Signal Separation Unraveling Hidden Structures In Data
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Signal Separation: Unraveling Hidden Structures in Data
Author: Pasquale De Marco
language: en
Publisher: Pasquale De Marco
Release Date: 2025-05-20
In the era of big data, where information floods our lives from countless sources, the ability to extract meaningful patterns and insights from complex signals is more critical than ever. Signal separation, a powerful technique that decomposes a mixture of signals into its constituent parts, has emerged as a key tool in addressing this challenge. This comprehensive book provides a thorough exploration of signal separation, guiding readers through its fundamental principles, advanced algorithms, and diverse applications. Written in an engaging and accessible style, it is suitable for readers with a basic understanding of linear algebra and probability theory. The book begins with an intuitive introduction to signal separation, laying the foundation for understanding more advanced concepts. It then delves into the three main signal separation techniques: independent component analysis (ICA), projection pursuit, and complexity pursuit. Each technique is thoroughly explained, with detailed mathematical derivations and clear geometric interpretations. Moving beyond the theoretical foundations, the book explores the practical applications of signal separation in various domains. These applications include: * Speech enhancement in noisy environments * Brain signal separation for brain-computer interfaces * Source localization in sensor networks * Blind source separation in telecommunications * Anomaly detection in financial markets * Fault diagnosis in industrial systems Each application is illustrated with real-world examples and case studies, showcasing the transformative impact of signal separation in addressing real-world challenges. To cater to readers with diverse backgrounds and interests, the book also includes advanced topics in signal separation, such as sparse component analysis, non-negative matrix factorization, and tensor-based signal separation. These topics are presented in a clear and concise manner, making them accessible to readers with a solid foundation in linear algebra and probability theory. Additionally, the book provides practical guidance on implementing signal separation algorithms using popular software tools. This hands-on approach allows readers to gain hands-on experience and apply the techniques discussed in the book to their own research or projects. With its comprehensive coverage of theory, applications, and practical implementation, this book is an invaluable resource for researchers, practitioners, and students alike. Whether you are new to the field or seeking to deepen your understanding, this book will provide you with the knowledge and tools you need to harness the power of signal separation in your work. If you like this book, write a review on google books!
Big Data Analytics
This book constitutes the thoroughly refereed conference proceedings of the Second International Conference on Big Data Analytics, BDA 2013, held in Mysore, India, in December 2013. The 13 revised full papers were carefully reviewed and selected from 49 submissions and cover topics on mining social media data, perspectives on big data analysis, graph analysis, big data in practice.
Personalized Psychiatry
This book integrates the concepts of big data analytics into mental health practice and research. Mental disorders represent a public health challenge of staggering proportions. According to the most recent Global Burden of Disease study, psychiatric disorders constitute the leading cause of years lost to disability. The high morbidity and mortality related to these conditions are proportional to the potential for overall health gains if mental disorders can be more effectively diagnosed and treated. In order to fill these gaps, analysis in science, industry, and government seeks to use big data for a variety of problems, including clinical outcomes and diagnosis in psychiatry. Multiple mental healthcare providers and research laboratories are increasingly using large data sets to fulfill their mission. Briefly, big data is characterized by high volume, high velocity, variety and veracity of information, and to be useful it must be analyzed, interpreted, and acted upon. As such, focus has to shift to new analytical tools from the field of machine learning that will be critical for anyone practicing medicine, psychiatry and behavioral sciences in the 21st century. Big data analytics is gaining traction in psychiatric research, being used to provide predictive models for both clinical practice and public health systems. As compared with traditional statistical methods that provide primarily average group-level results, big data analytics allows predictions and stratification of clinical outcomes at an individual subject level. Personalized Psychiatry – Big Data Analytics in Mental Health provides a unique opportunity to showcase innovative solutions tackling complex problems in mental health using big data and machine learning. It represents an interesting platform to work with key opinion leaders to document current achievements, introduce new concepts as well as project the future role of big data and machine learning in mental health.