This paper presents a new method for designing digital recursive integrators and differentiators by using nonlinear stochastic global optimization based on particle swarm optimization (PSO). A modified PSO is used to optimize the unknown coefficients of second-order and third-order recursive integrators in order to obtain a better magnitude response closer to the ideal integrator. The coefficients of the state-of-theart available filters in the literature are chosen as initial points in the PSO process. By choosing a good starting point, convergence to an optimal solution is greatly facilitated. A dynamic modification of the fitness function used in the PSO process leads to the design of a set of digital integrators each optimized for a specific frequency range. Then, second-order and third-order recursive digital differentiators are designed by inverting and stabilizing the transfer functions of the designed recursive integrators. The obtained stabilized differentiators are further optimized using PSO to further improve their performance. The magnitude responses of the designed filters outperform the existing integrators and differentiators.Additionally Al-Alaoui in [2] proposed an approach to design a digital differentiator by inverting the transfer function of an integrator with similar specifications, stabilizing the resulting transfer function, and compensating the resulting change in magnitude because of the stabilization step that consisted of reflecting the resulting poles, the zeros of the integrator, that lie outside the unit circle at a radius r in the z-plane to 1/r. In [3], he obtained a class of second-order integrators and the corresponding differentiators by interpolating the Simpson and the trapezoidal integration rules and inverting the transfer functions of the obtained integrators.Ngo [4] employed Newton-Cotes integration method to design digital integrators. Tseng-Lee [5] used fractional delay to design digital integrators and Pei and Hsu [6] proposed employing fractional delay filters to obtain new differentiators. Gupta, Jain, and Kumar [7-9] developed digital integrators by interpolating some of the popular digital integration techniques. Varshney, Gupta, and Visweswaran [10] also used the interpolation method to design new digital differentiators. Some recent publications [11][12][13][14] in this field have also been reported in the literature.Recursive digital integrator's design can be formulated as an optimization problem where one searches for the optimal vector of filter coefficients to minimize a certain error function between the designed filter and the ideal integrator given by 1/jw. The previously mentioned methods for designing digital integrators suffer from poor accuracy because only a small fraction of the entire set of candidate vectors of filter coefficients is considered and thus are easily trapped into local optima on the error surface. Papamarkos and Chamzas [15] have used linear programming optimization method to propose the design of digital integrators. Lai, Lin, and ...

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Motion estimation is a common tool used in all video coding standards. Fast and accurate algorithms are needed to target the real-time processing requirements of emerging applications. Many fast-search block motion estimation algorithms have been developed to reduce the computational cost required by the full-search algorithm. These techniques however often converge to a local minimum, which makes them subject to noise and matching errors. In the literature, several schemes were proposed to employ strategies of Particle Swarm Optimization (PSO) in the problem of motion estimation since PSO promises to alleviate the problem of being trapped in local minima. The existing schemes, however, still don't achieve the necessary improvement in terms of accuracy or speedup as compared to the existing fast searching methods. In this paper, we propose a novel fast and accurate block motion estimation scheme based on an improved parallel Particle Swarm Optimization algorithm. Unlike existing motion estimation algorithms which operate on blocks of the frame serially following the raster order, the proposed algorithm achieves parallelism since it performs motion estimation for all blocks of the frame in parallel. Simulation results showed that the proposed scheme could provide a higher accuracy and a remarkable speedup as compared to the wellknown fast searching techniques and to a recent PSO-based motion estimation algorithm.

In this paper, we propose and evaluate a new interference co-ordination scheme for the uplink of LTE systems. This presented approach mitigates the effects of ICI (Inter-Cell Interference) by finding suboptimal frequency and power assignments to all users in a given cellular network. The spectral efficiency of cell-edge users is enhanced in this scheme and simulation results show that our presented approach outperforms Samsung's Flexible Fractional Frequency Reuse (FFFR) algorithm which is one of the interference coordination schemes proposed for LTE systems.

Inter-prediction motion estimation (ME) is solved in a distributed manner by a network of cooperating and learning agents. The block motion estimation problem is formulated as the optimization of a global cost function that is the sum of individual sub-problems. A network of agents is then employed to address this problem where each agent iteratively solves its own subproblem using a modified Particle Swarm Optimization (PSO) algorithm. Diffusion strategies are employed to allow the agents to cooperate and diffuse information in real-time in order to reach the common minimizer of the global cost function.