Search

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]

Accurate modeling of selection effects is a key ingredient to the success of gravitational-wave astronomy. The detection probability plays a crucial role in both statistical population studies, where it enters the hierarchical Bayesian likelihood, and astrophysical modeling, where it is used to convert predictions from population-synthesis codes into observable distributions. We review the most commonly used approximations, extend them, and present some recipes for a straightforward implementation. These include a closed-form expression capturing both multiple detectors and noise realizations written in terms of the so-called Marcum Q -function and a ready-to-use mapping between signal-to-noise ratio (SNR) thresholds and false-alarm rates from state-of-the-art detection pipelines. The bias introduced by approximating the matched filter SNR with the optimal SNR is not symmetric: sources that are nominally below threshold are more likely to be detected than sources above threshold are to be missed. Using both analytical considerations and software injections in detection pipelines, we confirm that including noise realizations when estimating the selection function introduces an average variation of a few %. This effect is most relevant for large catalogs and specific subpopulations of sources at the edge of detectability (e.g. high redshifts). [ABSTRACT FROM AUTHOR]

Surface integrity problems during selective material removal processes are a very common limitation for process productivity and part quality, especially in difficult-to-machine materials like 5083 aluminium alloy (AA), which is known for its remarkable performance in extreme environments. In general, tuning the cutting-part material properties with cutter geometry and cutting parameters can optimize surface texture, increase parts accuracy and resistance in corrosion, and eliminate process noise and energy waste. This work is an experimental study of surface parameter optimization during finish end milling of an AA5083 under a specific range of three cutting parameters with an optimized two-flute carbide cutter by previous work. So, twenty-seven experiments were run having varied the radial depth of cut (RDOC), feed rate (f), and cutting speed (S). Surface roughness parameters (Ra and Rt) were measured in the direction of cutting speed at three different distances by the upper edge. The signal-to-noise (SN) ratios have been calculated, and the process was optimized following the analysis of means. Then, additive models with linear interactions were fitted on SN ratios, and the analysis of variances and residual normality plots were utilized to validate the models' goodness. The SN approach and analysis of means conclude that 0.5 mm RDOC, 6000 rpm speed, and 0.082 mm/tooth feed optimize the process and can effectively predict the Ra and Rt responses. The newly produced machinability data can benefit further applications of AA5083 in industrial applications such as shipbuilding and vehicle bodies. [ABSTRACT FROM AUTHOR]

The estimation of elastic properties of thin-bed formations from sonic logging is challenging. Standard slowness processing of sonic logging waveforms typically yields an average slowness log profile over the span of the receiver array, obscuring thin-layer features smaller than the array aperture. In order to enhance vertical resolution of the slowness logs, the subarray processing techniques have been developed. However, for the subarrays with smaller aperture, the semblance from subarray waveforms becomes susceptible to noise, which results in a low signal-to-noise (S/N) ratio for the processing slowness logs. To overcome the above drawbacks, we propose a slowness estimation method with the enhanced resolution ranging from the conventional array aperture resolution to the inter-receiver spacing based on the reconstruction of neighboring virtual traces (RNVTs). The method utilizes super-virtual interferometry to reconstruct a large number of waveforms for slowness extraction using redundant information from overlapping receiver subarrays. We validate the feasibility and effectiveness of the proposed method using synthetic numerical experiments. By adding different levels of noise to synthetic data, we conclude that the new method has better noise robustness. Finally, we apply this method to field data, and the estimated high-resolution slowness logs show good agreement in interbedded sand-shale sequences. Both numerical tests and examples of field data show that, the slowness logs estimated by the new method can be obtained with a high resolution as well as with a high S/N ratio, providing an effective method for assessing slowness properties from a borehole. [ABSTRACT FROM AUTHOR]

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

Random noise attenuation is a fundamental task in seismic data processing aimed at improving the signal‐to‐noise ratio of seismic data, thereby improving the efficiency and accuracy of subsequent seismic data processing and interpretation. To this end, model‐based and data‐driven seismic data denoising methods have been widely applied, including f–x deconvolution, K‐singular value decomposition, feed‐forward denoising convolutional neural network and U‐Net (an improved fully convolutional neural network structure), which have received widespread attention for their effectiveness in attenuating random noise. However, they often struggle with low‐signal‐to‐noise ratio data and complex noise environments, leading to poor performance and leakage of effective signals. To address these issues, we propose a novel approach for random noise attenuation. This approach employs a multi‐model stacking structure, where the parameters governing this structure are optimized using a particle swarm optimizer. In the model‐based denoising method, we choose the f–x deconvolution method, whereas in the data‐driven denoising method, we choose K‐singular value decomposition for shallow learning and U‐Net for deep learning as components of the multi‐model stacking structure. The optimal parameters for the multi‐model stacking structure are obtained using a particle swarm optimizer, guided by the proposed novel hybrid fitness function incorporating weighted signal‐to‐noise ratio, structural similarity and correlation parameters. Finally, the effectiveness of the proposed method is verified with three synthetic and two real seismic datasets. The results demonstrate that the proposed method is effective in attenuating random noise and outperforms the benchmark methods in denoising both synthetic and real seismic data. [ABSTRACT FROM AUTHOR]

As part of the Synergetic Utilisation of CO2$_2$ storage Coupled with geothermal EnErgy Deployment project, investigating CO2$_2$ reinjection with different seismic methods, both passive and active seismic surveys have been conducted at the geothermal power plant at Hellisheiði, Iceland. During the 2021 survey, two geophone lines recorded noise for a week. We process the passive‐source data with seismic interferometry to image the subsurface structure around the CarbFix2 reinjection reservoir. To improve image quality, we perform an illumination analysis to select only noise panels dominated by body‐wave energy. The results show that most noise panels are dominated by air‐wave energy arriving from the direction of the power plant. We use panels with a near‐vertical incidence to create a zero‐offset image and a larger selection of body‐wave‐dominated panels to create virtual common‐shot gathers. We process the gathers with a simple reflection seismology processing workflow to obtain stacked images. The zero‐offset images show a relatively lower signal‐to‐noise ratio and only horizontal reflectors. The stacked images show slightly dipping reflectors and possibly lateral amplitude variations around the expected injection region. This could indicate a region of interest for future research into the reinjection reservoir. [ABSTRACT FROM AUTHOR]

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

Filtering plays an essential role in event‐related potential (ERP) research, but filter settings are usually chosen on the basis of historical precedent, lab lore, or informal analyses. This reflects, in part, the lack of a well‐reasoned, easily implemented method for identifying the optimal filter settings for a given type of ERP data. To fill this gap, we developed an approach that involves finding the filter settings that maximize the signal‐to‐noise ratio for a specific amplitude score (or minimizes the noise for a latency score) while minimizing waveform distortion. The signal is estimated by obtaining the amplitude score from the grand average ERP waveform (usually a difference waveform). The noise is estimated using the standardized measurement error of the single‐subject scores. Waveform distortion is estimated by passing noise‐free simulated data through the filters. This approach allows researchers to determine the most appropriate filter settings for their specific scoring methods, experimental designs, subject populations, recording setups, and scientific questions. We have provided a set of tools in ERPLAB Toolbox to make it easy for researchers to implement this approach with their own data. Filtering can have a large impact on ERP data, influencing both statistical power and the validity of conclusions. However, there is no standardized, widely used method for determining optimal filter settings for cognitive and affective ERP research. Here, we provide a straightforward method along with tools that will allow researchers to easily determine the most appropriate filter settings for their data. [ABSTRACT FROM AUTHOR]

In research with event‐related potentials (ERPs), aggressive filters can substantially improve the signal‐to‐noise ratio and maximize statistical power, but they can also produce significant waveform distortion. Although this tradeoff has been well documented, the field lacks recommendations for filter cutoffs that quantitatively address both of these competing considerations. To fill this gap, we quantified the effects of a broad range of low‐pass filter and high‐pass filter cutoffs for seven common ERP components (P3b, N400, N170, N2pc, mismatch negativity, error‐related negativity, and lateralized readiness potential) recorded from a set of neurotypical young adults. We also examined four common scoring methods (mean amplitude, peak amplitude, peak latency, and 50% area latency). For each combination of component and scoring methods, we quantified the effects of filtering on data quality (noise level and signal‐to‐noise ratio) and waveform distortion. This led to recommendations for optimal low‐pass and high‐pass filter cutoffs. We repeated the analyses after adding artificial noise to provide recommendations for data sets with moderately greater noise levels. For researchers who are analyzing data with similar ERP components, noise levels, and participant populations, using the recommended filter settings should lead to improved data quality and statistical power without creating problematic waveform distortion. Filtering can have a large impact on ERP data, influencing both statistical power and the validity of conclusions. However, the field lacks recommended filter cutoffs for cognitive and affective ERP components that are quantitatively justified and empirically validated. Here, we provide recommended filter cutoffs for a broad range of ERP components and scoring methods. [ABSTRACT FROM AUTHOR]

We consider testing invariance of a distribution under an algebraic group of transformations, such as permutations or sign flips. As such groups are typically huge, tests based on the full group are often computationally infeasible. Hence, it is standard practice to use a random subset of transformations. We improve upon this by replacing the random subset with a strategically chosen, fixed subgroup of transformations. In a generalized location model, we show that the resulting tests are often consistent for lower signal-to-noise ratios. Moreover, we establish an analogy between the power improvement and switching from a t -test to a Z -test under normality. Importantly, in permutation-based multiple testing, the efficiency gain with our approach can be huge, since we attain the same power with many fewer permutations. [ABSTRACT FROM AUTHOR]

Aim: The aim of this study was to determine the usefulness of magnetic resonance imaging (MRI) characteristics in discriminating H3 K27M-mutant gliomas from wildtype gliomas in the spinal cord. Materials and methods: Fifty-eight patients with spinal cord gliomas were enrolled in this study. The H3 K27 gene status was identified by Sanger sequencing or immunohistochemistry test of resection tumor specimens. The MR imaging characteristics were evaluated and compared between H3 K27M-mutant and wildtype gliomas using the χ2 test and the Mann–Whitney U test. Results: Of 58 recruited patients, 23 (39.7%) were diagnosed with H3 K27M-mutant glioma. The H3 K27M-mutant gliomas were found to more likely occur in men compared with wildtype gliomas (87.0% vs. 42.9%, p = 0.001). On T2-weighted MR images, the signal-to-noise ratio (SNR) of H3 K27M-mutant gliomas was significantly lower than that of wildtype gliomas (103.9 ± 72.0 vs. 168.9 ± 86.8, p < 0.001). Of 35 wildtype tumors, 60% showed well-defined margin but this feature was not found in all mutant tumors (p < 0.001). The SNR of tumors on contrast-enhanced T1-weighted images of the H3 K27M-mutant gliomas was significantly lower than that of wildtype gliomas (187.7 ± 160.4 vs. 295.1 ± 207.8, p = 0.006). Receiver operating-characteristic analysis revealed that area under curve (AUC) of combination of 1/SNR on T2-weighted images, 1/SNR on contrast-enhanced T1-weighted images, ill-defined margin, and sex reached 0.937 (95% CI, 0.873–1.000) in discriminating H3 K27M-mutant gliomas. Conclusions: The MR imaging characteristics are valuable in discriminating H3 K27M-mutant from wildtype gliomas in the spinal cord and the combination of these imaging features with sex had a high strength in this discrimination. [ABSTRACT FROM AUTHOR]

In the context of temporal image forensics, it is not evident that a neural network, trained on images from different time-slots (classes), exploits solely image age related features. Usually, images taken in close temporal proximity (e.g. , belonging to the same age class) share some common content properties. Such content bias can be exploited by a neural network. In this work, a novel approach is proposed that evaluates the influence of image content. This approach is verified using synthetic images (where content bias can be ruled out) with an age signal embedded. Based on the proposed approach, it is shown that a deep learning approach proposed in the context of age classification is most likely highly dependent on the image content. As a possible countermeasure, two different models from the field of image steganalysis, along with three different preprocessing techniques to increase the signal-to-noise ratio (age signal to image content), are evaluated using the proposed method. • A novel approach in the context of XAI and deep learning image age approximation. • The approach evaluates the influence of image content on the classification accuracy. • The approach is verified using synthetic images with an age signal embedded. • Deep learning age approximation can be strongly biased by image content. • Different CNN models trained in the context of age classification are evaluated. [ABSTRACT FROM AUTHOR]

In the new radio wireless system, use cases for automation, automotive safety and virtual reality will require transmission and reception with very high reliability and very low latency. Radio channel fading and the onset of strong interference combine to make this task hard, which require new methods for mitigation. The alternative studied in this article is to introduce fast signal to interference and noise ratio (SINR) feedback control supported by feed‐forward control from interference estimates, thereby improving the robustness of the networked loop. This challenging nonlinear networked control problem involves delay and coarse control signal quantization. To guarantee that the deployed feedback loop instances are always well‐behaved, the SINR feedback controller is designed to be globally ℒ2$$ {\mathcal{L}}_2 $$‐stable using a new loop gain—loop delay product condition, derived from the classical circle criterion. Monte‐Carlo simulations show that the proposed controller is able to reduce the SINR back‐off significantly. [ABSTRACT FROM AUTHOR]

Spiking neural membrane systems (SN P systems) are a class of bio-inspired models inspired by the activities and connectivity of neurons. Extensive studies have been made on SN P systems with synchronization-based communication, while further efforts are needed for the systems with rhythm-based communication. In this work, we design an asynchronous SN P system with resonant connections where all the enabled neurons in the same group connected by resonant connections should instantly produce spikes with the same rhythm. In the designed system, each of the three modules implements one type of the three operations associated with the edge detection of digital images, and they collaborate each other through the resonant connections. An algorithm called EDSNP for edge detection is proposed to simulate the working of the designed asynchronous SN P system. A quantitative analysis of EDSNP and the related methods for edge detection had been conducted to evaluate the performance of EDSNP. The performance of the EDSNP in processing the testing images is superior to the compared methods, based on the quantitative metrics of accuracy, error rate, mean square error, peak signal-to-noise ratio and true positive rate. The results indicate the potential of the temporal firing and the proper neuronal connections in the SN P system to achieve good performance in edge detection. [ABSTRACT FROM AUTHOR]

To evaluate arterial enhancement, its depiction, and image quality in low-tube potential whole-body computed tomography (CT) angiography (CTA) with extremely low iodine dose and compare the results with those obtained by hybrid-iterative reconstruction (IR) and deep-learning image-reconstruction (DLIR) methods. This prospective study included 34 consecutive participants (27 men; mean age, 74.2 years) who underwent whole-body CTA at 80 kVp for evaluating aortic diseases between January and July 2020. Contrast material (240 mg iodine/ml) with simultaneous administration of its quarter volume of saline, which corresponded to 192 mg iodine/ml, was administered. CT raw data were reconstructed using adaptive statistical IR-Veo of 40% (hybrid-IR), DLIR with medium- (DLIR-M), and high-strength level (DLIR-H). A radiologist measured CT attenuation of the arteries and background noise, and the signal-to-noise ratio (SNR) was then calculated. Two reviewers qualitatively evaluated the arterial depictions and diagnostic acceptability on axial, multiplanar-reformatted (MPR), and volume-rendered (VR) images. Mean contrast material volume and iodine weight administered were 64.1 ml and 15.4 g, respectively. The SNRs of the arteries were significantly higher in the following order of the DLIR-H, DLIR-M, and hybrid-IR (p< 0.001). Depictions of six arteries on axial, three arteries on MPR, and four arteries on VR images were significantly superior in the DLIR-M or hybrid-IR than in the DLIR-H (p≤ 0.009 for each). Diagnostic acceptability was significantly better in the DLIR-M and DLIR-H than in the hybrid-IR (p< 0.001–0.005). DLIR-M showed well-balanced arterial depictions and image quality compared with the hybrid-IR and DLIR-H. • Low-tube-voltage whole-body CTA using the DLIR could achieve high SNR. • Arterial depictions in DLIR-M were almost the same or superior to the other groups. • Image quality in the DLIR-M were almost the same or better than the other groups. [ABSTRACT FROM AUTHOR]

In this paper, a streak camera system is used for obtaining the spectral images in laser-induced breakdown spectroscopy. Streak images are time-resolved, thus enabling tracking of the temporal development of the atomic and ionic emission lines from the plasma plume. To make our study more comprehensive, we have used a machine-learning approach to data analysis. We propose how to reduce the level of noise on streak images by applying the principal component analysis technique. The usual method of improving the signal-to-noise ratio by taking a large number of expositions in streak camera photon counting mode is often not practical. [ABSTRACT FROM AUTHOR]

In a clustered observational study, a treatment is assigned to groups and all units within the group are exposed to the treatment. We develop a new method for statistical adjustment in clustered observational studies using approximate balancing weights, a generalization of inverse propensity score weights that solve a convex optimization problem to find a set of weights that directly minimize a measure of covariate imbalance, subject to an additional penalty on the variance of the weights. We tailor the approximate balancing weights optimization problem to the clustered observational study setting by deriving an upper bound on the mean square error and finding weights that minimize this upper bound, linking the level of covariate balance to a bound on the bias. We implement the procedure by specializing the bound to a random cluster‐level effects model, leading to a variance penalty that incorporates the signal‐to‐noise ratio and penalizes the weight on individuals and the total weight on groups differently according to the the intra‐class correlation. [ABSTRACT FROM AUTHOR]

For nearly two decades, the Ozone Monitoring Instrument (OMI) aboard the NASA Aura spacecraft (launched in 2004) and the Ozone Mapping and Profiler Suite (OMPS) aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (SNPP) satellite (launched in 2011) have been providing global monitoring of SO2 column densities from both anthropogenic and volcanic activities. Here, we describe the version 1 NOAA-20 (N20)/OMPS SO2 product, aimed at extending the long-term climate data record. To achieve this goal, we apply a principal component analysis (PCA) retrieval technique, also used for the OMI and SNPP/OMPS SO2 products, to N20/OMPS. For volcanic SO2 retrievals, the algorithm is identical between N20 and SNPP/OMPS and produces consistent retrievals for eruptions such as the 2018 Kilauea and 2019 Raikoke. For anthropogenic SO2 retrievals, the algorithm has been customized for N20/OMPS, considering its greater spatial resolution and reduced signal-to-noise ratio as compared with SNPP/OMPS. Over background areas, N20/OMPS SO2 slant column densities (SCD) show relatively small biases, comparable retrieval noise with SNPP/OMPS (after aggregation to the same spatial resolution), and remarkable stability with essentially no drift during 2018–2023. Over major anthropogenic source areas, the two OMPS retrievals are generally well-correlated but N20/OMPS SO2 is biased low especially for India and the Middle East, where the differences reach ~20 % on average. The reasons for these differences are not fully understood but are partly due to algorithmic differences. Better agreement (typical differences of ~10–15 %) is found over degassing volcanoes. SO2 emissions from large point sources, inferred from N20/OMPS retrievals, agree well with those based on OMI, SNPP/OMPS, and TROPOspheric Monitoring Instrument (TROPOMI), with correlation coefficients > 0.98 and overall differences < 10 %. The ratios between the estimated emissions and their uncertainties offer insights into the ability of different satellite instruments to detect and quantify SO2 sources. While TROPOMI has the highest ratios among all four sensors, ratios from N20/OMPS are slightly greater than OMI and substantially greater than SNPP/OMPS. Overall, our results suggest that the version 1 N20/OMPS SO2 product will successfully continue the long-term OMI and SNPP/OMPS SO2 data records. Efforts currently underway will further enhance the consistency of retrievals between different instruments, facilitating the development of multi-decade, coherent global SO2 datasets across multiple satellites. [ABSTRACT FROM AUTHOR]

We evaluate the performance of the Legacy Survey of Space and Time Science Pipelines Difference Image Analysis (DIA) on simulated images. By adding synthetic sources to galaxies on images, we trace the recovery of injected synthetic sources to evaluate the pipeline on images from the Dark Energy Science Collaboration Data Challenge 2. The pipeline performs well, with efficiency and flux accuracy consistent with the signal-to-noise ratio of the input images. We explore different spatial degrees of freedom for the Alard–Lupton polynomial-Gaussian image subtraction kernel and analyze for trade-offs in efficiency versus artifact rate. Increasing the kernel spatial degrees of freedom reduces the artifact rate without loss of efficiency. The flux measurements with different kernel spatial degrees of freedom are consistent. We also here provide a set of DIA flags that substantially filter out artifacts from the DIA source table. We explore the morphology and possible origins of the observed remaining subtraction artifacts and suggest that given the complexity of these artifact origins, a convolution kernel with a set of flexible bases with spatial variation may be needed to yield further improvements. [ABSTRACT FROM AUTHOR]

Electrochemical aptamer‐based sensors support the high‐frequency, real‐time monitoring of molecules‐of‐interest in vivo. Achieving this requires methods for correcting the sensor drift seen during in vivo placements. While this correction ensures EAB sensor measurements remain accurate, as drift progresses it reduces the signal‐to‐noise ratio and precision. Here, we show that enzymatic cleavage of the sensor's target‐recognizing DNA aptamer is a major source of this signal loss. To demonstrate this, we deployed a tobramycin‐detecting EAB sensor analog fabricated with the DNase‐resistant "xenonucleic acid" 2'O‐methyl‐RNA in a live rat. In contrast to the sensor employing the equivalent DNA aptamer, the 2'O‐methyl‐RNA aptamer sensor lost very little signal and had improved signal‐to‐noise. We further characterized the EAB sensor drift using unstructured DNA or 2'O‐methyl‐RNA oligonucleotides. While the two devices drift similarly in vitro in whole blood, the in vivo drift of the 2'O‐methyl‐RNA‐employing device is less compared to the DNA‐employing device. Studies of the electron transfer kinetics suggested that the greater drift of the latter sensor arises due to enzymatic DNA degradation. These findings, coupled with advances in the selection of aptamers employing XNA, suggest a means of improving EAB sensor stability when they are used to perform molecular monitoring in the living body. [ABSTRACT FROM AUTHOR]

Electrochemical aptamer‐based sensors support the high‐frequency, real‐time monitoring of molecules‐of‐interest in vivo. Achieving this requires methods for correcting the sensor drift seen during in vivo placements. While this correction ensures EAB sensor measurements remain accurate, as drift progresses it reduces the signal‐to‐noise ratio and precision. Here, we show that enzymatic cleavage of the sensor's target‐recognizing DNA aptamer is a major source of this signal loss. To demonstrate this, we deployed a tobramycin‐detecting EAB sensor analog fabricated with the DNase‐resistant "xenonucleic acid" 2'O‐methyl‐RNA in a live rat. In contrast to the sensor employing the equivalent DNA aptamer, the 2'O‐methyl‐RNA aptamer sensor lost very little signal and had improved signal‐to‐noise. We further characterized the EAB sensor drift using unstructured DNA or 2'O‐methyl‐RNA oligonucleotides. While the two devices drift similarly in vitro in whole blood, the in vivo drift of the 2'O‐methyl‐RNA‐employing device is less compared to the DNA‐employing device. Studies of the electron transfer kinetics suggested that the greater drift of the latter sensor arises due to enzymatic DNA degradation. These findings, coupled with advances in the selection of aptamers employing XNA, suggest a means of improving EAB sensor stability when they are used to perform molecular monitoring in the living body. [ABSTRACT FROM AUTHOR]

Background: Traditional gel-based (wet) electrodes for biopotential recordings have several shortcomings that limit their practicality for real-world measurements. Dry electrodes may improve usability, but they often suffer from reduced signal quality. We sought to evaluate the biopotential recording properties of a novel mixed ionic–electronic conductive (MIEC) material for improved performance. Methods: We fabricated four MIEC electrode form factors and compared their signal recording properties to two control electrodes, which are electrodes commonly used for biopotential recordings (Ag-AgCl and stainless steel). We used an agar synthetic skin to characterize the impedance of each electrode form factor. An electrical phantom setup allowed us to compare the recording quality of simulated biopotentials with ground-truth sources. Results: All MIEC electrode form factors yielded impedances in a similar range to the control electrodes (all <80 kΩ at 100 Hz). Three of the four MIEC samples produced similar signal-to-noise ratios and interfacial charge transfers as the control electrodes. Conclusions: The MIEC electrodes demonstrated similar and, in some cases, better signal recording characteristics than current state-of-the-art electrodes. MIEC electrodes can also be fabricated into a myriad of form factors, underscoring the great potential this novel material has across a wide range of biopotential recording applications. [ABSTRACT FROM AUTHOR]

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

Reference signal reconstruction serves as a crucial technique for suppressing multipath interference and noise in the reference channel of passive radar. Aiming at the challenge of detecting Low-Slow-Small (LSS) targets using Digital Terrestrial Multimedia Broadcasting (DTMB) signals, this article proposes a novel segmented sliding window reference signal reconstruction method based on Fuzzy C-Means (FCM). By partitioning the reference signals based on the structure of DTMB signal frames, this approach compensates for frequency offset and sample rate deviation individually for each segment. Additionally, FCM clustering is utilized for symbol mapping reconstruction. Both simulation and experimental results show that the proposed method significantly suppresses constellation diagram divergence and phase rotation, increases the adaptive cancellation gain and signal-to-noise ratio (SNR), and in the meantime reduces the computation cost. [ABSTRACT FROM AUTHOR]

Underwater acoustic vector sensors (UAVSs) are increasingly utilized for remote passive sonar detection, but the accuracy of direction-of-arrival (DOA) estimation remains a challenging problem, particularly under low signal-to-noise ratio (SNR) conditions and complex background noise. In this paper, a comprehensive theoretical analysis is conducted on UAVS signal preprocessing subjected to gain-phase uncertainties for average acoustic intensity measurement (AAIM) and complex acoustic intensity measurement (CAIM)-based vector DOA estimation, aiming to explain the theoretical restrictions of intensity-based vector acoustic preprocessing approaches. On this basis, a generalized vector acoustic preprocessing optimization model is established in which the principle can be described as "maximizing the denoising performance under the constraints of an equivalent amplitude-gain response and phase-bias response". A novel vector acoustic preprocessing method named linear matched stochastic resonance (LMSR) is proposed within the framework of matched stochastic resonance theory, which can naturally guarantee the linear gain-phase restrictions, as well achieving effective denoising performance. Numerical analyses demonstrate the superior vector DOA estimation performance of our proposed LMSR-AAIM and LMSR-CAIM methods in comparison to classical intensity-based AAIM and CAIM methods, especially under low-SNR conditions and non-Gaussian impulsive noise circumstances. Experimental verification conducted in the South China Sea further verifies its the effectiveness for practical application. This work can lay a solid foundation to break through the challenges of underwater remote vector acoustic DOA estimation under low-SNR conditions and complex ocean ambient noise and can provide important guidance for future research work. [ABSTRACT FROM AUTHOR]

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

Background: Spatial molecular profiling depends on accurate cell segmentation. Identification and quantitation of individual cells in dense tissues, e.g. highly inflamed tissue caused by viral infection or immune reaction, remains a challenge. Methods: We first assess the performance of 18 deep learning-based cell segmentation models, either pre-trained or trained by us using two public image sets, on a set of immunofluorescence images stained with immune cell surface markers in skin tissue obtained during human herpes simplex virus (HSV) infection. We then further train eight of these models using up to 10,000+ training instances from the current image set. Finally, we seek to improve performance by tuning parameters of the most successful method from the previous step. Results: The best model before fine-tuning achieves a mean Average Precision (mAP) of 0.516. Prediction performance improves substantially after training. The best model is the cyto model from Cellpose. After training, it achieves an mAP of 0.694; with further parameter tuning, the mAP reaches 0.711. Conclusion: Selecting the best model among the existing approaches and further training the model with images of interest produce the most gain in prediction performance. The performance of the resulting model compares favorably to human performance. The imperfection of the final model performance can be attributed to the moderate signal-to-noise ratio in the imageset. [ABSTRACT FROM AUTHOR]

During the Water Vapor Lidar Network Assimilation (WaLiNeAs) campaign, 8 lidars specifically designed to measure water vapor mixing ratio (WVMR) profiles were deployed on the western Mediterranean coast. The main objectives were to investigate the water vapor content during case studies of heavy precipitation events in the coastal Western Mediterranean and assess the impact of high spatio-temporal WVMR data on numerical weather prediction forecasts by means of state–of–the–art assimilation techniques. Given the increasing occurrence of extreme events due to climate change, WaLiNeAs is the first program in Europe to provide network–like, simultaneous and continuous water vapor profile measurements. This paper focuses on the WVMR profiling datasets obtained from three of the lidars managed by the French component of the WaLiNeAs team. These lidars were deployed in the towns of Coursan, Grau du Roi and Cannes. This measurement setup enabled monitoring of the water vapor content within the low troposphere along a period of three months over autumn – winter 2022 and four months in summer 2023. The lidars measured the WVMR profiles from the surface up to approximately 6–10 km at night, and 1–2 km during daytime; with a vertical resolution of 100 m and a time sampling between 15 – 30 min, selected to meet the needs of weather forecasting with an uncertainty lower than 0.4 g kg-1. The paper presents details about the instruments, the experimental strategy, as well as the datasets given in NETcdf format. The final dataset is divided in two datasets, the first with a time resolution of 15 min, which contains a total of 26 423 WVMR vertical profiles and the second with a time resolution of 30 min to improve the signal to noise ratio and signal altitude range. [ABSTRACT FROM AUTHOR]

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]

Capillary electrophoresis with capacitively coupled contactless conductivity detection (CE-C4D) has the advantages of high throughput (simultaneous detection of multiple ions), high separation efficiency (higher than 105 theoretical plates) and rapid analysis capability (less than 5 min for common inorganic ions). A compact CE-C4D system is ideal for water quality control and on-site analysis. It is suitable not only for common cations (e.g. Na+, K+, Li+, NH4+, Ca2+, etc.) and anions (e.g. Cl−, SO42−, BrO3−, etc.) but also for some ions (e.g. lanthanide ions, Pb2+, Cd2+, etc.) that require complex derivatization procedures to be detected by ion chromatography (IC). However, an obvious limitation of the CE-C4D method is that its sensitivity (e.g. 0.3–1 μM for common inorganic ions) is often insufficient for trace analysis (e.g. 1 ppb or 20 nM level for common inorganic ions) without preconcentration. For this technology to become a powerful and routine analytical technique, the system should be made compact while maintaining trace analysis sensitivity. In this study, we developed an all-in-one version of the CE-C4D instrument with custom-made modular components to make it a convenient, compact and high-performance system. The system was designed using direct digital synthesis (DDS) technology to generate programmable sinusoidal waveforms with any frequency for excitation, a kilovolt high-voltage power supply for capillary electrophoresis separation, and an "effective" differential C4D cell with a low-noise circuitry for high-sensitivity detection. We characterized the system with different concentrations of Cs+, and even a low concentration of 20 nM was detectable without preconcentration. Moreover, the optimized CE-C4D setup was applied to analyse mixed ions at a trace concentration of 200 nM with excellent signal-to-noise ratios. In typical applications, the limits of detection based on the 3σ criterion (without baseline filtering) were 9, 10, 24, 5, and 12 nM for K+, Cs+, Li+, Ca2+, and Mg2+, respectively, and about 7, 6, 6 and 6 nM for Br−, ClO4−, BrO3− and SO42−, respectively. Finally, the setup was also applied for the analysis of all 14 lanthanide ions and rare-earth minerals, and it showed an improvement in sensitivity by more than 25 times. [ABSTRACT FROM AUTHOR]

The quest for higher spatial and/or temporal resolution in functional MRI (fMRI) while preserving a sufficient temporal signal-to-noise ratio (tSNR) has generated a tremendous amount of methodological contributions in the last decade ranging from Cartesian vs. non-Cartesian readouts, 2D vs. 3D acquisition strategies, parallel imaging and/or compressed sensing (CS) accelerations and simultaneous multi-slice acquisitions to cite a few. In this paper, we investigate the use of a finely tuned version of 3D-SPARKLING. This is a non-Cartesian CS-based acquisition technique for high spatial resolution whole-brain fMRI. We compare it to state-of-the-art Cartesian 3D-EPI during both a retinotopic mapping paradigm and resting-state acquisitions at 1mm3 (isotropic spatial resolution). This study involves six healthy volunteers and both acquisition sequences were run on each individual in a randomly-balanced order across subjects. The performances of both acquisition techniques are compared to each other in regards to tSNR, sensitivity to the BOLD effect and spatial specificity. Our findings reveal that 3D-SPARKLING has a higher tSNR than 3D-EPI, an improved sensitivity to detect the BOLD contrast in the gray matter, and an improved spatial specificity. Compared to 3D-EPI, 3D-SPARKLING yields, on average, 7% more activated voxels in the gray matter relative to the total number of activated voxels. [ABSTRACT FROM AUTHOR]

The metasurface is a promising technology that can help next-generation wireless communication systems not only improve the signal-to-noise ratio (SNR), but also increase security and mitigate interference. Further, introducing dual polarization (DP) in a metasurface can enhance its capabilities with polarization diversity, polarization multiplexing, and polarization-switched modulation. In this paper, we study a DP-metasurface-assisted single-user wireless communication system and propose a novel scheme that can improve the spectral efficiency (SE) and bit-error-rate (BER) performance compared to those of conventional schemes by exploiting the orthogonal property of dual-polarized waves. We employ the DP metasurface to increase the SNR at the receiver and create a specific phase difference between the polarized signals by controlling the transmit precoder and the phases of the metasurface reflecting elements representing some modulated bits. At the receiver, we use the recovered phase information to realign the modulated symbols in both polarizations, which are then added coherently. The simulation results demonstrate that the proposed scheme achieves significantly higher SE and BER performance than those of some closely related works. [ABSTRACT FROM AUTHOR]

Background: With recent advances in magnetic resonance imaging (MRI) technology, the practical role of lung MRI is expanding despite the inherent challenges of the thorax. The purpose of our study was to evaluate the current status of the concurrent dephasing and excitation (CODE) ultrashort echo-time sequence and the T1-weighted volumetric interpolated breath-hold examination (VIBE) sequence in the evaluation of thoracic disease by comparing it with the gold standard computed tomography (CT). Methods: Twenty-four patients with lung cancer and mediastinal masses underwent both CT and MRI including T1-weighted VIBE and CODE. For CODE images, data were acquired in free breathing and end-expiratory images were reconstructed using retrospective respiratory gating. All images were evaluated through qualitative and quantitative approaches regarding various anatomical structures and lesions (nodule, mediastinal mass, emphysema, reticulation, honeycombing, bronchiectasis, pleural plaque and lymphadenopathy) inside the thorax in terms of diagnostic performance in making specific decisions. Results: Depiction of the lung parenchyma, mediastinal and pleural lesion was not significant different among the three modalities (p > 0.05). Intra-tumoral and peritumoral features of lung nodules were not significant different in the CT, VIBE or CODE images (p > 0.05). However, VIBE and CODE had significantly lower image quality and poorer depiction of airway, great vessels, and emphysema compared to CT (p < 0.05). Image quality of central airways and depiction of bronchi were significantly better in CODE than in VIBE (p < 0.001 and p = 0.005). In contrast, the depiction of the vasculature was better for VIBE than CODE images (p = 0.003). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were significant greater in VIBE than CODE except for SNRlung and SNRnodule (p < 0.05). Conclusions: Our study showed the potential of CODE and VIBE sequences in the evaluation of localized thoracic abnormalities including solid pulmonary nodules. [ABSTRACT FROM AUTHOR]

For a narrow-brand seismograph with a flat response range limited, it cannot precisely record the signal of a ground motion and output the records with the low-frequency components cut down. A transfer function is usually used to spread the spectrum of the narrow-brand seismic records. However, the accuracy of the commonly used transfer function is not high. The authors derive a new transfer function based on the Laplace transform, bilinear transform, and Nyquist sampling theory to solve this problem. And then, the derived transfer function is used to correct the narrow-band velocity records from the Hi-net. The corrected velocity records are compared with the velocities integrated from the synchronously recorded broad-band acceleration at the same station with Hi-net. Meanwhile, the corrected records are compared with those corrected by the Nakata transfer function. The results show that the calculation accuracy of the derived transfer function is higher than the Nakata transfer function. However, when the signal-to-noise ratio is below 24, its accuracy diminishes, and it is unable to recover signals within the 0.05–0.78Hz frequency band. [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]

Underwater images often exhibit detail blurring and color distortion due to light scattering, impurities, and other influences, obscuring essential textures and details. This presents a challenge for existing super-resolution techniques in identifying and extracting effective features, making high-quality reconstruction difficult. This research aims to innovate underwater image super-resolution technology to tackle this challenge. Initially, an underwater image degradation model was created by integrating random subsampling, Gaussian blur, mixed noise, and suspended particle simulation to generate a highly realistic synthetic dataset, thereby training the network to adapt to various degradation factors. Subsequently, to enhance the network's capability to extract key features, improvements were made based on the symmetrically structured blind super-resolution generative adversarial network (BSRGAN) model architecture. An attention mechanism based on energy functions was introduced within the generator to assess the importance of each pixel, and a weighted fusion strategy of adversarial loss, reconstruction loss, and perceptual loss was utilized to improve the quality of image reconstruction. Experimental results demonstrated that the proposed method achieved significant improvements in peak signal-to-noise ratio (PSNR) and underwater image quality measure (UIQM) by 0.85 dB and 0.19, respectively, significantly enhancing the visual perception quality and indicating its feasibility in super-resolution applications. [ABSTRACT FROM AUTHOR]