Signal Processing Applications Research Articles

ICG signal denoising based on ICEEMDAN and PSO-VMD methods.

Impedance cardiography (ICG) plays a crucial role in clinically evaluating cardiac systolic and diastolic functions, along with various other cardiac parameters. However, its accuracy heavily depends on precisely identifying feature points reflecting cardiac function. Moreover, traditional signal processing techniques used to mitigate random noise and breathing artifacts may inadvertently distort the amplitude and temporal characteristics of ICG signals. To address this issue, this study investigates a noise and artifact elimination method based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Particle Swarm Optimization-based Variational Mode Decomposition Algorithm (PSO-VMD). The goal is to preserve the amplitude and temporal features of ICG signals to ensure accurate feature point extraction and computation of associated cardiac parameters. Comparative analysis with signal processing methods employing various wavelet families and Ensemble Empirical Mode Decomposition (EEMD) in ICG signal processing applications reveals that the proposed method achieves superior signal-to-noise ratio (SNR) and lower root-mean-square error (RMSE), while demonstrating enhanced correlation and waveform consistency with the original signal.

Dual-driving parametric locking of GHz phonon sources to sub-hertz linewidth in optomechanical systems

In the exploration of collective dynamics and advanced information processing, synchronization and frequency locking of mechanical oscillations are cornerstone phenomena. Traditional synchronization techniques, which typically involve a single mechanical mode, are limited by their inability to distinguish between intrinsic mechanical oscillations and external signals after locking. Addressing this challenge, we introduce a parametric approach that enables simultaneous frequency locking of two gigahertz mechanical modes within an optomechanical crystal cavity. By modulating the pump light to match the sum and difference frequencies of the mechanical modes, we significantly narrow their linewidths from tens of kilohertz to below 1 Hz at room temperature and ambient pressure. This dual-locking scheme also drastically reduces the phase noise of the mechanical modes by 76.6 dBc/Hz at a 100 Hz offset, while allowing flexible tuning of the locked modes’ frequencies via input signal adjustments. Our method not only facilitates direct observation of mechanical oscillations under the locking regime but also enriches the understanding of coherent phonons in multimode regimes, opening new avenues for optomechanical applications in signal processing.

Variable optical attenuator using a multi-path interferometer embedded in an optical cavity

Variable optical attenuators (VOAs) used in photonic integrated circuits suffer from trade-off between dynamic range of attenuation and large footprint and introduce inadvertent changes in optical phase while attenuating the input signal. In this work, we propose a VOA architecture using micro-ring resonators which provides ≥60dB optical attenuation of selected wavelengths, while preserving the input optical phase, in a compact footprint. It enables tailoring of the dynamic range and insertion loss for different circuit applications in photonic signal processing, programmable photonics, high performance computing, and datacenter communication.

Applications of Fourier transforms in engineering

The Fourier transform was proposed by Fourier in 1807. Fourier transform is a method of analyzing signals, which can analyze the components of the signal or synthesize the signal using these components. Many waveforms can be used as signal components, such as sine waves, square waves, sawtooth waves, etc. The Fourier transform uses sine waves as components of the signal. Due to its excellent properties, the Fourier transform has a wide range of applications in physics, number theory, combinatorial mathematics, signal processing, probability, statistics, cryptography, acoustics, optics, and other fields. This paper focuses on the study of fractional-order Fourier transform in the engineering field, for the equipment of the tiny fault diagnosis method, and according to some existing diagnostic methods, put forward the idea of diagnostic method enhancement.

High-performance power spectral/bispectral estimator for biomedical signal processing applications using novel memory-based FFT processor

This research paper proposes a novel robust high-performance power spectrum estimator, and bispactral power density analyzer that has outstanding capabilities in estimating noisy biomedical signal's power spectrum, and bispectrum. Biomedical signals usually are exposed to several sources of noises such as electrical noise from environmental noise from external sources, electrical equipment, and biological noise from the body. Therefore, accuracy and reliability are the most important feature of these systems in processing non-stationary biomedical signals. The proposed computationally-efficient architecture is based on a radix-8 memory-based 1024-point Blackman-Tuckey method power spectral density (PSD) estimator. The proposed nonparametric estimator uses a novel shared-resource CORDIC-Ⅱ unit to avoid multiplications in FFT computation, as well as filtering operations implemented in folded architectures. In order to merge two FFTs, the module uses bidirectional fractional delay filters to estimate half delay samples. By using modified safe-scaling, valid final output would be achieved, without any averaging operation. The proposed and competing designs are implemented on Artix-7 FPGA which is an ideal option for DSP applications. As final results demonstrate, the hardware has a remarkable capability in operating in short word-lengths which allows high-performance in low-power applications to compute the power spectrum and bicoherence of a vital signal.

Molecularization of Metasurfaces for Multifunctional Ultrafast All‐Optical Terahertz Waves

Hybrid metasurfaces incorporated by active materials hold great promise for state‐of‐the‐art terahertz functional devices. However, it is still a major challenge to achieve ultrafast, dynamic, and multifunctional effective control of THz waves via hybrid metasurfaces. Herein, a modulator consisting of split rings and cut‐wires is first demonstrated, with an amplitude of −35.6 dB at 0.524 THz. By embedding semiconductor silicon into specified locations to form a hybrid metasurface, the ultrastrong connectivity of the silicon bridges leads to rapid optical molecularization. Under photoexcitation, the frequency tuning range is 26.7%, the phase shifting reaches 357.5°, and the maximal modulation depth is 94.54%. Taking advantage of the rapid relaxation of photocarriers in the silicon bridges, the ultrafast frequency switching is within 1400 ps. More interestingly, by changing the positions of the silicon bridges, the frequency tuning range is further promoted to 60%, the phase shifting is 353.5°, the modulation depth of 100% is achieved, and the full recovery time is 1600 ps. Furthermore, the underlying mechanism of the ultrafast tuning process is elucidated. This work demonstrates the feasibility of all‐optical‐controlled hybrid metasurface to achieve multifunctional dynamic modulation of THz waves, which has tremendous potential for applications in optical switching, signal processing, and frequency conversion.

Application of deep learning-based speech signal processing technology in electronic communication

In recent years, the artificial intelligence boom triggered by deep learning is influencing and changing peoples lifestyles. People are no longer satisfied with human-computer interaction through simple text commands; instead, they look forward to more convenient and faster communication methods like voice interaction. Against the backdrop of innovative development, the application of speech signal processing systems is becoming increasingly widespread. Therefore, it is necessary to study the application of deep learning-based speech signal processing technology in electronic communication. This can provide more valuable references and assistance for future development, promoting the better development of deep learning-based speech signal processing technology in electronic communication. In this paper, we first review the application of deep learning in speech signal enhancement, speech recognition, and speech synthesis from a theoretical analysis perspective. Then, we discuss the application of deep learning-based speech signal processing in electronic communication, including the application of models such as Transformer, LAS (Listen, Attend and Spell), and GFT-conformer in speech signal processing. We also discuss some application scenarios of deep learning-based speech signal processing in electronic communication. Finally, we identify the need for deeper application of deep learning technology in speech signal processing and electronic communication, with continuous optimization and adjustment.

Design Efficient Vedic-Multiplier for Floating-Point MAC Module

Multiplication-accumulation (MAC) operation plays a crucial role in digital signal processing (DSP) applications, such as image convolution and filters, especially when performed on floating-point numbers to achieve high-level of accuracy. The performance of MAC module highly relies upon the performance of the multiplier utilized. This work offers a distinctive and efficient floating-point Vedic multiplier (VM) called adjusted-VM (AVM) to be utilized in MAC module to meet modern DSP demands. The proposed AVM is based on Urdhva-Tiryakbhyam-Sutra (UT-Sutra) approach and utilizes an enhanced design for the Brent-Kung carry-select adder (EBK-CSLA) to generate the final product. A (6*6)-bit AVM is designed first, then, it is extended to design (12*12)-bit AVM which in turns, utilized to design (24*24)-bit AVM. Moreover, the pipelining concept is used to optimize the speed of the offered (24*24)-bit AVM design. The proposed (24*24)-bit AVM can be used to achieve efficient multiplication for mantissa part in binary single-precision (BSP) floating-point MAC module. The proposed AVM architectures are modeled in VHDL, simulated, and synthesized by Xilinx-ISE14.7 tool using several FPGA families. The implementation results demonstrated a noticeable reduction in delay and area occupation by 33.16% and 42.42%, respectively compared with the most recent existing unpipelined design, and a reduction in delay of 44.78% compared with the existing pipelined design.

Specific Investigations Concerning the Improveable Multiplier Architecture of High-Speed and Area-Efficient Adders

Over the past few decades, the growth of portable devices such as laptops, mobile phone and personal digital assistant has resulted in increasing demand for complex functionality with effective computation. The present day technology is known for digital systems with very high computing capabilities. The demand for high speed, low power integrated circuits for portable devices has become crucial. The never ending growing complexities of integrated circuit for future devices pose a challenging task for integrated circuit designer. Cost effective integrated circuits requires the design meeting out the challenging task to optimize power, area with high performance. Hence this research focuses on the optimization of area, power and speed of Arithmetic circuits, Very Large Scale Integrated circuits (VLSI) are widely used in Arithmetic circuits, Digital Signal Processing (DSP), Image and Video Processing applications

Image Multiplication Using Novel 4:1 Approximate Compressor*

Energy-efficient designs are the need of the hour for signal and image processing applications that are error-tolerant. The paper proposes a new 4:1 approximate arithmetic-based compressor developed by analyzing the probability-based tactics that have a higher degree of precision when implemented. Approximate n × n multiplier is designed using the proposed varying probability-based compressors. To validate the effectiveness of the new compressor, a 16 × 16 multiplier is designed. The n × n bit multiplier will have 2 n columns of partial products(PP), in which the least significant n−1 columns are designed using the novel approximate 4:1 compressor and most n + 1 columns are designed with the exact compressor. The results of the 16-bit multiplier are superior to the performance of the exact multiplier in terms of power, area, power-delay-product (PDP), and area-delay-product (ADP) typically by 39 % , 20 % , 44 % , and 26 % respectively for Design 1 and 14 % , 24 % , 30 % , and 21 % , respectively for Design 2. The Peak Signal to Noise Ratio(PSNR), Mean Square Error(MSE), and Structural Similarity Index Measure(SSIM) for the output images processed by new approximate multipliers are acceptable when compared with precision and energy.

An Analytical Study of the Mikhailov–Novikov–Wang Equation with Stability and Modulation Instability Analysis in Industrial Engineering via Multiple Methods

Solitary waves, inherent in nonlinear wave equations, manifest across various physical systems like water waves, optical fibers, and plasma waves. In this study, we present this type of wave solution within the integrable Mikhailov–Novikov–Wang (MNW) equation, an integrable system known for representing localized disturbances that persist without dispersing, retaining their form and coherence over extended distances, thereby playing a pivotal role in understanding nonlinear dynamics and wave phenomena. Beyond this innovative work, we examine the stability and modulation instability of its gained solutions. These new solitary wave solutions have potential applications in telecommunications, spectroscopy, imaging, signal processing, and pulse modeling, as well as in economic systems and markets. To derive these solitary wave solutions, we employ two effective methods: the improved Sardar subequation method and the (℧′/℧, 1/℧) method. Through these methods, we develop a diverse array of waveforms, including hyperbolic, trigonometric, and rational functions. We thoroughly validated our results using Mathematica software to ensure their accuracy. Vigorous graphical representations showcase a variety of soliton patterns, including dark, singular, kink, anti-kink, and hyperbolic-shaped patterns. These findings highlight the effectiveness of these methods in showing novel solutions. The utilization of these methods significantly contributes to the derivation of novel soliton solutions for the MNW equation, holding promise for diverse applications throughout different scientific domains.

Dispersion aware matching pursuit decomposition of Lamb wave signals for structural health monitoring of thin plates structures

Structural Health Monitoring (SHM) techniques are key to monitor in real time the health state of engineering structures, where damage type, location and severity are to be estimated by applying signal processing and deep learning techniques to temporal signals measured by sensors coming from the structure under study. Among the most widely used signal processing techniques in the SHM community is the Matching Pursuit Method (MPM), which allows to extract key features from a measured signal. MPM simply consists in approximating temporal data as a sum of different terms composed of a delayed and scaled signal called atom. This signal decomposition approach is however limited to non-dispersive signals which is not the case when dealing with Lamb waves signals widely used in the SHM of thin structures. In this case, in addition of delays and attenuations, wave packed also endure dispersion caused by the fact that all the frequencies do not propagate at the same speed within the structures under study. In this context, an improved version of Matching Pursuit is proposed in the present work, which address the dispersion phenomena by decomposing a measured signal as delayed and dispersed impulse response of an atom, obtaining better features for Lamb wave measurements. The performance of the developed technique is tested for the approximation of real measured signals, showing its better performance compared to classical MPM, especially when dealing with dispersive signals. This improved signal decomposition can be very useful for SHM purposes.

Studies on the Marchenko–Pastur Law

In free probability, the theory of Cauchy–Stieltjes Kernel (CSK) families has recently been introduced. This theory is about a set of probability measures defined using the Cauchy kernel similarly to natural exponential families in classical probability that are defined by means of the exponential kernel. Within the context of CSK families, this article presents certain features of the Marchenko–Pastur law based on the Fermi convolution and the t-deformed free convolution. The Marchenko–Pastur law holds significant theoretical and practical implications in various fields, particularly in the analysis of random matrices and their applications in statistics, signal processing, and machine learning. In the specific context of CSK families, our study of the Marchenko–Pastur law is summarized as follows: Let K+(μ)={Qmμ(dx);m∈(m0μ,m+μ)} be the CSK family generated by a non-degenerate probability measure μ with support bounded from above. Denote by Qmμ•s the Fermi convolution power of order s>0 of the measure Qmμ. We prove that if Qmμ•s∈K+(μ), then μ is of the Marchenko–Pastur type law. The same result is obtained if we replace the Fermi convolution • with the t-deformed free convolution t.

SeisResoDiff: Seismic resolution enhancement based on a diffusion model

High resolution of post-stack seismic data assists in better interpretation of subsurface structures as well as high accuracy of impedance inversion. Therefore, geophysicists consistently strive to acquire higher resolution seismic images in petroleum exploration. Although there have been successful applications of conventional signal processing and machine learning for post-stack seismic resolution enhancement, there is limited reference to the seismic applications of the recent emergence and rapid development of generative artificial intelligence. Hence, we propose to apply diffusion models, among the most popular generative models, to enhance seismic resolution. Specifically, we apply the classic diffusion model—denoising diffusion probabilistic model (DDPM), conditioned on the seismic data in low resolution, to reconstruct corresponding high-resolution images. Herein the entire scheme is referred to as SeisResoDiff. To provide a comprehensive and clear understanding of SeisResoDiff, we introduce the basic theories of diffusion models and detail the optimization objective's derivation with the aid of diagrams and algorithms. For implementation, we first propose a practical workflow to acquire abundant training data based on the generated pseudo-wells. Subsequently, we apply the trained model to both synthetic and field datasets, evaluating the results in three aspects: the appearance of seismic sections and slices in the time domain, frequency spectra, and comparisons with the synthetic data using real well-logging data at the well locations. The results demonstrate not only effective seismic resolution enhancement, but also additional denoising by the diffusion model. Experimental comparisons indicate that training the model on noisy data, which are more realistic, outperforms training on clean data. The proposed scheme demonstrates superiority over some conventional methods in high-resolution reconstruction and denoising ability, yielding more competitive results compared to our previous research.

Machine learning assisted designing of conjugated organic chromophores, light absorption, and emission behavior prediction

Organic materials have several important characteristics that make them suitable for use in optoelectronics and optical signal processing applications. For absorption and emission maxima, the stabilities and photoactivities of conjugated organic chromophores can be tailored by selecting a suitable parent structure and incorporating substituents that predictably change the optical characteristics. However, a high-throughput design of efficient conjugated organic chromophores without using trial-and-error experimental approaches is required. In this study, machine learning (ML) is used to design and test the conjugated organic chromophores and predict light absorption and emission behavior. Many machine learning models are tried to select the best models for the prediction of absorption and emission maxima. Extreme gradient boosting regressor has appeared as the best model for the prediction of absorption maxima. Random forest regressor stands out as the best model for the prediction of emission maxima. Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) is used to generate 10,000 organic chromophores. Chemical similarity analysis is performed to obtain a deeper understanding of the characteristics and actions of compounds. Furthermore, clustering and heatmap approaches are utilized.

Switching and Frequency Response Assessment of Photovoltaic Drivers and Their Potential for Different Applications.

Newly introduced Photovoltaic (PV) devices, featuring a built-in chip with an illuminating Light Emitting Diode (LED), have emerged in the commercial market. These devices are touted for their utility as both low- and high-side power switch drivers and for data acquisition coupling. However, comprehensive knowledge and experimentation regarding the limitations of these Photovoltaic Drivers in both switching and signal processing applications remain underexplored. This paper presents a detailed characterization of a Photovoltaic Driver, focusing on its performance under resistive and capacitive loads. Additionally, it delineates the device's constraints when employed in signal processing. Through the analysis of switching losses across various power switches (Silicon and Silicon Carbide) in both series and parallel driver configurations, this study assesses the driver's efficacy in operating Junction Field-Effect Transistors (JFETs). Findings suggest that Photovoltaic Drivers offer a low-cost, compact solution for specific applications, such as high-voltage, low-bandwidth measurements, and low-speed turn-on with fast turn-off power switching scenarios, including solid-state switches and hot-swap circuits. Moreover, they present a straightforward, cost-effective method for driving JFETs, simplifying the circuit design and eliminating the need for an additional negative power source.

A Retina-inspired Sampling Method for Coding and Compression for Enhancement Image Based on Discrete Double Density Wavelet Transformer

The retina, a layer of light-sensitive tissue on the inner surface of the eye, is what allows for vision. That propose to design and analytically examine an image quantizer coding that is inspired by the way the retina manages and compresses visual features based on past studies. Retina-inspired coding is a new cipher algorithm which is very promising. Comparing the Double-Density Discrete Wavelet Transform (DDDWT) to earlier traditional approaches, it is more difficult but also produces excellent results. This efficient coding is credited to a streamlined account that, by the way, deals with 2-D and 3-D pictures, transformation matrices, and the conversion of the number DDWT as if via matrix multiplication. Naturally, this code makes a lot of interesting points of view available. In this proposal, we'll try to show how various theoretical model extensions can be applied to applications in video surveillance, information technology, and neuroscience. Therefore, that believe that using a multidisciplinary approach will help system achieve objectives. This strategy would apply signal processing methods with data from neurophysiology. There are hoping that our efforts will result in some innovative new coding algorithms. The proposed codes will be implemented in MATLAB for ease of experimentation. To implement this code more successfully, a low-level programming language is required. Index Terms— DDDWT, Retina-inspired, Coding.

Nonlinear photonics on integrated platforms

Abstract Nonlinear photonics has unveiled new avenues for applications in metrology, spectroscopy, and optical communications. Recently, there has been a surge of interest in integrated platforms, attributed to their fundamental benefits, including compatibility with complementary metal-oxide semiconductor (CMOS) processes, reduced power consumption, compactness, and cost-effectiveness. This paper provides a comprehensive review of the key nonlinear effects and material properties utilized in integrated platforms. It discusses the applications and significant achievements in supercontinuum generation, a key nonlinear phenomenon. Additionally, the evolution of chip-based optical frequency combs is reviewed, highlighting recent pivotal works across four main categories. The paper also examines the recent advances in on-chip switching, computing, signal processing, microwave generation, and quantum applications. Finally, it provides perspectives on the development and challenges of nonlinear photonics in integrated platforms, offering insights into future directions for this rapidly evolving field.

Universal machine learning approach to volcanic eruption forecasting using seismic features

Introduction: Volcano seismology has successfully predicted several eruptions and includes many reliable methods that have been adopted extensively by volcanic observatories; however, there are several problems that still lack solutions. Meanwhile, the overwhelming success of data-driven models to solve predictive complex real-world problems positions them as an effective addition to the monitoring systems deployed in volcanological observatories.Methods: By applying signal processing techniques on seismic records, we extracted four different seismic features, which usually change their trend when the system is approaching an eruptive episode. We built a temporal matrix with these parameters then defined a label for each temporal moment according to the real state of the volcanic activity (Unrest, Pre-Eruptive, Eruptive). To solve the remaining problem developing early warning systems that are transferable between volcanoes, we applied our methodology to databases associated with different volcanic systems, including data from both explosive and effusive episodes, recorded at several volcanic scenarios with open and closed conduits: Mt. Etna, Bezymianny, Volcán de Colima, Mount St. Helens and Augustine.Results and Discussion: This work proposes the use of Neural Networks to classify the volcanic state of alert based on the behaviour of these features, providing a probability of having an eruption. This approach offers a Machine Learning tool for probabilistic short-term volcanic eruption forecasting, transferable to different volcanic systems. This innovative method classifies the state of volcanic hazard in near real-time and estimates a probability of the occurrence of an eruption, resulting in a period from at least hours to several days to forecast an eruption.

Design and implementation of a nano-scale high-speed multiplier for signal processing applications

Digital signal processing (DSP) is an engineering field involved with increasing the precision and dependability of digital communications and mathematical processes, including equalization, modulation, demodulation, compression, and decompression, which can be used to produce a signal of the highest caliber. To execute vital tasks in DSP, an essential electronic circuit such as a multiplier plays an important role, continually performing tasks such as the multiplication of two binary numbers. Multiplier is a crucial component utilized to implement a wide range of DSP tasks, including convolution, Fourier transform, discrete wavelet transforms (DWT), filtering and dithering, multimedia information processing, and more. A multiplier device includes a clock and reset buttons for more flexible operational control. Each digital signal processor constitutes a multiplier unit. A multiplier unit functions entirely autonomously from the central processing unit (CPU); consequently, the CPU is burdened with a significantly reduced amount of work. Since DSP algorithms must constantly carry out multiplication tasks, the employment of a high-speed multiplier to execute fast-speed filtering processes is vital. The previous multipliers had lots of weaknesses, such as high energy, low speed, and high area, because they implemented this necessary circuit based on traditional technology such as complementary metal-oxide semiconductor (CMOS) and very large-scale integration (VLSI). To solve all previous drawbacks in this necessary circuit, we can use nanotechnology, which directly affects the performance of the multiplier and can overcome all previous issues. One of the alternative nanotechnologies that can be used for designing digital circuits is quantum dot cellular automata, which is high speed, low area, and low power. Therefore, this manuscript suggests a quantum technology-based multiplier for DSP applications. In addition, some vital circuits, such as half adder, full adder, and ripple carry adder (RCA), are suggested for designing a multiplier. Moreover, a systolic array, accumulator, and multiply and accumulate (MAC) unit are proposed based on the quantum technology-based multiplier. Nonetheless, each of the suggested frameworks has a coplanar configuration without rotated cells. The suggested structure is developed and verified utilizing the QCADesigner 2.0.3 tools. The findings showed that all circuits have no complicated configuration, including a higher number of quantum cells, latency, and an optimum area.