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In this paper the comparative optimal designs for maximum sidelobe level (SLL) reduction of three-ring concentric circular antenna array (CCAA) are determined using two novel Particle Swarm Optimization (PSO) techniques namely Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach (PSOCFIWA), Craziness based Particle Swarm Optimization (CRPSO) and Binary coded Genetic Algorithm...
Motion vector estimation is an important parameter for video segmentation. Effective video compression can be achieved by choosing a correct approach for the calculation of motion vector. Here in this paper we propose an optical flow motion vector estimation through iterative Lucas-Kanade pyramidal implementation for both large & small motion in image pyramid representation a group of pixel information...
A new optimization technique named as sliced particle swarm optimization (SPSO) is proposed. It introduces the slicing of search space into rectangular slices. It gives complete solution in terms of reduction in the computational cost and tracking minutely each sliced search space. It introduces the momentum factor which restricts the particle in a sliced search space. Linearly decreasing inertia...
Color selection in designing user interfaces is addressed by an interactive genetic algorithm. The proposed approach is aimed at finding the optimal trade-off between different and sometimes conflicting constraints, without any explicit model of user preferences and abilities. Experimentation investigates the algorithm convergence under several conditions and user behavior.
We consider the problem of joint decoding of signals transmitted from two correlated sources at a destination. In order to achieve high spectral efficiency at a high range of the channel signal-to-noise power (SNR), a high transmission rate is required. In this paper, we design a class of codes for joint-decoding by exploiting bit interleaved coded modulation with iterative decoding (BICM-ID) technique...
This paper presents an empirical analysis of the performance of differential evolution (DE) variants on different classes of unconstrained global optimization benchmark problems. This analysis has been undertaken to identify competitive DE variants which perform reasonably well on a range of problems with different features. Towards this, fourteen DE variants were implemented and tested on 14 high...
In this paper, the authors propose a new evolutionary optimization technique i.e. modified biogeography-based optimization (MBBO). This technique is an improved version of BBO with each solution is directly encoded by floating point. BBO is a new bio-inspired and population based optimization algorithm for global optimization. The exploitation ability of BBO method is good but it lacks in exploration...
In this paper we present an empirical, comparative performance, analysis of fourteen variants of Differential Evolution (DE) and Dynamic Differential Evolution (DDE) algorithms to solve unconstrained global optimization problems. The aim is to compare DDE, which employs a dynamic evolution mechanism, against DE and to identify the competitive variants which perform reasonably well on problems with...
Differential evolution (DE) is a simple and efficient scheme for global optimization over continuous spaces. DE is generally considered as a reliable, accurate, robust and fast optimization techniques. It outperforms many other optimization algorithms in terms of convergence speed and robustness over common benchmark problems and real world applications. However, the user is required to set the values...
This paper presents a new diversity guided particle swarm optimization algorithm (PSO) named beta mutation PSO or BMPSO for solving global optimization problems. The BMPSO algorithm makes use of an evolutionary programming based mutation operator to maintain the level of diversity in the swarm population, thereby maintaining a good balance between the exploration and exploitation phenomena and preventing...
Three term backpropagation (BP) network as proposed by Zweiri in 2003 has outperformed standard two term backpropagation. However, further studies on three term backpropagation in 2007 indicated that this network only surpassed standard BP for small scale datasets but not for medium and large scale datasets. It has also been observed that by using mean square error (MSE) as a cost function in three...
This paper proposes an affine projection algorithm (APA) using the inner product between input vectors. The existing APAs have fast convergence rate but large steady-state estimation errors. In order to reduce the estimation errors, the proposed algorithm adjusts the number of the input vectors by grouping of the input vectors. The grouping process uses the angle between a current input vector and...
In this paper the concepts of selective partial updates (SPU) and selective regressors (SR) in the affine projection (AP) adaptive filtering algorithm are combined and the family of affine projection algorithms with SPU and SR features are established. These algorithms are computationally efficient. We demonstrate the performance of the presented algorithms through simulations.
Different from gradient-based dynamics (GD), a special class of neural dynamics has been found, developed, generalized and investigated by Zhang et al, e.g., for online solution of time-varying and/or static nonlinear equations. The resultant Zhang dynamics (ZD) is designed based on the elimination of an indefinite error-function (instead of the elimination of a square-based positive or at least lower-bounded...
Three-dimensional reconstruction of cryo-electron tomography (cryo-ET) has emerged as the leading technique in analyzing structures of complex pleomorphic cellulars. A classical iterative method, simultaneous algebraic reconstruction technique (SART), has been employed to reconstruct volume images in cryo-ET. However, SART starts with an arbitrary approximation and takes into account only a weighted...
This paper presents a flexible method of achieving either fixed or self-adaptive antenna beamforming. It involves the use of an array image factor ??d, which interfaces an RLS and LMS sections in cascade to form the RLMS beamforming algorithm. It is shown that an accurate fixed beam can be obtained by prior setting the elements of ??d with prescribed values for the required direction. Moreover, the...
This work presents system identification using neural network approaches for modelling a laboratory based twin rotor multi-input multi-output system (TRMS). Here we focus on a memetic algorithm based approach for training the multilayer perceptron neural network (NN) applied to nonlinear system identification. In the proposed system identification scheme, we have exploited three global search methods...
This paper establishes a framework for designing fast, robust, and distributed algorithms for solving network utility maximization problems with coupled objective functions. We use two case studies in wireless communications to illustrate the key ideas: reverse-engineering the algorithm based on the KKT conditions of the optimization problem, and proving the properties of the algorithms using monotone...
This paper investigates signal and system representations in special rational orthogonal bases of the Hardy space H2. These bases are derived from the Blaschke-group associated to the hyperbolic group SH(n) of matrices. Using the concept of the unitary group representations over H2, and the voice-transform, specific hyperbolic wavelet constructions - Blaschke-wavelets and the associated Blaschke-Fourier...
In this paper, a fast blind equalization with a two-stage generalized multilevel modulus algorithm (GMMA) and soft decision-directed (SDD) scheme is proposed for high-order QAM (64/256/1024QAM) systems on the downstream wired cable channel. The proposed fast blind equalization algorithm applies the two-stage convergence scheme and the SDD part uses the scheme with an adaptively selected decision region...
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