Signal Processing Applications Research Articles

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.

Highly stable fiber-based photonic delay line with large and tunable delay.

A stable photonic delay line with large and tunable delay is essential for large-distance simulation, beamforming, and diverse photonic signal processing applications. Here, we demonstrate a fiber-based tunable photonic delay line (TPDL) with a maximum delay of 905 µs. Its environmental-related delay jitter is compensated for by a homodyne phase-locked loop (PLL). Precise delay tuning is realized by changing the phase of the reference with a minimum tuning step of 0.5 ps without breaking its locking state. The demonstrated delay line shows exceptional stability, as indicated by an overlapping Allan deviation (ADEV) of 2.06 × 10-17 at the averaging time of 1000 s and the delay jitter below 20 fs. Its high stability, wide delay range, wideband characteristics, and precise tunability make the TPDL an ideal photonic delay line for the above-mentioned applications.

Tuning the EKF using estimated measurements (EKF-EM) for balanced performances

In this paper, the conventional Extended Kalman Filter (EKF) algorithm has been systematically extended to the estimated measurement (EKF-EM) paradigm. The derivation of the EKF-EM state and state error estimates establishes the relation of the robustness and sensitivity metrics to the measurement projected Kalman gain. Recently, these metrics are being used ad-hoc for obtaining desired EKF performances in various signal processing applications. The EKF-EM is used to develop a process noise adjustment algorithm to tune the EKF for balanced performances. This proposed method is applied and validated in 3 diverse applications:(a) ensure convergent comparable filter performances for an originally divergent EKF in the 3D Lorenz attractor problem, (b) obtain spectral and audio improvement of a practical noise corrupted speech signal and (c) determine a suitable process model out of several options for balanced filter performances in a realistic 2D ballistic target tracking scenario.

Automatic classification of multi-carrier modulation signal using STFT spectrogram and deep CNN

In the realm of communication systems, categorizing Multi-Carrier Modulation (MCM) signals without cooperative communication poses a significant technical challenge. In this paper, we introduce a novel approach for accurately categorizing five distinct MCM signals, including Orthogonal Frequency Division Multiplexing (OFDM), Filter Bank Multicarrier (FBMC), Filtered Orthogonal Frequency Division Multiplexing (FOFDM), Windowed Orthogonal Frequency Division Multiplexing (WOLA), and Universal Filtered Multicarrier (UFMC). Each signal is considered with two types of subcarrier waveforms, Quadrature Amplitude Modulation 16 (QAM16) and Quadrature Amplitude Modulation 64 (QAM64), resulting in a total of 10 unique MCM signals for classification. Our proposed methodology leverages Short-Time Fourier Transform (STFT) spectrograms for feature extraction at the frontend, while at the backend, we employ three variants of Convolutional Neural Network (CNN) models; CNN, CNN with Dropout (CNN_d), CNN with both Dropout and L1 Regularization (CNN_dL1) and one deep CNN model; Xception, individually. We aim to provide an efficient and reliable means of categorizing MCM signals, with practical applications in signal processing and communication systems. Extensive simulations demonstrate the effectiveness of our approach, achieving remarkable accuracies. Notably, the Xception model exhibits the highest accuracy among the four models considered. Specifically, we attain an accuracy of 98% at 10 dB SNR using the Xception model. These results underscore the efficacy of our proposed methodology and highlight the potential for its deployment in real-world scenarios.

Microwave assisted efficient non-degenerate four-wave mixing in pulsed regime

We theoretically investigate a N-type 87Rb atomic system for efficient generation and control of a non-degenerate four wave mixing (FWM) signal in pulsed regime. The susceptibility of the atomic medium is customized as a gain profile by a weak probe pulse and two strong continuous wave control fields which allow us to generate the pulsed FWM signal. We study the propagation dynamics of the generated FWM signal inside the nonlinear medium. The FWM signal obtains the exact shape of the probe pulse and travels without changing the shape whereas, the probe pulse is absorbed inside the nonlinear medium. The conversion efficiency of this scheme without a MW field is 5.36%. However, a MW field that couples two metastable ground states enhances the conversion efficiency to 20.6%. The generation and control of such FWM signal in pulsed regime has important applications in signal processing, optical communication and information science.

Fine-tuning digital FIR filters with gray wolf optimization for peak performance

The design of optimum filters constitutes a fundamental aspect within the realm of signal processing applications. The process entails the calculation of ideal coefficients for a filter in order to get a passband with a flat response and an unlimited level of attenuation in the stopband. The objective of this work is to solve the FIR filter design problem and to compare the optimal solutions obtained from evolutionary algorithms. The design of optimal FIR low pass (LP), high pass (HP), and band stop (BS) filters is achieved by the utilization of nature-inspired optimization approaches, namely gray wolf optimization ,cuckoo search, particle swarm optimization, and genetic algorithm. The filters are evaluated in terms of their stop band attenuation, pass band ripples, and departure from the anticipated response. In addition, this study compares the optimization strategies applied in the context of algorithm execution time which is achievement of global optimal outcomes for the design of digital finite impulse response (FIR) filters. The results indicate that when the Gray wolf algorithm is applied to the development of a finite impulse response (FIR) filter, it produces a higher level of performance than other approaches, as supported by enhanced design precision, decreased execution time, and achievement of an optimal solution.

Structural, spectroscopic (FTIR, FT-Raman and UV–Vis) investigations, Fukui function analysis and thermodynamic properties of diethyl 3,3′-(ethane-1,2-diylbis(azanediyl))(2Z,2′Z)-bis(but-2-enoate)

Diethyl 3,3′-(ethane-1,2-diylbis(azanediyl))(2Z,2′Z)-bis(but‑2-enoate) (DBE) molecule is characterized by infrared, Raman and UV–visible spectroscopic techniques. The structural parameters obtained using density functional theory (DFT) with B3LYP/6-311++G(d,p) level of theory closely match the experimental data. The molecule's characteristic functional groups were identified through vibrational spectra analysis. The IR and Raman spectra vibrational peaks were assigned based on Potential Energy Distribution (PED) analysis. Excitation wavelengths, oscillator strengths and excitation energies were determined through time dependent density functional theory (TD-DFT) calculations and compared to experimental data. Nonlinear optical (NLO) properties were calculated to predict the electric dipole moment and first-order hyperpolarizability of the molecule. The significant nonlinear optical properties values indicated that these materials possess excellent nonlinear optical properties, making them suitable for applications in telecommunication and signal processing. The Mulliken atomic charge, chemical activity analysis and Fukui functions were utilized to investigate the electron-poor, rich and reactive sites in conjunction with the optimized structures using the DFT method with B3LYP function and 6-311++G(d,p) basis set. Thermodynamic investigations have shown that key molecular parameters such as entropy, heat capacity and enthalpy increase as temperature rises.

Tensor Golub–Kahan method based on Einstein product

The Singular Value Decomposition (SVD) of matrices is a widely used tool in scientific computing. In many applications of machine learning, data analysis, signal and image processing, the large datasets are structured into tensors, for which generalizations of SVD have already been introduced, for various types of tensor–tensor products. In this paper, we present innovative methods for approximating this generalization of SVD to tensors in the framework of the Einstein tensor product. These singular elements are called singular values and singular tensors respectively. The proposed method uses the tensor Lanczos bidiagonalization applied to the Einstein product. In most applications, as in the matrix case, the extremal singular values are of special interest. To enhance the approximation of the largest or the smallest singular triplets (singular values and left and right singular tensors), a restarted method based on Ritz augmentation is proposed. Numerical results are proposed to illustrate the effectiveness of the presented method. In addition, applications to video compression and facial recognition are presented.

Signal Processing Based on Butterworth Filter: Properties, Design, and Applications

This paper presents a comprehensive exploration of the properties, principles, design methodologies and practical applications of Butterworth filters. The Butterworth filters are acknowledged for their flat frequency response in the passband, which is at a maximal level, while introducing minimal distortion to the signal of interest. Therefore, they were commonly applied in various situations. The study centers on a detailed investigation of a specific case involving the analysis of a third-order Butterworth lowpass filter circuit using Sallen-Key circuit topology. The Sallen-Key topology was basically utilized to achieve the Butterworth filter in the second-order circuit design. Moreover, in order to complete the third-order Butterworth filter design, an additional circuit part was also established apart from the second-order Butterworth filter. The simulation of the circuit was done in the software LTspice. This article will provide an overview of the research, discussing the essential characteristics of Butterworth filters and their significance in signal processing. The findings of this study contribute to a deeper understanding of Butterworth filters and their role in various signal processing applications.

Advancements in Filter Technology: Evolution and Applications

This paper explores the historical trajectory and contemporary applications of signal processing filters. From the early days of analog filters to the current digital era, the paper traces their evolution, emphasizing key developments in active RC filters, monolithic integrated op amps, and the transition to switched-capacitor and continuous-time filters. Fundamentals of analog and digital filters are outlined, distinguishing their processing of continuous-time and discrete-time signals. The classification of filters, including low-pass, high-pass, band-pass, and band-stop, is discussed with insights into their specific applications. The paper delves into crucial applications in audio processing, video processing, communication systems, power management, and sensor signal processing, showcasing filters' pivotal role in enhancing signal quality across diverse fields. Addressing emerging technologies, the paper highlights adaptive and deep learning filters, projecting the future trends in filter technology. It anticipates developments in analog integrated filters, emphasizing the growing significance of integrated circuits technology. This comprehensive review aims to deepen readers' understanding and inspire continued innovation in signal processing.