Design of robust blind detector with application to watermarking
| dc.contributor.author | Anamalu, Ernest Sopuru | |
| dc.contributor.examiningcommittee | Yahampath, Pradeepa (Electrical & Computer Engineering) Liao, Simon (Applied Computer Science, University of Winnipeg) | en_US |
| dc.contributor.supervisor | Pawlak, Miroslaw (Electrical & Computer Engineering) | en_US |
| dc.date.accessioned | 2014-02-14T16:15:51Z | |
| dc.date.available | 2014-02-14T16:15:51Z | |
| dc.date.issued | 2014-02-14 | |
| dc.degree.discipline | Electrical and Computer Engineering | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | One of the difficult issues in detection theory is to design a robust detector that takes into account the actual distribution of the original data. The most commonly used statistical detection model for blind detection is Gaussian distribution. Specifically, linear correlation is an optimal detection method in the presence of Gaussian distributed features. This has been found to be sub-optimal detection metric when density deviates completely from Gaussian distributions. Hence, we formulate a detection algorithm that enhances detection probability by exploiting the true characterises of the original data. To understand the underlying distribution function of data, we employed the estimation techniques such as parametric model called approximated density ratio logistic regression model and semiparameric estimations. Semiparametric model has the advantages of yielding density ratios as well as individual densities. Both methods are applicable to signals such as watermark embedded in spatial domain and outperform the conventional linear correlation non-Gaussian distributed. | en_US |
| dc.description.note | May 2014 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/23303 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Signal detection | en_US |
| dc.subject | Parametric and nonparametric estimations | en_US |
| dc.subject | K-means | en_US |
| dc.subject | expectation maximization | en_US |
| dc.subject | maximum likelihood estimations | en_US |
| dc.subject | density ratio estimation | en_US |
| dc.subject | Gaussian mixture model | en_US |
| dc.subject | Logistic regression model | en_US |
| dc.title | Design of robust blind detector with application to watermarking | en_US |
| dc.type | master thesis | en_US |