Powerpoint Controller Using Speech Recognition

During presentation, it is hard to maintain the slide because we need to stand in front of the room and often not able to touch the computer. The presenters need to take attention at both their voice, and body language such eye contact, facial expression, posture, gesture, and body orientation. Microsoft PowerPoint is a simple but very useful tool to create digital presentation. Even though it is simple to use, but this application required the presenter to take control while using it, such as to star the slide show or moving it to the next slide. The purpose of this research is to minimize physical contact between user and the computer during the presentation by controlling the move of the slide using voice. This research will implement the Hidden Markov Model algorithm and Sphinx-4 library

Distributed Classification of Localization Attacks in Sensor Networks Using Exchange-Based Feature Extraction and Classifier

Secure localization under different forms of attack has become an essential task in wireless sensor networks. Despite the significant research efforts in detecting the malicious nodes, the problem of localization attack type recognition has not yet been well addressed. Motivated by this concern, we propose a novel exchange-based attack classification algorithm. This is achieved by a distributed expectation maximization extractor integrated with the PECPR-MKSVM classifier. First, the mixed distribution features based on the probabilistic modeling are extracted using a distributed expectation maximization algorithm. After feature extraction, by introducing the theory from support vector machine, an extensive contractive Peaceman-Rachford splitting method is derived to build the distributed classifier that diffuses the iteration calculation among neighbor sensors. To verify the efficiency of the distributed recognition scheme, four groups of experiments were carried out under various conditions. The average success rate of the proposed classification algorithm obtained in the presented experiments for external attacks is excellent and has achieved about 93.9% in some cases. These testing results demonstrate that the proposed algorithm can produce much greater recognition rate, and it can be also more robust and efficient even in the presence of excessive malicious scenario