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.

Aquest llibre segueix l'esquema basic d'una assignatura troncal de probabilitat i estadistica dels nous graus adaptats a l'Espai Europeu d'Educacio Superior. El text te l'objectiu introduir l'estudiant en els conceptes basics de la teoria de la probabilitat i l'estadistica matematica, des d'un enfocament alhora ame i rigoros. Els continguts s'estructuren en sis capitols, mes un annex amb taules. Al primer capitol, que serveix d'introduccio, s'hi exposen els conceptes basics de l'estadistica descriptiva. Al segon capitol es presenta la teoria axiomatica de la probabilitat i es repassen nocions de combinatoria. Les variables aleatories i els models probabilistics mes utilitzats es desenvolupen als capitols tercer i quart, respectivament. El mostreig i l'estimacio es tracten al capitol cinque. Finalment, el contrast d'hipotesis es presenta al sise capitol. Al final de cada capitol s'inclou una llista de problemes completament resolts.