Search

Beamforming technique can effectively improve the spectrum utilization in the multi‐antenna systems, while the dirty‐paper coding (DPC) technique can reduce the inter‐user interference. In this letter, it is aimed to maximize the weighted sum‐rate under the total power constraint in the multiple‐input‐single‐output (MISO) system with the DPC technique. However, the existing methods of beamforming optimization mainly rely on customized iterative algorithms, which have high computational complexity. To address this issue, the beamforming neural network (BFNNet) is devised by utilizing the deep learning technique and the uplink‐downlink duality and exploring the optimal solution structure, which includes the deep neural network module and the signal processing module. Simulation results show that the BFNNet can achieve near‐optimal solutions and significantly reduce computational complexity. [ABSTRACT FROM AUTHOR]

This Editorial introduces a Special Issue of papers from the SEE International Conference RADAR 2019, which took place in Toulon, France, 23–27 September 2019. The papers are expanded versions of ones that were judged in the reviewing process to be of the highest quality and relevance. Benefitting from the unique location of the conference centre on the French Riviera, the conference was given the theme 'Sensing from sea to space', attracting nearly 400 attendees from 26 countries from all over the world (Figure 1). Engineers and researchers coming from private or public sector (universities and laboratories) shared their knowledge and experience on radar topics of common interests from physics to signal processing, from sensor validation to system design, and also on emerging technologies based on artificial intelligence; all topics being supported by 200 papers presented either in oral (150 papers in 36 sessions), or poster sessions (50 papers). Figure 2 shows the range of topics of the papers. 1 FIGURE: Conference attendees by country 2 FIGURE: Conference paper topics [ABSTRACT FROM AUTHOR]

With the increasing use of surveillance cameras, face recognition is being studied by many researchers for security purposes. Although high accuracy has been achieved for frontal faces, the existing methods have shown poor performance for occluded and corrupt images. Recently, sparse representation based classification (SRC) has shown the state‐of‐the‐art result in face recognition on corrupt and occluded face images. Several researchers have developed extended SRC methods in the last decade. This paper mainly focuses on SRC and its extended methods of face recognition. SRC methods have been compared on the basis of five issues of face recognition such as linear variation, non‐linear variation, undersampled, pose variation, and low resolution. Detailed analysis of SRC methods for issues of face recognition have been discussed based on experimental results and execution time. Finally, the limitation of SRC methods have been listed to help the researchers to extend the work of existing methods to resolve the unsolved issues. [ABSTRACT FROM AUTHOR]

This paper proposes a digital morphological filtering method based on a novel structuring element (SE) formulated using fractional Fourier transform (FrFT) and cross-convolution of window functions. The highlighting feature of this newly formulated filter is its flexibility in adding an adaptive nature to classical morphological filtering. Until now, every method listed in the literature makes a convenient assumption that noises corrupting ECG signal are Gaussian and model the filter around this assumption. Addressing this shortcoming, the first-of-its-kind filter adopts the α -stable distribution model (of which Gaussian distribution is a special case) of noise to better replicate the real-time noises interfering with ECG signals. The designed filter can suppress the noise and adapt to the changes in ECG signal morphology for better reconstruction. The proposed filter is tested on MIT-BIH Arrhythmia Database. The simulation results show improved performance in several quantitative metrics, demonstrating the superiority of our suggested method over the currently used state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

The present work was focused to design a cheap, rapid, portable and easy to use filter paper based assay for the qualitative and quantitate analysis of acetone. Sodium alginate gel was loaded with the acetone specific optical signal probe, and subsequently coated onto filter paper surface to design portable colorimetric assays for acetone monitoring. The color of the paper sensor strip was observed to change from dark yellow to light yellowish in the presence of varying concentrations of acetone. Three different color analyzing models including RGB, HSV, and LAB were employed to probe the output optical signal, and their performance was compared in terms of better interpretation of the generated signal. The LAB model was found to provide better analytical figures of merit with a linear response for the acetone concentration ranging from 2.5 to 1500 ppm, and a limit of detection of 0.5 ppm. Furthermore, the specificity of the designed filter paper based sensor was demonstrated against different common interfering compounds. The results demonstrated the potential of our proposed filter paper based sensor as a novel tool for the analysis of acetone. [ABSTRACT FROM AUTHOR]

This paper analyzes the digital signal processing techniques and estimates induction motor (IM) rotational speed operating in stationary or non-stationary conditions. Rotor slot harmonics present in the stator current waveform are used to estimate the induction motor speed with a given or identified rotor slot numbers. This paper's contribution is the following: First, zoom improved short-time Chirp-Z transform is used to find supply frequency and the rotor slot harmonic frequency to improve the estimation accuracy without increasing computing complexity. Second, a technique is described that can be used to determine whether or not a motor can generate principal slot harmonics (PSH). Finally, an algorithm is designed to figure out the perfect window length and estimate the motors' speed. This proposed technique was investigated when the motor was fed by an inverter-fed supply driving a variable load and operating in non-stationary conditions. Experimental test results on 5.5 kW and 22 kW induction motors have confirmed the correctness of this method. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review of the use of computational bioacoustics as well as signal and speech processing techniques in the analysis of primate vocal communication. We explore the potential implications of machine learning and deep learning methods, from the use of simple supervised algorithms to more recent self‐supervised models, for processing and analyzing large data sets obtained within the emergence of passive acoustic monitoring approaches. In addition, we discuss the importance of automated primate vocalization analysis in tackling essential questions on animal communication and highlighting the role of comparative linguistics in bioacoustic research. We also examine the challenges associated with data collection and annotation and provide insights into potential solutions. Overall, this review paper runs through a set of common or innovative perspectives and applications of machine learning for primate vocal communication analysis and outlines opportunities for future research in this rapidly developing field. [ABSTRACT FROM AUTHOR]

The problem of crystal ice formation inside aircraft turbine engines is well‐documented, and poses a significant risk to safety. The problem is not only one of power loss in flight, but the very real possibility that a flame‐out event could occur due to ice accretion on compressor stators, with potentially catastrophic outcomes. Although many instrumentation systems have been developed for wing ice detection, incipient formation of crystal ice is somewhat more difficult to detect. This is compounded by the need for a noncontact sensor which is robust to in‐flight conditions. This paper proposes an approach to the detection of ice formation based on microwave transmission characteristics across the first and possibly the second stage of the compressor stator. It is shown that noncontact detection is feasible under realistic conditions. The contribution of this paper is twofold. First, the microwave transmission approach is motivated using wind tunnel measurements, and appropriate frequency bands are determined. Next, a signal processing approach involving higher‐order analysis of time‐frequency distribution characteristics is then put forward. Experimental results are presented to support the hypothesis that multiband detection offers a workable approach to the incipient crystal‐ice detection problem. [ABSTRACT FROM AUTHOR]

The echo traces in the ionograms contain key information about the ionosphere. Therefore, the accurate extraction of these traces is crucial for the subsequent work. This paper transforms the original signal processing problem into a semantic segmentation task, combines it with the currently popular deep learning techniques, and proposes a multiscale Transformer network to achieve pixel-level trace extraction. To train the proposed model, we built a dataset by discretizing the original echo data, labeling, and other preprocessing work. A series of advanced semantic segmentation networks are utilized for comparative experiments. The analysis of the results indicates that the proposed network excels in performance, achieving the highest scores on key semantic segmentation evaluation metrics, including mIoU, Kappa, Dice, and AUC-ROC. In addition, this paper also designs a series of ablation experiments to observe the changes in network performance and to evaluate the rationality of the network design. The experimental results demonstrate the effectiveness of the network in the trace extraction task, which plays a positive role in the subsequent electron density reversal work. [ABSTRACT FROM AUTHOR]

This research paper explores the realm of fault detection in distributed motors through the vision of the Internet of electrical drives. This paper aims at employing artificial neural networks supported by the data collected by the Internet of distributed devices. Cross-verification of results offers reliable diagnosis of industrial motor faults. The proposed methodology involves the development of a cyber–physical system architecture and mathematical modeling framework for efficient fault detection. The mathematical model is designed to capture the intricate relationships within the cyber–physical system, incorporating the dynamic interactions between distributed motors and their edge controllers. Fast Fourier transform is employed for signal processing, enabling the extraction of meaningful frequency features that serve as indicators of potential faults. The artificial neural network based fault detection system is integrated with the solution, utilizing its ability to learn complex patterns and adapt to varying motor conditions. The effectiveness of the proposed framework and model is demonstrated through experimental results. The experimental setup involves diverse fault scenarios, and the system's performance is evaluated in terms of accuracy, sensitivity, and false positive rates. [ABSTRACT FROM AUTHOR]

In vehicular communications, channel estimation is a complex problem due to the joint time–frequency selectivity of wireless propagation channels. To this end, several signal processing techniques as well as approaches based on neural networks have been proposed to address this issue. Due to the highly dynamic and random nature of vehicular communication environments, precise characterization of temporal correlation across a received data sequence can enable more accurate channel estimation. This paper proposes a new pilot constellation scheme in combination with a small feed‐forward neural network to improve the accuracy of channel estimation in V2X systems while keeping low the implementation complexity. The performance is evaluated in typical vehicular channels using simulated BER curves, and it is found superior to traditional channel estimation methods and state‐of‐the‐art neural‐network‐based implementations such as feed‐forward and super‐resolution. It is illustrated that the improvement becomes pronounced for small subcarrier spacings (or low 5G numerologies); hence, this paper contributes to the development of more reliable mobile services across rapidly varying vehicular communication channels with rich multi‐path interference. [ABSTRACT FROM AUTHOR]

Welded joints frequently endure the composite stress of steel components. The presence of defects within these welded joints can significantly jeopardise the safety and performance of the welded structure. Magnetic memory testing technology has garnered substantial attention due to its ability to evaluate welding defects. However, the conventional zero-point pole theory, which serves as the foundation for defect assessment in practical detection, may lead to defect location and omission errors. In response to this challenge, scholars have conducted extensive research to accurately pinpoint the location and identify the types of defect within welds. This paper systematically reviews the mechanisms of magnetic memory welding defect detection, the factors that influence it, signal characteristic parameters, noise reduction in magnetic memory signals and the application of machine learning for quantitative assessment. By summarising these research advancements, this paper aims to address the current issues and provide guidance for the precise quantitative evaluation of welding defects in the future using metal magnetic memory technology. [ABSTRACT FROM AUTHOR]

Signal processing is a very useful field of study in the interpretation of signals in many everyday applications. In the case of applications with time-varying signals, one possibility is to consider them as graphs, so graph theory arises, which extends classical methods to the non-Euclidean domain. In addition, machine learning techniques have been widely used in pattern recognition activities in a wide variety of tasks, including health sciences. The objective of this work is to identify and analyze the papers in the literature that address the use of machine learning applied to graph signal processing in health sciences. A search was performed in four databases (Science Direct, IEEE Xplore, ACM, and MDPI), using search strings to identify papers that are in the scope of this review. Finally, 45 papers were included in the analysis, the first being published in 2015, which indicates an emerging area. Among the gaps found, we can mention the need for better clinical interpretability of the results obtained in the papers, that is not to restrict the results or conclusions simply to performance metrics. In addition, a possible research direction is the use of new transforms. It is also important to make new public datasets available that can be used to train the models. [ABSTRACT FROM AUTHOR]

This study aims to demonstrate the feasibility of using a new wireless electroencephalography (EEG)–electromyography (EMG) wearable approach to generate characteristic EEG-EMG mixed patterns with mouth movements in order to detect distinct movement patterns for severe speech impairments. This paper describes a method for detecting mouth movement based on a new signal processing technology suitable for sensor integration and machine learning applications. This paper examines the relationship between the mouth motion and the brainwave in an effort to develop nonverbal interfacing for people who have lost the ability to communicate, such as people with paralysis. A set of experiments were conducted to assess the efficacy of the proposed method for feature selection. It was determined that the classification of mouth movements was meaningful. EEG-EMG signals were also collected during silent mouthing of phonemes. A few-shot neural network was trained to classify the phonemes from the EEG-EMG signals, yielding classification accuracy of 95%. This technique in data collection and processing bioelectrical signals for phoneme recognition proves a promising avenue for future communication aids. [ABSTRACT FROM AUTHOR]

Precoding is a critical signal processing technique used in wireless communication systems to enhance transmission performance. This paper initially provides a brief overview of various conventional precoding algorithms. The exploration includes various nonlinear precoding algorithms which outperform linear techniques in high signal-to-noise ratio (SNR) scenarios. Moreover, the paper delves into practical considerations and provides insights into selecting the most suitable technique for specific communication scenarios. The deep learning-based hybrid precoder is designed using the MDL-AltMin algorithm and simulated results show a spectral efficiency of (SE) 40.96 dB compared with different precoding algorithms. The precoder model is modified with a Hybrid Precoding Net algorithm with a spectral efficiency of 21.07 dB for high values of SNR such as −30 and again compared with deep learning precoders. The HPNet model is proposed with a spectral efficiency of 23.04 dB with and without CSI (Channel State Information) at the transmitter. Furthermore, the HPNet model is compared with optimal digital precoders. [ABSTRACT FROM AUTHOR]

Video processing is the most emergent area of image processing in Research and Development. This paper, we present some advanced technology interrelated to video and image processing, which will provide a super-resolution high-quality most prominent visible light video for better human visualization and background motions or gesticulations of the human brain. There are two approaches for increasing the current resolution level of an image. The first one is the improvement of spatial resolution by reducing the size of pixels through the techniques of sensor manufacturing by about 40 $ {\mathcal{M}}{\textrm{m}^2} $ M m 2 for a 0.35 $ {\mathcal{M}}\textrm{m} $ M m CMOS process and the second approach is the enhancement of chip size that also enhances the capacitance. In video processing, we mainly focus on object images, i.e. any form of signal processing in which the input is an image and the output is a photograph or a frame of video. So, it will be better to converge over techniques of digital image processing. In the proposed algorithm (MHI Simulink model), edge extraction is performed to find the precious area of MRI and CT-Scan images. Using FPGA, edge detection methods have been implemented over scanned images to get better accuracy. The accuracy of the proposed method has been compared with Prewitt and Sobel's edge detection techniques. The proposed method has given better accuracy than Prewitt and Sobel methods. Finally, this paper shows future directions for researchers to enhance the characteristics of digital image and video processing. [ABSTRACT FROM AUTHOR]

Abstract: A novel technology in the paper industry makes possible to produce paper by using a mineral powder and a polymer instead of cellulose fibers. This new product is called mineral paper, it presents some potential environmental advantages compared with conventional paper, while it exhibit a similar appearance and properties. The purpose of this work is to determine the possibilities of an air-coupled ultrasonic technique using wide band signals and spectral analysis to study this kind of materials. As no direct contact nor coupling fluids between the paper and the transducers is required, this technique is specially well suited to this problem. It also offers good perspectives for the development of a on-line quality control system. A through transmission technique (0.15–2.3MHz) is employed and Fourier analysis is performed to obtain both magnitude and phase spectra of the transmission coefficient. Properties in the thickness direction as well as in the paper plane has been determined by the excitation and analysis of thickness and plate resonances at several incident angles and different directions within the paper plane. Different paper grades (from 140 to 480g/m2) have been studied. Very high attenuation coefficients and very low propagation velocities (and hence elastic constant) have been obtained for most cases, this can be explained by considering the large porosity of this material (up to 50%) and the microstructure: a mixture of solid grains with a resin with a relatively large fraction of air-filled pores. Measurements show that unlike conventional cellulose machine made paper this material is transversely isotropic (isotropic in the paper plane) and that the degree of anisotropy (when in-plane directions are compared with the thickness direction) largely depends on the level of resin impregnation. [Copyright &y& Elsevier]

We try to assess the validity of assumptions taken when deriving drift velocity. We give simple formulas for characteristics of the spatiotemporal correlation function of the observed diffraction pattern for the frozen flow and the more general Briggs model. Using Low-Frequency Array (LOFAR) Cassiopeia intensity observation, we compare the experimental velocity scaling factor with a theoretical one to show that both models do not follow observations. We also give a qualitative comparison of our drift velocity estimates with SuperDARN convection maps. The article is essentially an extended version of the conference paper: "Determining ionospheric drift and anisotropy of irregularities from LOFAR core measurements", Signal Processing Symposium 2021 (SPSympo 2021). [ABSTRACT FROM AUTHOR]

Chemical equipment has to form a robust and efficient process space and was developed over the last centuries to very sophisticated, often highly specialized devices. With the upcoming information technology new opportunities arise with integrated sensors, and advanced control systems. Smart equipment is designed in form of intelligent modules. Furthermore, adaptive and integrated actuators convert the control signals to directly influence the process, thus, they can be described as responsive equipment. This contribution will give an overview of some activities in this field. Smart, responsive equipment for future chemical plants are discussed, e.g., a tubular reactor or crystallizer with integrated sensors, local microcontroller, and self‐adaptive settler. [ABSTRACT FROM AUTHOR]

Modeling the dynamics of glassy systems has been challenging in physics for several decades. Recent studies have shown the efficacy of Graph Neural Networks (GNNs) in capturing particle dynamics from the graph structure of glassy systems. However, current GNN methods do not take the dynamic patterns established by neighboring particles explicitly into account. In contrast to these approaches, this paper introduces a novel dynamical parameter termed "smoothness" based on the theory of graph signal processing, which explores the dynamic patterns from a graph perspective. Present graph-based approaches encode structural features without considering smoothness constraints, leading to a weakened correlation between structure and dynamics, particularly on short timescales. To address this limitation, we propose a Geometry-enhanced Graph Neural Network (Geo-GNN) to learn the smoothness of dynamics. Results demonstrate that our method outperforms state-of-the-art baselines in predicting glassy dynamics. Ablation studies validate the effectiveness of each proposed component in capturing smoothness within dynamics. These findings contribute to a deeper understanding of the interplay between glassy dynamics and static structure. [ABSTRACT FROM AUTHOR]

Application of the nonlinear Kalman filtering method to logarithmically amplified low-coherence fringe signals measured from paper samples is considered. Experimental results of dynamic fringe envelope recovery in optical coherence tomography (OCT) systems are presented. The analog fringe envelope and digital dynamic fringe envelope recoveries from noisy signals are compared. The results show that the nonlinear discrete Kalman filtering method can be applied to estimate envelopes of logarithmically transformed low-coherence fringe signals with high noise immunity. Logarithmic amplification reduces quantization error in dealing with small signal values. The experimental results obtained demonstrate the possibility of purely digital signal processing in OCT. [ABSTRACT FROM AUTHOR]

De-mining operations are of critical importance for humanitarian efforts and safety in conflict-affected regions. In this paper, we address the challenge of enhancing the accuracy and efficiency of mine detection systems. We present an innovative Deep Learning architecture tailored for pulse induction-based Metallic Mine Detectors (MMD), so called DL-MMD. Our methodology leverages deep neural networks to distinguish amongst nine distinct materials with an exceptional validation accuracy of 93.5%. This high level of precision enables us not only to differentiate between anti-personnel mines, without metal plates but also to detect minuscule 0.2-g vertical paper pins in both mineralized soil and non-mineralized environments. Moreover, through comparative analysis, we demonstrate a substantial 3% and 7% improvement (approx.) in accuracy performance compared to the traditional K-Nearest Neighbors and Support Vector Machine classifiers, respectively. The fusion of deep neural networks with the pulse induction-based MMD not only presents a cost-effective solution but also significantly expedites decision-making processes in de-mining operations, ultimately contributing to improved safety and effectiveness in these critical endeavors. [ABSTRACT FROM AUTHOR]

This paper follows a detection-theoretic approach for using synthetic-aperture measurements, made at multiple moving passive receivers, in order to form an image showing the locations of stationary sources that are radiating unknown electromagnetic or acoustic waves. The paper starts with a physics-based model for the propagating fields, and, following the general approach of McWhorter et al (2023 arXiv:2302.06816, IEEE Open J. Signal Process. 4 437–51), derives a detection statistic that is used for the image formation. This detection statistic is a quadratic function of the data. Each point in the scene is tested as a possible hypothesized location for a source, and the detection statistic is plotted as a function of location. Because this image formation process is nonlinear, the standard linear methods for determining resolution cannot be applied. This paper shows how to analyze the detection image by first writing the noiseless image as a coherent sum of shifted complex ambiguity functions of the source waveform. The paper then develops a technique for calculating image resolution; resolution is found to depend on the sensor-source geometry and also on the properties (bandwidth and temporal duration) of the source waveform. Optimal filtering of the image is given, but a simple example suggests that optimal filtering may have little effect. Analysis is also given for the case in which multiple sources are present. [ABSTRACT FROM AUTHOR]

Many medical reports and documents in printed form are available in huge sizes in archive units inside medical centers and hospitals around the world. The printed electrocardiogram paper is one type of archive data and is more powerful for expert cardiologists to study various cardiac diseases. On the other hand, the widespread development in the ability of intelligent computer systems to process digital signals increases the benefit of these data if converted to digital form. In this paper, a new approach for digital recovery of 12-lead electrocardiogram raw data from the printed colored scanned image has been proposed. This approach implements an algorithm with four steps including delineating effective regions, color filtering, contacting detected points, and sampling the resulted digital signals to reconstruct the digital electrocardiogram signal from the printed drawing of the same signal after digital scanning with significant resolution. Also, this algorithm is designed to process various kinds of printed electrocardiogram papers. The performance of the proposed approach is evaluated qualitatively by visual inspection of the recovered and original signals in the same graph. Also, the similarity of these signals is evaluated quantitatively using some standard evaluation metrics. The simulation results show the consistency and robustness of the proposed digital recovery approach to generate electrocardiogram digital data with a high percentage accuracy exceeding 98%. Also, plotting the recovered signal with the original printed signal on the same graph shows a significant percentage of congruence in time and amplitude. Finally, the proposed idea in this study opens the way for an unlimited bank of digital electrocardiogram data with different morphologies. [ABSTRACT FROM AUTHOR]

Incorporating style-related objectives into shape design has been centrally important to maximize product appeal. However, algorithmic style capture and reuse have not fully benefited from automated data-driven methodologies due to the challenging nature of design describability. This paper proposes an AI-driven method to fully automate the discovery of brand-related features. First, to tackle the scarcity of vectorized product images, this research proposes two data acquisition workflows: parametric modeling from small curve-based datasets, and vectorization from large pixel-based datasets. Second, this study constructs BIGNet, a two-tier Brand Identification Graph Neural Network, to learn from both scalar vector graphics' curve-level and chunk-level parameters. In the first case study, BIGNet not only classifies phone brands but also captures brand-related features across multiple scales, such as lens' location, as confirmed by AI evaluation. In the second study, this paper showcases the generalizability of BIGNet learning from a vectorized car image dataset and validates the consistency and robustness of its predictions given four scenarios. The results match the difference commonly observed in luxury versus economy brands in the automobile market. Finally, this paper also visualizes the activation maps generated from a convolutional neural network and shows BIGNet's advantage of being a more explainable style-capturing agent. [ABSTRACT FROM AUTHOR]

The fetal electrocardiogram (FECG) records changes in the graph of fetal cardiac action potential during conduction, reflecting the developmental status of the fetus in utero and its physiological cardiac activity. Morphological alterations in the FECG can indicate intrauterine hypoxia, fetal distress, and neonatal asphyxia early on, enhancing maternal and fetal safety through prompt clinical intervention, thereby reducing neonatal morbidity and mortality. To reconstruct FECG signals with clear morphological information, this paper proposes a novel deep learning model, CBLS-CycleGAN. The model's generator combines spatial features extracted by the CNN with temporal features extracted by the BiLSTM network, thus ensuring that the reconstructed signals possess combined features with spatial and temporal dependencies. The model's discriminator utilizes PatchGAN, employing small segments of the signal as discriminative inputs to concentrate the training process on capturing signal details. Evaluating the model using two real FECG signal databases, namely "Abdominal and Direct Fetal ECG Database" and "Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeat Annotations", resulted in a mean MSE and MAE of 0.019 and 0.006, respectively. It detects the FQRS compound wave with a sensitivity, positive predictive value, and F1 of 99.51%, 99.57%, and 99.54%, respectively. This paper's model effectively preserves the morphological information of FECG signals, capturing not only the FQRS compound wave but also the fetal P-wave, T-wave, P-R interval, and ST segment information, providing clinicians with crucial diagnostic insights and a scientific foundation for developing rational treatment protocols. [ABSTRACT FROM AUTHOR]

This document is an editorial for a special issue titled "Machine Learning in Computer Vision and Image Sensing: Theory and Applications." The editorial highlights the diverse applications of machine learning (ML) models in various domains such as medical imaging, signal processing, remote sensing, and human activity detection. The special issue includes 11 papers that cover topics such as image segmentation, fluvial navigation, Alzheimer's disease classification, pneumothorax detection, lung cancer malignancy prediction, amniotic fluid volume detection, COVID-19 detection, and Parkinson's disease detection. The papers showcase the progress and potential of ML models in computer vision applications and provide valuable insights for future research. [Extracted from the article]

The channel vocoder has become a useful tool to understand the impact of specific forms of auditory degradation—particularly the spectral and temporal degradation that reflect cochlear-implant processing. Vocoders have many parameters that allow researchers to answer questions about cochlear-implant processing in ways that overcome some logistical complications of controlling for factors in individual cochlear implant users. However, there is such a large variety in the implementation of vocoders that the term "vocoder" is not specific enough to describe the signal processing used in these experiments. Misunderstanding vocoder parameters can result in experimental confounds or unexpected stimulus distortions. This paper highlights the signal processing parameters that should be specified when describing vocoder construction. The paper also provides guidance on how to determine vocoder parameters within perception experiments, given the experimenter's goals and research questions, to avoid common signal processing mistakes. Throughout, we will assume that experimenters are interested in vocoders with the specific goal of better understanding cochlear implants. [ABSTRACT FROM AUTHOR]

In view of the increasing number of space objects, comprehensive high‐quality space surveillance becomes ever more important. Radar is a powerful tool that, in addition to detection and tracking of objects, also enables spatially high‐resolution imaging independent of daylight and most weather conditions. Together with the technique of Inverse Synthetic Aperture Radar (ISAR), very high‐resolution and distance‐independent two‐dimensional images can be obtained. However, advanced high‐performance radar imaging of space objects is a complex and demanding task, touching many technological and signal processing issues. Therefore, besides theoretical work, the Microwaves and Radar Institute of German Aerospace Center (DLR) has developed and constructed an experimental radar system called IoSiS (Imaging of Satellites in Space) for basic research on new concepts for the acquisition of advanced high‐resolution radar image products of objects in a low earth orbit. Based on pulse radar technology, which enables precise calibration and error correction, IoSiS has imaged space objects with a spatial resolution in the centimetre range, being novel in public perception and accessible literature. The goal of this paper is therefore to communicate and illustrate comprehensively the technological steps for the construction and successful operation of advanced radar‐based space surveillance. Besides the basic description of the IoSiS system design this paper outlines primarily useful theory for ISAR imaging of objects in space, together with relevant imaging parameters and main formulae. All relevant processing steps, necessary for very high‐resolution imaging of satellites in practice, are introduced and verified by simulation results. Finally, a unique measurement result demonstrates the practicability of the introduced processing steps and error correction strategies. [ABSTRACT FROM AUTHOR]

Background: Commercial electroencephalography (EEG) devices have become increasingly available over the last decade. These devices have been used in a wide variety of fields ranging from engineering to cognitive neuroscience. Purpose: The aim of this study was to chart peer-review articles that used consumer-grade EEG devices to collect neural data. We provide an overview of the research conducted with these relatively more affordable and user-friendly devices. We also inform future research by exploring the current and potential scope of consumer-grade EEG. Methods: We followed a five-stage methodological framework for a scoping review that included a systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched the following online databases: PsycINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, experimental research, validation, signal processing, and clinical) and location of use as indexed by the first author's country. Results: We identified 916 studies that used data recorded with consumer-grade EEG: 531 were reported in journal articles and 385 in conference papers. Emotiv devices were used most, followed by the NeuroSky MindWave, OpenBCI, interaXon Muse, and MyndPlay Mindband. The most common usage was for brain-computer interfaces, followed by experimental research, signal processing, validation, and clinical purposes. Conclusions: Consumer-grade EEG is a useful tool for neuroscientific research and will likely continue to be used well into the future. Our study provides a comprehensive review of their application, as well as future directions for researchers who plan to use these devices. [ABSTRACT FROM AUTHOR]

High-resolution and high-squint spaceborne spotlight synthetic aperture radar (SAR) has significant potential for extensive application in remote sensing, but its swath width effectiveness is constrained by a critical factor: severe range cell migration (RCM). To address this, pulse repetition interval (PRI) variation offers a practical scheme for raw data reception. However, the current designs for continuously varying PRI (CV-PRI) exhibit high complexity in engineering. In response to the issue, this paper proposes a novel strategy of stepwise varying PRI (SV-PRI), which demonstrates higher reconstruction accuracy compared with CV-PRI. Furthermore, confronting the azimuth non-uniform sampling characteristics induced by the PRI variation, this paper introduces a complete uniform reconstruction processing based on the azimuth partitioning methodology, which effectively alleviates the inherent contradiction between resolution and swath width. The processing flow, utilizing the temporal point remapping (TPR) concept, ensures the uniformity and coherence of dataset partitioning and reassembly in the context of the interpolation on non-uniform grids. Finally, according to the simulation results, the point target data, processed through the processing flow proposed in this study, have demonstrated effective focusing results. [ABSTRACT FROM AUTHOR]

Given the abundance of images related to operations that are being captured and stored, it behooves firms to innovate systems using image processing to improve operational performance that refers to any activity that can save labor cost. In this paper, we use deep learning techniques, combined with classic image/signal processing methods, to propose a pipeline to solve certain types of object counting and layer characterization problems in firm operations. Using data obtained by us through a collaborative effort with real manufacturers, we demonstrate that the proposed pipeline method is able to achieve higher than 93% accuracy in layer and log counting. Theoretically, our study conceives, constructs, and evaluates proof of concept of a novel pipeline method in characterizing and quantifying the number of defined items with images, which overcomes the limitations of methods based only on deep learning or signal processing. Practically, our proposed method can help firms significantly reduce labor costs and/or improve quality and inventory control by recording the number of products in real time, more accurately and with minimal up-front technological investment. The codes and data are made publicly available online through the INFORMS Journal on Computing GitHub site. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0260) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0260). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

In this survey paper, we review recent advances of compressed sensing applied to systems and control. Compressed sensing has been actively researched in the field of signal processing and machine learning. More recently, the method has been applied to systems and control problems, such as sparse feedback gain design, reduced-order control, and maximum hands-off control. This paper introduces these important applications of compressed sensing to systems and control. MATLAB programs for the numerical examples shown in this survey paper are available as supplementary materials. [ABSTRACT FROM AUTHOR]

This research introduces a novel method for emotion recognition using Electroencephalography (EEG) signals, leveraging advancements in emotion computing and EEG signal processing. The proposed method utilizes a semisupervised deep convolutional generative adversarial network (SSDNet) as the central model. The model fully integrates the feature extraction methods of the generative adversarial network (GAN), deep convolutional GAN (DCGAN), spectrally normalized GAN (SSGAN) and the encoder, maximizing the advantages of each method to construct a more accurate emotion classification model. In our study, we introduce a flow-form-consistent merging pattern, which successfully addresses mismatches between the data by fusing the EEG data with the features. Implementing this merging pattern not only enhances the uniformity of the input but also decreases the computational load on the network, resulting in a more efficient model. By conducting experiments on the DEAP and SEED datasets, we evaluate the SSDNet model proposed in this paper in detail. The experimental results show that the accuracy of the proposed algorithm is improved by 6.4% and 8.3% on the DEAP and SEED datasets, respectively, compared to the traditional GAN. This significant improvement in performance validates the effectiveness and feasibility of the SSDNet model. The research contributions of this paper are threefold. First, we propose and implement an SSDNet model that integrates multiple feature extraction methods, providing a more accurate and comprehensive solution for emotion recognition tasks. Second, by introducing a flow-form-consistent merging pattern, we successfully address the problem of interdata mismatches and improve the generalization performance of the model. Finally, we experimentally demonstrate that the method in this paper achieves a significant improvement in accuracy over the traditional GANs on the DEAP and SEED datasets, providing an innovative solution in the field of EEG-based emotion recognition. [ABSTRACT FROM AUTHOR]

Epilepsy is a neurological disorder, and its sudden seizures pose a serious threat to the quality of life of patients. Not only does this condition cause patients to potentially lose control and consciousness during seizures, leading to possible injuries or dangerous situations, but it also has a significant impact on their mental health, triggering issues such as anxiety and depression. An intelligent epilepsy diagnosis process based on electroencephalogram (EEG) signals offers notable advantages. First, it provides noninvasive brainwave signals that accurately monitor and record the patterns and characteristics of epileptic seizures, offering objective diagnostic criteria for doctors. Second, with the use of artificial intelligence and machine learning technologies, large amounts of EEG data can be efficiently analyzed, improving the speed and accuracy of diagnosis and providing timely and effective treatment plans for patients. Additionally, intelligent diagnostic systems can achieve real-time monitoring, promptly alert people to potential epileptic seizures, and provide a safer living environment for patients. In this context, this paper proposes an epilepsy diagnosis method based on a transferred AlexNet model and EEG signals. The main contributions of this paper are as follows. (1) A transfer learning mechanism is incorporated into the AlexNet model through the direct transfer of its neural network structure and the modification of some existing neural network structures, followed by collaborative training with the addition of a domain adaptation layer in the network. This introduced transfer mechanism can address small sample size issues. (2) The traditional AlexNet model suffers from redundant feature extraction, leading to slow training. This paper adds batch normalization (BN) layers after each convolutional layer in the AlexNet model to normalize the features extracted from the convolutional layers. This emphasizes the representation of the important features of EEG signals and enables the lower layers of the network to learn the features needed for EEG signal processing. (3) The transferred AlexNet model proposed in this paper is applied to extract the features of epilepsy EEG signals, and the extracted features are input into a support vector machine (SVM) classifier to obtain epilepsy diagnosis results. Comparative experiments show that the diagnostic method used in this paper yields superior results and shorter training times than those of the competing approaches. [ABSTRACT FROM AUTHOR]

Results of a comparative analysis of two non-dimensional coefficients, calculated based on signals emitted by partial discharges (PD) are presented in the paper. The PD were generated using setups for modeling of three basic types of PD sources, which can occur in paper-oil insulation in power transformers. The following measuring methods were considered: electrical, ultra-high frequency, acoustic emission, and optical spectrophotometry. All measurements, for each of the PD-source configuration using the various methods, were conducted simultaneously in a laboratory under same measurement conditions. Based on the gathered results it was stated that it is possible to recognize the partial discharges form based on the non-dimensional coefficients, which differ in value for all of the considered methods except for the acoustic emission. The main contribution of the research performed lies in the simplicity of the applied signal processing procedure, which can be adopted to a device or system for diagnosis of partial discharges occurring in paper-oil insulation. [ABSTRACT FROM AUTHOR]

Air-gap discharge in oil-paper insulation is one of the main types of partial discharge (PD) in power transformer. The discharge development stage for monitoring and diagnosis of transformer potential faults is a significant area of study. The method of wavelet packet energy entropy, which is based on different frequency bands energy distribution of PD signals at different insulation states, is provided to explore the variation characteristics of the whole PD process. In this paper, air-gap discharge model is built in the simulative transformer tank that collects PD signals based on constant voltage method. This model also utilizes wavelet packet decomposition method to partition PD signal bands obtaining signal energy distribution in each frequency band, as well as total signal energy tendency along with PD development process. Wavelet packet energy entropy, which is the new PD feature parameter describing the development process, represents the order degree of PD signals which corresponds to dielectric strength. Finally, because of the cyclic change of this method, the step points of wavelet packet entropy are taken as the way to effectively divide the PD development stage. [ABSTRACT FROM AUTHOR]

ABSTRACT This letter presents a high-voltage power amplifier for driving flexible paper speaker. Simple circuit complexity and high signal quality can be achieved. The operating principles and design considerations of the studied paper speaker power amplifier are analyzed and discussed. A laboratory prototype is built and tested. The experimental results are shown to verify the studied scheme. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Owing to the necessity of high computation amounts has emerged, interest in a neuromorphic computing system has significantly increased as a compelling alternative to conventional CMOS technology. This paper presents a neuromorphic hardware algorithm to finely reconfigure multi‐input signal processing, which can be implemented as an advanced processor for diverse external information, and the hydrogen explosion risk assessment system is demonstrated as a proof of concept. Hydrogen concentration and temperature are used as sensory inputs for the signal integration and the precise values of them are determined by offsetting the effect of temperature on the electrical signal from the hydrogen sensor through a sensor circuit. Each signal is then updated by the weight control circuit and converted into a postsynaptic current to represent the hydrogen explosion risk using a multi‐input artificial synapse. This simplicity of the circuitry renders the fabrication of all components and circuits compatible with simple inkjet printing methods, enabling cost‐effective and high‐throughput manufacturing. Additionally, the real‐time demonstration of the neuromorphic computing system is successfully conducted, offering insights into the practical application of neuromorphic computing. [ABSTRACT FROM AUTHOR]

The molecular movement in single particle tracking (SPT) experiments shows a crucial role of diffusion in many biological processes such as signaling, cellular organization, transport mechanisms, and more. The SPT analysis detects not only classical Brownian motion but diffusion with other features. These include directed diffusion and confined motion. The behavior remains a challenging problem for several reasons. Due to the action of many physical processes, random trajectories of cellular molecules are segmented in different diffusive modes. Often their study requires sophisticated algorithms for the analysis of statistical properties. In this paper we consider the segment analysis for trajectories of G proteins and coupled-receptors in living cells. Their movement is often transient and switches among free diffusion, confined diffusion, directed diffusion, and immobility. Moreover, the confined segments can have both Gaussian and non-Gaussian statistics. The types of alternation of diffusive modes along the trajectories of G proteins and coupled-receptors are analyzed. [ABSTRACT FROM AUTHOR]

In pipeline ultrasonic testing, the accurate identification of defect types is a prerequisite for quantifying defect sizes. To this end, this paper proposes a defect identification algorithm based on the combination of Wavelet Packet Transform (WPT), Distance Evaluation Technique (DET) and GA-SVM to precisely identify defect boundaries. First, the ultrasonic echo signals are processed using WPT to more accurately extract defect information. Then, DET is employed to eliminate redundant feature parameters, which are sorted by sensitivity to determine the input feature parameters for the GA-SVM. On this basis, a self-developed ultrasonic testing experimental system is used to collect ultrasonic echo signals from Q235 steel pipes with cylindrical, spherical, and conical defects at different spatial orientations of the probe and the pipe. After processing and identifying the signals, it was found that the proposed algorithm achieves an average identification accuracy of 99% for different types of defects, thereby realising automatic identification of pipeline defects. The results provide new ideas and methods for the intelligent identification of volumetric defect types in pipeline ultrasonic internal testing and promote the development of industrial-grade ultrasonic internal detectors. [ABSTRACT FROM AUTHOR]

The digital correlator is one of the most crucial data processing components of a radio telescope array. With the scale of radio interferometeric array growing, many efforts have been devoted to developing a cost-effective and scalable correlator in the field of radio astronomy. In this paper, a 192-input digital correlator with six CASPER ROACH2 boards and seven GPU servers has been deployed as the digital signal processing system for Tianlai cylinder pathfinder located in Hongliuxia observatory. The correlator consists of 192 input signals (96 dual-polarization), 125-MHz bandwidth, and full-Stokes output. The correlator inherits the advantages of the CASPER system, for example, low cost, high performance, modular scalability, and a heterogeneous computing architecture. With a rapidly deployable ROACH2 digital sampling system, a commercially expandable 10 Gigabit switching network system, and a flexible upgradable GPU computing system, the correlator forms a low-cost and easilyupgradable system, poised to support scalable large-scale interferometeric array in the future. [ABSTRACT FROM AUTHOR]

The precise separation of multicomponent signals encounters numerous challenges due to the complexity of signals and widespread interference. Synchrosqueezing Transform (SST) is one of the important technologies for improving the accurate separation of multicomponent signals, but it faces challenges in terms of the difficulty and effectiveness of squeezing. This paper introduces a multicomponent signal separation method based on innovative Fractional Synchrosqueezing Transform (FrSST). FrSST rearranges along the fractional frequency axis, improving the accuracy of time–frequency ridges and, consequently, enhancing the precision of multicomponent signal separation. In the signal reconstruction process, chirp multiplication and energy rearrangement compensate for chirp bases' effects, boosting energy concentration and reconstruction potential. Utilizing improved ridges from FrSST ensures effective signal reconstruction. Simulation comparisons demonstrate that, with varying SNRs from − 5 to 15 dB, the reconstructed components based on FrSST exhibit favorable approximation to the original signal components. Furthermore, as the sample size increases, the proposed algorithm shows satisfactory computational efficiency. [ABSTRACT FROM AUTHOR]

Unconstrained optimization problems have a long history in computational mathematics and have been identified as being among the crucial problems in the fields of applied sciences, engineering, and management sciences. In this paper, a new variant of the conjugate descent method for solving unconstrained optimization problems is introduced. The proposed algorithm can be seen as a modification of the popular conjugate descent (CD) algorithm of Fletcher. The algorithm of the proposed method is well-defined, and the sequence of the directions of search is shown to be sufficiently descending. The convergence result of the proposed method is discussed under the common standard conditions. The proposed algorithm together with some existing ones in the literature is implemented to solve a collection of benchmark test problems. Numerical experiments conducted show the performance of the proposed method is very encouraging. Furthermore, an additional efficiency evaluation is carried out on problems arising from signal processing and it works well. [ABSTRACT FROM AUTHOR]

This paper presents a method for improving the accuracy of determining a person's proximity to a building entrance in urban and indoor settings, where the Global Positioning System (GPS) and Wireless Fidelity (WiFi) signals are often interfered with. Fuzzy logic can be applied to variations in signal strengths in order to interpret the inverse relationship between GPS signals weakening and WiFi signals strengthening as a person approaches or enters a building. As a result, a fuzzy set for GPS signal strengths between 14 and 33 dBm and WiFi signal strengths between –68 and –31 dBm is created, separating them into weak, medium, and strong signals. By using fuzzy rules, the system can accurately determine if a user is 'far,' 'near,' or 'at' an entrance, mimicking real-life transitions from outdoor to indoor environments. The accuracy of this approach exceeded 90% based on real-world data, and it significantly improved user experience in navigation applications, particularly in cases where GPS does not work well. [ABSTRACT FROM AUTHOR]

Condition monitoring (CM) is the basis of prognostics and health management (PHM), which is gaining more and more importance in the industrial world. CM, which refers to the tracking of industrial equipment's state of health during operations, plays, in fact, a significant role in the reliability, safety, and efficiency of industrial operations. This paper proposes a data-driven CM approach based on the autoregressive (AR) modeling of the acquired sensor data and their analysis within frequency subbands. The number and size of the bands are determined with negligible human intervention, analyzing only the time–frequency representation of the signal of interest under normal system operating conditions. In particular, the approach exploits the synchrosqueezing transform to improve the signal energy distribution in the time–frequency plane, defining a multidimensional health indicator built on the basis of the AR power spectral density and the symmetric Itakura–Saito spectral distance. The described health indicator proved capable of detecting changes in the signal spectrum due to the occurrence of faults. After the initial definition of the bands and the calculation of the characteristics of the nominal AR spectrum, the procedure requires no further intervention and can be used for online condition monitoring and fault diagnosis. Since it is based on the comparison of spectra under different operating conditions, its applicability depends neither on the nature of the acquired signal nor on a specific system to be monitored. As an example, the effectiveness of the proposed method was favorably tested using real data available in the Case Western Reserve University (CWRU) Bearing Data Center, a widely known and used benchmark. [ABSTRACT FROM AUTHOR]

This paper presents a wideband signal (WS) beamforming method based on Inception convolutional neural network (ICNN), named as WS-ICNN algorithm. Firstly, an Inception module is constructed via some convolutional layers with feature maps of different sizes and a pooling layer. It can not only extract different scale information of covariance matrix, but also excavate the spatial correlation information about received wideband signals, so that the inception module can help neural network improve the beamforming output performance. On this basis, an ICNN model is established, which is suitable for wideband beamforming. Then, in order to obtain a good training label for the proposed ICNN model, a taper matrix and a second-order cone programming problem are introduced to calculate a wideband beamforming weight vector label. Based on this label, the training process of the proposed ICNN model is accomplished. Finally, the well-trained ICNN model accepts the input of the covariance matrix, and output the beamforming weight vector. Simulation results demonstrate the performance of the proposed algorithm in the cases of direction-of-arrival estimation error and sensor position error. [ABSTRACT FROM AUTHOR]

Traditional vibro-acoustic modulation methods for bolt looseness detection often use a probe wave with single frequency. However, using a single frequency probe wave not only reduces the detection accuracy but also increases the difficulty of selecting the frequency of the probe wave. To overcome these drawbacks, this paper proposes a bolt looseness detection method based the vibro-acoustic modulation using a frequency sweep probe wave. For the low-frequency pump wave, the excitation frequency is set as the resonant frequency of the bolted joint to enhance the modulation effect. For the high-frequency probe wave, a frequency sweep excitation is used to replace the single frequency excitation, which improves the detection accuracy and overcomes the difficulty of selecting the probe frequency. A signal processing method is proposed to extract modulation information from the complex response signal obtained under a frequency sweep excitation. A nonlinear modulation index is defined using the extracted modulation information to quantify the loosening level of bolts. An experimental setup is designed to implement the vibro-acoustic modulation detection method on a bolted joint. Experimental results demonstrate that the proposed method is effective for detecting bolt looseness and has high detection sensitivity and accuracy for the early bolt loosening. [ABSTRACT FROM AUTHOR]