Abstract: This study explores the potential of digital light processing to 3D print radioactive phantoms for high-resolution positron emission tomography (PET). Using a slightly modified desktop 3D ...

Abstract: Vegetation is a key component of biodiversity and ecosystem stability. The normalized difference vegetation index (NDVI) is widely used to monitor the vegetation growth status. Timely ...

Abstract: Time series classification is an important task in time series data mining, and has attracted great interests and tremendous efforts during last decades. However, it remains a challenging ...

Abstract: In this monograph, the authors present an introduction to the framework of variational autoencoders (VAEs) that provides a principled method for jointly learning deep latent-variable models ...

Abstract: The Internet of Things (IoT) system provides sensing and computing services via terrestrial networks. However, the restricted coverage of terrestrial networks, such as base stations, limits ...

Abstract: The fusion of the Internet of Things (IoT) with sixth-generation (6G) technology has significant potential to revolutionize the IoT landscape. With the ultrareliable and low-latency ...

Abstract: Super-resolution ultrasound (SRUS) has evolved significantly with the advent of Ultrasound Localization Microscopy (ULM). This technique enables sub-wavelength resolution imaging using ...

Abstract: In industrial production, precise detection of bearing defects is crucial for optimal machinery performance and maintenance, directly impacting the efficiency of industrial systems and the ...

Abstract: Based on the total least-squares (TLS) model, the gradient-descent TLS Euclidean direction search (GD-TLS-EDS) algorithm is proposed when both input and output signals are corrupted by ...

Abstract: To improve the tracking performance of Autonomous Underwater Vehicles (AUV), a sliding optimal tracking control method for linear continuous systems is proposed with Adaptive Dynamic ...

Abstract: Construction and analysis of functional brain networks (FBNs) with resting-state functional magnetic resonance imaging (rs-fMRI) is a promising method to diagnose functional brain diseases.