Face perception is a very important component of human cognition. We can judge the person's mood and mental status through his/her expressions. In other words, the most expressive way human display emotion is through facial expressions. And hence facial expression recognition has become an active research area in the field of human computer interaction. The work in this paper concentrates on images having different illuminations and analyzes the performance of canny edge detection method with two classifiers, Euclidian distance and neural network. The results are tested on JAFFE (Japanese Female Facial Expression) database, available in public domain and IFE (Indian Facial Expression) database which is self created.

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Cognitive Radio (CR) is an important technology which can enable the implementation of Dynamic Spectrum Access, which is a paradigm shift from the static spectrum access model. It is an intelligent wireless communication system which can sense the environment and can take decisions to effectively use the available radio resource without creating any interference to the Licensed Primary Users.   Hence sensing of the spectrum plays a very important role in the effective implementation of this technology. We propose a new spectrum sensing algorithm in this paper which is based on machine learning and uses a Multi Feature based Classifier (MFC) model for classification of the spectrum.