Ervin Sejdic (46 results)

Paperback. Condition: New.

Condition: New.

Condition: New. In English.

hardcover. Condition: Good. Hardcover without dust jacket. Bound in glossy pictorial paper over boards with white lettering on the covers and spine. Minor wear to back cover, but book remains in overall very good condition. Binding is tight and secure. Corners are slightly bumped. Pages are crisp, clean and bright. Springer. No date on title page. Copyright page dated 2019. 507 pages. A good copy. We ship everyday from a real neighborhood bookstore. This description is written by an actual person, who is holding the book in front of them to make sure it?s properly described. Please contact us with questions or if you would like to see photographs. …

Condition: New. In English.

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).…

Paperback. Condition: New.

Condition: New. pp. 391.

Condition: New. In English.

Hardcover. Condition: Brand New. 2nd edition. 9.25x6.25x1.25 inches. In Stock.

Condition: Sehr gut. Zustand: Sehr gut | Seiten: 322 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Condition: New. pp. 391.

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

Paperback. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces new methods to analyze vertex-varying graph signals. In many real-world scenarios, the data-sensing domain is not a regular grid, but a more complex network that consists of sensing points (vertices) and edges (relating the sensing points). Furthermore, sensing geometry or signal properties define the relation among sensed signal points. Even for the data sensed in the well-defined time or space domain, the introduction of new relationships among the sensing points may produce new insights in the analysis and result in more advanced data processing techniques. The data domain, in these cases and discussed in this book, is defined by a graph. Graphs exploit the fundamental relations among the data points.Although signal processing techniques for the analysis of time-varying signals are well established, the corresponding graph signal processing equivalent approaches are still in their infancy. This book presents novel approaches to analyze vertex-varying graph signals. The vertex-frequency analysis methods use the Laplacian or adjacency matrix to establish connections between vertex and spectral (frequency) domain in order to analyze local signal behavior where edge connections are used for graph signal localization. The book applies combined concepts from time-frequency and wavelet analyses of classical signal processing to the analysis of graph signals.This second edition has been revised and updated and has now been expanded to include new chapters on cutting-edge topics relevant to the analysis of graph signals such as machine learning.Covering analytical tools for vertex-varying applications, this book is of interest to researchers and practitioners in engineering, science, neuroscience, genome processing, just to name a few. It is also a valuable resource for postgraduate students and researchers looking to expand their knowledge of the vertex-frequency analysis theory and its applications.…

Condition: New.

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. 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.

Condition: New.

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

Condition: New.

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

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

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.