{"title":"各种短时信号的最优检测与分类","authors":"P. Baggenstoss","doi":"10.1109/IC2E.2014.96","DOIUrl":null,"url":null,"abstract":"Recent theoretical advances in class-dependent feature extraction are reviewed. These advances, culminating in the multi-resolution HMM (MR-HMM) statistical model are proposed for the detection and classification of transient signals that are composed of diverse components with widely varying structure and resolution.","PeriodicalId":273902,"journal":{"name":"2014 IEEE International Conference on Cloud Engineering","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Optimal Detection and Classification of Diverse Short-duration Signals\",\"authors\":\"P. Baggenstoss\",\"doi\":\"10.1109/IC2E.2014.96\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recent theoretical advances in class-dependent feature extraction are reviewed. These advances, culminating in the multi-resolution HMM (MR-HMM) statistical model are proposed for the detection and classification of transient signals that are composed of diverse components with widely varying structure and resolution.\",\"PeriodicalId\":273902,\"journal\":{\"name\":\"2014 IEEE International Conference on Cloud Engineering\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-03-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE International Conference on Cloud Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IC2E.2014.96\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE International Conference on Cloud Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IC2E.2014.96","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimal Detection and Classification of Diverse Short-duration Signals
Recent theoretical advances in class-dependent feature extraction are reviewed. These advances, culminating in the multi-resolution HMM (MR-HMM) statistical model are proposed for the detection and classification of transient signals that are composed of diverse components with widely varying structure and resolution.