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Abstract In engineering fields, time‐varying matrix inversion (TVMI) issue is often encountered. Zeroing neural network (ZNN) has been extensively employed to resolve the TVMI problem. Nevertheless, the original ZNN (OZNN) and the integral‐enhanced ZNN (IEZNN) usually fail to deal with the TVMI problem under unbounded noises, such as linear noises. Therefore, a neural network model that can handle the TVMI under linear noise interference is urgently needed. This paper develops a double integral‐enhanced ZNN (DIEZNN) model based on a novel integral‐type design formula with inherent linear‐noise tolerance. Moreover, its convergence and robustness are verified by derivation strictly. For comparison and verification, the OZNN and the IEZNN models are adopted to resolve the TVMI under multiple identical noise environments. The experiments proved that the DIEZNN model has excellent advantages in solving TVMI problems under linear noises. In general, the DIEZNN model is an innovative work and is proposed for the first time. Satisfyingly, the errors of DIEZNN are always less than 1 × 10−3 under linear noises, whereas the error norms of OZNN and IEZNN models are not convergent to zero. In addition, these models are applied to the control of the controllable permanent magnet synchronous motor chaotic system to indicate the superiority of the DIEZNN.

Abstract The solving of dynamic matrix square root (DMSR) problems is frequently encountered in many scientific and engineering fields. Although the original zeroing neural network is powerful for solving the DMSR, it cannot vanish the influence of the noise perturbations, and its constant‐coefficient design scheme cannot accelerate the convergence speed. Therefore, a noise‐tolerate and adaptive coefficient zeroing neural network (NTACZNN) is raised to enhance the robust noise immunity performance and accelerate the convergence speed simultaneously. Then, the global convergence and robustness of the proposed NTACZNN are theoretically analysed under an ideal environment and noise‐perturbed circumstances. Furthermore, some illustrative simulation examples are designed and performed in order to substantiate the efficacy and advantage of the NTACZNN for the DMSR problem solution. Compared with some existing ZNNs, the proposed NTACZNN possesses advanced performance in terms of noise tolerance, solution accuracy, and convergence rate.

Abstract The importance of hip adductor strength for injury prevention and performance benefits is well documented. The purpose of this study was to establish the intra‐ and inter‐day variability of peak force (PF) of a groin squeeze protocol using a custom‐designed compression strain gauge device. Sixteen semi‐professional soccer players completed three trials over three separate testing occasions with at least 24‐h rest between each session. The main findings were that the compression strain gauge was a reliable device for measuring PF within and between days. All intraclass correlations were higher than 0.80 and coefficients of variations were below 10% across the different sessions and trials. Due to the information gained through the compression strain gauge, the higher sampling frequency utilized, portability, and the relatively affordable price, this device offers an effective alternative for measuring maximal strength for hip adduction.

In order to improve the welfare of newborns, this study investigates the use of sound-recognition-based artificial intelligence (AI) approaches to the interpretation and monitoring of infant screams. Crying has long been a problem because it is the primary means of communication between infants and caregivers. The limitations of conventional interpretation techniques are discussed. These limitations include the subjective nature of interpretation and the inability to detect subtle variations in crying patterns. The goal of the research is to categorize crying patterns based on the cries of male and female infants and identify noises that are a sign of distress. The study utilized the Mel Frequency Cepstral Coefficients (MFCC) method to extract features from internet-sourced MP3 and WAV audio data. The technique successfully captured the unique qualities of each crying sound using various machine-learning models, including Random Forest and XGBoost. These models outperformed others with accuracy rates of 94.5% and 94.2%, respectively. These findings show how well these algorithms perform in correctly categorizing various newborn cries. The findings of this study establish the platform for possible Internet of Things (IoT) and healthcare framework implementations targeted at supporting parents in caring for their newborns by offering an insightful understanding of the distinctive vocalizations connected with weeping.

The paper describes low-cost hardware-based analog and digital real-time circuit simulators for the development of power electronics control circuits. During the process of designing and developing digital control circuits for power electronics systems, preliminary verification of control algorithms is required. For this purpose, software simulators such as Pspice, Psim, Matlab-Simulink, and many others are commonly used. Afterward, the developed control algorithm is implemented in the digital control system. For further verification of the implemented control algorithms, a hardware-based analog or digital simulator can be utilized. The paper presents the author’s proposed analog simulators. In the digital version of the simulator, TMS320F28388D microcontroller with 200 MHz clock was used. These simulators have demonstrated their usefulness in the development of power electronics systems.

Durational action timed automata (daTAs) are state transition systems like timed automata (TAs) that capture information regarding the concurrent execution of actions and their durations using maximality-based semantics. As the underlying semantics of daTAs are infinite due to the modeling of time progress, conventional model checking techniques become impractical for systems specified using daTAs. Therefore, a finite abstract representation of daTA behavior is required to enable model checking for such system specifications. For that, we propose a finite abstraction of the underlying semantics of a daTA-like region abstraction of timed automata. In addition, we highlight the unique benefits of daTAs by illustrating that they enable the verification of properties concerning concurrency and action duration that cannot be verified using the traditional TA model. We demonstrate mathematically that the number of states in durational action timed automata becomes significantly smaller than the number of states in timed automata as the number of actions increases, confirming the efficiency of daTAs in modeling behavior with action durations.

Recovering copper from end-of-life electronics, especially from printed circuit boards, provides significant economic benefits, reduces environmental impact, and supports a circular economy. This case study presents a data-driven approach to predicting copper recovery in the electrolysis stage of a bioleaching process by utilizing RGB sensor readings. We tested nine regression models using RGB values from experimental data. The gradient boosting model, optimized via response surface methodology (RSM), outperformed the others, with predictions matching 84% of observed patterns. These results demonstrate strong predictive capabilities, with scope for further accuracy enhancements. We offer an open-source, web-based digital twin designed specifically to monitor the bioleaching plant, enabling real-time and historical data analysis to support predictive maintenance. Our results underscore the potential to optimize the entire bioleaching process, marking a significant advancement for large-scale copper recovery. This study is the first to investigate predictive bioleaching continuous processes in a semi-industrial e-waste plant using RGB sensors, presenting a novel approach in the field.

In contemporary computer vision, deep learning-based real-time single image super-resolution approaches have gained significant attention for their ability to enhance the resolution of images in real time. These approaches are interconnected with various other computer vision domains, including image segmentation and object detection. Numerous surveys have summarized the state of the image SR domain. However, there is no survey that specifically addresses real-time single image SR on IoT devices. Therefore, in this study, we aim to explore strategies, identify the technical challenges, and outline the future directions of SR research, with a special emphasis on real-time super-resolution techniques. We begin with an overview of the core concepts related to real-time SR, recent challenges, and algorithm classification and delve into potential application scenarios that merit attention. Additionally, we explore the challenges and identify promising research areas related to real-time SR specifically related to IoT devices, highlighting potential advancements, limitations, and opportunities for future innovation in this rapidly evolving field.

This paper presents a hardware implementation of a one-dimensional convolutional neural network using depthwise separable convolution (DSC) on the VC707 FPGA development board. The design processes the one-dimensional rolling bearing current signal dataset provided by Paderborn University (PU), employing minimal preprocessing to maximize the comprehensiveness of feature extraction. To address the high parameter demands commonly associated with convolutional neural networks (CNNs), the model incorporates DSC, significantly reducing computational complexity and parameter load. Additionally, the DoReFa-Net quantization method is applied to compress network parameters and activation function outputs, thereby minimizing memory usage. The quantized DSC model requires approximately 22 KB of storage and performs 1,203,128 floating-point operations in total. The implementation achieves a power consumption of 527 mW at a clock frequency of 50 MHz, while delivering a fault diagnosis accuracy of 96.12%.

The operational performance of grid‐connected active front‐end converters (AFEs) faces challenges arising from the intricate interplay among phase‐locked loop (PLL) non‐linearities, grid impedance, and conventional control strategies, resulting in compromised stability. This study introduces a refined approach to dynamic model predictive control (MPC) by integrating recursive least squares (RLS) for the precise estimation of physical model parameters, thereby addressing stability concerns. Unlike conventional methodologies, the proposed enhanced RLS‐based MPC approach, equipped with an auto‐tuning feature, allows for the design of controllers without a prerequisite understanding of exact external dynamics. Notably, this technique exhibits exceptional disturbance rejection capabilities. The evaluation of the cost function at each sampling interval facilitates the determination of optimal switching states based on predicted variables. Gate pulses for the switches of the AFEs are generated accordingly. Employing a simulation platform, the proposed control structure's performance across varied conditions is comprehensively assessed, encompassing alterations in grid impedance and system non‐linearities. The method adeptly integrates inherent non‐linearities within the system, showcasing exceptional robustness in diverse dynamic scenarios. To further substantiate the efficacy of the proposed control system over conventional approaches, simulation results are validated using a laboratory hardware platform equipped with Typhoon HIL and dSPACE real‐time emulators, providing tangible evidence of the proposed control system's effectiveness in real‐world hardware setups. The multifaceted approach, encompassing precise parameter estimation, predictive control, auto‐tuning, disturbance rejection, robust design, and real‐time evaluation, collectively establishes a resilient foundation for enhancing and maintaining the overall stability of the system across diverse operating scenarios. [ABSTRACT FROM AUTHOR]

The advancement of unmanned aerial vehicles (UAVs) enables early detection of numerous disasters. Efforts have been made to automate the monitoring of data from UAVs, with machine learning methods recently attracting significant interest. These solutions often face challenges with high computational costs and energy usage. Conventionally, data from UAVs are processed using cloud computing, where they are sent to the cloud for analysis. However, this method might not meet the real-time needs of disaster relief scenarios. In contrast, edge computing provides real-time processing at the site but still struggles with computational and energy efficiency issues. To overcome these obstacles and enhance resource utilization, this paper presents a convolutional neural network (CNN) model with an early exit mechanism designed for fire detection in UAVs. This model is implemented using TSMC 40 nm CMOS technology, which aids in hardware acceleration. Notably, the neural network has a modest parameter count of 11.2 k. In the hardware computation part, the CNN circuit completes fire detection in approximately 230,000 cycles. Power-gating techniques are also used to turn off inactive memory, contributing to reduced power consumption. The experimental results show that this neural network reaches a maximum accuracy of 81.49% in the hardware implementation stage. After automatic layout and routing, the CNN hardware accelerator can operate at 300 MHz, consuming 117 mW of power. [ABSTRACT FROM AUTHOR]

Efficient animal detection is essential for biodiversity protection. Unmanned aerial vehicles (UAVs) have been widely used because of their low costs and minimal environmental intrusion. However, using UAVs for practical animal detection poses two challenges: (a) the UAV's fly highly to avoid disturbing animals, resulting in small object detection problems; (b) the limited processing power of UAVs makes large state‐of‐the‐art (SOTA) methods (e.g., You Only Look Once V7, YOLOv7) difficult to deploy. This work proposes the WILD‐YOLO based on YOLOv7 to deal with the two problems. To detect small objects, WILD‐YOLO improves upon YOLOv7 by adding a small object detection head in the head part. To enable real‐time animal detection in field environments with UAVs, the lighten FasterNet and GhostNet have been used to significantly reduce the model size. Compared to YOLOv7, WILD‐YOLO significantly reduces the number of parameters, making it suitable for lightweight deployment on UAVs. Additionally, comparisons with other lightweight models such as YOLOv7‐tiny, YOLOv5‐s, YOLOv4‐s and MobilenetV2 on the datasets are conducted. The experimental results demonstrate that this proposed WILD‐YOLO method outperforms other approaches and has great potential for effective detection of wildlife in complex environments encountered by UAVs. [ABSTRACT FROM AUTHOR]

In embedded systems, the execution semantics of the real-time operating system (RTOS), which is responsible for scheduling and timely execution of concurrent processes, is crucial for the correctness of the overall system. However, existing approaches for the formal verification of embedded systems typically abstract from the RTOS completely, or provide a detailed and synthesizable formal model of the RTOS. While the former may lead to unsafe systems, the latter is not compatible with industrial design processes. In this paper, we present an approach for reusable abstract formal models that can be configured for custom RTOS. Our key idea is to formally capture common execution mechanisms of RTOS like preemptive scheduling, event synchronization, and communication abstractly in configurable timed automata models. These abstract formal models can be configured for a concrete custom RTOS, and they can be combined into a formal system model together with a concrete application. Our reusable models significantly reduce the manual effort of defining a formal model that captures concurrency and real-time behavior, together with the functionality of an application. The resulting formal model enables analysis, verification, and graphical simulation. We validate our approach by formalizing and analyzing a rescue robot application running the custom open source RTOS EV3RT. [ABSTRACT FROM AUTHOR]

The multi-objective optimization (MOO) problem in wireless sensor networks (WSNs) is concerned with optimizing the operation of the WSN across three dimensions: coverage, connectivity, and lifetime. Most works in the literature address only one or two dimensions of this problem at a time, except for the randomized coverage-based scheduling (RCS) algorithm and the clique-based scheduling algorithm. More recently, a Hidden Markov Model (HMM)-based algorithm was proposed that improves on the latter two; however, the question remains open if further improvement is possible as previous algorithms explore solutions in terms of local minima and local maxima, not in terms of the full search space globally. Therefore, the main contribution of this paper is to propose a new scheduling algorithm based on bio-inspired computation (the bat algorithm) to address this limitation. First, the algorithm defines a fitness and objective function over a search space, which returns all possible sleep and wake-up schedules for each node in the WSN. This yields a (scheduling) solution space that is then organized by the Pareto sorting algorithm, whose output coordinates are the distance of each node to the base station and the residual energy of the node. We evaluated our results by comparing the bat and HMM node scheduling algorithms implemented in MATLAB. Our results show that network lifetime has improved by 30%, coverage by 40%, and connectivity by 26.7%. In principle, the obtained solution will be the best scheduling that guarantees the best network lifetime performance as well as the best coverage and connectedness for ensuring the dependability of safety-critical WSNs. [ABSTRACT FROM AUTHOR]

Current real-time systems applications are mostly large-scale, where tasks are intrinsically time-constrained and meaningfully dependent. Nevertheless, while the initial partitioning solution has a huge impact on scheduling results, most tasks-allocation problems are NP-complete and lack binding semantics. The scheduling analysis strategies are inevitably required to validate the system scheduling before its actual deployment. However, once the proposed scheduling is invalid, no possible corrections are provided, and a costing NP-complete partitioning regeneration is provoked as the only possible correction action. Whereas, sometimes nearby task re-allocations guarantee the scheduling correction. This paper proposes an approach to efficiently correct initial dependent task partitioning solutions while considering their allocation semantics. The ultimate objective is to conduct a correct schedule for multiprocessor real-time systems by means of a driven task re clustering and/or re-allocation without the need for extra complex partitioning regeneration. Simulation results show that our proposed approach is faster than classic partitioning regeneration, ameliorates the partitioning semantic results, and gives efficient scheduling results. [ABSTRACT FROM AUTHOR]

We present a near real-time solution for 3D reconstruction from aerial images captured by consumer UAVs. Our core idea is to simplify the multi-view stereo problem into a series of two-view stereo matching problems. Our method applies to UAVs equipped with only one camera and does not require special stereo-capturing setups. We found that the neighboring two video frames taken by UAVs flying at a mid-to-high cruising altitude can be approximated as left and right views from a virtual stereo camera. By leveraging GPU-accelerated real-time stereo estimation, efficient PnP correspondence solving algorithms, and an extended Kalman filter, our system simultaneously predicts scene geometry and camera position/orientation from the virtual stereo cameras. Also, this method allows for the user selection of varying baseline lengths, which provides more flexibility given the trade-off between camera resolution, effective measuring distance, flight altitude, and mapping accuracy. Our method outputs dense point clouds at a constant speed of 25 frames per second and is validated on a variety of real-world datasets with satisfactory results. [ABSTRACT FROM AUTHOR]

Component-based design encapsulates and isolates state and the operations on it, but timing semantics cross-cut these boundaries when a real-time task's control flow spans multiple components. Under priority-based scheduling, inter-component control flow should be coupled with priority information, so that task execution can be prioritized appropriately end-to-end. However, the CAmkES component architecture for the seL4 microkernel does not adequately support priority propagation across intercomponent requests: component interfaces are bound to threads that execute at fixed priorities provided at compile-time in the component specification. In this paper, we present a new library for CAmkES with a thread model that supports (1) multiple concurrent requests to the same component endpoint; (2) propagation and enforcement of priority metadata, such that those requests are appropriately prioritized; (3) implementations of Non-Preemptive Critical Sections, the Immediate Priority Ceiling Protocol, and the Priority Inheritance Protocol for components encapsulating critical sections of exclusive access to a shared resource; and (4) extensions of these mechanisms to support nested lock acquisition. We measure overheads and blocking times for these new features, use existing theory to discuss schedulability analysis, and present a new hyperbolic bound for rate-monotonic scheduling of tasks with blocking times that allows tasks to be assigned non-unique priorities. Evaluations on both Intel x86 and ARM platforms demonstrate that our library allows CAmkES to provide suitable end-to-end timing for real-time systems. [ABSTRACT FROM AUTHOR]

Software in autonomous systems like autonomous cars, robots or drones is often implemented on resource-constrained embedded systems with heterogeneous architectures. At the heart of such software are multiple feedback control loops, whose dynamics not only depend on the control strategy being used, but also on the timing behavior the control software experiences. But performing timing analysis for safety critical control software tasks, particularly on heterogeneous computing platforms, is challenging. Consequently, a number of recent papers have addressed the problem of stability analysis of feedback control loops in the presence of timing uncertainties (cf., deadline misses). In this paper, we address a different class of safety properties, viz., whether the system trajectory with timing uncertainties deviates too much from the nominal trajectory. Verifying such quantitative safety properties involves performing a reachability analysis that is computationally intractable, or is too conservative. To alleviate these problems we propose to provide statistical guarantees over the behavior of control systems with timing uncertainties. More specifically, we present a Bayesian hypothesis testing method that estimates deviations from a nominal or ideal behavior. We show that our analysis can provide, with high confidence, tighter estimates of the deviation from nominal behavior than using known reachability analysis methods. We also illustrate the scalability of our techniques by obtaining bounds in cases where reachability analysis fails, thereby establishing the practicality of our proposed method. [ABSTRACT FROM AUTHOR]

A real-time 3-D spatial localization system to track an unmanned aerial vehicle (UAV) using a single off-board conventional camera and an onboard circle-shaped colored light-emitting diode (LED) marker is presented. The image from the camera is color-segmented and then morphologically closed before being sent to the ellipse detector. The location of the drone is then estimated with geometric/optical calculations using the pixel coordinates provided by the real-time ellipse detector. The location data generated by this system was validated by a motion-capture system (MCS) that was simultaneously tracking the system in real time. The average position error was of 6 cm, while the processing speed achieved 15 locations/s, which are comparable with recent research references. The usage of a low-cost camera makes this method promising for most mobile UAV tracking applications. [ABSTRACT FROM AUTHOR]

The issue of noise filtering for 3D systems is extremely important in voluminous data transmission. The challenge of this method is to regenerate unknown 3D noisy objects with distant transmission channels. This canal is modeled by convolution system and deconvolution filter to rebuild the output 3D object. To resolve this issue, firstly, we use the hybrid moments based on three Tchibechef, Krawtchouk, and Hahn polynomials to extract the feature vectors for generating the input system with the minimum information. Next, we implement the system with the model of Fornasini–Marchesini for convolution and deconvolution. However, the free matrix variables are used to eliminate coupling between Lyapunov matrix and system matrices to obtain sufficient conditions in linear matrix inequality form to ensure the desired stability and performance of the error systems. Furthermore, the 3D filtering error system is asymptotically stable and satisfies the H ∞ performance index. A comparative study was carried out to show the robustness of the MSE of the proposed method of hybrid descriptor of optimal order obtained by the maximum of maximum entropy for parameters p = 50, α = 50, and β = 50 compared to Tchebichef, Krawtchouk for p = 50, Hahn descriptors for α = 50, and β = 50 parameters. The proposed method is more efficient with a test MSE of 3.45, also the PSNR = 2, and ETIR of 48.1137 for 3D object with Zero-mean Gaussian noise of variance= 0,1. [ABSTRACT FROM AUTHOR]

Introduction: Navigation satellite systems can fail to work or work incorrectly in a number of conditions: signal shadowing, electromagnetic interference, atmospheric conditions, and technical problems. All of these factors can significantly affect the localization accuracy of autonomous driving systems. This emphasizes the need for other localization technologies, such as Lidar. Methods: The use of the Kalman filter in combination with Lidar can be very effective in various applications due to the synergy of their capabilities. The Kalman filter can improve the accuracy of lidar measurements by taking into account the noise and inaccuracies present in the measurements. Results: In this paper, we propose a parallel Kalman algorithm in three- dimensional space to speed up the computational speed of Lidar localization. At the same time, the initial localization accuracy of the latter is preserved. A distinctive feature of the proposed approach is that the Kalman localization algorithm itself is parallelized, rather than the process of building a map for navigation. The proposed algorithm allows us to obtain the result 3.8 times faster without compromising the localization accuracy, which was 3% for both cases, making it effective for real-time decision-making. Discussion: The reliability of this result is confirmed by a preliminary theoretical estimate of the acceleration rate based on Ambdahl's law. Accelerating the Kalman filter with CUDA for Lidar localization can be of significant practical value, especially in real-time and in conditions where large amounts of data from Lidar sensors need to be processed. [ABSTRACT FROM AUTHOR]

Real-time systems experience many safety and performance issues at run time due to different uncertainties in the environment. Systems are now becoming highly interactive and must be able to execute in a changing environment without experiencing any failure. A real-time system can have multiple modes of operation such as safety and performance. The system can satisfy its safety and performance requirements by switching between the modes at run time. It is essential for the designers to ensure that a multi-mode real-time system operates in the expected mode at run time. In this paper, we present a verification model that identifies the expected mode at run time and checks whether the multi-mode real-time system is operating in the correct mode or not. To determine the expected mode, we present a monitoring module that checks the environment of the system, identifies different real-world occurrences as events, determines their properties and creates an event-driven dataset for failure analysis. The dataset consumes less memory in comparison to the raw input data obtained from the monitored environment. The event-driven dataset also facilitates onboard decision-making because the dataset allows the system to perform a safety analysis by determining the probability of failure in each environmental situations. We use the probability of failure of the system to determine the safety mode in different environmental situations. To demonstrate the applicability of our proposed scheme, we design and implement a real-time traffic monitoring system that has two modes: safety, and performance. The experimental analysis of our work shows that the verification model can identify the expected operating mode at run time based on the safety (probability of failure) and performance (usage) requirements of the system as well as allows the system to operate in performance mode (in 3295 out of 3421 time intervals) and safety mode (in 126 out of 3421 time intervals). The experimental result demonstrates that the volume of the dataset generated using our approach is significantly less in comparison to existing data compression techniques. [ABSTRACT FROM AUTHOR]

We consider the problem of meeting deadline constraints in wireless communication networks. Fulfilling deadlines depends heavily on the routing algorithm used. We study this dependence generically for a broad class of routing algorithms. For analyzing the impact of routing decisions on deadline fulfillment, we adopt a stochastic model from operations research to capture the source-to-destination delay distribution and the corresponding probability of successfully delivering data before a given deadline. Based on this model, we propose a decentralized algorithm that operates locally at each node and exchanges information solely with direct neighbors in order to determine the probabilities of achieving deadlines. A modified version of the algorithm also improves routing tables iteratively to progressively increase the deadline achievement probabilities. This modified algorithm is shown to deliver routing tables that maximize the deadline achievement probabilities for all nodes in a given network. We tested the approach by simulation and compared it with routing strategies based on established metrics, specifically the average delay, minimum hop count, and expected transmission count. Our evaluations encompass different channel quality and small-scale fading conditions, as well as various traffic load scenarios. Notably, our solution consistently outperforms the other approaches in all tested scenarios. [ABSTRACT FROM AUTHOR]

The solving of dynamic matrix square root (DMSR) problems is frequently encountered in many scientific and engineering fields. Although the original zeroing neural network is powerful for solving the DMSR, it cannot vanish the influence of the noise perturbations, and its constant‐coefficient design scheme cannot accelerate the convergence speed. Therefore, a noise‐tolerate and adaptive coefficient zeroing neural network (NTACZNN) is raised to enhance the robust noise immunity performance and accelerate the convergence speed simultaneously. Then, the global convergence and robustness of the proposed NTACZNN are theoretically analysed under an ideal environment and noise‐perturbed circumstances. Furthermore, some illustrative simulation examples are designed and performed in order to substantiate the efficacy and advantage of the NTACZNN for the DMSR problem solution. Compared with some existing ZNNs, the proposed NTACZNN possesses advanced performance in terms of noise tolerance, solution accuracy, and convergence rate. [ABSTRACT FROM AUTHOR]

In engineering fields, time‐varying matrix inversion (TVMI) issue is often encountered. Zeroing neural network (ZNN) has been extensively employed to resolve the TVMI problem. Nevertheless, the original ZNN (OZNN) and the integral‐enhanced ZNN (IEZNN) usually fail to deal with the TVMI problem under unbounded noises, such as linear noises. Therefore, a neural network model that can handle the TVMI under linear noise interference is urgently needed. This paper develops a double integral‐enhanced ZNN (DIEZNN) model based on a novel integral‐type design formula with inherent linear‐noise tolerance. Moreover, its convergence and robustness are verified by derivation strictly. For comparison and verification, the OZNN and the IEZNN models are adopted to resolve the TVMI under multiple identical noise environments. The experiments proved that the DIEZNN model has excellent advantages in solving TVMI problems under linear noises. In general, the DIEZNN model is an innovative work and is proposed for the first time. Satisfyingly, the errors of DIEZNN are always less than 1 × 10−3 under linear noises, whereas the error norms of OZNN and IEZNN models are not convergent to zero. In addition, these models are applied to the control of the controllable permanent magnet synchronous motor chaotic system to indicate the superiority of the DIEZNN. [ABSTRACT FROM AUTHOR]

Cloud computing has revolutionized IT delivery by offering scalable ondemand internet services encompassing software, platforms, and infrastructure. However, cloud services face significant performance challenges due to their susceptibility to failures given their vast operational scale. Implementing fault tolerance in dynamic cloud services is a key challenge, with complex configurations and dependencies complicating deployment. This paper introduces an innovative approach that combines double deep Q-learning (DDQL) with a dynamic fault-tolerant real-time scheduling algorithm (DFTRTSA) to enhance fault tolerance in real-time systems. DDQL, an extension of deep Q-learning, optimizes the faulttolerance decision-making process. The algorithm adjusts scheduling strategies dynamically based on system conditions and errors. The fusion of DDQL and DFTRTSA aims to create a resilient and adaptive fault-tolerant mechanism, ensuring uninterrupted operation while meeting real-time requirements. This adaptive approach efficiently manages resources, meets deadlines, and gracefully handles errors, as demonstrated through experiments. Our DDQL-DFTRTSA method outperforms conventional fault-tolerant mechanisms in defect tolerance, energy efficiency, downtime reduction, and system dependability. It proves to be an ideal solution for real-time systems in dynamic and unpredictable environments. [ABSTRACT FROM AUTHOR]

The proliferation of over-the-top (OTT) systems has led to a tremendous amount of video content over the Internet that users can watch them regardless of time or location restrictions. For sports games, highlight videos allow users to check the results of games without watching or listening in real-time. Despite these advances, the number of OTT companies broadcasting sports games in real-time is rising. This demonstrates that sports games are valuable video content to view in real-time. However, sports games generally last long, making continuous viewing difficult. To address this issue, X’s posts have been employed to support sports viewing to users. However, X’s limits on reading and posting and its introduction of paid features, an alternative to X need to be found. In this paper, we propose a novel approach as an alternative to X, using sports commentary to keep track of the state of a game in real-time. In order to classify players’ at-bat results from sports commentary, we combine pre-trained speech recognition and language models. In experiments, we used various pre-trained speech recognition models to create a dataset for fine-tuning, fine-tuned several pre-trained language models using this dataset, and compared the models to identify the best combination of pre-trained speech recognition and language models for classifying at-bat results. Moreover, by using both the speech recognition models and the fine-tuned language models, we compared in terms of real-time performance and classification accuracy across multiple games to verify their effectiveness.

We present a near real-time solution for 3D reconstruction from aerial images captured by consumer UAVs. Our core idea is to simplify the multi-view stereo problem into a series of two-view stereo matching problems. Our method applies to UAVs equipped with only one camera and does not require special stereo-capturing setups. We found that the neighboring two video frames taken by UAVs flying at a mid-to-high cruising altitude can be approximated as left and right views from a virtual stereo camera. By leveraging GPU-accelerated real-time stereo estimation, efficient PnP correspondence solving algorithms, and an extended Kalman filter, our system simultaneously predicts scene geometry and camera position/orientation from the virtual stereo cameras. Also, this method allows for the user selection of varying baseline lengths, which provides more flexibility given the trade-off between camera resolution, effective measuring distance, flight altitude, and mapping accuracy. Our method outputs dense point clouds at a constant speed of 25 frames per second and is validated on a variety of real-world datasets with satisfactory results.

Real-time systems mostly interact with the external world and each input operation must meet predetermined deadlines to be useful. However, in many real-time applications, a partial result is also acceptable. We developed a reward-based mixed criticality system based on the resource reservation approach to address the problem of ensuring the effective execution of low- and high-criticality tasks in both low- and high modes, even under heavy workloads. Using dedicated servers with pessimistic resource allocation for each high criticality task ensured their execution in both modes unaffected by low criticality tasks. The surplus resources are reclaimed and assigned to low critical tasks’ server by utilizing a greedy reclamation of unused bandwidth (GRUB) algorithm. Three strategies were suggested for server allocation to low criticality tasks: a dedicated server for all low criticality tasks, a single server for each low criticality task, and two servers (mandatory and optional) for each low criticality task. Our analysis revealed efficiency of the first approach by achieving 100% schedulability at a 1.1 target utilization, scheduling 20% and 50% more task sets than the second and third approaches, respectively. Moreover, the effectiveness of the proposed approach over existing imprecise mixed criticality approaches were demonstrated through comprehensive experimentation.

Real-time systems are becoming pervasive with the growing global connectivity and rising consumer demands. The need for real-time processing has become a crucial part of many business applications worldwide. A key factor that determines the time taken for an application to give out the result hinges on its ability to prioritize, manage, and execute real-time workloads. However, there are several difficulties and constraints connected with implementing tasks in a real-time context. This research study primarily focuses on load balancing for mixed real-time tasks on a multi-core system, one of the major challenges for executing real-time workloads. The purpose of load balancing is to distribute the load evenly among the processor(s) and maximize their utility while minimizing overall execution time. The goal of this paper is to present a critical analysis of existing load balancing techniques for both periodic and aperiodic tasks. The paper explores several factors including throughput, performance, migration time, response time, overhead, resource utilization, scalability, fault tolerance, power efficiency, and other variables that play a crucial role in assessing the efficacy of load balancing in real-time systems. The proposed has contributed in four folds. Firstly, the state-of-the-art of various load balancing algorithms are discussed followed by the architecture involved in real-time multi-core systems. Later, different load balancing based scheduling algorithms were compared on the basis of different schemas and metrics for algorithm evaluation is also provided. Finally, the paper also identifies areas that warrant further exploration or investigation, suggesting potential avenues for future research, and highlighting emerging trends or developments that may shape the field.

With an increase in the number of processing cores or systems, the high-performance edge-computing system’s power consumption along with its computational speed will increase, essentially. However, this comes at the expense of high-energy utilization. One notable solution to reduce the energy consumption of these systems is to execute these systems at the slowest feasible speed so that the job’s deadline times are met. Unfortunately, this method is at the expense of more response time and performance loss. To resolve this issue, in this paper, we propose a scheduling approach that associates the genetic algorithm (GA) with the first feasible speed (FiFeS) technique i.e. GA-FiFeS algorithm. This does not jeopardize real-time tasks’ deadlines. The GA-FiFeS algorithm proposes an energy-efficient schedule while still ensuring high response times. The results of the proposed approach, using plausible assumptions and experimental parameters, are compared with currently in-practice approaches, i.e. FiFeS and LeFeS (least feasible speed) approaches. Using numerical simulations and plausible assumptions, our investigation suggests that the proposed GA-FiFeS technique outperforms the FiFeS technique in terms of energy consumption (~18.56%) and response times (~2.78%). Furthermore, the GA-FiFeS has comparable outcomes with the LeFeS method while taking the expected time of execution as an assessment feature for analysis.

Abstract Real‐time estimating harmonic distortion levels near the Point of Interconnections of grid‐connected inverter‐based resources (IBRs) is critical for maintaining the operational reliability and security of the power grid, particularly during transients when IBRs generate excessive harmonics. Traditional methods like Total Harmonic Distortion (THD) are unsuitable for time‐varying waveforms as they suffer from aliasing, spectral leakage, and picket‐fence effects. This paper proposes a novel approach that utilizes convolution‐based measures for estimating harmonic distortion, which offers a more suitable way for ongoing real‐time applications. The first measure uses a 60 Hz sinusoidal function as the convolution kernel, while an enhanced measure employs the Gaussian function as the kernel. Two convolution‐based measures are proposed and compared with THD. The comparison criteria are based on the two significant features describing the variation of harmonics: min/max values and variation curvature of harmonics showing the accurate performance of proposed measures. Moreover, short‐term estimations of harmonic distortion are used to validate the effectiveness of the proposed measures, while long‐term estimations are used to determine the ability of the proposed measures to determine harmonic distortion contributions among power network components. The proposed technique's intrinsic characteristics provide a sampling window with low sensitivity to deviations in the fundamental frequency and signal's stationary condition to prevent aliasing and spectral leakage and rule out the impact of the picket‐fence effect on the harmonic distortion level estimation.

Abstract There is an important growth of unmanned aerial vehicles (UAVs) performing planned missions in urban environments, which poses significant challenges to the research community. The possibility of collisions represents a critical challenge. UAVs can suffer collisions due to different causes external or internal to their flight plans. In this context, dynamic geo‐fencing is a useful approach, whereby each UAV is able to provide a prediction of its future positions within a limited time. These predictions could be used to detect conflicts, allowing to dynamically modify the flight plans so as to avoid imminent collisions. In this work, a conflict detection algorithm/method is proposed, implemented and tested on a central server performing real‐time conflict analysis for a large number of UAVs flying in the aerial space of a city (U‐Space). The architecture assumes that UAVs send their future locations to a traffic controller. This controller compares the predicted positions of nearby vehicles to detect possible conflicts. The results of this work demonstrate the feasibility of the proposed conflict detection algorithm and its interest to improve the security and efficiency in U‐Space environments. The server is able to track thousands of UAVs in real time with a conflict anticipation around 11 s.

This paper presents linear model predictive control (MPC) for multiple kinds of constraint based on the linear complementarity problem (LCP) that gives the explicit upper bound of computational complexity. MPC generally solves constrained optimization problems. Its computational time should be strictly bounded for real-time applications. In a previous study, we proposed MPC based on the LCP for which a modified n-step vector successfully limits the number of iterations for the combinatorial problem. However, its class of applications is limited due to the existence of the modified n-step vector. In addition, MPC with a time-varying system is not included in this class since the modified n-step vector must be found for each problem at the corresponding time instance. This paper introduces a perturbation on constraints and applies a sequential LCP algorithm that gives a priori knowledge of the explicit upper bound of computational complexity and the accuracy of the solution. The iteration bounds are evaluated using the steering control of an autonomous driving vehicle for an obstacle avoidance manoeuvre.

This paper presents a real-time wearable system designed to assist Parkinson’s disease patients experiencing freezing of gait episodes. The system utilizes advanced machine learning models, including convolutional and recurrent neural networks, enhanced with past sample data preprocessing to achieve high accuracy, efficiency, and robustness. By continuously monitoring gait patterns, the system provides timely interventions, improving mobility and reducing the impact of freezing episodes. This paper explores the implementation of a CNN+RNN+PS machine learning model on a microcontroller-based device. The device operates at a real-time processing rate of 40 Hz and is deployed in practical settings to provide ‘on demand’ vibratory stimulation to patients. This paper examines the system’s ability to operate with minimal latency, achieving an average detection delay of just 261 milliseconds and a freezing of gait detection accuracy of 95.1%. While patients received on-demand stimulation, the system’s effectiveness was assessed by decreasing the average duration of freezing of gait episodes by 45%. These preliminarily results underscore the potential of personalized, real-time feedback systems in enhancing the quality of life and rehabilitation outcomes for patients with movement disorders.

Hearing loss is a common problem affecting the quality of life for thousands of people. However, many individuals with hearing loss are dissatisfied with the quality of modern hearing aids. Amplification is the main method of compensating for hearing loss in modern hearing aids. One common amplification technique is dynamic range compression, which maps audio signals onto a person's hearing range using an amplification curve. However, due to the frequency dependent nature of the human cochlea, compression is often performed independently in different frequency bands. This paper presents a real-time multirate multiband amplification system for hearing aids, which includes a multirate channelizer for separating an audio signal into eleven standard audiometric frequency bands, and an automatic gain control system for accurate control of the steady state and dynamic behavior of audio compression as specified by ANSI standards. The spectral channelizer offers high frequency resolution with low latency of 5.4 ms and about 14× improvement in complexity over a baseline design. Our automatic gain control includes a closed-form solution for satisfying any designated attack and release times for any desired compression parameters. The increased frequency resolution and precise gain adjustment allow our system to more accurately fulfill audiometric hearing aid prescriptions.

Abstract Real‐time passenger‐flow anomaly detection at all metro stations is a very critical task for advanced Internet management. Robust principal component analysis (RPCA) based method has often been employed for anomaly detection task of multivariate time series data. However, it ignores the spatio‐temporal features of regular passenger‐flow patterns, resulting in a decrease in the accuracy of anomaly detection. In this paper, RT‐STRPCA model integrating temporal periodicity and spatial similarity is proposed to address the above issues. RT‐STRPCA model detects anomalies by decomposing the observation data into normal component and anomaly component. The spatio‐temporal constraints are taken into account to extract anomalies more accurately. The real‐time anomaly detection are realized by a sliding window. The performance of RT‐STRPCA model is evaluated on synthetic datasets and real‐world datasets. The experimental results on synthetic datasets demonstrate that the method achieves more accurate real‐time anomaly detection than baseline approaches and the result on real‐world datasets verify the utility and effectiveness of the proposed method.

Abstract In this paper, a resonant converter model is proposed that takes into account the thermal and electrical dynamics of the main components. With respect to state‐of‐art, where model‐based design approaches are often implemented off‐line, the proposed model has a low computational complexity such that it can be simulated in real‐time. This real‐time electro‐thermal model enables also the design of a predictive control algorithm, that is proposed to optimize the current control of a SiC‐based resonant converter by handling both thermal and electrical operating limits of its components. To validate the interaction between the converter model and the predictive control algorithm, validation is proposed via real‐time processor‐in‐the‐loop, with application to the on‐board charger for high‐voltage Li‐ion battery pack, typical of modern fully electrified vehicles.

Abstract Time‐varying matrix inversion is an important field of matrix research, and lots of research achievements have been obtained. In the process of solving time‐varying matrix inversion, disturbances inevitably exist, thus, a model that can suppress disturbance while solving the problem is required. In this paper, an advanced continuous‐time recurrent neural network (RNN) model based on a double integral RNN design formula is proposed for solving continuous time‐varying matrix inversion, which has incomparable disturbance‐suppression property. For digital hardware applications, the corresponding advanced discrete‐time RNN model is proposed based on the discretisation formulas. As a result of theoretical analysis, it is demonstrated that the advanced continuous‐time RNN model and the corresponding advanced discrete‐time RNN model have global and exponential convergence performance, and they are excellent for suppressing different disturbances. Finally, inspiring experiments, including two numerical experiments and a practical experiment, are presented to demonstrate the effectiveness and superiority of the advanced discrete‐time RNN model for solving discrete time‐varying matrix inversion with disturbance‐suppression.

Introduction: Navigation satellite systems can fail to work or work incorrectly in a number of conditions: signal shadowing, electromagnetic interference, atmospheric conditions, and technical problems. All of these factors can significantly affect the localization accuracy of autonomous driving systems. This emphasizes the need for other localization technologies, such as Lidar.Methods: The use of the Kalman filter in combination with Lidar can be very effective in various applications due to the synergy of their capabilities. The Kalman filter can improve the accuracy of lidar measurements by taking into account the noise and inaccuracies present in the measurements.Results: In this paper, we propose a parallel Kalman algorithm in three-dimensional space to speed up the computational speed of Lidar localization. At the same time, the initial localization accuracy of the latter is preserved. A distinctive feature of the proposed approach is that the Kalman localization algorithm itself is parallelized, rather than the process of building a map for navigation. The proposed algorithm allows us to obtain the result 3.8 times faster without compromising the localization accuracy, which was 3% for both cases, making it effective for real-time decision-making.Discussion: The reliability of this result is confirmed by a preliminary theoretical estimate of the acceleration rate based on Ambdahl’s law. Accelerating the Kalman filter with CUDA for Lidar localization can be of significant practical value, especially in real-time and in conditions where large amounts of data from Lidar sensors need to be processed.

To improve the performance of real‐time systems, heterogeneous asymmetric multiprocessors are proposed. Heterogeneous asymmetric multiprocessor architecture allows the system to better match computational resources to meet the needs and dynamic workload of each application. The benefits of system performance and reduced power consumption cannot be fully exploited unless proper task scheduling and task allocation methods are implemented at the operating system level.The EDZL scheduling algorithm has been shown to be a better scheduling algorithm in heterogeneous multiprocessor platforms. In this paper, the BSF‐EDZL (best speed fit for earliest deadline first until zero‐laxity) performance asymmetric multiprocessor scheduling algorithm is proposed, and an effective schedulability analysis is performed. The algorithm selects an appropriate processor for non‐zero relaxation highest priority task rather than the fastest processor when assigning tasks. Experimental results show that the proposed BSF‐EDZL scheduling algorithm can schedule a larger set of tasks than the ordinary EDZL scheduling algorithm. [ABSTRACT FROM AUTHOR]

Experts warn that drivers who fail to take regular breaks when driving long distances are at high risk of becoming drowsy, which can often go unrecognized until it is too late. Studies show that approximately 25% of all serious motorway accidents are caused by tired drivers who need rest, making drowsy driving a more significant contributor to accidents than drunk driving. To address this issue, Attention Assist is a system that can alert drivers to inattentiveness and drowsiness across a wide range of speeds. It can also provide drivers with information about their current level of fatigue and the time elapsed since their last break. The system offers adjustable sensitivity and can provide warnings to drivers, indicating nearby service areas where they can take a break if necessary. By helping drivers recognize and address their drowsiness, this system can improve road safety and prevent accidents caused by tiredness while driving. [ABSTRACT FROM AUTHOR]

Verification of real-time system properties using formal models can improve system design and quality. The Timed Petri net is a formal model for modelling and designing real-time systems with time constraints. Furthermore, model checking is a formal verification method used to verify system properties using model checkers. This article proposes an automated transformation system for mapping a Timed Petri net into one of the common model checkers, the SPIN PROMELA model, to verify real-time system properties. This approach enables the combination of two modelling paradigms and supports system verification using system design models. In this study, the system properties were verified using both the simulation method and linear temporal logic formulas supported by the SPIN model checker. Timed Petri net models of two different case studies, a central server computer system and a manufacturing Kanban system were considered to verify the boundedness, liveness and system behavioral properties using the proposed transformation system. [ABSTRACT FROM AUTHOR]

The existing research on time‐triggered network reconfiguration neglects redundant traffic states, posing a challenge in effectively balancing traffic performance and reconfiguration efficiency in practical deployments. In response to these limitations, a novel joint optimization approach is introduced for reconfiguration and traffic planning. Initially, the efficiency of reconfiguration solutions is enhanced by sacrificing the delay performance of redundant flows and prioritizing compressed yet tailored constraints. Subsequently, a tabu‐based optimization algorithm is employed to provide more flexible enhancements to the delay performance of network‐wide traffic. The outcomes demonstrate that the proposed method significantly outperforms incremental reconfiguration, improving the solution efficiency of the reconfiguration recovery phase by over 75.38%. Furthermore, through multiple rounds of reconfiguration, the approach achieves an 8.38% improvement in the average delay performance of all flows, even with redundant flows sacrificed. [ABSTRACT FROM AUTHOR]

Surface electromyography (sEMG) is a promising technology for hand gesture recognition, yet faces challenges in user mobility and individual calibration. This paper introduces a novel dual-model adaptive continuous learning (DM-ACL) strategy for wrist-based sEMG real-time gesture recognition. The core of the DM-ACL strategy is a semi-supervised online learning algorithm that uses the kNN model to provide auxiliary labels for real-time sEMG signals, enhancing the robustness and adaptability of the deep learning model. Experimental results show that the DM-ACL strategy outperforms conventional transfer learning (TL) methods. Using the CNN-LSTM model as the baseline, the DM-ACL method achieved a recognition accuracy of 95.33% with an average of 33.6 s of sEMG data per gesture, while the conventional TL method attained an accuracy of 82.82%. With the CNN model as the baseline, the DM-ACL method achieved a recognition accuracy of 92.37% with an average of 48 s of sEMG data per gesture, while the conventional TL method attained an accuracy of 84.59%. The DM-ACL strategy efficiently improves performance for new users and maintains high accuracy across sessions, even in the presence of inter-session domain shifts. This enhances the practical usability of sEMG-based gesture recognition systems, particularly in real-time applications. [ABSTRACT FROM AUTHOR]

High mechanical loads generated during walking may accelerate the wear of the knee joint. General knee exoskeletons mainly reduce knee joint load by applying pure torque assistance to reduce the force required by knee extensor muscles. This study aims to further reduce the knee joint load during the early and middle stance phases through a gait intervention strategy that combines torque assistance and vertical force assistance. Comprehensive experiments were conducted to verify the gait intervention strategy in reducing the knee joint load. The results demonstrated that the strategy significantly reduced the maximum and mean knee joint force during the early and middle stance phases. Our studies indicated that the strategy may have the potential to reduce the knee joint load from multiple factors, including muscles, ligaments, and the ground reaction force. [ABSTRACT FROM AUTHOR]

Neuromusculoskeletal (NMS) models enable non-invasive estimation of clinically important internal biomechanics. A critical part of NMS modelling is the estimation of musculotendon kinematics, which comprise musculotendon unit lengths, moment arms, and lines of action. Musculotendon kinematics, which are partially dependent on joint angles, define the non-linear mapping of muscle forces to joint moments and contact forces. Currently, real-time computation of musculotendon kinematics requires creation of a per-individual surrogate model. The computational speed and accuracy of these surrogates degrade with increasing number of coordinates. We developed a feed-forward neural network that completely encodes musculotendon kinematics of a target model across a wide anthropometric range, enabling accurate real-time estimates of musculotendon kinematics without need for a priori creation of a per-individual surrogate model. Compared to reference, the neural network had median normalized errors ~0.1% for musculotendon lengths, <0.4% for moment arms, and <0.10° for line of action orientations. The neural network was employed within an electromyogram-informed NMS model to calculate hip contact forces, demonstrating little difference (normalized root mean square error $1.23\pm 0.15$ %) compared to using reference musculotendon kinematics. Finally, execution time was <0.04 ms per frame and constant for increasing number of model coordinates. Our approach to musculoskeletal kinematics may facilitate deployment of complex real-time NMS modelling in computer vision or wearable sensors applications to realize biomechanics monitoring, rehabilitation, and disease management outside the research laboratory. [ABSTRACT FROM AUTHOR]