http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Scaglione, Jamie B.,Akey, David L.,Sullivan, Rachel,Kittendorf, Jeffrey D.,Rath, Christopher M.,Kim, Eung-Soo,Smith, Janet L.,Sherman, David H. WILEY-VCH Verlag 2010 Angewandte Chemie Vol.49 No.33
<B>Graphic Abstract</B> <P>A narrow tunnel: Biochemical and structural analysis of the tautomycetin thioesterase (TE) has provided the first high-resolution structure of a linear-chain-terminating TE in polyketide biosynthesis, showing the enzyme to be stereoselective with a constrained substrate chamber relative to macrolactone-forming thioesterases. <img src='wiley_img_2010/14337851-2010-49-33-ANIE201000032-content.gif' alt='wiley_img_2010/14337851-2010-49-33-ANIE201000032-content'> </P>
The Expectation and Sparse Maximization Algorithm
Barembruch, Steffen,Scaglione, Anna,Moulines, Eric The Korea Institute of Information and Commucation 2010 Journal of communications and networks Vol.12 No.4
In recent years, many sparse estimation methods, also known as compressed sensing, have been developed. However, most of these methods presume that the measurement matrix is completely known. We develop a new blind maximum likelihood method-the expectation-sparse-maximization (ESpaM) algorithm-for models where the measurement matrix is the product of one unknown and one known matrix. This method is a variant of the expectation-maximization algorithm to deal with the resulting problem that the maximization step is no longer unique. The ESpaM algorithm is justified theoretically. We present as well numerical results for two concrete examples of blind channel identification in digital communications, a doubly-selective channel model and linear time invariant sparse channel model.
The Expectation and Sparse Maximization Algorithm
Steffen Barembruch,Anna Scaglione,Eric Moulines 한국통신학회 2010 Journal of communications and networks Vol.12 No.4
In recent years, many sparse estimation methods, also known as compressed sensing, have been developed. However,most of these methods presume that the measurement matrix is completely known. We develop a new blind maximum likelihood method—the expectation-sparse-maximization (ESpaM)algorithm—for models where the measurement matrix is the product of one unknown and one known matrix. This method is a variant of the expectation-maximization algorithm to deal with the resulting problem that the maximization step is no longer unique. The ESpaM algorithm is justified theoretically. We present as well numerical results for two concrete examples of blind channel identification in digital communications, a doubly-selective channel model and linear time invariant sparse channel model.
Automatic heart activity diagnosis based on Gram polynomials and probabilistic neural networks
Francesco Beritelli,Giacomo Capizzi,Grazia Lo Sciuto,Christian Napoli,Francesco Scaglione 대한의용생체공학회 2018 Biomedical Engineering Letters (BMEL) Vol.8 No.1
The paper proposes a new approach to heartactivity diagnosis based on Gram polynomials and probabilisticneural networks (PNN). Heart disease recognition isbased on the analysis of phonocardiogram (PCG) digitalsequences. The PNN provides a powerful tool for properclassification of the input data set. The novelty of theproposed approach lies in a powerful feature extractionbased on Gram polynomials and the Fourier transform. Theproposed system presents good performance obtainingoverall sensitivity of 93%, specificity of 91% and accuracyof 94%, using a public database of over 3000 heart beatsound recordings, classified as normal and abnormal heartsounds. Thus, it can be concluded that Gram polynomialsand PNN prove to be a very efficient technique using thePCG signal for characterizing heart diseases.