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Being an offline measurement technique, Polarization Depolarization Current (PDC) data is conventionally measured after the oil-paper insulation of the equipment reaches thermal equilibrium. Depending on the condition of the insulation, PDC data measurement time typically ranges from several minutes to few hours. It is understood that atmospheric conditions controls the value of ambient temperature. During field measurement, these conditions may not remain constant throughout the PDC recording process. Variation in ambient temperature disturbs the thermal equilibrium that the equipment may have attained at the start of the measurement procedure. Such a condition invariably affects the recorded dielectric response data. However, methods that are available for analyzing relaxation current assume that temperature equilibrium is maintained throughout the measurement period. Results presented in this paper show that dielectric response data is indeed affected if the ambient temperature increases/decreases monotonically. A method is also proposed in this paper using which the effect of such temperature variation on the recorded relaxation current data can be eliminated. This also ensures correct interpretation of PDC data for condition assessment. Analysis presented shows that the value of the equilibrium temperature does not affect the performance of the proposed technique. The applicability of the proposed methodology is first tested on laboratory samples. Thereafter, it is applied to data collected from a real life power transformer. [ABSTRACT FROM AUTHOR]

Quantitative analysis is a solid, well established mathematical technique that can be used to analyze the result of survey(s) in a specific field. Survey analysis is usually based on the study of the effect of independent variables on a dependent variable. Although quantitative analysis can use an R-Squared value as a method to measure the strength of the relationship between the independent and dependent variables, it does not capture the effect of participants’ ambiguity when answering questionnaires. The source of such ambiguity stems from the process of completing the survey, whereby the respondent may have answered most of the independent questions with ease, but has difficulty in responding to the overall dependent question (or vice versa). The objective of this paper is to demonstrate the use of fuzzy logic as a mechanism to measure the uncertainty faced by participants when filling a questionnaire. Based on the participants’ responses to the independent variables, the proposed technique uses fuzzy logic inference to measure the subjectivity (qualitative aspect) of the participants’ response to the dependent variable. Beyond quantitative analysis, augmentation with such a fuzzy module can provide clearer picture to analysts when analyzing the survey results. In this paper, Cloud acceptance survey will be used as a vehicle to provide step-by-step explanation of the proposed augmentation technique to unleash the hidden knowledge in similar cases to cloud computing long survey questionnaire where participants may change their mind at the end of the survey causing uncertainty represented in discrepancy of the collected data. The proposed technique would only be valid for long surveys like the presented Cloud computing acceptability where uncertainty in the data is inevitable. [ABSTRACT FROM AUTHOR]

Objective: This paper aims to analyse the human musculoskeletal and energetic adaptation mechanisms when physically interacting with a unilateral knee orthosis during treadmill walking. Methods: Test subjects participated in two walking trials, whereby the orthosis was controlled to deliver five predefined torque profiles of different duration (as % of a gait cycle). The adaptations to assistive torques of different duration were analysed in terms of gait parameters, metabolic effort, and muscle activity. Results: Orthotic assistance’s kinematic effects remain local to the assisted leg and joint, unlike the muscles spanning the knee joint, which engage in a balancing-out action to retain stability. Duration of assistive torque significantly affects only the timing of the knee joint’s peak flexion angle in the stance phase, while the observed joint kinematics and muscle activity demonstrate different recovery times upon changing robotic support (washout effects). Conclusion: Human body adaptations to external robotic knee joint assistance during walking take place on multiple levels and to a different extent in a joint effort to keep the gait stable. Significance: This paper provides important insights into the human body’s multiple adaptation mechanisms in the presence of external robotic assistance. [ABSTRACT FROM AUTHOR]

Spatial co-location pattern mining (SCPM) aims to discover subsets of spatial features frequently located together in proximate areas. Previous studies of SCPM solely concern the inter-features association of a pattern, but neglect the interesting intra-feature behavior. In this paper, we propose the task of spatial co-location pattern mining with coupling relation consideration (SCPM-CR) to capture complex relations embedded in a co-location. Specifically, InterPCI measure is designed to capture the inter-features coupling by considering the comprehensive interaction of objects for the features in a pattern, and luckily it possesses the anti-monotone property. Another measure, IntraCAI, is proposed to capture the congregating behavior of intra-feature objects under the restriction of a co-location. A general framework is designed for SCPM-CR task and experiments show that a large fraction of computation time is devoted to identifying the participating objects of a candidate pattern. To tackle this calculation bottleneck, a novel candidate-and-search algorithm is suggested, CS-HBS, equipped with heuristic backtracking search. Extensive experiments are conducted on real and synthetic datasets to demonstrate the superiority of SCPM-CR compared with traditional SCPM methods, and also to validate the efficiency and scalability of CS-HBS. Experimental results show that CS-HBS outperforms the baselines by several times or even orders of magnitude. [ABSTRACT FROM AUTHOR]

A tradeoff between sum rate and fairness for multiple-input single-output (MISO) broadcast communication employing dirty paper coding or zero-forcing dirty paper coding at physical layer is investigated in this paper. The tradeoff is based on a new design objective termed “tri-stage” approach as well as a new $\ell _1$ -based fairness measure that is much more robust than the well-known Jain's index for comparing fairness levels achieved by various design objectives at a much finer resolution in high SNR regime. The newly proposed tri-stage design also introduces a new concept of statistical power allocation that randomly allocates power to users based on an optimal probability distribution derived from the tradeoff between sum rate and fairness. Simulation results show that the proposed approach can simultaneously achieve a larger sum rate and better fairness than the reputable proportional fairness criterion. A performance upper bound is also given in the paper to show the excellent performance of the proposed approach at moderate and high SNR regimes as well as some potential for further improvement in low SNR regime. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach for the data-driven characterization of power system dynamics. The developed method of Extended Subspace Identification (ESI) is suitable for systems with output measurements when all the dynamics states are not observable. It is particularly applicable for power systems dynamic identification using Phasor Measurement Units (PMUs) measurements. As in the case of power systems, it is often expensive or impossible to measure all the internal dynamic states of system components such as generators, controllers and loads. PMU measurements capture voltages, currents, power injection and frequencies, which can be considered as the outputs of system dynamics. The ESI method is suitable for system identification, capturing nonlinear modes, computing participation factor of output measurements in system modes and identifying system parameters such as system inertia. The proposed method is suitable for measurements with a noise similar to realistic system measurements. The developed method addresses some of the known deficiencies of existing data-driven dynamic system characterization methods. The approach is validated for multiple network models and dynamic event scenarios with synthetic PMU measurements. [ABSTRACT FROM AUTHOR]

Data have significant economic or social value in many application fields including science, business, governance, etc. This naturally leads to the emergence of many data markets such as GBDEx and YoueData. As a result, the data trade through data markets has started to receive attentions from both industry and academia. During the data buying and selling, how to price the data is an indispensable problem. However, pricing incomplete data is more challenging, even though incomplete data exist pervasively in a vast lot of real-life scenarios. In this paper, we attempt to explore the pricing problem for queries over incomplete data. We propose a sophisticated pricing mechanism, termed as ${\sf iDBPricer}$ iDBPricer , which takes a series of essential factors into consideration, including the data contribution/usage, data completeness, and query quality. We present two novel price functions, namely, the usage, and completeness-aware price function (UCA price for short) and the quality, usage, and completeness-aware price function (QUCA price for short). Moreover, we develop efficient algorithms for deriving the query prices. Extensive experiments using both real and benchmark datasets demonstrate ${\sf iDBPricer}$ iDBPricer is of excellent performance in terms of effectiveness and scalability, compared with the state-of-the-art price functions. [ABSTRACT FROM AUTHOR]

Most of siamese network and correlation filter (CF) based trackers usually employ the context regression scheme to achieve appealing performance in both accuracy and efficiency. However, they are prone to drifting in challenging situations exhibiting occlusion, out-of-view and large-scale variations due to the lack of failure correction ability in these regressors. Particle filter based trackers can help to recover from tracking failures since several particles of high confidence about the target can be remained for the probability estimation of next frames, but need the large numbers of particles for each frame. In this paper, we propose a generic dynamic particle filter framework, which can reasonably control the number of particles in different scenarios, to improve the robustness of siamese and CF trackers by jointing the target classifier to relieve drifting. Our fundamental insight is that general scenarios are processed efficiently by the context regressor with few particles, while special scenarios are handled effectively by the target classifier with many particles. We propose a novel measure to determine whether to adopt the regressor and few particles to estimate target states with high measure scores, or increase the number of particles to prevent drifting with the proposed multi-template matching strategy in the classifier. In extensive experiments on eight large-scale benchmarks including OTB, UAV, TC128, VOT2017, VOT2019, LaSOT, TrackingNet and Got-10k, the proposed framework enables many basic siamese and CF trackers to operate at least over 24 frames per second and achieve superior tracking performance than themself, as well as the comparable accuracy with the state-of-the-art trackers. Furthermore, our framework is flexible and still has great potential for improvement and generalization. [ABSTRACT FROM AUTHOR]

In prior work, we have shown how the basic concepts and terms of quantum mechanics relate to factorizations and marginals of complex-valued quantum mass functions, which are generalizations of joint probability mass functions. In this paper, using quantum mass functions, we discuss the realization of measurements in terms of unitary interactions and marginalizations. It follows that classical measurement results strictly belong to local models, i.e., marginals of more detailed models. Classical variables that are created by marginalization do not exist in the unmarginalized model, and different marginalizations may yield incompatible classical variables. These observations are illustrated by the Frauchiger–Renner paradox, which is analyzed (and resolved) in terms of quantum mass functions. Throughout, the paper uses factor graphs to represent quantum systems/models with multiple measurements at different points in time. [ABSTRACT FROM AUTHOR]

The ability to monitor the quality of the metal forming process as well as the machine's condition is of significant importance in modern industrial processes. In the case where a physical device (i.e., sensor) cannot be deployed due to the characteristics of the system, models that rely on the estimation of both the applied force and the dynamic behavior of the machine (i.e., system) are adopted. The development of such models and the corresponding algorithms used to estimate the above-mentioned quantities has attracted the interest of the community. The main contribution of this paper is the estimation of a servo press force by employing a novel dual particle filter based algorithm, achieving a maximum relative error in the force estimation of 3.6%. Moreover, to address real-time performance requirements, this paper proposes a field programmable gate array based accelerator that improves the sampling rate by a factor of 200 compared to a processor-based solution, thus enabling the deployment of the system in many realistic scenarios. [ABSTRACT FROM AUTHOR]

The problem of recovering a pair of signals from their blind phaseless short-time Fourier transform measurements arises in several important phase retrieval applications, including ptychography and ultra-short pulse characterization. In this paper, we prove that in order to determine a pair of generic signals uniquely, up to trivial ambiguities, the number of phaseless measurements one needs to collect is, at most, five times the number of parameters required to describe the signals. This result improves significantly upon previous papers, which required the number of measurements to be quadratic in the number of parameters rather than linear. In addition, we consider the simpler problem of recovering a pair of generic signals from their blind short-time Fourier transform, when the phases are known. In this setting, which can be understood as a special case of the blind deconvolution problem, we show that the number of measurements required to determine the two signals, up to trivial ambiguities, equals exactly the number of parameters to be recovered. As a side result, we study the classical phase retrieval problem—that is, recovering a signal from its Fourier magnitudes—when some entries of the signal are known a priori. We derive a bound on the number of required measurements as a function of the size of the set of known entries. Specifically, we show that if most of the signal’s entries are known, then only a few Fourier magnitudes are necessary to determine a signal uniquely. [ABSTRACT FROM AUTHOR]

This paper proposes an approach to determine the participation of algebraic variables in power system modes. The approach is based on a new interpretation of the classical participation factors, as well as on the definition of adequate output variables of the system's state-space representation. The paper considers both the linear and generalized eigenvalue problems for the calculation of the participation factors and presents a theorem to cope with eigenvalue multiplicities. An illustrative example on the two-area system, as well as a study on a 1479-bus dynamic model of the all-Irish transmission system are carried out to support the theory and illustrate the features of the proposed approach. [ABSTRACT FROM AUTHOR]

It is known that particle size obtained from Brownian motion of a particle is called the diffusion coefficient equivalent size (DCES) and is close to the geometric size independent of the physical properties of a particle. In this paper, it is described that real time measurement of the particle size and particle number concentration by observing the Brownian motion of the particles from which the influence of the flow field has been eliminated by the instrument combining the flow particle tracking (FPT) method and the L-shaped flow cell. Furthermore, we realized in evaluating the refractive index, which is the physical property information of the particles, from the DCES, the Rayleigh scattering equation and the light scattering intensity of particle that physical properties are known. In this paper, we also discuss that improvement of particle size accuracy and refractive index accuracy in the measurement of DCES. [ABSTRACT FROM AUTHOR]

This paper is concerned with estimating remaining useful life (RUL) for a class of stochastic degrading systems with surviving degradation paths and uncertain measurements. The motivation comes from two engineering facts: the system's degradation state is discretely monitored and the measured degradation signals are taken from a survival degradation path, i.e., its lifetime is greater than the latest observation time, and the underlying degradation state cannot be perfectly observed due to the noise, disturbance, nonideal instruments, etc. Thus, the measured degradation signals are uncertain, but related to the underlying degradation state. Toward this end, we first present a general stochastic degradation modeling approach to account for the abovementioned facts. Then, a non-Gaussian degradation state transition equation is derived considering the constraint of the survival path and thus the particle filtering algorithm is used to estimate the underlying degradation state in real time from uncertain measurements. Furthermore, we derive the RUL distribution based on the first-passage time concept which incorporates the uncertainty of the estimation for the degradation state and can be real-time updated based on the available surviving yet uncertain measurements. The novelty of this paper is to allow us to exclude the probability of failure between discrete monitoring times and to account for the impacts of surviving and uncertain measurements on estimating both the degradation state and the RUL. To apply the presented methodology, a maximum likelihood estimation framework is provided to determine the model parameters based on the expectation maximization algorithm together with particle filtering and smoothing methods. As a special case fallen into the presented framework, a linear degradation modeling and prognostic realization is discussed. Finally, we demonstrate the proposed approach by a case study. [ABSTRACT FROM AUTHOR]

Cell selection is a critical issue in sparse mobile crowdsensing (MCS) systems. However, the sensing cost heterogeneity among different cells (subareas) has long been ignored by existing works. Moreover, the data provided by participants are not always trustworthy, and some malicious participants may intend to launch data positioning attacks, which raises a new challenge for cell selection. In this paper, to address these issues, we propose a trustworthy and cost-effective cell selection (TCECS) framework that takes cell heterogeneity and malicious participants into consideration simultaneously. To this end, we first offer to utilize an iterative statistical spatial interpolation technique to identify trustworthy participants with the help of a small portion of dedicated sensors. Furthermore, we employ the regularized mutual coherence (RMC) in compressive sensing (CS) theory to characterize the contribution to inference accuracy of measurements submitted by different trustworthy participants. Finally, the cell selection strategy, which consumes the least sensing cost while satisfying a given sensing quality, is determined via an RMC-constrained optimization problem. Extensive experiments on a real-world taxi GPS dataset demonstrate that the proposed approach can mitigate the adverse effects of malicious participants and outperforms the baselines with less sensing cost for the same required sensing quality. [ABSTRACT FROM AUTHOR]

Contribution: This paper found that design experiences can foster engineering identity and belongingness for early career electrical and computer engineering students. Students had different interpretations of what it meant to be an engineer (identity) and their belongingness in engineering. This paper provides novel insights into how students may be developing identities and belongingness in engineering, both critical for student retention and success. Background: Design experiences are crucial for engineering students, both for developing academic competencies and allowing students to see how they can become engineers. Existing literature has mixed results with respect to the influence of team-based design experiences on engineering identity and belongingness. Research Questions: 1) How do design experiences influence early career electrical and computer engineering students’ identification and belongingness in engineering? and 2) How do these students describe what it means to identify as an engineer and belong in engineering? Methodology: The beliefs of electrical and computer engineering students were examined using mixed methods to understand the intersection of design experiences, engineering identity, and belongingness. Findings: Students interpreted their engineering identity and belongingness differently, particularly, with respect to how design experiences can shape these attitudes. Whereas students interpreted engineering identity through their performance and interest in authentic engineering tasks, they interpreted belongingness as a means of comparing themselves to their peers. The findings have implications for engineering education researchers, and design instructors, to foster ways of being and belonging in engineering. [ABSTRACT FROM AUTHOR]

Land surface temperature and emissivity (LST&E) are key variables for a wide variety of surface–atmosphere studies, and it is very important to validate the accuracy of different LST&E retrieval algorithms. In this paper, the accuracy of the Advanced Spaceborne Thermal Emission and Reflection (ASTER) temperature emissivity separation (TES) algorithm with the water vapor scaling (WVS) method and the ASTER standard LSE&E products (without WVS) was validated using 12 ASTER scenes from May 2012 to September 2012 with concurrent ground LST and emissivity measurements collected in an arid area in northwest China during the Heihe Watershed Allied Telemetry Experimental Research experiment. Both the National Center for Environmental Prediction (NCEP) and MOD07 atmospheric profile products were employed to perform the atmospheric correction. The results showed that the WVSTES LSTs retrieved from the two profiles both demonstrate good accuracies, with average biases of 0.34 and 0.24 K and average root-mean-square errors (RMSEs) of 1.52 and 1.46 K for the NCEP and MOD07 profiles, respectively. When the WVS was not applied, we obtained an average bias of 1.26 K and an average RMSE of 2.05 K for the AST08 product. The emissivities from the WVSTES algorithm with the two profiles both showed good agreements with the ground CE312 measurements, with mean differences of the five ASTER bands that were less than 0.01. AST05 showed anomalous emissivity spectra and underestimated the emissivity values of graybody surfaces when compared with the ground data, with mean differences of greater than 0.015, which were more obvious for cases with high water vapor. This paper demonstrated that the WVS method is critical for retrieving ASTER TES LST with accuracies within 1.5 K and emissivity within 0.015 for a wide range of atmospheric conditions and land surface types. [ABSTRACT FROM AUTHOR]

Using the barometer for height estimation often requires the use of external reference to correct biases in measurement. These biases are often caused by the change of the ambient pressure environment. The barometric height estimation is especially challenging in tactical and rescue applications where high temperatures or sudden large pressure shocks can change the pressure rapidly. We assess the suitability of barometers for infrastructure-free navigation in tactical applications. First, this paper investigates the effect of transition in seamless indoor/outdoor navigation. Second, we measure the effects of pressure shocks, caused by explosions or firearms, on low-cost and lightweight micro-electromechanical barometers to ensure that the sensors are capable of operating under these conditions. Finally, we investigate the use of sonar measurement to estimate the vertical speed as an alternative to the reference barometer for infrastructure-free navigation. The fusion of barometer and sonar achieved on average 0.46-m root-mean-square error (RMSE) while simple barometric height estimation had a RMSE of 0.65 m. The fusion method had no errors over 1.5 m during the test. This accuracy is generally sufficient to find the correct floor level which is crucial for tactical situational awareness. The goal of this paper is to develop the methods for seamless indoor/outdoor navigation, and therefore the most important result of this paper is that the error caused when transitioning between outdoor and indoor environments is visibly reduced. [ABSTRACT FROM AUTHOR]

Smartphones have become an indispensable tool in the work and life of most people. Given the many kinds of sensors built into smartphones, solving indoor navigation problems using these phones has become feasible. This paper proposes such a solution, using a particle-filter-based indoor positioning method. This method improves the positioning accuracy and helps improve the user experience. The major features of the method are that it leverages and fuses the advantages of two different indoor positioning methods [pedestrian dead reckoning (PDR) and received-signal-strength-indicator-based indoor positioning], and considers the influence on positioning of the indoor spatial map. The smartphone built-in sensors are used as data sources and constitute a feasible method for PDR. The iBeacon devices are also used—because of the many advantages, they present in indoor environments, such as low energy consumption, convenient deployment, and popular applications—and their received signal strength indicator values are used as observations in the particle filter. The devices used in this paper are conventional, without any special equipment, and the experimental sites are common work environments. Therefore, we believe that the proposed method has a wide application potential for indoor navigation applications (e.g., museum guides), and is especially suitable for working with indoor navigation maps. [ABSTRACT FROM AUTHOR]

Interval scales are assumed by several basic descriptive statistics, such as mean and variance, and by many statistical significance tests which are daily used in IR to compare systems. Unfortunately, so far, there has not been any systematic and formal study to discover the actual scale properties of IR measures. Therefore, in this paper, we develop a theory of Information Retrieval (IR) evaluation measures, based on the representational theory of measurements, to determine whether and when IR measures are interval scales. We found that common set-based retrieval measures—namely Precision, Recall, and F-measure—always are interval scales in the case of binary relevance while this happens also in the case of multi-graded relevance only when the relevance degrees themselves are on a ratio scale and we define a specific partial order among systems. In the case of rank-based retrieval measures—namely AP, gRBP, DCG, and ERR—only gRPB is an interval scale when we choose a specific value of the parameter $p$ and define a specific total order among systems while all the other IR measures are not interval scales. Besides the formal framework itself and the proof of the scale properties of several commonly used IR measures, the paper also defines some brand new set-based and rank-based IR evaluation measures which ensure to be interval scales. [ABSTRACT FROM AUTHOR]

This paper discusses the design and implementation of a biomimetic, flexible antenna system consisting of flexible dipoles on Pyralux AP. They are implemented into a novel, airborne atmospheric sensor system. The antennas and substrate serve a secondary function as a rotor system, forcing the sensor nodes to autorotate when falling to maximize drag and decrease their fall speed. The designed antenna system works both in the industrial, scientific, and medical band and the GPS L1 band with the designed antennas having bandwidths of 100 and 130 MHz, respectively. This paper also presents a test procedure to characterize the antenna performance at different bend angles. [ABSTRACT FROM AUTHOR]

The infrared atmospheric sounder interferometer (IASI) onboard the Metop-A and Metop-B satellites is an essential instrument for numerical weather prediction centers. The IASI, with its 8461 channels, enables the observation of the surface and atmospheric geophysical variables twice a day around the world. This data volume will increase in the future, stressing the need for practical and efficient dimension reduction techniques for storage and transmission but also for inversion or assimilation. In a previous paper by Aires et al., a new dimension reduction method, the so-called bottleneck channel (BC), has been introduced. BCs are a good compromise between compression and channel selection techniques. In this paper, we introduce several new technical configurations of the BC approach, comparing their advantages/drawbacks. Comparisons are also made with two classical methodologies widely used in the satellite community: principal component analysis (PCA) compression and entropy reduction channel selection. Beyond the compression ability, the methodologies are tested on the temperature, water vapor, and ozone retrieval application using IASI measurements. Two approaches have been tested. First, theoretical retrieval improvement has been measured when using the various data reduction techniques. The BC achieves good theoretical improvements, comparable to noise-adjusted PCA (NAPCA), for the retrieval of the temperature, water vapor, and ozone. Second, a neural network inversion scheme is tested on real IASI observations using the several data reduction techniques. It is shown that BCs provide similar or better results than the other methods. The BCs present compression, denoising, and information content results comparable to the NAPCA, while preserving the physical meaning (i.e., actual channel brightness temperatures) of a channel selection approach. This has numerous advantages for inversion and assimilation: the mixing problem, contamination effects, minor constituents representation, or radiative transfer speed, and each of these will be discussed. [ABSTRACT FROM AUTHOR]

This paper reviews the main features of acoustic emission technique. First, the characteristics of acoustic signals and their physics are addressed. Then, the structure of acoustic sensors employed for capturing signals is studied. In the next step, the acoustic method of PD measurement is compared with the standard electrical method. Afterward, the applications of AET in PD measurement in different equipment are summarized. All acoustic method and combined acoustic-electrical method for PD localization in transformers are discussed. Moreover, the applications of AET in the monitoring of GIS systems are explained, along with the acoustic behaviors of moving particles in these systems. Finally, the general considerations in the signal processing of the acoustic signals are reviewed. [ABSTRACT FROM AUTHOR]

This paper investigates the particle filtering problem for a class of nonlinear/non-Gaussian systems under the dynamic event-triggered protocol. In order to avert frequent data transmission and reduce the communication overhead, a dynamic event-triggered transmission mechanism is adopted to decide whether the data should be transmitted or not. We first consider a scenario where all sensor nodes selectively transmit their newly obtained measurements to a central node, and a full likelihood function at the central node is derived by fusing the transmitted measurements and the information embodied in the non-triggered measurements. Based on the derived full likelihood function, a centralized auxiliary particle filtering algorithm is proposed to select those particles that are more likely to match the current measurement information. Next, based on the diffusion strategy, a distributed auxiliary particle filtering algorithm is further developed, where the local measurements and the local posteriors (approximated by the Gaussian mixture models) are exchanged among neighboring nodes under the dynamic event-triggered communication strategy. Finally, the effectiveness of the proposed filtering schemes is demonstrated via Monte Carlo simulations in a target tracking problem with received-signal-strength sensors. [ABSTRACT FROM AUTHOR]

This paper proposes a novel atmosphere-informed predictive satellite channel model for beyond the fifth-generation (B5G)/the sixth-generation (6G) satellite-terrestrial wireless communication systems at Q-band to model/predict channel attenuation at any specific time. The proposed channel model is a data-driven model based on either of two deep learning networks, i.e., multi-layer perceptron (MLP) and long short-term memory (LSTM). The accuracy of the proposed channel model is measured by cumulative density function (CDF) of absolute error and mean square error (MSE) between modeled/predicted and measured channel attenuation. The complexity of the proposed channel model is assessedby the training time, loading time, and test time of deep learning networks. To further improve the accuracy of the proposed channel model, weather classification is developed at the stage of database construction. Based on our established channel and weather measurement campaign, the performance of the proposed data-driven channel model based on different deep learning networks, e.g., MLP and LSTM, with or without the weather classification is investigated and analyzed comprehensively. Finally, the close agreement is achieved between the channel attenuation modeled/predicted from the proposed atmosphere-informed predictive satellite channel model and the one from real channel measurements, verifying the utility of proposed channel model. [ABSTRACT FROM AUTHOR]

In this paper, the problem of estimating the unknown degree of sparsity from compressive measurements without the need to carry out a sparse recovery step is investigated. While the sparsity order can be directly inferred from the effective rank of the observation matrix in the multiple snapshot case, this appears to be impossible in the more challenging single snapshot case. It is shown that specially designed measurement matrices allow to rearrange the measurement vector into a matrix such that its effective rank coincides with the effective sparsity order. In fact, it is proven that matrices that are composed of a Khatri–Rao product of smaller matrices generate measurements that allow to infer the sparsity order. Moreover, if some samples are used more than once, one of the matrices needs to be Vandermonde. These structural constraints reduce the degrees of freedom in choosing the measurement matrix, which may incur in a degradation in the achievable coherence. Thus, this paper also addresses suitable choices of the measurement matrices. In particular, Khatri–Rao and Vandermonde matrices are analyzed in terms of their coherence and a new design for Vandermonde matrices that achieves a low coherence is proposed. [ABSTRACT FROM AUTHOR]

This paper uses the meteorological conditions to predict neutron background counts as measured by a neutron detector. Such counts occur due to cosmic-ray interactions and temporal variations that are primarily dependent on meteorological factors. This paper presents a marginal autoregressive (AR) model constructed from eight months of hourly meteorological data that accurately predicts neutron background without being susceptible to the presence of source material. Nonmeteorological factors impacting neutron background (including cosmic-ray intensity) are not readily measurable in real time in a validated format, so a conditional AR model is also presented that can compensate for phenomena not included in the model while maintaining some independence from potential sources. This paper shows that the conditional AR model is more effective than the moving average, with lower susceptibility to source contamination, ensuring a consistent level of sensor effectiveness. [ABSTRACT FROM PUBLISHER]

This paper addresses the performance in the retrieval of 3-D mean deformation maps by exploiting simultaneous or quasi-simultaneous squinted synthetic aperture radar (SAR) interferometric acquisitions in a repeat-pass scenario. In multisatellite or multibeam low earth observation missions, the availability of two (or more) lines of sight (LOSs) allows the simultaneous acquisition of SAR images with different squint angles, hence improving the sensitivity to the north–south component of the deformation. Due to the simultaneity of the acquisitions, the troposphere will be highly correlated and, therefore, will tend to cancel out when performing the differential measurement between the interferograms obtained with the different LOSs, hence resulting in a practically troposphere-free estimation of the along-track deformation measurement. In practice, however, the atmospheric noise in the differential measurement will increase for increasing angular separations. This paper expounds the mathematical framework to derive the performance by properly considering the correlation of the atmospheric delays between the simultaneous acquisitions. To that aim, the hybrid Cramér–Rao bound is exploited making use of the autocorrelation function of the troposphere. Some performance examples are presented in the frame of future spaceborne SAR missions at C and L band. [ABSTRACT FROM AUTHOR]

This paper continues an overview of the validation of operational profile retrievals from the Suomi National Polar-Orbiting Partnership (SNPP), with focus here given to the infrared (IR) ozone profile environmental data record (EDR) product. The SNPP IR ozone profile EDR is retrieved using the cross-track IR sounder (CrIS), a Fourier transform spectrometer that measures high-resolution IR earth radiance spectra containing atmospheric state information, namely, vertical profiles of temperature, moisture, and trace gas constituents. The SNPP CrIS serves as the U.S. low earth orbit (LEO) satellite IR sounding system and will be featured on future Joint Polar Satellite System (JPSS) LEO satellites. The operational sounding algorithm is the National Oceanic and Atmospheric Administration-Unique Combined Atmospheric Processing System (NUCAPS), a legacy sounder science team algorithm that retrieves atmospheric profile EDR products, including ozone and carbon trace gases, with optimal vertical resolution under nonprecipitating (clear to partly cloudy) conditions. The NUCAPS ozone profile product is assessed in this paper using extensive global in situ truth data sets, namely, ozonesonde observations launched from ground-based networks and from ocean-based intensive field campaigns, along with numerical weather prediction model output. Based upon rigorous statistical analyses using these data sets, the NUCAPS ozone profile EDRs are determined to meet the JPSS Level 1 global performance requirements. [ABSTRACT FROM AUTHOR]

This paper analyzes the large positive bias of sea surface temperature (SST) retrievals of selected remotely sensed algorithms recorded during the simultaneous occurrence of upwelling and atmospheric subsidence along the coastal waters of Rio de Janeiro, Brazil. The optimal estimator (OE) for retrieving SST and the multichannel (MCSST) and nonlinear (NLSST) estimators are compared using Advanced Very High Resolution Radiometer-3 data. The in situ SST (SSTbuoy) data set used to validate the remotely sensed SST retrievals was collected from five moored buoys (four in the open sea and one in coastal waters). The principal results of this paper are as follows. First, the sensitivity analyses show that OE is quite susceptible to the first-guess SST rather than to the humidity profiles. Second, the comparison between the SSTOE and 365 cloud-free SSTbuoy measurements in open sea waters presents an root mean squared error (RMSE), bias, and standard deviation (STD) with the intervals of [0.5, 0.6], [−0.51, 0.13], and [0.27, 0.48], respectively. Third, the MCSST, NLSST, and OE SST produce a positive bias that can reach 5 K during simultaneous upwelling and atmospheric subsidence in coastal waters. Such unexpected errors are due to low SST values and water vapor compression in the lower layer of the atmosphere related to a temperature inversion. Fourth, an alternative approach using SSTbuoy obtained on the previous day as a first guess instead of the climatological SST significantly improves the errors (SSTOE–SSTbuoy) by reducing RMSE, bias, and STD by 58% (from 3.30 to 1.39 K), 73% (from 3.00 to 0.80 K), and 19% (from 1.38 to 1.12 K), respectively. [ABSTRACT FROM PUBLISHER]

Task interpretation is a critical first step for students in the process of self-regulated learning, and a key determinant when they set goals in their learning and select strategies in assigned work. This paper focuses on the explicit and implicit aspects of task interpretation based on Hadwin’s model. Laboratory activities improve students’ conceptual understanding, as they utilize cognitive ability to integrate the new experiences these provide. The purpose of this paper is to investigate how students’ interpretation of a task assigned during laboratory work may change during the task process, and how this relates to their conceptual understanding. A total of 143 students enrolled in an electronics course participated in this paper. Instruments to measure task interpretation and conceptual understanding were created, piloted, and applied before and after selected laboratory activities over the semester. Findings suggest that while students’ task interpretation changes during the task process, increasing after the completion of the laboratory activity levels of task interpretation are low. Previous research findings—that students generally have an incomplete understanding of the assigned tasks and struggle to establish a connection between laboratory activities and the theory—were confirmed. Lastly, this paper reports a significant relationship between students’ task interpretation and their conceptual understanding in laboratory work. Further investigation is necessary to unveil other factors related to these constructs in order to engage students in laboratory work. [ABSTRACT FROM AUTHOR]

This paper addresses 6-DOF (degree-of-freedom) tactile localization, i.e., the pose estimation of tridimensional objects using tactile measurements. This estimation problem is fundamental for the operation of autonomous robots that are often required to manipulate and grasp objects whose pose is a priori unknown. The nature of tactile measurements, the strict time requirements for real-time operation, and the multimodality of the involved probability distributions pose remarkable challenges and call for advanced nonlinear filtering techniques. Following a Bayesian approach, this paper proposes a novel and effective algorithm, named memory unscented particle filter (MUPF), which solves 6-DOF localization recursively in real time by only exploiting contact point measurements. The MUPF combines a modified particle filter that incorporates a sliding memory of past measurements to better handle multimodal distributions, along with the unscented Kalman filter that moves the particles toward regions of the search space that are more likely with the measurements. The performance of the proposed MUPF algorithm has been assessed both in simulation and on a real robotic system equipped with tactile sensors (i.e., the iCub humanoid robot). The experiments show that the algorithm provides accurate and reliable localization even with a low number of particles and, hence, is compatible with real-time requirements. [ABSTRACT FROM AUTHOR]

In this paper we prove two results related to the Gaussian optimizers conjecture for multimode bosonic system with gauge symmetry. First, we argue that the classical capacity of an arbitrary Gaussian observable is attained on a Gaussian ensemble of coherent states. This generalizes results previously known for heterodyne measurement in one mode. By using this fact and continuous variable version of ensemble-observable duality, we prove an old conjecture that accessible information of arbitrary Gaussian ensemble is attained on the multimode generalization of the heterodyne measurement. [ABSTRACT FROM AUTHOR]

A stochastic gradient (SG)-based particle filter (SG-PF) algorithm is developed for an ARX model with nonlinear communication output in this paper. This ARX model consists of two submodels, one is a linear ARX model and the other is a nonlinear output model. The process outputs (outputs of the linear submodel) transmitted over a communication channel are unmeasurable, while the communication outputs (outputs of the nonlinear submodel) are available, and both of the two-type outputs are contaminated by white noises. Based on the rich input data and the available communication output data, a SG-PF algorithm is proposed to estimate the unknown process outputs and parameters of the ARX model. Furthermore, a direct weight optimization method and the Epanechnikov kernel method are extended to modify the particle filter when the measurement noise is a Gaussian noise with unknown variance and the measurement noise distribution is unknown. The simulation results demonstrate that the SG-PF algorithm is effective. [ABSTRACT FROM AUTHOR]

The number of nodes in sensor networks is continually increasing, and maintaining accurate track estimates inside their common surveillance region is a critical necessity. Modern sensor platforms are likely to carry a range of different sensor modalities, all providing data at differing rates, and with varying degrees of uncertainty. These factors complicate the fusion problem as multiple observation models are required, along with a dynamic prediction model. However, the problem is exacerbated when sensors are not registered correctly with respect to each other, i.e., if they are subject to a static or dynamic bias. In this case, measurements from different sensors may correspond to the same target, but do not correlate with each other when in the same Frame of Reference (FoR), which decreases track accuracy. This paper presents a method to jointly estimate the state of multiple targets in a surveillance region, and to correctly register a radar and an Infrared Search and Track (IRST) system onto the same FoR to perform sensor fusion. Previous work using this type of parent-offspring process has been successful when calibrating a pair of cameras, but has never been attempted on a heterogeneous sensor network, or in a maritime environment. This article presents results on both simulated scenarios and a segment of real data that show a significant increase in track quality in comparison to using incorrectly calibrated sensors or single-radar only. [ABSTRACT FROM AUTHOR]

To optimize control and/or state estimation of industrial processes, information about measurement quality provided by sensors is required. In this paper, a probabilistic scheme is proposed in discrete-time nonlinear state space with the purpose of sensor monitoring. A quantitative index representing the measurement quality, as well as satisfied state estimates, is obtained by estimating the probability density functions (PDFs) of the states and the measurement noise covariance considered as a random variable using the variational Bayesian approach. To solve the intractable integrals of nonlinear PDFs in real time, a set of weighted particles is generated to overlap an empirical density of state, while the PDF of the measurement noise is still derived analytically. An example of localization and an experiment with a rotary flexible joint are supplied to demonstrate that the proposed algorithm significantly improves the applicability of existing methods and can monitor correlated sensors satisfactorily. [ABSTRACT FROM AUTHOR]

Satellite altimetry allows the estimation of accurate water surface heights only with accurate determination of all involved terms, namely, the wet tropospheric correction (WTC) or its symmetric value, the wet path delay (WPD). WPD is best determined from onboard microwave radiometer (MWR) measurements; however, the corresponding WPD retrievals become invalid close to land (e.g., coastal and inland waters). Alternative WPD sources are numerical weather models (NWMs), e.g., from the European Centre for Medium-Range Weather Forecasts (ECMWF). NWMs provide the parameters at 6-h intervals; however, ERA5 (the latest ECMWF reanalysis) provides hourly atmospheric parameters at $0.25^{\circ } \times 0.25^{\circ }$. The best spatial resolution is provided by the ECMWF operational model at $0.125^{\circ } \times 0.125^{\circ }$. Motivated by this new and improved temporal resolution, the focus of this paper is the global assessment of the impact of different temporal resolutions of ERA5 in the WPD computation. The same assessment is also performed concerning the spatial resolution of ERA5 and operational models. Aiming to identify the best compromise between spatial/temporal resolutions, accuracy, and computational time, WPDs computed using various combinations of these resolutions were inter-compared and compared with MWR-derived WPD. The results show that the global root mean square (RMS) of the differences between MWR and ERA5 is 1.2 cm and the effect of using 1-h intervals instead of 6-h intervals is small, significant only for latitudes 30°–60° S and 30°–60° N. Hourly intervals do not have a significant impact on the WPD from ERA5, being a temporal resolution of 3 h high enough to ensure the same accuracy of 1 h, showing that ERA5 cannot map the WPD short space and time scales. [ABSTRACT FROM AUTHOR]

Infrared atmospheric sounders, such as IASI, provide an unprecedented source of information for atmosphere monitoring and weather forecasting. Sensors provide rich spectral information that allows retrieval of temperature and moisture profiles. From a statistical point of view, the challenge is immense: on the one hand, "underdetermination" is common place as regression needs to work on high dimensional input and output spaces; on the other hand, redundancy is present in all dimensions (spatial, spectral and temporal). On top of this, several noise sources are encountered in the data. In this paper, we present for the first time the use of convolutional neural networks for the retrieval of atmospheric profiles from IASI sounding data. The first step of the proposed pipeline performs spectral dimensionality reduction taking into account the signal to noise characteristics. The second step encodes spatial and spectral information, and finally prediction of multidimensional profiles is done with deep convolutional networks. We give empirical evidence of the performance in a wide range of situations. Networks were trained on orbits of IASI radiances and tested out of sample with great accuracy over competing approximations, such as linear regression (+32%). We also observed an improvement in accuracy when predicting over clouds, thus increasing the yield by 34% over linear regression. The proposed scheme allows us to predict related variables from an already trained model, performing transfer learning in a very easy manner. We conclude that deep learning is an appropriate learning paradigm for statistical retrieval of atmospheric profiles. [ABSTRACT FROM AUTHOR]

This paper investigates the sensor selection problem in time difference of arrival (TDOA) tracking scenario to find the optimal sensor activation strategy for the upcoming time step. We propose a multiobjective optimization framework to minimize two conflicting objectives, i.e., tracking accuracy and quantity budget, which are represented by the trace of the conditional posterior Cramér Rao lower bound (CPCRLB) and the number of selected sensors, respectively. Due to the reduced measurement dimension and correlated noise caused by the common reference sensor (CRS), sensor selection algorithms for the general nonlinear model in existing literature cannot be applied to the TDOA tracking directly as they mostly assume that each sensor produces an independent measurement. Therefore, we introduce two Boolean vectors to indicate the CRS and ordinary sensors respectively. The sensor selection problem is then formulated as a multiobjective optimization problem (MOP), which can be further transformed as a single objective optimization problem (SOOP) using the linear weighted-sum method. We prove that the SOOP satisfies the rules of discrete monotonic optimization (DMO), and propose the polyblock outer approximation (POA)-based algorithms to seek for a globally optimal solution. For comparison, we introduce the conventional semidefinite program (SDP)-based algorithm to solve the SOOP with multi-step relaxation. Simulation results demonstrate that the proposed POA-based algorithms can considerably outperform the SDP-based ones in solving the optimization problem. [ABSTRACT FROM AUTHOR]

In this paper, we report measurements in Colorado and Utah of the disturbed very-low-frequency (VLF) signals from the NML Navy transmitter in North Dakota during the 2017 solar eclipse. Using an occultation mask of solar fluxes together with detailed chemistry and VLF propagation simulations, we quantify the D-region response to the eclipse, in terms of electron density variation, as well as the expected signatures of VLF transmitter signals. The VLF measurements, including an anomalous amplitude enhancement recorded in UT, can be quantitatively explained using the Wait and Spies ionospheric profile with a sharpness parameter of $\beta = 0.3$ km−1 above ~55 km and an increase in the D-region ionosphere height of $\Delta h' \simeq 8$ km. This sharpness parameter is consistent with previously reported rocket measurements and first-principles calculations. The best-fit results suggest a reduction of D-region electron density by ~90% during the eclipse in the D-region, implying an occultation of Lyman- $\alpha $ by nearly 99%. This finding agrees with detailed calculations of time-dependent obscuration factors utilizing the He 30.4-nm images from Solar Dynamics Observatory as a proxy for the distribution of Lyman- $\alpha $ across the solar disk and limb. Moreover, the present results show that subionospheric VLF propagation is sensitive to the sharpness parameter of the electron density profile in the D-region. Previously reported first-principles simulations have shown that the sharpness parameter is mostly controlled by the background concentration of minor neutral species. Thus, the VLF technique can be likely used to remotely sense these neutral species at and below the effective reflection altitudes of VLF waves. [ABSTRACT FROM AUTHOR]

Water vapor plays a vital role in the Arctic sea ice and climate change. The Atmospheric Infrared Sounder (AIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) are useful tools for studying the water vapor variations over the entire Arctic. In this paper, we discuss precipitable water vapor (PWV) variations from the AIRS and MODIS Level-3 data products in response to Arctic sea ice change during December 2002–November 2016. The results indicate that both AIRS- and MODIS-derived PWV measurements tend to be underestimated, and this underestimation is generally greater for the latter than the former over both land and ocean. Additionally, both the AIRS and MODIS retrievals suggest that the atmospheres are drier in the solid sea ice pack and, to a lesser extent, in the marginal ice zone, than in the open ocean in each season. Moreover, statistically significant correlations between the PWV and sea ice concentration (SIC) anomalies are generally observed in all seasons under different ice conditions except in the open ocean, where variations in the PWV may be attributed to the poleward moisture transport from lower latitudes. The Arctic PWV and SIC variation patterns are also dominated by the atmospheric and ice conditions in the solid sea ice pack, respectively. The monthly mean PWV and SIC time series analysis further reveals that the Arctic PWV variations tend to be affected by a delayed effect of approximately 1.2–1.3 months from SIC changes from a climatological perspective. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to describe a new algorithmic method to imitate 3-D geometries of soft magnetic composite materials. This method allows constructing various kinds of geometries for experimenting how certain geometric features affect different electromagnetic properties of the materials. Using the geometry imitations, we demonstrate that in order to explain the nonlinear static magnetic behavior of a certain composite, it is not necessary to assume contacts between the neighboring particles in the material. The contacts may instead be replaced by thin gaps. [ABSTRACT FROM AUTHOR]

A vision-based measurement (VBM) of dust concentration with high accuracy can be used to analyze dust pollution and reveal the distribution law of dust concentration in a production workshop. In conventional VBM, grayscale and morphological features of a dust image are commonly used. However, their measurement accuracy is limited by the atmospheric light scattering effect of surrounding light and the occlusion effect of dust particles. Thus, a high accuracy VBM system is investigated that is based on the relationship between dust concentration and image transmission (RCT) model. Meanwhile, a calculation method for image transmission that utilizes the atmospheric light scattering effect and the occlusion effect of dust particles is proposed in this paper. The experimental results show that in the proposed system, the relative error is ±3.0%, the relative uncertainty is 2.05%, the correlation is 0.99, the measurement range is 0.5–1000 mg/m3, and the measurement period is 2 s. The RCT tends to be a negative correlation, which is a quadratic polynomial. The impact of the atmospheric light scattering effect and the occlusion effect of dust particles on measurement accuracy can be eliminated by utilizing image transmission. The proposed system can be used as an alternative measurement of dust concentration in the workshop. [ABSTRACT FROM AUTHOR]

Pressure, temperature and humidity are the basic parameters of air, which affect the average mobility of ions in it. In this paper a series of the average mobilities of ions in corona discharge in air at different pressures, temperatures and humidities are measured using an apparatus for generating parallel plane ion flow field in a climatic test cabinet. The pressure, relative humidity, and temperature is from 80 to 110 kPa, 20 to 85%, and −10 to 40°C, respectively. The variation of the average mobility of ions in corona discharge in air, with respect to pressure, humidity and temperature, is discussed and calculated from the fundamental mobility equation. [ABSTRACT FROM AUTHOR]

In synthetic aperture radar (SAR), the separation of the height between the ground subsidence phase components and the atmospheric phase delay mixed in the global SAR interferometry (InSAR) phase information is an issue of primary concern in the remote sensing community. This paper describes a complete procedure to address the challenge to estimate the atmospheric phase screen and to separate the three-phase components by exploiting only one InSAR image couple. This solution has the capability to process persistent scatterers subsidence maps potentially using only two multitemporal InSAR couples observed in any atmospheric condition. The solution is obtained by emulating the atmosphere compensation technique that is largely used by the global positioning system where two frequencies are used in order to estimate and compensate the positioning errors due to atmosphere parameters’ variations. A sub-chirping and sub-Doppler algorithm for atmospheric compensation is proposed, which allows the successful separation of the height from the subsidence and the atmosphere parameters from the interferometric phase observed on one InSAR couple. The results are given processing images of two InSAR couples observed by the COSMO-SkyMed satellite system. [ABSTRACT FROM AUTHOR]

Urban black and odorous water (BOW) is a serious global environmental problem. Since these waters are often narrow rivers or small ponds, the detection of BOW waters using traditional satellite data and algorithms is limited both by a lack of spatial resolution and by imperfect retrieval algorithms. In this paper, we used the Chinese high-resolution remote sensing satellite Gaofen-2 (GF-2, 0.8 m). The atmospheric correction showed that the mean absolute percentage error of the derived remote sensing reflectance ($R_{\mathrm {rs}}$) in visible bands is 25.19%. We first measured $R_{\mathrm {rs}}$ spectra of two classes of BOW [BOW with high concentrations of iron (II) sulfide, i.e., BOW1 and BOW with high concentrations of total suspended matter, i.e., BOW2] and ordinary water in Shenyang. Then, in situ $R_{\mathrm {rs}}$ data were converted into $R_{\mathrm {rs}}$ corresponding to the wide GF-2 bands using the spectral response functions. We used the converted $R_{\mathrm {rs}}$ data to calculate several band combinations, including the baseline height, [ $R_{\mathrm {rs}}$ (green) $- R_{\mathrm {rs}}$ (red))/($R_{\mathrm {rs}}$ (green) $+ R_{\mathrm {rs}}$ (red)], and the color purity on a Commission Internationale de L’Eclairage (CIE) chromaticity diagram. The color purity was found to be the best index to extract BOW from ordinary water. Then, $R_{\mathrm {rs}}$ (645) was applied to categorize BOW into BOW1 and BOW2. We applied the algorithm to two synchronous GF-2 images. The recognition accuracy of BOW2 and ordinary water are both 100%. The extracted river water type near Weishanhu Road was BOW1, which agreed well with ground truth. The algorithm was further applied to other GF-2 data for Shenyang and Beijing. [ABSTRACT FROM AUTHOR]

The atmospheric correction approach currently being used operationally by NASA [termed as NASA standard atmospheric correction (NSAC) approach] to process ocean color data relies on traditional “black pixel” approach, with additional modifications to account for nonnegligible water-leaving radiance in the near-infrared (NIR) bands. The NSAC approach underestimates remote-sensing reflectance ($R_{\mathrm {rs}}$ , sr−1) in blue wavelengths in the presence of absorbing aerosols. Addressing this issue requires realistic absorbing-aerosol model and knowledge of the vertical distribution of aerosols, which are currently difficult to achieve. An alternative atmospheric correction approach has been evaluated in this paper for Moderate Resolution Imaging Spectroradiometer (MODIS) data. The approach is based on a previously developed spectra-matching optimization [POLYnomial-based approach established for the atmospheric correction of MERIS data (POLYMER)], where polynomial functions are used to express atmospheric contribution to the measured radiance and where a bio-optical model is used to estimate the water contribution. Evaluation against in situ data measured over the regions frequently affected by absorbing aerosols indicates that, compared with the NSAC approach, the POLYMER approach improves the $R_{\mathrm {rs}}$ retrievals in blue wavelengths while having a slightly worse performance in other wavelengths. Evaluation using NSAC-retrieved $R_{\mathrm {rs}}$ in adjacent days free of absorbing aerosols suggests that the POLYMER approach could improve the spectral shape and increase valid spatial coverage. When applied to time-series MODIS data, the POLYMER approach could generate more temporary coherent daily and monthly $R_{\mathrm {rs}}$ patterns than the NSAC approach. These results suggest that the POLYMER approach could be an alternative approach to partly correct for absorbing aerosols in the absence of explicit information on the aerosol type and the vertical distribution. [ABSTRACT FROM AUTHOR]

We consider the probabilistic group testing problem where $d$ random defective items in a large population of $N$ items are identified with high probability by applying binary tests. It is known that the $\Theta (d \log N)$ tests are necessary and sufficient to recover the defective set with vanishing probability of error when $d = O(N^{\alpha })$ for some $\alpha \in (0, 1)$. However, to the best of our knowledge, there is no explicit (deterministic) construction achieving $\Theta (d \log N)$ tests in general. In this paper, we show that a famous construction introduced by Kautz and Singleton for the combinatorial group testing problem (which is known to be suboptimal for combinatorial group testing for moderate values of $d$) achieves the order optimal $\Theta (d \log N)$ tests in the probabilistic group testing problem when $d = \Omega (\log ^{2}\,\,N)$. This provides a strongly explicit construction achieving the order optimal result in the probabilistic group testing setting for a wide range of values of $d$. To prove the order-optimality of Kautz and Singleton’s construction in the probabilistic setting, we provide a novel analysis of the probability of a non-defective item being covered by a random defective set directly, rather than arguing from combinatorial properties of the underlying code, which has been the main approach in the literature. Furthermore, we use a recursive technique to convert this construction into one that can also be efficiently decoded with only a log-log factor increase in the number of tests. [ABSTRACT FROM AUTHOR]

While formal and rigorous procedures are adopted to ensure the reliability of measurement results, the decision-making activities based on the information returned by measurement are often poorly structured and based on vague assumptions and reasoning, thus negatively impacting the reliability of the achieved conclusion. To overcome this problem, a structured multicriteria decision-making approach based on the analytic hierarchy process was recently proposed in the literature to properly support decision-making activities by exploiting the information provided by measurement results related to heterogeneous properties and its application was demonstrated in a case study. Using the same case study, this paper analyses the sensitivity of the obtained conclusion to various uncertainty sources that are embedded in that approach. In particular, the impact of uncertainties on both measurement and pairwise comparisons of the criteria is analyzed and their combined effect is evaluated by considering a worst case situation. Moreover, the effect of the formula adopted to aggregate the information returned by the measurement of heterogeneous properties on the final decision is considered. The performed analysis contributes to support a disciplined and methodologically sound implementation of decision-making activities, significantly improving both the repeatability and reproducibility of the whole evaluation process, as well as the confidence in the achieved conclusions. [ABSTRACT FROM AUTHOR]

Silicon nanowire (SiNW) field-effect transistors (SiNWFETs) are of great potential as a high-sensitivity charge sensor. The signal-to-noise ratio (SNR) of an SiNWFET sensor is ultimately limited by the intrinsic device noise generated by carrier trapping/detrapping processes at the gate oxide/silicon interface. This carrier trapping/detrapping-induced noise can be significantly reduced by replacing the noisy oxide/silicon interface with a Schottky junction gate (SJG) on the top of the SiNW. In this paper, we present a tri-SJG SiNWFET (Tri-SJGFET) with the SJG formed on both the top surface and the two sidewalls of the SiNW so as to enhance the gate control over the SiNW channel. Both experiment and simulation confirm that the additional sidewall gates in a narrow Tri-SJGFET indeed can confine the conduction path within the bulk of the SiNW channel away from the interfaces and significantly improve the immunity to the traps at the bottom buried oxide/silicon interface. Therefore, the optimal low-frequency noise performance can be achieved without the need for any substrate bias. This new gating structure holds promises for further development of robust SiNWFET-based charge sensors with low noise and low operation voltage. [ABSTRACT FROM AUTHOR]