书籍详情
格式
精装书
页数
241
语言
英语
已发布
Jan 1, 1990
出版商
Springer-Verlag Berlin Heidelberg GmbH & Co. KG
ISBN-10
3540972080
ISBN-13
9783540972082
描述
In the realm of signal processing, the intricate art of deconvolution stands as a critical component, enabling researchers and engineers to recover original signals from convoluted data. This work delves deep into the concept of maximum likelihood deconvolution, exploring its foundations and practical applications. Readers are invited on a comprehensive journey, unfolding the mathematical frameworks and intuitive approaches that underpin the methodology.
The authors, Jerry M. Mendel and C.S. Burrus, masterfully bridge theoretical aspects with real-world scenarios, ensuring that complex ideas are accessible to both seasoned professionals and newcomers. As they navigate through various techniques, they emphasize the importance of median-based principles, laying the groundwork for a robust understanding of how to manipulate and analyze complicated signals.
Throughout the pages, the narrative is punctuated with illustrative examples and thought-provoking exercises that challenge the reader's comprehension and application of the concepts presented. Each chapter builds upon the previous one, creating a cohesive thread that enhances learning and retention.
By the end of this insightful exploration, readers will not only grasp the intricacies of maximum likelihood deconvolution but also appreciate its significance in advancing signal processing as a field. This work stands as a valuable resource for those seeking to deepen their knowledge and apply these techniques in various domains.
The authors, Jerry M. Mendel and C.S. Burrus, masterfully bridge theoretical aspects with real-world scenarios, ensuring that complex ideas are accessible to both seasoned professionals and newcomers. As they navigate through various techniques, they emphasize the importance of median-based principles, laying the groundwork for a robust understanding of how to manipulate and analyze complicated signals.
Throughout the pages, the narrative is punctuated with illustrative examples and thought-provoking exercises that challenge the reader's comprehension and application of the concepts presented. Each chapter builds upon the previous one, creating a cohesive thread that enhances learning and retention.
By the end of this insightful exploration, readers will not only grasp the intricacies of maximum likelihood deconvolution but also appreciate its significance in advancing signal processing as a field. This work stands as a valuable resource for those seeking to deepen their knowledge and apply these techniques in various domains.