Coronal Mass Ejections (CMEs) and solar flares are energetic events taking place at the Sun that can affect the space weather or the near-Earth environment by the release of vast quantities of electromagnetic radiation and charged particles. Solar active regions are the areas where most flares and CMEs originate. Studying the associations among sunspot groups, flares, filaments, and CMEs is helpful in understanding the possible cause and effect relationships between these events and features. Forecasting space weather in a timely manner is important for protecting technological systems and human life on earth and in space. The research presented in this thesis introduces novel, fully computerised, machine learning-based decision rules and models that can be used within a system design for automated space weather forecasting. The system design in this work consists of three stages: (1) designing computer tools to find the associations among sunspot groups, flares, filaments, and CMEs (2) applying machine learning algorithms to the associations' datasets and (3) studying the evolution patterns of sunspot groups using time-series methods. Machine learning algorithms are used to provide computerised learning rules and models that enable the system to provide automated prediction of CMEs, flares, and evolution patterns of sunspot groups. These numerical rules are extracted from the characteristics, associations, and time-series analysis of the available historical solar data. The training of machine learning algorithms is based on data sets created by investigating the associations among sunspots, filaments, flares, and CMEs. Evolution patterns of sunspot areas and McIntosh classifications are analysed using a statistical machine learning method, namely the Hidden Markov Model (HMM).

Machine Learning Techniques for Space Weather provides a thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals. Additionally, it presents an overview of real-world applications in space science to the machine learning community, offering a bridge between the fields. As this volume demonstrates, real advances in space weather can be gained using nontraditional approaches that take into account nonlinear and complex dynamics, including information theory, nonlinear auto-regression models, neural networks and clustering algorithms. Offering practical techniques for translating the huge amount of information hidden in data into useful knowledge that allows for better prediction, this book is a unique and important resource for space physicists, space weather professionals and computer scientists in related fields. Collects many representative non-traditional approaches to space weather into a single volume Covers, in an accessible way, the mathematical background that is not often explained in detail for space scientists Includes free software in the form of simple MATLAB® scripts that allow for replication of results in the book, also familiarizing readers with algorithms

The new age space value chain is a complex interconnected system with diverse actors, which involves cross-sector and cross-border collaborations. This book helps to enrich the knowledge of Artificial Intelligence (AI) across the value chain in the space-related domains. Advancements of AI and Machine Learning have impactfully supported the space sector transformation as it is shown in the book. "This book embarks on a journey through the fascinating realm of AI in space, exploring its profound implications, emerging trends, and transformative potential." Prof. Dr. Oliver Ullrich - Director Innovation Cluster Space and Aviaton (UZH Space Hub), University of Zurich, Switzerland Aimed at space engineers, risk analysts, policy makers, technical experts and non-specialists, this book demonstrates insights into the implementation of AI in the space sector, alongside its limitations and use-case examples. It covers diverse AI-related topics applicable to space technologies or space big data such as AI-based technologies for improving Earth Observation big data, AI for space robotics exploration, AI for astrophysics, AI for emerging in-orbit servicing market, and AI for space tourism safety improvement. Key Features: Provides an interdisciplinary approach, with chapter contributions from expert teams working in the governmental or private space sectors, with valuable contributions from computer scientists and legal experts Presents insights into AI implementation and how to unlock AI technologies in the field Up-to-date with the latest developments and cutting-edge applications

As we rely more on satellites, communication systems and space research, the importance of space weather is increasing continuously. There are many space missions and ground based observatories providing continuous observation of the Sun at many different wavelengths to supply the demand for space weather forecast and research. All the forecasting strategies highly depend on experience of solar physicists and done manually. The results differ from observatories to observatories and subjective. There is a need for automated analysis of Sun and space weather forecasting. The solar activity is the driver of space weather. Thus it is important to be able to predict the violent eruptions such as coronal mass ejections and solar flares. In this book a hybrid system combining image processing and machine learning techniques for the automated short-term prediction of solar flares is presented. The system can also detect, group, and classify sunspots using solar images. The algorithms, implementation, and results are explained in this work.

Magnetic storms may cause damage to satellites, radiation hazard to astronauts, disruption of radio communications, and interruption of ground electric power lines. Space weather prediction becomes an important issue to be addressed in the twenty-first century. International Solar Terrestrial Program (ISTP) employs five satellites to probe the solar wind and magnetosphere, providing valuable information for space weather prediction. The Asia-Pacific region is becoming one of the economic centers in the world. The continuous drive for scientific and technological progress in parallel is evidenced by the establishment of many space research organizations in many countries of this area. In Taiwan, the National Space Program Office (NSPO) established her third satellite program -- COSMIC (Constellation Obsering Systems for Meteorology, Ionosphere and Climate), which is a science experiment to demonstrate the utility of atmospheric radio limb soundings from a constellation of six low-earth orbiting satellites in operational weather prediction, space weather monitoring, and climate monitoring and research.In order to provide a forum to discuss the many new results in this rapid-moving field and to forge international collaborations, a three-day COSPAR Colloquium on "Space Weather Study Using Multipoint Techniques" was held. This colloquium have provided a forum for experts from the international community to present new results on the timely topic "space weather".

Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.

Space weather has an enormous influence on modern telecommunication systems even though we may not always appreciate it. We shall endeavor throughout this monograph to expose the relationships between space weather factors and the performance (or lack thereof) of telecommunication, navigation, and surveillance systems. Space weather is a rather new term, having found an oMicial expression as the result of several government initiatives that use the term in the title of programs. But it is the logical consequence of the realization that space also has weather, just as the lower atmosphere has weather. While the weather in space will influence space systems that operate in that special environment, it is also true that space weather will influence systems that we understand and use here on terra firma. This brings space weather home as it were. It is not some abstract topic of interest to scientists alone; it is a topic of concern to all of us. I hope to make this clear as the book unfolds. Why have I written this book? First of all, I love the topic. While at the Naval Research Laboratory (NRL), I had the opportunity to do research on many topics including: Thomson scatter radar and satellite beacon studies of the ionosphere, utilization of the NASA Gemini platform for ionospheric investigations, microwave radar propagation studies, I-IF signal intercept and direction-finding experiments, and multi-disciplinary studies of certain physical phenomena relevant to weapon systems development.

The combination of bio-telemetry, sensor networks, communication networks and computing has opened up new areas in the medical field and provided the means for improved health care delivery. Over the past decade therefore reliance on information technology has become very prominent as doing so makes it a lot easier for health practitioners to offer much more efficient health services. This book is a compendium of emerging smart techniques using artificial intelligence for diagnosis, bio-informatics data analysis and biomedical systems. It details innovative applications of neural networks, computer vision, panoramic image processing, electroencephalography, electromyography and specialized information delivery based on smart sensors and communication to support the deaf, control of prosthetic limb, fall detection, cancer detection and fatigue detection. These tools and methods are presented for application in secure transportation, home-based health care and in medical establishments. The state-of-the art coverage provide also practical foundations for further research in biomedical informatics and engineering. Technical topics discussed in the book include: Active detection of driver drowsiness; Myoelectric Control of Limb Prostheses; Electromyography; Electroencephalography; Bio-Signal Telemetry Sensor Networks; Computer Vision in health care delivery; Applications of wireless communication devices in health care delivery Contents: Preface; 1. Neural Networks Based System for Cancer Diagnosis Support; 2. Myoelectric Control of Upper-Limb Prostheses and the Effects of Fatigue; 3. Using Game Consoles for Human Medical Data Collection: in-field applications; 4. An Approach to Fall Detection using Gaussian Distribution of Clustered Knowledge; 5. ZigBee Sensor Network Propagation Analysis for Health-care Application; 6. Dimensionality Reduction in Surface Electromyographic Signals for Pattern Recognition; 7. Assessing a potential electroencephalography based algorithm during a monotonous train driving task in train drivers; 8. Detecting Driver Drowsiness with Examples using EEG and Body Movement; 9. Cortical Width Measurement Based On Panoramic Radiographs Using Computer-Aided System; 10. Development of a Computer Vision Application for Surgical Skill Training and Assessment; 11. Information Delivery System for Deaf People at a Larger Disaster; Author Index; Keyword