Abstract:Abstract-Due to high-density routing under the CPU and DIMM areas, the original design of even and odd mode characteristic impedances changes. The occurrence of multi-drop problem between the CPU and memory chip causes over-and under-driven that reduce the eye opening. Furthermore, the different phase velocities of evenand odd-modes cause timing jitter at the receiver end. This paper proposes two steps to solve the complex issue of signal integrity for the multi-module memory bus. First, particle swarm optimiz… Show more
“…The technique is based on neural networks (NNs) [5,[18][19][20][21][22][23][24][25][26], which use training sets produced by a novel binary variant of Particle Swarm Optimization (PSO) [27][28][29][30][31][32][33], called Mutated Boolean PSO (MBPSO) [10]. In the MBPSO, the update of particle velocities and positions is performed using exclusively Boolean expressions, while former binary PSO variants update the particle velocities using real number expressions [34].…”
Abstract-A new adaptive beamforming technique based on neural networks (NNs) is proposed. The NN training is accomplished by applying a novel optimization method called Mutated Boolean PSO (MBPSO). In the beginning of the procedure, the MBPSO is repeatedly applied to a set of random cases to estimate the excitation weights of an antenna array that steer the main lobe towards a desired signal, place nulls towards several interference signals and achieve the lowest possible value of side lobe level. The estimated weights are used to train efficiently a NN. Finally, the NN is applied to a new set of random cases and the extracted radiation patterns are compared to respective patterns extracted by the MBPSO and a well-known robust adaptive beamforming technique called Minimum Variance Distortionless Response (MVDR). The aforementioned comparison has been performed considering uniform linear antenna arrays receiving several interference signals and a desired one in the presence of additive Gaussian noise. The comparative results show the advantages of the proposed technique.
“…The technique is based on neural networks (NNs) [5,[18][19][20][21][22][23][24][25][26], which use training sets produced by a novel binary variant of Particle Swarm Optimization (PSO) [27][28][29][30][31][32][33], called Mutated Boolean PSO (MBPSO) [10]. In the MBPSO, the update of particle velocities and positions is performed using exclusively Boolean expressions, while former binary PSO variants update the particle velocities using real number expressions [34].…”
Abstract-A new adaptive beamforming technique based on neural networks (NNs) is proposed. The NN training is accomplished by applying a novel optimization method called Mutated Boolean PSO (MBPSO). In the beginning of the procedure, the MBPSO is repeatedly applied to a set of random cases to estimate the excitation weights of an antenna array that steer the main lobe towards a desired signal, place nulls towards several interference signals and achieve the lowest possible value of side lobe level. The estimated weights are used to train efficiently a NN. Finally, the NN is applied to a new set of random cases and the extracted radiation patterns are compared to respective patterns extracted by the MBPSO and a well-known robust adaptive beamforming technique called Minimum Variance Distortionless Response (MVDR). The aforementioned comparison has been performed considering uniform linear antenna arrays receiving several interference signals and a desired one in the presence of additive Gaussian noise. The comparative results show the advantages of the proposed technique.
“…Particle swarm optimization (PSO) has recently become a popular optimization technique in electromagnetic applications [20][21][22][23][24][25][26][27]. PSO is a global optimization technique, and has demonstrated its ability to optimize complex, multidimensional, and discontinuous problems.…”
Section: Antenna Design By Particle Swarm Optimizationmentioning
Abstract-This study respectively uses two optimizers, iterative Taguchi's method and particle swarm optimization, combined with the method of moments to optimize a logotype planar antenna for multiband applications. The proposed antenna consists of four metal letters, NCNU, which is the abbreviation of the authors' university name. This antenna can be used for university logo or advertisement applications. The antenna also serves as an example to compare the optimization performance of these two optimizers. Optimization results show that Taguchi's method achieves much better optimization performance than particle swarm optimization. This study also investigates the electromagnetic characteristics of the proposed antenna by parametric study using simulation. The presented optimization methods could be applied to designing similar logotype antennas.
“…In particular, the SSN in a wide frequency range incidentally excites multiple resonance modes between the power/ground planes as in the parallel planes model, which would also introduce significant signal integrity problems, power integrity issues, and electromagnetic interference (EMI) [3][4][5]. If the SSN exceeds the noise margin, the IC may experience a functional failure.…”
Abstract-In this paper, a new method is proposed to estimate the simultaneous switching noise (SSN) directly from the power delivery network (PDN) frequency-domain impedance in order to reduce the time-domain simulation of SSN and computational burden, which is based on the periodic characteristics of the switching current and the SSN produced by one current pulse. The frequency-domain impedance is approximated with several single resonance circuits, which can capture the resonance characteristics of the PDN. The parameters of each resonance circuit are calculated with the rational function. It is also found that the SSN can be suppressed through adjusting the resonant frequencies and the period of switching current. Compared with the single resonance lumped circuit model and multi-resonance distributed circuit model, the performance of the new method for estimating the SSN is verified, which is more accurate than the target impedance method.
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