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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]

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]

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]

In recent years, opportunistic rainfall sensing exploiting commercial microwave links (CMLs) has become popular for real-time monitoring of rainfall events. Among such systems, satellite-to-Earth microwave links (SMLs) are gaining great interest. An important information to extract from the collected data is whether or not an SML observation corresponds to a rain condition. This letter proposes an online training procedure to keep the algorithms that classify this condition up to date, overcoming the problem of data seasonality. Specifically, we propose to retrain the algorithms after one day of observations by applying a forgetting mechanism, where the oldest data are removed from the training dataset. Data were collected by the Smart Rainfall System (SRS) SML-type sensors located in the city of Genoa (Italy). We compare three classification algorithms: one based on an anomaly detection method and two based on machine learning (ML) algorithms. The outcomes of this analysis pave the way for implementing artificial intelligence models on embedded systems with limited resources such as the electronic units of SML sensors, hence reducing the data flow from the peripheral sensor network to the central data acquisition and integration server. [ABSTRACT FROM AUTHOR]

Given a log and a specification, timed pattern matching aims at exhibiting for which start and end dates a specification holds on that log. For example, “a given action is always followed by another action before a given deadline”. This problem has strong connections with monitoring real-time systems. We address here timed pattern matching in the presence of an uncertain specification, i.e., that may contain timing parameters (e.g., the deadline can be uncertain or unknown). We want to know for which start and end dates, and for what values of the timing parameters, a property holds. For instance, we look for the minimum or maximum deadline (together with the corresponding start and end dates) for which the property holds. We propose two frameworks for parametric timed pattern matching. The first one is based on parametric timed model checking. In contrast to most parametric timed problems, the solution is effectively computable. The second one is a dedicated method; not only we largely improve the efficiency compared to the first method, but we further propose optimizations with skipping. Our experiment results suggest that our algorithms, especially the second one, are efficient and practically relevant. [ABSTRACT FROM AUTHOR]

Many IoT-based applications have inherited the artificial intelligence of things (AIoT) techniques to explore new services and benefits of smart recording and monitoring generated information. However, hundreds of hacking incidents caused by highly sophisticated attackers have generated serious risks, where they compromised various IoT sensors for their benefits, impeding the growth of AIoT. Various security schemes have been proposed in the literature; however, it is critical to determine the legitimacy of AIoT devices in real-time scenarios during the initial deployment of the network. Therefore, this article aims to provide a secure, reliable, and trusted decision-making scheme using multiattribute methods in collaborative AIoT. The proposed system uses backpropagation and Bayesian’s rule to ensure a fast and accurate decision. In addition, agent-based modeling and population-based modeling trust schemes are used to compute the legitimacy of the communicating model. Further, the proposed system is validated over various security measures against the various decision-based conventional methods such as Fuzzy c-means, REPTree, and random tree in terms of time, accuracy, replay attack, data falsification attack, recall, region of convergence, and F-Measure. The proposed mechanism achieves 93% improvement over accuracy and attack identification against existing mechanisms. [ABSTRACT FROM AUTHOR]

Blockchain creates a secure environment on top of strict cryptographic assumptions and rigorous security proofs. It permits on-chain interactions to achieve trustworthy properties such as traceability, transparency, and accountability. However, current blockchain trustworthiness is only confined to on-chain, creating a “trust gap” to the physical, off-chain environment. This is due to the lack of a scheme that can truthfully reflect the physical world in a real-time and consistent manner. Such an absence hinders further blockchain applications in the physical world, especially for the security-sensitive ones. In this paper, we propose a framework to extend blockchain trust from on-chain to off-chain, and take trustworthy vaccine tracing as an example scheme. Our scheme consists of 1) a Trusted Execution Environment (TEE)-enabled trusted environment monitoring system built with the Arm Cortex-M33 microcontroller that continuously senses the inside of a vaccine box through trusted sensors and generates anti-forgery data; and 2) a consistency protocol to upload the environment status data from the TEE system to blockchain in a truthful, real-time consistent, continuous and fault-tolerant fashion. Our security analysis indicates that no adversary can tamper with the vaccine in any way without being captured. We carry out an experiment to record the internal status of a vaccine shipping box during transportation, and the results indicate that the proposed system incurs an average latency of 84 ms in local sensing and processing followed by an average latency of 130 ms to have the sensed data transmitted to and been available in the blockchain. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT)-enabled electrocardiogram (ECG) monitoring system is an evolution in healthcare, and it is used to monitor heart conditions during daily living conditions. However, the ECG signals from the wearable device are often contaminated with noise, such as power line interference, baseline wander, and muscle artefact. The presence of these noises increases the communication cost of IoT-enabled devices by transmitting unusable noisy signals as well as affecting the clinical decision-making at the application level. A real-time ECG noise detection system at the IoT-enabled gateway can reduce this communication cost and improve the overall quality of the transmitted signal. In this article, we propose a tunable ECG noise localization system to detect noisy ECG segments with a selected percentage of noise-free ECG levels. The proposed system will hold the following: 1) deployable ECG noise detector in the low-computational devices, such as smart phone; 2) reduce the data dropping rate at the network layer; 3) reduce the transmission cost; and 4) improve the reliability of the automated analysis of ECG at the IoT-enabled gateway. We evaluated the performance of the proposed ECG noise detector using publicly available and real-time ECG datasets from wearable ECG sensors. We also measured the data drop rate and compared it with the state-of-the-art algorithms. The impact of reduced data dropping rate also justifies using two commonly used R-peak detection algorithms. The proposed system reduces the data dropping rate by 21.09% on average over public and real-time datasets. Similarly, it increases the number of R-Peak detection by 15.33% on average compared with the existing binary noise classification system by saving partial noisy ECG segments. Thus, the proposed noise detection system improves the reliability of the automated ECG analysis. In summary, the study results support the necessity of a noise detection system for wearable ECG monitoring in daily living conditions to improve the data acquisition efficiency. In addition, a noise localizer can be used for dropping a noisy portion of the signal or filtering the only noisy segment of the ECG signal without blindly imposing a filter on both noise-free and noisy ECG signals. [ABSTRACT FROM AUTHOR]

Prompt online detection of anomalies induced by malicious attacks enhances the efficacy of real-time operation and mitigation of attack, an indispensable part of any cyber-physical system (CPS) management. This article proposes a novel online rapid detection scheme that continuously monitors the data packet stream and infers the sequence of probability distributions, estimated as histograms, and alerts when a change in the histogram is detected, reporting both the attack as well as an estimate of its instant of commencement. A statistical data-driven attack model is proposed and employed that is general enough to represent two ubiquitous types of attacks on CPS: 1) replay and 2) bias-injection. The proposed detection framework relies on the fact that CPSs possess well-defined dynamics that are affected by quasistationary noise, which allows the histogram sequences of the system data packets to converge (to different distributions under the presence of the attack versus the absence of attack). The proposed online scheme detects an attack, and estimates the attack commencement time by relying on the computed distance between real-time estimated histogram versus apriori learned nominal histogram. Our formulation further sheds light on two different attack initiation-time-based subcases, “early” (attack starts before sufficient data of nominal behavior was collected to allow its histogram sequence to be closer to its nominal value) versus “late.” The designed algorithm of our scheme has linear time complexities in the dimension of data packets and algorithm parameters, which makes it suited for rapid detection. The proposed algorithm is implemented and validated on two real supervisory control and data acquisition system datasets, where a low detection delay demonstrates the effectiveness of the scheme. [ABSTRACT FROM AUTHOR]

Targeted digitalization for industrial cyberphysical systems is yet to be consistently deployed across the entire value chain. This is mainly due to the lack of implementable algorithms that integrate the information and operation technology layers. [ABSTRACT FROM AUTHOR]

The number of IoT devices on the Internet has surged recently, accompanied by a barrage of large-scale IoT malware infections breakouts. Designing security mechanisms for IoT devices poses significant challenges due to constantly changing malware variants that have numerous camouflage strategies, limited hardware resources, and heterogeneous architectures. In this article, we propose MIDAS, an adaptive safeguard framework for Linux-based IoT devices to defend against malwares with the real-time behavior auditing mechanism. First, we construct a stable and abstract behavior paradigm through behavioral characteristic extraction of 115 970 malwares. Then, based on the behavior paradigm, MIDAS can: 1) monitor suspicious behaviors of break-in programs in real-time driven by our built-in SELinux policy customized for malware defense; 2) aggregate behaviors of the program’s submodules with homology tracing; and 3) summarize these behaviors into abstract behavior pairs to unveil a possible IoT malware. Using the aforementioned real-time behavior auditing, MIDAS can constrain mutating and camouflaged malwares to protect discrepant IoT devices from being compromised while maintaining low overheads. We thoroughly evaluated the defense capabilities of MIDAS. On the benchmark dataset, MIDAS successfully constrained up to 94.46%, 91.79%, and 88.34% of 115970 malware samples on ARM, MIPS, and MIPSEL architectures, with less than 1.8 MiB of memory consumption and 0.54% CPU usage. Furthermore, we deployed virtual IoT devices worldwide to examine the performance of MIDAS when defending against real-world attacks. Over a duration of 25 days, these devices suffered from 971 951 attacks originating from 71 979 intruding malwares and 48 805 unique IPs distributed in 167 countries. For devices with MIDAS protection, the number of compromised incidents decreases by $343.1\times $ , and the duration of continuous operation is $179.2\times $ greater than devices without MIDAS on average. The evaluation results demonstrate that MIDAS can effectively safeguard IoT devices with minimal resource consumption. [ABSTRACT FROM AUTHOR]

Intermittently powered embedded systems are a foundational and growing component of the Internet of Things. It is essential to rigorously prove these systems’ correctness because they arise both in safety-critical applications and applications where quality-of-service is essential to social good. Such proofs are challenging because they are simultaneously cyber–physical and time-sensitive: correctness is affected by physical properties that change with time. This article introduces a new general-purpose formal verification approach for cyber–physical properties of intermittent systems. We define a high-level modeling and specification language for intermittent systems, define its formal semantics, and prove that the language reduces to hybrid games, enabling the application of existing theorem-proving software. Cold storage for COVID vaccines serves as a running example; we provide a machine-checked proof that safe temperatures are maintained under suitable assumptions. The crux of our proof approach is to identify power and timing assumptions under which sufficient power is available to complete time-sensitive tasks. Orthogonal to approaches that prove new guarantees on power or timing, our work rigorously shows which power and timing assumptions are needed for cyber–physical correctness. [ABSTRACT FROM AUTHOR]

Contribution: This work contributes to computer-supported collaborative learning (CSCL) offering support to the teaching presence among the enormous volume of interactions to provide a more active classroom posture in online environments. Background: Tracking interactions on collaborative learning environments (CLEs) with an increasing number of collaborative tools, offering real-time support for students, is one of the most intense and exhaustive challenges online teachers face. In this work context, social presence indicates a level of awareness of the other in the CLE, and it is necessary to improve and promote online social interactions. Research Questions: Aiming to answer the research question if the support of the teaching presence through Amadeus-SIMM helps to promote the learners’ social presence in a distance learning course, an experiment was conducted through a Python Programming distance course with Computer Science and Electrical Engineering learners. Methodology: The research methods adopted were a survey and the reports generated by Amadeus-SIMM. The statistical models used were the difference between means calculated in two moments on the same sample and the chi-square test with two criteria. Findings: The conclusion is that the significant differences in six of the eight survey-independent questions related to the dependent question indicate an increase in learners’ perception of social presence. The statistical analysis verified that the difference between the sample means in the two measurements of social behavior visibility, engagement, information intermediation, and prestige is statistically significant to confirm that Amadeus-SIMM supports teachers in promoting learners’ social presence. [ABSTRACT FROM AUTHOR]

Modulus is a critical parameter for evaluating the bearing capacity and remaining life of pavements. The prevalent modulus back-calculation method by using the pavement surface deflections captured from falling weight deflectometer (FWD) could characterize the overall bearing capacity of pavements. However, a non-uniqueness issue may occur when identifying the modulus of each layer from the perspective of mathematics. Alternatively, this paper presents a novel application to real-time modulus evaluation based on asphalt pavement health monitoring with built-in sensors. First, the sensor layout is optimized on the basis of the theoretical relationship between modulus and mechanical response inside the pavement. The proposed sensor layout, including input (random loads), output (mechanical responses), and environmental sensing modules, is illustrated in detail. Then, the procedure for real-time modulus evaluation is proposed, and the convergence and uniqueness of modulus evaluation results are investigated. Lastly, a case study of the realistic asphalt pavement health-monitoring system is conducted to demonstrate the effectiveness of the application to real-time modulus evaluation. The results indicate that the proposed modulus evaluation method has a potential to identify the modulus of each pavement structural layer in real time with a convergent and unique solution under a realistic traffic load. The evaluation process has the advantage of not interfering with traffic compared with the prevalent FWD-based modulus evaluation method. The pavement health monitoring with built-in sensors is recommended for newly built roads to facilitate long-term real-time performance assessment and maintenance decision making. [ABSTRACT FROM AUTHOR]

Surface microseismic monitoring is one of the most critical technologies for monitoring the dynamic subsurface reservoir processes. Due to the weak signals and poor signal-to-noise ratio (SNR), there are two major challenges to monitor the reservoir processes in an accurate and efficient way. One challenge is concerned with weak signals acquisition and how to evaluate the instrument’s abilities to record the microseismic events. The other one is how to realize real-time data monitoring during dynamic processes. Taking these challenges into consideration, a design approach to develop buried sensors consisting of three-component sensors and a low-cost, low-power consumption embedded device for microseismic monitoring data logger with the acquisition, synchronization, record, and transmission functions are presented. The circuits are divided into two printed circuit boards (PCBs). One is for a signal processing circuit, which consists of signal conditioning and analog-to-digital converter (ADC), and the other is for a digital circuit, which consists of the microcontroller and peripherals, including the global navigation satellite system (GNSS), Micro Secure Digital Memory Card (SD card), and Ethernet transceivers. The circuit for signal conditioning and ADC is a combination of a preamplifier, low-pass filter, programmable gain amplifier, and ADC. It has three modes: real-time monitoring, continuous recording, and combination. A software is designed to work in cooperation with the hardware to adjust the working parameters and modes. The self-noise of the data logger is tested in the laboratory, and the monitoring abilities of the data logger are evaluated. Finally, the surface microseismic monitoring system has been tested in the field to evaluate its performance for academic purposes. [ABSTRACT FROM AUTHOR]

The elderly population is growing, and the healthcare system is experiencing a strain on services provided to the elderly. The recent COVID-19 pandemic has increased this strain and has resulted in an increased risk of exposure during visits to elderly homes, increasing the desire to provide technological solutions to counteract this. Currently, there lack reliable real-time noninvasive sensing systems. This article makes use of radio-frequency (RF) sensing, where signal propagation is observed in channel state information (CSI) reports on activities of daily living (ADLs). Real-time data have been collected for three classifications: “movement,” “empty room,” and “no activity,” A filter is applied to reduce the noise of the CSI data. Then the mean, max, min, kurtosis, skew, and standard deviation features are extracted from the CSI data. A machine-learning model provides a classification for the real-time monitoring system allowing the detection of abnormalities in the expected ADLs of the elderly. The timing of classifications gives insights into the real-time capabilities of the system. The random forest algorithm is chosen to create the machine-learning model based on accuracy and timing capabilities. The model was able to achieve an accuracy of 100% on new unseen testing data with an average classification time of 7.31 ms. [ABSTRACT FROM AUTHOR]

Monitoring lactate levels is an established method for determining hyperlactatemia in critically ill patients and assessing aerobic fitness. It is a widely used gold-standard technique in both professional and serious amateur sports. Non-invasive real-time lactate monitoring offers significant advantages over the current technology of finger-prick blood sampling. Possible candidate technology for developing non-invasive real-time lactate monitoring should be highly sensitive, flexible, and capable of real-time monitoring of lactate levels in interstitial fluid or within specific working muscle groups depending on the type of sport. Herein we describe a planar, flexible, passive, chipless tag resonator that is electromagnetically coupled to a reader placed in proximity to the lactate sensor tag. The tag resonator is a thin metallic tracing that can be taped on the skin. The resonance frequency of the tag fluctuates proportionately with changing lactate concentrations in a solution mimicking human interstitial fluid with very high sensitivity. The spectrum of the tag is reflected in the spectrum of the reader, which is a planar microwave resonator designed at a different frequency. The reader could be embedded in a cellphone or an application-specific wearable device for data communication and processing. The tag can accurately and reproducibly measure lactate concentrations in the range of 1 to 10 mM, which is in the physiological range of lactate observed at rest and during intense physical activity. Furthermore, the chrematistics of this technology will allow monitoring of lactate in specific working muscle groups. [ABSTRACT FROM AUTHOR]

Disasters and the resulting emergencies are increasingly presenting situations where tele-communications infrastructure (e.g., functioning base stations) is often compromised, leading to diminished network capacity. In these situations, providing trapped populations in isolated areas with network access to be able to communicate with rescue and recovery personnel or volunteer teams is critical. Further, when network capacity is diminished, it is essential that mobile network users be alerted to “comply” and not overload the network with non-essential bandwidth-intensive traffic unrelated to the emergency unfolding. While the current evolution of the Integrated Public Alert and Warning System (IPAWS) is able to generate and disseminate such emergency alerts, there has been no analysis or evaluation of the effectiveness of such alerts as well as the design of mechanisms that can ensure such compliance to prevent network outage. In this work, we present a framework for Cognitive Public Alerts to Wireless Subscribers (CPAWS) that is able to predict and prevent mobile network overload/outage during emergencies using a mix of behavioral studies and machine learning tools that consider real-time monitoring information of the environmental status, network situation, and user compliance. Specifically, we study the efficacy of wireless emergency alerts (WEAs) in terms of saving network bandwidth by using surveys on Amazon MTurk to test the effectiveness of seven designed WEAs on reducing users' non-essential traffic across 12 designated cell phone applications. Additionally, we design complementary access and alert control strategies to ensure that the network load remains below the diminished available capacity during emergencies. CPAWS is also backward-compatible since it extends IPAWS by adding a feedback loop for real-time monitoring and control. [ABSTRACT FROM AUTHOR]

The better way to understand marine life and ecosystems is to surveil and analyze the activities of marine organisms. Recently, research on marine video surveillance is becoming increasingly popular. With the rapid development of deep learning (DL), convolutional neural networks (CNNs) have made remarkable progresses in image/video understanding tasks. In this article, we explore a visual attention and relation mechanism for marine organism detection, and propose a new way to apply an improved attention-relation (AR) module on an efficient marine organism detector (EMOD), which can well enhance the discrimination of organisms in complex underwater environments. We design our EMOD via integrating current state-of-the-art (SOTA) detection methods in order to detect organisms and surveil marine environments in a real time and fast fashion for high-resolution marine video surveillance. We implement our EMOD and AR on the annotated video data sets provided by the public data challenges in conjunction with the workshops (CVPR 2018 and 2019), which are supported by National Oceanic and Atmospheric Administration (NOAA) and their research works (NMFS-PIFSC-83). Experimental results and visualizations demonstrate that our application of AR module is effective and efficient, and our EMOD equipped with AR modules can outperform SOTA performance on the experimental data sets. For application requirements, we also provide the application suggestions of EMOD framework. Our code is publicly available at https://github.com/zhenglab/EMOD. [ABSTRACT FROM AUTHOR]

The evolution of headways (time intervals between consecutive vehicles) is an important performance indicator of public transportation systems. In this paper, we develop a data processing system that processes the expected arrival time data provided by the corresponding APIs of Madrid and London bus public transport systems with the aim of modeling and supervising their evolution along the time to detect their operational anomalies. By employing only estimates of the time headways between buses, we construct joint statistical modeling along the time of these headways within a stochastic process framework. Then, this model is employed to design a deviation detection procedure that provides a simple and adjustable anomaly detection scheme, as well as a complementary index for assessing the overall Quality of Service (QoS) provided by the operator. The proposed adjustable system can be easily tuned by managers or operators for online anomaly detection, which is an important asset to increase the efficiency of bus transportation services. [ABSTRACT FROM AUTHOR]

Traffic monitoring services collect traffic reports and respond to users’ traffic queries. However, the reports and queries may reveal the user’s identity and location. Although different anonymization techniques have been applied to protect user privacy, a new security threat arises, namely, n-by-1 jamming attack, in which an anonymous contributing driver impersonates $n$ drivers and uploads $n$ normal reports by using $n$ reporting devices. Such an attack will mislead the traffic monitoring service provider and further degrade the service quality. Existing traffic monitoring services do not support customized queries, and private information retrieval techniques cannot be applied directly in traffic monitoring. We formally define the new attack and propose a traffic monitoring scheme TraJ to defend the attack and achieve user-defined location privacy. Specifically, we bridge anonymous contributing drivers without disclosing their speed set by using private set intersection. Each RSU collects time traffic reports and structures a weighted proximity graph to filter out malicious colluding drivers. We design a user-defined privacy-preserving query method by encoding complex road network. We leverage the uploading phase from private aggregation to collect traffic conditions and allow requesting drivers to dynamically and privately query traffic conditions. We provide a formal analysis of TraJ to prove its privacy and security properties. We also construct a prototype based on a real-world dataset and Android smartphones to demonstrate its feasibility and efficiency. A formal analysis demonstrates the privacy and security properties. Extensive experiments illustrate the performance and defense efficacy. [ABSTRACT FROM AUTHOR]

The mission abort is an effective action to reduce the risk of casualties and enhance the survivability of mission-oriented systems, such as aircrafts, submarines, and unmanned aerial vehicles (UAV). A key target of operators in real mission environment is to strive for balance between the success possibility of a mission and system survivability (SS), via elaborate mission abort plans. In this article, we design mission abort policies based on two crucial information: 1) degradation degree of monitored health features, and 2) system age. Accordingly, the operators may abort the mission if the degradation attains a preset control limit at early system ages, or continue the task otherwise. We carry out loss analysis to determine the optimal abort action, by linking MSP and SS. Structural insights regarding the optimality of degradation control limit as well as age threshold are explored. For a comparative purpose, the performances of some heuristic abort policies are analytically evaluated. We make contribution by scheduling mission abort plans harnessing both condition monitoring and age information, which promotes the timeliness and robustness of risk control. A case study on inertial navigation systems of UAV executing line inspection missions is used to illustrate the applicability and superiority of the proposed abort policies. [ABSTRACT FROM AUTHOR]

Objective: This paper aims to introduce a wearable solution and a low-complexity algorithm for real-time continuous ambulatory respiratory monitoring. Methods: A wearable chest patch is designed using a bioimpedance (BioZ) sensor to measure the changes in chest impedance caused by breathing. Besides, a medical-grade infrared temperature sensor is utilized to monitor body temperature. The computing algorithm implemented on the patch enables computation of breath-by-breath respiratory rate and chest temperature in real-time. Two wireless communication protocols are included in the system, namely Bluetooth and Long Range (LoRa), which enable both short-range and long-range data transmission. Results: The breathing rate measured in static (i.e., standing, sitting, supine, and lateral lying) and dynamic (i.e., walking, running, and cycling) positions by our device yielded an accuracy of more than 97.8% and 98.5% relative to the ground truth, respectively. Additionally, the device’s performance is evaluated in real-world scenarios both indoors and outdoors. Conclusion: The proposed system is capable of measuring breathing rate throughout a variety of daily activities. To the best of our knowledge, this is the first BioZ-based wearable patch capable of detecting breath-by-breath respiratory rate in real-time remotely under unrestricted ambulatory conditions. Significance: This study establishes a strategy for continuous respiratory monitoring that could aid in the early detection of cardiopulmonary disorders in everyday life. [ABSTRACT FROM AUTHOR]

The health status of the running gear in high-speed trains changes dynamically with time in a complete life cycle. Running gear systems composed of many coupled components are complex, and health statuses of which are difficult to predict in real time through a traditional health status prediction scheme. Lately, belief rule base (BRB), which is able to combine quantitative information and expert knowledge, has shown excellent expression in modeling complex systems. In the procedure of health status prediction, expert expertise can sufficiently enhance the accuracy and efficiency of this model. Therefore, this paper puts forwards a real-time health status prediction framework based on a multi-layer BRB with priority scheduling strategies for running gears. In the first-layer BRB, a time-series prediction model of multiple module BRB considering complete features is established. In the second-layer model, grey relation analysis (GRA) is employed in priority scheduling strategies of features. The third-layer BRB is used for assessing the health status of running gears by combining the features. In addition, the initial parameters of all module BRB given by experts may not be precise. Accordingly, the initial parameters in the BRB are updated by the recursive algorithm online. Finally, the proposed method is tested on the testing platform in running gears. The results make it clear the proposed method can predict the health status of running gears with much accuracy in real time. [ABSTRACT FROM AUTHOR]

In this work, we investigate the issue of semantics in non real-time communication scenarios, that is, without stringent information delivery constraints. While semantics has been widely used in the context of communications, the definition of the semantic metrics has been limited to the real-time applications. We thus revise the basic concepts of the timeliness and the accuracy and propose their formal definitions in the case of non real-time applications characterized by a possibility of non-causal signal reconstruction at the destination. In particular, we link the concept of accuracy to the canonical first-order auto-regressive model of the signal, while the timeliness is defined by the time difference to reference samples of the signal. We compare the proposed semantic metrics in the case of different buffer management strategies and using numerical examples we show that the optimality of the transmission strategy changes when the real-time or non real-time applications are considered. [ABSTRACT FROM AUTHOR]

Real-time monitoring has received considerable attention recently due to its potential in automatic driving, tele-surgery, and factory automation in the 6G era. In remote estimation or reconstruction of stochastic processes, the statistical properties of stochastic processes to be monitored play a central role. Among the stochastic processes interested by real-time applications, the Brownian motion, also known as the Wiener processes is a typical one. In this paper, we are interested in how to monitor Brownian motions efficiently, timely, and reliably. To achieve this goal, we reveal that the real-time estimation error is jointly determined by the quantization error and freshness of data samples. Based on this observation, we present an optimal joint sampling and quantization scheme that efficiently balances the quantization distortion and the age-of-information (AoI). Furthermore, we find that the error accumulation will lead to infinite distortion as monitoring time increases. To overcome this, a multi-layer error correction method is presented for infinite-time monitoring, in which bounded distortion can be achieved with limited data rate. Finally, to conquer the accumulation of transmission errors in unreliable channels, we present an error correction mechanism based on periodic feedback. Diffusion approximation is then adopted to determine the optimal feedback rate and interval. [ABSTRACT FROM AUTHOR]

Real-time monitoring tools and their optimal measurement infrastructure are expected to play a critical role in smart grid applications. These applications require greater measurement precision and speed, which is not possible with conventional measurement devices. Due to their high economic burden and superseded technology, transmission level phasor measurement units (PMUs) are inappropriate for unbalanced active distribution networks (ADNs). Micro-synchrophasor technology is faster, less expensive with tighter accuracy tolerances. However, large-scale implementation of real-time monitoring instrumentation is economically unfeasible due to the size of ADNs, and successful deployment requires a new meter placement approach. In this paper, a micro-phasor measurement unit ($\mu $ PMU or micro-PMU) placement technique is proposed considering the unique traits of ADNs, which establishes a new concept of usable zero-injection phase strings (UZIP Strings). The method is evaluated using the IEEE-13, IEEE-34, and IEEE-123 node distribution test feeders. The fidelity of the placement algorithm is further verified via a fault detection and localization study. The results indicate better performance than previous literature with minor placement variations between deterministic and meta-heuristic algorithms for the presented holistic and UZIP String reduction methods. [ABSTRACT FROM AUTHOR]

Today, more and more enterprises are embarking on a digital transformation where most of their applications are hosted in the Cloud. As a result, a reliable Wide Area Network (WAN) has become a primary need to interconnect their distributed branch offices and data centers that accommodate those applications. Software-Defined Wide Area Network (SD-WAN) represents the most promising technology solution for next-generation enterprise networks, being able to increase network agility and reduce costs. In this paper, we present an experimental SD-WAN solution capable of running and optimizing delay-sensitive high-priority services, such as real-time video streaming, while minimizing downtime caused by network failures. This solution comprises a monitoring and a traffic engineering system for SD-WAN. The first consists of a Transport-layer Passive Monitoring (TPM) system based on extended Berkeley Packet Filter (eBPF) technology with the goal of monitoring TCP flows; the second consists of an application, running inside the SD-WAN controller, with the goal of orchestrating the network traffic in consideration of the monitoring measurements by ensuring rapid recovery and resilience in case of unexpected congestion events. We validate our solution over two SD-WAN testbeds: the first is hosted in our laboratory at Politecnico di Milano, while the second is deployed in a municipal network of an Italian city. Results show that our SD-WAN solution can increase the overall service availability while meeting the stringent QoS requirements of delay-sensitive services.s [ABSTRACT FROM AUTHOR]

Energy-efficient solar insecticidal lamp (SIL) management is an important way to promote the full deployment and application of SILs in intelligent farms. To improve processing latency and precision, an edge computing-based energy-efficient SILs management architecture (ECEEM-SIL) is presented. Considering architecture matching, an SILs management mechanism based on edge computing referring to real-time information of pests and energy (ECEEM-SIL-RRIPE) is proposed. Firstly, considering different weather conditions, the pest distribution model and its update method based on edge computing are established. Secondly, the scheduling strategy based on the maximum coverage of active pest periods with the minimum number of working SILs (MaxCAP-MinNWS) is proposed to address the turning on and off scheduling of SILs, while considering the joint optimization of energy consumption and insecticidal efficiency. Simulation results show that compared with the existing preset working period scheduling and unified scheduling strategies, the proposed strategy can improve utilization of energy resources in insecticidal by up to 20%. In addition, the energy supply and consumption of SILs can be more balanced, and the battery deep discharging of SILs can be relieved. [ABSTRACT FROM AUTHOR]

Positioning and navigation are critical functions of automated driving functions, which help autonomous vehicles determine their absolute and relative positions in the environment that they operate. Integrity Monitoring (IM) systems, which are intended to assess the reliability and trustworthiness of the information provided by the navigation systems, are crucial for ensuring the safety of automated driving functions. This paper provides a comprehensive review of the existing IM frameworks for safety-critical navigation applications and expands on the state-of-the-art of the most recent development of such systems for connected automated vehicles. We mainly focus on IM methods for Global Navigation Satellite Systems (GNSS) and Inertial Navigation Systems (INS). However, we also cover IM for map assisted and wireless signal augmented navigation systems, which are promising for high-performance navigation applications, such as automated driving functions. For each main category of solutions, key aspects such as the characteristics of measurement errors and faults related to various data sources are discussed to provide deeper insights into designing of reliable IM systems. Also, some of the major open research challenges to the best knowledge of the authors have been identified and discussed. [ABSTRACT FROM AUTHOR]

Dynamic voltage and frequency scaling (DVFS) has been the subject of extensive study for the past two decades. DVFS has been shown to dramatically reduce power consumption and thermal stress and is now routinely used in both embedded and general-purpose systems. However, DVFS research has largely focused on independently controlling voltage and frequency for individual cores. Less well studied is the case of global DVFS (GDVS), where multiple cores share the same voltage and clock. A very simple GDVS heuristic is presented in this letter; it is lightweight and effectively exploits task profiling information to appropriately sequence task executions. Simulation studies show its effectiveness. [ABSTRACT FROM AUTHOR]

This article discusses the design and performance of a single array of centrifugally tensioned metastable fluid detectors (CTMFDs) for real-time detection of shielded and unshielded neutron-emitting special nuclear materials (SNMs), both while moving and stationary, and at variable standoff distances. With the goal to maximize the detection rate of 0.02 eV to 12+ MeV energy range neutrons, the sensor fluid in each CTMFD unit was formulated to include natural boron. At a tensioned negative pressure ($P_{\mathrm {neg}}$) state of −7 bar, the 16 cc CTMFD intrinsic detection efficiency was measured for both shielded (in $12^{\prime \prime }$ OD paraffin) and unshielded configurations as ~35% against a Pu–Be ($\alpha $ , $n$) neutron source emitting $2\times 10^{6}$ n/s. An array of 13 CTMFDs stacked in a rectangular enclosure demonstrated conclusive real-time tracking of the Pu–Be source in the trunk of a car shielded in a 0.3-m ($12^{\prime \prime }$) OD paraffin bucket, or unshielded at variable standoffs, and while speeding at 20 mph (~32 kph). The measured detection counts range from ~2 to 12 times over background at closest approach standoffs ranging from 20 m (~70 ft) to 4 m (~13 ft), respectively. The experimental scenario was modeled and simulated to explain the paradoxical and nonintuitive experimental findings pertaining to shielded and unshielded SNM cases, both displaying similar detection rates. The relative effects of neutron down-scattering for shielded cases, which increased detection probability—compensating for shield-absorbed reductions, along with shadowing effects from detector panel background shielding, were found to be dominant contributors. [ABSTRACT FROM AUTHOR]

Sky-wave over-the-horizon radar (OTHR) propagates radio waves off the ionosphere to provide long-range surveillance around the Earth’s curvature. Frequency selection for high-latitude and polar OTHRs is challenging unless there is an environmental monitor that addresses the significant ionospheric variability in high-latitude regions, a spectrum monitor that finds unoccupied frequencies in the high-frequency (HF) radio waveband, and a frequency management system (FMS) that selects an optimal frequency from the merged results of the environmental monitor and spectrum monitor. This article describes the first FMS for high-latitude OTHR that merges results from the environmental monitor and spectrum monitor in real-time. The environmental monitor uses the assimilative Canadian high-arctic ionospheric model (A-CHAIM), which assimilates near-real-time data and is, to date, the most advanced ionospheric model for high-latitude regions. Despite remaining limitations, demonstrations of this real-time FMS between a transmitter and four targets during the day and night and during fall and summer show that it could be one of the tools for selecting frequencies for operational scenarios. [ABSTRACT FROM AUTHOR]

We constructed an open multi-access network platform using open source hardware and software. The open multi-access network platform is characterized by flexible utilization of network functions, integral management and control of wired and wireless access networks, zero-touch provisioning, intelligent resource monitoring, and dynamic task offloading. We also propose application-driven dynamic task offloading that utilizes intelligent resource monitoring to ensure effective task processing in edge and cloud servers. For this purpose, we developed a mobile application and server applications for the open multi-access network platform. To investigate the feasibility and availability of our developed platform, we experimentally and analytically evaluated the effectiveness of application-driven dynamic task offloading and intelligent resource monitoring. The experimental results demonstrate that application-driven dynamic task offloading could reduce real-time task response time and traffic over metro and core networks. [ABSTRACT FROM AUTHOR]

In this article, a periodic self-triggered impulsive (PSTI) control scheme is proposed to achieve synchronization of neural networks (NNs). Two kinds of impulsive gains with constant and random values are considered, and the corresponding synchronization criteria are obtained based on tools from impulsive control, event-driven control theory, and stability analysis. The designed triggering protocol is simpler, easier to implement, and more flexible compared with some previously reported algorithms as the protocol combines the advantages of the periodic sampling and event-driven control. In addition, the chaotic synchronization of NNs via the presented PSTI sampling is further applied to encrypt images. Several examples are also utilized to illustrate the validity of the presented synchronization algorithm of NNs based on PSTI control and its potential applications in image processing. [ABSTRACT FROM AUTHOR]

The accurate detection and quantification of submerged targets has been recognized as a key challenge in marine exploration, one that traditional census approaches cannot handle efficiently. Here we present a deep learning approach to detect the pattern of a moving fish from the reflections of an active acoustic emitter. To allow for real-time detection, we use a convolutional neural network, which provides the simultaneous labeling of a large buffer of signal samples. This allows to capture the structure of the reflecting signal from the moving target and to separate it from clutter reflections. We evaluate system performance both on synthetic (simulated) data, as well as on real data recorded over 50 sea experiments in a variety of sea conditions. When tested on real signals, the network trained on simulated patterns showed non-trivial detection capabilities, suggesting that transfer learning can be a viable approach in these scenarios, where tagged data is often lacking. However, training the network directly on the real reflections with data augmentation techniques allowed to reach a more favorable precision-recall trade-off, approaching an ideal detection bound. We also evaluate an alternative model based on recurrent neural networks which, despite exhibiting slightly inferior performance, could be applied in scenarios requiring on-line processing of the reflection sequence. [ABSTRACT FROM AUTHOR]

In this paper, a complete Lab-on-Chip (LoC) ion imaging platform for analysing Ion-Selective Membranes (ISM) using CMOS ISFET arrays is presented. An array of 128 × 128 ISFET pixels is employed with each pixel featuring 4 transistors to bias the ISFET to a common drain amplifier. Column-level 2-step readout circuits are designed to compensate for array offset variations in a range of up to $\pm$ 1 V. The chemical signal associated with a change in ionic concentration is stored and fed back to a programmable gain instrumentation amplifier for compensation and signal amplification through a global system feedback loop. This column-parallel signal pipeline also integrates an 8-bit single slope ADC and an 8-bit R-2R DAC to quantise the processed pixel output. Designed and fabricated in the TSMC 180 nm BCD process, the System-on-Chip (SoC) operates in real time with a maximum frame rate of 1000 fps, whilst occupying a silicon area of 2.3 mm × 4.5 mm. The readout platform features a high-speed digital system to perform system-level feedback compensation with a USB 3.0 interface for data streaming. With this platform we show the first reported analysis and characterisation of ISMs using an ISFETs array through capturing real-time high-speed spatio-temporal information at a resolution of 16 $\mu$ m in 1000 fps, extracting time-response and sensitivity. This work paves the way of understanding the electrochemical response of ISMs, which are widely used in various biomedical applications. [ABSTRACT FROM AUTHOR]

The Advanced Driver Assistance System (ADAS) features provide better safe driving dynamics reducing road accidents and collisions. Typically, these include lane assist, automatic night vision, adaptive cruise control, driver risk analysis primarily seen in high-end vehicles. Furthermore, such a proactive mechanism alerts the drivers in unsafe/ dangerous situations and enable drivers to respond with immediate actions. However, such ADAS-based solutions do not work very well in heterogeneous, less-disciplined, and chaotic road traffic environments, as seen in Asian countries such as India, Bangladesh, Burma, Nepal, etc. In this regard, this paper aims to propose an advanced integrated deep learning framework Dual-V-sense-Net (DVN) to provide recommendations for safe driving. A new dataset - Behavioral Understanding of Driver’s-Distraction Dataset for India (BUDDI), is proposed for the behavioral understanding of driver’s distraction. This proposed recommendation framework is designed in a two-step manner. The first step involves an in-vehicle driver distraction analysis with real-time frontal and cross-view naturalistic driver data for driver distraction analysis. Second step is to perform an exhaustive experimental evaluation is carried out for the tasks such as road traffic scene perception, road navigable area prediction with vehicular temporal dynamics, lane assist, safe drivable area navigation and driver distraction alert. A decision block modeling helps in safe/unsafe driving predictions considering the feature importance. We present comprehensive experimental results for road segmentation, vehicular road lane categorization, obstacle recognition, and driver behavioral pattern prediction using the BUDDI and Indian Driving Dataset (IDD) datasets. [ABSTRACT FROM AUTHOR]

People use various communication methods to interact with each other, such as speech and body movements. However, communication methods in babies differ according to their developmental periods. Babies may not be able to express themselves verbally, but they do have their own communication methods. Naturally, it becomes important for parents to understand these signs/poses. Human pose estimation is extensively employed in different applications such as video surveillance, sports analysis and medical support/aid. The goal of this research is to assist new parents by addressing pose-based real-time body movements of babies (such as arching back, head banging, kicking legs, rubbing eyes, stretching, sucking fingers) and making sense of their activities. This is the first study to estimate poses on babies. In this paper, a baby pose dataset is created from 156 video clips through online video sharing platforms. Key-points are obtained from three different pose estimators - OpenPose, AlphaPose, and KAPAO. Different LSTM models are used to recognize the babies’ activities and different performance metrics are used to compare these models. The best model has 99% accuracy and 0.0712 loss ratio. Also, babies are tracked in real-time via DeepSORT algorithm. Experimental results show that the proposed system is very promising and filling a gap in making sense of baby poses and monitoring them. [ABSTRACT FROM AUTHOR]

With the rapid advancement of in-process measurements and sensor technology driven by zero-defect manufacturing applications, high-dimensional heterogeneous processes that continuously collect distinct physical characteristics frequently appear in modern industries. Such large-volume high-dimensional data place a heavy demand on data collection, transmission, and analysis in practice. Thus, practitioners often need to decide which informative data streams to observe given the resource constraints at each data acquisition time, which poses significant challenges for multivariate statistical process control and quality improvement. In this article, we propose a generic online nonparametric monitoring and sampling scheme to quickly detect mean shifts occurring in heterogeneous processes when only partial observations are available at each acquisition time. Our innovative idea is to seamlessly integrate the Thompson sampling (TS) algorithm with a quantile-based nonparametric cumulative sum (CUSUM) procedure to construct local statistics of all data streams based on the partially observed data. Furthermore, we develop a global monitoring scheme of using the sum of top- ${r}$ local statistics, which can quickly detect a wide range of possible mean shifts. Tailored to monitoring the heterogeneous data streams, the proposed method balances between exploration that searches unobserved data streams for possible mean shifts and exploitation that focuses on highly suspicious data streams for quick shift detection. Both simulations and a case study are comprehensively conducted to evaluate the performance and demonstrate the superiority of the proposed method. Note to Practitioners—This paper is motivated by the critical challenge of online process monitoring by considering the cost-effectiveness and resource constraints in practice (e.g., limited number of sensors, limited transmission bandwidth or energy constraint, and limited processing time). Unlike the existing methodologies which rely on the restrictive assumptions (e.g., normally distributed, exchangeable data streams) or require historical full observations of all data streams to be available offline for training, this paper proposes a novel monitoring and sampling strategy that allows the practitioners to cost-effectively monitor high-dimensional heterogeneous data streams that contain distinct physical characteristics and follow different distributions. To implement the proposed methodology, it is necessary: (i) to identify sample quantiles for each data stream based on historical in-control data offline; (ii) to determine which data streams to observe at each acquisition time based on the resource constraints; and (iii) to automatically screen out the suspicious data streams to form the global monitoring statistic. Experimental results through simulations and a case study have shown that the proposed method has much better performance than the existing methods in reducing detection delay and effectively dealing with heterogeneous data streams. [ABSTRACT FROM AUTHOR]

Mixed-signal assertions need to be monitored over dense time during analog simulation because analog events are not aligned with clock boundaries in general. On the other hand, simulation of large integrated circuits regularly employs clocked approximations to accelerate the simulation of the analog content in the design. Migrating the analog assertions from the continuous setting to the discrete setting is necessary to monitor compliance in the digital–analog boundary, but such migration must ensure that assertion failures are not missed due to the loss in precision. We propose the formal basis for this migration and a methodology for choosing the granularity of the clock for an assertion in the digital setting. [ABSTRACT FROM AUTHOR]

Recently, IP over elastic optical network (IP-over-EON) has become a promising architecture for metro and core networks. This work studies how to visualize both layers of an IP-over-EON in real time, at different granularities (e.g., at flow-level, lightpath-level, and link-level), and with self-adaptivity. Specifically, we consider the multilayer application of in-band telemetry (INT) and propose entropy-driven adaptive INT (namely, EntropyINT). We introduce stateful processing to programmable data plane (PDP) switches for EntropyINT, such that they can make local decisions to determine whether and what type of telemetry data about the IP and EON layers should be encoded in each packet. The local decisions are designed to be based on the amount of information that can be conveyed by telemetry data to the network automation system. Meanwhile, we make EntropyINT cooperate with out-of-band monitoring, to detect and locate exceptions in the EON layer. Our proposal is implemented in a real-world testbed of IP-over-EON, to evaluate its assistance to network automation. Experimental results verify the effectiveness of our proposal, and indicate that the telemetry data collected by EntropyINT and out-of-band monitoring can better assist the machine learning in network automation, for status prediction and anomaly detection. [ABSTRACT FROM AUTHOR]

Real-time parking availability prediction is of great value to optimize the on-street parking resource utilization and improve traffic conditions, while the expensive costs of the existing parking availability sensing systems have limited their large-scale applications in more cities and areas. This paper presents the MePark system to predict real-time citywide on-street parking availability at fine-grained temporal level based on the readily accessible parking meter transactions data and other context data, together with the parking events data reported from a limited number of specially deployed sensors. We design an iterative mechanism to effectively integrate the aggregated inflow prediction and individual parking duration prediction for adequately exploiting the transactions data. Meanwhile, we extract discriminative features from the multi-source data, and combine the multiple-graph convolutional neural network (MGCN) and the long short-term memory (LSTM) network for capturing complex spatio-temporal correlations. The extensive experimental results based on a four-month real-world on-street parking dataset in Shenzhen, China demonstrate the advantages of our approach over various baselines. [ABSTRACT FROM AUTHOR]

Traffic guidance, traffic management and emergency vehicle traffic all require keeping abreast of traffic status. Intelligent Transportation Systems (ITS) is highly expected to provide real-time traffic condition information service. To achieve this, the capability of handling dynamic data stream collected from multi traffic monitoring sources and serving the public with information timely is essential for ITS. With the wide spread of Internet of Things technology, not only the amount, but also the spatial and temporal resolutions of real-time traffic data have explosive growth, thereby enhancing the difficulty of real-time traffic data processing in ITS. Web pyramid map tiles is wide accepted for massive spatial data service, and the latency of tile generation significantly reduces the timeliness of information transmission and the reliability of services. A Flink-based method for dynamic pyramid tile generation and updating is proposed here. Take advantages of combining grid indexes, employing data partition and window selection mechanisms, and applying iterative computational characteristics for resampling, the distributed dynamic pyramid map tile generation algorithm (DPTG), can quickly visualize real-time spatial traffic data with digital map tiles. Taking the national highway road data from China as an example, the experimental results show that the Flink-based DPTG method has high efficiency and scalability in both batch processing and stream processing mode, which highlights the capability of the proposed method to support real-time traffic monitoring data processing for timely large-scale public service in ITS. [ABSTRACT FROM AUTHOR]

In public transit networks, transfers occur when more than one route is used to connect origins and destinations. Much research has been done to investigate the design and management of transfers at the tactical and operational levels. However, very few studies have investigated the monitoring phase to check a posteriori if transfers are well planned and/or delivered according to archived automatic vehicle location (AVL) data. This article covers this gap by proposing the first offline framework. This framework preprocesses AVL raw data, performs a diagnosis of transfer reliability over all bus stops and time periods, and discloses the most common sources of transfer unreliability. Easy-to-read control dashboards show the viability of this framework on real bus routes with approximately 145,000 AVL data records to make an accurate transfer analysis and possible service adjustments. [ABSTRACT FROM AUTHOR]

The time-domain passivity approach (TDPA) has gained considerable attention (even beyond the fields of haptics and teleoperation) because it can ensure passivity while maintaining high system performance. Despite its simplicity and effectiveness, the practical applications of this method are limited by its high-frequency force modification, so-called chattering. Virtual mass-spring (VMS) filters have been proposed to overcome this limitation. However, such approaches also suffer from certain limitations attributable to the distorted forces. Herein, we propose a method to mitigate the chattering of the TDPA, while also retaining its merits. We introduce a non-zero velocity threshold, within which the adaptive damping of the TDPA is scaled down, to attenuate the chattering while maintaining a stable response significantly. The feasibility and effectiveness of the proposed method are evaluated using a time-delayed bilateral teleoperation system. We confirm that the proposed method can significantly outperform the conventional and VMS filter-based TDPAs. [ABSTRACT FROM AUTHOR]

Low intensity focused ultrasound (FUS) therapies use low intensity focused ultrasound waves, typically in combination with microbubbles, to non-invasively induce a variety of therapeutic effects. FUS therapies require pre-therapy planning and real-time monitoring during treatment to ensure the FUS beam is correctly targeted to the desired tissue region. To facilitate more streamlined FUS treatments, we present a system for pre-therapy planning, real-time FUS beam visualization, and low intensity FUS treatment using a single diagnostic imaging array. Therapy planning was accomplished by manually segmenting a B-mode image captured by the imaging array and calculating a sonication pattern for the treatment based on the user-input region of interest. For real-time monitoring, the imaging array transmitted a visualization pulse which was focused to the same location as the FUS therapy beam and ultrasonic backscatter from this pulse was used to reconstruct the intensity field of the FUS beam. The therapy planning and beam monitoring techniques were demonstrated in a tissue-mimicking phantom and in a rat tumor in vivo while a mock FUS treatment was carried out. The FUS pulse from the imaging array was excited with an MI of 0.78, which suggests that the array could be used to administer select low intensity FUS treatments involving microbubble activation. [ABSTRACT FROM AUTHOR]

The computing infrastructure to support the power grid monitoring and control evolves with the increasing system complexity and integration of distributed energy resources (DERs). The integration of DERs requires a stability assessment application to monitor and control at a faster rate compared to the legacy system. Distributed computing architecture provides opportunities for managing these key changes by introducing solutions within substation computers and digital devices such as phasor measurement unit. The proposed decentralized voltage stability using a distributed coordination platform is a secondary emergency control mechanism utilizing large amounts of data in real-time for faster monitoring and control with the legacy system. The developed algorithms have been validated using the IEEE 30, 57, 118, and 300 bus systems using offline and real-time hardware-in-the-loop testbed modeling and simulation. To demonstrate the flexibility of the distributed coordination blocks, multiple case studies and implementation are presented. With control decisions required under changing operating conditions, the IEEE 30 bus system is simulated in real-time using a digital simulator and a network simulator (NS-3). The impact of the SYN flood cyberattack and tolerance of the computing infrastructure to resulting missing data have also been illustrated. [ABSTRACT FROM AUTHOR]

Wind turbines play an increasingly important role in renewable power generation. To ensure the efficient production and financial viability of wind power, it is crucial to maintain wind turbines’ reliability and availability (uptime) through advanced real-time condition monitoring technologies. Given their plurality and evolution, this article provides an updated comprehensive review of the state-of-the-art condition monitoring technologies used for fault diagnosis and lifetime prognosis in wind turbines. Specifically, this article presents the major fault and failure modes observed in wind turbines along with their root causes, and thoroughly reviews the techniques and strategies available for wind turbine condition monitoring from signal-based to model-based perspectives. In total, more than 390 references, mostly selected from recent journal articles, theses, and reports in the open literature, are compiled to assess as exhaustively as possible the past, current, and future research and development trends in this substantial and active investigation area. [ABSTRACT FROM AUTHOR]