Signal Processing Algorithms Research Articles

Develop an Improved Mel-Frequency cepstral coefficients signal processing algorithms for enhancing underwater acoustic signal through wireless network

Develop an Improved Mel-Frequency cepstral coefficients signal processing algorithms for enhancing underwater acoustic signal through wireless network

Framework for Analyzing Spatial Interference in Vehicle-to-Vehicle Communication Networks with Positional Errors

This paper introduces a novel framework for evaluating vehicle-to-vehicle (V2V) communication systems, employing beamforming and null steering techniques. Addressing challenges such as electromagnetic interference from other vehicles within the network as well as diverse road conditions, the framework defines the simulation environment for V2V networks across different traffic scenarios to assess system reliability. The analytical components of the proposed framework are structured as follows: a comprehensive framework is developed, serving as the basis for implementing a simulator that leverages advanced spatial signal processing algorithms to evaluate V2V networks across specific scenarios, accounting for the effects of positional errors. The framework integrates multiple blocks, including road modeling, system modeling, and performance evaluation, providing adaptability for different algorithms or configurations. Positional inaccuracies are examined, highlighting their effects on system performance, particularly in scenarios where null steering accuracy is imperfect, thus underscoring the need for enhanced interference management strategies.

Self-homodyne 2-OTDM for doubling the baud rate in low-energy optical interconnect.

As a low-energy method to increase the data rate of optical links in data centers, we propose self-homodyne Nyquist optical time division multiplexing (OTDM). In Nyquist OTDM, spectrally efficient high-baud rate signals can be generated exceeding the limit of electronic signal processing. However, full integration of OTDM systems has not been reported, mainly because of the complicated signal detection scheme, which involves demultiplexing and clock recovery. In our proposal, the Nyquist pulse train is transmitted to the receiver as a local oscillator (LO) to leverage self-homodyne detection, which allows using large linewidth lasers and a simplified digital signal processing (DSP) algorithm. As the transmitted pulse train serves as an optical clock, demultiplexing and detection of the OTDM signal can be performed without using power-intensive high-bandwidth electronics and DSP. In this method, the LO pulse train needs to enter the coherent detector in exact synchronization with the OTDM signal for detecting the individual tributary correctly. For this purpose, we present a pulse delay control method suitable for photonic integration. A Nyquist pulse train with m carriers enables m-time multiplexing of optical signals. We explain and demonstrate the proposed concept in the case of m = 2, as it is the most feasible implementation. In the O-band where the chromatic dispersion (CD) is negligible, DSP-free operation can be achieved using the QPSK format. At the band edges where CD is non-negligible, it can be compensated by the DSP as in the conventional coherent detection. We verify this numerically and in an experiment involving the transmission of a 64-Gbaud QPSK signal at 1550 nm over a single-mode fiber. In terms of low energy, self-homodyne Nyquist OTDM is advantageous in wavelength division multiplexing (WDM). Taking it into consideration, we perform 4-channel WDM transmission of the 64-Gbaud QPSK signal over a 1-km dispersion shifted fiber without CD compensation. The results demonstrate a data rate of 512 Gb/s with a BER of <1 × 10-10.

Field-Programmable Gate Array (FPGA)-Based Lock-In Amplifier System with Signal Enhancement: A Comprehensive Review on the Design for Advanced Measurement Applications.

Lock-in amplifiers (LIAs) are critical tools in precision measurement, particularly for applications involving weak signals obscured by noise. Advances in signal processing algorithms and hardware synthesis have enabled accurate signal extraction, even in extremely noisy environments, making LIAs indispensable in sensor applications for healthcare, industry, and other services. For instance, the electrical impedance measurement of the human body, organs, tissues, and cells, known as bioelectrical impedance, is commonly used in biomedical and healthcare applications because it is non-invasive and relatively inexpensive. Also, due to its portability and miniaturization capabilities, it has great potential for the development of new point-of-care and portable testing devices. In this document, we highlight existing techniques for high-frequency resolution and precise phase detection in LIA reference signals from field-programmable gate array (FPGA) designs. A comprehensive review is presented under the key requirements and techniques for single- and dual-phase digital LIA architectures, where relevant insights are provided to address the LIAs' digital precision in measurement system configurations. Furthermore, the document highlights a novel method to enhance the spurious-free dynamic range (SFDR), thereby advancing the precision and effectiveness of LIAs in complex measurement environments. Finally, we summarize the diverse applications of impedance measurement, highlighting the wide range of fields that can benefit from the design of high performance in modern measurement technologies.

EVALUATION OF METHODS FOR RECOGNITION AND IDENTIFICATION OF AERIAL VEHICLES IN SURVEILLANCE RADAR SYSTEMS

The article addresses the critical issue of recognizing and identifying aerial vehicles, which plays a pivotal role in modern defense systems, particularly in the context of the ongoing russian-ukrainian war. The paper provides a comprehensive review of existing methods and approaches aimed at solving this problem. These methods include radar signature analysis, spectral analysis, trajectory-based identification, and the use of machine learning algorithms. Each approach is thoroughly examined, highlighting its strengths and limitations. The study emphasizes the significance of accurate recognition and identification of adversary aerial threats, such as drones, aircraft, and decoy targets, as these pose a significant challenge to air defense systems. The article underlines that traditional methods, such as trajectory-based identification, often fail to distinguish between real threats and decoy targets in complex operational environments, necessitating the development of more advanced solutions. Among the innovative approaches proposed, the use of autocorrelation function analysis of radar signals is highlighted as a promising method for aerial vehicle recognition and identification. This method allows for the extraction of unique features from radar signals, enabling more precise differentiation between various types of aerial targets. The article presents a detailed analysis of this method, discussing its advantages, including improved accuracy and reliability, and its potential to operate effectively under conditions of electronic warfare and signal interference. However, the study also acknowledges the technical constraints of the proposed method, such as the computational complexity involved in real-time signal processing and the need for high-quality radar data. These limitations are discussed in detail, alongside potential strategies for mitigating them, such as optimizing signal processing algorithms and leveraging advancements in radar technology. The paper concludes by highlighting the importance of further research and development in the field of aerial vehicle recognition and identification. It calls for the integration of advanced analytical techniques, such as autocorrelation function analysis, into modern radar systems to enhance their capability to counter evolving aerial threats effectively. The findings and recommendations presented in the study provide valuable insights for improving air defense systems in the face of contemporary challenges.

A Precise Oxide Film Thickness Measurement Method Based on Swept Frequency and Transmission Cable Impedance Correction.

Accurately measuring the thickness of the oxide film that accumulates on nuclear fuel assemblies is critical for maintaining nuclear power plant safety. Oxide film thickness typically ranges from a few micrometers to several tens of micrometers, necessitating a high-precision measurement system. Eddy current testing (ECT) is commonly employed during poolside inspections due to its simplicity and ease of on-site implementation. The use of swept frequency technology can mitigate the impact of interference parameters and improve the measurement accuracy of ECT. However, as the nuclear assembly is placed in a pool for inspection, a cable several dozen meters in length is used to connect the ECT probe to the instrument. The measurement is affected by the transmission line and its effect is a function of the operating frequencies, resulting in errors for swept frequency measurements. This paper proposes a method for precisely measuring oxide film thickness based on the swept frequency technique and long transmission line impedance correction. The signals are calibrated based on a transmission line model of the cable, effectively eliminating the influence of the transmission cable. A swept frequency signal-processing algorithm is developed to separate the parameters and calculate oxide film thickness. To verify the feasibility of the method, measurements are conducted on fuel cladding samples with varying conductivities. It is found that the method can accurately assess oxide film thickness with varying conductivity. The maximum error is 3.42 μm, while the average error is 1.82 μm. The impedance correction reduces the error by 66%. The experimental results indicate that this method can eliminate the impact of long transmission cables, and the algorithm can mitigate the influence of material conductivity. This method can be utilized to measure oxide film thickness in nuclear power maintenance inspections following extensive testing and engineering optimization.

Detailed Determination of Delamination Parameters in a Multilayer Structure Using Asymmetric Lamb Wave Mode.

A signal-processing algorithm for the detailed determination of delamination in multilayer structures is proposed in this work. The algorithm is based on calculating the phase velocity of the Lamb wave A0 mode and estimating this velocity dispersion. Both simulation and experimental studies were conducted to validate the proposed technique. The delamination having a diameter of 81 mm on the segment of a wind turbine blade (WTB) was used for verification of the proposed technique. Four cases were used in the simulation study: defect-free, delamination between the first and second layers, delamination between the second and third layers, and defect (hole). The calculated phase velocity variation in the A0 mode was used to determine the location and edge coordinates of the delaminations and defects. It has been found that in order to estimate the depth at which the delamination is, it is appropriate to calculate the phase velocity dispersion curves. The difference in the reconstructed phase velocity dispersion curves between the layers simulated at different depths is estimated to be about 60 m/s. The phase velocity values were compared with the delamination of the second and third layers and a hole drilled at the corresponding depth. The obtained simulation results confirmed that the drilled hole can be used as a defect corresponding to delamination. The WTB sample with a drilled hole of 81 mm was used in the experimental study. Using the proposed algorithm, detailed defect parameters were obtained. The results obtained using simulated and experimental signals indicated that the proposed new algorithm is suitable for the determination of delamination parameters in a multilayer structure.

Estimating Word Lengths for Fixed-Point DSP Implementations Using Polynomial Chaos Expansions

Efficient custom hardware motivates the use of fixed-point arithmetic in the implementation of digital signal-processing (DSP) algorithms. This conversion to finite precision arithmetic introduces quantization noise in the system, which affects the system’s performance. As a result, characterizing quantization noise and its effects within a DSP system is a challenge that must be addressed to avoid over-allocating hardware resources during implementation. Polynomial chaos expansion (PCE) is a method used to model uncertainty in engineering systems. Although it has been employed to analyze quantization effects in DSP systems, previous investigations have been limited in scope and scale. This paper introduces new techniques that allow the application of PCE to be scaled up to larger DSP blocks with many noise sources, as needed for building blocks in software-defined radios (SDRs). Design space exploration algorithms that leverage the accuracy of PCE to estimate bit widths for fixed-point implementations of DSP blocks in an SDR system are explored, and their advantages will be presented.

A piezoelectric coded-excitation scanning acoustic transducer for Lamb wave inspections

Abstract Several techniques for Guided Wave (GW) inspections have already been developed. Most of them rely on extensive sensor deployment and damage localization algorithms characterized by significant computational costs. However, there is a growing demand for simpler, more autonomous, and more affordable systems across various fields. In particular, the implementation of wireless, battery-powered systems with a reduced number of sensor nodes and simpler processing would greatly facilitate the transition of this inspection technology on the field. Following this direction, this work presents the design of a novel piezoelectric transducer composed of four different patches, i.e. with only four input/output channels, to scan a given area. The peculiar piezo-load distributions allow the association of different spectral binary sequences for each 6∘ discrete angular step. By evaluating the distance-of-flights (DoFs) of detected peaks, the range coordinates for multiple defects are identified. Meanwhile, the angular information is extracted by demodulating binary sequences of peaks with comparable DoFs across several frequency bands. Since the transducer is designed as an encoder, it is referred to as coded-excitation scanning acoustic transducer (CESAT). More specifically, the Gray code is used to generate spectral patterns to reduce the uncertainty between two adjacent angular steps. A new quantization procedure for the optimal generation of the piezo distributions is also proposed. Ad hoc signal processing algorithms, suitable for embedded applications, were developed to extract multi-target range and angle information. The processing is based on the frequency decomposition of the recorded signal using an FIR filter bank and on dispersion compensation procedures for pulse ‘re-compression’. The transducer encoder behavior is validated through a finite element analysis. Finally, numerical simulations were performed to assess the effectiveness of the CESAT and associated signal processing in multi-defect detection and localization tasks.

Detection of cardiac abnormalities in ECG signal using time-based signal processing algorithm

Detection of cardiac abnormalities in ECG signal using time-based signal processing algorithm

Detection of cardiac abnormalities in ECG signal using time-based signal processing algorithm

Detection of cardiac abnormalities in ECG signal using time-based signal processing algorithm

Space-Frequency Processing Methods for Satellite Navigation Signals

Relevance. Quite low power of the global satellite navigation systems’ useful informational signals near the Earth surface along with an ongoing noticeable increase of the number of easily available and efficient portable means of blocking wideband energetic interference radiation make the problem of radionavigational satellite devices antijamming capabilities improvement especially relevant both from practical and scientific points of view. Therefore, the goal of this research was to increase the antijamming capabilities of the global satellite navigation systems via processing of the corresponding receiving apparatus’ input signals by special spatial filters. To achieve the work goal the scientific task of researching on the antijamming capability improvement in radionavigational devices by means of space-frequency signal processing was solved. The methods used. During the research, different spatial signal processing algorithms were considered, among them both the ones functioning without any information about interference situation, external with respect to the receiving radionavigational system, and the ones using the knowledge about the number and relative disposition of the jamming sources. Additionally different methods of interference sources number and angular directions finding were studied, as well as modern cost function optimization algorithms which are used for signal sources’ location determination. Scientific novelty of this work consists of usage of new algorithms that implement separate signal processing stages and that provide necessary information to the filtering algorithms during the problem solution, as well as of combining known methods with new approaches to their design. The results. During the scientific task solution, the performance quality metrics comparison was carried out for all the considered algorithms via the computer modeling method that employed recordings of real satellite navigational signals with addition of varying number of uncorrelated energetic interferences sources. As a result of modeling, the performance quality measure values were obtained for all the investigated algorithms and the comparative analysis thereof was conducted, at the end whereof the methods with the best characteristics were picked out. The significance of the work results consists of possibility of using the considered algorithms in real antijamming satellite navigation devices design.

A NIRS-Based Technique for Monitoring Brain Tissue Oxygenation in Stroke Patients.

Stroke is a global health issue caused by reduced blood flow to the brain, which leads to severe motor disabilities. Measuring oxygen levels in the brain tissue is crucial for understanding the severity and evolution of stroke. While CT or fMRI scans are preferred for confirming a stroke due to their high sensitivity, Near-Infrared Spectroscopy (NIRS)-based systems could be an alternative for monitoring stroke evolution. This study explores the potential of fNIRS signals to assess brain tissue in chronic stroke patients along with rehabilitation therapy. To study the feasibility of this proposal, ten healthy subjects and three stroke patients participated. For signal acquisition, two NIRS sensors were placed on the forehead of the subjects, who were asked to remain in a resting state for 5 min, followed by a 30 s motor task for each hand, which consists of opening and closing the hand at a steady pace, with a 1 min rest period in between. Acomplete protocol for placing sensors and a signal processing algorithm are proposed. In healthy subjects, a measurable change in oxygen saturation was found, with statistically significant differences (females p = 0.016, males p = 0.005) between the resting-state and the hand movement conditions. This work showed the feasibility of the complete proposal, including the NIRS sensor, the placement, the tasks protocol, and signal processing, for monitoring the state of the brain tissue cerebral oxygenation in stroke patients undergoing rehabilitation therapy. Thus this is a non-invasive barin assessment test based on fNIRS with the potential to be implemented in non-controlled clinical environments.

Deep Learning-Based Algorithms for Real-Time Lung Ultrasound Assisted Diagnosis

Lung ultrasound is an increasingly utilized non-invasive imaging modality for assessing lung condition but interpreting it can be challenging and depends on the operator’s experience. To address these challenges, this work proposes an approach that combines artificial intelligence (AI) with feature-based signal processing algorithms. We introduce a specialized deep learning model designed and trained to facilitate the analysis and interpretation of lung ultrasound images by automating the detection and location of pulmonary features, including the pleura, A-lines, B-lines, and consolidations. Employing Convolutional Neural Networks (CNNs) trained on a semi-automatically annotated dataset, the model delineates these pulmonary patterns with the objective of enhancing diagnostic precision. Real-time post-processing algorithms further refine prediction accuracy by reducing false-positives and false-negatives, augmenting interpretational clarity and obtaining a final processing rate of up to 20 frames per second with accuracy levels of 89% for consolidation, 92% for B-lines, 66% for A-lines, and 92% for detecting normal lungs compared with an expert opinion.

Multi-Approach Learning with Embedded Sensors Application in Gesture Recognition

The increased attention to human daily activities in academic circles has proven highly valuable, serving various specific needs and producing desired outcomes across different fields. Evaluating human activity data opens up numerous possibilities for researchers, facilitating personalized support options such as timely stress interventions, real-time feedback mechanisms, and applications for assisting individuals with disabilities or monitoring mental health. This paper presents a comprehensive approach integrating multiple sensors to recognize human body movements, applicable to real-life scenarios such as classrooms, driving, and kitchen-related activities. Our focus is to enhance the precision of motion classification and improve motion classification rates by merging acceleration and rotation signals and analyzing an enhanced array of features using various high-caliber machine-learning models. This methodology achieves exceptional performance and flexibility, with accuracy rates ranging between 96% and 98%, substantiating activity recognition within diverse contexts. It aims to reduce system recognition errors, improve the classification process, and promote the advanced utilization of artificial intelligence algorithms in signal processing and in controlling and enhancing bionic hands.

Electrocardiogram-Based Driver Authentication Using Autocorrelation and Convolutional Neural Network Techniques

This study presents a novel driver authentication system utilizing electrocardiogram (ECG) signals collected through dry electrodes embedded in the steering wheel. Traditional biometric authentication methods are sensitive to environmental changes and vulnerable to replication, but this study addresses these issues by leveraging the unique characteristics and forgery resistance of ECG signals. The proposed system is designed using autocorrelation profiles (ACPs) and a convolutional neural network and is optimized for real-time processing even in constrained hardware environments. Additionally, advanced signal processing algorithms were applied to refine the ECG data and minimize noise in driving environments. The system’s performance was evaluated using a public dataset of 154 participants and a real-world dataset of 10 participants, achieving F1-Scores of 96.8% and 96.02%, respectively. Furthermore, an ablation study was conducted to analyze the importance of components such as ACPs, normalization, and filtering. When all components were removed, the F1-Score decreased to 60.1%, demonstrating the critical role of each component. These findings highlight the potential of the proposed system to deliver high accuracy and efficiency not only in vehicle environments but also in various security applications.

Human pressure-volume loops from the impedance catheter: Development and validation of semi-automated beat-by-beat analysis

Introduction Pressure-volume (PV) loops offer a comprehensive evaluation of cardiac function. Impedance catheters enable the acquisition of synchronised intracardiac electrocardiogram (ECG), pressure, and volume data with high temporal resolution. However, current calibration methods are impractical and data interpretation is often inconsistent. Methods In the PREFER-CMR prospective, cohort study, 15 patients with suspected heart failure and preserved ejection fraction underwent same-day cardiac magnetic resonance (CMR) imaging and invasive impedance catheter studies. Signal processing algorithms were developed to semi-automatically determine PV-loop phases and calibrate impedance catheter volumes to CMR. Results of beat-by-beat and average loop analysis approaches were compared with reference methods and between each other. Results The second-order differential of the pressure-volume trace identified PV-loop phases on a beat-by-beat basis, but gradient smoothing prevented detection in average loops. Calibrated impedance catheter volumes, including left ventricular end diastolic (LVEDV) and end systolic (LVESV) volumes, correlated with CMR (r≥0.95, p&lt;0.001) using both analysis methods. However, the average loop LVESV was overestimated by 8.1ml (p=0.031). For left ventricular end diastolic pressure, both beat-by-beat (r=0.73, p=0.002) and average loop (r=0.69, p=0.005) methods correlated with the fluid-filled manometer reference. Maximum pressure correlation was strong for both beat-by-beat (r=0.85, p&lt;0.001) and average loop (r=0.80, p&lt;0.001) methods, but was 10.1mmHg (p=0.040) lower in the average loop method. Between methods, significant correlations (r=0.73–0.99) were found across all pressures and volumes. Stroke work (r=0.94) and potential energy (r=0.96) significantly correlated (p&lt;0.001) between methods, although Bland-Altman subgroup analysis suggested underestimation of stroke work in atrial fibrillation using the average loop method. Conclusions Impedance catheter volumes can be accurately calibrated using CMR. PV-loop phases can be robustly detected with a semi-automated algorithm. Both beat-by-beat and average loop approaches are viable for analysing multiple cardiac cycles, though beat-by-beat analysis may offer advantages for phase identification, pressure assessment, and in irregular rhythms. Trials registration ClinicalTrials.gov: NCT05114785. Registration date: 05/11/2021. https://clinicaltrials.gov/study/NCT05114785

Surfing beta burst waveforms to improve motor imagery-based BCI

Abstract Our understanding of motor-related, macroscale brain processes has been significantly shaped by the description of the event-related desynchronization (ERD) and synchronization (ERS) phenomena in the mu and beta frequency bands prior to, during, and following movement. The demonstration of reproducible, spatially- and band-limited signal power changes has, consequently, attracted the interest of non-invasive brain-computer interface (BCI) research for a long time. BCIs often rely on motor imagery (MI) experimental paradigms that are expected to generate brain signal modulations analogous to movement-related ERD and ERS. However, a number of recent neuroscience studies has questioned the nature of these phenomena. Beta band activity has been shown to occur, on a single-trial level, in short, transient, and heterogeneous events termed bursts rather than sustained oscillations. In a previous study, we established that an analysis of hand MI binary classification tasks based on beta bursts can be superior to beta power in terms of classification score. In this article, we elaborate on this idea, proposing a signal processing algorithm that is comparable to- and compatible with state-of-the-art techniques. Our pipeline filters brain recordings by convolving them with kernels extracted from beta bursts and then applies spatial filtering before classification. This data-driven filtering allowed for a simple and efficient analysis of signals from multiple sensors, thus being suitable for online applications. By adopting a time-resolved decoding approach, we explored MI dynamics and showed the specificity of the new classification features. In accordance with previous results, beta bursts improved classification performance compared to beta band power, while often increasing information transfer rate compared to state-of-the-art approaches.

Long short-term-memory-based depth of anesthesia index computation for offline and real-time clinical application in pigs

We here present a deep-learning approach for computing depth of anesthesia (DoA) for pigs undergoing general anesthesia with propofol, integrated into a novel general anesthesia specialized MatLab-based graphical user interface (GAM-GUI) toolbox. This toolbox permits the collection of EEG signals from a BIOPAC MP160 device in real-time. They are analyzed using classical signal processing algorithms combined with pharmacokinetic and pharmacodynamic (PK/PD) predictions of anesthetic concentrations and their effects on DoA and the prediction of DoA using a novel deep learning-based algorithm. Integrating the DoA estimation algorithm into a supporting toolbox allows for the clinical validation of the prediction and its immediate application in veterinary practice. This novel, artificial-intelligence-driven, user-defined, open-access software tool offers a valuable resource for both researchers and clinicians in conducting EEG analysis in real-time and offline settings in pigs and, potentially, other animal species. Its open-source nature differentiates it from proprietary platforms like Sedline and BIS, providing greater flexibility and accessibility.

Bioacoustic Signatures - Revolutionizing User Authentication in the Digital Age

This white paper explores the concept of bioacoustic authentication in continuous user verification for cybersecurity. As traditional authentication methods become increasingly vulnerable to sophisticated attacks, we propose leveraging the unique internal sounds produced by the human body - such as heartbeats, blood flow, and joint movements as a novel biometric identifier. We examine the technical challenges and potential solutions in capturing and processing these subtle bioacoustic signals, including the development of specialized sensors and advanced signal processing algorithms. The use of machine learning techniques for real-time analysis and user verification is explored, with a focus on accuracy, adaptability, and fraud prevention. We explore practical applications across various sectors, from high-security environments to personal devices, and its potential integration with existing cybersecurity frameworks. By harnessing the body's internal rhythms, bioacoustic authentication presents a promising avenue for enhancing cybersecurity measures, offering a unique blend of security, convenience, and continuous protection against unauthorized access.