Classical Signal Processing Techniques Research Articles

Machine Learning in the Parkinson’s disease smartwatch (PADS) dataset

The utilisation of smart devices, such as smartwatches and smartphones, in the field of movement disorders research has gained significant attention. However, the absence of a comprehensive dataset with movement data and clinical annotations, encompassing a wide range of movement disorders including Parkinson’s disease (PD) and its differential diagnoses (DD), presents a significant gap. The availability of such a dataset is crucial for the development of reliable machine learning (ML) models on smart devices, enabling the detection of diseases and monitoring of treatment efficacy in a home-based setting. We conducted a three-year cross-sectional study at a large tertiary care hospital. A multi-modal smartphone app integrated electronic questionnaires and smartwatch measures during an interactive assessment designed by neurologists to provoke subtle changes in movement pathologies. We captured over 5000 clinical assessment steps from 504 participants, including PD, DD, and healthy controls (HC). After age-matching, an integrative ML approach combining classical signal processing and advanced deep learning techniques was implemented and cross-validated. The models achieved an average balanced accuracy of 91.16% in the classification PD vs. HC, while PD vs. DD scored 72.42%. The numbers suggest promising performance while distinguishing similar disorders remains challenging. The extensive annotations, including details on demographics, medical history, symptoms, and movement steps, provide a comprehensive database to ML techniques and encourage further investigations into phenotypical biomarkers related to movement disorders.

Efficient quantum interpolation of natural data

We present efficient methods to interpolate data with a quantum computer that complement uploading techniques and quantum post-processing. The quantum algorithms are supported by the efficient Quantum Fourier Transform (QFT) and classical signal and imaging processing techniques, and open the door of quantum advantage to relevant families of data. We showcase a QFT interpolation method, a Quantum Cosine Transform (QCT) interpolation geared towards natural data, and we improve upon them by utilizing a quantum circuit's capabilities of processing data in superposition. A novel circuit for the QCT is presented. We demonstrate the methods on probability distributions and quantum encoded images, and discuss the precision of the resulting interpolations.

A Survey on EEG Signal Processing Techniques and Machine Learning: Applications to the Neurofeedback of Autobiographical Memory Deficits in Schizophrenia

In this paper, a general overview regarding neural recording, classical signal processing techniques and machine learning classification algorithms applied to monitor brain activity is presented. Currently, several approaches classified as electrical, magnetic, neuroimaging recordings and brain stimulations are available to obtain neural activity of the human brain. Among them, non-invasive methods like electroencephalography (EEG) are commonly employed, as they can provide a high degree of temporal resolution (on the order of milliseconds) and acceptable space resolution. In addition, it is simple, quick, and does not create any physical harm or stress to patients. Concerning signal processing, once the neural signals are acquired, different procedures can be applied for feature extraction. In particular, brain signals are normally processed in time, frequency, and/or space domains. The features extracted are then used for signal classification depending on its characteristics such us the mean, variance or band power. The role of machine learning in this regard has become of key importance during the last years due to its high capacity to analyze complex amounts of data. The algorithms employed are generally classified in supervised, unsupervised and reinforcement techniques. A deep review of the most used machine learning algorithms and the advantages/drawbacks of most used methods is presented. Finally, a study of these procedures utilized in a very specific and novel research field of electroencephalography, i.e., autobiographical memory deficits in schizophrenia, is outlined.

Interactive Learning of Signal Processing Through Music: Making Fourier Analysis Concrete for Students

In this artaicle, we illustrate how music may serve as a vehicle to support education in signal processing. Using Fourier analysis as a concrete example, we demonstrate how the music domain provides motivating and tangible applications that make learning signal processing an interactive pursuit. Furthermore, we indicate how software tools, originally developed for music analysis, provide students multiple entry points to delve deeper into classical signal processing techniques while bridging the gap between education and cutting-edge research.

Marked point process representation of oscillatory dynamics underlying working memory

Objective. Computational models of neural activity at the meso-scale suggest the involvement of discrete oscillatory bursts as constructs of cognitive processing during behavioral tasks. Classical signal processing techniques that attempt to infer neural correlates of behavior from meso-scale activity employ spectral representations of the signal, exploiting power spectral density techniques and time–frequency (T–F) energy distributions to capture band power features. However, such analyses demand more specialized methods that incorporate explicitly the concepts of neurophysiological signal generation and time resolution in the tens of milliseconds. This paper focuses on working memory (WM), a complex cognitive process involved in encoding, storing and retrieving sensory information, which has been shown to be characterized by oscillatory bursts in the beta and gamma band. Employing a generative model for oscillatory dynamics, we present a marked point process (MPP) representation of bursts during memory creation and readout. We show that the markers of the point process quantify specific neural correlates of WM. Approach. We demonstrate our results on field potentials recorded from the prelimbic and secondary motor cortices of three rats while performing a WM task. The generative model for single channel, band-passed traces of field potentials characterizes with high-resolution, the timings and amplitudes of transient neuromodulations in the high gamma (80–150 Hz, γ) and beta (10–30 Hz, β) bands as an MPP. We use standard hypothesis testing methods on the MPP features to check for significance in encoding of task variables, sensory stimulus and executive control while comparing encoding capabilities of our model with other T–F methods. Main Results. Firstly, the advantages of an MPP approach in deciphering encoding mechanisms at the meso-scale is demonstrated. Secondly, the nature of state encoding by neuromodulatory events is determined. Third, we demonstrate the necessity of a higher time resolution alternative to conventionally employed T–F methods. Finally, our results underscore the novelty in interpreting oscillatory dynamics encompassed by the marked features of the point process. Significance. An MPP representation of meso-scale activity not just enables a rich, high-resolution parameter space for analysis but also presents a novel tool for diverse neural applications.

Random Node-Asynchronous Graph Computations: Novel Opportunities for Discrete-Time State-Space Recursions

For the analysis of data defined over irregular domains, the recent area of graph signal processing extends classical signal processing techniques to the case of graphs. A prominent idea in this context is the notion of graph shift, which provides a foundation for distributed signal processing over networks. Despite its distributed nature, successive implementations of graph shift require a synchronization over the network, which can be a restriction in practical applications. The main goal of this article is to review the recent advancements in random and asynchronous computations over graphs with a specific emphasis on asynchronous graph-filtering applications. This article describes the behavior of the randomized asynchronous graph shift from a linear system theory viewpoint while making connections with asynchronous fixed-point iterations and Markov jumpswitching systems. In the context of graph signal processing, two specific applications are considered. The first one combines polynomial filtering and asynchronous updates to compute the Fiedler vector for spectral clustering. The second one considers the asynchronous implementation of a given rational graph filter. Asynchronous graph filtering is also extended to implement polynomial graph filter banks and graph neural networks in an asynchronous manner. The considered applications have proven mean-squared convergence guarantees.

Signal Detection and Enhancement for Seismic Crosscorrelation Using the Wavelet-Domain Kalman Filter

Crosscorrelation is a classical signal-processing technique that plays an important role in exploration and earthquake geophysics. Seismic velocity estimation utilizes the crosscorrelation between observed and predicted seismic records in traveltime tomography. The crosscorrelation between two stations represents the Green’s functions retrieved from ambient noises in passive seismic interferometry. It can be used to estimate the subsurface velocity and amplitude information. The calculation of crosscorrelation usually assumes that the input data are stationary; however, the real seismic data are often non-stationary, due to the presence of multiple wave-modes and background noises. The seismic crosscorrelations often have low signal-to-noise ratio and frequently fail to provide correct information for subsequent processing. To address this problem, we develop a comprehensive technique to reduce contamination and improve the quality of crosscorrelation in the wavelet domain. The new procedure includes the forward wavelet transformation of raw records, the crosscorrelation between wavelet coefficients, single-channel image object detection, multi-channel Kalman-filter object tracking, and inverse wavelet transformation to produce the new crosscorrelation gathers. We effectively remove the unwanted components associated with contaminated wave-modes as the proposed detection and tracking algorithm can accurately extract the target wave-mode. We validate the method for three datasets: a marine streamer survey, a borehole survey, and a broadband dataset from seismology stations. We demonstrate that the proposed method can significantly improve the signal-to-noise ratio of the seismic crosscorrelations, considerably enhancing the quality of the data for subsequent advanced crosscorrelation-based seismic processing.

Low-Pass NGD Voice Signal Sensoring With Passive Circuit

Nowadays, the speech signal processing is still challenging with the multimedia ubiquity. Classical signal processing techniques as filtering were developed for the speech enhancement to overcome unintentional effects as the propagation channel. However, the delay effect remains an open challenge for audio signal processing engineers. A voice/song signal (VSS) prediction is investigated theoretically and experimentally in this paper. This VSS engineering allows to predict an arbitrary waveform signal by using a lowpass negative group delay (NGD) function. This outstanding function presents the VSS advance properties when its parameters are matched with the operated signals. This NGD engineering analytical approach is described with the audio signal processing. The low-pass NGD analysis and property are deeply investigated with two NGD RC-and RL-passive cells. NGD transient study is performed with parametric analytical signals presenting raised cosine waveform. The introduced VSS NGD sensoring is verified with experimental real-time tests of microphone VSS generator. VSS time-advance of approximately 50 μs was observed with a good agreement between simulations and measurements.

Intracellular signaling dynamics and their role in coordinating tissue repair.

Tissue repair is a complex process that requires effective communication and coordination between cells across multiple tissues and organ systems. Two of the initial intracellular signals that encode injury signals and initiate tissue repair responses are calcium and extracellular signal‐regulated kinase (ERK). However, calcium and ERK signaling control a variety of cellular behaviors important for injury repair including cellular motility, contractility, and proliferation, as well as the activity of several different transcription factors, making it challenging to relate specific injury signals to their respective repair programs. This knowledge gap ultimately hinders the development of new wound healing therapies that could take advantage of native cellular signaling programs to more effectively repair tissue damage. The objective of this review is to highlight the roles of calcium and ERK signaling dynamics as mechanisms that link specific injury signals to specific cellular repair programs during epithelial and stromal injury repair. We detail how the signaling networks controlling calcium and ERK can now also be dissected using classical signal processing techniques with the advent of new biosensors and optogenetic signal controllers. Finally, we advocate the importance of recognizing calcium and ERK dynamics as key links between injury detection and injury repair programs that both organize and execute a coordinated tissue repair response between cells across different tissues and organs.This article is categorized under:Models of Systems Properties and Processes > Mechanistic ModelsBiological Mechanisms > Cell SignalingLaboratory Methods and Technologies > ImagingModels of Systems Properties and Processes > Organ, Tissue, and Physiological Models

A novel FastICA algorithm based on improved secant method for Intelligent drive

Blind Source Separation(BSS) is one of the research hotspots in the field of signal processing. In order to improve the accuracy of speech recognition in driving environment, the driver’s speech signal must be enhanced to improve its signal to noise ratio(SNR). Independent component analysis (ICA) algorithm is the most classical and efficient blind statistical signal processing technique. Compared with other improved ICA algorithms, fixed-point algorithm (FastICA) is well known for its fast convergence speed and good robustness. However, the convergence of FastICA algorithm is comparatively susceptible to the initial value selection of the original demixing matrix and the calculation of the iterative process is relatively large. In this paper, the gradient descent method is used to reduce the effect of initial value. What’s more, the improved secant method is proposed to speed up the convergence rate and reduce the amount of computation. As the results of mixed speech separation experiment turn out, the improved algorithm is of better performance relative to the standard FastICA algorithm. Experimental results show that the proposed algorithm improves the speech quality of the target driver. It is suitable for speech separation in driving environment with low SNR.

Vibration-based diagnostics of epicyclic gearboxes – From classical to soft-computing methods

The paper presents up-to-date multidisciplinary review of scientific knowledge and industrial guidelines concerning the issue of technical assessment of epicyclic gearboxes on the basis of vibrational measurement. The paper covers norms, patents, signal processing methods, technical features, diagnostic methodologies, and high-level soft computing. The review classifies recognized papers according to different aspects, like case difficulty, applied methods or studied objects. Moreover, the paper discusses recent development of diagnostic techniques taking into account changing operational conditions and requiring powerful data processing units not available hitherto. Since in recent years many partial review papers have been published in different journals, the current paper presents their individual scope. Because diagnostics of epicyclic gearboxes generally covers and extends the classical signal processing techniques and classical machine condition monitoring functionality, the current paper might serve as a descriptive database of sources of information and bibliography or as a reference to individual techniques.

Wavelet transform‐based feature extraction for detection and classification of disturbances in an islanded micro‐grid

As one of the most classical digital signal processing (DSP) techniques, the wavelet transformation (WT) has been applied to detect and classify power system disturbances for many years. However, its performance is easily affected by harmonics. This makes it difficult to detect and classify disturbances occurring in an islanded micro-grid, especially when non-linear loads are involved. To improve the performance of WT, in this article, the Renyi entropy is used with WT to detect and classify seven types of disturbance. To demonstrate the efficacy of Renyi entropy in the wavelet domain, a comparison is made by detecting and classifying disturbances using the raw data with the time-domain-based Renyi entropy. Other comparisons are also performed to show how the performance of Renyi is affected by different order values of Renyi and the non-linear loading level. Detection and classification are made by using the support vector machine (SVM) and K-nearest neighbour (KNN) classifiers. The results demonstrate the effectiveness of WT-based Renyi entropy and show that the performance accuracy improves with the increase in the percentage of non-linear loads.

Multi-Level Downsampling of Graph Signals via Improved Maximum Spanning Trees

Graph signal processing (GSP) is an emerging field in the signal processing community. Novel GSP-based transforms, such as graph Fourier transform and graph wavelet filter banks, have been successfully utilized in image processing and pattern recognition. As a rapidly developing research area, graph signal processing aims to extend classical signal processing techniques to signals with irregular underlying structures. One of the hot topics in GSP is to develop multi-scale transforms such that novel GSP-based techniques can be applied in image processing or other related areas. For designing graph signal multi-scale frameworks, downsampling operations that ensuring multi-level downsampling should be specifically constructed. Among the existing downsampling methods in graph signal processing, the state-of-the-art method was constructed based on the maximum spanning tree (MST). However, when using this method for multi-level downsampling of graph signals defined on unweighted densely connected graphs, such as social network data, the sampling rates are not close to [Formula: see text]. This phenomenon is summarized as a new problem and called downsampling unbalance problem in this paper. Due to the unbalance, MST-based downsampling method cannot be applied to construct graph signal multi-scale transforms. In this paper, we propose a novel and efficient method to detect and reduce the downsampling unbalance generated by the MST-based method. For any given graph signal, we apply the graph density to construct a measurement of the downsampling unbalance generated by the MST-based method. If a graph signal has large unbalance possibility, the multi-level downsampling is conducted after the MST is improved. The experimental results on synthetic and real-world social network data show that downsampling unbalance can be efficiently detected and then reduced by our method.

Recovery of Differential Equations from Impulse Response Time Series Data for Model Identification and Feature Extraction

Time recordings of impulse-type oscillation responses are short and highly transient. These characteristics may complicate the usage of classical spectral signal processing techniques for (a) describing the dynamics and (b) deriving discriminative features from the data. However, common model identification and validation techniques mostly rely on steady-state recordings, characteristic spectral properties and non-transient behavior. In this work, a recent method, which allows reconstructing differential equations from time series data, is extended for higher degrees of automation. With special focus on short and strongly damped oscillations, an optimization procedure is proposed that fine-tunes the reconstructed dynamical models with respect to model simplicity and error reduction. This framework is analyzed with particular focus on the amount of information available to the reconstruction, noise contamination and nonlinearities contained in the time series input. Using the example of a mechanical oscillator, we illustrate how the optimized reconstruction method can be used to identify a suitable model and how to extract features from uni-variate and multivariate time series recordings in an engineering-compliant environment. Moreover, the determined minimal models allow for identifying the qualitative nature of the underlying dynamical systems as well as testing for the degree and strength of nonlinearity. The reconstructed differential equations would then be potentially available for classical numerical studies, such as bifurcation analysis. These results represent a physically interpretable enhancement of data-driven modeling approaches in structural dynamics.

EMD-Based Methodology for the Identification of a High-Speed Train Running in a Gear Operating State.

An efficient maintenance is a key consideration in systems of railway transport, especially in high-speed trains, in order to avoid accidents with catastrophic consequences. In this sense, having a method that allows for the early detection of defects in critical elements, such as the bogie mechanical components, is a crucial for increasing the availability of rolling stock and reducing maintenance costs. The main contribution of this work is the proposal of a methodology that, based on classical signal processing techniques, provides a set of parameters for the fast identification of the operating state of a critical mechanical system. With this methodology, the vibratory behaviour of a very complex mechanical system is characterised, through variable inputs, which will allow for the detection of possible changes in the mechanical elements. This methodology is applied to a real high-speed train in commercial service, with the aim of studying the vibratory behaviour of the train (specifically, the bogie) before and after a maintenance operation. The results obtained with this methodology demonstrated the usefulness of the new procedure and allowed for the disclosure of reductions between 15% and 45% in the spectral power of selected Intrinsic Mode Functions (IMFs) after the maintenance operation.

RF Dataset of Incumbent Radar Signals in the 3.5 GHz CBRS Band.

This Radio Frequency (RF) dataset consists of synthetically generated waveforms of incumbent 3.5GHz radar systems. The intended use of the dataset is for developing and evaluating detectors for the 3.5GHz Citizens Broadband Radio Service (CBRS) \cite{cbrs} or similar bands where the primary users of the band are Federal radar systems. The dataset can be used for developing and testing radar detection algorithms using machine learning/deep learning techniques. The algorithm aims to detect whether the radar signal is present or absent regardless of the signal type. The target signals have a variety of modulation types and parameters chosen from wide ranges. In addition, the start time and the center frequency of the radar signals are randomized in the waveform. The variety of signals and their random parameters makes the detection problem more challenging when using non-naive (e.g., energy detector is a naive signal detector) classical signal processing techniques.

Dynamic synchrophasor estimation by Taylor–Prony method in harmonic and non‐harmonic conditions

Dynamic phasor is suitable for analysis of non-stationary signals (such as power swings) in phasor measurement unit. In this study, dynamic phasor of the generated signals during power swing is analysed by a classical signal processing technique known as Prony. The Prony analysis is a useful technique to model a linear sum of damped complex exponential signals. In this study, a combination of the least square-based Prony analysis and Taylor expansion called as Taylor–Prony is proposed to estimate the dynamic phasor. Zeroth-order Taylor–Prony, i.e. static phasor is compared with the second-order Taylor–Prony, i.e. dynamic phasor for illustrating the dynamic phasor capability. Sinusoidal and step changes of amplitude and phase, harmonic condition, frequency tracking tests, noise infiltration and computation time are different tests which are used to validate the proposed method. First, the method is explained theoretically, and then its potential is demonstrated by simulating various numerical signals in MATLAB and measured signals during power swing from three-machine power system simulated in PSCAD. Simulation results show that the proposed method can estimate the dynamic phasor during power swing accurately. Ultimately, the proposed method is compared with six methods which have already presented to verify the capability of the proposed method.

ECG Signal Analysis Using DCT-Based DOST and PSO Optimized SVM

Signal processing techniques are an obvious choice for real-time analysis of electrocardiography (ECG) signals. However, classical signal processing techniques are unable to deal with the nonstationary nature of the ECG signal. In this context, this paper presents a new approach, i.e., discrete orthogonal stockwell transform using discrete cosine transform for efficient representation of the ECG signal in time–frequency space. These time–frequency features are further reduced in lower dimensional space using principal component analysis, representing the morphological characteristics of the ECG signal. In addition, the dynamic features (i.e., RR-interval information) are computed and concatenated to the morphological features to constitute the final feature set, which is utilized to classify the ECG signals using support vector machine (SVM). In order to improve the classification performance, particle swarm optimization technique is employed for gradually tuning the learning parameters of the SVM classifier. In this paper, ECG data exhibiting 16 classes of the most frequently occurring arrhythmic events are taken from the benchmark MIT-BIH arrhythmia database for the validation of the proposed methodology. The experimental results yielded an improved overall accuracy, sensitivity (Sp), and positive predictivity (Pp) of 98.82% in comparison with the existing approaches available in the literature.

Shallow water sound source localization using the iterative beamforming method in an image framework

Shallow water is a complicated sound propagation medium due to multiple reflections by water surface and bottom, imprecisely measured sound speed, noisy environment, etc. Therefore, in order to localize a shallow water sound source, classical signal processing techniques must be improved by taking these complexities into account. In this work, the multiple reflections and uncertain reflectivity of water bottom are explicitly modeled. In the proposed model, a measured signal is a mixture of the direct propagation from the source and the multiple reflections. Instead of solving the Helmholtz equation with boundary conditions of reflections, each signal is interpreted as a superposition of signals emitting from the physical source and its image sources in a free space, which results in a fast computation of sound propagation. Then, the source location, along with its amplitude, reflection paths and power loss of bottom reflection, is estimated via the iterative beamforming (IB) method, which alternatively estimates the source contributions and performs beamforming on these estimates until convergence. This approach does not need to compute the sound propagation for all the possible source locations in a large space, which thus leads to a low computational cost. Finally, numerical simulations are introduced to illustrate the advantage of the proposed model and the source estimation method. The sensitivity of the proposed method with respect to model parameter uncertainties is also investigated via a full uncertainty quantification analysis. The localization error of IB is proved to be acceptable in the given error range of sound speed and water depth. Besides, the IB source estimate is more sensitive to the sound speed while the matched-field processing methods have a stronger sensitivity to the water depth: this result can guide the choice of source localization method in different cases of model parameter uncertainties.

Detection of ULF Geomagnetic Anomalies Associated to Seismic Activity Using EMD Method and Fractal Dimension Theory

Over the past few years, analysis of anomalies associated to seismic activity in ultra-low frequency (ULF) geomagnetic signals has been the task of many researchers around the world. For the analysis, classical and recent signal processing techniques have been used; yet, despite obtaining promising results, the development and application of new techniques and methodologies that offer suitable and reliable results in terms of complexity and performance are still open issues due to the inherent relevance of this topic for human being. In this work, the analysis of ULF geomagnetic signals using empirical mode decomposition (EMD) method and fractal dimension (FD) theory is carried out. The signals have been obtained at the Juriquilla station, which is localized in Queretaro, Mexico (geographic coordinates: longitude 100.45° E and latitude 20.70° N). First, EMD method is applied as a pre-processing technique, where the goals are to remove background noise and highlight non-visible frequency components. Then, three FD algorithms named Katz, Higuchi, and Box are applied in a separated way in order to assess which algorithm provides more information related to anomalies into the ULF signal. The obtained results show that the EMD-FD-based analysis is a suitable tool to detect geomagnetic anomalies related with seismic activity