Deterministic Signals Research Articles

Adaptive Control With Internal Model for High-Performance Precision Motion Control and Its Application to a Fast-Acting Piezoelectric Actuator

This paper proposes an adaptive control scheme that minimizes the least-mean-square (LMS) value of the plant output while meeting the constraints of canceling deterministic exogenous signals generated by a priori dynamics. This scheme may be applied to a broad range of applications in which the exogenous input signals to the plant contain both deterministic and stochastic components. The adaptive control includes both feedback and previewed feedforward actions. In both actions, the deterministic signal model is included as a constraint of the dynamics from the external input to the plant output by determining solutions for a Bezout identity. The proposed scheme is applied to a fast-acting piezoelectric actuator (PZT) to generate precise dynamic motion profiles. This paper presents the experimental results to demonstrate the effectiveness of the proposed adaptive control scheme.

Segregation of complex acoustic scenes based on temporal coherence

In contrast to the complex acoustic environments we encounter everyday, most studies of auditory segregation have used relatively simple signals. Here, we synthesized a new stimulus to examine the detection of coherent patterns ('figures') from overlapping 'background' signals. In a series of experiments, we demonstrate that human listeners are remarkably sensitive to the emergence of such figures and can tolerate a variety of spectral and temporal perturbations. This robust behavior is consistent with the existence of automatic auditory segregation mechanisms that are highly sensitive to correlations across frequency and time. The observed behavior cannot be explained purely on the basis of adaptation-based models used to explain the segregation of deterministic narrowband signals. We show that the present results are consistent with the predictions of a model of auditory perceptual organization based on temporal coherence. Our data thus support a role for temporal coherence as an organizational principle underlying auditory segregation. DOI:http://dx.doi.org/10.7554/eLife.00699.001.

Features for Fault Diagnosis and Prognosis of Gearbox

Good features are critical for both fault diagnosis and prognosis of gearbox. Most traditional features are effective only when the gearbox is under stationary operating condition. Some features which are modified based on the traditional features are no longer sensitive to load changes and remain sensitive to fault propagation. However, it is only permit the load changing in a very small range. In engineering applications, some machines usually work in a non-stationary operating condition; both speed and load are varying over a wide range (e.g. wind turbine). So, in order to solve this dilemma, we propose using the energy ratio between residual signal and deterministic periodic signal which is separated by autoregressive model as the condition indicator for fault diagnosis and prognosis. The effectiveness of this feature is demonstrated and compared to other traditional features using two run-to-failure data sets of gearbox collected in laboratory.

Stochastic-Deterministic MMSE STFT Speech Enhancement With General A Priori Information

A wide range of Bayesian short-time spectral amplitude (STSA) speech enhancement algorithms exist, varying in both the statistical model used for speech and the cost functions considered. Current algorithms of this class consistently assume that the distribution of clean speech short time Fourier transform (STFT) samples are either randomly distributed with zero mean or deterministic. No single distribution function has been considered that captures both deterministic and random signal components. In this paper a Bayesian STSA algorithm is proposed under a stochastic-deterministic (SD) speech model that makes provision for the inclusion of a priori information by considering a non-zero mean. Analytical expressions are derived for the speech STFT magnitude in the MMSE sense, and phase in the maximum-likelihood sense. Furthermore, a practical method of estimating the a priori SD speech model parameters is described based on explicit consideration of harmonically related sinusoidal components in each STFT frame, and variations in both the magnitude and phase of these components between successive STFT frames. Objective tests using the PESQ measure indicate that the proposed algorithm results in superior speech quality when compared to several other speech enhancement algorithms. In particular it is clear that the proposed algorithm has an improved capability to retain low amplitude voiced speech components in low SNR conditions.

RFI Mitigation Using Two-Scale Estimators for Statistical Variance

The well-known sample variance estimator utilizes <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> samples from a random process to first estimate the process mean. The estimator then uses the same <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> samples to estimate variance from this mean. Process variance could also be estimated by first using less than <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> samples to estimate the mean, followed by using all <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> samples to estimate variance. Two-scale estimators of this type, both causal and noncausal, are defined. Statistics for these estimators are derived, which are valid for samples from any statistical distribution. These statistics are used to improve analysis of a previously reported device called the double detector. In microwave radiometry, the double detector senses the presence of deterministic signals, often called radio-frequency interference, that corrupt the usual measurement consisting only of Planck radiation.

Towards a unified treatment of gravitational-wave data analysis

We present a unified description of gravitational-wave data analysis that unites the template-based analysis used to detect deterministic signals from well-modeled sources, such as binary-black-hole mergers, with the cross-correlation analysis used to detect stochastic gravitational-wave backgrounds. We also discuss the connection between template-based analyses and those that target poorly-modeled bursts of gravitational waves, and suggest a new approach for detecting burst signals.

Accurate measurement of RF exposure from emerging wireless communication systems

Isotropic broadband probes or spectrum analyzers (SAs) may be used for the measurement of rapidly varying electromagnetic fields generated by emerging wireless communication systems. In this paper this problematic is investigated by comparing the responses measured by two different isotropic broadband probes typically used to perform electric field (E-field) evaluations. The broadband probes are submitted to signals with variable duty cycles (DC) and crest factors (CF) either with or without Orthogonal Frequency Division Multiplexing (OFDM) modulation but with the same root-mean-square (RMS) power. The two probes do not provide accurate enough results for deterministic signals such as Worldwide Interoperability for Microwave Access (WIMAX) or Long Term Evolution (LTE) as well as for non-deterministic signals such as Wireless Fidelity (WiFi). The legacy measurement protocols should be adapted to cope for the emerging wireless communication technologies based on the OFDM modulation scheme. This is not easily achieved except when the statistics of the RF emission are well known. In this case the measurement errors are shown to be systematic and a correction factor or calibration can be applied to obtain a good approximation of the total RMS power.

Microvascular Smooth Muscle Gene Program is Highly Sensitive to Blood Flow

Microvascular smooth muscle (MVSM) is phenotypically and functionally distinct from that of the large arteries yet the deterministic input signals are not known. We use Myosin Phosphatase as a model to address this question as it is a primary mediator of vasomotor responses. Isoforms of the myosin phosphatase regulatory subunit (Mypt1) are generated by alternative splicing of exon 24 (E24) and determine MVSM responses to NO/cGMP. Mesenteric artery MVSM expresses the E24+ (fast) isoform; large arteries express the E24‐ (slow) isoform. Ligation of the downstream MA2s causes switching of the upstream MA1s to the E24‐ isoform suggesting the hypothesis that the unique (fast) contractile phenotype of MVSM is determined by blood flow. To test this we developed a model of reduced perfusion pressure in which placement of an occluder around the abdominal aorta allows for transient reductions in blood flow within the rat mesenteric arcade. Brief (20 &amp; 60min) and repetitive (60min; 5days) perfusion pressure reduction via the balloon occluder causes a switch in the splicing of E24 in Mypt1 of mesenteric arteries from the E24+ to E24‐ isoform 6 hours after pressure reduction in both brief (20% E24+ vs 80% control; p&lt;0.05) and repetitive (45% E24+; p&lt;0.05) protocols. Given these effects of reduced perfusion pressure we propose that the unique properties of the MVSM are highly dependent upon the pulsatile blood flow generated by the heart

Improvement of TDOA Position Fixing Using the Likelihood Curvature

A conventional multilateration is a two-step process where, in the first step the time difference of arrivals (TDOA) of a signal at multiple sensors are estimated, and in the second step, these TDOAs are used in some position fixing technique to estimate the location of an emitter. Several estimators have been proposed over the years for the estimation of the TDOAs. Many techniques have been proposed for position fixing as well. Much of the research on position fixing has been focused on obtaining a simplified closed form solution. For the unknown deterministic signal model, Stein had derived the maximum-likelihood estimator (MLE) for the TDOA between two sensors, which is the peak location of the cross-correlation function. Since the asymptotic variance of an MLE approaches the Cramer-Rao lower bound (CRLB), which is the inverse of the negative of the expected value of the curvature of the log-likelihood function, using this as a motivation, we propose a weighted least squares type position fixing technique where the weights are computed from the curvature of the log-likelihood function.

Advanced Waveform Decomposition for High-Speed Videoendoscopy Analysis

This article presents a novel approach to analyze nonperiodic vocal fold behavior of high-speed videoendoscopy (HSV) data. Although HSV can capture true vibrational motions of the vocal folds, its clinical advantage over the videostroboscopy has not widely been accepted. One of the key advantages of the HSV over the videostroboscopy is its ability to capture vocal folds' nonperiodic behavior, which is more prominent in pathological vocal folds. However, such nonperiodicity in the HSV data has not been fully explored quantitatively beyond simple perturbation analysis. This article presents an advanced waveform modeling and decomposition technique for HSV-based waveforms. Waveforms are modeled to have three components: harmonic signal, deterministic nonharmonic signal, and random nonharmonic signal. This decomposition is motivated by the fact that voice disorders introduce signal content that is nonharmonic but carries deterministic quality such as subharmonic or modulating content. The proposed model is aimed to isolate such disordered behaviors as deterministic nonharmonic signal and quantify them. In addition to the model, the article outlines model parameter estimation procedures and a family of harmonics-to-noise ratio (HNR) parameters. The proposed HNR parameters include harmonics-to-deterministic-noise ratio (HDNR) and harmonics-to-random-noise ratio. A preliminary study demonstrates the effectiveness of the extended model and its HNR parameters. Vocal folds with and without benign lesions (Nwith=13; Nwithout=20) were studied with HSV glottal area waveforms. All three HNR parameters significantly distinguished the disordered condition, and the HDNR reported the largest effect size (Cohen's d=2.04).

One-Bit Quantizer Design for Multisensor GLRT Fusion

In this letter, we consider a decentralized detection problem in which a number of sensor nodes collaborate to detect the presence of an unknown deterministic signal. Due to stringent power/bandwidth constraints, each sensor quantizes its local observation into one bit of information. The binary data are then sent to the fusion center (FC), where a generalized likelihood ratio test (GLRT) detector is employed to make a global decision. In this context, we study one-bit quantizer design and analyze the asymptotic performance of the one-bit GLRT detector for cases where the quantized data are sent to the FC via perfect or imperfect channels. Simulation results are carried out to corroborate our theoretical analysis and to illustrate the performance of the proposed scheme.

Application of Correlation and Coherence Functions in Diagnostic Systems

Main problem in operation of diagnostic systems of power transmission systems is analysis of the energy of these harmonic components of the signal, which could be considered as diagnostic symptoms. Measured values can be affected i.a. by energy of random effects or other, less important components of the signal. Coherence and correlation functions are ones of classical methods of signal analysis being applied to analyze deterministic signals affected by random noise. In time domain autocorrelation function allows to determine time cohesion between adjacent fragments of analyzed process, shifted by different time values. In the paper author describes the method of minimization the impact of disturbances on signals being analyzed using basic properties of autocorrelation function. For the analysis of deterministic phenomena (including nonlinear) ordinary coherence function was applied. Showed examples were implemented and solved in MATLAB environment. The method was verified by analysis of model signals and signals (vibration accelerations) recorded on real object.

Output feedback control of a class of feedforward nonlinear systems in the presence of sensor noise

SUMMARY We propose an output feedback controller for a class of feedforward nonlinear systems under sensor noise. The sensor noise is any signal whose DC component is finite, which covers not only deterministic signals but also random signals including many practical noises. We introduce a notion of virtual state, then propose a measurement output feedback controller that utilizes a gain scaling factor. The gain scaling factor is commonly employed by the observer and controller. Through analysis, we show that all system states and output remain to be bounded in the presence of sensor noise, and the bound of states except output can be made arbitrarily small. Moreover, if the DC component of sensor noise is zero, the ultimate bound of the states and output can be made arbitrarily small by increasing the gain scaling factor in the presence of sensor noise. Copyright © 2013 John Wiley & Sons, Ltd.

F‐Wave Suppression Method for Improvement of Locating T‐Wave Ends in Electrocardiograms during Atrial Fibrillation

This article is to propose an algorithm for improving T-wave ends location during atrial fibrillation (AF). The traditional algorithms do not take the irregular baseline fibrillation of AF into consideration, so their location accuracy is relatively low. Based on simple assumptions that AF is a random signal while T waves and QRS complexes are deterministic signals, we suggest a novel method to suppress f wave for improving location of T-wave ends during AF. We firstly define a new cardiac cycle and then match R peaks and T peaks in the three adjacent cardiac cycles. Finally, we suppress the interference of the f wave by averaging. When evaluating with the PhysioNet QT database and simulated AF signals in terms of the mean and the standard deviation of the T-wave ends location errors, the proposed algorithm improves the performance of existing popular methods. Besides, the clinical significance of the proposed method is illustrated.

An algorithm for on-the-fly generation of samples of non-stationary Gaussian processes based on a sampling theorem

A Monte Carlo algorithm is developed for generating samples of real-valued non-stationary Gaussian processes. The method is based on a generalized version of Shannon's sampling theorem for bandlimited deterministic signals, as well as an efficient algorithm for generating conditional Gaussian variables. One feature of the method that is attractive for engineering applications involving stochastic loads is the ability of the algorithm to be implemented “on-the-fly” meaning that, given the value of the sample of the process at the current time step, it provides the value for the sample of the process at the next time step. Theoretical arguments are supported by numerical examples demonstrating the implementation, efficiency, and accuracy of the proposed Monte Carlo simulation algorithm.

On the temporal variability of the Sea Surface Temperature on the Tropical Southwest Atlantic Continental Shelf

ABSTRACT Teixeira, C. E.P. and Machado, G. T., 2013. On the temporal variability of the Sea Surface Temperature on the Tropical Southwest Atlantic Continental Shelf In this study, we used 29 years of daily 4-km AVHRR Pathfinder Version 5.2 (PFV5.2) data to study the SST temporal variability over the continental shelf in the Southwestern Tropical Atlantic Ocean (SWTA) (1 to 6° S and 41 to 35 W). Firstly, we evaluated the cloud cover and assessed the annual and semi-annual deterministic signals using a harmonic analysis. The annual plus the semi-annual cycles were removed from the original data and a low-pass filter was used to estimate the contribution of the inter-annual component. Finally, the inter-annual signal was also removed and the variance associated to the remaining residuals was calculated to address the intra-annual component. We show that the region is covered by clouds – and SST data unavailable - between 40% and 60% of the time. The annual signal explains most of the variability over the are...

Comparative Analysis of SSRLS and SSRLS with Adaptive Memory for Wireless Channel Equalization

Wireless radio channels cause severe distortion in the signals. These distortions can be reduced by using different channel equalization techniques. In this paper, we compare the equalization performance of state-space recursive least squares (SSRLS) and state-space recursive least squares with adaptive memory (SSRLSWAM) to offset the effect of a linear dispersive channel. These adaptive filters are well-suited for the estimation of deterministic signals corrupted by the observation noise. We consider the equalization of both the linear time invariant and linear time varying systems to compare the performance of these filters. The performance is affected by the observation noise variance, choice of the forgetting factor and the model mismatch. We present simulation results to compare these cases by tracking a deterministic signal. It has been shown that SSRLSWAM outperforms SSRLS in both the time invariant and time varying channels.

Staged miRNA re-regulation patterns during reprogramming

BackgroundMiRNAs often operate in feedback loops with transcription factors and represent a key mechanism for fine-tuning gene expression. In transcription factor-induced reprogramming, miRNAs play a critical role; however, detailed analyses of miRNA expression changes during reprogramming at the level of deep sequencing have not been previously reported.ResultsWe use four factor reprogramming to induce pluripotent stem cells from mouse fibroblasts and isolate FACS-sorted Thy1- and SSEA1+ intermediates and Oct4-GFP+ induced pluripotent stem cells (iPSCs). Small RNAs from these cells, and two partial-iPSC lines, another iPSC line, and mouse embryonic stem cells (mES cells) were deep sequenced. A comprehensive resetting of the miRNA profile occurs during reprogramming; however, analysis of miRNA co-expression patterns yields only a few patterns of change. Dlk1-Dio3 region miRNAs dominate the large pool of miRNAs experiencing small but significant fold changes early in reprogramming. Overexpression of Dlk1-Dio3 miRNAs early in reprogramming reduces reprogramming efficiency, suggesting the observed downregulation of these miRNAs may contribute to reprogramming. As reprogramming progresses, fewer miRNAs show changes in expression, but those changes are generally of greater magnitude.ConclusionsThe broad resetting of the miRNA profile during reprogramming that we observe is due to small changes in gene expression in many miRNAs early in the process, and large changes in only a few miRNAs late in reprogramming. This corresponds with a previously observed transition from a stochastic to a more deterministic signal.

Single channel blind source separation of deterministic sinusoidal signals with independent component analysis

Single channel blind source separation of deterministic sinusoidal signals with independent component analysis

Detection of continuous-time quaternion signals in additive noise

Different kinds of quaternion signal detection problems in continuous-time by using a widely linear processing are dealt with. The suggested solutions are based on an extension of the Karhunen-Loeve expansion to the quaternion domain which provides uncorrelated scalar real-valued random coefficients. This expansion presents the notable advantage of transforming the original four-dimensional eigen problem to a one-dimensional problem. Firstly, we address the problem of detecting a quaternion deterministic signal in quaternion Gaussian noise and a version of Pitcher’s Theorem is given. Also the particular case of a general quaternion Wiener noise is studied and an extension of the Cameron-Martin formula is presented. Finally, the problem of detecting a quaternion random signal in quaternion white Gaussian noise is tackled. In such a case, it is shown that the detector depends on the quaternion widely linear estimator of the signal.