This paper investigates the design of joint transmit and reflective beamforming for dual intelligent reflecting surfaces (IRSs) aided integrated sensing and communication (ISAC) system. We aim to minimize the Cram...
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In contemporary times cardiovascular disease has emerged as a predominant health concern. It underscores a critical necessity for early and precise predictions to facilitate effective prevention and intervention strat...
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The concept of smart grids was released a long time ago, and since then the development in clever grids have changed our conventional energy machine with new advanced features like real-time monitoring power distribut...
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This paper will explore current studies and developments in consumer interfaces for records visualization and analysis. Mainly that specialize in how innovative consumer interfaces can make choices better than present...
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Medical Field are growing tremendously and using various IT related services for data analysis and diagnoses. The numerous amounts of computer assisted systems are available to measure accurate results. The clinical d...
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Roads are essential for daily transportation worldwide, but their aging and usage patterns can cause deterioration of the road surface, leading to a decline in quality. This deterioration often results in the formatio...
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Coded computation has garnered considerable attention for its ability to eliminate the stragglers' effect. Most existing works of coded computation are designed for linear tasks. They cannot handle the nonlinear t...
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Extracting specific information from diverse and complex image types is a challenging task, especially in real-world cases where images often contain noise, low resolution, occlusion, or poor contrast. Traditional app...
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Early detection of fire is a must to prevent the dangerous accidents it could cause. Traditional fire detection systems use hardware like sensors to detect the presence of fire. A more automated approach is provided w...
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Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail,which finds wide applications in climate modeling,numerical weather forecasting,and renewable *** dee...
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Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail,which finds wide applications in climate modeling,numerical weather forecasting,and renewable *** deeplearning-based downscaling methods effectively capture the complex nonlinear mapping between meteorological data of varying scales,the supervised deep-learning-based downscaling methods suffer from insufficient high-resolution data in practice,and unsupervised methods struggle with accurately inferring small-scale specifics from limited large-scale inputs due to small-scale *** article presents DualDS,a dual-learning framework utilizing a Generative Adversarial Network–based neural network and subgrid-scale auxiliary information for climate *** a learning method is unified in a two-stream framework through up-and downsamplers,where the downsampler is used to simulate the information loss process during the upscaling,and the upsampler is used to reconstruct lost details and correct errors incurred during the *** dual learning strategy can eliminate the dependence on high-resolution ground truth data in the training process and refine the downscaling results by constraining the mapping *** findings demonstrate that DualDS is comparable to several state-of-the-art deep learning downscaling approaches,both qualitatively and ***,for a single surface-temperature data downscaling task,our method is comparable with other unsupervised algorithms with the same dataset,and we can achieve a 0.469 dB higher peak signal-to-noise ratio,0.017 higher structural similarity,0.08 lower RMSE,and the best correlation *** summary,this paper presents a novel approach to addressing small-scale uncertainty issues in unsupervised downscaling processes.
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