2D Signals Research Articles

Fundamental Concepts of Bipolar and High-Density Surface EMG Understanding and Teaching for Clinical, Occupational, and Sport Applications: Origin, Detection, and Main Errors.

Surface electromyography (sEMG) has been the subject of thousands of scientific articles, but many barriers limit its clinical applications. Previous work has indicated that the lack of time, competence, training, and teaching is the main barrier to the clinical application of sEMG. This work follows up and presents a number of analogies, metaphors, and simulations using physical and mathematical models that provide tools for teaching sEMG detection by means of electrode pairs (1D signals) and electrode grids (2D and 3D signals). The basic mechanisms of sEMG generation are summarized and the features of the sensing system (electrode location, size, interelectrode distance, crosstalk, etc.) are illustrated (mostly by animations) with examples that teachers can use. The most common, as well as some potential, applications are illustrated in the areas of signal presentation, gait analysis, the optimal injection of botulinum toxin, neurorehabilitation, ergonomics, obstetrics, occupational medicine, and sport sciences. The work is primarily focused on correct sEMG detection and on crosstalk. Issues related to the clinical transfer of innovations are also discussed, as well as the need for training new clinical and/or technical operators in the field of sEMG.

The effects on classifier performance of 2D discrete wavelet transform analysis and whale optimization algorithm for recognition of power quality disturbances

Power Quality (PQ) is becoming more and more important day by day in the electric network. Signal processing, pattern recognition and machine learning are increasingly being studied for the automatic recognition of any disturbances that may occur during the generation, transmission, and distribution of electricity. There are three main steps to identify the PQ disturbances. These include the use of signal processing methods to calculate the features representing the disturbances, the selection of those that are more useful than these feature sets to prevent the creation of a complex classification model, the creating a classification model that recognizes multiple classes using the selected feature subsets. In this study, one-dimensional (1D) PQ disturbances signals are transformed into two-dimensional (2D) signals, 2D discrete wavelet transforms (2D-DWT) are used to extract the features. The features are extracted by using the wavelet families such as Daubechies, Biorthogonal, Symlets, Coiflets and Fejer-Korovkin in 2D-DWT to analyze PQ disturbances. Whale Optimization Algorithm (WOA) and k-nearest neighbor (KNN) classifier determine the feature subsets. Then, WOA and k nearest neighbor (KNN) classifier are used to determine the feature group. By using KNN and Support Vector Machines (SVM) classification methods, Classifier models that distinguish PQ disturbances are formed. The main aim of the study is to determine the features derived from 2D wavelet coefficients for different wavelet families and to determine which of them has a better classification performance to distinguish PQ disturbances signals. At the same time, different classification methods are simulated and a model which can classify PQ disturbances signals with high performance is created. Also, the generated models are analysed for their performance in terms of different noise levels (40 dB, 30 dB, 20 dB). The result of this simulation study shows that the model developed to classify PQ disturbances is superior to conventional models and other 2D signal processing methods in the literature. In addition, it was concluded that the proposed method can cope better with noisy signals by low computational complexity and higher classification rate.

The relationship between graph Fourier transform (GFT) and discrete cosine transform (DCT) for 1D signal and 2D image

The relationship between graph Fourier transform (GFT) and discrete cosine transform (DCT) for 1D signal and 2D image

Alzheimer's Disease Analysis Algorithm Based on No-threshold Recurrence Plot Convolution Network.

Alzheimer's disease is a neurological disorder characterized by progressive cognitive dysfunction and behavioral impairment that occurs in old. Early diagnosis and treatment of Alzheimer's disease is great significance. Electroencephalography (EEG) signals can be used to detect Alzheimer's disease due to its non-invasive advantage. To solve the problem of insufficient analysis by single-channel EEG signal, we analyze the relationship between multiple channels and build PLV framework. To solve the problem of insufficient representation of 1D signal, a threshold-free recursive plot convolution network was constructed to realize 2D representation. To solve the problem of insufficient EEG signal characterization, a fusion algorithm of clinical features and imaging features was proposed to detect Alzheimer's disease. Experimental results show that the algorithm has good performance and robustness.

A Wavelet Plancherel Theory with Application to Multipliers and Sparse Approximations

We introduce an extension of continuous wavelet theory that enables an efficient implementation of multiplicative operators in the coefficient space. In the new theory, the signal space is embedded in a larger abstract signal space – the so called window–signal space. There is a canonical extension of the wavelet transform to an isometric isomorphism between the window–signal space and the coefficient space. Hence, the new framework is called a wavelet-Plancherel theory, and the extended wavelet transform is called the wavelet-Plancherel transform. Since the wavelet-Plancherel transform is an isometric isomorphism, any operation in the coefficient space can be pulled-back to an operation in the window–signal space. It is then possible to improve the computational complexity of methods that involve a multiplicative operator in the coefficient space, by performing all computations directly in the window–signal space. As one example application, we show how continuous wavelet multipliers (also called Calderón–Toeplitz operators), with polynomial symbols, can be implemented with linear complexity in the resolution of the 1D signal. As another example, we develop a framework for efficiently computing greedy sparse approximations to signals based on elements of continuous wavelet systems.

Laser-Assisted Growth of Carbon-Based Materials by Chemical Vapor Deposition

Carbon-based materials (CBMs) such as graphene, carbon nanotubes (CNT), highly ordered pyrolytic graphite (HOPG), and pyrolytic carbon (PyC) have received a great deal of attention in recent years due to their unique electronic, optical, thermal, and mechanical properties. CBMs have been grown using a variety of processes, including mechanical exfoliation, pulsed laser deposition (PLD), and chemical vapor deposition (CVD). Mechanical exfoliation creates materials that are irregularly formed and tiny in size. On the other hand, the practicality of the PLD approach for large-area high-quality CMB deposition is quite difficult. Thus, CVD is considered as the most effective method for growing CBMs. In this paper, a novel pulsed laser-assisted chemical vapor deposition (LCVD) technique was explored to determine ways to reduce the energy requirements to produce high quality CBMs. Different growth parameters, such as gas flow rate, temperature, laser energy, and deposition time were considered and studied thoroughly to analyze the growth pattern. CBMs are grown on Si and Cu substrates, where we find better quality CBM films on Cu as it aids the surface solubility of carbon. Raman spectroscopy confirms the presence of high-quality PyC which is grown at a temperature of 750 °C, CH4 gas flow rate of 20 sccm, a laser frequency of 10 Hz, and an energy density of 0.116 J/cm2 per pulse. It is found that the local pulsed-laser bombardment helps in breaking the carbon-hydrogen bonds of CH4 at a much lower substrate temperature than its thermal decomposition temperature. There is no significant change in the 2D peak intensity in the Raman spectrum with the further increase in temperature which is the indicator of the number of the graphene layer. The intertwined graphene flakes of the PyC are observed due to the surface roughness, which is responsible for the quenching in the Raman 2D signal. These results will provide the platform to fabricate a large area single layer of graphene, including the other 2D materials, on different substrates using the LCVD technique.

Long distance high resolution FMCW laser ranging with phase noise compensation and 2D signal processing.

A long distance high resolution frequency-modulated continuous wave (FMCW) laser rangefinder with phase noise compensation and two-dimensional (2D) data processing skills is developed. Range-finding ladar consists of a continuously chirped laser source, an auxiliary reference interferometer, and a monostatic optical transceiver for target illumination and return photon collection. To extend the range unambiguity and lower the electronic processing bandwidth, a two-step laser frequency chirping scheme is adopted, where a long pulse width, small frequency bandwidth laser chirping signal are used in step 1 for coarse distance estimation, and a short pulse width and large frequency bandwidth laser chirping signal are applied afterwards for step 2 high resolution distance realization. An auxiliary reference interferometer is to record the phase noise originated from the laser source to compensate for phase errors induced in the target return photons. The 2D data processing skill helps to coherently sum up all the phase noise removed echo photons to achieve high resolution range peak extraction with high detection sensitivity. Experimental demonstration shows that the proposed FMCW ladar at 1550 nm wavelength with a laser chirping bandwidth of 10 GHz and electronic processing bandwidth of 200 MHz can measure a corner cube test target in an outdoor atmospheric environment, and the measurement results are 12013.905 m with a 2.4 cm range resolution under strong return photon levels and 12013.920 m with a 2.5 cm range resolution under weak return photon levels.

Iterative 2D sparse signal reconstruction with masked residual updates for automotive radar interference mitigation

Compressive sensing has attracted considerable attention in automotive radar interference mitigation. However, these algorithms usually cannot be applied directly to commercial automotive radar as most of them are computationally intense. In this paper, we therefore introduce a computationally efficient two-dimensional masked residual updates (2D MRU) compressive sensing framework. By utilizing the sparsity of the beat signal in the frequency domain, the range-Doppler (RD) spectrum can be reconstructed with the help of undistorted samples in the beat signal. Unlike the other schemes, where a 2D signal measurement is vectorized into a 1D signal, the proposed 2D MRU can directly take a 2D signal measurement and reconstruct the corresponding RD spectrum. Furthermore, the 2D MRU framework can be easily integrated into well-known optimization schemes such as basis pursuit, iterative hard thresholding, iterative soft thresholding, orthogonal matching pursuit, and approximate message-passing algorithm. In addition to the standard iterative thresholding algorithms, we propose a novel prior-model-based iterative thresholding method to further reduce the computation time and reconstruction error. Theoretical analysis shows that the proposed framework can successfully reconstruct the RD spectrum with high probability. Moreover, numerical experiments demonstrate the superiority of the proposed framework in terms of computational complexity.

HOS-FingerCode: Bispectral invariants based contactless multi-finger recognition system using ridge orientation and feature fusion

Fingerprints are a commonly used biometric for civil and forensic applications and the majority of them are captured by contact sensors. More recently, there is a trend toward the use of high resolution digital and video cameras to acquire contactless images. However, the processing of such images is challenging because of intra-class variations such as pose, rotation, translation and scaling due to hand orientation and deformation. They can, however, facilitate the use of full finger or multiple fingers or hand information rather than the fingertip alone. Although some recent research has investigated representation of full finger and hand information under relaxed conditions, current state-of-the-art methods are unable to address transformation issues without manual intervention. To overcome the issues of relaxed constrained nature, geometrical variations and invariant feature encoding regardless of finger orientation, we have used higher order spectral features (HOS-FingerCode) from ridge patterns. We employ an automated method to detect key points and constructs a graph of key-lines. Ridge orientation profiles along with the selected key-lines provide a 1D signal from which bispectral invariant features are extracted. In addition, two fusion schemes are proposed where fusion from multiple key lines combinations and feature fusion from multiple key line configurations are investigated to extract ridge pattern profile for a better performance. The features are pooled from multiple fingers by concatenation or compact bilinear pooling to yield the HOS-FingerCode for multi-finger biometrics. For a single-finger system using 3369 high resolution index and middle finger images, the algorithm better performs when different key lines configurations are fused. For geometrical variations of the images, the system is most impacted by pose than rotation and scale changes of the images, and is tolerant to such variations. For the multi-finger system using 3111 images from both fingers, the algorithm has achieved 93.8% TPR and 97.95% classification accuracy at a setting of 2% FPR. For the feature fusion scheme proposed for multi-finger system using concatenation and compact bilinear pooling, concatenation of features performs better than the compact bilinear pooling. Since multi-finger recognition system performed better than the single finger biometrics system, it can help to diminish the high traffic scenario in busy premises and will facilitate the soft identity verification.

Classification of 1D and 2D EEG Signals for Seizure Detection in the Newborn Using Convolutional Neural Networks

Newborns do not always show clinical symptoms during seizures unlike adults. Therefore, uncontrolled seizures lead to serious brain damage. Timely detection of seizures plays a vital role for newborn babies. In this study, a deep transfer learning approach was proposed for automatic seizure detection on the C4-P4 channel using electroencephalography (EEG) signals of newborns. EEG signals have been used in 1D and 2D dimensions to ensure performance, robust functionality, and a clinically acceptable level of detection accuracy. Pre-trained deep learning models Alexnet, ResNet, GoogleNet and VggNet were used in the study. Spectrograms were obtained by converting 1-dimensional signal data to 2-dimensional images, and then the classification was made on both 1D and 2D data set. In 1D classification, the highest performance was obtained from VggNet architecture with 91.67%, while 2D classification was obtained from AlexNet and ResNet architecture with 95.83%. The use of spectrograms has greatly improved the classification performance and made seizure detection and decision clinically more reliable in newborns.

Prediction of misfire location for SI engine by unsupervised vibration algorithm

The early detection of faults by monitoring can prevent further damage to the internal combustion engine and avoid further causalities. The faults lead to reducing the engine performance and increasing the harmfulpollution. In this paper, a novel unsupervised vibration-based detection algorithm for predicting the location of the misfire of the spark-ignited engine using Khonen self-organizing map (SOM) is introduced. The purpose is to map the misfire location on self-organizing neural network with the best quality comparing 1D, 2D and 2D vibration signals. Four fault locations of the 4-cylinder engine are clustered and mapped at normal and misfire conditions. The behavior of the SOM for the engine vibration signals is investigated. The clustering neural network with a competitive learning algorithm is used to decide which cylinder has the misfire. The output map is used to cluster misfires using a tri-axial accelerometer to record vibration signals for three operation conditions. Also, the mean quantization error is used to measure the quality of each map. It is found that the ability of the SOM to discern misfire locations at three-dimensional vibration signals. Finally, it is found that the 3D vibration signal is improved the clustering with minimum errors 0.0025, 0.0011, 0.00043 for three different operation conditions. In additions, the maximum accuracy in detecting the misfire is 93.55% for 3D vibration signal at 3000 rpm.

A Novel Deep-Learning-Based Framework for the Classification of Cardiac Arrhythmia.

Cardiovascular diseases (CVDs) are the primary cause of death. Every year, many people die due to heart attacks. The electrocardiogram (ECG) signal plays a vital role in diagnosing CVDs. ECG signals provide us with information about the heartbeat. ECGs can detect cardiac arrhythmia. In this article, a novel deep-learning-based approach is proposed to classify ECG signals as normal and into sixteen arrhythmia classes. The ECG signal is preprocessed and converted into a 2D signal using continuous wavelet transform (CWT). The time–frequency domain representation of the CWT is given to the deep convolutional neural network (D-CNN) with an attention block to extract the spatial features vector (SFV). The attention block is proposed to capture global features. For dimensionality reduction in SFV, a novel clump of features (CoF) framework is proposed. The k-fold cross-validation is applied to obtain the reduced feature vector (RFV), and the RFV is given to the classifier to classify the arrhythmia class. The proposed framework achieves 99.84% accuracy with 100% sensitivity and 99.6% specificity. The proposed algorithm outperforms the state-of-the-art accuracy, F1-score, and sensitivity techniques.

Ultrasonic diagnosis method for stainless steel weld defects based on multi-domain feature fusion

PurposeThis paper aims to propose a new ultrasonic diagnosis method for stainless steel weld defects based on multi-domain feature fusion to solve two problems in the ultrasonic diagnosis of austenitic stainless steel weld defects. These are insufficient feature extraction and subjective dependence of diagnosis model parameters.Design/methodology/approachTo express the richness of the one-dimensional (1D) signal information, the 1D ultrasonic testing signal was derived to the two-dimensional (2D) time-frequency domain. Multi-scale depthwise separable convolution was also designed to optimize the MobileNetV3 network to obtain deep convolution feature information under different receptive fields. At the same time, the time/frequent-domain feature extraction of the defect signals was carried out based on statistical analysis. The defect sensitive features were screened out through visual analysis, and the defect feature set was constructed by cascading fusion with deep convolution feature information. To improve the adaptability and generalization of the diagnostic model, the authors designed and carried out research on the hyperparameter self-optimization of the diagnostic model based on the sparrow search strategy and constructed the optimal hyperparameter combination of the model. Finally, the performance of the ultrasonic diagnosis of stainless steel weld defects was improved comprehensively through the multi-domain feature characterization model of the defect data and diagnosis optimization model.FindingsThe experimental results show that the diagnostic accuracy of the lightweight diagnosis model constructed in this paper can reach 96.55% for the five types of stainless steel weld defects, including cracks, porosity, inclusion, lack of fusion and incomplete penetration. These can meet the needs of practical engineering applications.Originality/valueThis method provides a theoretical basis and technical reference for developing and applying intelligent, efficient and accurate ultrasonic defect diagnosis technology.

Analysis of single channel electroencephalographic signals for visual creativity: A pilot study

Analysis of creativity has important clinical applications in studying neurological disorders. Among the available tools, the simplest and the most portable device is the EEG headset. In this study we explore the potential of a single channel EEG to assess varying levels of Visual Creativity via sketching. Three tasks requiring different levels of creativity are designed: Max Creative(TMC)-sketching, Less Creative(TLC)-repetitive geometric patterns, and Nil Creative(TNC)-tally marks. Normalized and artifact-filtered EEG signals are used for analyses. Three different types of analysis are carried out using the features extracted from three different paradigms: (1) Chaos Analysis, (2) Distribution Analysis, and (3) Statistical Analysis. The 4-class classification scenario (Rest vs TMC vs TLC vs TNC) is compared with LSTM which is the popular technique for 1D signal. Mean accuracy is reported over 5-fold cross-validation over 10 runs. Among various feature and classifier combinations, peak performances are seen in the following scenarios. In 4-class classification, chaos features result in a peak mean accuracy of 52% while other features show significantly less accuracy. LSTM for 4-class classification results in only 38% accuracy in the current setting. When levels of creativity(TMC vs TLC vs TNC) are analyzed, Hjorth mobility and complexity, LLE and distribution features result in 45% accuracy. As expected, it is observed that Rest(R) is distinguished from Non-Rest with a mean accuracy > 90% using Standard deviation, MAD and LLE, individually. However, distribution-based features result in 69-74% accuracy. It is observed that chaos analysis results in a higher accuracy for the classes considered.

Wrapping based curvelet transform approach for ECG watermarking in telemedicine application

Patient information is currently maintained separately from the physiological signals received in telemedicine. Furthermore, in recent years, the usage of portable devices to obtain such medical information has become more common. This form of storing can lead to several other authentication issues and compromise the accuracy of diagnostics. To address this issue, we propose a watermarking method for embedding patient information in the ECG signal in this study. In our technique, the watermark is a QR code that contains the patient's information as well as the Electrocardiogram signal. The 1D ECG signal is transformed to a 2D signal in the proposed technique. A fast discrete curvelet transform is used to extract the signal's frequency content, and then Schur decomposition is used to the frequency sub-bands obtained. Finally, the watermark bits are integrated by modifying the Eigen values produced. According to imperceptibility experiments, this method produces a watermarked signal that is identical to the originals, keeping the diagnostic content of the signal. Robustness test indicated that the watermark could overcome the most popular watermarking attacks.

Analysis of the Measurement Matrix in Directional Predictive Coding for Compressive Sensing of Medical Images

Compressive sensing of 2D signals involves three fundamental steps: sparse representation, linear measurement matrix, and recovery of the signal. This paper focuses on analyzing the efficiency of various measurement matrices for compressive sensing of medical images based on theoretical predictive coding. During encoding, the prediction is efficiently chosen by four directional predictive modes for block-based compressive sensing measurements. In this work, Gaussian, Bernoulli, Laplace, Logistic, and Cauchy random matrices are used as the measurement matrices. While decoding, the same optimal prediction is de-quantized. Peak-signal-to-noise ratio and sparsity are used for evaluating the performance of measurement matrices. The experimental result shows that the spatially directional predictive coding (SDPC) with Laplace measurement matrices performs better compared to scalar quantization (SQ) and differential pulse code modulation (DPCM) methods. The results indicate that the Laplace measurement matrix is the most suitable in compressive sensing of medical images.

EGHWT: The Extended Generalized Haar\u2013Walsh Transform

Extending computational harmonic analysis tools from the classical setting of regular lattices to the more general setting of graphs and networks is very important, and much research has been done recently. The generalized Haar–Walsh transform (GHWT) developed by Irion and Saito (2014) is a multiscale transform for signals on graphs, which is a generalization of the classical Haar and Walsh–Hadamard transforms. We propose the extended generalized Haar–Walsh transform (eGHWT), which is a generalization of the adapted time–frequency tilings of Thiele and Villemoes (1996). The eGHWT examines not only the efficiency of graph-domain partitions but also that of “sequency-domain” partitions simultaneously. Consequently, the eGHWT and its associated best-basis selection algorithm for graph signals significantly improve the performance of the previous GHWT with the similar computational cost, O(N log N), where N is the number of nodes of an input graph. While the GHWT best-basis algorithm seeks the most suitable orthonormal basis for a given task among more than (1.5)^N possible orthonormal bases in mathbb {R}^N, the eGHWT best-basis algorithm can find a better one by searching through more than 0.618cdot (1.84)^N possible orthonormal bases in mathbb {R}^N. This article describes the details of the eGHWT best-basis algorithm and demonstrates its superiority using several examples including genuine graph signals as well as conventional digital images viewed as graph signals. Furthermore, we also show how the eGHWT can be extended to 2D signals and matrix-form data by viewing them as a tensor product of graphs generated from their columns and rows and demonstrate its effectiveness on applications such as image approximation.

Classification of audio signals using spectrogram surfaces and extrinsic distortion measures

Representation of one-dimensional (1D) signals as surfaces and higher-dimensional manifolds reveals geometric structures that can enhance assessment of signal similarity and classification of large sets of signals. Motivated by this observation, we propose a novel robust algorithm for extraction of geometric features, by mapping the obtained geometric objects into a reference domain. This yields a set of highly descriptive features that are instrumental in feature engineering and in analysis of 1D signals. Two examples illustrate applications of our approach to well-structured audio signals: Lung sounds were chosen because of the interest in respiratory pathologies caused by the coronavirus and environmental conditions; accent detection was selected as a challenging speech analysis problem. Our approach outperformed baseline models under all measured metrics. It can be further extended by considering higher-dimensional distortion measures. We provide access to the code for those who are interested in other applications and different setups (Code: https://github.com/jeremy-levy/Classification-of-audio-signals-using-spectrogram-surfaces-and-extrinsic-distortion-measures).

Reconstruction of Connected Digital Lines Based on Constrained Regularization.

This paper presents a new approach for reconstruction of disconnected digital lines (DDLs) based on a constrained regularization model which ensures connectivity of the digital lines (DLs) in the discrete image plane. The first step in this approach is to determine the order of given pixels of the DDL. To determine connectivity of pixels, we use the usual 8-neighbor connectivity in discrete images. For any neighboring pixels of the DDL that are not connected, we determine a number of new pixel values that need to be reconstructed between these pixels. Next, the integer-valued x - and y -coordinates of the location of the pixels of the DDLs are segregated into two 1D signal vectors. Then the x - and y -coordinates of the missing pixels of the DDLs are estimated using a new constrained regularization. While the solution of this constrained minimization problem provides real values for the x - and y -coordinates of pixels positions, the imposed constraint ensures connectivity of the resulting DLs in the image plane after transforming the computed values from [Formula: see text] to [Formula: see text]. The proposed regularization approach forces connected lines with small curvature. The experimental results demonstrate that the proposed technique improves DL intersection detection, as well. Moreover, this technique has a high potential to be used as a fast approach in binary image inpainting particularly overcoming the shortcomings of conventional methods which cause destruction of thin objects and blurring in the recovered regions.

Static-shift suppression and anti-interference signal processing for CSAMT based on Guided Image Filtering

Shallow conductive heterogeneity can lead to static shifts ain the apparent resistivity sounding curve of controlled-source audio-frequency magnetotellurics (CSAMT). The static effect will shift the apparent resistivity curves along with axial log-log coordinates. Such an effect, if not properly processed, can distort the resistivity of rock formation and the depth of interfaces, and even make the geological structures unrecognizable. In this paper, we discuss the reasons and characteristics of the static shift and summarize the previous studies regarding static shift correction. Then, we propose the Guided Image Filtering algorithm to suppress static shifts in CSAMT. In detail, we use the multi-window superposition method to superimpose 1D signals into a 2D matrix image, which is subsequently processed with Guided Image Filtering. In the synthetic model study and field examples, the Guided Image Filtering algorithm has effectively corrected and suppressed static shifts, and finally improved the precision of data interpretation.