Multichannel blind deconvolution (MCBD) algorithms are known to suffer from an extensive computational complexity problem, which makes them impractical for blind source separation (BSS) of speech and audio signals. This problem is even more serious with noncausal MCBD algorithms that must be used in many frequently occurring BSS setups. In this paper, we propose a novel frequency domain algorithm for the efficient implementation of noncausal multichannel blind deconvolution. A block-wise formulation is first developed for filtering and adaptation of filter coefficients. Based on this formulation, we present a modified overlap-save procedure for noncausal filtering in the frequency domain. We also derive update equations for training both causal and anti-causal filters in the frequency domain. Our evaluations indicate that the proposed frequency domain implementation reduces the computational requirements of the algorithm by a factor of more than 100 for typical filter lengths used in blind speech separation. The algorithm is employed successfully for the separation of speech mixtures in a reverberant room. Simulation results demonstrate the superior performance of the proposed algorithm over causal MCBD algorithms in many potential source and microphone positions. It is shown that in BSS problems, causal MCBD algorithms with center-spike initialization do not always converge to a delayed form of the desired noncausal solution, further revealing the need for an efficient noncausal MCBD algorithm.

Solar panels are power sources in photovoltaic applications. Solar panels I-V curves depend on environmental conditions such as irradiance, temperature, load and degradation level. In this paper, design and implementation of simultaneous fuzzy-based maximum power point tracker (MPPT) and sun tracker are presented for deployable solar panels. A digital controller was implemented by an AVR microcontroller. Results showed that the proposed system ensure to have photovoltaic system with higher efficiency. Finally, we observed that, using the proposed fuzzy-based MPP tracking and sun tracking simultaneously, solar panel output power can be remarkably increased leading in turn to reduction of the size, weight and cost of solar panels in photovoltaic systems.

License plate location is an important phase in vehicle license plate recognition for intelligent transport systems. This paper presents a robust and real time method of license plate location. The proposed algorithm consists of some stages. In the first stage, we extract vertical edges of the input image using Sobel mask. In the next stage, histogram analysis is used for finding the candidate regions of license plate. Candidate regions are also verified by defined compact factor. In the last stage, we locate the license plate exactly with some morphological operators. Experiments have been conducted for this algorithm. 400 images taken from various scenes were employed, including diverse angles, different lightening conditions. The algorithm can quickly and correctly detect the region of license plate. The license plate detecting rate of success is 83.50%.