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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, a novel multimodal large language model-based fault detection and diagnosis framework that addresses the limitations of traditional fault detection and diagnosis approaches is proposed. The proposed framework leverages the Generative Pre-trained Transformer-4-Preview model to improve its scalability, generalizability, and efficiency in handling complex systems and various fault scenarios. Moreover, synthetic datasets generated via large language models augment the knowledge base and enhance the accuracy of fault detection and diagnosis of imbalanced scenarios. In the framework, a hybrid architecture that integrates online and offline processing, combining real-time data streams with fine-tuned large language models for dynamic, accurate, and context-aware fault detection suited to industrial settings, particularly focusing on security concerns, is introduced. This comprehensive approach aims to address traditional fault detection and diagnosis challenges and advance the field toward more adaptive and efficient fault diagnosis systems. This paper presents a detailed literature review, including a detailed taxonomy of fault detection and diagnosis methods and their applications across various industrial domains. This study discusses case study results and model comparisons, exploring the implications for future developments in industrial fault detection and diagnosis systems within Industry 4.0 technologies. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

Advancements in neural network approaches have enhanced the effectiveness of surface Electromyography (sEMG)-based hand gesture recognition when measuring muscle activity. However, current deep learning architectures struggle to achieve good generalization and robustness, often demanding significant computational resources. The goal of this paper was to develop a robust model that can quickly adapt to new users using Transfer Learning. We propose a Multi-Scale Convolutional Neural Network (MSCNN), pre-trained with various strategies to improve inter-subject generalization. These strategies include domain adaptation with a gradient-reversal layer and self-supervision using triplet margin loss. We evaluated these approaches on several benchmark datasets, specifically the NinaPro databases. This study also compared two different Transfer Learning frameworks designed for user-dependent fine-tuning. The second Transfer Learning framework achieved a 97% F1 Score across 14 classes with an average of 1.40 epochs, suggesting potential for on-site model retraining in cases of performance degradation over time. The findings highlight the effectiveness of Transfer Learning in creating adaptive, user-specific models for sEMG-based prosthetic hands. Moreover, the study examined the impacts of rectification and window length, with a focus on real-time accessible normalizing techniques, suggesting significant improvements in usability and performance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper presents an energy-efficient and high-accuracy sampling synchronization approach for real-time synchronous data acquisition in wireless sensor networks (saWSNs). A proprietary protocol based on time-division multiple access (TDMA) and deep energy-efficient coding in sensor firmware is proposed. A real saWSN model based on 2.4 GHz nRF52832 system-on-chip (SoC) sensors was designed and experimentally tested. The obtained results confirmed significant improvements in data synchronization accuracy (even by several times) and power consumption (even by a hundred times) compared to other recently reported studies. The results demonstrated a sampling synchronization accuracy of 0.8 μs and ultra-low power consumption of 15 μW per 1 kb/s throughput for data. The protocol was well designed, stable, and importantly, lightweight. The complexity and computational performance of the proposed scheme were small. The CPU load for the proposed solution was <2% for a sampling event handler below 200 Hz. Furthermore, the transmission reliability was high with a packet error rate (PER) not exceeding 0.18% for TXPWR ≥ −4 dBm and 0.03% for TXPWR ≥ 3 dBm. The efficiency of the proposed protocol was compared with other solutions presented in the manuscript. While the number of new proposals is large, the technical advantage of our solution is significant. [ABSTRACT FROM AUTHOR]

The automotive industry has recently adopted multicore processors and microcontrollers to meet the requirements of new features, such as autonomous driving, and comply with the latest safety standards. However, inter-core communication poses a challenge in ensuring real-time requirements such as time determinism and low latencies. Concurrent access to shared buffers makes predicting the flow of data difficult, leading to decreased algorithm performance. This study explores the integration of Logical Execution Time (LET) and Time-Division Multiple Access (TDMA) models in multicore embedded systems to address the challenges in inter-core communication by synchronizing read/write operations across different cores, significantly reducing latency variability and improving system predictability and consistency. Experimental results demonstrate that this integrated approach eliminates data loss and maintains fixed operation rates, achieving a consistent latency of 11 ms. The LET-TDMA method reduces latency variability to approximately 1 ms, maintaining a maximum delay of 1.002 ms and a minimum delay of 1.001 ms, compared to the variability in the LET-only method, which ranged from 3.2846 ms to 8.9257 ms for different configurations. [ABSTRACT FROM AUTHOR]

To validate safety-related automotive software systems, experimental tests are conducted at different stages of the V-model, which are referred as "X-in-the-loop (XIL) methods". However, these methods have significant drawbacks in terms of cost, time, effort and effectiveness. In this study, based on hardware-in-the-loop (HIL) simulation and real-time fault injection (FI), a novel testing framework has been developed to validate system performance under critical abnormal situations during the development process. The developed framework provides an approach for the real-time analysis of system behavior under single and simultaneous sensor/actuator-related faults during virtual test drives without modeling effort for fault mode simulations. Unlike traditional methods, the faults are injected programmatically and the system architecture is ensured without modification to meet the real-time constraints. Moreover, a virtual environment is modeled with various environmental conditions, such as weather, traffic and roads. The validation results demonstrate the effectiveness of the proposed framework in a variety of driving scenarios. The evaluation results demonstrate that the system behavior via HIL simulation has a high accuracy compared to the non-real-time simulation method with an average relative error of 2.52. The comparative study with the state-of-the-art methods indicates that the proposed approach exhibits superior accuracy and capability. This, in turn, provides a safe, reliable and realistic environment for the real-time validation of complex automotive systems at a low cost, with minimal time and effort. [ABSTRACT FROM AUTHOR]

In the scheduling of hard real-time systems on multicore platforms, significant unpredictability arises from interference caused by shared hardware resources. The objective of this paper is to offer a schedulability analysis for such systems by assuming a general model that introduces interference as a time parameter for each task. The analysis assumes constrained deadlines and is provided for fixed priorities. It is based on worst-case response time analysis, which exists in the literature for monocore systems. We demonstrate that the worst-case response time is an upper bound, and we evaluate our proposal with synthetic loads and execution on a real platform. [ABSTRACT FROM AUTHOR]

A new approach for output feedback L 1 adaptive control based on a proportional adaptation law is presented. The effectiveness of this design is assessed in simulation and validated through real-time testing on an airfoil pitch control wind tunnel experimental rig. Experimental evaluation of the robustness of the controllers, assessed by introducing various disturbances into the control signals, shows that the adaptive control has a better performance compared to PID control, particularly in scenarios with reduced control effectiveness and time-varying disturbances. The experimental results demonstrate the efficacy of the proposed method in practical applications. [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]

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]

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]

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]

Edge computing can provide core functions such as data collection and analysis without connecting to a centralized server. The convergence of edge computing and IoT devices has enabled medical institutions to collect patient data in real time, improving the efficiency of short- and long-term patient management. Medical equipment measures a large amount of biosignal data for analyzing diseases and patient health conditions. However, analyzing and monitoring biosignal data using a centralized server or cloud limit the medical institutions' ability to analyze patients' conditions in real time, preventing prompt treatment. Therefore, edge computing can enhance the efficiency of patient biosignal data collection and analysis for patient management systems. Analyzing biosignals using edge computing can eliminate the wait time present in cloud computing. Hence, this study aims to develop an IoT pulse oximeter using edge computing for medical institutions and proposes an architecture for providing a real-time monitoring service. The proposed system utilizes five types of raw (IR AC, IR DC, red AC, red DC, AMB), pulse, and SpO2 data measured using IoT pulse oximeters. Edge nodes are installed in every hospital ward to collect, analyze, and monitor patient biosignal data through a wireless network. The collected biosignal data are transmitted to the cloud for managing and monitoring the data of all patients. This system enables medical institutions to collect and analyze raw biosignal data in real time, by which an integrated management system can be established by connecting various types of IoT-based medical equipment. [ABSTRACT FROM AUTHOR]

Electronic waste (e-waste) contains toxic elements causing an important impact on environmental and human health. However, the presence of valuable metals, such as copper or gold, among others, make recycling a necessity for obtaining an alternative source of raw materials. Conventional metal recovery methods are environmentally unsound, prompting the exploration of greener alternatives like bioleaching, which utilizes the activity of microorganisms for a more sustainable recovery. However, the mechanisms involved in the process and the conditions to optimize the metabolic paths are still not completely known. Monitorization and automatization of the different stages composing the global process are crucial for advancing in the implementation of this novel technology at an industrial scale. For the first time, an open-source industrial IoT system is designed to enhance and regulate bioleaching by implementing real-time monitoring and control within the plant's infrastructure. This system includes an Android app that displays real-time plant data from sensors and a robust server featuring a flexible application programming interface (API) for future applications. The app caters to specific needs such as remote sensor reading, actuator control, and real-time bioleaching alerts, ensuring secure access and proactive event management. By utilizing collected data, it minimizes downtime, equipment failures, and supply chain disruptions. The server maintains seamless communication with the plant controller, enabling efficient pump activation and sensor data transmission. A telegram bot demonstrates the API's flexibility by forwarding plant alerts to users. During validation with concurrent remote user access, the application demonstrated its ability to prevent irreversible plant failures through an advanced alarm system. Ultimately, this IIoT system amplifies plant performance, safety, and efficiency by optimizing processes and decision-making capabilities. It emerges as a pivotal open-source tool, securing remote oversight and management of large-scale bioleaching plants, promising adaptability for future enhancements. [ABSTRACT FROM AUTHOR]

Technological advances in the internet of things (IoT) allowed a low-cost, yet small sensor device to operate with limited power in a dynamic harsh environment where human intervention is impossible. The wireless sensor network (WSN) is an example of the IoT in which physical devices' software and sensors can interconnect to provide application services. It is important that such applications be dependable to meet the required quality of service (QoS) and function as expected. Consequently, the multi-objective optimization (MOO) problem in WSNs aims to address the trade-off among coverage, connectivity, and network lifetime requirements. Node scheduling is one approach of many used to optimize energy in WSNs. The contribution of this work is the proposal of a self-organizing feature map (SOFM) to enhance the node scheduling in WSNs. The proposed SOFM node-scheduling algorithm aims to spatially explore the state space domain and obtain an optimal solution. In our experiment, the proposed SOFM node-scheduling algorithm is evaluated against a comparable algorithm, namely the BAT node-scheduling algorithm, via MATLAB simulator. The results showed that the SOFM node-scheduling algorithm outperformed the latter by 27% and 28% for the maximum and minimum coverage, respectively, with similar performance of 99% of connectivity and network lifetime. [ABSTRACT FROM AUTHOR]

In various industrial domains, machinery plays a pivotal role, with bearing failure standing out as the most prevalent cause of malfunction, contributing to approximately 41% to 44% of all operational breakdowns. To address this issue, this research employs a lightweight neural network, boasting a mere 8.69 K parameters, tailored for implementation on an FPGA (field-programmable gate array). By integrating an incremental network quantization approach and fixed-point operation techniques, substantial memory savings amounting to 63.49% are realized compared to conventional 32-bit floating-point operations. Moreover, when executed on an FPGA, this work facilitates real-time bearing condition detection at an impressive rate of 48,000 samples per second while operating on a minimal power budget of just 342 mW. Remarkably, this system achieves an accuracy level of 95.12%, showcasing its effectiveness in predictive maintenance and the prevention of costly machinery failures. [ABSTRACT FROM AUTHOR]

The multiple objective optimisation (MOO) challenges encountered in the context of wireless sensor networks (WSNs) present a formidable NP-hard problem. These issues primarily arise from the constraints imposed by critical factors such as connectivity, coverage, and, most notably, energy consumption. Simultaneously fulfilling these three requirements is no longer considered the standard approach for enhancing system dependability. To illustrate, a prospective solution may optimise one or two of these requirements while bolstering overall network energy efficiency. Nonetheless, prior endeavours documented in the extant literature reveal unexplored avenues for enhancement. Hence, this paper introduces a new methodology aimed at alleviating MOO concerns and thereby enhancing the quality of service (QoS) in WSNs. A long short-term memory (LSTM) model is proposed as an analytical tool to deliver an energy-efficient scheduling solution that aligns and optimises WSN parameters, striving to attain the most favourable system performance. The LSTM algorithm's effectiveness is assessed through the iterative application of periods, confirming the desired QoS levels. The unique feature of LSTM lies in its capability to observe specific event sequences and subsequently establish them as the system's default configuration for its entire operational lifespan. Once these favourable parameters are identified, LSTM automatically ensures consistent service availability and reliability throughout the network's lifespan. The results obtained demonstrate the superiority of the proposed LSTM-based scheduling algorithm in comparison to the self-organising map (SOFM)-based node scheduling algorithm. The LSTM-based approach outperforms the SOFM-based alternative by a remarkable 75% in terms of coverage and exhibits a 20% enhancement in network lifetime, all while maintaining equivalent levels of connectivity (i.e., 99%) in both algorithms. [ABSTRACT FROM AUTHOR]

Vessel detection and tracking is of utmost importance to river traffic. Efficient detection and tracking technology offer an effective solution to address challenges related to river traffic safety and congestion. Traditional image-based object detection and tracking algorithms encounter issues such as target ID switching, difficulties in feature extraction, reduced robustness due to occlusion, target overlap, and changes in brightness and contrast. To detect and track vessels more accurately, a vessel detection and tracking algorithm based on the LiDAR point cloud was proposed. For vessel detection, statistical filtering algorithms were integrated into the Euclidean clustering algorithm to mitigate the effect of ripples on vessel detection. Our detection accuracy of vessels improved by 3.3% to 8.3% compared to three conventional algorithms. For vessel tracking, L-shape fitting of detected vessels can improve the efficiency of tracking, and a simple and efficient tracking algorithm is presented. By comparing three traditional tracking algorithms, an improvement in multiple object tracking accuracy (MOTA) and a reduction in ID switch times and number of missed detections were achieved. The results demonstrate that LiDAR point cloud-based vessel detection can significantly enhance the accuracy of vessel detection and tracking. [ABSTRACT FROM AUTHOR]

This paper introduces a one-dimensional convolutional neural network (CNN) hardware accelerator. It is crafted to conduct real-time assessments of bearing conditions using economical hardware components, implemented on a field-programmable gate array evaluation platform, negating the necessity to transfer data to a cloud-based server. The adoption of the down-sampling technique augments the visible time span of the signal in an image, thereby enhancing the accuracy of the bearing condition diagnosis. Furthermore, the proposed method of quaternary quantization enhances precision and shrinks the memory demand for the neural network model by an impressive 89%. Provided that the current signal data sampling rate stands at 64 K samples/s, the proposed design can accomplish real-time fault diagnosis at a clock frequency of 100 MHz. Impressively, the response duration of the proposed CNN hardware system is a mere 0.28 s, with the fault diagnosis precision reaching a remarkable 96.37%. [ABSTRACT FROM AUTHOR]

This article presents the development of a geo-visualization tool, which provides police officers or any other type of law enforcement officer with the ability to conduct the spatiotemporal predictive geo-visualization of criminal activities in short and continuous time horizons, according to the real events that are happening: that is, for those geographical areas, time slots, and dates that are of interest to users, with the ability to consider individual events or groups of events. This work used real data collected by the Colombian National Police (PONAL); it constitutes a tool that is especially effective when applied to Real-Time Systems for crime deterrence, prevention, and control. For its creation, the spatial and temporal correlation of the events is carried out and the following deep learning techniques are employed: CNN-1D (Convolutional Neural Network-1D), MLP (multilayer perceptron), LSTM (long short-term memory), and the classical technique of VAR (vector autoregression), due to its appropriate performance in the multi-step and multi-parallel forecasting of multivariate time series with sparse data. This tool was developed with Open-Source Software (OSS) as it is implemented in the Python programming language with the corresponding machine learning libraries. It can be implemented with any geographic information system (GIS) and used in relation to other types of activities, such as natural disasters or terrorist activities. [ABSTRACT FROM AUTHOR]

In modern telemedicine systems, the data path can be exceedingly complex, with data passing through a number of phases and processes before reaching its ultimate state. It is extremely difficult to predict the effects of lacking data or improperly processed data on the final outcome. In real-time systems, and particularly in the field of telemedicine, it is crucial to rapidly identify and rectify issues in order to prevent the loss of large amounts of data and the degradation of data quality. A basic simulation is insufficient for a comprehensive examination of the system; instead, modeling approaches are required. However, a minor system's state space can be immense. We present a methodology and a hybrid framework that incorporate simulation, emulation, and modeling in order to evaluate the state space and potential consequences of a sufficiently large system in a more targeted and condensed manner. In this paper, we demonstrate the structure and operation of our framework using an actively researched telemedicine use case, as well as how data quality can fluctuate and new anomalies can emerge if data is corrupted during an intermediate phase. According to our real-time Atrial Fibrillation (AF) and classification use case, data loss can be as high as 15%. [ABSTRACT FROM AUTHOR]

Two emerging trends for designing a complex, cyber-physical systems are the component-based and mixed-criticality (MC) approaches. A component-based approach independently develops individual components and subsequently integrates them to reduce system complexity. This approach provides strong isolation among components but incurs resource inefficiency. Alternatively, an MC approach integrates components of different criticality with different levels of guarantee for resource efficiency, while components are not isolated. To leverage MC and component-based approaches, we investigate how to balance component isolation and resource efficiency under component-based MC systems. We introduce the concept of component-MC schedulability, where isolated tasks are protected from external events outside the component, and shared tasks may be suspended for the critical events of other components. Under component-MC schedulability, we propose a component-based mixed-criticality scheduling framework with dynamic resource allocation (CMC-DRA), which suspends low-criticality tasks differently depending on internal or external component behavior. We also develop scheduling semantics and analyze the schedulability for CMC-DRA. Through simulation on synthetic workloads, we demonstrate that CMC-DRA has up to 88.3% higher schedulability than existing approaches and reduces the deadline miss ratio by up to 47.7%. [ABSTRACT FROM AUTHOR]

Anomaly detection in real-time data is accepted as a vital area of research. Clustering techniques have effectively been applied for the detection of anomalies several times. As the datasets are real time, the time of data generation is important. Most of the existing clustering-based methods either follow a partitioning or a hierarchical approach without addressing time attributes of the dataset distinctly. In this article, a mixed clustering approach is introduced for this purpose, which also takes time attributes into consideration. It is a two-phase method that first follows a partitioning approach, then an agglomerative hierarchical approach. The dataset can have mixed attributes. In phase one, a unified metric is used that is defined based on mixed attributes. The same metric is also used for merging similar clusters in phase two. Tracking of the time stamp associated with each data instance is conducted simultaneously, producing clusters with different lifetimes in phase one. Then, in phase two, the similar clusters are merged along with their lifetimes. While merging the similar clusters, the lifetimes of the corresponding clusters with overlapping cores are merged using superimposition operation, producing a fuzzy time interval. This way, each cluster will have an associated fuzzy lifetime. The data instances either belonging to sparse clusters, not belonging to any of the clusters or falling in the fuzzy lifetimes with low membership values can be treated as anomalies. The efficacy of the algorithms can be established using both complexity analysis as well as experimental studies. The experimental results with a real world dataset and a synthetic dataset show that the proposed algorithm can detect the anomalies with 90% and 98% accuracy, respectively. [ABSTRACT FROM AUTHOR]

The emergence of Explainable Artificial Intelligence (XAI) has enhanced the lives of humans and envisioned the concept of smart cities using informed actions, enhanced user interpretations and explanations, and firm decision-making processes. The XAI systems can unbox the potential of black-box AI models and describe them explicitly. The study comprehensively surveys the current and future developments in XAI technologies for smart cities. It also highlights the societal, industrial, and technological trends that initiate the drive towards XAI for smart cities. It presents the key to enabling XAI technologies for smart cities in detail. The paper also discusses the concept of XAI for smart cities, various XAI technology use cases, challenges, applications, possible alternative solutions, and current and future research enhancements. Research projects and activities, including standardization efforts toward developing XAI for smart cities, are outlined in detail. The lessons learned from state-of-the-art research are summarized, and various technical challenges are discussed to shed new light on future research possibilities. The presented study on XAI for smart cities is a first-of-its-kind, rigorous, and detailed study to assist future researchers in implementing XAI-driven systems, architectures, and applications for smart cities. [ABSTRACT FROM AUTHOR]

In crop and livestock management within the framework of precision agriculture, scenarios full of sensors and devices are deployed, involving the generation of a large volume of data. Some solutions require rapid data exchange for action or anomaly detection. However, the administration of this large amount of data, which in turn evolves over time, is highly complicated. Management systems add long-time delays to the spatio-temporal data injection and gathering. This paper proposes a novel spatio-temporal semantic data model for agriculture. To validate the model, data from real livestock and crop scenarios, retrieved from the AFarCloud smart farming platform, are modeled according to the proposal. Time-series Database (TSDB) engine InfluxDB is used to evaluate the model against data management. In addition, an architecture for the management of spatio-temporal semantic agricultural data in real-time is proposed. This architecture results in the DAM&DQ system responsible for data management as semantic middleware on the AFarCloud platform. The approach of this proposal is in line with the EU data-driven strategy. [ABSTRACT FROM AUTHOR]

The Internet of things (IoT) paradigm promotes the emergence of solutions to enable energy-management strategies. However, these solutions may favor the disposal or replacement of outdated but still necessary systems. Thus, a proposal that advocates the retrofit of pre-existing systems would be an alternative to implement energy monitoring. In this sense, this work presents a strategy for monitoring electrical parameters in real time by using IoT solutions, cloud-resident applications, and retrofitting of legacy building electrical systems. In this implementation, we adapted the SmartLVGrid metamodel to systematize the insertion of remote monitoring resources in low-voltage circuits. For this, we developed embedded platforms for monitoring the circuits of a building electrical panel and application for visualization and data storage in the cloud. With this, remote monitoring of the consumer unit was carried out in relation to energy demand, power factor, and events of variations of electrical parameters in the circuits of the legacy distribution board. We also carried out a case study with the proposed system, identifying events of excess demand in the consumer unit, mitigating the individual contribution of the installation circuits in this process. Therefore, our proposal presents an alternative to enable energy management and maximum use of existing resources. [ABSTRACT FROM AUTHOR]

One of the most important applications of sensors is feedback control, in which an algorithm is applied to data that are collected from sensors in order to drive system actuators and achieve the desired outputs of the target plant. One of the most challenging applications of this control is represented by magnetic confinement fusion, in which real-time systems are responsible for the confinement of plasma at a temperature of several million degrees within a toroidal container by means of strong electromagnetic fields. Due to the fast dynamics of the underlying physical phenomena, data that are collected from electromagnetic sensors must be processed in real time. In most applications, real-time systems are implemented in C++; however, Python applications are now becoming more and more widespread, which has raised potential interest in their applicability in real-time systems. In this study, a framework was set up to assess the applicability of Python in real-time systems. For this purpose, a reference operating system configuration was chosen, which was optimized for real time, together with a reference framework for real-time data management. Within this framework, the performance of modules that computed PID control and FFT transforms was compared for C++ and Python implementations, respectively. Despite the initial concerns about Python applicability in real-time systems, it was found that the worst-case execution time (WCET) could also be safely defined for modules that were implemented in Python, thereby confirming that they could be considered for real-time applications. [ABSTRACT FROM AUTHOR]

In recent years, modern industry has been exploring the transition to cyber physical system (CPS)-based smart factories. As intelligent industrial detection and control technology grows in popularity, massive amounts of time-sensitive applications are generated. A cutting-edge computing paradigm called edge-cloud collaborative computing was developed to satisfy the need of time-sensitive tasks such as smart vehicles and automatic mechanical remote control, which require substantially low latency. In edge-cloud collaborative computing, it is extremely challenging to improve task scheduling while taking into account both the dynamic changes of user requirements and the limited available resources. The current task scheduling system applies a round-robin policy to cyclically select the next server from the list of available servers, but it may not choose the best-suited server for the task. To satisfy the real-time task flow of industrial production in terms of task scheduling based on deadline and time sensitivity, we propose a hierarchical architecture for edge-cloud collaborative environments in the Industrial Internet of Things (IoT) and then simplify and mathematically formulate the time consumption of edge-cloud collaborative computing to reduce latency. Based on the above hierarchical model, we present a dynamic time-sensitive scheduling algorithm (DSOTS). After the optimization of DSOTS, the dynamic time-sensitive scheduling algorithm with greedy strategy (TSGS) that ranks server capability and job size in a hybrid and hierarchical scenario is proposed. What cannot be ignored is that we propose to employ comprehensive execution capability (CEC) to measure the performance of a server for the first time and perform effective server load balancing while satisfying the user's requirement for tasks. In this paper, we simulate an edge-cloud collaborative computing environment to evaluate the performance of our algorithm in terms of processing time, SLA violation rate, and cost by extending the CloudSimPlus toolkit, and the experimental results are very promising. Aiming to choose a more suitable server to handle dynamically incoming tasks, our algorithm decreases the average processing time and cost by 30% and 45%, respectively, as well as the average SLA violation by 25%, when compared to existing state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

As the large amount of video surveillance data floods into cloud data center, achieving load balancing in a cloud network has become a challenging problem. Meanwhile, we hope the cloud data center maintains low latency, low consumption, and high throughput performance when transmitting massive amounts of data. OpenFlow enables a software-defined solution through programing to control the scheduling of data flow in the cloud data center. However, the existing scheduling algorithm of the data center cannot cope with the congestion of the network center effectively. Even for some dynamic scheduling algorithms, adjustments can only be made after congestion occurs. Hence, we propose a proactive and dynamically adjusted mixed-flow load-balanced scheduling (MFLBS) algorithm, which not only takes into account the different sizes of flows in the network but also maintains maximum throughput while balancing the load. In this paper, the MFLBS problem was formulated, along with a set of heuristic algorithms for real-time feedback and adjustment. Experiments with mesh and tree network models show that our MFLBS is significantly better than other dynamic scheduling algorithms, including one-hop DLBS and static scheduling algorithm FCFS. The MFLBS algorithm can effectively reduce the delay of small flows and average delay while maintaining high throughput. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of real-time model predictive control (MPC) in the integrated guidance and control (IGC) of missile systems. When the primal-dual interior point method (PD-IPM), which is a convex optimization method, is used as an optimization solution for the MPC, the real-time performance of PD-IPM degenerates due to the elevated computation time in checking the Karush–Kuhn–Tucker (KKT) conditions in PD-IPM. This paper proposes a graphics processing unit (GPU)-based method to parallelize and accelerate PD-IPM for real-time MPC. The real-time performance of the proposed method was tested and analyzed on a widely-used embedded system. The comparison results with the conventional PD-IPM and other methods showed that the proposed method improved the real-time performance by reducing the computation time significantly. [ABSTRACT FROM AUTHOR]

The interaction of digital systems with dynamic systems requires synchrony and the accomplishment of time constrains, so the simulation of physical processes needs an implementation by means of real-time systems (RTS). However, as it can be expected, every simulation and/or implementation might demand too many computational resources, surpassing the capacity of the processor used by computational systems. This is the reason for the need to perform a temporary computational complexity analysis based on the study of the behavior of the execution times of the implemented simulation. In this regard, the Real-Time Operating Systems (RTOS) feature time managing tools, which allow their precise measurement and the establishment of scheduling criteria in process execution. Therefore, this research proposes accomplishing a temporary computational complexity analysis of the real-time simulation by an embedded system (ES) of an unmanned aerial vehicle (UAV) propelled by four rotors. Derived from this analysis, formal definitions are elaborated and proposed, which establish a close relationship between the temporary computational complexity and typical real-time temporary constraints. To the best of the author's knowledge, the definitions presented in this article have not been reported in the literature. Furthermore, to perform the temporary computational complexity analysis of the UAV, the mathematical modeling based on the Euler–Lagrange approach is presented in detail. Finally, simulations were performed using a real-time system implemented on the Embedded Computer System (ECS) Raspberry Pi 2 Model B+, in order to validate the suggested definitions. [ABSTRACT FROM AUTHOR]

The modeling of system non-functional properties is a broad field. Among these properties, dependability is an important one for real-time and embedded systems. On the other hand, UML offers the profiling mechanism to address specific modeling domains. In particular, the DAM (dependability analysis and modeling) profile provides a modeling framework for dependability in the model-driven paradigm. This work is for practitioners to understand the basics of dependability modeling, using DAM. In this sense, the paper digests the literature to understand the concept of the UML profile, the MARTE profile and to obtain a practical guide on dependability modeling using DAM. The modeling approach is illustrated through a case study taken from the literature. [ABSTRACT FROM AUTHOR]

Multicore and multithreaded architectures increase the performance of computing systems. The increase in cores and threads, however, raises further issues in the efficiency achieved in terms of speedup and parallelization, particularly for the real-time requirements of Internet of things (IoT)-embedded applications. This research investigates the efficiency of a 32-core field-programmable gate array (FPGA) architecture, with memory management unit (MMU) and real-time operating system (OS) support, to exploit the thread level parallelism (TLP) of tasks running in parallel as threads on multiple cores. The research outcomes confirm the feasibility of the proposed approach in the efficient execution of recursive sorting algorithms, as well as their evaluation in terms of speedup and parallelization. The results reveal that parallel implementation of the prevalent merge sort and quicksort algorithms on this platform is more efficient. The increase in the speedup is proportional to the core scaling, reaching a maximum of 53% for the configuration with the highest number of cores and threads. However, the maximum magnitude of the parallelization (66%) was found to be bounded to a low number of two cores and four threads. A further increase in the number of cores and threads did not add to the improvement of the parallelism. [ABSTRACT FROM AUTHOR]

An increasing number of battery-powered devices that are used outdoors or in mobile systems put emphasis on the power and energy efficiency as a form of trade-off between application performance and system power consumption. However, lack of objective metrics for the evaluation of application performance degradation poses difficulties for managing such trade-offs in real-time applications. The proposed methodology introduces metrics for modeling of application performance and the technique for its control, enabling more efficient power–performance trade-off management. The methodology allows for selective system performance degradation and fine-grained control of system behavior in the power–performance domain by extending the set of operating point parameters controllable through real-time application. The utilization and the effectiveness of the proposed methodology is evaluated in a simulated environment for different scenarios of the application execution, including system operation above the utilization bounds. [ABSTRACT FROM AUTHOR]

In recent years, we have witnessed the emergence of the implementation and integration of significant working solutions in transportation, especially within the smart city concept. A lot of cities in Europe and around the world support this initiative of making their cities smarter for enhanced mobility and a sustainable environment. In this paper, we present a case study of Tartu city, where we developed and designed a daily real-time system for extracting and performing a modal split analysis. Our web-based platform relied on an optimization approach for calibrating our simulation in order to perform the analysis with the use of real data streams from IoT devices installed around the city. The results obtained from our system demonstrated acceptable performance versus the quality of the available data source. In addition, our platform provides downloadable OD matrices for each mode of mobility for the community. [ABSTRACT FROM AUTHOR]

Face-swap-attacks (FSAs) are a new threat to face recognition systems. FSAs are essentially imperceptible replay-attacks using an injection device and generative networks. By placing the device between the camera and computer device, attackers can present any face as desired. This is particularly potent as it also maintains liveliness features, as it is a sophisticated alternation of a real person, and as it can go undetected by traditional anti-spoofing methods. To address FSAs, this research proposes a noise-verification framework. Even the best generative networks today leave alteration traces in the photo-response noise profile; these are detected by doing a comparison of challenge images against the camera enrollment. This research also introduces compression and sub-zone analysis for efficiency. Benchmarking with open-source tampering-detection algorithms shows the proposed compressed-PRNU verification robustly verifies facial-image authenticity while being significantly faster. This demonstrates a novel efficiency for mitigating face-swap-attacks, including denial-of-service attacks. [ABSTRACT FROM AUTHOR]

There is a significant interest in facial emotion recognition in the fields of human–computer interaction and social sciences. With the advancements in artificial intelligence (AI), the field of human behavioral prediction and analysis, especially human emotion, has evolved significantly. The most standard methods of emotion recognition are currently being used in models deployed in remote servers. We believe the reduction in the distance between the input device and the server model can lead us to better efficiency and effectiveness in real life applications. For the same purpose, computational methodologies such as edge computing can be beneficial. It can also encourage time-critical applications that can be implemented in sensitive fields. In this study, we propose a Raspberry-Pi based standalone edge device that can detect real-time facial emotions. Although this edge device can be used in variety of applications where human facial emotions play an important role, this article is mainly crafted using a dataset of employees working in organizations. A Raspberry-Pi-based standalone edge device has been implemented using the Mini-Xception Deep Network because of its computational efficiency in a shorter time compared to other networks. This device has achieved 100% accuracy for detecting faces in real time with 68% accuracy, i.e., higher than the accuracy mentioned in the state-of-the-art with the FER 2013 dataset. Future work will implement a deep network on Raspberry-Pi with an Intel Movidious neural compute stick to reduce the processing time and achieve quick real time implementation of the facial emotion recognition system. [ABSTRACT FROM AUTHOR]

Facial recognition is fundamental for a wide variety of security systems operating in real-time applications. Recently, several deep neural networks algorithms have been developed to achieve state-of-the-art performance on this task. The present work was conceived due to the need for an efficient and low-cost processing system, so a real-time facial recognition system was proposed using a combination of deep learning algorithms like FaceNet and some traditional classifiers like SVM, KNN, and RF using moderate hardware to operate in an unconstrained environment. Generally, a facial recognition system involves two main tasks: face detection and recognition. The proposed scheme uses the YOLO-Face method for the face detection task which is a high-speed real-time detector based on YOLOv3, while, for the recognition stage, a combination of FaceNet with a supervised learning algorithm, such as the support vector machine (SVM), is proposed for classification. Extensive experiments on unconstrained datasets demonstrate that YOLO-Face provides better performance when the face under an analysis presents partial occlusion and pose variations; besides that, it can detect small faces. The face detector was able to achieve an accuracy of over 89.6% using the Honda/UCSD dataset which runs at 26 FPS with darknet-53 to VGA-resolution images for classification tasks. The experimental results have demonstrated that the FaceNet+SVM model was able to achieve an accuracy of 99.7% using the LFW dataset. On the same dataset, FaceNet+KNN and FaceNet+RF achieve 99.5% and 85.1%, respectively; on the other hand, the FaceNet was able to achieve 99.6%. Finally, the proposed system provides a recognition accuracy of 99.1% and 49 ms runtime when both the face detection and classifications stages operate together. [ABSTRACT FROM AUTHOR]

Various development and validation methods for cyber-physical systems such as Controller-Hardware-in-the-Loop (C-HIL) testing strongly benefit from a seamless integration of (hardware) prototypes and simulation models. It has been often demonstrated that linking discrete event-based control systems and hybrid plant models can advance the quality of control implementations. Nevertheless, high manual coupling efforts and sometimes spurious simulation artifacts such as glitches and deviations are observed frequently. This work specifically addresses these two issues by presenting a generic, standard-based infrastructure referred to as virtual component, which enables the efficient coupling of simulation models and automation systems. A novel soft real-time coupling algorithm featuring event-accurate synchronization by extrapolating future model states is outlined. Based on considered standards for model exchange (FMI) and controls (IEC 61499), important properties such as real-time capabilities are derived and experimentally validated. Evaluation demonstrates that virtual components support engineers in efficiently creating C-HIL setups and that the novel algorithm can feature accurate synchronization when conventional approaches fail. [ABSTRACT FROM AUTHOR]

Electric-vehicle technology is an emerging area offering several benefits such as economy due to low running costs. Electric vehicles can also help to significantly reduce CO2 emission, which is a vital factor for environmental pollution. Modern vehicles are equipped with driver-assistance systems that facilitate drivers by offloading some of the tasks a driver does while driving. Human beings are prone to errors. Therefore, accidents and fatalities can happen if the driver fails to perform a particular task within the deadline. In electric vehicles, the focus has always been to optimize the power and battery life, and thus, any additional hardware can affect their battery life significantly. In this paper, the design of driver-assistance systems has been introduced to automate and assist in some of the vital tasks, such as a braking system, in an optimized manner. We revamp the idea of the traditional driver-assistance system and propose a generic lightweight system based on the leading factors and their impact on accidents. We model tasks for these factors and simulate a low-cost driver-assistance system in a real-time context, where these scenarios are investigated and tasks schedulability is formally proved before deploying them in electric vehicles. The proposed driver-assistance system offers many advantages. It decreases the risk of accidents and monitors the safety of driving. If, at some point, the risk index is above a certain threshold, an automated control algorithm is triggered to reduce it by activating different actuators. At the same time, it is lightweight and does not require any dedicated hardware, which in turn has a significant advantage in terms of battery life. Results show that the proposed system not only is accurate but also has a very negligible effect on energy consumption and battery life. [ABSTRACT FROM AUTHOR]

Data networks are naturally prone to interferences that can corrupt messages, leading to performance degradation or even to critical failure of the corresponding distributed system. To improve resilience of critical systems, time-triggered networks are frequently used, based on communication schedules defined at design-time. These networks offer prompt error detection, but slow error recovery that can only be compensated with bandwidth overprovisioning. On the contrary, the Flexible Time-Triggered (FTT) paradigm uses online traffic scheduling, which enables a compromise between error detection and recovery that can achieve timely recovery with a fraction of the needed bandwidth. This article presents a new method to recover transmission errors in a time-triggered Controller Area Network (CAN) network, based on the Flexible Time-Triggered paradigm, namely FTT-CAN. The method is based on using a server (traffic shaper) to regulate the retransmission of corrupted or omitted messages. We show how to design the server to simultaneously: (1) meet a predefined reliability goal, when considering worst case error recovery scenarios bounded probabilistically by a Poisson process that models the fault arrival rate; and, (2) limit the direct and indirect interference in the message set, preserving overall system schedulability. Extensive simulations with multiple scenarios, based on practical and randomly generated systems, show a reduction of two orders of magnitude in the average bandwidth taken by the proposed error recovery mechanism, when compared with traditional approaches available in the literature based on adding extra pre-defined transmission slots. [ABSTRACT FROM AUTHOR]

In the Infrastructure-as-a-Service cloud computing model, virtualized computing resources in the form of virtual machines are provided over the Internet. A user can rent an arbitrary number of computing resources to meet their requirements, making cloud computing an attractive choice for executing real-time tasks. Economical task allocation and scheduling on a set of leased virtual machines is an important problem in the cloud computing environment. This paper proposes a greedy and a genetic algorithm with an adaptive selection of suitable crossover and mutation operations (named as AGA) to allocate and schedule real-time tasks with precedence constraint on heterogamous virtual machines. A comprehensive simulation study has been done to evaluate the performance of the proposed algorithms in terms of their solution quality and efficiency. The simulation results show that AGA outperforms the greedy algorithm and non-adaptive genetic algorithm in terms of solution quality. [ABSTRACT FROM AUTHOR]

Minimizing power consumption to prolong battery life has become an important design issue for portable battery-operated devices such as smartphones and personal digital assistants (PDAs). On a Dynamic Voltage Scaling (DVS) enabled processor, power consumption can be reduced by scaling down the operating frequency of the processor whenever the full processing speed is not required. Real-time task scheduling is a complex and challenging problem for DVS-enabled multiprocessor systems. This paper first formulates the real-time task scheduling for DVS-enabled multiprocessor systems as a combinatorial optimization problem. It then proposes a genetic algorithm that is hybridized with the stochastic evolution algorithm to allocate and schedule real-time tasks with precedence constraints. It presents specialized crossover and perturb operations as well as a topology preserving algorithm to generate the initial population. A comprehensive simulation study has been done using synthetic and real benchmark data to evaluate the performance of the proposed Hybrid Genetic Algorithm (HGA) in terms of solution quality and efficiency. The performance of the proposed HGA has been compared with the genetic algorithm, particle swarm optimization, cuckoo search, and ant colony optimization. The simulation results show that HGA outperforms the other algorithms in terms of solution quality. [ABSTRACT FROM AUTHOR]

Big data are visually cluttered by overlapping data points. Rather than removing, reducing or reformulating overlap, we propose a simple, effective and powerful technique for density cluster generation and visualization, where point marker (graphical symbol of a data point) overlap is exploited in an additive fashion in order to obtain bitmap data summaries in which clusters can be identified visually, aided by automatically generated contour lines. In the proposed method, the plotting area is a bitmap and the marker is a shape of more than one pixel. As the markers overlap, the red, green and blue (RGB) colour values of pixels in the shared region are added. Thus, a pixel of a 24-bit RGB bitmap can code up to 224 (over 1.6 million) overlaps. A higher number of overlaps at the same location makes the colour of this area identical, which can be identified by the naked eye. A bitmap is a matrix of colour values that can be represented as integers. The proposed method updates this matrix while adding new points. Thus, this matrix can be considered as an up-to-time knowledge unit of processed data. Results show cluster generation, cluster identification, missing and out-of-range data visualization, and outlier detection capability of the newly proposed method. [ABSTRACT FROM AUTHOR]

This paper is about better engineering of cyber-physical systems (CPSs) through better models. Deterministic models have historically proven extremely useful and arguably form the kingpin of the industrial revolution and the digital and information technology revolutions. Key deterministic models that have proven successful include differential equations, synchronous digital logic and single-threaded imperative programs. Cyber-physical systems, however, combine these models in such a way that determinism is not preserved. Two projects show that deterministic CPS models with faithful physical realizations are possible and practical. The first project is PRET, which shows that the timing precision of synchronous digital logic can be practically made available at the software level of abstraction. The second project is Ptides (programming temporally-integrated distributed embedded systems), which shows that deterministic models for distributed cyber-physical systems have practical faithful realizations. These projects are existence proofs that deterministic CPS models are possible and practical. [ABSTRACT FROM AUTHOR]