Today, metro is one of the urban infrastructure and plays an important role in urban transport. The safety and health of people in a city are always important, and transport in the metro should also be safe. When subway trains operate, it is possible to occur various accidents such as an exit from the rails or collision with a possible obstacle on the rails (human or another train). In this paper, a model is proposed based on RFID technology in which the train is equipped with a RFID reader, and a control circuit with a microcontroller as well as placing RFID active tags at specific points of the path. When the train approaches the tagged points of the route, the train tag reader scans the tag number, and then the microcontroller identifies the status of the environment by retrieving the information from the internal database. Then, the control circuit adjusts the rotational speed of the electric motor and the train speed consequently based on that data. In this way, the speed of the train is adapted automatically and promptly to the conditions and the probability of an accident in unforeseen circumstances is reduced.
This paper gives a novel approach of automatic speaker recognition technology, with an emphasis on text-dependent speaker recognition. Speaker recognition has been studied actively for several decades. In fact, Speaker recognition system may be viewed as working in four stages, namely, analysis, feature extraction, modeling and testing. After some preprocessing modules, we apply MFCC, as one of the most important feature extraction methods in this field of works, to speech signals independently in order to extract feature vectors. Afterwards, obtained vectors are used by training system to find codewords for ten users in our Persian database by LBG VQ. Finally, we use DTW technique for recognizing a speaker among all. Our experience strongly indicates that the identification rate over 96% can be achieved by the proposed algorithm.
General TermsSpeeches analyze,Speaker recognition.
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