Abstract
This work reports on preliminary findings about the learning process of a diverse set of users playing an immersive Virtual Reality serious game based on hand tracking to complement conventional occupational therapy in hand rehabilitation. Two test sessions with a one-day break between them were carried out with seven able-bodied participants. Participants went through twenty minutes guided functional task practice followed by a Semi-structured interview. The System Usability Scale (SUS) scores obtained for the first (75.36) and second (76.43) test sessions positioned the serious game above the average in terms of usability. Insights about the learning process., SUS and the obtained trial results., confirm the potential of the developed solution as a viable complement to conventional hand rehabilitation and highlight possible points for improvement. © 2021 IEEE.

Abstract
Up to one third of the global food production depends on the pollination of honey bees, making them vital. This study defines a methodology to create a bee hive health monitoring system through image processing techniques. The approach consists of two models, where one performs the detection of bees in an image and the other classifies the detected bee's health. The main contribution of the defined methodology is the increased efficacy of the models, whilst maintaining the same efficiency found in the state of the art. Two databases were used to create models based on Convolutional Neural Network (CNN). The best results consist of 95% accuracy for health classification of a bee and 82% accuracy in detecting the presence of bees in an image, higher than those found in the state-of-the-art.

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The signal processing (SP) landscape has been enriched by recent advances in artificial intelligence (AI) and machine learning (ML), yielding new tools for signal estimation, classification, prediction, and manipulation. Layered signal representations, nonlinear function approximation and nonlinear signal prediction are now feasible at very large scale in both dimensionality and data size. These are leading to significant performance gains in a variety of long-standing problem domains like speech and Image analysis. As well as providing the ability to construct new classes of nonlinear functions (e.g., fusion, nonlinear filtering). This book will help academics, researchers, developers, graduate and undergraduate students to comprehend complex SP data across a wide range of topical application areas such as social multimedia data collected from social media networks, medical imaging data, data from Covid tests etc. This book focuses on AI utilization in the speech, image, communications and yirtual reality domains. © 2021 River Publishers. All rights reserved.

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[No abstract available]