Record Nr.

UNINA9910782676203321

Titolo

Annual review of gerontology and geriatrics [[electronic resource] ] . Volume 20 Focus on the end of Life : scientific and social issues / / M. Powell Lawton, volume editor

Pubbl/distr/stampa


New York, : Springer Pub. Co., c2001

ISBN

1-281-80622-6

9786611806224

0-8261-1661-2

Descrizione fisica

1 online resource (345 p.)

Collana

Annual review of gerontology and geriatrics, , 0198-8794 ; ; v. 20

Altri autori (Persone)

LawtonM. Powell <1923-2001.> (Mortimer Powell)

Disciplina

362.6

Soggetti

Advance directives (Medical care)

Death - Social aspects

Hospice care

Right to die

Terminal care

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di contenuto

Contents; Acknowledgments; Contributors; Forthcoming Contents for Volume 21; 1. An Overview of the End of Life; 2. Ethics and Spirituality: Strangers at the End of Life?; 3. Medical Decision Making Toward the End of Life: Ethical, Economic, and Health Policy Implications; 4. Quality-of-Life Trajectories of Elders in the End of Life; 5. Measuring Quality of Medical Care for Dying Persons and Their Families: Preliminary Suggestions for Accountability; 6. Comfort in Older Adults at the End of Life; 7. Friendship at the End of Life

8. Healthy Elders' Early Decisions for End-of-Life Living and Dying9. Hospice and Its Role in Improving End-of-Life Care; 10. End of Life in Nursing Homes; 11. The Ethnography of the End of Life: The Nursing Home and Other Residential Settings; 12. Clinical Aspects of End-of-Life Care; Index

Sommario/riassunto

The study of ""the end of life"" has become a major focus on medicine, the social sciences, ethics, and religion. This volume brings together the latest research on issues around death and dying, life's attributes

as it nears death, planning and preparation for death, and care and intervetion-related issues. This evidence-based finding of this volume will help shape how we approach the topic for years to come.

Record Nr.

UNINA9910794514003321

Autore

Haigh Karen

Titolo

Cognitive Electronic Warfare : An Artificial Intelligence Approach

Pubbl/distr/stampa


Norwood : , : Artech House, , 2021
©2021

ISBN

1-5231-4584-6

1-63081-812-7

Edizione

[1st ed.]

Descrizione fisica

1 online resource (261 pages)

Collana

Artech House electronic warfare library

Altri autori (Persone)

AndrusenkoJulia

Disciplina

623.043

Soggetti

Artificial intelligence

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

Intro -- Cognitive Electronic Warfare: An Artificial Intelligence Approach -- Contents -- Foreword -- Preface -- 1 Introduction to Cognitive EW -- 1.1 What Makes a Cognitive System? -- 1.2 A Brief Introduction to EW -- 1.3 EW Domain Challenges Viewed from an AI Perspective -- 1.3.1 SA for ES and EW BDA -- 1.3.2 DM for EA, EP, and EBM -- 1.3.3 User Requirements -- 1.3.4 Connection between CR and EW Systems -- 1.3.5 EW System Design Questions -- 1.4 Choices: AI or Traditional? -- 1.5 Reader's Guide -- 1.6 Conclusion -- References -- 2 Objective Function -- 2.1 Observables That Describe the Environment -- 2.1.1 Clustering Environments -- 2.2 Control Parameters to Change Behavior -- 2.3 Metrics to Evaluate Performance -- 2.4 Creating a Utility Function -- 2.5 Utility Function Design Considerations -- 2.6 Conclusion -- References -- 3 ML Primer -- 3.1 Common ML Algorithms -- 3.1.1 SVMs -- 3.1.2 ANNs -- 3.2 Ensemble Methods -- 3.3 Hybrid ML -- 3.4 Open-Set Classification -- 3.5

 Generalization and Meta-learning -- 3.6 Algorithmic Trade-Offs -- 3.7 Conclusion -- References -- 4 Electronic Support -- 4.1 Emitter Classification and Characterization -- 4.1.1 Feature Engineering and Behavior Characterization -- 4.1.2 Waveform Classification -- 4.1.3 SEI -- 4.2 Performance Estimation -- 4.3 Multi-Intelligence Data Fusion -- 4.3.1 Data Fusion Approaches -- 4.3.2 Example: 5G Multi-INT Data Fusion for Localization -- 4.3.3 Distributed-Data Fusion -- 4.4 Anomaly Detection -- 4.5 Causal Relationships -- 4.6 Intent Recognition -- 4.6.1 Automatic Target Recognition and Tracking -- 4.7 Conclusion -- References -- 5 EP and EA -- 5.1 Optimization -- 5.1.1 Multi-Objective Optimization -- 5.1.2 Searching Through the Performance Landscape -- 5.1.3 Optimization Metalearning -- 5.2 Scheduling -- 5.3 Anytime Algorithms -- 5.4 Distributed Optimization -- 5.5 Conclusion.

References -- 6 EBM -- 6.1 Planning -- 6.1.1 Planning Basics: Problem Definition, and Search -- 6.1.2 Hierarchical Task Networks -- 6.1.3 Action Uncertainty -- 6.1.4 Information Uncertainty -- 6.1.5 Temporal Planning and Resource Management -- 6.1.6 Multiple Timescales -- 6.2 Game Theory -- 6.3 HMI -- 6.4 Conclusion -- References -- 7 Real-Time In-mission Planning and Learning -- 7.1 Execution Monitoring -- 7.1.1 EW BDA -- 7.2 In-Mission Replanning -- 7.3 In-Mission Learning -- 7.3.1 Cognitive Architectures -- 7.3.2 Neural Networks -- 7.3.3 SVMs -- 7.3.4 Multiarmed Bandi -- 7.3.5 MDPs -- 7.3.6 Deep Q-Learning -- 7.4 Conclusion -- References -- 8 Data Management -- 8.1 Data Management Process -- 8.1.1 Metadata -- 8.1.2 Semantics -- 8.1.3 Traceability -- 8.2 Curation and Bias -- 8.3 Data Management -- 8.3.1 Data in an Embedded System -- 8.3.2 Data Diversity -- 8.3.3 Data Augmentation -- 8.3.4 Forgetting -- 8.3.5 Data Security -- 8.4 Conclusion -- References -- 9 Architecture -- 9.1 Software Architecture: Interprocess -- 9.2 Software Architecture: Intraprocess -- 9.3 Hardware Choices -- 9.4 Conclusion -- References -- 10 Test and Evaluation -- 10.1 Scenario Driver -- 10.2 Ablation Testing -- 10.3 Computing Accuracy -- 10.3.1 Regression and Normalized RMSE -- 10.3.2 Classification and Confusion Matrices -- 10.3.3 Evaluating Strategy Performance -- 10.4 Learning Assurance: Evaluating a Cognitive System -- 10.4.1 Learning Assurance Process -- 10.4.2 Formal Verification Methods -- 10.4.3 Empirical and Semiformal Verification Methods -- 10.5 Conclusion -- References -- 11 Getting Started: First Steps -- 11.1 Development Considerations -- 11.2 Tools and Data -- 11.2.1 ML Toolkits -- 11.2.2 ML Datasets -- 11.2.3 RF Data-Generation Tools -- 11.3 Conclusion -- References -- Acronyms -- About the Authors -- Index.

Sommario/riassunto

This comprehensive book gives an overview of how cognitive systems and artificial intelligence (AI) can be used in electronic warfare (EW). Readers will learn how EW systems respond more quickly and effectively to battlefield conditions where sophisticated radars and spectrum congestion put a high priority on EW systems that can

characterize and classify novel waveforms, discern intent, and devise and test countermeasures. Specific techniques are covered for optimizing a cognitive EW system as well as evaluating its ability to learn new information in real time. The book presents AI for electronic support (ES), including characterization, classification, patterns of life, and intent recognition. Optimization techniques, including temporal tradeoffs and distributed optimization challenges are also discussed. The issues concerning real-time in-mission machine learning and suggests some approaches to address this important challenge are presented and described. The book covers electronic battle management, data management, and knowledge sharing. Evaluation approaches, including how to show that a machine learning system can learn how to handle novel environments, are also discussed. Written by experts with first-hand experience in AI-based EW, this is the first book on in-mission real-time learning and optimization.