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This paper is an investigation on negative emotions states recognition by employing of Fuzzy Adaptive Resonance Theory (Fuzzy-ART) considering the changes in activities of autonomic nervous system (ANS). Specific psychological experiments were designed to induce appropriate physiological responses on individuals in order to acquire a suitable database for training, validating and testing the proposed procedure. In this research, the three physiological applied signals are Galvanic Skin Response (GSR), Heart Rate (HR) and Respiration Rate (RR). The first experiment which is named Shock was designed to determine a criterion for the change of physiological signals of each individual. In the second one, a combination of two sets of questions has been asked from the subjects to induce their emotions. Finally, Physiological responses were analyzed by Fuzzy-ART to recognize which question excites the negative emotions. Detecting negative emotions from neutral is obtained with total accuracy of 94%

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One of the main renewable energy sources for the future is photovoltaic (PV) energy. Hence, working of the PV systems at maximum efficiency is taken into consideration in recent years. In this paper, for improving the performance of the global maximum power point tracking under partial shading conditions and uncertainty in parameters of DC-DC converter, a two-level adaptive control scheme is proposed. The proposed controller is capable of efficiently handling the uncertainties in the PV systems and the perturbations in the environment. The first level is global perturbation-based extremum seeking control (GPESC), and the second level is model reference adaptive control (MRAC). GPESC is used to find global maximum power point and MRAC is utilized to handle the dynamics of the DC-DC converter. Adequate difference in the time constants of control levels, causes decoupled control levels, which in turn makes it easy to design the controller. The performance of the proposed control scheme is evaluated through simulation based on four indicators: tracking accuracy, tracking efficiency, tracking speed and searching resolution for different irradiance patterns. The results are compared with GPESC and GPESC with PID controller.

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Active noise control (ANC) is based on the principle of superposition of waves. It means that an algorithm is used to tune a secondary source to make an anti-noise with equal amplitude but opposite phase with the primary source. In this paper, a wavelet function and network (WAVENET) approach is designed for ANC. The algorithm is used to train parameters of an anti-noise filter for omitting the undesired noise. FXLMS and NLMS are the conventional methods of ANC that need complex acoustic plant models and these necessities make the methods complex and inaccurate. In the WAVENET approach, this complexity can be accounted for. Numerical simulations for a WAVENET approach are presented to demonstrate the performance of the WAVENET approach scheme.

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