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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]

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]

To effectively manage the growing number of distributed energy resources, more extensive monitoring is needed at distribution network levels, where a challenging combination of constraints and cost-effectiveness requirements must be taken into account. In this paper, we consider the idea of combining accurate phasor measurement with the capability to analyze harmonic and interharmonic phasors. This creates the need to deal with multiple phasor components having different amplitudes, including interharmonics with unknown frequency locations. For this purpose, we propose an enhanced compressive sensing-based algorithm for the estimation of Taylor–Fourier coefficients in a multifrequency dynamic phasor model. We characterize its accuracy in harmonic and interharmonic phasor measurement and discuss reporting rate and latency performance. We show that very good accuracy can also be achieved as a standard phasor measurement unit, allowing to combine multiple functions into a promising new-generation measurement, monitoring, and diagnostic device. [ABSTRACT FROM AUTHOR]

The onsite partial discharge (PD) detection of cable insulation is of great importance for power system maintenance. To preserve more basic features of denoised PD signal, in this paper, a PD denoising by a new class of affine projection algorithm (APA) is proposed based on the high-order error power (HOEP) criterion. We propose a new affine projection least mean absolute third algorithm (APLMATA), which exploits the HOEP criterion ($p=3$) and can achieve reliable performance under Gaussian interference. We further develop a modified APLMATA (MAPLMATA) to reduce the misadjustment with moderate computational burden. Analytical models are derived for MAPLMATA for Gaussian signals. To enhance the stability of the algorithm in generalized Gaussian noise scenarios, an affine project least mean $p$ th power algorithm (APLMPA) that includes the $p$ th power dependence on the error is presented. In particular, we apply the proposed APA-based algorithms to suppress the noise from PD signal in power cables. Simulation and experimental results demonstrate the effectiveness of the proposed algorithms for system identification and PD denoising. [ABSTRACT FROM AUTHOR]

This paper presents the design and testbed implementation of a tree-based algorithm, which classifies many of the linearly modulated signals, such as binary phase-shift keying (PSK), quadrature PSK (QPSK), offset QPSK, $\pi $ /4-QPSK, 8-PSK, minimum shift keying, and 16-quadrature amplitude modulation. The proposed modulation classification algorithm is applicable to spatially multiplexed multiple antenna systems over frequency-selective fading channels. It works in the presence of timing, phase, and frequency offsets, without having prior knowledge of the channel state information. Classification is performed by utilizing the combined properties of the correlation functions, cyclic cumulant (CC), and cumulant. The correlation function of the received baseband signal exhibits peaks at a particular set of time lag, which represents distinctive features for the modulation formats. The CC uses the position of nonzero cycle frequency of the received baseband signals to classify modulation formats, while the cumulant employs threshold values. To authenticate the proposed algorithm, implementation and measurement are performed in an indoor propagation environment by using a National Instrument testbed setup. The performance of the proposed algorithm is compared with existing methods via Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

This paper deals with an advanced approach for the monitoring of elders and people with neurological pathologies (e.g., Alzheimer). The system that adopts a multisensor approach is able to recognize critical events, such as falls or prolonged inactivity, to monitor the user posture, and to notify the alerts to caregivers. In particular, this paper focuses on smart algorithms developed for the activities of daily living (ADL) classification, which use the information provided by inertial sensors embedded in the user device. In particular, a novel multisensor data-fusion approach, combining data from an accelerometer and a gyroscope, is presented. Apart from alert management, the information provided by this system is useful to track the evolution of the user pathology, also during rehabilitation tasks. Results obtained during tests with users demonstrate suitable performances of the adopted paradigm, in terms of sensitivity and specificity, in performing falls and ADL classification tasks. The average value of the sensitivity index for the classes of falls and ADL considered through this paper is 0.81, while the average value of the specificity index is 0.98. [ABSTRACT FROM AUTHOR]

In the recent years, the scientific community is paying great attention to the development of phasor measurement units (PMU) and, particularly, to novel digital signal processing algorithms for these instruments. While the performance of estimation algorithms is usually evaluated through simulations on a PC, the overall PMU accuracy depends on measurement hardware as well (e.g., transducers, data converters, and synchronization circuitry). However, the relationship between estimation algorithms accuracy and metrological characteristics of hardware equipment is very difficult to predict or to simulate. Therefore, the performance of different algorithms can be hardly compared when they are actually implemented in real instruments and used in the field. This paper attempts to address this problem by presenting an open testbed for PMU estimation algorithms. The key distinctive feature of the testbed is its ability to evaluate and to compare algorithm accuracy under experimental conditions that include not only the disturbances specified in the IEEE Standard C37.118.1-2011 and its Amendment IEEE C37.118.1a-2014, but also the effects of given uncertainty contributions due to different hardware components. A thorough metrological characterization of the testbed, properly supported by a noise propagation model, is performed in order to quantify such uncertainty contributions and their impact on the estimates of synchrophasor magnitude, phase, fundamental frequency, and rate of change of frequency returned by different algorithms under test. As a case study, three state-of-the-art estimation algorithms are tested in a variety of conditions. [ABSTRACT FROM AUTHOR]

The instantaneous vital sign rates, which are related to physiological dynamics, are important indicators of human health condition. This paper presents a noncontact way to measure the human instantaneous vital signs using digital-intermediate frequency (IF) Doppler radar. The synchrosqueezing transform-based algorithm has been proposed to get a concentrated time-frequency (TF) distribution, so that the high-resolution instantaneous heartbeat and respiratory rates and the time-domain signals can be acquired. Moreover, the developed radar with customized radio frequency module employs the direct IF sampling technique to achieve high sensitivity to capture the tiny vital sign variations. Experiments with different human subjects and physiological conditions have been carried out. Compared with the landmark-based method and traditional TF algorithms, the results show that instantaneous vital signs can be acquired more accurately within 3 m at a −13 dBm transmit power by the proposed method. Therefore, the radar can be used for evaluating the physiological dynamics and assessing health condition. [ABSTRACT FROM PUBLISHER]

Accurate spectral testing plays a crucial role in modern high-precision analog-to-digital converters’ (ADCs’) evaluation process. One of the challenges is to be able to cost-effectively test the continually higher resolution ADCs accurately. Due to its stringent test requirement, the standard test method for ADCs can be difficult to implement with low cost. This paper proposes an algorithm that relaxes the requirements of precise control over source amplitude and frequency, and of the need to achieve coherent sampling. The algorithm divides the output data into segments, and estimates drift fundamental via Newton iteration. By removing the estimated drift fundamental and replacing with a coherent, nondrift fundamental in time domain, accurate spectral results can be achieved. Various simulation results have validated the accuracy of the proposed algorithm. The proposed algorithm is capable of tolerating various test condition variations such as any-level of noncoherency, various input frequency range and different numbers of segmentations. In addition, several measurement results from different ADCs have verified the accuracy of the proposed algorithm, which is able to accurately obtain spectral performance of an 18 b high-resolution ADC. Such algorithm relaxes the standard test requirement such as precise control over source frequency and amplitude, which dramatically reduces the test setup complexity and cost. [ABSTRACT FROM PUBLISHER]

This paper presents a method for the detection of series arc faults in electrical circuits, which has been developed starting from an experimental characterization of the arc fault phenomenon and an arcing current study in several test conditions. Starting from this, the authors have found that is it possible to suitably detect arc faults by means of a high-resolution low-frequency harmonic analysis of current signal, based on chirp zeta transform, and a proper set of indicators. The proposed method effectiveness is shown by means of experimental tests, which were carried in both arcing and nonarcing conditions and in the presence of different loads, chosen according to the UL 1699 standard requirements. [ABSTRACT FROM PUBLISHER]

This paper presents a versatile low-cost procedure for complex permittivity measurements of liquids from 400 MHz to 5 GHz. Our procedure uses handheld instrumentation and custom software for data acquisition and postprocessing. The purpose is to replace the benchtop vector network analyzer (VNA) generally used in such applications with a portable and cheaper handheld spectrum analyzer used in the VNA mode. Postprocessing software based on homomorphic deconvolution is used to remove possible inaccuracies in the permittivity spectra coming from the reduced performance of the handheld VNA with respect to benchtop models. Our measurements are evaluated by comparison with those of the National Physical Laboratory (NPL). The results of the real and imaginary parts of the permittivity spectra of two well characterized organic compounds, ethanediol and 2-propanol, are in very good agreement with the NPL reference standards, showing a relative root-mean-square error (RRMSE) always less than 5%. Low values of the RRMSE, together with an expanded uncertainty less than 3%, ensure that our permittivity measurements are repeatable and accurate. Thanks to its versatility, portability, and at least half of the cost of commercial models, our system is suitable for on-site measurements in different applications, including food quality monitoring and control of medical treatments and biological procedures. [ABSTRACT FROM AUTHOR]

Bearing fault diagnosis is becoming more and more important for current rotating machinery. How to extract bearing fault signals submerged by heavy background noise is still a challenging problem, especially in the case of multiple faults coupled with each other. In this paper, a novel multifault model called sparse multiperiod group lasso (SMPGL) is proposed to extract the fault feature of every single fault from multifault signals based on the sparsity within and across groups (SWAG) property and the separably periodic prior. Moreover, a fast algorithm is deduced based on the majorization–minimization (MM) algorithm for solving the proposed multifault model and its convergence condition is also analyzed. We investigate the parameter selection thoroughly and provide a deterministic rule for the parameter selection of SMPGL. The main advantages of the proposed method are that users can set the number of compound faults freely, the algorithm is very fast, and parameters are set adaptively. The effectiveness and robustness of SMPGL are verified by simulation studies and two experiment cases. Furthermore, the comparison study shows that the proposed SMPGL method gives more satisfying results than other state-of-the-art methods, including the L1-based method and spectral kurtosis (SK). [ABSTRACT FROM AUTHOR]

The accurate detection and classification of power quality (PQ) disturbances in power systems is a key step to determine the causes of these events before any proper countermeasure could be taken. This paper presents a new algorithm for detection and classification of PQ disturbances based on the combination of double-resolution S-transform (DRST) and directed acyclic graph support vector machines (DAG-SVMs). The proposed method first employs DRST for an effective feature extraction from power signals. Then, the DAG-SVMs are used to predict the classes of PQ disturbances. The DRST not only has better time–frequency localization and stronger robustness but also reduces the computational complexity without losing the useful information of the original signal in comparison with the traditional S-transform. Through the combined use of DRST and DAG-SVMs, the algorithm can be easily implemented in embedded real-time applications. Finally, the implementation of the proposed algorithm in a digital signal processor + advanced reduced instruction set computing machine-based hardware test platform is introduced. The effectiveness of the proposed method is demonstrated by means of computer simulations and practical experiments with single and combined PQ disturbances. [ABSTRACT FROM PUBLISHER]

Machine fault diagnosis collects massive amounts of vibration data about complex mechanical systems. Performing feature detection from these data sets has already led to a major challenge. Compressive sensing theory is a new sampling framework that provides an alternative to the well-known Shannon sampling theory. This theory enables the recovery of sparse or compressible signals from a small set of nonadaptive linear measurements. However, it is suboptimal to recover the whole signals from the compressive measurements and then solve feature identification problems through traditional DSP techniques. Thus, a novel mechanical feature identification method is proposed in this paper. Its main advantage is that fault features are extracted directly in the compressive measurement domain without sacrificing accuracy, while a significant reduction in the dimensionality of the measurement data is achieved. Moreover, Gaussian white noises are significantly alleviated, which dramatically enhances the reliability of machine fault diagnosis. Parameter analysis is also profoundly investigated through a set of numerical experiments. Numerical simulations and experiments are further performed to prove the reliability and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In-service inspection of complex systems such as nuclear steam generator (SG) tubes and their surrounding support structures is challenged by overlapping degradation modes. In these complex systems the simultaneous and accurate measurement of more than two interdependent parameters is difficult using standard statistical regression analysis tools. Recently, artificial neural networks (ANNs) have been investigated for dealing with the complex relation between inspection data and defect properties. In this paper, pulsed eddy current data were obtained using a single driver with an array of eight pick-up coils configured for inspection of Alloy-800 SG tube fretting, accompanied by tube offset within a simulated corroding ferromagnetic support structure. Time-voltage data were processed by a modified principal component analysis (MPCA) to reduce data dimensionality, and MPCA scores were input into an ANN that simultaneously targeted four parameters associated with support structure hole size, tube off-centering in two dimensions, and fret depth. The neural network was trained, tested, and validated on experimental data and provided estimates within 2% of hole inner diameter (ID) and 3% of fret depth targets. The estimates of hole ID and tube position were further improved when fret depth was used as an input, as might occur if fret depth inspection results are available. [ABSTRACT FROM PUBLISHER]

Ultrasonic tomography (UT) is a nondestructive technique that is widely applied in industrial process imaging. In this paper, a novel sector diffusion method for construction of a sensitivity matrix is proposed to improve the performance of a linear back-projection (LBP) algorithm. The so-called sector-diffusion-matrix-based LBP (SLBP) was applied to reconstruct gas/liquid two-phase flow. A series of models has been established to evaluate the effectiveness of the method. The images reconstructed by traditional LBP and SLBP are presented and compared. Two quantitative evaluation criteria including position error and image correlation coefficient ($I_{\mathrm {cr}}$) were defined and used to evaluate the quality of the reconstructed images. The imaging results indicate that the SLBP method is valid and feasible to improve the quality of reconstructed images, especially for a UT system with a small number of transducers. The evaluation criterion $I_{\mathrm {cr}}$ is improved by 26%, 20%, 9%, and 4% for four different flow patterns with an eight-transducer UT system in $128\times128$ pixels and improved by 26%, 20%, 10%, and 1% in $40\times40$ resolution. [ABSTRACT FROM AUTHOR]

This paper describes the application of a pulse pileup correction algorithm based on maximum likelihood estimation to simulated and experimentally acquired signals from a pyroelectric ultrasound sensor. The sensor forms part of a prototype phase-insensitive ultrasound computed tomography (piUCT) system for breast cancer detection that generates quantitative maps of acoustic attenuation. The effectiveness of the piUCT technique has been previously demonstrated through imaging of cylindrical, polyurethane phantoms. For the technique to be applied clinically, the system must be capable of measuring high acoustic attenuations (>30 dB) and completing a scan in a clinically acceptable time frame of approximately 5 min. High attenuations present a challenge because the sensor responds directly to acoustic power and therefore, an attenuation of 30 dB causes a drop in signal level of three orders of magnitude, resulting in a large dynamic range in the received signal set. Completion of a scan in 5 min requires a period between measurements shorter than the response time of the sensor, causing pyroelectric pulse pileup. The maximum likelihood estimation method recovers the amplitudes of closely spaced superimposed pyroelectric signals while improving the coefficient of variation of the measurement by a factor of two compared to direct pulse amplitude measurement, simultaneously increasing the maximum measurable attenuation and measurement rate of the system. [ABSTRACT FROM AUTHOR]

Supervision of the physical layer of optical networks is an extremely relevant subject. To detect fiber faults, single-ended solutions, such as the optical time-domain reflectometry (OTDR), allow for precise measurements of fault profiles. Combining the OTDR with a signal processing approach for high-dimensional sparse parameter estimation allows for automated and reliable results in reduced time. In this paper, a measurement system composed of a photon-counting OTDR data acquisition unit and a processing unit based on a linearized Bregman iterations’ algorithm for automatic fault finding is proposed. An in-depth comparative study of the proposed algorithm’s fault-finding prowess in the presence of noise is presented. Characteristics, such as sensitivity, specificity, processing time, and complexity, are analyzed in simulated environments. Real-life measurements that are conducted using the photon-counting OTDR subsystem for data acquisition and the linearized Bregman-based processing unit for automated data analysis demonstrated accurate results. It is concluded that the proposed measurement system is particularly well-suited to the task of fault finding. The natural characteristic of the algorithm fosters embedding the solution in digital hardware, allowing for reduced costs and processing time. [ABSTRACT FROM AUTHOR]

In this paper, a chirp $z$ -transform (CZT)-based algorithm for frequency-modulated continuous wave (FMCW) radar applications is presented. The proposed algorithm is optimized for real-time implementation in field-programmable gate arrays. To achieve a very high accuracy, the FMCW radar uses an additional phase evaluation. Therefore, a phase calculation based on the CZT algorithm is derived and compared with a correlation based algorithm. For a better classification of the algorithm, the respective Cramér–Rao bounds are calculated. The performance of the algorithm is shown by the evaluation of different radar measurements with a K-band radar. In the measurements, an accuracy of $5~\mu $ m with a mean standard deviation of 774 nm is achieved, which nearly matches the theoretically predicted mean standard deviation of 160 nm. [ABSTRACT FROM PUBLISHER]

The acoustic recognition module of the unattended ground sensor (UGS) system applied in wild environments is faced with the challenge of complicated noise interference. In this paper, a small-aperture microphone array (MA)-based acoustic target classification system, including the system hardware architecture and classification algorithm scheme, is designed as a node-level sensor for the application of UGS in noisy situation. Starting from the analysis of signature of the acoustic signal in wild environments and the merits of small-aperture array in noise reduction, a closely arranged microelectromechanical systems MA is designed to improve the signal quality. Considering the similarities between speaker discrimination and acoustic target recognition, a classification algorithm scheme, consisting of a simplified Mel-frequency cepstrum coefficients and the Gaussian mixture model, is developed to distinguish acoustic targets’ patterns. The proposed classification algorithm has been implemented on embedded system after being tested on training datasets. By combining the small-aperture array and low-complexity classification algorithm, the presented acoustic classification prototype system is portable and efficient. To demonstrate the efficiency of the design, the prototype system is verified in a practical situation with the employment of wheeled and tracked vehicles. Evaluation of the system performances in comparison with other state-of-the-art methods indicates that the proposed design is practical for the acoustic target classification and may be widely adopted by UGS. [ABSTRACT FROM PUBLISHER]

This paper describes the measurement and nonparametric estimation of phase difference with stronger inhibition of spectral leakage. The phase difference is estimated by discrete Fourier transform coefficients around component peaks using Rife–Vincent windows class I (RV1) and the minimum side-lobe level (MSL) windows. Selecting a suitable window order, MSL windows demonstrate a very low bias error and lower noise error propagation than RV1 windows. A practical example evaluates the accuracy of the used windows in estimations. [ABSTRACT FROM PUBLISHER]

An appropriate wavelet network (WN) approach is introduced for detecting damage location and severity of structures based on measured guided ultrasonic wave (GUW) signals. An algorithm for establishing a multiple-input multiple-output fixed grid wavelet network (FGWN) is proposed. This algorithm consists of three main stages: 1) formation of wavelet latticel; 2) formation of wavelet matrix; and 3) optimizing the wavelet structure by means of orthogonal least square algorithm. Three damage-sensitive features are extracted from the GUW signals: 1) time of flight; 2) normalized damage wave amplitude; and 3) normalized damage wave area. These features are considered as the FGWN inputs and the damage location and severity are estimated. The established FGWN is used for identifying damage location and severity in a structural beam. The beam is investigated and simulated in different damaged conditions. Computed finite element method (FEM) simulation signals are used for training the FGWN. Some other FEM simulation signals, as well as measured experimental ones are used for testing. The proposed damage identification method is compared with three artificial neural network (ANN)-based algorithms. In addition to some other benefits of the proposed WN-based algorithm over ANN-based methods discussed in this paper, the results show that our approach performs better in both damage location and severity detections than other methods. [ABSTRACT FROM PUBLISHER]

Compressive sensing (CS) shows that, when a signal is sparse or compressible with respect to some basis, a small number of compressive measurements of the original signal can be sufficient for exact (or approximate) recovery. Distributed CS (DCS) takes advantage of both intra- and intersignal correlation structures to reduce the number of measurements required for multisignal recovery. In most cases of audio signal processing, only mixtures of the original sources are available for observation under the DCS framework, without prior information on both the source signals and the mixing process. To recover the original sources, estimating the mixing process is a key step. The underlying method for mixing matrix estimation reconstructs the mixtures by a DCS approach first and then estimates the mixing matrix from the recovered mixtures. The reconstruction step takes considerable time and also introduces errors into the estimation step. The novelty of this paper lies in verifying the independence and non-Gaussian property for the compressive measurements of audio signals, based on which it proposes a novel method that estimates the mixing matrix directly from the compressive observations without reconstructing the mixtures. Numerical simulations show that the proposed method outperforms the underlying method with better estimation speed and accuracy in both noisy and noiseless cases. [ABSTRACT FROM PUBLISHER]

Multiscale noise tuning stochastic resonance (MSTSR) has been proved to be an effective method for enhanced fault diagnosis by taking advantage of noise to detect the incipient faults of the bearings and gearbox. This paper addresses a sequential algorithm for the MSTSR method to detect the train bearing faults in an embedded system through the acoustic signal analysis. Specifically, the energy operator, digital filter array, and fourth rank Runge–Kutta equation methods are designed to realize the signal demodulation, multiscale noise tuning, and bistable stochastic resonance in sequence. The merit of the sequential algorithm is that it reduces the memory consumption and decreases the computation complexity, so that it can be efficiently implemented in the embedded system based on a low-cost, low-power hardware platform. After the sequential algorithm, the real-valued fast Fourier transform is used to calculate the power spectrum of the analyzed signal. The proposed method has been verified in algorithm performance and hardware implementation by three kinds of practical acoustic signals from defective train bearings. An enhanced performance of the proposed fault diagnosis method is confirmed as compared with several traditional methods, and the hardware performance is also validated. [ABSTRACT FROM PUBLISHER]

This paper describes the design and realization of a 5.6-GHz ultrawide-bandwidth-based position measurement system. The system was entirely made using off-the-shelf components and achieves centimeter-level accuracy in an indoor environment. It is based on asynchronous modulated pulse round-trip time measurements. Both system level and realization details are described along with experimental results including estimates of measurement uncertainties. [ABSTRACT FROM PUBLISHER]

State estimation process is one of the major concerns for controlling and monitoring systems in industry which requires high-cost measurements or unmeasurable variables of nonlinear systems. These drawbacks can be highly eliminated by designing systems without using any kind of sensors. In this paper, sensorless speed control of a dc motor was performed by using extended Kalman filter (EKF) and particle filter (PF). The speed information is estimated by using armature current data measured from a dc motor which is controlled in various speed references with a closed-loop controller. Furthermore, a performance comparison of the EKF and the PF by taking into consideration their estimation errors under the same conditions was realized in a simulation environment. The comparison results showed that the estimation performance of the PF is more accurate but slower than the EKF. The quantitative values of accurateness and slowness are depended on the particle number of the PF. The obtained computation times of the PF having ten particles and the EKF are 180 and 15 \mu \s, respectively. [ABSTRACT FROM PUBLISHER]

Signal demodulation is a fundamental procedure in many situations during a spectral analysis. Through an envelope analysis, it becomes possible to identify fault frequencies that are embedded in a modulated signal and that are not clearly visible only by directly applying some signal processing techniques such as the Fourier transform and filtering. In this paper, a very simple and empirical technique for demodulation is proposed. It is based only on the analysis of local extremes from a modulated time sequence to find a new time sequence that carries the wanted relevant fault data. The method of analysis is a good alternative tool for electrical fault detection in induction motors. The numerical and experimental results demonstrate the effectiveness of the proposed technique. [ABSTRACT FROM PUBLISHER]

This paper presents a new method for the automated processing of surface electromyography (SEMG) signals, particularly suited for the detection of muscle activation timing. The method has an intermediate level of complexity between simpler (but less performing) and more complex (but in general slower) methods, and is successfully used in the development of biomedical devices for rehabilitation carried out by our group. The method proposed here is based on a statistical approach for threshold computation that is implemented without the need of maximum voluntary contraction or relaxed state, usually required to overcome the difficulty in obtaining the threshold value. The method is compared with 10 popular automated standard methods using different types of simulated signals that approximate the behavior of real SEMG signals. Both the number of activations detected and the onset time measured are analyzed. The algorithm is then applied to real SEMG signals acquired from healthy subjects. The results are finally compared with the literature values. The results show that the proposed algorithm is the best performing method when both the number of activations and the activation timing are considered. In real applications, the algorithm gives the results compatible with the well-agreed literature data. [ABSTRACT FROM PUBLISHER]

Accurate harmonics estimation has become a key issue in power quality assessment. This paper deals with a discrete Fourier transform (DFT)-based measurement technique, which can be easily employed to accurately determine the harmonic components of a distorted signal, i.e., voltage or current. The proposed method is based on a modulated sliding DFT algorithm, which is unconditionally stable and does not accumulate errors due to finite precision representation, and a variable sampling period technique (VSPT) to achieve a frequency adaptive mechanism. It is worth noting that the VSPT changes the sampling period for a variable grid frequency condition, leading to a constant sampling frequency under steady-state conditions. The proposed method provides: 1) high degree of accuracy; 2) structural/performance robustness; and 3) frequency adaptability. Given the modular nature of the method, it is implemented on a field programmable gate array. Simulations and experimental tests are shown to verify the performance of the proposed method. [ABSTRACT FROM PUBLISHER]

Accurate estimation of power system frequency is essential for monitoring and operation of the smart grid. Traditionally, this has been done using discrete Fourier transform (DFT) coefficients of the positive fundamental frequency. Such DFT-based frequency estimation has been used successfully in phasor measurement units and frequency disturbance recorders in North America. Frequency errors in DFT-based algorithms for single-phase signals arise mainly due to noise and the leakage effect of the negative fundamental frequency. In this paper, a DFT-based frequency estimation algorithm is proposed to introduce three digital filters for reduction of estimate error due to noise and the leakage effect. This algorithm calculates the frequency estimate from the magnitude ratios of DFT coefficients to avoid the leakage effect. It compensates the estimate error, which is induced from the DFT magnitude ratios of three filtered outputs. The enhancement of signal-to-noise ratios is verified through simulations. [ABSTRACT FROM AUTHOR]

Voltage flicker analysis is one of the elementary tools of power quality assessment in power systems. A new algorithm, fast S-transform (FST) is proposed and is used to analyze the time-varying voltage flicker in this paper. A voltage flicker waveform envelope, which captures the main flicker characteristic, is obtained by Teager–Kaiser energy operator instantaneously. FST achieves much lower computational complexity in comparison to the S-transform. FST is used to extract both the time and the frequency domain information of each flicker component. The proposed algorithm can be easily implemented with hardware multipliers, making the method a good choice for real-time applications for the time-frequency analysis of flicker. The implementation of the proposed algorithm in the dual-core processor-digital signal processor and advanced RISC machines-based voltage flickermeter is also introduced. The simulation and application results validate the accuracy and efficiency of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

In terms of signal denoising, filtering out high-frequency noise while effectively preserving the useful high-frequency information is a hot topic for research. In this paper, we propose a wavelet modulus maxima spatially selective correlative filtration (SSCF) algorithm to filter out the high-frequency noise mixed in the useful signals. The proposed algorithm is different from traditional noise filtration algorithms in that it need not estimate noise and is not easily affected by noise. The SSCF algorithm first searches the modulus maxima from the multiscale wavelet coefficients of the signal and analyzes the corresponding edge information. Then, it identifies the noise components and useful components from the edge information. For noise components, the algorithm smoothes them away. For useful edge information, the algorithm aligns the corresponding modulus maxima in the wavenumber domain using the shift-related technique. This process amends the “drift” phenomenon of modulus maxima across scales. Next, the proposed algorithm multiplies the aligned multiscale wavelet coefficients that can strengthen the useful signal and attenuate the noise information. We apply the proposed SSCF algorithm to denoise the infrared absorption spectrum of electric insulation gas SF6. Experiments show that the proposed scheme can effectively suppress noise and, at the same time, preserving the useful components. [ABSTRACT FROM PUBLISHER]

The realization principle of digital input electricity meters and the present situation of the conventional power algorithm in the field of signal processing are introduced. According to the onsite operating conditions, formulas are derived for changes in active power and reactive power with frequency offset or phase change, aiming at the influence of nonsynchronization sampling created by the frequency offset. The corresponding analysis was simulated. The theory and simulated analysis indicate that the influence caused by frequency offset could cause excessive errors in digital input electricity meters at substation sites. A weighted algorithm is proposed to effectively reduce the influence to a negligible level. Error measurements of the frequency offset in the laboratory verify the analysis results. Finally, rationalization proposals are given for the software algorithm in digital input electricity meters. [ABSTRACT FROM AUTHOR]

Vortex flowmeter is installed in piping to measure the fluid flow rate. The piping is easily disturbed by strong transient impact in the application fields, such as other components impacting on the pipeline, manual knocking on the pipe, and opening and closing of valves. The strong transient impact will be picked up by the vortex flow sensor, which makes the vortex flowmeter output wrong measurement results. The current commonly used digital signal processing method can do nothing about it. To solve this problem, an experimental setup is built in our laboratory to study the impact interference, and a large number of vortex flow sensor output signals are collected. The characteristics of transient impacts outputted by the vortex sensor are analyzed so as to describe this type of signal quantitatively. The pattern of transient impact is that the signal amplitudes increase suddenly and then attenuate gradually. A segmented Kalman filter based digital signal processing method is proposed according to this pattern. The data segment containing strong transient impact is found first; the transient impact power is reduced by filtering the data segment second; and through spectral analysis of the filtered signal, the maximum peak of the spectrum is extracted as the vortex flow signal finally. The algorithm is implemented in real time by a low-power microcontroller. Both gas and water flow experiments have been conducted to verify the validity and reliability of the antistrong transient impact algorithm. [ABSTRACT FROM PUBLISHER]

A simple algorithm (SAL) and a modified SAL (MSAL) for the estimation of the phase difference between two sine-wave signals are proposed. The results obtained by SAL and MSAL in simulations and on real samples are given. A comparison between the phase difference results estimated by MSAL, quadrature delay estimator (QDE), unbiased QDE (UQDE), three-parameter sine-fitting algorithm (3PSF), four-parameter sine-fitting algorithm (4PSF), and seven-parameter sine-fitting algorithm (7PSF) is presented. The requirements for the application of SAL and MSAL are analyzed. [ABSTRACT FROM PUBLISHER]

Built-in test (BIT) is widely employed in equipment state detection. However, the high false alarm rate (FAR) of conventional BIT leads to troubles for manufacturers and users. To solve this problem, a novel BIT analog signal recognition algorithm that integrates long short-term memory (LSTM) and biologically inspired neural network (BINN) is proposed in this article. The effects of signal noise and intermittent faults on BIT results are effectively reduced. The proposed algorithm is mainly used for the BIT analog signal of heavy-duty gas turbine controllers, LSTM is used for preliminary modeling of analog signals, and the BINN is introduced into the extra signal state recognition, serving as a classifier based on stimulation transmission among neurons. Furthermore, the gravitational search algorithm (GSA) is employed to optimize the parameters of the proposed algorithm to improve its performance. By employing BIT temperature signal data from heavy-duty gas turbine controllers, extensive experimental results were given to show that the proposed approach was reasonable and accurate by comparison with conventional BIT. By comparing with other common neural network algorithms, the developed approach can provide lower FAR and more accurate recognition results. [ABSTRACT FROM AUTHOR]

This article proposes a method of phasor estimation called the Morlet wavelet-based two-point finite-impulse response (FIR) filter (MW-FIR), showing advantages on algorithm complexity, customizability, and zero overshoot. The method focuses on amplitude and frequency estimation, while phase and rate of frequency of change (ROCOF) estimation can be performed using the standard signal processing model with an FIR filter. The mathematical principle behind the method is presented. The causes of error are discussed, and the parameter selection procedure is introduced to minimize error and adjust algorithm performance according to different application requirements. The proposed method has been discussed and compared with the three-point interpolated discrete Fourier transform (ipDFT) method and the FIR method using a flat-top (FT) windowing function, under the static and dynamic situations given by the IEC/IEEE 60255-188-1 and noise immunity evaluation. Finally, the major advantages of the proposed frequency estimation method compared with the conventional FIR-based method using the derivative of angular position are discussed. It can be concluded that this method becomes a substitute for ipDFT in most cases and shows several advantages over the FIR-based method with an FT sequence. [ABSTRACT FROM AUTHOR]

This article presents the digital design of a versatile and low-power broadband impedance spectroscopy (IS) system based on the pseudorandom binary sequence (PRBS) excitation. The PRBS technique allows fast and low-power estimation of the impedance spectrum over a wide bandwidth with adequate accuracy, proving to be a good candidate for portable medical devices, especially. This article covers the low-power design of the firmware algorithms and implements them on a versatile and reconfigurable digital platform that can be easily adjusted to the specific application. It will analyze the digital platform with the aim of reducing power consumption while maintaining adequate accuracy of the estimated spectrum. This article studies two main algorithms (time-domain and frequency-domain) used for PRBS-based IS and implements both of them on the ultralow- power GAP-8 digital platform. They are compared in terms of accuracy, measurement time, and power budget, while the general design tradeoffs are drawn out. The time-domain algorithm demonstrated the best accuracy, while the frequency-domain one contributes more to save power and energy. However, the analysis of the energy-per-error FOM revealed that the time-domain algorithm outperforms the frequency-domain algorithm, offering better accuracy for the same energy consumption. Numerical methods and microprocessor resources are exploited to optimize the implementation of both algorithms, achieving a 27-ms processing time, the power consumption of as low as 1.4 mW, and a minimum energy consumption per measurement of 0.5 mJ, for a dense impedance spectrum estimation of 214 points. [ABSTRACT FROM AUTHOR]

A complete description of a field-programmable gate array (FPGA)-based smart energy meter is presented in this article. By implementing the power quantities definitions of the IEEE 1459–2010 Standard, a novel power quality analyzer capable of performing remote monitoring of electric power systems operating under sinusoidal, nonsinusoidal, balanced, and/or unbalanced conditions was developed. In order to ensure portability, the digital signal processing algorithms implemented on the FPGA device were entirely coded in hardware description languages, and all equations of them are fully described to be applied to any digital signal processing environment. These algorithms are completely defined as functions of the grid fundamental frequency, and by adjusting the sampling frequency, the adaptability of all modules developed is ensured. Experimental results were obtained using the developed platform in a three-phase four-wire system operating under several conditions, which were defined by the presence of harmonic components imposed by nonlinear loads and supply voltages, as well as by setting bidirectional power flow in distributed generation scenarios by employing a photovoltaic system. Real-time and parallel processes are validated as well. [ABSTRACT FROM AUTHOR]

This article presents two displacement calibration algorithms for Doppler radar demodulated data, which contribute to accurate monitoring of small vibrations even when the conventional arctangent demodulated displacement exhibits abrupt jumps. The field structural health monitoring of a traffic signal support structure was carried out by a 5.8-GHz Doppler radar to verify the effectiveness of each algorithm. The radar sensor captures and downconverts the microwave signal, which is phase modulated by the mast arm motion after being reflected on the ground. When conventional nonlinear phase demodulation is employed to retrieve the mast arm displacement from the Doppler radar baseband responses, sudden jumps are encountered in the displacement measurement. Two novel algorithms, referred to as the adaptive low-pass filtering algorithm (ALFA) and the adaptive joint signal processing algorithm (AJSPA), respectively, are suggested to eliminate jumps in phase-demodulated Doppler radar data. These strategies are compared in terms of accuracy and computational cost. The simulated and experimental results are provided to validate the proposed techniques. The presented methods can have wide applications in radar-based vibration monitoring. [ABSTRACT FROM AUTHOR]

Patients undergoing presurgical evaluation for resective epilepsy surgery require the localization of the epileptogenic zone. The detection and analysis of interictal and ictal epileptiform spikes are of major importance for identifying this area. The “irritative zone” of the cortex with interictal spikes is usually revealed intraoperatively during acute electrocorticography (ECoG). Since ECoG recordings cannot be completely visually reviewed in a reasonable amount of time, computer algorithms for the automatic detection of seizures and spikes were developed. We proposed the spike detection algorithm based on the definition of the physical properties of the sought spikes (duration and amplitude). The parameters are easily interpreted by neurophysiologists. The detection sensitivity was 93%, and the precision was 99%. In addition, based on the number of spikes detected on individual electrodes, it is possible to draw the “frequency of spike occurrence” map. The algorithm proved to be so effective in analyzing ECoG records, as it is allowed to be used in practice. [ABSTRACT FROM AUTHOR]

To analyze the valuable frequency component for time-varying frequency-modulated (FM) signals, component extraction is necessary in most applications. Considering the advantage of parameterized demodulation (PD) in transforming FM signals to be stationary, a novel component extraction method based on PD and singular value decomposition (PD-SVD) for both monocomponent and multicomponent signals is proposed. By extending the idea of PD, the time-varying term of the continuous phase function for the interested FM component can be removed, thus resulting in a highly self-correlated component with constant phase. Then, the extraction of the target component from noise or other components can be realized by SVD. Compared with the existing methods, the proposed algorithm is able to analyze the multicomponent signal with crossed instantaneous frequency trajectories and effectively improve the signal-to-noise ratio of the extracted component. The effectiveness of the proposed method is demonstrated by applying it on several numerical signals and the radial vibration signal of a hydroturbine rotor, indicating the potential of analyzing many practical FM signals. [ABSTRACT FROM AUTHOR]

Vortex flowmeter is apt to be disturbed by pipe vibration in industrial applications. It is a very difficult problem to be solved, and affects the applications of vortex flowmeter. To solve this key problem, the frequency features of the flow signal and vibration noise are analyzed. A frequency-variance algorithm is proposed to deal with the vortex flow sensor output signal. A variance threshold is set to distinguish the vortex flow signal from the vibration noise interference. A signal processing system based on an ultralow-power microcontroller is developed to implement the algorithm and to extract the flow rate information from the signal containing strong vibration noise. At the same time, this system meets the requirements of low-power and two-wire mode, and can be used in process control. The strong vibration experiments are conducted to validate the effectiveness of both antistrong vibration algorithm and the system. Only one vortex flow sensor is required in the vortex flowmeter, which avoids the design and installation of the vibration sensor and saves the costs of meter. [ABSTRACT FROM PUBLISHER]

A novel digital signal processing algorithm for online estimation of the fundamental frequency of the distorted power system signals is presented. The basic algorithm relies on the development of an efficient variance reduction algorithm; and design of a new stable bandpass infinite impulse response (IIR) second-degree digital integrator (SDDI) with reduced approximation error. Compared with the well-established technique such as the enhanced-phase-locked-loop (EPLL) system, the proposed algorithm provides the following: 1) higher degree of immunity and insensitivity to harmonics and noise and 2) faster response during step frequency change. Structural simplicity, wide range of application, controls over speed and accuracy, and parameter robustness are other salient features of the method. The only limitation as compared with the EPLL system is its slower transient response during step change in signal magnitude. Based on simulation studies, performances of the proposed algorithm at different operating conditions have been presented, and its accuracy and response time have been compared with the EPLL systems. [ABSTRACT FROM AUTHOR]