Much attention has recently been paid to methods of shared secret key generation that exploit the random characteristics of the amplitude and phase of a received signal and common channel symmetry in wireless communication systems. Protocols based on the phase of a rece More
Much attention has recently been paid to methods of shared secret key generation that exploit the random characteristics of the amplitude and phase of a received signal and common channel symmetry in wireless communication systems. Protocols based on the phase of a received signal, due to the uniform distribution phase of fading channel, are suitable in both static and dynamic environments and, they have a key generation rate (KGR) higher than protocols based on received signal strength (RSS).In addition, previous works have generally focused on key generation protocol for single-antenna (SISO) systems but these have not produced a significant KGR. So in this paper to increase the randomness and key generation rate are used received signal phase estimations on multiple-antenna (MIMO) systems because they have the potential to present more random variables in key generation compared to SISO systems. The results of simulation show that the KGR of the proposed protocol is 4 and 9 times more than the KGR of a SISO system, when the numbers of transmitter and receiver antennas are the same and equal to 2 and 3, respectively. Also, the key generation rate will increase considerably, when to extract the secret key bits using multilevel quantization.
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Recent developments in interactive and robotic systems have motivated researchers for recognizing human’s emotion from speech. The present study aimed to classify emotional speech signals using a two stage classifier based on arousal-valence emotion model. In this metho More
Recent developments in interactive and robotic systems have motivated researchers for recognizing human’s emotion from speech. The present study aimed to classify emotional speech signals using a two stage classifier based on arousal-valence emotion model. In this method, samples are firstly classified based on the arousal level using conventional prosodic and spectral features. Then, valence related emotions are classified using the proposed non-linear dynamics features (NLDs). NLDs are extracted from the geometrical properties of the reconstructed phase space of speech signal. For this purpose, four descriptor contours are employed to represent the geometrical properties of the reconstructed phase space. Then, the discrete cosine transform (DCT) is used to compress the information of these contours into a set of low order coefficients. The significant DCT coefficients of the descriptor contours form the proposed NLDs. The classification accuracy of the proposed system has been evaluated using the 10-fold cross-validation technique on the Berlin database. The average recognition rate of 96.35% and 87.18% were achieved for females and males, respectively. By considering the total number of male and female samples, the overall recognition rate of 92.34% is obtained for the proposed speech emotion recognition system.
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