This thesis explores the use of the Naïve Bayesian classifier as a method of determining high-level information about secondary users in a Dynamic Spectrum Access (DSA) network using a low complexity channel sensing method.  With a growing number of users generating an increased demand for broadband access, determining an efficient method for utilizing the limited available broadband is a developing current and future issue.  One possible solution is DSA, which we simulate using the Universal DSA Network Simulator (UDNS), created by our team at Virginia Tech. However, DSA requires user devices to monitor large amounts of bandwidth, and the user devices are often limited in their acceptable size, weight, and power.  This greatly limits the usable bandwidth when using complex channel sensing methods.  Therefore, this thesis focuses on energy detection for channel sensing. Constraining computing requirements by operating with limited spectrum sensing equipment allows for efficient use of limited broadband by user devices.  The research on using the Naïve Bayesian classifier coupled with energy detection and the UDNS serves as a strong starting point for supplementary work in the area of radio classification. / Master of Science

We investigate the factors that determine the performance of text-based language identification, with a particular focus on the 11 official languages of South Africa. Our study uses n-gram statistics as features for classification. In particular, we compare support vector machines, Naïve Bayesian and difference-in-frequency classifiers on different amounts of input text and various values of n, for different amounts of training data. For a fixed value of n the support vector machines generally outperforms the other classifiers, but the simpler classifiers are able to handle larger values of n. The additional computational complexity of training the support vector machine classifier may not be justified in light of importance of using a large value of n, except possibly for small sizes of the input window when limited training data is available. We find that it is more difficult to discriminate languages within language families then those across families. The accuracy on small input strings is low due to this reason, but for input strings of 100 characters or more there is only a slight confusion within families and accuracies as high as 99.4% are achieved. For the smallest input strings studied here, which consist of 15 characters, the best accuracy achieved is only 83%, but when the languages in different families are grouped together, this corresponds to a usable 95.1% accuracy. The relationship between the amount of training data and the accuracy achieved is found to depend on the window size – for the largest window (300 characters) about 400 000 characters are sufficient to achieve close-to-optimal accuracy, whereas improvements in accuracy are found even beyond 1.6 million characters of training data. Finally, we show that the confusions between the different languages in our set can be used to derive informative graphical representations of the relationships between the languages. / Dissertation (MEng)--University of Pretoria, 2008. / Electrical, Electronic and Computer Engineering / unrestricted