Digital Signal Processing Methods Research Articles

Detection of Extremely Weak and Wideband Bio-Magnetic Signals in Non-Shielded Environments Using Passive Coil Sensors

We report a coil-based passive sensing system and associated Digital Signal Processing (DSP) capable of detecting extremely weak and wideband bio-magnetic signals without the need for shielding. Our previous work showed potential in this direction but was limited to detecting magnetocardiography (MCG) signals that are the strongest emanated by the human body as well as narrowband. In a major step forward, we advance our DSP with notch and Empirical Mode Decomposition filters, in addition to bandpass filtering and averaging utilized in the past and refine our coil design. We characterize the system's noise performance, analyze effectiveness of the DSP methods, and validate performance <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vitro</i> . The proposed system considerably outperforms our previous design: noise levels at 10 Hz and 100 Hz are reduced by ∼46% and ∼92%, respectively, while detection sensitivity is improved by ∼94% across a ∼3333% wider signal bandwidth. That is, signals as low as magnetomyography (MMG) and evoked Compound Action Potentials (eCAP) and of frequency range comparable to magnetoneurography (MNG) can now be retrieved passively, in non-shielded environments. As a proof-of-concept, we utilize a single sensor and 24 minutes of recording; however, these parameters are scalable, as is the strength and frequency range of detectable bio-magnetic signals. To this end, we include discussions on how the hardware and DSP components can be parameterized and adjusted to fit diverse clinical needs. The proposed system can empower seamless detection of MCG, MMG, eCAP, and MNG, among others, opening unexplored opportunities for the future of medical diagnostics, monitoring, and treatment.

Algorithms and Measuring Complex for Classification of Seismic Signal Sources, Determination of Distance and Azimuth to the Point of Excitation of Surface Waves

Machine learning and digital signal processing methods are used in various industries, including in the analysis and classification of seismic signals from surface sources. The developed wave type analysis algorithm makes it possible to automatically identify and, accordingly, separate incoming seismic waves based on their characteristics. To distinguish the types of waves, a seismic measuring complex is used that determines the characteristics of the boundary waves of surface sources using special molecular electronic sensors of angular and linear oscillations. The results of the algorithm for processing data obtained by the method of seismic observations using spectral analysis based on the Morlet wavelet are presented. The paper also describes an algorithm for classifying signal sources, determining the distance and azimuth to the point of excitation of surface waves, considers the use of statistical characteristics and MFCC (Mel-frequency cepstral coefficients) parameters, as well as their joint application. At the same time, the following were used as statistical characteristics of the signal: variance, kurtosis coefficient, entropy and average value, and gradient boosting was chosen as a machine learning method; a machine learning method based on gradient boosting using statistical and MFCC parameters was used as a method for determining the distance to the signal source. The training was conducted on test data based on the selected special parameters of signals from sources of seismic excitation of surface waves. From a practical point of view, new methods of seismic observations and analysis of boundary waves make it possible to solve the problem of ensuring a dense arrangement of sensors in hard-to-reach places, eliminate the lack of knowledge in algorithms for processing data from seismic sensors of angular movements, classify and systematize sources, improve prediction accuracy, implement algorithms for locating and tracking sources. The aim of the work was to create algorithms for processing seismic data for classifying signal sources, determining the distance and azimuth to the point of excitation of surface waves.

Survey on compressed sensing reconstruction method for3Ddata

SummaryThe information society has higher and higher requirements for the collection, transmission and storage of digital signals, and signal utilization efficiency has become an increasingly important part of the digital signal processing process. The traditional digital signal processing needs to satisfy the Nyquist sampling theorem to ensure the restoration of the signal, while the digital signal processing method based on compressed sensing can sample and reconstruct the signal under the conditions that much lower than the Nyquist sampling theorem. Undoubtedly, the utilization efficiency of digital signals has been greatly accelerated. In this article, taking hyperspectral images and videos as examples, we review the basic theory, reconstruction model, and reconstruction algorithm of 3D data compressed sensing. We analyze, summarize, and discuss the existing literature. Finally, the research status of compressive sensing reconstruction methods for hyperspectral images and videos is compared, and several important research directions for compressive sensing reconstruction of 3D data in the future are proposed.

Support Vector Machine-Based Soft Decision for Consecutive-Symbol-Expanded 4-Dimensional Constellation in Underwater Visible Light Communication System

Nowadays, underwater visible light communication (UVLC) has become one of the key technologies for high-speed underwater wireless communication. Because of the limited modulation bandwidth and nonlinearity of the optoelectronic devices in the UVLC system, the combination of inter-symbol interference and nonlinear impairment will inevitably degrade the transmission performance. Advanced digital signal processing methods including equalization and decoding are required. In the past few years, Support vector machine (SVM) has been widely investigated in quadrature amplitude modulation (QAM) for soft decision in the decoding process. However, previous works only consider 2-dimensional (2-D) separate symbol, ignoring the correlation between consecutive symbols. In this paper, we propose to use SVM for soft decision with a 4-dimensional (4-D) constellation by concatenating two consecutive symbols. To deal with the increasing computational complexity in the SVM training phase, bit-based binary SVM multi-class strategy and an edge-detection-based data pre-processing method are employed. In this paper, we demonstrate a carrierless amplitude and phase (CAP) 16-QAM UVLC system. Experimental results indicate that the performance is greatly improved when using consecutive-symbol-expanded 4-D constellation with SVM for soft decision.

Review of Fundamental Active Current Extraction Techniques for SAPF.

The field of advanced digital signal processing methods is one of the fastest developing scientific and technical disciplines, and is important in the field of Shunt Active Power Filter control methods. Shunt active power filters are highly desirable to minimize losses due to the increase in the number of nonlinear loads (deformed power). Currently, there is rapid development in new adaptive, non-adaptive, and especially hybrid methods of digital signal processing. Nowadays, modern methods of digital signal processing maintain a key role in research and industrial applications. Many of the best practices that have been used to control shunt active power in industrial practice for decades are now being surpassed in favor of new progressive approaches. This systematic research review classifies the importance of using advanced signal processing methods in the field of shunt active power filter control methods and summarizes the extant harmonic extraction methods, from the conventional approach to new progressive methods using genetic algorithms, artificial intelligence, and machine learning. Synchronization techniques are described and compared as well.

Research and Analysis on the Cultivation of Intelligent Transportation Technology Professionals under the Background of Integration of Production and Education

In order to improve the training effect of transportation technology professionals, this paper combines the intelligent digital signal processing method to process the data of the integration of production and education, and explores the training process of transportation technology professionals. In the process of data processing of industry-education fusion, this paper demodulates the nuclear magnetic resonance signal obtained after A/D conversion from the carrier frequency signal and places it at zero frequency. At the same time, this paper realizes the separation of the real part and the imaginary part of the nuclear magnetic resonance signal and constructs the digital sine table and digital cosine table of the carrier reference frequency. In addition, the data output by the ADC is subjected to quadrature detection to become two data signals, which are the in-phase component and the quadrature component. Finally, this paper constructs an intelligent analysis model. The simulation results show that the talent training method of intelligent transportation technology based on the integration of production and education has good effects, which can effectively improve the training effect of transportation technology professionals.

Development of an EEG artefact detection algorithm and its application in grading neonatal hypoxic-ischemic encephalopathy

ObjectiveThe primary aim of this study is to develop and evaluate algorithms for neonatal EEG artefact detection. The secondary aim is to subsequently assess its application as a post-processing routine for automated EEG grading of background abnormalities in neonatal hypoxic-ischemic encephalopathy (HIE). Methods: A database of neonatal EEG with expertly annotated artefacts was used to train and validate machine learning models to automatically identify EEG epochs containing artefacts. Three approaches were developed and compared, specifically, a simple threshold-based digital signal processing (DSP) method, a machine learning method, and a deep learning method. The artefact detection classifier was subsequently assessed as a post-processing tool to assist in the application of automated EEG grading of HIE. A new deep learning model for grading the EEG was developed by training an existing network on a large, multi-centre dataset. The artefact detection algorithm was integrated into the grading algorithm through a post-processing routine. Results: Using a database containing 19 h of EEG from 51 patients with per-channel and per-second annotations of artefacts, a deep learning convolutional neural network solution achieved best performance for artefact detection with an area under the operating characteristic curve (AUC) of 0.84, compared to an AUC of 0.68 and 0.82 for a DSP method and a random-kernel ridge-classifier model, respectively. The automated EEG grading algorithm was trained and tested on 653 h of EEG from 181 patients, which achieved an accuracy of 82.8 % (95 % CI: 80.5 % to 85.2 %). The percentage of detected artefacts in the misclassified epochs was not statistically different (p = 0.568) compared to that of correctly classified epochs. Using artefact detection, a small number of epochs were removed from grading, resulting in a minor increase in accuracy for the EEG grading algorithm from 82.6 % to 83.6 %. Conclusion: Deep learning methods achieved highest classification performance for neonatal EEG artefact detection, although a ridge classifier using random kernels achieved comparable performance without significant parameter tuning or training time. The inclusion of artefact detection in automated EEG grading does not significantly improve accuracy in our curated dataset, but does allow for a quality measure to be presented alongside the automated EEG grades which may increase user confidence in its real-world application.

Компенсация искажений сигналов при распространении по трансионосферным радиолиниям с использованием глобальной модели земной ионосферы

A description is given of distortions of complex envelopes of digital signals during their propagation along transionospheric channels, the effect of which is equivalent to linear filtering. It is shown that signal distortions due to the dispersion properties of the earth's ionosphere cause the occurrence of interference noise which reduces the reliability of information transmission. It is noted that the effect of digital signal processing methods at reception, which reduce the distorting effect of interference is to form a linear filter with an inverse transmission coefficient relative to the transionospheric channels. A method for forming the inverse filter using the global model of the earth's ionosphere Klobuchar, developed and intensively used in the GPS satellite navigation system to improve the accuracy of navigation measurements is considered. A detailed description of this method is given, which is based on estimating the total electron content of a transionospheric channels using the considered Klobuchar model. Limitations in the application of the method under consideration are determined.

Special Issue on Mathematics and Digital Signal Processing

Modern computer technology has opened up new opportunities for the development of digital signal processing methods [...]

In-cylinder pressure statistical analysis and digital signal processing methods for studying the combustion of a natural gas/diesel heavy-duty engine at low load conditions

In-cylinder pressure analysis is one of the most important tools for combustion diagnosis. The dual-fuel compression ignition engines present a different in-cylinder pressure evolution for consecutive cycles due to the high cyclic variability. One source of the cyclic variability is a long ignition delay caused by a low-temperature combustion chamber (at low load conditions) and this effect led to lower efficiency, higher emissions, and driveability problems. In the present study, a six-cylinder turbocharged heavy-duty diesel engine (6.7 L) was used to acquire the in-cylinder pressure signal at low-load operating conditions. Then a statistical methodology is proposed to process the experimental pressure signal for a combustion diagnosis approach obtained at different loads for diesel mode and dual fuel mode. First, a representative sample number of consecutive thermodynamic cycles is determined, and then the cut-off frequencies for in-cylinder pressure signal digital filtering are selected by the analysis of the Fourier Transform spectrum for the test engine using only diesel fuel and natural gas/diesel mode. The results show an effective high-frequency noise diminish (related to the resonance combustion chamber) on the filtered pressure signals. Therefore, a high-quality curve of the heat release rate can be reached, which allows identifying the combustion process at low-loads operating conditions.

Digital RIS (DRIS): The Future of Digital Beam Management in RIS-Assisted OWC Systems

Reconfigurable intelligent surfaces (RIS) have been recently introduced to optical wireless communication (OWC) networks to resolve skip areas and improve the signal-to-noise ratio at the user’s end. In OWC networks, RIS are based on mirrors or metasurfaces. Metasurfaces have evolved significantly over the last few years. As a result, coding, digital, programmable, and information metamaterials have been developed. The advantage of these materials is that they can enable digital signal processing (DSP) techniques. For the first time, this paper proposes the use of digital RIS (DRIS) in OWC systems. We discuss the concept of DRIS and the application of DSP methods to the physical material. In addition, we examine metamaterials for optical DRIS with liquid crystals serving as the front row material. Finally, we present a design example and discuss future research directions.

Parameterization of user functions in digital signal processing for obtaining angular superresolution

Objectives. One of the most important tasks in the development of goniometric systems is improving resolution in terms of angular coordinates. This can be achieved in two ways: firstly, by increasing the aperture, which is very expensive and often technically challenging to implement; secondly, with the help of digital signal processing methods. If the recorded signal sources are located close to each other and not resolved by the Rayleigh criterion, it can be impossible to determine their number, location and reflection characteristics. The aim of the present work is to develop a digital signal processing algorithm for obtaining angular superresolution.Methods. Mathematical methods for solving inverse problems are used to overcome the Rayleigh criterion, i.e., obtain angular superresolution. These problems are unstable, since there is an infinite number of approximate solutions and false targets may occur. The search for the optimal solution is carried out by minimizing the standard deviation.Results. A description of a mathematical model for a goniometric system is presented. A signal processing algorithm is developed based on existing methods according to the principle of parameterization of user functions. Results of numerical experiments for achieving superresolution by algebraic methods are given along with an estimation of solution stability. The accuracy and correspondence of the amplitude of the obtained objects to the initial parameters are measured. The degree of excess of the Rayleigh criterion by the obtained solution is estimated.Conclusions. Algebraic methods can be used to obtain stable solutions with angular superresolution. The results obtained correctly reflect the location of objects with a minor error. Errors in the distribution of the signal amplitude are small, appearing false targets have negligible amplitude.

Pulsed Laser Ranging Techniques Based on Digital Signal Processing Methods for Proximity Fuze

Laser fuze systems play an emerging part in military technology bases nowadays. In the paper, a solution is developed to improve the accuracy of the laser rangefinder system in laser fuze without changing the hardware design of the digital signal processor. The proposed algorithm will combine real-time pulse processing techniques used in target range measurement. The new algorithm interpolates returned data using a relatively low sampling rate to a higher one. It applies a cross-correlation technique using high-resolution reference waveform models to restore the position of the super-sampled start and return pulses. Based on the Monte Carlo method and simulating the waveform in the noise environments, 100 trials were judged on the standard deviation of the range estimations. With the presentation of detection performance comparison results between range estimation algorithms of laser fuzes, this paper can provide guidance and references for the application in the proximity fuze.

Kinetically Consistent Data Assimilation for Plant PET Sparse Time Activity Curve Signals

Time activity curve (TAC) signal processing in plant positron emission tomography (PET) is a frontier nuclear science technique to bring out the quantitative fluid dynamic (FD) flow parameters of the plant vascular system and generate knowledge on crops and their sustainable management, facing the accelerating global climate change. The sparse space-time sampling of the TAC signal impairs the extraction of the FD variables, which can be determined only as averaged values with existing techniques. A data-driven approach based on a reliable FD model has never been formulated. A novel sparse data assimilation digital signal processing method is proposed, with the unique capability of a direct computation of the dynamic evolution of noise correlations between estimated and measured variables, by taking into explicit account the numerical diffusion due to the sparse sampling. The sequential time-stepping procedure estimates the spatial profile of the velocity, the diffusion coefficient and the compartmental exchange rates along the plant stem from the TAC signals. To illustrate the performance of the method, we report an example of the measurement of transport mechanisms in zucchini sprouts.

Colorless Coherent TDM-PON Based on a Frequency-Comb Laser

Coherent reception becomes an interesting option when data rates in time-division-multiplexed (TDM) passive optical networks (PONs) grow beyond 50 Gbit/s. Controlling the wavelength, i.e., the optical frequency, and the phase of the laser acting as local oscillator (LO) is one of the main technical challenges in the design of coherent TDM PONs. In the optical network units (ONUs), low-cost lasers are required, which come at the expense of wavelength variations and drifts over multiple nanometers due to fabrication imperfections, and temperature variations. This contradicts the requirement of wavelength-stable LOs in coherent receivers. The use of a wavelength locker circuit and a temperature controller is considered as too complex for applications in access networks. In this work, we propose a novel colorless coherent architecture with high resilience to ONU laser wavelength drifts of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 4 nm ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 0.5 THz) for future 100 Gbit/s PON. It allows the use of distributed feedback lasers at the ONU side. This is rendered possible by generating a frequency comb with carefully chosen free spectral range in a quantum-dash mode-locked laser diode at the optical line terminal. In upstream operation, the frequency comb serves as an LO, whereas the same information is modulated onto all comb lines for the case of downstream. As a result, the ONU laser can drift over the entire comb bandwidth without substantial performance penalty. We experimentally demonstrate downstream and upstream operation with an aggregated raw data rate of 96 Gbit/s, respectively. We further introduce advanced digital signal processing (DSP) methods including a coarse frequency offset compensation (CFOC) and a multiple-input multiple-output (MIMO) equalizer to improve the performance of our concept. We show that the receiver sensitivity can be increased by 3 dB for a high-bandwidth receiver when using a 6 × 2 MIMO equalizer scheme. A 4 × 2 MIMO equalizer scheme enables colorless reception even with a limited-bandwidth receiver.

Fault Diagnosis Algorithms of Distribution Network Based on Convolutional Neural Network

Fault diagnosis is very important for power restoration. This paper proposed a basic network architecture design, using a simplified residual connection technology, using Focal Loss as the objective function for supervised training, adding a BatchNormalization layer to the network for optimization, reducing parameters based on ShuffleNet network, and improving accuracy based on attention mechanism, a process that can automatically determine the appropriate CNN architecture for fault diagnosis problems. The CNN used in this paper takes the current information sampled from the fault recorder of each node in the distribution network as the input directly, and does not need to use digital signal processing methods to extract frequency domain features and manually select features. The proposed algorithm is evaluated on the IEEE 34-node system, and the fault classification accuracy of more than 99% is achieved on different lines.

EEG analysis and classification based on cardinal spline empirical mode decomposition and synchrony features.

Dementia is a major cause of disability and dependency among older adults. Diagnosis is most effective at an early stage of the disease, as patients can start early treatment to delay progressive cognitive decline. While other diagnostic methods for dementia are available, electroencephalography (EEG) is noninvasive, more accessible, and less complicated than other biomarker measurements. Moreover, it may be orders of magnitude less expensive, thereby offering the possibility of low-cost mass screening. This paper presents a novel digital signal processing method called cardinal spline empirical mode decomposition (CS-EMD) for EEG processing. This new method uses a different signal envelope interpolation algorithm to separate EEG signals into constituent components, called intrinsic mode functions (IMFs), with better signal decomposition properties than classical empirical mode decomposition (EMD). The IMFs obtained from the new method are then used to compute longitudinal and transversal synchrony measures, which are explored as features for healthy and dementia classification using a support vector machine (SVM). The performance of the proposed method is studied on a publicly available EEG dataset. The results show that using multiple synchrony measures of both longitudinal and transversal EEG channels on five IMFs produces the best classification result of 90% accuracy, 96.67% specificity, 83.33% sensitivity, and 96.15% precision, outperforming the classical EMD method and various other approaches. This new, data-driven CS-EMD method shows good potential as a dementia screening tool. CS-EMD may also be applied in processing other nonlinear and nonstationary biosignals.

Minimizing the coincidence error in particle size spectrometers with digital signal processing techniques

Aerosol quantification is highly coveted for many applications ranging from quality monitoring to healthcare. Optical particle size spectrometers are one of the popular measurement devices used in aerosol characterization, which provide information about the size distribution and concentration of the particles. One drawback of this method is that the spectrometers are limited to applications with relatively low concentrations. At high concentrations, the number concentration is underestimated, while the size distribution is shifted towards larger particles. This phenomenon is commonly referred as coincidence error. However, the counting efficiency, above the lower detection limit, depends i. a. on the detector dead time. In order to minimize the probability of coincidence events, the dead time of the detector has to be as small as possible. The contribution of this work is to minimize the coincidence error by purposefully reducing the detector dead time. Therefore, various digital signal processing methods, to minimize dead time, are investigated and experimentally verified. Wavelet and derivative-based peak detection algorithms were found to be able to separate even strongly overlapping scattered light pulses from each other. The results show that the detector dead time can be reduced by 65% with digital signal processing techniques. Allowing the quantification of aerosols with more than 2.9 times the maximum concentration of current optical aerosol spectrometers, without increasing the coincidence error.

Optimized design of battery pole control system based on dual-chip architecture.

At present, the global demand for lithium batteries is still in a high growth state, and the traditional lithium battery pole mill control system is still dominated by ARM (Artificial Intelligence Enhanced Computing), DSP (Digital Signal Processing), and other single-chip control methods. There are problems such as poor anti-interference ability and insufficient real-time online analysis of production data. This paper adopts the dual-chip control system architecture based on "ARM+DSP", starting from the mechanical characteristics and operating signal features of the pole mill. The hardware system adopts a three-unit joint control hardware structure, which separates the control unit from the data processing unit and improves the operation of the system. The software system adopts fuzzy PID algorithm to realize deflection control and tension control, and verifies that the Fuzzy PID (Proportion Integration Differentiation) control algorithm can effectively improve the anti-interference ability of the deflection system and tension system. The results show that the data loss rate is low with the SPI communication between DSP and ARM. The tension error of the "ARM+DSP" control system does not exceed 5%, and the deviation of the correction band is within ±4mm. The dedicated dual-chip hardware architecture effectively improves the robustness and operation efficiency of the pole mill, solves the problem of low tension control accuracy, and provides a theoretical basis for the application of the dual-roll mill.

New Methods for the Acoustic-Signal Segmentation of the Temporomandibular Joint.

(1) Background: The stethoscope is one of the main accessory tools in the diagnosis of temporomandibular joint disorders (TMD). However, the clinical auscultation of the masticatory system still lacks computer-aided support, which would decrease the time needed for each diagnosis. This can be achieved with digital signal processing and classification algorithms. The segmentation of acoustic signals is usually the first step in many sound processing methodologies. We postulate that it is possible to implement the automatic segmentation of the acoustic signals of the temporomandibular joint (TMJ), which can contribute to the development of advanced TMD classification algorithms. (2) Methods: In this paper, we compare two different methods for the segmentation of TMJ sounds which are used in diagnosis of the masticatory system. The first method is based solely on digital signal processing (DSP) and includes filtering and envelope calculation. The second method takes advantage of a deep learning approach established on a U-Net neural network, combined with long short-term memory (LSTM) architecture. (3) Results: Both developed methods were validated against our own TMJ sound database created from the signals recorded with an electronic stethoscope during a clinical diagnostic trail of TMJ. The Dice score of the DSP method was 0.86 and the sensitivity was 0.91; for the deep learning approach, Dice score was 0.85 and there was a sensitivity of 0.98. (4) Conclusions: The presented results indicate that with the use of signal processing and deep learning, it is possible to automatically segment the TMJ sounds into sections of diagnostic value. Such methods can provide representative data for the development of TMD classification algorithms.