The attention mechanism in the proposed ABPN allows for the learning of efficient representations from the fused features. The knowledge distillation (KD) technique is applied to compact the proposed network, resulting in comparable outputs compared to the large model. The proposed ABPN is a newly integrated feature of the VTM-110 NNVC-10 standard reference software. In contrast to the VTM anchor, the BD-rate reduction of the lightweight ABPN reaches 589% on the Y component under random access (RA) and 491% under low delay B (LDB), respectively.
Perceptual redundancy reduction, a common application of the just noticeable difference (JND) model, accounts for the visibility limits of the human visual system (HVS), essential to perceptual image/video processing. Existing JND models commonly adopt a uniform approach to the color components across the three channels, causing their estimation of the masking effect to fall short. Visual saliency and color sensitivity modulation are integrated into the JND model in this paper to achieve enhanced performance. Firstly, we painstakingly integrated contrast masking, pattern masking, and edge-preservation techniques to precisely measure the masking influence. The HVS's visual salience was subsequently employed to adjust the masking effect in a flexible way. Finally, we engineered color sensitivity modulation, drawing inspiration from the perceptual sensitivities of the human visual system (HVS), to fine-tune the sub-JND thresholds applicable to the Y, Cb, and Cr components. As a result, a model built upon color sensitivity for quantifying just-noticeable differences (JND), specifically called CSJND, was constructed. Extensive experiments, complemented by thorough subjective testing, were conducted to validate the effectiveness of the CSJND model. We observed a higher degree of concordance between the CSJND model and HVS than was seen in previous cutting-edge JND models.
Advances in nanotechnology have led to the design of novel materials, exhibiting unique electrical and physical properties. This development in the electronics industry yields a noteworthy advancement with implications spanning several fields. We present a method for fabricating nanomaterials into stretchable piezoelectric nanofibers, which can power connected bio-nanosensors in a wireless body area network. Energy from the body's mechanical movements, encompassing arm actions, joint movements, and the heart's rhythmic beats, is the energy source for powering the bio-nanosensors. A collection of these nano-enhanced bio-nanosensors can be employed to construct microgrids for a self-powered wireless body area network (SpWBAN), which finds application in diverse sustainable health monitoring services. We examine and present a system model for an SpWBAN, incorporating an energy harvesting MAC protocol, leveraging fabricated nanofibers with particular characteristics. Analysis of simulation results reveals the SpWBAN's enhanced performance and prolonged lifespan compared to non-self-powered WBAN counterparts.
This study's novel approach identifies the temperature response from the long-term monitoring data, which includes noise and various action-related effects. The proposed method utilizes the local outlier factor (LOF) to transform the initial measured data, finding the optimal LOF threshold by minimizing the variance in the modified dataset. The Savitzky-Golay convolution smoothing technique is also employed to remove noise from the processed data. In addition, this research introduces the AOHHO optimization algorithm. This algorithm, a hybridization of the Aquila Optimizer (AO) and Harris Hawks Optimization (HHO), is designed to identify the optimal threshold value within the LOF. The AOHHO integrates the AO's exploratory power with the HHO's exploitative capability. Evaluation using four benchmark functions underscores the stronger search ability of the proposed AOHHO in contrast to the other four metaheuristic algorithms. Proteasome inhibitor The performances of the proposed separation method are evaluated through numerical examples and concurrent in-situ measurements. The results demonstrate superior separation accuracy for the proposed method, exceeding the wavelet-based approach, employing machine learning techniques across various time windows. The maximum separation errors of the other two methods are roughly 22 times and 51 times larger than the proposed method's maximum separation error, respectively.
Infrared (IR) small-target detection performance poses a significant obstacle to the advancement of infrared search and track (IRST) systems. Existing detection approaches, unfortunately, often lead to missed detections and false alarms when facing complex backgrounds and interference. Their emphasis on target location, while ignoring the distinctive features of target shape, hinders the classification of IR targets into specific categories. To ensure a consistent execution time, a weighted local difference variance metric (WLDVM) algorithm is proposed to handle these concerns. Using the concept of a matched filter, initial pre-processing of the image involves Gaussian filtering to improve the target's prominence and suppress the noise. The target zone is then divided into a new tri-layered filtering window, aligning with the target area's spatial distribution, and a window intensity level (WIL) is introduced to reflect the complexity of each layer's structure. Secondly, a local difference variance measure, LDVM, is proposed, which removes the high-brightness background using difference calculation, and further employs local variance to increase the visibility of the target area. The background estimation is then used to establish the weighting function, which, in turn, determines the shape of the actual small target. Subsequently, a rudimentary adaptive thresholding technique is employed on the WLDVM saliency map (SM) to locate the precise target. Experiments conducted on nine sets of IR small-target datasets with intricate backgrounds showcase the proposed method's effectiveness in resolving the preceding challenges, offering superior detection performance compared to seven widely adopted, classic methods.
The continuing ramifications of Coronavirus Disease 2019 (COVID-19) on various aspects of life and global healthcare systems necessitate the deployment of rapid and effective screening protocols to limit the further spread of the virus and reduce the pressure on healthcare systems. Visual inspection of chest ultrasound images, achievable through the affordable and easily accessible point-of-care ultrasound (POCUS) technique, allows radiologists to identify symptoms and assess their severity. Deep learning's application to medical image analysis, empowered by recent computer science advancements, has shown encouraging results, enabling a faster diagnosis of COVID-19 and reducing the stress on healthcare professionals. Nevertheless, the scarcity of extensive, meticulously labeled datasets presents a significant obstacle to the creation of potent deep neural networks, particularly concerning rare ailments and emerging epidemics. This issue is tackled by introducing COVID-Net USPro, an explainable few-shot deep prototypical network, which is designed to ascertain the presence of COVID-19 cases from just a few ultrasound images. Intensive quantitative and qualitative assessments highlight the network's remarkable performance in identifying COVID-19 positive cases, facilitated by an explainability component, while also demonstrating that its decisions stem from the true representative characteristics of the disease. Remarkably, the COVID-Net USPro model, trained on a mere five samples, achieved outstanding results for COVID-19 positive cases with 99.55% accuracy, 99.93% recall, and 99.83% precision. Our contributing clinician, with extensive experience interpreting POCUS data, independently verified the network's COVID-19 diagnostic decisions, based on clinically relevant image patterns, in conjunction with the quantitative performance assessment, confirming the analytic pipeline and results. The successful implementation of deep learning in medical care requires not only network explainability but also crucial clinical validation. The COVID-Net initiative is making its network open-source, available to the public, to enable reproducibility and encourage further innovation.
The design of active optical lenses for arc flashing emission detection is presented within this paper. Proteasome inhibitor The characteristics and nature of arc flash emissions were the subject of much contemplation. The methods of preventing these emissions within electric power systems were also explored. A section dedicated to commercially available detectors is included in the article, with a focus on their comparisons. Proteasome inhibitor A substantial portion of the paper is dedicated to analyzing the material properties of fluorescent optical fiber UV-VIS-detecting sensors. This study's primary focus was the construction of an active lens based on photoluminescent materials, which acted to transform ultraviolet radiation into visible light. The research examined active lenses, consisting of materials such as Poly(methyl 2-methylpropenoate) (PMMA) and phosphate glass that was doped with lanthanide ions, specifically terbium (Tb3+) and europium (Eu3+), as part of the overall work. The lenses, acting in conjunction with commercially available sensors, facilitated the creation of optical sensors.
Pinpointing the origin of propeller tip vortex cavitation (TVC) noise requires isolating nearby sound sources. The sparse localization methodology for off-grid cavitations, explored in this work, seeks to estimate precise locations while maintaining a favorable computational footprint. Two separate grid sets (pairwise off-grid), employing a moderate grid interval, are used to generate redundant representations for noise sources located close to each other. The pairwise off-grid scheme (pairwise off-grid BSBL), leveraging a block-sparse Bayesian learning approach, estimates the off-grid cavitation locations by iteratively updating grid points using Bayesian inference. Following this, experimental and simulation results verify that the presented method successfully isolates nearby off-grid cavities with reduced computational demands, whereas other methods exhibit a substantial computational burden; regarding the separation of adjacent off-grid cavities, the pairwise off-grid BSBL approach consistently required a significantly shorter duration (29 seconds) compared to the conventional off-grid BSBL method (2923 seconds).