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Accurate traffic prediction is vital for effective traffic management during hurricane evacuation. This article proposes a predictive modeling system that integrates multilayer perceptron and long short-term memory models to capture both long-term congestion patterns and short-term speed patterns. Leveraging various input variables, including archived traffic data, spatial-temporal road network information, and hurricane forecast data, the framework is designed to address challenges posed by heterogeneous human behaviors, limited evacuation data, and hurricane event uncertainties. Deployed in a real-world traffic prediction system in Louisiana, USA, the model achieved an 82% accuracy in predicting long-term congestion states over a 6-h period during a seven-day hurricane-impacted duration. The short-term speed prediction model exhibited mean absolute percentage errors ranging from 7% to 13% across evacuation horizons from 1 to 6 h. The evaluation results underscore the model’s potential to enhance traffic management during hurricane evacuations, and real-world deployment highlights its adaptability and scalability in diverse hurricane scenarios within extensive transportation networks. [ABSTRACT FROM AUTHOR]

This article explores the critical challenges and limitations of modern communication-based train control (CBTC) systems, particularly focusing on the dynamic and uncertain nature of train–ground communication. The concept of the Age of Information (AoI) is introduced, highlighting the discrepancies between the actual and derived states used for control, which can compromise system performance and safety. Existing solutions, such as wireless local area networking technologies and reinforcement learning handoff schemes, are discussed, emphasizing their contributions to reducing handoff latency and improving the freshness of information between the train and access points. However, their limitations in adequately addressing issues related to the AoI are critically examined. The article argues for the development of novel control approaches that consider these effects to minimize the information gap and enhance the overall performance of CBTC systems. [ABSTRACT FROM AUTHOR]

Machine learning methods have been used to solve real-time scheduling problems but none has yet made an architecture that utilizes influences between real-time tasks as input features. This letter proposes a novel approach to partitioned scheduling in real-time systems using graph machine learning. We present a graph representation of real-time task sets that enable graph machine-learning schemes to capture the influence between real-time tasks. By using a graph attention network (GAT) with this method, our model successfully partitioned-schedule task sets that were previously deemed unschedulable by state-of-the-art partitioned scheduling algorithms. The GAT is used to establish relationships between nodes in the graph, which represent real-time tasks, and to learn how these relationships affect the schedulability of the system. [ABSTRACT FROM AUTHOR]

The current quality assessment for fruits and vegetables relies on subjective human judgment and manual inspection, resulting in inconsistencies and inefficiencies. Due to that, there is a need for a real-time system that can accurately and efficiently assess the quality of fruits and vegetables by analyzing various parameters, such as color, texture, size, and blemishes, to ensure consistency and reduce waste in the food supply chain. This study presents the development of a real-time tomato classification system using a hybrid model that combines convolutional neural network (CNN) and support vector machines (SVMs) deployed on the embedded single-board NVIDIA Jetson TX1. The selected CNN model EfficientNetB0 was used for feature extraction and SVM for classification. Notably, the EfficientNetB0-SVM hybrid model demonstrated impressive efficiency, achieving an average accuracy of 93.54% for classifying static tomato images stored in a board into healthy or reject with a testing time of 0.0216-s per image. Also, during real-time implementation, the proposed hybrid model attained an average inference speed of 15.6 frames per second (15.6 FPS), with an accuracy of 78.57% in classifying actual tomatoes into healthy or reject. The classification decision was taken based on 5 images for each tomato captured at different angles to ensure the detection of any blemishes from almost all sides of the tomato. The performance of the proposed model outperforms that of the state-of-the-art (SOTA) methods in accuracy, testing time per image, and real-time prediction accuracy. [ABSTRACT FROM AUTHOR]

Efficiently deploying deep neural networks on resource-limited embedded systems is crucial to meet real-time and power consumption requirements. Utilizing metareasoning as a higher-level controller along with tiny machine learning (TinyML) can enhance energy efficiency and reduce latency on such systems by overseeing available resources. This study introduces MetaTinyML, a comprehensive metareasoning framework for self-guided navigation on TinyML platforms. The framework adapts its decision-making process by factoring in environmental changes to select the most suitable algorithms for the current scenario. Implementation of MetaTinyML on an NVIDIA Jetson Nano 4-GB system integrated with a Jetbot ground vehicle demonstrated up to 50% power consumption enhancement. View a video demonstration of the MetaTinyML framework at: Video. [ABSTRACT FROM AUTHOR]

Perception-based autonomous system design methods are widely adopted in various domains like transportation, industrial robotics, etc. However, attaining safe and predictable execution in such systems depends on the platform-level integration of perception and control tasks. This letter presents a novel methodology to co-optimize these tasks, assuming a CPU-GPU-based real-time platform, a common choice of compute resource in this domain. Unlike the traditional methods that separately address AI-based sensing and control concerns, we consider that the overall performance of the system depends on the inferencing accuracy of the perception tasks and the performance of the control tasks iteratively executing in a feedback loop. We propose a design-space exploration methodology that considers the above concern and validates the same on an autonomous driving use case using a novel simulation setup. [ABSTRACT FROM AUTHOR]

As the demand for edge computing applications continues to rise, the need for efficient training of resource-constrained devices becomes paramount. This letter proposes message queuing telemetry transport (MQTT)-based implementation of distributed estimation strategies in the context of the Internet of Things (IoT), namely incremental, consensus, and diffusion strategies. The use of Raspberry Pi Pico W in the emulation environment is motivated by its advanced capability, while the MQTT data protocol is employed to address the constraints associated with conventional HTTP/HTTPs protocols. Synchronization in an IoT network is achieved by the integration of a novel methodology that entails the use of the wait-for-slowest (WFS) protocol and the MQTT protocol. Furthermore, the development of a graphical user interface supported by the Django application allows for adjusting parameters in distributed strategies through the HTTP REST API, along with SQLite. The results acquired from hardware experiments exhibit a strong correlation between the mean-square performance achieved from simulation studies. The distributed estimation strategy is compared with state-of-the art centralized and noncooperation estimation strategies, demonstrating its superior performance. In addition, a study is conducted on the resilience of these IoT networks in the face of several network threats, such as node failure and model poisoning attacks. A theoretical analysis is provided to explain the relationship between the number of iterations and node failure. [ABSTRACT FROM AUTHOR]

Traffic congestion is a problem that burdens urban cities daily. Despite the development of numerous research methodologies and technological solutions, the problem still persists. This article presents a system that combines traffic and demand management mechanisms to avoid the emergence of congestion by sustaining the occupancy of each road in the network up to a threshold, using a reservation scheme and keeping track of the future states of the network. Traffic management is responsible for navigating vehicles through congestion-free paths, while demand management is responsible for identifying the time when each vehicle will enter the network. Considering the size of actual urban networks and the number of vehicles utilizing the infrastructure, this is not an easy task. This work designs and validates a responsive and scalable real-life system for online traffic and demand management, while addressing the challenge of managing and processing large numbers of requests and data. The complexity and feasibility of the system are evaluated through microsimulations of real and artificial road networks. Its traffic efficiency is also evaluated by running extensive simulations of a real city, emulating realistic conditions in different scenarios. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAV) can capture multiview images of large-scale scenes, and then using aerial light field (ALF)-rendering technology, they can render high-quality, large-scale 3D scenes. However, the reconstruction of large-scale scenes poses challenges, such as rendering distortion and high memory consumption. In this article, we study the multiview image-capturing and novel view-rendering method of UAV sampling to address these issues. First, we present an ALF sampling model using the spectral analysis of light field and obtain an exact spectrum expression of the ALF. Through the spectral support of ALF, we determine the bandwidth and calculate the minimum required UAV sampling rate. Finally, we demonstrate that our sampling and rendering methods can improve the rendering quality of UAV 3D reconstruction and reduce the minimum sampling rate. [ABSTRACT FROM AUTHOR]

Visual analytics is vital for identifying targets by differentiating their structure and texture from unmanned aerial vehicles (UAVs). Object sensing disturbance and swift texture differentiation are tedious due to the UAV displacements. For improving the accuracy of target detection, this article introduces a learning optimizer-based visual analytics method. The proposed method assimilates deep learning and a gradient descent algorithm for feature differentiation and error minimization concurrently. The captured images are identified using multiple structural feature variations and are correlated with similar stored images. The features are extracted at different displacement and structural changing instances for leveraging accuracy. The learning process trains the similarity features during different differentiation factors. In the feature extraction, the minimum slope points are identified using a gradient descent algorithm by assigning random weights. As the differentiation increases using similar features, the minimum similarity value is detection. Postdetection, the weights are incremental and linear across different feature slopes. Therefore, the accuracy increases under varying displacement instances, preventing target misdetection. The gradient function is invariable between the minimum and maximum values for identifying high-precision features. This ensures optimal detection of different buildings and structures with high accuracy. [ABSTRACT FROM AUTHOR]

The integration of cloud computing (CC) and Internet of Things (IoT) technologies in the healthcare industry has significantly boosted the importance of real-time remote patient monitoring. The Internet of Medical Things (IoMT) systems facilitate the seamless transfer of health records to data centers, allowing medical professionals and caregivers to analyze, process, and access them. This data is often stored in cloud-based systems. Nevertheless, the transmission of data and execution of computations in a cloud environment may lead to delays and affect the efficiency of real-time healthcare services. In addition, the use of edge computing (EC) layers has become prevalent in performing local data processing and storage to reduce service response times for IoMT applications. The main objective of this article is to develop an adaptive EC infrastructure for IoMT systems, with a specific emphasis on maintaining optimal performance for real-time health services. It also designs a model to predict the server resources required to meet service level agreements (SLAs) regarding response time. Simulation results demonstrate that EC significantly improves service response time for real-time IoMT applications. The proposed model can accurately and efficiently predict the computing resources required for medical data services to achieve SLAs under varying workload conditions. [ABSTRACT FROM AUTHOR]

The diffusion of electric vehicles (EVs) is recently gaining great attention in the road transport and automotive sectors as an attempt to bring in an emission-free world. EVs are considered a key to future clean transportation systems. However, these vehicles still suffer from limited battery capacity and range anxiety. Therefore, EVs manufacturers are focusing on reducing energy consumption and CO2 emissions. In addition, research in the context of intelligent transportation systems embedding information and communication technologies are focusing on the optimization of the energy consumption as a valuable solution to foster the wide diffusion of EVs. In this article, we propose a simulation platform for eco-routing services based on estimating EV energy consumption to provide the most energy-efficient routes for the EV while traveling. We provide an energy map that can be used for eco-routing through a real-time data collection of the EV energy consumption. The energy map was established in the traffic simulator Simulation of Urban MObility (SUMO) to show the efficiency of the proposed eco-routing strategy compared to the other strategies based on establishing the fastest routes. This map will be exploited as good support, in the future, for advanced research on the EV concept. [ABSTRACT FROM AUTHOR]

This letter introduces the design of an estimator for parameters of Middleton Class A noise using its canonical formula and classical numerical methods. The main focus is to acquire parameters to characterize communication channels in intelligent systems or those based on cognitive paradigms. A comprehensive analysis of the first-order characteristics of the Middleton Class A noise model is conducted to establish the foundational understanding necessary for developing the presented estimator model, named impulsive noise estimator with minimization methods (INEMM). Subsequently, the method is introduced, substantiated, and compared to various established estimators concerning precision and complexity. Results show a distinct advantage in terms of overall performance. [ABSTRACT FROM AUTHOR]

In the literature, macroscopic fundamental diagram (MFD)- or network fundamental diagram-based perimeter control approaches have been demonstrated as cost-effective means to improve traffic efficiency for urban traffic networks. This article proposes a new perimeter control approach, which extends previous approaches in two aspects. First, the proposed approach optimizes the allocation of perimeter metered flow considering the heterogeneity of network traffic flow distribution, which is inversely related to network production. Second, queue management is considered by the proposed approach so as to prevent queue spillback induced by perimeter control. The proposed control approach is well advanced for field application using only loop detector data. It adopts a hierarchical framework, where the upper level controller determines the total flow entering the protected network through perimeter links based on an MFD-based feedback regulator, while the lower level controller optimizes the distribution of the total metered flow among the perimeter links based on real-time network heterogeneity and perimeter queues. The proposed approach is tested and compared with state-of-the-art perimeter control approaches, using a real traffic network developed in the microscopic simulation software VISSIM. Results demonstrate that the proposed approach significantly reduces the overall network delay. Moreover, it outperforms other perimeter control approaches by maintaining low network heterogeneity within the protected network and preventing (or delaying) queue spillback at perimeter links. [ABSTRACT FROM AUTHOR]

Emotion recognition systems are typically trained to classify a given psychophysiological state into emotion categories. Current platforms for emotion ground-truth collection show limitations for real-world scenarios of long-duration content (e.g. $> $ > 10 minutes), namely: 1) Real-time annotation tools are distracting and become exhausting; 2) Perform retrospective annotation of the whole content in bulk (providing highly coarse annotations); or 3) Are used by external experts (depending on the number of annotators and their subjective experience). We explore a novel approach, the EmotiphAI Annotator, that allows undisturbed content visualisation and simplifies the annotation process by using segmentation algorithms that select brief clips for emotional annotation retrospectively. We compare three methods for content segmentation based on physiological data (Electrodermal Activity (EDA), emotion-based), scene (time-based), and random (control) selection. The EmotiphAI Annotator attained a B+ System Usability Scale score and low-average mental workload as per the NASA Task Load Index (40%). The reliability of the self-report was analysed by the inter-rater agreement (STD $< $ < 0.75), coherence across time segmentation methods (STD $< $ < 0.17), comparison against the state-of-the-art ground truth (STD $< $ < 0.7), and correlation to EDA ($> $ > 0.3 to 0.8), where the EDA-based method obtained the overall best performance. [ABSTRACT FROM AUTHOR]

Emotions play an essential role in affecting the performance of cognitive abilities in continuous cognitive tasks. Most previous studies share a common issue in that the evoked emotions are simply presumed to be real emotions, without taking into account the observation that emotions may be changed when carrying out cognitive activities. This may lead to the inaccurate detection of true emotions, which further adversely affects the investigation of interactions between emotion and cognition. To address this challenging problem, the present work develops an innovative study using EEG measurement to investigate the interaction between dynamic affection and cognitive ability. In particular, a real-time emotion detection model by the use of physiological signals (i.e., EEG) is constructed, to dynamically monitor the current emotional state. Given the observed emotion, the analysis of the interaction between cognitive abilities and dynamic emotions is undertaken from the perspectives of both behavioral performance and brain mechanisms. Research outcomes indicate that emotions are not stable, and are indeed dynamically changed by cognitive performance. Meanwhile, cognitive activities also influence the brain activation pattern revealed under different emotions, which validates the necessity of introducing the dynamic emotion monitoring model. In addition, the best performance has been found when the emotional state is neutral in terms of accuracy and response time. The results of this study provide a potential basis for assessing the cognitive abilities of individuals with different emotions in a variety of applications of cognitive scenarios. [ABSTRACT FROM AUTHOR]

An affective brain-computer interface (aBCI) can detect affective states based on brain signals and might assist people in improving their emotion regulation abilities. However, individual differences in emotional brain patterns make cross-subject emotion identification extremely challenging. Traditional supervised single-subject classification schemes require considerable calibration samples from new individuals to train subject-dependent models. Individuals are easily fatigued with long-term EEG collection processes, which may affect performance in subsequent online experiments. In this study, we propose a real-time aBCI system using domain-fusion-based multisource style transfer mapping (DF-MS-STM) to detect positive, neutral, and negative emotional states without the need for additional training sessions. Sixteen subjects participated in our online experiments to test the performance of our aBCI system and an average online prediction accuracy of 72.17±12.25% was obtained for three-class emotion recognition tasks in the last three experimental sessions. Our proposed algorithm significantly outperformed numerous baseline methods in terms of cross-subject emotion classification. In addition, we identified distinct brain patterns in response to different emotional stimuli based on the results of event-related spectral perturbation (ERSP) analyses. These neural patterns might provide new insights for emotional brain mechanistic studies and related aBCIs. [ABSTRACT FROM AUTHOR]

The advent of 6G communication systems has enabled, through integrated sensing and communications (ISAC), the vision of the intelligent connection of everything. Since such ISAC systems will be utilizing the sub-6-GHz and millimeter-wave (mm-wave) bands, appropriate hybrid, i.e., sensing and communication channel models, are required for their proper operation. This article first presents a comprehensive survey of channel models used in ISAC applications viewed from different modeling perspectives by identifying the challenges encountered in these modeling approaches. A novel hybrid approach for ISAC channel modeling is then introduced wherein the modeling methodology is partitioned into three elements: targets, clutters, and interferences. The advantage of such a flexible modeling approach using these three elements enhances the proposed model’s ability to effectively deal with the specific sensing and communication operational requirements. Finally, further performance enhancement methods, based upon real-time 3D environment reconstruction techniques as well as artificial intelligence (AI), are proposed and discussed. [ABSTRACT FROM AUTHOR]

In the current subsea industry scenario, autonomous underwater vehicles (AUVs) are widely used for expeditions and explorations. However, the mission duration is limited due to the limitations in the battery capacity. To increase the endurance, there is a need for a submerged docking station (DS) to charge the battery, also to update the next mission profile. In this letter, deep learning (DL) technique aided short-range vision guidance is envisaged for a reliable and precise AUV homing operation. Intelligent control algorithms with an efficient DL-based you only look once (YOLO) v5-image processing techniques are used for DS detection and tracking and deployed in an edge computer integrated into AUV prototype. The developed illuminated DS and AUV prototype with high-definition camera has been demonstrated in test tank at depth of 2 m. An analysis was conducted on the DS data set, which comprised 132 images of clear and turbid water, 13 were designated for testing, 40 for validation, and 79 for training purposes. The results were observed that the probability of detecting the DS is 95%, detection range is 5 m, the probability of homing toward the DS is CEP 90 with the position error of 5% in less-turbid waters and in high-turbid waters, 60% is the probability of DS detection with position error up to 25%, detectable range is 1 m. The proposed embedded hardware is extremely useful for underwater reliable homing applications. [ABSTRACT FROM AUTHOR]

Edge devices are increasingly utilized for deploying deep learning applications on embedded systems. The real-time nature of many applications and the limited resources of edge devices necessitate latency-targeted neural network compression. However, measuring latency on real devices is challenging and expensive. Therefore, this letter presents a novel and efficient framework, named EvoLP, to accurately predict the inference latency of models on edge devices. This predictor can evolve to achieve higher latency prediction precision during the network compression process. Experimental results demonstrate that EvoLP outperforms previous state-of-the-art approaches by being evaluated on three edge devices and four model variants. Moreover, when incorporated into a model compression framework, it effectively guides the compression process for higher model accuracy while satisfying strict latency constraints. We open-source EvoLP at https://github.com/ntuliuteam/EvoLP. [ABSTRACT FROM AUTHOR]

Modern real-time systems started leveraging runtime reconfigurable architectures to reduce size, weight, and power while ensuring predictability. However, the time to reconfigure a platform (referred to as reconfiguration delay) is non-negligible and subject to fluctuations caused by conflicts in accessing shared components during the reconfiguration process. Different from existing solutions that impact either system resources or system performance, in this letter, we propose an approach, together with its supporting tools, allowing to characterize the reconfiguration delay and to be able to capture and produce evidence of the actual impact of the resource contention. In addition, we report the experiments of the proposed approach in a representative configuration on a Xilinx Zynq Ultrascale+ MPSoC; results show that conflicts can impact the reconfiguration delay at most by 230%. [ABSTRACT FROM AUTHOR]

In intelligent transportation systems, one key challenge for managing ride-hailing services is the balancing of traffic supply and demand while meeting passenger needs within vehicle availability constraints. Accurate origin–destination (OD) demand predictions can empower platforms to execute timely reallocation of cruising vehicles and improve ride-sharing services. Nonetheless, the complexity of OD-based demand prediction arises from intricate spatiotemporal dependencies and a higher need for precision compared to zone-based predictions, which leads to many unprecedented OD pairs. To tackle this issue, we design a comprehensive set of 102 features, including travel demand, passenger count, travel volume, liveliness, weather, and cross features. We also introduce an enhanced conformer model, which is composed of a single conformer block that integrates feedforward layers, multihead self-attention mechanisms, and depth-wise separable convolution layers. To address the cold-start problem and manage large values, we design a specific algorithm for OD pairs lacking training data and apply a technique to handle larger values. Our approach demonstrates a marked improvement in prediction performance, with an 18% decrease in the total travel demand error and up to a 47% reduction for certain larger values in some cases. Through extensive experiments on a dataset collected from a city, provided by a ride-hailing platform, our proposed methods significantly outperform the most advanced models. [ABSTRACT FROM AUTHOR]

Navigation or route guidance systems are designed to provide drivers with real-time travel information and the associated recommended routes for their trips. Classical route choice models typically rely on utility theory to represent drivers’ route choice behavior. Such choices, however, may not be optimal from both the individual and the system perspectives. This is simply due to the fact that drivers usually have imperfect knowledge about the time-varying traffic conditions. In this article, we explore and propose a new model-free deep reinforcement learning (DRL) approach to solving the adaptive route guidance problem based on microsimulation. The proposed approach consists of three interconnected algorithms, including a network edge labeling algorithm, a routing plan identification algorithm, and an adaptive route guidance algorithm. Simulation experiments on both a toy network and a real-world network of Suzhou, China, are performed to demonstrate the effectiveness of the proposed approach in terms of guiding a single vehicle as well as multiple vehicles through complex traffic environments. Comparative results confirm that the DRL approach outperforms the traditional shortest path method by further reducing the average travel time in the network. [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 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.

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.

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]

Recently, pressure-sensing mats have been widely used to capture static and dynamic pressure over sleep for posture recognition. Both a full-size mat with a low-density sensing array for figuring out the structure of the whole body and a miniature scale mat with a high-density sensing array for identifying the local characteristics around the chest have been investigated. However, both of the mat systems may face the challenge in the trade-off between the computational complexity (involving the size, density, etc. of the mat) and the performance of sleep posture recognition, where high performance may requires overcomplex computation and result in time latency in real-time sleep posture monitoring. In this paper, a lightweight neural network named ConcatNet, is proposed to realize sleep postures (supine, left, right, and prone) recognition in real time while maintaining a favorable performance. In ConcatNet, the inception module is proposed to extract the image features under multiple receptive fields, while the multi-layer feature fusion module is utilized to fuse deep and shallow features to enhance the model performance. To further improve the efficiency of the model, the depthwise convolution is adopoted. ConcatNet models in 3 different scales (ConcatNet-S, ConcatNet-M, and ConcatNet-L) are built to explore the impact of the sensor density on sleep posture recognition performance. Experimental results exhibit that ConcatNet-M corresponding to medium sensor density (${16}\times {16}$) achieved the best comprehensive performance, with short-term data cross-validation accuracy at 95.56% and overnight data testing accuracy at 94.68%. The model size is 7.91KB, FLOPs is 56.47K, and the inference time is only 0.38ms, which shows an outstanding performance of real-time sleep posture recognition with minimum consumption, indicating the potential to be deployed in mobile devices. [ABSTRACT FROM AUTHOR]

Surface electromyography (sEMG), a human-machine interface for gesture recognition, has shown promising potential for decoding motor intentions, but a variety of nonideal factors restrict its practical application in assistive robots. In this paper, we summarized the current mainstream gesture recognition strategies and proposed a gesture recognition method based on multimodal canonical correlation analysis feature fusion classification (MCAFC) for a nonideal condition that occurs in daily life, i.e., posture variations. The deep features of the sEMG and acceleration signals were first extracted via convolutional neural networks. A canonical correlation analysis was subsequently performed to associate the deep features of the two modalities. The transformed features were utilized as inputs to a linear discriminant analysis classifier to recognize the corresponding gestures. Both offline and real-time experiments were conducted on eight non-disabled subjects. The experimental results indicated that MCAFC achieved an average classification accuracy, average motion completion rate, and average motion completion time of 93.44%, 94.05%, and 1.38 s, respectively, with multiple dynamic postures, indicating significantly better performance than that of comparable methods. The results demonstrate the feasibility and superiority of the proposed multimodal signal feature fusion method for gesture recognition with posture variations, providing a new scheme for myoelectric control. [ABSTRACT FROM AUTHOR]

Individuals with high-level spinal cord injuries often face significant challenges in performing essential daily tasks due to their motor impairments. Consequently, the development of reliable, hands-free human-computer interfaces (HCI) for assistive devices is vital for enhancing their quality of life. However, existing methods, including eye-tracking and facial electromyogram (FEMG) control, have demonstrated limitations in stability and efficiency. To address these shortcomings, this paper presents an innovative hybrid control system that seamlessly integrates gaze and FEMG signals. When deployed as a hybrid HCI, this system has been successfully used to assist individuals with high-level spinal cord injuries in performing activities of daily living (ADLs), including tasks like eating, pouring water, and pick-and-place. Importantly, our experimental results confirm that our hybrid control method expedites the performance in pick-place tasks, achieving an average completion time of 34.3 s, which denotes a 28.8% and 21.8% improvement over pure gaze-based control and pure FEMG-based control, respectively. With practice, participants experienced up to a 44% efficiency improvement using the hybrid control method. This state-of-the-art system offers a highly precise and reliable intention interface, suitable for daily use by individuals with high-level spinal cord injuries, ultimately enhancing their quality of life and independence. [ABSTRACT FROM AUTHOR]

Various measures have been proposed to quantify upper-limb use through wrist-worn inertial measurement units. The two most popular traditional measures of upper-limb use – thresholded activity counts (TAC) and the gross movement (GM) score suffer from high sensitivity and low specificity, and vice versa. We previously proposed a hybrid version of these two measures – the GMAC – that showed better overall detection performance than TAC and GM. In this paper, we answer two critical questions to improve the GMAC measure’s usefulness: (a) can it be implemented using only the accelerometer data? (b) what are its optimal parameter values? Here, we propose a modified GMAC using only the accelerometer data and optimize its parameters to develop: (a) a generic measure that is both limb- and subject-independent, and (b) limb-specific measures that were only subject-independent. The optimized GMAC showed better detection performance than the previous GMAC and surprisingly had comparable performance to the best-performing machine learning-based measure (random forest inter-subject model). In hemiparetic data, its performance was similar to the previous GMAC and the random forest inter-subject model; the limb-specific GMAC measure, however, had a better performance than the generic measure. The optimized limb-specific GMAC is a simple, interpretable alternative to a machine learning-based inter-subject model. The optimized GMAC can be a valuable measure for offline or real-time detection and feedback of upper limb use. The preliminary results of this study, based on a small dataset, need to be validated on a larger dataset to evaluate its generalizability. [ABSTRACT FROM AUTHOR]

To overcome the challenges posed by the complex structure and large parameter requirements of existing classification models, the authors propose an improved extreme learning machine (ELM) classifier for human locomotion intent recognition in this study, resulting in enhanced classification accuracy. The structure of the ELM algorithm is enhanced using the logistic regression (LR) algorithm, significantly reducing the number of hidden layer nodes. Hence, this algorithm can be adopted for real-time human locomotion intent recognition on portable devices with only 234 parameters to store. Additionally, a hybrid grey wolf optimization and slime mould algorithm (GWO-SMA) is proposed to optimize the hidden layer bias of the improved ELM classifier. Numerical results demonstrate that the proposed model successfully recognizes nine daily motion modes including low-, mid-, and fast-speed level ground walking, ramp ascent/descent, sit/stand, and stair ascent/descent. Specifically, it achieves 96.75% accuracy with 5-fold cross-validation while maintaining a real-time prediction time of only 2 ms. These promising findings highlight the potential of onboard real-time recognition of continuous locomotion modes based on our model for the high-level control of powered knee prostheses. [ABSTRACT FROM AUTHOR]

In neurodegenerative disorders, neuronal firing patterns and oscillatory activity are remarkably altered in specific brain regions, which can serve as valuable biomarkers for the identification of deep brain regions. The subthalamic nucleus (STN) has been the primary target for DBS in patients with Parkinson’s disease (PD). In this study, changes in the spike firing patterns and spectral power of local field potentials (LFPs) in the pre-STN (zona incerta, ZI) and post-STN (cerebral peduncle, cp) regions were investigated in PD rats, providing crucial evidence for the functional localization of the STN. Sixteen-channel microelectrode arrays (MEAs) with sites distributed at different depths and widths were utilized to record neuronal activities. The spikes in the STN exhibited higher firing rates than those in the ZI and cp. Furthermore, the LFP power in the delta band in the STN was the greatest, followed by that in the ZI, and was greater than that in the cp. Additionally, increased LFP power was observed in the beta bands in the STN. To identify the best performing classification model, we applied various convolutional neural networks (CNNs) based on transfer learning to analyze the recorded raw data, which were processed using the Gram matrix of the spikes and the fast Fourier transform of the LFPs. The best transfer learning model achieved an accuracy of 95.16%. After fusing the spike and LFP classification results, the time precision for processing the raw data reached 500 ms. The pretrained model, utilizing raw data, demonstrated the feasibility of employing transfer learning for training models on neural activity. This approach highlights the potential for functional localization within deep brain regions. [ABSTRACT FROM AUTHOR]

Individuals with Parkinson’s disease (PD) are characterized by gait and balance disorders limiting their independence and quality of life. Home-based rehabilitation programs, combined with drug therapy, demonstrated to be beneficial in the daily-life activities of PD subjects. Sensorized shoes can extract balance- and gait-related data in home-based scenarios and allow clinicians to monitor subjects’ activities. In this study, we verified the capability of a pair of sensorized shoes (including pressure-sensitive insoles and one inertial measurement unit) in assessing ground-level walking and body weight shift exercises. The shoes can potentially be combined with a sensory biofeedback module that provides vibrotactile cues to individuals. Sensorized shoes have been assessed in terms of the capability of detecting relevant gait events (heel strike, flat foot, toe off), estimating spatiotemporal parameters of gait (stance, swing, and double support duration, stride length), estimating gait variables (vertical ground-reaction force, vGRF; coordinate of the center of pressure along the longitudinal axes of the feet, yCoP; and the dorsiflexion angle of the feet, Pitch angle). The assessment compared the outcomes with those extracted from the gold standard equipment, namely force platforms and a motion capture system. Results of this comparison with 9 PD subjects showed an overall median absolute error lower than 0.03 s in detecting the foot-contact, foot-off, and heel-off gait events while performing ground-level walking and lower than 0.15 s in body weight shift exercises. The computation of spatiotemporal parameters of gait showed median errors of 1.62 % of the stance phase duration and 0.002 m of the step length. Regarding the estimation of vGRF, yCoP, and Pitch angle, the median across-subjects Pearson correlation coefficient was 0.90, 0.94, and 0.91, respectively. These results confirm the suitability of the sensorized shoes for quantifying biomechanical features during body weight shift and gait exercises of PD and pave the way to exploit the biofeedback modules of the bidirectional interface in future studies. [ABSTRACT FROM AUTHOR]

Automatic identification of visual learning style in real time using raw electroencephalogram (EEG) is challenging. In this work, inspired by the powerful abilities of deep learning techniques, deep learning-based models are proposed to learn high-level feature representation for EEG visual learning identification. Existing computer-aided systems that use electroencephalograms and machine learning can reasonably assess learning styles. Despite their potential, offline processing is often necessary to eliminate artifacts and extract features, making these methods unsuitable for real-time applications. The dataset was chosen with 34 healthy subjects to measure their EEG signals during resting states (eyes open and eyes closed) and while performing learning tasks. The subjects displayed no prior knowledge of the animated educational content presented in video format. The paper presents an analysis of EEG signals measured during a resting state with closed eyes using three deep learning techniques: Long-term, short-term memory (LSTM), Long-term, short-term memory–convolutional neural network (LSTM-CNN), and Long-term, short-term memory–Fully convolutional neural network (LSTM-FCNN). The chosen techniques were based on their suitability for real-time applications with varying data lengths and the need for less computational time. The optimization of hypertuning parameters has enabled the identification of visual learners through the implementation of three techniques. LSTM-CNN technique has the highest average accuracy of 94%, a sensitivity of 80%, a specificity of 92%, and an F1 score of 94% when identifying the visual learning style of the student out of all three techniques. This research has shown that the most effective method is the deep learning-based LSTM-CNN technique, which accurately identifies a student’s visual learning style. [ABSTRACT FROM AUTHOR]

Micro-expression recognition based on ima- ges has made some progress, yet limitations persist. For instance, image-based recognition of micro-expressions is affected by factors such as ambient light, changes in head posture, and facial occlusion. The high temporal resolution of electroencephalogram (EEG) technology can record brain activity associated with micro-expressions and identify them objectively from a neurophysiological standpoint. Accordingly, this study introduces a novel method for recognizing micro-expressions using node efficiency features of brain networks derived from EEG signals. We designed a real-time Supervision and Emotional Expression Suppression (SEES) experimental paradigm to collect video and EEG data reflecting micro- and macro-expression states from 70 participants experiencing positive emotions. By constructing functional brain networks based on graph theory, we analyzed the network efficiencies at both macro- and micro-levels. The participants exhibited lower connection density, global efficiency, and nodal efficiency in the alpha, beta, and gamma networks during micro-expressions compared to macro-expressions. We then selected the optimal subset of nodal efficiency features using a random forest algorithm and applied them to various classifiers, including Support Vector Machine (SVM), Gradient-Boosted Decision Tree (GBDT), Logistic Regression (LR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). These classifiers achieved promising accuracy in micro-expression recognition, with SVM exhibiting the highest accuracy of 92.6% when 15 channels were selected. This study provides a new neuroscientific indicator for recognizing micro-expressions based on EEG signals, thereby broadening the potential applications for micro-expression recognition. [ABSTRACT FROM AUTHOR]

Based on delay-Doppler maps (DDMs) in raw counts generated by spaceborne global navigation satellite system reflectometry (GNSS-R) receivers, retrieving ocean surface wind speeds is feasible, so several spaceborne GNSS-R missions have been carried out. However, it is currently troubled by global data latency of several hours or even more due to the bottleneck in the satellite downlink. Consequently, this article, for the first time, presents an algorithm for spaceborne GNSS-R receivers to conduct the DDM calibration in orbit and then to retrieve ocean surface wind speeds in real time, which contributes to not only lightening the burden on downloading a wealth of scientific data but also broadcasting real-time ocean surface wind speeds to users. Since there is a power correlation between direct and reflected signals from the same GNSS satellite with respect to the GNSS-R receiver, this algorithm calibrates direct signal power first, and then it estimates the real-time GNSS transmitter effective isotropic radiated power at the reflected signal according to the normalized antenna pattern of the corresponding GNSS satellite. Afterward, DDMs in raw counts are calibrated. Finally, ocean surface wind speeds are computed using pretrained geophysical model functions. Exploiting the scientific data from the GNOS-II onboard China's FY-3E satellite, this algorithm is validated carefully, and final retrieved ocean surface wind speeds against collocated European Centre for Medium-Range Weather Forecasts wind speeds have an overall root-mean-square error of 1.68 m/s and 1.50 m/s for GPS-R and BDS-R, respectively. [ABSTRACT FROM AUTHOR]

The vehicular metaverse is expected to provide a widely connected virtual Internet of Vehicles (IoV), where extended reality (XR) is one of the critical infrastructures. However, the combination of XR and automated vehicle (AV) networks brings several significant challenges, e.g., low-latency XR panoramic video transmission, high bandwidth, and the high mobility of vehicles. This article introduces digital twin (DT) and artificial intelligence (AI)-empowered panoramic video streaming for XR-assisted connected AVs to reduce transmission latency and intelligently respond to user requirements. Specifically, we propose a DT-enabled distributed XR service management framework to provide low-latency and smooth XR services across different domains in the vehicular metaverse. In addition, we present a case study on XR streaming-based virtualized resource allocation and propose a novel deep reinforcement learning (DRL)-based method to minimize transmission latency. Quantitative experimental results demonstrate that the positive role of AI in connected AV networks can be enhanced by DTs. Finally, open issues and potential research directions for the XR-assisted vehicular metaverse are discussed. [ABSTRACT FROM AUTHOR]

A decomposed dynamic dispatching and preventive maintenance (D-DDPM) framework is presented to overcome the computational challenges of solving large DDPM problems. Stochastic deterioration of machine health is explicitly considered. By efficient allocation of individual machine’s capacity under different machine health, the D-DDPM framework decomposes the global DDPM problems into single-machine DDPM problems while ensuring throughput optimality. Intelligent machine agents are then designed to solve the single-machine DDPM problems efficiently and to perform real-time decisions for individual machines. After the decomposition, the overall computational complexity grows only linearly with the number of machines and the proposed framework could be adopted in large systems. In the numerical study, the D-DDPM framework is near-optimal in small systems that have optimal DDPM solutions. In which, the computation time is reduced by 90.72% and the average cycle time is within 6% of optimal. For large systems without optimal DDPM solutions, numerical results show that the D-DDPM maintains throughput optimality and reduces average cycle time by up to 90% against other control policies. [ABSTRACT FROM AUTHOR]

Once a freight train is delayed on a busy railway under a quasi-moving-block signaling system, the speed of its following trains may fluctuate, without proper adjustment. Updating the reference speed profile of the delayed freight train is an urgent task to improve the train operation performance and rail utilization ratio. To this end, this article proposes a speed profile optimization method for following delayed trains that is based on the idea of model predictive control (MPC). The state change time of the block section ahead is predicted for each following train according to the speed profile of its preceding train, based on which the distance between the following train and each speed protection curve (DTC) is predicted. The DTC is taken as a key optimization index for the speed profile optimization, and the other optimization indices are train delay and energy consumption. A two-step hierarchical optimization algorithm is proposed in this article. In the upper level, each prediction step is defined as the traveling time of the preceding train in each block section ahead, and the train target cruising speed sequence is calculated, with the dimension decided by the prediction horizon, using particle swarm optimization. In the lower level, the optimal speed profile is calculated based on the rolling optimization algorithm of the train speed curve in the prediction horizon. The proposed algorithm repeats the optimization process with updated train information after each control horizon. Three simulations are presented, which consider a downhill scenario, steep uphill scenario, and temporary speed limit, respectively. Then, the MPC parameters are analyzed and the optimized speed profiles are compared with the other algorithm. [ABSTRACT FROM AUTHOR]

Traffic congestion has become a major concern in most cities all over the world. The proper guidance of cars with an effective route planning method has become a fundamental and smart way to alleviate congestion under existing urban road facilities. Current route planning methods mainly focus on a single car, but ignoring the dynamic effect between cars may lead to severe congestion during the actual driving guidance. In this article, we extend the study of route planning to the case of multiple cars and present a novel multicar shortest travel-time routing problem. The objective is to minimize the average travel time by considering the dynamic effect of the induced traffic congestion on travel speed, while ensuring that each car’s travel distance is within an acceptable range. We construct a time-hierarchical graph model for structuring the spatiotemporal dynamic properties of the urban road network and then develop a two-level multicar route planning optimization method for complex problem solving. The experimental results show that our path recommendations reduce the average travel time by 51.74% and 38.87% on average compared to two representative methods. Our research will become more important in the years ahead as self-driving cars become more commonplace. [ABSTRACT FROM AUTHOR]

Virtual coupling allows multiple trains to run synchronously in a virtually coupled formation via train-to-train (T2T) wireless communication. The distance between virtual coupling trains can be reduced greatly to improve the line capacity. However, the time delay in T2T communication cannot be avoided, and the delay may fluctuate due to the uncertainties in the train operation, which will negatively impact the virtual coupling trains. In this article, a particle swarm optimization (PSO)-based online optimization approach for virtual coupling that considers the nondeterministic delay in T2T communication is presented. The optimization objectives include energy consumption, ride comfort, operation speed, and the distance between trains. The PSO algorithm is adopted to solve the problem online so that the train can be controlled according to the optimized traction/braking acceleration. The simulation was conducted with data from the existing Beijing subway line, and the results show that the proposed method reduces the impact of nondeterministic delay on virtual coupling significantly. The speed error of the follower train is below 0.3 m/s, and the tracking distance error is below 0.8 m. Moreover, the requirements of real-time control and ride comfort are satisfied. [ABSTRACT FROM AUTHOR]

Even though one-stage detectors have advantages for resource-constrained and real-time applications, they face the disadvantage of mediocre performance. Thus, this article proposes a novel one-stage parking slot detection method that achieves comparable performance to two (or multi-)stage detectors. The proposed method extracts the components and properties of the parking slots from input images. As the extracted components and properties are unorganized, it is significantly important to combine them correctly. To this end, this article introduces the component linkages that provide sufficient information for connecting the extracted components and properties. By the guide of the component linkages, the components and properties of the parking slots are progressively assembled to produce precise detection results. In experiments, the proposed method was evaluated using two large-scale parking slot detection datasets and showed state-of-the-art performances. Specifically, in the Seoul National University (SNU) dataset, the proposed method achieved 96.73% recall and 96.75% precision while maintaining a fast processing speed of 134 frames per second. In addition, this article provides a new set of labels for the SNU dataset, which covers more than 60,000 parking slots with high-quality annotations. [ABSTRACT FROM AUTHOR]

Modern avionics systems exhibit numerous networked electronic components ranging from sensors and actuators to dedicated subsystems, resulting in aircraft capable of processing and responding to information accurately, reliably, and in a timely fashion. Assuring the cyber security of these systems is a continual challenge and an active area of research; in the case where an aircraft has been compromised by a malicious actor, digital forensics can be utilized to investigate what and how the incident occurred. This research answers a simple, yet fundamental question on the security of aircraft: whether useful digital forensic artifacts be obtained from embedded real-time systems on aircraft. The highly reliable file system (HRFS) utilized by VxWorks was analyzed and described to align with the generalized descriptions of file system formats accepted in academia. The Sleuth Kit (TSK), an open-source forensic toolkit, was analyzed and extended to include functionality to support this file system, and a proof-of-concept implementation to obtain digital forensic artifacts from real-time operating systems on aircraft was developed. This research finds that the proposed implementation can perform file analysis and recovery from a VxWorks generated HRFS-formatted file system and can be generalized to show that embedded real-time systems can provide useful digital forensic artifacts. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are one important enabler for the evolution of sustainable transportation sector. However, one major barrier to the widespread adoption of EVs is their limited battery capacity and the excessive time required to fully charge depleted batteries. Although techniques such as charging with a rapid charger and battery swapping may partially alleviate such concerns, the corresponding high capital investment may not be profitable for areas with low EV adoption rates. Moreover, the diversified charging demands of randomly distributed EVs, both spatially and temporally, restrict high-quality services from current fixed charging stations (FCSs). To resolve such challenges, mobile charging services (MCSs) have been investigated as a supplemental charging method for EVs, wherein energy replenishment is provided by mobile charging vehicles (MCVs). Under the framework of the Internet of EVs (IoEV), this article describes the concept of MCSs and investigates their charging merits under some distinct charging scenarios, e.g., real-time response service and emergency rescue service. A case study concerning real-time response services is investigated to illustrate the influence of MCSs on the current charging system. Simulation results demonstrate the ability of MCSs to improve the quality of charging service by reducing the average service time and service acceptance ratio. Finally, technical challenges, future research issues, and possible methodologies regarding the MCSs for EVs are discussed. [ABSTRACT FROM AUTHOR]

The signal-to-noise ratio of optical emission spectroscopy (OES) data has decreased as the plasma etching process has advanced. As a result, not only the advanced endpoint detection method was required, but also the selection of more informative wavelengths. This paper proposes an improved endpoint detection algorithm by combining data-driven wavelength selection and a Gaussian mixture model (GMM). The data-driven wavelength selection algorithm finds the correlation between training data and a sigmoid function of time. Then, using the fitted GMM of the training data in latent space, the endpoint of the test data is determined in real-time. The proposed algorithm’s performance was evaluated using real OES data, comprised of seven operations. The correlation-based wavelength selection algorithm significantly reduced detection error by 70.2% when compared to the conventional method, which selects a few wavelengths manually based on prior knowledge. Additionally, the GMM detection method clustered OES data from low open area wafers much more clearly than the recently proposed method using GMM. This demonstrates that combining correlation-based wavelength selection with GMM is an effective method for detecting endpoints during plasma etching of small open area wafers. [ABSTRACT FROM AUTHOR]