Signal Processing Approach Research Articles

A quantum approach for digital signal processing

A quantum approach for digital signal processing

METHODS AND ALGORITHMS OF COMPLEXING IMAGES AND THERMAL SIGNALS

This paper considers possible approaches to improving the quality of image signal formation both in the light and in the dark period of the day, as well as reducing the influence of noise, interference and artifacts on the characteristics of image signals. It is proposed to use the wavelet domain for the analysis of thermal and image signals with their subsequent possible complexation. The main features of the formation of such signals are indicated. It is shown that the proposed approach allows to improve the quality characteristics of image signals, especially in the dark period of time, as well as to ensure their effective processing related to filtering, compression, scaling and contrast change. A comparison of the obtained results of the proposed method with other existing similar signal processing approaches is given. Keywords – Thermal signals, video signals, complexation of signals, wavelet transformation.

State-of-the-Art Review into Signal Processing and Artificial Intelligence-based Approaches Applied in Gearbox Defect Diagnosis

State-of-the-Art Review into Signal Processing and Artificial Intelligence-based Approaches Applied in Gearbox Defect Diagnosis

Non‐contact vital signs monitoring in broiler chickens

AbstractMeasuring the heartbeat and respiration of animals continuously can provide valuable information about their health status. Respiratory‐related diseases are very common in the poultry industry and unfortunately, there is no non‐contact respiratory monitoring system for measuring the breathing rate and heart rate of the broiler chicken. In this letter, the authors explored and tested the feasibility of utilizing a 24‐GHz continuous‐wave (CW) radar module for monitoring the vital signs (breathing rate and heart rate) of broiler chickens. A signal processing approach has been developed to extract vital signs of broiler chicken from Radar‐captured signals. The experiment was carried out on three different normal broiler chickens with the ages of 25–30 days, weight 1.18–1.6 kg where a 24‐GHz radar module was mounted at a distance of 0.2 m above the chest surface of the chicken, and this particular experiment was repeated for 20 times. The authors also used a reference ECG module (Biopac System) for extracting the breathing rate and heart rate of the broiler chicken and compared the accuracy of their proposed system. Experimental results demonstrated that the radar measurement closely matches the Biopac ECG acquisition module measurement and showed an accuracy of 96% for a short‐scale study.

Effective reduction of sidelobes in pulse compression radars using NLFM signal processing approaches

Pulse compression techniques are widely used in modern radar systems to enhance range resolution and detection capability. As signal design is one of the basic factors of an efficient radar system, the problem of designing a radar signal with good characteristics using pulse compression is addressed in this paper. A linear frequency modulated (LFM) waveform has been widely used for conventional radars. However, it has a higher sidelobe level which makes the detection of weak targets very difficult in presence of strong targets returns and as a result the problem of masking occurs. In order to get suppressed sidelobes of radar matched filter output as well as preserve the main lobe resolution and level to overcome the problem of masking, nonlinear frequency modulated (NLFM) signals are used in modern radars. In this paper, we will introduce two different approaches to design an optimized NLFM signal which is characterized by optimized sidelobe level (SLL). The first is the exponential piecewise linear function (EPWL) which is the modification of piecewise linear (PWL) functions relying on an exponential predistortioning function. The second is the odd-term polynomial approximation (OTPA) in which the generation of NLFM signal depends only on odd-powered terms of the polynomial function. Furthermore, the simulation results of autocorrelation functions (ACF) of the proposed signals show its superiority over the traditional LFM signals and significantly enhancement compared to the recent background work. The Doppler sensitivity of the designed signals has been evaluated, revealing that the first approach offers Doppler tolerance and is suitable for surveillance radar systems, while the second approach with a lower sidelobe level is used in applications such as Synthetic Aperture Radar (SAR). Finally, the ambiguity function of the designed signals has been measured to illustrate the effect of the Doppler effect relative to different velocities.

A systematic tomography framework for thickness mapping of pipes using helical guided waves

Pipe wall loss caused by corrosion is of growing interest in the petrochemical industry. A systematic tomography framework using helical guided waves is developed in this paper to conduct a thickness mapping. In this work, the thickness under investigation is reconstructed using an objective function derived from the acoustic Helmholtz equation. The main approach consists of two parts. Firstly, the parametric dictionary is designed to separate the overlapped guided waves travelling in helical paths. After that, the scattering field can be extracted as the input of the distorted born iteration method. The imaging result is exemplified numerically and experimentally, with the strengths and drawbacks explained thoroughly. Remarkably, the thickness error of the simple defect is still within 0.5 mm when the input data is poor. A clear qualitative description of complex defects can be achieved through iterations even in the absence of an initial objective function. The framework established in this paper contributes a comprehensive imaging algorithm and the corresponding signal processing approach, all of which are conducive to providing some reference for engineering applications in nondestructive testing and structural health monitoring.

A Multimodal Social Signal Processing Approach to Team Interactions

Social signal processing develops automated approaches to detect, analyze, and synthesize social signals in human–human as well as human–machine interactions by means of machine learning and sensor data processing. Most works analyze individual or dyadic behavior, while the analysis of group or team interactions remains limited. We present a case study of an interdisciplinary work process for social signal processing that can develop automatized measures of complex team interaction dynamics, using team task and social cohesion as an example. In a field sample of 25 real project team meetings, we obtained sensor data from cameras, microphones, and a smart ID badge measuring acceleration. We demonstrate how fine-grained behavioral expressions of task and social cohesion in team meetings can be extracted and processed from sensor data by capturing dyadic coordination patterns that are then aggregated to the team level. The extracted patterns act as proxies for behavioral synchrony and mimicry of speech and body behavior which map onto verbal expressions of task and social cohesion in the observed team meetings. We reflect on opportunities for future interdisciplinary or collaboration that can move beyond a simple producer–consumer model.

Identifying a Suitable Signal Processing Technique for MI EEG Data

Motor imagery (MI) electroencephalography (EEG) technology is acquiring great attention from researchers due to its remarkable real-world applications. EEG signals inherit a high degree of non-stationarity, making their analysis not modest. Hence, choosing an appropriate signal processing approach becomes crucial. This comparative paper aims to identify a suitable signal processing method among famous approaches, namely short-time Fourier transform (STFT), continuous wavelet transform (CWT), and two variations of discrete wavelet transform maximal overlap DWT (MODWT) and MODWT multiresolution analysis (MODWTMRA). Different mother wavelet basis filters experimented with wavelet methods: Morse, Amor, Bump, Symlets, Daubechies, Coiflets, and Fejér-Korovkin. The different methods were tested on the classification of the right-hand and left-hand motor imagery tasks using the brain-computer interface (BCI) competition IV 2b dataset. A shallow convolutional neural network containing a single convolution layer was first trained and then used for classification. The experimental outcomes verified that MI EEG signals can be better analyzed and recognized using the maximal overlap-based signal processing methods. The classification accuracy proved that MODWT and MODWTMRA with the Symlets wavelet outperformed the other methods.

Genomic Signal Processing Methods in DNA Mapping Schemes for Prediction of Exon in a Gene Using Digital Filters

Genomic signal processing (GSP) is an engineering domain involved with the analysis of genomic data using digital signal processing (DSP) approaches after transformation of the sequence of genome to numerical sequence. One challenge of GSP is how to minimize the error of detection of the protein coding region in a specified deoxyribonucleic acid (DNA) sequence with a minimum processing time. Since the type of numerical representation of a DNA sequence extremely affects the prediction accuracy and precision. The impact of different DNA statistical representations on the identification of coding sequences (exons) was researched. In this study using the IIR inverse Chepyshev filter for twenty benchmark human genes. In order to accomplish this, the sensitivity, specificity, and correlation coefficient of the four most modern DNA numerical representation schemes GCC, FNO, atomic number, and 2-bit binary were measured and contrasted with those of EIIP, the most used technique for locating protein-coding regions.

On the PMU Placement Optimization for the Detection of False Data Injection Attacks

The graph signal processing (GSP) approaches could facilitate the design of detectors for false data injection attacks (FDIAs) on smart grids. However, enhancing the performance of FDIA detection by optimizing the placement of phasor measurement units (PMUs) on smart grids remains an open problem. We formulate herein the detection of FDIAs as a binary hypothesis testing problem within the Grid-GSP framework and develop a subspace-based projection detection (SPD) algorithm, incorporating a quasi-optimal PMU placement strategy and two double thresholds. The proposed quasi-optimal PMU placement strategy enhances the performance of the SPD in FDIA detection. We further propose to enhance the SPD with the dynamic double threshold for high dynamic load profiles beyond a simple yet efficient static double threshold. Illustrative simulations based on the IEEE 118 bus test grid and the ERCOT dataset validate the superior performance of the proposed quasi-optimal PMU placement strategy along with the two double thresholds.

Exploration on the application of electronic information technology in signal processing based on big data

Abstract Mobile phones are the most commonly used electronic devices in people’s daily life. The image, voice, and other information in these devices need to be processed through signal transmission. The role of signal processing is to process the acquired information in a certain way to get the final result. In order to ensure that the whole processing program can work normally, it is necessary to implement good control to achieve the desired effect. However, with the continuous progress and development of science and technology, its requirements are becoming increasingly strict. The traditional signal processing method is unreliable, has poor real time, and has error-prone characteristics, which can no longer meet the accuracy requirements of current information acquisition equipment. Therefore, people begin to study more complex and precise information processing methods and apply these algorithms to various advanced electronic devices to achieve better results. From the perspective of big data, electronic information technology is generated and developed based on massive data processing. It not only has a strong storage function but also has strong computing power and a wide range of application scenarios. It has strong applicability in real life. In this article, the signal to be processed was divided into several wavelet components in different frequency ranges by empirical mode decomposition technology, and then the signal was denoised by combining three wavelet denoising methods to obtain noise data with good signal-to-noise ratio and high classification accuracy. Finally, the corresponding feature information was extracted according to the signal-receiving model to improve the system recognition rate. This article compared the traditional signal processing methods with the signal processing approaches from the perspective of electronic information technology. The results showed that the processing method had a high computing speed and could better solve the problem of detection performance degradation caused by interference. User satisfaction had also increased by 2.87%, which showed that signal processing based on big data and information processing technology had broad application prospects in communication systems. The core of open computer science is to build a unified, efficient, and scalable computing platform based on massive data processing and use signal processing and computer technology to manage and optimize the scheduling of information resources to better meet various business needs.

Island detection for grid connected photovoltaic distributed generations via integrated signal processing and machine learning approach

The applications of distributed generators in distribution networks have proven to be highly reliable and cost-effective. Accidental islanding is currently regarded as an undesirable operational mode in utility practice because it can harm individuals and connected systems. Therefore, the occurrence of islanding must be detected, and then distributed generators must be disconnected from the main network. In this article, a fast and accurate island detection method is proposed for photovoltaic distributed generations with a near-zero non-detection zone. A new island detection approach is developed by combining signal processing and machine learning techniques. Variational mode decomposition is used as a signal processing technique. Whereas the ensemble bagged-trees method is used as a machine learning technique. Variational mode decomposition is used to process positive- and negative-sequence component voltage signals along with power signal measurements acquired from the point of common coupling in order to identify intrinsic-mode functions. Next, the ensemble bagged-trees method is utilized to detect islanding during active and reactive power mismatch events and inconvenient quality factors. The results demonstrate that the suggested technique is able to discriminate between islanding and non-islanding events such as capacitive switching, fault emulation, and distribution generation cut-off. Besides, it has a minimum non-detection zone of less than 4% and a 4.8 ms detection time. Therefore, it is a reliable and reasonable solution for the distribution grid.

Electron counting detectors in scanning transmission electron microscopy via hardware signal processing

Transmission electron microscopy is a pivotal instrument in materials and biological sciences due to its ability to provide local structural and spectroscopic information on a wide range of materials. However, the electron detectors used in scanning transmission electron microscopy are often unable to provide quantified information, that is the number of electrons impacting the detector, without exhaustive calibration and processing. This results in arbitrary signal values with slow response times that cannot be used for quantification or comparison to simulations. Here we demonstrate and optimise a hardware signal processing approach to augment electron detectors to perform single electron counting.

Improving speech perception for hearing-impaired listeners using audio-to-tactile sensory substitution with multiple frequency channels

Cochlear implants (CIs) have revolutionised treatment of hearing loss, but large populations globally cannot access them either because of disorders that prevent implantation or because they are expensive and require specialist surgery. Recent technology developments mean that haptic aids, which transmit speech through vibration, could offer a viable low-cost, non-invasive alternative. One important development is that compact haptic actuators can now deliver intense stimulation across multiple frequencies. We explored whether these multiple frequency channels can transfer spectral information to improve tactile phoneme discrimination. To convert audio to vibration, the speech amplitude envelope was extracted from one or more audio frequency bands and used to amplitude modulate one or more vibro-tactile tones delivered to a single-site on the wrist. In 26 participants with normal touch sensitivity, tactile-only phoneme discrimination was assessed with one, four, or eight frequency bands. Compared to one frequency band, performance improved by 5.9% with four frequency bands and by 8.4% with eight frequency bands. The multi-band signal-processing approach can be implemented in real-time on a compact device, and the vibro-tactile tones can be reproduced by the latest compact, low-powered actuators. This approach could therefore readily be implemented in a low-cost haptic hearing aid to deliver real-world benefits.

Misalignment-Related Defect Detection using Discrete Wavelet Transform

Induction motors are most commonly used in many industries, including petrochemicals, oil, and steel. A single failure in any of the induction motor's components or sub-components can result in a plant shutdown. The plant will suffer significant financial losses as a result. It is crucial to diagnose different types of faults in induction motors. Various condition monitoring techniques diagnose faults in induction motors in the early stages. Vibration analysis is most commonly used among different condition monitoring techniques due to its higher accuracy than other methods. Vibration analysis is used to detect various types of faults in induction motors. The acceleration vibration data corresponding to multiple types of defects are gathered from publicly available web resources. The primary objective of this research work is to explore the severity of horizontal and vertical misalignment defects utilizing a signal processing approach. To achieve this objective, Discrete Wavelet Transform (DWT) is used to detect abnormal behavior of the induction motor. The Daubechies-4(db4) wavelet is chosen as a mother wavelet. As Daubechies wavelet is an orthogonal wavelet, the percentage energy in all decomposed sub-band will equal the original energy of the signal. The energy level of sub-bands is compared with the healthy condition of the motor to detect significant changes in motor fault.

Statistical Validation of An Automated Method for Calculating Time Domain Heart Rate Variability on The QT Dataset.

Heart rate variability measures calculated from electrocardiography recordings reflect social competence. Clinical assessments of social skills have found that reduced heart rate variability is related to differences in the development of social skills in children and increase the risk of mental disorders. Limited by widespread manual signal processing and R-peak detection in current clinical assessments, most literature reports only short-term baseline studies, with fewer studies reporting social interaction settings with prolonged recording. There is an urgent need for an automated physiological signal processing toolbox to detect R-peaks and perform heart rate variability measurements in social settings. This paper proposes a modified automated Neurokit2 toolbox with signal processing procedures similar to the MindWare software that requires manual inspection of R-peak locations. We calculate time domain heart rate variability metrics from the publicly available QT database by PhysioNet collected at resting states and under stress tests, mimicking social interaction stress scenarios. Statistical analysis conveys that heart rate variability metrics calculation applying both signal processing approaches using the Neurokit2 toolbox are statistically equivalent in comparison to the hand-labelled R-peaks from the QT database (n= 10 in the normal sinus rhythm group, and n= 6 in the ST Change group). Such validation results are crucial for the adoption of automated toolboxes for heart rate variability measures in social interaction assessments, where more movement and mood changes of participants are expected.Clinical Relevance- This contributes to the body of evidence of the reliability of the Neurokit2 toolbox for automatic cleaning of prolonged cardiac electrophysiological signals and calculation of heart rate variability in time-domain characterization in social interaction stress assessment.

Novel Multi-Parametric Sensor System for Comprehensive Multi-Wavelength Photoplethysmography Characterization.

Photoplethysmography (PPG) is widely used to assess cardiovascular health. However, its usage and standardization are limited by the impact of variable contact force and temperature, which influence the accuracy and reliability of the measurements. Although some studies have evaluated the impact of these phenomena on signal amplitude, there is still a lack of knowledge about how these perturbations can distort the signal morphology, especially for multi-wavelength PPG (MW-PPG) measurements. This work presents a modular multi-parametric sensor system that integrates continuous and real-time acquisition of MW-PPG, contact force, and temperature signals. The implemented design solution allows for a comprehensive characterization of the effects of the variations in these phenomena on the contour of the MW-PPG signal. Furthermore, a dynamic DC cancellation circuitry was implemented to improve measurement resolution and obtain high-quality raw multi-parametric data. The accuracy of the MW-PPG signal acquisition was assessed using a synthesized reference PPG optical signal. The performance of the contact force and temperature sensors was evaluated as well. To determine the overall quality of the multi-parametric measurement, an in vivo measurement on the index finger of a volunteer was performed. The results indicate a high precision and accuracy in the measurements, wherein the capacity of the system to obtain high-resolution and low-distortion MW-PPG signals is highlighted. These findings will contribute to developing new signal-processing approaches, advancing the accuracy and robustness of PPG-based systems, and bridging existing gaps in the literature.

A Deep Echo State Network-Based Novel Signal Processing Approach for Underwater Wireless Optical Communication System with PAM and OFDM Signals

Underwater wireless optical communication (UWOC) plays key role in the underwater wireless sensor networks (UWSNs), which have been widely employed for both scientific and commercial applications. UWOC offers high transmission data rates, high security, and low latency communication between nodes in UWSNs. However, significant absorption and scattering loss in underwater channels, due to ocean water conditions, can introduce highly non-linear distortion in the received signals, which can severely deteriorate communication quality. Consequently, addressing the challenge of processing UWOC signals with low optical signal-to-noise ratios (OSNRs) is critical for UWOC systems. Increasing the transmitting optical power and investigating more advanced signal processing technologies to recover transmitted symbols are two primary approaches to improve system tolerance in noisy UWOC signal channels. In this paper, we propose and demonstrate the application of deep echo state networks (DeepESNs) for channel equalization in high-speed UWOC systems to enhance system performance with both PAM and QPSK-OFDM modulations. Our experimental results demonstrate the effectiveness of DeepESNs in UWOC systems, achieving error-free underwater transmission over 40.5 m with data rates up to 167 Mbps. Moreover, we compare the performance of DeepESNs to conventional echo state networks and provide suggestions on the configuration of a DeepESN for UWOC signals.

A novel rolling bearing fault diagnosis method based on parameter optimization variational mode decomposition with feature weighted reconstruction and multi-target attention convolutional neural networks under small samples.

To enhance the precision of rolling bearing fault diagnosis, an intelligent hybrid approach is proposed in this paper for signal processing and fault diagnosis in small samples. This approach is based on advanced techniques, combining parameter optimization variational mode decomposition weighted by multiscale permutation entropy (MPE) with maximal information coefficient and multi-target attention convolutional neural networks (MTACNN). First, an improved variational mode decomposition (VMD) is developed to denoise the raw signal. The whale optimization algorithm was used to optimize the penalty factor and mode component number in the VMD algorithm to obtain several intrinsic mode functions (IMFs). Second, separate MPE calculations are performed for both the raw signal and each of the IMF components obtained from the VMD decomposition; the results are used to calculate the maximum information coefficient (MIC). Subsequently, each MIC is normalized and converted to a weight coefficient for signal reconstruction. Ultimately, the reconstructed signals serve as input to the MTACNN for diagnosing rolling bearing faults. Results demonstrate that the signal processing approach exhibits superior noise reduction capability through simple processing. Furthermore, compared to several similar approaches, The method proposed for fault diagnosis achieves superior performance levels in the fault pattern recognition target and the fault severity recognition target.

Multi-Task Learning Radar Transformer (MLRT): A Personal Identification and Fall Detection Network Based on IR-UWB Radar.

Radar-based personal identification and fall detection have received considerable attention in smart healthcare scenarios. Deep learning algorithms have been introduced to improve the performance of non-contact radar sensing applications. However, the original Transformer network is not suitable for multi-task radar-based applications to effectively extract temporal features from time-series radar signals. This article proposes the Multi-task Learning Radar Transformer (MLRT): a personal Identification and fall detection network based on IR-UWB radar. The proposed MLRT utilizes the attention mechanism of Transformer as its core to automatically extract features for personal identification and fall detection from radar time-series signals. Multi-task learning is applied to exploit the correlation between the personal identification task and the fall detection task, enhancing the performance of discrimination for both tasks. In order to suppress the impact of noise and interference, a signal processing approach is employed including DC removal and bandpass filtering, followed by clutter suppression using a RA method and Kalman filter-based trajectory estimation. An indoor radar signal dataset is generated with 11 persons under one IR-UWB radar, and the performance of MLRT is evaluated using this dataset. The measurement results show that the accuracy of MLRT improves by 8.5% and 3.6% for personal identification and fall detection, respectively, compared to state-of-the-art algorithms. The indoor radar signal dataset and the proposed MLRT source code are publicly available.