The relatively complex task of detecting 3D objects is essential in the realm of autonomous driving. The related algorithmic processes generally produce an output that consists of a series of 3D bounding boxes that are placed around specific objects of interest. The related scientific literature usually suggests that the data that are generated by different sensors or data acquisition devices are combined in order to work around inherent limitations that are determined by the consideration of singular devices. Nevertheless, there are practical issues that cannot be addressed reliably and efficiently through this strategy, such as the limited field-of-view, and the low-point density of acquired data. This paper reports a contribution that analyzes the possibility of efficiently and effectively using 3D object detection in a cooperative fashion. The evaluation of the described approach is performed through the consideration of driving data that is collected through a partnership with several car manufacturers. Considering their real-world relevance, two driving contexts are analyzed: a roundabout, and a T-junction. The evaluation shows that cooperative perception is able to isolate more than 90% of the 3D entities, as compared to approximately 25% in the case when singular sensing devices are used. The experimental setup that generated the data that this paper describes, and the related 3D object detection system, are currently actively used by the respective car manufacturers’ research groups in order to fine tune and improve their autonomous cars’ driving modules.
The continuously increasing number of mobile devices actively being used in the world amounted to approximately 6.8 billion by 2022. Consequently, this implies a substantial increase in the amount of personal data collected, transported, processed, and stored. The authors of this paper designed and implemented an integrated personal health data management system, which considers data-driven software and hardware sensors, comprehensive data privacy techniques, and machine-learning-based algorithmic models. It was determined that there are very few relevant and complete surveys concerning this specific problem. Therefore, the current scientific research was considered, and this paper comprehensively analyzes the importance of deep learning techniques that are applied to the overall management of data collected by data-driven soft sensors. This survey considers aspects that are related to demographics, health and body parameters, and human activity and behaviour pattern detection. Additionally, the relatively complex problem of designing and implementing data privacy mechanisms, while ensuring efficient data access, is also discussed, and the relevant metrics are presented. The paper concludes by presenting the most important open research questions and challenges. The paper provides a comprehensive and thorough scientific literature survey, which is useful for any researcher or practitioner in the scope of data-driven soft sensors and privacy techniques, in relation to the relevant machine-learning-based models.
This paper explores the foundations regarding the systematic usage of the concept of interface in order to sketch a methodological approach, in which the fundamental perspectives that guide the abstracting of a software system solution (referred to as UP, SP and BP in the paper) are unified, with the goal to optimally derive the behaviour of the system from its structure. Moreover, this is very useful for opening new avenues in order to address the shortcomings that are provoked by changes, considering a software system that is conceived at an industrial scale.
In this chapter, the authors systematically relate to the question: “What are the main ideas that should be considered when elaborating software Systems for the communication’s streamlining and diversification (CSD) between the actors of a learning system?” The broader perspective within which these ideas are debated is represented by the context that is created through the inception of what, in the specialized literature, is called social media (as a problematic universe) and Web 2.0 (as a fundamental technological universe). Naturally, the authors will not miss some considerations that highlight the impact of the phenomenon “social media” on the information systems of the near future.
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