Sea target detection is a vital application for military and navigation purposes. A new supervised clustering method based on the combination of the PSO and FCM techniques is presented for the sea target detection problem. The color components of the target and non-target pixels in the RGB color space are used as features to train the classification algorithm. The new classifier is presented in the form of a new color space which we call the Target-based Color Space (TCS); in fact the RGB color space is converted to this new space through a 3 × 3 matrix. The Particle Swarm Optimization (PSO) algorithm is then used to search for the optimum weights of the conversion matrix which results in a more discriminating clustering space between the target and non-target pixels. In other words, solving the optimization problem, minimization of the objective function of the FCM clustering technique in linear and quadratic transform domain (with a NP-hard problem in quadratic conversion), is done using the PSO algorithm. The main objective of this work is to demonstrate the efficiency of using just color features, as well as color space conversion in the classification domain. Experimental results show the efficiency of new method in finding sea targets in color images.
Due to the good tracking behaviour of the LMS adaptive filter in a noisy environment, the FX-LMS algorithm is proposed in the literature as a method of active noise control, ANC. But each of the LMS and RLS algorithms have their own advantages and disadvantages. In this paper, a new approach based on a mixture of the RLS and LMS algorithms, RLMS, is presented. The optimum weights of the mixture are derived and it is proved that the MMSE of the proposed system is reduced compared to those of the RLS and LMS algorithms. Then, the proposed RLMS algorithm is employed for active noise cancellation to form the FX-RLMS algorithm, in a duct. Experimental results show better performance of the RLMS algorithm compared to both the RLS and LMS algorithms of convergence and tracking behaviour in the system identification problem and noisy chirp tracking. The FX-RLMS algorithm shows better results in active noise cancellation compared to the FX-LMS algorithm.
A new algorithm is proposed for updating the weights of an adaptive filter. The proposed algorithm is a modification of an existing method, namely, the clipped LMS, and uses a three-level quantization ( ) scheme that involves the threshold clipping of the input signals in the filter weight update formula. Mathematical analysis shows the convergence of the filter weights to the optimum Wiener filter weights. Also, it can be proved that the proposed modified clipped LMS (MCLMS) algorithm has better tracking than the LMS algorithm. In addition, this algorithm has reduced computational complexity relative to the unmodified one. By using a suitable threshold, it is possible to increase the tracking capability of the MCLMS algorithm compared to the LMS algorithm, but this causes slower convergence. Computer simulations confirm the mathematical analysis presented.
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