161 research outputs found

    Data Mining Applications to Fault Diagnosis in Power Electronic Systems: A Systematic Review

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    Advanced Sensing, Fault Diagnostics, and Structural Health Management

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    Advanced sensing, fault diagnosis, and structural health management are important parts of the maintenance strategy of modern industries. With the advancement of science and technology, modern structural and mechanical systems are becoming more and more complex. Due to the continuous nature of operation and utilization, modern systems are heavily susceptible to faults. Hence, the operational reliability and safety of the systems can be greatly enhanced by using the multifaced strategy of designing novel sensing technologies and advanced intelligent algorithms and constructing modern data acquisition systems and structural health monitoring techniques. As a result, this research domain has been receiving a significant amount of attention from researchers in recent years. Furthermore, the research findings have been successfully applied in a wide range of fields such as aerospace, manufacturing, transportation and processes

    Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021

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    As almost all human activities have been moved online due to the pandemic, novel robust and efficient approaches and further research have been in higher demand in the field of computer science and telecommunication. Therefore, this (reprint) book contains 13 high-quality papers presenting advancements in theoretical and practical aspects of computer recognition, pattern recognition, image processing and machine learning (shallow and deep), including, in particular, novel implementations of these techniques in the areas of modern telecommunications and cybersecurity

     Ocean Remote Sensing with Synthetic Aperture Radar

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    The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

    Applications in Electronics Pervading Industry, Environment and Society

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    This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs

    Point Cloud Processing for Environmental Analysis in Autonomous Driving using Deep Learning

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    Autonomous self-driving cars need a very precise perception system of their environment, working for every conceivable scenario. Therefore, different kinds of sensor types, such as lidar scanners, are in use. This thesis contributes highly efficient algorithms for 3D object recognition to the scientific community. It provides a Deep Neural Network with specific layers and a novel loss to safely localize and estimate the orientation of objects from point clouds originating from lidar sensors. First, a single-shot 3D object detector is developed that outputs dense predictions in only one forward pass. Next, this detector is refined by fusing complementary semantic features from cameras and joint probabilistic tracking to stabilize predictions and filter outliers. The last part presents an evaluation of data from automotive-grade lidar scanners. A Generative Adversarial Network is also being developed as an alternative for target-specific artificial data generation.One of the main objectives of leading automotive companies is autonomous self-driving cars. They need a very precise perception system of their environment, working for every conceivable scenario. Therefore, different kinds of sensor types are in use. Besides cameras, lidar scanners became very important. The development in that field is significant for future applications and system integration because lidar offers a more accurate depth representation, independent from environmental illumination. Especially algorithms and machine learning approaches, including Deep Learning and Artificial Intelligence based on raw laser scanner data, are very important due to the long range and three-dimensional resolution of the measured point clouds. Consequently, a broad field of research with many challenges and unsolved tasks has been established. This thesis aims to address this deficit and contribute highly efficient algorithms for 3D object recognition to the scientific community. It provides a Deep Neural Network with specific layers and a novel loss to safely localize and estimate the orientation of objects from point clouds. First, a single shot 3D object detector is developed that outputs dense predictions in only one forward pass. Next, this detector is refined by fusing complementary semantic features from cameras and a joint probabilistic tracking to stabilize predictions and filter outliers. In the last part, a concept for deployment into an existing test vehicle focuses on the semi-automated generation of a suitable dataset. Subsequently, an evaluation of data from automotive-grade lidar scanners is presented. A Generative Adversarial Network is also being developed as an alternative for target-specific artificial data generation. Experiments on the acquired application-specific and benchmark datasets show that the presented methods compete with a variety of state-of-the-art algorithms while being trimmed down to efficiency for use in self-driving cars. Furthermore, they include an extensive set of standard evaluation metrics and results to form a solid baseline for future research.Eines der Hauptziele führender Automobilhersteller sind autonome Fahrzeuge. Sie benötigen ein sehr präzises System für die Wahrnehmung der Umgebung, dass für jedes denkbare Szenario überall auf der Welt funktioniert. Daher sind verschiedene Arten von Sensoren im Einsatz, sodass neben Kameras u. a. auch Lidar Sensoren ein wichtiger Bestandteil sind. Die Entwicklung auf diesem Gebiet ist für künftige Anwendungen von höchster Bedeutung, da Lidare eine genauere, von der Umgebungsbeleuchtung unabhängige, Tiefendarstellung bieten. Insbesondere Algorithmen und maschinelle Lernansätze wie Deep Learning, die Rohdaten über Lernzprozesse direkt verarbeiten können, sind aufgrund der großen Reichweite und der dreidimensionalen Auflösung der gemessenen Punktwolken sehr wichtig. Somit hat sich ein weites Forschungsfeld mit vielen Herausforderungen und ungelösten Problemen etabliert. Diese Arbeit zielt darauf ab, dieses Defizit zu verringern und effiziente Algorithmen zur 3D-Objekterkennung zu entwickeln. Sie stellt ein tiefes Neuronales Netzwerk mit spezifischen Schichten und einer neuartigen Fehlerfunktion zur sicheren Lokalisierung und Schätzung der Orientierung von Objekten aus Punktwolken bereit. Zunächst wird ein 3D-Detektor entwickelt, der in nur einem Vorwärtsdurchlauf aus einer Punktwolke alle Objekte detektiert. Anschließend wird dieser Detektor durch die Fusion von komplementären semantischen Merkmalen aus Kamerabildern und einem gemeinsamen probabilistischen Tracking verfeinert, um die Detektionen zu stabilisieren und Ausreißer zu filtern. Im letzten Teil wird ein Konzept für den Einsatz in einem bestehenden Testfahrzeug vorgestellt, das sich auf die halbautomatische Generierung eines geeigneten Datensatzes konzentriert. Hierbei wird eine Auswertung auf Daten von Automotive-Lidaren vorgestellt. Als Alternative zur zielgerichteten künstlichen Datengenerierung wird ein weiteres generatives Neuronales Netzwerk untersucht. Experimente mit den erzeugten anwendungsspezifischen- und Benchmark-Datensätzen zeigen, dass sich die vorgestellten Methoden mit dem Stand der Technik messen können und gleichzeitig auf Effizienz für den Einsatz in selbstfahrenden Autos optimiert sind. Darüber hinaus enthalten sie einen umfangreichen Satz an Evaluierungsmetriken und -ergebnissen, die eine solide Grundlage für die zukünftige Forschung bilden

    Electronic systems-1. Lecture notes

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    The discipline «Electronic Systems» belongs to the cycle of professional and practical training of bachelors in the educational program «Electronic Components and Systems» is read over one semester (7) and is one of the final subjects of the bachelor's degree. In the process of studying the course, students get acquainted with the informational assessments of the ES; a description of the signals used in different purposes of the ES; methods of their processing, storage and transformation; principles of construction and operation of the ES - the selection, transformation, transmission, reception, registration and display of information. The basics of device design based on programmable logic integrated circuits (FPGA) are considered. Lecture notes contain theoretical information for up to 18 lectures and a list of recommended reading

    Digital Signal Processing (Second Edition)

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    This book provides an account of the mathematical background, computational methods and software engineering associated with digital signal processing. The aim has been to provide the reader with the mathematical methods required for signal analysis which are then used to develop models and algorithms for processing digital signals and finally to encourage the reader to design software solutions for Digital Signal Processing (DSP). In this way, the reader is invited to develop a small DSP library that can then be expanded further with a focus on his/her research interests and applications. There are of course many excellent books and software systems available on this subject area. However, in many of these publications, the relationship between the mathematical methods associated with signal analysis and the software available for processing data is not always clear. Either the publications concentrate on mathematical aspects that are not focused on practical programming solutions or elaborate on the software development of solutions in terms of working ‘black-boxes’ without covering the mathematical background and analysis associated with the design of these software solutions. Thus, this book has been written with the aim of giving the reader a technical overview of the mathematics and software associated with the ‘art’ of developing numerical algorithms and designing software solutions for DSP, all of which is built on firm mathematical foundations. For this reason, the work is, by necessity, rather lengthy and covers a wide range of subjects compounded in four principal parts. Part I provides the mathematical background for the analysis of signals, Part II considers the computational techniques (principally those associated with linear algebra and the linear eigenvalue problem) required for array processing and associated analysis (error analysis for example). Part III introduces the reader to the essential elements of software engineering using the C programming language, tailored to those features that are used for developing C functions or modules for building a DSP library. The material associated with parts I, II and III is then used to build up a DSP system by defining a number of ‘problems’ and then addressing the solutions in terms of presenting an appropriate mathematical model, undertaking the necessary analysis, developing an appropriate algorithm and then coding the solution in C. This material forms the basis for part IV of this work. In most chapters, a series of tutorial problems is given for the reader to attempt with answers provided in Appendix A. These problems include theoretical, computational and programming exercises. Part II of this work is relatively long and arguably contains too much material on the computational methods for linear algebra. However, this material and the complementary material on vector and matrix norms forms the computational basis for many methods of digital signal processing. Moreover, this important and widely researched subject area forms the foundations, not only of digital signal processing and control engineering for example, but also of numerical analysis in general. The material presented in this book is based on the lecture notes and supplementary material developed by the author for an advanced Masters course ‘Digital Signal Processing’ which was first established at Cranfield University, Bedford in 1990 and modified when the author moved to De Montfort University, Leicester in 1994. The programmes are still operating at these universities and the material has been used by some 700++ graduates since its establishment and development in the early 1990s. The material was enhanced and developed further when the author moved to the Department of Electronic and Electrical Engineering at Loughborough University in 2003 and now forms part of the Department’s post-graduate programmes in Communication Systems Engineering. The original Masters programme included a taught component covering a period of six months based on two semesters, each Semester being composed of four modules. The material in this work covers the first Semester and its four parts reflect the four modules delivered. The material delivered in the second Semester is published as a companion volume to this work entitled Digital Image Processing, Horwood Publishing, 2005 which covers the mathematical modelling of imaging systems and the techniques that have been developed to process and analyse the data such systems provide. Since the publication of the first edition of this work in 2003, a number of minor changes and some additions have been made. The material on programming and software engineering in Chapters 11 and 12 has been extended. This includes some additions and further solved and supplementary questions which are included throughout the text. Nevertheless, it is worth pointing out, that while every effort has been made by the author and publisher to provide a work that is error free, it is inevitable that typing errors and various ‘bugs’ will occur. If so, and in particular, if the reader starts to suffer from a lack of comprehension over certain aspects of the material (due to errors or otherwise) then he/she should not assume that there is something wrong with themselves, but with the author

    Machine Learning in Sensors and Imaging

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    Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

    Advances in the Field of Electrical Machines and Drives

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    Electrical machines and drives dominate our everyday lives. This is due to their numerous applications in industry, power production, home appliances, and transportation systems such as electric and hybrid electric vehicles, ships, and aircrafts. Their development follows rapid advances in science, engineering, and technology. Researchers around the world are extensively investigating electrical machines and drives because of their reliability, efficiency, performance, and fault-tolerant structure. In particular, there is a focus on the importance of utilizing these new trends in technology for energy saving and reducing greenhouse gas emissions. This Special Issue will provide the platform for researchers to present their recent work on advances in the field of electrical machines and drives, including special machines and their applications; new materials, including the insulation of electrical machines; new trends in diagnostics and condition monitoring; power electronics, control schemes, and algorithms for electrical drives; new topologies; and innovative applications
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