Erik Blasch – författare

This authoritative resource presents a comprehensive illustration of modern Artificial Intelligence / Machine Learning (AI/ML) technology for radio frequency (RF) data exploitation. It identifies technical challenges, benefits, and directions of deep learning (DL) based object classification using radar data, including synthetic aperture radar (SAR) and high range resolution (HRR) radar. The performance of AI/ML algorithms is provided from an overview of machine learning (ML) theory that includes history, background primer, and examples. Radar data issues of collection, application, and examples for SAR/HRR data and communication signals analysis are discussed. In addition, this book presents practical considerations of deploying such techniques, including performance evaluation, energy-efficient computing, and the future unresolved issues.

This book constitutes the refereed proceedings of the Third International Conference on Dynamic Data Driven Application Systems, DDDAS 2020, held in Boston, MA, USA, in October 2020.The 21 full papers and 14 short papers presented in this volume were carefully reviewed and selected from 40 submissions. They cover topics such as: digital twins; environment cognizant adaptive-planning systems; energy systems; materials systems; physics-based systems analysis; imaging methods and systems; and learning systems.

This book constitutes the refereed proceedings of the 4th International Conference on Dynamic Data Driven Applications Systems, DDDAS 2022, which took place in Cambridge, MA, USA, during October 6–10, 2022.The 31 regular papers in the main track and 5 regular papers from the Wildfires panel, as well as one workshop paper, were carefully reviewed and selected for inclusion in the book. They were organized in following topical sections: DDAS2022 Main-Track Plenary Presentations; Keynotes; DDDAS2022 Main-Track: Wildfires Panel; Workshop on Climate, Life, Earth, Planets.

This handbook is an authoritative reference on the Dynamic Data Driven Applications Systems paradigm, pioneered by Dr. Darema, and contains DDDAS-based work conducted by contributing co-authors in numerous areas, to benefit science and technology researchers and practitioners developing DDDAS-based technologies.Beginning with general concepts and history of the DDDAS paradigm in the introductory chapter, this Third Volume in the series contains 33 chapters contributed by leading experts and is organized in nine thematic areas, spanning natural, engineered, or societal systems to enable an accurate understanding, analysis, and control of corresponding complex systems. The thematic areas cover fundamental and foundational methods; materials, structural, and energy systems; healthcare and situation awareness systems; air and space, communications, and cyber systems.   The work presented shows how DDDAS unifies the computational and instrumentation aspects of an application system, extends computing notions to span from the high-end to the edge computing and real-time data acquisition and control, adaptively manages the operation of complex, distributed, multimodal systems through high-dimensional, systems-cognizant model-coordination and multi-objective optimization of resources and service delivery; supports methodologies for achieving autonomic and  enhanced AI capabilities; DDDAS is foundational concept for Predictive Digital Twins and Dynamic Digital Twins.The authorsexplain how DDDAS unifies the computational and instrumentation aspects of an application system, extends the notion of Smart Computing to span from the high-end to the real-time data acquisition and control, and manages Big Data exploitation with high-dimensional model coordination.

This book constitutes the refereed proceedings of the 5th International Conference on Dynamic Data Driven Applications Systems, DDDAS/Infosymbiotics for Reliable AI 2024, held in New Brunswick, NJ, USA, during November 6–8, 2024.The 43 full papers included in this book were carefully reviewed and selected from 52 submissions. By combining DDDAS and typical AI approaches, the papers address state-of-the-art efforts to create frameworks for enabling new and advanced Science and Technology capabilities to address challenges and create opportunities in important areas, spanning a wide set of areas, such as: materials and aerospace systems; communications networks; energy infrastructures; cyber-security; adverse environmental situations; societal dynamics; computer vision; robotics; laboratory automation; bio-informatics and pharmaceuticals design; and more.

This book constitutes the refereed proceedings of the 5th International Conference on Dynamic Data Driven Applications Systems, DDDAS/Infosymbiotics for Reliable AI 2024, held in New Brunswick, NJ, USA, during November 6–8, 2024.The 43 full papers included in this book were carefully reviewed and selected from 52 submissions. By combining DDDAS and typical AI approaches, the papers address state-of-the-art efforts to create frameworks for enabling new and advanced Science and Technology capabilities to address challenges and create opportunities in important areas, spanning a wide set of areas, such as: materials and aerospace systems; communications networks; energy infrastructures; cyber-security; adverse environmental situations; societal dynamics; computer vision; robotics; laboratory automation; bio-informatics and pharmaceuticals design; and more.

This text reviews the fundamental theory and latest methods for including contextual information in fusion process design and implementation. Chapters are contributed by the foremost international experts, spanning numerous developments and applications. The book highlights high- and low-level information fusion problems, performance evaluation under highly demanding conditions, and design principles. A particular focus is placed on approaches that integrate research from different communities, emphasizing the benefit of combining different techniques to overcome the limitations of a single perspective. Features: introduces the terminology and core elements in information fusion and context; presents key themes for context-enhanced information fusion; discusses design issues in developing context-aware fusion systems; provides mathematical grounds for modeling the contextual influences in representative fusion problems; describes the fusion of hard and soft data; reviews a diverse range of applications.

This text reviews the fundamental theory and latest methods for including contextual information in fusion process design and implementation. Chapters are contributed by the foremost international experts, spanning numerous developments and applications. The book highlights high- and low-level information fusion problems, performance evaluation under highly demanding conditions, and design principles. A particular focus is placed on approaches that integrate research from different communities, emphasizing the benefit of combining different techniques to overcome the limitations of a single perspective. Features: introduces the terminology and core elements in information fusion and context; presents key themes for context-enhanced information fusion; discusses design issues in developing context-aware fusion systems; provides mathematical grounds for modeling the contextual influences in representative fusion problems; describes the fusion of hard and soft data; reviews a diverse range of applications.

This text reviews the fundamental theory and latest methods for including contextual information in fusion process design and implementation. Chapters are contributed by the foremost international experts, spanning numerous developments and applications. The book highlights high- and low-level information fusion problems, performance evaluation under highly demanding conditions, and design principles. A particular focus is placed on approaches that integrate research from different communities, emphasizing the benefit of combining different techniques to overcome the limitations of a single perspective. Features: introduces the terminology and core elements in information fusion and context; presents key themes for context-enhanced information fusion; discusses design issues in developing context-aware fusion systems; provides mathematical grounds for modeling the contextual influences in representative fusion problems; describes the fusion of hard and soft data; reviews a diverse range of applications.