Biosensors Journal
Open Access

Editorial

Engineering Modelling of Data Acquisition and Digital Instrumentation for Intelligent Learning and Recognition

Jun Qin^1* and Lin Xu²

¹Department of Electrical and Computer Engineering, Southern Illinois University, Carbondale, IL 62901, USA

²College of Computer and Control Engineering, Nankai University, Tianjin, China

Corresponding Author:

Jun Qin
Ph.D. Assistant Professor
Department of Electrical and Computer Engineering
Southern Illinois University,Carbondale
Mail Code 6603, 1230 Lincoln Drive, Carbondale, IL 62901, USA
Tel: (618)453-3460
Fax: (618) 453-79
E-mail: jqin@siu.edu

Received date: March 06, 2015; Accepted date: March 10, 2015; Published date: March 30, 2015

Citation: Qin J, Xu L (2015) Engineering Modelling of Data Acquisition and Digital Instrumentation for Intelligent Learning and Recognition. Biosens J 4:e103. doi:10.4172/2090-4967.1000e103

Copyright: © 2015 Qin J, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

In data acquisition and digital instrumentation fields, it is essential to understand the learning and recognition to acquire data and information of objects to be studied. In recent years, engineering modelling and simulation contribute greatly to the understanding of intelligent learning and recognition problems. The ability to learn is one of the central features of intelligence, which makes it an important concern for both cognitive psychology and artificial intelligence. In this paper, definitions and modelling aspects of learning are discussed. Fundamentals of learning and recognition and their applications are investigated and described. Illustrations are given to demonstrate the increasing applications of learning and recognition with engineering modelling in data acquisition and digital instrumentation fields.

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