Artificial Intelligence Applied To Target Tracking Phase I
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Artificial Intelligence Applied to Target Tracking
Author | : Peter Scarlett |
Publisher | : |
Total Pages | : |
Release | : 1997 |
Genre | : Automatic tracking |
ISBN | : |
Artificial Intelligence Applied to Target Tracking, Phase II
Author | : Tarun Bhattacharya |
Publisher | : |
Total Pages | : |
Release | : 1995 |
Genre | : |
ISBN | : |
Describes the implementation of a multiple target tracker for marine vessels which incorporates radar, geographic, and vessel-specific information along with artificial intelligence techniques for resolving potential tracking errors. Features include: a multiple hypothesis tracking subsystem that maintains up to a user-specified number of tracking hypotheses at once, thus avoiding mis-associations and loss of track; a geographic information system subsystem, incorporating channel and vessel routing knowledge to main track as targets turn from one channel to the next; and a dynamic transmit model which implements an expert system that predicts how vessels will manoeuvre as they move through channels, along ferry routes, or into close proximity with each other. Sections of the document cover system functional descriptions, software requirements, development environment, software design, and test results using real radar data and simulations. Appendices include software documentation and specifications.
Artificial Intelligence Applied to Target Tracking
Author | : Raytheon Canada Limited |
Publisher | : The Centre |
Total Pages | : |
Release | : 1995 |
Genre | : Artificial intelligence |
ISBN | : |
Probabilistic Search for Tracking Targets
Author | : Irad Ben-Gal |
Publisher | : John Wiley & Sons |
Total Pages | : 367 |
Release | : 2013-03-25 |
Genre | : Mathematics |
ISBN | : 1118597044 |
Presents a probabilistic and information-theoretic framework for a search for static or moving targets in discrete time and space. Probabilistic Search for Tracking Targets uses an information-theoretic scheme to present a unified approach for known search methods to allow the development of new algorithms of search. The book addresses search methods under different constraints and assumptions, such as search uncertainty under incomplete information, probabilistic search scheme, observation errors, group testing, search games, distribution of search efforts, single and multiple targets and search agents, as well as online or offline search schemes. The proposed approach is associated with path planning techniques, optimal search algorithms, Markov decision models, decision trees, stochastic local search, artificial intelligence and heuristic information-seeking methods. Furthermore, this book presents novel methods of search for static and moving targets along with practical algorithms of partitioning and search and screening. Probabilistic Search for Tracking Targets includes complete material for undergraduate and graduate courses in modern applications of probabilistic search, decision-making and group testing, and provides several directions for further research in the search theory. The authors: Provide a generalized information-theoretic approach to the problem of real-time search for both static and moving targets over a discrete space. Present a theoretical framework, which covers known information-theoretic algorithms of search, and forms a basis for development and analysis of different algorithms of search over probabilistic space. Use numerous examples of group testing, search and path planning algorithms to illustrate direct implementation in the form of running routines. Consider a relation of the suggested approach with known search theories and methods such as search and screening theory, search games, Markov decision process models of search, data mining methods, coding theory and decision trees. Discuss relevant search applications, such as quality-control search for nonconforming units in a batch or a military search for a hidden target. Provide an accompanying website featuring the algorithms discussed throughout the book, along with practical implementations procedures.
Artificial Intelligence in Healthcare
Author | : Adam Bohr |
Publisher | : Academic Press |
Total Pages | : 385 |
Release | : 2020-06-21 |
Genre | : Computers |
ISBN | : 0128184396 |
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data