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      Demodulation of Chaos Phase Modulation Spread Spectrum Signals Using Machine Learning Methods and Its Evaluation for Underwater Acoustic Communication

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          Abstract

          The chaos phase modulation sequences consist of complex sequences with a constant envelope, which has recently been used for direct-sequence spread spectrum underwater acoustic communication. It is considered an ideal spreading code for its benefits in terms of large code resource quantity, nice correlation characteristics and high security. However, demodulating this underwater communication signal is a challenging job due to complex underwater environments. This paper addresses this problem as a target classification task and conceives a machine learning-based demodulation scheme. The proposed solution is implemented and optimized on a multi-core center processing unit (CPU) platform, then evaluated with replay simulation datasets. In the experiments, time variation, multi-path effect, propagation loss and random noise were considered as distortions. According to the results, compared to the reference algorithms, our method has greater reliability with better temporal efficiency performance.

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          The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses

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            An introduction to matched filters

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              Tumor classification by partial least squares using microarray gene expression data.

              One important application of gene expression microarray data is classification of samples into categories, such as the type of tumor. The use of microarrays allows simultaneous monitoring of thousands of genes expressions per sample. This ability to measure gene expression en masse has resulted in data with the number of variables p(genes) far exceeding the number of samples N. Standard statistical methodologies in classification and prediction do not work well or even at all when N < p. Modification of existing statistical methodologies or development of new methodologies is needed for the analysis of microarray data. We propose a novel analysis procedure for classifying (predicting) human tumor samples based on microarray gene expressions. This procedure involves dimension reduction using Partial Least Squares (PLS) and classification using Logistic Discrimination (LD) and Quadratic Discriminant Analysis (QDA). We compare PLS to the well known dimension reduction method of Principal Components Analysis (PCA). Under many circumstances PLS proves superior; we illustrate a condition when PCA particularly fails to predict well relative to PLS. The proposed methods were applied to five different microarray data sets involving various human tumor samples: (1) normal versus ovarian tumor; (2) Acute Myeloid Leukemia (AML) versus Acute Lymphoblastic Leukemia (ALL); (3) Diffuse Large B-cell Lymphoma (DLBCLL) versus B-cell Chronic Lymphocytic Leukemia (BCLL); (4) normal versus colon tumor; and (5) Non-Small-Cell-Lung-Carcinoma (NSCLC) versus renal samples. Stability of classification results and methods were further assessed by re-randomization studies.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                01 December 2018
                December 2018
                : 18
                : 12
                : 4217
                Affiliations
                [1 ]State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
                [2 ]University of Chinese Academy of Sciences, Beijing 100190, China
                [3 ]LE2I EA7508, Université Bourgogne Franche-Comté, 21078 Dijon, France; Franck.Marzani@ 123456u-bourgogne.fr (F.M.); fanyang@ 123456u-bourgogne.fr (F.Y.)
                Author notes
                [* ]Correspondence: chao.li.1986@ 123456ieee.org
                Author information
                https://orcid.org/0000-0002-3782-7955
                https://orcid.org/0000-0003-0963-1565
                Article
                sensors-18-04217
                10.3390/s18124217
                6308446
                30513748
                9279622c-332c-48e4-b4f1-cc1182fef8a2
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 25 September 2018
                : 28 November 2018
                Categories
                Article

                Biomedical engineering
                underwater acoustic communication,direct sequence spread spectrum,chaos phase modulation sequence,partial least square regression,machine learning

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