When creating mission-critical distributed systems using off-the-shelf components, it is important to assess the dependability

This paper describes the results of real deployment of A 2 S which consists of WSN(Wireless Sensor Network) to monitor and control the environments and a management subsystem to manage the WSN and provide various and convenient services to consumers with hand-held devices such as a PDA living a farming village. The WSN were deployed in greenhouses with melon and cabbage in Dongbu Handong Seed Research Center. A 2 S was used to monitor the growing process of them and control the environment of the greenhouses. We acquired valuable experiences and ideas from this real deployment and operation of A 2 S and believe that they can be useful in consumer electronics field such as home network as well as automated agriculture field. .

Despite the popularity of current Radio Frequency Identification (RFID) and Wireless Sensor Networks (WSN), current research fails to propose the global vision that is needed for truly pervasive computing. In this paper we introduce our effort to build a global standard infrastructure for WSN and RFID based on the EPCglobal standard Architecture Framework. By leveraging the EPC Network infrastructure, our proposed EPC Sensor Network will effectively provide standardized WSN/RFID integration framework to support sensor data sharing.

In an integration system of RFID and wireless sensor network (WSN)

BACKGROUND AND PURPOSE: Limited evidence has suggested that a deep learning automatic brain segmentation and classification method, based on T1-weighted brain MR images, can predict Alzheimer disease. Our aim was to develop and validate a deep learning-based automatic brain segmentation and classification algorithm for the diagnosis of Alzheimer disease using 3D T1-weighted brain MR images. MATERIALS AND METHODS: A deep learning-based algorithm was developed using a dataset of T1-weighted brain MR images in consecutive patients with Alzheimer disease and mild cognitive impairment. We developed a 2-step algorithm using a convolutional neural network to perform brain parcellation followed by 3 classifier techniques including XGBoost for disease prediction. All classification experiments were performed using 5-fold cross-validation. The diagnostic performance of the XGBoost method was compared with logistic regression and a linear Support Vector Machine by calculating their areas under the curve for differentiating Alzheimer disease from mild cognitive impairment and mild cognitive impairment from healthy controls. RESULTS: In a total of 4 datasets, 1099, 212, 711, and 705 eligible patients were included. Compared with the linear Support Vector Machine and logistic regression, XGBoost significantly improved the prediction of Alzheimer disease (P , .001). In terms of differentiating Alzheimer disease from mild cognitive impairment, the 3 algorithms resulted in areas under the curve of 0.758-0.825. XGBoost had a sensitivity of 68% and a specificity of 70%. In terms of differentiating mild cognitive impairment from the healthy control group, the 3 algorithms resulted in areas under the curve of 0.668-0.870. XGBoost had a sensitivity of 79% and a specificity of 80%. CONCLUSIONS: The deep learning-based automatic brain segmentation and classification algorithm allowed an accurate diagnosis of Alzheimer disease using T1-weighted brain MR images. The widespread availability of T1-weighted brain MR imaging suggests that this algorithm is a promising and widely applicable method for predicting Alzheimer disease. ABBREVIATIONS: AD ¼ Alzheimer disease; ADNI ¼ Alzheimer's Disease Neuroimaging Initiative; AUC ¼ area under the curve; CNN ¼ convolutional neural network; MCI ¼ mild cognitive impairment; OASIS ¼ Open Access Series of Imaging Studies; SVM ¼ Support Vector Machine A lzheimer disease (AD) is the most common cause of dementia, with mild cognitive impairment (MCI) regarded as a transitional state between normal cognition and early stages of dementia. 1 Although current therapeutic and preventive options are only moderately effective, a reliable decision-making diagnostic approach is important during early stages of AD. 2,3 The guidelines of the National Institute on Aging-Alzheimer's Association suggest that MR imaging is a supportive imaging tool in the diagnostic work-up of patients with AD and MCI. 2,3 Imaging biomarkers play an important role in the diagnosis of AD, both in