Digital Signal Processing Methods Research Articles

Novel Wearable System to Recognize Sign Language in Real Time.

The aim of this study is to develop a practical software solution for real-time recognition of sign language words using two arms. This will facilitate communication between hearing-impaired individuals and those who can hear. We are aware of several sign language recognition systems developed using different technologies, including cameras, armbands, and gloves. However, the system we propose in this study stands out for its practicality, utilizing surface electromyography (muscle activity) and inertial measurement unit (motion dynamics) data from both arms. We address the drawbacks of other methods, such as high costs, low accuracy due to ambient light and obstacles, and complex hardware requirements, which have limited their practical application. Our software can run on different operating systems using digital signal processing and machine learning methods specific to this study. For the test, we created a dataset of 80 words based on their frequency of use in daily life and performed a thorough feature extraction process. We tested the recognition performance using various classifiers and parameters and compared the results. The random forest algorithm emerged as the most successful, achieving a remarkable 99.875% accuracy, while the naïve Bayes algorithm had the lowest success rate with 87.625% accuracy. The new system promises to significantly improve communication for people with hearing disabilities and ensures seamless integration into daily life without compromising user comfort or lifestyle quality.

A review in advanced digital signal processing systems

Digital Signal Processing (DSP) is a powerful technology that helps in making sense of various signals, like sounds and images, using computers. This review paper explains the meaning of DSP, shows how it works to process and enhance signals. It explores the wide range of signal processing methods, categorizing them from basic noise reduction to advanced machine learning algorithms, and how they are used today to improve the quality of audio, images, medical data, and other control systems. The paper further examines into signal processing techniques, providing a comprehensive understanding of the diverse methodologies employed in DSP applications. Additionally, it addresses not only the advancements but also the drawbacks associated with advanced DSP systems, offering insightful recommendations for overcoming challenges and optimizing performance. This review also includes categories of DSP methods, providing a structured overview of the different approaches within the field. It offers a clear and concise understanding of DSP, its practical uses, and its exciting potential in the digital age

Comparative Performance Analysis of Metaheuristic Feature Selection Methods for Speech Emotion Recognition

Abstract Emotion recognition systems from speech signals are realized with the help of acoustic or spectral features. Acoustic analysis is the extraction of digital features from speech files using digital signal processing methods. Another method is the analysis of time-frequency images of speech using image processing. The size of the features obtained by acoustic analysis is in the thousands. Therefore, classification complexity increases and causes variation in classification accuracy. In feature selection, features unrelated to emotions are extracted from the feature space and are expected to contribute to the classifier performance. Traditional feature selection methods are mostly based on statistical analysis. Another feature selection method is the use of metaheuristic algorithms to detect and remove irrelevant features from the feature set. In this study, we compare the performance of metaheuristic feature selection algorithms for speech emotion recognition. For this purpose, a comparative analysis was performed on four different datasets, eight metaheuristics and three different classifiers. The results of the analysis show that the classification accuracy increases when the feature size is reduced. For all datasets, the highest accuracy was achieved with the support vector machine. The highest accuracy for the EMO-DB, EMOVA, eNTERFACE’05 and SAVEE datasets is 88.1%, 73.8%, 73.3% and 75.7%, respectively.

Ataxic person prediction using feature optimized based on machine learning model

Ataxic gait monitoring and assessment of neurological disorders belong to important areas that are supported by digital signal processing methods and artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) techniques. This paper uses spatio-temporal data from Kinect sensor to optimize machine learning model to distinguish between ataxic and normal gait. Existing ML-based methodologies fails to establish feature correlation between different gait parameters; thus, exhibit very poor performance. Further, when data is imbalanced in nature the existing ML-based methodologies induces higher false positive. In addressing the research issues this paper introduces an extreme gradient boost (XGBoost)-based classifier and enhanced feature optimization (EFO) by modifying the standard cross validation (SCV) mechanism. Experiment outcome shows the proposed ataxic person identification model achieves very good result in comparison with existing ML-based and DL-based ataxic person identification methodologies.

Algorithm for estimating the absolute total electron content of the ionosphere from dual-frequency phase and range satellite measurements

Objectives. The problem of developing an algorithm for estimating the absolute total electron content of the ionosphere from dual-frequency phase and range satellite measurements for a single receiving station of global navigation satellite systems is being solved.Methods. To obtain an estimate the phase measurement data are corrected using digital signal processing methods, well known total electron content formulas for phase and range measurements are applied and combined, and also the differential code bias of the receiving station is estimated using the least squares method.Results. It is shown that the total electron content calculated from phase measurements provides high accuracy, but up to an unknown constant, but the content calculated from range measurements allows one to obtain the absolute value, but with a large noise component and differential code bias of a satellite and receiver equipment. An algorithm for estimating the absolute total electron content of the ionosphere has been developed, its description and diagram are given. The algorithm was used to estimate the total electronic content within six months of observations, and the average error of the resulting estimate was calculated.Conclusion. The developed algorithm can be used to estimate the absolute total electron content of the ionosphere for a single receiving station of global navigation satellite systems. In contrast to theoretically known formulas for phase and range measurements, this article contains information about adjusting phase measurements and estimating the differential code delay of receiving station. Further research may be related to the adaptive selection of parameters and testing of the algorithm for working with nanosatellites of the CubeSat format.

Online digital compensation of pump dithering induced phase and amplitude distortions in transmission links with cascaded fibre-optical parametric amplifiers

We present an advanced online digital signal processing (DSP) method for correcting the phase and amplitude distortions caused by the phase modulation of the pump source and its interaction with the dispersive fibre channel in transmission systems using cascaded fibre-optical parametric amplifiers. The proposed algorithm is numerically demonstrated to achieve significant (up to 3.7 dB for a four-tone pump-phase modulation scheme) Q2-factor performance improvement over conventional DSP in 16 quadrature-amplitude modulation signal transmission.

Exploration of Four-Channel Coherent Optical Chaotic Secure Communication with the Rate of 400 Gb/s Using Photonic Reservoir Computing Based on Quantum Dot Spin-VCSELs

In this work, we present a novel four-channel coherent optical chaotic secure communication (COCSC) system, incorporating four simultaneous photonic reservoir computers in tandem with four coherent demodulation units. We employ a quartet of photonic reservoirs that capture the chaotic dynamics of four polarization components (PCs) emitted by a driving QD spin-VCSEL. These reservoirs are realized utilizing four PCs of a corresponding reservoir QD spin-VCSEL. Through these four concurrent photonic reservoir structures, we facilitate high-quality wideband-chaos synchronization across four pairs of PCs. Leveraging wideband chaos synchronization, our COCSC system boasts a substantial 4 × 100 GHz capacity. High-quality synchronization is pivotal for the precise demasking or decoding of four distinct signal types, QPSK, 4QAM, 8QAM and 16QAM, which are concealed within disparate chaotic PCs. After initial demodulation via correlation techniques and subsequent refinement through a variety of digital signal processing methods, we successfully reconstruct four unique baseband signals that conform to the QPSK, 4QAM, 8QAM and 16QAM specifications. Careful examination of the eye diagrams, bit error rates, and temporal trajectories of the coherently demodulated baseband signals indicates that each set of baseband signals is flawlessly retrieved. This is underscored by the pronounced eye openings in the eye diagrams and a negligible bit error rate for each channel of baseband signals. Our results suggest that delay-based optical reservoir computing employing a QD spin-VCSEL is a potent approach for achieving multi-channel coherent optical secure communication with optimal performance and enhanced security.

Analysis of Eeg Data Using Different Techniques of Digital Signal Processing

This paper explores the application of digital signal processing (DSP) techniques in the examination of electroencephalogram (EEG) data. DSP encompasses a collection of mathematical algorithms designed to employ signals, such as EEG recordings, and finds application in diverse domains, including sleep medicine, neuroscience, and biomedical engineering. Employing DSP methods for EEG data analysis enables the extraction of pertinent insights from EEG signals, the identification of event-related patterns, and the enhancement of diagnostic and therapeutic practices across various disciplines. This article provides an overview of prevalent DSP methodologies employed in EEG signal processing, encompassing filtering, power spectral analysis, wavelet analysis, independent component analysis, and artifact removal.

A convenient acoustic measurement of femtosecond filamentation based on a cell phone

The acoustic diagnostic method has been widely used in diagnosing femtosecond filamentation. In this work, acoustic measurements of femtosecond laser filamentation and weakly air-ionized region are conducted using a readily available portable device - a cell phone. The signal is extracted by employing digital signal processing method of Fourier transformation filtering. The results demonstrate that the method can be used for effective measurements even in noisy environments. This greatly enhances the accuracy and portability of the acoustic method, providing a practical approach for improving acoustic diagnostics.

Digital Signal Processing Approaches in the field of Genomics: A Recent Trend

Digital signal processing (DSP) techniques have emerged as powerful tools in the field of genomics, enabling researchers to extract valuable insights from complex genetic data. This research paper presents a comprehensive analysis of the recent trends and advance- ments in applying DSP approaches to genomics. The objective is to provide an overview of the transformative role of DSP in genomic data analysis, variant calling, and interpretation. By leveraging DSP methods such as filtering, feature extraction, time-frequency analysis, and machine learning algorithms, researchers can enhance the quality of genetic signals, identify genetic variants, and gain a deeper understanding of genomic processes. The paper highlights key applications of DSP in genomics, including DNA sequence analysis, RNA expression pro- filing, epigenetics, and genome-wide association studies. Additionally, the challenges associated with applying DSP techniques in genomics, such as signal noise, data in- tegration, and computational complexity, are discussed. This research paper serves as a valuable resource for researchers, bioinformaticians, and geneticists seeking to harness the power of DSP in genomics, advancing our knowledge of genetic diseases and paving the way for personalized medicine and precision healthcare. Keywords: Digital signal processing, Genome analysis, Feature extraction, DNA sequence analysis, RNA expression profiling.

Mitigating the Strong Inter-Core Crosstalk during MCF Transmissions by Neural-Network-Equalizer-Based MIMO-DSP and Approaches for Its Simplification

In this paper, we report our recent progress related to advanced digital signal processing (DSP) technologies to address the strong inter-core crosstalk (IC-XT) during multicore fiber (MCF) optical transmissions. MCF transmission technology has significant potential to break through the capacity crunch of single-mode fiber (SMF) transmissions. However, strong coupling among cores, namely, inter-core crosstalk (IC-XT), is unavoidable for high-density space-division multiplexing (SDM) transmissions using MCFs with the standard cladding size. To deal with this issue, we propose some novel DSP structures to eliminate IC-XT with considerable simplicity, based on the neural network equalizer (NNE)-based multiple-input and multiple-output digital signal processing (MIMO-DSP). The traditional NNE-based MIMO-DSP method has the ability to process the coupled SDM tributaries transmitted over MCFs; however, it exhibits complexity limitations for practical implementations. The implementation complexity of the NNE-based method is mainly attributed to the time-consumption of the training process and the large weight (neurons) numbers of the equalizers. Thus, we propose two main approaches to simplify NNE-based MIMO-DSP for the practical implementation of MCF transmissions: (1) To reduce the time-consumption of the training process in NNE-based MIMO-DSP, the idea of transfer learning (TL) is employed for initializing the weights, resulting in the faster convergence of the equalizers. (2) IC-XT cancellation is performed along with MIMO-DSP; thus, the dimensionality of MIMO-DSP could be reduced. To validate the performance improvement of the proposed machine learning DSP methods, both simulations and experiments related to transmissions and reception over MCFs were conducted. The results indicate that the proposed novel DSP structures possess the advantages of reduced complexity and improved robustness to IC-XT, which is beneficial for the next-generation high-density SDM transmissions.

An Optical Frequency Domain Reflectometer's (OFDR) Performance Improvement via Empirical Mode Decomposition (EMD) and Frequency Filtration for Smart Sensing.

We describe a method for reducing the cost of optical frequency domain reflectometer (OFDR) hardware by replacing two reference channels, including an auxiliary interferometer and a gas cell, with a single channel. To extract useful information, digital signal processing methods were used: digital frequency filtering, as well as empirical mode decomposition. It is shown that the presented method helps to avoid the use of an unnecessary analog-to-digital converter and photodetector, while the OFDR trace is restored by the equal frequency resampling (EFR) algorithm without loss of high resolution and with good measurement repeatability.

ASSESSING THE INFLUENCE OF THE SCALING FACTOR OF THE BASIC WAVELET TRANSFORM FUNCTION ON THE DEGREE OF MANIFESTATION OF THE MAIN FAULTS OF CENTRIFUGAL PUMPING UNITS IN WAVELET PATTERNS

ASSESSING THE INFLUENCE OF THE SCALING FACTOR OF THE BASIC WAVELET TRANSFORM FUNCTION ON THE DEGREE OF MANIFESTATION OF THE MAIN FAULTS OF CENTRIFUGAL PUMPING UNITS IN WAVELET PATTERNS

Increasing the energy efficiency of a fishing vessel by estimating the motion parameters of a towed object

The optimal mode of towing fishing gear has a direct impact on the energy efficiency of the fishing vessel. To build the combined traction characteristics of the vessel and trawl systems, data are needed on the nominal moments on the winch and engine drum, as well as the traction force in the warp. Estimation of the movement parameters of the towed object will make it possible to haul warps without overloading the trawl winch and the main engine. The paper proposes a block diagram of the algorithm for estimating the motion parameters of a trawl system. Processing of the current measurements of the warp tension in real time is carried out by digital signal processing methods based on the constructed optimal linear filter, which will be used to obtain the tension on the winch without taking into account the noise that occurs both as a result of the operation of the equipment and under the influence of the random nature of the warp tension process itself. and external hydrometeorological factors. Estimation and prediction of dynamic parameters is necessary for the development of an automated control system for a ship's winch, regardless of its purpose. Therefore, the results presented in this paper can be used in solving problems of automation of lifting equipment operating in non-stationary conditions.

Enhancing Outdoor Moving Target Detection: Integrating Classical DSP with mmWave FMCW Radars in Dynamic Environments

This paper introduces a computationally inexpensive technique for moving target detection in challenging outdoor environments using millimeter-wave (mmWave) frequency-modulated continuous-wave (FMCW) radars leveraging traditional signal processing methodologies. Conventional learning-based techniques for moving target detection suffer when there are variations in environmental conditions. Hence, the work described here leverages robust digital signal processing (DSP) methods, including wavelet transform, FIR filtering, and peak detection, to efficiently address variations in reflective data. The evaluation of this method is conducted in an outdoor environment, which includes obstructions like woods and trees, producing an accuracy score of 92.0% and precision of 91.5%. Notably, this approach outperforms deep learning methods when it comes to operating in changing environments that project extreme data variations.

INVESTIGATION ON CHARACTERISTICS OF TURBOCHARGER VIBRATION USING DIGITAL SIGNAL PROCESSING TECHNIQUE

High-speed rotating machinery such as a turbocharger is always subject to imperfection that causes strong vibrations. The problem is that there is no way of knowing the root causes of these vibrations by just using our five senses. This problem is significant since unattended heavy vibrations on a turbocharger may lead to permanent damage and a high cost to fix them. The purpose of this study is to investigate the vibration characteristics of a running turbocharger using the Digital Signal Processing (DSP) method, hence identifying the sources of such vibration profiles. In this context, DSP is the process of taking vibration signals that have been digitized and then mathematically manipulating them. Usually, it involves converting time domain data into frequency domain using Fast Fourier Transform (FFT). To identify the causes of turbocharger vibrations, a literature review was done by studying past research of the same methodology. An experiment was conducted by running the machine at different speeds and operating conditions across the choke, regular, and surge conditions. Vibrational data was obtained by mounting a piezoelectric accelerometer onto the compressor housing. The data was then later post-processed using MATLAB to execute FFT. The result shows high energy peaks at various frequencies associated with an unbalanced rotor, oil whirl, looseness, misalignment, and surging noise. On this basis, it is concluded that the vibrational data of a turbocharger can be identified using DSP.

A prince for the sleeping beauty - NFT for soliton signal processing

The soliton, the eigen-solution of nonlinear Schrodinger equation (NLSE), has been investigated in many branches of physics. The first prediction of a soliton in optical fiber could be dated back to 1973. Since then, numerous theoretical and experimental works on solitons have been carried out, including soliton communication and soliton generation in fiber lasers. However, due to the constraints of hardware and digital signal processing methods, research on solitons have been stalled for decades. Recently, the nonlinear Fourier transform (NFT) has attracted significant attention in the field of optical fiber communications and soliton-based signal processing, which provides a powerful mathematical framework for analyzing and understanding the behavior of solitons in optical fibers, enabling the development of novel signal processing techniques. The NFT technique has brought the soliton back to life. It has been widely recognized that in nonlinear frequency domain, the nonlinear crosstalk resulting from the Kerr effect is practically negligible, and fiber nonlinearity damage caused by the Kerr effect can be accounted for as a linear transform factor. Moreover, NFT has been demonstrated for the analysis of laser radiation, highlighting its potential in characterizing ultrafast pulses in the nonlinear frequency domain. In this submission, we review the advancements of NFT concerning both in soliton communication and characterization, focusing on practical implementation aspects. With many practical implementation aspects still being open, our mini review is aimed to assist researchers in evaluating the potential, identifying challenges, and envisioning future directions in NFT-based signal processing technologies. And wake up the sleeping beauty – the soliton!

Direction-of-arrival estimation for acoustic signals based on direction-dependent parameter tuning of a bioinspired binaural coupling system

Bioinspired methods for sound source localization offer opportunities for resource reduction as well as concurrent performance improvement in contrast to conventional techniques. Usually, sound source localization requires a large number of microphones arranged in irregular geometries, and thus has high resource requirements in terms of space and data processing. Motivated by biology and using digital signal processing methods, an approach that adapts the coupled hearing system of the fly Ormia ochracea with a minimally distant two-microphone array is presented. Despite its physiology, the fly is able to overcome physical limitations in localizing low-frequency sound sources. By exploiting the filtering effect of the coupling system, the direction-of-arrival of the sound is determined with two microphones at an intermediate distance of 0.06 m. For conventional beamforming algorithms, these physical limitations would result in degraded localization performance. In this work, the bioinspired coupling system is analyzed and subsequently parameterized direction-sensitive for different directions of incidence of the sound. For the parameterization, an optimization method is presented which can be adopted for excitation with plane as well as spherical sound wave propagation. Finally, the method was assessed using simulated and measured data. For 90% of the simulated scenarios, the correct direction of incidence could be determined with an accuracy of less than 1∘ despite the use of a minimal distant two-microphone array. The experiments with measured data also resulted in a correct determination of the direction of incidence, which qualifies the bioinspired method for practical use in digital hardware systems.

Restoration of a blurred photographic image of a moving object obtained at the resolution limit

Objectives. When processing images of the Earth’s surface obtained from satellites, the problem of restoring a blurry image of a moving object is of great practical importance. The aim of this work is to study the possibility of improving the quality of restoration of blurry images obtained at the limit of the resolution of the camera.Methods. Digital signal processing methods informed by the theory of incorrect and ill-conditioned problems were used.Results. The proposed method for restoring a blurred photographic image of a moving object differs from traditional approaches in that the discrete convolution equation, to which the problem of restoring a blurred image is reduced, is obtained by approximating the corresponding integral equation based on the Kotelnikov interpolation series rather than on the traditional basis of the quadrature formula. In the work, formulas are obtained for calculating the kernel of the convolution obtained using the Kotelnikov interpolation series. The discrete convolution inversion problem, which belongs to the class of ill-posed problems, requires regularization. Results of traditional approaches to restoring blurred images using the quadrature formula with Tikhonov regularization and the proposed method based on the Kotelnikov interpolation series are compared. Although the quality of the blurred image restoration is almost the same in both cases, in the quadrature formula the blur value is expressed as an integer number of pixels, while, when using the Kotelnikov series, this value can also be specified in fractions of a pixel.Conclusions. The expediency of discretizing the convolution describing the image distortion of the blur type on the basis of the Kotelnikov interpolation series when processing a blurred image obtained at the limit of the resolution of the camera is demonstrated. In this case, the amount of blur can be expressed in fractions of a pixel. This situation typically arises when processing satellite photography of the Earth’s surface.

Optical label-based cost-effective DWDM optical network performance monitoring using low-bandwidth PD with novel SRS mitigation DSP

Large-scale dense wavelength division multiplexing (DWDM) multi-channel performance monitoring is one of the indispensable technologies for the flexible optical networks. The existing Labelbased monitoring scheme requires expensive optical demultiplexing components/equipment to avoid the influence of stimulated Raman scattering (SRS), which is not only costly and bulky, but also could not monitor the wavelength channels simultaneously. In this paper, a low-cost, high-accuracy monitoring scheme based on Optical Label Method is proposed for DWDM networks, where the optical channel power and node identification (ID), as the main monitoring targets that both can indicate or evaluate the channel connection status, could be efficiently monitored. In the scheme, a novel digital signal processing (DSP) method of SRS mitigation is proposed and demonstrated, and an asynchronous code-division multiple access (A-CDMA) based digital label encoding and decoding method is adopted to distinguish the node ID so that channel initial added node can be accurately verified, thereby wavelength connection status can be reliably monitored by combining the channel power and node ID information. The simulation results show that each wavelength channel power and node ID can be accurately monitored only by low bandwidth photoelectric detector (PD) under the condition of 80 wavelengths and 10 spans at C-band.