Real-time Architecture Research Articles

A Real-Time End-to-End Framework with a Stacked Model Using Ultrasound Video for Cardiac Septal Defect Decision-Making

Echocardiography is the gold standard for the comprehensive diagnosis of cardiac septal defects (CSDs). Currently, echocardiography diagnosis is primarily based on expert observation, which is laborious and time-consuming. With digitization, deep learning (DL) can be used to improve the efficiency of the diagnosis. This study presents a real-time end-to-end framework tailored for pediatric ultrasound video analysis for CSD decision-making. The framework employs an advanced real-time architecture based on You Only Look Once (Yolo) techniques for CSD decision-making with high accuracy. Leveraging the state of the art with the Yolov8l (large) architecture, the proposed model achieves a robust performance in real-time processes. It can be observed that the experiment yielded a mean average precision (mAP) exceeding 89%, indicating the framework’s effectiveness in accurately diagnosing CSDs from ultrasound (US) videos. The Yolov8l model exhibits precise performance in the real-time testing of pediatric patients from Mohammad Hoesin General Hospital in Palembang, Indonesia. Based on the results of the proposed model using 222 US videos, it exhibits 95.86% accuracy, 96.82% sensitivity, and 98.74% specificity. During real-time testing in the hospital, the model exhibits a 97.17% accuracy, 95.80% sensitivity, and 98.15% specificity; only 3 out of the 53 US videos in the real-time process were diagnosed incorrectly. This comprehensive approach holds promise for enhancing clinical decision-making and improving patient outcomes in pediatric cardiology.

Mechanisms of inducing cancer extracellular vesicles secretion and transfer for cancer malignancy

Assistant Professor Nao Nishida-Aoki, Waseda Institute for Advanced Study (WIAS), is investigating the role of extracellular vesicles (EVs) in physiological tumour tissue environments in order to improve cancer treatments. Communication tools like EVs are used by cancer cells to educate the surrounding cells and make them supportive to cancer cell growth and metastasis. Nishida-Aoki’s research focuses on understanding EV-mediated communication within the complex tissue environment of patient tumours, where they are secreted in densely packed cells and non-cellular components forming a 3D structure. She has developed ex vivo brain and lung metastatic models using organotypic tissue slice culture (TSC). This enables her to observe EV transfer within tissue architecture in real time, identify target cells affected by cancer EVs and determine how they are distributed. Nishida-Aoki is also exploring the mechanisms of inducible secretion of cancer extracellular vesicles upon cell recognition and contribution to cancer malignancy. Her research is investigating the molecular mechanics by which cancer cells regulate and the extent of interactions with neighbouring cells using EVs at metastatic initiation. She has hypothesised that cancer cells should increase communication frequency using EVs when they meet other types of cells in a new environment to benefit themselves. By elucidating the molecular basis of cancer-normal cell interactions mediated by EVs, Nishida-Aoki aims to develop novel cancer therapeutic strategies.

ResLMFFNet: a real-time semantic segmentation network for precision agriculture

Lightweight multiscale-feature-fusion network (LMFFNet), a proficient real-time CNN architecture, adeptly achieves a balance between inference time and accuracy. Capturing the intricate details of precision agriculture target objects in remote sensing images requires deep SEM-B blocks in the LMFFNet model design. However, employing numerous SEM-B units leads to instability during backward gradient flow. This work proposes the novel residual-LMFFNet (ResLMFFNet) model for ensuring smooth gradient flow within SEM-B blocks. By incorporating residual connections, ResLMFFNet achieves improved accuracy without affecting the inference speed and the number of trainable parameters. The results of the experiments demonstrate that this architecture has achieved superior performance compared to other real-time architectures across diverse precision agriculture applications involving UAV and satellite images. Compared to LMFFNet, the ResLMFFNet architecture enhances the Jaccard Index values by 2.1% for tree detection, 1.4% for crop detection, and 11.2% for wheat-yellow rust detection. Achieving these remarkable accuracy levels involves maintaining almost identical inference time and computational complexity as the LMFFNet model. The source code is available on GitHub: https://github.com/iremulku/Semantic-Segmentation-in-Precision-Agriculture.

Digital banking fortification: a real-time isolation forest architecture for detecting online transaction fraud

Since the use of the Internet has increased exponentially, numerous organizations, including the financial industry, offer services online. As a result, financial scams are expanding in quantity and complexity worldwide, resulting in massive revenue losses and making digital fraudulent transactions a severe issue. Abnormal attempts and illegal access are instances of these dangers that fraudulent activity detection systems must identify. Machine learning and data mining approaches have been extensively used to address this issue in recent years. However, these approaches must be enhanced regarding real-time detection speed, tackling enormous amounts of data, and finding undiscovered attack patterns. Consequently, the present study provides a real-time architecture for averting and identifying digital transaction fraud, which relies on the Isolation Forest (IForest) approach and big data analytic tools, including Spark Streaming, sparkling water, Kafka, and PostgreSQL. This architecture seeks to improve present detection strategies by increasing accuracy for detection when considering enormous amounts of data. Two real datasets of online transactional fraud are used to assess the proposed architecture, and the findings are compared to relevant studies. The investigation results showed that IForest performed flawlessly, achieving an accuracy of 0.99 in two datasets.

Hardware motion estimation methods survey and example real-time architecture design

Motion estimation is essential in video processing, computer vision, and other related fields due to its applications in improving data compression, enhancing image/video quality, increasing computer vision task accuracy, and conserving computational resources. Typical motion estimation methods include FS (Full Search) and DS (Diamond Search). Traditional motion estimation methods will face the challenge of high complexity in practical real-time applications. Therefore, this paper focuses on fast motion estimation algorithms and hardware architecture design for real-time applications survey and research. Aiming at the need for 1920x1080@60fps processing speed, this paper explores the key ideas of FS and DS hardware structure design based on the Vivado HLS high-level synthesis tool and provides a feasible hardware scheme to meet the real-time requirements.

Scalable and Multi-Channel Real-Time Low Cost Monitoring System for PEM Electrolyzers Based on IoT Applications

This paper discusses and evaluates a novel multi-channel real-time architecture aimed at monitoring a Proton Exchange Membrane (PEM) electrolyzer, both at the individual cell and stack levels. The proposed solution includes two primary subsystems: a hardware subsystem dedicated to data acquisition (DAQ) and a software subsystem focused on monitoring purposes. The DAQ subsystem utilizes an Arduino platform, being an affordable and open-source solution. The real-time monitoring data can be encoded in JSON format, widely used as a light-weight inter-exchange data format between a variety of IoT applications. They are also available to be transferred to Excel. Indeed, and to enhance convenience, the proposed system integrates graphs displaying a template based on Excel spreadsheets, which are commonly used in industrial environments. The current, voltage, temperature, and pressure data of both individual cells and stacks were monitored and collected, being configurable under a variety of ranges. As a case study, the validation of the system involved static and dynamic operational modes using a 1.2 kW PEM electrolyzer prototype (100 A, 1 A/cm2). The results successfully provided the monitored variables across individual cells and within the stack. The proposed approach exhibits relevant key characteristics such as scalability, flexibility, user-friendliness, versatility, and affordability and are suitable to monitor PEM electrolyzers in real-time at both the cell and stack levels.

Model predictive real-time architecture for secondary voltage control of microgrids

The integration of new elements and technologies to the power grid, driven by the expansion of electric microgrids, brings various challenges requiring a suitable control architecture for systems to operate ensuring economic and reliable access to electricity. This paper presents the development of a time-varying constraint real-time model predictive secondary voltage control (MPVC) strategy for microgrids built up in a multi-class Python environment with InfluxDB and SQLExpress databases storing the controller and microgrid data, and its experimental implementation with Modbus communication to physical devices at the Universidad Pontificia Bolivariana (UPB) campus microgrid. The microgrid characteristics are described and the experimental setup is presented. The mathematical model of the resource used as case study is obtained through experimental identification, and it is integrated to the MPVC scheme. Polytopic invariant sets are used as terminal sets in the MPVC scheme to ensure stability of the solution, and time-varying constraints of reactive power availability are considered. Validation studies of the proposed control system in computational simulation are presented prior to real application in the UPB campus microgrid. Controller performance results are described for three different implementation scenarios of low, medium and high variability of external disturbances, presenting adequate voltage regulation around the reference value and considerable dependence on the meteorological forecast quality. The model predictive voltage controller is finally benchmarked against a commercial microgrid controller to evaluate its performance, standing out as a more comprehensive and tailor-made solution.

A Fuzzy Logic based Solution for Network Traffic Problems in Migrating Parallel Crawlers

Search engines are the instruments for website navigation and search, because the Internet is big and has expanded greatly. By continuously downloading web pages for processing, search engines provide search facilities and maintain indices for web documents. Online crawling is the term for this process of downloading web pages. This paper proposes solution to network traffic problem in migrating parallel web crawler. The primary benefit of a parallel web crawler is that it does local analysis at the data's residence rather than inside the web search engine repository. As a result, network load and traffic are greatly reduced, which enhances the performance, efficacy, and efficiency of the crawling process. Another benefit of moving to a parallel crawler is that as the web gets bigger, it becomes important to parallelize crawling operations in order to retrieve web pages more quickly. A web crawler will produce pages of excellent quality. When the crawling process moves to a host or server with a specific domain, it begins downloading pages from that domain. Incremental crawling will maintain the quality of downloaded pages and keep the pages in the local database updated. Java is used to implement the crawler. The model that was put into practice supports all aspects of a three-tier, real-time architecture. An implementation of a parallel web crawler migration is shown in this paper. The method for efficient parallel web migration detects changes in the content and structure using neural network-based change detection techniques in parallel web migration. This will produce high-quality pages and detection for changes will always download new pages. Either of the following strategies is used to carry out the crawling process: either crawlers are given generous permission to speak with one another, or they are not given permission to communicate with one another at all. Both strategies increase network traffic. Here, a fuzzy logic-based system that predicts the load at a specific node and the path of network traffic is presented and implemented in MATLAB using the fuzzy logic toolbox.

End-to-End Real-time Architecture for Fraud Detection in Online Digital Transactions

End-to-End Real-time Architecture for Fraud Detection in Online Digital Transactions

Enhanced Adaptive Brain-Computer Interface Approach for Intelligent Assistance to Disabled Peoples

Assistive devices for disabled people with the help of Brain-Computer Interaction (BCI) technology are becoming vital bio-medical engineering. People with physical disabilities need some assistive devices to perform their daily tasks. In these devices, higher latency factors need to be addressed appropriately. Therefore, the main goal of this research is to implement a real-time BCI architecture with minimum latency for command actuation. The proposed architecture is capable to communicate between different modules of the system by adopting an automotive, intelligent data processing and classification approach. Neuro-sky mind wave device has been used to transfer the data to our implemented server for command propulsion. Think-Net Convolutional Neural Network (TN-CNN) architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data classification. Data collection and processing are the responsibility of the central integrated server for system load minimization. Testing of implemented architecture and deep learning model shows excellent results. The proposed system integrity level was the minimum data loss and the accurate commands processing mechanism. The training and testing results are 99% and 93% for custom model implementation based on TN-CNN. The proposed real-time architecture is capable of intelligent data processing unit with fewer errors, and it will benefit assistive devices working on the local server and cloud server.

6G-oriented ultra-wideband fiber-THz-fiber seamless converged communication system: architecture, key techniques and verification

Terahertz (THz) communication is widely regarded as a key component of the future 6G mobile communication system. This paper proposes a novel ultra-wideband fiber-THz-fiber seamless converged real-time architecture that fully exploits the commercially mature digital coherent optical module (DCO) to realize ultra-high capacity THz real-time wireless communication by conducting a comparative analysis for some of the main existing technical routes of THz up- and down-conversion in THz wireless communication systems. (1) The proposed architecture employs the techniques of dual-polarization photonic up-conversion for THz generation and hybrid optoelectronic down-conversion for THz reception, respectively, to enable seamless integration of optical fiber and THz communications. (2) Due to the limited bandwidth of the optoelectronic devices, multi-dimensional modulation techniques are adopted for ultra-wideband terahertz signals to improve the spectral efficiency and transmission capacity. (3) An intelligent nonlinear joint compensation technique based on deep neural network (DNN) is proposed, which can effectively improve the signal-to-noise ratio of the time-varying hybrid fiber-THz-fiber channel. Based on the above-mentioned investigations, we for the first time realize the photonics-aided record-high 100/200 GbE real-time THz wireless transmission at 360 (∼) 430 GHz band, with a capacity of 10 to 20 times that of 5G. The proposed fiber-THz-fiber architecture can realize the smooth conversion between high-speed THz signal and lightwave signal. Furthermore, the architecture can significantly reduce research complexity and development costs and thus greatly accelerate the commercialization of 6G THz technology by highly reusing commercial DCO modules, which is compatible with the physical layer transmission protocols such as IEEE 802.3 and ITU-T G.798. Finally, this paper discusses some potential research and development directions for higher-capacity, longer-distance, and more-integrated fiber-THz-fiber seamless communication.

Design of Adaptive-RST Controller for Nonlinear Magnetic Levitation System Using Multiple Zone-Model Approach in Real-Time Experimentation

A system with multiple controllers and a multiple-model architecture is one of the most effective solutions for the real-time control of nonlinear systems. The employment of these structures necessitates the resolution of certain difficulties, such as selecting the optimal algorithm or switching control algorithms. Based on the concepts of auto-transfer, the paper provides a way for switching the numerous controller structures’ algorithms. This paper presents a real-time dynamic model and platform of a magnetic levitation system (Maglev). The method’s applicability was demonstrated by utilizing a real-time architecture with an RST controller mechanism and real. In conclusion, the software was implemented and demonstrated by using the LabVIEW platform in real-time, and the results reveal that this solution can stabilize the ball’s location and has strong disturbance rejection because of the multi-zone effect.

RAMi: A New Real-Time Internet of Medical Things Architecture for Elderly Patient Monitoring

The aging of the world’s population, the willingness of elderly to remain independent, and the recent COVID-19 pandemic have demonstrated the urgent need for home-based diagnostic and patient monitoring systems to reduce the financial and organizational burdens that impact healthcare organizations and professionals. The Internet of Medical Things (IoMT), i.e., all medical devices and applications that connect to health information systems through online computer networks. The IoMT is one of the domains of IoT where real-time processing of data and reliability are crucial. In this paper, we propose RAMi, which is a Real-Time Architecture for the Monitoring of elderly patients thanks to the Internet of Medical Things. This new architecture includes a Things layer where data are retrieved from sensors or smartphone, a Fog layer built on a smart gateway, Mobile Edge Computing (MEC), a cloud component, blockchain, and Artificial Intelligence (AI) to address the specific problems of IoMT. Data are processed at Fog level, MEC or cloud in function of the workload, resource requirements, and the level of confidentiality. A local blockchain allows workload orchestration between Fog, MEC, and Cloud while a global blockchain secures exchanges and data sharing by means of smart contracts. Our architecture allows to follow elderly persons and patients during and after their hospitalization. In addition, our architecture allows the use of federated learning to train AI algorithms while respecting privacy and data confidentiality. AI is also used to detect patterns of intrusion.

Automatically search an optimal face detector for a specific deployment environment

Face detection plays an important role in many artificial intelligence applications, such as identity recognition, facial expression recognition, and gender/age recognition. Recently, the development of deep learning techniques has greatly improved face detection’s performance. However, it is still ineffective and time-consuming to manually design hyperparameters of face detectors for different deployment environments with diverse distributions. Besides, due to the limited computation capability, many previous networks are hard to meet the latency requirements in deployment environments, and the improved resolution of current cameras further increases the computation burden. Motivated by the above problems, we propose a searching framework aiming to automatically search a real-time face detector architecture with a fixed complexity constraint, to adapt a specific deployment environment. We model the whole searching space into two parts, including the hyperparameters of the network and the detector. Instead of only searching the network structure, the proposed method considers the whole model’s hyperparameters space which contains the preprocessing and postprocessing parameters. The evolutionary algorithm is employed to find the optimal solution, and new evolutionary operations are proposed to explore architecture space. During the whole searching procedure, we guarantee the computation cost is under the restrictions. The advantages of the proposed framework are that it considers a hard computation cost constraint and the preprocessing and postprocessing hyperparameters, leading to a fully automatic design style and global optimization. Finally, we evaluate the proposed model on the most popular Widerface and FDDB datasets. The proposed detector significantly surpasses the existing lightweight face detectors in the comprehensive performances, and the average latency is twice as shorter as the best competitor.

Research on Electrical Automation Monitoring System Model of Power Plant Based on CAN Bus

Using the traditional manual operation mode to control the electrical equipment of the power plant was not only inefficient and unsafe but also difficult to adapt to the use of new equipment. Therefore, this article puts forward the research on the model of power plant electrical automation monitoring system based on CAN bus, which was of great significance to improve the operation monitoring ability and management efficiency of power plant electrical equipment. Based on the analysis of the design mode and main functions of electrical equipment automation control system in power plant, this article expounded on the characteristics of CAN bus communication mode and its advantages in realizing electrical equipment automation control. According to the hierarchical management and structural characteristics of the electrical automation monitoring system, the real-time database architecture of the monitoring system was established, the real-time data monitoring and processing method of the electrical monitoring system was given, and then the electrical automation monitoring system model of the power plant was proposed. The experimental results showed that the electrical automation monitoring system proposed in this article was effective and feasible for real-time monitoring of the operation of electrical equipment. The model construction method of the electrical automation monitoring system proposed in this article can provide theoretical reference and technical support for improving the control mode of electrical equipment in power plants.

Implementation and integration of image processing blocks in a real-time bottle classification system

A practical solution to the problems caused by the water, air, and soil pollution produced by the large volumes of waste is recycling. Plastic and glass bottle recycling is a practical solution but sometimes unfeasible in underdeveloped countries. In this paper, we propose a high-performance real-time hardware architecture for bottle classification, that process input image bottles to generate a bottle color as output. The proposed architecture was implemented on a Spartan-6 Field Programmable Gate Array, using a Hardware Description Language. The proposed system was tested for several input resolutions up to 1080 p, but it is flexible enough to support input video resolutions up to 8 K. There is no evidence of a high-performance bottle classification system in the state-of-the-art. The main contribution of this paper is the implementation and integration of a set of dedicated image processing blocks in a high-performance real-time bottle classification system. These hardware modules were integrated into a compact and tunable architecture, and was tested in a simulated environment. Concerning the image processing algorithm implemented in the FPGA, the maximum processing rate is 60 frames per second. In practice, the maximum number of bottles that can be processed would be limited by the mechanical aspects of the bottle transportation system.

A NOVEL TRUE REAL-TIME SPATIOTEMPORAL DATA STREAM PROCESSING FRAMEWORK

The ability to interpret spatiotemporal data streams in real-time is critical for a range of systems. However, processing vast amounts of spatiotemporal data out of several sources, such as online traffic, social platforms, sensor networks, and other sources, is a considerable challenge. The major goal of this study is to create a framework for processing and analyzing spatiotemporal data from multiple sources with irregular shapes so that researchers can focus on data analysis instead of worrying about the data sources' structure. We introduced a novel spatiotemporal data paradigm for true-real-time stream processing, which enables high-speed and low-latency real-time data processing, with these considerations in mind. A comparison of two state-of-the-art real-time process architectures was offered, as well as a full review of the various open-source technologies for real-time data stream processing, and their system topologies were also presented. Hence, this study proposed a brand-new framework that integrates Apache Kafka for spatiotemporal data ingestion, Apache flink for true real-time processing of spatiotemporal stream data, as well as machine learning for real-time predictions, and Apache Cassandra at the storage layer for distributed storage in real-time. The proposed framework was compared with others from the literature using the following features: Scalability (Sc), prediction tools (PT), data analytics (DA), multiple event types (MET), data storage (DS), Real-time (Rt), and performance evaluation (PE) stream processing (SP), and our proposed framework provided the ability to handle all of this task.

On-Line FPGA Hardware in the Loop Validation of Based Fuzzy-STPWM Induction Motor Control

This work is part of the study and implementation of a fuzzy PWM control structure (fuzzy pulse width modulation) of an induction machine (MAS) on a circuit-based platform reconfigurable FPGA type. We first presented a strategy for the hardware implementation of a fuzzy inference system on a programmable logic circuit of FPGA type, through the hardware description language (VHDL) and Xilinx generator system (XSG). Secondly we proposed Fuzzy-PWM architecture for the improvement of response time of an asynchronous machine (MAS) and finally we validate the proposed hardware co-simulation architecture in real time on the ML402 development kit (based on FPGA Xilinx Virtex-4) and Simulink / Matlab.

Guest Editorial: Special Section on New Trends in Parallel and Distributed Computing for Human Sensible Applications

The papers in this special section focus on collecting high-quality scientific contributions from the research community working in the fields of parallel and distributed computing, data analytics algorithms and big data frameworks, application specific processing. Specifically, the main focus is on emerging new computing trends that affect the concrete human life, the so-called “Human Sensible Applications”. Problems in parallel computing related to implement precision medicine and novel therapeutical targets, real-time architectures for biomedical IoT, computational biology and chemical compounds simulations, realistic modelling of human body organs, bioimaging processing, but even emerging computing systems for Human Sustainability, including weather and climate changes monitoring/prediction, resources management, safety, disaster prediction and prevention, belong to this scenario.

Toward smart distributed renewable generation via multi-uncertainty featured non-intrusive interactive energy monitoring

Quantities of advanced technologies have been utilized to realize the carbon neutrality, among which the smart implementations at energy user side are highlighted due to the extensive coverage. Supported by multiple energy management technologies, the integration of distributed renewable energy is prominent, but the compatibility problem of diverse technologies still exists especially considering the newly-developed features. In this paper, an interactive energy monitoring via non-intrusive way is allocated to analyze the roof-top photovoltaic generation and individual appliance consumption from integral energy measurements, and a complete solution is proposed to tackle the distributed generation intelligence with smart voltage maintenance. At first stage, aiming at the domestic reactive power management of photovoltaic inverters, the framework and strategy of coordination between smart meter and intelligent photovoltaic generation are proposed, and then the unified energy signature formulation of photovoltaic generation is presented. Afterwards, the energy disaggregation model is formularized by modified dictionary learning scheme with hybrid validation strategy. Lastly, a sparse coding and auxiliary optimization-based solution is proposed to solve the problem, achieving the robustness under real time architecture. Both the specific low voltage network simulator platform and the practical field measurement scenarios are utilized to verify the effectiveness of the proposed study, and results demonstrate that the proposed study provides an efficient monitoring solution to the intelligent photovoltaic generation, which helps to move toward the smart energy user implementation.