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Incorporating cavity magnonics has opened up a new avenue in controlling non-reciprocity. This work examines a yttrium iron garnet sphere coupled to a planar microwave cavity at millikelvin temperature. Non-reciprocal device behavior results from the cooperation of coherent and dissipative coupling between the Kittel mode and a microwave cavity mode. The device's bi-directional transmission was measured and compared to the theory derived previously in the room temperature experiment. Investigations are also conducted into key performance metrics such as isolation, bandwidth, and insertion loss. The findings point to the coexistence of coherent and dissipative interactions at cryogenic conditions, and one can leverage their cooperation to achieve directional isolation. This work foreshadows the application of a cavity magnonic isolator for on-chip readout and signal processing in superconducting circuitry. [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]

Spherical harmonics provide a smooth, orthogonal, and symmetry-adapted basis to expand functions on a sphere, and they are used routinely in physical and theoretical chemistry as well as in different fields of science and technology, from geology and atmospheric sciences to signal processing and computer graphics. More recently, they have become a key component of rotationally equivariant models in geometric machine learning, including applications to atomic-scale modeling of molecules and materials. We present an elegant and efficient algorithm for the evaluation of the real-valued spherical harmonics. Our construction features many of the desirable properties of existing schemes and allows us to compute Cartesian derivatives in a numerically stable and computationally efficient manner. To facilitate usage, we implement this algorithm in sphericart, a fast C++ library that also provides C bindings, a Python API, and a PyTorch implementation that includes a GPU kernel. [ABSTRACT FROM AUTHOR]

The energy band structure is crucial for solid insulation research. The traditional thermally stimulated current is used mostly for direct trap estimation but has some limitations, which can be overcome by the space charge decay method. With the help of a modified pulsed-electro acoustic system, we perform the test under different low temperatures and obtain a more comprehensive band spectrum including shallow traps and different polarities. This study chooses isotactic polypropylene (PP) to investigate in a temperature range of −120 to 20 °C. Signal processing and recovery are considered. Two basic trap estimation methods are compared and the one based on the modified isothermal relaxation current theory is used here. The electron and hole trap densities near different electrode interfaces are considered in the experiment. In the preliminary results, it is found that the traps tend to move toward a shallow direction with a decrease in temperature. A model of the retrapping process caused by phonon scattering is put forward to explain the results. The retrapping possibility is, therefore, assumed to follow a power relation with temperature. An attempt to rectify the trap depth is performed on such a model by numerical approximation and reaches the result that the retrapping possibility may be dependent upon the second power of temperature. Finally, the broadband energy spectrum is calculated after correction. In the normalized coordinate system, the traps in PP are mainly distributed in a deep range; few shallow traps are found maybe because the space charge density is low. [ABSTRACT FROM AUTHOR]

Photoluminescence imaging of field-installed photovoltaic modules has the potential to be a high throughput on-site inspection technique. A contribution to this development is a noninvasive photoluminescence imaging method that has been proposed recently. It is based on acquiring images during a progressing current–voltage curve sweep, resulting in a detection of a continuously changing photoluminescence signal. From this follows the necessity to employ an alternative algorithm based on the Pearson correlation coefficient, which will contribute to efficient, unsupervised image processing and, thus, easier (in real-time) implementation. The new algorithm separates photoluminescence from the reflected sunlight, similar to the conventional subtraction algorithm. However, it is robust to varying solar irradiance and can process the photoluminescence signal emitted from multiple asynchronized strings. We predict that it will enable unsupervised real-time surveillance and detection of functional anomalies at a low computational cost. It is also more sensitive at segmenting ground-reflected photoluminescence. [ABSTRACT FROM AUTHOR]

Owing to the advanced fabrication technology of silicon, silicon waveguides are particularly attractive for implementing all-optical signal processing devices and switches. Therefore, in this paper, a silicon-on-silica waveguide that consists of four slots arranged in the shape of letter E is proposed to be employed as the building block for simulating fundamental all-optical logic gates (AOLGs), including XOR, AND, OR, NOT, NOR, NAND, and XNOR, at 1.55 μm telecommunications wavelength. The operation concept of these logic gates relies on the constructive and destructive interference that results from the phase difference induced by optical beams that are incident on the E-shaped waveguide. The performance of the target logic gates is assessed against the contrast ratio (CR) metric. Moreover, the dependence of the spectral transmission on the device's key operating parameters is investigated and assessed. Compared to other reported designs, the results obtained by conducting simulations using the finite-difference-time-domain in lumerical commercial software show that the proposed waveguide can operate at a higher speed of 80 Gb/s and attain higher CRs of 36, 39, 35.5, 28.8, 30, 38, and 36.7 dB for logic XOR, AND, OR, NOT, NOR, NAND, and XNOR, respectively. This suggests that by using the proposed scheme, AOLGs could be realized more feasibly with greater performance and faster operation toward satisfying the present and future needs of light wave circuits and systems. [ABSTRACT FROM AUTHOR]

Coaxial cables and optical fibers are two types of cylindrical waveguides used in telecommunications. Fiber Bragg gratings (FBGs) have found successful applications in various fields, such as optical communications, fiber lasers, and fiber-optic sensing. In this paper, we propose and numerically investigate the implementations of various fiber Bragg configurations, including uniform, chirped, apodized, and phase-shifted configurations, on coaxial cables to generate the corresponding special types of coaxial cable Bragg gratings (CCBGs). The simulation results of different CCBGs match well with the well-known FBG theories. It is demonstrated that the reflection spectrum of a CCBG can be flexibly tailored by introducing various quasi-periodic perturbations in the permittivity of the dielectric layer along the coaxial cable. The proposed special types of CCBGs with unique characteristics could find potential applications in radio frequency signal processing, communication, and sensing fields. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) of metal components is expanding as a developing technique for fabricating high-value and large-scale metal parts. Among various metal AM procedures, wire arc additive manufacturing (WAAM) received special attention in recent years due to its unique potential for fabricating complex geometries in industrial scale and applications. In this study, a step forward for developing a continuous, multi-layer in-situ monitoring technique based on acoustic signatures recorded by acoustic emission nondestructive method over the deposition process is presented. The major goal of this research is to investigate if previously proven single-layer monitoring procedures based on acoustic signatures can be expanded toward a robust multi-layer and continuous monitoring method. Two different types of materials have been used in a WAAM process equipped with acoustic emission sensors, and recorded signals were analyzed by traditional statistical assessment as well as a K-mean clustering machine learning algorithm. The findings affirm the effectiveness of acoustic signals in monitoring processes during the continuous deposition of material and indicate that acoustic signals can reliably identify distinct process states across all layers. This underscores the reliability of acoustic signals as a multi-layer process monitoring method. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a condition that degenerates over time and impairs speech and pronunciation because brain cells have died. This research work aims to predict parkinson disease using the voice features extracted from speech signals recorded from PD individuals with dysphonic speech disorders by employing deep learning algorithms. PD is challenging to diagnose early on in the clinical presentation. To address the issue in machine learning methods, this paper proposes a neural network model by processing speech signals to classify PD using the University of California Irvine (UCI) machine learning repository dataset. Initially, a pre-loss reduction module is created by using pre-sampling to make the dataset balanced by reducing the dimensionality and maintaining the size of the space without influencing the learning process for data preparation. The relevant features are derived using a novel multi-agent salp swarm (MASS) algorithm, and a novel Parkinson classification neural network (PCNN) is proposed to classify Parkinson's patients with high accuracy employing these derived features. The result shows that the models that use MASS-PCNN produce higher classification accuracy of 99.1%, precision of 97.8%, recall of 94.7% and F1-score of 0.995 when paralleled to the existing models. As an outcome, the suggested model will perform superior to common convolutional neural networks. [ABSTRACT FROM AUTHOR]

Reservoir monitoring is essential to guarantee safe operations for all activities involving the production and injection of fluids into the subsurface, such as hydrocarbon production, gas storage and the exploitation of geothermal reservoirs. For this purpose, microseismic monitoring networks are operated in real time in order to identify and locate any possible seismic events in the vicinity of the reservoir. The goal of this study is to investigate whether the large amount of ambient seismic noise recorded by seismic reservoir monitoring networks can be used to infer a one‐dimensional shear‐wave velocity profile representative of the area covered by the network. Shear‐wave velocities are generally difficult to characterize and constrain, yet they are key to precisely locate seismic events. The adopted workflow consists of three steps: first, the cross‐correlation functions between all station pairs are retrieved, using 1 year of continuous data; second, the average group‐ and phase velocity dispersion curves are extracted; third, a joint group and phase velocity inversion is done. For validation, the obtained average shear‐wave velocity profile is compared with a regional model of the area as well as with local shear‐wave velocity measurements from a sonic log. [ABSTRACT FROM AUTHOR]

Deep learning has gained attraction amongst geophysicists for solving complex longstanding problems. Nevertheless, proper hyperparameter optimization methodologies remain critically underexplored in geophysical deep learning research. This paper attempts to first highlight the importance of hyperparameter optimization and then showcase two geophysics‐related deep learning examples where a grid search and Optuna framework (an automated optimization approach) were used for hyperparameter optimization. We consider two geophysical problems related to denoising seismic traces and the inversion of seismic traces for velocity information. In both cases, models created based on Optuna hyperparameter optimization were able to perform better than those created through grid search. The most significant advantage of Optuna, however, is having quantifiable results to justify the choice of a neural network architecture, depth and other hyperparameters rather than relying on inefficient methods of exploring the hyperparameter space such as a trial‐and‐error or grid search. This study aims to stimulate further exploration and adoption of these frameworks, pushing the boundaries of current deep learning based geophysical problem‐solving methodologies towards full automation. [ABSTRACT FROM AUTHOR]

The presence of coherent noise in seismic data leads to errors and uncertainties, and as such it is paramount to suppress noise as early and efficiently as possible. Self‐supervised denoising circumvents the common requirement of deep learning procedures of having noisy‐clean training pairs. However, self‐supervised coherent noise suppression methods require extensive knowledge of the noise statistics. We propose the use of explainable artificial intelligence approaches to 'see inside the black box' that is the denoising network and use the gained knowledge to replace the need for any prior knowledge of the noise itself. This is achieved in practice by leveraging bias‐free networks and the direct linear link between input and output provided by the associated Jacobian matrix; we show that a simple averaging of the Jacobian contributions over a number of randomly selected input pixels provides an indication of the most effective mask to suppress noise present in the data. The proposed method, therefore, becomes a fully automated denoising procedure requiring no clean training labels or prior knowledge. Realistic synthetic examples with noise signals of varying complexities, ranging from simple time‐correlated noise to complex pseudo‐rig noise propagating at the velocity of the ocean, are used to validate the proposed approach. Its automated nature is highlighted further by an application to two field data sets. Without any substantial pre‐processing or any knowledge of the acquisition environment, the automatically identified blind masks are shown to perform well in suppressing both trace‐wise noise in common shot gathers from the Volve marine data set and coloured noise in post‐stack seismic images from a land seismic survey. [ABSTRACT FROM AUTHOR]

Marine vibrators represent an alternative seismic source technology that could come to market in the near future. A key challenge related to marine vibrator seismic data is the effect that phase dispersion from source motion has on the signal during transmission. As such, the recorded moving vibrator data will benefit from being phase corrected, so that the data appear as if they had been shot with a stationary source. This transformation is called the source motion correction. Previous source motion corrections used either specific dephasing operators for specific sweep types or pre‐correlation methods for any sweep type. A source motion correction dephasing operator has been derived, demonstrated and applied to real seismic data collected during a Marine Vibrator Joint Industry Project field trial of an array of marine vibrators. This dephasing operator has been made more general so that any strictly monotonic sweep through time may be used for this correction regardless of sweep type (e.g. linear or exponential). Moreover, the correction uses the pilot sweep as a direct input, measuring from it, the instantaneous frequency which can then be used to build the dephasing operator. This new more general form brings two key advantages over previous dephasing operator corrections: (1) Non‐analytically defined sweeps can now be source motion corrected, and (2) even when an analytically defined sweep (e.g. linear) is used, the transmitted sweep from the marine vibrator can vary from the theoretical input sweep; this correction can account for these changes. Furthermore, given that this source motion correction is a dephasing operator, it can be applied pre‐ or post‐correlation of the raw data with the pilot sweep, thus allowing greater flexibility in the processing. Lastly, some previously derived source motion corrections based on dephasing operators contained errors in their derivation; this new derivation addresses these errors resulting in an improved correction. [ABSTRACT FROM AUTHOR]

Geophysical prospecting signals encompass subsurface structural information and incorporate textual messages generated in accordance with a specific pattern. These signals can be employed in places without radio access to ensure public and worker safety. Therefore, the use of seismic signals to transmit information through the earth has attracted the attention of researchers in the last decade. Presently, achievements in seismic communication are mainly in the coding, generation, and propagation of seismic signals. Little work has been done on methods to convert seismic signals generated by vibroseis sources into text and broadcast them vocally. Therefore, we built a seismic communication system with 6‐bit code using the American Standard Code for Information Interchange. To better recommend the seismic communication system scheme, the origin, state and principles of seismic communication system are illustrated in detail. Then, a seismic transmitting system is devised with a 500 N vibroseis source, which compiles seismic signals through amplitude modulation. After seismic signals propagate through the earth, they are received by geophones and recorded in seismographs. Through data acquisition based on a compression algorithm, seismic signals are converted into text and voice signals, which significantly reduces the storage and transmission of seismic data. [ABSTRACT FROM AUTHOR]

Deghosting is an important technique in the marine seismic industry, as it plays a crucial role in mitigating the effects of ghost reflections from the sea surface, which can significantly impact the accuracy and resolution of subsurface imaging. In recent years, various acquisition‐based techniques have been developed to tackle the challenge of removing receiver–ghost reflections, which is the focus of our paper. These state‐of‐the‐art approaches, such as dual‐sensor or multicomponent towed streamer acquisitions, have demonstrated exceptional accuracy by combining pressure and particle motion data. However, such methods face limitations when dealing with low frequencies due to heavy noise contamination in the particle motion data. Consequently, ghost‐free data reconstruction at low frequencies typically relies on processing‐based approaches, which exclusively utilize recorded pressure data. This study presents a novel deghosting method for low‐frequency applications based on parabolic dictionary learning, which relies solely on recorded pressure data. The proposed method has the advantage of being applicable directly in a compressed domain, eliminating the need for data decompression prior to the deghosting process when compression is applied before the processing steps. This not only reduces costs related to data storage and transfer but also provides a cost‐effective alternative to conventional deghosting by operating directly on the compressed data format, which is smaller in size. The effectiveness of the proposed method was evaluated using both synthetic and field datasets. The results obtained from a synthetic data example indicate that the proposed method achieves similar results to an industry‐standard frequency–wavenumber method, while achieving a compression rate of over 7. Furthermore, the method was tested using a field dataset consisting of a full sail‐line of marine seismic acquisition. The comparison of 2D pre‐stack migrated images between the proposed method and the industry‐standard frequency–wavenumber method revealed insignificant differences, while achieving a compression ratio higher than 5 when our method was used. [ABSTRACT FROM AUTHOR]

We propose an approach for measuring seismic attenuation at ultrasonic frequencies on laboratory‐scale samples. We use a Gaussian filter to select a bandwidth of frequencies to identify the attenuation in a small window and, by moving the window across the frequency content of the data, we determine the frequency‐dependent attenuation function. We assess the validity of the method with three‐dimensional numerical simulations of seismic wave propagation across different sample geometries, using free surface boundary conditions. We perform the simulations using viscoelastic media under various seismic attenuation models. Our numerical results indicate that we can successfully recover the representative viscoelastic attenuation parameters of the media, regardless of the sample geometry, by processing the seismic signal recorded either within the volume or at the boundaries. Due to the equipartition phenomenon, the energy of S‐waves is consistently higher in seismic records than that of P‐waves. Therefore, we systematically recover the attenuating properties of S‐waves in the medium. We also conduct experiments of seismic wave propagation on samples of aluminum and Fontainebleau sandstone to validate our approach with real data. The quality factor of the S‐wave Qs$Q_s$ in the aluminum medium increases from 300 to 7000 between 60 kHz and 1.2 MHz. The Fontainebleau sandstone, which is more attenuating, exhibits a Qs$Q_s$ that increases from 200 at 60 kHz to 1000 at 1.2 MHz. With our approach, we are not only able to recover the attenuation property but also identify the frequency‐dependent attenuation model of the samples. Our method allows for seismic attenuation recovery at ultrasonic frequencies in low‐attenuating media. [ABSTRACT FROM AUTHOR]

Estimating clean seismic signals from noisy single‐channel records is a hot research topic in the field of microseismic data processing. Due to the existence of strong random noise, the signal‐to‐noise ratio of such data is low, presenting a challenge for signal restoration. In this paper, we propose a denoising method of microseismic data based on a single‐channel phase space reconstruction and independent component analysis algorithm. Specifically, we first apply the phase space reconstruction to transform the one‐dimensional seismic signal into a high‐dimensional phase space in which signal and noise have different trajectories. We then employ independent component analysis to the reconstructed data for noise separation. Experimental results on real microseismic data verify that our proposed denoising method is useful for noise suppression and can enhance the signal‐to‐noise ratio of data, which can be further used for signal identification and event positioning in microseismic monitoring. [ABSTRACT FROM AUTHOR]

Summary: In this communication, two new positive lossy series inductor simulator circuits using single current differencing buffered amplifier (CDBA), one capacitor, and three resistors are presented. In both the proposed circuits, the inductance value is independently controllable through a grounded resistor, which may be implemented using a voltage‐controlled resistor (VCR), facilitating electronic tuning. Moreover, one of the circuits presented utilizes the intrinsic resistance (internal resistance available at input ports p and n) of the CDBA, thereby enabling electronic control and establishing the inductor simulator as canonical. Both the circuits are analyzed for non‐ideal behavior by taking into account the parasitic elements of CDBA and compared with their ideal counterparts. To demonstrate the efficacy of the proposed series RL simulator circuits, SPICE simulations are performed using CMOS CDBA, and the performance of the proposed circuits is evaluated by designing a lead compensator. The simulation and experimental results of the proposed circuits, along with the application examples employing CDBA realized with the off‐the‐shelf available IC AD844, have also been corroborated. [ABSTRACT FROM AUTHOR]

Summary: To ensure smooth power exchange in a three‐phase inverter, it is important to accurately detect the faults of insulated gate bipolar transistor (IGBT) switches. However, the fault characteristics of power tubes are complex, the fault diagnosis results are affected by the load, and the recognition accuracy of some fault diagnosis models is low. To tackle this issue, this paper proposes an inverter open circuit fault diagnosis method named SR‐WOA‐ELM, which integrates signal reconstruction (SR), the whale optimization algorithm (WOA), and an extreme learning machine (ELM). First, the three‐phase output current is processed by the signal reconstruction method to eliminate the effect of load fluctuation, the average value of low frequency coefficients and wavelet entropy is extracted from the reconstructed three‐phase currents using the improved wavelet packet transform, and the fault feature vector is constructed by fusing the above two feature parameters. Second, the parameters of the ELM fault diagnosis model are optimized using WOA. Finally, the fault feature vector is added in the SR‐WOA‐ELM model to obtain the fault diagnosis results. Through simulation and experimental verification, the model can accurately identify the single‐tube and double‐tube faults of the inverter and is not affected by load fluctuations. [ABSTRACT FROM AUTHOR]

We propose and analyze a continuous-time firing-rate neural network, the positive firing-rate competitive network (PFCN), to tackle sparse reconstruction problems with non-negativity constraints. These problems, which involve approximating a given input stimulus from a dictionary using a set of sparse (active) neurons, play a key role in a wide range of domains, including, for example, neuroscience, signal processing, and machine learning. First, by leveraging the theory of proximal operators, we relate the equilibria of a family of continuous-time firing-rate neural networks to the optimal solutions of sparse reconstruction problems. Then we prove that the PFCN is a positive system and give rigorous conditions for the convergence to the equilibrium. Specifically, we show that the convergence depends only on a property of the dictionary and is linear-exponential in the sense that initially, the convergence rate is at worst linear and then, after a transient, becomes exponential. We also prove a number of technical results to assess the contractivity properties of the neural dynamics of interest. Our analysis leverages contraction theory to characterize the behavior of a family of firing-rate competitive networks for sparse reconstruction with and without non-negativity constraints. Finally, we validate the effectiveness of our approach via a numerical example. [ABSTRACT FROM AUTHOR]

Inconel 718 possesses exceptional properties that negatively affect tool life and other key indicators of the cutting mechanism. Owing to the unprecedented failure states of carbide inserts under the synergistic impact of processing conditions and the complex superalloy's metallurgical properties, the imagery signals contain smeared noise, which affects the predictive efficiency of data analytics during tool condition monitoring (TCM). Previous studies applied image processing techniques, such as edge segmentation, detection, auto-encoders, textural, fractal, Fourier, and wavelet analysis, to extract features from tool wear signals despite being inefficient under complex wear morphology. Therefore, this study applies a more efficient data-mining analytic called the multi-sectional singular value decomposition (multi-sectional SVD) for dimensionality reduction and extraction of features from the complex imagery signals, enhancing the predictive efficiency of machine learning (ML) during TCM. To achieve this, an interrupted climb-milling of Inconel 718 was conducted at various speeds, feeds, and axial depth of cut to generate the dataset through the in-process acquisition of tool wear images, as well as measurement of the progressive VB for the PVD-TiAlN/NbN-coated carbide inserts. Then, the multi-sectional SVD-based ML was employed to process the imagery signals and extract latent features that combined with process parameters to predict VB. After validating the predicted against the actual VB values, the model yielded a mean average percentage error (MAPE) of 2.36%, indicating its effectiveness in predicting VB profile under complex flank wear morphology. Furthermore, the system was utilized to design an effective cutting condition, where the multi-stage adjustment of speed and feed was employed to reduce the VB rate in the early cutting stage. [ABSTRACT FROM AUTHOR]

In this paper, a new method based on the Tufts–Kumaresan (TK) algorithm is used to process nonstationary signals and extract system modes. This new algorithm for mode estimation is proposed to improve estimation accuracy under dynamic conditions. The key contribution in TK is the use of multiple orthogonal sliding windows rather than a single pair of sliding windows. Simulation results under various scenarios, such as different types of faults and load changes are used to evaluate the performance of the proposed method. The proposed method will accurately extract system modes. This method will also reduce the required memory and the calculation time of estimation in nonstationary signals. In complement to successful applications in studying the dynamic behavior of the power system, identifying and analyzing low-frequency electromechanical oscillations, and removing signal noise, this method can accurately estimate the modes of a power system. To validate the accuracy of the proposed method, the Wavelet and the Prony methods have been used for comparison. The proposed method is implemented using simulated ringdown data of standard two-area power system and real measurement data of the WSCC system breakup on 10 August 1996. [ABSTRACT FROM AUTHOR]

Challenges arise in accessing archived signal outputs due to proprietary software limitations. There is a notable lack of exploration in open-source mandibular EMG signal conversion for continuous access and analysis, hindering tasks such as pattern recognition and predictive modelling for temporomandibular joint complex function. To Develop a workflow to extract normalised signal parameters from images of mandibular muscle EMG and identify optimal clustering methods for quantifying signal intensity and activity durations. A workflow utilising OpenCV, variational encoders and Neurokit2 generated and augmented 866 unique EMG signals from jaw movement exercises. k-means, GMM and DBSCAN were employed for normalisation and cluster-centric signal processing. The workflow was validated with data collected from 66 participants, measuring temporalis, masseter and digastric muscles. DBSCAN (0.35 to 0.54) and GMM (0.09 to 0.24) exhibited lower silhouette scores for mouth opening, anterior protrusion and lateral excursions, while K-means performed best (0.10 to 0.11) for temporalis and masseter muscles during chewing activities. The current study successfully developed a deep learning workflow capable of extracting normalised signal data from EMG images and generating quantifiable parameters for muscle activity duration and general functional intensity. [ABSTRACT FROM AUTHOR]

Convolutional neural networks possess the capability of feature learning and nonlinear mapping, which has significant advantages in classifying and recognizing optical fiber vibration signals. In order to further enhance the recognition rate of vibration signals, this paper combines wavelet transform with convolutional neural networks and designs a convolutional layer based on parameterized wavelets. In this layer, the initial signal is convolved with parameterized Laplace wavelet dictionaries to complete the wavelet transform. Such customized filters make more sense than filters with randomly initialized parameters for traditional CNNs. Simultaneously, we introduce the channel attention mechanism to enhance the features of the filtered signals. Subsequently, standard CNNs are employed to extract and process signal features, ultimately utilizing a multi‐layer perceptron for recognition and classification. The experimental results show that the network model possesses better recognition refinement. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

A capacitance multiplier is an active circuit designed specifically to increase the capacitance of a passive capacitor to a significantly higher capacitance level. In this paper, the use of a voltage differencing differential difference amplifier (VDDDA), an electronically controllable active device for designing grounded and floating capacitance multipliers, is proposed. The capacitance multipliers proposed in this study are extremely simple and consist of a VDDDA, a resistor, and a capacitor. The multiplication factor ( K c ) can be electronically controlled by adjusting the external bias current ( I B ). It offers an easy way of controlling it by utilizing a microcontroller for modern analog signal processing systems. The multiplication factor has the potential to be adjusted to a value that is either less than or greater than one, hence widening the variety of uses. The grounded capacitance multiplier can be easily transformed into a floating one by utilizing Zc-VDDDA. PSpice simulation and experimentation with a VDDDA realized from commercially available integrated circuits were used to test the performance of the proposed capacitance multipliers. The multiplication factor is electronically adjustable, ranging in approximation from 0.56 to 13.94. The operating frequency range is approximately three frequency decades. The realization of the lagging and leading phase shifters using the proposed capacitance multiplier is also examined and proven. The results reveal that the lagging and leading phase shifts are electronically tuned via the multiplication factor of the proposed capacitance multipliers. [ABSTRACT FROM AUTHOR]

We experimentally demonstrate a simple design for a spin-wave frequency demultiplexer based on submicrometer-width yttrium iron garnet waveguides intersecting at an angle of 30°. We show that, depending on the frequency, spin waves excited in the input arm of the device are predominantly directed to one of the two output arms. This spin-wave routing is characterized by a large extinction ratio of about 10. The frequency response of the demultiplexer can be efficiently controlled by changing the static magnetic field and the geometry of the device. Due to the small intersection angle and symmetry of the device, its operation does not require conversion between different types of spin-wave modes. This results in a high efficiency of the device and allows its facile integration into magnonic networks for complex signal processing and computing with spin waves. [ABSTRACT FROM AUTHOR]

Statistical modeling of epidemiological curves to capture the course of epidemic processes and to implement a signaling system for detecting significant changes in the process is a challenging task, especially when the process is affected by political measures. As previous monitoring approaches are subject to various problems, we develop a practical and flexible tool that is well suited for monitoring epidemic processes under political measures. This tool enables monitoring across different epochs using a single statistical model that constantly adapts to the underlying process, and therefore allows both retrospective and on‐line monitoring of epidemic processes. It is able to detect essential shifts and to identify anomaly conditions in the epidemic process, and it provides decision‐makers a reliable method for rapidly learning from trends in the epidemiological curves. Moreover, it is a tool to evaluate the effectivity of political measures and to detect the transition from pandemic to endemic. This research is based on a comprehensive COVID‐19 study on infection rates under political measures in line with the reporting of the Robert Koch Institute covering the entire period of the pandemic in Germany. [ABSTRACT FROM AUTHOR]

Preprocessing plays a key role in Raman spectral analysis. However, classical preprocessing algorithms often have issues with reducing Raman peak intensities and changing the peak shape when processing spectra. This paper introduces a unified solution for preprocessing based on a convolutional autoencoder to enhance Raman spectroscopy data. One is a denoising algorithm that uses a convolutional denoising autoencoder (CDAE model), and the other is a baseline correction algorithm based on a convolutional autoencoder (CAE+ model). The CDAE model incorporates two additional convolutional layers in its bottleneck layer for enhanced noise reduction. The CAE+ model not only adds convolutional layers at the bottleneck but also includes a comparison function after the decoding for effective baseline correction. The proposed models were validated using both simulated spectra and experimental spectra measured with a Raman spectrometer system. Comparing their performance with that of traditional signal processing techniques, the results of the CDAE-CAE+ model show improvements in noise reduction and Raman peak preservation. [ABSTRACT FROM AUTHOR]

In synthetic aperture radar (SAR) signal processing, compared with the raw data of level-0, level-1 SAR images are more readily accessible and available in larger quantities. However, an amount of level-1 images are affected by radio frequency interference (RFI), which typically originates from Linear Frequency Modulation (LFM) signals emitted by ground-based radars. Existing research on interference suppression in level-1 data has primarily focused on two methods: transforming SAR images into simulated echo data for interference suppression, or focusing interference in the frequency domain and applying notching filters to reduce interference energy. However, these methods overlook the effective utilization of the interference parameters or are confined to suppressing only one type of LFM interference at a time. In certain SAR images, multiple types of LFM interference manifest bright radiation artifacts that exhibit varying lengths along the range direction while remaining constant in the azimuth direction. It is necessary to suppress multiple LFM interference on SAR images when original echo data are unavailable. This article proposes a joint sparse recovery algorithm for interference suppression in the SAR image domain. In the SAR image domain, two-dimensional LFM interference typically exhibits differences in parameters such as frequency modulation rate and pulse width in the range direction, while maintaining consistency in the azimuth direction. Based on this observation, this article constructs a series of focusing operators for LFM interference in SAR images. These operators enable the sparse representation of dispersed LFM interference. Subsequently, an optimization model is developed that can effectively suppress multi-LFM interference and reduce image loss with the assistance of a regularization term in the image domain. Simulation experiments conducted in various scenarios validate the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

Hyper-spectral imaging (HSI) systems can be divided into two main types as follows: a group of systems that includes a dedicated dispersion/filtering component whose role is to physically separate the different wavelengths and a group of systems that sample all wavelengths in parallel, so that the separation into wavelengths is performed by signal processing (interferometric method). There is a significant advantage to systems of the second type in terms of the integration time required to obtain a signal with a high signal-to-noise ratio since the signal-to-noise ratio of methods based on scanning interferometry (Windowing method) is better compared to methods based on dispersion. The current research deals with the feasibility study of a new concept for an HSI system that is based on scanning interferometry using the "push-broom" method. In this study, we investigated the viability of incorporating a simple birefringent plate into a scanning optical system. By exploiting the motion of the platform on which the system is mounted, we extracted the spectral information of the scanned region. This approach combines the benefits of scanning interferometry with the simplicity of the setup. According to the theory, a chirped cosine-shaped interferogram is obtained for each wavelength due to the nonlinear behavior of the optical path difference of light in the birefringent plate as a function of the angle. An algorithm converts the signal from a superposition of chirped cosine signals to a scaled interferogram such that Fourier transforming (FT) the interferogram retrieves the spectral information. This innovative idea can turn a simple monochrome camera into a hyperspectral camera by adding a relief lens and a birefringent plate. [ABSTRACT FROM AUTHOR]

In previous research, we presented an apparatus designed for comprehensive and systematic surveillance of trees against borers. This apparatus entailed the insertion of an uncoated waveguide into the tree trunk, enabling the transmission of micro-vibrations generated by moving or digging larvae to a piezoelectric probe. Subsequent recordings were then transmitted at predetermined intervals to a server, where analysis was conducted manually to assess the infestation status of the tree. However, this method is hampered by significant limitations when scaling to monitor thousands of trees across extensive spatial domains. In this study, we address this challenge by integrating signal processing techniques capable of distinguishing vibrations attributable to borers from those originating externally to the tree. Our primary innovation involves quantifying the impulses resulting from the fracturing of wood fibers due to borer activity. The device employs criteria such as impulse duration and a strategy of waiting for periods of relative quietness before commencing the counting of impulses. Additionally, we provide an annotated large-scale database comprising laboratory and field vibrational recordings, which will facilitate further advancements in this research domain. [ABSTRACT FROM AUTHOR]

In environments where silent communication is essential, such as libraries and conference rooms, the need for a discreet means of interaction is paramount. Here, we present a single-electrode, contact-separated triboelectric nanogenerator (CS-TENG) characterized by robust high-frequency sensing capabilities and long-term stability. Integrating this TENG onto the inner surface of a mask allows for the capture of conversational speech signals through airflow vibrations, generating a comprehensive dataset. Employing advanced signal processing techniques, including short-time Fourier transform (STFT), Mel-frequency cepstral coefficients (MFCC), and deep learning neural networks, facilitates the accurate identification of speaker content and verification of their identity. The accuracy rates for each category of vocabulary and identity recognition exceed 92% and 90%, respectively. This system represents a pivotal advancement in facilitating secure and efficient unobtrusive communication in quiet settings, with promising implications for smart home applications, virtual assistant technology, and potential deployment in security and confidentiality-sensitive contexts. [ABSTRACT FROM AUTHOR]

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) technology is widely used in integrated radar and communication systems (IRCSs). Moreover, index modulation (IM) is a reliable OFDM transmission scheme in the field of communication, which transmits information by arranging several distinguishable constellations. In this paper, we propose a sparse reconstruction-based joint signal processing scheme for integrated MIMO-OFDM-IM systems. Combining the advantages of MIMO and OFDM-IM technologies, the integrated MIMO-OFDM-IM signal design is realized through the reasonable allocation of bits and subcarriers, resulting in better intercarrier interference (ICI) resistance and a higher transmission efficiency. Taking advantage of the sparseness of OFDM-IM, an improved target parameter estimation method based on sparse signal reconstruction is explored to eliminate the influence of empty subcarriers on the matched filtering at the receiver side. In addition, an improved sequential Monte Carlo signal detection method is introduced to realize the efficient detection of communication signals. The simulation results show that the proposed integrated system is 5 dB lower in the peak sidelobe ratio (PSLR) and 1.5 × 10 5 lower in the number of complex multiplications than the latest MIMO-OFDM system and can achieve almost the same parameter estimation performance. With the same spectral efficiency, it has a lower bit error rate (BER) than existing methods. [ABSTRACT FROM AUTHOR]

Magnetic resonance sounding is a geophysical method for quantitatively determining the state for groundwater storage that has gained international attention in recent years. However, the practical acquisition of magnetic resonance sounding signals, which are on the nanovolt scale, is susceptible to various types of interference, such as power-line harmonics, random noise, and spike noise. Such interference can degrade the quality of magnetic resonance sounding signals and, in severe cases, be completely drowned out by noise. This paper introduces an adaptive Gaussian filtering algorithm that is well-suited for handling intricate noise signals due to its adaptive solving characteristics and iterative sifting approach. Notably, the algorithm can process signals without relying on prior knowledge. The adaptive Gaussian filtering algorithm is applied for the envelope extraction of noisy magnetic resonance sounding signals, and the reliability and effectiveness of the method are rigorously validated. The simulation results reveal that, even under strong noise interference (with original signal-to-noise ratios ranging from −7 dB to −25 dB), the magnetic resonance sounding signal obtained after algorithmic processing is compared to the ideal signal, with 16 sets of data statistics, and the algorithm ensures an initial amplitude uncertainty within 4nV and restricts the uncertainty of the relaxation time within a 6 ms range. The signal-to-noise ratio can be boosted by up to 53 dB. The comparative assessments with classical algorithms such as empirical mode decomposition and the harmonic modeling method confirm the superior performance of the adaptive Gaussian filtering algorithm. The processing of the field data also fully proved the practical application effects of the algorithm. [ABSTRACT FROM AUTHOR]

Wire rope is playing a significant role in various engineering fields. Although a great number of non-destructive testing methods are applied to wire rope defect detection, the results are greatly influenced by the signal noises and the quantitative defect detection of wire rope is consequently limited. Therefore, a new accurate and quantitative wire rope defect magnetic flux leakage (MFL) recognition method based on improved Hilbert transform and long short-term memory (LSTM) neural network is proposed. The theoretical background of the proposed signal processing models and preliminary results, as well as the influence of the main model parameters are analyzed first. Then, different wire rope defect classification principles through LSTM neural network and performance evaluation based on the improved signal processing and machine learning combined method are presented. Finally, the defect inspection and classification comparison results between the proposed method and other deep learning neural networks and pattern recognition strategies are given, which manifest that the proposed method has higher classification accuracy and shorter running time for five different wire rope defects under various servicing conditions, and is promising in accurate defect inspection for multi-scenario wire rope. Additionally, the main conclusions, disadvantages of the proposed method are summarized. [ABSTRACT FROM AUTHOR]

Within the islets of Langerhans, beta cells orchestrate synchronized insulin secretion, a pivotal aspect of metabolic homeostasis. Despite the inherent heterogeneity and multimodal activity of individual cells, intercellular coupling acts as a homogenizing force, enabling coordinated responses through the propagation of intercellular waves. Disruptions in this coordination are implicated in irregular insulin secretion, a hallmark of diabetes. Recently, innovative approaches, such as integrating multicellular calcium imaging with network analysis, have emerged for a quantitative assessment of the cellular activity in islets. However, different groups use distinct experimental preparations, microscopic techniques, apply different methods to process the measured signals and use various methods to derive functional connectivity patterns. This makes comparisons between findings and their integration into a bigger picture difficult and has led to disputes in functional connectivity interpretations. To address these issues, we present here a systematic analysis of how different approaches influence the network representation of islet activity. Our findings show that the choice of methods used to construct networks is not crucial, although care is needed when combining data from different islets. Conversely, the conclusions drawn from network analysis can be heavily affected by the pre-processing of the time series, the type of the oscillatory component in the signals, and by the experimental preparation. Our tutorial-like investigation aims to resolve interpretational issues, reconcile conflicting views, advance functional implications, and encourage researchers to adopt connectivity analysis. As we conclude, we outline challenges for future research, emphasizing the broader applicability of our conclusions to other tissues exhibiting complex multicellular dynamics. Author summary: Islets of Langerhans, multicellular microorgans in the pancreas, are pivotal for whole-body energy homeostasis. Hundreds of beta cells within these networks synchronize to produce insulin, a crucial hormone for metabolic control. Coordinated activity disruptions in these multicellular networks contribute to irregular insulin secretion, a hallmark of diabetes. Recognizing the significance of collective activity, network science approaches have been increasingly applied in islet research. However, variations in experimental setups, imaging techniques, signal processing, and connectivity analysis methods across different research groups pose challenges for integrating findings into a comprehensive picture. Therefore, we present here a systematic analysis of various approaches impacting results in islet activity network representation. We find that methods for constructing functional connectivity maps aren't critical, but caution is necessary when aggregating data from different islets. Network analysis conclusions are notably influenced by factors such as time series pre-processing, the oscillatory component of signals, and experimental preparation. Despite these challenges, this paper advocates for the adoption of connectivity analysis in future islet research, emphasizing that the insights gained extend beyond pancreatic islets to provide valuable contributions for understanding connectivity in other multicellular systems. [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]

Organic photoelectric neuromorphic devices that mimic the brain are widely explored for advanced perceptual computing. However, current individual neuromorphic synaptic devices mainly focus on utilizing linear models to process optoelectronic signals, which means that there is a lack of effective response to nonlinear structural information from the real world, severely limiting the computational efficiency and adaptability of networks to static and dynamic information. Here, a feedforward photoadaptive organic neuromorphic transistor with mixed‐weight plasticity is reported. By introducing the potential of the space charge to couple gate potential, photoexcitation, and photoinhibition occur successively in the channel under the interference of constant light intensity, which enables the device to transform from a linear model to a nonlinear model. As a result, the device exhibits a dynamic range of over 100 dB, exceeding the currently reported similar neuromorphic synaptic devices. Further, the device achieves adaptive tone mapping within 5 s for static information and achieves over 90% robustness recognition accuracy for dynamic information. Therefore, this work provides a new strategy for developing advanced neuromorphic devices and has great potential in the fields of intelligent driving and brain‐like computing. [ABSTRACT FROM AUTHOR]

This paper explores if plants are capable of responding to human movement by changes in their electrical signals. Toward that goal, we conducted a series of experiments, where humans over a period of 6 months were performing different types of eurythmic gestures in the proximity of garden plants, namely salad, basil, and tomatoes. To measure plant perception, we used the plant SpikerBox, which is a device that measures changes in the voltage differentials of plants between roots and leaves. Using machine learning, we found that the voltage differentials over time of the plant predict if (a) eurythmy has been performed, and (b) which kind of eurythmy gestures has been performed. We also find that the signals are different based on the species of the plant. In other words, the perception of a salad, tomato, or basil might differ just as perception of different species of animals differ. This opens new ways of studying plant ecosystems while also paving the way to use plants as biosensors for analyzing human movement. [ABSTRACT FROM AUTHOR]

Recent studies have highlighted the possibility of using surface electromyographic (EMG) signals to develop human–computer interfaces that are also able to recognize complex motor tasks involving the hand as the handwriting of digits. However, the automatic recognition of words from EMG information has not yet been studied. The aim of this study is to investigate the feasibility of using combined forearm and wrist EMG probes for solving the handwriting recognition problem of 30 words with consolidated machine-learning techniques and aggregating state-of-the-art features extracted in the time and frequency domains. Six healthy subjects, three females and three males aged between 25 and 40 years, were recruited for the study. Two tests in pattern recognition were conducted to assess the possibility of classifying fine hand movements through EMG signals. The first test was designed to assess the feasibility of using consolidated myoelectric control technology with shallow machine-learning methods in the field of handwriting detection. The second test was implemented to assess if specific feature extraction schemes can guarantee high performances with limited complexity of the processing pipeline. Among support vector machine, linear discriminant analysis, and K-nearest neighbours (KNN), the last one showed the best classification performances in the 30-word classification problem, with a mean accuracy of 95% and 85% when using all the features and a specific feature set known as TDAR, respectively. The obtained results confirmed the validity of using combined wrist and forearm EMG data for intelligent handwriting recognition through pattern recognition approaches in real scenarios. [ABSTRACT FROM AUTHOR]

Since bearing fault signal in complex running status is usually characterized as nonlinear and non-stationary, it is difficult to extract accurate affluent features and achieve effective fault identification via conventional signal processing tools. In this article, a rolling bearing fault diagnosis technique based on variational mode decomposition and weighted multidimensional feature entropy fusion is proposed to address this issue, which is mainly composed of three procedures. First, the original signal undergoes the variational model decomposition. Next, the signal features are extracted by weighted multidimensional feature entropy as the input of the diagnosis model. Finally, the classification is performed by a convolutional neural network. The method is applied in simulation and experimental analysis. The experimental results show that the proposed method, which demonstrates strong immunity to noise and robustness, can more effectively and adaptively extract the fault features of rolling bearings and achieve the goal of identifying the rolling bearing fault category and damage degree under variable operating conditions. Meanwhile, this approach exhibits superior accuracy and identification performance to some similar entropy-based hybrid approaches referred to in this article, with a promising prospect in industrial application. [ABSTRACT FROM AUTHOR]

Coordination of functions in multi‐body or multi‐component systems requires communication among its parts and is a prerequisite for achieving complex tasks. While electromagnetic (network) communication provides a key ingredient for modern robotics, its molecular equivalent is largely missing for soft robots. With advancements in programmed cargo release, DNA strand‐displacement reactions, enzymatic cascades, and genetic circuits, budding solutions to such chemical network communication have emerged with the potential to drive function in soft machines. In order for such chemical communication to be useful, however, new control concepts, (orthogonal) communication protocols, and molecular solutions should be found. Herein, we provide a critical perspective on the current state‐of‐the‐art chemical communication and identify key challenges towards autonomous soft materials, including signal processing, dealing with noise, switching signaling modalities, and limitations in time and length scales that determine material design. Building on an emerging body of examples, we illustrate gaps, synergies, and new concepts that may provide possible solutions to achieve standardized and reliable molecular information exchange for regulating (soft) robotic function. [ABSTRACT FROM AUTHOR]

Accurate predictions of photovoltaic generation are essential for effectively managing power system resources, particularly in the face of high variability in solar radiation. This is especially crucial in microgrids and grids, where the proper operation of generation, load, and storage resources is necessary to avoid grid imbalance conditions. Therefore, the availability of reliable prediction models is of utmost importance. Authors address this issue investigating the potential benefits of a machine learning approach in combination with photovoltaic power forecasts generated using weather models. Several machine learning methods have been tested for the combined approach (linear model, Long Short-Term Memory, eXtreme Gradient Boosting, and the Light Gradient Boosting Machine). Among them, the linear models were demonstrated to be the most effective with at least an RMSE improvement of 3.7% in photovoltaic production forecasting, with respect to two numerical weather prediction based baseline methods. The conducted analysis shows how machine learning models can be used to refine the prediction of an already established PV generation forecast model and highlights the efficacy of linear models, even in a low-data regime as in the case of recently established plants. [ABSTRACT FROM AUTHOR]

Coda waves are highly sensitive to changes in medium properties and can serve as a tool for structural health monitoring (SHM). However, high sensitivity also makes them susceptible to noise, leading to excessive dispersion of monitoring results. In this paper, a coda wave multi-feature extraction method is proposed, in which three parameters, the time shift, the time stretch, and the amplitude variation of the wave trains within the time window, are totally derived. These three parameters are each mapped to the temperature variations of concrete beams, and then combined together with their optimal weight coefficients to give a best-fitted temperature–multi-parameter relationship that has the smallest errors. Coda wave signals were collected from an ultrasonic experiment on concrete beams within an environmental temperature range of 14 °C~21 °C to verify the effectiveness of the proposed method. The results indicate that the combination of multi-features derived from coda wave signals to quantify the medium temperature is feasible. Compared to the relationship established by a single parameter, the goodness-of-fit is improved. During identification, the method effectively reduces the dispersion of identification errors and mitigates the impact of noise interference on structural state assessment. Both the identification accuracy and stability are improved by more than 50%, and the order of magnitude of the identification accuracy is improved from 1 °C to 0.1 °C. [ABSTRACT FROM AUTHOR]