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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]

Noise power estimation is central to efficient radio resource allocation in modern wireless communication systems. In the literature, there exist many noise power estimation methods that can be classified based on underlying theoretical principle; the most common are spectral averaging, eigenvalues of sample covariance matrix, information theory, and statistical signal analysis. However, how these estimation methods compare against each other in terms of accuracy, stability, and complexity is not well studied, and the focus instead remains on the enhancement of individual methods. In this paper, we adopt a common simulation methodology to perform a detailed performance evaluation of the prominent estimation techniques. The basis of our comparison is the signal-to-noise ratio estimation in the simulated industrial, scientific and medical band transmission, while the reference noise signal is acquired from an industrial production plant using a software-defined radio platform, USRP-2932. In addition, we analyze the impact of different techniques for noise-samples’ separation on the estimation process. As a response to defects in the existing techniques, we propose a novel noise-samples’ separation algorithm based on the adaptation of rank-order filtering. Our analysis shows that the proposed solution, apart from its low complexity, has a very good root-mean-squared error of 0.5 dB and smaller than 0.1-dB resolution, thus achieving a performance comparable with the methods exploiting information theory concepts. [ABSTRACT FROM AUTHOR]

Accurate detection and localization of unintended emissions from multiple radio-controlled electronic devices have a wide range of security applications. First, this paper introduces a cost-effective mobile array detector. Subsequently, a novel scheme is presented for detection and localization of multiple devices emitting unintended passive emissions. Peak detection is employed in detecting the devices with superheterodyne receivers (SHRs). Experimental results demonstrate that the proposed scheme outperforms the traditional methodologies with a single antenna. A 2-D array is also proposed in this paper for localization of devices with SHRs using the angle of arrival. A new, synthetic aperture radar (SAR)-based scheme called edge synthetic aperture radar (ESAR) is also introduced in this paper for localization. Finally, the results for localization and error in localization are analyzed. The ESAR method reduces the error in localization up to 75% and increases accuracy in detection of multiple devices. [ABSTRACT FROM PUBLISHER]

A field-programmable gate array (FPGA)-based robust speech measurement and recognition system is the focus of this paper, and the environmental noise problem is its main concern. To accelerate the recognition speed of the FPGA-based speech recognition system, the discrete hidden Markov model is used here to lessen the computation burden inherent in speech recognition. Furthermore, the empirical mode decomposition is used to decompose the measured speech signal contaminated by noise into several intrinsic mode functions (IMFs). The IMFs are then weighted and summed to reconstruct the original clean speech signal. Unlike previous research, in which IMFs were selected by trial and error for specific applications, the weights for each IMF are designed by the genetic algorithm to obtain an optimal solution. The experimental results in this paper reveal that this method achieves a better speech recognition rate for speech subject to various environmental noises. Moreover, this paper also explores the hardware realization of the designed speech measurement and recognition systems on an FPGA-based embedded system with the System-On-a-Chip (SOC) architecture. Since the central-processing-unit core adopted in the SOC has limited computation ability, this paper uses the integer fast Fourier transform (FFT) to replace the floating-point FFT to speed up the computation for capturing speech features through a mel-frequency cepstrum coefficient. The result is a significant reduction in the calculation time without influencing the speech recognition rate. It can be seen from the experiments in this paper that the performance of the implemented hardware is significantly better than that of existing research. [ABSTRACT FROM PUBLISHER]

Carouseling is an efficient method to mitigate the measurement errors of inertial sensors, particularly microelectromechanical systems (MEMS) gyroscopes. In this paper, the effect of carouseling on the most significant stochastic error processes of an MEMS gyroscope, i.e., additive bias, white noise, $1/f$ noise, and rate random walk (RRW) is investigated. Variance propagation equations for these processes under averaging and carouseling are defined. Furthermore, a novel approach to generating $1/f$ noise is presented. The experimental results show that carouseling reduces the contributions of additive bias, $1/f$ noise, and RRW significantly compared with plain averaging, which can be used to improve the accuracy of dead reckoning systems. [ABSTRACT FROM PUBLISHER]

When estimating the input-output characteristic of a static nonlinear function, input-referred noise may induce estimation bias, if model identification based on simple averages of input and output measurement data is performed. This paper considers estimation of static nonlinearities based on polynomial functions and input-output averaged data. It first illustrates how the estimation bias originates and then it describes a procedure to compensate its effects. Both simulation and experimental results are shown. Experiments are carried out to estimate the voltage-to-voltage characteristic of a diode-based electrical circuit. Practical considerations are made regarding the minimum number of samples needed to perform compensation effectively. [ABSTRACT FROM PUBLISHER]

Schumann resonances are very weak natural electromagnetic signals produced in the earth–ionosphere cavity located in the extremely low frequency (ELF) band (7–60 Hz), and the sensors that measure them produce amplitudes of few microvolts. Strong signals from power lines (50–60 Hz) occur in the same frequency range. Amplification techniques play a key role in acquiring resonance modes with the best signal-to-noise (S/N) ratio. This paper presents a study of the various structures of amplification systems that optimize the S/N ratio for the signal of interest. The aim of this paper is to measure all possible resonance modes with low time acquisition. To this end, we compare four instrumentation amplifiers and design a new indirect method for obtaining the noise floor of the system with sensors manufactured on magnetic cores that are several meters long. We present the measurements of the Schumann resonance achieved using these techniques at the ELF electromagnetic wave observatory at Calar Alto (Spain). The solutions adopted allow measurement of seven resonance modes with an acquisition time of 30 min, where the S/N ratio in the fundamental mode was 39 dB. [ABSTRACT FROM PUBLISHER]

The prediction of the clock offset plays an important role in the generation of a time scale, clock steering, and time offset prediction of the onboard clocks. The mathematical model method based on the stochastic differential equations has been dealt with the clock prediction problem. In this paper, we examine the method from a theoretical point of view. We derive the analytic expressions of the theoretical prediction uncertainties when the clocks are modeled by the sum of a white frequency modulation noise, a random walk frequency modulation noise in both the linear model case and the quadratic polynomial model case. Simulations and an experiment for predicting the time deviations of International Atomic Time-TA(National Time Service Center) are used to validate the theoretical analyses. The simulations and the experiment show that the prediction performances agree with the theoretical evaluations; therefore, we consider that the research is useful in applications. Furthermore, we illustrate that the prediction uncertainty can be minimized only by choosing an optimal observation interval. From simulations, for a typical cesium clock, the method is effective for short-term prediction, whereas for a typical hydrogen maser, for all prediction times the uncertainties of the deterministic part are predominant, and we thus consider that the method in the quadratic polynomial model case deserves further investigation. [ABSTRACT FROM PUBLISHER]

In quite some applications, the linear dynamics of the system under test evolve over time in a smooth nonperiodic manner. The dynamic behavior of such systems is uniquely described by the time-variant frequency response function (TV-FRF). However, since most real-life systems behave nonlinearly to some extent, it is important to detect and quantify the deviation between the ideal linear time-variant (LTV) framework and the true non-LTV dynamics. Therefore, in this paper, a nonparametric estimation procedure is developed for detecting and quantifying the noise level and the level of the nonlinear distortions in TV-FRF measurements using random-phase multisine excitations. As a result, the users can decide whether or not the LTV framework is accurate enough to describe the true dynamics in their particular application. The proposed measurement procedure is illustrated on a time-variant electronic circuit. [ABSTRACT FROM PUBLISHER]

This paper proposes an error vector magnitude (EVM) measurement method for a wideband code-division multiple-access source using a real-time oscilloscope. The EVM values are extracted from the measured waveform using a signal processing that finds the appropriate carrier phase and symbol timing. Measurement uncertainty is also evaluated based on a Monte Carlo simulation, where the errors from the real-time oscilloscope and the signal processing are taken into account. The measured EVM of a source at 900 MHz is (0.2586 ± 0.0040)%, (0.2617 ± 0.0060)%, and (0.2543 ± 0.0078)% at 95% confidence level when the real-time oscilloscope has a bandwidth of 2, 4, and 20 GHz, respectively. [ABSTRACT FROM PUBLISHER]

In this paper, a 3D indoor localization demonstrator on the basis of impulse-radio ultrawideband (UWB) technology and time-difference-of-arrival (TDOA) principle is developed and analyzed. The parameters of the transmitter and receiver hardware components are investigated to determine their influence on the localization performance. The signal detection method based on a comparator and the precise time measurement unit was examined. Two effects, namely the threshold-trigger offset and the TDOA variance errors, were quantified. Corrections methods of both these phenomena have been proposed, which delivered very good results in the experiments. With the identified and modeled inaccuracies, the Cramer–Rao lower bound for a 3D TDOA localization system is derived for the first time and verified by measurements, performed in a large-scale industrial environment. The results obtained from measurements follow closely the variance predicted by the CRLB. Moreover, the comparison with the literature published up to date proves the excellent performance of the system presented here. [ABSTRACT FROM PUBLISHER]

This paper presents a nonparametric time-domain estimation method for linear slowly time-varying systems. The proposed method uses a 2-D impulse response function. That is modeled by a generalized B-spline smoothing technique. This means that double smoothing is applied: once over the different realizations (referring to the system behavior) and once over the global time (referring to the system memory). With respect to the system dynamics using the proposed method, it is possible to reduce the model order and to decrease the effect of the disturbing noise. [ABSTRACT FROM PUBLISHER]

This paper proposes a strategy for suppressing noise via empirical mode decomposition (EMD)-based hierarchical multiresolution analysis. First, an intrinsic mode function (IMF) index that can differentiate the noise-dominant IMFs and the information-dominant IMFs in the first level of decomposition is determined by the conventional EMD-based denoising method. Next, the EMD is applied to those discarded IMFs in the first level of decomposition to obtain the IMFs in the second level of decomposition. Then, the detrended fluctuation analysis (DFA) is employed to define the IMF index separating the noise-dominant IMFs and the information-dominant IMFs in the second level of decomposition. Information-dominant IMFs in both the first and second levels of decompositions are summed together to obtain the denoised signal. The study is limited to signals that were corrupted by additive white Gaussian noise (AWGN). Extensive computer numerical simulations are conducted on both synthetic signals and practical signals. The results show that our proposed method is promising in some cases, especially at high-input SNR levels. For example, in the case of the CH4 signal with 12-dB input SNR, the processed SNR based on our proposed strategy with the consecutive mean squared error (CMSE) is 23.27 dB, while that based on the conventional CMSE method is only 23.14 dB. Also, our proposed method can extract information from more than one level of decomposition to reconstruct the denoised signal compared to only one level of decomposition in the conventional EMD-based denoising method. [ABSTRACT FROM AUTHOR]

In high-speed analog-to-digital converters (ADCs), two main factors contribute to high power consumption. The first is the super linear relationship with the sampling rate; i.e., by doubling the sampling rate, the power consumption more than doubles. The second factor arises from the consumption of analog circuitry responsible to mitigate the jitter noise. By employing a multichannel sampling system, one can achieve high sampling rates by incorporating multiple low sampling-rate channels, which results in a linear scaling of power consumption with the number of channels. The main drawback of this system is the timing mismatch between the sampling channels. In this paper, we intend to jointly compensate the jitter noise and the timing mismatch between the channels using statistical methods. We first approximate the acquisition system and derive a stochastic model. Then, we propose an iterative maximum likelihood algorithm to estimate the parameters of the input signal. We further evaluate the Cramér-Rao lower bound (CRLB) for the estimation error to examine the proposed algorithm. Simulation results indicate that our algorithm is capable of closely following the CRLB curve for reasonable values of jitter noise and a wide range of timing mismatch errors. Moreover, it is shown that the mismatch-compensated multichannel sampling system performs almost equivalently to a single-channel high rate sampler without having its shortcomings. [ABSTRACT FROM AUTHOR]

This paper introduces a novel measurement procedure for the concomitant detection of type and concentration of unknown target species employing a combination of noise spectra analysis and resonant frequency characteristics. An algorithm was proposed incorporating a machine learning phase (for the creation of a database) followed by a detection phase. In the machine learning phase, with the exposure to the known target species with known concentrations, three distinctive features of the device were measured, namely: 1) randomness on the estimation in capacitance; 2) impedance variation (with respect to the air) profile; and 3) capacitive response magnitude (CRM). In this regard, reduced graphene oxide–titanium dioxide nanotubes hybrid sensor was employed as the test device, and consequently, low-frequency noise (i.e., randomness) on the capacitance estimation with the exposure to different known target species (methanol, ethanol, and 2-propanol as test species) was measured, and the distinctive signature frequency for each one of them was obtained. A shift in the resonant frequency toward the higher frequency regime was also observed with the increase in target species concentrations, by which the concentration of target species was predicted effectively. Furthermore, a principal component analysis was performed on the CRM for cross-validation of obtained target species type and concentration. In the detection phase, the above-mentioned parameters of the device were measured for an unknown species and compared with the existing database. Moreover, a multivariate data analysis was carried out for the proposed method, and the maximum error was found to be within 13.25% at higher concentrations. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel technique for the measurement of noise wave correlation matrix (NWCM) of a two-port network using measured noise power ratios. The ratio of two measured noise powers is observed to show significantly reduced fluctuations compared with absolutely measured noise powers. Based on this observation, the equations to compute the NWCM are formulated using the noise power ratios. Since the NWCM has four unknowns, five absolute output noise powers of a device under test (DUT) are measured to obtain four noise power ratios. The conventional noise parameters can then be obtained by converting the NWCM. In order to demonstrate this technique, we measured the NWCM of an active DUT with a noise figure of less than 1 dB. The noise parameters resulting from the measured NWCM show minimal fluctuations as expected. [ABSTRACT FROM AUTHOR]

This paper presents a method of digital stochastic measurement (DSM) of nonstationary signals. The method is based on stochastic analog-to-digital (A/D) conversion and accumulation, with a hardware structure based on a field-programmable gate array and a low-resolution A/D converter. The characteristic of previous implementations of DSM was the measurement of stationary signal harmonics. This paper shows how DSM can be extended and also used when it is necessary to measure the time series of nonstationary signals. An electroencephalography signal is selected as an example of a real nonstationary signal, and its DSM is tested by simulations and experiments. Tests are done without adding noise and with adding a noise-varying signal-to-noise ratio (SNR) from 10 to -10 dB. The results of simulations and experiments are compared versus theory calculations, and the comparison confirms the theory. The presented method provides control of the measurement uncertainty even at low SNR values, by controlling the sample rate of the used A/D converter. This enables designers of measurement systems to choose fast A/D converters with low resolution to achieve higher measurement accuracy. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive genetic algorithm (AGA) for an active noise control (ANC) system. The conventional ANC system often implements the filtered extended least mean square (FXLMS) algorithm to update the coefficients of the linear finite-impulse response (FIR) and nonlinear Volterra filters, owing to its simplicity; meanwhile, the FXLMS algorithm may converge to local minima. In this paper, the FXLMS algorithm is replaced with an AGA to prevent the local minima problem. Additionally, the proposed AGA method does not require identifying the secondary path for the ANC, explaining why no plant measurement is necessary when designing an AGA-based ANC system. Simulation results demonstrate that the effectiveness of the proposed AGA method can suppress the nonlinear noise interference under several situations without clearly identifying the secondary path. [ABSTRACT FROM PUBLISHER]

This paper presents a practical wavelet-based approach to denoising coarsely quantized signals. Such signals can arise from the status data collected within large-scale engineering plants employing traditional limit checking fault detection and identification (FDI). Transitioning such plants to more advanced FDI techniques requires that the coarsely quantized data be accurately denoised. As FDI by its nature is concerned with the analysis of nonstationary signals, wavelets offer an appropriate denoising framework. Existing techniques for optimal wavelet denoising presuppose Gaussian noise contamination and, hence, are suboptimal for coarsely quantized signals. In this paper, a secondary correction stage is added to the standard wavelet-denoising process to improve its denoising performance on coarsely quantized signals. This correction stage exploits a priori knowledge of the known coarsely quantized signal dependencies to ‘tune’ the wavelet thresholds. The effectiveness of the approach is demonstrated through the analysis of real-world data collected from an operational large-scale engineering plant. [ABSTRACT FROM AUTHOR]

This paper presents a sequential fault detection and identification algorithm for detecting a bias fault in the measurements of partial discharge (PD) in a transformer insulation system using acoustic signals. The algorithm identifies a bias fault in any of the ultrasonic sensors by computing the probability of having that bias fault given a carefully constructed measurement residual. The constructed measurement residual is a function of the measurement noise and the possible measurement fault. A set of bias hypotheses is assumed and initially given equal alarm probability. It is assumed that only one sensor will acquire a bias at any given time. Once the probability of a hypothesis approaches 1, that hypothesis is declared as the correct hypothesis and the bias associated with the hypothesis is removed from the sensors’ reading. Therefore, this will enable high-accuracy estimation of the location of PD. First, the algorithm is verified in a simulation environment. Subsequently, the accuracy of the proposed algorithm is verified using the experimental results. [ABSTRACT FROM AUTHOR]

Good initial values are crucial to obtain solutions of nonconvex optimization problems. When estimating the transfer function of physical systems from measured noisy data, obtaining good initial parameter estimates is therefore a primordial step. In this paper, it is shown that smoothing the measured frequency response function of a linear time-invariant system enhances the construction of initial estimates significantly, resulting in the optimization schemes to converge to a better optimum. This is achieved with minimal user interaction. Two smoothing techniques, the time-truncated local polynomial method and the regularized finite impulse response, are compared with the existing generalized total least squares and the bootstrapped total least squares initial estimates. The improvement attributable to smoothing is demonstrated by a simulation and by measurements of an electrical filter. The results ultimately show that the parametric models obtained using the proposed starting values are much more likely to give a good description of the measured system and hence lead to more useful models. [ABSTRACT FROM PUBLISHER]

A new adaptive scheme for joint time delay and frequency estimation of two sinusoidal signals that are received at two spatially separated sensors is proposed in this paper. The proposed method is based on adding a new mechanism to the existing structure of a second-order generalized integrator. The main feature of the proposed algorithm, which makes it distinguished from some similar works and appealing from a practical viewpoint, is that it does not require any a priori knowledge on the input signal frequency and therefore works at various sampling frequencies. Averaging theory is used to analyze the dynamic behavior of the proposed algorithm. Various simulations are provided to illustrate the effectiveness of the algorithm and to demonstrate the ability of the algorithm to track variations of parameters of the input signals. [ABSTRACT FROM PUBLISHER]

On-line estimation of low-frequency electromechanical oscillations in an interconnected power system has attracted much attention as this provides vital information about the stability condition of the power system. In this paper, a measurement-based method for the estimation of the frequency, magnitude, and damping of an oscillation is proposed. The method can be applied to the phasor measurement data to extract further information about the power system. The proposed method is derived by applying modifications to the existing concept of second-order generalized integrator and adaptive notch filter. This paper details the mathematical derivation and stability properties of the proposed method and presents the simulation results to confirm the analytical derivations and the desirable performance of the proposed method. [ABSTRACT FROM PUBLISHER]

The ability to track pedestrians without any infrastructure support is required by numerous applications in the healthcare, augmented reality, and entertainment industries. In this paper, we present a simple self-contained pedestrian tracking method using a foot-mounted inertial and magnetic sensor module. Traditional methods normally incorporate double integration of the measured acceleration, but such methods are susceptible to the acceleration noise and integration drift. To avoid this issue, alternative approaches which make use of walking dynamics to aggregate individual stride have been explored. The key for stride aggregating is to accurately and reliably detect stride boundary and estimate the associated heading direction for each stride, but it is still not well solved yet due to sensor noise and external disturbance. In this paper, we propose to make use of the inertial sensor and magnetometer measurements for stride detection and heading direction determination. In our method, a simple and reliable stride detection method, which is resilient to random bouncing motions and sensor noise, is designed based on gyroscope and accelerometer measurements. Heading direction is then determined from the foot’s orientation which fuses all the three types of sensor information together. The proposed pedestrian tracking method has been evaluated using experiments, including both short distance walking with different patterns and long distance walking performed indoors and outdoors. The good experimental results have illustrated the effectiveness of the proposed pedestrian tracking method. [ABSTRACT FROM PUBLISHER]

Accurate voltage and current phasor measurements are required to be provided by phasor measurement units placed at various buses in the system. This paper presents an effective method for estimation of alternating current signal fundamental phasor under dynamic conditions. The model-order estimation of the current signal has been carried out to separate the signal space and the noise space in the signal correlation matrix. The Vandermonde matrix of the total least square estimation of signal parameters via rotational invariance techniques method is extended to second-order Taylor’s series approximation to estimate the dynamic phasor of the current signal. The suggested technique for the phasor estimation has been tested on simultaneous multimode amplitude and phase oscillations with decaying dc offset and noninteger harmonics, in the presence of noise and also for a fault scenario in New England 39-bus system. This paper also provides a few suggestions for a few possible amendments in the IEEE synchrophasor standard. [ABSTRACT FROM PUBLISHER]

This paper presents a design approach to concurrent dual-band subsampling receivers for wireless certification and testing interoperability. The proposed technique allows the optimization of the signal-to-noise and distortion ratio and improves the subsampling receiver performance, usually limited by the clock jitter and folded thermal noise effects. Furthermore, the proposed design approach considers the presence of spurious nonlinear distortions and/or interferer's signals, which can alias over the desired signal bandwidth and reduce the valid subsampling frequency ranges. The proposed architecture allows seamless reconfigurability for simultaneous multistandard signal reception, reducing the costs in test equipment. As a validation of the proposed design methodology, a dual-band multiple clocking subsampling receiver operating in a nonlinear environment is developed, and an algorithm to find the optimal frequency plan is proposed and validated experimentally. [ABSTRACT FROM PUBLISHER]

This paper presents modeling of wave propagation in pavements from a system identification point of view. First, a model based on the physical structure is derived. Second, experiment design and evaluation are discussed and maximum-likelihood estimators for estimating the model parameters are introduced, assuming an error-in-variables setting. Finally, the proposed methods are applied to measurement data from two experiments under varying environmental conditions. It is found that the proposed methods can be used to estimate the dispersion curves of the considered waveguide and the results can be used for further analysis. [ABSTRACT FROM PUBLISHER]

Recently methods have been developed to detect and quantify the influence of time-variation in frequency response function measurements using random excitations. These methods can also be applied to random phase multisine excitations, provided uniformly spaced excited harmonics. However, in quite a few applications, a nonuniform harmonic spacing is required, for example, a quasi-logarithmic spacing to cover a large frequency range, and a random spacing to detect and quantify nonlinear distortions. Therefore, in this paper, the results are extended to random phase multisines with nonuniformly spaced harmonics. An in-depth study of the noise sensitivity (bias and variance) of the proposed procedure is made. [ABSTRACT FROM PUBLISHER]

This paper describes interpolated discrete Fourier transform (IpDFT) for parameter estimation of sinusoidal and damped sinusoidal signals analyzed with a \sin^\alpha(x) window. For \alpha=0,2,4,\ldots\sin^\alpha(x) windows are Rife–Vincent class I (RVI) windows, for which IpDFT algorithms are known. We present a new IpDFTs for \alpha=1,3,5,\ldots. The bias-variance trade-off of the proposed IpDFT fits between results offered by RVI windows, e.g., for \alpha=1, we get higher noise immunity than Hann (RVI order 1, \alpha=2) window and lower bias than rectangular (RVI order 0, \alpha=0) window. [ABSTRACT FROM PUBLISHER]

In this paper, a robustness metric and a sensitivity metric have been defined, which can be used to determine a suitable combination of the filter tuning parameters of the extended Kalman filter. These metrics are related to the innovation covariance and their derivation necessitates a change of paradigm from the estimated states to the estimated measurements. The characteristics of these metrics have been inferred in detail and these have been used to predict the root-mean-squared error (RMSE) performances in a 2-D falling body problem. To do so, a general method has been proposed in this paper to obtain an initial choice of the filter tuning parameters based on the available literature. The RMSE performances are then obtained for a range of variation of the most critical tuning parameter, namely the filter process noise covariance. In general, the characteristics predicted from the metrics correlate significantly with the RMSE performances, and hence these can be used to obtain the desired tradeoff between robustness and sensitivity in various filter applications. [ABSTRACT FROM PUBLISHER]

This paper considers least square (LS) based estimation of the amplitude and square amplitude of a quantized sine wave, done by considering random initial record phase. Using amplitude- and frequency-domain modeling techniques, it is shown that the estimator is inconsistent, biased, and has a variance that may be underestimated if the simple model of quantization is applied. The effects of both sine wave offset values and additive Gaussian noise are considered. General estimator properties are derived, without making simplifying assumptions on the role of the quantization process, to allow assessment of measurement uncertainty, when this LS procedure is used. [ABSTRACT FROM PUBLISHER]

This paper introduces a new signal-filtering, which combines the empirical mode decomposition (EMD) and a similarity measure. A noisy signal is adaptively broken down into oscillatory components called intrinsic mode functions by EMD followed by an estimation of the probability density function (pdf) of each extracted mode. The key idea of this paper is to make use of partial reconstruction, the relevant modes being selected on the basis of a striking similarity between the pdf of the input signal and that of each mode. Different similarity measures are investigated and compared. The obtained results, on simulated and real signals, show the effectiveness of the pdf-based filtering strategy for removing both white Gaussian and colored noises and demonstrate its superior performance over partial reconstruction approaches reported in the literature. [ABSTRACT FROM PUBLISHER]

This paper deals with fundamental properties of the least-squares-based estimation algorithms of power frequency, such as immunity and insensitivity to harmonics and noise. This paper is motivated by a recent study that dealt with an optimal case of the distance between equidistantly spaced samples. This paper extends and generalizes that approach by decoupling of task of canceling dc component from the least-squares estimation task. Two different algorithms derived as particular cases of this generalized approach are investigated. In addition, besides the basic least-squares (LS) method, the Prony-based method previously formulated by Lobos and Rezmer has also been investigated. The task of estimation frequency from a pure sine signal without dc component presence is also considered. Finally, the third method is derived and its basic properties are shown. [ABSTRACT FROM PUBLISHER]

This paper investigates the behavior of the spectral resonator-based spectral observer (RBO) for lossy measurements. The RBO is an efficient real-time method designed for measuring the harmonic components of periodic signals. Recently, some experiments have shown that RBO can tolerate some data loss, but no exact criteria of the convergence have been available. The main contribution of this paper is a set of conditions which enable to predict whether the observer is able to find the harmonic components or not. The necessary condition of convergence helps to recognize the situations when the error-free estimation of the harmonic components is not possible. The sufficient conditions of convergence give safe regions of data loss parameters (e.g., data loss ratio) where the unbiased harmonic estimation can always be ensured. Simulations are also presented to demonstrate the effect of data loss in different situations. [ABSTRACT FROM PUBLISHER]

This paper presents the probability distribution function (PDF) of the ratio of two random waves. This result is used to obtain the PDF of S-parameters random errors in magnitude (in decibels) and phase, which are the quantities that most engineers work with. These results are further used on the development of a Monte Carlo simulation method in order to predict the variability of frequency-domain measurements. Experiments are performed to identify and characterize frequency-domain random errors, such as instrument noise, connector repeatability, and calibration variations, in measurement systems. By comparing with real measurement data, it is shown that random-error effects can be accurately estimated by the PDF's obtained and the Monte Carlo technique. [ABSTRACT FROM PUBLISHER]

The goal of this paper is to construct an approximate maximum-likelihood estimator to accurately estimate the period and spectral contents of a noisy periodic signal that has many frequency components. This is accomplished without user interaction. The signal data record has a total number of periods that is not necessarily an integer but is greater than four. Furthermore, the number of samples per period may not necessarily be an integer number. It is shown that the accuracy of the estimated results is superior to estimates that are devoid of variance weighting, such as those engendered by the least squares estimator. [ABSTRACT FROM PUBLISHER]

The paper presents a pre-processing scheme to enhance the resolution of data acquisition systems (DASs) in the presence of band-pass input signals. The proposed solution permits to acquire a seamless data stream with improved resolution and is designed to be efficient in terms of hardware requirements and processing time. In the paper, details on the designed architecture are given, along with an analytical comparison of the resolution improvement achievable with respect to existing techniques. The performance of the solution is assessed through a number of simulative and experimental tests on band-pass signals. [ABSTRACT FROM PUBLISHER]

Noise figures (NFs) are defined at each frequency of interest over a 1-Hz bandwidth. Their measurements are often performed over bandwidths of a few kilohertzs to a few megahertzs to accelerate the process and to reduce measurement jitter. These wider bandwidths introduce NF errors that can be substantial [>1 dB measured for a 0.4-dB NF low-noise amplifier] if the measurement equipment exhibits propagation delays as demonstrated in this paper. Similarly, propagation delays affect receiver noise and can cause inaccuracies in receiver sensitivity estimates. [ABSTRACT FROM AUTHOR]

In this paper, details are given on a novel measurement device for radio propagation-channel measurements. To avoid measurement errors due to the conductive cables on small terminal antennas, as well as to improve the handling of the prototypes under investigation, an optical measurement device has been developed. It utilizes thin, light, and flexible glass fibers as opposed to heavy, stiff, and conductive coaxial cables. This paper looks at the various system parameters such as overall gain, noise figure, and dynamic range and compares the solution to other methods. An estimate of the device accuracy is also given. Selected parts of the circuitry are given in more detail. Typical measurement results are also shown. [ABSTRACT FROM AUTHOR]

This paper presents a nonparametric method to measure an improved frequency response function (FRF) of a linear dynamic system excited by a random input. Recently, the local polynomial method (LPM) has been proposed as a technique to reduce the leakage errors on FRF measurements. The noise sensitivity of the LPM was similar to that of the classical windowing methods, while the leakage rejection is improved from an O(N-1) to an O(N-3). This paper shows a methodology, to automatically tune the parameter of the LPM, viz., the local bandwidth that sets how many neighboring frequency lines are combined in a single-point estimate, to get lower noise sensitivity by increasing the smoothening of the original LPM, without any user interaction. The balance between noise reduction and bias error will be automatically retrieved. [ABSTRACT FROM AUTHOR]

Over the past few decades, centralized computing systems have been replaced by decentralized systems, consisting of simple nodes interconnected by a communication network. The time reference of the distributed nodes must be synchronized in order to be able to coordinate the operation or compare the data collected by the different nodes. Several synchronization protocols have been developed to be used instead of global positioning system or dedicated synchronization systems. For example, the network time protocol (NTP) is a very popular synchronization protocol used to synchronize computers over wide area networks, like the Internet. In addition, IEEE 1588, a synchronization protocol dedicated to high-performance synchronization in local networks, was recently established. However, submicroseconds synchronization can be obtained only using dedicated hardware devices, thereby increasing the cost of each node. For these reasons, PTP software-only implementations are quite common in real systems, with a resulting synchronization uncertainty varying from a few to hundreds of microseconds and quite burdensome to estimate or measure. The work presented in this paper focuses on the analysis of the major uncertainty contributions in a software-only implementation. Particularly, a careful analysis of timestamp mechanism and time management in a PC platform is carried out. In addition, a method for the experimental evaluation of uncertainty contributions is proposed. A test case based on a software implementation of the IEEE 1588 (the so-called PTPd) is presented. The experimental tests presented in the paper highlight that the main uncertainty source of a software-only synchronization approach is the timestamp method. Timestamping accuracy can be affected by the computational load of the node itself: in normal conditions, the maximum uncertainty introduced by the timestamp mechanism is in the order of 10 \mu\s, but in case of high computational load, it can raise up to 224 \mu\s. [ABSTRACT FROM AUTHOR]

An improved fundamental identification and replacement technique is presented in this paper. The proposed method achieves fast and accurate spectral testing results for approximate sinusoidal signals without requiring coherent sampling. The new method uses data record lengths comprising only prime factors 2 or 3, which improves the computational efficiency. Furthermore, a new algorithm for counting the integer cycles in the data record is developed, which guarantees the robustness even when the signal frequency is close to Nyquist frequency and/or when noise is high. The new method provides an alternative option for testing engineers to achieve a good result in the case of noncoherent sampling without carefully choosing the windows. Theoretical analysis, simulation, and experimental results collaborate to prove the validity of the proposed method. Comparative studies demonstrate that the proposed algorithm achieves spectral testing accuracies similar to those obtained using perfect coherent sampling method. The proposed method is well qualified for high precision spectral testing. [ABSTRACT FROM AUTHOR]

In this paper, a nonparametric estimation procedure is presented in order to track the evolution of the dynamics of continuous (discrete)-time (non)-linear periodically time-varying (PTV) systems. Multisine excitations are applied to a PTV system since this kind of excitation signals allows us to discriminate between the noise and the nonlinear distortion from a single experiment. The key idea is that a linear PTV system can be decomposed into an (in)finite series of transfer functions, the so-called harmonic transfer functions (HTFs). Moreover, a systematic methodology to determine the number of significant branches is provided in this paper as well. Making use of the local polynomial approximation, a method that was recently developed for multivariable (non)-linear time invariant systems, the HTFs, together with their uncertainties embedded in an output-error framework, are then obtained from only one single experiment. From these nonparametric estimates, the evolution of the dynamics, described by the instantaneous transfer function (ITF), can then be achieved in a simple way. The effectiveness of the identification scheme will be first illustrated through simulations before a real system will be identified. Eventually, the methodology is applied to a weakly nonlinear PTV electronic circuit. [ABSTRACT FROM AUTHOR]

Gyroscopes are integral components of inertial measurement units, which are used for guidance and stabilization of many platforms. This paper presents an algorithm for estimating the statistical parameters that govern the performance of rate gyros, i.e., the spectral densities R and Q of the angle random walk and rate random walk components, respectively. Previous work on gyro modeling is based on computing the Allan variance of a gyro signal and using a well-known formula for its mean. The algorithm in this paper uses these as well as the following quantities, which are derived in this paper: the theoretical variance of the Allan variance and the covariance between different Allan variance points. The algorithm is developed using the formulation of the best linear unbiased estimator from statistical estimation theory. The performance of the algorithm is demonstrated using simulated and experimental data. A bound on the error in the integral of the gyro output, as a function of Q and R, is also derived. [ABSTRACT FROM PUBLISHER]

Performances in network-based synchronization depend on several related factors, including the instability of local clocks, the rate at which timing information is exchanged, and the accuracy of the resulting correction estimates. This paper analyzes these effects and their relationships, showing how these may affect the design of an IEEE 1588 Precision Time Protocol synchronization scheme. This paper introduces a state-variable clock model for which realistic parameters can be obtained for different kinds of clocks from experimental measurements of Allan variance plots. A Kalman-filter-based clock servo employing this model is developed, and a simulation analysis of the behavior of clock regulation and the effect of parameter variations on its performances is presented. [ABSTRACT FROM PUBLISHER]

The measurement of many signal parameters using digital sampling relies on synchronization between the sampling clock and the signal under analysis. Deviations in the frequency of the measured signal from that expected can lead to significant measurement errors. Many techniques for overcoming these problems have been developed. Appraisals of the performance of these algorithms usually involve relatively benign steady-state signals, and the treatment is often heavily mathematical. This paper describes the testing of several algorithms for accurately recovering frequency and harmonic amplitude and phase information from mains-borne waveforms. This paper focuses on their practical use while avoiding abstract mathematical concepts. [ABSTRACT FROM AUTHOR]

The wavelet transform (WT) technique has been proposed for detecting and localizing a transient disturbance in power systems. The disturbance is detected by comparing the transformed signal with an empirically given threshold. However, as the signal under analysis contains noises, especially the white noise with flat spectrum, the threshold is difficult to give. Due to the nature of the flat spectrum, a filter cannot just get rid of the noise without removing the significant disturbance signals together. To enhance the WT technique in processing the noise-riding signals, this paper proposes a noise-suppression algorithm. The abilities of the WT in detecting and localizing the disturbances can hence be restored. Finally, this paper employed the actual data obtained from the practical power systems of Taiwan Power Company to test the effectiveness of the developed noise-suppression method. [ABSTRACT FROM AUTHOR]

Temperature measurement based on infrared radiation depends on correctly adjusted emissivity. However, emissivity configuration is complex as emissivity is not normally known with precision; it is influenced by radiation reflections and can also vary with the temperature. The wedge method is a temperature measurement method which assures the selection of the correct emissivity configuration using an infrared camera to take images of a wedge region. Within the wedge, a virtually closed cavity for radiation is created. Although this method outperforms traditional infrared measurement, no comparison has yet been carried out under real industrial conditions which would provide information about emissivity variations. This paper proposes a method to measure temperature using the wedge method in industrial environments and compares the results with the temperature measurement acquired from a calibrated infrared line scanner. Using the wedge method, it is possible to accurately estimate emissivity profiles under real working conditions. A method to apply these profiles for emissivity compensation is also proposed in this paper. Conclusions give the analysis of the strengths and weaknesses of the two methods and provide recommendations and guidelines for technicians interested in temperature measurement and emissivity estimation and compensation. [ABSTRACT FROM AUTHOR]

This paper describes a highly accurate dose control circuit (DCC) for the emission of a desired number of electrons from vertically aligned carbon nanofibers (VACNFs) in a massively parallel maskless e-beam lithography system. The parasitic components within the VACNF device cause a premature termination of the electron emission, resulting in underexposure of the photoresist. In this paper, we compensate for the effects of the parasitic components and noise while reducing the area of the chip and achieving a precise count of emitted electrons from the VACNFs to obtain the optimum dose for the e-beam lithography. [ABSTRACT FROM PUBLISHER]

This work proposes a novel prototype-based engine fault classification scheme employing the audio signature of engines. In this scheme, Fourier transform and correlation methods have been used. Notably, automated audio classification has immense significance in the present times, used in both audio-based content retrieval and audio indexing in multimedia industry. Likewise, it is also becoming increasingly important in automobile industries. It has been observed that real world automobile engine audio data are contaminated with substantial noise and out fliers. Hence, it is challenging to categorize different fault types in different engines. Accordingly, the present paper discusses a methodology where a set of algorithms checks the state of an unknown engine as either healthy or faulty. Fault categorizing algorithm is based on its cross- and autocorrelation coefficient values. Appropriately, in this study, the engine amplitude–frequency values of fast Fourier transform are calculated and subdivided into bands to calculate the correlation coefficient matrix. The correlation coefficient matrix for the unknown engine is then calculated and matched with this “prototype” engine matrix to categorize it into a single or multiple fault(s). It is worth mentioning here that although a rank-based maximum close scheme is adopted for finding the unknown engine's fault, the work can be extended to any other parametric and neural network-based classification scheme. Keeping this background in mind, the present paper discusses the proposed methodology to find a prototype engine, unknown engine classification, implementation on real audio signal for single cylinder engine data, and its results. [ABSTRACT FROM PUBLISHER]