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We introduce and discuss a new approach to the phase retrieval (PR) of fields radiated by continuous aperture sources having circular support, which is of interest in many applications, including the detection of shape deformations on reflector antennas. The approach is based on a decomposition of the actual 2-D problem into a number of 1-D PR problems along diameters and concentric rings of the visible part of the spectrum. In particular, the 1-D problems are effectively solved by using the spectral factorization method, while discrimination arguments at the crossing points allow completing the retrieval of the 2-D complex field. The proposed procedure just requires a single set of far-field amplitudes (but for an additional bit of information) and takes advantage from up-to-now unexplored field properties. The developed technique is assessed in the case of reflector aperture fields. [ABSTRACT FROM AUTHOR]

This paper addressesthe problem of real-time state estimation in European-type four-wire low-voltage distribution networks with advanced metering infrastructure based on power line communication (PLC). The main drawback of this type of infrastructure is its high latency and a sequential data sampling process. Both factors make it difficult to obtain instantaneous values from the smart meters in a timely and adequate manner to estimate the real-time state of the system. This problem may be solved in the future with the deployment of metering systems based on other technologies such as 5G. However, PLC technology prevails in many countries, and is not likely to be replaced in the medium term. In the meantime, a solution is needed to provide acceptable real-time state estimates in low voltage networks, not to hinder the active management of these assets in the context of smart grids. In this work, a solution is proposed that allows estimating the state of the low voltage network with a reasonable accuracy considering the low quality of the measurements provided by the deployed infrastructure. The system is validated using a portion of a real pilot network located in the north of Spain fed by a transformer station with seven feeders. [ABSTRACT FROM AUTHOR]

With the power system operating close to its stability margin, the increased penetration of renewable resources and loads with diverse characteristics, appropriate stability studies, and control decisions have become crucial. Due to the importance of accurate representation of aggregate loads in such studies, research interests for load modeling have increased. This paper puts forward a novel measurement-based load modeling approach that adapts itself based on the complex dynamics exhibited by the installed aggregate loads. The proposed approach primarily utilizes the collected measurements to gain insight regarding the dynamic trend in the measurements and subsequently determines the optimal order of an equivalent system or load model capable of effectively representing the aggregate load dynamics. An advanced signal pre-processing technique is employed, which effectively suppresses the noise and also preserves the transients present in the measurements. Afterward, an adaptive dynamic load model (ADLM) accurately represents the installed aggregate load. Lastly, the Refined Instrumental Variable (RIV) approach estimates the equivalent dynamic load model parameters. The results on a 2 bus and the IEEE 118 bus networks in DIgSILENT Powerfactory and a 7 bus network in Real-Time Digital Simulator (RTDS) reveal the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

The Prague texture segmentation data-generator and benchmark (mosaic.utia.cas.cz) is a web-based service designed to mutually compare and rank (recently nearly 200) different static and dynamic texture and image segmenters, to find optimal parametrization of a segmenter and support the development of new segmentation and classification methods. The benchmark verifies segmenter performance characteristics on potentially unlimited monospectral, multispectral, satellite, and bidirectional texture function (BTF) data using an extensive set of over forty prevalent criteria. It also enables us to test for noise robustness and scale, rotation, or illumination invariance. It can be used in other applications, such as feature selection, image compression, query by pictorial example, etc. The benchmark’s functionalities are demonstrated in evaluating several examples of leading previously published unsupervised and supervised image segmentation algorithms. However, they are used to illustrate the benchmark functionality and not review the recent image segmentation state-of-the-art. [ABSTRACT FROM AUTHOR]

Stereo matching is one of the most popular techniques to estimate dense depth maps by finding the disparity between matching pixels on two, synchronized and rectified images. Alongside with the development of more accurate algorithms, the research community focused on finding good strategies to estimate the reliability, i.e., the confidence, of estimated disparity maps. This information proves to be a powerful cue to naively find wrong matches as well as to improve the overall effectiveness of a variety of stereo algorithms according to different strategies. In this paper, we review more than ten years of developments in the field of confidence estimation for stereo matching. We extensively discuss and evaluate existing confidence measures and their variants, from hand-crafted ones to the most recent, state-of-the-art learning based methods. We study the different behaviors of each measure when applied to a pool of different stereo algorithms and, for the first time in literature, when paired with a state-of-the-art deep stereo network. Our experiments, carried out on five different standard datasets, provide a comprehensive overview of the field, highlighting in particular both strengths and limitations of learning-based strategies. [ABSTRACT FROM AUTHOR]

Detector dead time losses, caused by both physical components in the detection system and the electronic data acquisition system, are perhaps the most prominent effect in non-ideal detector behavior. Most mathematical modeling methods consider the detector as a single unit with a single dead time parameter, while a true physical detector is often comprised of several serially connected components, each suffering from independent dead time losses. In this study, we develop new formulas for evaluating the dead time losses in a detection system with two serially connected components, each component with a non paralyzing dead time, while assuming that the original count rate has a Poisson distribution. The analytic results obtained are first compared with numeric simulations (showing a very good correspondence) and are then used for numeric investigation of global boundaries of the dead time effect in serially connected systems. [ABSTRACT FROM AUTHOR]

Fault diagnosability evaluation is important for monitoring and control system design. The evaluation results provide knowledge of the achievable performance of concerned systems from the fault diagnosis perspective. In this article, the diagnosability analysis of Markov jump systems with multiple time delays is addressed by using statistics. Specifically, cases with both completely and partially known transition probabilities are considered. First, the characteristics of the multiple time delays and the Markov process are extracted based on a constructed system dynamic. This step is followed by defining the fault detectability and isolability of the considered system. On this basis, Kullback–Leibler divergence-based fault diagnosability measures for different fault cases are given, and the relations between these measures are investigated. For cases with completely known transition probabilities, due to random variation in the structure, a quantitative fault diagnosability method is proposed by simultaneously considering the fault diagnosis performance and the importance of different system structures. For cases with partially known or even completely unknown transition probabilities, instead of a scalar measure, an interval measure and the corresponding evaluation method are developed to take advantage of as much available information as possible. Finally, the effectiveness of the developed measures is verified via simulation. [ABSTRACT FROM AUTHOR]

Understanding the inter-relationships between traffic flow, density, and speed through the study of the fundamental diagram of road traffic is critical for traffic modelling and management. Consequently, over the last 85 years, a wealth of models have been developed for its functional form. However, there has been no clear answer as to which model is the most appropriate for observed (i.e. empirical) fundamental diagrams and under which conditions. A lack of data has been partly to blame. Motivated by shortcomings in previous reviews, we first present a comprehensive literature review on modelling the functional form of empirical fundamental diagrams. We then perform fits of 50 previously proposed models to a high quality sample of 10 150 empirical fundamental diagrams pertaining to 25 cities. Comparing the fits using information criteria, we find that the non-parametric Sun model greatly outperforms all of the other models. The Sun model maintains its winning position regardless of road type and congestion level. Our study, the first of its kind when considering the number of models tested and the amount of data used, finally provides a definitive answer to the question “Which model for the functional form of an empirical fundamental diagram is currently the best?”. The word “currently” in this question is key, because previously proposed models adopt an inappropriate Gaussian noise model with constant variance. We advocate that future research should shift focus to exploring more sophisticated noise models. This will lead to an improved understanding of empirical fundamental diagrams and their underlying functional forms. [ABSTRACT FROM AUTHOR]

Cooperative-Intelligent Transportation System (C-ITS) safety applications depend on reliable location information timely exchanged by road users. Due to inter-vehicle communication delays and sampling frequency, there always exists a time gap between the state observation update time and safety decision time. Predicting the vehicle’s locations into a future time epoch common to both host and subject vehicles enables real-time collision detection. Current studies of vehicle positioning performance mostly focus on the accuracy and availability of vehicle navigation solutions at equal observation intervals. Location error propagation over the prediction time intervals and dependence on various factors is not much understood. In this paper, we analyzed how the accuracy of the location prediction degrades depending on prediction intervals and state estimate errors from the measurement updates. We adopted the Kalman Filter method to predict locations with two representative location data sets collected in real road environments. Results from a dual-frequency Global Navigation Satellite System (GNSS)/Real-time Kinematic (RTK) receiver show that the Root Mean Square Error (RMSE) of prediction locations grow from a few centimeters at the state updates to about 50 and 100 cm within the prediction intervals of 1 and 2 seconds, respectively. This implies that GNSS/RTK positioning capability is a prerequisite for C-ITS safety applications. The experimental results from a surveying-grade GNSS/Inertial Navigation System (INS) receiver show that the RMSE can remain within 10 cm for the prediction interval of 2 s. High-rate INS velocity measurements provide significant advantages in efficient control of the error growth of the predicted vehicle locations. [ABSTRACT FROM AUTHOR]

Natural disasters affect millions of people every year. Finding missing persons in the shortest possible time is of crucial importance to reduce the death toll. This task is especially challenging when victims are sparsely distributed in large and/or difficult-to-reach areas and cellular networks are down. In this paper we present SARDO, a drone-based search and rescue solution that leverages the high penetration rate of mobile phones in the society to localize missing people. SARDO is an autonomous, all-in-one drone-based mobile network solution that does not require infrastructure support or mobile phones modifications. It builds on novel concepts such as pseudo-trilateration combined with machine-learning techniques to efficiently locate mobile phones in a given area. Our results, with a prototype implementation in a field-[1], show that SARDO rapidly determines the location of mobile phones ($\sim \!3$ ∼ 3 min/UE) in a given area with an accuracy of few tens of meters and at a low battery consumption cost ($\sim \!5\%$ ∼ 5 % ). State-of-the-art localization solutions for disaster scenarios rely either on mobile infrastructure support or exploit onboard cameras for human/computer vision, IR, thermal-based localization. To the best of our knowledge, SARDO is the first drone-based cellular search-and-rescue solution able to accurately localize missing victims through mobile phones. [ABSTRACT FROM AUTHOR]

In this article, a fuzzy clustering model for multivariate time series based on the quantile cross-spectral density and principal component analysis is extended by including: 1) a weighting system which assigns a weight to each principal component in accordance with its importance concerning the underlying clustering structure and 2) a penalization term allowing to take into account the spatial information. The iterative solutions of the new model, which employs the exponential distance in order to gain robustness against outlying series, are derived. A simulation study shows that the weighting system substantially enhances the effectiveness of the former approach. The behavior of the extended model in terms of the spatial penalization term is also analyzed. An application involving multivariate time series of mobility indicators concerning COVID-19 pandemic highlights the usefulness of the proposed technique. [ABSTRACT FROM AUTHOR]

Granular computing has been an intense research area over the past two decades, focusing on acquiring, processing, and interpreting information granules. In this study, we focus on the granulation of time series and discover the overall structure of the original time series by clustering the granular time series. During the granulation process, when time series exhibit some trend (up trend, equal trend, or down trend) or consist of a variety of tendencies, the trend is essential to be involved to construct the granular time series. Following the principle of justifiable granularity, we propose to form a series of trend-based information granules to describe the original time series and effectively reduce its dimensionality. Then, the similarity measure between trend-based information granules is provided, and considering the dynamic feature of time-series data, dynamic time warping (DTW) distance is generalized to measure the distance for granular time series. In sum, we show here a novel way of forming trend-based granular time series and the corresponding similarity measure, then based on this, the hierarchical clustering of granular time series is realized. The proposed approach can capture the main essence of time series and help to reduce the computing overhead. Experimental results show that the designed approach can reveal meaningful trend-based information granules, and provide promising clustering results on UCR and real-world datasets. [ABSTRACT FROM AUTHOR]

This letter focuses on whether accurate over-the-air (OTA) performance of large-form-factor (LFF) Internet-of-Things (IoT) devices (especially for vehicles) can be obtained from medium-sized anechoic chambers (ACs). For LFF IoT devices under test (DUTs), antenna locations are usually deviated significantly from the center of the OTA testing system due to mechanical positioner or chamber constraints in medium-sized ACs, which will lead to distortion for assessed OTA performance. This work evaluates the effect of offsetting the antenna under test integrated in LFF IoT DUTs from the center of the quiet zone on OTA integrated-type quantities. A new figure of merit ROFF has been defined to characterize the off-center extent, while the influence of which on OTA performance as well as associated simulations is presented based on statistical analysis. It can be found that the performance deviation is determined by ROFF rather than absolute off-center distance. The relationship between ROFF and performance deviation can be derived by using the least-squares fitting method. Additionally, it is acquired that for off-center scenario, performance deviation cannot be eliminated by increasing measurement grid density, yet can be reduced significantly by introducing off-center correction. [ABSTRACT FROM AUTHOR]

This article presents a novel observer synthesis for nonuniform observable uncertain nonlinear systems with sampled (constant and non-uniform intervals) and disturbed delayed output. An observer with a cascade structure is proposed in order to obtain (in a continuous way) the estimated free-delay state vector. The base of the cascade structure is a continuous-discrete observer that achieve the continuous estimation of the delayed state, which reduces the convergence time and allows to reduce the remaining subsystems in the cascade. The rest of the cascade structure is composed by predictors that compensate the delay induced by the original signal. The performance of the observer is evaluated by estimating the state of an uncertain chaotic system with delayed measurements by considering different scenarios. [ABSTRACT FROM AUTHOR]

State estimation is a fundamental task in power systems. Although distribution systems are increasingly equipped with sensing devices and smart meters, measurements are typically reported at different rates and asynchronously; these aspects pose severe strains on workhorse state estimation algorithms, which are designed to process batches of data collected in a synchronous manner from all the measurement units. In this paper, we develop a novel state estimation algorithm to continuously update the estimate of the state based on measurements received in an asynchronous manner from measurement units. The synthesis of the algorithm hinges on a proximal-point type method, implemented in an online fashion, and capable of processing measurements received sequentially from sensors. A performance analysis is presented by providing bounds on the estimation error in terms of the mean and variance that hold at each iteration and asymptotically. The scheme is also compared with a more traditional Weighted Least Squares estimator that compensates for the lack of measurement data by using, as pseudo measurements, the measurement retrieved during a certain time window. Numerical simulations on the IEEE 37-bus feeder corroborate the analytical findings. [ABSTRACT FROM AUTHOR]

This paper describes BigBen, a network telemetry processing system designed to enable accurate and timely reporting of Internet events (e.g., outages, attacks and configuration changes). BigBen is distinct from other Internet-wide event detection systems in its use of passive measurements of Network Time Protocol (NTP) traffic. We describe the architecture of BigBen, and a cloud-based implementation developed to process large NTP data sets and provide accurate daily event reporting. We demonstrate BigBen on a 15.5TB corpus of NTP data. We show that BigBen identifies a wide range of Internet events characterized by their location, scope and duration. We compare the events detected by BigBen vs. events detected by a large active probe-based detection system. We find only modest overlap between the two datasets and show how BigBen provides details on events that are not available from active measurements. Finally, we report on the perspective that BigBen provides on Internet events that were reported by third parties. In each case, BigBen confirms the event and provides details that were not available in prior reports, highlighting the utility of the passive, NTP-based approach. [ABSTRACT FROM AUTHOR]

Information-Centric Networking is an emerging alternative to host-centric networking designed for large-scale content distribution and stricter privacy requirements. Recent research on Information-Centric Networking focused on the protection of the network from attacks targeting the content delivery protocols, while assuming genuine content can always be retrieved from trustworthy nodes. In this paper, we depart from the assumption of the trustworthiness of network nodes and propose a novel certification methodology for information-centric networks that supports continuous security verification of non-functional properties. Our methodology provides a complete and detailed view of the network security status, increasing the trustworthiness of the network and its services. The proposed approach builds on an enhanced certification model capturing the evolution of the system over time. It also defines certification services that fully integrate with existing networks to collect evidence on the target of certification and carry out the certification process. It finally proposes two certification processes, centralized and decentralized, balancing the impact on the network and the system performance. Efficiency, performance, and soundness of our approach are experimentally evaluated in a simulated Named Data Networking (NDN) network targeting property availability. [ABSTRACT FROM AUTHOR]

This is the first time that the feasibility of space charge measurements on full-sized HVDC cable joints has been explicitly considered. In this article we also consider whether the PEA and TSM techniques could be used. [ABSTRACT FROM AUTHOR]

This brief addresses the event-triggered $H_{\infty }$ filtering problem for networked unmanned surface vehicles (USVs) subject to transmission delays. First, a novel event-triggered scheme based on the average measurement output (AMO) is proposed for scheduling the network transmissions of sensor measurements from a USV. Unlike traditional event-triggering conditions that adopt only the current measurement information, an average of consecutive sensor measurements over given time period $T$ is used to verify the triggering condition. This allows the triggering scheme to employ historical measurements and make more reasonable decisions regarding network transmissions. As a consequence, the proposed triggering scheme has the potential to avoid unexpected events arising from stochastic disturbance and noise or malicious attacks. To solve the filtering problem, AMO-based event-triggered $H_{\infty }$ filtering is then developed. Sufficient design criteria are also derived, from which the filter gain parameters can be readily determined. The efficacy of the proposed filter design method is illustrated via simulations using practical USV parameters. [ABSTRACT FROM AUTHOR]

The Fourier transform (FT) of the electric field (EF) radiated by a lightning return stroke is shown to include a distributional term at zero frequency, which prevents numerical algorithms, such as fast FT, from working properly. Inspired by the finite duration of any field measurement in real applications, we introduce a time-windowing strategy, whereby the integration time-interval of the FT becomes bounded. As a result, the distributional term is removed from the FT, whereas the functional part of the FT is modified throughout the whole range of frequencies, in such a way that a good performance of the numerical algorithms can be restored. The dependence of the modified spectra of the EFs on the length of the time window is also investigated and three numerical experiments with different channel-base currents are performed to validate the whole resulting framework. [ABSTRACT FROM AUTHOR]

The article presents the results of calculating time responses of a meander line (ML) turn to pulse excitation. The responses were calculated at the ML nodes analytically and numerically, and demonstrated coincidence. Besides, the created prototype of the ML turn was measured and showed good qualitative and acceptable quantitative agreement with the simulation results. Analytical models were found to be promising since they may significantly accelerate the design of such MLs in contrast to numerical methods which provide costly multivariate simulation of the time response during optimization. [ABSTRACT FROM AUTHOR]

Fault diagnosis and prognosis (FDP) is critical for ensuring system reliability and reducing operation and maintenance (O&M) costs. Lebesgue sampling based FDP (LS-FDP) is an event-based approach with the advantages of cost-efficiency, uncertainty management, and less computation. In previous works, LS-FDP approaches are mainly model-based. However, fault dynamic modeling is difficult and time consuming for some complex systems and this severely hinders the applications of LS-FDP. To address this problem, this article presents a data-driven based LS-FDP framework in which deep belief networks (DBN) and particle filter (PF) are integrated to achieve fault state estimation and remaining useful life prediction. In the proposed approach, DBN learns the state evolution model and the Lebesgue time transition model, which are used as diagnostic and prognostic models in PF for FDP. A series of offline and online experiments are conducted on lithium-ion batteries to verify the proposed method. Experimental results and comparison studies show that the proposed approach has higher efficiency in terms of computation and better performance in terms of FDP accuracy and precision. [ABSTRACT FROM AUTHOR]

Supraharmonic (SH) disturbances are generated mainly by power electronics whose usage has increased significantly in the last decades. Consequently, supraharmonics have increased as well. The SH frequency range from 2 kHz to 150 kHz still lacks a normative measurement method. Compatibility levels and emission limits of SH are commonly presented as quasi-peak (QP) detector values for a bandwidth (BW) of 200 Hz measured by CISPR 16 receivers. In this context, this paper proposes an optimized method emulating the CISPR 16 receiver with QP detector for measuring grid disturbance levels. It makes innovative use of PLL (Phase-Locked Loop) units for estimating the highest distortion levels in the SH frequency range of the test signal, and in sequence, the QP detector with the appropriate specifications in the CISPR 16 standard is applied. Different cases of both synthetic and real signals were applied to the proposed method, which showed to be efficient in evaluating SH levels for current and voltage signals. An analysis of maximum permissible distortion levels is performed considering an individual SH distortion. In addition, the proposed method presents a computational cost reduction of approximately 91% in relation to the CISPR 16 method considering a specific case of signal containing SH distortions used for power quality analysis. [ABSTRACT FROM AUTHOR]

Compared to the probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters, the trajectory PHD (TPHD) and trajectory CPHD (TCPHD) filters are for sets of trajectories, and thus are able to produce trajectory estimates with better performance. In this paper, we develop the TPHD and TCPHD filters which can adaptively learn the history of the unknown target detection probability, and therefore they can perform more robustly in scenarios where targets are with unknown and time-varying detection probabilities. These filters are referred to as the unknown TPHD (U-TPHD) and unknown TCPHD (U-TCPHD) filters. By minimizing the Kullback-Leibler divergence (KLD), the U-TPHD and U-TCPHD filters can obtain, respectively, the best Poisson and independent identically distributed (IID) density approximations over a set of augmented trajectories. For computational efficiency, we also propose the U-TPHD and U-TCPHD filters that only consider the unknown detection profile at the current time. Specifically, the Beta-Gaussian mixture method is adopted for the implementations of proposed filters, which are referred to as the BG-U-TPHD and BG-U-TCPHD filters. The $L$ -scan approximations of these filters with much lower computational burden are also presented. Finally, various simulation results demonstrate that the BG-U-TPHD and BG-U-TCPHD filters can achieve robust tracking performance to adapt to unknown detection profile. Besides, it also shows that usually a small value of $L$ in the $L$ -scan approximation has similar performance than with a large value at a fraction of the computational cost. [ABSTRACT FROM AUTHOR]

Condition-monitoring systems are used to determine the best timing for predictive maintenance of power electronics converters. These monitoring systems can significantly reduce the converter repair time and increase system availability. In this article, an investigation has been conducted on the possibility of using the dissipation factor as a lifetime indicator of electrolytic capacitors. A criterion is also proposed based on the dissipation factor to detect electrolytic capacitors’ end of useful life. A method has been presented for measuring the dc-link capacitor dissipation factor in a back-to-back converter. Using this technique, it is possible to estimate the switching component of the capacitor current by measuring the output currents of the converter without a need to measure the capacitor current directly. The proposed method is simulated, and the impact of various factors, such as switching frequency, capacitor series inductor, and the network filter on the accuracy of the dissipation factor measurement, is discussed. Experimental results are also provided to validate the acceptable accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

We study the problem of transmitting a source sample with minimum distortion over an infinite-bandwidth additive white Gaussian noise channel under an energy constraint. To that end, we construct a joint source–channel coding scheme using analog pulse position modulation (PPM) and bound its quadratic distortion. We show that this scheme outperforms existing techniques, since its quadratic distortion attains both the exponential and polynomial decay orders of Burnashev’s outer bound. We supplement our theoretical results with numerical simulations and comparison to existing schemes. [ABSTRACT FROM AUTHOR]

This article is concerned with the development of a new event-triggered parity space fault detection (FD) scheme. A linear discrete-time system model with varying sampling periods is presented for handling the problem of event-triggered FD and a new parity relation is established. Based on this, an event-triggered residual generator is constructed and the generated residual is completely decoupled from event-triggered transmission error. The design of the parity matrix is formulated into an optimization problem and an optimal solution of the parity matrix is obtained by using singular value decomposition. The issue of residual evaluation is also considered in the event-triggering implementation. The novelties of this article are twofold. First, a new event-triggered parity relation is obtained and the parity space-based residual signal achieves complete decoupling with the event-triggered transmission error. Second, the calculation of the parity matrix is independent of event parameters. So the design of the parity space-based residual generator and event generator can be carried out independently. Finally, a simulation example is considered to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Many time series data mining problems can be solved with repeated use of distance measure. Examples of such tasks include similarity search, clustering, classification, anomaly detection and segmentation. For over two decades it has been known that the Dynamic Time Warping (DTW) distance measure is the best measure to use for most tasks, in most domains. Because the classic DTW algorithm has quadratic time complexity, many ideas have been introduced to reduce its amortized time, or to quickly approximate it. One of the most cited approximate approaches is FastDTW. The FastDTW algorithm has well over a thousand citations and has been explicitly used in several hundred research efforts. In this work, we make a surprising claim. In any realistic data mining application, the approximate FastDTW is much slower than the exact DTW. This fact clearly has implications for the community that uses this algorithm: allowing it to address much larger datasets, get exact results, and do so in less time. [ABSTRACT FROM AUTHOR]

Partial discharge inception voltage (PDIV) at different frequencies is of great interest for PD tests conducted at nonpower frequency. However, in the existing literature, there is no agreement on PDIV characteristics with respect to frequency. There is a lack of comprehensive analysis of frequency-dependent PDIV characteristics either. In this article, the PDIV of a cylindrical void at different frequencies (from 0.5 to 300 Hz) is investigated from the perspective of the first PD time lag. First, an appropriate time interval between consecutive measurements is experimentally determined. Then, PDIV measurements at different frequencies are conducted by increasing voltage at a fixed ramp rate. Furthermore, the PDIVs defined by peak voltage and instantaneous voltage are compared. After that, a bipolar square wave is applied to measure the first PD time lag. Finally, based on the first PD time-lag distribution with respect to voltage at different frequencies, the cumulative probability of the first PD under sinusoidal voltage is computed and analyzed. The results suggest that the first PD time lag has a significant influence on the measured PDIV at frequencies above 10 Hz. This is because the mean time lag is nearly half the time period of half voltage cycle at low voltages. Thus, high voltages (HVs) are needed to obtain a high probability of the first PD, i.e., a large measured PDIV. [ABSTRACT FROM AUTHOR]

A museum hybrid space combines physical artifacts co-located with virtual and augmented reality displays. Although the technology exists to provide museums with hybrid space, there are no empirical studies on effectiveness of the museum hybrid space in terms of learning and enjoyment. This article takes an experimental approach and measures the enjoyment and learning (dependent variables) of participants in response to selected environments (independent variables) including a traditional environment (based on photos and labels), a video-enhanced environment (based on projected video clips), and a VR-enhanced environment (based on video game). The main outcome of this article is demonstrating that the use of VR technology and the resulting hybrid space (i.e., VR-enhanced environment) results in novel museum experiences that provide greater impacts on audience in terms of learning and enjoyment. [ABSTRACT FROM AUTHOR]

We present a novel analytical model to approximate the travel-time distribution of vehicles traversing a freeway corridor that experiences random quality of service degradations due to non-recurrent incidents. The proposed model derives the generating function of travel times in closed-form using clearance time, incident frequency and severity, and other ordinary traffic characteristics. We validate the model using data from a freeway corridor where weather events and traffic accidents serve as the principal causes of service degradation. The resulting model is equivalent in performance to widely used methodologies while uniquely providing a clear connection on how incidents affect travel time distribution. With this connection, the model readily yields travel time reliability measures for alternative roadway behaviors, providing crucial information for long-term planning. [ABSTRACT FROM AUTHOR]

In this article, an event-triggered control problem for partial differential equation systems with pointwise measurements is investigated via relaxed Lyapunov–Krasovskii functionals. First, the Takagi–Sugeno fuzzy model is introduced to describe the nonlinear systems and a fuzzy event-triggered pointwise controller is proposed with pointwise measurements, which can make a tradeoff between the system’s performance and implementation complexity subject to limited transmission bandwidth. Second, some relaxed conditions are established to ensure the closed-loop system’s stability by using the Lyapunov method and inequality techniques. Finally, two simulation examples are provided to demonstrate the effectiveness and practicability of the designed controller. [ABSTRACT FROM AUTHOR]

Fault detection for distributed parameter processes (heat processes, fluid processes, etc.) is vital for safe and efficient operation. On one hand, the existing data-driven methods neglect the evolution dynamics of the processes and cannot guarantee that they work for highly dynamic or transient processes; on the other hand, model-based methods reported so far are mostly based on the backstepping technique, which does not possess enough redundancy for fault detection since only the boundary measurement is considered. Motivated by these considerations, we intend to investigate the robust fault detection problem for distributed parameter processes in a model-based perspective covering both boundary and in-domain measurement cases. A real-time fault detection filter (FDF) is presented, which gets rid of a large amount of data collection and offline training procedures. Rigorous theoretic analysis is presented for guiding the parameters selection and threshold computation. A time-varying threshold is designed such that the false alarm in the transient stage can be avoided. Successful application results on a hot strip mill cooling system demonstrate the potential for real industrial applications. [ABSTRACT FROM AUTHOR]

In modern medicine, smart wireless connected devices are gaining an increasingly important role in aiding doctors’ job of monitoring patients. More and more complex systems, with a high density of sensors capable of monitoring many biological signals, are arising. Merging the data offers a great opportunity for increasing the reliability of diagnosis. However, a huge problem is constituted by synchronization. Multi-board wireless-connected monitoring systems are a typical example of distributed systems and synchronization has always been a challenging issue. In this paper, we present a distributed full synchronized system for monitoring patients’ health capable of heartbeat rate, oxygen saturation, gait and posture analysis, and muscle activity measurements. The time synchronization is guaranteed thanks to the Fractional Low-power Synchronization Algorithm (FLSA). [ABSTRACT FROM AUTHOR]

Based on the framework of distributed optimization algorithms, this brief studies distributed optimal formation behaviors of multi-agent systems modeled as a group of uncertain Euler-Lagrange systems with collision avoidance. Assume that each agent can only obtain measured position-based gradient value of a local objective function. By integrating a modified distributed optimization algorithm and an adaptive control law, the proposed distributed optimal formation controllers can lead the positions of agents asymptotically converging to the optimal solution to the total objective function with collision avoidance. The proposed design is verified by a numerical example of mobile relays for communication. [ABSTRACT FROM AUTHOR]

Scalability and performance requirements are driving tenants to increasingly move their applications to public clouds. Unfortunately, cloud providers do not provide a view of their networking infrastructure to the tenants, rather only provide some generic service level agreements (SLAs). Tenants are, therefore, forced to plan the deployments of their applications based on these SLAs. This limits the performance that the tenants can achieve. Keeping this in view, we present a detailed network measurement study of the largest public cloud, Amazon Web Services (AWS). We collected network data to characterize the availability and latency of AWS over a period of 100 days and studied various temporal trends across several geographical locations of AWS throughout the world. We performed our study at all three levels of cloud hierarchy: inside availability zones (AZs), across AZs, and across regions. Our results show that network behavior varies significantly over time at different geographical locations, levels of hierarchy, and temporal granularities. For example, while we observed high availability at monthly granularity, it deteriorates at daily and hourly granularities. This and many other such observations that we present have significant implications for cloud tenants. We further implemented our measurement approach on Google Cloud Platform (GCP) to demonstrate that it can be deployed on any cloud platform and present some preliminary comparative observations from this implementation. Based on our observations, we present several recommendations that tenants can use to better deploy their applications. [ABSTRACT FROM AUTHOR]

Cloud operators require timely identification of Heavy Hitters (HH) and Hierarchical Heavy Hitters (HHH) for applications such as load balancing, traffic engineering, and attack mitigation. However, existing techniques are slow in detecting new heavy hitters. In this paper, we present the case for identifying heavy hitters through sliding windows. Sliding windows are quicker and more accurate to detect new heavy hitters than current interval-based methods, but to date had no practical algorithms. Accordingly, we introduce, design, and analyze the Memento family of sliding window algorithms for the HH and HHH problems in the single-device and network-wide settings. We use extensive evaluations to show that our single-device solutions are orders of magnitude faster than existing sliding window techniques and comparable in speed to state-of-the-art non-windowed sampling based technique. Furthermore, we exemplify our network-wide HHH detection capabilities on a realistic testbed. To that end, we implemented Memento as an open-source extension to the popular HAProxy cloud load-balancer. In our evaluations, using an HTTP flood by 50 subnets, our network-wide approach detected the new subnets faster and reduced the number of undetected flood requests by up to $37\times $ compared to the alternatives. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal scheduling policy for broadcasting spatio-temporally dependent observations to two remote estimators over a finite time horizon. The system comprises a scheduler that can broadcast one observation from one out of two spatio-temporally dependent processes at each time instant. Since the number of broadcasting instants for each sensor is constrained, the scheduler must plan the broadcasting that minimizes the time-averaged estimation error. As the scheduler cannot observe the measurements, it determines the expected estimation error based on the age-of information (AoI). Using AoI as a state variable, we derive a set of optimal scheduling policies that minimizes the average mean squared error (MSE) for any given time horizon. The policies provide the optimal number of transmission instances for each sensor and time-varying AoI thresholds for when to be scheduled. By studying how the MSE evolves with respect to the AoI generated by a given scheduling sequence, we can obtain an optimal policy using a low-complexity numerical method. Numerical results validate the theory and demonstrate how utilizing spatio-temporal dependencies together with AoI can enhance the estimation accuracy in a communication-constrained sensor network. [ABSTRACT FROM AUTHOR]

In this article, a new approach is proposed to improve the speed of near-field measurement of antennas. The near-field measurements are mainly performed using a planar near-field scanner, named RFX2. In RFX2, which is an electronically switched probe array, the probes in the $x$ and $y$ directions cannot be fabricated at the same place. At each point, only one tangential component of the magnetic field is measured, and the other component is estimated numerically using an interpolation technique. Here, an adaptive data acquisition technique is proposed that sequentially collects samples from the field in the areas with highly dynamic behavior and skips the regions that have smooth near-field variations. Since the newly introduced points at each iteration of the algorithm are not necessarily laid on the probe locations of the RFX2, the values of the data at these points are calculated using an interpolation method. The proposed adaptive algorithm in this work requires remarkably fewer samples to reach the same accuracy as uniform sampling. This method is also utilized in spherical NF measurement over $\theta $ and $\phi $ plane. The validity of the approach is verified using various numerical and measurement results. [ABSTRACT FROM AUTHOR]

This letter discusses performance degradation of traditional supervisory control and data acquisition (SCADA)-based static state estimation (SSE) caused by violation of the static assumption. The sensitivity of non-synchronized sampling errors against oscillations is tested through real measurement data and grid model from a subsystem of the North American power gird. The test results show that as an important reason leading to the failure of SCADA-based SSE, the non-synchronized sampling error of SCADA is sensitive to oscillations in that the error rapidly increases when oscillations occur and vanishes when the oscillations cease. [ABSTRACT FROM AUTHOR]

This article presents a novel information-based mission planner for a drone tasked to monitor a spatially distributed dynamical phenomenon. For the sake of simplicity, the area to be monitored is discretized. The insight behind the proposed approach is that, due to the spatiotemporal dependencies of the observed phenomenon, one does not need to collect data on the entire area, which is one of the main limiting factors in unmanned aerial vehicle (UAV) applications due to their limited autonomy. In fact, unmeasured states can be estimated using an estimator, such as a Kalman filter. In this context, the planning problem becomes the one of generating a flight plan that maximizes the quality of the state estimation while satisfying the flight constraints (e.g., flight time). The first result of this article is the formulation of this problem as a special orienteering problem where the cost function is a measure of the quality of the estimation. This results in a mixed-integer semidefinite formulation, which can be optimally solved for small instances of the problem. For larger instances, a heuristic is proposed, which provides suboptimal results. Simulations numerically demonstrate the capabilities and efficiency of the proposed path-planning strategy. We believe that this approach has the potential to increase dramatically the area that a drone can monitor, thus increasing the number of applications where monitoring with drones can become economically convenient. Note to Practitioners—This article was motivated by the problem of performing large-scale field monitoring activities using unmanned aerial vehicle (UAV), which at the moment is very time-consuming and limits the definitive adoption of UAVs for this kind of activities. This problem is caused by the limited autonomy of commercial UAVs and the lack of systematic ways to plan missions so as to maximize the amount of information collected. This work starts from the observation that, in many applications, the phenomena that one wants to observe have dynamics and statistical properties. Accordingly, data that are not directly measured can be estimated with a characterizable observation error. In this article, we develop the theoretical foundations for an information-based path planning and define the problem of designing the optimal mission as an optimization problem based on the knowledge of the monitored phenomenon. The presented results have the potential to dramatically improve the effectiveness of drones for monitoring applications. [ABSTRACT FROM AUTHOR]

The rapid development of sensing technologies has enabled sensors embedded into systems and generated continuous online data for anomaly monitoring and protection of the system. However, due to transmission errors and environmental noises, the online measurements collected by sensors are usually inaccurate and cannot be applied directly. A small amount of precise in situ measurements, which are accurate but costly to acquire, are usually fused with online measurements for accuracy improvement. Over the past years, there are many studies concerning such data fusion approaches for obtaining reliable fused data. However, most existing works assume the availability of in situ measurements, without providing a strategy for sampling the expensive in situ measurements. As a result, they may undersample or oversample the in situ measurements that result in large fusion errors or unnecessary costs. To address this problem, this article proposes a dynamic policy for sampling the in situ measurements adaptively using the information that the online measurements provide. Specifically, the proposed policy models the dependence between in situ sampling decisions and influencing factors using a policy network, predicts the probability of recommending in situ samplings with the policy network, and guides the sampling of in situ measurements dynamically. Theoretical and experimental results both verify the effectiveness of the proposed approach. Note to Practitioners—In modern industrial systems, a large number of online measurements of equipment parameters usually can be acquired through sensors at a small cost. However, such measurements are normally corrupted with noise and not appropriate for high-accuracy monitoring of equipment. In practice, a small number of in situ measurements, which are accurate but costly to acquire, are usually collected and fused with online measurements for accuracy improvement. Many studies have been conducted on such data fusion approaches, but most of them focus on the fusion models and overlook the role of the sampling policy of in situ measurement on the fusion results. This article addresses the problem of sampling a few expensive in situ measurements to be fused with online measurements for obtaining high-accuracy and low-cost fused data. To support this objective, a dynamic sampling policy is proposed to guide the sampling of in situ measurements and balance the benefit and cost brought by in situ samplings. Experimental results on the real-world data set show the benefits of the proposed policy compared to relevant methods. [ABSTRACT FROM AUTHOR]

This article proposes model-based homogeneity (MBH) to extend compressed sensing (CS) for landmine-detection ground penetrating radar (GPR). Conventional CS methods have difficulty in distinguishing landmines from clutter, since it principally pays attention to signal magnitude. In contrast, our method visualizes landmines based on homogeneity of high-dimensional scattering features in a spatial model. It realizes both the exclusion of clutter and the reduction of measurement points. Experiments demonstrate that the total measurement and processing time are reduced to one-twentieth of a conventional dense measurement case. We also investigate the influence of model size and number of landmines on the performance. The proposed method is capable of visualizing any objects having respective shapes by configuring corresponding models. [ABSTRACT FROM AUTHOR]

The rate of total electron contents (ROT) calculated from global navigation satellite system (GNSS) observables has mainly been used as a quantity that can be averaged into an ROT index (ROTI), which is unable to detect small-scale, fast-moving disturbances. In this study, we demonstrate that a unique signature on ROT time series can be used to estimate the horizontal velocity and dimension of the irregularities when GNSS signals propagate through ionospheric irregularities (IIs). This was achieved by estimating the time lags and durations of the signatures observed by an array of closely spaced GNSS receivers. We applied this ROT-based estimation method to a three-element GNSS receiver array yielding high-rate carrier phase measurements in Poker Flat, Alaska. The velocities were estimated with a precision of 10 m/s and 2° in speed and direction, respectively. In summary, this method can be used to better describe spatial and temporal distributions of IIs. Furthermore, such distributions can be used to project or predict ionospheric fluctuations for receivers located in the directions of the ionospheric disturbance drift. [ABSTRACT FROM AUTHOR]

In this letter, we address the multistatic object localization problem when the transmitter position and signal propagation speed are not known. By transforming the time delay measurement model, we first formulate a non-convex weighted least squares problem, which is then relaxed into a convex semidefinite programming (SDP) problem by applying semidefinite relaxation. It turns out that the relaxed SDP problem is too loose and fails to achieve the Cramer-Rao lower bound (CRLB) performance. To address this problem, we propose to tighten the relaxed SDP problem by adding a second-order cone constraint to reach better solutions. Simulation results show that the proposed method is able to achieve the CRLB. [ABSTRACT FROM AUTHOR]

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Technologies already provide people with sophisticated and powerful digital ecosystems for lifelong and life-wide education. This article identifies grand challenges for ubiquitous and pervasive computing researchers to transform future education. It begins with a scenario to characterize learning on a day in 2030. Then it overviews broader educational needs and the pervasive computing elements for realizing them: mobile devices; embedded devices; importantly pervasive data infrastructures; and multidisciplinary foundations. Then it presents the grand challenges: 1) smart learning environments that feel safe, trusted, scrutable and controllable; 2) harness long-term, rich data for self-knowledge and shared understanding; 3) long-term personal digital learning companions; 4) augment classroom teacher's awareness and memory; 5) smart classrooms that seamlessly fit into teaching and learning; 6) metaresearch to understand if and when technology really supports learning. [ABSTRACT FROM AUTHOR]