Hamim Zafar | Jadavpur University (original) (raw)
“Somewhere, something incredible is waiting to be known.”- Dr.Carl Sagan
I am a member of the research group in "Digital Control and Image Processing Laboratory" guided by Dr.Swagatam Das.For two years I have been working in the area of Optimization and its diverse applications in engineering.I have applied different optimization algorithms to solve problems in the fields of Communication,Control,Signal Processing and Electromagnetics.I have undergone a summer project on "Assessment of Electric Power Quality in Distribution Network" under Dr.B.K.Panigrahi in Electrical Engineering department of IIT,Delhi.I am particularly interested in Intelligent and Adaptive Control and Communication system.Presently I am working on the development of an efficient algorithm for application in controller and digital filter design.
Supervisors: Dr. Swagatam Das, Dr. B.K.Panigrahi, and Dr. P.N.Suganthan
Phone: 03512250175(resi)
Address: Bibigram,
PO.& Dist.- Malda
West Bengal
Pin-732101
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Papers by Hamim Zafar
This paper proposes an improved variant of the Differential Evolution (DE) algorithm called MJADE... more This paper proposes an improved variant of the Differential Evolution (DE) algorithm called MJADE_pBX (Modified Adaptive DE with pBest crossover) for the design of two channel Quadrature Mirror Filters (QMF) with linear phase characteristics. To match the ideal system response characteristics, the algorithm is employed to optimize the values of the filter bank coefficients. The filter response is optimized in both pass band and stop band. The overall filter bank response consists of objective functions termed as reconstruction error, mean square error in pass band and mean square error in stop band. Effective designing can be achieved by efficiently minimizing the objective function. The proposed algorithm is able to perform much better than the other existing design methods. Five different design examples are presented here to validate the effectiveness of the proposed approach over other existing design techniques found in literature.
… Computation (CEC), 2011 …, Jan 1, 2011
Particle Swarm Optimization (PSO) is arguably one of the most popular nature-inspired algorithms ... more Particle Swarm Optimization (PSO) is arguably one of the most popular nature-inspired algorithms for real parameter optimization at present. In this article, we introduce a new variant of PSO referred to as Hierarchical D-LPSO (Dynamic Local Neighborhood based Particle Swarm Optimization). In this new variant of PSO the particles are arranged following a dynamic hierarchy. Within each hierarchy the particles search for better solution using dynamically varying sub-swarms i.e. these sub-swarms are regrouped frequently and information is exchanged among them. Whether a particle will move up or down the hierarchy depends on the quality of its so-far bestfound result. The swarm is largely influenced by the good particles that move up in the hierarchy. The performance of Hierarchical D-LPSO is tested on the set of 25 numerical benchmark functions taken from the competition and special session on real parameter optimization held under IEEE Congress on Evolutionary Computation (CEC) 2005. The results have been compared to those obtained with a few best-known variants of PSO as well as a few significant existing evolutionary algorithms.
Swarm, Evolutionary, and Memetic Computing, Jan 1, 2010
In the paper, a simple approach is used to design the beampattern of a nonuniform linear array wi... more In the paper, a simple approach is used to design the beampattern of a nonuniform linear array with maximum mainlobe to average sidelobe ratio. The basic idea of the approach is to minimise the mean square value of the array response in certain directional regions while imposing one or several directional null constraints with respect to an error element position vector. A new estimated array geometry is then obtained by adding the error array geometry onto the nominal linear array geometry which is normally assumed to be the uniformly spaced linear array geometry. To verify the performance of the proposed technique, numerical results are obtained to compare the response of an array designed using the proposed approach with that of the conventional array. It is shown that, with the proposed technique, one is able to perturb the conventional linear array slightly to achieve better array pattern with no additional cost involved in terms of practical implementation.
This paper proposes an improved variant of the Differential Evolution (DE) algorithm called MJADE... more This paper proposes an improved variant of the Differential Evolution (DE) algorithm called MJADE_pBX (Modified Adaptive DE with pBest crossover) for the design of two channel Quadrature Mirror Filters (QMF) with linear phase characteristics. To match the ideal system response characteristics, the algorithm is employed to optimize the values of the filter bank coefficients. The filter response is optimized in both pass band and stop band. The overall filter bank response consists of objective functions termed as reconstruction error, mean square error in pass band and mean square error in stop band. Effective designing can be achieved by efficiently minimizing the objective function. The proposed algorithm is able to perform much better than the other existing design methods. Five different design examples are presented here to validate the effectiveness of the proposed approach over other existing design techniques found in literature.
… Computation (CEC), 2011 …, Jan 1, 2011
Particle Swarm Optimization (PSO) is arguably one of the most popular nature-inspired algorithms ... more Particle Swarm Optimization (PSO) is arguably one of the most popular nature-inspired algorithms for real parameter optimization at present. In this article, we introduce a new variant of PSO referred to as Hierarchical D-LPSO (Dynamic Local Neighborhood based Particle Swarm Optimization). In this new variant of PSO the particles are arranged following a dynamic hierarchy. Within each hierarchy the particles search for better solution using dynamically varying sub-swarms i.e. these sub-swarms are regrouped frequently and information is exchanged among them. Whether a particle will move up or down the hierarchy depends on the quality of its so-far bestfound result. The swarm is largely influenced by the good particles that move up in the hierarchy. The performance of Hierarchical D-LPSO is tested on the set of 25 numerical benchmark functions taken from the competition and special session on real parameter optimization held under IEEE Congress on Evolutionary Computation (CEC) 2005. The results have been compared to those obtained with a few best-known variants of PSO as well as a few significant existing evolutionary algorithms.
Swarm, Evolutionary, and Memetic Computing, Jan 1, 2010
In the paper, a simple approach is used to design the beampattern of a nonuniform linear array wi... more In the paper, a simple approach is used to design the beampattern of a nonuniform linear array with maximum mainlobe to average sidelobe ratio. The basic idea of the approach is to minimise the mean square value of the array response in certain directional regions while imposing one or several directional null constraints with respect to an error element position vector. A new estimated array geometry is then obtained by adding the error array geometry onto the nominal linear array geometry which is normally assumed to be the uniformly spaced linear array geometry. To verify the performance of the proposed technique, numerical results are obtained to compare the response of an array designed using the proposed approach with that of the conventional array. It is shown that, with the proposed technique, one is able to perturb the conventional linear array slightly to achieve better array pattern with no additional cost involved in terms of practical implementation.