Real-time System Applications Research Articles

Thermal radiation sensing image resolution and heterogeneous clustering application in real-time basketball training system

Thermal radiation sensing image resolution and heterogeneous clustering application in real-time basketball training system

SHIELD: S ecurity-Aware Sc h edul i ng for R e a l -Time D AGs on Heterogeneous Systems

Many control applications in real-time cyber-physical systems are represented as Directed Acyclic Graphs ( DAGs ) due to complex interactions among their functional components, and executed on distributed heterogeneous platforms. Data communication between dependent task nodes running on different processing elements are often realized through message transmission over a public network, and are hence susceptible to multiple security threats such as snooping , alteration , and spoofing . Several alternative security protocols having varying security strengths and associated implementation overheads are available in the market, for incorporating confidentiality , integrity , and authentication on the transmitted messages. While message size and correspondingly its associated transmission overheads may be marginally increased due to the assignment of security protocols, significant computation overheads must be incurred for securing the message at the location of its source task node and for unlocking security/message extraction at the destination. Obtained security strengths and associated computation overheads vary depending on the set of protocols chosen for a given message from an available pool of protocols. Given lower bounds on the security demands of an application’s messages, selecting the appropriate protocols for each message such that a system’s overall security is maximized while satisfying constraints related to the resource, task precedence and deadline, is a challenging and computationally hard problem. In this article, we propose an efficient heuristic strategy called SHIELD for security-aware real-time scheduling of DAG-structured applications to be executed on distributed heterogeneous systems. The efficacy of the proposed scheduler is exhibited through extensive simulation-based experiments using two DAG-structured application benchmarks. Our performance evaluation results demonstrate that SHIELD significantly outperforms two greedy baseline strategies SHIELDb in terms of solution generation times (i.e., runtimes) and SHIELDf in terms of achieved security utility. Additionally, a case study on the Traction Control application in automotive systems has been included to exhibit the applicability of SHIELD in real-world settings.

Semantic Logical Relations for Timed Message-Passing Protocols

Many of today’s message-passing systems not only require messages to be exchanged in a certain order but also to happen at a certain time or within a certain time window . Such correctness conditions are particularly prominent in Internet of Things (IoT) and real-time systems applications, which interface with hardware devices that come with inherent timing constraints. Verifying compliance of such systems with the intended timed protocol is challenged by their heterogeneity —ruling out any verification method that relies on the system to be implemented in one common language, let alone in a high-level and typed programming language. To address this challenge, this paper contributes a logical relation to verify that its inhabitants (the applications and hardware devices to be proved correct) comply with the given timed protocol. To cater to the systems’ heterogeneity, the logical relation is entirely semantic , lifting the requirement that its inhabitants are syntactically well-typed. A semantic approach enables two modes of use of the logical relation for program verification: (i) once-and-for-all verification of an arbitrary well-typed application, given a type system, and (ii) per-instance verification of a specific application / hardware device (foreign code). To facilitate mode (i) , the paper develops a refinement type system for expressing timed message-passing protocols and proves that any well-typed program inhabits the logical relation (fundamental theorem). A type checker for the refinement type system has been implemented in Rust, using an SMT solver to check satisfiability of timing constraints. Then, the paper demonstrates both modes of use based on a small case study of a smart home system for monitoring air quality, consisting of a controller application and various environment sensors.

Application and optimization of FPGA-based real-time motion control systems in robotics

Application and optimization of FPGA-based real-time motion control systems in robotics

Compounded real-time operating systems for rich real-time applications

Compounded real-time operating systems for rich real-time applications

Improved Localization and Recognition of Handwritten Digits on MNIST Dataset with ConvGRU

Video location prediction for handwritten digits presents unique challenges in computer vision due to the complex spatiotemporal dependencies and the need to maintain digit legibility across predicted frames, while existing deep learning-based video prediction models have shown promise, they often struggle with preserving local details and typically achieve clear predictions for only a limited number of frames. In this paper, we present a novel video location prediction model based on Convolutional Gated Recurrent Units (ConvGRU) that specifically addresses these challenges in the context of handwritten digit sequences. Our approach introduces three key innovations. Firstly, we introduce a specialized decoupling model using modified Generative Adversarial Networks (GANs) that effectively separates background and foreground information, significantly improving prediction accuracy. Secondly, we introduce an enhanced ConvGRU architecture that replaces traditional linear operations with convolutional operations in the gating mechanism, substantially reducing spatiotemporal information loss. Finally, we introduce an optimized parameter-tuning strategy that ensures continuous feature transmission while maintaining computational efficiency. Extensive experiments on both the MNIST dataset and custom mobile datasets demonstrate the effectiveness of our approach. Our model achieves a structural similarity index of 0.913 between predicted and actual sequences, surpassing current state-of-the-art methods by 1.2%. Furthermore, we demonstrate superior performance in long-term prediction stability, with consistent accuracy maintained across extended sequences. Notably, our model reduces training time by 9.5% compared to existing approaches while maintaining higher prediction accuracy. These results establish new benchmarks for handwritten digit video prediction and provide practical solutions for real-world applications in digital education, document processing, and real-time handwriting recognition systems.

Optimal power scheduling in real-time distribution systems using crow search algorithm for enhanced microgrid performance

Microgrids (MGs) have gained significant attention over the past two decades due to their advantages in service reliability, easy integration of renewable energy sources, high efficiency, and enhanced power quality. In India, low-voltage side customers face significant challenges in terms of power supply continuity and voltage regulation. This paper presents a novel approach for optimal power scheduling in a microgrid, aiming to provide uninterrupted power supply with improved voltage regulation (VR). To address these challenges, a crow search algorithm is developed for effective load scheduling within the distribution system. The proposed method minimizes the total operating cost (TOC) and maximizes VR under varying loading conditions and distributed generation (DG) configurations. A case study in Tamil Nadu, India, is conducted using a microgrid composed of three distributed generation sources (DGs), modeled and simulated using the Electrical Transient Analyzer Program (ETAP) environment. The proposed approach is tested under three operational scenarios: grid-connected mode, islanded mode, and grid-connected mode with one DG outage. Results indicate that the crow search algorithm significantly optimizes load scheduling, leading to a substantial reduction in power loss and enhancement in voltage profiles across all scenarios. The islanded mode operation using the crow search algorithm demonstrates a remarkable reduction in TOC and maximizes voltage regulation compared to other modes. The main contributions of this work include: (1) developing a new meta-heuristic approach for power scheduling in microgrids using the crow search algorithm, (2) achieving optimal power flow and load scheduling to minimize TOC and improve VR, and (3) successfully implementing the proposed methodology in a real-time distribution system using ETAP. The findings showcase the effectiveness of the crow search algorithm in microgrid power management and its potential for application in other real-time power distribution systems.

Digital Twin-Driven Optimization Of Bioenergy Production From Waste Materials

This study explores the transformative role of digital twin technology in optimizing bioenergy production, focusing on its applications in real-time monitoring, process simulation, predictive maintenance, and hybrid energy systems. Utilizing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, a total of 94 peer-reviewed articles were systematically identified, screened, and analyzed to ensure a comprehensive and rigorous review. The findings highlight the advancements in digital twin technology, including its integration with IoT-enabled sensors and machine learning algorithms, which enable dynamic system optimization and predictive capabilities. The study also examines the role of digital twins in hybrid energy systems, demonstrating their potential to enhance energy efficiency by up to 30% through seamless integration with solar and wind energy. Despite these advancements, the review identifies critical challenges, such as high computational demands, data integration issues, and economic and policy barriers, which limit the scalability and widespread adoption of digital twins in industrial bioenergy applications. This study contributes to the growing body of knowledge by offering a comprehensive synthesis of existing research, identifying key gaps, and emphasizing the need for interdisciplinary collaboration and policy support to fully harness the potential of digital twins in achieving sustainable energy goal.

Kinematic matrix: One-shot human action recognition using kinematic data structure

Kinematic matrix: One-shot human action recognition using kinematic data structure

A low-complexity adaptive speech dereverberation approach based on multichannel linear prediction

The adaptive multichannel linear prediction (AMCLP) technique is one of the most effective methods for speech dereverberation in real-time application systems. However, the computational complexity of the associated AMCLP algorithms is very high. In this paper, we propose a low-complexity speech dereverberation approach based on AMCLP. The coefficient matrix of the multichannel dereverberation filter is decomposed into a linear combination of a set of low-dimensional sub-filter matrices and a set of low-dimensional sub-filter vectors through Kronecker product. According to these sub-filter models, we define two sets of signal models and cost functions, from which the adaptive speech dereverberation algorithm is established. This solution not only leads to effective dereverberation performance, but also reduces the computational cost, which is very conducive to realistic speech systems.

A class of regularized adaptive multichannel equalization algorithms for speech dereverberation

The multiple input/output inverse theorem (MINT) is a typical speech dereverberation technique based on acoustic channel equalization. Although the adaptive MINT (A-MINT) method has been developed to meet the requirements of real-time application systems, its performance often deteriorates due to the effect of the identification errors of room impulse responses (RIRs). In this paper, we present a regularized A-MINT approach to reduce the sensitivity of the adaptive equalizer to the RIR identification errors. According to the regularized MINT criterion, Newton's and quasi-Newton methods are used to establish two adaptive speech dereverberation algorithms, respectively, which obtain faster convergent rate and better dereverberation performance. The effectiveness of the proposed algorithms is validated through numerical experiments with the RIRs measured in real acoustic environments.

COMPREHENSIVE EVALUATION OF REAL-TIME OBJECT DETECTION ALGORITHM BASED ON EXTENDED CRITERIA

In this study, we implemented YOLO (You Only Look Once) for real-time object detection and evaluated its performance based on key metrics such as processing speed, frame rate, and object detection accuracy. Our approach emphasizes both the precision and efficiency of YOLO, focusing on its ability to detect objects in real-world scenarios while maintaining a low computational cost. To identify and count objects, the YOLO algorithm was applied to analyze three images. It divided each image into a grid, and each cell predicted bounding boxes and confidence scores for potential objects. Following the processing of these predictions using non-max suppression to remove duplicates, each image contained an accurate count of the items that were detected. The model achieved a processing time of 17.68 seconds, with an average of 0.25 seconds per frame, demonstrating the system's capability for rapid object detection in near real-time applications. On average, 1.32 objects were detected per frame, with a maximum of 1.67 objects in a single frame and a minimum of 1 object per frame, indicating consistent detection across the dataset. The standard deviation of objects per frame (0.113) shows a low variability in object detection rates, reflecting the robustness of the model in handling diverse input frames. The achieved frame rate of 4.2 FPS demonstrates the model's potential for real-time applications, particularly in environments where processing speed is critical. The scientific novelty of this work lies in demonstrating YOLO’s adaptability for efficient object detection while maintaining high detection rates and consistent performance across varying scenarios. This study contributes to the field by showcasing YOLO's applicability in real-time systems, where object detection speed and accuracy are paramount. Our findings provide a foundation for further optimization in high-performance, low-latency object detection tasks, as well as its scalability for more complex detection systems. The results underscore YOLO's potential in both academic and industrial settings.

WITHDRAWN: Non-Contact Blood Pressure Monitoring from Radar Signals by a Temporal-Spatial Feature Fusion Network

WITHDRAWN: Non-Contact Blood Pressure Monitoring from Radar Signals by a Temporal-Spatial Feature Fusion Network

SMS Scam Detection Application Based on Optical Character Recognition for Image Data Using Unsupervised and Deep Semi-Supervised Learning.

The growing problem of unsolicited text messages (smishing) and data irregularities necessitates stronger spam detection solutions. This paper explores the development of a sophisticated model designed to identify smishing messages by understanding the complex relationships among words, images, and context-specific factors, areas that remain underexplored in existing research. To address this, we merge a UCI spam dataset of regular text messages with real-world spam data, leveraging OCR technology for comprehensive analysis. The study employs a combination of traditional machine learning models, including K-means, Non-Negative Matrix Factorization, and Gaussian Mixture Models, along with feature extraction techniques such as TF-IDF and PCA. Additionally, deep learning models like RNN-Flatten, LSTM, and Bi-LSTM are utilized. The selection of these models is driven by their complementary strengths in capturing both the linear and non-linear relationships inherent in smishing messages. Machine learning models are chosen for their efficiency in handling structured text data, while deep learning models are selected for their superior ability to capture sequential dependencies and contextual nuances. The performance of these models is rigorously evaluated using metrics like accuracy, precision, recall, and F1 score, enabling a comparative analysis between the machine learning and deep learning approaches. Notably, the K-means feature extraction with vectorizer achieved 91.01% accuracy, and the KNN-Flatten model reached 94.13% accuracy, emerging as the top performer. The rationale behind highlighting these models is their potential to significantly improve smishing detection rates. For instance, the high accuracy of the KNN-Flatten model suggests its applicability in real-time spam detection systems, but its computational complexity might limit scalability in large-scale deployments. Similarly, while K-means with vectorizer excels in accuracy, it may struggle with the dynamic and evolving nature of smishing attacks, necessitating continual retraining.

Robust Mixed-Rate Region-of-Interest-Aware Video Compressive Sensing for Transmission Line Surveillance Video

Classic video compression methods usually suffer from long encode time and requires large memories, making it hard to deploy on edge devices; thus, video compressive sensing, which requires less resources during encoding, is receiving more attention. We propose a robust mixed-rate ROI-aware video compressive sensing algorithm for transmission line surveillance video compression. The proposed method compresses foreground targets and background frames separately and uses reversible neural network to reconstruct original frames. The result on transmission line surveillance video data shows that the proposed compressive sensing method can achieve 26.47, 34.71 PSNR and 0.6839, 0.9320 SSIM higher than existing methods on 1.5% and 15% measurement rates, and the proposed ROI extraction net can precisely retrieve regions under high noise levels. This research not only demonstrates the potential for a more efficient video compression technique in resource-constrained environments, but also lays a foundation for future advancements in video compressive sensing techniques and their applications in various real-time surveillance systems.

Internet of Things (IoT) Driven Water Management System for Efficient Level Control

Water is an essential and valuable resource in daily life, making its conservation crucial to prevent adverse effects. Storing water for domestic, industrial, agricultural, and other purposes is particularly important. Safe drinking water is increasingly becoming polluted due to the growing population and their demands for urbanization and industrialization. At the household level, some people leave electric water pumps running and either go to work or sleep, forgetting to turn off the pumps when the water container is full. This highlights the need for a reliable and continuous water supply. IoT plays a significant role in environmental monitoring, particularly in disaster management, early warning systems, and environmental data analytics. One major challenge in urban cities is water management, especially with the rapid growth of urbanization, necessitating sustainable urban development plans. To address these issues, we propose a "Water Level Monitoring System" solution. This paper presents an IoT-based water monitoring system for real-time applications. The system's sensors measure the water level in the tank, and the data is sent to a cloud server, allowing users to view it on a remote dashboard. This system can be used efficiently by both homeowners and industrial users, as well as other water utilities.

Advances in ground robotic technologies for site-specific weed management in precision agriculture: A review

Advances in ground robotic technologies for site-specific weed management in precision agriculture: A review

High-resolution non-confocal non-line-of-sight imaging based on spherical-slice transform from spatial and temporal frequency to space and time.

Current non-confocal non-line-of-sight (NLOS) imaging faces the problems of low resolution and limited scene adaptability. We propose a non-confocal NLOS imaging method based on spherical-slice transform from spatial and temporal frequency to space and time. Simulation and experimental results show that the proposed method has high-resolution reconstruction without artifact interference, shape distortion, and position offset. Furthermore, it has strong scene adaptability. After GPU acceleration, the reconstruction time of the proposed method can be reduced to several hundred milliseconds for the PF32 photon array camera with 32 × 32 detection units. In the future, the proposed method has great potential for application in real-time NLOS imaging systems.

Leveraging Business Analytics for Strategic Merchandising Decisions in Fashion Retail

The application of business analytics to strategic merchandising decisions in the retail fashion industry is investigated in this study. Using data-driven insights has become essential for improving product assortments, pricing strategies, and inventory management as the business faces increasing complexity due to rapidly changing consumer preferences, seasonal demand changes, and more competition. The main goal of this study is to examine how fashion retailers may use business analytics to strengthen their merchandising plans, increase operational effectiveness, and increase profitability. The application of real-time inventory management systems, dynamic pricing models, and predictive analytics in the retail fashion industry is highlighted. This study attempts to provide actionable insights that enable retailers to make better-educated, data-centric decisions by addressing the opportunities and difficulties related to these sophisticated analytical tools. Despite its potential to improve retail operations, a major problem identified in this study is the underutilization of business analytics in merchandising decisions. To solve this issue, a qualitative research methodology was used to evaluate the current state of analytics integration in fashion retail using secondary data from industry reports, case studies, literature reviews, and other pertinent sources. The results show that using analytics has many benefits, including better demand forecasting, more accurate pricing, and more effective inventory management. However, the study also identifies barriers such as difficulties integrating data, the need for qualified staff, and moral dilemmas about the privacy of customer data. By shedding light on the efficient use of business analytics in the retail fashion industry, this study enhances theoretical knowledge and real-world implementation. It makes recommendations for removing adoption hurdles while highlighting the necessity of combining analytics with human knowledge and creativity. A better understanding of how data-driven decision-making affects merchandising procedures in fashion retail is one of the theoretical ramifications. Practically, the study suggests tactics for retailers to maximize their use of analytics to stay competitive in a market that is changing quickly. This study admits some limitations despite its insightful findings, most notably its reliance on secondary data that might not accurately represent the most recent advancements in technology or industry trends. Future studies should investigate how analytics affect consumer behavior and retail performance over the long run, as well as how small and medium-sized businesses may use these technologies efficiently.

Blockchain Sensor Network Based CPS Analysis in Healthcare System for Real Time Applications Using 6G IoT Model and Deep Learning Techniques

Blockchain Sensor Network Based CPS Analysis in Healthcare System for Real Time Applications Using 6G IoT Model and Deep Learning Techniques