Falk Tiago (17 results)

Hardcover. Condition: Very Good. 1st Edition. Oversized hardcover, weight: 1840g, xviii + 605 pages, NOT ex-library. Printed and bound in the UK. Interior is clean and bright throughout, with unmarked text, free of inscriptions and stamps, firmly bound. Boards show gentle shelfwear and small scuff-marks. Issued without a dust jacket. -- This reference work addresses the critical intersection of massive medical datasets and advanced computational analysis. The text serves as a bridge for researchers and students, detailing how new signal processing paradigms can unlock the potential of "big data" to improve clinical outcomes and patient quality of life. Thematic Structure: The book is organized into two primary segments to balance fundamental theory with practical implementation: - Theoretical Foundations: These chapters focus on signal processing tools specifically designed for large-scale data environments. Topics include data quality, compression, and statistical and graph signal processing techniques. - Application-Driven Research: The second half explores existing deployments of machine learning and signal processing across diverse medical domains, including neuroimaging, cardiac monitoring, retinal analysis, and genomic sequencing. Key Technical Areas: - Bio-Signal Modalities: Detailed discussions on capturing data through various sensors, addressing the challenges of differing sample rates, high dimensionality, and massive storage requirements; - Machine Learning Integration: The text explores the transition from traditional algorithms to deep learning models for predictive analytics in critical care, sleep studies, and rehabilitation; - Clinical Impact: A significant focus is placed on using expert domain knowledge to enhance algorithms for patient outcome prediction and real-time monitoring in intensive care units (ICUs).…

Condition: As New. Unread book in perfect condition.

Condition: As New. Unread book in perfect condition.

Hardcover. Condition: new. Hardcover. Within the healthcare domain, big data is defined as any ``high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.'' Such data is crucial because within it lies vast amounts of invaluable information that could potentially change a patient's life, opening doors to alternate therapies, drugs, and diagnostic tools. Signal Processing and Machine Learning for Biomedical Big Data thus discusses modalities; the numerous ways in which this data is captured via sensors; and various sample rates and dimensionalities. Capturing, analyzing, storing, and visualizing such massive data has required new shifts in signal processing paradigms and new ways of combining signal processing with machine learning tools. This book covers several of these aspects in two ways: firstly, through theoretical signal processing chapters where tools aimed at big data (be it biomedical or otherwise) are described; and, secondly, through application-driven chapters focusing on existing applications of signal processing and machine learning for big biomedical data. This text aimed at the curious researcher working in the field, as well as undergraduate and graduate students eager to learn how signal processing can help with big data analysis. It is the hope of Drs. Sejdic and Falk that this book will bring together signal processing and machine learning researchers to unlock existing bottlenecks within the healthcare field, thereby improving patient quality-of-life. Provides an overview of recent state-of-the-art signal processing and machine learning algorithms for biomedical big data, including applications in the neuroimaging, cardiac, retinal, genomic, sleep, patient outcome prediction, critical care, and rehabilitation domains. Provides contributed chapters from world leaders in the fields of big data and signal processing, covering topics such as data quality, data compression, statistical and graph signal processing techniques, and deep learning and their applications within the biomedical sphere. This books material covers how expert domain knowledge can be used to advance signal processing and machine learning for biomedical big data applications. This will be a comprehensive, multi-contributed reference work that will detail the latest research and developments in biomedical signal processing related to big data medical analysis. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condition: New.

Condition: New.

Condition: New.

Condition: New. Ervin Sejdic is currently an Assistant Professor with the Department of Electrical Engineering and Biomedical Engineering at the University of Pittsburg. He has extensive research experience in biomedical and theoretical signal processin.

Condition: New.

Hardcover. Condition: Brand New. 500 pages. 11.00x8.50x1.50 inches. In Stock.

Hardcover. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Condition: New.

Condition: New. 2018. 1st Edition. hardcover. . . . . .

Buch. Condition: Neu. Neuware - Within the healthcare domain, big data is defined as any ``high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.'' Such data is crucial because within it lies vast amounts of invaluable information that could potentially change a patient's life, opening doors to alternate therapies, drugs, and diagnostic tools. Signal Processing and Machine Learning for Biomedical Big Data thus discusses modalities; the numerous ways in which this data is captured via sensors; and various sample rates and dimensionalities. Capturing, analyzing, storing, and visualizing such massive data has required new shifts in signal processing paradigms and new ways of combining signal processing with machine learning tools. This book covers several of these aspects in two ways: firstly, through theoretical signal processing chapters where tools aimed at big data (be it biomedical or otherwise) are described; and, secondly, through application-driven chapters focusing on existing applications of signal processing and machine learning for big biomedical data. This text aimed at the curious researcher working in the field, as well as undergraduate and graduate students eager to learn how signal processing can help with big data analysis. It is the hope of Drs. Sejdic and Falk that this book will bring together signal processing and machine learning researchers to unlock existing bottlenecks within the healthcare field, thereby improving patient quality-of-life. - Provides an overview of recent state-of-the-art signal processing and machine learning algorithms for biomedical big data, including applications in the neuroimaging, cardiac, retinal, genomic, sleep, patient outcome prediction, critical care, and rehabilitation domains. - Provides contributed chapters from world leaders in the fields of big data and signal processing, covering topics such as data quality, data compression, statistical and graph signal processing techniques, and deep learning and their applications within the biomedical sphere. - This book's material covers how expert domain knowledge can be used to advance signal processing and machine learning for biomedical big data applications.…

Hardcover. Condition: Brand New. 500 pages. 11.00x8.50x1.50 inches. In Stock.

Condition: New. 2018. 1st Edition. hardcover. . . . . . Books ship from the US and Ireland.

Hardcover. Condition: new. Hardcover. Within the healthcare domain, big data is defined as any ``high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.'' Such data is crucial because within it lies vast amounts of invaluable information that could potentially change a patient's life, opening doors to alternate therapies, drugs, and diagnostic tools. Signal Processing and Machine Learning for Biomedical Big Data thus discusses modalities; the numerous ways in which this data is captured via sensors; and various sample rates and dimensionalities. Capturing, analyzing, storing, and visualizing such massive data has required new shifts in signal processing paradigms and new ways of combining signal processing with machine learning tools. This book covers several of these aspects in two ways: firstly, through theoretical signal processing chapters where tools aimed at big data (be it biomedical or otherwise) are described; and, secondly, through application-driven chapters focusing on existing applications of signal processing and machine learning for big biomedical data. This text aimed at the curious researcher working in the field, as well as undergraduate and graduate students eager to learn how signal processing can help with big data analysis. It is the hope of Drs. Sejdic and Falk that this book will bring together signal processing and machine learning researchers to unlock existing bottlenecks within the healthcare field, thereby improving patient quality-of-life. Provides an overview of recent state-of-the-art signal processing and machine learning algorithms for biomedical big data, including applications in the neuroimaging, cardiac, retinal, genomic, sleep, patient outcome prediction, critical care, and rehabilitation domains. Provides contributed chapters from world leaders in the fields of big data and signal processing, covering topics such as data quality, data compression, statistical and graph signal processing techniques, and deep learning and their applications within the biomedical sphere. This books material covers how expert domain knowledge can be used to advance signal processing and machine learning for biomedical big data applications. This will be a comprehensive, multi-contributed reference work that will detail the latest research and developments in biomedical signal processing related to big data medical analysis. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…