{"title":"Not all Vinnicombe metric neighbourhoods are homotopically connected","authors":"Bruce Anderson, T. Brinsmead","doi":"10.1109/IDC.2002.995361","DOIUrl":"https://doi.org/10.1109/IDC.2002.995361","url":null,"abstract":"We prove by counterexample that even for two transfer functions which are close in the Nu-gap metric of Vinnicombe (1993, 1999), there does not necessarily exist a Vinnicombe metric homotopy from one transfer function to the other, such that intermediate transfer functions in the homotopy remain close to the transfer function at the beginning of the homotopy. This implies that the Vinnicombe metric neighbourhoods of some transfer functions in L-infinity space, are not connected.","PeriodicalId":385351,"journal":{"name":"Final Program and Abstracts on Information, Decision and Control","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125407511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A note on the classification error of an SVM in one dimension","authors":"T. Cooke","doi":"10.1109/IDC.2002.995376","DOIUrl":"https://doi.org/10.1109/IDC.2002.995376","url":null,"abstract":"There are many algorithms available for detecting noise corrupted signals in background clutter. In cases where the exact statistics of the noise and clutter are unknown, the optimal detector may be estimated from a set of samples of each. One method for doing this is the support vector machine (SVM), which has a detection performance that is dependent on some regularisation parameter C, and cannot be determined a-priori. The standard method of choosing C is by trying values and choosing the one which minimises the detection error on a cross-validation set. It is often assumed that as the size of the training set increases, the resulting discriminant will give the best possible detection rate on an independent test set. This paper investigates two simple 1D examples: uniform and normal distributions. An example is provided where the optimum detection rate cannot be achieved by an SVM regardless of the C chosen value.","PeriodicalId":385351,"journal":{"name":"Final Program and Abstracts on Information, Decision and Control","volume":"36 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125728277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Flight parameter identification from cepstrum tracks","authors":"Y. Gao, G. Pulford, J. Sendt, A. Maguer","doi":"10.1109/IDC.2002.995436","DOIUrl":"https://doi.org/10.1109/IDC.2002.995436","url":null,"abstract":"By using a single microphone located above the ground, it is possible to determine the flight parameters of an aircraft fly-over. This technique utilises the asymmetry of the Lloyds mirror rings (LMR) that have been converted into a primary rahmonic in the cepstrogram of the acoustic data. Unlike previous techniques, the spectrogram is not needed. The cepstrum data are automatically processed by a hidden Markov model tracker that provides input to the flight parameter estimation stage. The Levenberg-Marquardt optimisation procedure is then applied to derive the aircraft speed along with the time, horizontal distance and height of the closest point of approach. Reliable cepstrogram estimates are obtainable when at least three LMR's are present in the spectrogram data.","PeriodicalId":385351,"journal":{"name":"Final Program and Abstracts on Information, Decision and Control","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126637785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Building fusion systems","authors":"E. Shahbazian","doi":"10.1109/IDC.2002.995418","DOIUrl":"https://doi.org/10.1109/IDC.2002.995418","url":null,"abstract":"Information and data fusion is a discipline that provides the methods and techniques to build observe-orient-decide-act (OODA) capabilities for various applications. There are many ways in which these methods and techniques can be chosen to provide capabilities in each phase of the OODA decision-making cycle, and there are different fusion architectures, i.e., ways these methods and techniques can be applied, grouped and integrated. How one chooses the most appropriate set of methods, techniques and fusion architecture depends on a number of factors. This paper discusses the compromises and trade-offs of combining the methods and techniques and selecting the architecture for a specific system for each of the four phases of the OODA decision-making cycle. This is done based on the actual experiences of development fusion systems at Lockheed Martin (LM) Canada over the last ten years.","PeriodicalId":385351,"journal":{"name":"Final Program and Abstracts on Information, Decision and Control","volume":"557 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113982163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}