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.

This book contains revised and extended versions of selected papers from the 6th International Conference on Pattern Recognition, ICPRAM 2017, held in Porto, Portugal, in February 2017. The 13 full papers presented were carefully reviewed and selected from 139 initial submissions. They aim at making visible and understandable the relevant trends of current research on pattern recognition.

This book constitutes the thoroughly refereed post-conference proceedings of the 4th International Conference on Pattern Recognition, ICPRAM 2015, held in Lisbon, Portugal, in January 2015. The 20 revised full papers were carefully reviewed and selected from 145 submissions and describe up-to-date applications of pattern recognition techniques to real-world problems, interdisciplinary research, experimental and/or theoretical studies yielding new insights that advance pattern recognition methods.

This edited book includes extended and revised versions of a set of selected papers from the First International Conference on Pattern Recognition (ICPRAM 2012), held in Vilamoura, Algarve, Portugal, from 6 to 8 February, 2012, sponsored by the Institute for Systems and Technologies of Information Control and Communication (INSTICC) and held in cooperation with the Association for the Advancement of Artificial Intelligence (AAAI) and Pattern Analysis, Statistical Modelling and Computational Learning (PASCAL2). The conference brought together researchers, engineers and practitioners interested on the areas of Pattern Recognition, both from theoretical and application perspectives.

The nature of handwriting in our society has significantly altered over the ages due to the introduction of new technologies such as computers and the World Wide Web. With increases in the amount of signature verification needs, state of the art internet and paper-based automated recognition methods are necessary. Pattern Recognition Technologies and Applications: Recent Advances provides cutting-edge pattern recognition techniques and applications. Written by world-renowned experts in their field, this easy to understand book is a must have for those seeking explanation in topics such as on- and offline handwriting and speech recognition, signature verification, and gender classification.

The many different mathematical techniques used to solve pattem recognition problems may be grouped into two general approaches: the decision-theoretic (or discriminant) approach and the syntactic (or structural) approach. In the decision-theoretic approach, aset of characteristic measurements, called features, are extracted from the pattems. Each pattem is represented by a feature vector, and the recognition of each pattem is usually made by partitioning the feature space. Applications of decision-theoretic approach indude character recognition, medical diagnosis, remote sensing, reliability and socio-economics. A relatively new approach is the syntactic approach. In the syntactic approach, ea ch pattem is expressed in terms of a composition of its components. The recognition of a pattem is usually made by analyzing the pattem structure according to a given set of rules. Earlier applications of the syntactic approach indude chromosome dassification, English character recognition and identification of bubble and spark chamber events. The purpose of this monograph is to provide a summary of the major reeent applications of syntactic pattem recognition. After a brief introduction of syntactic pattem recognition in Chapter 1, the nin e mai n chapters (Chapters 2-10) can be divided into three parts. The first three chapters concem with the analysis of waveforms using syntactic methods. Specific application examples indude peak detection and interpretation of electro cardiograms and the recognition of speech pattems. The next five chapters deal with the syntactic recognition of two-dimensional pictorial pattems.

This monograph is intended to cover several major applications of pattern recognition. After a brief introduction to pattern recognition in Chapter 1, the two major approaches, statistical approach and syntactic approach, are reviewed in Chapter 2, and 3, respectively. Other topics include the application of pattern recognition to seismic wave interpretation, to system reliability problems, to medical data analysis, as well as character and speech recognition.

Data assimilation of satellite-based observations of hydrological variables with full numerical physics models can be used to downscale these observations from coarse to high resolution to improve microwave sensor-based soil moisture observations. Moreover, assimilation can also be used to predict related hydrological variables, e.g., precipitation products can be assimilated in a land information system to estimate soil moisture. High quality spatio-temporal observations of these processes are vital for a successful assimilation which in turn needs a detailed analysis and improvement. In this research, pattern recognition and adaptive signal processing methods are developed for the spatio-temporal analysis and enhancement of soil moisture and precipitation datasets. These methods are applied to accomplish the following tasks: (i) a consistency analysis of level-3 soil moisture data from the Advanced Microwave Scanning Radiometer - EOS (AMSR-E) against in-situ soil moisture measurements from the USDA Soil Climate Analysis Network (SCAN). This method performs a consistency assessment of the entire time series in relation to others and provides a spatial distribution of consistency levels. The methodology is based on a combination of wavelet-based feature extraction and oneclass support vector machines (SVM) classifier. Spatial distribution of consistency levels are presented as consistency maps for a region, including the states of Mississippi, Arkansas, and Louisiana. These results are well correlated with the spatial distributions of average soil moisture, and the cumulative counts of dense vegetation; (ii) a modified singular spectral analysis based interpolation scheme is developed and validated on a few geophysical data products including GODAE's high resolution sea surface temperature (GHRSST). This method is later employed to fill the systematic gaps in level-3 AMSR-E soil moisture dataset; (iii) a combination of artificial neural networks and vector space transformation function is used to fuse several high resolution precipitation products (HRPP). The final merged product is statistically superior to any of the individual datasets over a seasonal period. The results have been tested against ground based measurements of rainfall over our study area and average accuracies obtained are 85% in the summer and 55% in the winter 2007.

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition.

Pattern recognition continued to be one of the important research fields in computer science and electrical engineering. Lots of new applications are emerging, and hence pattern analysis and synthesis become significant subfields in pattern recognition. This book is an edited volume and has six chapters arranged into two sections, namely, pattern recognition analysis and pattern recognition applications. This book will be useful for graduate students, researchers, and practicing engineers working in the field of machine vision and computer science and engineering.