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Objective. This study aims to enhance positron emission tomography (PET) imaging systems by developing a continuous depth-of-interaction (DOI) measurement technique using a single-ended readout. Our primary focus is on reducing the number of readout channels in the scintillation detectors while maintaining accurate DOI estimations, using a high-pass filter-based signal multiplexing technique combined with artificial neural networks (ANNs). Approach. Instead of reading out all 64 signals from an 8 × 8 silicon photomultiplier (SiPM) array for DOI estimation, the proposed method technique reduces the signals into just four channels by applying high-pass filters with different time constants. To recover the original signal amplitudes, an ANN is used to demultiplex the multiplexed signals. Specifically, the ANN processes the sampled waveforms of these four multiplexed signals and estimates the energy information of the original 8 × 8 SiPM channels. In this study, two DOI estimation strategies were explored for a continuous DOI (cDOI) PET detector utilizing triangular teeth-shaped reflectors: a 'single-step estimation' method directly estimating DOI from multiplexed signals, and a 'two-stage cascade estimation' method that first demultiplexes the signals and then estimates DOI. The performances of proposed strategies were validated using data irradiated at five steps (2, 6, 10, 14, and 18 mm). Results. The signal amplitude of row/column summed signals, which were recovered using the proposed ANN-based de-multiplexing, showed strong correlation with ground truth (e.g. R 2 = 0.98 for 125 MHz digitizer sampling rate). Moreover, both the single-step and two-stage estimation methods achieved high accuracy in DOI estimation, with an average DOI resolution of 4.86 and 5.11 mm respectively. Significance. This novel signal multiplexing technique significantly reduces the number of required readout channels, making cDOI PET more cost-effective. [ABSTRACT FROM AUTHOR]

In this paper, the application is investigated of fast Fourier transform filtering (FFT-FR) to high spatial resolution digital terrain models (HR-DTM) derived from LiDAR sensors, assessing its efficacy in identifying genuine relief elements, including both natural geological features and anthropogenic landforms. The suitability of the derived filtered geomorphic references (FGRs) is evaluated through spatial correlation with ground truths (GTs) extracted from the topographical and geological geodatabases of Santander Bay, Northern Spain. In this study, it is revealed that existing artefacts, derived from vegetation or human infrastructures, pose challenges in the units' construction, and large physiographic units are better represented using low-pass filters, whereas detailed units are more accurately depicted with high-pass filters. The results indicate a propensity of high-frequency filters to detect anthropogenic elements within the DTM. The quality of GTs used for validation proves more critical than the geodatabase scale. Additionally, in this study, it is demonstrated that the footprint of buildings remains uneliminated, indicating that the model is a poorly refined digital surface model (DSM) rather than a true digital terrain model (DTM). Experiments validate the DTM's capability to highlight contacts and constructions, with water detection showing high precision (≥60%) and varying precision for buildings. Large units are better captured with low filters, whilst high filters effectively detect anthropogenic elements and more detailed units. This facilitates the design of validation and correction procedures for DEMs derived from LiDAR point clouds, enhancing the potential for more accurate and objective Earth surface representation. [ABSTRACT FROM AUTHOR]

Copyright of Technisches Messen is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In low intermediate frequency (low-IF) receivers, image interference rejection is one of the core tasks to be accomplished. Conventional active polyphase filters (APPFs) are unable to have a sufficient image rejection ratio (IRR) at high operating frequencies due to the degradation of the IRR by the amplitude and phase imbalances produced by the secondary pole. The proposed solution to the above problem is a frequency-dependent image rejection enhancement technique based on secondary pole compensation. By adjusting the dominant pole frequency of the high-pass filter (HPF) appropriately, the proposed technique can theoretically completely reject the image interference signal even in the presence of the secondary pole. The proposed APPF is simulated and fabricated in a 180-nm CMOS process. The simulation results show that the proposed technique can improve the IRR of the APPF by more than 30 dB at the operating frequency of hundreds of MHz. The measured IRR is better than −31 dB at the frequency from 95 to 105 MHz. Unlike conventional schemes, the proposed design is from the perspective of frequency correlation, which makes the operating frequency no longer limited by the secondary pole frequency. In addition, the proposed design also has an excellent IRR for quadrature input signals with phase imbalance. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To quickly and accurately locate the fault location and fault parameter deviation of analog circuits, a novel incipient fault diagnosis method based on multi-channel one-dimensional residual networks (MC-1D-ResNet) and wavelet packet transform(WPT) is proposed in this paper. The WPT is employed to preprocess the time-domain response signals of analog circuit, and the proposed MC-1D-ResNet is utilized for feature mining and fault classification.The two-level WPT is first carried out on the time-domain response signal to generate one approximate signal and three detailed signals. Secondly, MC-1D-ResNet further performs feature mining on approximate signals and three detailed signals, and realizes fault diagnosis. Through simulation analysis, the proposed method is fully evaluated with the Sallen-Key bandpass filter circuit and the four-op-amp biquad high-pass filter circuit. Even in complex Four-op-amp biquad high-pass filtering circuits, the diagnostic accuracy can reach 99.74%. This article also designs a hardware testing platform based on FPGA, and conduct actual fault diagnosis tests on the four-op-amp biquad high-pass filter circuit. The results show that the average accuracy of 50 actual diagnoses for each type of fault in the circuit was 97.80%. [ABSTRACT FROM AUTHOR]

This study proposes a descriptor-based approach combined with deep learning, which recognizes facial emotions for safe driving. Paying attention to the driver's facial expressions is crucial to address the increasing road accidents. This project aims to develop a Facial Emotion Recognition (FER) system that monitors the driver's facial expressions to identify emotions and provide instant assistance for safety control. In the initial stage, Viola-Jones face detection was employed to detect the facial region, followed by Butterworth high-pass filtering to enhance the identified region for locating the eye, nose, and mouth regions, using Viola-Jones face detection. Secondly, the Local Binary Patterns (LBP) feature descriptor is utilized to extract features from the identified eye, nose, and mouth regions. Using 3 RGB channels, the extracted features from these three regions are fed into RessNet-50 and EfficientNet deep networks. The outputs of the two deep learning models' classifiers are combined and integrated using two ensemble methods: ensemble maximum voting and ensemble mean. Based on these combining classifier rules, the performance was evaluated on the JAFFE and KMU-FED databases. The experimental results demonstrate that the proposed method can effectively and with higher accuracy than other competitors recognize emotions in the JAFFE and KMU-FED datasets. The novelty and originality of this paper lie in its significant application in the automotive industry. Implementing our proposed method in a system capable of high accuracy and precision can help mitigate numerous driving hazards. Our approach has achieved 99% and 98% accuracy on the JAFFE and KMUFED databases, respectively. This high level of accuracy, coupled with its practical relevance, underscores the innovative nature of our work. [ABSTRACT FROM AUTHOR]

This paper proposes an Adaptive Grey Wolf Optimizer based on Transfer function Inertia Weight of Second-order High-pass Filter (SAGWO), aiming to overcome the limitations of Grey Wolf Optimizer (GWO) in terms of convergence speed and solution accuracy. SAGWO enhances the algorithm's dynamic adaptability and greatly speeds up convergence by incorporating the transfer function of the second-order high-pass filter into the modifications of inertia and leader wolves' weight. Furthermore, SAGWO incorporates an adaptive random search component, which significantly enhances the search range and the ability to explore globally. The performance of SAGWO is assessed and compared against three prominent Swarm Intelligence Algorithms (ALO, WOA, and EHO), along with GWO and GWO optimized by PSO. This evaluation is conducted through trials on the CEC2017 standard test set. The experimental results indicate that SAGWO outperforms in terms of both convergence speed and solution correctness. Moreover, SAGWO is utilized to address practical engineering problems such as pressure vessel design and robot gripper design, showcasing its remarkable effectiveness once more. This research enhances the theoretical development of Swarm Intelligence Algorithms and demonstrates the significant utility of SAGWO in practical applications, establishing it as a powerful instrument for scientific investigation and engineering optimization. [ABSTRACT FROM AUTHOR]

In many integrated circuit applications, where large passive resistors are prohibited, a tunable active resistor is necessary. This paper presents a compact and wide-range voltage-controlled grounded resistor that employs MOS transistors only. The design’s foundation is an active adjustable resistor that controls the NMOS transistor’s mode of operation. The proposed design is implemented using 0.18μm TSMC CMOS technology and is validated through post-layout simulations conducted in the Cadence Virtuoso environment. Furthermore, a tunable high-pass filter utilizing the proposed active resistor is also shown. The circuit is powered by 1.5V DC. According to the simulation findings, the resistance varies between 33k Ω and 36G Ω, for an input voltage range −0.7–0.6v. [ABSTRACT FROM AUTHOR]

Because oil and gas pipelines reside in high-temperature, high-pressure and highly acidic environments for a long time, it is very common for such pipes to experience corrosion thinning. To solve the long-term online monitoring problem of piezoelectric ultrasonic dry coupling of monitored objects with different surface roughnesses, the effects of different thicknesses of silver plates, different surface roughnesses and different coaxial loads on piezoelectric ultrasonic dry coupling were studied here. To improve the low SNR of the acquired signal, a Batworth high-pass filter and zero-phase digital filter are proposed for online ultrasonic monitoring. The effects of three kinds of roughness and axial load on the piezoelectric ultrasonic dry coupling signal are studied, and the relationship between the roughness, thickness of the silver plate and axial load is obtained. The results show that under different roughness conditions, the SNR of the ultrasonic signal increases with increasing axial load. When the axial load reaches a certain magnitude, the SNR of the ultrasonic signal tends to be stable. When the surface roughness is Ra = 1.6 µm and the thickness of the silver plate is 0.05 mm, the SNR of the ultrasonic signal reaches the best value, and the required axial load is minimal. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to address the challenges in hydroacoustic signal detection, signal distortion and target localization caused by baseline drift. The authors propose a combined algorithm that integrates short-time Fourier transform (STFT) detection, smoothness priors approach (SPA), attitude calibration and direction of arrival (DOA) estimation for micro-electro-mechanical system vector hydrophones. Design/methodology/approach: Initially, STFT method screens target signals with baseline drift in low signal-to-noise ratio environments, facilitating easier subsequent processing. Next, SPA is applied to the screened target signal, effectively removing the baseline drift, and combined with filtering to improve the signal-to-noise ratio. Then, vector channel amplitudes are corrected using attitude correction with 2D compass data. Finally, the absolute target azimuth is estimated using the minimum variance distortion-free response beamformer. Findings: Simulation and experimental results demonstrate that the SPA outperforms high-pass filtering in removing baseline drift and is comparable to the effectiveness of variational mode decomposition, with significantly shorter processing times, making it more suitable for real-time applications. The detection performance of the STFT method is superior to instantaneous correlation detection and sample entropy methods. The final DOA estimation achieves an accuracy within 2°, enabling precise target azimuth estimation. Originality/value: To the best of the authors' knowledge, this study is the first to apply SPA to baseline drift removal in hydroacoustic signals, significantly enhancing the efficiency and accuracy of signal processing. It demonstrates the method's outstanding performance in the field of underwater signal processing. In addition, it confirms the reliability and feasibility of STFT for signal detection in the presence of baseline drift. [ABSTRACT FROM AUTHOR]

Accurate time series analysis is essential for studying variable astronomical sources, where detecting periodicities and characterizing power spectral density (PSD) are crucial. The Lomb–Scargle periodogram, commonly used in astronomy for analyzing unevenly sampled time series data, often suffers from noise introduced by irregular sampling. This paper presents a new high-pass filter (HPF) periodogram, a novel implementation designed to mitigate this sampling-induced noise. By applying a frequency-dependent HPF before computing the periodogram, the HPF method enhances the precision of PSD estimates and periodicity detection across a wide range of signal characteristics. Simulations and comparisons with the Lomb–Scargle periodogram demonstrate that the HPF periodogram improves accuracy and reliability under challenging sampling conditions, making it a valuable complementary tool for more robust time series analysis in astronomy and other fields dealing with unevenly sampled data. [ABSTRACT FROM AUTHOR]

In recent years, interest in airships for cargo transport and stratospheric platforms has increased, necessitating accurate dynamic modeling for stability analysis, autopilot design, and mission planning, specifically through the calculation of stability derivatives, like added mass and inertia. Despite the several CFD methods and analytical solutions available to calculate added masses, experimental validation remains essential. This study introduces a novel methodology to measure these in a wind tunnel, comparing the results with prior studies that utilized towing tanks. The approach involves designing the test model and a crank-slider mechanism to generate motion within the wind tunnel, considering load cell sensitivity, precision, frequency range, and Reynolds numbers. A revolution ellipsoid model, made from extruded polystyrene, was used to validate analytical solutions. The test model, measuring 1 m in length with an aspect ratio of 6, weighing 482 g, was moved along rails by the crank-slider system. By increasing the motion frequency, structural vibrations affecting load cell measurements were minimized. Proper signal processing, including high-pass filtering and second-order Fourier series fitting, enabled successful virtual mass calculation, showing only a 2.1% deviation from theoretical values, significantly improving on previous studies with higher relative errors. [ABSTRACT FROM AUTHOR]

A compact, low loss microstrip ultra-wideband (UWB) bandpass filter (BPF) with a narrow notch band is proposed in this article for using wireless communication applications within the unlicensed UWB range reported by the Federal Communications Commission. A systematic approach is presented to establish the UWB-BPF structure followed by a narrow notch within it. The UWB-BPF response is achieved by integrating a defected structures-based lowpass filter and a short-stepped impedance stub-based highpass filter. The narrow notch band is obtained by embedding two-stepped impedance stubs. The physical dimensions of the stubs can be optimized to select a specific frequency for the notch band. The UWB-BPF structure without the notched band, having a center frequency of 3.69 GHz, provides a fractional bandwidth (FBW) of 157%. The stopband suppression is −26 dB and it is broadened up to 15 GHz. On the other hand, the notched band response is obtained at 5.8 GHz with an FBW of 49.78% which is used for avoiding interference from the wireless local area network. The proposed structures are fabricated and measured for experimental validation. [ABSTRACT FROM AUTHOR]

Circuit noise is a critical factor that affects the performances of an MEMS gyroscope. Therefore, it is essential to analyze and suppress the noises in the key analog circuits, which are the main noise sources. This study presents an optimized front-end readout circuit and noise suppression methods. First, the noise analysis of the front-end readout circuit is carried out with theoretical derivation to clarify the main noise contributors. To suppress the output noise, an improved readout circuit based on the T-resistor networks is proposed, and the corresponding noise equation is derived in detail. In addition, the noise analysis of the critical circuits of the detection and control system, such as the inverting amplifiers, the first-order low-pass filters, and the first-order high-pass filters, is carried out, and the noise suppression strategy with the optimization of the resistances and is proposed. Taking the inverting amplifier as an example, the theoretical derivation is verified by measuring and comparing the output noises of different resistance schemes. In addition, the output noises of the gyroscope before and after circuit optimization are measured. Experimental results demonstrate that the output noise with the circuit optimization is reduced from 60 μV/Hz1/2 to 30 μV/Hz1/2 and the bias instability is reduced from 3.8 deg/h to 1.38 deg/h. In addition, the ARW is significantly improved from 0.035 deg/h1/2 to 0.018 deg/h1/2, which indicates that the proposed noise analysis and suppression methods are effective and feasible. [ABSTRACT FROM AUTHOR]

The advantage of constant resistance filters, as compared to active filters, is that any number of such filters can be cascaded - either to sharpen or broaden the response or for designing other kinds of filters, without being sensitive to temperature or other environmental effects. Also, although using lossy elements involves a larger number of components as compared to that using lossless components, the former is a practical design, while the latter is an ideal one and cannot be realized in practice. It is proved here that the circuit for constant resistance filter designed with dissipative elements, proposed earlier in the literature, is a versatile one, in that by properly designing or reorienting, it can perform the functions of a band-pass, band-stop, low-pass as well as high-pass filters. [ABSTRACT FROM AUTHOR]

The circuit of an operational amplifier notch filter, using a simulated inductance, is revisited, to reveal its versatility in that, by suitable design and choice of the output point, one can obtain all kinds of filters – all-pass, low-pass, high-pass, band-pass, and band-stop – null as well as notch. Detailed analysis is followed by simulation, to bring out the important characteristics, and considerations in design for a prescribed specification. [ABSTRACT FROM AUTHOR]

Direct numerical simulation (DNS) data of flame–wall interaction (FWI) has been utilized to analyze the multiscale nature of turbulent Reynolds stresses and dissipation rate tensor anisotropies within turbulent reacting flow boundary layers across a broad range of scales. The DNS data of head-on quenching of premixed flames propagating through turbulent boundary layers, representative of friction Reynolds numbers R e τ of 110 and 180, has been explicitly filtered using both two- and three-dimensional Gaussian filter kernels for the purpose of multiscale analysis. The low-pass filter results demonstrate the transition from a 2-component limit to a 1-component limit near the wall with increasing filter width, accompanied by a decrease in isotropy, suggesting a significant alteration in dominant flow patterns and a diminishing tendency toward isotropy. The high-pass filter results indicate a progressive increase in anisotropy with the progress of FWI at the channel center, emphasizing the anisotropy of the large scales with the progress of FWI. Furthermore, behaviors of the second and third invariants of the Reynolds stress tensor remain qualitatively similar to that of the dissipation rate tensor at all stages of FWI, suggesting a link between viscous dissipation and Reynolds stress distributions; notably, there is a stronger isotropic tendency in the dissipation rate tensor when the flame is away from the wall, intensifying with an increase in Reynolds numbers. However, as FWI progresses, the shift in the trend toward the 1-component limit indicates an increase in anisotropy within the turbulent reacting flow for the region near the center of the channel. [ABSTRACT FROM AUTHOR]

In recent times, deep learning-based steganalysis classifiers have become popular due to their state-of-the-art performance. Most deep steganalysis classifiers usually extract noise residuals using high-pass filters as preprocessing steps and feed them to their deep model for classification. It is observed that recent steganographic embedding does not always restrict their embedding in the high-frequency zone; instead, they distribute it as per embedding policy. Therefore, besides noise residual, learning the embedding zone is another challenging task. In this work, unlike the conventional approaches, the proposed model first extracts the noise residual using learned denoising kernels to boost the signal-to-noise ratio. After preprocessing, the sparse noise residuals are fed to a novel Multi-Contextual Convolutional Neural Network (M-CNET) that uses heterogeneous context size to learn the sparse and low-amplitude representation of noise residuals. The model performance is further improved by incorporating the Self-Attention module to focus on the areas prone to steganalytic embedding. A set of comprehensive experiments is performed to show the proposed scheme's efficacy over the prior arts. Besides, an ablation study is given to justify the contribution of various modules of the proposed architecture. [ABSTRACT FROM AUTHOR]

Due to the advancement in biomedical technologies, to diagnose problems in people, a number of psychological signals are extracted from patients. We should be able to ensure that psychological signals are not altered by adversaries and it should be possible to relate a patient to his/her corresponding psychological signal. As far as our awareness extends, none of the existing methods possess the capability to both identify and verify the authenticity of the ECG signals. Consequently, this paper introduces an innovative dual-layer data-embedding approach for electrocardiogram (ECG) signals, aiming to achieve both signal identification and authenticity verification. Since file name-based signal identification is vulnerable to modifications, we propose a robust watermarking method which will embed patient-related details such as patient identification number, into the medically less-significant portion of the ECG signals. The proposed robust watermarking algorithm adds data into ECG signals such that the patient information hidden in an ECG signal can resist the filtering attack (such as high-pass filtering) and noise addition. This is achieved via the use of error buffers in the embedding algorithm. Further, modification-sensitive fragile watermarks are added to ECG signals. By extracting and checking the fragile watermark bits, we can determine whether an ECG signal is modified or not. To ensure the security of the proposed mechanism, two secret keys are used. Our evaluation demonstrates the usefulness of the proposed system. [ABSTRACT FROM AUTHOR]

The negative high-pass filter feedback of the grid current (NFGCF) can offer active damping for the LCL-type grid-connected inverter. Due to the control delay in digital control systems, this damping can cause the system to exhibit non-minimum phase behavior within specific frequency ranges. This study proposes a joint active damping approach that combines grid current feedback and the point of common coupling (PCC) voltage unit feedforward. The proposed method introduces a dynamic damping region that varies with grid impedance. By developing suitable damping loop control parameters, this region can span the entire frequency range, even exceeding the Nyquist frequency fs/2. The research results demonstrate that the proposed approach enhances robustness against variations in grid impedance and eliminates non-minimum phase behavior. Simulation and experimental outcomes validate the effectiveness of this joint active damping method. [ABSTRACT FROM AUTHOR]

The field of computer vision has witnessed significant interest in the area of salient object recognition. The utilization of this technology becomes advantageous in various applications, such as image segmentation, image attention retargeting, image cropping, and image understanding. The primary challenges encountered by underwater images pertain to diminished contrast, distorted colors, and an overall suboptimal visual appearance. The present study introduces an innovative and resilient method for detecting saliency in underwater photos. The RGB picture input undergoes enhancement and is subsequently subjected to gradient domain filtration. The filtered image is subjected to a pyramid decomposition. The computation of saliency is performed on three distinct scales using a process consisting of two parallel phases. In the first stage, saliency is calculated by employing cellular automata at various scales. Initially, the filtered image is subjected to a decomposition process into superpixels. Subsequently, the k-means clustering technique is employed to calculate matrices that represent color dissimilarity and geodesic distance. The application of cellular automata follows the fusion of the matrices. The map undergoes additional optimization and filtering processes in order to achieve saliency at a specific scale. The computation of a main saliency map involves the process of scale integration. In the second stage, the computation of saliency is achieved by applying a homomorphic filter to each scale. The natural logarithm of the filtered image is computed. The spatial domain of the image undergoes a transformation to the frequency domain. The Butterworth high-pass filter is utilized. The frequency domain of the image is subsequently converted back to its spatial domain. The computation of the inverse logarithm of the image is performed. The guided filter is employed to acquire saliency on a specific scale. The computation of the secondary saliency map involves the process of scale integration. The final output is obtained by applying multiplicative fusion to both primary and secondary saliency maps. Utilizing cutting-edge methodologies, a thorough evaluation that includes both qualitative and quantitative analyses evaluates the effectiveness of the suggested strategy. In comparison to alternative state-of-the-art methodologies, the findings indicate that the suggested methodology exhibits high levels of precision and dependability. [ABSTRACT FROM AUTHOR]

To enhance the accuracy of dynamic monitoring based on the global navigation satellite system real-time kinematic (GNSS-RTK) technique, an improved hybrid denoising method was proposed in this study. The improved hybrid denoising method is composed of a Type II Chebyshev high-pass filter, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), and wavelet soft threshold denoising. Detrended fluctuation analysis (DFA) was applied as a judgment to identify the noise of GNSS-RTK caused by the multipath effects and the receiver's internal error in the proposed method. A simulation signal containing noise was designed to evaluate the performance of the proposed method. The signal-to-noise ratio (SNR) obtained by the proposed method was 7.1619 dB, which was the largest among the results obtained by the Chebyshev filter, ICEEMDAN, and the proposed method. The root-mean square error (RMSE) obtained by the proposed method was 3.3 mm, which was the smallest among the results obtained by the Chebyshev filter, ICEEMDAN, and the proposed method. The SNR and the RMSE proved the proposed method outperformed the Chebyshev filter and ICEEMDAN. GNSS-RTK stability experiment revealed the noise of GNSS-RTK caused by the multipath effects and the receiver's internal error. Compared with the signal obtained by wavelet denoiser, the signal obtained by the proposed method was more uniformly distributed around the ideal signal and had a smaller displacement range in the GNSS-RTK stability experiment. It demonstrated the proposed method was more suitable than wavelet soft threshold denoising for GNSS-RTK monitoring denoising. The proposed method was applied in a super-high-rise building with a structure height of 596.2 m. The results demonstrate the proposed improved hybrid denoising method has a significant effect on noise reduction in monitoring the super-high-rise building based on GNSS-RTK. The results also present that the vertical dynamic deformation of the super-high-rise building is greater than the planar dynamic deformation. The first-order natural frequency obtained from processed monitoring data was 0.1926 Hz. The difference is only 1.37% compared with the result obtained by finite-element analysis (FEA). It proves the reliability of the proposed method in monitoring super-high-rise building based on the GNSS-RTK technique. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the applicability of the high-resolution WaveFront Phase Imaging Sensor (WFPI) in eyes with Fuchs' Endothelial Corneal Dystrophy (FECD) through qualitative and quantitative analysis using a custom-designed Automatic Guttae Detection Method (AGDM). The ocular phase was measured using the t · eyede aberrometer and then was processed to obtain its High-Pass Filter Map (HPFM). The subjects were pathological and healthy patients from the Fundación Jiménez-Díaz Hospital (Madrid, Spain). The AGDM was developed and applied in pupils with 3 and 5 mm of diameter. A set of metrics were extracted and evaluated like the Root-Mean-Square error (RMS), Number of guttae, Guttae Area, and Area of Delaunay Triangulation (DT). Finally, a Support Vector Machine (SVM) model was trained to classify between pathological and healthy eyes. Quantitatively, the HPFM reveals a dark spots pattern according to the ophthalmologist's description of the slit-lamp examination of guttae distribution. There were significant statistical differences in all the metrics when FECD and Healthy groups were compared using the same pupil size; but comparing both pupil sizes for the same group there were significant differences in most of the variables. This sensor is a value tool to objectively diagnose and monitor this pathology through wavefront phase changes. [ABSTRACT FROM AUTHOR]

The electrical activity produced by muscles is recorded by the electromyography signal, a dynamic biosignal. This dynamic nature represents complex muscular behaviour patterns. Electromyography -based recognition systems have far-reaching effects, revolutionizing humancomputer interactions, enabling sign language detection, and giving amputees greater control over their devices. This study aims to predict specific hand movements by analyzing electromyography signals from a minimal number of channels. The computational cost efficiency of this approach makes it suitable for real-time applications. Electromyography signals were collected from the forearm muscles of 15 healthy subjects (5 women and 10 men) aged between 19 and 24. Preprocessing tasks were applied to the raw signals, including high-pass filtering, Butterworth filtering, notch filtering, and signal rectification. The dataset consists of recordings from four channels, of which two channels were selected for predicting hand movements. Two 500 ms time windows from both channels were used as inputs for a weighted K-Nearest Neighbors algorithm. The task involved predicting and classifying the intentions of three hand movements: pinching, finger abduction, and grasping. The procedure's overall results demonstrate 83.1% detection accuracy for the weighted K-Nearest Neighbors indicating high precision and a relatively short response time for predicting these hand movements in healthy subjects. [ABSTRACT FROM AUTHOR]

Extreme ultraviolet (XUV) radiation is a tool of choice for studying ultrafast processes and atomic physics. Most experiments employing sources of XUV radiation, such as high harmonic generation (HHG) or x-ray lasers, benefit from knowing the number of photons delivered to target in every single shot, because of the possible shot-to-shot pulse fluctuation of the sources. Nonetheless, many setups lack noninvasive XUV pulse energy diagnostics, hindering the simultaneous measurement of pulse energy and utilization of the XUV beam for applications. We present an online XUV pulse energy monitoring method based on the detection of photoelectric charge from thin metallic foil filters transmitting the XUV beam, which can be easily implemented at every pulsed XUV source that includes a high-pass filter system in the form of metallic filters. Consequently, implementation of our method is as straightforward as connecting the filter to an oscilloscope. In the paper, we describe all the physics aspects of such measurement and show the dependence of measured photoelectric charge on the incident pulse energy. To prove the versatility of our approach, we performed this measurement on two different high-flux HHG beamlines, taking consecutive shots at 1 kHz. [ABSTRACT FROM AUTHOR]

We have designed a flexible photoelectric artificial synapse with an oxide/mixed perovskite/polymer N-I-P structure that exhibits essential synaptic plasticity. Formamidinium lead triiodide FAPbI3 perovskite doped with bromine and methylammonium (FAxMA1−xPbI2Br) is employed as the intrinsic layer to improve the optical properties of devices. Without requiring a power source in reaction to outside optical spikes, multiple pulse-dependent plasticity is reproduced on the synaptic devices, and the image's edges are sharpened using high-pass filtering. Additionally, the classical conditioning and spatiotemporal learning are copied under the electric pulse excitation. Significant negative differential resistance is evident, even after 1500 flex/flat mechanical operation. The recognition rate of letters in the visual system is as high as 92%, and the walking distance in the efferent neuromuscular system is controllable. The flexible optoelectronic N-I-P synaptic device is designed to facilitate energy-efficient information processing for neuromorphic computing. [ABSTRACT FROM AUTHOR]

This paper presents a new current-mode first-order versatile active filter employing one extra-x second-generation current controlled current conveyor (EX-CCCII) and one grounded capacitor. The proposed filter can realize first-order filtering functions of a low-pass filter (LPF), high-pass filter (HPF), and all-pass filter (APF) within the same topology with low-input and high-output impedances required for current-mode circuits. This multiple-output EX-CCCII-based filter can provide six transfer functions as both non-inverting and inverting filtering functions of the LPF, HPF, and APF are obtained. The filter also offers electronic control of the pole frequency of all filtering. The proposed current-mode filter can be applied to work as a mixed-mode active filter, namely in the transadmittance-mode (TAM), transimpedance-mode (TIM), and voltage-mode (VM). Each operation mode can provide six transfer functions. The proposed filter was simulated and designed using SPICE and 0.18 µm CMOS technology. Experimental results using the commercially available integrated circuit AD844 were used to confirm the functionality of the new circuits. [ABSTRACT FROM AUTHOR]

In this study, we propose an innovative concept that applies an AI-based approach using the random forest algorithm integrated with fractal and discrete wavelet transform features of nuclear chromatin. This strategy could be employed to identify subtle structural changes in cells that are in the early stages of programmed cell death. The code for the random forest model is developed using the Scikit-learn library in Python and includes hyperparameter tuning and cross-validation to optimize performance. The suggested input data for the model are chromatin fractal dimension, fractal lacunarity, and three wavelet coefficient energies obtained through high-pass and low-pass filtering. Additionally, the code contains several methods to assess the performance metrics of the model. This model holds potential as a starting point for designing simple yet advanced AI biosensors capable of detecting apoptotic cells that are not discernible through conventional microscopy techniques. [ABSTRACT FROM AUTHOR]

In this article, a low loss and compact microstrip ultra-wideband bandpass filter (UWB-BPF) with a narrow-notched band is recommended for suitable applications in ultra-wideband wireless communication systems. The design methodology is presented to develop the microstrip structure of the UWB-BPF pursued by a single notched band (NB) in the passband of the UWB filter. An array of I-shaped defected ground structures and L-shaped defected microstrip structures based lowpass filters is cascaded with a shorted stub based highpass filter to achieve a UWB-BPF response. The two identically folded open stubs are loaded to obtain the narrow NB. The center frequency of the UWB-BPF without the NB is 3.5 GHz, and the stopband is suppressed up to 14.5 GHz. The fractional bandwidth achieved is 160%, with a circuit area of 0.11 ${\rm{\lambda }}_{\rm{g}}^{\rm{2}}$ λ g 2 . Furthermore, the single NB is attained at 5.7 GHz to reject interferences of signal from the 5G network with an attenuation level of 59 dB. The electromagnetic simulated and experimental responses of the designed structures demonstrate a well-matched behavior among them. The NB at 5.7 GHz in the UWB-BPF passband region suppresses the signal interference from the 5G network (5500 MHz), WLAN (5900 MHz), and WiMAX (5800 MHz). [ABSTRACT FROM AUTHOR]

Objectives: As a continuation of mining subsidence, the surface secondary subsidence (including sinking and uplift) of closed mines poses a potential threat to the safety of surface buildings-structures in mining areas. However, at this stage, the law analysis of the surface secondary subsidence of closed mines under different geological mining conditions is not yet comprehensive. Due to the high underground water level, thick Quaternary loose layers, and multiple coal seams in the Huainan mining area, monitoring and analyzing the surface secondary subsidence of closed mines in the Huainan mining area has important theoretical and practical values. Methods: First, in order to verify the reliability of the monitoring results, the StaMPS software is employed to simultaneously perform persistent scatterers interferometry and small baseline subset processing. Amplitude dispersion index and amplitude difference dispersion index are used to select coherent points, respectively. Then, the unwrapped phases of the coherent points are obtained by a three-dimensional phase unwrapping algorithm. Finally, the surface subsidence of the coherent points are obtained by using temporal-domain low-pass and spatial-domain high-pass filtering. Results: The results show that: (1) The surface uplifts after the closure of Xinjisan and Lizuizi-Xinzhuangzi-Xieyi mines, with a maximum uplift rate of 51.1 mm/a, and is located in Xinzhuangzi mine, while the surface of Panyi mine is still sinking due to its late closure in September 2018. (2) The surface secondary subsidence law of closed mines in Huainan mining area is sinking stage‐stable stage‐uplift stage, which is consistent with the surface subsidence law of closed mines in Xuzhou mining area, but the sinking, stable and uplift stages do not necessarily occur successively over time. (3) There may be a hydraulic connection between Lizuizi, Xinzhuangzi, and Xieyi mines. The groundwater first rises from the junction of Xinzhuangzi and Xieyi mines and then flows to the southeast and northwest sides. Conclusions: Although the law of surface subsidence in the closed mines of Huainan and Xuzhou mines is relatively consistent, there are differences in the law of surface uplift due to different hydrogeological mining conditions. Therefore, in future work, we will continue to pay attention to the law of surface secondary subsidence of closed mines under different geological mining conditions [ABSTRACT FROM AUTHOR]

Synthetic ground motions (GMs) play a fundamental role in both deterministic and probabilistic seismic engineering assessments. This paper shows that the family of filtered and modulated white noise stochastic GM models overlooks a key parameter—the high‐pass filter's corner frequency, fc$f_c$. In the simulated motions, this causes significant distortions in the long‐period range of the linear‐response spectra and in the linear‐response spectral correlations. To address this, we incorporate fc$f_c$ as an explicitly fitted parameter in a site‐based stochastic model. We optimize fc$f_c$ by individually matching the long‐period linear‐response spectrum (i.e., Sa(T)$Sa(T)$ for T≥1s$T \ge 1\,{\rm {s}}$) of synthetic GMs with that of each recorded GM. We show that by fitting fc$f_c$ the resulting stochastically simulated GMs can precisely capture the spectral amplitudes, variability (i.e., variances of log(Sa(T))$\log (Sa(T))$), and the correlation structure (i.e., correlation of log(Sa(T))$\log (Sa(T))$ between distinct periods T1$T_1$ and T2$T_2$) of recorded GMs. To quantify the impact of fc$f_c$, a sensitivity analysis is conducted through linear regression. This regression relates the logarithmic linear‐response spectrum (log(Sa(T))$\log (Sa(T))$) to 7 GM parameters, including the optimized fc$f_c$. The results indicate that the variance of fc$f_c$ observed in natural GMs, along with its correlation with the other GM parameters, accounts for 26% of the spectral variability in long periods. Neglecting either the fc$f_c$ variance or fc$f_c$ correlation typically results in an important overestimation of the linear‐response spectral correlation. [ABSTRACT FROM AUTHOR]

The presence of noise and artifacts often has a significant impact on electrocardiogram (ECG) data. Signal corruptions of this nature impact the precise interpretation of ECG signals, necessitating the removal of noise and artifacts during the preprocessing stage. The present paper provides a thorough pre-processing phase that removes motion distortions and noise before identifying and recovering entirely distorted ECG signal segments. The initial technique, referred to as the WLNH method, is created by employing Wavelet multiresolution analysis (MRA), the Lilliefors test, NLM, and a high pass filter. The second approach involves replacing the wavelet MRA decomposition with the variational mode decomposition (VMD), while keeping all other steps from the previous approach. The abbreviation VLWNH is used to represent this approach. The two proposed methods distinguish themselves from existing methods by initially employing the Lilliefors test to determine if a component exhibits white Gaussian noise characteristics, and subsequently use the High Pass Filter to remove any motion irregularities. The simulation results demonstrate the effectiveness of the proposed strategies, especially in addressing white Gaussian noise and baseline wander (BW) noise. [ABSTRACT FROM AUTHOR]

Automated diagnosis and evolving CNN architectures are improving diagnostic quality in digital breast cancer histopathology images. The study predominantly focuses on classifying the histopathological images of the BreakHis breast cancer dataset into distinct categories: benign and malignant. A primary challenge in this task is the uneven class distribution and limited training samples, which introduce bias and compromise the model's non-malignant classification accuracy. The study utilizes wavelet decomposition on benign images to address class imbalance and enhance the model's ability to accurately classify breast cancer histopathological images. This technique begins by filtering the image with high-pass and low-pass filters, followed by downsampling. The process is then repeated to generate four images representing different components of the original image, enabling precise localization of essential features and denoising. The DenseNet201 convolutional network is chosen for image classification due to its efficiency and accuracy. Our proposal involves concatenating features extracted from specific blocks of the pre-trained DenseNet201 model: pool3_pool, pool4_pool, and conv5_block32_conca. The proposed framework achieves an impressive overall accuracy in classifying both benign and malignant images, maintaining high accuracy rates of 99% in both multi-scale and magnification-independent classifications. These promising results indicate the potential clinical application of this approach in diagnosing diseases. [ABSTRACT FROM AUTHOR]

This paper presents a 6-bit phase shifter operating in the 11∼13 GHz band. The designed phase shifter is fabricated using a 130-nm SOI process to validate its performance. For fine phase adjustment, the phase shifter employs six single-bit phase shifters ranging from 5.625° to 180°. To expand the bandwidth and minimize the phase and gain errors, the 90° bit uses low-pass and high-pass filters. Additionally, the 180° bit uses a transformer-based switch structure. The fabricated phase shifter measures 0.87×0.4 mm², with a return loss of more than 10 dB, an average insertion loss of 6.41 dB, RMS phase errors ranging from 3.19° to 4.13°, and RMS gain errors between 0.52 dB and 0.56 dB. [ABSTRACT FROM AUTHOR]

In this paper, a new voltage-mode (VM) multifunction filter including only two grounded capacitors, three resistors, and two current feedback operational amplifiers is introduced. This filter with one high input impedance and two low output impedances can simultaneously provide band-pass filter (BPF) and low-pass filter (LPF) responses with an adjustable gain. A universal VM filter topology with three inputs and one output generating high-pass filter (HPF), BPF, LPF, notch filter, and all-pass filter (APF) responses is obtained from the multifunction one. It has the same number of active and passive elements as the multifunction and a low output impedance. Sensitivities of both introduced filters are low enough. However, the APF of the introduced universal filter has a passive element-matching problem. As an example, parasitic impedance effects are considered for the LPF of the universal filter, while non-ideal gain analyses are given for the HPF. To see how the introduced universal filter works, countless simulations are performed through the SPICE (Simulation Program with Integrated Circuit Emphasis) program in which 0.13 μ m IBM CMOS technology parameters are utilized. DC supply voltages are selected as ± 0.9 V. Also, several experiments using commercially available AD844s with ± 10 V supply voltages are carried out to see the performance. [ABSTRACT FROM AUTHOR]

Brain-inspired flexible neuromorphic devices are of great significance for next-generation high-efficiency wearable sensing and computing systems. In this paper, we propose a flexible organic electrochemical transistor using poly[(bithiophene)-alternate-(2,5-di(2-octyldodecyl)- 3,6-di(thienyl)-pyrrolyl pyrrolidone)] (DPPT-TT) as the organic semiconductor and poly(methyl methacrylate) (PMMA)/LiClO4 solid-state electrolyte as the gate dielectric layer. Under gate voltage modulation, an electric double layer (EDL) forms between the dielectric layer and the channel, allowing the device to operate at low voltages. Furthermore, by leveraging the double layer effect and electrochemical doping within the device, we successfully mimic various synaptic behaviors, including excitatory post-synaptic currents (EPSC), paired-pulse facilitation (PPF), high-pass filtering characteristics, transitions from short-term plasticity (STP) to long-term plasticity (LTP), and demonstrate its image recognition and storage capabilities in a 3 × 3 array. Importantly, the device's electrical performance remains stable even after bending, achieving ultra-low-power consumption of 2.08 fJ per synaptic event at −0.001 V. This research may contribute to the development of ultra-low-power neuromorphic computing, biomimetic robotics, and artificial intelligence. [ABSTRACT FROM AUTHOR]

Accurate data acquisition is crucial in embedded systems. This study aimed to evaluate the data acquisition ability of the ESP32 Analog to Digital Converter (ADC) module when combined with the I2S module to collect high-frequency data. Sine waves at various frequencies and white noise were recorded in this mode. The recorded data were analyzed by the fast Fourier transform (FFT) to assess the accuracy of the recorded data and evaluate the generated noise. Digital filters are proposed to improve the quality of the collected signals. A 2D spectrogram imaging algorithm is proposed to convert the data to time-frequency domain images. The results showed that the ADC module could effectively collect signals at frequencies up to 96 kHz; frequency errors were proportional to the sampling rate, and the maximum was 79.6 Hz, equivalent to 0.38%. The execution time of the lowpass and highpass filters was about 6.83 ms and for the bandpass filter about 5.97 ms; the spectrogram imaging time was 40 ms; while the calculation time for an FFT transform was approximately 1.14 ms, which is appropriate for real-time running. These results are significant for data collection systems based on microcontrollers and are a premise for deploying TinML networks on embedded systems. [ABSTRACT FROM AUTHOR]

When constructing multivariate framelets, it is often unavoidable to work with matrices of multivariate trigonometric polynomials and complicated matrix decomposition problems. These problems become even harder when good properties such as high-order vanishing moments are required on the framelets. In this paper, we establish a new method for constructing multivariate dual framelets with high-order vanishing moments. The underlying scheme of our algorithm is the famous Mixed Extension Principle that allows us to derive the high-pass filters (or framelet generators) from a given pair of refinable filters with high-order linear-phase moments. Our method only involves two steps: (1) directly constructing the first few pairs of high-pass filters by using the linear-phase moment conditions of the refinement filters; (2) solving a system of linear equations to obtain the rest of the high-pass filters. Both are easy to implement for scientific computation, regardless of what dimension or dilation matrix we work with. Apart from high-order vanishing moments, we will see that if the refinement filters take coefficients from some subfield of ℂ that is closed under complex conjugation, so do the high-pass filters. Furthermore, our algorithm gives the upper bounds for the number of high-pass filters in arbitrary dimensions. At the end of the paper, we will give several illustrative examples, from which we can also see that the support sizes of the high-pass filters are comparable with those of the refinement filters. [ABSTRACT FROM AUTHOR]

This paper presents the design and simulation of radio frequency (RF) filters to mitigate colocation interference between high-frequency (HF) and very high-frequency (VHF) tactical radios used in military ground vehicles with minimal insertion loss. The interference occurs due to the proximity of the HF and VHF radio antennas, with HF signals distorting the VHF radio signals and vice versa. In order to minimise the interference, low- and high-pass filters are designed and simulated to achieve steep roll-off, low passband ripple and complete rejection in the stop-band region. The Advanced Design System (ADS) simulator is used in the filter design, which starts with preliminary studies to choose the best method for designing the RF filter based on military communication system specifications. This includes selecting the insertion loss method, filter topology and passband ripple. Based on this, the RF filter implements a Chebyshev type 1 design with passband ripple of 0.15 dB and passive lumped elements. Therefore, 19th-order Chebyshev low- and high-pass filters are designed and simulated using ADS. From the simulation, a low-pass filter with a cut-off frequency of 29.73 MHz at -3.00 dB was designed. This filter exhibits a sharp suppression of about -114.83 dB in the stop-band at 40 MHz and a return loss of -14.7 dB. Similarly, a high-pass filter with cut-off frequency of 29.27 MHz at -3.00 dB was designed. This filter also shows a sharp suppression of about -114.82 dB in the stop-band at 21.76 MHz and has return loss of -14.7 dB, making it effective for eliminating co-location interference. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning (DL)-assisted high-pass-filter (HPF)-based fixed-threshold decision (FTD) for free-space optical (FSO) communication. HPF is applied to reduce the scintillation effect by filtering out the low-frequency components of the received signal. However, the performance is limited owing to the signal distortion from HPF and remnant scintillation effect due to insufficient filtering. Therefore, the DL model is adopted to improve the performance of HPF-based scintillation effect compensation. The multilayer perceptron (MLP) model is used to adaptively select the peak frequency component of the received signal as the optimized cutoff frequency of HPF. Furthermore, recurrent neural network (RNN) and long short-term memory (LSTM) models are cascaded after HPF to compensate for the remnant scintillation effect and recover the signal distortion without the optimization of HPF cutoff frequency. The simulation was conducted under different turbulence channels and data rates. Simulation results showed that MLP-assisted adaptive optimized cutoff frequency and cascaded LSTM and HPF methods were close to the adaptive-threshold decision with precise channel state information under various turbulence channel degrees. [ABSTRACT FROM AUTHOR]

Current challenges in visible and infrared image fusion include color information distortion, texture detail loss, and target edge blur. To address these issues, a fusion algorithm based on double-domain transform filter and nonlinear contrast transform feature extraction (DDCTFuse) is proposed. First, for the problem of incomplete detail extraction that exists in the traditional transform domain image decomposition, an adaptive high-pass filter is proposed to decompose images into high-frequency and low-frequency portions. Second, in order to address the issue of fuzzy fusion target caused by contrast loss during the fusion process, a novel feature extraction algorithm is devised based on a novel nonlinear transform function. Finally, the fusion results are optimized and color-corrected by our proposed spatial-domain logical filter, in order to solve the color loss and edge blur generated in the fusion process. To validate the benefits of the proposed algorithm, nine classical algorithms are compared on the LLVIP, MSRS, INO, and Roadscene datasets. The results of these experiments indicate that the proposed fusion algorithm exhibits distinct targets, provides comprehensive scene information, and offers significant image contrast. [ABSTRACT FROM AUTHOR]

This paper describes a new method for automatic preparation of a noisy seismic record for further analysis using expert information systems. The method is based on seismogram fingerprints, which, due to their concise but informative pattern, allow the development of a reliable algorithm for finding important noise characteristics. The search for the optimal cutoff frequency for a high-pass filter is especially important under conditions of partial overlap of the signal and noise spectra, when the latter have high intensity. It is precisely this difficult case that this study aims to address. The article analyzes the methodology developed here in application to several hundred records of regional earthquakes and explosions. The analysis showed that reliable results can be achieved in more than 90% of the cases. In addition, the defects and limitations of the method are mentioned, which are logical consequences of its advantages. The Appendix to this article contains a detailed description of the algorithm underlying the method. [ABSTRACT FROM AUTHOR]