Signal Processing Research Research Articles

The art of selecting the ECG input in neural networks to classify heart diseases: a dual focus on maximizing information and reducing redundancy.

Accurate diagnosis of cardiovascular diseases often relies on the electrocardiogram (ECG). Since the cardiac vector is located within a three-dimensional space and the standard ECG comprises 12 projections or leads derived from it, redundant information is inherently present. This study aims to quantify this redundancy and its impact on classification tasks using Convolutional Neural Networks (CNNs) in cardiovascular diseases. We employed signal theory and mutual information to introduce a novel redundancy metric and explored techniques for redundancy augmentation and reduction. This involved lead selection and transformation to evaluate the effects on neural network performance. Our findings indicate that optimizing input configurations through redundancy reduction techniques can enhance the performance of deep learning models in cardiovascular diagnostics, provided that the information is preserved and minimally distorted. For the first time, this research has quantified the redundancy present in the input by validating various redundancy reduction techniques using a CNN. This discovery paves the way for advancing biomedical signal processing research, simplifying model complexity, and enhancing diagnostic performance in cardiovascular medicine within reduced lead systems, such as Holter monitors or wearables.

Effect of Modulation Domain Coupled KalmanSpectral Filter on Speech Enhancement over Wireless Voiced Communication System

Noise suppression in speech signal has received great attention in signal processing research community. However, existing methods of noise suppression such as the time-domain Kalman filter suffers from the psychoacoustic and physiological characteristic feature while the spectral subtraction technique performed in the modulation domain is associated with remnant noise in the enhanced speech signal. Therefore, in this paper, a Modulation-Domain coupled Kalman-Spectral Filtering (MD-KSF) in a single channel system is proposed to address the aforementioned shortcomings in the two existing techniques. The simulation of the proposed coupled MD-KSF was performed in MATLAB software environment. The evaluation of the proposed coupled MD-KSF technique in terms of Spectral waveform, Mean Square Error (MSE) and Log Spectral Distance (LSD) was performed. Furthermore, Perceptual Evaluation of Speech Quality (PESQ) and Short-Time Objective Intelligibility (STOI) tests were employed to validate the quality and intelligibility of the proposed coupled MD- KSF using the NOIZEUS corpus data set. The proposed technique shows significant noise suppression over the existing techniques.

Research of signal processing and detection of satellite telemetry data based on EMD and MBSE

Abstract Based on the characteristics of abnormal data and high Signal-to-Noise Ratio in satellite in-orbit telemetry data, the empirical mode decomposition algorithm is used to remove the abnormal data, and the method is effective. By using the empirical mode decomposition algorithm, trend prediction of telemetry data can also be achieved, and its practicality can be demonstrated through signal construction and development. The combination of the empirical mode decomposition algorithm for telemetry data processing with the satellite’s digital model constructed by MBSE software has expanded the application scope of the digital satellite model.

Audio Classification on the FSDKaggle2018 Dataset using Tensorflow, Keras

This project explores audio classification leveraging representation learning within the TensorFlow framework. The methodology focuses on the initial engineering of wave features derived from raw audio data, which are subsequently used to train convolutional neural networks (CNNs) for effective representation learning. By transforming the audio signals into a structured format amenable to convolutional processing, our system is designed to capture the intrinsic properties and patterns embedded in the sound waves. The feature engineering process is detailed where various envelope features such as the homomorphic envelogram, hilbert envelogram and wavelet decompositions help in extracting meaningful information from the raw audio signals. These engineered features provide a robust foundation for the subsequent layers of convolutional networks. The CNNs are meticulously architected to learn hierarchical representations, effectively capturing both low-level and high-level audio characteristics.This study attempts to reinforces the significance of tailored feature engineering in deep learning and demonstrate an effective audio classification pipeline using representation learning and open up new avenues for research in audio signal processing and machine learning.

An Exciting Juncture for Signal Processing Research: On Building Bridges, Challenges, and Opportunities [From the Editor

An Exciting Juncture for Signal Processing Research: On Building Bridges, Challenges, and Opportunities [From the Editor

Deep Learning, Ensemble and Supervised Machine Learning for Arabic Speech Emotion Recognition

Today, automatic emotion recognition in speech is one of the most important areas of research in signal processing. Identifying emotional content in Arabic speech is regarded as a very challenging and intricate task due to several obstacles, such as the wide range of cultures and dialects, the influence of cultural factors on emotional expression, and the scarcity of available datasets. This study used a variety of artificial intelligence models, including Xgboost, Adaboost, KNN, DT, and SOM, and a deep-learning model named SERDNN. ANAD was employed as a training dataset, which contains three emotions, "angry", "happy", and "surprised", with 844 features. This study aimed to present a more efficient and accurate technique for recognizing emotions in Arabic speech. Precision, accuracy, recall, and F1-score metrics were utilized to evaluate the effectiveness of the proposed techniques. The results showed that the Xgboost, SOM, and KNN classifiers achieved superior performance in recognizing emotions in Arabic speech. The SERDNN deep learning model outperformed the other techniques, achieving the highest accuracy of 97.40% with a loss rate of 0.1457. Therefore, it can be relied upon and deployed to recognize emotions in Arabic speech.

A Collaborative Platform for Advancing Automatic Interpretation in ECG Signals.

Numerous papers report the efficiency of the automatic interpretation capabilities of commercial algorithms. Unfortunately, these algorithms are proprietary, and academia has no means of directly contributing to these results. In fact, nothing at the same stage of development exists in academia. Despite the extensive research in ECG signal processing, from signal conditioning to expert systems, a cohesive single application for clinical use is not ready yet. This is due to a serious lack of coordination in the academic efforts, which involve not only algorithms for signal processing, but also the signal acquisition equipment itself. For instance, the different sampling rates and the different noise levels frequently found in the available signal databases can cause severe incompatibility problems when the integration of different algorithms is desired. Therefore, this work aims to solve this incompatibility problem by providing the academic community with a diagnostic-grade electrocardiograph. The intention is to create a new standardized ECG signals database in order to address the automatic interpretation problem and create an electrocardiography system that can fully assist clinical practitioners, as the proprietary systems do. Achieving this objective is expected through an open and coordinated collaboration platform for which a webpage has already been created.

The Graduate Program in Acoustics at Penn State

The Graduate Program in Acoustics at Penn State offers graduate degrees (M.Eng., M.S., Ph.D.) in Acoustics, with courses and research opportunities in a wide variety of subfields. Our 820 alumni are employed around the world in military and government labs, academic institutions, consulting firms, and consumer audio and related industries. Our 40 + faculty from several disciplines conduct research and teach courses in structural acoustics, nonlinear acoustics, architectural acoustics, signal processing, aeroacoustics, biomedical ultrasound, transducers, computational acoustics, noise and vibration control, acoustic metamaterials, psychoacoustics, and underwater acoustics. Course offerings include fundamentals of acoustics and vibration, electroacoustic transducers, signal processing, acoustics in fluid media, sound and structure interaction, digital signal processing, experimental techniques, acoustic measurements and data analysis, ocean acoustics, architectural acoustics, noise control engineering, nonlinear acoustics, outdoor sound propagation, computational acoustics, biomedical ultrasound, flow induced noise, spatial sound and three-dimensional audio, and the acoustics of musical instruments. Distance education students pursuing the M.Eng. degree join resident students in a hybrid classroom environment. This poster highlights faculty research areas, laboratory facilities, student demographics, successful graduates, and recent enrollment and employment trends.

Acoustics at UMass Dartmouth

The University of Massachusetts Dartmouth has a long history spanning 5 decades of graduate courses offerings and research in underwater acoustics, transduction and signal processing leading to M.S. and Ph.D. degrees in Electrical Engineering. Collaborations between Marine Science, Physics, Mechanical, Bioengineering, and Electrical Engineering departments offer many interdisciplinary opportunities in Acoustics. Courses include Fundamentals of Acoustics, Vibrations, Underwater Acoustics, Electroacoustic Transducers, Medical Ultrasonics, Sonar, Digital Signal Processing, Array Processing, Random Signals, Information Theory, Communications, Detection and Estimation. Many unique facilities including an Underwater Acoustic Test Tank, Open-Ocean water access, and Unmanned Underwater Vehicles support both undergraduate and graduate projects. Research focuses include transducers and transduction science, materials characterization, calibration, array and sonar signal processing, animal bioacoustics, communications, detection and estimation, active and passive sonar funded by Office of Naval Research and Industry.

Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced Error

Inter-beat interval (IBI) measurement enables estimation of heart-tare variability (HRV) which, in turn, can provide early indication of potential cardiovascular diseases (CVDs). However, extracting IBIs from noisy signals is challenging since the morphology of the signal gets distorted in the presence of noise. Electrocardiogram (ECG) of a person in heavy motion is highly corrupted with noise, known as motion-artifact, and IBI extracted from it is inaccurate. As a part of remote health monitoring and wearable system development, denoising ECG signals and estimating IBIs correctly from them have become an emerging topic among signal-processing researchers. Apart from conventional methods, deep-learning techniques have been successfully used in signal denoising recently, and diagnosis process has become easier, leading to accuracy levels that were previously unachievable. We propose a deep-learning approach leveraging tiramisu autoencoder model to suppress motion-artifact noise and make the R-peaks of the ECG signal prominent even in the presence of high-intensity motion. After denoising, IBIs are estimated more accurately expediting diagnosis tasks. Results illustrate that our method enables IBI estimation from noisy ECG signals with SNR up to -30 dB with average root mean square error (RMSE) of 13 milliseconds for estimated IBIs. At this noise level, our error percentage remains below 8% and outperforms other state-of-the-art techniques.

The STATiX pipeline for the detection of X-ray transients in three dimensions

ABSTRACT The recent serendipitous discovery of a new population of short duration X-ray transients, thought to be associated with collisions of compact objects or stellar explosions in distant galaxies, has motivated efforts to build up statistical samples by mining X-ray telescope archives. Most searches to date, however, do not fully exploit recent developments in the signal and imaging processing research domains to optimize searches for short X-ray flashes. This paper addresses this issue by presenting a new source detection pipeline, STATiX (Space and Time Algorithm for Transients in X-rays), which directly operates on three-dimensional X-ray data cubes consisting of two spatial and one temporal dimension. The algorithm leverages wavelet transforms and the principles of sparsity to denoise X-ray observations and then detect source candidates on the denoised data cubes. The light curves of the detected sources are then characterized using the Bayesian blocks algorithm to identify flaring periods. We describe the implementation of STATiX in the case of XMM–Newton data, present extensive validation and performance verification tests based on simulations and also apply the pipeline to a small subset of seven XMM–Newton observations, which are known to contain transients sources. In addition to known flares in the selected fields, we report a previously unknown short duration transient found by our algorithm that is likely associated with a flaring Galactic star. This discovery demonstrates the potential of applying STATiX to the full XMM–Newton archive.

A modal estimation method of rotating blade based on compressed sensing and blade tip timing

Structural modal estimation has consistently remained one of the most crucial areas of research in mechanical vibration analysis and signal processing. It is imperative to accurately monitor the vibration signals of rotor blades and their corresponding structural modal parameters. Blade tip timing (BTT) is a promising approach used to measure vibration and monitor the health of blades. However, most existing BTT methods focus on frequency accuracy, neglecting damping, a key physical quantity that represents the strength of the structure. This research focuses on damping modal estimation of blade vibration and proposes a sparse reconstruction algorithm based on a two-dimensional Laplace wavelet family, which addresses the inherent under-sampled problem of BTT technology while enabling estimations of both the structural modal frequency and modal damping of the blades. Firstly, the proposed algorithm is achieved by designing a Laplace wavelet dictionary based on prior information and using a convex optimization objective function with a regularization term. Secondly, Laplace wavelet dictionary matches the signal better than the traditional Fourier dictionary based on the similarity between the damped vibration signal and the Laplace wavelet, then a sparser representation vector can be obtained. Moreover, the research object is not limited to a single blade, but can be easily expanded to multiple stages and multiple rotating blades monitored simultaneously. Finally, the simulation and physical test results indicate that the proposed method exhibits high reconstruction accuracy, reliability, and anti-noise abilities.

Virtual practicum for research of mathematical algorithms using Digital Signal Processing

The article describes an approach to the organisation of a virtual laboratory practicum for research in the Digital Signal Processing (DSP) field. Such an approach may be useful in implementing a part of the educational process in a distant mode. The laboratory setting simulation method based on LabVIEW software was used to solve the research tasks.
 It is advisable to create and implement this integrated educational solution — virtual laboratory practicum — in the educational process of universities that train engineers. It will allow students to have laboratory and practical training to improve specialists’ qualifications and carry out control activities. Using such an environment will allow extension course students, teachers, and trainees to perform assigned tasks without time limit, both in virtual form and in the form of training before working with real equipment. It will also allow students to carry out additional research on their own in the Digital Signal Processing sphere, which will increase their interest.

On derivative-free extended Kalman filtering and its Matlab-oriented square-root implementations for state estimation in continuous-discrete nonlinear stochastic systems

Recent research in nonlinear filtering and signal processing has suggested an efficient derivative-free Extended Kalman filter (EKF) designed for discrete-time stochastic systems. Such approach, however, has failed to address the estimation problem for continuous-discrete models. In this paper, we develop a novel continuous-discrete derivative-free EKF methodology by deriving the related moment differential equations (MDEs) and sample point differential equations (SPDEs). Additionally, we derive their Cholesky-based square-root MDEs and SPDEs and obtain several numerically stable derivative-free EKF methods. Finally, we propose the MATLAB-oriented implementations for all continuous-discrete derivative-free EKF algorithms derived. They are easy to implement because of the built-in fashion of the MATLAB numerical integrators utilized for solving either the MDEs or SPDEs in use, which are the ordinary differential equations (ODEs). More importantly, these are accurate derivative-free EKF implementations because any built-in MATLAB ODE solver includes the discretization error control that bounds the discretization error arisen and makes the implementation methods accurate. Besides, this is done in automatic way and no extra coding is required from users. The new filters are particularly effective for working with stochastic systems with highly nonlinear and/or nondifferentiable drift and observation functions, i.e. when the calculation of Jacobian matrices are either problematical or questionable. The performance of the novel filtering methods is demonstrated on several numerical tests.

IEEE Signal Processing Society Flagship Conferences Over the Past 10 Years

Throughout the IEEE Signal Processing Society’s (SPS’s) history, conferences have functioned as a main way to connect within the Society, bringing together the signal processing research community to discuss and debate, establish research collaborations, and have a good time. These immersive conference experiences, to which attendees travel from all over the world to be together for a set period of time, have certainly been challenged over these past years, but SPS leadership was able to guide and steer through the constant unanticipated changes, with continued financial stability and growing momentum for a more inclusive future. This article gives an overview of the evolution of SPS conferences in the past decade and presents the challenges ahead.

Three More Decades in Array Signal Processing Research: An optimization and structure exploitation perspective

The signal processing community is currently witnessing the emergence of sensor array processing and direction-of-arrival (DoA) estimation in various modern applications, such as automotive radar, mobile user and millimeter wave indoor localization, and drone surveillance, as well as in new paradigms, such as joint sensing and communication in future wireless systems. This trend is further enhanced by technology leaps and the availability of powerful and affordable multiantenna hardware platforms.

Inferring Causal Factors of Core Affect Dynamics on Social Participation through the Lens of the Observer

A core endeavour in current affective computing and social signal processing research is the construction of datasets embedding suitable ground truths to foster machine learning methods. This practice brings up hitherto overlooked intricacies. In this paper, we consider causal factors potentially arising when human raters evaluate the affect fluctuations of subjects involved in dyadic interactions and subsequently categorise them in terms of social participation traits. To gauge such factors, we propose an emulator as a statistical approximation of the human rater, and we first discuss the motivations and the rationale behind the approach.The emulator is laid down in the next section as a phenomenological model where the core affect stochastic dynamics as perceived by the rater are captured through an Ornstein-Uhlenbeck process; its parameters are then exploited to infer potential causal effects in the attribution of social traits. Following that, by resorting to a publicly available dataset, the adequacy of the model is evaluated in terms of both human raters' emulation and machine learning predictive capabilities. We then present the results, which are followed by a general discussion concerning findings and their implications, together with advantages and potential applications of the approach.

Preface

4th National Conference on Communication Systems (NCOCS-2022) December 23rd, 2022 ABOUT THE CONFERENCE National Conference on Communication Systems (NCOCS-2022) is fourth in the series of conferences intended to be held every year at NITPY, aimed to disseminate knowledge on the latest technological developments and cutting-edge research in wireless communication, signal processing, antenna and RF design, optical communication, internet of things and sensor networks & electronics among academicians, researchers, scientists, industry personnel, and entrepreneurs across India. This conference will provide the premier interdisciplinary forum relevant with Electronics and Communication Engineering to discuss the recent technologies, innovations, recent research findings, challenges, solutions, and perspectives of the future directions of Communication Engineering through keynote lectures and paper presentations. Young researchers are encouraged to participate and utilize the opportunity to network with others who seek to develop their research abilities and experience. OBJECTIVE & SCOPE OF THE CONFERENCE The objective of the NCOCS 2022 is to provide a platform to perceive, share, and exchange the recent trends related to various aspects of Communication Engineering. The main aim of this conference is to bring together researchers, young faculties and practitioners working in multi-disciplinary fields to discuss and deliberate on challenges, opportunities and strategies involved for next-generation communication systems. The conference will provide a forum to academic researchers and practitioners to discuss critical issues concerning latest technologies towards 6G and mmWave communications. Conference Chairperson Dr. M. Surendar Assistant Professor, Department of ECE,National Institute of Technology Puducherry, Karaikal – 609 609, IndiaConference Website: https://sites.google.com/view/ncocs-2022/home?authuser=0List of Conference Committee is available in this Pdf.

A Guide to Computational Reproducibility in Signal Processing and Machine Learning [Tips & Tricks

A computational experiment is deemed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reproducible</i> if the same data and methods are available to replicate quantitative results by any independent researcher, anywhere and at any time, granted that they have the required computing power. Such computational reproducibility is a growing challenge that has been extensively studied among computational researchers as well as within the signal processing and machine learning research community <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> .

Reaching Out to Members in the Middle East and India [President’s Message

As I am writing this article, I am wrapping up a trip as IEEE Signal Processing Society (SPS) president to Doha, Qatar (9–11 January), to speak at the 2022 IEEE Spoken Language Technology (SLT) Workshop, and India (12–16 January), for technical talks and meetings with local signal processing researchers and SPS local Chapter chairs.