A method for detecting non-stationary oscillations in process plants

This paper proposes a method for detecting oscillations in non-stationary timeseries based on the statistical properties of zero-crossings. The main development presented is atechnique to remove non-stationary trend component from the analysed signals before applyingan oscillation detection procedure. First, the method extracts the signal's baseline that is utilizedto stationarize the signal. Then, an index describing the regularity of the stationarized signal'szero-crossings is computed in order to determine the presence of oscillation. The propertiesand performance of the method are analysed in simulation studies. Furthermore, the methodis comprehensively tested with industrial data in a comparative study in which the proposedmethod is tested against other oscillation detection methods using industrial benchmark dataand in tests on paperboard machine process. Finally, the simulation and industrial results areanalysed and discussed.Peer reviewe

Enhancing Missing Data Imputation of Non-stationary Signals with Harmonic Decomposition

Dealing with time series with missing values, including those afflicted by low quality or over-saturation, presents a significant signal processing challenge. The task of recovering these missing values, known as imputation, has led to the development of several algorithms. However, we have observed that the efficacy of these algorithms tends to diminish when the time series exhibit non-stationary oscillatory behavior. In this paper, we introduce a novel algorithm, coined Harmonic Level Interpolation (HaLI), which enhances the performance of existing imputation algorithms for oscillatory time series. After running any chosen imputation algorithm, HaLI leverages the harmonic decomposition based on the adaptive nonharmonic model of the initial imputation to improve the imputation accuracy for oscillatory time series. Experimental assessments conducted on synthetic and real signals consistently highlight that HaLI enhances the performance of existing imputation algorithms. The algorithm is made publicly available as a readily employable Matlab code for other researchers to use

Neural Network-Based Multi-Target Detection within Correlated Heavy-Tailed Clutter

This work addresses the problem of range-Doppler multiple target detection in a radar system in the presence of slow-time correlated and heavy-tailed distributed clutter. Conventional target detection algorithms assume Gaussian-distributed clutter, but their performance is significantly degraded in the presence of correlated heavy-tailed distributed clutter. Derivation of optimal detection algorithms with heavy-tailed distributed clutter is analytically intractable. Furthermore, the clutter distribution is frequently unknown. This work proposes a deep learning-based approach for multiple target detection in the range-Doppler domain. The proposed approach is based on a unified NN model to process the time-domain radar signal for a variety of signal-to-clutter-plus-noise ratios (SCNRs) and clutter distributions, simplifying the detector architecture and the neural network training procedure. The performance of the proposed approach is evaluated in various experiments using recorded radar echoes, and via simulations, it is shown that the proposed method outperforms the conventional cell-averaging constant false-alarm rate (CA-CFAR), the ordered-statistic CFAR (OS-CFAR), and the adaptive normalized matched-filter (ANMF) detectors in terms of probability of detection in the majority of tested SCNRs and clutter scenarios.Comment: Accepted to IEEE Transactions on Aerospace and Electronic System

Reconstructing time-dependent dynamics

The usefulness of the information extracted from biomedical data relies heavily on the underlying theory of the methods used in its extraction. The assumptions of stationarity and autonomicity traditionally applied to dynamical systems break down when considering living systems, due to their inherent time-variability. Living systems are thermodynamically open, and thus constantly interacting with their environment. This results in highly nonlinear, time-dependent dynamics. The aim of signal analysis is to gain insight into the behaviour of the system from which the signal originated. Here, various analysis methods for the characterization of signals and their underlying non-autonomous dynamics are presented, incorporating time-frequency analysis, time-domain decomposition of nonlinear modes, and methods to study mutual interactions and couplings using dynamical Bayesian inference, wavelet-bispectral and time-localised coherence, and entropy and information-based analysis. The recent introduction of chronotaxic systems provides a theoretical framework in which dynamical systems can have amplitudes and frequencies which are time-varying, yet stable, matching well the characteristics of living systems. We demonstrate that considering this theory of chronotaxic systems whilst applying the presented methods results in an approach for the reconstruction of the dynamics of living systems across many scales

Nonlinear ultrasound image modeling: Development of a complete end-to-end model for qualitative and quantitative analysis of medical ultrasound

Finite amplitude sound propagation undergoes nonlinear distortion due to continuous path interaction with the propagation medium. This distortion tends to defocus the beam causing significant lateral and contrast resolution degradation. Fundamental understanding of this interaction requires development of computational models that accurately predict the nonlinear interaction - development of media-borne harmonics - as well as produce an ultrasound image - introduction of transducer effects, interface transitions, and innovative image processing to extract harmonics. Most computational models of ultrasound propagation assume axial symmetry for computational expediency. Two notable exceptions are the K-Z-K and NLP models. A new endto- end model, NUPROP, is introduced that also incorporates non-axially symmetric geometries and simplified transducer responses to accurately predict ultrasound RF signals for image reconstruction. Nonlinearities are modeled using either the Fubini solution or Burgers\u27 Equation coupled with angular spectrum propagation or Lommel formulation, appropriately masked by the transducer frequency response. Comparative analyses are performed on NUPROP results with high correlation with the literature. Parameter sensitivity analyses are performed to determine harmonic signal characteristics as a function of propagation distance. A-line and B-scan images are produced

The future of primordial features with large-scale structure surveys

Primordial features are one of the most important extensions of the Standard Model of cosmology, providing a wealth of information on the primordial Universe, ranging from discrimination between inflation and alternative scenarios, new particle detection, to fine structures in the inflationary potential. We study the prospects of future large-scale structure (LSS) surveys on the detection and constraints of these features. We classify primordial feature models into several classes, and for each class we present a simple template of power spectrum that encodes the essential physics. We study how well the most ambitious LSS surveys proposed to date, including both spectroscopic and photometric surveys, will be able to improve the constraints with respect to the current Planck data. We find that these LSS surveys will significantly improve the experimental sensitivity on features signals that are oscillatory in scales, due to the 3D information. For a broad range of models, these surveys will be able to reduce the errors of the amplitudes of the features by a factor of 5 or more, including several interesting candidates identified in the recent Planck data. Therefore, LSS surveys offer an impressive opportunity for primordial feature discovery in the next decade or two. We also compare the advantages of both types of surveys.AstronomyPhysic

Effects of non-simultaneous masking on the binaural masking level difference

The present study sought to clarify the role of non-simultaneous masking in the binaural masking level difference for maskers that fluctuate in level. In the first experiment the signal was a brief 500-Hz tone, and the masker was a bandpass noise (100–2000 Hz), with the initial and final 200-ms bursts presented at 40-dB spectrum level and the inter-burst gap presented at 20-dB spectrum level. Temporal windows were fitted to thresholds measured for a range of gap durations and signal positions within the gap. In the second experiment, individual differences in out of phase (NoSπ) thresholds were compared for a brief signal in a gapped bandpass masker, a brief signal in a steady bandpass masker, and a long signal in a narrowband (50-Hz-wide) noise masker. The third experiment measured brief tone detection thresholds in forward, simultaneous, and backward masking conditions for a 50- and for a 1900-Hz-wide noise masker centered on the 500-Hz signal frequency. Results are consistent with comparable temporal resolution in the in phase (NoSo) and NoSπ conditions and no effect of temporal resolution on individual observers’ ability to utilize binaural cues in narrowband noise. The large masking release observed for a narrowband noise masker may be due to binaural masking release from non-simultaneous, informational masking

Independent component analysis (ICA) applied to ultrasound image processing and tissue characterization

As a complicated ubiquitous phenomenon encountered in ultrasound imaging, speckle can be treated as either annoying noise that needs to be reduced or the source from which diagnostic information can be extracted to reveal the underlying properties of tissue. In this study, the application of Independent Component Analysis (ICA), a relatively new statistical signal processing tool appeared in recent years, to both the speckle texture analysis and despeckling problems of B-mode ultrasound images was investigated. It is believed that higher order statistics may provide extra information about the speckle texture beyond the information provided by first and second order statistics only. However, the higher order statistics of speckle texture is still not clearly understood and very difficult to model analytically. Any direct dealing with high order statistics is computationally forbidding. On the one hand, many conventional ultrasound speckle texture analysis algorithms use only first or second order statistics. On the other hand, many multichannel filtering approaches use pre-defined analytical filters which are not adaptive to the data. In this study, an ICA-based multichannel filtering texture analysis algorithm, which considers both higher order statistics and data adaptation, was proposed and tested on the numerically simulated homogeneous speckle textures. The ICA filters were learned directly from the training images. Histogram regularization was conducted to make the speckle images quasi-stationary in the wide sense so as to be adaptive to an ICA algorithm. Both Principal Component Analysis (PCA) and a greedy algorithm were used to reduce the dimension of feature space. Finally, Support Vector Machines (SVM) with Radial Basis Function (RBF) kernel were chosen as the classifier for achieving best classification accuracy. Several representative conventional methods, including both low and high order statistics based methods, and both filtering and non-filtering methods, have been chosen for comparison study. The numerical experiments have shown that the proposed ICA-based algorithm in many cases outperforms other algorithms for comparison. Two-component texture segmentation experiments were conducted and the proposed algorithm showed strong capability of segmenting two visually very similar yet different texture regions with rather fuzzy boundaries and almost the same mean and variance. Through simulating speckle with first order statistics approaching gradually to the Rayleigh model from different non-Rayleigh models, the experiments to some extent reveal how the behavior of higher order statistics changes with the underlying property of tissues. It has been demonstrated that when the speckle approaches the Rayleigh model, both the second and higher order statistics lose the texture differentiation capability. However, when the speckles tend to some non-Rayleigh models, methods based on higher order statistics show strong advantage over those solely based on first or second order statistics. The proposed algorithm may potentially find clinical application in the early detection of soft tissue disease, and also be helpful for better understanding ultrasound speckle phenomenon in the perspective of higher order statistics. For the despeckling problem, an algorithm was proposed which adapted the ICA Sparse Code Shrinkage (ICA-SCS) method for the ultrasound B-mode image despeckling problem by applying an appropriate preprocessing step proposed by other researchers. The preprocessing step makes the speckle noise much closer to the real white Gaussian noise (WGN) hence more amenable to a denoising algorithm such as ICS-SCS that has been strictly designed for additive WGN. A discussion is given on how to obtain the noise-free training image samples in various ways. The experimental results have shown that the proposed method outperforms several classical methods chosen for comparison, including first or second order statistics based methods (such as Wiener filter) and multichannel filtering methods (such as wavelet shrinkage), in the capability of both speckle reduction and edge preservation