Journal Description
Signals
Signals
is an international, peer-reviewed, open access journal on signals and signal processing published quarterly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), Inspec, and other databases.
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 35.1 days after submission; acceptance to publication is undertaken in 6.8 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: APC discount vouchers, optional signed peer review, and reviewer names published annually in the journal.
- Signals is a companion journal of Electronics.
Latest Articles
ApeTI: A Thermal Image Dataset for Face and Nose Segmentation with Apes
Signals 2024, 5(1), 147-164; https://doi.org/10.3390/signals5010008 - 15 Mar 2024
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The ApeTI dataset was built with the aim of retrieving physiological signals such as heart rate, breath rate, and cognitive load from thermal images of great apes. We want to develop computer vision tools that psychologists and animal behavior researchers can use to
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The ApeTI dataset was built with the aim of retrieving physiological signals such as heart rate, breath rate, and cognitive load from thermal images of great apes. We want to develop computer vision tools that psychologists and animal behavior researchers can use to retrieve physiological signals noninvasively. Our goal is to increase the use of a thermal imaging modality in the community and avoid using more invasive recording methods to answer research questions. The first step to retrieving physiological signals from thermal imaging is their spatial segmentation to then analyze the time series of the regions of interest. For this purpose, we present a thermal imaging dataset based on recordings of chimpanzees with their face and nose annotated using a bounding box and nine landmarks. The face and landmarks’ locations can then be used to extract physiological signals. The dataset was acquired using a thermal camera at the Leipzig Zoo. Juice was provided in the vicinity of the camera to encourage the chimpanzee to approach and have a good view of the face. Several computer vision methods are presented and evaluated on this dataset. We reach mAPs of 0.74 for face detection and 0.98 for landmark estimation using our proposed combination of the Tifa and Tina models inspired by the HRNet models. A proof of concept of the model is presented for physiological signal retrieval but requires further investigation to be evaluated. The dataset and the implementation of the Tina and Tifa models are available to the scientific community for performance comparison or further applications.
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Open AccessArticle
A Complete Pipeline for Heart Rate Extraction from Infant ECGs
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Harry T. Mason, Astrid Priscilla Martinez-Cedillo, Quoc C. Vuong, Maria Carmen Garcia-de-Soria, Stephen Smith, Elena Geangu and Marina I. Knight
Signals 2024, 5(1), 118-146; https://doi.org/10.3390/signals5010007 - 13 Mar 2024
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Infant electrocardiograms (ECGs) and heart rates (HRs) are very useful biosignals for psychological research and clinical work, but can be hard to analyse properly, particularly longform (≥5 min) recordings taken in naturalistic environments. Infant HRs are typically much faster than adult HRs, and
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Infant electrocardiograms (ECGs) and heart rates (HRs) are very useful biosignals for psychological research and clinical work, but can be hard to analyse properly, particularly longform (≥5 min) recordings taken in naturalistic environments. Infant HRs are typically much faster than adult HRs, and so some of the underlying frequency assumptions made about adult ECGs may not hold for infants. However, the bulk of publicly available ECG approaches focus on adult data. Here, existing open source ECG approaches are tested on infant datasets. The best-performing open source method is then modified to maximise its performance on infant data (e.g., including a 15 Hz high-pass filter, adding local peak correction). The HR signal is then subsequently analysed, developing an approach for cleaning data with separate sets of parameters for the analysis of cleaner and noisier HRs. A Signal Quality Index (SQI) for HR is also developed, providing insights into where a signal is recoverable and where it is not, allowing for more confidence in the analysis performed on naturalistic recordings. The tools developed and reported in this paper provide a base for the future analysis of infant ECGs and related biophysical characteristics. Of particular importance, the proposed solutions outlined here can be efficiently applied to real-world, large datasets.
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Open AccessArticle
Study of Time–Frequency Domain of Acoustic Emission Precursors in Rock Failure during Uniaxial Compression
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Gang Jing, Pedro Marin Montanari and Giuseppe Lacidogna
Signals 2024, 5(1), 105-117; https://doi.org/10.3390/signals5010006 - 29 Feb 2024
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Predicting rock bursts is essential for maintaining worker safety and the long-term growth of subsurface infrastructure. The purpose of this study is to investigate the precursor reactions and processes of rock instability. To determine the degree of rock damage, the research examines the
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Predicting rock bursts is essential for maintaining worker safety and the long-term growth of subsurface infrastructure. The purpose of this study is to investigate the precursor reactions and processes of rock instability. To determine the degree of rock damage, the research examines the time-varying acoustic emission (AE) features that occur when rocks are compressed uniaxially and introduces AE parameters such as the b-value, γ-value, and βt-value. The findings suggest that the evolution of rock damage during loading is adequately reflected by the b-value, γ-value, and βt-value. The relationships between b-value, γ-value, and βt-value are studied, as well as the possibility of using these three metrics as early-warning systems for rock failure.
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Open AccessArticle
Object Detection with Hyperparameter and Image Enhancement Optimisation for a Smart and Lean Pick-and-Place Solution
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Elven Kee, Jun Jie Chong, Zi Jie Choong and Michael Lau
Signals 2024, 5(1), 87-104; https://doi.org/10.3390/signals5010005 - 26 Feb 2024
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Pick-and-place operations are an integral part of robotic automation and smart manufacturing. By utilizing deep learning techniques on resource-constraint embedded devices, the pick-and-place operations can be made more accurate, efficient, and sustainable, compared to the high-powered computer solution. In this study, we propose
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Pick-and-place operations are an integral part of robotic automation and smart manufacturing. By utilizing deep learning techniques on resource-constraint embedded devices, the pick-and-place operations can be made more accurate, efficient, and sustainable, compared to the high-powered computer solution. In this study, we propose a new technique for object detection on an embedded system using SSD Mobilenet V2 FPN Lite with the optimisation of the hyperparameter and image enhancement. By increasing the Red Green Blue (RGB) saturation level of the images, we gain a 7% increase in mean Average Precision (mAP) when compared to the control group and a 20% increase in mAP when compared to the COCO 2017 validation dataset. Using a Learning Rate of 0.08 with an Edge Tensor Processing Unit (TPU), we obtain high real-time detection scores of 97%. The high detection scores are important to the control algorithm, which uses the bounding box to send a signal to the collaborative robot for pick-and-place operation.
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Open AccessArticle
Investigation of LASSO Regression Method as a Correction Measurements’ Factor for Low-Cost Air Quality Sensors
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Ioannis Christakis, Elena Sarri, Odysseas Tsakiridis and Ilias Stavrakas
Signals 2024, 5(1), 60-86; https://doi.org/10.3390/signals5010004 - 02 Feb 2024
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Air quality is a subject of study, particularly in densely populated areas, as it has been shown to affect human health and the local ecosystem. In recent years, with the rapid development of technology, low-cost sensors have emerged, with many people interested in
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Air quality is a subject of study, particularly in densely populated areas, as it has been shown to affect human health and the local ecosystem. In recent years, with the rapid development of technology, low-cost sensors have emerged, with many people interested in the quality of the air in their area turning to the procurement of such sensors as they are affordable. The reliability of measurements from low-cost sensors remains a question in the research community. In this paper, the determination of the correction factor of low-cost sensor measurements by applying the least absolute shrinkage and selection operator (LASSO) regression method is investigated. The results are promising, as following the application of the correction factor determined through LASSO regression the adjusted measurements exhibit a closer alignment with the reference measurements. This approach ensures that the measurements from low-cost sensors become more reliable and trustworthy.
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Open AccessArticle
Online Detection and Fuzzy Clustering of Anomalies in Non-Stationary Time Series
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Changjiang He, David S. Leslie and James A. Grant
Signals 2024, 5(1), 40-59; https://doi.org/10.3390/signals5010003 - 24 Jan 2024
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We consider the challenge of detecting and clustering point and collective anomalies in streaming data that exhibit significant nonlinearities and seasonal structures. The challenge is motivated by detecting problems in a communications network, where we can measure the throughput of nodes, and wish
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We consider the challenge of detecting and clustering point and collective anomalies in streaming data that exhibit significant nonlinearities and seasonal structures. The challenge is motivated by detecting problems in a communications network, where we can measure the throughput of nodes, and wish to rapidly detect anomalous traffic behaviour. Our approach is to train a neural network-based nonlinear autoregressive exogenous model on initial training data, then to use the sequential collective and point anomaly framework to identify anomalies in the residuals generated by comparing one-step-ahead predictions of the fitted model with the observations, and finally, we cluster the detected anomalies with fuzzy c-means clustering using empirical cumulative distribution functions. The autoregressive model is sufficiently general and robust such that it provides the nearly (locally) stationary residuals required by the anomaly detection procedure. The combined methods are successfully implemented to create an adaptive, robust, computational framework that can be used to cluster point and collective anomalies in streaming data. We validate the method on both data from the core of the UK’s national communications network and the multivariate Skoltech anomaly benchmark and find that the proposed method succeeds in dealing with different forms of anomalies within the nonlinear signals and outperforms conventional methods for anomaly detection and clustering.
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Open AccessArticle
The Effect of Jittered Stimulus Onset Interval on Electrophysiological Markers of Attention in a Brain–Computer Interface Rapid Serial Visual Presentation Paradigm
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Daniel Klee, Tab Memmott and Barry Oken
Signals 2024, 5(1), 18-39; https://doi.org/10.3390/signals5010002 - 09 Jan 2024
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Brain responses to discrete stimuli are modulated when multiple stimuli are presented in sequence. These alterations are especially pronounced when the time course of an evoked response overlaps with responses to subsequent stimuli, such as in a rapid serial visual presentation (RSVP) paradigm
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Brain responses to discrete stimuli are modulated when multiple stimuli are presented in sequence. These alterations are especially pronounced when the time course of an evoked response overlaps with responses to subsequent stimuli, such as in a rapid serial visual presentation (RSVP) paradigm used to control a brain–computer interface (BCI). The present study explored whether the measurement or classification of select brain responses during RSVP would improve through application of an established technique for dealing with overlapping stimulus presentations, known as irregular or “jittered” stimulus onset interval (SOI). EEG data were collected from 24 healthy adult participants across multiple rounds of RSVP calibration and copy phrase tasks with varying degrees of SOI jitter. Analyses measured three separate brain signals sensitive to attention: N200, P300, and occipitoparietal alpha attenuation. Presentation jitter visibly reduced intrusion of the SSVEP, but in general, it did not positively or negatively affect attention effects, classification, or system performance. Though it remains unclear whether stimulus overlap is detrimental to BCI performance overall, the present study demonstrates that single-trial classification approaches may be resilient to rhythmic intrusions like SSVEP that appear in the averaged EEG.
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(This article belongs to the Special Issue Advancing Signal Processing and Analytics of EEG Signals)
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Complex Parameter Rao and Wald Tests for Assessing the Bandedness of a Complex-Valued Covariance Matrix
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Zhenghan Zhu
Signals 2024, 5(1), 1-17; https://doi.org/10.3390/signals5010001 - 04 Jan 2024
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Banding the inverse of a covariance matrix has become a popular technique for estimating a covariance matrix from a limited number of samples. It is of interest to provide criteria to determine if a matrix is bandable, as well as to test the
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Banding the inverse of a covariance matrix has become a popular technique for estimating a covariance matrix from a limited number of samples. It is of interest to provide criteria to determine if a matrix is bandable, as well as to test the bandedness of a matrix. In this paper, we pose the bandedness testing problem as a hypothesis testing task in statistical signal processing. We then derive two detectors, namely the complex Rao test and the complex Wald test, to test the bandedness of a Cholesky-factor matrix of a covariance matrix’s inverse. Furthermore, in many signal processing fields, such as radar and communications, the covariance matrix and its parameters are often complex-valued; thus, it is of interest to focus on complex-valued cases. The first detector is based on the complex parameter Rao test theorem. It does not require the maximum likelihood estimates of unknown parameters under the alternative hypothesis. We also develop the complex parameter Wald test theorem for general cases and derive the complex Wald test statistic for the bandedness testing problem. Numerical examples and computer simulations are given to evaluate and compare the two detectors’ performance. In addition, we show that the two detectors and the generalized likelihood ratio test are equivalent for the important complex Gaussian linear models and provide an analysis of the root cause of the equivalence.
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Open AccessArticle
Automatic Detection of Electrodermal Activity Events during Sleep
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Jacopo Piccini, Elias August, Sami Leon Noel Aziz Hanna, Tiina Siilak and Erna Sif Arnardóttir
Signals 2023, 4(4), 877-891; https://doi.org/10.3390/signals4040048 - 18 Dec 2023
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Currently, there is significant interest in developing algorithms for processing electrodermal activity (EDA) signals recorded during sleep. The interest is driven by the growing popularity and increased accuracy of wearable devices capable of recording EDA signals. If properly processed and analysed, they can
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Currently, there is significant interest in developing algorithms for processing electrodermal activity (EDA) signals recorded during sleep. The interest is driven by the growing popularity and increased accuracy of wearable devices capable of recording EDA signals. If properly processed and analysed, they can be used for various purposes, such as identifying sleep stages and sleep-disordered breathing, while being minimally intrusive. Due to the tedious nature of manually scoring EDA sleep signals, the development of an algorithm to automate scoring is necessary. In this paper, we present a novel scoring algorithm for the detection of EDA events and EDA storms using signal processing techniques. We apply the algorithm to EDA recordings from two different and unrelated studies that have also been manually scored and evaluate its performances in terms of precision, recall, and score. We obtain scores of about 69% for EDA events and of about 56% for EDA storms. In comparison to the literature values for scoring agreement between experts, we observe a strong agreement between automatic and manual scoring of EDA events and a moderate agreement between automatic and manual scoring of EDA storms. EDA events and EDA storms detected with the algorithm can be further processed and used as training variables in machine learning algorithms to classify sleep health.
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(This article belongs to the Special Issue Advanced Methods of Biomedical Signal Processing)
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Open AccessArticle
Benford’s Law and Perceptual Features for Face Image Quality Assessment
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Domonkos Varga
Signals 2023, 4(4), 859-876; https://doi.org/10.3390/signals4040047 - 05 Dec 2023
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The rapid growth in multimedia, storage systems, and digital computers has resulted in huge repositories of multimedia content and large image datasets in recent years. For instance, biometric databases, which can be used to identify individuals based on fingerprints, facial features, or iris
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The rapid growth in multimedia, storage systems, and digital computers has resulted in huge repositories of multimedia content and large image datasets in recent years. For instance, biometric databases, which can be used to identify individuals based on fingerprints, facial features, or iris patterns, have gained a lot of attention both from academia and industry. Specifically, face image quality assessment (FIQA) has become a very important part of face recognition systems, since the performance of such systems strongly depends on the quality of input data, such as blur, focus, compression, pose, or illumination. The main contribution of this paper is an analysis of Benford’s law-inspired first digit distribution and perceptual features for FIQA. To be more specific, I investigate the first digit distributions in different domains, such as wavelet or singular values, as quality-aware features for FIQA. My analysis revealed that first digit distributions with perceptual features are able to reach a high performance in the task of FIQA.
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(This article belongs to the Special Issue The 2022 7th International Conference on Intelligent Information Processing)
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Open AccessArticle
Integrating Data from Multiple Nondestructive Evaluation Technologies Using Machine Learning Algorithms for the Enhanced Assessment of a Concrete Bridge Deck
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Mustafa Khudhair and Nenad Gucunski
Signals 2023, 4(4), 836-858; https://doi.org/10.3390/signals4040046 - 04 Dec 2023
Cited by 1
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Several factors impact the durability of concrete bridge decks, including traffic loads, fatigue, temperature changes, environmental stress, and maintenance activities. Detecting problems such as corrosion, delamination, or concrete degradation early on can lower maintenance costs. Nondestructive evaluation (NDE) techniques can detect these issues
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Several factors impact the durability of concrete bridge decks, including traffic loads, fatigue, temperature changes, environmental stress, and maintenance activities. Detecting problems such as corrosion, delamination, or concrete degradation early on can lower maintenance costs. Nondestructive evaluation (NDE) techniques can detect these issues at early stages. Each NDE method, meanwhile, has limitations that reduce the accuracy of the assessment. In this study, multiple NDE technologies were combined with machine learning algorithms to improve the interpretation of half-cell potential (HCP) and electrical resistivity (ER) measurements. A parametric study was performed to analyze the influence of five parameters on HCP and ER measurements, such as the degree of saturation, corrosion length, delamination depth, concrete cover, and moisture condition of delamination. The results were obtained through finite element simulations and used to build two machine learning algorithms, a classification algorithm and a regression algorithm, based on Random Forest methodology. The algorithms were tested using data collected from a bridge deck in the BEAST® facility. Both machine learning algorithms were effective in improving the interpretation of the ER and HCP measurements using data from multiple NDE technologies.
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(This article belongs to the Special Issue Health Monitoring of Cement-Based Structures/Materials: Signal Processing and Artificial Intelligence Techniques)
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EEG-Based Seizure Detection Using Variable-Frequency Complex Demodulation and Convolutional Neural Networks
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Yedukondala Rao Veeranki, Riley McNaboe and Hugo F. Posada-Quintero
Signals 2023, 4(4), 816-835; https://doi.org/10.3390/signals4040045 - 28 Nov 2023
Cited by 2
Abstract
Epilepsy is a complex neurological disorder characterized by recurrent and unpredictable seizures that affect millions of people around the world. Early and accurate epilepsy detection is critical for timely medical intervention and improved patient outcomes. Several methods and classifiers for automated epilepsy detection
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Epilepsy is a complex neurological disorder characterized by recurrent and unpredictable seizures that affect millions of people around the world. Early and accurate epilepsy detection is critical for timely medical intervention and improved patient outcomes. Several methods and classifiers for automated epilepsy detection have been developed in previous research. However, the existing research landscape requires innovative approaches that can further improve the accuracy of diagnosing and managing patients. This study investigates the application of variable-frequency complex demodulation (VFCDM) and convolutional neural networks (CNN) to discriminate between healthy, interictal, and ictal states using electroencephalogram (EEG) data. For testing this approach, the EEG signals were collected from the publicly available Bonn dataset. A high-resolution time–frequency spectrum (TFS) of each EEG signal was obtained using the VFCDM. The TFS images were fed to the CNN classifier for the classification of the signals. The performance of CNN was evaluated using leave-one-subject-out cross-validation (LOSO CV). The TFS shows variations in its frequency for different states that correspond to variation in the neural activity. The LOSO CV approach yields a consistently high performance, ranging from 90% to 99% between different combinations of healthy and epilepsy states (interictal and ictal). The extensive LOSO CV validation approach ensures the reliability and robustness of the proposed method. As a result, the research contributes to advancing the field of epilepsy detection and brings us one step closer to developing practical, reliable, and efficient diagnostic tools for clinical applications.
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(This article belongs to the Special Issue Advanced Methods of Biomedical Signal Processing)
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Open AccessArticle
Nearly Linear-Phase 2-D Recursive Digital Filters Design Using Balanced Realization Model Reduction
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Abdussalam Omar, Dale Shpak and Panajotis Agathoklis
Signals 2023, 4(4), 800-815; https://doi.org/10.3390/signals4040044 - 27 Nov 2023
Abstract
This paper presents a new method for the design of separable-denominator 2-D IIR filters with nearly linear phase in the passband. The design method is based on a balanced realization model reduction technique. The nearly linear-phase 2-D IIR filter is designed using 2-D
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This paper presents a new method for the design of separable-denominator 2-D IIR filters with nearly linear phase in the passband. The design method is based on a balanced realization model reduction technique. The nearly linear-phase 2-D IIR filter is designed using 2-D model reduction from a linear-phase 2-D FIR filter, which serves as the initial filter. The structured controllability and observability Gramians and serve as the foundation for this technique. onal positive-definite matrices that satisfy 2-D Lyapunov equations. An efficient method is used to compute these Gramians by minimizing the traces of and under linear matrix inequality (LMI) constraints. The use of these Gramians ensures that the resulting 2-D IIR filter preserves stability and can be implemented using a separable-denominator 2-D filter with fewer coefficients than the original 2-D FIR filter. Numerical examples show that the proposed method compares favorably with existing techniques.
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(This article belongs to the Topic Research on the Application of Digital Signal Processing)
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Open AccessTechnical Note
Evaluating the Feasibility of Euler Angles for Bed-Based Patient Movement Monitoring
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Jonathan Mayer, Rejath Jose, Gregory Kurgansky, Paramvir Singh, Chris Coletti, Timothy Devine and Milan Toma
Signals 2023, 4(4), 788-799; https://doi.org/10.3390/signals4040043 - 14 Nov 2023
Abstract
In the field of modern healthcare, technology plays a crucial role in improving patient care and ensuring their safety. One area where advancements can still be made is in alert systems, which provide timely notifications to hospital staff about critical events involving patients.
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In the field of modern healthcare, technology plays a crucial role in improving patient care and ensuring their safety. One area where advancements can still be made is in alert systems, which provide timely notifications to hospital staff about critical events involving patients. These early warning systems allow for swift responses and appropriate interventions when needed. A commonly used patient alert technology is nurse call systems, which empower patients to request assistance using bedside devices. Over time, these systems have evolved to include features such as call prioritization, integration with staff communication tools, and links to patient monitoring setups that can generate alerts based on vital signs. There is currently a shortage of smart systems that use sensors to inform healthcare workers about the activity levels of patients who are confined to their beds. Current systems mainly focus on alerting staff when patients become disconnected from monitoring machines. In this technical note, we discuss the potential of utilizing cost-effective sensors to monitor and evaluate typical movements made by hospitalized bed-bound patients. To improve the care provided to unaware patients further, healthcare professionals could benefit from implementing trigger alert systems that are based on detecting patient movements. Such systems would promptly notify mobile devices or nursing stations whenever a patient displays restlessness or leaves their bed urgently and requires medical attention.
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(This article belongs to the Special Issue Advanced Methods of Biomedical Signal Processing)
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Open AccessArticle
High-Quality and Reproducible Automatic Drum Transcription from Crowdsourced Data
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Mickaël Zehren, Marco Alunno and Paolo Bientinesi
Signals 2023, 4(4), 768-787; https://doi.org/10.3390/signals4040042 - 10 Nov 2023
Abstract
Within the broad problem known as automatic music transcription, we considered the specific task of automatic drum transcription (ADT). This is a complex task that has recently shown significant advances thanks to deep learning (DL) techniques. Most notably, massive amounts of labeled data
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Within the broad problem known as automatic music transcription, we considered the specific task of automatic drum transcription (ADT). This is a complex task that has recently shown significant advances thanks to deep learning (DL) techniques. Most notably, massive amounts of labeled data obtained from crowds of annotators have made it possible to implement large-scale supervised learning architectures for ADT. In this study, we explored the untapped potential of these new datasets by addressing three key points: First, we reviewed recent trends in DL architectures and focused on two techniques, self-attention mechanisms and tatum-synchronous convolutions. Then, to mitigate the noise and bias that are inherent in crowdsourced data, we extended the training data with additional annotations. Finally, to quantify the potential of the data, we compared many training scenarios by combining up to six different datasets, including zero-shot evaluations. Our findings revealed that crowdsourced datasets outperform previously utilized datasets, and regardless of the DL architecture employed, they are sufficient in size and quality to train accurate models. By fully exploiting this data source, our models produced high-quality drum transcriptions, achieving state-of-the-art results. Thanks to this accuracy, our work can be more successfully used by musicians (e.g., to learn new musical pieces by reading, or to convert their performances to MIDI) and researchers in music information retrieval (e.g., to retrieve information from the notes instead of audio, such as the rhythm or structure of a piece).
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(This article belongs to the Topic Research on the Application of Digital Signal Processing)
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Open AccessArticle
Radix-22 Algorithm for the Odd New Mersenne Number Transform (ONMNT)
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Yousuf Al-Aali, Mounir T. Hamood and Said Boussakta
Signals 2023, 4(4), 746-767; https://doi.org/10.3390/signals4040041 - 23 Oct 2023
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This paper introduces a new derivation of the radix- fast algorithm for the forward odd new Mersenne number transform (ONMNT) and the inverse odd new Mersenne number transform (IONMNT). This involves introducing new equations and functions in finite fields, bringing particular
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This paper introduces a new derivation of the radix- fast algorithm for the forward odd new Mersenne number transform (ONMNT) and the inverse odd new Mersenne number transform (IONMNT). This involves introducing new equations and functions in finite fields, bringing particular challenges unlike those in other fields. The radix- algorithm combines the benefits of the reduced number of operations of the radix-4 algorithm and the simple butterfly structure of the radix-2 algorithm, making it suitable for various applications such as lightweight ciphers, authenticated encryption, hash functions, signal processing, and convolution calculations. The multidimensional linear index mapping technique is the conventional method used to derive the radix- algorithm. However, this method does not provide clear insights into the underlying structure and flexibility of the radix- approach. This paper addresses this limitation and proposes a derivation based on bit-unscrambling techniques, which reverse the ordering of the output sequence, resulting in efficient calculations with fewer operations. Butterfly and signal flow diagrams are also presented to illustrate the structure of the fast algorithm for both ONMNT and IONMNT. The proposed method should pave the way for efficient and flexible implementation of ONMNT and IONMNT in applications such as lightweight ciphers and signal processing. The algorithm has been implemented in C and is validated with an example.
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Open AccessArticle
Restoration for Intensity Nonuniformities with Discontinuities in Whole-Body MRI
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Stathis Hadjidemetriou, Ansgar Malich, Lorenz Damian Rossknecht, Luca Ferrarini and Ismini E. Papageorgiou
Signals 2023, 4(4), 725-745; https://doi.org/10.3390/signals4040040 - 18 Oct 2023
Abstract
The reconstruction in MRI assumes a uniform radio-frequency field. However, this is violated due to coil field nonuniformity and sensitivity variations. In whole-body MRI, the nonuniformities are more complex due to the imaging with multiple coils that typically have different overall sensitivities that
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The reconstruction in MRI assumes a uniform radio-frequency field. However, this is violated due to coil field nonuniformity and sensitivity variations. In whole-body MRI, the nonuniformities are more complex due to the imaging with multiple coils that typically have different overall sensitivities that result in sharp sensitivity changes at the junctions between adjacent coils. These lead to images with anatomically inconsequential intensity nonuniformities that include jump discontinuities of the intensity nonuniformities at the junctions corresponding to adjacent coils. The body is also imaged with multiple contrasts that result in images with different nonuniformities. A method is presented for the joint intensity uniformity restoration of two such images to achieve intensity homogenization. The effect of the spatial intensity distortion on the auto-co-occurrence statistics of each image as well as on the joint-co-occurrence statistics of the two images is modeled in terms of Point Spread Function (PSF). The PSFs and the non-stationary deconvolution of these PSFs from the statistics offer posterior Bayesian expectation estimates of the nonuniformity with Bayesian coring. Subsequently, a piecewise smoothness constraint is imposed for nonuniformity. This uses non-isotropic smoothing of the restoration field to allow the modeling of junction discontinuities. The implementation of the restoration method is iterative and imposes stability and validity constraints of the nonuniformity estimates. The effectiveness and accuracy of the method is demonstrated extensively with whole-body MRI image pairs of thirty-one cancer patients.
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(This article belongs to the Special Issue Whole Body MRI: Restoration and Analysis with Signal/Image Processing Principles)
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Open AccessArticle
Quantitative Electroencephalography: Cortical Responses under Different Postural Conditions
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Marco Ivaldi, Lorenzo Giacometti and David Conversi
Signals 2023, 4(4), 708-724; https://doi.org/10.3390/signals4040039 - 18 Oct 2023
Abstract
In this study, the alpha and beta spectral frequency bands and amplitudes of EEG signals recorded from 10 healthy volunteers using an experimental cap with neoprene jacketed electrodes were analysed. Background: One of the main limitations in the analysis of EEG signals during
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In this study, the alpha and beta spectral frequency bands and amplitudes of EEG signals recorded from 10 healthy volunteers using an experimental cap with neoprene jacketed electrodes were analysed. Background: One of the main limitations in the analysis of EEG signals during movement is the presence of artefacts due to cranial muscle contraction; the objectives of this study therefore focused on two main aspects: (1) validating a tool capable of decreasing movement artefacts, while developing a reliable method for the quantitative analysis of EEG data; (2) using this method to analyse the EEG signal recorded during a particular motor activity (bi- and monopodalic postural control). Methods: The EEG sampling frequency was 512 Hz; the signal was acquired on 16 channels with monopolar montage and the reference on Cz. The recorded signals were processed using a specifically written Matlab script and also by exploiting open-source software (Eeglab). Results: The procedure used showed excellent reliability, allowing for a significant decrease in movement artefacts even during motor tasks performed both with eyes open and with eyes closed. Conclusions: This preliminary study lays the foundation for correctly recording EEG signals as an additional source of information in the study of human movement.
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(This article belongs to the Special Issue Advancing Signal Processing and Analytics of EEG Signals)
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Open AccessReview
Exploitation Techniques of IoST Vulnerabilities in Air-Gapped Networks and Security Measures—A Systematic Review
by
Razi Hamada and Ievgeniia Kuzminykh
Signals 2023, 4(4), 687-707; https://doi.org/10.3390/signals4040038 - 13 Oct 2023
Cited by 1
Abstract
IP cameras and digital video recorders, as part of the Internet of Surveillance Things (IoST) technology, can sometimes allow unauthenticated access to the video feed or management dashboard. These vulnerabilities may result from weak APIs, misconfigurations, or hidden firmware backdoors. What is particularly
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IP cameras and digital video recorders, as part of the Internet of Surveillance Things (IoST) technology, can sometimes allow unauthenticated access to the video feed or management dashboard. These vulnerabilities may result from weak APIs, misconfigurations, or hidden firmware backdoors. What is particularly concerning is that these vulnerabilities can stay unnoticed for extended periods, spanning weeks, months, or even years, until a malicious attacker decides to exploit them. The response actions in case of identifying the vulnerability, such as updating software and firmware for millions of IoST devices, might be challenging and time-consuming. Implementing an air-gapped video surveillance network, which is isolated from the internet and external access, can reduce the cybersecurity threats associated with internet-connected IoST devices. However, such networks can also be susceptible to other threats and attacks, which need to be explored and analyzed. In this work, we perform a systematic literature review on the current state of research and use cases related to compromising and protecting cameras in logical and physical air-gapped networks. We provide a network diagram for each mode of exploitation, discuss the vulnerabilities that could result in a successful attack, demonstrate the potential impacts on organizations in the event of IoST compromise, and outline the security measures and mechanisms that can be deployed to mitigate these security risks.
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(This article belongs to the Special Issue Internet of Things for Smart Planet: Present and Future)
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Open AccessArticle
Beyond Staircasing Effect: Robust Image Smoothing via ℓ0 Gradient Minimization and Novel Gradient Constraints
by
Ryo Matsuoka and Masahiro Okuda
Signals 2023, 4(4), 669-686; https://doi.org/10.3390/signals4040037 - 26 Sep 2023
Abstract
In this paper, we propose robust image-smoothing methods based on gradient minimization with novel gradient constraints to effectively suppress pseudo-edges. Simultaneously minimizing the gradient, i.e., the number of nonzero gradients in an image, and the data fidelity
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In this paper, we propose robust image-smoothing methods based on gradient minimization with novel gradient constraints to effectively suppress pseudo-edges. Simultaneously minimizing the gradient, i.e., the number of nonzero gradients in an image, and the data fidelity results in a smooth image. However, this optimization often leads to undesirable artifacts, such as pseudo-edges, known as the “staircasing effect”, and halos, which become more visible in image enhancement tasks, like detail enhancement and tone mapping. To address these issues, we introduce two types of gradient constraints: box and ball. These constraints are applied using a reference image (e.g., the input image is used as a reference for image smoothing) to suppress pseudo-edges in homogeneous regions and the blurring effect around strong edges. We also present an gradient minimization problem based on the box-/ball-type gradient constraints using an alternating direction method of multipliers (ADMM). Experimental results on important applications of gradient minimization demonstrate the advantages of our proposed methods compared to existing gradient-based approaches.
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(This article belongs to the Topic Research on the Application of Digital Signal Processing)
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