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We have observed that Variational Mode Decomposition (VMD) exhibits instability in the denoising and separation of noisy multitone signals. Specifically, minor changes in factors such as signal-to-noise ratio, frequency spacing, sampling rate, and probability distribution can significantly impact the decomposition results. To address this issue, we have developed the Dual-VMD-correlation algorithm. This algorithm effectively mitigates the impact of beat effects when harmonics of closely spaced frequencies are superimposed, enabling stable denoising of noisy multitone signals and the separation of each individual tone signal. The algorithm holds promise for applications in frequency-modulated continuous wave laser detection. It can address challenges related to denoising laser echo signals interfered with by aerosols and the difficulty in separating backscatter interference from the target reflected signal spectrum. [ABSTRACT FROM AUTHOR]

Within aerosol-rich environments, efficient simulation of frequency-modulated continuous wave (FMCW) laser detector echo characteristics is crucial. Conventional methods often need more efficiency. To address this, we propose a photon matrix-based approach for simulating intricate photon scattering processes, enhancing simulation accuracy. This study focuses on short-range FMCW laser detection under aerosol interference, assessing performance via signal-to-noise ratio (SNR). We analyze the impact of amplitude modulation coefficient and photon count on SNR. Surprisingly, the photon count minimally affects SNR, while the amplitude modulation coefficient significantly influences it. These findings shed light on optimizing FMCW laser detection in aerosol-laden environments. Attention to the amplitude modulation coefficient can notably enhance SNR and overall detection efficiency. [ABSTRACT FROM AUTHOR]

We demonstrate an approach to two-dimensional electronic spectroscopy (2DES) that combines the benefits of shot-to-shot detection at high-repetition rates with the simplicity of a broadband white light continuum input and conventional optical elements to generate phase-locked pump pulse pairs. We demonstrate this through mutual synchronization between the laser repetition rate, the acousto-optical deflector, the pump delay stage, and the CCD line camera, which allows for rapid scanning of pump optical delay synchronously with the laser repetition rate, while the delay stage is moved at a constant velocity. The resulting shot-to-shot detection scheme is repetition rate scalable and only limited by the CCD line rate and the maximum stage velocity. Using this approach, we demonstrate the measurement of an averaged 2DES absorptive spectrum in as much as 1.2 s of continuous sample exposure per 2D spectrum. We achieve a signal-to-noise ratio of 6.8 for optical densities down to 0.05 with 11.6 s of averaging at 100 kHz laser repetition rate. Combining rapid scanning of mechanical delay lines with shot-to-shot detection as demonstrated here provides a viable alternative to acousto-optic pulse shaping approaches that is repetition-rate scalable, has comparable throughput and sensitivity, and minimizes sample exposure per 2D spectrum with promising micro-spectroscopy applications. [ABSTRACT FROM AUTHOR]

We demonstrated a stable passively Q-switched fiber laser operation at 1531.57 nm using a double-transition metal MAX phase TiNbAlC saturable absorber. The prepared saturable absorber's modulation depth, saturation intensity, and non-saturation loss are 1.75%, 0.089 MW/cm 2 , and 32.81%, respectively. Using the TiNbAlC saturable absorber within an erbium-doped fiber laser ring cavity, stable Q-switched pulses with a central wavelength of 1531.57 nm, and a 3-dB bandwidth of 1.43 nm are acquired. The minimum pulse duration and the maximum single pulse energy are 3.18 μ s and 51.56 nJ, respectively. Furthermore, the maximum repetition rate is 37.9 kHz with a signal-to-noise ratio of 50 dB. Our results indicate that double-transition metal MAX phase TiNbAlC can be an excellent saturable absorber candidate for an ultrashort pulse fiber laser. [ABSTRACT FROM AUTHOR]

Single-molecule and related experiments yield time series of an observable as it fluctuates due to thermal motion. In such data, it can be difficult to distinguish fluctuating signal from fluctuating noise. We present a method of separating signal from noise using nonlinear-correlation functions. The method is fully nonparametric: No a priori model for the system is required, no knowledge of whether the system is continuous or discrete is needed, the number of states is not fixed, and the system can be Markovian or not. The noise-corrected, nonlinear-correlation functions can be converted to the system's Green's function; the noise-corrected moments yield the system's equilibrium-probability distribution. As a demonstration, we analyze synthetic data from a three-state system. The correlation method is compared to another fully nonparametric approach—time binning to remove noise, and histogramming to obtain the distribution. The correlation method has substantially better resolution in time and in state space. We develop formulas for the limits on data quality needed for signal recovery from time series and test them on datasets of varying size and signal-to-noise ratio. The formulas show that the signal-to-noise ratio needs to be on the order of or greater than one-half before convergence scales at a practical rate. With experimental benchmark data, the positions and populations of the states and their exchange rates are recovered with an accuracy similar to parametric methods. The methods demonstrated here are essential components in building a complete analysis of time series using only high-order correlation functions. [ABSTRACT FROM AUTHOR]

β-Ga2O3 has attracted much recent attention as a promising ultrawide bandgap semiconductor. Hydrogen can affect the conductivity of β-Ga2O3 through the introduction of shallow donors and the passivation of deep acceptors. The introduction of H or D into β-Ga2O3 by annealing in an H2 or D2 ambient at elevated temperature produces different classes of O–H or O–D centers. This work is a study of the interaction of D with V Ga 1 and V Ga 2 deep acceptors as well as other impurities and native defects in Ga2O3 by infrared spectroscopy and the complementary theory. (We focus primarily on the deuterium isotope of hydrogen because the vibrational modes of O–D centers can be detected with a higher signal-to-noise ratio than those of O–H.) O–D centers in β-Ga2O3 evolve upon annealing in an inert ambient and are transformed from one type of O–D center into another. These reactions affect the compensation of unintentional shallow donors by deep acceptors that are passivated by D. Defects involving additional impurities in β-Ga2O3 compete with V Ga deep acceptors for D and modify the deuterium-related reactions that occur. The defect reactions that occur when D is introduced by annealing in a D2 ambient appear to be simpler than those observed for other introduction methods and provide a foundation for understanding the D-related reactions that can occur in more complicated situations. [ABSTRACT FROM AUTHOR]

The results of a study of methods for processing optoelectronic images of the Earth’s surface are presented. The application of fractal transformations to solve the problems of automated and automatic analysis of terrain images, ensuring the separation of natural and anthropogenic objects without the use of machine learning, is shown. The analysis of existing works has shown the absence of studies linking the result of fractal transformation with the image quality recorded in real conditions of optoelectronic photography. There is no justification for choosing a specific fractal transformation for the applied processing of images with certain typical distortions. The purpose of this work was to identify the dependence of the signal-to-noise ratio of fractal dimension on the quality of the source images, to determine the type of fractal transformation that is most resistant to the effects of the considered negative factors. Methods of fractal transformations for thematic image processing are defined, which include the prism method and the differential cube counting method, and their description is presented. To study the selected methods, real images of the Earth’s surface were used, simulating distorted images of the terrain. Image distortions determined by the instability of shooting conditions and the properties of the optoelectronic complex are considered: defocusing, smudging and noise. The mathematical models used to describe them are summarized. A technique for analyzing the signal-to-noise ratio of fractal transformation is described, involving the processing of reference and distorted images of the terrain. The aspects of distortion modeling and indicators characterizing the level of image distortion are indicated. To implement the experiment, images of the area were selected characterized by various plots. For each plot, the dependences of the signal-to-noise ratio on the indicators characterizing the studied distortions are obtained. By estimating the signal-tonoise ratio, the analysis of the influence of distorting factors on the fractal dimension field being formed was performed. The results of the experiment confirmed the possibility of using fractal transformations for thematic processing of distorted optoelectronic images. It is shown that the dependence of the signal-to-noise ratio on the distortion index has a pronounced nonlinear character. It is established that for distortions of the defocusing and smearing type, the prism method is more stable, and in the presence of noise, the differential cube method is more stable. For processing images of an area represented mainly by images of forest vegetation, the best result is shown by using the differential cube counting method.

Abstract 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 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

The research paper proposes a novel approach for signal detection in cognitive radio networks, aiming to improve spectrum utilization and overall performance. The approach combines matched filter-based detection and the Salp Swarm Algorithm (SSA)-optimized energy detection.Matched filtering is a technique used to detect the presence of a known signal. It correlates the received signal with a reference waveform to determine if the signal is present. In the proposed approach, matched filtering is utilized to detect known signals in the cognitive radio network.On the other hand, energy detection is employed to identify unknown signals. Energy detection measures the energy level of the received signal and compares it to a predetermined threshold. If the energy exceeds the threshold, it is considered as a signal. In this approach, energy detection is optimized using the Salp Swarm Algorithm (SSA). SSA is a metaheuristic algorithm inspired by the behavior of salps in nature, and it is used to find an optimal energy threshold for energy detection in order to improve detection accuracy. The proposed approach is evaluated through simulations, and the results demonstrate its superiority over existing methods in terms of probability of detection, probability of false alarm, and receiver operating characteristics. This indicates that the proposed hybrid approach offers better performance in detecting both known and unknown signals, leading to more efficient spectrum utilization.

Kinematic reconstruction of lower-limb movements using electroencephalography (EEG) has been used in several rehabilitation systems. However, the nonlinear relationship between neural activity and limb movement may challenge decoders in real-time Brain-Computer Interface (BCI) applications. This paper proposes a nonlinear neural decoder using an Unscented Kalman Filter (UKF) to infer lower-limb kinematics from EEG signals during pedaling. The results demonstrated maximum decoding accuracy using slow cortical potentials in the delta band (0.1-4 Hz) of 0.33 for Pearson's r-value and 8 for the signal-to-noise ratio (SNR). This leaves an open door to the development of closed-loop EEG-based BCI systems for kinematic monitoring during pedaling rehabilitation tasks. [ABSTRACT FROM AUTHOR]

[Display omitted] Near-infrared (NIR)-emitting persistent luminescence nanoparticles (PLNPs) are ideal optical imaging contrast reagents characterized by autofluorescence-free optical imaging for their frontier applications in long-term bioimaging. Preparation of uniform small-sized PLNPs with excellent luminescence performance is crucial for biomedical applications, but challenging. Here, we report a facile magnesium doping strategy to achieve size-independent boost of NIR persistent luminescence in typical and most concerned ZnGa 2 O 4 :Cr3+ PLNPs. This strategy relies on the doping of Mg2+ ions that with similar size of Zn2+ ions in the host lattice matrix, and concomitant to the electron traps tailoring tuned by varying the feed ratio of Mg2+. The optimum Mg2+-doped PLNPs give a long afterglow time (signal-to-noise ratio (SNR) = 31.6 at 30 d) without changing the desirable uniform sub-10 nm size of the original nanocrystals. The appropriate increase of the depth and concentration of electron trap contribute jointly to the enhancement of lifetime (488 % longer, 20.57 s) and afterglow time for 700 nm persistent luminescence. Meanwhile, these PLNPs keep the original excellent rechargeability and promote over 60 times increase of SNR in renewable in vivo imaging. This simple strategy provides a basis for new opportunities to address the critical challenge of effective optical performance boost in small-sized PLNPs. [ABSTRACT FROM AUTHOR]

Summary: It has been confirmed that the aerial intelligent reflecting surface (AIRS) can dramatically improve the coverage and quality of service of wireless communication systems. This paper proposes to exploit an AIRS to enhance the physical layer security (PLS) of wireless communication systems. In particular, the AIRS flies at convenient positions for enhancing legitimate channel gain and reducing eavesdropping channel gain. Moreover, the worst‐case security is determined where the legitimate user is affected by hardware impairments (HIs) while the eavesdropper hardware is ideal (ID). The analytical expression of secrecy outage probability (SOP) of the considered systems (referred to as the AIRS‐HI systems) is derived over realistic Nakagami‐ m channels recommended for application in the fifth and beyond generations (5G & B5G). To clarify the behaviors of the considered systems, the asymptotic expressions of signal‐to‐distortion‐plus‐noise ratio (SDNR) at the legitimate user, signal‐to‐noise ratio (SNR) at the eavesdropper, and SOP are also provided. Monte‐Carlo simulations are employed to confirm the accuracy of the derived theorem. Numerical results confirm that the SOP of the AIRS‐HI systems is dramatically higher than the SOP of the AIRS‐ID systems. In other words, a strong effect of HIs on the SOP of the AIRS‐HI systems is clearly indicated. Consequently, utilizing the high transmit power is not an effective solution to improve the secrecy performance of the AIRS‐HI systems. In contrast, it is essential to employ an appropriate transmit power tailored to the specific system parameters. Furthermore, the considerable advantages of incorporating AIRS are demonstrated through a comparison between the SOPs of AIRS‐HI systems and HI systems lacking AIRS. Additionally, a comprehensive examination of AIRS‐HI system behaviors is conducted by varying various system parameters, including the predefined secrecy rate, HI level, the number of reflecting elements (REs) in AIRS, WiFi network frequency, AIRS altitude, bandwidth, and channel fading order. These observations lead to the identification of numerous recommended solutions for enhancing the SOP performance of the considered AIRS‐HI systems. [ABSTRACT FROM AUTHOR]

Summary: LEDs are a prominent technique for a pervasive communication system using visible light communication (VLC), since LED includes long life expectancy, fast switching, and being visible light and is less expensive, which is safe for the human body, though still many technical problems presented to be addressed, especially in an indoor environment. Due to this, the major aim of this task is to afford a hybridized optimization algorithm that is highly suitable for enabling the signal to noise ratio (SNR) and received signal strength (RSS) maximization in VLC. Here, the coordinate of each point of the receiver photo detector in the VLC system is tuned by the new hybridized algorithm. The fusion of two optimization algorithms like Deer Hunting Optimization Algorithm (DHOA) and Dragonfly Algorithm (DA) termed as Fitness‐oriented Dragonfly‐based DHOA (FD‐DHOA) is used to attain the objective concerning the RSS or received power and SNR. The proposed model tries to attain maximum RSS and SNR at the receiver instead of calculating it at every angle. Moreover, the experimental analysis proves that the developed model validates the orientation that generates an optimum RSS and SNR value at the receiver. [ABSTRACT FROM AUTHOR]

• Children had longer latencies than adults in all listening conditions for P1 and N1. • There were fewer identifiable wave peak components in children than in adults. • /da/ was the most resistant stimulus to noise, especially in white noise. • P1, P2 and N2 amplitudes were reduced, and latencies were elongated in noise. • N1 amplitude was significantly more negative in the noise conditions than in quiet for /da/ Children are disadvantaged compared to adults when they perceive speech in a noisy environment. Noise reduces their ability to extract and understand auditory information. Auditory-Evoked Late Responses (ALRs) offer insight into how the auditory system can process information in noise. This study investigated how noise, signal-to-noise ratio (SNR), and stimulus type affect ALRs in children and adults. Fifteen participants from each group with normal hearing were studied under various conditions. The findings revealed that both groups experienced delayed latencies and reduced amplitudes in noise but that children had fewer identifiable waves than adults. Babble noise had a significant impact on both groups, limiting the analysis to one condition: the /da/ stimulus at +10 dB SNR for the P1 wave. P1 amplitude was greater in quiet for children compared to adults, with no stimulus effect. Children generally exhibited longer latencies. N1 latency was longer in noise, with larger amplitudes in white noise compared to quiet for both groups. P2 latency was shorter with the verbal stimulus in quiet, with larger amplitudes in children than adults. N2 latency was shorter in quiet, with no amplitude differences between the groups. Overall, noise prolonged latencies and reduced amplitudes. Different noise types had varying impacts, with the eight-talker babble noise causing more disruption. Children's auditory system responded similarly to adults but may be more susceptible to noise. This research emphasizes the need to understand noise's impact on children's auditory development, given their exposure to noisy environments, requiring further exploration of noise parameters in children. [ABSTRACT FROM AUTHOR]

Under global warming, the summer surface air temperature (SAT) change signal in the northern mid‐latitudes is generally the most significant, while the SAT change in North China (NC) shows distinct local characteristics. Using simulations from the Coupled Model Intercomparison Project Phase 6 and observation from Berkeley Earth Surface Temperature, this study reveals an unusually weak signal of summer SAT change in NC since the 1960s. This uniquely weak signal can be attributed to the small net contribution of external forcings, mainly from anthropogenic greenhouse gases (GHG) and aerosols (AA). Compared to other land regions in the same latitudinal band, the GHG‐induced warming was weaker and AA‐induced cooling was stronger in NC, resulting in the weakest SAT change signal and thus the lowest signal‐to‐noise ratio. This weaker GHG‐induced warming plays a more important role in the SAT change difference between NC and other land regions in the same latitudinal zone. Under different emission scenarios in the future, the signal‐to‐noise ratio in NC will become as large as those in the other land regions in the same latitudinal band and the northern hemisphere, which is partly due to the smaller relative differences in the SAT change signal between NC and the other two regions. The projections of SAT change indicate that climate change in NC will probably become more violent and vulnerable. [ABSTRACT FROM AUTHOR]

Seismic images are essential for understanding the subsurface geological structure and resource distribution. However, the accuracy and certainty of geological analysis using seismic images are limited by the resolution and signal-to-noise ratio. Simultaneously improving resolution and suppressing random noise with traditional methods can be quite challenging. This research proposes a new approach called SeisGAN which leverages a generative adversarial network to address the challenge at hand. Due to the lack of high-resolution noiseless and low-resolution noisy seismic data, stochastic parameter control is employed to simulate a vast range of diverse, paired seismic data for SeisGAN training. The results on the synthetic dataset demonstrate that the proposed method is effective in enhancing the resolution and suppressing the random noise in the original images. Spectrum analysis shows that the proposed method increases the bandwidth of the original data, primarily at high frequencies. Ablation experiments reveal that, under similar conditions, SeisGAN outperforms traditional convolutional neural networks. Incorporating the VGG loss in the generator loss function improves the model's ability to recover high-frequency details. The application of the technique on two publicly available field seismic datasets indicates SeisGAN's excellent generalizability, despite being trained only on synthetic seismic data. Compared with bicubic interpolation and traditional noise suppression and resolution enhancement methods, SeisGAN is capable of effectively suppressing the random noise and enhancing the dominant frequency of field seismic data, making it easier to identify adjacent thin layers and fault features, even for small-scale faults. The zoomed images are clearer and easier to interpret. Furthermore, an example of automatic machine fault identification demonstrates the significant contribution of the SeisGAN-enhanced image to accurate fault recognition. [ABSTRACT FROM AUTHOR]

Background: MRI-based synthetic CT (sCT) generates CT-like images from MRI data. Objective: To evaluate equivalence, inter- and intraobserver reliability, and image quality of sCT compared to conventional (cCT) for assessing hip morphology and maturity in pediatric patients. Materials and methods: We prospectively enrolled patients <21 years old with cCT and 3T MRI of the hips/pelvis. A dual-echo gradient-echo sequence was used to generate sCT via a commercially available post-processing software (BoneMRI v1.5 research version, MRIguidance BV, Utrecht, NL). Two pediatric musculoskeletal radiologists measured seven morphologic hip parameters. 3D surface distances between cCT and sCT were computed. Physeal status was established at seven locations with cCT as reference standard. Images were qualitatively scored on a 5-point Likert scale regarding diagnostic quality, signal-to-noise ratio, clarity of bony margin, corticomedullary differentiation, and presence and severity of artifacts. Quantitative evaluation of Hounsfield units (HU) was performed in bone, muscle, and fat tissue. Inter- and intraobserver reliability were measured by intraclass correlation coefficients. The cCT-to-sCT intermodal agreement was assessed via Bland-Altman analysis. The equivalence between modalities was tested using paired two one-sided tests. The quality parameter scores of each imaging modality were compared via Wilcoxon signed-rank test. For tissue-specific HU measurements, mean absolute error and mean percentage error values were calculated using the cCT as the reference standard. Results: Thirty-eight hips in 19 patients were included (16.6 ± 3 years, range 9.9–20.9; male = 5). cCT- and sCT‐based morphologic measurements demonstrated good to excellent inter- and intraobserver correlation (0.77

Advances in hardware capabilities and big data technologies over the past decade have enabled the application of deep learning techniques to address the challenge of super-resolution in remote-sensing images. Recently, while deep-learning-based methods have outperformed traditional methods, the abundance of information in remote-sensing images creates an imbalance between performance and computational resource consumption in current deep-learning-based methods. This paper introduces a stable super-resolution algorithm based on parameter reconstructive optimization to address these issues. First, based on the stable super-resolution generative adversarial network (SSRGAN), the algorithm employs a generator with networks using residual connections to reconstruct images with enhanced resolution. Next, it extracts content, adversarial, and regularization losses using the discriminator from a stable super-resolution generative adversarial network, which in turn guides the training of the network. Finally, upon completion of training, structural re-parameterization is conducted to optimize the multi-branch trained network into a plain inference network. This inference network can serve as an individual new generator. The results of qualitative and quantitative experimental comparisons with the models Bicubic, super-resolution convolutional neural network, very deep super-resolution convolutional network, deep recursive residual network, super-resolution generative adversarial network (SRGAN), enhanced SRGAN, and stable SRGAN on the Gaofen-5 AHSI satellite dataset suggest that this algorithm achieves improved evaluation indices with a 4× magnification ratio, reaching a peak signal-to-noise ratio of 30.7207 dB and structural similarity index measure of 0.8178. Compared with the trained but unconverted generator, which can also work independently, implementing re-parameterization results in approximately a 10% reduction in the number of parameters, indicating lower resource consumption, while the reconstruction effect is minimally influenced. Furthermore, the super-resolution results exhibit richer detail, increased contrast, and better scene adaptability. [ABSTRACT FROM AUTHOR]

Rank‐reduction‐based simultaneous random noise attenuation and three‐dimensional seismic‐data interpolation has recently become a hot topic in reflection seismology. However, the rank of traditional methods is fixed without considering the variation of signal‐to‐noise ratio on different frequency components, leading to serious residual noise and further affecting the following processing and interpretation tasks. In addition, traditional methods also heavily rely on the application of singular value decomposition technique for rank reduction, which is proven to be computationally expensive for large‐scale data. Thus, a fast‐adaptive rank‐reduction method is proposed in this study. First, the information entropy theory is introduced to adaptively select the optimal rank at various frequencies by calculating the increment of singular entropy. Second, we propose a fast Random Block Krylov algorithm and a subspace multiplexing technique to replace the singular value decomposition algorithm used in traditional methods. The proposed method can significantly improve computational efficiency and yield better seismic‐data reconstruction performance than traditional methods. Applications of the proposed approach on both synthetic and field seismic data demonstrate its superior performance over a well‐known rank‐reduction‐based method, that is the random multi‐channel singular spectrum analysis, in terms of recovered signal‐to‐noise ratio and visual view. [ABSTRACT FROM AUTHOR]

The source vessel noise is a very common noise type in offshore seismic surveys. The state‐of‐art deep learning‐based methods provide an end‐to‐end framework for seismic data denoising. The denoising performance of a pretrained network is, however, highly dependent on the completeness of the training set. When training a denoising network with only field data, especially for attenuating erratic noise, it is hard to obtain a noise‐free data as the training target for the network. Transfer learning, by combining the synthetic and field data, is an alternative solution for improving the generalization capabilities of the network, although being able to model such erratic noise represents also a challenge. Although the denoising results by traditional methods are not accurate enough for creating a complete training set, the features in residual noise by subtracting the denoised data from noisy data are enough for the network to learn. Considering the aforementioned factors, we develop a deep learning‐based workflow for the attenuation of the erratic source vessel noise from ocean bottom node 4C data. Instead of using denoising results directly, we use the conventional methods to extract noise and add them to the high signal‐to‐ratio region of the field data. The created noisy dataset is different from the original noisy data in noise regions; thus, the pretrained network can also be used for predicting the same original data. The denoising results of synthetic and field data all show that even the network is trained on a noisy labelled dataset, we still can obtain high signal‐to‐noise ratio denoising result. Besides, when compare with the results by filtering‐based methods, our proposed method can attenuate the vessel noise more effectively and preserve the near offsets reflections. [ABSTRACT FROM AUTHOR]

Ocean bottom seismometer data usually contain a large amount of random noise, which seriously reduces the signal‐to‐noise ratio of the data and affects subsequent imaging. Hence, random noise attenuation is one of the most essential steps in ocean bottom seismometer data processing. In this paper, a novel approach is proposed to attenuate the marine seismic random noise of ocean bottom seismometers based on a six‐dense‐layer denoising autoencoder. We input the t−x$t-x$ domain data into the denoising autoencoder, the encoder compresses the signal and noise and extracts the main features and the decoder finally reconstructs the denoised data with the same dimension as the input. In this approach, because few raw labelled examples are available, we first constructed the pretraining, training and test data sets by patch processing. Then, we pretrained the encoder based on clean synthetic seismic data through unsupervised learning and pretrained the decoder based on noisy synthetic seismic data through supervised learning. Next, the pretrained model was fine‐tuned with the encoder–decoder on a raw seismic data set in an unsupervised manner. Finally, we used the model to attenuate the random noise in raw ocean bottom seismometer data for testing. Synthetic and raw examples are used to compare the f−x$f-x$ deconvolution, multichannel singular spectrum analysis, deep denoising autoencoder and substep deep denoising autoencoder approaches. Experimental tests demonstrate that the proposed method has higher processing efficiency and precision. [ABSTRACT FROM AUTHOR]

Refraction seismic data acquired by wide‐angle ocean‐bottom seismometers are typically used to invert geological structures. Accurate and reliable first‐arrival traveltime picks are therefore critical to successful refraction data processing. Unfortunately, signal‐to‐noise ratios of refraction data acquired by wide‐angle ocean‐bottom seismometers at far offsets are usually low; thus, conventional methods such as the energy ratio method and correlation analysis method usually fail to accurately and efficiently determine the refraction phase. We therefore develop a novel first‐arrival wave recognition method based on the optimal dominant energy spectrum to improve the accuracy of refraction wave picking on wide‐angle ocean‐bottom seismometer data. The method consists of three steps. The first step is to obtain the optimal dominant energy spectrum according to the power spectrum density of sample scanning. Second, the initial position of the first‐arrival wave is recognized by combining the range of the optimal dominant energy spectrum and the maximum point of its derivative. Third, the modified cross‐correlation method adjusts the initial position to obtain a more accurate arrival time. In this paper, we illustrate the workflow and feasibility of the proposed method via testing on model data. Then, the method is used to determine the first‐arrival time of field wide‐angle ocean‐bottom seismometer data acquired in the Southwest Pacific Ocean and Xisha Islands. The results show that our method can accurately pick the refraction phase at offsets as far as 80 km, thus significantly reducing the uncertainties in first‐arrival phase picking. [ABSTRACT FROM AUTHOR]

Sub‐surface characterization in fractured aquifers is challenging due to the co‐existence of contrasting materials namely matrix and fractures. Transient hydraulic tomography (THT) is proved to be an efficient and robust technique to estimate hydraulic (Km, Kf) and storage (Sm, Sf) properties in such complex hydrogeologic settings. However, performance of THT is governed by data quality and optimization technique used in inversion. We assessed the performance of gradient and gradient‐free optimizers with THT inversion. Laboratory experiments were performed on a two‐dimensional, granite rock (80 cm × 45 cm × 5 cm) with known fracture pattern. Cross‐hole pumping experiments were conducted at 10 ports (located on fractures), and time‐drawdown responses were monitored at 25 ports (located on matrix and fractures). Pumping ports were ranked based on weighted signal‐to‐noise ratio (SNR) computed at each observation port. Noise‐free, good quality (SNR > 100) datasets were inverted using Levenberg–Marquardt: LM (gradient) and Nelder–Mead: NM (gradient‐free) methods. All simulations were performed using a coupled simulation‐optimization model. Performance of the two optimizers is evaluated by comparing model predictions with observations made at two validation ports that were not used in simulation. Both LM and NM algorithms have broadly captured the preferential flow paths (fracture network) via K and S tomograms, however LM has outperformed NM during validation (RLM2=0.76,RMSELM=1.75cm,RNM2=0.73,RMSENM=1.77cm). Our results conclude that, while method of optimization has a trivial effect on model predictions, exclusion of low quality (SNR ≤ 100) datasets can significantly improve the model performance. Article impact statement: We assessed the role of two optimizers as well as signal‐to‐noise ratio in estimating hydraulic and storage properties of fractured aquifers using hydraulic tomography. [ABSTRACT FROM AUTHOR]

The patchiness in the reionization process alters the statistics of cosmic microwave background (CMB), with the kinematic Sunyaev–Zeldovich (kSZ) effect in the CMB temperature power spectrum being a notable consequence. In this work, we aim to explore the potential of future kSZ power spectrum measurements in inferring the details of the reionization process. In this pursuit, we capitalize on the recent developments in foreground mitigation techniques using the Cross-Internal Linear Combination (Cross-ILC) technique, which enables robust detection of the kSZ power spectrum with signal-to-noise ratio roughly 20σ–30σ in this decade by South pole Telescope (SPT-3G) and Simons Observatory (SO); and ≥80σ by CMB-S4 – substantially improving on the recent evidence for kSZ binned at ℓ = 3000 using SPT-SZ + SPTpol surveys. We use a fiducial kSZ power spectrum along with realistic error bars expected from the above technique for SPT-3G, SO, and CMB-S4 to constrain the parameter space for a physical model of reionization. We find that with the improved error bars it will be possible to place stringent constraints on reionization using solely the Cross-ILC recovered SPT-3G kSZ without imposing any prior on τ in the Bayesian inference. Notably, high-fidelity kSZ measurements from CMB-S4 coupled with τ measurements through LiteBIRD will enable unprecedented constraint on the midpoint of reionization with an error bar of ∼0.25 and the duration of reionization with an error bar at ∼0.21 exclusively using CMB data. This study highlights the need to capture kSZ power spectrum on a broad range of multipoles to gain insights into the inhomogeneous reionization era. [ABSTRACT FROM AUTHOR]

In this paper, based on reversibility and approximation concepts, full adder (FA) circuits are designed for fault-tolerant applications. Area saving along with power reduction is the main objectives in the design and implementation of the proposed FAs. So, the basic reversible gates including Toffoli, Fredkin, Feynman, and Thapliyal Ranganathan (TR) are realized based on the gate diffusion input (GDI) technique. Subsequently, three approximate reversible FAs are designed with 3, 2, and 1 gates, respectively, which have special features such as 0 constant input (CI) and minimum garbage output and transistor counts. The circuitry performance and the quality of output images are extracted, when the FAs are placed in the approximate ripple carry adder (RCA) for image addition. The results show the superiority of the proposed FAs. Comprehensive results are extracted from the defined figure of merits (FoMs), which consists of area, power-delay product (PDP), peak signal-to-noise ratio (PSNR), and normalized mean error distance (NMED). The Pro-2 FA is selected as the best option due to its significant superiority compared to other circuits. The Pro-2 has 61.47% and 73.51% savings of PDP and PDP-area product (PDAP), respectively, compared to the exact circuit. [ABSTRACT FROM AUTHOR]

The aim of this study is to predict the total ergodic capacity of near users in a cognitive radio (CR)-based non-orthogonal multiple access (NOMA) system model using the proposed artificial neural network (ANN) architecture. The input dataset used in this study was collected from the CR-NOMA system model and consists of the path loss coefficient, power allocation coefficient, signal-to-noise ratio, the distance between the source-relay-destination, and the ratio of the power of the secondary user to that of the primary user. Using a supervised learning method, the output data are trained and input into the ANN to estimate the ergodic capacity of nearby users using test data. The trained system model demonstrates an accuracy of 96.43% for training data, 96.34% for validation data, and 95.66% for test data when estimating the total ergodic capacity. [ABSTRACT FROM AUTHOR]

Due to the limitation of high power amplifier's (HPA) physical structure, challenges such as nonlinearity and memory distortions which impact the transmitted signal in HPA system remain as an open research problem. As such, this paper introduces a simple and low complex Hammerstein model design as a digital predistorter (DPD) which can handle both of the mentioned challenges. This model first estimates the reverse error to deal with the nonlinearity effect. Subsequently, the DPD complexity is eliminated by removing the order of the memory polynomial. In order to reduce the memory effect resulting from HPA, the proposed model deploys traditional filter such as Finite Impulse Response to the acquired signal in the Hammerstein model. The simulation results show that the proposed DPD outperforms the current best practices for nonlinearity by 96 % from the ideal signal, and memory effect compensation by 95 % from the original signal position in space. Based on that, the proposed DPD can be utilized as conventional HPA for transmitting a standard signal with minimum risk of distortion, and achieving high signal to noise ratio against state-of-the-art models. [ABSTRACT FROM AUTHOR]

The weather has a detrimental effect on outdoor vision systems and raises the probability of traffic crashes and road accidents. The scattering of atmospheric particles degrades outdoor images captured in poor weather conditions such as haze and fog. The reduced visibility has a significant impact on driving assistance systems designed for automatic vehicles. As a result, clear visibility is critical for outdoor computer vision systems. Image dehazing is one of the ill-posed problems because evaluating transmission depth is challenging. It is essential to estimate transmission depth with the greatest degree of accuracy. In order to estimate or optimize the transmission depth, this paper employs the adaptive Gaussian notch filter and the concept of gamma correction to recover the final scene radiance. The results of the experiments are assessed and compared both quantitatively and qualitatively with state-of-the-art techniques. The experimental results demonstrate that the proposed indicators ensure high consistency in qualitative and quantitative evaluation using six performance metrics: two blind assessment indicators (e, r), contrast gain (C gain) , visual contrast measure (VCM), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and probability. [ABSTRACT FROM AUTHOR]

Femtosecond fiber lasers with a high peak power are of great interest due to their various real-world applications. This work uses a double-clad ytterbium-doped fiber (DC-YDF) as the gain medium to successfully generate mode-locked pulses with a high peak power of hundreds of kilowatts at the 1 μm wavelength. The pulse width of the mode-locked pulses was as short as 108 fs, with a fundamental frequency of 1.9 MHz. The highest average power output, energy pulse, and peak power of the mode-locked pulses were 114.5 mW, 58.4 nJ, and 540.7 kW, respectively. The mode-locked fiber laser operates at a center wavelength of 1052 nm with normal net-cavity dispersion, which shows characteristics of a dissipative soliton. The mode-locked pulses exhibit good stability, as demonstrated by the measurement of the signal-to-noise ratio (SNR) of 40 dB. It is the very first instance of high-power short pulses mode-locked in the 1 μm range being shown utilizing a DC-YDF with a two-dimensional (2D) material-based saturable absorber (SA) into the laser cavity. [ABSTRACT FROM AUTHOR]

Visible light communication (VLC) can serve as a supplement to the radio frequency to address its inherent constraints. VLC can be easily implemented in vehicles using their existing LED components. Sunlight interference can cause significant variation in the background noise powers, leading to signal-to-noise ratio (SNR) degradation in vehicular VLC (VVLC) communication. Hence, it is necessary to improve the performance besides interference. Allan variance is a temporal analysis approach that can extract the noise from the received data. In this article, an Allan variance-based adaptive noise canceller for a VVLC system is proposed and evaluated. Based on the Allan variance obtained, the step size and the number of taps of the adaptive filter are adjusted for superior performance. This proposed filter effectively mitigates the pronounced autocorrelation induced by the noise interference. It yields about an 85% bit error rate (BER) reduction compared to the conventional filter. This improved convergence underscores the potential for enhanced communication performance and reliability. The balanced distribution of the Manchester encoded signal outperforms NRZ encoding. Thus, this work contributes a novel adaptive noise canceller design for a VVLC system and also provides valuable insights to the significance of Allan variance in the VLC system performance. [ABSTRACT FROM AUTHOR]

In this paper, a stabilized and wavelength-selective erbium-doped fiber (EDF) laser architecture with single-longitudinal-mode, high optical signal to noise ratio (OSNR), stability, wide tunability and narrower linewidth is investigated experimentally. To suppress the dense longitudinal-mode fluctuations and reduce the output linewidth, the proposed four-fiber-ring with Rayleigh backscattering feedback injection and an EDF based saturable absorber is added in the ring cavity. So, the presented EDF laser configuration can provide a 68 nm wide wavelength-selective bandwidth of 1513.0–1581.0 nm by operating the original C-band gain-medium and also achieve a flattened power output with 0.3 dB power fluctuation in the bandwidth of 1529.0–1573.0 nm. Moreover, the related output characteristics of the proposed fiber laser are executed and discussed. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of the Spectral Amplitude Coding (SAC) Optical Code Division Multiple-Access (OCDMA) system utilizing the Flexible Double Weight (FDW) code and a newly developed detection technique, the Direct AND (D-AND) detection technique. The newly proposed Direct AND (D-AND) technique comprises three decoder components: a primary decoder, a subtractive decoder (S-decoder), and an addition decoder (A-decoder). The numerous noises investigated in the theoretical research included phase-induced intensity noise, shot noise, thermal noise, and multiple-access interference effects. Bit Error Rate (BER) and signal-to-noise ratio (SNR) were used to gauge the system's performance. The Detection (DD) and Single-Photodiode Detection (SPD) results were compared to those of the study of the new detection technique. The newly developed detection technique, which was tested using the FDW code, can support more active users and improve system performance when compared to SPD and DD approaches. The simulation experiment demonstrates that the system's BER performance with the new detection technique is much better than with the earlier techniques. The new detection technique outperforms the previous technique, supporting 284 concurrent users compared to DD 172 concurrent users and SPD 116 concurrent users at a 10 Gbps bit rate. [ABSTRACT FROM AUTHOR]

This study examines the feature space of seismic waveforms often used in machine learning applications for seismic event detection and classification problems. Our investigation centres on the southern Alaska region, where the seismic record captures diverse seismic activity, notably from the calving of marine-terminating glaciers and tectonic earthquakes along active plate boundaries. While the automated discrimination of earthquakes and glacier quakes is our nominal goal, this data set provides an outstanding opportunity to explore the general feature space of regional seismic phases. That objective has applicability beyond ice quakes and our geographic region of study. We make a noteworthy discovery that features rooted in the spectral content of seismic waveforms consistently outperform statistical and temporal features. Spectral features demonstrate robust performance, exhibiting resilience to class imbalance while being minimally impacted by factors such as epicentral distance and signal-to-noise ratio. We also conduct experiments on the transferability of the model and find that transferability primarily depends on the appearance of the waveforms. Finally, we analyse misclassified events and find examples that are identified incorrectly in the original regional catalogue. [ABSTRACT FROM AUTHOR]

The prediction of fundus fluorescein angiography (FFA) images from fundus structural images is a cutting-edge research topic in ophthalmological image processing. Prediction comprises estimating FFA from fundus camera imaging, single-phase FFA from scanning laser ophthalmoscopy (SLO), and three-phase FFA also from SLO. Although many deep learning models are available, a single model can only perform one or two of these prediction tasks. To accomplish three prediction tasks using a unified method, we propose a unified deep learning model for predicting FFA images from fundus structure images using a supervised generative adversarial network. The three prediction tasks are processed as follows: data preparation, network training under FFA supervision, and FFA image prediction from fundus structure images on a test set. By comparing the FFA images predicted by our model, pix2pix, and CycleGAN, we demonstrate the remarkable progress achieved by our proposal. The high performance of our model is validated in terms of the peak signal-to-noise ratio, structural similarity index, and mean squared error. [ABSTRACT FROM AUTHOR]

Recursive Least Square (RLS) is a popular algorithm for noise cancellation in non-stationary signals; however, it demands more computational resources and more difficult mathematical operations. Also, RLS has less performance stability. The present research explores a novel idea of an RLS adaptive noise cancellation through a modified method that uses an RLS adaptive filter by introducing an additional constant multiplier, along with their in-depth analysis. The proposed algorithm is analyzed using three primary performance metrics: mean square error (MSE), signal-to-noise ratio (SNR), and convergence rate. The obtained results demonstrate that the proposed algorithm has reduced the MSE by 79.65%, which leads to an improvement in SNR by 86.16% compared to the traditional RLS algorithm. The additional constant multiplier is optimized for SNR, and the optimized value is found to be equal to 0.65, which gives the best possible SNR value of 19.38 dB. Also, the proposed algorithm has been successfully applied to a real-world scenario in acoustic echo cancellation (AEC). The experimental setup for the echo canceller is simulated on MATLAB to measure the echo canceller efficiency in terms of MSE and echo return loss enhancement. Based on performance evaluation, the proposed algorithm has been found to better echo cancellation in AEC as compared to traditional RLS. [ABSTRACT FROM AUTHOR]

With the development of deep learning techniques, the field of direction of arrival (DOA) estimation has also made significant progress. However, the accuracy of DOA estimation using end-to-end neural networks (NNs) heavily relies on the classification step of the networks, which necessitates the use of large and representative datasets. Additionally, conventional speech presence probability (SPP) estimation methods based on the ideal ratio mask (IRM) may misclassify time-frequency (T-F) bins dominated by non-speech and noise, which hinders the accurate extraction of directional information. To improve the robustness of existing DOA estimation algorithms, this paper proposes a DOA estimation method with T-F bin selection. In terms of output, instead of using IRM-based SPP, our proposed approach focuses on the a posteriori SPP, a deliberate choice aimed at circumventing potential confusion. For input optimization, we construct features that encompass spatial, temporal, and directional information concurrently, and these are coupled with a frequency bin-wise recurrent neural network (RNN) model to attain precise multi-channel SPP estimation. Subsequently, these SPP estimates are utilized to extract local information for DOA estimation. Moreover, the cascaded structure ensures that the model has the ability to complete out-of-label tasks, effectively reducing the dataset requirements by training only a subset of direction information to achieve omnidirectional DOA estimation. Besides, this contributes to the algorithm's ability to eliminate its reliance on the step size, setting it apart from other end-to-end methods. Simulation results validate that the proposed method achieves higher accuracy and lower error compared to both NN-based end-to-end approaches and traditional full-band approaches under various conditions of reverberation and signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Speech presence probability (SPP) and gain functions such as Wiener filter or MMSE estimators require an estimate of the a-priori signal-to-noise ratio (SNR). However, the estimation of the a-priori SNR is computationally involved and sensitive to noise variations. This paper proposes to approximate the SPP and the overall gain function of a speech enhancement system by using sigmoid functions to reduce the need of estimating the a-prior SNR. By applying an approximation via the sigmoid functions it is shown that only the a-posteriori estimate of SNR is needed, resulting in a low complexity system. The sigmoid function is designed with an optimization algorithm to optimize its parameters with respect to speech quality measures. The optimization algorithm is based on the idea that the solution obtained for a given problem should move towards the best solution and avoid the worst solution. The proposed algorithm requires minimal control parameters and does not require any algorithm specific parameters. Simulation results show that the proposed sigmoid functions achieve good results in terms of speech quality measures when compared with existing methods while providing significantly lower complexity for implementation. [ABSTRACT FROM AUTHOR]

In this work, we propose and study an inertial Krasnosel'skii-Mann iterative algorithm with step-size parameters involving nonexpansive mapping to find a solution of a fixed point problem of a nonexpansive mapping in the frame work of Hilbert spaces. Strong convergence of the new proposed algorithm is proved under some useful properties of a nonexpansive mapping and inequalities on real Hilbert spaces together with the appropriate conditions of scalar controls without relying on the concept of viscosity methods. For the applications, we employ the obtained results to find a zero point of some monotone inclusion problems and then apply to solve image restoration problems. For representing the advantage of the new algorithm, the signal to noise ratio (SNR) with various types of blurring operators and some numerical experiments are presented to compare and illustrate the behavior of the new algorithm with numerical results of some previous existing algorithms. [ABSTRACT FROM AUTHOR]

To investigate the improvement in image quality of triple-low-protocol (low radiation, low contrast medium dose, low injection speed) renal artery computed tomography (CT) angiography (RACTA) using deep-learning image reconstruction (DLIR), in comparison with standard-dose single- and dual-energy CT (DECT) using adaptive statistical iterative reconstruction-Veo (ASIR-V) algorithm. Ninety patients for RACTA were divided into different groups: standard-dose single-energy CT (S group) using ASIR-V at 60% strength (60%ASIR-V), DECT (DE group) with 60%ASIR-V including virtual monochromatic images at 40 keV (DE40 group) and 70 keV (DE70 group), and the triple-low protocol single-energy CT (L group) with DLIR at high level (DLIR-H). The effective dose (ED), contrast medium dose, injection speed, standard deviation (SD), signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of abdominal aorta (AA), and left/right renal artery (LRA, RRA), and subjective scores were compared among the different groups. The L group significantly reduced ED by 37.6% and 31.2%, contrast medium dose by 33.9% and 30.5%, and injection speed by 30% and 30%, respectively, compared to the S and DE groups. The L group had the lowest SD values for all arteries compared to the other groups (p <0.001). The SNR of RRA and LRA in the L group, and the CNR of all arteries in the DE40 group had highest value compared to others (p <0.05). The L group had the best comprehensive score with good consistency (p <0.05). The triple-low protocol RACTA with DLIR-H significantly reduces the ED, contrast medium doses, and injection speed, while providing good comprehensive image quality. • The DLIR can improve image quality of RACTA at triple-low protocol. • The DLIR could be used as a surrogate for the IR method. • The DLIR-H for RACTA provided good comprehensive image quality. [ABSTRACT FROM AUTHOR]

This paper proposes an improved matching pursuit (MP) method with a multicomponent interference dictionary (MCID) based on a pipeline guided wave reflection model. The proposed method separates the overlapped time domain reflections from the notch edges and extracts parameters such as amplitude, time of flight (TOF), and phase to identify the number and axial dimensions of notches in pipes. Firstly, the performance of the method in identifying parameters of overlapped components affected by noise and reverberation under different signal-to-noise ratios (SNRs) and frequencies is analyzed using four error metrics. Secondly, finite-element (FE) models of pipes with a single notch and double notches are established, and the accuracy of the reflection model is validated by comparing the predicted amplitudes, TOF, and phase of reflections with the FE results. Finally, experimental validation is conducted on aluminum pipes with multiple notches. The consistency between the experimental results, theoretical results, and FE results in wave packet parameters confirmed the accuracy of the proposed reflection models. The method accurately captures the parameters of the reflections from each notch edge in the experimental pipes, enabling the identification of the number and axial dimensions of the notches. The method holds the potential for identifying a greater number of notches in pipelines and characterizing their axial dimensions. This can provide a reliable reference for developing maintenance plans based on pipeline operational conditions, thereby preventing major safety incidents. [ABSTRACT FROM AUTHOR]