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Moisture accumulates with the growing aging progress of oil-paper insulation and further shortens the remaining life of the transformer. The frequency-domain spectroscopy (FDS) technique can be used to realize the moisture estimation. However, the moisture estimation results would be unreliable once the aging effect on FDS was ignored. Given this issue, an alternative model including the aging effect is thus proposed using FDS and intelligent algorithm. In this work, the feature parameters of FDS data are used to build the databases for characterizing the aging degree and moisture. Then, the moisture estimation models are developed using the weighted K-nearest neighbor (K-NN) algorithm. The accuracy and applicability of the proposed models are finally discussed in laboratory and field conditions. In that respect, the findings reveal that the reported model is available for moisture estimation of transformer oil-paper insulation under various aging degrees and test temperatures. [ABSTRACT FROM AUTHOR]

This paper considers a problem of in situ estimation of the weight values of individual items that are moving as a bulk on a conveyor in a nonoriented and nonsingulated way. In logistics and material handling processes, it is a challenge to measure the weight of each package moving on a conveyor without halting the conveyor operation. This is not only because measuring weight requires physical contact between the target item and the measuring instrument, but also because each item must be separated (or singulated) from others for it to be weighed. Such a singulation task causes a significant delay in the transport process, especially when the conveyor is heavily loaded with items. Because of this issue, despite the importance of weight information for correct pricing and safe loading (for both air and ground vehicles), the weight has largely been ignored in many logistical sites such as sorting facilities or distribution centers. To address these issues, this paper proposes a new instrumentation device called the in-motion weight sensor (IMWS) array that consists of a grid of mini-checkweighers operating separately from each other as independent weight measurement cells. Each IMWS unit is equipped with a mini-conveyor mounted on a load cell, and provides the composite load data for a group of items being carried within the IMWS system. A depth camera mounted above the IMWS surface identifies both the location and the orientation of each package and provides the real-time measurement of its partial areas that intersect with the surface of load cells in contact with each package. The load cell measurements and the vision data are then fused to construct the measurement equation that is linear in the unknown weight values of all the packages being transported within the IMWS system. To extract individual weight values from these composite load data, we implement the recursive least square (RLS) estimation to the IMWS array. The feasibility of the proposed idea has been verified with experimental tests using the full-scale prototype IMWS array system. The results show that IMWS array is capable of estimating the unknown weight with the mean error around ±3% error and the standard deviation of 9 ~ 13% error for cases where 3 ~ 5 randomly oriented packages are moving in a nonsingulated way. [ABSTRACT FROM AUTHOR]

Reliability of any electrical system heavily depends on the accuracy of the model used for representing the integrated device components. The design of renewable power sources and energy management systems using ultracapacitor demands adoption of precise mathematical representation of ultracapacitor dynamics. An appropriate model of ultracapacitor not only allows an accurate assessment of its charge/discharge characteristics but also provides a precise estimation of energy storage and power delivery capabilities. However, due to the lack in the usability of data/information provided by the manufacturer, conventional calculus-based ultracapacitor modeling and energy estimation approach often leads to its inaccurate and inefficient utility. In this regard, this paper has focused on fractional calculus-based analytical formulation for power, energy, and efficiency measurement of ultracapacitor and has deployed an upgraded model of the ultracapacitor, which in turn provides a better estimation of its energy efficiency. The work presented in this paper has been supported by extensive experimentation with the ultracapacitor under different input excitations, considering the dependence of energy storage (state of energy) and power delivery capacity of ultracapacitor on the charge/discharge profile. Furthermore, the hybrid optimization-based analysis recommends the requirement and challenges for the design and development of optimal charging/discharging of ultracapacitors for achieving higher energy efficiency. [ABSTRACT FROM AUTHOR]

Time-of-flight (TOF) measures are valuable in the estimation of displacements, distances, and moving fluids’ speed, e.g., the wind. Of the different types of methods that measure wind speed, the ultrasound is highlighted. As part of the calculation required to acquire the wind speed, one must find the time that an ultrasonic wave takes to travel from one particular place to another. This time is known as TOF. The biggest challenge in measuring TOF is to determine the correct moment when the ultrasonic wave arrived at the reception because it will be immersed in noise and its envelope probably will be corrupted by the actions of the wind. The methodology proposed by this paper presents a combined algorithm between an extended Kalman Filter (EKF) and a zero-crossing detector (ZCD) technique. The EKF method is responsible for the noise elimination stage and estimation of the real ultrasound’s envelope, while the ZCD detects the correct TOF of the estimated signal. This paper also presents the flaws and solutions found for each technique. It was shown that for speeds up to 75 km/h, the coefficient that was found to be $R^{2}\,\,=0.99977$. [ABSTRACT FROM AUTHOR]

Synchrophasor estimation is typically performed by means of spectral analysis based on the discrete Fourier transform (DFT). Traditional DFT approaches, though, suffer from several uncertainty contributions due to the stationarity assumption, spectral leakage effects, and the finite-grid resolution. This paper addresses these limitations, by proposing a joint application of the Hilbert transform (HT) and the interpolated DFT (IpDFT) technique. Specifically, the HT enables the suppression of the spectral leakage generated by the negative image of the tones under analysis, whereas the IpDFT limits the effects of spectrum granularity. In order to relax the constraint in terms of measurement reporting latency, the proposed estimator can adopt a window length of 40 ms and yet provides a noticeable estimation accuracy with a worst-case total vector error and frequency error equal to 0.02% and 4 mHz, respectively, in steady-state conditions. In this context, this paper discusses the most suitable setting of the algorithm parameters and their effect on spurious component rejection. Moreover, a thorough metrological characterization of the algorithm estimation accuracy and responsiveness with respect to the IEEE Std. C37.118.1 is carried out in order to detect the main uncertainty sources as well as possible room for enhancement. Finally, a comparison with two consolidated IpDFT approaches shows the actual performance enhancement provided by the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a modified version of the sine wave histogram test for analog-to-digital converter (ADC) characterization. To accurately estimate the transition levels, the input signal has to fulfill strict conditions, which are coherent sampling and uniform distribution of the phases. Noncoherent sampling of the sine wave leads to systematic errors in the transition level estimation, while the nonuniform distribution of the phases increases the variance of the results using the original sine wave histogram test method. The source of the errors is the improper modeling of the histogram of the measured signal. This paper shows that the true histogram of the measured sine wave can be reconstructed by estimating the phases of the sampled signal. It is shown that integrating this information into the estimation of the transition levels can significantly decrease the effect of noncoherent sampling, thus the ADC can be characterized accurately even if the conditions are not fulfilled. The proposed method is verified on both simulated and real measurement data. [ABSTRACT FROM AUTHOR]

This paper is an extension of the procedure pointed out to evaluate the distortion of the exponential signal, modeled as multiexponential signal with different amplitudes and time constants. This procedure evaluates the distortion by applying the Prony-like method to the signal reconstructed by zero-crossing time evaluated on the difference between the exponential original signal and the reference sinusoidal one. The extension proposed in this paper concerns with the use of the low-chirp signal as reference instead of the sinusoidal signal. The low-chirp signal permits to increase the number of the zero-crossing time in the section of exponential signal with the higher slope and decreasing in the section with the lower slope. This setting permits to estimate the distortion of the exponential signal with higher accuracy and precision. The parameters’ amplitude, phase, and start frequency of the reference low-chirp signal are used to reconstruct the exponential signal in the zero-crossing time in order to overcome the problem of the high-resolution sampling. Their estimation is carried out by the time-varying sine-fitting algorithm that processes the resulting signal that is the difference between the exponential original signal and the low-chirp signal. Advantages of the proposed extended procedure regard the direct estimation of the reference low-chirp signal parameters from the resulting signal over-sampled at low resolution, and the use of only one input acquisition channel that permits to overcome the synchronization problem between the reference and the resulting signal. The numerical and experimental tests validate the extended procedure. [ABSTRACT FROM AUTHOR]

Timing skews among channels degrade seriously the time-interleaved analog-to-digital converter (TIADC) performance, which can be improved by the blind timing skew estimation (TSE) technique. In this paper, we proposed the all-phase fast Fourier transform (ApFFT) based on spectrum sparsity signal phase relationship blind TSE (ApFFT-SSPR-BLTSE) algorithm. The ApFFT-SSPR-BLTSE algorithm reduces computational complexity based on the phase relationship of the total output from TIADC and the corresponding reference channel output compared with the existing spectrum sparsity blind TSE (SS-BLTSE) algorithm. We also utilized the ApFFT technique to increase the accuracy of phase spectral estimation. Simulation results show that the proposed ApFFT-SSPR-BLTSE algorithm, which as a reduced number of fast Fourier transforms (FFTs) and low hardware complexity, has higher accuracy for blind TSE compared to the existing SS-BLTSE algorithm. In addition, this paper presents an efficient hardware architecture of the ApFFT-SSPR-BLTSE algorithm on the Xilinx Virtex-6 vlx550tff1759 field-programmable gate array (FPGA) chip for the blind TSE of the four-channel 400-MHz 14-bit TIADC real system. The validation results show that the proposed algorithm uses only a few percent of the hardware resources of the FPGA chip, and the mismatch spurs were suppressed to better than −81.54 dB. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to enhance a random bearing vibration signal (repetitive character with a random part) corrupted by an oscillatory gear vibration signal. This approach, called the Taylor- $H_{\infty }$ (TH) estimator, is proposed in a variable speed condition. The latter is designed using the mathematical modeling of the raw signal in the state space. It is used to remove the oscillatory signal from the raw signal. The obtained residual signal is the enhanced signal of the bearing contribution. Different from the Vold–Kalman (VK) estimator, which results from the minimization of the mean squared error, the proposed method is based on the minimax optimization. The minimax approach for the TH estimator leads to the minimization of the estimation error for the worst possible amplification of the random signals. No prior knowledge about the statistical properties of the random signals is required. The proposed approach is assessed using both synthetic and experimental nonstationary vibration data. In addition, the squared envelope spectrum of the residual signal is analyzed to diagnose the bearings state. This paper shows the [ABSTRACT FROM AUTHOR]

This paper designed a novel multiresolution analysis method and input waveform classification scheme. The proposed techniques serve as preprocessing procedures of input waveforms in order to facilitate synchrophasor estimation. Simple, scalable and time-shifted “pseudowavelets” (PWs) are employed to repeatedly calculate the correlation coefficients between the proposed PWs and input power waveforms. By scrutinizing the correlation factors with respect to frequency and time, the proposed method is capable of revealing the temporal trajectories of frequency and amplitude features of input waveforms. Such features are then leveraged to classify input waveform types. The result of such waveform classification can be applied to perform accurate synchrophasor estimation. Contrary to traditional approaches, where a single algorithm is designed to compute synchrophasor accurately for all input signal types, in this paper, a framework is proposed which enables adaptive switching of synchrophasor algorithms, so that the most suitable algorithm can be used for the identified waveform. The efficacy and efficiency of proposed methods are validated with standardized phasor measurement unit testing waveforms and simulated power system waveforms. The results prove that the PW-based waveform classification method is capable of distinguishing between power system dynamic waveforms. [ABSTRACT FROM AUTHOR]

This paper presents an innovative in-field coarse bias estimation (CBE) method for stationary intermediate-grade inertial measurement units (IMUs), which is based on the existence of normality and orthogonality errors in the attitude matrix supplied by the traditional three-axis attitude determination stationary coarse alignment (CA) stage. Differently from the currently existing in-field bias estimation formulations, the proposed CBE method is conceived to be conducted in stationary conditions, jointly with the CA, and it allows the down accelerometer bias and the north and down angular rate sensor biases to be adequately estimated. As a main contribution of this paper, we demonstrate that the proposed CBE method is able to greatly accelerate the estimation process of the aforementioned biases, contributing, hence, to shorten the IMU setup time. A comprehensive error analysis is presented, and the conclusions are twofold: 1) the proposed CBE method is valid regardless of the IMU orientation and 2) its estimation accuracy is mainly constrained by the north accelerometer bias and the position location uncertainty, which are, in general, very reduced. Results from simulated and experimental test confirm the adequacy of the outlined verifications, also attesting the superiority of the proposed CBE method in situations where, due to time constraints, stationary fine alignment procedures are not allowed to be carried out. [ABSTRACT FROM PUBLISHER]

Recently, the application of the adaptive notch filter (ANF) concept to the analysis of damped oscillations and the estimation of frequency and damping factor of such oscillations was introduced. This paper presents the application of the enhanced phase-locked loop (EPLL) to the same problem. A modified version of the EPLL is developed that includes the estimation of damping factor in addition to the already estimated variables of amplitude, phase angle, and frequency. The structure, mathematical formulation, and theoretical stability analysis of the proposed method, including guidelines for its parameter tuning, are presented. Simulation results verify the analytical derivations and the desirable performance of the proposed method. This paper concludes that the EPLL-based approach can address the problem as efficient as the ANF approach while offering more robust and accurate results at low sampling rate applications. [ABSTRACT FROM AUTHOR]

In spectral analysis, achieving coherent sampling, especially when signals have large distortion, has been a challenge for many years. This paper introduces three algorithms to resolve this issue. In comparison to previous algorithms, and two widely used methods in industry: windowing and four-parameter sine wave fit, these new algorithms are capable of obtaining accurate spectral results of the signal, while achieving high accuracy as well as computational efficiency. The novel contribution of this paper is that not only does it propose two new algorithms, but also it analyzes their advantages and limitations in detail, providing their trade-offs and different fields of applications. Extensive simulations and measurements are performed to validate these algorithms. Combined with their high accuracy, computational efficiency, and robustness against signal purity, these algorithms are readily available to be implemented for bench or on-chip testing. In addition, it is suitable for data converter spectral testing when noncoherent sampling is present, and spectrally pure test signal source is not available. [ABSTRACT FROM PUBLISHER]

Power quality is described by voltage fluctuation measures. Voltage fluctuation is understood as “a series of voltage changes or a continuous variation of the rms or peak value of the voltage.” Voltage variation is most often manifested by flicker, which might become obnoxious at a high enough intensity. One of the standard measures of voltage fluctuation is \boldsymbol Pst indicator. One of its major flaws is its limited diagnostic capabilities. Voltage fluctuation indices are the measure, which provides much bigger range of diagnostic capabilities. However, it has not been acknowledged as a standard measure. The initial part of this paper includes the description of the accepted method of reproducing voltage variation. It also presents general characteristics of the \boldsymbol Pst indicator and voltage fluctuation indices. The next part of this paper describes the methods of converting voltage fluctuation indices into \boldsymbol Pst indicator and vice versa. The capability of estimating the \boldsymbol Pst indicator on the basis of the indices has been confirmed. Three examples of measurement results of both these measures in low-voltage (LV) power grids have been presented. These results have proved the connection between the estimated values (on the basis of voltage fluctuation indices) and the results of the \boldsymbol Pst indicator measurements. [ABSTRACT FROM AUTHOR]

This paper presents an instrument for finding the location of an object in 3-D space. The location is determined using estimates of the object’s distance from static anchor points with known coordinates. This process involves three main challenges: 1) a way of measuring the Euclidean distance between two points; 2) estimating system parameters such as the position of the anchor points, the speed of sound, and data acquisition delay; and 3) computing the position of the object from its distance to the anchor points. This paper addresses each of these challenges. A wideband acoustic signal is used for distance estimation. Nonlinear regression techniques are used to estimate the positions of the anchors and jointly optimize other system parameters. After calibration, a similar method is also used, this time using the optimized parameter set, to estimate the position of the target object. The design and performance of a prototype system, based on the proposed method, are discussed. The prototype, using five anchors and 13 reference locations, achieved a mean positioning accuracy of 0.86 mm over a test volume spanning 0.5 m $\times0.5$ m $\times0.25$ m. [ABSTRACT FROM AUTHOR]

Expansion displacement of switch rail is very important for the comfort and safety of the high-speed railway. For safety considerations, it is reasonable to take a noncontact measurement to monitor it online. The visual measurement method is suitable for the monitoring system of high-speed railway infrastructure. However, it is a big challenge to monitor such displacement in a visual system because of uneven illumination, low light, camera shake, and the constrained data acquisition condition in all-weather. In this paper, an Automatic Visual Monitoring System for Expansion Displacement of Switch Rail (AVMS-EDSR) is presented. First, AVMS-EDSR captures switch rail images through an image acquisition and transportation system. Then, a novel Spatial Support Convolutional Neural Networks (SSCNN) is proposed for ruler detection in switch rail images. At last, AVMS-EDSR outputs expansion displacement by Automatic Interpretation Method (AIM), which is faster and more precise than manual interpretation. Experimental results demonstrate that AVMS-EDSR can automatically monitor the expansion displacement of switch rail, report abnormal results, and interpret the ruler of the switch rail image in 1 s with the average difference less than 1 mm at a distance of 5 m, which can meet the requirement for real-time automatic monitoring of expansion displacement of switch rail. [ABSTRACT FROM AUTHOR]

In a 3-D gaze tracking system, the system calibration is used to determine the positions of the light sources and the screen in the camera coordinate system, which plays an important role in the estimation of the 3-D gaze and the point of regard (PoR). The traditional way for the system calibration is the mirror-based method, in which a mirror is used to make the light sources and the screen can be captured by the camera through specular reflection. However, the mirror-based method is imprecise and complex. In this paper, a novel system-calibration method for the 3-D gaze tracking system is proposed. On the basis of the system camera, a calibration camera is placed on the opposite of the screen to constitute a multi-camera system. The images are captured by the two cameras to estimate the light sources and the screen of the system camera coordinate frame. Simulations and experiments were carried out to illustrate the validation of the method. This method solves the problem that the light sources and the screen are not in view of the system camera. In addition, it is easy to implement while ensuring high accuracy and providing a new method for other similar calibration systems. [ABSTRACT FROM AUTHOR]

A novel and practical combined weighted (COM-W) method for target localization based on time difference of arrival (TDOA) is proposed in this paper. For 2-D situations with more than three nodes, the main idea of the proposed method is to obtain multiple preliminary localization results corresponding to the different three-node combinations first, and then use the weighted averaging technique based on the Cramér–Rao lower bound (CRLB) to obtain the final estimation. Considering target localization with a set of fixed-positioning nodes employing TDOA measurements as an example, the performance of the proposed method is evaluated by both simulations and acoustic indoor positioning experiments. The experimental results show that the COM-W method is a closed-form and computationally efficient method. It has an excellent positioning performance under both narrow and square geometric arrangements, specifically in the central cross-shaped area and in the vicinity of the nodes. Compared to the three classic weighted linear least squares (WLS)-based positioning methods, the proposed COM-W method is less sensitive to the measurement noise and the NLOS environment. The proposed method has an important application value for the realization of a TDOA-based indoor target positioning system. [ABSTRACT FROM AUTHOR]

Fast harmonic phasor measurements are very useful in power system protection applications, e.g., high-impedance fault location and intelligent method-based islanding detection. To address this problem, a novel harmonic phasor estimator is proposed in this paper, especially for P-class phasor measurement units (PMUs). The exponential functions are used to model the decaying dc (ddc) component, and the optimal time constant (TC) set for the ddc model is searched based on an enumeration method. In addition, the dynamic harmonic phasors are modeled based on the Shannon sampling theorem. As a result, a filter bank is designed for harmonic phasor and frequency estimations. The rate of change of frequency (ROCOF) of the harmonic is estimated based on a second-order fitting method. The response time of the proposed method for harmonic phasor estimation is always no longer than 40 ms. Simulation test results indicate that the proposed method is more accurate than the Taylor–Fourier transform (TFT) under ddc offset and frequency deviation conditions. In particular, the proposed method is more robust to ddc’s TC variations. An experimental test is implemented to demonstrate the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a compact, low-cost, and noninvasive system for real-time estimation of plasma sodium concentration ([Na] $_{Pl}$) during a hemodialysis (HD) session with state-of-the-art accuracy. It is based on the electrical impedance spectroscopy (EIS) performed with a capacitively coupled impedance sensing cell and a high-frequency measurement device, both custom-built. The EIS data are processed to infer the resistance of the liquid inside the cell, which is used together with an optical hemoglobin sensor to estimate the [Na] $_{\mathrm{Pl}}$. Validation of the EIS was performed by estimating the conductivity of the blood-mimicking fluid (BMF). The complete method was validated using the whole bovine blood, comparing the results to those obtained with standard instruments. The system was able to estimate the [Na] $_{\mathrm{Pl}}$ with sufficient accuracy (root-mean-square error (RMSE) of 3.0 mol/ $m^{3}$ with respect to reference data] to provide clinically useful information. The proof-of-concept hardware can be converted to a cheap and compact circuit board for integration into an HD machine. [ABSTRACT FROM AUTHOR]

This paper focuses on the time-difference-of-arrival (TDOA)-based direction finding for a low-altitude source with an acoustic sensor array. The source bearing in space must be estimated by the joint azimuth-elevation estimation. The azimuth may be the only concern for some scenarios, where the elevation is trivial; however, an extra parameter about elevation needs to be estimated in the joint estimation. When a planar array is introduced to replace the spatial array, the azimuth-only estimation solution with the original TDOA measurements is proposed. This solution is simpler than the joint estimation by excluding the elevation calculation. The mathematical relation between the TDOAs of spatial and planar arrays is derived, and their azimuth Cramer–Rao lower bounds (CRLBs) are compared. The CRLBs together with the pseudo-linear estimation and Taylor series least squares method are evaluated for estimating the source azimuth by simulations as well as real-life data. The results demonstrate that the azimuth-only estimation would create a modest precision improvement over the joint azimuth-elevation estimation. The effect of array altitude variation is also examined, suggesting that the relative heights among sensors have little influence on the estimation of source azimuth. [ABSTRACT FROM AUTHOR]

Tomographic imaging based on acoustic wave time-of-flight (TOF) measurement is considered to be a promising technique for monitoring temperature fields due to it being noninvasive, relatively low cost, having a high temporal resolution, and being easy to use. Accurate estimation of the TOF is key in improving the quality of the reconstructed images. In this paper, an improved TOF estimation method based on the Akaike information criterion (AIC) is developed. Two improvements are studied in comparison to the conventional weighted AIC TOF estimation methods against crosstalk interference and sound reflections: an adaptive window is applied for accurate AIC TOF estimation; a phase correction method is used to further improve the temporal resolution and noise robustness of the TOF estimation. The feasibility and effectiveness of the developed methods are validated in two experiments. [ABSTRACT FROM AUTHOR]

Rapid voltage changes (RVCs) are power quality (PQ) events characterized by small and fast transitions between two steady-state root-mean-square (rms) voltage levels. RVCs occur quite often at the distribution level and are expected to be even more frequent in the future due to the increasing penetration of dynamic loads and renewable-based generators in the smart grid. Unlike other PQ events, RVCs are less critical, but also more difficult to detect than dips/sags and swells, due to their smaller voltage variations. Nevertheless, they can be harmful to generators’ control systems and electronic equipment in general. Moreover, they strongly affect flicker. The IEC Standard 61000-4-3:2015 clearly describes an algorithm for RVC detection. However, this approach is poorly characterized in the scientific literature. In fact, it suffers from some drawbacks. In this paper, some of them (e.g., rate-dependent detection limits and detection delays) are analyzed in depth. In addition, an alternative approach based on the estimation of the rate of change of rms voltage is proposed. Multiple simulation results show that the approach considered is more sensitive to noise, but also faster, especially when not so fast RVCs occur. Moreover, it allows measuring the rate of change of rms voltage, which is currently disregarded in the IEC Standard. [ABSTRACT FROM AUTHOR]

Estimation of periodic signals, based on quantized data, is a topic of general interest in the area of instrumentation and measurement. Although several methods are available, new applications require low-power, low-complexity, and adequate estimation accuracy. In this paper, we consider the simplest possible quantization, that is, binary quantization, and describe a technique to estimate the parameters of a sampled periodic signal, using a fast algorithm. By neglecting the possibility that the sampling process is triggered by some signal-derived event, sampling is assumed to be asynchronous, that is, the ratio between the signal and the sampling periods is defined to be an irrational number. To preserve enough information at the quantizer output, additive Gaussian input noise is assumed as the information encoding mechanism. With respect to the published techniques addressing the same problem, the proposed approach does not rely on the numerical estimation of the maximum likelihood function but provides solutions that are very close to this estimate. At the same time, since the main estimator is based on matrix inversion, it proves to be less time-consuming than the numerical maximization of the likelihood function, especially when solving problems with a large number of parameters. The estimation procedure is described in detail and validated using both simulation and experimental results. The estimator performance limitations are also highlighted. [ABSTRACT FROM AUTHOR]

Natural disasters affect structural health of buildings, thus directly impacting public safety. Continuous structural monitoring can be achieved by deploying an Internet of things network of distributed sensors in buildings to capture floor movement. These sensors can be used to compute the displacements of each floor, which can then be employed to assess building damage after a seismic event. The peak relative floor displacement is computed, which is directly related to damage level according to the United States federal agencies standards. With this information, the building inventory can be classified into immediate occupancy, life safety, or collapse prevention categories. In this paper, we propose a zero velocity update technique to minimize displacement estimation error. Theoretical derivation and experimental validation are presented. In addition, we investigate modeling sensor error and interstory drift ratio distribution. Moreover, we discuss the impact of sensor error on the achieved building classification accuracy. [ABSTRACT FROM AUTHOR]

This paper introduces sparse low-power sensor networks using passive electric potential sensors for crowd-aware smart buildings. The proposed networks can detect human presence, track their positions, and estimate room occupancy by measuring the electric field disturbances caused by human movement. In addition, the proposed passive sensing system can remotely monitor human physiological signals of nearby stationary occupants (standoff ~1.5 m) when the ambient 60-Hz fields are filtered out. Based on an experimentally calibrated source range model, we further propose an improved multiple-occupant near-field indoor localization method using a maximum likelihood algorithm combined with dividing rectangular search. The tradeoff between search efficiency and localization accuracy is also discussed. Experimental results in two different rooms for 1 week duration show the effectiveness of our proposed sensing system: human identification with an accuracy of 98.3% on six participants, occupant localization methods with room occupancy estimation accuracy of 89.03%, and average localization error less than 0.3 m. These results open up new possibilities in security, indoor navigation or positioning systems, human–machine interfaces, and tele-health applications. [ABSTRACT FROM AUTHOR]

The availability of real-time information about the biological activity is a valuable matter in wastewater treatment employing activated sludge processes, and this is represented by the oxygen uptake rate (OUR). In these processes, OUR measurements have been used as a fast indicator of changes in waste load or influx of toxic organic compounds and for monitoring of the process efficiency. In this paper, we propose a method for OUR estimation by using Kalman filters and PWM control of the dissolved oxygen (DO) concentration in the activated sludge reactor. The proposed method presents a shorter response time than the standard online procedure and a lower measurement uncertainty compared to a previous feedback control method, which estimates the OUR from values of the applied control signal and the DO concentration measurements. Simulations are presented to illustrate the method and experiments are performed in a bench scale activated sludge reactor to show the applicability of the proposed estimator in a real system. The results confirm the fast response of the proposed method and its consistency with respect to the standard procedure for OUR computation. [ABSTRACT FROM AUTHOR]

There is a growing trend to use a digital camera as an instrument to measure velocity instead of a regular sensor approach. This paper introduces a new proposal for estimating kinematic quantities, namely, the angle and the relative speed, from a single motion blur image using the discrete cosine transform (DCT). Motion blur is a common phenomenon present in images due to the relative movement between the camera and the objects, during sensor exposure to light. Today, this source of kinematic data is mostly dismissed. The introduced technique focuses on cases where the camera moves at a constant linear velocity while the background remains unchanged. 2250 motion blur pictures were shot for the angle experiments and 500 for the speed estimation experiments, in a light and distance controlled environment, using a belt motor slider driven at angles between 0° and 90° and 10 preset speeds. The DCT Hough and DCT Radon results were compared to discrete Fourier transform (DFT) Hough and DFT Radon algorithms for angle estimation. The mean absolute error of the DCT Radon method for direction estimation was 4.66°. In addition, the mean relative error for speed estimation of the DCT Pseudocepstrum was 5.15%. The innovative DCT frequency analysis proposals were more accurate than all competitors evaluated for the reconstruction of the point spread function that enables calculation of relative velocity and motion direction. These results demonstrate that cameras as an instrument can be used to measure velocity even using a single linear motion blur degraded image. [ABSTRACT FROM AUTHOR]

In modern power systems, the Rate-of-Change-of-Frequency (ROCOF) may be largely employed in Wide Area Monitoring, Protection, and Control (WAMPAC) applications. However, a standard approach toward ROCOF measurements is still missing. In this paper, we investigate the feasibility of Phasor Measurement Units (PMUs) deployment in ROCOF-based applications, with a specific focus on Under-Frequency Load-Shedding (UFLS). For this analysis, we select three state-of-the-art window-based synchrophasor estimation algorithms and compare different signal models, ROCOF estimation techniques, and window lengths in datasets inspired by real-world acquisitions. In this sense, we are able to carry out a sensitivity analysis of the behavior of a PMU-based UFLS control scheme. Based on the proposed results, PMUs prove to be accurate ROCOF meters, as long as the harmonic and interharmonic distortion within the measurement pass-bandwidth is scarce. In the presence of transient events, the synchrophasor model loses its appropriateness as the signal energy spreads over the entire spectrum and cannot be approximated as a sequence of narrow-band components. Finally, we validate the actual feasibility of PMU-based UFLS in a real-time simulated scenario where we compare two different ROCOF estimation techniques with a frequency-based control scheme, and we show their impact on the successful grid restoration. [ABSTRACT FROM AUTHOR]

Air pollution has become a worldwide concerned issue and automatical estimation of air quality can provide a positive guidance to both individual and industrial behaviors. Given that the traditional instrument-based method requires high economic, labor costs on instrument purchase and maintenance, this paper proposes an effective, efficient, and cheap photo-based method for the air quality estimation in the case of particulate matter (PM2.5). The success of the proposed method lies in extracting two categories of features (including the gradient similarity and distribution shape of pixel values in the saturation map) by observing the photo appearances captured under different PM2.5 concentrations. Specifically, the gradient similarity is extracted to measure the structural information loss with the consideration that PM2.5 attenuates the light rays emitted from the objects and accordingly distorts the structures of the formed photo. Meanwhile, the saturation map is fit by the Weibull distribution to quantify the color information loss. By combining two features, a primary PM2.5 concentration estimator is obtained. Next, a nonlinear function is adopted to map the primary one to the real PM2.5 concentration. Sufficient experiments on real data captured by professional PM2.5 instrument demonstrate the effectiveness and efficiency of the proposed method. Specifically, it is highly consistent with real sensor’s measures and requires low implementation time. [ABSTRACT FROM AUTHOR]

This paper deals with a gain scheduling synchronous demodulation scheme useful to obtain speed and position measurements from resolver position sensors. The proposed algorithm is devoted to a field-programmable gate array implementation in order to provide the elaborated information for very low latency control loops. The presented design allows getting accurate estimations in a wide range of rotational speeds without requiring costly off-the-shelf integrated circuits and leads to higher accuracy at low speed if compared to commercial solutions. To this purpose, the resolver excitation circuit has been simplified working directly with a square wave signal, and the resolver frequency behavior due to the nonsinusoidal excitation has been considered. [ABSTRACT FROM AUTHOR]

Quantifying the level of nonlinear distortions and time-varying effects in frequency response function measurements is a first step toward the selection of an appropriate parametric model structure. In this paper, we tackle this problem in the presence of missing data, which is an important issue in large-scale low-cost wireless sensor networks. The proposed method is based on one experiment with a special class of periodic excitation signals. [ABSTRACT FROM AUTHOR]

The effective management of future smart grids is strictly related to the accurate knowledge of the network operating conditions via distribution system state estimation (DSSE). To achieve this target, the measurement infrastructure of the distribution systems needs a substantial upgrade. However, obvious economic limitations prevent a massive deployment of measurement instruments on the field in short times. As a consequence, ad hoc meter placement techniques have to be applied to define location and type of a minimum set of measurements required to obtain the desired accuracy performance. This paper presents a mathematical analysis showing the impact of current and power measurements on the accuracy of DSSE results. The goal is to provide the analytical tools to identify the best placement for additional current or power measurements when applying an incremental deployment of meters in the distribution system. The analysis has a general validity but has also a clear impact on practical situations when the monitoring systems are upgraded starting from an existing infrastructure. Tests on a 95-bus sample grid are presented in order to validate the found mathematical results and to highlight the associated benefits in a meter placement perspective. [ABSTRACT FROM AUTHOR]

Modern power systems produce nonstationary harmonics which bring harm to the electric equipment. Real-time and accurate parameter estimation is the key step of the harmonic elimination. This paper proposes a novel technique of the parameter estimation for fundamental and harmonic using nonergodic $S$ -transform (NEST), which can effectively eliminates the estimated parameter error caused by nonsynchronous sampling. First, the signal is decomposed into the fundamental and harmonics NEST vectors (NESTVs) by NEST, and the amplitudes are detected accurately by extracting the magnitude of NESTVs, which are optimized by the Gaussian window width coefficient of the NEST kernel function. Second, the overlapping and abandoning method is used to remove the end effect of NESTVs so as to obtain the effective and complete data for the parameter estimation. Then, the estimated instantaneous frequency and phase are obtained using the linear phase characteristic of NESTVs. Finally, simulations and experiments prove that the proposed method can not only estimate the stationary signal parameters accurately but also trace the nonstationary signal parameters in the real time under the spectrum leakage condition. [ABSTRACT FROM AUTHOR]

Heart rate (HR) estimation and monitoring is of great importance to determine a person’s physiological and mental status. Recently, it has been demonstrated that HR can be remotely retrieved from facial video-based photoplethysmographic signals captured using professional or consumer-level cameras. Many efforts have been made to improve the detection accuracy of this noncontact technique. This paper presents a timely, systematic survey on such video-based remote HR measurement approaches, with a focus on recent advancements that overcome dominating technical challenges arising from illumination variations and motion artifacts. Representative methods up to date are comparatively summarized with respect to their principles, pros, and cons under different conditions. Future prospects of this promising technique are discussed and potential research directions are described. We believe that such a remote HR measurement technique, taking advantages of unobtrusiveness while providing comfort and convenience, will be beneficial for many healthcare applications. [ABSTRACT FROM AUTHOR]

This paper describes a new measuring method of digital signal processing for detecting bee swarm phenomenon. The new method uses a comparison of the similarity between the estimated distribution of unknown sound samples with some estimated pattern distributions of known sound samples. The value of similarity of distributions was determined using statistical tools. The new method was tested in MATLAB and its results were compared with the results of a well-known classification method in the diagnostic space state. All calculations were made on recorded, real bee sounds. After testing in MATLAB, the method was successfully implemented in a prototype embedded system as an automated diagnostic system. The use of the method makes it possible to automatically detect the phenomenon of swarming. The authors see the possibility of using the developed method in other technical fields. [ABSTRACT FROM AUTHOR]

Accelerometers are used in a wide variety of applications including inertial navigation systems and activity monitors. In order to reduce systematic and random errors and improve accuracy and precision, this paper proposes a differential accelerometer system where two triaxial accelerometers are combined in opposite directions. The differential system allows to create additional constraints that can be used for improving calibration and estimation. Calibration methods based on nonlinear optimization are suggested to determine the offset and the scaling vectors. The differential system also permits to determine and update the offset and the scaling factors dynamically based on the system’s constraints. Sensor fusion is accomplished using a weighted least squares method. Experiments are carried out to evaluate and validate the system and the proposed methods including comparison with a single accelerometer system. It is shown that the differential system facilitates and improves acceleration estimation in terms of accuracy and precision. [ABSTRACT FROM AUTHOR]

This paper introduces a new dynamic harmonic synchrophasor estimator (DHSE) based on the Shannon sampling theorem. Each dynamic harmonic phasor is modeled as a weighted sum of a series of sinc interpolation functions. Based on this, a bank of finite impulse response filters is designed. They are used to estimate not only the dynamic harmonic synchrophasor but also the harmonic frequency and rate of change of frequency (ROCOF). A model parameter is properly selected to adapt filters’ passband and stopband performances for different order harmonics. Frequency responses and simulation tests are used to compare the performance of the DHSE and the Taylor–Fourier transform (TFT). The results show that the DHSE has lower passband ripples and higher stopband attenuation than the TFT. Moreover, under frequency deviation, harmonic oscillation, and frequency ramp conditions, the DHSE is more accurate than the TFT in dynamic harmonic synchrophasor, frequency, and ROCOF estimations. [ABSTRACT FROM AUTHOR]

In this paper, we discuss nonparametric estimation of the probability density function (PDF) of a univariate random variable. This problem has been the subject of a vast amount of scientific literature in many domains, while statisticians are mainly interested in the analysis of the properties of proposed estimators, and engineers treat the histogram as a ready-to-use tool for a data set analysis. By considering histogram data as a numerical sequence, a simple approach for PDF estimation is presented in this paper. It is based on basic notions related to the reconstruction of a continuous-time signal from a sequence of samples. When estimating continuous PDFs, it is shown that the proposed approach is as accurate as kernel-based estimators, widely adopted in the statistical literature. Conversely, it can provide better accuracy when the PDF to be estimated exhibits a discontinuous behavior. The main statistical properties of the proposed estimators are derived and then verified by simulations related to the common cases of normal and uniform density functions. The obtained results are also used to derive optimal, i.e., minimum integral of the mean square error, estimators. [ABSTRACT FROM AUTHOR]

An estimate of a measurand using a nonlinear function of uncorrelated input quantities can be done by either applying the nonlinear function to the means of the input quantities (Method 1) or calculating the mean of a set of values obtained from propagating individual measurement values through the nonlinear function (Method 2). This paper proposes an improvement over the standard Method 2 procedures when the input quantities are assumed to be statistically independent and the nonlinear function has a general sum-of-product form, which covers many common measurement models. This paper shows that the proposed new approach (called Method 2S), if applicable, always produces a mean-squared error smaller than that of the conventional Method 2 procedures. The proposed approach improves the efficiency of Type-A evaluation as well as the Monte Carlo method. It also well complements the mainstream practices in the measurement uncertainty evaluation. [ABSTRACT FROM AUTHOR]

This paper presents a new time–frequency (TF) analysis method called matching synchrosqueezing wavelet transform (MSWT) to signals with fast varying instantaneous frequency (IF). The original synchrosqueezing wavelet transform (SWT) can effectively improve the readability of TF representation (TFR) of signals with slowly varying IF. However, SWT still suffers from TF blurs for signals with fast varying IF. Moreover, the variable operating conditions of the aeroengine always make the vibration a signal with fast varying IF, especially when it comes to significant speed changes, which results in the obscure TFR for aeroengine vibration monitoring. In this paper, the MSWT introduces a chirp rate estimation into a comprehensive IF estimation to match the TF structure of the signals with fast varying IF and thus to achieve a highly concentrated TFR as the standard TF reassignment methods. Most importantly, the MSWT retains the reconstruction benefit like the SWT. The proposed MSWT is validated by both numerical simulation and applications in a bat echolocation signal analysis. Finally, a case study of a dual-rotor turbofan engine is given to illustrate the effectiveness of the proposed method for aeroengine vibration monitoring. [ABSTRACT FROM AUTHOR]

As an important part of prognostics and health management, accurate remaining useful life (RUL) prediction for lithium (Li)-ion batteries can provide helpful reference for when to maintain the batteries in advance. This paper presents a novel method to predict the RUL of Li-ion batteries. This method is based on the framework of improved particle learning (PL). The PL framework can prevent particle degeneracy by resampling state particles first with considering the current measurement information and then propagating them. Meanwhile, PL is improved by adjusting the number of particles at each iteration adaptively to reduce the running time of the algorithm, which makes it suitable for online application. Furthermore, the kernel smoothing algorithm is fused into PL to keep the variance of parameter particles invariant during recursive propagation with the battery prediction model. This entire method is referred to as PLKS in this paper. The model can then be updated by the proposed method when new measurements are obtained. Future capacities are iteratively predicted with the updated prediction model until the predefined threshold value is triggered. The RUL is calculated according to these predicted capacities and the predefined threshold value. A series of case studies that demonstrate the proposed method is presented in the experiment. [ABSTRACT FROM AUTHOR]

Next-generation phasor measurement units (PMU) are expected to be more accurate than existing ones, especially to address the stricter requirements of future active distribution grids. From this perspective, the influence of acquisition wideband noise (which includes multiple contributions both in amplitude and in phase) has to be carefully evaluated. In this context, the contribution of this paper is twofold. First, it provides a general framework to evaluate the effect of wideband noise on synchrophasor amplitude, phase, frequency, and rate of change of frequency (ROCOF) estimation. The results of this analysis show that the influence of wideband noise can become critical for compliance with the requirements reported in the IEEE standards C37.118.1-2011 and C37.118.1a-2014, especially for frequency and ROCOF estimation. In addition, this paper reports general guidelines to choose the PMU effective resolution and sampling rate for which the impact of wideband noise on both P Class and M Class PMUs is negligible. [ABSTRACT FROM PUBLISHER]

In the modern very large scale integration systems that demand high reliability, a carefully designed power/ground network is essential in order to provide stable power supply to the internal circuits. Although the power supply connection is always checked before tapeout, after the chip fabrication it is quite difficult to evaluate its actual quality and reliability. This paper proposes a novel way to evaluate the on-chip power supply network based on the current estimation using magnetic field emitted from LSI. The proposed method estimates the actual current flow in the network from the magnetic field measurement results and enables us to find defects or design faults such as VIA/wire disconnections and/or current concentration with noninvasive and low-cost way. Experimental results using an electromagnetic field simulator demonstrate that the proposed method precisely estimates the current flow in the supply network. The feasibility of the proposed method is also verified through the actual measurement results of the test structure, which is designed and fabricated in a 0.18- $\mu \text{m}$ 1P5M CMOS process. Near-field magnetic field measurement of the chip was achieved by our high-precision magnetic sensing system and the VIA fault locations on the power supply network were clearly detected by the proposed method. [ABSTRACT FROM AUTHOR]

Indoor positioning is an emerging technology with wide applications. Augmented and mixed reality need accurate and real-time positioning of user’s limbs or direction of sight in real time, and reference points within the real environment. Positioning is also required for mall navigation, elderly people movements monitoring, and many others contexts. Indoor positioning of mobile units (MUs) can be provided by multilateration techniques that compute the location of an MU starting from distance measurements between the MU and a set of beacons. In this paper, a nonlinear closed-form solution for the trilateration problem is employed to avoid heavy numerical iterative algorithms. The closed-form solution is allowed by a particular arrangement of the beacons, placed at the vertex of a square. A positioning system is presented where MUs, exploiting the light algorithm of the closed-form positioning, are able to autonomously and privately calculate their own positions. A prototype of the positioning system has been designed, realized, and characterized for an average $4 \times 4 \times 3\,\,\text {m}^{3}$ home or office room. It includes four beacons that emit a sequence of ultrasonic chirp signals, several MUs, and a master unit that provides time synchronization via ANT transceivers onboard each MU and the master unit. Thanks to the adopted closed form solution, each tiny battery-operated MU is able to carry out all the computations onboard, including analog and digital signal processing, beacon-MU distance estimation, and finally MU positioning at a rate of 2 Hz. [ABSTRACT FROM AUTHOR]

This paper proposes models for the reference phasor in the context of phasor measurement unit calibration, under conditions of step discontinuities in magnitude and phase, along with methods to estimate the model parameters. Two parametric mathematical models, which are proposed to represent these signals, are fitted to signal samples via an iterative numerical method. The proposed approach does not require any time adjustment of the analysis window to skip the discontinuity. The estimated parameters can be used to calculate the frequency, the magnitude, and the phase during magnitude or phase steps. During a window with the step transient, we propose reference phasors with an appropriate definition. Moreover, we quantify the errors of the phasor-related estimates, for both computer-simulated cases and laboratory measurements. [ABSTRACT FROM AUTHOR]

This paper proposes a method to estimate power quantities based on IEEE 1459–2010 using measurement window with length as close as possible to the length of the changing signal period and the nonparametric estimation approach in the frequency domain. Parameters are calculated from the largest local discrete Fourier transformation (DFT) coefficients of the particular frequency components. The improvement in reducing the influences of the harmonic components is the estimation around one signal period. When the time is shortened below the signal period, the dc coefficients of the DFT are taken into account using two Rife–Vincent windows class I with different orders. First, the frequency is estimated by the quotient of the dc coefficients on the sampling points from the measurement interval of around half of the signal period, and upon this, the fine estimation of the signal period, number of samples in this period, and belonging relative frequency with displacement can be effectively estimated. Effectiveness of estimations and tracking of the power quantities is demonstrated by simulations and on the real signals of the short-circuit conditions in the power distribution system. [ABSTRACT FROM AUTHOR]

This paper presents a modified sine-fitting algorithms which can be used for high-accuracy sampling measurements of complex voltage ratio of sinusoidal signals. The proposed algorithms provide a significant reduction in the measurement uncertainty of phase shift when synchronizing the sampler and signal source is impossible. [ABSTRACT FROM AUTHOR]

The Wiener–Hammerstein (W–H) system is a popular and easy to understand class of Volterra nonlinear dynamical system. It consists of a static nonlinearity positioned between two dynamical subsystems. The main identification challenge resides in separating two linear filters as a rational form of poles and zeros. According to previous studies, an initial guess of the separated dynamics is made by browsing through all possible combinations of pole–zero. Then, based on the root-mean-square error (RMSE), good partitions are selected and later all parameters are optimized mutually to determine the best model. As the RMSE before and after optimization behaves erratically, this paper proposes the use of the Spearman correlation to select good models for optimizing. The proposed technique avoids the estimation of the local nonlinearity for each partition and optimizes only few good models. Thus, a massive speedup in processing time is achieved without any prior knowledge about the system. The theoretical verification complies with the simulation study and the measurement analysis. [ABSTRACT FROM AUTHOR]

Atmospheric ice accretion is considered a severe safety issue for technical systems such as communication infrastructure, power lines, blades of wind turbines, and so on in cold climate regions. Among different sensing systems to detect and provide reliable information about the state of ice, capacitive sensing has been reported as a suitable technique. In capacitive sensing, measured capacitances are influenced due to ice accretion. Signal processing for ice sensors requires to estimate parameters for the state of ice from the data. In this paper, we present a model-based estimation approach for capacitive ice sensors. We analyze different electrode topologies for ice sensing and provide a statistical analysis of the sensor behavior. We hereby consider the random nature of natural ice accretion. From simulations, we show how a statistical model for the measurement process can be derived and demonstrate the construction of estimation algorithms within the Bayesian framework. We will demonstrate the capability of our approach for automated ice detection by means of long-term field data showing the ability to estimate ice layers with a precision better than 1 mm. [ABSTRACT FROM AUTHOR]