Structural health monitoring is an exciting new field on the frontier of applied engineering. With the purpose of examining the health of standing buildings, aircrafts, and other complex structures using pre-installed sensors that provide stress and pressure data in real-time, this field allows structural-health experts to act as engineering "doctors." These specialists diagnose potential problems in advance using complex algorithms and statistical modeling so that steps may be taken to prevent structural damage-or worse, loss of life. Emerging Design Solutions in Structural Health Monitoring Systems seeks to advance cutting-edge research in the field, with a special focus on cross-disciplinary work involving recent advances in IT. This research has enabled structural-health experts to wield groundbreaking new models of artificial intelligence as a diagnostic tool capable of identifying future problems before they even appear. This publication serves as a broad overview of structural management science, as well as an on-ramp for a general engineering audience, including students, educators, and researchers.

The implementation of data and information analysis has become a trending solution within multiple professions. New tools and approaches are continually being developed within data analysis to further solve the challenges that come with professional strategy. Pattern recognition is an innovative method that provides comparison techniques and defines new characteristics within the information acquisition process. Despite its recent trend, a considerable amount of research regarding pattern recognition and its various strategies is lacking. Pattern Recognition Applications in Engineering is an essential reference source that discusses various strategies of pattern recognition algorithms within industrial and research applications and provides examples of results in different professional areas including electronics, computation, and health monitoring. Featuring research on topics such as condition monitoring, data normalization, and bio-inspired developments, this book is ideally designed for analysts; researchers; civil, mechanical, and electronic engineers; computing scientists; chemists; academicians; and students.

The implementation of data and information analysis has become a trending solution within multiple professions. New tools and approaches are continually being developed within data analysis to further solve the challenges that come with professional strategy. Pattern recognition is an innovative method that provides comparison techniques and defines new characteristics within the information acquisition process. Despite its recent trend, a considerable amount of research regarding pattern recognition and its various strategies is lacking. Pattern Recognition Applications in Engineering is an essential reference source that discusses various strategies of pattern recognition algorithms within industrial and research applications and provides examples of results in different professional areas including electronics, computation, and health monitoring. Featuring research on topics such as condition monitoring, data normalization, and bio-inspired developments, this book is ideally designed for analysts; researchers; civil, mechanical, and electronic engineers; computing scientists; chemists; academicians; and students.