Search

Crop disease detection and crop baking stage judgement require large image data to improve accuracy. However, the existing crop disease image datasets have high asymmetry, and the poor baking environment leads to image acquisition difficulties and colour distortion. Therefore, we explore the potential of the self-attention mechanism on crop image datasets and propose an innovative crop image data-enhancement method for recurrent generative adversarial networks (GANs) fused with the self-attention mechanism to significantly enhance the perception and information capture capabilities of recurrent GANs. By introducing the self-attention mechanism module, the cycle-consistent GAN (CycleGAN) is more adept at capturing the internal correlations and dependencies of image data, thus more effectively capturing the critical information among image data. Furthermore, we propose a new enhanced loss function for crop image data to optimise the model performance and meet specific task requirements. We further investigate crop image data enhancement in different contexts to validate the performance and stability of the model. The experimental results show that, the peak signal-to-noise ratio of the SM-CycleGAN for tobacco images and tea leaf disease images are improved by 2.13% and 3.55%, and the structural similarity index measure is improved by 1.16% and 2.48% compared to CycleGAN, respectively. [ABSTRACT FROM AUTHOR]

Purpose: Bayesian penalised likelihood (BPL) reconstruction, which incorporates point-spread-function (PSF) correction, provides higher signal-to-noise ratios and more accurate quantitation than conventional ordered subset expectation maximization (OSEM) reconstruction. However, applying PSF correction to brain PET imaging is controversial due to Gibbs artefacts that manifest as unpredicted cortical uptake enhancement. The present study aimed to validate whether BPL without PSF would be useful for amyloid PET imaging. Methods: Images were acquired from Hoffman 3D brain and cylindrical phantoms for phantom study and 71 patients administered with [18F]flutemetamol in clinical study using a Discovery MI. All images were reconstructed using OSEM, BPL with PSF correction, and BPL without PSF correction. Count profile, %contrast, recovery coefficients (RCs), and image noise were calculated from the images acquired from the phantoms. Amyloid β deposition in patients was visually assessed by two physicians and quantified based on the standardised uptake value ratio (SUVR). Results: The overestimated radioactivity in profile curves was eliminated using BPL without PSF correction. The %contrast and image noise decreased with increasing β values in phantom images. Image quality and RCs were better using BPL with, than without PSF correction or OSEM. An optimal β value of 600 was determined for BPL without PSF correction. Visual evaluation almost agreed perfectly (κ = 0.91–0.97), without depending on reconstruction methods. Composite SUVRs did not significantly differ between reconstruction methods. Conclusion: Gibbs artefacts disappeared from phantom images using the BPL without PSF correction. Visual and quantitative evaluation of [18F]flutemetamol imaging was independent of the reconstruction method. The BPL without PSF correction could be the standard reconstruction method for amyloid PET imaging, despite being qualitatively inferior to BPL with PSF correction for [18F]flutemetamol amyloid PET imaging. [ABSTRACT FROM AUTHOR]

Nitroxyl (HNO) plays a vital role in various biological functions and pharmacological activities, so the development of an excellent near-infrared fluorescent (NIRF) and photoacoustic (PA) dual-modality probe is crucial for understanding HNO-related physiological and pathological progression. Herein, we proposed and synthesized a novel NIRF/PA dual probe (QL-HNO) by substituting an indole with quinolinium in hemicyanine for the sensitive detection of exogenous and endogenous HNO in vivo. The designed probe showed the highest sensitivity in NIRF mode and a desirable PA signal-to-noise ratio for HNO detection in vitro and was further applied for NIRF/PA dual-modal imaging of HNO with high contrast in living cells and tumor-bearing animals. Based on the excellent performance of QL-HNO, we believe that this study provides a promising molecular tool for further understanding of HNO-related physiological and pathological progression. [ABSTRACT FROM AUTHOR]

Objective. Multi-contrast magnetic resonance imaging (MC MRI) can obtain more comprehensive anatomical information of the same scanning object but requires a longer acquisition time than single-contrast MRI. To accelerate MC MRI speed, recent studies only collect partial k-space data of one modality (target contrast) to reconstruct the remaining non-sampled measurements using a deep learning-based model with the assistance of another fully sampled modality (reference contrast). However, MC MRI reconstruction mainly performs the image domain reconstruction with conventional CNN-based structures by full supervision. It ignores the prior information from reference contrast images in other sparse domains and requires fully sampled target contrast data. In addition, because of the limited receptive field, conventional CNN-based networks are difficult to build a high-quality non-local dependency. Approach. In the paper, we propose an Image-Wavelet domain ConvNeXt-based network (IWNeXt) for self-supervised MC MRI reconstruction. Firstly, INeXt and WNeXt based on ConvNeXt reconstruct undersampled target contrast data in the image domain and refine the initial reconstructed result in the wavelet domain respectively. To generate more tissue details in the refinement stage, reference contrast wavelet sub-bands are used as additional supplementary information for wavelet domain reconstruction. Then we design a novel attention ConvNeXt block for feature extraction, which can capture the non-local information of the MC image. Finally, the cross-domain consistency loss is designed for self-supervised learning. Especially, the frequency domain consistency loss deduces the non-sampled data, while the image and wavelet domain consistency loss retain more high-frequency information in the final reconstruction. Main results. Numerous experiments are conducted on the HCP dataset and the M4Raw dataset with different sampling trajectories. Compared with DuDoRNet, our model improves by 1.651 dB in the peak signal-to-noise ratio. Significance. IWNeXt is a potential cross-domain method that can enhance the accuracy of MC MRI reconstruction and reduce reliance on fully sampled target contrast images. [ABSTRACT FROM AUTHOR]

Intraductal T2 mapping based on a catheter receiver is proposed as a method of visualizing the extent of intraductal and periductal cholangiocarcinoma (CCA). Compared to external receivers, internal receivers provide locally enhanced signal-to-noise ratios by virtue of their lower field-of-view for body noise, allowing smaller voxels and higher resolution. However, inherent radial sensitivity variation and segmentation for patient safety both distort image brightness. We discuss simulated T2 weighted images and T2 maps, and in vitro images obtained using a thin film catheter receiver of a freshly resected liver specimen containing a polypoid intraductal tumor from a patient with CCA. T2 mapping provides a simple method of compensating non-uniform signal reception patterns of catheter receivers, allowing the visualization of tumor extent without contrast enhancement and potentially quantitative tissue characterization. Potential advantages and disadvantages of in vivo intraductal imaging are considered. [ABSTRACT FROM AUTHOR]

Continuous glucose monitoring systems (CGMs) are critical toward closed-loop diabetes management. The field's progress urges next-generation CGMs with enhanced antinoise ability, reliability, and wearability. Here, we propose a coin-sized, fully integrated, and wearable CGM, achieved by holistically synergizing state-of-the-art interdisciplinary technologies of biosensors, minimally invasive tools, and hydrogels. The proposed CGM consists of three major parts: (i) an emerging biochemical signal amplifier, the organic electrochemical transistor (OECT), improving the signal-to-noise ratio (SNR) beyond traditional electrochemical sensors; (ii) a microneedle array to facilitate subcutaneous glucose sampling with minimized pain; and (iii) a soft hydrogel to stabilize the skin-device interface. Compared to conventional CGMs, the OECT-CGM offers a high antinoise ability, tunable sensitivity and resolution, and comfort wearability, enabling personalized glucose sensing for future precision diabetes health care. Last, we discuss how OECT technology can help push the limit of detection of current wearable electrochemical biosensors, especially when operating in complicated conditions. [ABSTRACT FROM AUTHOR]

Wearable long-term monitoring applications are becoming more and more popular in both the consumer and the medical market. In wearable ECG monitoring, the data quality depends on the properties of the electrodes and on how they interface with the skin. Dry electrodes do not require any action from the user. They usually do not irritate the skin, and they provide sufficiently high-quality data for ECG monitoring purposes during low-intensity user activity. We investigated prospective motion artifact–resistant dry electrode materials for wearable ECG monitoring. The tested materials were (1) porous: conductive polymer, conductive silver fabric; and (2) solid: stainless steel, silver, and platinum. ECG was acquired from test subjects in a 10-min continuous settling test and in a 48-h intermittent long-term test. In the settling test, the electrodes were stationary, whereas both stationary and controlled motion artifact tests were included in the long-term test. The signal-to-noise ratio (SNR) was used as the figure of merit to quantify the results. Skin–electrode interface impedance was measured to quantify its effect on the ECG, as well as to leverage the dry electrode ECG amplifier design. The SNR of all electrode types increased during the settling test. In the long-term test, the SNR was generally elevated further. The introduction of electrode movement reduced the SNR markedly. Solid electrodes had a higher SNR and lower skin–electrode impedance than porous electrodes. In the stationary testing, stainless steel showed the highest SNR, followed by platinum, silver, conductive polymer, and conductive fabric. In the movement testing, the order was platinum, stainless steel, silver, conductive polymer, and conductive fabric. [ABSTRACT FROM AUTHOR]

In robotics, echolocation has been used to detect acoustic reflectors, e.g., walls, as it aids the robotic platform to navigate in darkness and also helps detect transparent surfaces. However, the transfer function or response of an acoustic system, e.g., loudspeakers/emitters, contributes to non-ideal behavior within the acoustic systems that can contribute to a phase lag due to propagation delay. This non-ideal response can hinder the performance of a time-of-arrival (TOA) estimator intended for acoustic reflector localization especially when the estimation of multiple reflections is required. In this paper, we, therefore, propose a robust expectation-maximization (EM) algorithm that takes into account the response of acoustic systems to enhance the TOA estimation accuracy when estimating multiple reflections when the robot is placed in a corner of a room. A non-ideal transfer function is built with two parameters, which are estimated recursively within the estimator. To test the proposed method, a hardware proof-of-concept setup was built with two different designs. The experimental results show that the proposed method could detect an acoustic reflector up to a distance of 1.6 m with 60 % accuracy under the signal-to-noise ratio (SNR) of 0 dB. Compared to the state-of-the-art EM algorithm, our proposed method provides improved performance when estimating TOA by 10 % under a low SNR value. [ABSTRACT FROM AUTHOR]

In this study, we investigate the effectiveness of noise reduction in electron holography, based on the wavelet hidden Markov model (WHMM), which allows the reasonable separation of weak signals from noise. Electron holography observations from a Nd2Fe14B thin foil showed that the noise reduction method suppressed artificial phase discontinuities generated by phase retrieval. From the peak signal-to-noise ratio, it was seen that the impact of denoising was significant for observations with a narrow spacing of interference fringes, which is a key parameter for the spatial resolution of electron holography. These results provide essential information for improving the precision of electron holography studies. [ABSTRACT FROM AUTHOR]

In current times, reconstruction of remote sensing images using super-resolution is a prominent topic of study. Remote sensing data have a complex spatial distribution. Compared with natural pictures, remote sensing pictures often contain subtler and more complicated information. Most super-resolution reconstruction algorithms cannot restore all the information contained in remote sensing images when reconstructing them. The content of some areas in the reconstructed images may be too smooth, and some areas may even have color changes, resulting in lower quality reconstructed images. In response to the problems presenting in current reconstruction algorithms about super-resolution, this article proposes the SRGAN-MSAM-DRC model (SRGAN model with multi-scale attention mechanism and dense residual connection). This model roots in generative adversarial networks and incorporates multi-scale attention mechanisms and dense residual connections into the generator. Furthermore, residual blocks are incorporated into the discriminator. We use some remote sensing image datasets of real-world data to evaluate this model, and the results indicate the SRGAN-MSAM-DRC model has shown enhancements in three evaluation metrics for reconstructed images about super-resolution. Compared to the basic SRGAN model, the SSIM (structural similarity), PSNR (peak signal-to-noise ratio), and IE (image entropy) increase by 5.0%, 4.0%, and 4.1%, respectively. From the results, we know the quality of the reconstructed images of remote sensing using the SRGAN-MSAM-DRC model is better than basic SRGAN model, and verifies that the model has good applicability and performance in reconstruction of remote sensing images using super-resolution. [ABSTRACT FROM AUTHOR]

Monitoring atmospheric concentrations of greenhouse gases (GHGs) like carbon dioxide and methane in near real time and with good spatial resolution is crucial for enhancing our understanding of the sources and sinks of these gases. A novel approach can be proposed using a constellation of small satellites equipped with miniaturized spectrometers having a spectral resolution of a few nanometers. The objective of this study is to describe expected results that can be obtained with a single satellite named Uvsq-Sat NG. The SolAtmos end-to-end simulator and its three tools (IRIS, OptiSpectra, and GHGRetrieval) were developed to evaluate the performance of the spectrometer of the Uvsq-Sat NG mission, which focuses on measuring the main GHGs. The IRIS tool was implemented to provide Top-Of-Atmosphere (TOA) spectral radiances. Four scenes were analyzed (pine forest, deciduous forest, ocean, snow) combined with different aerosol types (continental, desert, maritime, urban). Simulated radiance spectra were calculated based on the wavelength ranges of the Uvsq-Sat NG, which spans from 1200 to 2000 nm. The OptiSpectra tool was used to determine optimal observational settings for the spectrometer, including Signal-to-Noise Ratio (SNR) and integration time. Data derived from IRIS and OptiSpectra served as input for our GHGRetrieval simulation tool, developed to provide greenhouse gas concentrations. The Levenberg–Marquardt algorithm was applied iteratively to fine-tune gas concentrations and model inputs, aligning observed transmittance functions with simulated ones under given environmental conditions. To estimate gas concentrations (CO2, CH4, O2, H2O) and their uncertainties, the Monte Carlo method was used. Based on this analysis, this study demonstrates that a miniaturized spectrometer onboard Uvsq-Sat NG is capable of observing different scenes by adjusting its integration time according to the wavelength. The expected precision for each measurement is of the order of a few ppm for carbon dioxide and less than 25 ppb for methane. [ABSTRACT FROM AUTHOR]

In this paper, a lightweight deep neural network (DNN) for direction of arrival (DOA) estimation is proposed, of which the input vector is designed to remove data redundancy as well as remaining DOA information. By exploring the Vandermonde property of the steering vector of a uniform linear array (ULA), the size of the newly designed input vector is greatly reduced. Furthermore, the DOA estimation is designed as a regression problem instead of a classification problem; that is, the lightweight DNN designs the output vector as the estimated DOAs of sources, of which the size is much shorter than that of the spatial spectrum used as the output vector in the conventional DNN. The reductions in the sizes of input and output vectors lead to a reduction in the sizes of hidden layers, achieving lightweightness of the neural network. The analysis illustrates that when the number of sensors is 22, the number of parameters in the lightweight DNN is three orders of magnitude less than that in the conventional DNN. The simulation results demonstrate the lightweight DNN can provide high DOA estimation accuracy with the shortest testing time. It performs better than the conventional DNN. Furthermore, it is superior to traditional solutions such as the multiple signal classification (MUSIC) method and conventional beamforming (CBF) method in harsh conditions like low signal-to-noise ratios (SNRs), closely spaced sources, and few snapshots. [ABSTRACT FROM AUTHOR]

Multi-rotor aircraft have the advantages of a simple structure, low cost, and flexible operation in the unmanned aerial vehicle (UAV) family, and have developed rapidly in recent years. Radar surveillance and classification of the growing number of multi-rotor aircraft has become a challenging problem due to their low-slow-small (LSS) characteristics. Estimation of the blade number is an important step in distinguishing LSS targets. However, most of the current research on micro-motion parameters estimation has focused on the analysis of rotational frequency, length, and the initial phase of blades with a prior of blade number, affecting its ability to identify LSS targets. In this article, a micro-motion parameters estimation method for multi-rotor targets without a prior is proposed. On the basis of estimating the flashing frequency of the blades, a validation function is constructed through spectral analysis to judge the number of blades, and then the rotational frequency is estimated. The blade length is calculated by estimating the maximum Doppler shift. Moreover, the variational mode decomposition (VMD)-based atomic scaling orthogonal matching pursuit (AS-OMP) method is jointly applied to estimate the blade length when suffering from the low PRF and insufficient SNR conditions. Extensive experiments on the simulated and measured data demonstrate that the proposed method outperforms robust micro-motion parameter estimation capability in low PRF and insufficient SNR conditions compared to the traditional time-frequency analysis methods. [ABSTRACT FROM AUTHOR]

To address the issue associated with poor accuracy rates for specific emitter identification (SEI) under low signal-to-noise ratio (SNR) conditions, where the single-dimension radar signal characteristics are severely affected by noise, we propose an attention-enhanced dual-branch residual network structure based on the adaptive large-margin Softmax (ALS). Initially, we designed a dual-branch network structure to extract features from one-dimensional intermediate frequency data and two-dimensional time–frequency images, respectively. By assigning different attention weights according to their importance, these features are fused into an enhanced joint feature for further training. This approach enables the model to extract distinctive features across multiple dimensions and achieve good recognition performance even when the signal is affected by noise. In addition, we have introduced L-Softmax to replace the original Softmax and propose the ALS. This approach adaptively calculates the classification margin decision parameter based on the angle between samples and the classification boundary and adjusts the margin values of the sample classification boundaries; it reduces the intra-class distance for the same class while increasing the inter-class distance between different classes without the need for cumbersome experiments to determine the optimal value of decision parameters. Our experimental findings revealed that, in comparison to alternative methods, our proposed approach markedly enhances the model's capability to extract features from signals and classify them in low-SNR environments, thereby effectively diminishing the influence of noise. Notably, it achieves the highest recognition rate across a range of low-SNR conditions, registering an average increase in recognition rate of 4.8%. [ABSTRACT FROM AUTHOR]

Fault-finding diagnostics is a model-driven approach that identifies a system's malfunctioning portion. It uses residual generators to identify faults, and various methods like isolation techniques and structural analysis are used. However, diagnostic equipment doesn't measure the remaining signal-to-noise ratio. Residual selection identifies fault-detecting generators. Fault detective diagnostic (FDD) approaches have been investigated and implemented for various industrial processes. However, industrial operations make it difficult to implement FDD techniques. To bridge the gap between theoretical methodologies and implementations, hybrid approaches and intelligent procedures are needed. Future research should focus on improving fault prognosis, allowing for accurate prediction of process failures and avoiding safety hazards. Real-time and comprehensive FDD strategies should be implemented in the age of big data. [ABSTRACT FROM AUTHOR]

The presence of green areas in urbanized cities is crucial to reduce the negative impacts of urbanization. However, these areas can influence the signal quality of IoT devices that use wireless communication, such as LoRa technology. Vegetation attenuates electromagnetic waves, interfering with the data transmission between IoT devices, resulting in the need for signal propagation modeling, which considers the effect of vegetation on its propagation. In this context, this research was conducted at the Federal University of Pará, using measurements in a wooded environment composed of the Pau-Mulato species, typical of the Amazon. Two machine learning-based propagation models, GRNN and MLPNN, were developed to consider the effect of Amazonian trees on propagation, analyzing different factors, such as the transmitter's height relative to the trunk, the beginning of foliage, and the middle of the tree canopy, as well as the LoRa spreading factor (SF) 12, and the co-polarization of the transmitter and receiver antennas. The proposed models demonstrated higher accuracy, achieving values of root mean square error (RMSE) of 3.86 dB and standard deviation (SD) of 3.8614 dB, respectively, compared to existing empirical models like CI, FI, Early ITU-R, COST235, Weissberger, and FITU-R. The significance of this study lies in its potential to boost wireless communications in wooded environments. Furthermore, this research contributes to enhancing more efficient and robust LoRa networks for applications in agriculture, environmental monitoring, and smart urban infrastructure. [ABSTRACT FROM AUTHOR]

The prevalence of Low Probability of Interception (LPI) and Low Probability of Exploitation (LPE) radars in contemporary Electronic Warfare (EW) presents an ongoing challenge to defense mechanisms, compelling constant advances in protective strategies. Noise radars are examples of LPI and LPE systems that gained substantial prominence in the past decade despite exhibiting a common drawback of limited Doppler tolerance. The Advanced Pulse Compression Noise (APCN) waveform is a stochastic radar signal proposed to amalgamate the LPI and LPE attributes of a random waveform with the Doppler tolerance feature inherent to a linear frequency modulation. In the present work, we derive closed-form expressions describing the APCN signal's ambiguity function and spectral containment that allow for a proper analysis of its detection performance and ability to remove range ambiguities as a function of its stochastic parameters. This paper also presents a more detailed address of the LPI/LPE characteristic of APCN signals claimed in previous works. We show that sophisticated Electronic Intelligence (ELINT) systems that employ Time Frequency Analysis (TFA) and image processing methods may intercept APCN and estimate important parameters of APCN waveforms, such as bandwidth, operating frequency, time duration, and pulse repetition interval. We also present a method designed to intercept and exploit the unique characteristics of the APCN waveform. Its performance is evaluated based on the probability of such an ELINT system detecting an APCN radar signal as a function of the Signal-to-Noise Ratio (SNR) in the ELINT system. We evaluated the accuracy and precision of the random variables characterizing the proposed estimators as a function of the SNR. Results indicate a probability of detection close to 1 and show good performance, even for scenarios with a SNR slightly less than − 10 dB. The contributions in this work offer enhancements to noise radar capabilities while facilitating improvements in ESM systems. [ABSTRACT FROM AUTHOR]

The recently explored synergistic combination of graphene-based materials and deep eutectic solvents (DESs) is opening novel and effective avenues for developing sensing devices with optimized features. In more detail, remarkable potential in terms of simplicity, sustainability, and cost-effectiveness of this combination have been demonstrated for sensors, resulting in the creation of hybrid devices with enhanced signal-to-noise ratios, linearities, and selectivity. Therefore, this review aims to provide a comprehensive overview of the currently available scientific literature discussing investigations and applications of sensors that integrate graphene-based materials and deep eutectic solvents, with an outlook for the most promising developments of this approach. [ABSTRACT FROM AUTHOR]

Heteroepitaxial chemical vapor deposition is the most promising option to fabricate wafer-scale monocrystalline diamonds for quantum applications. Previously, we demonstrated the feasibility to manufacture functional micrometer-sized pyramids on as-grown heteroepitaxial diamond as well as their quantum optical characteristics. Due to high background signals and microfabrication challenges, these pyramids could not compete with homoepitaxially grown structures. In this study, we overcame these problems with a nominally undoped buffer layer between the heteroepitaxial substrate and the pyramidal microstructure to reduce the signal-to-noise ratio from the substrate on the spin measurements of the nitrogen-vacancy (NV) center. Moreover, the microfabrication was improved to reach a higher angle of the pyramidal side plane, corresponding to the {111} facets. These improvements lead to pyramids on which each facet contains almost purely only one of the four possible NV orientations as shown by optically detected magnetic resonance (ODMR). ODMR shows a very high contrast of 19% without an external magnet and of 13% for a single spin resonance in the presence of a magnetic field. The contrast is more than doubled compared to our previous study. The T 2 * dephasing time of the NV centers of the samples ranges from 0.02 to 0.16 μs. The P1 center is a single substitutional nitrogen center, and the P1 densities range from 1.8 to 5 ppm. [ABSTRACT FROM AUTHOR]

NIR luminescent materials have garnered widespread attention because of their exceptional properties, with high tissue penetration, low absorption and high signal-to-noise ratio in the field of optical imaging. However, producing nanophosphors with high quantum yields of emitting infrared light with wavelengths above 1000 nm remains a significant challenge. Here, we prepared a nanoscale ZnGa2O4:xCr3+,yNi2+ phosphor with good luminescence performance in near-infrared emission, which was synthesized via a hydrothermal method and subsequent calcination process. By co-doping with Cr3+ and Ni2+, the ZnGa2O4 phosphor shows a strong broadband emission of 1100-1600 nm in the second near-infrared (NIR-II) region, owing to the energy transfer from Cr3+ to Ni2+ with an efficiency up to 90%. Meanwhile, a near-infrared phosphor-conversion LED (NIR pc-LED) device is fabricated based on the ZnGa2O4:0.8% Cr3+,0.4%Ni2+ nanophosphor, which has under 100 mA input current, an output power of 23.99 mW, and a photoelectric conversion efficiency of 7.53%, and can be effectively applied in imaging and nondestructive testing. Additionally, the intensity ratio of INi/ICr of ZnGa2O4:0.8% Cr3+,0.4%Ni2+ with its high sensitivity value of 4.21% K-1 at 453 K under 410 nm excitation, indicates its potential for thermometry application. [ABSTRACT FROM AUTHOR]

Several state-of-the-art convolutional neural networks (CNNs)-based methods are available for image denoising tasks. CNNs are typically trained using the backpropagation algorithm, which requires all operations in the network to be differentiable. Most CNN operations satisfy this requirement and can be applied to backpropagation-based training algorithms. However, some transforms, including wavelet transform, which is useful for speeding up CNN computations as well as performing multi-resolution analysis, are not strictly differentiable. This paper addresses this challenge by proposing a wavelet-like transform that is differentiable. This new design is, in fact, a new CNN architecture named semi-wavelet, specific edge convolutional neural network (SW/SE-CNN), consisting of three newly designed layers. The first layer is a Semi-Wavelet (SW)-based layer which is a differential down-sampling operator for wavelet approximation. That is, the SW layer converts the input image into four channels. Three of these channels are estimations of the vertical, horizontal, and diagonal edges of the original image; and the fourth channel is a down-sampled version of it. The second proposed layer, called Semi-Wavelet Inverse (SWI), is to restore the original image by using the four SW output channels. Additionally, a specific edge extractor (SE), as another new layer, is designed on the basis of the well-known Sobel operator to extract specific edges of the image. The reason behind proposing the SE layer is to provide more edge information for the network; and the motive for including the SW layer is to speed the network up as well as multi-resolution analysis. Then, the new SW/SE-CNN architecture is implemented for Gaussian image denoising. The experimental results show that the new SW/SE-CNN outperformed the state-of-the-art methods for Gaussian image denoising based on the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) measurements for grayscale as well as color images. [ABSTRACT FROM AUTHOR]

Quadratic Phase Coupling (QPC) serves as an essential statistical instrument for evaluating nonlinear synchronization within multivariate time series data, especially in signal processing and neuroscience fields. This study explores the precision of QPC detection using numerical estimates derived from cross-bicoherence and bivariate Granger causality within a straightforward, yet noisy, instantaneous multiplier model. It further assesses the impact of accidental statistically significant bifrequency interactions, introducing new metrics such as the ratio of bispectral quadratic phase coupling and the ratio of bivariate Granger causality quadratic phase coupling. Ratios nearing 1 signify a high degree of accuracy in detecting QPC. The coupling strength between interacting channels is identified as a key element that introduces nonlinearities, influencing the signal-to-noise ratio in the output channel. The model is tested across 59 experimental conditions of simulated recordings, with each condition evaluated against six coupling strength values, covering a wide range of carrier frequencies to examine a broad spectrum of scenarios. The findings demonstrate that the bispectral method outperforms bivariate Granger causality, particularly in identifying specific QPC under conditions of very weak couplings and in the presence of noise. The detection of specific QPC is crucial for neuroscience applications aimed at better understanding the temporal and spatial coordination between different brain regions. [ABSTRACT FROM AUTHOR]

This paper proposes a VCO-based ADC with first-order noise shaping for EEG signal recording front ends. Addressing the challenge of applying analog integrators in advanced processes due to low voltage issues, a multi-phase quantizer structure is introduced based on V-F conversion within the VCO structure, resulting in lower analog power consumption at the same output bit-width. By introducing a form of Gray code encoding, errors caused by circuit metastability are limited to within 1 bit. Considering the effects of motion artifacts and the electrode DC offset, the circuit achieves a wide input range of 500 m V p p by adjusting the feedback coefficients. A prototype ADC is fabricated using 180 nm CMOS technology, operating at a 1.8 V/1 V power supply voltage, with power consumption of 17.1 μ W, while achieving a 62.1 dB signal-to-noise and distortion ratio (SNDR) and 55.2 dB dynamic range (DR). The proposed ADC exhibits input noise of 8.64 μ V r m s within a bandwidth of 0.5 Hz–5 kHz. [ABSTRACT FROM AUTHOR]

In the dynamic visual cabinet, the occurrence of motion blur when consumers take out commodities will reduce the accuracy of commodity detection. Recently, although Transformer-based video deblurring networks have achieved results compared to Convolutional Neural Networks in some blurring scenarios, they are still challenging for the non-uniform blurring problem that occurs when consumers pick up the commodities, such as the problem of difficult alignment of blurred video frames of small commodities and the problem of underutilizing the effective information between the video frames of commodities. Therefore, an improved Transformer video deblurring network is proposed. Firstly, a multi-scale Transformer feature extraction method is utilized for non-uniform blurring. Secondly, for the problem of difficult alignment of small-item-blurred video frames, a temporal interactive attention mechanism is designed for video frame alignment. Finally, a feature recurrent fusion mechanism is introduced to supplement the effective information of commodity features. The experimental results show that the proposed method has practical significance in improving the accuracy of commodity detection. Moreover, compared with the recent Transformer deblurring algorithm Video Restoration Transformer, the Peak Signal-to-Noise Ratio of this paper's algorithm is higher than that of the Deep Video Deblurring dataset and the Fuzzy Commodity Dataset by 0.23 dB and 0.81 dB, respectively. [ABSTRACT FROM AUTHOR]

As the application of machine learning technology in predicting and optimizing material performance continues to grow, handling the electromagnetic data of magnetic materials, especially in removing unavoidable data noise and accurately extracting resonance peaks in the imaginary part of electromagnetic information, has become a significant challenge. These steps are crucial for revealing the deep electromagnetic behavior of materials and optimizing their performance. In response to this challenge, this study introduces an innovative approach—Genetic Algorithm-Optimized Variational Mode Decomposition for Signal Enhancement (GAO-VMD-SE). This method, through the Variational Mode Decomposition (VMD) technique optimized by genetic algorithms, not only effectively reduces noise in the data, thereby improving the Signal-to-Noise Ratio (SNR) and reducing the Mean Absolute Error (MAE), but also significantly enhances the hidden resonance peak information in complex permittivity and permeability data to achieve a comprehensive improvement in key performance indicators. Experimental results prove that this method surpasses traditional analysis techniques in key performance metrics such as the peak width ratio, peak overlap ratio, and the number of peaks. Especially in identifying characteristic peaks related to the Snoek limit, GAO-VMD-SE can effectively reveal the peak features hidden in complex data, thus providing important insights for evaluating the performance of materials at specific frequencies. Moreover, the effectiveness of this method in denoising not only enhances the quality and accuracy of material data analysis but also achieves a 1% to 10% enhancement in peak information extraction. This optimized data processing capability and versatility make GAO-VMD-SE not only suitable for evaluating the performance of magnetic materials but also show significant practical application value in processing spectral data and other time series signal data applications. [ABSTRACT FROM AUTHOR]

Ocean Bottom Node (OBN) acquisition is a technique for marine seismic survey that has gained increased attention in recent years. The removal of shear wave noise from the vertical component of receivers plays a crucial role in the subsequent processing and interpretation of OBN data. Previous solutions suffer from noise residue or signal impairment for complex noise and signal overlap scenarios. In this work, we present and explore a self-supervised deep learning approach to attenuate shear wave noise in OBN data. It applies a deep neural network (DNN) to perform adaptive subtraction and comprises two steps to remove the noise associated with the two horizontal components of receivers, respectively. The two horizontal components are considered as noise reference and are sequentially fed into the DNN, and the DNN predicts the actual leaked noise from the contaminated vertical components data. The self-supervised method achieves improvements in the signal-to-noise ratio (SNR) on a set of synthetic data. The implementation of our method on field data demonstrates that it effectively attenuates the shear wave noise and preserves the valid signal. [ABSTRACT FROM AUTHOR]

The ability to capture and record high-resolution images over long distances is essential for a wide range of applications, including connected and autonomous vehicles, defense and security operations, as well as agriculture and mining industries. Here, we demonstrate a self-assembled bistatic long-range imaging LiDAR system. Importantly, to achieve high signal-to-noise ratio (SNR) data, we employed a comprehensive suite of denoising methods including temporal, spatial, spectral, and polarization filtering. With the aid of these denoising technologies, our system has been validated to possess the capability of imaging under various complex usage conditions. In terms of distance performance, the test results achieved ranges of over 4000 m during daylight with clear weather, 19,200 m at night, 6700 m during daylight with haze, and 2000 m during daylight with rain. Additionally, it offers an angular resolution of 0.01 mrad. These findings demonstrate the potential to offer comprehensive construction strategies and operational methodologies to individuals seeking long-range LiDAR data. [ABSTRACT FROM AUTHOR]

In space laser communication, the wide divergence angle of beacon light leads to substantial spatial losses, compounded by background light and detector noise; this results in compromised precision in the detection of the beacon light position. To solve this problem, a high-precision detection technique and communication composite technology employing a four-quadrant detector (QD) with beacon spread-spectrum modulation are proposed. Pseudo-random sequences (PRNs) are employed to spread the beacon communication spectrum, with the spread-spectrum signal utilized to modulate the intensity of the transmitted beacon light at the transmitter end. At the receiver, QD photocurrent signals are cross-correlated with an identical PRN that is used for modulation. The strong auto-correlation properties of PRNs, which are uncorrelated with noise, enhance the output signal-to-noise ratio (SNR), enabling precise position detection and beacon communication under high-SNR conditions. Theoretical analysis is used to explore the effects of spreading gain on the sensitivity of system detection and the precision of position detection. The experimental results demonstrate that the beacon spread-spectrum modulation scheme effectively detects the position of the light spot. At a received optical power of −37 dBm and spreading sequence PRN depths of 1023, 127, and 31, the root-mean-square error (RMSE) values are 0.983 μm, 2.876 μm, and 7.275 μm, respectively. This corresponds to improvements of 14.96 dB, 10.29, dB, and 6.26 dB compared to direct detection precision (30.811 μm). Additionally, under an identical signal bandwidth, the sensitivity improves by 14.6 dB, 10.1 dB, and 6.4 dB, respectively. The proposed beacon spread-spectrum scheme mitigates the limitations of hardware reception sensitivity and position-detection precision, demonstrating its potential application in high-precision detection in long-distance interstellar laser communication. [ABSTRACT FROM AUTHOR]

Automatic speech recognition (ASR) has been widely used to realize daily human–machine interactions. Face masks have become everyday wear in our post-pandemic life, and speech through masks may have impaired the ASR. This study explored the effects of different kinds of face masks (e.g., surgical mask, KN95 mask, and cloth mask) on the Mandarin word accuracy of two ASR systems with or without noises. A mouth simulator was used to play speech audio with or without wearing a mask. Acoustic signals were recorded at distances of 0.2 m and 0.6 m. Recordings were mixed with two noises at a signal-to-noise ratio of +3 dB: restaurant noise and speech-shaped noise. Results showed that masks did not affect ASR accuracy without noise. Under noises, masks did not significantly influence ASR accuracy at 0.2 m but had significant effects at 0.6 m. The activated-carbon mask had the most significant impact on ASR accuracy at 0.6 m, reducing the accuracy by 18.5 percentage points compared to that without a mask, whereas the cloth mask had the least effect on ASR accuracy at 0.6 m, reducing the accuracy by 0.9 percentage points. The acoustic attenuation of masks on the high-frequency band at around 3.15 kHz of the speech signal attributed to the effects of masks on ASR accuracy. When training ASR models, it may be important to consider mask robustness. [ABSTRACT FROM AUTHOR]

Industrial computed tomography (CT) images reconstructed directly from projection data using the filtered back projection (FBP) method exhibit strong metal artifacts due to factors such as beam hardening, scatter, statistical noise, and deficiencies in the reconstruction algorithms. Traditional correction approaches, confined to either the projection domain or the image domain, fail to fully utilize the rich information embedded in the data. To leverage information from both domains, we propose a joint deep learning framework that integrates UNet and ResNet architectures for the correction of metal artifacts in CT images. Initially, the UNet network is employed to correct the imperfect projection data (sinograms), the output of which serves as the input for the CT image reconstruction unit. Subsequently, the reconstructed CT images are fed into the ResNet, with both networks undergoing a joint training process to optimize image quality. We take the projection data obtained by analytical simulation as the data set. The resulting optimized industrial CT images show a significant reduction in metal artifacts, with the average Peak Signal-to-Noise Ratio (PSNR) reaching 36.13 and the average Structural Similarity Index (SSIM) achieving 0.953. By conducting simultaneous correction in both the projection and image domains, our method effectively harnesses the complementary information from both, exhibiting a marked improvement in correction results over the deep learning-based single-domain corrections. The generalization capability of our proposed method is further verified in ablation experiments and multi-material phantom CT artifact correction. [ABSTRACT FROM AUTHOR]

Numerous single-image dehazing algorithms have been developed, employing a spectrum of techniques ranging from intricate physical computations to state-of-the-art deep-learning methodologies. However, conventional deep-learning approaches, particularly those based on standard convolutional neural networks (CNNs), often result in the persistence of residual fog patches when applied to images featuring high fog concentration or heterogeneous fog distribution. In response to this challenge, we propose an innovative solution known as the multi-feature fusion image dehazing network (MFID-Net). This approach employs an end-to-end methodology to directly capture the mapping relationship between hazy and fog-free images. Central to our approach is the introduction of a novel multi-feature fusion (MF) module, strategically designed to address channel and pixel characteristics in regions with uneven or high fog concentrations. Notably, this module achieves effective haze reduction while minimizing computational resources, thereby mitigating the issue of residual fog patches. Experimental results underscore the superior performance of our algorithm compared to similar dehazing methods, as evidenced by higher scores in structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and computational velocity. Moreover, MFID-Net exhibits significant advancements in restoring details within expansive monochromatic areas, such as skies and white walls. [ABSTRACT FROM AUTHOR]

Underwater spectral detection plays an important role in the study of the underwater environment, ecology, oceanography, and environmental monitoring. A kind of underwater spectral radiometer that can observe the distribution of underwater spectral radiation bidirectionally has been developed. The flat-field concave holographic grating is used as the only optical component, the optical design parameters are optimized, and the system is miniaturized. The mechanism of stray light generation in the spectrometer is studied, and a method of suppressing stray light is proposed and simulated. The level of stray light in the system is suppressed to the order of 10−4, the signal-to-noise ratio of the system is improved, and the ability to detect weak light is enhanced. The wavelength calibration experiment was completed, and the experimental results show that the wavelength resolution of the underwater spectral radiometer is better than 3 nm. Finally, the quantitative relationship between spectral irradiance and digital output is obtained through radiation in the calibration of the system. [ABSTRACT FROM AUTHOR]

The Atmospheric LAser Doppler INstrument (ALADIN) onboard Aeolus, was the first high-spectral-resolution lidar (HSRL) in space. It was launched in 2018 and re-entered in 2023. The feature mask (A-FM) and extinction profile algorithms (A-PRO) developed for the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) HSRL Atmospheric Lider (ATLID) have been adapted to Aeolus, called AEL-FM and AEL-PRO, respectively. These algorithms have been purpose built to process low signal-to-noise ratio space-based lidar signals. A short description of the AEL-FM and AEL-PRO algorithms is provided in this paper. AEL-FM and AEL-PRO prototype products (v1.7) have been evaluated using collocated Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Vertical Feature Mask (VFM) product and Level 2 aerosol profile product for two months of data in October 2018 and May 2019. Aeolus and CALIPSO are both polar orbiting satellites but they have different overpass time. The evaluations are focused on desert dust aerosols over Africa. These types of scenes are often stable in space (tens of km) and time (on the order of 0.5–1 hr), and thus, a useful number of col-located cases can be collected. We have found that AEL-FM feature mask and the CALIPSO VFM show similar aerosol patterns in the collocated orbits but AEL-FM does not separate aerosol and cloud features. Aeolus and CALIPSO have good agreement for the extinction coefficients for the dust aerosols, especially for the cloud-free scenes. The Aeolus aerosol optical thickness (AOT) is larger than CALIPSO AOT mainly due to cloud contamination. Because of missing a cross polar channel, it is difficult to distinguish aerosols and thin ice clouds by using the Aeolus extinction coefficients alone. The AEL-FM and AEL-PRO algorithms have been implemented in the Aeolus level 2A (L2A) processor. The findings here are applicable to the AEL-FM and AEL-PRO products in the L2A Baseline 17. This is the first time the AEL-FM and AEL-PRO products have been evaluated using CALIPSO data. [ABSTRACT FROM AUTHOR]

Neuroimaging biomarkers have shown high potential to map the disease processes in the application to neurodegenerative diseases (NDD), e.g., diffusion tensor imaging (DTI). For DTI, the implementation of a standardized scanning and analysis cascade in clinical trials has potential to be further optimized. Over the last few years, various approaches to improve DTI applications to NDD have been developed. The core issue of this review was to address considerations and limitations of DTI in NDD: we discuss suggestions for improvements of DTI applications to NDD. Based on this technical approach, a set of recommendations was proposed for a standardized DTI scan protocol and an analysis cascade of DTI data pre-and postprocessing and statistical analysis. In summary, considering advantages and limitations of the DTI in NDD we suggest improvements for a standardized framework for a DTI-based protocol to be applied to future imaging studies in NDD, towards the goal to proceed to establish DTI as a biomarker in clinical trials in neurodegeneration. [ABSTRACT FROM AUTHOR]

Fast screening strategies that enable high‐throughput evaluation and identification of desired variants from diversified enzyme libraries are crucial to tailoring biocatalysts for the synthesis of D‐allulose, which is currently limited by the poor catalytic performance of ketose 3‐epimerases (KEases). Here, the study designs a minimally equipment‐dependent, high‐throughput, and growth‐coupled in vivo screening platform founded on a redesigned D‐allulose‐dependent biosensor system. The genetic elements modulating regulator PsiR expression levels undergo systematic optimization to improve the growth‐responsive dynamic range of the biosensor, which presents ≈30‐fold facilitated growth optical density with a high signal‐to‐noise ratio (1.52 to 0.05) toward D‐allulose concentrations from 0 to 100 mm. Structural analysis and evolutionary conservation analysis of Agrobacterium sp. SUL3 D‐allulose 3‐epimerase (ADAE) reveal a highly conserved catalytic active site and variable hydrophobic pocket, which together regulate substrate recognition. Structure‐guided rational design and directed evolution are implemented using the growth‐coupled in vivo screening platform to reprogram ADAE, in which a mutant M42 (P38N/V102A/Y201L/S207N/I251R) is identified with a 6.28‐fold enhancement of catalytic activity and significantly improved thermostability with a 2.5‐fold increase of the half‐life at 60 °C. The research demonstrates that biosensor‐assisted growth‐coupled evolutionary pressure combined with structure‐guided rational design provides a universal route for engineering KEases. [ABSTRACT FROM AUTHOR]

Purpose: A 2D image navigator (iNAV) based 3D whole-heart sequence has been used to perform MRI and PET non-rigid respiratory motion correction for hybrid PET/MRI. However, only the PET data acquired during the acquisition of the 3D whole-heart MRI is corrected for respiratory motion. This study introduces and evaluates an MRI-based respiratory motion correction method of the complete PET data. Methods: Twelve oncology patients scheduled for an additional cardiac 18F-Fluorodeoxyglucose (18F-FDG) PET/MRI and 15 patients with coronary artery disease (CAD) scheduled for cardiac 18F-Choline (18F-FCH) PET/MRI were included. A 2D iNAV recorded the respiratory motion of the myocardium during the 3D whole-heart coronary MR angiography (CMRA) acquisition (~ 10 min). A respiratory belt was used to record the respiratory motion throughout the entire PET/MRI examination (~ 30–90 min). The simultaneously acquired iNAV and respiratory belt signal were used to divide the acquired PET data into 4 bins. The binning was then extended for the complete respiratory belt signal. Data acquired at each bin was reconstructed and combined using iNAV-based motion fields to create a respiratory motion-corrected PET image. Motion-corrected (MC) and non-motion-corrected (NMC) datasets were compared. Gating was also performed to correct cardiac motion. The SUVmax and TBRmax values were calculated for the myocardial wall or a vulnerable coronary plaque for the 18F-FDG and 18F-FCH datasets, respectively. Results: A pair-wise comparison showed that the SUVmax and TBRmax values of the motion corrected (MC) datasets were significantly higher than those for the non-motion-corrected (NMC) datasets (8.2 ± 1.0 vs 7.5 ± 1.0, p < 0.01 and 1.9 ± 0.2 vs 1.2 ± 0.2, p < 0.01, respectively). In addition, the SUVmax and TBRmax of the motion corrected and gated (MC_G) reconstructions were also higher than that of the non-motion-corrected but gated (NMC_G) datasets, although for the TBRmax this difference was not statistically significant (9.6 ± 1.3 vs 9.1 ± 1.2, p = 0.02 and 2.6 ± 0.3 vs 2.4 ± 0.3, p = 0.16, respectively). The respiratory motion-correction did not lead to a change in the signal to noise ratio. Conclusion: The proposed respiratory motion correction method for hybrid PET/MRI improved the image quality of cardiovascular PET scans by increased SUVmax and TBRmax values while maintaining the signal-to-noise ratio. Trial registration METC162043 registered 01/03/2017. [ABSTRACT FROM AUTHOR]

Knowledge of chemical composition and mixing state of aerosols at a single particle level is critical for gaining insights into atmospheric processes. One common tool to make these measurements is single particle mass spectrometry. There remains a need to compare the performance of different single particle mass spectrometers (SPMSs). An intercomparison of SPMSs was conducted at the Aerosol Interaction and Dynamics in the Atmosphere (AIDA) chamber at the Karlsruhe Institute of Technology (KIT) in November 2014, as the first phase of the Fifth International Workshop on Ice Nucleation (FIN-01). In this paper we compare size distributions and mass spectra of atmospherically relevant particle types measured by five SPMSs. These include different minerals, desert and soil dusts, soot, bioaerosol (Snomax; protein granule), secondary organic aerosol (SOA) and SOA coated mineral particles. All SPMSs reported similar vacuum aerodynamic diameter (dva) within typical instrumental ranges from ~100‒200 nm (lower limit) to ~2‒3 μm (upper limit). In general, all SPMSs exhibited a wide dynamic range (up to ~103) and high signal to noise ratio (up to ~104) in mass spectra. Common spectral features with small diversities in mass spectra were found with high average Pearson's correlation coefficients, i.e., for average positive spectra ravg-pos = 0.74 ± 0.12 and average negative spectra ravg-neg = 0.67 ± 0.22. The highest correlation of average mass spectra across all instruments was observed for Snomax (ravg-pos = 0.92 ± 0.04 and ravg-neg = 0.90 ± 0.05), attributed to the prevalence of common markers and similar spectral patterns. The poorest correlation was found for propane soot (ravg-pos = 0.51 ± 0.23 and ravg-neg = 0.35 ± 0.26), primarily because of low detection efficiency (DE) due to small particle size. We found that instrument-specific DE was more dependent on particle size than particle type. We also found that particle identification favored the use of bipolar, rather than monopolar, instruments. Particle classification from "blind experiments" showed that all instruments differentiated SOA, soot, and soil dust, and detected subtle changes in the particle internal mixing, but had difficulties differentiating among specific mineral types and dusts. This study helps to further understand the capabilities and limitations of the single particle mass spectrometry technique in general, as well as the specific instrument performance in characterizing atmospheric aerosol particles. We propose that intercomparison workshops should continue as new SPMSs are developed and, ideally, should include both laboratory and field activities. [ABSTRACT FROM AUTHOR]

Objectives To evaluate the clinical value of using a split-bolus contrast injection protocol in improving image quality consistency and diagnostic accuracy in lower extremity CT angiography (CTA). Methods Fifty (mean age, 66 ± 12 years) and 39 (mean age, 66 ± 11 years) patients underwent CTA in the lower extremity arteries using split-bolus and fixed-bolus injection schemes, respectively. The objective and subjective image quality of the 2 groups were compared and the diagnostic efficacy for the degree of vessel stenosis was compared using digital subtraction angiography as the gold standard. A P  <  .05 was considered statistically significant. Results In comparison with the fixed-bolus scheme, the split-bolus scheme greatly improved the consistency of image quality of the low extremities by significantly increasing the arterial enhancement (337.87 ± 64.67HU vs. 254.74 ± 71.58HU, P  <  .001), signal-to-noise ratio (22.58 ± 11.64 vs. 7.14 ± 1.98, P  <  .001), and contrast-to-noise ratio (37.21 ± 10.46 vs. 31.10 ± 15.40, P  =  .041) in the infrapopliteal segment. The subjective image quality was better (P  <  .001) and the diagnostic accuracy was higher in the split-bolus group than in the fixed-bolus group (96.00% vs. 91.67%, P  <  .05, for diagnosing >50% stenosis, and 97.00% vs. 89.10%, P  <  .05, for diagnosing occlusion) for the infrapopliteal segment arteries. Conclusions Compared with the fixed-bolus injection scheme, the split-bolus injection scheme improves the image quality consistency and diagnostic accuracy especially for the infrapopliteal segment arteries in lower extremity CTA. Advances in knowledge (1) The split-bolus injection scheme of CTA of the lower extremity arteries improves the overall image quality, uniformity of contrast enhancement. (2) Compared with the fixed-bolus injection scheme, the split-bolus injection scheme especially improves the infrapopliteal segment arteries image quality and diagnostic efficacy. [ABSTRACT FROM AUTHOR]

With the rapid development of computer network technology, it is often necessary to collect weak signals to collect favorable information. The development of signal detection technology is ongoing; however, various issues arise during the detection process. These issues include low efficiency and a high signal noise threshold. However, many problems will be encountered in the process of detection. In order to solve these problems, the nonlinear chaos theory is introduced to detect signals, and the simulation experiments of weak pulse signals and weak partial discharge signals are carried out respectively. The experimental results showed that the detection effect was remarkable in the quasi periodic state, and it had a good detection effect for weak pulse signals. At a signal-to-noise ratio of - 25 dB, double coupling system, two-way ring coupling system, and single ring coupling system displayed detection success rates exceeding 98%. Meanwhile, the detection success rate of the strong coupling system was only 12%. Even at a noise signal ratio as low as - 40 dB, the dual coupling system still maintained a detection success rate above 80%. The simulation results of partial discharge signal detection showed that there was a high fluctuation only at 2 ms, and the rest was basically stable at about 0 V, indicating that the system had a strong suppression effect on Gaussian white noise. When comparing the simulation results of the detection of the new chaotic system and the double coupling system, it was found that the new chaotic system has a superior impact in detecting weakly attenuated partial discharge signals. Through analysis of the system's dynamic behavior, the research confirms its rich dynamic characteristics and sheds light on the reasons for phase state mutation and missed detection. The noise system is utilized for comparing the performance of various systems, with the goal of enhancing the system's detection capability. [ABSTRACT FROM AUTHOR]

The characteristic frequency of a rub-impact fault is usually very complex and may contain higher harmonics and subharmonics. Due to the uncertainty of harmonic components and the complexity of signal-to-noise ratio (SNR) operation, the general scale transformation stochastic resonance (GSTSR) has certain limitations in the identification of rub-impact faults. To solve this problem, the paper starts with complexity and proposes a rub-impact fault identification method combining a swarm intelligence optimized algorithm (SIOA) with Hjorth parameters and GSTSR. The complexity of vibration signals will change greatly before and after rub-impact faults. The complexity parameter in Hjorth parameters can effectively embody the complexity of signals and is invulnerable to noise interference. Therefore, the complexity parameter in the Hjorth parameters is taken as the objective function of SIOA and combined with GSTSR. Vibration signals from cases are taken as input to adaptive stochastic resonant (ASR) systems, and the system parameters are adaptively and synchronously adjusted to realize the maximal resonant effect. Finally, the spectrum analysis of signals obtained from ASR is used to extract failure features and recognize faults in the rotor–stator rub-impact. The proposed method is verified by comparing it with other schemes under different SIOAs and different operating conditions. The result of the comparison shows that the complexity parameter of the Hjorth parameters can be taken as the objective function of SIOA to accurately identify the rub-impact fault. Meanwhile, the proposed method, compared with the method of taking SNR as an objective function, has a better effect on reducing time costs and strengthening fault characteristics. [ABSTRACT FROM AUTHOR]

In the pursuit of precise diagnostics for measuring negative ion density in a helicon plasma source (HPS), a new approach utilizing a radio frequency (RF) broadband transformer-based Langmuir probe is developed specifically for laser photo-detachment (LPD) analysis. This inductively coupled LPD technique is useful for high power RF systems in which capacitive RF noise is in the same scale as the pulsed photo-detachment signal. The signal acquired by this transformer-based probe is compared against the conventional Langmuir probe-based LPD technique, revealing a remarkable enhancement in signal fidelity through an improved signal-to-noise ratio (SNR) achieved by the RF broadband transformer methodology. In addition, the localized hydrogen negative ion density measurements obtained through this probe are harmoniously aligned with the line-averaged negative ion density derived from the cavity ringdown spectroscopy (CRDS) technique. These concurrence measurements highlight the RF broadband transformer-based approach's accuracy in capturing localized negative ion density during helicon mode operation in an HPS setup. Furthermore, the correlation of negative ion density values with RF input exhibits a consistent trend in tandem with background plasma density. Notably, both CRDS and LPD measurements ascertain negative ion densities ranging from ∼5 to 6×1016 m−3 under an RF power of 500–700 W and a pressure of 8 × 10−3 mbar, all under the influence of a 55 G axial magnetic field. These specific parameters represent the optimal operational configuration for effective negative ion production with the present experimental HPS setup. Due to its better SNR, the RF broadband transformer-based Langmuir probe emerges as a useful tool for LPD diagnostics, particularly in the presence of pervasive RF noise. [ABSTRACT FROM AUTHOR]

In the inversion part of the full-waveform inversion (FWI) that brings high resolution in finding a convergence point in the model space, a localized numerical optimization technique reduces the objective function employing the ℓ 2 norm in a least-squares approach. Given the ℓ 2 norm's vulnerability to outliers and noise, this strategy might frequently yield imprecise imaging outcomes. Consequently, it's essential to introduce a novel regulatory approach that incorporates a more applicable form of relaxation to address the issue of overfitting, specifically, the K-support norm, characterized by its more logical and stringent restrictions. Unlike the least-squares method that targets the ℓ 2 norm reduction, the ℓ 1 norm is celebrated for its ability to ensure sparsity, durability, and superior noise reduction capabilities; thus, we consider a new regularization form, the K-support norm, which combines the ℓ 2 and the ℓ 1 norms in minimization. Subsequently, a quadratic penalty approach is utilized to expedite the convergence process and prevent entrapment in local minima, linearizing the nonlinear issue to reduce computational demands. This study presents the K-support norm concept, incorporating it with the quadratic penalty approach to enhance both convergence speed and resilience to ambient noise. In the numerical demonstration, three synthetic models are evaluated to demonstrate the K-support norm's superiority over Tikhonov regularization using two distinct noise data sets. Experimental results indicate that the modified FWI improves inversion accuracy by enhancing lateral resolution in deeper parts even with data with a low signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

To monitor damage developments in structures, various structural health monitoring methods based on different principles are used. The common aspect of elastic wavebased methods is to place appropriate sensors on the structure, to detect acoustic wave propagation and to analyze these signals the sensors transformed. The arrival time of these recorded signals to the sensors is the most significant parameter used to determine critical information such as the time and location of the damage. Therefore, the accurate calculation of the arrival time affects the accuracy of the damage detection. In this study, effects of the signal-to-noise ratio (SNR), sampling frequency, length of the signal, and length of the focal window on determining the arrival time of the signals to the sensors were investigated. For this purpose, an energy-traced arrival time picking approach (Akaike Information Criterion, AIC), which is the frequently used method in the literature, has been applied to a typical acoustic signal originated from a concrete cracking. The results of the study suggest the necessity of noise elimination, the optimum level of data logging and the ratios of focal window lengths for accurate time of arrival detection in the field monitoring of the structures using acoustic methods. [ABSTRACT FROM AUTHOR]

Femtosecond pulsed lasers are widely used in applied technologies, such as laser processing, nonlinear optical microscopy, and time-of-flight measurement, wherein narrowing the pulse duration is particularly important for improving the processing efficiency and signal-to-noise ratio of measurement systems. This paper proposes a method for shortening temporal pulse waveforms using spectral intensity filtering. Symmetrical spectral filtering was specifically applied at the central wavelength of the femtosecond pulsed laser spectrum. To investigate the pulse-narrowing effect, we numerically simulated the pulse duration as a function of the filtering width and cut-off wavelength position. The results of these simulations showed that the pulse duration decreased as the filter wavelength approached the central wavelength of the light. Furthermore, increasing the filter width reduced the pulse duration. Additionally, we implemented a spatial light modulator-based pulse-shaping system to realize a spectral intensity filter. We experimentally demonstrated that the duration of a telecom-band pulse was reduced by 9.8% when using a spectral intensity filter with a width of 1 nm. [ABSTRACT FROM AUTHOR]

Summary Segmentation of neural somata is a crucial and usually the most time-consuming step in the analysis of optical functional imaging of neuronal microcircuits. In recent years, multiple auto-segmentation tools have been developed to improve the speed and consistency of the segmentation process, mostly, using deep learning approaches. Current segmentation tools, while advanced, still encounter challenges in producing accurate segmentation results, especially in datasets with a low signal-to-noise ratio. This has led to a reliance on manual segmentation techniques. However, manual methods, while customized to specific laboratory protocols, can introduce variability due to individual differences in interpretation, potentially affecting dataset consistency across studies. In response to this challenge, we present ViNe-Seg: a deep-learning-based semi-automatic segmentation tool that offers (i) detection of visible neurons, irrespective of their activity status; (ii) the ability to perform segmentation during an ongoing experiment; (iii) a user-friendly graphical interface that facilitates expert supervision, ensuring precise identification of Regions of Interest; (iv) an array of segmentation models with the option of training custom models and sharing them with the community; and (v) seamless integration of subsequent analysis steps. Availability and implementation ViNe-Seg code and documentation are publicly available at https://github.com/NiRuff/ViNe-Seg and can be installed from https://pypi.org/project/ViNeSeg/. [ABSTRACT FROM AUTHOR]

The horizontal records of ocean‐bottom seismometers (OBS) are usually highly noisy, generally due to ocean‐bottom currents tilting the instrument, which greatly limits their practical usage in ocean‐bottom seismology. In shallow water, water waves with energy concentration around 0.07 Hz induce additional noise on OBSs. Such noise is not well understood. In this article, we propose a noise model to explain the horizontal noise around 0.07 Hz. The noise model consists of three types of noise, that is, water‐wave‐induced noise, current noise with a relatively constant orientation, and background random noise. The wave‐induced horizontal acceleration is theoretically shown to be proportional to the time derivative of ocean‐bottom pressure. We validate the noise model and related theories using realistic observations. Results are potentially applicable to determine the propagation direction of water waves nearshore, and also provide constraints on the underlying Earth structure. The results can also be applied to the removal of wave‐induced noise, achieving a typical maximum improvement in the signal‐to‐noise ratio of 10–20 dB for time periods with strong wave noise. Plain Language Summary: Ocean Bottom Seismometers (OBSs) are important instruments for seafloor seismology. However, OBS recordings are highly noisy, and the noise mechanism is not well understood. Currently, most research focuses on the noise in the vertical channel, and understanding of the horizontal noise has been limited. In this study we investigate one specific type of OBS noise, which is commonly observed in shallow water is associated with water waves. We propose a noise model to explain the noise, and also develop analytical theories for the noise generation. The noise model and related theories are verified by realistic recordings. Based on the theories, we propose potential applications, for example, derivation of the water‐wave propagation direction and subsurface structure inversion using the noise. A method of noise removal is also developed, which highly improves the signal‐to‐noise ratio. Key Points: We propose a noise model to explain the horizontal OBS noise around 0.07 Hz in shallow waterThe noise model and related theories are validated by realistic observationsPotential applications to determine water‐wave direction, Earth structure inversion and noise removal are discussed [ABSTRACT FROM AUTHOR]

Traditional fault detection methods in acoustic signal feature extraction of rolling bearings often make the signal denoising process complex due to low signal-to-noise ratio and weak fault features, making this method difficult to meet real-time requirements. Therefore, a fault detection model based on Fast-Renoriented SIFT feature extraction is proposed, which can quickly extract a large number of features from the original signal without the need for noise reduction processing and can effectively improve the efficiency and accuracy of fault detection. At the same time, to adapt to the fault detection of rolling bearings under multiple working conditions, this study also proposes an adaptive extended word bag model that combines local kurtosis and local 2-dimensional information entropy features, improving the adaptability and flexibility of the new model. It obtained a 100% overall recognition rate and a fault detection time of no more than 0.5 seconds in a 5-fold cross-validation experiment, verifying the excellent recognition accuracy, stability, and operational efficiency of the detection model. Its recognition accuracy in the multi-working condition rolling bearing fault detection experiment was above 97%, which was improved by about 21.25% compared to the traditional word bag model and had significant advantages in fault recognition accuracy and computational efficiency. [ABSTRACT FROM AUTHOR]

Existing methods for recognizing partial discharge patterns in power cables do not utilize fuzzy clustering of the discharge signals, resulting in poor quality and low recall and precision of the pattern recognition. To address this, we propose a new approach for partial discharge pattern recognition in cables using Gustafson-Kessel(GK) Fuzzy Clustering. The method involves acquiring signals from a power cable partial discharge monitoring system and then processing the signals with GK fuzzy clustering. The clustered discharge signals are filtered with wavelet packet transforms before input into an improved adaptive resonance theory(ART) neural network for final pattern recognition. Experiments demonstrate the new technique achieves up to 98.7% recall and 85.6% precision for discharge pattern recognition, with discharge signal Signal Noise Ratio(SNR) between 55 dB and 62 dB and maximum recognition accuracy reaching 98%. The proposed fuzzy clustering-based pattern recognition approach significantly enhances partial discharge diagnostics for power cable monitoring. [ABSTRACT FROM AUTHOR]

Epilepsy is considered a most general neurological disorder related to brain activity disruption. In epileptic seizures detection and classification, EEG (Electroencephalogram) measurements that record the brain's electrical activities are used frequently. Generally, physicians investigate the abnormalities in the brain. However, this technique is time-consuming, faced complexity in seizure detection, and poor consistency because of data imbalance. To overcome these difficulties, Improved Empirical Mode Decomposition for feature extraction and Improved Weight Updated KNN (K-Nearest Neighbor) algorithm for classification are proposed. In the case of pre-processing, a rule-based filter, namely a wiener scalar filter with integer wavelet transform is used for multiple channels conversion and further signal to noise ratio is increased. Further in feature extraction, better features are extracted using an improved empirical mode decomposition-based bandpass filter. By using the Improved Weight updated KNN, feature extracted samples are classified incorrect manner, avoiding data imbalance issues. Feature vectors' effective classification is performed attains higher computational speed and sensitivity. The EEG input signal of the proposed study utilizing the BONN dataset and different performance metrics such as accuracy, sensitivity, specificity, recall, f-score, and error values were performed and compared with various existing studies. From the results, it is clear that the proposed method provides effective detection for seizure and non-seizure patients compared with existing studies. [ABSTRACT FROM AUTHOR]

Road extraction is a typical task in the semantic segmentation of remote sensing images, and one of the most efficient techniques for solving this task in recent years is the vision transformer technique. However, roads typically exhibit features such as uneven scales and low signal-to-noise ratios, which can be understood as the asymmetry between the road and the background category and the asymmetry in the transverse and longitudinal shape of the road. Existing vision transformer models, due to their fixed sliding window mechanism, cannot adapt to the uneven scale issue of roads. Additionally, self-attention, based on fully connected mechanisms for long sequences, may suffer from attention deviation due to excessive noise, making it unsuitable for low signal-to-noise ratio scenarios in road segmentation, resulting in incomplete and fragmented road segmentation results. In this paper, we propose a road extraction based on deformable self-attention computation, termed DOCswin-Trans (Deformable and Overlapped Cross-Window Transformer), to solve these problems. On the one hand, we develop a DOC-Transformer block to address the scale imbalance issue, which can utilize the overlapped window strategy to preserve the overall contextual semantic information of roads as much as possible. On the other hand, we propose a deformable window strategy to adaptively resample input vectors, which can direct attention automatically to the foreground areas relevant to roads and thereby address the low signal-to-noise ratio problem. We evaluate the proposed method on two popular road extraction datasets (i.e., DeepGlobe and Massachusetts datasets). The experimental results demonstrate that the proposed method outperforms baseline methods. On the DeepGlobe dataset, the proposed method achieves an IoU improvement ranging from 0.63% to 5.01% compared to baseline methods. On the Massachusetts dataset, our method achieves an IoU improvement ranging from 0.50% to 6.24% compared to baseline methods. [ABSTRACT FROM AUTHOR]