Signal Processing Applications Research Articles

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.

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.

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.

Hybrid Radix-16 booth encoding and rounding-based approximate Karatsuba multiplier for fast Fourier transform computation in biomedical signal processing application

Multiplication is an essential biomedical signal processing function implemented in the Digital Signal Processing (DSP) cores. To enhance the speed, area and energy efficiency of DSP cores, approximate multiplication is used. Also, low power multiplier unit design is one of the requirements of DSP processor to meet the increasing demands. To balance both the design and error metrics of a multiplier design, an efficient Hybrid Radix-16 Booth Encoding and rounding-based approximate Karatsuba Multiplier (RBEKM-16) is proposed. This research introduces an Approximate Karatsuba multiplier based on rounding, utilizing rounding approximation to compute the least significant part of the product. Simple operators, like adders and multiplexers, replace complex and costly conventional Floating-Point (FP) multipliers in this process. Radix-4 logarithms are incorporated to further minimize hardware complexity and calculate the product's most significant part. Subsequently, an approximate 4-2 compressor is applied in the partial product reduction stage to generate the most significant bit result. In the experimental scenario, the efficiency of the multiplier is evaluated in terms of energy efficiency, area utilization and error rate by using Xilinx ISE 8.1i tool. The results from the experiments indicate that the suggested multiplier demonstrates improved energy efficiency, utilizes space more effectively, and performs well in applications related to biomedical signal processing. Further, the accomplished area utilization of the proposed 16-bit multiplier is 1068 μm2, delay is 3.01 ns, power consumption is 0.021 mW and power delay product is 119 fJ.

Designing signal processing for robotic olfaction based on electroantennogram

ABSTRACT In this study, we experimentally evaluated a signal-processing method for the stable use of an insect antenna as an odor sensor for a robot because of its excellent sensitivity and selectivity to odors. Changes in potential in response to an odor stimulus can be measured when the electrodes are connected to both ends of an insect antenna. This is called an electroantennogram (EAG), and is a type of physiological response. Hence, we must extract only the EAG waveform using appropriate signal processing because it has a low signal-to-noise ratio. Accordingly, we applied signal processing based on an exponentially weighted moving average (EWMA), which demonstrates excellent real-time performance, to an EAG to eliminate high-frequency noise. Moreover, we experimentally investigated the sampling rate to properly measure the EAG. From the results, the EAG-driven robot with the designed signal processing demonstrated a search performance of more than 70 % , even in odor-source localization experiments with different types of moths and environments.

3C-SiC phononic waveguide for manipulating mechanical wave propagation

We present experimental demonstration and modeling of mechanical wave propagation in a quasi-one-dimensional (quasi-1D) phononic crystal (PnC) waveguide (WG) constructed from a periodic array of single-crystal cubic-silicon carbide (3C-SiC) coupled micromechanical resonators, with an exceptional dynamic range exceeding 92 dB. The PnC design comprises 50 periodic cells, enabling the propagation of flexural mechanical waves in high-frequency and very-high-frequency bands, featuring a broad PnC bandgap spanning approximately 24–27.5 MHz. Furthermore, the 3C-SiC PnC WG exhibits excellent characteristics, including a high group velocity of 350 m/s and a low transmission loss of 0.69 dB/mm, enabling efficient guidance and support for mechanical waves across extended distances before reaching the noise level of the device. These attributes of the PnC WG, as demonstrated in this study, may open possibilities for the development of device platforms with applications in on-chip signal processing, sensing, and quantum transducer technologies.

Towards multidimensional attentive voice tracking—estimating voice state from auditory glimpses with regression neural networks and Monte Carlo sampling

Selective attention is a crucial ability of the auditory system. Computationally, following an auditory object can be illustrated as tracking its acoustic properties, e.g., pitch, timbre, or location in space. The difficulty is related to the fact that in a complex auditory scene, the information about the tracked object is not available in a clean form. The more cluttered the sound mixture, the more time and frequency regions where the object of interest is masked by other sound sources. How does the auditory system recognize and follow acoustic objects based on this fragmentary information? Numerous studies highlight the crucial role of top-down processing in this task. Having in mind both auditory modeling and signal processing applications, we investigated how computational methods with and without top-down processing deal with increasing sparsity of the auditory features in the task of estimating instantaneous voice states, defined as a combination of three parameters: fundamental frequency F0 and formant frequencies F1 and F2. We found that the benefit from top-down processing grows with increasing sparseness of the auditory data.

Multiplicative Basis Function Based Adaptive DOA Estimation in Optimal MIMO Sparse Antenna Reconfiguration Model

Direction of arrival (DOA) estimation using sparse signal representation has gained significant attention in recent decades. The spatial signals utilized for DOA estimation are reconstructed through a novel compressive sensing (CS) approach. In this study, we used a CS approach to ascertain the adaptive underdetermined DOA for multiple inputs and multiple output (MIMO) sparse media. Accurate DOA estimation through the CS approach contributes to enhanced signal investigation and the suppression of undesired noise in a sparse channel. The method proposed herein encompasses the development and execution of a novel multiplicative basis function known as the multi-kernel supported non-negative sparse Bayesian learning (NNSBL) algorithm, which is implemented on predefined grid values. Concurrently, stochastic grey wolf optimization (GWO) is virtually applied to increase the degrees of freedom (DOF) on a minimum redundancy array (MRA) while considering an optimal antenna reconfiguration model. The advantages of this proposed approach include the generation of a distinct manifold matrix for precise beam steering, resulting in improved DOA estimation accuracy. Additionally, an augmentation of DOF using GWO was observed, with low computational complexity. The simulated result of proposed algorithm leads to the attainment of optimal minimum root mean square error (RMSE) across various optimal wavelengths of the stochastic sources. Finally, a comparative analysis of RMSE and convergence plots confirms the superior performance and high potentiality of the proposed method in comparison to existing techniques across different SNR values. Notably, there is significant reduction in RMSE which is suitable for precise DOA estimation in signal processing applications.

Condition Monitoring of a Cartesian Robot with a Mechanically Damaged Gear to Create a Fuzzy Logic Control and Diagnosis Algorithm

The detection of faults during an operational process constitutes a crucial objective within the framework of developing a control system to monitor the structure of industrial mechanisms. Even minor faults can give rise to significant consequences that require swift resolution. This research investigates the impact of overtension in the tooth belt transmission and heating of the screw transmission worm on the vibration signals in a robotic system. Utilizing FFT techniques, distinct frequency characteristics associated with different faults were identified. Overtension in the tooth belt transmission caused localized oscillations, addressed by adjusting the acceleration and deceleration speeds. Heating of the screw transmission worm led to widespread disturbances affecting servo stress and positioning accuracy. A fuzzy logic algorithm based on spectral analysis was proposed for adaptive control, considering the vibration’s frequency and amplitude. The simulation results demonstrated effective damage mitigation, reducing wear on the mechanical parts. The diagnostic approach, relying on limited data, emphasized the feasibility of identifying transmission damage, thereby minimizing maintenance costs. This research contributes a comprehensive and adaptive solution for robotic system diagnostics and control, with the proposed fuzzy logic algorithm showing promise for efficient signal processing and machine learning applications.

Acoustic Resonance Tuning by High-Order Lorentzian Mixing.

Nanoscale mechanical resonators have attracted a great deal of attention for signal processing, sensors, and quantum applications. Recent progress in ultrahigh-Q acoustic cavities in nanostructures allows strong interactions with various physical systems and advanced functional devices. Those acoustic cavities are highly sensitive to external perturbations, and it is hard to control those resonance properties since those responses are determined by the geometry and material. In this paper, we demonstrate a novel acoustic resonance tuning method by mixing high-order Lorentzian responses in an optomechanical system. Using weakly coupled phononic-crystal acoustic cavities, we achieve coherent mixing of second- and third-order Lorentzian responses, which is capable of the fine-tunability of the bandwidth and peak frequency of the resonance with a tuning range comparable to the acoustic dissipation rate of the device. This novel resonance tuning method can be widely applied to Lorentzian-response systems and optomechanics, especially active compensation for environmental fluctuation and fabrication errors.

A complexity efficient penta-diagonal quantum smoothing filter for bio-medical signal denoising: a study on ECG

Extracting information-bearing signal from a noisy environment has been a practical challenge in both classical and quantum computing formalism, especially in critical signal processing applications. To filter out the effect of noise, we propose a quantum smoothing filter built upon quantum formalism-based circuits applied for electrocardiogram signal denoising. The proposed quantum filter is a conceptually novel framework with an advantage in computational complexity as compared to the existing classical filters, such as discrete wavelet transform and empirical mode decomposition, whereas it achieves similar performance metrics for the accuracy of the filter. Further, we exploit the penta-diagonal Toeplitz structure of the smoothing filter, which gives approximately 48%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$48\\%$$\\end{document} gate cost reduction for 10 qubit circuit compared to the standard Hamiltonian simulation without structure. The run-time complexity using the quantum matrix inversion technique for the structured matrix is given by O~κ2poly(logN)εP\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ilde{{\\mathscr {O}}}\\left( \\frac{\\kappa ^2 \ ext {poly}(\\log {N})}{\\varepsilon _P}\\right)$$\\end{document} for condition number κ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\kappa$$\\end{document} of the N×N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N\ imes N$$\\end{document} filter matrix within precision εP\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varepsilon _P$$\\end{document}. Embedding fixed sparsity of the banded matrix, the quantum filter shows potentially better run-time complexity than classical filtering techniques. For the quantifiable research results of our work, we have shown several performance metrics, such as mean-square error and peak signal-to-noise ratio analysis, with a bound of error due to observation noise, simulation error and quantum measurement uncertainty.

Low-Power Approximate Unsigned and Signed Multipliers with Configurable Error Recovery

In the field of Digital Signal Processing and similar applications, Approximate circuits are being explored as a means to enhance performance and energy efficiency by sacrificing a degree of accuracy. Within these circuits, Multipliers play a crucial role and are being investigated for their potential impact on overall system optimization. In this paper, a novel approximate multiplier with a low power consumption and a short critical path is proposed for high-performance DSP applications. This multiplier leverages a newly designed approximate adder that limits its carry propagation to the nearest neighbours for fast partial product accumulation. Different levels of accuracy can be achieved by using either OR gates or the proposed approximate adder in a configurable error recovery. The multipliers using these two error reduction strategies are referred to as Approximate Multiplier 1 (AM1) and Approximate Multiplier 2 (AM2), respectively. Both AM1 and AM2 have a low mean error distance, i.e., most of the errors are not significant in magnitude. Compared with a Unsigned multiplication multiplier, with the signed multiplication. The signed multiplication tends to have lower power consumption is observed. By utilizing an appropriate error recovery, the proposed approximate multipliers achieve similar processing accuracy as traditional exact multipliers, but with significant improvements in power.

Review Study on Recent Advancements in Islanding Detection and Diagnosis in Microgrids Using Signal Processing and Machine Learning Techniques

The integration of renewable energy sources and microgrids has become a key focus in the pursuit of sustainable and resilient power systems. Microgrids, being decentralized and often operating in isolation from the main grid, face unique challenges, including the need for accurate islanding detection and diagnosis to ensure safe and efficient operations. This review article comprehensively investigates and evaluates the application of signal processing and machine learning techniques in the context of islanding detection and diagnosis within microgrids. The significance of islanding detection and diagnosis is highlighted in this review study which emphasizes grid stability, safety risk mitigation, and energy efficiency enhancement during islanding. Further, the study explores the technical aspects, covering signal processing techniques and machine learning approaches used for islanding detection, including harmonic analysis, wavelet transforms, and neural networks. This research explores the most recent developments in the domain, encompassing a variety of strategies that harness sophisticated algorithms and data analysis to improve the dependability and effectiveness of microgrid operations. Subsequently, this review sheds light on the state-of-the-art methodologies, challenges, and promising avenues in islanding detection and diagnosis, ultimately contributing to the advancement of microgrid technology and the broader vision of sustainable and resilient energy systems. As the transition toward renewable energy sources accelerates, microgrids represent a promising solution for enhancing grid resilience and integrating distributed generation. However, ensuring the safety and efficiency of microgrid operations during islanding events is a critical concern. This study explores the intersection of signal processing and machine learning, offering a comprehensive examination of islanding detection and diagnosis techniques in microgrids.

Development of a high-performance arithmetic and logic unit for efficient digital signal processing based on reversible logic and quantum dots

Digital Signal Processing (DSP) finds a wide range of applications in various fields, including telecommunications, audio and video processing, biomedical engineering, radar systems, and image processing. Previous DSP designs faced limitations in available processing power and computational resources. Insufficient processing power could result in slower execution times, an inability to handle complex algorithms, or limited capacity to process high-speed or large-scale signals. As the demand for minimal power consumption in DSP circuits continues to grow, reversible logic and quantum-dot cellular automata (QCA) have emerged as promising technologies due to their inherent ability to reduce energy loss. Within this landscape, the arithmetic and logic unit (ALU) plays a vital role in complex circuitry, serving as a key component in digital signal processing applications. However, challenges persist, including high quantum cost and the need to limit the number of cells in the ALU design. To address these challenges, our research aims to develop an efficient ALU by integrating reversible logic and QCA technology. Our focus will be on generating essential components, such as Feynman gates, Fredkin gates, and full adder circuits, which serve as foundational building blocks for reversible logic and QCA designs. These components will be combined to construct a comprehensive ALU capable of performing 20 different operations. Our implementation efforts will be centered around QCADesigner, with a specific emphasis on digital signal processing systems that prioritize energy efficiency and optimal utilization of occupied areas.

Design Wallace Tree Multiplier Using Reversible Gates

Abstract: Multipliers are essential components in digital circuit design. They have wide spread applications in digital signal processing and communication systems. Circuit designers in VLSI design seek compact, small scale circuits with low power consumption and minimal delay. The Wallace tree multiplier is an advanced version of tree based multipliers. Numerous algorithms have been developed to create the fastest multipliers, and the Wallace tree multiplier is one such example. It utilizes reversible compressors, full adders, and half adders. The results demonstrate that the Wallace tree multiplier using reversible gates outperforms traditional multipliers in terms of power dissipation and delay, with values of 0.027W and 29.327nS re­spectively.

Construction of non-stationary biorthogonal Wavelets based on cardinal Chebyshev B-spline

In this paper, we propose a novel method for designing non-stationary biorthogonal wavelets that leverages Bezout equations to compute wavelet filter coefficients. Our approach integrates cardinal Chebyshev B-splines of varying degrees (second, third, and fourth) as bases for constructing these wavelets, providing explicit formulations for both low-pass and high-pass filters at all levels. By employing this method, we streamline the design process, making it more efficient and accessible while accommodating diverse processing requirements. The utilization of Bezout equations offers a systematic framework for deriving wavelet filter coefficients, enhancing the reproducibility and reliability of the design process. This systematic approach ensures that the resulting wavelets possess desired properties such as vanishing moments, smoothness, and optimal localization. Moreover, by incorporating cardinal Chebyshev B-splines, we can effectively capture intricate signal and image features, addressing challenges encountered in traditional wavelet design methods. Our method not only simplifies the design process but also provides a comprehensive solution by explicitly formulating filters and dual filters across all levels. This comprehensive approach ensures that the resulting wavelets meet the specific demands of applications in signal and image processing, including compression, denoising, and pattern recognition. These advancements hold significant implications for various domains, including information technology, healthcare, and engineering, where efficient and reliable processing techniques are crucial. Moving forward, it is imperative to refine algorithms, assess parameter robustness, and conduct thorough empirical validations across diverse datasets and application scenarios. This will ensure the efficacy and scalability of our proposed method and pave the way for further advancements in non-stationary wavelet design and its applications. By continuously improving and validating our approach, we can foster innovation and enhance practical outcomes in a wide range of fields.

Optimized wireless communication for 6G signal processing using memory‐augmented deep unfolding network

SummaryIn this manuscript, optimized wireless communication for 6G signals processing using memory‐augmented deep unfolding network (WC‐6G‐SP‐MADUN) is proposed. The aim of this work is to give a memory‐augmented deep unfolding network approach with a focus on belief propagation decoding of error correction codes and multiple‐input multiple‐output (MIMO) wireless systems. In contrast, compares and tabulates the performance of memory‐augmented deep unfolding methodologies with classic principled procedures, which could potentially enable 6G. Here, the wireless communication for 6G signal processing methods was used for a variety of receiver functions, including self‐interference cancelation, signal estimation and detection using memory‐augmented deep unfolding Network (MADUN), and advanced error correction by clouded leopard optimization algorithm (CLOA). Furthermore, outline the general framework of memory‐augmented deep unfolding method for any kind of signal processing application and talk about cutting edge research paths that will make it possible to build communication networks in the future. The proposed approach is implemented in MATLAB; the performance is analyzed with performance metrics such as reliability, complexity, SNR gain, convergence rate, hardware efficiency, and quantization bit width. The proposed approach attains higher SNR gain 17.96%, 24.75%, and 30.09%; lower complexity 17.92%, 23.41%, and 30.13%; and higher convergence 18.01%, 25.41%, and 31.39% when analyzed with existing techniques such as redefined wireless communication for 6G: signal processing meets deep learning and deep unfolding (RWC‐6G‐SP‐DU), lower complication deep unfolded neural network receiver for MIMO under probability data association detector (LC‐DUNN‐MIMO‐PDAD), and deep learning in physical layer communications: evolution with prospects on 5G and 6G networks (DL‐PLC‐6G), respectively.

Investigating the existence, uniqueness, and stability of solutions in boundary value problem of fractional differential equations

This study uses fixed point theory and the Banach contraction principle to prove the existence, uniqueness, and stability of solutions to boundary value problems involving a Ψ-Caputo-type fractional differential equation. The conclusions are supported by illustrative cases, which raise the theoretical framework’s legitimacy. Fractional calculus is widely used in scientific fields, as seen by its applications in beam deflection analysis, groundwater pollution, and biomedical signal processing.

Valley Pseudospin Polarized Evanescent Coupling between Microwave Ring Resonator and Waveguide in Phononic Topological Insulators.

A coupled ring-waveguide structure is at the core of bosonic wave-based information processing systems, enabling advanced wave manipulations such as filtering, routing, and multiplexing. However, its miniaturization is challenging due to momentum conservation issues in rings with larger curvature that induce significant backscattering and radiation leakage and hampering stable operation. Here, we address it by taking an alternative approach of using topological technology in wavelength-scale and microwave ring-waveguide coupled systems built in nanoengineered phononic crystals. Our approach, which leverages pseudospin conservation in valley topological systems, eliminates phonon backscattering and achieves directional evanescent coupling. The resultant hypersonic waves in the tiny ring exhibit robust transport and resonant circulation. Furthermore, the ring-waveguide hybridization enables critical coupling, where valley-dependent ring-waveguide interference blocks the transmission. Our findings reveal the capability of topological phenomena for managing ultrahigh-frequency phonons in nano/microscale structures and pave the way for advanced phononic circuits in classical and quantum signal processing applications.