe. Using wireless sensor network, intelligent home system can realize the real-time collection of object location and state information, remote operation of home equipment, as well as the monitoring of the information of personnel activities, etc. However, how to efficiently analyze and integrate these information to be applied to the intelligent home system is one of the problems that need to be solved; further study of the smart home system of personal behavior identifies fire monitoring and remote monitoring of mining applications. Such information collection and integration of technology and method are given based on wireless sensor network transfer of key technologies; intelligent home security monitoring system based on Bayesian networks is proposed and a card. According to the theoretical fire monitoring method, and based on the Bayesian belief network personal behavior identification model, this paper provides the theory and method for the remote safety monitoring and control in the intelligent home environment. In this paper, based on the multisensor data fusion of smart home security, real-time monitoring alarm platform is built. Through the multisensor detection module for the home fire gas human infrared monitoring, the detected data is transmitted to the data fusion center for processing; processed results are uploaded to the cloud through the Internet. When abnormal conditions occur, users can log in to the mobile phone client to view abnormal alarm results.

Design and Implementation of Real-Time Localization System (RTLS) Based on UWB and TDoA Algorithm.

Nowadays, accurate localization plays an essential role in many fields, such as target tracking and path planning. The challenges of indoor localization include inadequate localization accuracy, unreasonable anchor deployment in complex scenarios, lack of stability, and the high cost. So, the universal positioning technologies cannot meet the real application requirements scarcely. To overcome these shortcomings, a comprehensive ultra wide-band (UWB)-based real-time localization system (RTLS) is presented in this paper. We introduce the architecture of a real-time localization system, then propose a new wireless clock synchronization (WCS) scheme, and finally discuss the time difference of arrival (TDoA) algorithm. We define the time-base selection strategy for the TDoA algorithm, and we analyze the relationship between anchor deployment and positioning accuracy. The extended Kalman filter (EKF) method is presented for non-linear dynamic localization estimation, and it performs well in terms of stability and accuracy on moving targets.

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.

Design of Real-Time Temperature Monitoring and Control System for Multimodal Ablation

Design of Real-Time Temperature Monitoring and Control System for Multimodal Ablation

Parallelised Multithreaded Applications on a 4-core Field Programmable Gate Array (FPGA) Architecture

Abstract:The challenges in real-time multithreading, particularly in the efficiency of multithreaded applications running concurrently on multiple cores, have evolved significantly due to the increase in IoT, cloud and edge computing applications. The continuous increase in cores depth adds further research issues related to the efficiency of such multicore systems and their applications. Therefore, further research is still required.Background:Multicore systems can achieve higher performance running in parallel multiple multithreaded applications. However, efficient parallelisation of multiple threads among many cores is not an easy task. Field Programmable Gate Arrays (FPGAs) is a preferred technology for the rapid design and experimentation with such architectures, based primarily on softcore processors.Objective:The purpose of this research is to investigate the efficiency of running in parallel and concurrently multithreaded applications on a 4-core FPGA multicore architecture.Methods:The design of a 4-core FPGA architecture is implemented with Nios II/f soft processors on a Cyclone IV series chip, having real-time Linux operating system (OS) support. A multithreaded application with specific compute-intensive tasks is developed in C, and is used to obtain measurements in specific efficiency metrics under different core configurations.Results:The reliability of the proposed 4-core FPGA architecture is validated against 4-core and 2- core development platforms, respectively, on Raspberry Pi4 and BeagleBone AI single board computers. The results have been analysed and evaluated upon performance metrics, including execution time, response time, speedup, and cores usage. The experimental tests demonstrate the validity and efficiency of the approach to using FPGA for experimentations with multithreaded applications.Conclusion:The obtained results show that the proposed FPGA architecture stands well both in terms of timing and efficiency metrics. Execution times are about 50% lower, and the average speedup at 21% is fairly close to that of 33% for the Raspberry Pi4, and higher than BeagleBone AI (10%). The proposed measurements approach and evaluation methodology could benefit the design and development of real-time systems utilizing operating systems with real-time support in emerging areas, such as embedded devices in real-time control.

Design of an FPGA-Based High-Quality Real-Time Autonomous Dehazing System

Image dehazing, as a common solution to weather-related degradation, holds great promise for photography, computer vision, and remote sensing applications. Diverse approaches have been proposed throughout decades of development, and deep-learning-based methods are currently predominant. Despite excellent performance, such computationally intensive methods as these recent advances amount to overkill, because image dehazing is solely a preprocessing step. In this paper, we utilize an autonomous image dehazing algorithm to analyze a non-deep dehazing approach. After that, we present a corresponding FPGA design for high-quality real-time vision systems. We also conduct extensive experiments to verify the efficacy of the proposed design across different facets. Finally, we introduce a method for synthesizing cloudy images (loosely referred to as hazy images) to facilitate future aerial surveillance research.

Data-driven models for train control dynamics in high-speed railways: LAG-LSTM for train trajectory prediction

The construction of an accurate train control model (TCM) is crucial to the design of automatic train operation and real-time traffic management systems in high-speed railways. Traditional physical-driven models usually fail to reflect the “true” dynamics of high-speed trains (HSTs) because of the strong nonlinearity and uncertainty due to air resistance, frequently switching working conditions, and variations in external influencing factors such as weather and temperature. Although some data-driven deep learning models have recently been proposed for environmental adaptation, they are all “black-box” models, which cannot explain how the input of the models affects the HST output. To overcome these issues, this study constructs a novel long short-term memory with lagged information (LAG-LSTM) model by combining the physical-driven HST model and an “interpretable” deep learning model. Specifically, our LAG-LSTM model contains three modules: a time-delay variable module to model the transform delay of control variables, state variable enhancement module to extract the key features among high-dimensional input data, and Pre-LSTM module to predict the future train trajectory with given control variables. We collected field data from the Beijing-Shanghai high-speed railway and developed a data filter method and a normalization procedure to overcome the positioning errors of HSTs and construct a standard data set. Finally, we tested the effectiveness of our LAG-LSTM by comparing it with six deep learning structures, including fully connected neural network, recursive neural network, standard LSTM, and LSTM with convolutional layers. The results show that LAG-LSTM can accurately predict the trajectories of HSTs and outperforms other deep learning models. Regarding prediction accuracy, LAG-LSTM improved the performance of the traditional LSTM by 13.5% to 23.3%.

Design of efficient single target real-time beam tracking system

This paper builds a system for real-time tracking of a single target Radio Frequency (RF) signal source, in which the transmitter uses a low-cost HackRF One software radio transceiver platform, and the receiving antenna array uses two monopole antennas to form a 1×2 receiving array. The receiver uses Xilinx 7-Series development board Zedboard and AD9361 RF transceiver to build a wireless receiving platform to process the received signal. In this RF beam tracking system, it needs to perform Direction of Arrival (DOA) estimation on the calibrated signal, the DOA algorithm plays a crucial role in the accuracy of beam pointing and system running time, the MUSIC algorithm takes some time to conduct spectrum peak search, so the Root-Music algorithm is used here to avoid the spectrum peak search problem. To enable the Root-MUSIC algorithm to be better executed on this two-element receiving platform, this work simplifies the entire calculation process, requiring only a small amount of calculation and running time to complete the DOA estimation step, which effectively save FPGA processing time and computing resources. It provides a guarantee for the real-time performance of the system.

Design of multi-parameter real-time acquisition system of human movement based on nanosensor

Aiming at the problems of low accuracy of parameter collection in traditional systems, low comprehensiveness of collection results, and poor system robustness, a Nanosensors-based multi-parameter real-time collection system of human body motion is designed in this work. According to the overall frame structure of the parameter acquisition system, the system hardware design is realized by using Nanosensors and nodes, radiofrequency chip MC13201, and a multi-parameter acquisition circuit. Based on hardware design, the time synchronization algorithm based on virtual time stamp is used to realize the time synchronization among sub-nodes of the sensor, and the COPS algorithm is used to identify the bad parameters and control the parameter acquisition error, to realize the effective collection of multiple parameters of human motion. The experimental results show that the system has an 80% higher accuracy rate, 83% higher degree of comprehensiveness, better robustness, and higher practical application value.

Internet-of-thing based real-time electrical market monitoring system design

Electricity price forecasting is a good way of estimating the future electricity prices to a large extent in safe and peaceful conditions. Knowing an acceptable range of commodity prices in the future can positively reduce investment risk (for both producers and customers) and give more stability to the trading market. In this paper, the electricity price is forecasted using the autoregressive moving average method and the Generalized Autoregressive Conditional Heteroscedasticity model. These methods have been developed and applied to the Australian electricity market data, and in some case studies. The proposed learning model is capable of modeling the variance error in the highly correlated market data as well as dealing with the price of the electricity in real-time. The results are compared with the actual data that is collected with Internet of Things approach and show the convincing evidence of the model's validity and accuracy.

A Distributed Real-Time Transcoding CDN System Design and Performance Simulation Study.

Content Delivery Network (CDN) technology is one of the core technologies for performance optimization of mobile WEB applications, but it often encounters bottlenecks in video storage. In this paper, based on studying the basic technical model of distributed real-time transcoding CDN, we propose an overall technical architecture of a distributed real-time transcoding CDN system, present the business process of distributed real-time transcoding CDN resource negotiation in the model, design a resource storage policy model and algorithm, including the hot storage ratio resource storage value evaluation method and storage resource allocation algorithm. Through simulation experiments, we verify the technical efficiency of the distributed real-time transcoding CDN system by simulation experiments.

Design of real-time data acquisition system for interferometer-based electron density diagnostics on EAST.

Laser-aided interferometers are employed as the common diagnostics for electron density measurements. On the EAST (Experimental Advanced Superconducting Tokamak), we have four main electron density diagnostics-the far-infrared hydrogen cyanide interferometer, polarimeter/interferometer, carbon dioxide (CO2) dispersion interferometer, and solid-state source interferometer. For interferometers, the phase difference brought by the change in electron density can be acquired via mixers. A real-time data acquisition (DAQ) system is needed to calculate the highly precise density values. In this paper, a field-programmable gate array is employed to implement the system with fast Fourier transform-based demodulation technology. A highly precise analog/digital converter is employed for readout and digitization of the waveform including multiple frequency signals from high-performance Schottky mixers. The system has been simulated, and the results show that it can meet the requirements of the accuracy of interferometers on the EAST. Furthermore, the framework of the DAQ system also offers a reference of the data processing method to the future laser-aided interferometers.

Design of Multi-channel Real-time Signal Acquisition System

In order to improve the accuracy of the real-time signal acquisition, a real-time signal acquisition system based on high speed ADC and FPGA is proposed in this paper. AD9208 is used to realize high speed acquisition and DDC processing of original signal. FPGA 7V690T is used to realize digital channelization and fine FFT processing. This system can realize massive data storage and real-time signal display. The test results show that this system can effectively realize real-time signal acquisition and processing, and has strong anti-interference ability.

Design of moisture real-time detection system for grain dryer

STM32 MCU is used as the core control chip, and the parallel plate is designed as the data acquisition terminal of capacitance sensor. The parallel plate is a capacitive sensor to collect the moisture data of grain in the dryer, through AD conversion, signal processing. Through RS485 communication, the signal will be transmitted to the top computer for real-time display. The operator can calibrate grain moisture in real time through the upper computer. The influence of grain temperature and volume on capacitance was studied, and a mathematical model describing capacitance and moisture content was established, and the model was verified. Through field experiments, the reliability of the system was confirmed, and the accuracy of online monitoring could reach within 0.5%, which fully met the requirements.

Statement of Retraction: Real-time image recognition algorithm and system design of Android mobile devices

This article refers to:Retracted Article: Real-time image recognition algorithm and system design of Android mobile devices

Design and Development of Real-Time Measurement System for Dynamic Torque of Rotating Shaft

Design and Development of Real-Time Measurement System for Dynamic Torque of Rotating Shaft