Signal Processing Applications Research Articles

An efficient infinity norm minimization algorithm for under-determined inverse problems

The problem of solving under-determined systems of linear equations with minimum peak magnitude (ℓ∞ norm) has numerous applications in signal processing. These include Peak-to-Average Power Ratio (PAPR) reduction in MIMO-OFDM systems, vector quantization, approximate nearest neighbor search, optimal control in robotics, and power grid optimization. Several methods have been proposed to address this problem, but they often face limitations in computational speed or representation accuracy. Some methods also impose constraints on the frame matrix, such as restrictions on the type of its entries or its aspect ratio. In this paper, we present the Fast Iterative Peak Shrinkage Algorithm (FIPSA), which iterates over feasible solutions to consistently reduce peak magnitude and provably converge to near-optimal solutions. Our experimental results, conducted across various frame matrix types and aspect ratios, demonstrate that FIPSA consistently achieves near-minimal ℓ∞ norm values. In addition, it operates 1.3 to 7.3 times faster than previous methods, while maintaining an average representation error of 10−15. Notably, these advancements are achieved without imposing any constraints on the frame matrix.

Application of ECG signal processing and classification based on CNN in wearable devices

Application of ECG signal processing and classification based on CNN in wearable devices

Design of energy-efficient full subtractor circuit at near threshold computing for signal processing application

Abstract Energy Efficiency is a critical factor while designing integrated circuits. Therefore, a 1-bit full subtractor (FS) cell is proposed for lower power application by employing Gate Level Body Biasing (GLBB) scheme for Near Threshold Computing (NTC) application to conquer a unique module for achieving full swing borrow output.We evaluate power, delay, energy and the product of energy with delay (EDP) metrics with respect to C-CMOS full subtractor. The proposed feedback based with FS with GLBB technique has a total die area of 60.02 μm2, while the average power, delay, and energy are 1138 pW, 242 ns, and 27.53 aJ, respectively. The results revealed that our proposed subthreshold hybrid FS circuit with GLBB scheme successfully achieved more than 10.46% average power consumption, 26.58% energy consumption reductions, and 17.98% EDP savings compared to conventional CMOS configuration and other hybrid counterparts. GLBB circuits with FS achieve performance levels that are not affordable in C-CMOS, DTMOS,and GLBB with full adder configurations. Therefore, the FS circuit serves as an efficient divider circuit in terms of detecting objects for image processing applications.

Robust Characterization of Photonic Integrated Circuits

AbstractPhotonic integrated circuits (PICs) offer ultra‐broad optical bandwidths that enable unprecedented data throughputs for signal processing applications. Dynamic reconfigurability enables compensation of fabrication flaws and fluctuating external environments, tuning for adaptive equalization and training of optical neural networks. The initial step in PIC reconfiguration entails measuring its dynamic performance, often described by its frequency response. While measuring the amplitude response is straightforward, e.g. using a tunable laser and optical power meter, measuring the phase response presents challenges due to various factors, including phase variations in test connections and instrumentation limitations. To address these challenges, a universal and robust characterization technique is proposed, which uses an on‐chip reference path coupled to the signal processing core (SPC), with a delay gap larger than the total delay across the signal processing paths. A Fourier transform of the chip's power response reveals the SPC's impulse response. The method is more robust against low reference‐path power and imprecise delays. Experiments using a finite‐impulse‐response (FIR) structure demonstrate rapid SPC training, overcoming thermal crosstalk and device imperfections. This approach offers a promising solution for PIC characterization, facilitating expedited physical parameter training for advanced applications in communications and optical neural networks.

Moiré photonic superlattice-induced transparency at commensurate angle in a terahertz metasurface composed of triple layer square lattices

The control of the speed of terahertz waves is always a challenge since the bandgap of most optical materials is much larger beyond meV with subtle nonlinear susceptibility. Moiré metasurfaces are shown to exhibit wide tunable optical properties and extraordinary physical phenomena at specific commensurate angles. These can be achieved by a careful design of the metasurface to manipulate terahertz slow light. Herein, we demonstrate a triple layer Moiré metasurface with a distinct electromagnetically induced transparency (EIT) phenomenon at commensurate angles. The proposed metasurface is composed of an intrinsic square lattice embedded into another Moiré photonic superlattice made of twisted square lattice at commensurate angles of 10.39° and 7.63°. The coupling between adjacent meta-atoms on the combined metasurface leads to destructive interference of dual trapped lattice modes, which results in a transparency window at the terahertz band. A maximum group delay of 9.76 ps is found at the transparent window of 0.84 THz when the commensurate angle is 10.39°. When the commensurate angle reduces to 7.63°, the transparency window shifts to 0.57 THz with a 5.96 ps group delay. The coupled Lorentz oscillator model indicates that the nonlinear optical susceptibility at transparency windows is above zero. Our results create an approach to tune the EIT as well as slow light in the terahertz band. Our device can have potential applications in terahertz signal processing and storage.

البرمجة الضبابية وتحسين أداء الخوارزميات في بيئة متعددة المتغيرات الرتيبة

In many situations of daily life, when we start statistical analysis, we encounter data that we suffer from inaccuracy in its measurement, either due to observation or measurement errors or the lack of appropriate conditions for collecting this data, such as failure times of machines, equipment, electrical devices, medical, engineering and economic data. In many medical studies, the researcher may have a strong conviction that the regression function used to describe the relationship between two variables has a certain form or formula that can be characterized by certain ordering restrictions such as monotonicity; monotonic regression has important applications in statistics, operations research, and signal processing, and these applications are often characterized by a very large value of n. For such large-scale problems, it is of great practical importance to develop algorithms whose complexity does not increase rapidly with increasing n. It is a nonparametric regression technique that preserves order. In this thesis, the proposed fuzzy autoregressive monotonic method, fuzzy autoregressive monotonic least squares (FILSR), fuzzy autoregressive M estimator (FIM), and fuzzy autoregressive maximum likelihood estimator (FIML) were proposed. The best method for estimating the autoregressive function is the fuzzy autoregressive maximum likelihood method with a mean square error of (0.51455).

Stress Engineering of Magnetization Fluctuation and Noise Spectra in Low-Barrier Nanomagnets Used as Analog and Binary Stochastic Neurons.

A single-domain nanomagnet, shaped like a thin elliptical disk with small eccentricity, has a double-well potential profile with two degenerate energy minima separated by a small barrier of a few kT (k = Boltzmann constant and T = absolute temperature). The two minima correspond to the magnetization pointing along the two mutually anti-parallel directions along the major axis of the ellipse. At room temperature, the magnetization fluctuates randomly between the two minima, mimicking telegraph noise. This makes the nanomagnet act as a "binary" stochastic neuron (BSN) with the neuronal state encoded in the magnetization orientation. If the nanomagnet is magnetostrictive, then the barrier can be depressed further by applying (electrically generated) uniaxial stress along the ellipse's major axis, thereby gradually eroding the double-well shape. When the barrier almost vanishes, the magnetization begins to randomly assume any arbitrary orientation (not just along the major axis), making the nanomagnet act as an "analog" stochastic neuron (ASN). The magnetization fluctuation then begins to increasingly resemble white noise. The full width at half maximum (FWHM) of the noise auto-correlation function decreases with increasing stress, as the fluctuation gradually transforms from telegraph noise to white noise. Consistent with this trend, the noise spectral density exhibits a 1/fβ spectrum (at high frequencies) with β decreasing from 2.00 to 1.88 with increasing stress. Stress can thus not only reconfigure a BSN to an ASN, which has its own applications, but it can also perform "noise engineering", i.e., tune the auto-correlation function and power spectral density, having applications in signal processing.

Development of Machine Learning Algorithms Using EEG Data to Detect the Presence of Chronic Pain.

Chronic pain impacts more than one in five adults in the United States (US) and the costs associated with the condition amount to hundreds of billions of dollars annually. Despite the tremendous impact of chronic pain in the US and worldwide, the standard of care for diagnosis depends on subjective self-reporting of pain state, with no effective objective assessment procedure available. This study investigated the application of signal processing and machine learning to electroencephalography (EEG) data for the development of classification algorithms capable of differentiating subjects in pain from pain free subjects. In this study, nineteen (19) channels of EEG data were obtained from subjects in an eyes closed resting state, and ultimately data from 186 participants were used for algorithm development, including 35 healthy controls and 151 chronic pain patients. Signal processing was applied to identify noise free segments of EEG data and 6375 quantitative EEG (qEEG) measures were calculated for each subject. Various machine learning methodologies were applied to the data, with Elastic Net chosen as the optimal methodology. The final classifier developed using Elastic Net contained 34 qEEG features with non-zero weights. The classifier was able to differentiate pain versus no pain subjects with an accuracy of 79.6%, sensitivity of 82.2%, and specificity of 66.7%. The features used in the classifier were evaluated and found to align well with contemporary literature regarding changes in neurological characteristics associated with chronic pain.

Investigation of pnn inverter-based low PDP 12T gnrfet full adder for vlsi signal processing applications

Investigation of pnn inverter-based low PDP 12T gnrfet full adder for vlsi signal processing applications

Addressing Imbalanced EEG Data for Improved Microsleep Detection: An ADASYN, FFT and LDA-Based Approach

Microsleep, brief lapses in consciousness lasting less than 15 seconds, are often accompanied by feelings of fatigue and are detectable through a deceleration in electroencephalogram (EEG) signal frequencies. Accurate identification of microsleep is critical for assessing driver alertness and preventing accidents. This paper introduces a novel approach to detecting driver microsleep by leveraging EEG signals and advanced machine learning techniques. The methodology begins with preprocessing raw EEG data to improve quality and balance, utilizing the ADASYN algorithm to address dataset imbalances. After preprocessing, features are extracted using Fast Fourier Transform (FFT), which provides a comprehensive frequency domain analysis of the EEG signals. For classification, Linear Discriminant Analysis (LDA) is employed to effectively distinguish between microsleep events and normal wakefulness based on the extracted features. The proposed framework was rigorously validated using a well-established publicly available EEG dataset, which included recordings from 76 healthy individuals. The validation results revealed a high testing accuracy of 92.71% in detecting microsleep episodes, demonstrating the effectiveness of the proposed approach. These results underscore the potential of combining EEG signal analysis with machine learning models for practical applications in monitoring driver alertness. The framework could significantly enhance driver safety by providing an effective tool for detecting microsleep and thereby reducing the risk of accidents caused by drowsy driving. This research highlights the promising application of advanced signal processing and machine learning techniques in the field of driver alertness monitoring.

Optimized power and speed of Split-Radix, Radix-4 and Radix-2 FFT structures

Fast Fourier transform (FFT) is a fundamental building block for digital signal processing applications where high processing speed is crucial. Resource utilization in implementing FFT structures can be minimized by optimizing the performance of multipliers and adders used within the design. FFTs are also widely used in various machine learning algorithms. To achieve increased processor efficiency and reduced resource utilization, we propose a hardware design for Radix-2, Radix-4, and Split-Radix FFT architectures that utilizes a novel parallel prefix adder. This design offers lower power consumption, smaller chip area, and faster operation compared to existing architectures. Our performance analysis focuses on metrics such as power consumption, clock speed, and hardware complexity for Radix-2, Radix-4, and Split-Radix FFT algorithms implemented with the proposed adder. We compare these metrics using our proposed arithmetic structure against existing adder designs. The results indicate that the Split-Radix FFT architecture achieves lower power consumption and smaller chip area compared to Radix-4 and Radix-2 methods. Additionally, the Split-Radix FFT exhibits a higher clock speed. Therefore, based on these findings, the Split-Radix algorithm appears to be a compelling choice for implementation on field-programmable gate arrays due to its high speed and lower power consumption.

Interoperable Integration of Automatic ECG Processing Using DICOMweb and the AcuWave Software Suite.

Established cardiovascular risk scores are typically based on items from structured clinical data such as age, sex, or smoking status. Cardiovascular risk is also assessed from physiological measurements such as electrocardiography (ECG). Although ECGs are standard diagnostic tools in clinical care, they are scarcely integrated into clinical information systems. To overcome this roadblock, we propose the integration of an automatic workflow for ECG processing using the DICOMweb interface to transfer ECGs in a standardised way. We implemented the workflow using non-commercial software and tested it with about 150,000 resting ECGs acquired in a maximum-care hospital. We employed Orthanc as DICOM server and AcuWave as signal processing application and implemented a fully-automated workflow which reads the ECG data and computes heart rate-related parameters. The workflow is evaluated on off-the-shelf hardware and results in an average run time of approximately 40 ms for processing a single ECG.

Separation of overlapping non-stationary signals and compressive sensing-based reconstruction using instantaneous frequency estimation

Compressive sensing uses an incomplete sample set to consider signal reconstruction with sparse basis representations over a specific transform domain. This work examines the difficulties encountered in various signal processing applications, such as data overlap with the target signal in time and frequency domains. The conventional filtering and windowing processes fail to promote better results in recovering the desired signal. The non-stationary signal with time-frequency representations is highly significant for certain valuable applications. An efficient signal separation and reconstruction method based on instantaneous frequency estimate is proposed to improve the non-stationary signal outcome. The proposed research includes speech signal data acquisition, signal component decomposition, instantaneous frequency estimation, overlapped signal separation, and reconstruction. Two datasets, Dacon Overlapped Speech and LJ Speech Dataset, are utilized to generate a new overlapped signal dataset. Initially, the generated speech signals are collected and decomposed into several monocomponents using Synchrosqueezing Wavelet Transform (SynWav), Renyi's quadratic entropy (RQuad), Narrow bandpass filtering (NBand) and single frequency filtering (SFreq). The instantaneous frequencies are estimated using the Hilbert-Huang transform (HilHuT) to generate a time-frequency representation of multi-component signals. The overlapped signals are separated and reconstructed to a desired signal using a Compressive sensing-based Discrete Cosine transform with amended intrinsic chirp separation (CDcT_AIChirS). The proposed model is simulated using PYTHON to examine the performances. In comparison, the mean square error is calculated to be 2.71, the root mean square error is calculated to be 1.64, and the signal to noise ratio is calculated to be 32dB.

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.