Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles

The damaging effects of cyberattacks to an industry like the Cooperative Connected and Automated Mobility (CCAM) can be tremendous. From the least important to the worst ones, one can mention for example the damage in the reputation of vehicle manufacturers, the increased denial of customers to adopt CCAM, the loss of working hours (having direct impact on the European GDP), material damages, increased environmental pollution due e.g., to traffic jams or malicious modifications in sensors’ firmware, and ultimately, the great danger for human lives, either they are drivers, passengers or pedestrians. Connected vehicles will soon become a reality on our roads, bringing along new services and capabilities, but also technical challenges and security threats. To overcome these risks, the CARAMEL project has developed several anti-hacking solutions for the new generation of vehicles. CARAMEL (Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles), a research project co-funded by the European Union under the Horizon 2020 framework programme, is a project consortium with 15 organizations from 8 European countries together with 3 Korean partners. The project applies a proactive approach based on Artificial Intelligence and Machine Learning techniques to detect and prevent potential cybersecurity threats to autonomous and connected vehicles. This approach has been addressed based on four fundamental pillars, namely: Autonomous Mobility, Connected Mobility, Electromobility, and Remote Control Vehicle. This book presents theory and results from each of these technical directions

Advanced Techniques and Efficiency Assessment of Mechanical Processing

Mechanical processing is just one step in the value chain of metal production, but to some exten,t it determines an effectiveness of separation through suitable preparation of the raw material for beneficiation processes through production of required particle sze composition and useful mineral liberation. The issue is mostly related to techniques of comminution and size classification, but it also concerns methods of gravity separation, as well as modeling and optimization. Technological and economic assessment supplements the issue

Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles

The damaging effects of cyberattacks to an industry like the Cooperative Connected and Automated Mobility (CCAM) can be tremendous. From the least important to the worst ones, one can mention for example the damage in the reputation of vehicle manufacturers, the increased denial of customers to adopt CCAM, the loss of working hours (having direct impact on the European GDP), material damages, increased environmental pollution due e.g., to traffic jams or malicious modifications in sensors’ firmware, and ultimately, the great danger for human lives, either they are drivers, passengers or pedestrians. Connected vehicles will soon become a reality on our roads, bringing along new services and capabilities, but also technical challenges and security threats. To overcome these risks, the CARAMEL project has developed several anti-hacking solutions for the new generation of vehicles. CARAMEL (Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles), a research project co-funded by the European Union under the Horizon 2020 framework programme, is a project consortium with 15 organizations from 8 European countries together with 3 Korean partners. The project applies a proactive approach based on Artificial Intelligence and Machine Learning techniques to detect and prevent potential cybersecurity threats to autonomous and connected vehicles. This approach has been addressed based on four fundamental pillars, namely: Autonomous Mobility, Connected Mobility, Electromobility, and Remote Control Vehicle. This book presents theory and results from each of these technical directions

Analysis of textural image features for content based retrieval

Digital archaelogy and virtual reality with archaeological artefacts have been quite hot research topics in the last years 55,56 . This thesis is a preperation study to build the background knowledge required for the research projects, which aim to computerize the reconstruction of the archaelogical data like pots, marbles or mosaic pieces by shape and ex ural features. Digitalization of the cultural heritage may shorten the reconstruction time which takes tens of years currently 61 ; it will improve the reconstruction robustness by incorporating with the literally available machine vision algorithms and experiences from remote experts working on a no-cost virtual object together. Digitalization can also ease the exhibition of the results for regular people, by multiuser media applications like internet based virtual museums or virtual tours. And finally, it will make possible to archive values with their original texture and shapes for long years far away from the physical risks that the artefacts currently face. On the literature 1,2,3,5,8,11,14,15,16 , texture analysis techniques have been throughly studied and implemented for the purpose of defect analysis purposes by image processing and machine vision scientists. In the last years, these algorithms have been started to be used for similarity analysis of content based image retrieval 1,4,10 . For retrieval systems, the concurrent problems seem to be building efficient and fast systems, therefore, robust image features haven't been focused enough yet. This document is the first performance review of the texture algorithms developed for retrieval and defect analysis together. The results and experiences gained during the thesis study will be used to support the studies aiming to solve the 2D puzzle problem using textural continuity methods on archaelogical artifects, Appendix A for more detail. The first chapter is devoted to learn how the medicine and psychology try to explain the solutions of similiarity and continuity analysis, which our biological model, the human vision, accomplishes daily. In the second chapter, content based image retrieval systems, their performance criterias, similiarity distance metrics and the systems available have been summarized. For the thesis work, a rich texture database has been built, including over 1000 images in total. For the ease of the users, a GUI and a platform that is used for content based retrieval has been designed; The first version of a content based search engine has been coded which takes the source of the internet pages, parses the metatags of images and downloads the files in a loop controlled by our texture algorithms. The preprocessing algorithms and the pattern analysis algorithms required for the robustness of the textural feature processing have been implemented. In the last section, the most important textural feature extraction methods have been studied in detail with the performance results of the codes written in Matlab and run on different databases developed

Acoustic classification of Australian frogs for ecosystem survey

Novel bioacoustics signal processing techniques have been developed to classify frog vocalisations in both trophy and field recordings. The research is useful in helping ecologists monitor frog community activity and species richness over long-term. Two major contributions are the construction of novel feature descriptors in the Cepstral domain, and the design of novel classification systems for multiple simultaneously vocalising frog species

Survey of FPGA applications in the period 2000 – 2015 (Technical Report)

Romoth J, Porrmann M, Rückert U. Survey of FPGA applications in the period 2000 – 2015 (Technical Report).; 2017.Since their introduction, FPGAs can be seen in more and more different fields of applications. The key advantage is the combination of software-like flexibility with the performance otherwise common to hardware. Nevertheless, every application field introduces special requirements to the used computational architecture. This paper provides an overview of the different topics FPGAs have been used for in the last 15 years of research and why they have been chosen over other processing units like e.g. CPUs

The Space and Earth Science Data Compression Workshop

This document is the proceedings from a Space and Earth Science Data Compression Workshop, which was held on March 27, 1992, at the Snowbird Conference Center in Snowbird, Utah. This workshop was held in conjunction with the 1992 Data Compression Conference (DCC '92), which was held at the same location, March 24-26, 1992. The workshop explored opportunities for data compression to enhance the collection and analysis of space and Earth science data. The workshop consisted of eleven papers presented in four sessions. These papers describe research that is integrated into, or has the potential of being integrated into, a particular space and/or Earth science data information system. Presenters were encouraged to take into account the scientists's data requirements, and the constraints imposed by the data collection, transmission, distribution, and archival system

Image and Video Coding Techniques for Ultra-low Latency

The next generation of wireless networks fosters the adoption of latency-critical applications such as XR, connected industry, or autonomous driving. This survey gathers implementation aspects of different image and video coding schemes and discusses their tradeoffs. Standardized video coding technologies such as HEVC or VVC provide a high compression ratio, but their enormous complexity sets the scene for alternative approaches like still image, mezzanine, or texture compression in scenarios with tight resource or latency constraints. Regardless of the coding scheme, we found inter-device memory transfers and the lack of sub-frame coding as limitations of current full-system and software-programmable implementations.publishedVersionPeer reviewe