Monitoring vital signs such as breathing rate (BR) and heart rate (HR) is crucial for early detection of health issues and supports a wide range of health-related applications. Traditional monitoring methods often inv...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Monitoring vital signs such as breathing rate (BR) and heart rate (HR) is crucial for early detection of health issues and supports a wide range of health-related applications. Traditional monitoring methods often involve body-attached medical devices, which can be intrusive and inconvenient for continuous use in daily life. Contactless monitoring using radio frequency (RF) signals has emerged as a promising alternative, but acquiring precise vital sign measurements remains challenging due to the limited sensing resolution of RF devices. In this paper, we design a high-resolution contactless vital sensing system by leveraging advanced beamforming in combination with machine learning (ML) methods. The key idea of our system is to reconstruct fine-grained vital sign measurements from RF signals, achieving low estimation error, comparable to that of dedicated medical devices such as photoplethysmography sensors, respiration monitoring belts. To enhance the reconstruction performance, we integrate an antenna array with double phase shifters to acquire RF data that captures precise chest displacement of human subjects. An encoder-decoder model based on a 1D convolutional neural network is then developed to map the RF signals into vital sign measurements. Extensive evaluations show that our system has low errors of 0.3 beat per minute (BPM) for BR estimation and 2.7 BPM for HR estimation.
When the robot arm grasps across media, continuously grasping and placing the objects inevitably causes water flow fluctuations, thus interfering with subsequent visual quality. This results in the inability to correc...
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This paper considers the distributed information bottleneck (D-IB) problem for a primitive Gaussian diamond channel with two relays and MIMO Rayleigh fading. The channel state is an independent and identically distrib...
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Real-time object tracking necessitates a delicate balance between speed and accuracy, a challenge exacerbated by the computational demands of deep learning methods. In this paper, we introduce Confidence-Triggered Det...
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Towards achieving efficient computational approaches, the perspective of Cellular Automata (CAs) appears to be highly potent, owing to its performance hidden in the parallel computing capabilities inherent in its loca...
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TOTEM is a framework that allows users to execute their own code on access restricted datasets with controlled computation. It provides data security by restricting the exchange of data across the network, instead, th...
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Signal multiscale decomposition (SMD) is an effective analysis for the identification of modal information in time-domain signals. So far, various SMD approaches, such as the Multiresolution Wavelet Transform (MWT), t...
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This paper presents an in-depth investigation into the verification of a software design model, focusing on the context of Electronic Medical Record (EMR) systems. Verification plays a critical role in ensuring the co...
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Conformal prediction is an uncertainty quantification method that constructs a prediction set for a previously unseen datum, ensuring the true label is included with a predetermined coverage probability. Adaptive conf...
ISBN:
(纸本)9798331314385
Conformal prediction is an uncertainty quantification method that constructs a prediction set for a previously unseen datum, ensuring the true label is included with a predetermined coverage probability. Adaptive conformal prediction has been developed to address data distribution shifts in dynamic environments. However, the efficiency of prediction sets varies depending on the learning model used. Employing a single fixed model may not consistently offer the best performance in dynamic environments with unknown data distribution shifts. To address this issue, we introduce a novel adaptive conformal prediction framework, where the model used for creating prediction sets is selected 'on the fly' from multiple candidate models. The proposed algorithm is proven to achieve strongly adaptive regret over all intervals while maintaining valid coverage. Experiments on real and synthetic datasets corroborate that the proposed approach consistently yields more efficient prediction sets while maintaining valid coverage, outperforming alternative methods.
Wireless sensor networks (WSNs) have been increasingly deployed in hydrometeorological stations to enhance the collection and transmission of environmental data. LoRa-based Wireless Sensor Networks is a wireless commu...
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