Quality control in molecular optical sectioning microscopy is indispensable for transforming acquired digital images from qualitative descriptions to quantitative data. Although numerous tools, metrics, and phantoms h...
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High-performance and reliable wearable devices for healthcare are in high demand for the health monitoring of infants,ensuring that life-threatening events can be addressed ***,the continuous monitoring of infant resp...
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High-performance and reliable wearable devices for healthcare are in high demand for the health monitoring of infants,ensuring that life-threatening events can be addressed ***,the continuous monitoring of infant respiration for preventing sudden infant death syndrome(SIDS)is proposed using high-performance flexible piezoresistive sensors(FPS).The thorny challenges associated with FPS,including the signal drift and poor repeatability,are progressively improved via the employment of high-Tg matrix,the strengthening of in situ graft-on conducting polyaniline layer byβ-cyclodextrin(β-CD),and the nanostructure interlocking between the piezoresistive layer and *** sensor presents high linear sensitivity(30.7 kPa^(−1)),outstanding recoverability(low hysteresis up to 1.98%FS),static stability(4.00%signal drift after 24 h at 2.4 kPa)and dynamic stability(1.92%decay of signal intensity after 50,000 cycles).A wireless infant respiration monitoring system is *** patterns and the real-time respiration rate are displayed on the *** are implemented when abnormal status such as bradypnea and tachypnea is detected.
Ultra-wideband (UWB) is a popular technology for indoor positioning. However, non-line-of-sight (NLOS) propagation caused by human occlusion dramatically affects the accuracy of UWB-based indoor positioning. Most of t...
Ultra-wideband (UWB) is a popular technology for indoor positioning. However, non-line-of-sight (NLOS) propagation caused by human occlusion dramatically affects the accuracy of UWB-based indoor positioning. Most of the existing works focused on mitigating NLOS errors in the scenarios with building structure occlusion, but the NLOS mitigation under human occlusion has not been well addressed. To this end, this paper aims to mitigate the NLOS impact of human occlusion, and then proposes a method of NLOS mitigation to reduce NLOS ranging errors under human occlusion with low-cost UWB devices (i.e., NM-LUD). It takes a set of signal features extracted from low-cost devices as input, calculates their weights and mitigates the ranging error based on the features' weights. Experiment results on the two data sets collected by LinkTrack S and UML1 UWB devices show that the method reduces ranging error from 0.9009 m to 0.1435 m (about by 84.07%) and from 0.7466 m to 0.0792 m (about by 89.39%) respectively, which greatly mitigates the influence of NLOS caused by human occlusion.
Mission critical communications require ultra-high reliability guarantees. It is a natural idea to combine massive multiple-input multiple-output (MIMO) and repetition transmission to further improve the reliability. ...
Mission critical communications require ultra-high reliability guarantees. It is a natural idea to combine massive multiple-input multiple-output (MIMO) and repetition transmission to further improve the reliability. In this paper, we combine the irregular repetition scheme with the massive MIMO with grant-free access. By introducing random transmission patterns for replicas, the collision problem of grant-free access is mitigated. Considering finite block-length coding, as well as sporadic and periodic arrivals of mission critical traffic, the reliability is thoroughly analyzed and derived in closed form. The theoretical analysis is finally verified through computer simulations. It is observed that the reliability of periodic traffic is superior to the sporadic one in large MIMO systems.
Smoking is one of the significant avoidable risk factors for premature death. Most smokers make multiple quit attempts during their lifetime but smoking dependence is not easy and many people eventually failed quit at...
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Smoking is one of the significant avoidable risk factors for premature death. Most smokers make multiple quit attempts during their lifetime but smoking dependence is not easy and many people eventually failed quit attempts. Predicting the likelihood of success in smoking cessation program is necessary for public health. In recent years, a few numbers of decision support systems have been developed for dealing with smoking cessation based on machine learning techniques. However, the class imbalance problem is increasingly recognized as serious in real-world applications. Therefore, this paper presents a synthetic minority over-sampling technique (SMOTE) based decision support framework in order to predict the success of smoking cessation program using Korea National Health and Nutrition Examination Survey (KNHANES) dataset. We carried out experiments as follows: I) the unnecessary instances and variables have been eliminated, II) then we employed three variations of SMOTE, III) also the prediction models have been constructed. Finally, compare the prediction models to obtain the best model. Our experimental results showed that SMOTE improved the prediction performance of machine learning classifiers among evaluation metrics. Moreover, SMOTE regular based Random Forest (RF) and Naïve Bayes (NB) classifiers were determined the best prediction models in real-world smoking cessation dataset. Consequently, our decision support framework can interpret the important risk factors of smoking cessation using multivariate regression analysis. Copyright: The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted distribution provided the original author and source are cited.
Federated learning (FL) has been widely regarded as a promising distributed machine learning technology that utilizes on-device computation while protecting clients' data privacy. To adapt FL to wireless networks,...
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ISBN:
(纸本)9781665435413
Federated learning (FL) has been widely regarded as a promising distributed machine learning technology that utilizes on-device computation while protecting clients' data privacy. To adapt FL to wireless networks, the over-the-air (OTA) computation, which employs the superposition nature of wireless waveforms, can prevent excessive consumption of the communication resources. However, energy harvesting technology can overcome the energy limitation of clients to realize durable computation. Despite the existing works devoted to OTA FL from various aspects, they mostly neglect jointly performing client selection and energy management for energy harvesting devices. In this paper, we investigate the combined problem of client selection and energy management for OTA FL and formulate it as a nonlinear integer programming (NIP) problem to minimize the optimality gap. To solve the NIP problem, we propose a client selection scheme that jointly considers channel state information, residual battery capacities, and dataset size. Our simulation results show that the proposed solution outperforms other comparison schemes within various parameter settings.
Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortalit...
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ISBN:
(数字)9798350351552
ISBN:
(纸本)9798350351569
Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag.
Visible light positioning (VLP) is an accurate indoor positioning technology that uses luminaires as transmitters. In particular, circular luminaires are a common source type for VLP, that are typically treated only a...
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Visible light positioning (VLP) is an accurate indoor positioning technology that uses luminaires as transmitters. In particular, circular luminaires are a common source type for VLP, that are typically treated only as point sources for positioning, while ignoring their geometry characteristics. In this paper, the arc feature of the circular luminaire and the coordinate information obtained via visible light communication (VLC) are jointly used for positioning, and a novel perspective arcs approach is proposed for VLC-enabled indoor positioning. The proposed approach does not rely on any inertial measurement unit and has no tilted angle limitaion at the user. First, a VLC assisted perspective circle and arc algorithm (V-PCA) is proposed for a scenario in which a complete luminaire and an incomplete one can be captured by the user. Based on plane and solid geometry theory, the relationship between the luminaire and the user is exploited to estimate the orientation and the coordinate of the luminaire in the camera coordinate system. Then, the pose and location of the user in the world coordinate system are obtained by single-view geometry theory. Considering the cases in which parts of VLC links are blocked, an anti-occlusion VLC assisted perspective arcs algorithm (OA-V-PA) is proposed. In OA-V-PA, an approximation method is developed to estimate the projection of the luminaire's center on the image and, then, to calculate the pose and location of the user. Simulation results show that the proposed indoor positioning algorithm can achieve a 90th percentile positioning accuracy of around 10 cm. Moreover, an experimental prototype is implemented to verify the feasibility. In the established prototype, a fused image processing method is proposed to simultaneously obtain the VLC information and the geometric information. Experimental results in the established prototype show that the average positioning accuracy is less than 5 cm for different tilted angles of the user.
Networked micro-grid topologies are important for several reasons. First, they make it possible to connect decentralized energy sources like solar PV installations, micro- and mini-scale wind turbines, etc., to the ma...
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