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Detection of random signals in dependent Gaussian noise [electronic resource]

Detection of random signals in dependent Gaussian noise [electronic resource]

자료유형
E-Book(소장)
개인저자
Gualtierotti, Antonio F.
서명 / 저자사항
Detection of random signals in dependent Gaussian noise [electronic resource] / Antonio F. Gualtierotti.
발행사항
Cham :   Springer International Publishing :   Imprint: Springer,   2015.  
형태사항
1 online resource (xxxiv, 1176 p.).
ISBN
9783319223155
요약
The book presents the necessary mathematical basis to obtain and rigorously use likelihoods for detection problems with Gaussian noise. To facilitate comprehension the text is divided into three broad areas –  reproducing kernel Hilbert spaces, Cramér-Hida representations and stochastic calculus – for which a somewhat different approach was used than in their usual stand-alone context. One main applicable result of the book involves arriving at a general solution to the canonical detection problem for active sonar in a reverberation-limited environment. Nonetheless, the general problems dealt with in the text also provide a useful framework for discussing other current research areas, such as wavelet decompositions, neural networks, and higher order spectral analysis. The structure of the book, with the exposition presenting as many details as necessary, was chosen to serve both those readers who are chiefly interested in the results and those who want to learn the material from scratch. Hence, the text will be useful for graduate students and researchers alike in the fields of engineering, mathematics and statistics.
일반주기
Title from e-Book title page.  
내용주기
Prolog -- Part I: Reproducing Kernel Hilbert Spaces -- Part II: Cramér-Hida Representations -- Part III: Likelihoods -- Credits and Comments -- Notation and Terminology -- References -- Index.
서지주기
Includes bibliographical references and index.
이용가능한 다른형태자료
Issued also as a book.  
일반주제명
Random noise theory.
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020 ▼a 9783319223155
040 ▼a 211009 ▼c 211009 ▼d 211009
050 4 ▼a TK5102.5
082 0 4 ▼a 003.54 ▼2 23
084 ▼a 003.54 ▼2 DDCK
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100 1 ▼a Gualtierotti, Antonio F.
245 1 0 ▼a Detection of random signals in dependent Gaussian noise ▼h [electronic resource] / ▼c Antonio F. Gualtierotti.
260 ▼a Cham : ▼b Springer International Publishing : ▼b Imprint: Springer, ▼c 2015.
300 ▼a 1 online resource (xxxiv, 1176 p.).
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references and index.
505 0 ▼a Prolog -- Part I: Reproducing Kernel Hilbert Spaces -- Part II: Cramér-Hida Representations -- Part III: Likelihoods -- Credits and Comments -- Notation and Terminology -- References -- Index.
520 ▼a The book presents the necessary mathematical basis to obtain and rigorously use likelihoods for detection problems with Gaussian noise. To facilitate comprehension the text is divided into three broad areas –  reproducing kernel Hilbert spaces, Cramér-Hida representations and stochastic calculus – for which a somewhat different approach was used than in their usual stand-alone context. One main applicable result of the book involves arriving at a general solution to the canonical detection problem for active sonar in a reverberation-limited environment. Nonetheless, the general problems dealt with in the text also provide a useful framework for discussing other current research areas, such as wavelet decompositions, neural networks, and higher order spectral analysis. The structure of the book, with the exposition presenting as many details as necessary, was chosen to serve both those readers who are chiefly interested in the results and those who want to learn the material from scratch. Hence, the text will be useful for graduate students and researchers alike in the fields of engineering, mathematics and statistics.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Random noise theory.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=http://dx.doi.org/10.1007/978-3-319-22315-5
945 ▼a KLPA
991 ▼a E-Book(소장)

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No. 1 소장처 중앙도서관/e-Book 컬렉션/ 청구기호 CR 003.54 등록번호 E14028412 도서상태 대출불가(열람가능) 반납예정일 예약 서비스 M

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