Parameters Of Multiple Signals Research Articles

DELAMINATION DETECTION IN THIN-WALLED COMPOSITE STRUCTURES USING ACOUSTIC PITCH-CATCH TECHNIQUE WITH FIBER BRAGG GRATING SENSORS

Structural health monitoring (SHM) of a composite structure is essential in maintaining the integrity of the structure. Over the years, various studies have reported on the use of conventional electrical sensors in analysing acoustic wave propagation for delamination detection. However, electrical sensors are associated with drawbacks such as high signal attenuation, are prone to electromagnetic interference (EMI) and are not suitable for harsh environments. Therefore, this paper reported on the use of fiber Bragg grating (FBG) sensors for delamination detection. Two composite structures with delamination sizes of 10 cm × 2 cm and 10 cm × 6 cm were fabricated. Two FBGs were bonded before and after the delamination. In addition, three trials of impacts were induced at the centre of the structure. Multiple signal parameters were obtained and analysed, which were the time delay, amplitude difference and velocity difference. The experimental results revealed that the time delay, amplitude and velocity analysis varied for both the delamination sizes with an average percentage of 42.36%, 97.09% and 42.39%, respectively. Therefore, it was confirmed that the increase in delamination size resulted in a longer time delay, higher signal amplitude attenuation and slower wave propagation.

Correlation-based local detection for deceptive interference mitigation in multi-parameter modulated radar

To improve the ability of resisting the deceptive interference for radar, a multi-parameter modulated radar signal is investigated and its compressed sensing model is constructed in this paper. Unlike the traditional frequency modulated continuous wave (FMCW) signal model, the established model has natural advantages in mitigating the deceptive interference due to the agility of multiple signal parameters. Simultaneously, to reduce the impact of the deceptive interference on the target echo, the compressed recovery algorithm combined with the proposed correlation-based local detection is introduced. Before recovering the target echo, the local dictionary matrix is inspected by using the correlation to ensure that the expected echo signal is obtained merely. Simulation results show that the proposed method significantly improves the performance in mitigating the deceptive interference. By coherently integrating the signals over multiple periods, the relative pure range profile or the range-Doppler plane could be acquired. It helps radar system to extract the distance or velocity information in the interference environment.

Toward Quantification: Microstructure and Magnetic Resonance Fingerprinting.

Quantitative magnetic resonance imaging (MRI) is a long-standing challenge. We advocate that the origin of the problem is the simplification applied in commonly used models of the MRI signal relation to the target parameters of biological tissues. Two research fields are briefly reviewed as ways to respond to the challenge of quantitative MRI, both experiencing an exponential growth right now. Microstructure MRI strives to build physiology-based models from cells to signal and, given the signal, back to the cells again. Magnetic resonance fingerprinting aims at efficient simultaneous determination of multiple signal parameters. The synergy of these yet disjoined approaches promises truly quantitative MRI with specific target-oriented diagnostic tools rather than universal imaging methods.

Joint Angle-Amplitude Estimation for Multiple Signals with L-Structured Arrays Using Bioinspired Computing

The aim of this work is to estimate jointly the elevation and azimuth angles along with the amplitudes of multiple signals impinging on 1-L- and 2-L-shape arrays. An efficient mechanism based on hybrid Bioinspired techniques is proposed for this purpose. The global search optimizers such as Differential Evolution (DE) and Particle Swarm optimization (PSO) are hybridized with a local search optimizer called pattern search (PS). Approximation theory in Mean Square Error sense is exploited to develop a fitness function of the problem. The unknown parameters of multiple signals transmitted by far-field sources are estimated with the strength of hybrid DE-PS and PSO-PS. The effectiveness of the proposed techniques is tested in terms of estimation accuracy, proximity effect, convergence, and computational complexity.

Recursive RANSAC: Multiple Signal Estimation with Outliers

The random sample consensus (RANSAC) algorithm is frequently used in computer vision to estimate the parameters of a signal in the presence of noisy and even spurious observations called gross errors. Instead of just one signal, we desire to estimate the parameters of multiple signals, where at each time step a set of observations of generated from the underlying signals and gross errors are received. In this paper, we develop the recursive RANSAC (RRANSAC) algorithm to solve the inherent data association problem and recursively estimate the parameters of multiple signals without prior knowledge of the number of true signals. We compare the performance of RRANSAC with several existing algorithms, and also demonstrate the capabilities of RRANSAC in an aerial geolocation problem.

Improved resolution DoA estimation through shrunk projections on the signal sub-space

Maximum likelihood (ML) estimation of the Direction of Arrival (DoA) parameters of multiple signals impinging on a sensor array is known to provide best performances among existing techniques, under general signal and system assumptions. However, even the ML estimation performance deteriorates severely in system conditions where the angular separations between signal sources are small and the SNR/sample size are low. In an effort to improve on the ML performance in such challenging conditions, the present communication investigates DoA estimators obtained by performing shrunk (non-orthogonal) projections on the signal sub-space (SS). It is argued that suitable selections of the introduced shrinkage parameters help to limit the chance of outlier estimates occurring, which account for the rapid deterioration of ML at low SNR. Simulation results show that a proposed two-stage estimation approach based on the Shrunk Projections (SP) estimator, offers significant performance gains relative to ML.

Which Call Parameters Signal Threat to Conspecifics in White‐Throated Magpie‐Jay Mobbing Calls?

Abstract Variation in signals often encodes additional information beyond that provided by the main signal. Many species communicate degree of threat using such signal variation. However, multiple signal parameters often covary with threat level, and it is unclear whether receivers are using variation along one or more parameters when assessing threat level. White‐throated magpie‐jays (Calocitta formosa) vary several call parameters when mobbing, and here I report on an experiment testing whether such variation contains information used by conspecific receivers, and which parameters are salient to receivers. I collected data on natural mobbing sequences in two threat contexts and found that mobbing calls were highly variable in both length and inter‐call interval, but on average, both parameters were shorter in high threat contexts, while call frequency did not differ. To determine how jays respond to such between‐context variation, I conducted a playback experiment in which call length and inter‐call interval were independently varied in a factorial design. This is one of the first studies to my knowledge to deconstruct and independently test covarying signal parameters in an anti‐predator signaling system. Magpie‐jays responded more often to treatments with either short call lengths or short inter‐call intervals, in concordance with the natural calling data. However, the two parameters did not appear to act independently; responses were not additive, and short calls at short inter‐call intervals elicited no stronger of a response than short calls at long intervals or long calls at short intervals. Receivers can use either parameter when assessing threat level. These data demonstrate that mobbing can signal threat level, that call length and call rate are redundant, and that receivers need not use both to determine whether to approach.

Estimating the Parameters of General Frequency Modulated Signals

A general frequency modulated (GFM) signal characterizes the vibrations produced by compressors, turbines, propellers, gears, and other rotating machines in a dynamic environment. A GFM signal is defined as the composition of a real or complex, periodic, or almost-periodic carrier function with a real, differentiable modulation function. A GFM signal therefore contains sinusoids whose frequencies are (possibly nonintegral) multiples of a fundamental; to distinguish a GFM signal from a set of unrelated sinusoids, it is necessary to track them as a group. This paper develops the general frequency modulation tracker (GFMT) for one or more GFM signals in noise using the expectation/conditional maximization (ECM) algorithm that is an extension of the expectation-maximization (EM) algorithm. Three advantages of this approach are that the ratios (harmonic numbers) of the carrier functions do not need to be known a priori, that the parameters of multiple signals are estimated simultaneously, and that the GFMT algorithm exploits knowledge of the noise spectrum so that a separate normalization procedure is not required. Several simulated examples are presented to illustrate the algorithm's performance.

Analysis of joint angle-frequency estimation using ESPRIT

High-resolution parameter estimation techniques have been applied to jointly estimate multiple signal parameters. we consider the problem of determining the directions and center frequencies of a number of narrowband sources in a band of interest. We present a joint angle-frequency estimation method, based on the multidimensional ESPRIT algorithm. A perturbation error analysis gives bounds on the parameter estimates and provides optimal values for the temporal and spatial smoothing parameters. The analysis is shown to be consistent with simulation results.

Listening in the dark: Behavioral and neural mechanisms of acoustic recognition in a singing fish

Vocalizing species of teleost fish have a simple repertoire of species-typical signals central to their social and reproductive behavior. Male midshipman fish (‘‘the California singing fish’’) acoustically court females using ‘‘hums’’ that are long duration (secs 1h) and spectrally simple. Individual hums exhibit an essentially flat envelope consisting of a low fundamental frequency (90–100 Hz) and several harmonics. By contrast, naturally occurring acoustic beats show envelope modulations: the hums of neighboring males overlap to produce a beat with a difference frequency (0–8 Hz) in which amplitude and phase modulations result from the continual shifting of the two fundamental frequencies. Behavioral playback (phonotaxis) studies show that individual midshipman can use multiple signal parameters including hum duration, gap duration between successive hums, fundamental frequency, and intensity to distinguish hums from each other, while the effectiveness of beats as playback signals depends upon both beat modulation rates and depth of modulation. Neurophysiological evidence shows that auditory neurons in the midshipmans VIIIth nerve and midbrain encode the physical attributes of these signals mainly using spike rate and spike synchronization, mechanisms that we propose contribute to the neural templates adapted for both hum and beat recognition. [Research supported by NIH.]

Behavioral assessment of acoustic parameters relevant to signal recognition and preference in a vocal fish.

Acoustic signal recognition depends on the receiver's processing of the physical attributes of a sound. This study takes advantage of the simple communication sounds produced by plainfin midshipman fish to examine effects of signal variation on call recognition and preference. Nesting male midshipman generate both long duration (> 1 min) sinusoidal-like "hums" and short duration "grunts." The hums of neighboring males often overlap, creating beat waveforms. Presentation of humlike, single tone stimuli, but not grunts or noise, elicited robust attraction (phonotaxis) by gravid females. In two-choice tests, females differentiated and chose between acoustic signals that differed in duration, frequency, amplitude, and fine temporal content. Frequency preferences were temperature dependent, in accord with the known temperature dependence of hum fundamental frequency. Concurrent hums were simulated with two-tone beat stimuli, either presented from a single speaker or produced more naturally by interference between adjacent sources. Whereas certain single-source beats reduced stimulus attractiveness, beats which resolved into unmodulated tones at their sources did not affect preference. These results demonstrate that phonotactic assessment of stimulus relevance can be applied in a teleost fish, and that multiple signal parameters can affect receiver response in a vertebrate with relatively simple communication signals.

Power and timing parameter estimation of multiple cyclostationary signals from sensor array data

The problem of estimating power and timing parameters of multiple signals impinging on an array of sensor is addressed. The proposed technique, which assumes that the signals of interest exhibit cyclostationarity, is based on the properties of the array cyclic correlation matrix of the received signals. Unlike the conventional methods, the new algorithm allows one to estimate also signal timing parameters. Simulation results show the capability of the technique to assure satisfactory performances in severely adverse interference environments.

Quantification of the difference between detection and resolution thresholds for multiple closely spaced emitters

The problem of estimating the parameters of multiple signals closely spaced in either space or frequency has been much researched because it is a difficult case for modern spectral analysis techniques. Based on empirical evidence, it has been noted by others that signal-to-noise ratio (SNR) detection thresholds typically are lower than resolution thresholds. That is, one can detect the occurrence of n closely spaced signals at SNRs lower than those at which one can resolve the signals and reliably estimate their parameters. The difference between detection and resolution thresholds is quantified for the direction-finding problem in which source locations are specified by a single angular coordinate (e.g., azimuth).< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>