Signal Processing Research Articles

ESTIMATION OF FORMANT INFORMATION USING AUTOCORRELATION FUNCTION OF VOICE SIGNAL

Context. The current scientific problem of extracting biometric characteristics of a user of a voice authentication system, which can significantly increase its reliability, is considered. There has been performed estimation of formant information from the voice signal, which is a part of the user template in the voice authentication system and is widely used in the processing of speech signals in other applications, including in the presence of interfering noise components. The work is distinguished by the investigation of a polyharmonic signal. Objective. The purpose of the work is to develop procedures for generating formant information based on the results of calculating the autocorrelation function of the analyzed fragment of the voice signal and their subsequent spectral analysis. Method. The procedures for generating formant information in the process of digital processing of voice signal are proposed. Initially, the autocorrelation function of the analyzed fragment of the voice signal is calculated. Based on the results of the autocorrelation function estimation, the amplitude-frequency spectrum is calculated, from which the formant information is extracted, for example, by means of threshold processing. When the signal-to-noise ratio of the analyzed voice signal fragment is low, it is advisable to iteratively calculate the autocorrelation function. The latter allows increasing the signal-to-noise ratio and the efficiency of formant information extraction. However, each subsequent iteration of the autocorrelation function calculation is associated with an increase in the required computational resource. The latter is conditioned by the doubling of the amount of processed data at each iteration. Results. The developed procedures for generating formant information were investigated both in the processing of model and experimental voice signals. The model signals had a low signal-to-noise ratio. The proposed procedures allow to determine more precisely the width of the spectrum of extracted formant frequencies, significantly increase the number of extracted formants, including cases at low signal-to-noise ratio. Conclusions. The conducted model experiments have confirmed the performance and reliability of the proposed procedures for extracting formant information both in the processing of model and experimental voice signals. The results of the research allow to recommend their use in practice for solving problems of voice authentication, speaker differentiation, speech and gender recognition, intelligence, counterintelligence, forensics and forensic examination, medicine (diseases of the speech tract and hearing). Prospects for further research may include the creation of procedures for evaluating formant information based on phase data of the processed voice signal.

Active Impulsive Noise Control with Missing Input Data Based on FxImdMCC Algorithm

In this study, we address the challenge of noise reduction in environments characterized by impulsive noise and missing input data in active noise control (ANC) systems, where existing algorithms often fail to deliver satisfactory results. Background noise, especially impulsive noise, poses a significant obstacle to signal processing and noise reduction. Furthermore, data loss during transmission or acquisition further complicates the noise reduction task. In this paper, a filtered-x imputation of the missing data maximum correntropy criterion (FxImdMCC) algorithm is proposed based on an imputation model, least mean square, and the maximum correntropy criterion (MCC), which can effectively reduce the impact of outliers and missing input data. The simulation results demonstrate the efficacy of the proposed FxImdMCC algorithm, which significantly outperforms existing algorithms in the context of active impulsive noise control.

Chuprov invariant for vector-scalar fields of multipole sources in a shallow sea

A computational and theoretical study of the properties of the well-known waveguide invariant S.D. Chuprova (IC) was carried out in a plane-parallel Pekeris waveguide. Unlike earlier works, in which predominantly non-directional (monopole) sources were used as a source, and sound pressure fields (scalar fields) were studied, in this work not only scalar, but also vector fields formed in the waveguide by directional - combined multipole sources with directivity in both horizontal and vertical planes. A differential equation has been obtained that makes it possible to fairly accurately calculate the IC values under different conditions of signal propagation and different depths of sources and receivers. This makes it possible, in a simpler way than “full computer modeling,” to predict the invariance (stability) of the IC when varying both the hydrophysical conditions in the waveguide and the geometry of the experiment. It is shown that the directionality of sources in the horizontal plane has virtually no effect on the properties of the IC, and the directionality in the vertical plane leads to a shift in the fan structure of the signal amplitude fields, but has little effect on the IC values. The properties of the fan structure change in a similar way when using vertical projections of the oscillatory velocity vector - despite the fact that another analytical relation, different from scalar fields, is used to calculate the IC, the IC value is close to (+1) at all frequencies and distances, except those at which new modes or dislocations arise. At these frequencies and in these zones, alternating emissions with different signs and magnitudes occur. It is concluded that the stability of IC allows the application of signal processing algorithms developed for scalar fields and non-directional sources to vector-scalar fields generated, including using directional sources.

Quantitative assessment of the effectiveness of adaptive spatial processing algorithms in searching for low-noise underwater vehicles in surface shipping conditions of different density

The article describes the methodology and provides the results of a model quantitative assessment of the effectiveness of solving the problem of detecting and tracking a low-noise underwater object using three algorithms for spatial signal processing at the output of a multi-element antenna — the non-adaptive Bartlett algorithm, the Capon algorithm, and the Capon algorithm combined with a projection procedure limiting the signal power of strong local sources.

Preprocessing and Denoising Techniques for Electrocardiography and Magnetocardiography: A Review

This review systematically analyzes the latest advancements in preprocessing techniques for Electrocardiography (ECG) and Magnetocardiography (MCG) signals over the past decade. ECG and MCG play crucial roles in cardiovascular disease (CVD) detection, but both are susceptible to noise interference. This paper categorizes and compares different ECG denoising methods based on noise types, such as baseline wander (BW), electromyographic noise (EMG), power line interference (PLI), and composite noise. It also examines the complexity of MCG signal denoising, highlighting the challenges posed by environmental and instrumental interference. This review is the first to systematically compare the characteristics of ECG and MCG signals, emphasizing their complementary nature. MCG holds significant potential for improving the precision of CVD clinical diagnosis. Additionally, it evaluates the limitations of current denoising methods in clinical applications and outlines future directions, including the potential of explainable neural networks, multi-task neural networks, and the combination of deep learning with traditional methods to enhance denoising performance and diagnostic accuracy. In summary, while traditional filtering techniques remain relevant, hybrid strategies combining machine learning offer substantial potential for advancing signal processing and clinical diagnostics. This review contributes to the field by providing a comprehensive framework for selecting and improving denoising techniques, better facilitating signal quality enhancement and the accuracy of CVD diagnostics.

Effects of the two-pore potassium channel subunit Task5 on neuronal function and signal processing in the auditory brainstem

Processing of auditory signals critically depends on the neuron’s ability to fire brief, precisely timed action potentials (APs) at high frequencies and high fidelity for prolonged times. This requires the expression of specialized sets of ion channels to quickly repolarize neurons, prevent aberrant AP firing and tightly regulate neuronal excitability. Although critically important, the regulation of neuronal excitability has received little attention in the auditory system. Neuronal excitability is determined to a large extent by the resting membrane potential (RMP), which in turn depends on the kind and number of ion channels open at rest; mostly potassium channels. A large part of this resting potassium conductance is carried by two-pore potassium channels (K2P channels). Among the K2P channels, the subunit Task5 is expressed almost exclusively in the auditory brainstem, suggesting a specialized role in auditory processing. However, since it failed to form functional ion channels in heterologous expression systems, it was classified “non-functional” for a long time and its role in the auditory system remained elusive. Here, we generated Task5 knock-out (KO) mice. The loss of Task5 resulted in changes in neuronal excitability in bushy cells of the ventral cochlear nucleus (VCN) and principal neurons of the medial nucleus of the trapezoid body (MNTB). Moreover, auditory brainstem responses (ABRs) to loud sounds were altered in Tasko5-KO mice. Thus, our study provides evidence that Task5 is indeed a functional K2P subunit and contributes to sound processing in the auditory brainstem.

Nonlinear acoustic modulation utilizing designed acoustic bubble array.

Acoustic modulation has attracted significant investigative interest for their outstanding promising application scenes. Furthermore, acoustic bubble array has shown anticipated foreground in signal processing and acoustic manipulation. Here, we demonstrate a nonlinear acoustic modulation method via designed acoustic bubble array. Numerical calculations have been conducted to analyze several influential parameters and the corresponding effects on the vibrational behaviors of the acoustic bubbles. Appropriate corrections have been added on the numerical model to elucidate the physical scene. Experimental validation has confirmed the practicability and validity of the designation. Potential applications in biological tissue imaging and unidirectional sound transmission can be expected with further research.

Improved automated parallel implementation of GMM background subtraction on a multicore digital signal processor

<p><span>Scene segmentation is an essential step in a wide range of video processing applications, for instance, object recognition and tracking. The Gaussian mixture model (GMM) for background subtraction (BS) has gained widespread usage in scene segmentation, despite its known computational intensity. To tackle this challenge, we propose a practical solution to accelerate processing through a parallel implementation on an embedded multicore platform. In this paper, we present an improved automated parallel implementation of the GMM algorithm using the Orphan directive provided by open multiprocessing (OpenMP). Experimental assessments conducted on the eight cores of the C6678 digital signal processor (DSP) demonstrate significant advancements in parallel efficiency, particularly when handling high-resolution frames, including high-definition (HD) and full-HD resolutions. The achieved parallel efficiency surpasses the results obtained with classical OpenMP scheduling modes, encompassing dynamic, static, and guided approaches. Specifically, the parallel efficiency reaches approximately 82% for full-HD resolution frames and, 99.3% for low-resolution frames, respectively.</span></p>

A Review of the Challenges of Adaptive Filtering Technology in High-fidelity Audio Signal Processing

Abstract. High-fidelity audio signal processing plays an important role in modern audio technology. With the increasing demand for this technology in various audio application scenarios, the optimization of adaptive filters has emerged as a significant challenge in this field. This paper focuses on improving the performance of adaptive filters in high-fidelity audio signal processing which aims to improve the adaptability and efficiency of filters in complex audio environments by improving algorithms. In this study, the adaptive filtering algorithm based on wavelet transform and particle swarm optimization is used to verify the audio data processing by simulation and real data processing. The research data was collected using the standard audio signal database and the actual collected high-fidelity audio samples. The results show that the improved adaptive filter can significantly improve the performance of complex audio environments, improve the clarity and fidelity of audio signals and reduce the computational complexity. It is also suitable for real-time processing scenarios with limited resources. The conclusion shows that this method provides an efficient solution for high-fidelity audio signal processing and has a wide application prospect.

A Novel Beam-Domain Direction-of-Arrival Tracking Algorithm for an Underwater Target

Underwater direction-of-arrival (DOA) tracking using a hydrophone array is an important research subject in passive sonar signal processing. In this study, a DOA tracking algorithm based on a novel beam-domain signal processing technique is proposed to ensure robust DOA tracking of an interested underwater target under a low signal-to-noise ratio (SNR) environment. Firstly, the beam-based observation is designed and proposed, which innovatively applies beamforming after array-based observation to achieve specific spatial directivity. Next, the proportional–integral–differential (PID)-optimized Olen–Campton beamforming method (PIDBF) is designed and proposed in the beamforming process to achieve faster and more stable sidelobe control performance to enhance the SNR of the target. The adaptive dynamic beam window is designed and proposed to focusing the observation on more likely observation area. Then, by utilizing the extended Kalman filter (EKF) tracking framework, a novel PIDBF-optimized beam-domain DOA tracking algorithm (PIDBF-EKF) is proposed. Finally, simulations with different SNR scenarios and comprehensive analyses are made to verify the superior performance of the proposed DOA tracking approach.

Automatic control strategy of electronic and electrical engineering based on FPGA

Field Programmable Gate Array (FPGA) have demonstrated significant potential in the field of automation control due to their flexibility, programmability, and high performance. This study designs an FPGA-based automation control strategy for motor control systems, encompassing four core modules: signal acquisition, signal processing, control algorithm, and output drive. The signal acquisition module employs the AD9280 high-speed, high-precision ADC chip to achieve rapid and accurate signal acquisition. The signal processing module utilizes digital filtering and FFT analysis within the FPGA to extract key information such as motor speed. The control algorithm module adopts an adaptive fuzzy control strategy that fine-tunes control rules in real-time to ensure precise control. The output drive module generates PWM signals via the FPGA to drive the motor, guaranteeing efficient and stable operation. Experimental results indicate that the FPGA-based control strategy significantly outperforms traditional methods in terms of response speed and stability, capable of meeting the high-performance requirements of modern industrial motor control systems. This research provides new technical insights and practical references for the field of automation control in electrical and electronic engineering.

A Novel Approach to Solving Fractional Diffusion Equations Using Fractional Beta Derivative

This paper introduces a novel application of fractional beta derivatives in solving fractional diffusion equations with an emphasis on systems exhibiting anomalous diffusion and memory effects. The work explores the fractional beta derivative as an extension of classical fractional derivatives by incorporating a parameter β (beta) that controls the system’s memory behavior. We investigate both the analytical and numerical solutions of these equations, demonstrating the superior flexibility of fractional beta derivatives in modeling complex diffusion processes. Additionally, we provide a comparison between classical fractional derivatives and the new fractional beta approach to highlight the advantages in terms of accuracy and computational efficiency. We begin by reviewing the theoretical background of fractional derivatives and proceed to introduce the beta derivative as a modification that provides additional flexibility in modeling complex systems. Applications in fields such as control theory, signal processing, and bioengineering are highlighted. Furthermore, numerical methods for solving fractional beta differential equations are discussed, along with potential areas for future research.

Robust continuous-variable quantum key distribution in the finite-size regime

Quantum key distribution (QKD) has been proven to be theoretically unconditionally secure. However, any theoretical security proof relies on certain assumptions. In QKD, the assumption in the theoretical proof is that the security of the protocol is considered under the asymptotic case where Alice and Bob exchange an infinite number of signals. In the continuous-variable QKD (CV-QKD), the finite-size effect imposes higher requirements on block size and excess noise control. However, the local local oscillator (LLO) CV-QKD system cannot be considered time-invariant under long blocks, especially in cases of environmental disturbances. Thus, we propose an LLO CV-QKD scheme with time-variant parameter estimation and compensation. We first establish an LLO CV-QKD theoretical model under the temporal modes of continuous-mode states. Then, a robust method is used to compensate for arbitrary frequency shift and arbitrary phase drift in CV-QKD systems with longer blocks, which cannot be achieved under traditional time-invariant parameter estimation. Besides, the digital signal processing method predicated on high-speed reference pilots can achieve a time complexity of O(1). In the experiment, the frequency shift is up to 89.05 MHz/s and phase drift is up to 3.036 Mrad/s using a piezoelectric transducer (PZT) to simulate the turbulences in the practical channel. With a signal-to-interference ratio (SIR) of −51.67 dB, we achieve a secret key rate (SKR) of 0.29 Mbits/s with an attenuation of 16 dB or a standard fiber of 80 km. This work paves the way for future long-distance field-test experiments in the finite-size regime.

CMOS Pulse Oximeter

Abstract: This paper presents the design and implementation of a non-invasive pulse oximeter based on Complementary MetalOxide-Semiconductor (CMOS) technology, aimed at enhancing the accuracy and accessibility of blood oxygen saturation measurements. Pulse oximetry is a critical tool in clinical and personal health monitoring, providing vital information about respiratory function. The integration of CMOS technology allows for miniaturization and energy efficiency, facilitating the development of compact, wearable devices capable of continuous monitoring. The proposed system utilizes advanced signal processing techniques, including lock-in detection and dual-wavelength illumination, to improve the reliability of photoplethysmographic (PPG) signal acquisition under various conditions. Experimental validation demonstrates the device's capability to deliver accurate SpO2 readings even in the presence of motion artifacts and ambient light interference. Furthermore, the calibration methods employed ensure that measurements are consistent and precise across different users. By leveraging the advantages of CMOS technology, this study not only highlights the potential for improved patient comfort and compliance but also opens new avenues for the application of pulse oximetry in diverse environments. The findings underscore the effectiveness of CMOS-based pulse oximeters in advancing non-invasive health monitoring solutions, ultimately contributing to enhanced patient care and outcomes.

О возможности проведения контроля состояния асинхронных электродвигателей с частотно-регулируемым приводом методом спектрального анализа

One of the important tasks of current energy power industry is to increase the reliability of induction motors operation. A promising direction for monitoring their technical condition is application of methods of spectral analysis of signals. However, very little attention is paid to the diagnostic assessment of induction motors as a part of a variable frequency drive, as well as the use of frequency converters for diagnostic purposes. Therefore, the issues related to monitoring the condition of induction motors with variable frequency drives are relevant and require scientific research. The purpose of the study is experimental evaluation of the possibility to use the spectral analysis method to assess the technical condition of induction motors driven by frequency converters. The research has been carried out on AIR71A6 induction motor with a serviceable rotor winding and one broken rotor bar rotor winding. The internal magnetic field is considered as a diagnostic signal in steady-state and start-up mode. The condition has been evaluated based on the determination of harmonics in the amplitude spectrum from the dummy rotor winding. A Short Time Fourier Transform is used for signal processing. The spectra of the internal magnetic field have been obtained at start-up and in steady-state mode when the engine is fed by an autotransformer and frequency converter. A diagnostic sign of the presence of broken bars, that is an increase of harmonic amplitudes from the dummy rotor winding, manifested itself not only when the electric motor is fed by an autotransformer, but also frequency driven. Comparing the amplitudes of harmonics in the presence and absence of a frequency converter allows us to conclude that interference of the converter slightly affect the amplitudes of specific harmonics. Using the example of an internal magnetic field signal, it is shown that spectral analysis methods can be used to diagnose the induction motors condition with a variable speed drive.

Underground Cable Fault Detection

Underground cables play a crucial role in delivering electricity, ensuring a dependable power supply in both urban and industrial regions. However, detecting and resolving faults in these cables can be difficult due to their hidden placement. This project aims to develop an effective underground cable fault detection system that accurately identifies fault locations, reducing downtime. The system will combine electrical principles with modern sensing technologies to detect faults such as short circuits, open circuits, and insulation issues. By injecting a small current into the cable and analyzing the resulting voltage drop, the system will calculate the distance to the fault from the measurement point. Using a microcontroller along with sensors and signal processing units, it will determine and display the fault location in kilometers. Real-time fault detection will significantly decrease maintenance time and costs by quickly locating problem areas, enabling faster repairs and reducing power outages. This project aims to improve the efficiency and reliability of power transmission systems. Additionally, the system can be integrated with communication technologies for remote monitoring, allowing utility companies to detect and manage faults from centralized control centers.

Inertial Measurement Unit Self-Calibration by Quantization-Aware and Memory-Parsimonious Neural Networks

This paper introduces a methodology to compensate inertial Micro-Electro-Mechanical System (IMU-MEMS) time-varying calibration loss, induced by stress and aging. The approach relies on a periodic assessment of the sensor through specific stimuli, producing outputs which are compared with the response of a high-precision sensor, used as ground truth. At any re-calibration iteration, differences with respect to the ground truth are approximated by quantization-aware trained tiny neural networks, allowing calibration-loss compensations. Due to the unavailability of aging IMU-MEMS datasets, a synthetic dataset has been produced, taking into account aging effects with both linear and nonlinear calibration loss. Also, field-collected data in conditions of thermal stress have been used. A model relying on Dense and 1D Convolution layers was devised and compensated for an average of 1.97 g and a variance of 1.07 g2, with only 903 represented with 16 bit parameters. The proposed model can be executed on an intelligent signal processing inertial sensor in 126.4 ms. This work represents a step forward toward in-sensor machine learning computing through integrating the computing capabilities into the sensor package that hosts the accelerometer and gyroscope sensing elements.

Improvement of multiscale decomposition for space-based gravitational wave signal processing technology.

During the process of detecting gravitational waves in space, addressing noise issues caused by terrestrial vibrations, natural environmental changes, and the factors intrinsic to the detectors, this paper proposes a multiscale variational mode adaptive denoising algorithm based on momentum gradient descent. This algorithm integrates momentum factors and multiscale concepts into the variational mode algorithm to resolve the issue of multiple local optima encountered during operation, reduce oscillations in regions with large or unstable gradient changes, and improve convergence speed. Additionally, the algorithm combines the least mean squares algorithm to automatically adjust weights, thereby mitigating the impact of noise, addressing the issue of noise from multiple and random sources, effectively suppressing noise in the gravitational wave signal, and enhancing the quality and reliability of the gravitational wave signal. Experimental results demonstrate that this algorithm performs better than other algorithms in noise suppression, effectively reducing noise in gravitational wave signals and meeting the noise suppression requirements for space-based gravitational wave detection.

Dopaminergic modulation of visual attention in the prefrontal cortex

An essential cognitive ability that enables creatures to interpret pertinent information from their surroundings only is visual attention. The prefrontal cortex (PFC) is involved in modulating visual attention, and this review focuses on the dopaminergic aspects of this process. The PFC is crucial for top-down attention management, improving visual responses, and coordinating neural activity. This is especially true of the frontal eye field (FEF). Dopamine is an important neuromodulator that directly influences the PFC's visual signal processing by adjusting sensory input and the variability of neuronal response. The substantia nigra (SN) and ventral tegmental area (VTA) send dopaminergic pathways to the prefrontal cortex, where D1 and D2 receptors have different functions in attention regulation. Dopamine receptor modification has been shown in studies involving rodents and primates to have a major effect on visual attention and cognitive task performance. The exact mechanisms underlying dopamine's involvement in executive control are still unknown despite a great deal of research, which calls for more study, especially in rodent models. The present review highlights the significance of dopamine in the domain of high-level cognitive regulation and advocates for more investigation to clarify its mechanisms in visual attention.

Bioinspired cooperation in a heterogeneous robot swarm using ferrofluid artificial pheromones for uncontrolled environments.

This article presents a novel bioinspired technology for the cooperation and coordination of heterogeneous robot swarms in uncontrolled environments, utilizing an artificial pheromone composed of magnetized ferrofluids. Communication between different types of robots is achieved indirectly through stigmergy, where messages are inherently associated with specific locations. This approach is advantageous for swarm experimentation outside controlled laboratory spaces, where localization is typically managed through centralized camera systems (e.g., infrared, RGB). Applying pheromone principles has also proven beneficial for various swarm behaviors. We introduce a detection methodology for the artificial ferrofluid pheromone using low-cost magnetic sensors, along with signal processing and parameter characterization. Experiments involved a heterogeneous swarm consisting of two types of robots: one equipped with camera and image processing capabilities and the other with basic sensor technologies. Validation in multiple uncontrolled environments (with varying floor surfaces, wind, and light conditions) demonstrated successful cooperation among robots with differing technological complexities using the proposed technology.