A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision

Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision. This paper presents a comprehensive survey of deep learning in sports performance, focusing on three main aspects: algorithms, datasets and virtual environments, and challenges. Firstly, we discuss the hierarchical structure of deep learning algorithms in sports performance which includes perception, comprehension and decision while comparing their strengths and weaknesses. Secondly, we list widely used existing datasets in sports and highlight their characteristics and limitations. Finally, we summarize current challenges and point out future trends of deep learning in sports. Our survey provides valuable reference material for researchers interested in deep learning in sports applications

Survey of computer vision algorithms and applications for unmanned aerial vehicles

This paper presents a complete review of computer vision algorithms and vision-based intelligent applications, that are developed in the field of the Unmanned Aerial Vehicles (UAVs) in the latest decade. During this time, the evolution of relevant technologies for UAVs; such as component miniaturization, the increase of computational capabilities, and the evolution of computer vision techniques have allowed an important advance in the development of UAVs technologies and applications. Particularly, computer vision technologies integrated in UAVs allow to develop cutting-edge technologies to cope with aerial perception difficulties; such as visual navigation algorithms, obstacle detection and avoidance and aerial decision-making. All these expert technologies have developed a wide spectrum of application for UAVs, beyond the classic military and defense purposes. Unmanned Aerial Vehicles and Computer Vision are common topics in expert systems, so thanks to the recent advances in perception technologies, modern intelligent applications are developed to enhance autonomous UAV positioning, or automatic algorithms to avoid aerial collisions, among others. Then, the presented survey is based on artificial perception applications that represent important advances in the latest years in the expert system field related to the Unmanned Aerial Vehicles. In this paper, the most significant advances in this field are presented, able to solve fundamental technical limitations; such as visual odometry, obstacle detection, mapping and localization, et cetera. Besides, they have been analyzed based on their capabilities and potential utility. Moreover, the applications and UAVs are divided and categorized according to different criteria.This research is supported by the Spanish Government through the CICYT projects (TRA2015-63708-R and TRA2013-48314-C3-1-R)

Seeds of phase transition to thermoacoustic instability

Tackling the problem of emissions is at the forefront of scientific research today. While industrial engines designed to operate in stable regimes produce emissions, attempts to operate them at 'greener' conditions often fail due to a dangerous phenomenon known as thermoacoustic instability. Hazardous high amplitude periodic oscillations during thermoacoustic instability lead to the failure of these engines in power plants, aircraft, and rockets. To prevent this catastrophe in the first place, identifying the onset of thermoacoustic instability is required. However, detecting the onset is a major obstacle preventing further progress due to spatiotemporal variability in the reacting field. Here, we show how to overcome this obstacle by discovering a critical condition in certain zones of the combustor, which indicates the onset of thermoacoustic instability. In particular, we reveal the critical value of the local heat release rate that allows us to distinguish stable operating regimes from hazardous operations. We refer to these zones as seeds of the phase transition because they show the earliest manifestation of the impending instability. The increase in correlations in the heat release rate between these zones indicates the transition from a chaotic state to a periodic state. Remarkably, we found that observations at the seeds of the phase transition enable us to predict when the onset occurs, well before the emergence of dangerous large-amplitude periodic acoustic pressure oscillations. Our results contribute to the operation of combustors in more environment-friendly conditions. The presented approach is applicable to other systems exhibiting such phase transitions.Indian Institute of Technology Madrashttps://doi.org/10.13039/501100003845Federal Ministry for the Environment, Nature Conservation and Nuclear Safety and the International Climate Initiative GermanyDepartment of Science and Technology IndiaRussian Foundation for Basic Researchhttps://doi.org/10.13039/501100002261Peer Reviewe