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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]

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]

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]

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]

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]

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]

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]

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]

The interest of industry and academia in medical measurements continues to increase, due to technology advancements that allow new sensors, measurement systems, and methodologies, supporting a wide number of medical applications. [ABSTRACT FROM PUBLISHER]

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]

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]

Air-borne micrometer and submicrometer particles produced by anthropogenic sources contaminate atmospheric air, especially in large cities where both population and industrial activities are higher leading to a reduced air quality. Recent research has pointed out particles less than 2.5 \mum in diameter (PM2.5) as a potential health hazard. To address this issue, stricter legislation has been put into force to reduce PM2.5 emissions. This paper is focused on the development of an impulsive microelectrostatic precipitation technology for charging and removal of fine air-borne particles in an economically feasible way. In this paper, a compact coaxial precipitator has been developed for possible indoor air cleaning applications. High-voltage impulses together with dc voltage have been used for energization of the reactor as it has been shown to enhance the precipitation efficiency. This precipitation system has been used for removal of fumes and fine air-borne particles from ambient air. In addition to the experimental part, analytical work has been conducted to optimize the electrostatic precipitation process and to reduce its power consumption. [ABSTRACT FROM PUBLISHER]

This paper describes the preflight spectral calibration methods and results for the Orbiting Carbon Observatory 2 (OCO-2), following the approach developed for the first OCO. The instrument line shape (ILS) function and dispersion parameters were determined through laser-based spectroscopic measurements, and then further optimized by comparing solar spectra recorded simultaneously on the ground by the OCO-2 flight instrument and a collocated high-resolution Fourier transform spectrometer (FTS). The resulting ILS profiles and dispersion parameters, when applied to the FTS solar data, showed agreement between the spectra recorded by the spectrometers and FTS to approximately 0.2% RMS, satisfying the preflight spectral calibration accuracy requirement of <0.25% RMS. Specific changes to the OCO-2 instrument and calibration process, compared to the original OCO, include stray-light protection; improved laser setup; increased spectral sampling; enhanced data screening, and incremental improvements in the ILS, dispersion, and FTS optimization analyses. [ABSTRACT FROM PUBLISHER]

Testability analysis is a fundamental prerequisite to any fault location or parameter identification technique (FLPIT) for analog circuits. However, differently from the linear time-invariant field, this topic has been poorly investigated with regard to nonlinear circuits. In this paper, for the first time, to the best of our knowledge, the question is addressed with reference to periodically switched networks with dc stimuli (PSNDSs), a class of inherently nonlinear time-varying circuits that subsumes dc-dc converters as noteworthy members. A rigorous testability measure at both a circuit and a component level is thereby obtained, which provides information as to how many and which components can be unambiguously diagnosed or estimated from time-domain measurements. Consequently, it yields an upper bound, independent of the parameter values and the test instants, to the performance of whatever method actually employed as an FLPIT. This theoretical testability metric has been converted into an efficient computer program for the fully automated testability analysis of PSNDSs presented herein. Such a computer program represents the first practical testability gauge for PSNDSs: it can be employed by both test engineers and circuit designers as a guide to properly selecting injection and measurement points as well as estimating the number of necessary measurement instants, so as to shape their own FLPITs. Several examples are provided for which an in-depth pencil-and-paper testability analysis is contrasted with its automated counterpart provided by the aforementioned software: in particular, popular dc-dc converter topologies are thoroughly analyzed. Comparisons with a previously proposed frequency-domain testability measure are also considered. [ABSTRACT FROM PUBLISHER]

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 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]

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]

This paper presents the analysis of all driving during 1 year by 14 older drivers from the Candrive study. Data analytic techniques have been applied to this unique big data set that includes global positioning system and geographic information system data with an average of 1567 trips and 20 200 km for each driver. The drivers have stable general, cognitive, and physical health over the year. This paper identifies trip features and tests their potential to be used to distinguish between driver pairs and shows that trip length and duration allow 90.1% and 90.1% of the 91 driver pairs (14 drivers) to be distinguished ( $p<5$ %). Driver velocity histograms are also shown to allow driver pairs to be distinguished. This paper explores the deceleration habits of the drivers by locating all deceleration events with a net velocity drop of >=4 km/h (average of 30 156 events per driver) and finds that the event mean and minimum deceleration values have different two-phase relationships for each driver. Trips taken by a driver are compared with the drivers’ two-phase relationships and this paper shows that the measures achieve their maximum performance for trips with >=8 events. The minimum measure is better (correlation of 0.74) than the mean measure (0.54). These relationships provide a measure of a driver’s unique behaviors and could allow more personalized autonomous driving systems and capture features of the deceleration habits for the driver that can be used to distinguish between drivers. [ABSTRACT FROM PUBLISHER]

This paper presents a comprehensive study of the impact of visual-haptic spatial discrepancies on human performance in a targeting task conducted in a visual-haptic virtual and augmented environment. Moreover, it explores whether the impact of this effect varies with two additional variables: 1) haptic wall stiffness and 2) visual cursor diameter. Finally, we discuss the relative dominance of visual and haptic cues during a targeting task. The results indicate that while the spatial discrepancies studied exerted a small effect on the time required to perform targeting, they impacted the absolute errors considerably. Additionally, we report that haptic wall stiffness has a significant effect on absolute errors while the visual cursor diameter has a significant effect on movement time. Finally, we conclude that while both visual and haptic cues are important during targeting tasks, haptic cues played a more dominant role than visual cues. The results of this paper can be used to predict how human targeting performance will vary between systems, such as those using haptically enabled virtual reality or augmented reality technologies that feature visual-haptic spatial discrepancies. [ABSTRACT FROM PUBLISHER]

In this paper, a new analytical model is proposed to describe the behavior of uncertainty for a generic vision-based measurement system applied to mechanical vibration measurement. In particular, this paper proposes a novel way to evaluate the effects of motion blur. The theoretical framework presented here takes into account the camera acquisition parameters, the dynamics of the measurand (in terms of instantaneous speed), and the image scaling factor to develop a physical model of uncertainty when measuring a mainly monomodal vibration. Uncertainty is evaluated starting from the normalized discrepancy between a measured position and the actual one. The analytical model proposed here has been validated extensively with the help of a test stand that can generate biaxial monomodal vibrations. The motion of a target mounted on the test stand has been simultaneously monitored by a stereo vision rig and a set of triangulation lasers. Model validation has been carried out using two measurement techniques: stereo vision blob analysis and digital image correlation. The experimental results show that the proposed model is able to represent the behavior of uncertainty for both the techniques. After the model has been validated, it is shown how it is possible to exploit it to estimate the uncertainty of a vision rig starting from the nominal characteristics of both the vision system and the target. This can be a tool to facilitate the design of vision-based measurement systems. [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]

In this paper, we propose a method for vehicle tracking on roadways using measurements of magnetometers and accelerometers. The measurements are used to build a low-cost, low-complexity vehicle tracking sensor platform for highway traffic monitoring. First, the problem is formulated by introducing the process model for the motion of the vehicle on the road and two measurement models: one for each of the sensors. Second, it is shown how the measurements of the sensors can be fused using particle filtering. The standard sampling importance resampling (SIR) particle filter is extended for processing of multirate sensor measurements and models that employ unknown static parameters. The latter are treated by Rao–Blackwellization. The performance of the method is demonstrated by computer simulations. It is found that it is feasible to fuse the two sensors for vehicle tracking and that the proposed multirate particle filter performs better than particle filters that process only measurements of one of the sensors. The main contribution of this paper is the novel approach of fusing the measurements of road-mounted magnetometers and accelerometers for vehicle tracking and traffic monitoring. [ABSTRACT FROM AUTHOR]

This paper presents a method that has been developed to determine the quantity of accumulated magnetic particles in mice tissues using magnetization measurements. Dispersions of platelet Fe3O4 particles with the size of 30–50 nm and a saturation magnetization of $\sim 80$ Am2/kg were intravenously administered to mice. Primary tissues were dried to measure the magnetization. The amounts of Fe3O4 particles accumulated in the tissues were obtained by dividing the magnetization of tissues by the magnetization of Fe3O4 particles under a magnetic field of 39.8 kA/m. A remarkable accumulation of particles was observed in the liver and the spleen, being supported by the observation of tissues using Prussian blue staining. Total Fe3O4 particles accumulated in primary tissues were $\sim 38$ –40 and 40–44 wt% against the particles in administered dispersions with 3 and 0.4 wt% contents, respectively. The method developed in this paper is considered to be effective for verifying magnetic hyperthermia and thermoablation therapies, in which the quantity of accumulated particles directly reflects the heating power required for those therapies. [ABSTRACT FROM AUTHOR]

This paper presents the results from a one-year study of 12 patients with moderate dementia in an adult day program who played a novel whack-a-mole game-based measurement instrument for cognitive behavior and performance. The ongoing measurement of cognition and changes associated with dementia is a challenge for healthcare providers. Measurement methods based on a tablet-based instrument are proposed. Partnership with the adult day program greatly eased recruitment: all but 1 eligible participant joined our study, compared to one in five, or lower, for previous studies with similar populations. There are three unique aspects to the design of our game: first, it has two distinct targets requiring different actions, which increases the cognitive processing for the users; second, each level is systematically more difficult; third, it records and analyzes player performance. The results show that the patients’ game performance improves over the first few weeks; this indicates that they are learning the game and retaining ability gains from week-to-week, suggesting some procedural learning is still intact. Over the year, 4 participants showed cognitive decline, 4 were stable and 3 improved based on their Minimental State Exam (MMSE) score. Two measures are proposed based on level progression within the sessions and mole hit performance. The level progression measure identifies declining participants with one false negative (FN) and one false positive error. The mole hit performance measure identifies declining participants with one FN error. These results demonstrate the potential for the proposed instrument to provide an ongoing measurement as an alternative for the repeated application of the MMSE. [ABSTRACT FROM AUTHOR]

Accommodation of a situation when a prior map is not available in an indoor localization system is valuable to cost-effective operations by removing a need for map drawing and map updating. This paper suggests a trajectory learning method using crowdsourced measurements in order to support the absence of map. A localization framework based on a particle filter is formalized by machine-learning-based feature extraction and Gaussian process (GP) regression. The feature extraction algorithm reduces dimensionality of sparse measurement vector, and it is applied to detect floor level and designated landmarks. Also, the combination of the feature extraction and the GP regression is used for modeling nonlinear relationship between location and measurement. By this combination, locations of Wi-Fi access points are not required to be known. From the field experimental results, we confirm that the detections of floor level and landmarks are accurate, the learned trajectories are close to the true map, and positioning accuracy is improved thanks to the learning-aided localization. [ABSTRACT FROM PUBLISHER]

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]

In this paper, we extend the notion of Cauchy–Schwarz divergence to point processes and establish that the Cauchy–Schwarz divergence between the probability densities of two Poisson point processes is half the squared L^2 -distance between their intensity functions. Extension of this result to mixtures of Poisson point processes and, in the case where the intensity functions are Gaussian mixtures, closed form expressions for the Cauchy–Schwarz divergence are presented. Our result also implies that the Bhattacharyya distance between the probability distributions of two Poisson point processes is equal to the square of the Hellinger distance between their intensity measures. We illustrate the result via a sensor management application where the system states are modeled as point processes. [ABSTRACT FROM PUBLISHER]

In order to improve the availability of wind turbines and to avoid catastrophic consequences, the detection of faults in their earlier occurrence is fundamental. This paper proposes the development of a fault diagnosis scheme relying on identified fuzzy models. The fuzzy theory is exploited since it allows approximating uncertain models and managing noisy data. These fuzzy models, in the form of Takagi–Sugeno prototypes, represent the residual generators used for fault detection and isolation (FDI). A wind turbine benchmark is used to validate the achieved performances of the designed FDI scheme. Finally, extensive comparisons with different fault diagnosis methods highlight the features of the suggested solution. [ABSTRACT FROM PUBLISHER]

In this paper, we report the vertical profiles of CO2 and CH4 measured with a cavity ring-down spectrometer (CRDS) on a research aircraft from near-ground level to 8 km above mean sea level. The airborne platform employed in this paper is an Alpha Jet aircraft operated from NASA's Ames Research Center. Flights were undertaken to Railroad Valley, NV, USA, to coincide with overpasses of the Greenhouse Gases Observing Satellite (GOSAT). Ground-based CO2 and CH4 were simultaneously measured using CRDS, at the time and location of the airborne and satellite measurements. Results of three GOSAT coordinated aircraft profiles and ground-based measurements in June 2011 are presented and discussed in this paper. The accuracy of the CO2 and CH4 measurements has been determined based upon laboratory calibrations (World Meteorological Organisation traceable standard) and pressure/temperature flight simulations in a test chamber. The overall uncertainty for the airborne measurements ranged from 0.31 to 0.39 ppm for CO2 and from 3.5 to 5.6 ppb for CH4. Our column-averaged CO2 and CH4 measurements, which include about 61% of the total atmospheric mass, are extrapolated, using different techniques, to include the remainder of the tropospheric and stratospheric CO2 and CH4. The CO2 data are then analyzed using the Atmospheric CO2 Observations from Space 2.9 and 3.3 algorithms. For methane data, the RemoTeC v2.1 algorithm was used in its full physics setup. Column-averaged CO2 and XCO2, measured by GOSAT and analyzed from our data, ranged from 388.1 to 396.4 ppm, and XCH4 ranged from 1.743 to 1.822 ppm. The agreement of the satellite and aircraft CO2 mixing ratios, as well as ground measurements, falls within the uncertainties of the methods employed to acquire these numbers. [ABSTRACT FROM PUBLISHER]

This paper presents a simultaneous localization and mapping (SLAM) method that utilizes the measurement of ambient magnetic fields present in all indoor environments. In this paper, an improved exponentially weighted particle filter was proposed to estimate the pose distribution of the object and a Kriging interpolation method was introduced to update the map of the magnetic fields. The performance and effectiveness of the proposed algorithms were evaluated by simulations on MATLAB based on a map with magnetic fields measured manually in an indoor environment and also by tests on the mobile devices in the same area. From the tests, two interesting phenomena have been discovered; one is the shift of location estimation after sharp turning and the other is the accumulated errors. While the latter has been confirmed and investigated by a few researchers, the reason for the first one still remains unknown. The tests also confirm that the interpolated map by using the proposed method improves the localization accuracy. [ABSTRACT FROM PUBLISHER]

This is the second of two papers that quantify the high added value of frequent 3-D radar observations of the atmosphere to capture the dynamics of weather systems. Recent advances in small-satellite and radar technologies, such as the “Radar in Cubesat” developed at the Jet Propulsion Laboratory, are paving the way for the design of convoys of spaceborne radars that can directly observe the evolution of severe weather at very fine temporal scales. The analyses presented here are to establish the relation between such observations to the underlying cloud variables and processes, and to quantify the sensitivity to the different physical and instrument parameters. In this paper, we quantify the uncertainty in the relation between the measured radar reflectivities Z and their time derivatives d_{t} Z , on one hand, and the underlying rate of change of the condensed-water mass M$ , and fluxes of dry and moist air in convection, on the other hand. The uncertainties are due to the variability of the atmospheric parameters as well as the constraints of an observation strategy that would use pairs of spaceborne instruments. We specifically analyze the sensitivities for pairs of satellites, each carrying a Ka-band profiling radar. Our simulations show that, with a convoy of two spacecraft separated by ~90 s, each with a pointing accuracy of ~0.025° in rms error, a sensitivity of 17 dBZ and a precision of 1 dBZ, the proposed observation strategy would capture more than 70% of the tropical convection between 5 and 10 km of altitude and resolve the air-mass and condensed-water fluxes. [ABSTRACT FROM AUTHOR]

Biosensors are prominent in several areas, such as medical diagnosis, food preparation, pharmaceutical industries, and clinical analysis; high performances are required in spite of attaining accuracy, sensitivity, low cost, easy handling, and portability, but the major parameter that is always representing a challenge for biosensors is specificity. High sensitivity and specificity can be obtained by combining appropriate transduction methods together with immunoassay techniques. In this paper, integrated inductive biosensors for the magnetic immunoassay process, which use magnetic beads as markers for biomolecules, are presented with potential applications to proteins and DNA measurements. The working principle and a dedicated fabrication technology, which also embeds thermal actuation and control, are described; in particular, an improved sensing architecture is proposed here, which allows one to expand the microsensor operative field toward very low bead concentrations. The sensor characterization results are presented together with the analytical model of the transduction principle; a detection limit of approximately 300 beads has been demonstrated. The microsensor is also capable of operating by measuring up to 450 000 beads with a good linearity. Therefore, the sensor architecture proposed here has been demonstrated for wide operating field with high resolution and good linearity; the results proposed confirm the suitability of the devices developed for magnetic immunoassay applications. [ABSTRACT FROM AUTHOR]

Devise stability is another important problem for hindering the commercial applications of organic field-effect transistors (OFETs) besides the mobility. In this paper, an abnormal instability was observed in the OFET with a pure polymer poly(methyl methacrylate) (PMMA) dielectric due to the minority carriers trapped at the pentacene/PMMA interface. By inserting a tetratetracontane modification layer between PMMA and pentacene, not only the morphology of the deposited pentacene was greatly improved, but also the trap density at the pentacene/dielectric interface was reduced, demonstrated by the investigation of the atomic force microscopy, and the analysis of the subthreshold swing and flicker noise, respectively. As a result, both the stability as well as the mobility of the OFET was obviously improved. [ABSTRACT FROM AUTHOR]

Since there exist discharge problems in gas insulated metal-enclosed switchgear due to internal metal particle movement, the study in this paper thus, conducted an experiment and simulation research on the movement characteristics of particles, where the charge on particle and the collision recovery coefficient (CRC) was obtained. When the electrode is brass, the charge on the particle is close to the theory one and the CRC of aluminum, stainless steel and particle are 0.52, 0.75 and 0.79 respectively. Accordingly, the study, in order to take the particle charge and its collision process into consideration, established a particle movement model, and conducted a test has thereof. The results show that there exists excellent consistency between charge and its collision. According to the research, the relationship between particle size, material and collision characteristics was revealed. The result shows that under DC voltage the metal particle collision frequency decreases with the increase of particle size when the material of the particles is the same. The time difference between particles' moving to high voltage electrode and ground electrode becomes greater with the increase of the size of the particle. When the particle material is different, the collision characteristics are influenced by its density and collision recovery coefficient of restitution. Particle motion velocity loss becomes less before and after the collision when its collision recovery coefficient of restitution is larger. So the collision frequency is higher. The research results in this paper can offer some reference value to particles suppression measures in gas insulated metal-enclosed transmission line. [ABSTRACT FROM PUBLISHER]

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]

Multibaseline (MB) synthetic aperture radar interferometry (InSAR) has the potential to improve the traditional single-baseline InSAR from the ill-posed problem to the well-posed problem. It is because MB InSAR phase unwrapping (PU) can take advantage of the baseline diversity to significantly increase the ambiguity intervals of interferometric phases, so it completely overcomes the limitation of the phase continuity assumption. However, due to the mathematical foundation of most of the MB PU methods, i.e., the Chinese remainder theorem (CRT), has poor measurement bias robustness (measurement bias could be caused by surface deformation, atmospheric artifact, and phase noise), CRT is sensitive to the normal baseline length. In other words, even if we choose the same CRT-based MB PU method, different system baseline lengths could result in different PU performances. Therefore, how to choose the baseline lengths to optimize CRT performance is crucial to all the CRT-based MB PU methods. In this paper, a nonlinear mixed-integer programming-based baseline design criterion (referred to NIP criterion) is proposed to maximize the measurement bias tolerance of the CRT-based MB PU. The optimality condition of the NIP criterion quantitatively provides important instructions for the CRT-based MB PU applications and offers significant guidance in the development of the practical MB InSAR system. The experiment results are shown to verify the effectiveness of the NIP criterion by using three representative CRT-based MB PU methods. [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]

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]

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]

In modern semiconductor manufacturing, one type of measured particle count data contains excess zeros, and the ratio of zeros in the measurements is usually larger than 50%. This type of particle count sample data cannot be well modeled by popular defect models such as Poisson, zero-inflated Poisson, generalized zero-inflated Poisson, and Neyman and Gamma-Poisson models. In this paper, a threshold-Poisson model was proposed to describe the particles with excess zero counts, and the method for parameter estimation was developed. Via comparison with those popular models by using 15 measured samples, it showed that the measurements are better modeled by the threshold-Poisson model. A control chart called threshold-c control chart was proposed and the control limits were derived. A reasonable minimum sample size for constructing control chart was also discussed based on simulations. [ABSTRACT FROM AUTHOR]

When the pressure and temperature of a fluid exceed a critical point, the fluid enters into the supercritical (SC) region. In this region, the physical properties are believed to be in favor of a good current interruption medium. This paper focuses on the arc voltage characteristics of nitrogen arcs burning inside cylindrical tubes at different filling pressures: 1, 20, 40, and 80 bar, thus covering the SC region. Two different tube materials have been used in the experiments, alumina and polytetrafluoroethylene. Arc voltages are measured for arcs burning inside tubes of 2, 4, 8, and 15 mm inner diameters. In addition, free-burning arcs have been investigated at the same filling pressures. The arc current was 150 A at 350 Hz throughout the study. The arc voltage is found to increase with decreasing inner diameter of the tube at atmospheric pressure. At higher filling pressures (i.e., 20, 40, and 80 bar), however, such a simple relationship is not observed. The arc temperature and radius have been calculated based on the “simple theory of free-burning arcs” and the “two-zone ablation arc model.” The calculated arc radius decreases with increasing gas pressure. Furthermore, due to increased absorption of radiation at high filling pressures, ablation is found less significant for ultrahigh-pressure nitrogen arcs compared to atmospheric pressure arcs. This is in line with the observations from optical micrographs of the inner surfaces of the tubes exposed to arcs at different filling pressures. [ABSTRACT FROM AUTHOR]

This paper presents a design in which a common, off-the-shelf, low-cost dust sensor, normally aimed at providing the measurements of the total dust concentration, is employed as the core of a more sophisticated instrument, object of this design, which is able to measure also the particle size of sampled airborne particulate, thus providing the particle size analysis of the sampled air. This result has been achieved by introducing some modifications to the sensor’s operating directions as specified by the manufacturer. These modifications mainly concern: a different geometry for the sampled air flow (to enable the detection of single particles), a more accurate processing of the output signal (to maximize the signal/noise ratio), and a simple yet an accurate metrological calibration, which exploits the statistical properties of commonly available polydispersed particulate (talc powder), instead of needing monodispersed material (expensive and difficult to employ). The experimental results show that, under the proper conditions, the calibrated instrument is indeed able to measure the optical size of the detected particles and therefore to determine the particle size distribution (i.e., the granulometric analysis) of the sampled particulate. [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]

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]

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]

The theory of compressed sensing (CS) asserts that an unknown signal x\in \mathbb R ^p can be accurately recovered from an underdetermined set of n linear measurements with n\ll p provided that x is sufficiently sparse. However, in applications, the degree of sparsity is typically unknown, and the problem of directly estimating \|x\|_{0} has been a longstanding gap between theory and practice. A closely related issue is that \|x\|_{0} is a highly idealized measure of sparsity, and for real signals with entries not equal to 0, the value \|x\|0=p is not a useful description of compressibility. In our previous conference paper [1] that examined these problems, we considered an alternative measure of soft sparsity, \|x\|1^{2}/\|x\|2^{2} , and designed a procedure to estimate \|x\|1^{2}/\|x\|2^{2} that does not rely on sparsity assumptions. This paper offers a new deconvolution-based method for estimating unknown sparsity, which has wider applicability and sharper theoretical guarantees. In particular, we introduce a family of entropy-based sparsity measures sq(x)=\big ({\|x\|q}/{\|x\|1}\big )^{({q}/{1-q})} parameterized by q\in [0,\infty ] . This family interpolates between \|x\|_{0}=s_{0}(x) and \|x\|_{1}^{2}/\|x\|_{2}^{2}=s_{2}(x) as q ranges over [0, 2]. For any q\in (0,2]\setminus \{1\} , we propose an estimator \hat {s}_{q}(x) whose relative error converges at the dimension-free rate of 1/\sqrt n , even when p/n\to \infty . Our main results also describe the limiting distribution of \hat sq(x) , as well as some connections to basis pursuit denosing, the Lasso, deterministic measurement matrices, and inference problems in CS. [ABSTRACT FROM PUBLISHER]

This paper presents the results of a recent study of the interaction between charged dust particles and plasma ions through the electrostatic and ion drag forces in a dc glow discharge plasma. Measurements of the dust particles motion are carried out using particle image velocimetry (PIV). When an electrostatic perturbation is applied to the dust cloud, the particle motion, in response to the perturbation, is shown to reverse direction as the gas pressure is increased. An analysis of the dust particle motion and background plasma parameters suggests that there is a change in the equilibrium position and competition between the electrostatic force and the ion drag force on the particles that lead to the asymmetric motion of the cloud. The ion drag and electric forces on the dust particles are calculated at each pressure using detailed measurements of the plasma parameters, as measured by a single Langmuir probe. The analysis of these measurements suggests that the reversal of direction is a result of the change in the electric field in the plasma, but that the effectiveness of the perturbation at displacing the dust cloud is controlled by the ion drag force. [ABSTRACT FROM PUBLISHER]

This paper presents a new achievable rate-distortion region for the general $L$ channel multiple descriptions (MDs) problem. A well-known general region for this problem is due to Venkataramani, Kramer, and Goyal (VKG). Their encoding scheme is an extension of the El Gamal-Cover (EC) and Zhang–Berger (ZB) coding schemes to the $L$ channel case and includes a combinatorial number of refinement codebooks, one for each subset of the descriptions. As in ZB, the scheme also allows for a single common codeword to be shared by all descriptions. This paper proposes a novel encoding technique involving combinatorial message sharing (CMS), where every subset of the descriptions may share a distinct common message. This introduces a combinatorial number of shared codebooks along with the refinement codebooks of. These shared codebooks provide a more flexible framework to tradeoff redundancy across the messages for resilience to descriptions loss. We derive an achievable rate-distortion region for the proposed technique, and show that it subsumes the VKG region for general sources and distortion measures. We further show that CMS provides a strict improvement of the achievable region for any source and distortion measures for which some two-description subset is such that ZB achieves points outside the EC region. We then show a more surprising result: CMS outperforms VKG for a general class of sources and distortion measures, including scenarios where the ZB and EC regions coincide for all two-description subsets. In particular, we show that CMS strictly improves on VKG, for the $L$ -channel quadratic Gaussian MD problem, for all $L\geq 3$ , despite the fact that the EC region is complete for the corresponding two-descriptions problem. Consequently, the correlated quantization scheme (an extreme special case of VKG) that has been proven to be optimal for several cross sections of the $L$ -channel quadratic Gaussian MD problem is strictly suboptimal in general. Using the encoding principles derived, we show that the CMS scheme achieves the complete rate-distortion region for several asymmetric cross sections of the $L$ -channel quadratic Gaussian MD problem. [ABSTRACT FROM AUTHOR]

Particle filters provide a general framework for dynamic state estimation (DSE) in nonlinear systems. The scope for particle filter-based DSE can be significantly enhanced by exploiting data from phasor measurement units (PMUs) when available at higher sampling frequencies. In this paper, we present a particle filtering approach to dynamically estimate the states of a synchronous generator in a multi-machine setting considering the excitation and prime mover control systems. The filter relies on typical output measurements assumed available from PMUs stationed at generator buses. The performance of the proposed filter is illustrated with dynamic simulations on IEEE 14-bus system including: 1) generators models with subtransient dynamics, 2) excitation units (IEEE DC1A, DC2A, AC5A), and 3) turbine-governor models (steam and hydro). The estimation accuracy of the proposed filter is assessed for three classes of disturbances assuming noisy PMUs' measurements and comparative results are presented with the unscented Kalman filter (UKF). The accuracy-computational burden trade-off is also analyzed and the results strengthen the feasibility of using particle filters for dynamic state estimation. [ABSTRACT FROM PUBLISHER]

This paper is aimed at expressing the expanded combined measuring uncertainty of capacitive divider with concentrated capacitance on a high-voltage scale. The tested capacitive divider is constructed to have a minimal measuring uncertainty. Measuring uncertainty of type A is expressed by means of the statistical processing of an experimentally determined random variable of pulse voltage amplitude and random variable of step voltage amplitude uncertainty budget of type B was derived from: 1) the influence of uncertainty values of a high-voltage and low-voltage capacitances on the transmission ratio; 2) the influence of frequency on the transmission ratio; and 3) measuring uncertainty type B of the used instrument. The experiments were performed under well-controlled conditions. Measuring uncertainty of type B for the first budget component is obtained theoretically using the Monte Carlo method, for the second budget component by using experimental-analytical method, while for the third budget component it is taken from the instructions manual of the manufacturer. The Gaussian distribution is attributed to the influential effects of the instruments measuring uncertainty type B. The results obtained show that most impact on measuring uncertainty of type B has the measuring uncertainty type B of the used instrument and the measuring uncertainty type B of divider by the influence of frequency. Values of expanded combined measuring uncertainty for 95% of coverage probability are 5.2% for frequencies equal to 50 MHz, 6.1% for frequencies equal to 1 GHz, and 7.1% for frequencies equal to 4 GHz. Comparing to our results for relative error of capacitive divider for measuring fast pulse voltages, it is shown that the tested capacitive divider has very good characteristics. [ABSTRACT FROM AUTHOR]