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Molecular quantum-dot cellular automata (QCA) may provide high speed, low-power, classical information processing in the post-CMOS era. The readout of molecular QCA bits is challenging because the molecules may be much smaller than transistors and even single-electron transistors. This paper builds on a recent proposal for the spectroscopic readout of bits from asymmetric QCA molecules. Here, we propose circuits for fanning out a bit onto a large QCA circuit to increase the spectroscopic signal-to-noise ratio. As the number of molecules in a fanout circuit grows, the internal bias of each asymmetric cell accumulates, and the circuit may become stuck, tolerating only a very small internal bias. We also propose the use of an applied electric field to compensate for a candidate molecule's internal bias, thereby restoring switchability, even when the internal bias is significant. [ABSTRACT FROM AUTHOR]

The two-dimensional laser-based Ångstrom method measures the in-plane thermal properties for anisotropic film-like materials. It involves periodic laser heating at the center of a suspended film sample and records its transient thermal response by infrared imaging. These spatiotemporal temperature data must be analyzed to extract the unknown thermal conductivity values in the orthotropic directions, an inverse parameter fitting problem. Previous demonstration of the metrology technique used a least-squares fitting method that relies on numerical differentiation to evaluate the second-order partial derivatives in the differential equation describing transient conduction in the physical system. This fitting approach is susceptible to measurement noise, introducing high uncertainty in the extracted properties when working with noisy data. For example, when noise of a signal-to-noise ratio of 10 is added to simulated amplitude and phase data, the error in the extracted thermal conductivity can exceed 80%. In this work, we introduce a new alternative inverse parameter fitting approach using physics-informed neural networks (PINNs) to increase the robustness of the measurement technique for noisy temperature data. We demonstrate the effectiveness of this approach even for scenarios with extreme levels of noise in the data. Specifically, the PINN-approach accurately extracts the properties to within 5% of the true values even for high noise levels (a signal-to-noise ratio of 1). This offers a promising avenue for improving the robustness and accuracy of advanced thermal metrology tools that rely on inverse parameter fitting of temperature data to extract thermal properties. [ABSTRACT FROM AUTHOR]

Piezoresponse force microscopy (PFM) is one of the most widespread methods for investigating and visualizing ferroelectric domain structures down to the nanometer length scale. PFM makes use of the direct coupling of the piezoelectric response to the crystal lattice, and hence, it is most often applied to spatially map the three-dimensional (3D) near-surface domain distribution of any polar or ferroic sample. Nonetheless, since most samples investigated by PFM are at least semiconducting or fully insulating, the electric ac field emerging from the conductive scanning force microscopy (SFM) tip penetrates the sample and, hence, may also couple to polar features that are deeply buried into the bulk of the sample under investigation. Thus, in the work presented here, we experimentally and theoretically explore the contrast and depth resolution capabilities of PFM, by analyzing the dependence of several key parameters. These key parameters include the depth of the buried feature, i.e., here a domain wall (DW), as well as PFM-relevant technical parameters such as the tip radius, the PFM drive voltage and frequency, and the signal-to-noise ratio. The theoretical predictions are experimentally verified using x-cut periodically poled lithium niobate single crystals that are specially prepared into wedge-shaped samples, in order to allow the buried feature, here the DW, to be "positioned" at any depth into the bulk. This inspection essentially contributes to the fundamental understanding in PFM contrast analysis and to the reconstruction of 3D domain structures down to a 1 μ m-penetration depth into the sample. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The disco-vertebral junction (DVJ) of the lumbar spine contains thin structures with short T2 values, including the cartilaginous endplate (CEP) sandwiched between the bony vertebral endplate (VEP) and the nucleus pulposus (NP). We previously demonstrated that ultrashort-echo-time (UTE) MRI, compared to conventional MRI, is able to depict the tissues at the DVJ with improved contrast. In this study, we sought to further optimize UTE MRI by characterizing the contrast-to-noise ratio (CNR) of these tissues when either single echo or echo subtraction images are used and with varying echo times (TEs). METHODS: In four cadaveric lumbar spines, we acquired 3D Cones (a UTE sequence) images at varying TEs from 0.032 ms to 16 ms. Additionally, spin echo T1- and T2-weighted images were acquired. The CNRs of CEP-NP and CEP-VEP were measured in all source images and 3D Cones echo subtraction images. RESULTS: In the spin echo images, it was challenging to distinguish the CEP from the VEP, as both had low signal intensity. However, the 3D Cones source images at the shortest TE of 0.032 ms provided an excellent contrast between the CEP and the VEP. As the TE increased, the contrast decreased in the source images. In contrast, the 3D Cones echo subtraction images showed increasing CNR values as the second TE increased, reaching statistical significance when the second TE was above 10 ms (p < 0.05). CONCLUSIONS: Our study highlights the feasibility of incorporating UTE MRI for the evaluation of the DVJ and its advantages over conventional spin echo sequences for improving the contrast between the CEP and adjacent tissues. Additionally, modulation of the contrast for the target tissues can be achieved using either source images or subtraction images, as well as by varying the echo times.

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]

BACKGROUND: The monitoring and analysis of quasi-periodic biological signals such as electrocardiography (ECG), intracranial pressure (ICP), and cerebral blood flow velocity (CBFV) waveforms plays an important role in the early detection of adverse patient events and contributes to improved care management in the intensive care unit (ICU). This work quantitatively evaluates existing computational frameworks for automatically extracting peaks within ICP waveforms. METHODS: Peak detection techniques based on state-of-the-art machine learning models were evaluated in terms of robustness to varying noise levels. The evaluation was performed on a dataset of ICP signals assembled from 700 h of monitoring from 64 neurosurgical patients. The groundtruth of the peak locations was established manually on a subset of 13, 611 pulses. Additional evaluation was performed using a simulated dataset of ICP with controlled temporal dynamics and noise. RESULTS: The quantitative analysis of peak detection algorithms applied to individual waveforms indicates that most techniques provide acceptable accuracy with a mean absolute error (MAE) ≤ 10 ms without noise. In the presence of a higher noise level, however, only kernel spectral regression and random forest remain below that error threshold while the performance of other techniques deteriorates. Our experiments also demonstrated that tracking methods such as Bayesian inference and long short-term memory (LSTM) can be applied continuously and provide additional robustness in situations where single pulse analysis methods fail, such as missing data. CONCLUSION: While machine learning-based peak detection methods require manually labeled data for training, these models outperform conventional signal processing ones based on handcrafted rules and should be considered for peak detection in modern frameworks. In particular, peak tracking methods that incorporate temporal information between successive periods of the signals have demonstrated in our experiments to provide more robustness to noise and temporary artifacts that commonly arise as part of the monitoring setup in the clinical setting.

BACKGROUND: Global shortages of iodinated contrast media (ICM) during COVID-19 pandemic forced the imaging community to use ICM more strategically in CT exams. PURPOSE: The purpose of this work is to provide a quantitative framework for preserving iodine CNR while reducing ICM dosage by either lowering kV in single-energy CT (SECT) or using lower energy virtual monochromatic images (VMI) from dual-energy CT (DECT) in a phantom study. MATERIALS AND METHODS: In SECT study, phantoms with effective diameters of 9.7, 15.9, 21.1, and 28.5 cm were scanned on SECT scanners of two different manufacturers at a range of tube voltages. Statistical based iterative reconstruction and deep learning reconstruction were used. In DECT study, phantoms with effective diameters of 20, 29.5, 34.6, and 39.7 cm were scanned on DECT scanners from three different manufacturers. VMIs were created from 40 to 140 keV. ICM reduction by lowering kV levels for SECT or switching from SECT to DECT was calculated based on the linear relationship between iodine CNR and its concentration under different scanning conditions. RESULTS: On SECT scanner A, while matching CNR at 120 kV, ICM reductions of 21%, 58%, and 72% were achieved at 100, 80, and 70 kV, respectively. On SECT scanner B, 27% and 80% ICM reduction was obtained at 80 and 100 kV. On the Fast-kV switch DECT, with CNR matched at 120 kV, ICM reductions were 35%, 30%, 23%, and 15% with VMIs at 40, 50, 60, and 68 keV, respectively. On the dual-source DECT, ICM reductions were 52%, 48%, 42%, 33%, and 22% with VMIs at 40, 50, 60, 70, and 80 keV. On the dual-layer DECT, ICM reductions were 74%, 62%, 45%, and 22% with VMIs at 40, 50, 60, and 70 keV. CONCLUSIONS: Our work provided a quantitative baseline for other institutions to further optimize their scanning protocols to reduce the use of ICM.

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]

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]

This paper proposes a model for encrypted images that depend on chaotic maps. This scheme uses eight chaotic maps to perform the encryption process: Logistic, Gauss, Circle, Sine, Singer, Piecewise, Tent, and Chebyshev. The two major processes of the suggested model are chaotic confusion and pixel diffusion. Chaotic maps are used to permute the pixel positions during the confusion process. In the diffusion process, the value of the image pixel is changed. To evaluate the suggested model, some performance metrics were used, such as execution time, peak signal-to-noise ratio, entropy, key sensitivity, noise attack, the number of pixels change rate (NPCR), unified average changing intensity (UACI), histogram analysis, and cross-correlation. According to experimental analysis, images encrypted with the suggested system have correlation coefficient values that are almost zero, NPCR of 99.6%, UACI of 32.9%, the key space of 10^(80), the histogram analysis showed that the encrypted images have almost similar pixels, an execution time of 0.1563 ms, the, and entropy of 7.9973. All prior results have verified the robustness and efficiency of the suggested algorithm. [ABSTRACT FROM AUTHOR]

Modern security faces challenges in detecting unauthorized and potentially harmful items in luggage, despite X-ray baggage scanning frameworks and research on efficiently screening highly disguised items. In response to this gap, a groundbreaking Modified Encoder-Decoder-based model has been introduced. This innovative model takes X-ray scan images as input and generates distinct feature representations for both suspicious and non-suspicious baggage materials. A key focus of the model is to address the denoising challenge inherent in X-ray images which reduces the models efficiency. This is achieved through the implementation of a Poisson Noise Reduction method during the preprocessing stage. Following preprocessing, the model effectively segments the non-threat image, identifying potential threats from the denoised input. The model showcases superior performance, as evidenced by high Peak Signal-to-Noise Ratio (PSNR) and low Mean Squared Error (MSE) values, outperforming existing filtering techniques. Rigorous testing on publicly available SIXray and GDXray datasets validates the effectiveness of the proposed methodology. Performance metrics for the SIXray dataset, including mAP, IoU, and DC values of 97.32%, 73.14%, and 85.12%, respectively, underscore the model's efficacy. Notably, the framework attains an impressive accuracy of 99.17% on the SIXray dataset, affirming its robustness in addressing contemporary security challenges. [ABSTRACT FROM AUTHOR]

To address this issue of streak artifacts and noise caused by faster scanning in this paper, this paper presents a prunable U-NET++ with hybrid attention and loss function (HAL-UNET++) deep learning network, which is a U-shaped framework-based network for industrial CT image reconstruction. In this reconstruction network, the prior knowledge that CT images are sparse in the gradient domain is utilized, and a hybrid loss function is designed to combine the L0 norm of gradient image and the image signal-to-noise ratio. This ensures that the network's learning process places greater emphasis on the noise content and sparsity of the image gradient domain. Subsequently, an adaptable convolutional block attention module has been incorporated into the shallow network layers to ensure the preservation of finer details in the CT images that are reconstructed. Finally, experimental objects comprising cables and rock samples with internal defects were used. The results indicate that the present reconstruction network, through pruning techniques, achieves rapid image reconstruction. Under the constraint of maintaining reconstruction accuracy, the pruned network demonstrates a reduction of approximately fourfold in network parameter volume compared to the unpruned network, resulting in a time saving of 0.24 seconds for the reconstruction of a single CT image. [ABSTRACT FROM AUTHOR]

Rotary encoder signal has been attracting considerable attention for health estimation of the rotating elements, since its advantages in lower testing costs and sensitive to torsional stiffness loss caused by incipient faults. However, the practical encoder signal often keeps low signal-to-noise ratio due to its instantaneous angular acceleration conventional method, which brings new challenges for the commonly used feature extraction methods. Sparse decomposition is an efficient method for fault impact features extracting of rotating machinery. However, limited by the quality of initial dictionary and the learning efficiency of K-SVD algorithm, commonly used sparse decomposition is susceptible to the signal noise, especially when unsuitable initial atoms are applied. To overcome this problem, a simple but effective framework term Generalized Gini indices-enhanced sparsity decomposition is established in this paper for practical encoder signal analysis. Firstly, the de-trended and frequency domain weighting methods are used for instantaneous angular acceleration estimation. After that, a Generalized Gini indices guided tunable Q-factor wavelet filter is introduced for the atom selection. Then, the shift-invariant K-SVD method is introduced for dictionary updating and sparse coefficients calculation. Through the analysis of simulation and experiment, the transient characteristics of the gear can be extracted well which provides an alternative methodology for incipient fault detection of rotating machinery. [ABSTRACT FROM AUTHOR]

Reconstruction of missing seismic traces is one of the key steps in seismic data processing. Deep neural network-based interpolation methods for seismic trace reconstruction have attracted much attention in recent years. However, the currently widely used supervised-based deep neural network interpolation models require the support of labeled data, which is difficult to obtain in real seismic exploration. In this study, we propose a novel unsupervised deep learning interpolation framework SSTI (Self-supervised Seismic Trace Interpolation), designed to address the limitations of existing supervised-based models by avoiding the need of labeled data. SSTI employs a unique combination of two semi-supervised models: temporal ensembling model and mean teacher model. The temporal ensembling model and mean teacher model jointly predict the missing traces by integrating the outputs and weights of each training epoch through Exponential Moving Average method, respectively. Furthermore, a joint loss function that combines consistency loss and self-supervised loss is designed to guide the training process of SSTI. We evaluated the interpolation performance of SSTI on synthetic and field seismic data. Comparative analyses demonstrate that our proposed method outperforms the traditional theory-guided methods in terms of mean square error, peak signal to noise ratio, structural similarity, and quality of the reconstructed traces. Notably, although it does not achieve the interpolation performance of supervised deep learning methods, SSTI has shown great potential since there is only a marginal performance difference between SSTI and supervised-based methods. Our findings suggest that SSTI presents a promising advancement in the field of unsupervised deep learning methods for seismic trace interpolation. The proposed architecture holds great potential for recovering the missing and corrupted traces at a low cost in field seismic exploration. [ABSTRACT FROM AUTHOR]

The technology for classifying and recognizing underwater targets is crucial for supporting underwater acoustic information countermeasures. The research focus is on the extraction and classification of features of underwater targets. Researchers have conducted an in-depth study from various perspectives. Due to the influence of ambient noise and various operating conditions of different targets, the signal-to-noise ratio of underwater acoustic signals is generally meager. Additionally, the components of these signals are complex, often requiring specific signal pre-processing techniques such as signal enhancement and decomposition. In current methods, there is a primary focus on extracting and classifying features of underwater acoustic signals after multi-step preprocessing. However, these methods do not effectively integrate feature extraction and classification. To address these limitations, we propose a new model called Depthwise Separable Convolutional Attention (DSCA) and use multiple instances of DSCA to construct a neural network, which we call DSCANet. The DSCANet integrates feature extraction and target classification for underwater acoustic targets. The 'target' in our work should be mentioned as it refers to underwater sources of sound. The structure of DSCANet is unified and simple, and no specific pre-processing of the underwater acoustic signal is necessary. The DSCANet is trained and validated on ShipsEars, an open dataset. It achieves a classification accuracy of 93%, which is the highest in the contrast experiment. [ABSTRACT FROM AUTHOR]

This paper presents an image demosaicing based on an optimization-driven deep learning model, namely the Autoregressive Water Wave Optimization algorithm (Autoregressive-WWO). The proposed method is devised by assimilating the Wave Optimization algorithm (WWO), and the Conditional autoregressive value at risk (CAViaR) model. Here, the input images are subjected to Autoregressive WWO-based local polynomial approximation and intersection of confidence intervals (LPA-ICI) filter, and Deep Convolution neural network (Deep CNN) in a concurrent manner. The filter coefficients are obtained from the proposed Autoregressive WWO-based LPA-ICI filter and the residual image is obtained from Deep CNN. In order to create the demosaiced image, these two outputs are combined using an entropy measure. The proposed method offered superior performance with the highest Peak signal to noise ratio (PSNR) of 40.049dB, the highest Second derivative measure of enhancement (SDME) of 50.168dB, and highest Structural Index Similarity (SSIM) of 0.9056. [ABSTRACT FROM AUTHOR]

Using songs to facilitate speech processing in noisy environments seems appealing and practical. However, current research suggests otherwise, possibly due to the simplicity of sung speech contours. This study investigates the effects of contour tonality on sung speech intelligibility in noisy environments. A cohort of 20 trained musicians and 20 nonmusicians were tested on the intelligibility of Mandarin sentences sung on tonal, atonal, and fixed-pitch melodies or normally spoken under three signal-to-noise ratios (SNRs: −3, −6, and −9 dB). Perceptual musical skills related to speech-in-noise perception were also assessed. Results showed that overall speech-in-noise intelligibility decreased as the SNR decreased, with spoken speech being more intelligible than sung speech. Sung speech intelligibility was higher for fixed- than variable-pitch contours, with no difference between tonal and atonal melodies. No musician advantage was reported for spoken speech. Musicians, nonetheless, outperformed nonmusicians in identifying sung speech across all melodic contour types. Moreover, the musician sung speech advantage correlated with enhanced music perception abilities on pitch and accent. These results suggest that musicians have an advantage in sung speech in noisy environments. However, melody tonality provided no additional benefits, suggesting that imposing tonality on sung speech does not improve speech perception in noisy environments. [ABSTRACT FROM AUTHOR]

PURPOSE: The primary results of phase III oncology trials may be challenging to interpret, given that results are generally based on P value thresholds. The probability of whether a treatment is beneficial, although more intuitive, is not usually provided. Here, we developed and released a user-friendly tool that calculates the probability of treatment benefit using trial summary statistics. METHODS: We curated 415 phase III randomized trials enrolling 338,600 patients published between 2004 and 2020. A phase III prior probability distribution for the treatment effect was developed on the basis of a three-component zero-mean mixture distribution of the observed z-scores. Using this prior, we computed the probability of clinically meaningful benefit (hazard ratio [HR] <0.8). The distribution of signal-to-noise ratios and power of phase III oncology trials were compared with that of 23,551 randomized trials from the Cochrane Database. RESULTS: The signal-to-noise ratios of phase III oncology trials tended to be much larger than randomized trials from the Cochrane Database. Still, the median power of phase III oncology trials was only 49% (IQR, 14%-95%), and the power was <80% in 65% of trials. Using the phase III oncology-specific prior, only 53% of trials claiming superiority (114 of 216) had a ≥90% probability of clinically meaningful benefits. Conversely, the probability that the experimental arm was superior to the control arm (HR <1) exceeded 90% in 17% of trials interpreted as having no benefit (34 of 199). CONCLUSION: By enabling computation of contextual probabilities for the treatment effect from summary statistics, our robust, highly practical tool, now posted on a user-friendly webpage, can aid the wider oncology community in the interpretation of phase III trials. A novel prior facilitates estimation of phase III treatment effect probabilities from summary statistics. [ABSTRACT FROM AUTHOR]

This paper aims to enhance the surface quality of digital light processing (DLP) printed lead zirconate titanate (PZT) components by controlling the content of BYK-111 dispersant, thereby improving the electrical properties of the resulting printed piezoelectric ceramics and devices. 55 vol% high solid loading PZT slurries with different dispersant contents were prepared and assessed for their dispersion stability, rheological behavior and curing characteristics. The slurry containing 1 wt% BYK-111 demonstrated ideal rheological properties, notably achieving a low viscosity of 1.37 Pa·s at 100 s−1. The surface roughness of the printed ceramic component was reduced to 69.3 nm, representing a 57.1 % improvement in surface quality compared to other dispersant levels. The printed PZT ceramic exhibited superior piezoelectric performance, with a piezoelectric constant d 33 reaching 445 pC/N, surpassing most existing 3D-printed macro-sized piezoelectric ceramics. Furthermore, a DLP-printed 1–3 piezoelectric array was encapsulated to fabricate an acoustic emission (AE) sensor, which achieved a low substrate noise level of 31 dB and a high signal-to-noise ratio of 62 dB, outperforming commercial sensors. The optimal BYK-111 content not only ensures favorable dispersion stability and rheological properties in the slurry, but also significantly enhances the surface quality and density of the printed ceramic components, ultimately leading to the production of high-performance piezoelectric ceramics and devices. [ABSTRACT FROM AUTHOR]

We provide a comprehensive technical analysis of the data acquisition process with oscillating delay lines for Terahertz-time domain spectroscopy. The utilization of these rapid stages, particularly in high-repetition-rate systems, is known to enable an effective reduction of noise content through averaging. However, caution must be exercised to optimize the data averaging process, with the goal of significantly optimizing the dynamic range (DR) and signal-to-noise ratio (SNR). Here we discuss some pitfalls to avoid and the effect of improper data handling on the dynamic range obtainable. A free and open-source program, called parrot (Processing All Rapidly & Reliably Obtained THz-traces), is provided alongside this publication to overcome the discussed pitfalls and facilitate the acceleration of experimental setups and data analysis, thereby enhancing signal fidelity and reproducibility. [ABSTRACT FROM AUTHOR]

Image denoising has gained in relevance as a component of image preprocessing due to the increased use of digital images in a range of applications, as well as the degradation of image quality caused by noise introduced by unavoidable occurrences. This work suggests a novel two-stage filter to remove salt and pepper noise from the images. It operates in two stages, the first stage uses an enhanced adaptive type-2 fuzzy noise identifier to identify the corrupted pixel, and the second stage uses a simple linear regression-based approach filter to denoise the corrupted pixel. We first identify a pixel as corrupted or uncorrupted using an improved adaptive type-2 fuzzy-based Gaussian membership function with variables both mean and variance for a specific corrupted image frame. The second step is denoising the damaged pixel using a linear regression-based technique. Herein, we propose a novel co-design method that uses the Gaussian membership function for detection and a linear regression-based denoising technique without any parameter tuning, resulting in better time efficiency. We validate the proposed improved adaptive type-2 fuzzy detection and linear regression-based filter (IAFDLRBF) on a variety of standard images and real-time images with varying noise density. We compare the simulation results with various state-of-the-art methods in terms of various assessment metrics. The results demonstrate the effectiveness of the proposed filter even at high noise densities by providing better detail and edge preservation of an image. [ABSTRACT FROM AUTHOR]

Objectives: To assess a deep learning-based reconstruction algorithm (DLRecon) in zero echo-time (ZTE) MRI of the shoulder at 1.5 Tesla for improved delineation of osseous findings. Methods: In this retrospective study, 63 consecutive exams of 52 patients (28 female) undergoing shoulder MRI at 1.5 Tesla in clinical routine were included. Coronal 3D isotropic radial ZTE pulse sequences were acquired in the standard MR shoulder protocol. In addition to standard-of-care (SOC) image reconstruction, the same raw data was reconstructed with a vendor-supplied prototype DLRecon algorithm. Exams were classified into three subgroups: no pathological findings, degenerative changes, and posttraumatic changes, respectively. Two blinded readers performed bone assessment on a 4-point scale (0-poor, 3-perfect) by qualitatively grading image quality features and delineation of osseous pathologies including diagnostic confidence in the respective subgroups. Quantitatively, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of bone were measured. Qualitative variables were compared using the Wilcoxon signed‐rank test for ordinal data and the McNemar test for dichotomous variables; quantitative measures were compared with Student's t-testing. Results: DLRecon scored significantly higher than SOC in all visual metrics of image quality (all, p < 0.03), except in the artifact category (p = 0.37). DLRecon also received superior qualitative scores for delineation of osseous pathologies and diagnostic confidence (p ≤ 0.03). Quantitatively, DLRecon achieved superior CNR (95 CI [1.4–3.1]) and SNR (95 CI [15.3–21.5]) of bone than SOC (p < 0.001). Conclusion: DLRecon enhanced image quality in ZTE MRI and improved delineation of osseous pathologies, allowing for increased diagnostic confidence in bone assessment. [ABSTRACT FROM AUTHOR]

The thick loess sediments produce a lot of noise during seismic acquisition, typically surface wave. Conventional methods are difficult while suppressing 3D surface waves since they are hyperbolic in the far-array. This study proposed a new model-based, data-driven surface wave attenuation method, which uses dispersion curve analysis of surface wave and joint inversion based on genetic algorithm (GA) and conjugate gradient algorithm, to build an accurate surface wave model, and then to subtract the model data from seismic data to improve the signal to noise ratio (S/N ratio). Located in the southern part of the Ordos Basin, the Loess Plateau has the characteristics of large thickness of loess sedimentary layer, diverse geomorphology, complex geological conditions and developed surface waves. After applying the method to the field seismic data, numerical tests show that joint inversion of dispersion waves was stable, accurate, and efficient in multi-type models, including models with a low-velocity layer and with a high-velocity layer. The result showed that the 3D surface wave attenuation method is effective in surface wave suppression. The low-frequency information and the reflected signal are efficiently preserved. Seismic faces are more apparent for reservoir characterization. This method provided credible data for the exploration of the loess tableland area. [ABSTRACT FROM AUTHOR]

This paper studies the interference impact on the performance of reconfigurable intelligent surface (RIS)-equipped decode-and-forward (DF) relay networks, where RIS is used as internal part of both the source and relay nodes. For that purpose, approximate closed-form expression is derived for the system outage probability assuming Rayleigh fading channels and opportunistic relaying scheme. Furthermore, to get more insights at the system performance, an approximate but accurate expression is achieved for the outage probability at the high signal-to-noise ratio (SNR) regime, where the system diversity order and coding gain are obtained and analyzed. The findings show that the system can achieve a diversity order of G d = min (N 1 , N 2) K , where N 1 and N 2 are the numbers of reflecting elements at the source and relays, respectively, and K is the number of relays. Additionally, results illustrate that for the same diversity order, utilizing one relay with multiple reflecting elements gives better performance than utilizing multiple relays each with a single reflecting element. Moreover, findings show that the number of relays not only affects the system diversity order, but also the coding gain of the first hop when it is dominating the system performance. Furthermore, results illustrate that when N 1 = N 2 , the system performance is dominated by the largest interference at either the relay node or the destination. With unequal interference powers, changing number of interferers at the node where interference is larger affects the performance, whereas changing number of interferers at the other node has no effect on the system behavior. On the other hand, when both nodes have the same interference power, changing number of interferers at either node by the same amount results in the same effect on the system performance. [ABSTRACT FROM AUTHOR]

Under the foggy environment, lane line images are obscured by haze, which leads to lower detection accuracy, higher false detection of lane lines. To address the above problems, a multi-layer feature fusion dehazing network based on CycleGAN architecture is proposed. Firstly, the foggy image is enhanced to remove the fog in the image, and then the lane line detection network is used for detection. For the dehazing network, a multi-layer feature fusion module is used in the generator to fuse the features of different coding layers of U-Net to enhance the network's recovery of information such as details and edges, and a frequency domain channel attention mechanism is added at the key nodes of the network to enhance the network's attention to different fog concentrations. At the same time, to improve the discriminant effect of the discriminator, the discriminator is extended to a global and local discriminator. The experimental results show that the dehaze effect on Reside and other test data sets is better than the comparison method. The peak signal-to-noise ratio is improved by 2.26 dB compared to the highest GCA-Net algorithm. According to the lane detection of fog images, it is found that the proposed network improves the accuracy of lane detection on foggy days. [ABSTRACT FROM AUTHOR]

Neutrosophic sets (NS) have referred to as interval fuzzy sets applied in minimizing the uncertainty and fuzziness in computer-vision and machine-learning communities and hence employed for several applications. As far as medical image processing applications are concerned NSs are obtained as an important technique for de-noising. Also, fuzzy segmentation with machine and deep learning is determined as a familiar procedure that splits input image into distinct regions for precise learning. Several research works conducted in different image-processing domains. However, less works was focused on denoising and segmentation of medical image processing with minimal time complexity and accuracy. In this work we plan to develop a Kalman--Bucy Filtered Neutrosophic Neuro Fuzzy Image Denoising (KBF-NNFID) method with the objective of reducing the noisy artifacts with higher peak signal-to-noise ratio in a computationally efficient manner. First, medical images obtained from Brain MRI LGG segmentation dataset are subjected to filtering employing Kalman Bucy Filtering algorithm with series of measurements examined. Second with the filtered medical images provided as input, uncertainty is handled by utilizing Neutrosophic Neuro Fuzzy set (NNFS) with help of the membership grade. With the aid of three membership grades, i.e., truth, indeterminacy and falsity, uncertainty involved in noisy image are said to be handled in a time efficient manner. By this way, an efficient image denoising process is performed with better PSNR. Experimental evaluation is carried out using medical images with different performance metrics such as enhanced PSNR and true positive rate up to 13%, 14% as well minimum execution time by 38% using medical images. [ABSTRACT FROM AUTHOR]

Frequency encoded reconfigurable logic gates OR and AND are designed using interferometric switch based on quantum dot semiconductor optical amplifier (QDSOA) and simulated using MATLAB for the first time as far as our knowledge goes. The amplitude modulation (AM) and quality factor ( Q fe ) are calculated using numerical simulations confirming practical feasibility of the logic gates. This paper also investigates the effect of amplified spontaneous emission (ASE) noise on AM, Q fe and signal-to-noise ratio (SNR) values. AM below 0.005 dB and Q fe more than 30 dB ensure error free operation of the logic gates. [ABSTRACT FROM AUTHOR]

The aim of this study was to optimise the vessel angle as well as the stack number from the profiles of carbon dioxide digital subtraction angiography (CO2-DSA) images of a water phantom containing an artificial vessel tilted at different angles which imitate arteries in the body. The artificial vessel was tilted at 0°, 15°, and 30° relative to the horizontal axis with its centre as the pivot point, and CO2-DSA images were acquired at each vessel tilt angle. The maximum opacity method was used to stack up to four images of the next frame one by one. The signal-to-noise ratio (SNR) was determined from the profile curves. The Wilcoxon rank sum test was used to evaluate whether the profile curve and SNR differed depending on the vessel tilt angle or stack number, and a p-value of less than 0.05 was considered statistically significant. Images acquired at 0° had a significantly lower SNR than images acquired at 15° (p = 0.10). When the vessel angle was 30°, the profile curves were significantly improved (p < 0.05) when two or more images were stacked over the original image. Images with a good SNR were acquired at the vessel tilt angle of 15°, and the shape of the profile curve was improved when two or more images were stacked on the original image. This study demonstrates that the quality of images acquired using CO2-DSA can be significantly improved through parameter optimisation for image acquisition and post-processing. [ABSTRACT FROM AUTHOR]

In the quest for high-speed, reliable and cost-effective backhaul solutions for modern cellular networks, the hybrid free space optical (FSO) and radio frequency (RF) communication system is envisaged to be a promising technology. The hybrid system merges the benefits of both RF and FSO subsystems, delivering high data rates and reliability. The integration of both technologies improves the communication system's performance by addressing the inherent limitations of each. This study proposes a singlethreshold hard-switching hybrid FSO/RF system for reliable pico-macrocell backhauling applications. We formulated closed-form expressions for the cumulative density functions (CDFs), probability density functions (PDFs), and outage probability (OP) for RF-only, FSO-only and hybrid FSO/RF links. The rician fading and gamma-gamma (G-G) channel distributions were utilized, respectively. The average received signal-to-noise ratio (SNR) determines the switching mechanism based on the defined threshold and atmospheric condition. Simulation results and analysis demonstrated that, at any average SNR above the defined threshold, the hybrid system's OP outperforms that of the RF-only and FSO-only links under most conditions. The analysis illustrates that employing the hybrid FSO/RF system enhances reliability and boosts overall system performance in pico-macrocell backhauling scenarios, surpassing the performance of standalone FSO-only or RF-only links. [ABSTRACT FROM AUTHOR]

Numerous remote sensing applications rely heavily on hyperspectral imagery, but it is frequently plagued by noise, which degrades the data quality and hinders subsequent analysis. In this research paper, we present an in-depth analysis of noise removal techniques for hyperspectral imagery, specifically for data acquired from the Hyperion EO-1 sensor. Setting off with obtaining Hyperion data and the pre-processing stages, the paper discusses the acquisition and denoising of Hyperion data. The hyperspectral data considered is in the high dynamic range (HDR) format, which maintains the original imagery's complete dynamic range. The study explores various noise reduction methods, such as minimum noise fraction (MNF), principal component analysis (PCA), wavelet denoising, non-local means (NLM), and denoising autoencoders, aimed at enhancing the signal-to-noise ratio. The effectiveness of these techniques is evaluated through visual quality, mean square error (MSE), and peak signal-to-noise ratio (PSNR), alongside their impact on mineral exploration. Furthermore, the paper investigates the application of machine learning algorithms on denoised data for mineral identification, highlighting the potential of integrating denoising techniques with machine learning for improved mineral exploration. This comparative analysis aims to identify the most efficient noise removal methods for hyperspectral imagery, facilitating higher quality data for subsequent analysis. [ABSTRACT FROM AUTHOR]

Active and passive sonar are the two types of empirical underwater acoustic channel models (UWACMs). Passive sonar UWACMs have applications in military, ocean exploration, and search and rescue (SAR) activities. However, high transmission loss (TL), multipath propagation, and ambient noise pose significant challenges to signal-to-noise ratio (SNR) and communication effectiveness. To address these challenges, this paper develops a UWACM based on the passive sonar equation method to determine SNR in deep-sea environments, specifically for SAR operations. Determining SNR involves characterizing signal propagation in terms of TL. Existing models lack analysis of TL and SNR for various deep-sea multipath propagation scenarios relevant to SAR applications. Therefore, this paper analyses TL and SNR for both direct and various multipath propagation modes, including surface reflection (SR), surface duct (SD), bottom bounce (BB), convergence zone (CZ), deep sound channel (DSC), and reliable acoustic paths (RAPs) in the deep sea. This work aims to quantify the detection capabilities of underwater location beacons (ULBs) under various deep-sea scenarios and configurations. By analyzing ULB signal propagation characteristics, this research seeks to address key challenges related to ULB performance and ultimately improve SAR operations. The results of the proposed model significantly correlate with existing literature, confirming its accuracy. [ABSTRACT FROM AUTHOR]

The demand for broadband data services on high-speed trains is rapidly growing as more people commute between their homes and workplaces. However, current radio frequency (RF) technology cannot adequately meet this demand. In order to address the bandwidth constraint, a technique known as free space optics (FSO) has been proposed. This paper presents a mathematical derivation and formulation of curve track G2T-FSO (Ground-to-train Free Space Optical) model, where the track radius characteristics is 2667 m, divergence angle track is 1.5° for train velocity at V = 250 km/h. Multiple transmitter configurations are proposed to maximize coverage range and enhance curve track G2T-FSO link performance under varying weather conditions. The curved track G2T-FSO model was evaluated in terms of received power, signal-to-noise ratio (SNR), bit error rate (BER), and eye diagrams. The results showed maximum coverage lengths of 618, 505, 365, and 240 m for 4Tx/1Rx, 3Tx/1Rx, 2Tx/1Rx, and 1Tx/1Rx configurations, respectively. The analyzed results demonstrate that the G2T-FSO link can be effectively implemented under various weather conditions. [ABSTRACT FROM AUTHOR]

Direct illumination rendering is an important technique in computer graphics. Precomputed radiance transfer algorithms can provide high quality rendering results in real time, but they can only support rigid models. On the other hand, ray tracing algorithms are flexible and can gracefully handle animated models. With NVIDIA RTX and the AI denoiser, we can use ray tracing algorithms to render visually appealing results in real time. Visually appealing though, they can deviate from the actual one considerably. We propose a visibility-boundary edge oriented infinite triangle bounding volume hierarchy (BVH) traversal algorithm to dynamically generate visibility in vector form. Our algorithm utilizes the properties of visibility-boundary edges and infinite triangle BVH traversal to maximize the efficiency of the vector form visibility generation. A novel data structure, temporal vectorized visibility, is proposed, which allows visibility in vector form to be shared across time and further increases the generation efficiency. Our algorithm can efficiently render close-to-reference direct illumination results. With the similar processing time, it provides a visual quality improvement around 10 dB in terms of peak signal-to-noise ratio (PSNR) w.r.t. the ray tracing algorithm reservoir-based spatiotemporal importance resampling (ReSTIR). [ABSTRACT FROM AUTHOR]

A pulsar's pulse profile gets broadened at low frequencies due to dispersion along the line of sight or due to multipath propagation. The dynamic nature of the interstellar medium makes both of these effects time-dependent and introduces slowly varying time delays in the measured times-of-arrival similar to those introduced by passing gravitational waves. In this article, we present an improved method to correct for such delays by obtaining unbiased dispersion measure (DM) measurements by using low-frequency estimates of the scattering parameters. We evaluate this method by comparing the obtained DM estimates with those, where scatter-broadening is ignored using simulated data. A bias is seen in the estimated DMs for simulated data with pulse-broadening with a larger variability for a data set with a variable frequency scaling index, |$\alpha$|⁠ , as compared to that assuming a Kolmogorov turbulence. Application of the proposed method removes this bias robustly for data with band averaged signal-to-noise ratio larger than 100. We report the measurements of the scatter-broadening time and |$\alpha$| from analysis of PSR J1643 |$-$| 1224, observed with upgraded Giant Metrewave Radio Telescope as part of the Indian Pulsar Timing Array experiment. These scattering parameters were found to vary with epoch and |$\alpha$| was different from that expected for Kolmogorov turbulence. Finally, we present the DM time-series after application of this technique to PSR J1643 |$-$| 1224. [ABSTRACT FROM AUTHOR]

We present a novel approach to extracting dwarf galaxies from photometric data to measure their average halo mass profile with weak lensing. We characterize their stellar mass and redshift distributions with a spectroscopic calibration sample. By combining the |${\sim} 5000\,\mathrm{deg}^2$| multiband photometry from the Dark Energy Survey and redshifts from the Satellites Around Galactic Analogs Survey with an unsupervised machine learning method, we select a low-mass galaxy sample spanning redshifts |$z\lt 0.3$| and divide it into three mass bins. From low to high median mass, the bins contain [146 420, 330 146, 275 028] galaxies and have median stellar masses of |$\log _{10}(M_*/\text{M}_\odot)=\left[8.52\substack{+0.57 -0.76},\, 9.02\substack{+0.50 -0.64},\, 9.49\substack{+0.50 -0.58}\right]$|  . We measure the stacked excess surface mass density profiles, |$\Delta \Sigma (R)$|⁠ , of these galaxies using galaxy–galaxy lensing with a signal-to-noise ratio of [14, 23, 28]. Through a simulation-based forward-modelling approach, we fit the measurements to constrain the stellar-to-halo mass relation and find the median halo mass of these samples to be |$\log _{10}(M_{\rm halo}/\text{M}_\odot)$| = [ |$10.67\substack{+0.2 -0.4}$|⁠ ,  |$11.01\substack{+0.14 -0.27}$|⁠ ,  |$11.40\substack{+0.08 -0.15}$| ]. The cold dark matter profiles are consistent with NFW (Navarro, Frenk, and White) profiles over scales |${\lesssim} 0.15 \, {h}^{-1}$|  Mpc. We find that |${\sim} 20$| per cent of the dwarf galaxy sample are satellites. This is the first measurement of the halo profiles and masses of such a comprehensive, low-mass galaxy sample. The techniques presented here pave the way for extracting and analysing even lower mass dwarf galaxies and for more finely splitting galaxies by their properties with future photometric and spectroscopic survey data. [ABSTRACT FROM AUTHOR]

In the growing field of the Internet of Medical Things (IoMT), securing the transmission of medical images over public networks is a critical challenge. Medical images, being highly sensitive and often containing personally identifiable information, require robust protection against unauthorized access and tampering. This paper addresses this challenge by introducing a novel cryptosystem specifically tailored to the resource limitations inherent in IoMT environments. To meet the demand for protecting sensitive information in medical images, the proposed system integrates two layers of security: spatial watermarking and asymmetric encryption. At the core of our approach lies a newly developed, cost-effective spatial watermarking algorithm that seamlessly embeds watermarks within host images to facilitate tamper detection. Complementing this, we employ a resource-efficient Twin Message Fusion (TMF) encryption scheme to ensure confidentiality and integrity during transmission. The proposed cryptosystem is evaluated for both watermarking effectiveness and encryption robustness. Additionally, we assess the system's suitability for IoMT environments in terms of cost-effectiveness. Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) metrics have been used to assess watermarking resilience against various attacks, while encryption efficacy is analyzed through histogram and entropy evaluations. Furthermore, we compare the complexity of our cryptosystem with five recently proposed techniques in both traditional and IoMT environments, demonstrating superior execution time, reduced image size, and enhanced encryption efficiency. [ABSTRACT FROM AUTHOR]

The emergence of on-machine measurement (OMM) technology has opened up new possibilities for error compensation in complex surface machining. Due to the harsh working environments of machine tools, the error data obtained from OMM often contains a considerable degree of random errors. To separate the systematic errors from the random errors and improve the effectiveness of the measurement results and the accuracy of surface parts machining, a method known as ICEEMDAN-VMD is proposed. This method, based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), permutation entropy, correlation coefficient, power spectral entropy, and variational mode decomposition (VMD), is entirely data-driven and does not necessitate manual intervention, making it well-suited for OMM. The accuracy of error separation using the ICEEMDAN-VMD method was assessed through simulation, utilizing root mean square error and signal-to-noise ratio as evaluation metrics. Application of this method in machining experiments resulted in a substantial reduction of 73.58% in the average error, 64.33% in the maximum error, and 67.86% in the standard deviation on surfaces. This implementation notably enhanced surface consistency and effectively avoided over-cutting. The results conclusively demonstrated the theoretical feasibility, high accuracy, and practical effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a study of noise reduction of an electrochemical sensor is presented. This study is initiated based on the fact that the signal from the electrochemical sensor has quite high noise. The current signal from the sensor is very weak and noisy due to high impedance and complex process on the sensor and measured media such as a solution of dissolved oxygen. The current is usually converted using a transimpedance amplifier with high voltage-to-current ratio by a feedback resistance, which usually also produces large noise due to high feedback resistance value. This will contribute to an additional noise to the natural noise of the sensor and measured media, causing more noisy result. A modified transimpedance amplifier is proposed to reduce the noise from the sensor and converter to increase signal-to-noise ratio. The modification employs a current compensation, so that the current noise is suppressed at the converter input. The noise and signal measurements at the output of the compensated transimpedance amplifier show noise reduction compared to the standard transimpedance amplifier and measurement error reduction of the measured dissolved oxygen down to 0.89%. [ABSTRACT FROM AUTHOR]

Epsilon‐near‐zero (ENZ) materials have shown strong refractive nonlinearities that can be fast in an absolute sense. While continuing to advance fundamental science, such as time varying interactions, the community is still searching for an application that can effectively make use of the strong index modulation offered. Here, the effect of strong space‐time index modulation in ENZ materials is combined with the beam deflection technique to introduce a new approach to optical pulse characterization that is termed a space‐time knife edge. It is shown that in this approach, temporal and spatial information of a Gaussian beam can be extracted with only two time resolved measurements. The approach achieves this without phase‐matching requirements (<1 µm thick) and can achieve a high signal to noise ratio by combining the system with lock‐in detection, facilitating the measurement of weak refractive index changes (Δn ≈$ \approx $ 10−5) for low intensity beams. Thus, the space‐time knife edge can offer a new avenue for ultrafast light measurement and demonstrates a use case of ENZ materials. In support of this, temporal dynamics for refractive index changes in non‐colinear experiments opening avenues are outlined for better theoretical understanding of both the spatial and temporal dynamics of emerging ENZ films. [ABSTRACT FROM AUTHOR]

Reliable, repeatable, and flexible testing is crucial for assessing system performance and ensuring the quality of communication. In reverberation chambers (RC), real-life propagation environments can be emulated by loading absorbers, facilitating controlled testing of the system. This work presents over-the-air (OTA) testing of the LTE-A PHY layer in the RC. We assessed the performance of the LTE-A link using key performance indicators (KPIs) such as error vector magnitude (EVM), bit error rate (BER), and signal-to-noise ratio (SNR) for varying transmitter (Tx) and receiver (Rx) gains. Additionally, we have compared the results both in an empty RC and when the RC was loaded with RF absorbers. We used software-defined radio (SDR) for OTA transmission of LTE-A frames. The measurement results indicate that loading the RC with RF absorbers improves EVM, BER, and SNR. We also quantified the performance of the LTE-A link by changing the position of RF absorbers. Results showed that loading absorbers yielded up to 72.8% improvement in EVM, and placing absorbers closer to Rx helped reduce the amount of multipath, resulting in better transmission performance. [ABSTRACT FROM AUTHOR]

Advancements in digital imaging and video processing are often challenged by low-light environments, leading to degraded visual quality. This affects critical sectors such as medical imaging, aerospace, and underwater exploration, where uneven lighting can compromise safety and clarity. To enhance image quality in low-light conditions using a computationally efficient system. This paper introduces an FPGA-based system utilizing the Retinex algorithm for low-light image enhancement, implemented on a Coarse-Grained Reconfigurable Architecture (CGRA). The system is designed using Verilog HDL on a Xilinx FPGA, prioritizing hardware optimization to achieve high-quality outputs with minimal latency. The system achieves a processing rate of 60 frames per second (fps) for images with a resolution of 720 × 576. Quantitative evaluations show a Peak Signal-to-Noise Ratio (PSNR) improvement to 43.18 dB, a Structural Similarity Index (SSIM) of 0.92, and a Mean Squared Error (MSE) reduction, demonstrating significant enhancements in image quality. The design also achieves a low power consumption of 0.186 W and efficient resource utilization, with only 2.2% of Slice LUTs and Slice Registers used. The FPGA-based system demonstrates significant improvements in image quality with high computational efficiency, proving beneficial for critical applications in various sectors. [ABSTRACT FROM AUTHOR]

In real industrial environments, the data collected is often subject to noise interference, which leads to challenges for traditional deep learning fault diagnosis models, including insufficient autonomous learning capabilities, complex feature extraction processes, and sensitivity to noise. To address these issues, this paper proposes a bearing fault diagnosis convolutional neural network based on multi-scale and soft-threshold denoising (MSTD). In this model, Poly Kernel Feature Fusion (PKFF) is utilised to extract high-quality features from raw vibration data, enhancing the model’s ability to recognise complex fault patterns; Residual anchor attention (RAA) reduces redundancy and irrelevant information between channels, optimising inter-channel information to ensure the model’s effectiveness when handling multi-channel data; Soft threshold CG (ST-CG) utilises a soft-threshold denoising method to suppress noise after feature extraction, thereby more clearly revealing fault characteristics. Soft-threshold processing not only enhances the model’s noise resistance but also preserves critical fault information, enabling the model to maintain high diagnostic accuracy even under low signal-to-noise ratio conditions. The proposed model demonstrates outstanding performance on the CWRU and MFS-MG datasets, exhibiting superior feature extraction capabilities and robustness in high-noise environments compared to other models, significantly improving diagnostic accuracy and noise resistance. [ABSTRACT FROM AUTHOR]

A remaining challenge for genetically encoded voltage indicators (GEVIs) is the reliable detection of excitatory postsynaptic potentials (EPSPs). Here, we developed ASAP5 as a GEVI with enhanced activation kinetics and responsivity near resting membrane potentials for improved detection of both spiking and subthreshold activity. ASAP5 reported action potentials (APs) in vivo with higher signal-to-noise ratios than previous GEVIs and successfully detected graded and subthreshold responses to sensory stimuli in single two-photon trials. In cultured rat or human neurons, somatic ASAP5 reported synaptic events propagating centripetally and could detect ∼1-mV EPSPs. By imaging spontaneous EPSPs throughout dendrites, we found that EPSP amplitudes decay exponentially during propagation and that amplitude at the initiation site generally increases with distance from the soma. These results extend the applications of voltage imaging to the quantal response domain, including in human neurons, opening up the possibility of high-throughput, high-content characterization of neuronal dysfunction in disease. [Display omitted] • Multiparametric screening generated a GEVI with higher gain and faster onset, ASAP5 • ASAP5 exhibits higher responsivity to action potentials and mEPSPs than other GEVIs • ASAP5 reveals mEPSP dendritic propagation and network activity in rat and human neurons • ASAP5 demonstrated high-SNR voltage recordings in flies, fish, and mice Hao, Lee, et al. report ASAP5, a GEVI that enhances responsivity and accelerates kinetics. It enables single-trial recordings of sub- and suprathreshold activities in flies, mice, and human stem-cell-derived neurons, offering new possibilities to study the dynamics of membrane potentials from 1 to 100 mV. [ABSTRACT FROM AUTHOR]

We study a magnitude-limited sample of 36 broad-lined type Ic supernovae (SNe Ic-BL) from the Zwicky Transient Facility Bright Transient Survey (detected between 2018 March and 2021 August), which is the largest systematic study of SNe Ic-BL done in literature thus far. We present the light curves (LCs) for each of the SNe and analyze the shape of the LCs to derive empirical parameters, along with the explosion epochs for every event. The sample has an average absolute peak magnitude in the r band of M ¯ r , max = − 18.51 ± 0.15 mag. Using spectra obtained around peak light, we compute expansion velocities from the Fe ii 5169 Å line for each event with high enough signal-to-noise ratio spectra, and find an average value of v ph ¯ = 16 , 100 ± 1100 km s−1. We also compute bolometric LCs, study the blackbody temperature and radii evolution over time, and derive the explosion properties of the SNe. The explosion properties of the sample have average values of M ¯ Ni = 0.37 − 0.06 + 0.08 M ⊙ , M ¯ ej = 2.45 − 0.41 + 0.47 M ⊙ , and E ¯ K = (4.02 − 1.00 + 1.37) × 10 51 erg. Thirteen events have radio observations from the Very Large Array, with eight detections and five non-detections. We find that the populations that have radio detections and radio non-detections are indistinct from one another with respect to their optically inferred explosion properties, and there are no statistically significant correlations present between the events' radio luminosities and optically inferred explosion properties. This provides evidence that the explosion properties derived from optical data alone cannot give inferences about the radio properties of SNe Ic-BL and likely their relativistic jet formation mechanisms. [ABSTRACT FROM AUTHOR]

The accretion/ejection processes in T Tauri stars are fundamental to their physical evolution, while also impacting the properties and evolution of the circumstellar material at a time when planet formation takes place. To date, the characterization of ongoing accretion processes in stellar pairs at 5–50 au scales has been challenging as high-angular resolution spectrographs are required to extract the spectral features of each component. We present the analysis of spectroscopic observations of the tight (160 mas, 25 au) T Tauri system HT Lup A/B, obtained with MUSE at the Very Large Telescope in 2021 March and July. We focus on constraining the accretion/ejection processes and variability of the secondary component HT Lup B by searching for accretion tracers by applying high-resolution spectral differential imaging techniques. We retrieve strong (signal-to-noise ratio > 5) H α, H β, and [O i ] λ 6300 emission in both epochs. The H α and H β line fluxes showcase high variability, with variations up to 200%–300% between epochs. The fluxes are consistent with accretion rates of 3× 10−9 M ⊙ yr−1 and 8 × 10−10 M ⊙ yr−1 for the first and second epochs, respectively. We attribute the increased accretion activity during the first night to a "burst-like" event, followed by a relaxation period more representative of the common accretion activity of the system. The [O i ] λ 6300 line profiles remain relatively similar between epochs and suggest ejection rates on the order of 10−9−10−10 M ⊙ yr−1, compatible with moderate disk wind emission. Our results also indicate that the accretion processes of HT Lup B are compatible with Classical T Tauri stars, unlike previous classifications. [ABSTRACT FROM AUTHOR]

Organic luminophores have been widely utilized in cells and in vivo fluorescence imaging but face extreme challenges, including a low signal-to-noise ratio (SNR) and even false signals, due to non-negligible background signals derived from real-time excitation lasers. To overcome these challenges, in the last decade, functionalized organic long-persistent luminophores have gained much attention. Such luminophores could not only overcome the biological toxicity of inorganic long-persistent luminescent materials (metabolic toxicity and leakage risk of inorganic heavy metals), but also continue to emit long-persistent luminescence after removing the excitation source, thus effectively improving imaging quality. More importantly, organic long-persistent luminophores have good structure tailorability for the construction of activable probes, which is favorable for biosensing. Recently, the development of reactive oxygen species (ROS)-mediated long-persistent (ROSLP) luminophores (especially organic small-molecule ROSLP luminophores) is still in the rising stage. Notably, ROSLP luminophores for in vivo imaging have experienced from two-component separated nano-systems to integrated uni-luminophores, which obtained gradually better designability and biocompatibility. In this review, we summarize the progress and challenges of organic long-persistent luminophores, focusing on their development history, long-persistent luminescence working mechanisms, and biomedical applications. We hope that these insights will help scientists further develop functionalized organic long-persistent luminophores for the biomedical field. [ABSTRACT FROM AUTHOR]

Background: Multipin dry electrodes (dry EEG) provide faster and more convenient application than wet EEG, enabling extensive data collection. This study aims to compare task-related time-frequency representations and resting-state connectivity between wet and dry EEG methods to establish a foundation for using dry EEG in investigations of brain activity in neuropsychiatric disorders. Methods: In this counterbalanced cross-over study, we acquired wet and dry EEG in 33 healthy participants [ n = 22 females, mean age (SD) = 24.3 (± 3.4) years] during resting-state and an auditory oddball paradigm. We computed mismatch negativity (MMN), theta power in task EEG, and connectivity measures from resting-state EEG using phase lag index (PLI) and minimum spanning tree (MST). Agreement between wet and dry EEG was assessed using Bland–Altman bias. Results: MMN was detectable with both systems in time and frequency domains, but dry EEG underestimated MMN mean amplitude, peak latency, and theta power compared to wet EEG. Resting-state connectivity was reliably estimated with dry EEG using MST diameter in all except the very low frequencies (0.5–4 Hz). PLI showed larger differences between wet and dry EEG in all frequencies except theta. Conclusion: Dry EEG reliably detected MMN and resting-state connectivity despite a lower signal-to-noise ratio. This study provides the methodological basis for using dry EEG in studies investigating the neural processes underlying psychiatric and neurological conditions. [ABSTRACT FROM AUTHOR]

Image Compression is one of the emerging techniques of a Digital System for storing and retrieving of digital information. The main challenge in implementing Image Compression is to maintain the accuracy of the retrieved data. As the encoding techniques used for data compression are computationally intensive, new hardware architectures are required so that the processing of image consumes less space with increase in computation speed, reduction in area and power consumption. In this paper we address this problem and developed a Dynamic Adaptive coding technique based on probability of occurrence,where the variable and fixed length code are fused to generate a code word for eliminating the extra bit encountered at the entropy coder. Here the Entropy code has a maximum search overhead of 6 match per 4-bit pattern. Wherein a maximum of 5-bit search is observed in proposed approach. This reduces a search overhead of (N⨉m)-1 iterations. Here N is the number of unique patterns and m is the block size. The proposed architecture is developed using Very high speed integrated circuit Hardware Descriptive Language (VHDL) and implemented using Xilinx Aldec's Field Programmable Gate Array (FPGA). The adaptive coding approach attains a compression of 35% more as compared to the entropy coding. The implementation on to a targeted Xilinx FPGA results in power minimization and area coverage reduction. The speed of operation is observed to be improved by 135 MHz. The validation of proposed approach is made on image data to observe the coding accuracy. The mean square error of the output image is reduced by 35% with an increase in the signal to noise ratio of the output image. [ABSTRACT FROM AUTHOR]