Signals

Dynamic mode decomposition (DMD) is a powerful tool for separating the background and foreground in videos. This algorithm decomposes a video into dynamic modes, called DMD modes, to facilitate the extraction of the near-zero mode, which represents the stationary background. Simultaneously, it captures the evolving motion in the remaining modes, which correspond to the moving foreground components. However, when applied to noisy video, this separation leads to degradation of the background and foreground components, primarily due to the noise-induced degradation of the DMD mode. This paper introduces a novel noise removal method for the DMD mode in noisy videos. Specifically, we formulate a minimization problem that reduces the noise in the DMD mode and the reconstructed video. The proposed problem is solved using an algorithm based on the plug-and-play alternating direction method of multipliers (PnP-ADMM). We applied the proposed method to several video datasets with different levels of artificially added Gaussian noise in the experiment. Our method consistently yielded superior results in quantitative evaluations using peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM) compared to naive noise removal methods. In addition, qualitative comparisons confirmed that our method can restore higher-quality videos than the naive methods. Full article

X-ray photoelectron spectroscopy (XPS) remains a fundamental technique in materials science, offering invaluable insights into the chemical states and electronic structure of a material. However, the interpretation of XPS spectra can be complex, requiring deep expertise and often sophisticated curve-fitting methods. In this study, we present a novel approach to the analysis of XPS data, integrating the utilization of large language models (LLMs), specifically OpenAI’s GPT-3.5/4 Turbo to provide insightful guidance during the data analysis process. Working in the framework of the CIRCE-NAPP beamline at the CELLS ALBA Synchrotron facility where data are obtained using ambient pressure X-ray photoelectron spectroscopy (APXPS), we implement robust curve-fitting techniques on APXPS spectra, highlighting complex cases including overlapping peaks, diverse chemical states, and noise presence. Post curve fitting, we engage the LLM to facilitate the interpretation of the fitted parameters, leaning on its extensive training data to simulate an interaction corresponding to expert consultation. The manuscript presents also a real use case utilizing GPT-4 and Meta’s LLaMA-2 and describes the integration of the functionality into the TANGO control system. Our methodology not only offers a fresh perspective on XPS data analysis, but also introduces a new dimension of artificial intelligence (AI) integration into scientific research. It showcases the power of LLMs in enhancing the interpretative process, particularly in scenarios wherein expert knowledge may not be immediately available. Despite the inherent limitations of LLMs, their potential in the realm of materials science research is promising, opening doors to a future wherein AI assists in the transformation of raw data into meaningful scientific knowledge. Full article

In gasoline engines, the combustion process involves a flame’s propagation from the spark plug to the cylinder walls, resulting in the localized heating and pressurization of the cylinder content ahead of the flame, which can lead to the autoignition of the gasoline and air. The energy release from the autoignition event causes the engine cylinder to resonate, causing an unpleasant noise and eventual engine damage. This process is termed as knock. Avoiding knock has resulted in limiting the maximum engine pressures, and hence limiting the maximum efficiencies of the engine. Modern engines employ knock sensors to detect resonances, adjusting the spark plug timing to reduce pressures and temperatures, albeit at the expense of engine performance. This paper sets out to review the different signals that can be measured from an engine to detect the start of knock. These signals traditionally consist of the in-cylinder pressure, the vibrations of the engine block, and acoustic noise. This paper reviews each of these techniques, with a focus on recent advances. A number of novel methods are also presented, including identifying perturbations in the engine speed or exhaust temperature; measuring the ion charge across the spark plug leads; and using artificial intelligence to build models based on engine conditions. Each of these approaches is also reviewed and compared to the more traditional approaches. This review finds that in-cylinder pressure measurements remain as the most accurate for detecting knock in modern engines; however, their usage is limited to research settings. Meanwhile, new sensors and processing techniques for vibration measurements will more accurately detect knock in modern vehicles in the short term. Acoustic measurements and other novel approaches are showing promise in the long term. Full article

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. Full article

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. Full article

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 β_t-value. The findings suggest that the evolution of rock damage during loading is adequately reflected by the b-value, γ-value, and β_t-value. The relationships between b-value, γ-value, and β_t-value are studied, as well as the possibility of using these three metrics as early-warning systems for rock failure. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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 $F_1$ score. We obtain $F_1$ 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. Full article

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. Full article

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. Full article

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. Full article

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 $P^s$ and $Q^s$ 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 $P^s$ and $Q^s$ 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. Full article

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. Full article

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). Full article

This paper introduces a new derivation of the radix-$2^2$ 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-$2^2$ 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-$2^2$ algorithm. However, this method does not provide clear insights into the underlying structure and flexibility of the radix-$2^2$ 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. Full article