In this paper we introduce the concept of semantic Wireless Body Area Network (sWBAN). First the method for semantic interpretation of body sensor data is developed. This method is then illustrated for the case of ECG monitoring, providing the user with real-time monitoring and interpretation of heart activity. Finally, possible extensions of the method to data fusion and context-aware monitoring are discussed.
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http://dx.doi.org/10.1109/IEMBS.2008.4649938 | DOI Listing |
World J Gastroenterol
December 2024
School of Computer Science Technology, Changchun University, Changchun 130022, Jilin Province, China.
Background: Wireless capsule endoscopy (WCE) has become an important noninvasive and portable tool for diagnosing digestive tract diseases and has been propelled by advancements in medical imaging technology. However, the complexity of the digestive tract structure, and the diversity of lesion types, results in different sites and types of lesions distinctly appearing in the images, posing a challenge for the accurate identification of digestive tract diseases.
Aim: To propose a deep learning-based lesion detection model to automatically identify and accurately label digestive tract lesions, thereby improving the diagnostic efficiency of doctors, and creating significant clinical application value.
Sensors (Basel)
October 2024
School of Computer Science, National University of Defense Technology, Changsha 410000, China.
The advancement of 6G (6th Generation Mobile Networks) communication technology has posed challenges for traditional communication network architectures in meeting the demands for communication efficiency and quality. Semantic communication technology, characterized by its "understand before transmit" approach, has emerged as a pivotal technology driving the progress of 6G due to its ability to enhance communication efficiency and quality. The Wireless Image Transmission Transformer (WITT) model, which operates as a semantic communication system leveraging vision transformer technology for the transmission of semantic images, has shown efficacy in transmitting input images through processes of feature extraction and channel adaptation.
View Article and Find Full Text PDFIEEE Trans Neural Netw Learn Syst
August 2024
The rapid growth of the Internet of Things (IoT) has led to the widespread adoption of the IoT networks in numerous digital applications. To counter physical threats in these systems, automatic modulation classification (AMC) has emerged as an effective approach for identifying the modulation format of signals in noisy environments. However, identifying those threats can be particularly challenging due to the scarcity of labeled data, which is a common issue in various IoT applications, such as anomaly detection for unmanned aerial vehicles (UAVs) and intrusion detection in the IoT networks.
View Article and Find Full Text PDFComput Med Imaging Graph
September 2024
Department of Radiology, Zhuhai People's Hospital(Zhuhai Clinical Medical College of Jinan University), Zhuhai 519000, China.
Intracranial aneurysm (IA) is a prevalent disease that poses a significant threat to human health. The use of computed tomography angiography (CTA) as a diagnostic tool for IAs remains time-consuming and challenging. Deep neural networks (DNNs) have made significant advancements in the field of medical image segmentation.
View Article and Find Full Text PDFSci Rep
July 2024
Mechatronics Department, Faculty of Engineering, University of Debrecen, Ótemető U. 4-5, Debrecen, 4028, Hungary.
Advancements in cloud computing, flying ad-hoc networks, wireless sensor networks, artificial intelligence, big data, 5th generation mobile network and internet of things have led to the development of smart cities. Owing to their massive interconnectedness, high volumes of data are collected and exchanged over the public internet. Therefore, the exchanged messages are susceptible to numerous security and privacy threats across these open public channels.
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