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The lunar calendar is the first one established in history. The lunar calendar gives nature's weekly and monthly changes more precisely than the solar one. In restoring such a calendar, we distinguished between proven facts and hypotheses and legends, which were preserved with the help of records based on the testimony of individuals who lived in northwestern Slovenia during the first part of the 20th century. Their eternal lunar calendar is said to be over 300 years old. Its users believed in the initial force, which, according to them, governs everything that exists in this world. Considering the natural cycles increases the quality of our life and ensures its longest possible length.

Future wireless network technology will provide automobiles with a connectivity feature to consolidate the concept of vehicular networks that collaborate in conducting cooperative driving tasks. The full potential of connected vehicles, which promises road safety and a quality driving experience, can be leveraged if machine learning (ML) models guarantee robustness in performing core functions, including localization and controls. Location awareness, in particular, lends itself to the deployment of location-specific services and improvement of the operation performance. Localization entails direct communication to the network infrastructure, and the resulting centralized positioning solutions readily become intractable as the network scales up. As an alternative to the centralized solutions, this article addresses a decentralized principle of vehicular localization reinforced by ML techniques in dense urban environments with frequent inaccessibility to reliable measurement. As such, the collaboration of multiple vehicles enhances the positioning performance of ML approaches. A virtual testbed is developed to validate this ML model for real-map vehicular networks. Numerical results demonstrate the universal feasibility of cooperative localization, in particular, for dense urban area configurations. [ABSTRACT FROM AUTHOR]

Animals living at high latitudes are exposed to prominent seasonal changes to which they need to adapt to survive. By applying Zeitgeber cycles of different periods and photoperiods we show here that high-latitude D. ezoana flies possess evening oscillators and highly damped morning oscillators that help them adapting their activity rhythms to long photoperiods. In addition, the damped morning oscillators are involved in timing diapause. The flies measure night length and use external coincidence for timing diapause. We discuss the clock protein TIMELESS (d-TIM) as the molecular correlate and the small ventrolateral clock neurons (s-LNvs) as the anatomical correlates of the components measuring night length. [ABSTRACT FROM AUTHOR]

The identity and nature of the photoperiodic photoreceptors are now quite well known, as is the nature of the endocrine regulation of the resulting diapauses. The central problem of time measurement—how the photoperiodic clock differentiates long from short days—however, is still obscure, known only from whole-animal experiments and abstract models, although it is clearly a function of the insect circadian system. This review describes some of these experiments in terms of oscillator entrainment and two widely applicable photoperiodic clock models, external and internal coincidence, mainly using data from experiments on flesh flies (Sarcophaga spp) and the parasitic wasp, Nasonia vitripennis. [ABSTRACT FROM AUTHOR]

Copyright of ISTEM is the property of ISTEM and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Satellite navigation systems enable long‐distance and high‐precision time transfers. The authors proposed an undifferenced precise time transfer method with information constraints in this study. A more accurate receiver clock offset can be obtained by constraining the station coordinate parameters and receiver clock offset parameters to achieve a precise time transfer. In this example, the research object is the time transfer link established by the Global Positioning System observation station using a caesium clock, rubidium clock, and hydrogen clock. The clock difference sequence of the time‐transfer link was calculated by combining the observation data and precise products. Following analysis of the examples, the following conclusions were reached. The undifferenced precise time transfer method based on information constraints can effectively be used to smooth the clock difference sequence of the receiver and the clock difference sequence of the time transfer link. To some extent, this method can also be used to reflect the performance of atomic clocks at Global Positioning System stations and is better suited for the precise time transfer of the equal‐clock long‐baseline time transfer link. Simultaneously, the frequency stability of this method is improved significantly in the short term compared to the traditional scheme without any constraints. The improvement of frequency stability is helpful to improve the accuracy of time transfer.

Satellite navigation systems enable long‐distance and high‐precision time transfers. The authors proposed an undifferenced precise time transfer method with information constraints in this study. A more accurate receiver clock offset can be obtained by constraining the station coordinate parameters and receiver clock offset parameters to achieve a precise time transfer. In this example, the research object is the time transfer link established by the Global Positioning System observation station using a caesium clock, rubidium clock, and hydrogen clock. The clock difference sequence of the time‐transfer link was calculated by combining the observation data and precise products. Following analysis of the examples, the following conclusions were reached. The undifferenced precise time transfer method based on information constraints can effectively be used to smooth the clock difference sequence of the receiver and the clock difference sequence of the time transfer link. To some extent, this method can also be used to reflect the performance of atomic clocks at Global Positioning System stations and is better suited for the precise time transfer of the equal‐clock long‐baseline time transfer link. Simultaneously, the frequency stability of this method is improved significantly in the short term compared to the traditional scheme without any constraints. The improvement of frequency stability is helpful to improve the accuracy of time transfer. [ABSTRACT FROM AUTHOR]

Time and frequency measurement is among the most widely used types of measurement. Data on the exact time and the national time scale is in great demand among a variety of consumers, from commercial power metering systems, where the required synchronization accuracy is a few seconds, to space navigation systems, which require synchronization at the level of nanoseconds. In addition, consumer requirements for the accuracy of time and frequency measurement, as well as the efficiency of obtaining frequency and time data, are steadily increasing, which entails the need to modernize the means of reproducing, maintaining, and disseminating time and frequency units and the time scale. In order to meet the modern requirements of consumers for the accuracy of time and frequency measurement, technical means of reproducing, maintaining, and disseminating units have been introduced into GET 1‑2022 State Primary Standard for time and frequency units and the national time scale, which allow the contribution of GET 1‑2022 to the Coordinated Universal Time (UTC) to be significantly increased. The authors provide a brief overview of the composition of GET 1‑2022, perform a comparative analysis of the contribution of national time standards from different countries to UTC, as well as carry out an analysis of frequency instability and time scale shifts in the considered standards relative to UTC. It is shown that from September 2022 to March 2023, the contribution of GET 1‑2022 to UTC increased significantly and exceeded that of the standard of the U.S. Naval Observatory; presently, the contributions of these standards are comparable. According to such indicators as frequency instability and average contribution to UTC, the atomic standards comprising GET 1‑2022 are significantly superior to similar instruments included in the national standards of other countries. The national coordinated time scale of the Russian Federation UTC(SU) was found to be one of the best national UTC realizations, while the national atomic time scale TA(SU) holds the leading position in terms of stability among the time scales realized by leading foreign time laboratories. [ABSTRACT FROM AUTHOR]

Detecting the charge particles at Giga hertz rate is one of the applications of UFSD (Ultra-Fast Silicon Detectors). The UFSD test in front of the proton beam to count the beam particles and use it for a more precise Dose Delivery System for the treatment of the cancerous tumor by charge particles can become an effective step for the development of cancer treatment. In fact, this assessment is a prerequisite and guarantees the use of these detectors in the dose delivery system. In this regard, the best method for time measurement, which was CFD, was chosen. MATLAB software was used to measure and analyze the UFSD output signals. The results of the 50-μm-thick UFSD test and the various geometries that we performed in several different experiments at the CNAO Cancer Treatment Center in Italy, as well as the data obtained at the laboratory of CERN accelerator in Germany, were analyzed by the CFD method. The results of many different runs of programs in MATLAB for many registered signals show: 1- These sensors are reliable to count the proton particles in Giga hertz rate. 2-The CFD devices could be used to record the UFSD output signals. Therefore, they can be used in the correction of the dose delivery system as a counter for the proton number of the proton beam.

Time and frequency are quantities that have seen a proliferation and diffusion of tools, unimaginable until a few decades ago, and whose application implications are multiplying in a digital society, now characterized by an absolute lack of temporal and spatial limits. Today’s world requires a perfect synchronism of human activities, both for the need to identify with certainty the moment of commercial transactions and to accurately describe biological phenomenologies, which affect the social life of individuals to the point of having repercussions on issues such as safety, production and manufacturing organization. In this regard, the recent award of the Nobel Prize for Medicine for the discovery of the gene capable of controlling our internal biological clock is significant. This paper describes the social implications connected to time measurements, analyzing some very original application effects, ranging from the typical cadences of production activities to sports applications, going so far as to highlight its apparent anomaly of adopting, unlike all other physical quantities, duodecimal and/or sexagesimal scales. Real time and perceived time can both converge and diverge, and this is almost never objectifiable, as it varies from individual to individual, according to individual experiences or sensitivities. This paper is a point of reflection attempting to understand how the chronology of major historical events influenced the organization of time as it is known today and how we arrived at actual measuring instruments so accurate and interconnected with the social sphere. The evolution of calendars and instruments for measuring relative time is described in terms of their specificity.

Urban railway systems in many cities are facing increasing levels of crowding and operating near capacity. Crowding at stations and on trains is a concern due to its impact on safety, service quality, and operating efficiency. Denied boarding is becoming a key measure of the impact of near-capacity operations on customers, and it is fundamental for calculating other performance metrics, such as the expected waiting time. Several approaches have been proposed to infer denied boarding using smart card and train movement data. They formulate the inference as a maximum-likelihood estimation problem on observed trip journey times, with an a priori model assumption on independent journey time components, and they require extensive ground truth data collection and model calibration for practical deployment. This article proposes a data-driven unsupervised clustering-based approach to robustly infer denied boarding probabilities for access-plus-waiting times by decomposing trip journey times (instead of directly on journey times). The approach is applicable to closed fare collection systems and consists of two main steps: grouping passengers to trains via trip exit information by using a probabilistic model and inferring denied boarding probabilities by using a structured mixture distribution model with physical constraints and systematic parameter initialization. The method is data driven and requires neither observations of denied boarding nor assumptions about model components’ independence and parameter calibrations. Case studies validate the proposed method by using actual data and comparing it with state-of-the-art models and survey data. The results demonstrate the proposed model’s robustness and applicability for estimating denied boarding under both normal and abnormal operation conditions. [ABSTRACT FROM AUTHOR]

Some different time measurement methods have been presented for similar signals with the same trigger that one can choose by considering signal conditions. Finding the best time measurement method for the output signals from the UFSD (Ultra Fast Silicon Detectors) is our goal because we wanted to use these sensors to detect protons in the proton therapy system to treat cancerous tumors. Some different methods include Constant Fraction Discriminator (CFD), Cross-Correlation (CC), and Time over Threshold (TOT) by different data. These kinds of data include Data from Weightfield2 which is a UFSD signal simulator, Data from simulated signals in practice inside the laboratory, and experiment data, which are real data from the UFSD test in front of a Pico laser beam. Several programs in MATLAB software have been written and run to analyze and compare the different methods. The results are shown that CFD is the best method for time measurement finally

Time and frequency are quantities that have seen a proliferation and diffusion of tools, unimaginable until a few decades ago, and whose application implications are multiplying in a digital society, now characterized by an absolute lack of temporal and spatial limits. Today's world requires a perfect synchronism of human activities, both for the need to identify with certainty the moment of commercial transactions and to accurately describe biological phenomenologies, which affect the social life of individuals to the point of having repercussions on issues such as safety, production and manufacturing organization. In this regard, the recent award of the Nobel Prize for Medicine for the discovery of the gene capable of controlling our internal biological clock is significant. This paper describes the social implications connected to time measurements, analyzing some very original application effects, ranging from the typical cadences of production activities to sports applications, going so far as to highlight its apparent anomaly of adopting, unlike all other physical quantities, duodecimal and/or sexagesimal scales. Real time and perceived time can both converge and diverge, and this is almost never objectifiable, as it varies from individual to individual, according to individual experiences or sensitivities. This paper is a point of reflection attempting to understand how the chronology of major historical events influenced the organization of time as it is known today and how we arrived at actual measuring instruments so accurate and interconnected with the social sphere. The evolution of calendars and instruments for measuring relative time is described in terms of their specificity. [ABSTRACT FROM AUTHOR]

Quality monitoring is an essential element of defect detection in semiconductor manufacturing processes, but semiconductor companies use virtual metrology (VM) in addition to actual metrology to prevent productivity degradation due to the time and costs required to obtain measurements. Past VM studies aimed to predict average wafer measurement values via equipment sensor data and focused on achieving improved predictive performance by selecting or extracting important variables among high-dimensional variables such as equipment sensor data. However, the management of wafer chip quality requires not only average measurement values but also measurement value predictions for chips located at the edges, which are vulnerable to defects. In this paper, we therefore propose a graph attention (GAT) network-based VM model that predicts the measurement values of chips located at wafer edges by constructing graph data with measurement data (i.e., the measurement location information in wafers and the measurement values). To verify the performance of the proposed model, we conduct a comparative experiment with conventional machine learning methods. The experimental results show that the proposed VM model contributes to a predictive performance improvement in terms of the measurement values of chips located at wafer edges. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

After eons of evolution, biological systems have developed excellent actuating methodology in micro-nano scale, which inspired the design and development of advanced microrobots. Cardiomyocytes have received extensive attention following the development of bio-syncretic robots in terms of its spontaneous contraction and high energy efficiency. However, the multipoint, high-precision synchronized, and comprehensive acquisition of the axial motion parameters of cardiomyocytes still constitutes the major factor hindering the development of quantitative control methods for bio-syncretic robots. Herein, off-focus imaging method was proposed to quantify the axial beating of cardiomyocyte. The spontaneously beating of cardiomyocytes were quantitative analyzed based on the combination of cross-correlation and off-focusing imaging. We also validated the use of the proposed SiO2 microspheres in cardiotoxicity test. Following the administration of isoproterenol, cardiomyocytes exhibited positive inotropic and chronotropic effects. Finally, we analyzed the beating responses of individual cardiomyocytes at different locations, and we validated the feasibility of multipoint, synchronized measurements. In the future, this study can also be extended to the analysis of the motion coupling relationship between individual cardiomyocytes and cardiomyocyte clusters, thus providing a data acquisition platform for the dynamic analysis and modeling of bio-syncretic robot actuators. [ABSTRACT FROM AUTHOR]

We propose to use the State Estimation by Sum-of-Norms Regularisation (STATESON-)algorithm for recovering the tip-sample interaction in high-speed tapping mode atomic force microscopy (AFM). This approach enables accurate sample height estimation for each independent cantilever oscillation period, provided that the tip-sample interaction dominates the noise. The entire course of the cantilever deflection signal is compared to a modelled counterpart in subsequent convex minimisations, such that the sparse tip-sample interaction can be recovered. Afterwards, the sample height is determined using the minimum smoothed cantilever deflection per cantilever oscillation period. Results from simulation experiments are in favour of the proposed approach as it consistently reveals sharp edges in sample height, as opposed to both the conventional and a closely related existing approach. However, the non-processed cantilever deflection provided most accurate sample height estimation. It is recommended to implement the STATESON-algorithm in the form of a filter to use it in feedback control of the scanner and cantilever excitation. [ABSTRACT FROM AUTHOR]

Many navigation, sensing, and time-transfer applications call for the high-precision timestamping of events that occur in different digital clock domains. However, state-of-the-art techniques such as digital dual mixer time difference (DDMTD) typically require that the measured clocks be syntonized, i.e., derived from a common reference oscillator and identical in frequency. This article introduces a novel, all-digital technique that can provide accurate collinear timestamps for asynchronous clock domains of arbitrary frequencies. The vernier-referenced digital asynchronous collinear timestamp (VERDACT) system allows timestamps to be generated and compared across any number of arbitrary clock domains with sub-picosecond precision. The novel circuit can be implemented on most field programmable gate arrays (FPGAs) without the need for external components. Under laboratory conditions, an open-source prototype has demonstrated end-to-end precision better than 0.8 ps-rms on a Zynq UltraScale+ FPGA. Potential applications include sub-nanosecond two-way time-transfer using precision time protocol (PTP / IEEE-1588) over an asynchronous multiport Ethernet switch.

This study aimed to determine the mistakes of first-year primary students in online education about time measurement, which is the sub-learning area of measurement in the Maths class. According to this aim, the study was conducted as a case study, which is one of the qualitative methods. In order to collect the data in the study, nonparticipant observations made online in the classroom environment, semi-structured interviews with the students individually via video conference, and all the homework assigned to the students by the classroom teacher during the process related to the subject were utilised as document review. In the analysis of the data obtained, content analysis was used. In light of the findings, it was determined that the students made mistakes in showing and saying half hours and whole hours involving hour and minute hand, in the yesterday-today-tomorrow relationship, and in the week-month relationship. Supporting time measurement activities with concrete materials (examples of clock, calendar) and making sense of time measures by associating them with fractions, numbers, and geometry can help reduce mistakes. [ABSTRACT FROM AUTHOR]

The multi-round sequential memory coal drawing technology can improve the recovery rate of top coal and gangue content in the fully mechanized working face. But it needs to accurately measure and control the time of each round of coal drawing in field application. In the practical application of the automatic coal drawing technology based on the top coal migration tracker, the top coal movement tracker is only used as a mark point and is arranged in the top coal. The top coal movement tracker can not obtain more top coal movement information. In view of the above problems, based on the top coal movement tracker, a top coal migration time measurement system based on accelerometer is designed. The system includes three parts: tag, collector and central computer. The label is placed inside the top coal, and moves along with the top coal in the coal drawing process. Through the built-in accelerometer, the specific force data is collected in real-time. The time measurement algorithm is called to realize the monitoring of top coal migration. Then the different coal drawing stages are determined. The top coal migration time information of different stages is calculated. When the tag is released from the coal chute, it collides with the scraper conveyor belt, and sends the top coal migration time information outward to the collector through the RF signal. The information is further transmitted to the central computer through the field bus to guide the fully mechanized working face to realize multi-round of sequential coal drawing on site. The hardware and software design of the time measurement label of top coal migration is introduced in detail. The functions of real-time acquisition of specific force value, wireless signal transmission and data storage are realized. A calibration platform with 3D turntable as the core and Gauss-Newton method as the calibration algorithm is built. The calibration of the accelerometer is completed. The calibrated accelerometer can accurately collect the specific force data of the top coal migration time measurement label. According to the migration characteristics of top coal in the process of coal drawing, the time measurement algorithm based on threshold and the time measurement algorithm based on long-term and short-term memory (LSTM) are proposed. The time measurement algorithm based on threshold realizes the time identification of motion stage by introducing static threshold and maximum threshold. The time measurement algorithm based on LSTM identifies the dynamic changes of the specific force vector sum in the time domain, finds the mutation point, and realizes the time identification of the motion stage. The performance test of the two time measurement algorithms is completed through the tag free falling experiment. The time measurement variance is 0.000 6 and 0.000 2 respectively. The time measurement error is 13.07% and 5.22% respectively. The results meet the on-site top coal migration time measurement requirements. And the time measurement algorithm based on LSTM has obvious application advantages in top coal migration time measurement.

Gallium nitride high electron mobility transistors outperform Silicon devices due to their excellent physical properties. However, being an immature technology, it exhibits dynamic on-state resistance ($R_{\text{ds}, \text{on}}$). This causes increased conduction loss and leads to reduced operating efficiency. To evaluate the dynamic $R_{\text{ds}, \text{on}}$ of the device, a measurement circuit is required. In literature, a standardized double pulse test is widely used for the measurement of $R_{\text{ds}, \text{on}}$. However, due to the uncontrolled off-state time and inability to independently adjust, the test conditions result in restricted measurement. This article proposes a measurement circuit of the device in power electronics operations. The circuit uses an H-Bridge-derived topology with capacitive storage to enable forward and reverse conduction mode measurement of the device. In all the modes, the circuit allows measurement from the first switching cycle and independent control over the testing conditions by using a hysteresis current control. The proposed measurement circuit is validated using simulation and experimentation. Finally, analytical efficiency compared with the state-of-the-art measurement circuits is provided. [ABSTRACT FROM AUTHOR]

The presence of asynchronous absolute and relative measurements has posed a great challenge to the current multisensor positioning method in robotic systems. Although traditional factor graph methods possess a capability of plug-and-play, only a compromised performance can be obtained when dealing with an increasing number of asynchronous observations. In this article, a novel plug-and-play factor graph method for asynchronous absolute/relative measurements fusion in multisensor positioning is proposed. Different from traditional methods, a fixed-rate graph model is formed. The variable nodes in the graph are built at a fixed update rate to do the optimization, which is not affected by the arrival of measurements. Asynchronous absolute and relative measurements between two successive variable nodes are associated with corresponding variable nodes in the graph via the propagation of closed-form inertial measurement unit (IMU) preintegration. The simulations, datasets, and field tests are carried out to validate the proposed method. The results indicate that the closed-form IMU preintegration method has better dynamic adaptability in fusion system to formulate the association with asynchronous measurements. On this basis, the proposed method can integrate asynchronous measurements in a plug-and-play manner and achieve better performance compared to current methods. Meanwhile, the computational load can also be reduced. [ABSTRACT FROM AUTHOR]

Network measurements provide critical inputs for a wide range of network management. Existing network-wide monitoring methods face the challenge of incurring a high measurement cost. Some recent studies show that network-wide measurement data such as end-to-end latency and flow traffic, have hidden spatio-temporal correlations and thus low-rank features. Taking advantage of the low-rank feature, enlightened by tensor model's strong capability of information representation and extracting, this paper studies a novel sparse measurement scheduling problem which selects a proportion of Origin and Destination (OD) pairs to take measurements in the future time slots, while ensuring the data of the remaining un-measured OD pairs be accurately inferred through tensor completion. It is challenging to find the optimal sampling points (OD pairs) without knowing the structure of the future data and also infer the un-measured data in the presence of noise in the measurement samples. To conquer the challenges, we propose several techniques: a tripartite graph to illustrate the relationship between sample locations and tensor factorization, a graph-based sample selection algorithm, and a graph-based robust tensor completion algorithm. We have conducted extensive experiments based on two real network latency monitoring traces (PlanetLab and Harvard) and two other network monitoring traces (including a traffic trace Abilene and a throughput trace WS-Dream). Our results demonstrate that, even with a sampling ratio of less than 5%, our scheme can accurately obtain the complete network-wide monitoring data by inferring the missing ones based on the samples taken. To achieve similar recovery performance, the best peer tensor completion algorithm needs a significantly larger number of samples, with the sampling ratio up to 25-150 times ours. [ABSTRACT FROM AUTHOR]

The rapid developments in Internet of Medical Things open up new avenues for personalized healthcare. Continuously monitored physiological data can be collected by wearable devices and are transmitted to a remote server for real-time monitoring and diagnosis. This article concerns a risk assessment problem of acute mountain sickness (AMS) with data transmitted according to an event-triggered transmission schedule. An event-triggered signal processing approach is introduced to reconstruct the untransmitted information, based on which, a dynamic SpO $_{\bf 2}$ index (DSI) is further proposed for AMS risk evaluation. The performance of the proposed approach is analyzed through physiological data collected in a proof-of-the-concept study (N=12). Statistical significant correlation of the DSI with AMS ground truth including Lake Louise score, deep sleep duration, deep sleep ratio, and mean SpO $_{\bf 2}$ during sleep is observed. More importantly, it is observed that the proposed event-triggered signal processing procedure can dramatically reduce the data transmission rate while maintaining the performance of the DSI assessment, through comparison of the DSI obtained using the proposed event-triggered approach with those obtained based on event-triggered raw data and continuously transmitted time-triggered data. The obtained results indicate the feasibility of adopting event-triggered data scheduling and signal processing to achieve AMS risk evaluation using data from wearable devices with limited communication/battery resources. [ABSTRACT FROM AUTHOR]

The increased use of light detection and ranging (LiDAR) systems for distance determination requires the investigation of mutual interference. In this article, we describe the conditions for the occurrence of LiDAR interference. We outline suppression methods for different LiDAR types identifying pulse-position modulation (PPM) as a solution for time-of-flight LiDAR with time-correlated single photon counting (TCSPC) histograms. Based on PPM, we present a suppression method, which randomly varies the laser pulse emission times. For optimal suppression, we switch on the suppression only when interference is present. To recognize the occurrence of LiDAR interference, we develop a multipulse detection algorithm that can also extract all pulse positions. Simulations show that the algorithm can be applied for a signal-to-noise ratio greater than 3. Determining the heights of all recognized pulse signatures, an appropriate suppression level can be chosen. We successfully show the optimized interference suppression for an example LiDAR measurement. For safe use by multiple systems, we suggest random numbers. We reuse the TCSPC histograms to generate random numbers, whose generation probability is calculated theoretically and confirmed by simulation and measurement data. For nearly all histogram distributions consisting of background- and laser-generated data, a sufficient amount of random numbers is produced. [ABSTRACT FROM AUTHOR]

Fluorescence lifetime imaging is a valuable technique for probing characteristics of wide ranging samples and sensing of the molecular environment. However, the desire to measure faster and reduce effects such as photo bleaching in optical photon-count measurements for lifetime estimation lead to inevitable effects of convolution with the instrument response functions and noise, causing a degradation of the lifetime accuracy and precision. To tackle the problem, this paper presents a robust and computationally efficient framework for recovering fluorophore sample decay from the histogram of photon-count arrivals modelled as a decaying single-exponential function. In the proposed approach, the temporal histogram data is first decomposed into multiple bins via an adaptive multi-bin signal representation. Then, at each level of the multi-resolution temporal space, decay information including both the amplitude and the lifetime of a single-exponential function is rapidly decoded based on a novel statistical estimator. Ultimately, a game-theoretic model consisting of two players in an “amplitude-lifetime” game is constructed to be able to robustly recover optimal fluorescence decay signal from a set of fused multi-bin estimates. In addition to theoretical demonstrations, the efficiency of the proposed framework is experimentally shown on both synthesised and real data in different imaging circumstances. On a challenging low photon-count regime, our approach achieves about 28% improvement in bias than the best competing method. On real images, the proposed method processes data on average around 63 times faster than the gold standard least squares fit. Implementation codes are available to researchers. [ABSTRACT FROM AUTHOR]

When the single-phase grounding fault is removed due to interruptions in circuit breakers, a secondary arc emerges and continues to burn, which weakens the continuity and stability of electrical power supplies. Therefore, analyses of the influencing factors of the arcing time are necessary to find suppression technologies for secondary arcs. Scatter plots of the arcing time as a function of the relevant parameters for each experiment are plotted to determine the distribution rule for the scattered points. In addition, the correlation coefficients between the arcing time and the relevant parameters are calculated through statistical correlation algorithm processing. Finally, multivariate fitting is performed between the arcing time and the relevant parameters, and a function is obtained from the fitting for testing. The results show that the arc length and secondary current are significantly positively correlated with the arcing time, while the maximum recovery voltage is significantly negatively correlated. However, the arc area and diameter have little correlation with the arcing time. The proposed method not only provides theoretical support and suppression measures for secondary arcs but can also be applied as an effective means to improve the continuity and security of electrical power supplies. [ABSTRACT FROM AUTHOR]

This paper introduces experimental and numerical analysis of nozzle ablation in SF6 gas circuit breakers. Ablation and changes in the diameter of a nozzle were measured in the experiments. In addition, an ablation model was developed and simulations of nozzle ablation were carried out. The relationship between geometric changes to the nozzle due to ablation and the injection energy was investigated using both experimental measurements and data predicted by simulation. The predicted values for mass loss and geometric changes to the nozzles tested show similar tendencies to the experimental results. Also, the developed ablation model was applied to the mixture of fluoronitrile and CO2 gas. By predicting flow and pressure changes due to the ablation effect, the interruption performance of gas circuit breakers after repeated testing can be analyzed. [ABSTRACT FROM AUTHOR]

This article presents a novel secure-control framework against sensor deception attacks. The vulnerability of cyber-physical systems with respect to sensor deceptive attacks makes that all sensor measurements are not reliable until the system security is assured by an attack detection module. Most of the active attack detection strategies require some time to assess the system security, while injecting a watermark signal to ease the detection. However, the injection of watermark signals deteriorates the performance and stability of the plant. The proposed control framework consists of a dual-rate control (DRC) that is able to stabilize the plant using: 1) a model predictive controller that operates at a slower sampling time; 2) a state-feedback predictor-based controller that operates in the nominal sampling time disregarding the use of the untrustworthy measurements until the attack detector is able to certify the security; and 3) a reconfiguration block (RB) for palliating the effect of the watermarking. Simulation results indicate the efficacy of the proposed DRC framework to defend the system from cyber-attacks and the ability of the RB to improve the closed-loop performance during the watermark injection. [ABSTRACT FROM AUTHOR]

Feature-based algorithm selection aims to automatically find the best one from a portfolio of optimization algorithms on an unseen problem based on its landscape features. Feature-based algorithm selection has recently received attention in the research field of black-box numerical optimization. However, there is still room for the analysis of algorithm selection for black-box optimization. Most previous studies have focused only on whether an algorithm selection system can outperform the single-best solver (SBS) in a portfolio. In addition, a benchmarking methodology for algorithm selection systems has not been well investigated in the literature. In this context, this article analyzes algorithm selection systems on the 24 noiseless black-box optimization benchmarking functions. First, we demonstrate that the first successful performance measure is more reliable than the expected runtime measure for benchmarking algorithm selection systems. Then, we examine the influence of randomness on the performance of algorithm selection systems. We also show that the performance of algorithm selection systems can be significantly improved by using sequential least squares programming as a presolver. We point out that the difficulty of outperforming the SBS depends on algorithm portfolios, cross-validation methods, and dimensions. Finally, we demonstrate that the effectiveness of algorithm portfolios depends on various factors. These findings provide fundamental insights for algorithm selection for black-box optimization. [ABSTRACT FROM AUTHOR]

We consider state estimation for networked systems (NSs), where measurements from sensor nodes are contaminated by outliers. A new hierarchical measurement model is formulated for outlier detection by integrating an outlier-free measurement model with a binary indicator variable for each sensor. The binary indicator variable, which is assigned a beta-Bernoulli prior, is utilized to characterize if the sensor’s measurement is nominal or an outlier. Based on the proposed outlier-detection measurement model, both centralized and decentralized information fusion filters are developed. Specifically, in the centralized approach, all measurements are sent to a fusion center where the state and outlier indicators are jointly estimated by employing the mean-field variational Bayesian (VB) inference in an iterative manner. In the decentralized approach, however, every node shares its information, including the prior and likelihood, only with its neighbors based on a hybrid consensus strategy. Then each node independently performs the estimation task based on its own and shared information. In addition, a distributed solution with an approximation is proposed to reduce the local computational complexity and communication overhead. Simulation results reveal that the proposed algorithms are effective in dealing with outliers compared with several recent robust solutions. [ABSTRACT FROM AUTHOR]

This article presents the design and test results of a waveform sampling application-specific integrated circuit (ASIC) based on the switched capacitor array (SCA). It integrates eight differential-input channels, and each channel consists of a 256-cell capacitor array, 256 12-bit Wilkinson analog-to-digital converters (ADCs), and a serial data readout. A sampling rate of up to 5 Gsps is achieved with a low-jitter delay-locked loop (DLL). Two shared coarse counters running at the rising and falling edges of the reference clock are used to extend the time measurement range. Several crosstalk reduction strategies were adopted in the circuit design, especially in the clock generation circuit and the digitization circuit. The ASIC has been fabricated in 180-nm CMOS process. The test results show that the random noise is approximately 0.7-mV rms after the fixed-pattern noise correction and that the precision of waveform timing is better than 7-ps rms with the sampling interval correction. [ABSTRACT FROM AUTHOR]

The power distribution network is undergoing tremendous transformation due to an increase in the penetration of renewable energy resources and electric vehicles. These changes have resulted in greater uncertainty and dynamics in the distribution grid states. Therefore, the ability to track and monitor system states has become a critical need for accurate and timely control actions. In this article, we propose two dynamic sparsity-based state estimation approaches for distribution systems: 1) locally weighted matrix completion (LW-MC), and 2) Bayesian matrix completion with Kalman filter prediction (BMC-KF). The performance of the proposed dynamic state estimation strategies is compared with the classic/static matrix completion (static-MC) approach using the IEEE 37 and IEEE 123 bus test systems. Results indicate that BMC-KF approach outperforms both LW-MC as well as static-MC even when 30% of the measurement data is available. Computational complexity associated with both approaches is quantified. [ABSTRACT FROM AUTHOR]

We present two model-based neural network architectures purposed for sporadic user detection and channel estimation in massive machine-type communications. In the scenario under consideration, a base station assigns the users a set of pilot sequences that is linearly dependent, but because user activity is sporadic the detection/estimation problem is amenable to sparse recovery algorithms. Further, we consider a millimeter-wave wireless channel, so that the channel vectors are sparse in a known dictionary. We apply the deep unfolding framework to design custom neural network layers by unrolling two iterative optimization algorithms: (1) linearized alternating direction method of multipliers, which we apply to a constrained convex problem, and (2) vector approximate message passing featuring a novel denoiser based on the iterative shrinkage thresholding algorithm. The networks thus inherit domain knowledge as encapsulated by the signal model, and suitable operations as informed by the algorithms—in the same spirit as convolutional networks that exploit structure inherent in images and audio, except grounded in optimization and statistics. The networks, trained on synthetic data generated from the block-fading millimeter-wave multiple access channel model, offer improved complexity and accuracy relative to their iterative counterparts, and are potentially a boon to cell-free MIMO systems. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicles (AUVs) commonly employ a Doppler velocity log (DVL) to achieve an accurate navigation solution. In real-world scenarios, like when operating in complex environments, situations of complete DVL outages may occur, leading to rapid degradation in the navigation solution. To circumvent such situations, this article proposes an approach to estimate the platform’s velocity vector based on past DVL measurements and a motion model, for short time periods. At-sea experiments were conducted using two types of AUVs, each with a different DVL, to collect data for a total time duration of 90 min. This dataset was employed to show the benefits of using the proposed approach. [ABSTRACT FROM AUTHOR]

Activation energy represents the average rate of interaction between aging by-products and cellulose. Activation energy is a crucial parameter that can be used to identify the remaining life of insulation in high voltage (HV) equipment. Existing noninvasive methods take a significantly longer time to sense activation energy for given insulation. This is primarily due to the volume of data required for such analysis, which generally takes significant time to measure. This work reports a noninvasive and effective approach to predict activation energy of oil–paper insulation using a dielectric response that is recorded for a very short span of time. The proposed method requires polarization current data sensed for a few seconds (15–20 s) to operate. The initial decay rate (DR) of the sensed data is found to be sensitive to the activation energy. This feature of the initial DR is utilized to sense the value of activation energy within a short duration. The proposed technique utilizes the current sensor (present within an electrometer) more efficiently. This facilitates the measurement of a highly accurate polarization profile and ensures reliable activation energy estimation. The proposed methodology has been successfully applied to data collected from a few real-life transformers. Reported results show that the suggested method provides satisfactory results with good accuracy. [ABSTRACT FROM AUTHOR]

State estimation algorithms deployed in modern grids are vulnerable towards false data injection attacks. This work demonstrates a novel attack vector (AV) formulation scheme by exploiting the low-rank subspace of the topology matrix. With constraints over the state deviation vector, this work showcases that the developed AV can effectively bypass the residue test for a demonstrated test bench. [ABSTRACT FROM AUTHOR]

Phasor measurement units (PMUs) used in the smart grids are vulnerable to time synchronization attacks (TSA), whose attack target is the global positioning systems (GPS). The attacker manipulates phase measurements by sending deceptive GPS signals to introduce errors in state estimation, which can affect bus power flow calculation and economic dispatching of the power system. Considering the case of multiple attacked PMUs in AC, a novel false phase data detection method is proposed in this paper. The method uses “phase coding” to extract and encode the potential relationship between amplitude and phase angle, and then builds a TSAs detection model based on a vector neural network that learns the encoded relationship vectors through dynamic routing. Finally, simulation experiments are carried out on IEEE 9, 14, 30, and 118 bus systems and real measurements provided by a province in China. The results show that the proposed method achieves the best performance among the detection methods based on the learning model. [ABSTRACT FROM AUTHOR]

Existing robust state estimation methods are generally unable to distinguish model uncertainties (state outliers) from measurement outliers as they only exploit the current measurement. In this article, the measurements in a sliding window are, therefore, utilized to better distinguish them, and an adaptive method is embedded, leading to a sliding window variational outlier-robust Kalman filter based on Student’s t-noise modeling. Target tracking simulations and experiments show that the tracking accuracy and consistency of the proposed filter are superior to those of the existing state-of-the-art outlier-robust methods thanks to the improved ability to identify the outliers but at a cost of greater computational burden. [ABSTRACT FROM AUTHOR]

Techniques for high accuracy tracking of the 3-D velocity, position, and cellular carrier frequency bias of unmanned aerial vehicles (UAVs) that communicate using a cellular network are presented. The tracking algorithm does only rely on asynchronous measurements of Doppler shift in a minimum of four base station sites. This number can be reduced to three, by combination with a round-trip time measurement from one site. The tracking is therefore independent of any capability of the UAV transceiver, like the global positioning system that may be disabled in the UAV by the pilot. Another significant advantage is that high precision time synchronization between base stations is not needed. The algorithm exploits interacting multiple model filtering, with the nonlinear measurement equations handled by extended Kalman filters. The low computational complexity, low sampling rate, and the uplink Doppler shift measurement resources enable joint tracking of several UAVs per cell. This makes the tracking technique relevant for supervision of UAV traffic using cellular connectivity, for example for border surveillance and surveillance of sensitive areas like airports where illegal flying may be extremely dangerous and costly. Theoretically, an observability analysis treats measurement configurations based on different combinations of Doppler shift and round-trip time. Simulations are used to illustrate the performance and to assess the initialization properties. [ABSTRACT FROM AUTHOR]

The project management literature has typically focused on project efficiency measures, such as time, cost, and quality, but rarely examined broader measures of project success, such as customer satisfaction and customer loyalty. In this article, we apply a resource-based theory framework and examine both ordinary and dynamic capabilities of the firm and their influence on customer satisfaction and repurchase intentions. We also include the firm's corporate reputation and explore its moderating influences on variables in the model. Data from 491 project managers indicate that one dynamic capability (relational) is three times stronger at predicting customer satisfaction than one of the ordinary capabilities (project). Another dynamic capability (product) is not a significant predictor of customer satisfaction. Customer satisfaction is the strongest determinant of repurchase intentions and fully mediate the impact of the firm's dynamic and ordinary capabilities on repurchase intentions. When the sample is split according to corporate reputation, for the group who saw the focal firm reputation ranked first in industry, the project (ordinary) capabilities and relational (dynamic) capabilities have similar influences on satisfaction. However, for the group who saw other firms’ reputation ranked first, relational capabilities are stronger than project capabilities at predicting satisfaction. [ABSTRACT FROM AUTHOR]

This article presents the degradation effects of long-term use of a dual vibrating intrinsic reverberation chamber (dual VIRC) for shielding effectiveness (SE) measurements. At the beginning with a new fresh dual VIRC setup it is possible to achieve a very high dynamic range (DR) but the DR degrades due to long-term use of the dual VIRC. The effect of 10 years use of the dual VIRC on the DR is shown as function of usage time. Also, the effect on the DR of a new dual VIRC with better conductive cloth has been presented. [ABSTRACT FROM AUTHOR]

In many papers, random modulation of power electronic converters is described as an electromagnetic interference (EMI) mitigation technique, which contributes to the assurance of electromagnetic compatibility (EMC) of systems consisting of power electronic converters and serial transmission-based control and measuring devices. However, experimental results as well as theoretical analyses of physical phenomenon suggest that the EMI reduction is ostensible and results from the measuring procedure adopted in standards. This article presents the first known approach to compare the influence of deterministic and random modulation of transmission errors rate in serial communication systems. The comparison is based on the especially developed mathematical models, which are validated experimentally, using a simple setup with a dc/dc converter injecting a noise into the USB/RS232 converter representing a serial transmission scheme. The comparison reveals that for the studied switching frequency range from 10 kHz to 1 MHz, on average, there is no significant difference between random and deterministic modulations with respect to the probability of error rate in serial communication systems. Thus, in such systems, random modulation should not be considered as a valid EMC-assuring technique. [ABSTRACT FROM AUTHOR]

This article studies the attack detection problem of cyber–physical systems (CPSs) with the event-triggered (ET) mechanism. A switching-based moving-target defense (MTD) strategy is developed to detect malicious false-data-injection (FDI) attacks, which is characterized by the average dwell-time (ADT) property. Even if attacks may remain stealthy when the MTD mechanism is adopted, the impact of these stealthy attacks on the system performance is relatively small when the switching signal is unknown to the adversary. In order to reduce the cost of data transmission, an ET mechanism is added into the system. Furthermore, we prove that there exists a lower bound of the execution interval, which indicates that the Zeno phenomenon is excluded. Finally, numerical examples are employed to illustrate the effectiveness of the main results. [ABSTRACT FROM AUTHOR]

Time series similarity computation is a fundamental primitive that underpins many time series data analysis tasks. However, many existing time series similarity measures have a high computation cost. While there has been much research effort for reducing the computational cost, such effort is usually specific to one similarity measure. We propose NeuTS (Neural metric learning for Time Series) to accelerate time series similarity computation in a generic fashion. NeuTS computes the similarity of a given time series pair in linear time and generic to handle any existing similarity measures. NeuTS samples a number of seed time series from the given database, and then uses their pair-wise similarities as guidance to approximate the similarity function with a neural metric learning framework. NeuTS features two novel modules to achieve accurate approximation of the similarity function: (1) a local attention memory module that augments existing recurrent neural networks for time series encoding; and (2) a distance-weighted ranking loss that effectively transcribes information from the seed-based guidance. With these two modules, NeuTS can yield high accuracies and fast convergence rates even if the training data is small. Our experiments with five real-life datasets and four similarity measures (Fréchet, Hausdorff, ERP and DTW) show that NeuTS outperforms baselines consistently and significantly. Specifically, it achieves over 80 percent accuracies in most settings, while obtaining 50x-1000x speedup over bruteforce methods and 3x-350x speedup over approximate algorithms for top-k similarity search. [ABSTRACT FROM AUTHOR]

Information granules can discover interpretable and meaningful relationships offering a full description for time series. This article presents an information granulation method with rectangular information granules and applies it to time-series similarity measurement. First, the fuzzy $c$ -means clustering algorithm transforms the time series and its first-order difference time series to data clusters. With the maximum volume of rectangular information granules viewed as the criterion, the optimal rectangular information granules are formed using the data cluster by the principle of justifiable granularity and the gravitational search algorithm. The time-series similarity is measured by calculating the similarity between the upper and lower bounds of the optimal rectangular information granules built from the time series. Finally, an experiment is performed on a public dataset to verify the feasibility of the proposed method. The result shows that the rectangular information granulation method can capture the change characteristics of time series. The similarity measurement method can effectively evaluate the similarity of the time series belonging to different classes. [ABSTRACT FROM AUTHOR]