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      Distributed Water Pollution Source Localization with Mobile UV-Visible Spectrometer Probes in Wireless Sensor Networks

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          Abstract

          Pollution accidents that occur in surface waters, especially in drinking water source areas, greatly threaten the urban water supply system. During water pollution source localization, there are complicated pollutant spreading conditions and pollutant concentrations vary in a wide range. This paper provides a scalable total solution, investigating a distributed localization method in wireless sensor networks equipped with mobile ultraviolet-visible (UV-visible) spectrometer probes. A wireless sensor network is defined for water quality monitoring, where unmanned surface vehicles and buoys serve as mobile and stationary nodes, respectively. Both types of nodes carry UV-visible spectrometer probes to acquire in-situ multiple water quality parameter measurements, in which a self-adaptive optical path mechanism is designed to flexibly adjust the measurement range. A novel distributed algorithm, called Dual-PSO, is proposed to search for the water pollution source, where one particle swarm optimization (PSO) procedure computes the water quality multi-parameter measurements on each node, utilizing UV-visible absorption spectra, and another one finds the global solution of the pollution source position, regarding mobile nodes as particles. Besides, this algorithm uses entropy to dynamically recognize the most sensitive parameter during searching. Experimental results demonstrate that online multi-parameter monitoring of a drinking water source area with a wide dynamic range is achieved by this wireless sensor network and water pollution sources are localized efficiently with low-cost mobile node paths.

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          The appropriate use of approximate entropy and sample entropy with short data sets.

          Approximate entropy (ApEn) and sample entropy (SampEn) are mathematical algorithms created to measure the repeatability or predictability within a time series. Both algorithms are extremely sensitive to their input parameters: m (length of the data segment being compared), r (similarity criterion), and N (length of data). There is no established consensus on parameter selection in short data sets, especially for biological data. Therefore, the purpose of this research was to examine the robustness of these two entropy algorithms by exploring the effect of changing parameter values on short data sets. Data with known theoretical entropy qualities as well as experimental data from both healthy young and older adults was utilized. Our results demonstrate that both ApEn and SampEn are extremely sensitive to parameter choices, especially for very short data sets, N ≤ 200. We suggest using N larger than 200, an m of 2 and examine several r values before selecting your parameters. Extreme caution should be used when choosing parameters for experimental studies with both algorithms. Based on our current findings, it appears that SampEn is more reliable for short data sets. SampEn was less sensitive to changes in data length and demonstrated fewer problems with relative consistency.
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            Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                16 February 2018
                February 2018
                : 18
                : 2
                : 606
                Affiliations
                [1 ]Chinese Research Academy of Environmental Sciences, Beijing 100012, China; majunjie85@ 123456gmail.com (J.M.); mengfs@ 123456craes.org.cn (F.M.); zhouyuexi@ 123456263.net (Y.Z.)
                [2 ]China National Environmental Monitoring Centre, Beijing 100012, China
                [3 ]Yiwen Environmental Science Technology Co., Ltd., Guangzhou 510663, China; shiping@ 123456yiwenkeji.com
                Author notes
                [* ]Correspondence: yeyaowang@ 123456163.com ; Tel.: +86-10-8494-3007
                Author information
                https://orcid.org/0000-0002-6024-0423
                https://orcid.org/0000-0002-4395-8927
                Article
                sensors-18-00606
                10.3390/s18020606
                5855095
                29462929
                5f61386d-9943-4ffb-90c9-86641a9d11ee
                © 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
                : 29 November 2017
                : 13 February 2018
                Categories
                Article

                Biomedical engineering
                pollution source localization,wireless sensor networks,mobile nodes,uv-visible spectroscopy,water quality multi-parameter,distributed algorithm,particle swarm optimization

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