Asoke K. Nandi – författare

In the signal-processing research community, a great deal of progress in higher-order statistics (HOS) began in the mid-1980s. These last 15 years have witnessed a large number of theoretical developments as well as real applications. This book focuses on the blind estimation area and records some of the major developments in this field. It provides an addition to the few books on the subject of HOS and is devoted to covering blind estimation using HOS. The book provides the reader with an introduction to HOS and goes on to illustrate its use in blind signal equalization (which has many applications including (mobile) communications), blind system identification, and blind sources separation (a generic problem in signal-processing with many applications including radar, sonar and communications). There is also a chapter devoted to robust cumulant estimation, an important problem where HOS results have been encouraging.

Automatic Modulation Classification (AMC) has been a key technology in many military, security, and civilian telecommunication applications for decades. In military and security applications, modulation often serves as another level of encryption; in modern civilian applications, multiple modulation types can be employed by a signal transmitter to control the data rate and link reliability.This book offers comprehensive documentation of AMC models, algorithms and implementations for successful modulation recognition. It provides an invaluable theoretical and numerical comparison of AMC algorithms, as well as guidance on state-of-the-art classification designs with specific military and civilian applications in mind.Key Features: Provides an important collection of AMC algorithms in five major categories, from likelihood-based classifiers and distribution-test-based classifiers to feature-based classifiers, machine learning assisted classifiers and blind modulation classifiersLists detailed implementation for each algorithm based on a unified theoretical background and a comprehensive theoretical and numerical performance comparisonGives clear guidance for the design of specific automatic modulation classifiers for different practical applications in both civilian and military communication systemsIncludes a MATLAB toolbox on a companion website offering the implementation of a selection of methods discussed in the book

Clustering techniques are increasingly being put to use in the analysis of high-throughput biological datasets. Novel computational techniques to analyse high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery.This book details the complete pathway of cluster analysis, from the basics of molecular biology to the generation of biological knowledge. The book also presents the latest clustering methods and clustering validation, thereby offering the reader a comprehensive review of clustering analysis in bioinformatics from the fundamentals through to state-of-the-art techniques and applications.Key Features: Offers a contemporary review of clustering methods and applications in the field of bioinformatics, with particular emphasis on gene expression analysisProvides an excellent introduction to molecular biology with computer scientists and information engineering researchers in mind, laying out the basic biological knowledge behind the application of clustering analysis techniques in bioinformaticsExplains the structure and properties of many types of high-throughput datasets commonly found in biological studiesDiscusses how clustering methods and their possible successors would be used to enhance the pace of biological discoveries in the futureIncludes a companion website hosting a selected collection of codes and links to publicly available datasets

Provides an extensive, up-to-date treatment of techniques used for machine condition monitoringClear and concise throughout, this accessible book is the first to be wholly devoted to the field of condition monitoring for rotating machines using vibration signals. It covers various feature extraction, feature selection, and classification methods as well as their applications to machine vibration datasets. It also presents new methods including machine learning and compressive sampling, which help to improve safety, reliability, and performance. Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines starts by introducing readers to Vibration Analysis Techniques and Machine Condition Monitoring (MCM). It then offers readers sections covering: Rotating Machine Condition Monitoring using Learning Algorithms; Classification Algorithms; and New Fault Diagnosis Frameworks designed for MCM. Readers will learn signal processing in the time-frequency domain, methods for linear subspace learning, and the basic principles of the learning method Artificial Neural Network (ANN). They will also discover recent trends of deep learning in the field of machine condition monitoring, new feature learning frameworks based on compressive sampling, subspace learning techniques for machine condition monitoring, and much more. Covers the fundamental as well as the state-of-the-art approaches to machine condition monitoring�guiding readers from the basics of rotating machines to the generation of knowledge using vibration signalsProvides new methods, including machine learning and compressive sampling, which offer significant improvements in accuracy with reduced computational costsFeatures learning algorithms that can be used for fault diagnosis and prognosisIncludes previously and recently developed dimensionality reduction techniques and classification algorithmsCondition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines is an excellent book for research students, postgraduate students, industrial practitioners, and researchers.

Image Segmentation Summarizes and improves new theory, methods, and applications of current image segmentation approaches, written by leaders in the field The process of image segmentation divides an image into different regions based on the characteristics of pixels, resulting in a simplified image that can be more efficiently analyzed. Image segmentation has wide applications in numerous fields ranging from industry detection and bio-medicine to intelligent transportation and architecture. Image Segmentation: Principles, Techniques, and Applications is an up-to-date collection of recent techniques and methods devoted to the field of computer vision. Covering fundamental concepts, new theories and approaches, and a variety of practical applications including medical imaging, remote sensing, fuzzy clustering, and watershed transform. In-depth chapters present innovative methods developed by the authors—such as convolutional neural networks, graph convolutional networks, deformable convolution, and model compression—to assist graduate students and researchers apply and improve image segmentation in their work. Describes basic principles of image segmentation and related mathematical methods such as clustering, neural networks, and mathematical morphology.Introduces new methods for achieving rapid and accurate image segmentation based on classic image processing and machine learning theory.Presents techniques for improved convolutional neural networks for scene segmentation, object recognition, and change detection, etc.Highlights the effect of image segmentation in various application scenarios such as traffic image analysis, medical image analysis, remote sensing applications, and material analysis, etc.Image Segmentation: Principles, Techniques, and Applications is an essential resource for undergraduate and graduate courses such as image and video processing, computer vision, and digital signal processing, as well as researchers working in computer vision and image analysis looking to improve their techniques and methods.

Featuring the latest research on computational signal processing and analysis, the book is useful to researchers, professionals, and students working in the core areas of electronics and their applications, especially signal processing, embedded systems, and networking.