In this paper, a graphene-dielectric metasurface is proposed for effective optical biosensing. The structure is composed of a periodic array of double-slit split-ring resonators (SRRs) adjacent to silicon bars over a ...
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This paper presents a thorough review of various methodologies employed in heart sound classification, combining both conventional machine learning (ML) algorithms and complex deep learning (DL) approaches. We systema...
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Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing t...
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Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing the accuracy of automatic sleep staging, certain challenges remain, as follows: 1) optimizing the utilization of multi-modal information complementarity, 2) effectively extracting both long- and short-range temporal features of sleep information, and 3) addressing the class imbalance problem in sleep data. To address these challenges, this paper proposes a two-stream encode-decoder network, named TSEDSleepNet, which is inspired by the depth sensitive attention and automatic multi-modal fusion (DSA2F) framework. In TSEDSleepNet, a two-stream encoder is used to extract the multiscale features of electrooculogram (EOG) and electroencephalogram (EEG) signals. And a self-attention mechanism is utilized to fuse the multiscale features, generating multi-modal saliency features. Subsequently, the coarser-scale construction module (CSCM) is adopted to extract and construct multi-resolution features from the multiscale features and the salient features. Thereafter, a Transformer module is applied to capture both long- and short-range temporal features from the multi-resolution features. Finally, the long- and short-range temporal features are restored with low-layer details and mapped to the predicted classification results. Additionally, the Lovász loss function is applied to alleviate the class imbalance problem in sleep datasets. Our proposed method was tested on the Sleep-EDF-39 and Sleep-EDF-153 datasets, and it achieved classification accuracies of 88.9% and 85.2% and Macro-F1 scores of 84.8% and 79.7%, respectively, thus outperforming conventional traditional baseline models. These results highlight the efficacy of the proposed method in fusing multi-modal information. This method has potential for application as an adjunct tool for diagnosing sleep disorde
Reliable classification of grasp types from human limbs has become an important aspect used by applications with humanoid robotic systems, because of their high-accuracy implementations in human movement replication a...
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This paper presents a new CAN (Controller Area Network) receiver, which is suited for multi-bit communication. Two alternative designs are described. The former provides better performance in terms of threshold voltag...
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Deep learning has been broadly applied to imaging in scattering applications.A common framework is to train a descattering network for image recovery by removing scattering *** achieve the best results on a broad spec...
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Deep learning has been broadly applied to imaging in scattering applications.A common framework is to train a descattering network for image recovery by removing scattering *** achieve the best results on a broad spectrum of scattering conditions,individual“expert”networks need to be trained for each ***,the expert’s performance sharply degrades when the testing condition differs from the *** alternative brute-force approach is to train a“generalist”network using data from diverse scattering *** generally requires a larger network to encapsulate the diversity in the data and a sufficiently large training set to avoid ***,we propose an adaptive learning framework,termed dynamic synthesis network(DSN),which dynamically adjusts the model weights and adapts to different scattering *** adaptability is achieved by a novel“mixture of experts”architecture that enables dynamically synthesizing a network by blending multiple experts using a gating *** demonstrate the DSN in holographic 3D particle imaging for a variety of scattering *** show in simulation that our DSN provides generalization across a continuum of scattering *** addition,we show that by training the DSN entirely on simulated data,the network can generalize to experiments and achieve robust 3D *** expect the same concept can find many other applications,such as denoising and imaging in scattering ***,our dynamic synthesis framework opens up a new paradigm for designing highly adaptive deep learning and computational imaging techniques.
This paper proposes a highly supply voltage-scalable, low-power and compact-area temperature sensor, which satisfies the requirements for Systems-on-Chip (SoCs) and microprocessors hotspots monitoring. The proposed no...
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Accurate removal of brain tumors is always one of the most important challenges for surgeons, as the continuous change of the brain state after opening the skull and releasing the resulting pressure causes the tumor s...
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The global population is aging due to increased life expectancy and declining birth rates. As a result, there is a growing prevalence of chronic diseases such as heart disease, hypertension, and diabetes, among the ol...
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In recent years, synthetic data generation has become a topic of growing interest, especially in healthcare, where they can support the development of robust Artificial Intelligence (AI) tools. Additionally, synthetic...
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