Signal Classification Algorithm Research Articles

Characterizing the consistency of motion of spermatozoa through nanoscale motion tracing

To demonstrate nanoscale motion tracing of spermatozoa and present analysis of the motion traces to characterize the consistency of motion of spermatozoa as a complement to progressive motility analysis. Anonymized sperm samples videographed under quantitative phase microscope, followed by generating and analyzing super-resolution motion traces of individual spermatozoa. Centrifuged human sperm samples. Precision of motion trace of individual sperms, presence of helical pattern in the motion trace, and mean and standard deviations of helical periods and radii of sperm motion traces, speed of progression. Spatially sensitive quantitative phase imaging with a super-resolution computational technique MUltiple SIgnal Classification ALgorithm (MUSICAL) allowed achieving motion precision of 340 nm using 10×, 0.25 NA lens whereas the diffraction limited resolution at this setting was 1320 nm. The motion traces thus derived facilitated new kinematic features of sperm, namely the statistics of helix period and radii per sperm. Through the analysis, 47 sperms with speed >25 μm/sec randomly selected from the same healthy donor's semen sample, it is seen that the kinematic features did not correlate with the speed of the sperms. Also, it is noted that spermatozoa may experience changes in the periodicity and radius of the helical path over time. Further, some very fast sperms (for example >70 μm/sec) may demonstrate irregular motion, needs further investigation. Presented computational analysis can be used directly for sperm samples from both fertility patients with normal and abnormal sperm cell conditions. We note that MUSICAL is an image analysis technique which may vaguely fall under machine learning category, but the conventional metrics for reporting found in EQUATOR do not apply. Alternative suitable metrics are reported, and bias is avoided through random selection of regions for analysis. Detailed methods are included for reproducibility. Kinematic features derived from nanoscale motion traces of spermatozoa contain information complementary to the speed of the sperms, allowing further distinction among the progressively motile sperms. Some highly progressive spermatozoa may have irregular motion pattern, and whether irregularity of motion indicate poor quality regarding artificial insemination needs further investigation. Presented technique can be generalized for sperm analysis for a variety of fertility conditions.

A tailor-made cylindrical concrete implantable module for damage imaging in plate-like concrete structures

In this research, a tailor-made cylindrical concrete implantable module (CCIM) was proposed for damage imaging in concrete structures. The CCIM can be directly implanted into inspected structure to transmit and receive probing stress waves, which allows conveniently removing and updating CCIM when devices failed to work. Moreover, a CCIM-based multiple signal classification (MUSIC) algorithm is proposed to realize two-dimensional damage visualization. Based on meso-level finite element simulation, considering concrete as a three-phase composite material, the stress wave propagation characteristics can be revealed and the effectiveness of CCIM-based MUSIC imaging method has been investigated. To verify the proposed method experimentally, a pair of CCIMs were fabricated and implanted into two concrete specimens with different configurations. The results could effectively pinpoint damage location within inspected area. The proposed hardware and algorithm has great potential to overcome the disadvantage of short service period and could be applied for damaging imaging in concrete structures.

Robust DOA estimation for two‐level nested arrays in unknown mutual coupling

AbstractAiming at solving the problem that the capability of direction of arrival method for estimation of nested arrays decreases sharply under unknown mutual coupling, an iterative weighted low‐rank matrix reconstruction algorithm is proposed here. First, the received data covariance matrix is extended, and a weighted low‐rank matrix recovery problem is formulated for joint estimations of the Toeplitz matrix and mutual coupling coefficients. Next, the direction of arrival from recovered Toeplitz matrix is retrieved by using the root multiple signal classification algorithm. Simulation results show that iterative weighted low‐rank matrix reconstruction algorithm can effectively avoid the influence of mutual coupling while solving the grid‐mismatch problem so that the accuracy and the resolution of estimation are improved.

Multiple Signal Classification Algorithm Combined with Volume Reflectivity Models to Improve Accuracy of the Estimated Vegetation Height in Synthetic Aperture Radar Tomography

Multiple Signal Classification Algorithm Combined with Volume Reflectivity Models to Improve Accuracy of the Estimated Vegetation Height in Synthetic Aperture Radar Tomography

Kalman-filter based hierarchical channel estimation for RIS-aided mmWave communication systems

Reconfigurable intelligent surface (RIS) technology has emerged as a promising solution to enhance network coverage and spectral efficiency in mmWave communication systems, where the RIS-aided localization has attracted much attention for its capability of simultaneously obtaining two geographically separated copies of steering vectors of users at one base station (BS). However, the estimation of the cascaded channel parameters of RISs still remains a challenging problem due to the passive nature of RISs and the large number of channel links between the BS and RIS. To tackle this issue, we propose to jointly employ the multiple signal classification (MUSIC) algorithm and the Kalman filter (KF) to hierarchically estimate the azimuth angle and the path loss with the help of an anchor node. The simulation results indicate that the proposed method is significantly superior to the benchmark technique in terms of accuracy and computational complexity, and reduces the pilot frequency overhead by over 40%.

Localization of Dual Partial Discharge in Transformer Windings Using Fabry–Pérot Optical Fiber Sensor Array

The power transformer is one of the most critical core devices for energy exchange in power systems, and its safe and stable operation is directly related to the reliability of the power grid. Partial discharge is the main cause of insulation degradation and failure of high-voltage electrical equipment. Online monitoring and accurate localization of partial discharge can provide information on the aging of power equipment, which is of great value for improving the safe operation and maintenance of the power grid. Internal dual partial discharge in transformer windings is a more complex type of fault. Since it is located inside the windings, the signal is attenuated and distorted, making it difficult for traditional monitoring methods to capture such partial discharge signal The Fabry–Perot optical fiber sensor is an ultrasonic detection method that can be built into the transformer interior. This sensor has high sensitivity and a small size, enabling flexible placement at different locations inside the transformer for precise partial discharge detection. Especially for the narrow space inside the high and low voltage windings, F–P sensors can form an array, utilizing the array’s directivity to locate the fault points. In this study, an ultrasonic detection system based on the F–P optical fiber sensor array was developed. The system utilizes a directional cross-localization algorithm based on the multiple signal classification (MUSIC) algorithm to accurately locate dual partial discharge sources. This partial discharge detection system was applied to a 35 kV single-phase transformer, enabling the localization of dual partial discharge sources within the high and low voltage windings. Combined with experimental results, this method exhibits high localization accuracy and is particularly suitable for detecting partial discharge phenomena that occur within or between transformer windings.

The angle of arrival estimation of frequency-hopping cooperative object based on software-defined radio

The primary objective of this research is to propose and examine a technique for accurately determining the azimuth angle of cooperative objects. The proposed methodology aims were to achieve fast processing, increased precision, and enhanced safety. A transmitter emitting a continuous wave with a hopping frequency was utilized in combination with interferometry techniques to measure the angle of arrival (AOA) between two antennas. The efficient method incorporates a coarse-to-fine strategy that improves processing speed and azimuth angle accuracy and to effectively eliminate any signal interference, a Fourier bandpass filter is utilized. The coarse estimation is performed using the fast Fourier transform (FFT) algorithm, while the fine estimation is achieved using the multiple signal classification (MUSIC) algorithm. The fine estimation involves utilizing coarse angle input and a scan limit of 2.5° that is determined from the largest simulated root mean square error (RMSE) value. Modelling and outdoor testing using software defined radio (SDR) have been carried out to assess the proposed methodology. The results from the analysis indicate that the proposed method produces the desired RMSE estimation of less than 1°, thereby validating its accuracy and effectiveness.

Enhanced angle estimation in MIMO radar: Combine RD-MUSIC and SDP optimization

Multiple Input Multiple Output (MIMO) radars are increasingly employed. However, with a growing array of antenna components, the dimension of the sampling covariance matrix and the computational complexity of traditional Direction of Arrival (DOA) and Direction of Departure (DOD) estimation algorithms increase exponentially. In order to accurately and effectively identify and locate signal sources and to optimize the computational complexity, this paper presents an innovative approach for detecting target numbers based on a Semi-Definite Programming (SDP) problem, which generates efficient solutions even for higher dimensions. In addition, the SDP’s robustness is developed through shrinkage adaptation using Large Covariance Matrices (LCMs), which are crucial for angle estimation. Following the effective detection of a target, the tapering estimation is applied to the LCMs under the computationally efficient Reduced-Dimension Multiple Signal Classification (RD-MUSIC) algorithm, which is more accurate and less computationally intensive than 2D search algorithms. Simulation outcomes demonstrate that the efficacy of our proposed model achieves high success rates in target detection and enhanced precision in DOA and DOD estimations.

Direction of Arrival Estimation Method Based on Eigenvalues and Eigenvectors for Coherent Signals in Impulsive Noise

In this paper, a Toeplitz construction method based on eigenvalues and eigenvectors is proposed to combine with traditional denoising algorithms, including fractional low-order moment (FLOM), phased fractional low-order moment (PFLOM), and correntropy-based correlation (CRCO) methods. It can improve the direction of arrival (DOA) estimation of signals in impulsive noise. Firstly, the algorithm performs eigenvalue decomposition on the received covariance matrix to obtain eigenvectors and eigenvalues, and then the Toeplitz matrix is created according to the eigenvectors corresponding to its eigenvalues. Secondly, the spatial averaging method is used to obtain an unbiased estimate of the Toeplitz matrix, which is then weighted and added based on the corresponding eigenvalues. Next, the noise subspace of the Toeplitz matrix is reconstructed to obtain the one that has less angle information. Finally, the DOA of the coherent signal is estimated using the Multiple Signal Classification (MUSIC) algorithm. The improved method based on the Toeplitz matrix can not only suppress the effect of impulsive noise but can also solve the problem of aperture loss due to its decoherence. A series of simulations have shown that they have better performances than other algorithms.

Emotion recognition with EEG-based brain-computer interfaces: a systematic literature review

AbstractElectroencephalography (EEG)-based Brain-Computer Interface (BCI) systems for emotion recognition have the potential to assist the enrichment of human–computer interaction with implicit information since they can enable understanding of the cognitive and emotional activities of humans. Therefore, these systems have become an important research topic today. This study aims to present trends and gaps on this topic by performing a systematic literature review based on the 216 published scientific literature gathered from various databases including ACM, IEEE Xplore, PubMed, Science Direct, and Web of Science from 2016 to 2020. This review gives an overview of all the components of EEG based BCI system from the signal stimulus module which includes the employed device, signal stimuli, and data processing modality, to the signal processing module which includes signal acquisition, pre-processing, feature extraction, feature selection, classification algorithms, and performance evaluation. Thus, this study provides an overview of all components of an EEG-based BCI system for emotion recognition and examines the available evidence in a clear, concise, and systematic way. In addition, the findings are aimed to inform researchers about the issues on what are research trends and the gaps in this field and guide them in their research directions.

On the Generalization of Deep Learning Models for AoA Estimation in Bluetooth Indoor Scenarios

Indoor positioning remains as one of the major challenges of the Internet of Things (IoT), where assets are usually deployed over environments where the Global Navigation Satellite Systems (GNSS) are denied. Angle-of-arrival (AoA) estimation techniques are among the most prominent candidates to address this challenge and multiple techniques based on conventional signal processing, such as the Multiple Signal Classification (MUSIC) algorithm, have been extensively studied. However, they require a deep knowledge of the elements involved in the AoA system, such as the characterization of the receivers’ antennas. On the other hand, Deep Learning (DL) models are considered as promising solutions, as they can provide AoA estimations without resorting to a deep knowledge of the physical system. Instead, they only need to be trained with labeled AoA signal measurements. Nevertheless, in the training process, non-desired effects like overfitting appear, yielding models with poor generalization capabilities. Although these models have already been considered in the literature, the problem of DL models generalization has not been addressed in depth. Therefore, this manuscript first compares the performance of DL models to the MUSIC algorithm as a baseline, to then analyze their generalization to different situations. These include changing the position of the receivers within the same area, considering measurements at different time instants and deployments in new scenarios, specifically in locations different from the training one. The assessment showed that the generalization of the DL models is weak compared to the MUSIC algorithm, especially when applied to environments not previously observed.

Research on Direction Finding Method under Impulsive Noise Based on Nonuniform Linear Array

Direction of arrival (DOA) estimation under impulsive noise has always been an important research area in array signal processing. The traditional methods under impulsive noise mostly rely on prior parameters and have high computational complexity. Based on the filtering theory, we present an effective pretreatment filtering technology to cut out the impulse mixed in the array received data and employ the nonuniform linear array to improve the estimation performance further. First, according to the amplitude characteristics of impulse noise, the pretreatment filtering technology is proposed to cut out the impulse based on the median filter and sliding average filter, which is valid for both strong and weak impulsive noise. Second, the minimum redundant array is adopted to carry out array virtual expansion so that the array aperture can be increased and the estimation performance can be improved. Finally, based on the idea of matrix reconstruction, we propose the improved estimation of signal parameters via rotational invariance techniques algorithm and an improved root multiple signal classification algorithm for DOA estimation. Theoretical analysis and simulation results show that the proposed method has a simple processing process, small calculation load, good array expansion ability, and excellent noise adaptability. Moreover, the proposed methods greatly improve the direction-finding performance under the condition of low signal-to-noise ratio and strong impulsive noise.

Research on Local Field Potential Signal Classification Algorithm Based on Transfer Learning

Research on Local Field Potential Signal Classification Algorithm Based on Transfer Learning

Evaluation of Antenna Pattern Measurement of HF Radar using Drone

The High-Frequency Radar (HFR) is an equipment designed to measure real-time surface ocean currents in broad maritime areas.It emits radio waves at a specific frequency (HF) towards the sea surface and analyzes the backscattered waves to measure surface current vectors (Crombie, 1955; Barrick, 1972).The Seasonde HF Radar from Codar, utilized in this study, determines the speed and location of radial currents by analyzing the Bragg peak intensity of transmitted and received waves from an omnidirectional antenna and employing the Multiple Signal Classification (MUSIC) algorithm. The generated currents are initially considered ideal patterns without taking into account the characteristics of the observed electromagnetic wave propagation environment. To correct this, Antenna Pattern Measurement (APM) is performed, measuring the strength of signals at various positions received by the antenna and calculating the corrected measured vector to radial currents.The APM principle involves modifying the position and phase information of the currents based on the measured signal strength at each location. Typically, experiments are conducted by installing an antenna on a ship (Kim et al., 2022). However, using a ship introduces various environmental constraints, such as weather conditions and maritime situations. To reduce dependence on maritime conditions and enhance economic efficiency, this study explores the possibility of using unmanned aerial vehicles (drones) for APM. The research conducted APM experiments using a high-frequency radar installed at Dangsa Lighthouse in Dangsa-ri, Wando County, Jeollanam-do. The study compared and analyzed the results of APM experiments using ships and drones, utilizing the calculated radial currents and surface current fields obtained from each experiment.

Antenna Pattern Calibration Method for Phased Array of High-Frequency Surface Wave Radar Based on First-Order Sea Clutter

The problem of accurate source localization has been an area of focus in high-frequency surface wave radar (HFSWR) applications. However, antenna pattern distortion (APD) decreases the direction-of-arrival (DOA) estimation performance of the multiple signal classification (MUSIC) algorithm. Up to now, limited studies have been conducted on the calibration of antenna pattern distortion for phased arrays in HFSWR. In this paper, we first analyze the effect of APD on the performance of the MUSIC algorithm through estimation of accuracy and angular resolution. We demonstrate that using the actual pattern (or say APD) can improve DOA estimation performance. Based on this proposition, we propose a novel iterative calibration method that employs the first-order sea clutter data and can jointly estimate DOA and APD in an iterative way. To obtain available calibration points, we introduce the extraction methods of the first-order sea clutter spectrum and single-DOA spectrum points. Meanwhile, in each iteration, the Beamspace MUSIC algorithm and artificial hummingbird algorithm (AHA) are utilized to estimate the DOA and APD, respectively. Numerical results reveal a good coincidence between the actual pattern and the estimated APD. We also apply this method to process the experimental data of HFSWR. We obtain the APD vector of the real phased array and improve the direction-finding performance of several real ship targets using this vector. Both numerical and experimental results prove the correctness of our proposed calibration method.

Performance evaluation of sound source localisation and tracking methods using multiple drones

This study evaluates methods to improve sound source localisation and tracking performance using microphone arrays from multiple drones. Previous studies have demonstrated the feasibility of multi-drone sound source tracking by triangulating their respective direction estimations using the MUltiple SIgnal Classification (MUSIC) algorithm. In addition, we evaluate techniques to reduce the influence of rotor noise. Namely, this includes a Wiener filter that utilises rotor noises' power spectral density (PSD) estimations and a noise correlation matrix (NCM) estimation scheme exploiting the geometries of the drone and the microphone array. This is to reduce the rotor noise's influence on the microphone array input correlation matrix prior to localising with MUSIC. Results show that applying the rotor noise reduction method improves localisation and tracking accuracy under a wider range of signal-to-noise ratios, regardless of the sound source tracking method.

Localization of mechanical and electrical defects in dry-type transformers using an optimized acoustic imaging approach.

This paper presents an acoustic imaging localization system designed to pinpoint common defects in dry-type transformers by analyzing the unique sounds they produce during operation. The system includes an optimized microphone array and an improved multiple signal classification algorithm. Sound signal characteristics of typical defects, such as foreign object intrusion, screw loosening, and partial discharge, are investigated. A 64-element, 8-arm spiral microphone array is designed using a particle swarm optimization algorithm. The multiple signal classification algorithm enhances acoustic imaging quality in field environments by transforming the input from time-domain to preprocessed frequency-domain signals. The power spectra of subarray and main array are combined, forming the optimization algorithm's output. Experimental results demonstrate the system's effectiveness and accuracy.

Exploration on 2D DOA estimation of linear array motion: Uniform linear motion

Generally, due to the limitation of the dimension of the array aperture, linear arrays cannot achieve two-dimensional (2D) direction of arrival (DOA) estimation. But the emergence of array motion provides a chance for that. In this paper, a generalized motion scheme and a novel method of 2D DOA estimation are proposed by exploring the linear array motion. To be specific, the linear arrays are controlled to move along an arbitrary direction at a constant velocity and snap per fixed time delay. All the received signals are processed to synthesize the comprehensive observation vector for an extended 2D virtual aperture. Subsequently, since most of 2D DOA estimation methods are not universal to our proposed motion scheme and the reduced-dimensional (RD) method fails to handle the case of the coupled parameters, a decoupled reduced-complexity multiple signals classification (DRC MUSIC) algorithm is designed specifically. Simulation results demonstrate that: a) our proposed scheme can achieve underdetermined 2D DOA estimation just by the linear arrays; b) our designed DRC MUSIC algorithm has the good properties of high accuracy and low complexity; c) our proposed motion scheme with the DRC method has better universality in the motion direction.

Application and Development of EEG Acquisition and Feedback Technology: A Review.

This review focuses on electroencephalogram (EEG) acquisition and feedback technology and its core elements, including the composition and principles of the acquisition devices, a wide range of applications, and commonly used EEG signal classification algorithms. First, we describe the construction of EEG acquisition and feedback devices encompassing EEG electrodes, signal processing, and control and feedback systems, which collaborate to measure faint EEG signals from the scalp, convert them into interpretable data, and accomplish practical applications using control feedback systems. Subsequently, we examine the diverse applications of EEG acquisition and feedback across various domains. In the medical field, EEG signals are employed for epilepsy diagnosis, brain injury monitoring, and sleep disorder research. EEG acquisition has revealed associations between brain functionality, cognition, and emotions, providing essential insights for psychologists and neuroscientists. Brain-computer interface technology utilizes EEG signals for human-computer interaction, driving innovation in the medical, engineering, and rehabilitation domains. Finally, we introduce commonly used EEG signal classification algorithms. These classification tasks can identify different cognitive states, emotional states, brain disorders, and brain-computer interface control and promote further development and application of EEG technology. In conclusion, EEG acquisition technology can deepen the understanding of EEG signals while simultaneously promoting developments across multiple domains, such as medicine, science, and engineering.

Deep Learning-Based Classification of Epileptic Electroencephalography Signals Using a Concentrated Time-Frequency Approach.

ConceFT (concentration of frequency and time) is a new time-frequency (TF) analysis method which combines multitaper technique and synchrosqueezing transform (SST). This combination produces highly concentrated TF representations with approximately perfect time and frequency resolutions. In this paper, it is aimed to show the TF representation performance and robustness of ConceFT by using it for the classification of the epileptic electroencephalography (EEG) signals. Therefore, a signal classification algorithm which uses TF images obtained with ConceFT to feed the transfer learning structure has been presented. Epilepsy is a common neurological disorder that millions of people suffer worldwide. Daily lives of the patients are quite difficult because of the unpredictable time of seizures. EEG signals monitoring the electrical activity of the brain can be used to detect approaching seizures and make possible to warn the patient before the attack. GoogLeNet which is a well-known deep learning model has been preferred to classify TF images. Classification performance is directly related to the TF representation accuracy of the ConceFT.The proposed method has been tested for various classification scenarios and obtained accuracies between 95.83% and 99.58% for two and three-class classification scenarios. High results show that ConceFT is a successful and promising TF analysis method for non-stationary biomedical signals.