Showing papers by "Durbadal Mandal published in 2014"
TL;DR: Extensive simulation results affirm that the proposed approach using GSA outperforms over its counterparts not only in terms of quality output, i.e., sharpness at cut-off, smaller pass band ripple, higher stop band attenuation, but also the fastest convergence speed with assured stability.
Abstract: This paper presents a global heuristic search optimization technique known as Gravitation Search Algorithm (GSA) for the design of 8th order Infinite Impulse Response (IIR), low pass (LP), high pass (HP), band pass (BP) and band stop (BS) filters considering various non-linear characteristics of the filter design problems. This paper also adopts a novel fitness function in order to improve the stop band attenuation to a great extent. In GSA, law of gravity and mass interactions among different particles are adopted for handling the non-linear IIR filter design optimization problem. In this optimization technique, searcher agents are the collection of masses and interactions among them are governed by the Newtonian gravity and the laws of motion. The performances of the GSA based IIR filter designs have proven to be superior as compared to those obtained by real coded genetic algorithm (RGA) and standard Particle Swarm Optimization (PSO). Extensive simulation results affirm that the proposed approach using GSA outperforms over its counterparts not only in terms of quality output, i.e., sharpness at cut-off, smaller pass band ripple, higher stop band attenuation, but also the fastest convergence speed with assured stability.
44 citations
TL;DR: The simulation results obtained justify the efficacy of the proposed system identification approach using CRPSO over RGA, PSO and DE in terms of convergence speed, unknown plant coefficients and mean square error (MSE) values produced for both the same order and reduced order models of adaptive IIR filters.
Abstract: In this paper a variant of particle swarm optimization (PSO), called craziness based particle swarm optimization (CRPSO) technique is applied to the infinite impulse response (IIR) system identification problem. A modified version of PSO, called CRPSO adopts a number of random variables for having better and faster exploration and exploitation in multidimensional search space. Incorporation of craziness factor in the basic velocity expression of PSO not only brings diversity in particles but also ensures convergence to optimal solution. The proposed CRPSO based system identification approach has alleviated from the inherent drawbacks of premature convergence and stagnation, unlike real coded genetic algorithm (RGA), particle swarm optimization (PSO) and differential evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed system identification approach using CRPSO over RGA, PSO and DE in terms of convergence speed, unknown plant coefficients and mean square error (MSE) values produced for both the same order and reduced order models of adaptive IIR filters.
44 citations
TL;DR: The simulation results show that the ADEPSO outperforms PSO, ADE, and DE in combination with PSO not only in magnitude response but also in the convergence speed and thus proves itself to be a promising candidate for designing the FIR filters.
Abstract: This paper presents an efficient way of designing linear phase finite impulse response (FIR) low pass and high pass filters using a novel algorithm ADEPSO. ADEPSO is hybrid of fitness based adaptive differential evolution (ADE) and particle swarm optimization (PSO). DE is a simple and robust evolutionary algorithm but sometimes causes instability problem; PSO is also a simple, population based robust evolutionary algorithm but has the problem of sub-optimality. ADEPSO has overcome the above individual disadvantages faced by both the algorithms and is used for the design of linear phase low pass and high pass FIR filters. The simulation results show that the ADEPSO outperforms PSO, ADE, and DE in combination with PSO not only in magnitude response but also in the convergence speed and thus proves itself to be a promising candidate for designing the FIR filters.
37 citations
TL;DR: The detailed analysis of simulation results emphasizes the strength of HS algorithm to find the near-global optimal solution, quality of convergence profile and the speed of convergence while tested against standard benchmark examples for same and reduced order models.
31 citations
TL;DR: The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed OHS based system identification approach over GA, PSO and DE in terms of convergence speed, identifying the system plant coefficients and mean square error (MSE) fitness values produced for both same order and reduced order models of adaptive IIR filters.
Abstract: In this paper a population based evolutionary optimization methodology called Opposition based Harmony Search Algorithm (OHS) is applied for the optimization of system coefficients of adaptive infinite impulse response (IIR) system identification problem. The original Harmony Search (HS) algorithm is chosen as the parent one and opposition based approach is applied to it with an intention to exhibit accelerated near global convergence profile. During the initialization, for choosing the randomly generated population/solution opposite solutions are also considered and the fitter one is selected as apriori guess for having faster convergence profile. Each solution in Harmony Memory (HM) is generated on the basis of memory consideration rule, a pitch adjustment rule and a re-initialization process which gives the optimum result corresponding to the least error fitness in multidimensional search space. Incorporation of different control parameters in basic HS algorithm results in balancing of exploration and exploitation of search space. The proposed OHS based system identification approach has alleviated from inherent drawbacks of premature convergence and stagnation, unlike Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed OHS based system identification approach over GA, PSO and DE in terms of convergence speed, identifying the system plant coefficients and mean square error (MSE) fitness values produced for both same order and reduced order models of adaptive IIR filters.
28 citations
TL;DR: An optimized hyper beamforming method is presented based on a hyper beam exponent parameter for receiving linear antenna arrays using a new meta-heuristic search method based on the Firefly algorithm (FFA).
Abstract: In this paper, an optimized hyper beamforming method is presented based on a hyper beam exponent parameter for receiving linear antenna arrays using a new meta-heuristic search method based on the Firefly algorithm (FFA). A hyper beam is derived from the sum and difference beam patterns of the array, each raised to the power of a hyper beam exponent parameter. As compared to the conventional hyper beamforming of the linear antenna array, FFA applied to the hyper beam of the same array can achieve much more reduction in sidelobe level (SLL) and improved first null beam width (FNBW), keeping the same value of the hyper beam exponent. As compared to the uniformly excited linear antenna array with inter-element spacing of λ/2, conventional non-optimized hyper beamforming and optimal hyper beamforming of the same obtained by real-coded genetic algorithm, particle swarm optimization and Differential evolution, FFA applied to the hyper beam of the same array can achieve much greater reduction in SLL and same or less FNBW, keeping the same value of the hyper beam exponent parameter. The whole experiment has been performed for 10-, 14-, and 20-element linear antenna arrays.
23 citations
TL;DR: The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed system identification approach using DEWM over GA, PSO and DE in terms of convergence speed, plant coefficients and mean square error (MSE) values produced for both the same order and reduced order models of adaptive IIR filters.
16 citations
TL;DR: The simulation results show PSOWM outperforms GA, PSO, and BBO in the optimal design of three non-uniform circular antenna arrays by achieving much greater reduction in SLL and much more improved first null beamwidth (FNBW) and 3 dB beamwidth.
Abstract: In this paper, a novel particle swarm optimisation with wavelet mutation (PSOWM) has been applied for the optimal designs of three non-uniform circular antenna arrays with optimal side lobe level (SLL) reduction. Circular array antennas laid on x-y plane are assumed. PSOWM incorporates a new definition of swarm updating with the help of wavelet theory. Wavelet mutation enhances the PSO to explore the solution space more effectively compared to the other optimisation methods. Thus, PSOWM is apparently free from getting trapped at local optima and premature convergence. The approach is illustrated through 8-, 10-, and 12-element circular antenna arrays. Various simulation results are presented and radiation pattern performances are analysed. The simulation results show PSOWM outperforms GA (Panduro et al., 2006), PSO (Sahib et al., 2008), SA (Rattan et al., 2009), and BBO (Singh and Kamal, 2011) in the optimal design of three non-uniform circular antenna arrays by achieving much greater reduction in SLL and much more improved first null beamwidth (FNBW) and 3 dB beamwidth.
15 citations
14 May 2014
TL;DR: Simulation results of static and dynamic power dissipations and power delay product of the proposedSRAM cell have been determined and compared to those of some other exiting models of SRAM cell.
Abstract: This paper focuses on the static and dynamic power dissipations and power delay product of a proposed novel low power MTCMOS based 12T SRAM cell. In the proposed structure two voltage sources are used, one connected with the Bit line and the other one connected with the Bit bar line in order to reduce the swing voltage at the output nodes of the bit and the bit bar lines. Reduction in swing voltage causes the reduction in dynamic power dissipation during switching activity. Because of MTCMOS technology, the SRAM cell is having low V
T
(LVT) transistors and there are two high V
T
(HVT) sleep transistors as well. Sleep transistors and a LVT Transmission gate (TG) in conjunction are used for reducing the wake up power during transition from sleep mode to active mode and sleep power during transition from sleep mode to active mode for writing operations of the SRAM cell. This reduces the static power dissipation of the SRAM cell. Simulation results of static and dynamic power dissipations and power delay product of the proposed SRAM cell have been determined and compared to those of some other exiting models of SRAM cell. The proposed SRAM cell dissipates less dynamic power at different frequencies, less static power during transition modes. Simulation has been done in 45nm CMOS environment with the help of Microwind 3.1.
14 citations
10 Nov 2014
TL;DR: Analog environment virtuoso (cadence) simulator is used for analysis of the power associated with CMOS SRAM cell for 180nm technology and theSRAM cell with sleep transistor shows better leakage reduction approach than stack approaches.
Abstract: Leakage power is a major issue for short channel devices. As the technology is shrinking (i.e., 180nm, 90nm, 45nm. etc.) the leakage current is increasing very fast. So, several methods and techniques have been proposed for leakage reduction in CMOS digital integrated circuits. Leakage power dissipation has become a sizable proportion of the total power dissipation in integrated circuit. This paper demonstrates the ideas of 6T, 8T and 10T models with sleep transistors. This proposed SRAM cells give the advantages over basic 6T, 8T and 10T transistor models. The SRAM cell with sleep transistor shows better leakage reduction approach than stack approaches. Here in this paper Analog environment virtuoso (cadence) simulator is used for analysis of the power associated with CMOS SRAM cell for 180nm technology.
11 citations
01 Jan 2014
TL;DR: The simulation results show CRPSO outperforms RGA and PSO in the optimal hyper beamforming by achieving much greater reduction in sidelobe level (SLL) and much more improved first null beam width (FNBW) keeping the same value of hyper beam exponent.
Abstract: In this paper, various evolutionary optimization based algorithms like real coded genetic algorithm (RGA), conventional particle swarm optimization (PSO), a proposed craziness particle swarm optimization (CRPSO) have been applied for the optimal design of hyper beamforming of linear antenna array. Hyper beam is derived from sum and difference beam patterns each raised to the power of the hyper beam exponent parameter for the array. CRPSO uses new definition for the velocity vector. The simulation results show CRPSO outperforms RGA and PSO in the optimal hyper beamforming by achieving much greater reduction in sidelobe level (SLL) and much more improved first null beam width (FNBW) keeping the same value of hyper beam exponent. The optimized hyper beam is achieved by optimization of current excitation weights and uniform inter-element spacing. The approach is illustrated through 10-, 14-, and 20-element linear antenna arrays.
13 Jan 2014
01 Oct 2014
TL;DR: The side lobe level of time modulated linear antenna arrays can be reduced significantly by optimal switching time sequence of each element by evolutionary algorithms.
Abstract: In this paper time modulation technique is implied to linear antenna array. The evolutionary optimization algorithm like real coded genetic algorithm (RGA) and particle swarm optimization (PSO) is used to get the optimal radiation pattern by controlling the switching time sequence of each element of the array. The time modulation period is divided into numerous minimal time steps, where the ON-OFF status for each time step is optimized by evolutionary algorithms. The side lobe level of time modulated linear antenna arrays can be reduced significantly by optimal switching time sequence of each element. The approach is illustrated through 16 element time modulated linear antenna arrays. Various results are presented to show the advantages of this approach considering maximal side lobe level reduction.
03 Apr 2014
TL;DR: A combination of recently proposed evolutionary algorithm Backtracking Search Algorithm (BSA) with another widely accepted evolutionary algorithm, namely, Differential Evolution (DE) is considered for finding near optimal solutions of interference suppression of linear antenna arrays.
Abstract: A problem of interference suppression of linear antenna arrays is dealt with in this work. Problem of reduction of sidelobe level relative to main beam is considered. For simplicity antenna arrays are assumed to have ideal elements and hence, mutual coupling effect is neglected. A combination of recently proposed evolutionary algorithm Backtracking Search Algorithm (BSA) with another widely accepted evolutionary algorithm, namely, Differential Evolution (DE) is considered for finding near optimal solutions. It is seen that, combined search outperforms in this interference suppression problem.
Journal Article•
01 Jan 2014-World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering
TL;DR: Experimental results show considerable reductions of both the SLL and FNBW with respect to those of the uniform case and some standard algorithms GA, PSO and SA applied to the same problem.
Abstract: A design problem of non-uniform circular antenna arrays for maximum reduction of both the side lobe level (SLL) and first null beam width (FNBW) is dealt with. This problem is modeled as a simple optimization problem. The method of Firefly algorithm (FFA) is used to determine an optimal set of current excitation weights and antenna inter-element separations that provide radiation pattern with maximum SLL reduction and much improvement on FNBW as well. Circular array antenna laid on x-y plane is assumed. FFA is applied on circular arrays of 8-, 10-, and 12elements. Various simulation results are presented and hence performances of side lobe and FNBW are analyzed. Experimental results show considerable reductions of both the SLL and FNBW with respect to those of the uniform case and some standard algorithms GA, PSO and SA applied to the same problem. Keywords—Circular arrays, First null beam width, Side lobe level, FFA.
TL;DR: The simulation results obtained justify the efficacy of the proposed system identification approach using CAB over RGA, PSO and DE in terms of convergence speed, unknown plant coefficients and mean square error (MSE) values produced for IIR system models of both the same order and reduced order.
Abstract: In this paper a novel optimization technique which is developed on mimicking the collective animal behaviour (CAB) is applied to the infinite impulse response (IIR) system identification problem. Functionality of CAB is governed by occupying the best position of an animal according to its dominance in the group. Enrichment of CAB with the features of randomness, stochastic and heuristic search nature has made the algorithm a suitable tool for finding the global optimal solution. The proposed CAB has alleviated from the defects of premature convergence and stagnation, shown by real coded genetic algorithm (RGA), particle swarm optimization (PSO) and differential evolution (DE) in the present system identification problem. The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed system identification approach using CAB over RGA, PSO and DE in terms of convergence speed, unknown plant coefficients and mean square error (MSE) values produced for IIR system models of both the same order and reduced order.
03 Apr 2014
TL;DR: PSO-CFIWA is an improved particle swarm optimization (PSO) that proposes a new definition for the velocity vector and swarm updating and hence the solution quality is improved and can be efficiently used for CMOS inverter design.
Abstract: In this paper, symmetric switching characteristics of CMOS inverter are realized using an evolutionary optimization technique called Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach (PSO-CFIWA). PSO-CFIWA is an improved particle swarm optimization (PSO) that proposes a new definition for the velocity vector and swarm updating and hence the solution quality is improved. The performance of PSO-CFIWA is studied with the comparison of real coded genetic algorithm (RGA), a conventional PSO reported in the literature. PSO-CFIWA based design results have been compared also to those of the PSPICE results. The comparative simulation results show that the PSO-CFIWA is superior to other aforementioned evolutionary algorithms for the employed examples and can be efficiently used for CMOS inverter design.
Journal Article•
TL;DR: It is shown that the proposed SRAM cell has better static noise margin and dissipates lesser power in comparison to other SRAM cells.
18 Dec 2014
TL;DR: Object of this work is to obtain good quality design parameters for uniformly excited concentric hexagonal array to achieve low sidelobe pencil beam radiation pattern and high directivity.
Abstract: Research in the evolutionary optimization algorithm (EA) has turned its focus towards solving real life and complex multi-objective problems (MOP). Objective of this work is to obtain good quality design parameters for uniformly excited concentric hexagonal array to achieve low sidelobe pencil beam radiation pattern and high directivity. The optimizing variables are the inter-ring gaps and inter-element gaps in each ring. The objective function vector comprises of three pattern parameters relative peak sidelobe level, peak directivity and the population of the array. Widely accepted multi-objective evolutionary algorithm, namely, Elitist Non-dominated Sorting based Genetic Algorithm (NSGA II) is utilized to achieve these solutions. Optimized design parameters are found better than un-optimized design parameters in every aspect.
08 Feb 2014
TL;DR: The proposed SRAM cell dissipates less power at different temperatures and better stability at different pull-up ratios than the other SRAM models, and the stability of data retention is also enhanced.
Abstract: This paper focuses on the power dissipations at different temperatures and stability analysis at different pull-up ratios of a novel low power 12T MTCMOS SRAM cell. Because of MTCMOS technology, the SRAM cell is having low VT (LVT) transistors and there are two high VT (HVT) Sleep transistors as well. Sleep transistors and a LVT Transmission gate (TG) in conjunction are used for reducing the wake up power during transition from sleep mode to active mode and sleep power during transition from sleep mode to active mode for writing operations of the SRAM cell. This reduces the static energy dissipation of the cell. In the proposed structure two additional voltage sources are also used, one connected with the bit line and the other one connected with the bitbar line in order to reduce the swing voltage at the output nodes of the bit and the bitbar lines. The reduction in swing causes the reduction in dynamic power dissipation. Because of very low leakage currents in MTCMOS technology, the stability of data retention is also enhanced. Simulation results of power dissipation and stability of the proposed SRAM cell have been determined and compared to those of some other exiting models of SRAM cell. The proposed cell dissipates less power at different temperatures and better stability at different pull-up ratios than the other SRAM models. Simulation has been done in 45nm CMOS environment. Microwind 3.1 is used for schematic design and layout design purpose.
01 Oct 2014
TL;DR: A novel meta-heuristic search method based on social emotional optimization algorithm (SEOA) are applied to determine the best optimal current excitation weights and optimal inter-element spacing of optimized hyper beamforming of linear antenna arrays.
Abstract: Antenna array optimization in electromagnetics has thrown a growing influence on the communication systems In this paper, a novel meta-heuristic search method based on social emotional optimization algorithm (SEOA) are applied to determine the best optimal current excitation weights and optimal inter-element spacing of optimized hyper beamforming of linear antenna arrays Hyper beam is derived from sum and difference beam patterns of the array, each raised to the power of a hyper beam exponent parameter SEOA is a population-based stochastic optimization algorithm where each individual simulates one natural person All individuals communicate among them through cooperation and competition to increase the social status The winner with the highest status is the final solution As compared to uniformly excited linear antenna array with inter-element spacing of λ/2, conventional non-optimized hyper beamforming and optimal hyper beamforming of the same obtained by FFA [18], SEOA applied to the hyper beam of the same array can achieve much greater reduction in SLL and same or less first null beam width (FNBW), keeping the same value of hyper beam exponent parameter The whole experiment has been performed for 10-, 14-, and 20-element linear antenna arrays
01 Dec 2014
TL;DR: The simulation results show that the number of antenna array elements can be brought down more than 50% of total isotropic elements with simultaneous reduction in Side Lobe Level (SLL) with an approximately fixed first null beam width (FNBW).
Abstract: In this paper, the optimal thinning of two-ring Concentric Hexagonal Array (CHA) of uniformly excited isotropic antennas which can generate directive beam with minimum relative Side Lobe Level (SLL) is described. The Improved Particle Swarm Optimization (IPSO) method, which represents a new approach for optimization problems in electromagnetic, is used in the optimization process. To determine an optimal set of ‘ON-OFF’ elements that provide a radiation pattern with maximum SLL reduction, the IPSO algorithm is used. The simulation results show that the number of antenna array elements can be brought down more than 50% of total isotropic elements with simultaneous reduction in Side Lobe Level (SLL) with an approximately fixed first null beam width (FNBW). Particle Swarm Optimization (PSO), as well is also adopted to compare the results of above IPSO.
TL;DR: Simulation results affirm that the proposed DEWM algorithm outperforms its counterparts not only in terms of quality output, i.e., sharpness at cut-off, pass band ripple and stop band attenuation but also in convergence speed with assured stability.
Abstract: In this paper, an improved version of differential evolution (DE) algorithm which incorporates wavelet-based mutation strategy called differential evolution with wavelet mutation (DEWM) is proposed for the design of digital infinite impulse response (IIR) filters. Unlike fixed value of scaling factor in standard DE, the proposed optimisation technique DEWM adopts iteration dependent scaling factor governed by the wavelet function during the mutation process. This modification in the mutation process not only ensures the faster searching in the multidimensional search space but also the solution produced is very close to the global optimal solution. The effectiveness of this algorithm is justified with a comparative study of some well established algorithms, namely, real coded genetic algorithm (RGA), conventional particle swarm optimisation (PSO) and standard DE with a superior DEWM-based outcome for the designed 8th order IIR low pass (LP), high pass (HP), band pass (BP) and band stop (BS) filters. Simulation results affirm that the proposed DEWM algorithm outperforms its counterparts not only in terms of quality output, i.e., sharpness at cut-off, pass band ripple and stop band attenuation but also in convergence speed with assured stability.
TL;DR: The proposed SEOA based system identification approach has resolved the inherent drawbacks of premature convergence and stagnation, unlike genetic algorithm (GA), particle swarm optimisation (PSO) and differential evolution (DE).
Abstract: In this paper an evolutionary optimisation methodology based on social emotional optimisation algorithm (SEOA) is applied to the infinite impulse response (IIR) system identification problem. In SEOA methodology, behaviour of human beings for achieving higher social status in society is structured. In this virtual world, the individual with the highest rank in society gives the optimal solution in multidimensional search space. Earning the highest social status by means of cooperation and competition with others not only results in better exploration and exploitation of problem space but also ensures faster convergence to optimal solution. The proposed SEOA based system identification approach has resolved the inherent drawbacks of premature convergence and stagnation, unlike genetic algorithm (GA), particle swarm optimisation (PSO) and differential evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed system identification approach using SEOA over GA, PSO and DE in terms of convergence speed, unknown plant coefficients and mean square error (MSE) values produced for both the same order and reduced order models of adaptive IIR filters.
01 Apr 2014
TL;DR: A comparison of simulation results reveals the optimization superiority of the proposed technique over the other optimization techniques for the solution of FIR low pass LP, high pass HP, band pass BP and band stop BS filter designs.
Abstract: In this paper, Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach is hybridized with Wavelet Mutation PSOCFIWA-WM strategy for the optimal design of linear phase FIR filters. Real coded genetic algorithm RGA, particle swarm optimization PSO and particle swarm optimization with constriction factor and inertia weight PSOCFIWA have also been adopted for the sake of comparison. PSOCFIWA-WM incorporates a new definition of swarm updating in PSOCFIWA with the help of wavelet based mutation. Wavelet mutation enhances the effectiveness of PSOCFIWA to explore the multidimensional solution space more effectively. In this design approach, filter length, pass band and stop band edge frequencies, feasible pass band and stop band ripple sizes are specified. A comparison of simulation results reveals the optimization superiority of the proposed technique over the other optimization techniques for the solution of FIR low pass LP, high pass HP, band pass BP and band stop BS filter designs.
Journal Article•
01 Jan 2014-World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering
TL;DR: In this paper, the Particle Swarm Optimization (PSO) method was used to determine the optimal set of ON-OFF elements that provided a radiation pattern with maximum SLL reduction.
Abstract: This paper describes optimal thinning of an Elliptical Cylindrical Array (ECA) of uniformly excited isotropic antennas which can generate directive beam with minimum relative Side Lobe Level (SLL). The Particle Swarm Optimization (PSO) method, which represents a new approach for optimization problems in electromagnetic, is used in the optimization process. The PSO is used to determine the optimal set of ‘ON-OFF’ elements that provides a radiation pattern with maximum SLL reduction. Optimization is done without prefixing the value of First Null Beam Width (FNBW). The variation of SLL with element spacing of thinned array is also reported. Simulation results show that the number of array elements can be reduced by more than 50% of the total number of elements in the array with a simultaneous reduction in SLL to less than -27dB. Keywords—Thinned array, Particle Swarm Optimization, Elliptical Cylindrical Array, Side Lobe Label.
03 Apr 2014
TL;DR: Experimental results show considerable reductions of both the SLL and FNBW with respect to those of the uniform case and some standard algorithms GA, PSO, and SA applied to the same problem of non-uniform circular antenna arrays.
Abstract: In this paper synthesis of single ring non-uniform circular antenna for maximum side lobe level (SLL) reduction and improved first null beamwidth (FNBW) is dealt with. Evolution based Differential Evolution (DE) is used to determine an optimal set of current excitation weights and antenna inter-element spacing that provides optimal radiation pattern. Circular array antenna laying on x-y plane is assumed. DE is applied on circular arrays of 8-, 10-, and 12- elements. Various simulation results are presented and hence performances of SLL and FNBW are analyzed. Experimental results show considerable reductions of both the SLL and FNBW with respect to those of the uniform case and some standard algorithms GA [12], PSO [14] and SA [11] applied to the same problem of non-uniform circular antenna arrays.
TL;DR: In this article, the authors presented a novel, control parameter independent evolutionary search technique known as Seeker Optimization Algorithm (SOA) for the design of a eighth order Infinite Impulse Response (IIR) Band Pass (BP) filter.
Abstract: This paper presents a novel, control parameter independent evolutionary search technique known as Seeker Optimization Algorithm (SOA) for the design of a eighth order Infinite Impulse Response (IIR) Band Pass (BP) filter. A new fitness function has also been adopted in this paper to improve the stop band attenuation to a great extent. The performance of the SOA based IIR BP filter design has proven to be much superior as compared to those obtained by real coded genetic algorithm (RGA) and standard particle swarm optimization (PSO) in terms of highest sharpness at cut-off, smallest pass band ripple, highest stop band attenuation, smallest stop band ripple and also the fastest convergence speed with assured stability recognized by the pole-zero analysis of the designed optimized IIR filter.
14 Nov 2014
TL;DR: The SOA based optimal hyper beam forming designs have proven to be superior in achieving the greatest reduction in SLL and much more improved FNBW, keeping the same value of hyper beam exponent.
Abstract: This paper presents Seeker Optimization Algorithm (SOA) to the optimization of current excitation weights and uniform inter-element spacing for the optimal design of hyper beam forming of linear antenna arrays. Hyper beam forming is based on sum and difference beam patterns, each raised to the power of hyper beam exponent parameter for linear antenna arrays. In the SOA, the act of human searching capability and understanding are exploited for the purpose of optimization of the hyper beam pattern. In this algorithm, the search direction is based on empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. The simulation experiment is performed on 10-, 14-, and 20-element linear antenna arrays with an objective of obtaining maximum Side Lobe Level (SLL) reduction and much more improved first null beam width (FNBW) for SOA. Finally, the SOA based optimal hyper beam forming designs have proven to be superior in achieving the greatest reduction in SLL and much more improved FNBW, keeping the same value of hyper beam exponent.
TL;DR: In this article, a compact dumbbell-shaped split-ring DGS is introduced between array elements of a sixteen-element microstrip array in order to reduce the mutual coupling between antenna elements and eliminate the scan blindness.
Abstract: A compact dumbbell-shaped split-ring DGS is introduced between array elements of a sixteen-element microstrip array in order to reduce the mutual coupling between antenna elements and eliminate the scan blindness. The proposed DGS is inserted between the adjacent rectangle-shaped slotted microstrip antenna elements separated by 0.35λ, as a technique to suppress the radiation in the horizontal direction. Simulated results show that a reduction in mutual coupling of 36 dB is obtained between elements at the operation frequency of 2.45 GHz (WLAN band). The scan properties of microstrip array with and without DGS have been studied, and the result indicates that the scan blindness of the array has been well eliminated because of the effect of the DGS. We have developed experimental models that have proved the concept of scan blindness elimination. Finally, the influence of other antenna parameters at the presence of DGS in the array system has been studied. Prototype antennas of sixteen-element array with and without resonator have been fabricated, measured, and the idea has been verified. A good agreement is observed between measured and simulated results.