Utilizing an attention mechanism, the proposed ABPN is constructed to learn efficient representations of the fused features. In addition, a knowledge distillation (KD) method is utilized to reduce the size of the proposed network, ensuring results comparable to those of the large model. The standard reference software for VTM-110 NNVC-10 now contains the integrated proposed ABPN. The lightweight ABPN's BD-rate reduction on the Y component, measured against the VTM anchor, demonstrates a 589% improvement under random access (RA) and a 491% improvement under low delay B (LDB).

Perceptual image/video processing often employs the just noticeable difference (JND) model, a reflection of human visual system (HVS) limitations. This model is frequently applied for removing perceptual redundancy. Existing JND models are often constructed with an assumption of equal importance among the color components of the three channels, which ultimately results in an inadequate estimation of the masking effect. We propose an improved JND model in this paper that utilizes visual saliency and color sensitivity modulation. In the first instance, we meticulously combined contrast masking, pattern masking, and edge protection methods to evaluate the masking effect. Following this, the visual salience of the HVS was considered to adjust the masking effect in an adaptive manner. In the final stage, we created color sensitivity modulation systems based on the perceptual sensitivities of the human visual system (HVS), meticulously adjusting the sub-JND thresholds for the Y, Cb, and Cr components. Subsequently, a JND model, based on color-discrimination capability, now known as CSJND, was developed. Subjective assessments and extensive experimentation were employed to ascertain the effectiveness of the CSJND model. The CSJND model exhibited improved consistency with the HVS, surpassing the performance of current best-practice JND models.

Electrical and physical characteristics are now integral to novel materials, a result of advancements in nanotechnology. This development, a significant leap for the electronics industry, has applications across a wide array of fields. For energy harvesting to power bio-nanosensors within a Wireless Body Area Network (WBAN), we propose the fabrication of nanotechnology-based, stretchable piezoelectric nanofibers. Body movements, such as arm gestures, joint articulations, and cardiac contractions, provide the energy source for the bio-nanosensors' operation. These nano-enriched bio-nanosensors, when assembled, can form microgrids for a self-powered wireless body area network (SpWBAN), enabling various sustainable health monitoring services. The energy harvesting-based medium access control in an SpWBAN system, coupled with a model using fabricated nanofibers with unique characteristics, is presented and evaluated. In simulations, the SpWBAN's performance and operational lifetime outperform comparable WBAN systems lacking self-powering technology.

This research introduces a separation method to extract the temperature-driven response from the long-term monitoring data, which is contaminated by noise and responses to other actions. The local outlier factor (LOF) is applied to the original measured data in the proposed method, and the threshold for the LOF is determined by minimizing the variance of the processed data. For the purpose of filtering the noise in the modified dataset, Savitzky-Golay convolution smoothing is used. This study further suggests an optimization approach, the AOHHO, which integrates the Aquila Optimizer (AO) and the Harris Hawks Optimization (HHO) strategies to achieve the ideal threshold value of the Local Outlier Factor (LOF). The AO's exploratory capacity and the HHO's exploitative skill are integrated within the AOHHO. The superior search ability of the proposed AOHHO, relative to the other four metaheuristic algorithms, is verified by four benchmark functions. read more An assessment of the proposed separation method's performance is carried out by employing in-situ measured data and numerical examples. The proposed method's separation accuracy surpasses the wavelet-based method's, leveraging machine learning across diverse time windows, as evidenced by the results. The proposed method exhibits approximately 22 times and 51 times less maximum separation error than the two alternative methods, respectively.

Development of infrared search and track (IRST) systems is hampered by the limitations of infrared (IR) small-target detection performance. Existing methods of detection frequently lead to missed detections and false alarms when faced with complicated backgrounds and interference. These methods, focusing narrowly on target location, disregard the critical shape characteristics, ultimately hindering the classification of IR targets into distinct categories. To achieve consistent runtime, a weighted local difference variance method (WLDVM) is designed to tackle these problems. Initially, Gaussian filtering, leveraging the matched filter approach, is used to improve the target's visibility while minimizing the presence of noise in the image. Following the initial step, the target region is separated into a fresh tri-layered filtration window, depending on the distribution characteristics of the target area, and a window intensity level (WIL) is introduced to gauge the complexity of each window stratum. Following on, a local difference variance measure (LDVM) is developed, capable of removing the high-brightness background through a difference calculation, and subsequently enhancing the target area by utilizing local variance. To determine the form of the real small target, the background estimation is used to derive the weighting function. A simple adaptive thresholding operation is performed on the obtained WLDVM saliency map (SM) to isolate the desired target. Nine groups of IR small-target datasets, each with complex backgrounds, were used to evaluate the proposed method's capability to address the previously discussed issues. Its detection performance significantly outperforms seven established, frequently used methods.

Given the persistent influence of Coronavirus Disease 2019 (COVID-19) across diverse aspects of daily life and global healthcare systems, the adoption of swift and effective screening methods is vital to prevent further viral propagation and ease the burden on healthcare facilities. The point-of-care ultrasound (POCUS) imaging modality, widely accessible and economical, allows radiologists to visually interpret chest ultrasound images, thereby identifying symptoms and evaluating their severity. Deep learning's efficacy in medical image analysis, bolstered by recent innovations in computer science, has showcased promising outcomes in accelerating COVID-19 diagnoses, thereby easing the burden on healthcare professionals. A key impediment to the effective development of deep neural networks is the scarcity of large, well-annotated datasets, notably in the case of rare diseases and recent pandemics. To effectively manage this challenge, we present COVID-Net USPro, an easily understandable deep prototypical network employing few-shot learning, crafted to identify COVID-19 cases utilizing a minimal number of ultrasound images. Quantitative and qualitative assessments of the network reveal its exceptional ability to detect COVID-19 positive cases, employing an explainability component, and further show that its decisions are based on the true representative patterns of the disease. In a demonstration of its efficacy, the COVID-Net USPro model, trained using only five examples, achieved an exceptional 99.55% accuracy, coupled with 99.93% recall and 99.83% precision for COVID-19 positive cases. To validate the network's COVID-19 diagnostic decisions, which are rooted in clinically relevant image patterns, our contributing clinician with extensive POCUS experience corroborated the analytic pipeline and results, beyond the quantitative performance assessment. Deep learning's successful application in medicine necessitates the integration of network explainability and clinical validation as essential components. To encourage further innovation and promote reproducibility, the COVID-Net network has been open-sourced, granting public access.

The design of active optical lenses for arc flashing emission detection is presented within this paper. read more The properties of arc flash emissions and the phenomenon itself were subjects of our contemplation. The methods of preventing these emissions within electric power systems were also explored. A comparative overview of available detectors is provided in the article, in addition to other information. read more A substantial portion of the paper is dedicated to analyzing the material properties of fluorescent optical fiber UV-VIS-detecting sensors. The primary function of this work was the design of an active lens comprising photoluminescent materials, with the capability to convert ultraviolet radiation into visible light. An analysis of active lenses was conducted, utilizing Poly(methyl 2-methylpropenoate) (PMMA) and phosphate glass doped with lanthanides like terbium (Tb3+) and europium (Eu3+) ions, within the context of the ongoing project. These lenses were a key element in the construction of optical sensors, with further support provided by commercially available sensors.

The challenge of pinpointing propeller tip vortex cavitation (TVC) noise lies in distinguishing the diverse sound sources in the immediate vicinity. A sparse localization technique for off-grid cavitation, detailed in this work, aims to precisely estimate cavitation locations while maintaining acceptable computational cost. Two different grid sets (pairwise off-grid) are adopted with a moderate spacing, creating redundant representations for neighboring noise sources. Off-grid cavitation position estimation utilizes a block-sparse Bayesian learning method (pairwise off-grid BSBL), which iteratively adjusts grid points through Bayesian inference in the context of the pairwise off-grid scheme. The subsequent simulation and experimental results indicate that the proposed method effectively isolates neighboring off-grid cavities, achieving this with reduced computational costs, while the alternative approach suffers from a substantial computational load; the pairwise off-grid BSBL approach, for the separation of adjacent off-grid cavities, was significantly faster (29 seconds) than the conventional off-grid BSBL method (2923 seconds).