Hai Liu, Zhaoli Zhang, Sanya Liu, Jiangbo Shu, Tingting Liu
{"title":"基于最大熵约束的参数谱信号复原及其应用","authors":"Hai Liu, Zhaoli Zhang, Sanya Liu, Jiangbo Shu, Tingting Liu","doi":"10.1109/DSP-SPE.2015.7369579","DOIUrl":null,"url":null,"abstract":"In this paper, we will propose a new framework which can estimate the desired signal and the instrument response function (IRF) simultaneously from the degraded spectral signal. Firstly, the spectral signal is considered as a distribution, thus, new entropy (called differential-entropy, DE) is defined to measure the distribution with a uniform distribution, which allows negative value existing. Moreover, the IRF is parametrically modeled as a Lorentzian function. Comparative results manifest that the proposed method outperforms the conventional methods on peak narrowing and noise suppression. The deconvolution IR spectrum is more convenient for extracting the spectral feature and interpreting the unknown chemical mixtures.","PeriodicalId":91992,"journal":{"name":"2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE)","volume":"48 1","pages":"353-357"},"PeriodicalIF":0.0000,"publicationDate":"2015-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Parametric spectral signal restoration via maximum entropy constraint and its application\",\"authors\":\"Hai Liu, Zhaoli Zhang, Sanya Liu, Jiangbo Shu, Tingting Liu\",\"doi\":\"10.1109/DSP-SPE.2015.7369579\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we will propose a new framework which can estimate the desired signal and the instrument response function (IRF) simultaneously from the degraded spectral signal. Firstly, the spectral signal is considered as a distribution, thus, new entropy (called differential-entropy, DE) is defined to measure the distribution with a uniform distribution, which allows negative value existing. Moreover, the IRF is parametrically modeled as a Lorentzian function. Comparative results manifest that the proposed method outperforms the conventional methods on peak narrowing and noise suppression. The deconvolution IR spectrum is more convenient for extracting the spectral feature and interpreting the unknown chemical mixtures.\",\"PeriodicalId\":91992,\"journal\":{\"name\":\"2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE)\",\"volume\":\"48 1\",\"pages\":\"353-357\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DSP-SPE.2015.7369579\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DSP-SPE.2015.7369579","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parametric spectral signal restoration via maximum entropy constraint and its application
In this paper, we will propose a new framework which can estimate the desired signal and the instrument response function (IRF) simultaneously from the degraded spectral signal. Firstly, the spectral signal is considered as a distribution, thus, new entropy (called differential-entropy, DE) is defined to measure the distribution with a uniform distribution, which allows negative value existing. Moreover, the IRF is parametrically modeled as a Lorentzian function. Comparative results manifest that the proposed method outperforms the conventional methods on peak narrowing and noise suppression. The deconvolution IR spectrum is more convenient for extracting the spectral feature and interpreting the unknown chemical mixtures.