Papers by Kyriaki Tychola
Beyond Flight: Enhancing the Internet of Drones with Blockchain Technologies
Drones, May 26, 2024
The visual computer/The visual computer, Jan 29, 2024
Point clouds consist of 3D data points and are among the most considerable data formats for 3D re... more Point clouds consist of 3D data points and are among the most considerable data formats for 3D representations. Their popularity is due to their broad application areas, such as robotics and autonomous driving, and their employment in basic 3D vision tasks such as segmentation, classification, and detection. However, processing point clouds is challenging compared to other visual forms such as images, mainly due to their unstructured nature. Deep learning (DL) has been established as a powerful tool for data processing, reporting remarkable performance enhancements compared to traditional methods for all basic 2D vision tasks. However new challenges are emerging when it comes to processing unstructured 3D point clouds. This work aims to guide future research by providing a systematic review of DL on 3D point clouds, holistically covering all 3D vision tasks. 3D technologies of point cloud formation are reviewed and compared to each other. The application of DL methods for point cloud processing is discussed, and state-of-the-art models' performances are compared focusing on challenges and solutions. Moreover, in this work the most popular 3D point cloud benchmark datasets are summarized based on their task-oriented applications, aiming to highlight existing constraints and to comparatively evaluate them. Future research directions and upcoming trends are also highlighted. To the best of the authors' knowledge, this review is the first to holistically cover DL-based tasks, including segmentation, classification, detection and tracking, registration, completion, and compression, and to combine point cloud fundamentals, DL research advances on point clouds for all tasks, challenges, solutions, datasets, and future research directions, as opposed to already existing reviews. Performance comparison results of DL algorithms on 3D point cloud processing tasks can be found in . Within the context of this work, a systematic literature review took place by using the Kitchenham approach to identify the status of research in DL on 3D point clouds, based on six basic research questions: RQ1: What are the challenges regarding point cloud data processing? RQ2: What are the challenges that DL models face with 3D point cloud data? RQ3: What is the status of 3D point cloud datasets for DL-based applications? RQ4: In which applications does it make sense to apply point clouds? RQ5: To what extent do different sensors affect the point cloud resolution?
Telecommunication Systems
The Tactile Internet (TI) is a recently emerging field that has been developing and evolving to d... more The Tactile Internet (TI) is a recently emerging field that has been developing and evolving to date, since its communications parallel the sense of human touch. Lately, the revolutionized concept, Metaverse, draws attention due to the evolved immersive experience of human perception of the surrounding environment. This technology supports the ultimate union between the physical and virtual world, facilitated by 5G and beyond communication networks. Users are capable of interacting with machines and devices in real-time, remotely, resembling the actions of their physical counterparts. The particular approaches are still in their infancy and expected to produce spectacular results in various sectors such as industry, healthcare, autonomous vehicles, etc. This immersion is further assisted by the Internet of Things, while expecting full wireless support by 5G networks. In this article, a systematic review studies the domains of TI, 5G and beyond networks, as well as their relations wi...
Electronics
Quantum computing has been proven to excel in factorization issues and unordered search problems ... more Quantum computing has been proven to excel in factorization issues and unordered search problems due to its capability of quantum parallelism. This unique feature allows exponential speed-up in solving certain problems. However, this advantage does not apply universally, and challenges arise when combining classical and quantum computing to achieve acceleration in computation speed. This paper aims to address these challenges by exploring the current state of quantum machine learning and benchmarking the performance of quantum and classical algorithms in terms of accuracy. Specifically, we conducted experiments with three datasets for binary classification, implementing Support Vector Machine (SVM) and Quantum SVM (QSVM) algorithms. Our findings suggest that the QSVM algorithm outperforms classical SVM on complex datasets, and the performance gap between quantum and classical models increases with dataset complexity, as simple models tend to overfit with complex datasets. While ther...
Technologies
The buildings in a city are of great importance. Certain historic buildings are landmarks and ind... more The buildings in a city are of great importance. Certain historic buildings are landmarks and indicate the city’s architecture and culture. The buildings over time undergo changes because of various factors, such as structural changes, natural disaster damages, and aesthetic interventions. The form of buildings in each period is perceived and understood by people of each generation, through photography. Nevertheless, each photograph has its own characteristics depending on the camera (analog or digital) used for capturing it. Any photo, even depicting the same object, is impossible to capture in the same way in terms of illumination, viewing angle, and scale. Hence, to study two or more photographs depicting the same object, first they should be identified and then properly matched. Nowadays, computer vision contributes to this process by providing useful tools. In particular, for this purpose, several feature detection and description algorithms of homologous points have been devel...
Digital
The representation of the physical world is an issue that concerns the scientific community study... more The representation of the physical world is an issue that concerns the scientific community studying computer vision, more and more. Recently, research has focused on modern techniques and methods of photogrammetry and stereoscopy with the aim of reconstructing three-dimensional realistic models with high accuracy and metric information in a short time. In order to obtain data at a relatively low cost, various tools have been developed, such as depth cameras. RGB-D cameras are novel sensing systems that capture RGB images along with per-pixel depth information. This survey aims to describe RGB-D camera technology. We discuss the hardware and data acquisition process, in both static and dynamic environments. Depth map sensing techniques are described, focusing on their features, pros, cons, and limitations; emerging challenges and open issues to investigate are analyzed; and some countermeasures are described. In addition, the advantages, disadvantages, and limitations of RGB-D camer...
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Papers by Kyriaki Tychola