A novel approach for reducing the computational cost of GA in synthesizing low sidelobe unequally spaced linear arrays

2011 ◽  
Vol 21 (2) ◽  
pp. 221-227 ◽  
Author(s):  
Shuai Zhang ◽  
Shu-Xi Gong ◽  
Ying Guan ◽  
Bao Lu
Keyword(s):  
Computational Cost ◽  
Linear Arrays ◽  
Unequally Spaced ◽  
Novel Approach ◽  
Low Sidelobe

scholarly journals A NOTE ON PHASING LONG GENOMIC REGIONS USING LOCAL HAPLOTYPE PREDICTIONS

2006 ◽  
Vol 04 (03) ◽  
pp. 639-647 ◽  
Author(s):  
ELEAZAR ESKIN ◽  
RODED SHARAN ◽  
ERAN HALPERIN
Keyword(s):  
Large Scale ◽  
Computational Cost ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Novel Approach ◽  
Maximum Likelihood Criterion ◽  
The Common ◽  
Genomic Regions ◽  
High Computational Cost ◽  
Combining Information

The common approaches for haplotype inference from genotype data are targeted toward phasing short genomic regions. Longer regions are often tackled in a heuristic manner, due to the high computational cost. Here, we describe a novel approach for phasing genotypes over long regions, which is based on combining information from local predictions on short, overlapping regions. The phasing is done in a way, which maximizes a natural maximum likelihood criterion. Among other things, this criterion takes into account the physical length between neighboring single nucleotide polymorphisms. The approach is very efficient and is applied to several large scale datasets and is shown to be successful in two recent benchmarking studies (Zaitlen et al., in press; Marchini et al., in preparation). Our method is publicly available via a webserver at .

Experimental and Novel Analytical Trajectory Optimization of a 7-DOF Baxter Robot: Global Design Sensitivity and Step Size Analyses

2017 ◽  
Author(s):  
Mostafa Bagheri ◽  
Peiman Naseradinmousavi ◽  
Rasha Morsi
Keyword(s):  
Global Optimization ◽  
Cost Function ◽  
Trajectory Optimization ◽  
Computational Cost ◽  
Joint Space ◽  
Design Sensitivity ◽  
Target Object ◽  
Size Analysis ◽  
Step Size ◽  
Novel Approach

In this paper, we present a novel nonlinear analytical coupled trajectory optimization of a 7-DOF Baxter manipulator validated through experimental work utilizing global optimization tools. The robotic manipulators used in network-based applications of industrial units and even homes, for disabled patients, spend significant lumped amount of energy and therefore, optimal trajectories need to be generated to address efficiency issues. We here examine both heuristic (Genetics) and gradient based (GlobalSearch) algorithms for a novel approach of “S-Shaped” trajectory (unlike conventional polynomials), to avoid being trapped in several possible local minima along with yielding minimal computational cost, enforcing operational time and torque saturation constraints. The global schemes are utilized in minimizing the lumped amount of energy consumed in a nominal path given in the collision-free joint space except an impact between the robot’s end effector and a target object for the nominal operation. Note that such robots are typically operated for thousands of cycles resulting in a considerable cost of operation. Due to the expected computational cost of such global optimization algorithms, step size analysis is carried out to minimize both the computational cost (iteration) and possibly cost function by finding an optimal step size. Global design sensitivity analysis is also performed to examine the effects of changes of optimization variables on the cost function defined.

Low-Sidelobe Patterns From Small, Low-Loss Uniformly Fed Linear Arrays Illuminating Parasitic Dipoles

2009 ◽  
Vol 57 (5) ◽  
pp. 1584-1586 ◽  
Author(s):  
M. Alvarez-Folgueiras ◽  
J. A. Rodriguez-Gonzalez ◽  
F. Ares-Pena
Keyword(s):  
Low Loss ◽  
Linear Arrays ◽  
Low Sidelobe

scholarly journals Radiation Pattern Performance of Unequally Linear Arrays with Parasitic Element

2017 ◽  
Vol 6 (1) ◽  
pp. 110
Author(s):  
Noor Ainniesafina Zainal ◽  
Muhammad Ramlee Kamarudin ◽  
Yoshihide Yamada ◽  
Norhudah Seman
Keyword(s):  
Radiation Pattern ◽  
Base Station ◽  
Sidelobe Level ◽  
Linear Arrays ◽  
Parasitic Element ◽  
Array Configuration ◽  
Grating Lobe ◽  
Base Station Antennas ◽  
Low Sidelobe ◽  
Mobile Base

<p>For next generation of 5G mobile base station antennas, multibeam, multifrequency and low sidelobe characteristics requested. Simplify the feeding network will contribute a low feeder loss and frequency dependent. From the previous research by the author, low sidelobe level reported by density tapered array configuration from -13 dB to -16 dB and the result maintained for wideband operation frequency at 28 GHz, 42 GHz, and 56 GHz. However, the grating lobe has occurred due to element spacing larger than a wavelength of higher frequency (56 GHz). In this paper, an investigation was made of the performance of radiation pattern for unequally microstrip linear array antenna in frequency 42 GHz and 56 GHz by loading parasitic elements. The effect of parasitic element to the impedance, gain, and sidelobe level of unequally microstrip linear spaced tapered array also examined. The design has been simulated using Ansoft High Frequency Structural Simulator (HFSS) ver 16.0.</p>

scholarly journals A Spline Kernel-Based Approach for Nonlinear System Identification with Dimensionality Reduction

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 940
Author(s):  
Wanxin Zhang ◽  
Jihong Zhu
Keyword(s):  
Nonlinear Systems ◽  
Dimensionality Reduction ◽  
Nonlinear System Identification ◽  
Computational Cost ◽  
Feature Space ◽  
Training Data ◽  
Accurate Identification ◽  
Novel Approach ◽  
New States ◽  
Identification Approach

This paper proposes a novel approach for identification of nonlinear systems. By transforming the data space into a feature space, kernel methods can be used for modeling nonlinear systems. The spline kernel is adopted to produce a Hilbert space. However, a problem exists as the spline kernel-based identification method cannot deal with data with high dimensions well, resulting in huge computational cost and slow estimation speed. Additionally, owing to the large number of parameters to be estimated, the amount of training data required for accurate identification must be large enough to satisfy the persistence of excitation conditions. To solve the problem, a dimensionality reduction strategy is proposed. Transformation of coordinates is made with the tool of differential geometry. The purpose of the transformation is that no intersection of information with relevance to the output will exist between different new states, while the states with no impact on the output are extracted, which are then abandoned when constructing the model. Then, the dimension of the kernel-based model is reduced, and the number of parameters to be estimated is also reduced. Finally, the proposed identification approach was validated by simulations performed on experimental data from wind tunnel tests. The identification result turns out to be accurate and effective with lower dimensions.

Landcover Change Detection Using PSO-Evaluated Quantum CA Approach on Multi-Temporal Remote-Sensing Watershed Images

2018 ◽  
pp. 178-212
Author(s):  
Kalyan Mahata ◽  
Rajib Das ◽  
Subhasish Das ◽  
Anasua Sarkar
Keyword(s):  
Cellular Automata ◽  
Discrete Dynamical System ◽  
Computational Cost ◽  
Ground Truth ◽  
Rule Base ◽  
Quantum Cellular Automata ◽  
Landcover Change ◽  
Novel Approach ◽  
Mixed Pixels ◽  
Multi Temporal

Computer science plays a major role in image segmentation and image processing applications. Despite the computational cost, PSO evaluated QCA approaches perform comparable to or better than their crisp counterparts. This novel approach, proposed in this chapter, has been found to enhance the functionality of the CA rule base and thus enhance the established potentiality of the fuzzy-based segmentation domain with the help of quantum cellular automata. This new unsupervised method is able to detect clusters using 2-dimensional quantum cellular automata model based on PSO evaluation. As a discrete, dynamical system, cellular automaton explores uniformly interconnected cells with states. In the second phase, it utilizes a 2-dimensional cellular automata to prioritize allocations of mixed pixels among overlapping land cover areas. The authors experiment on Tilaya Reservoir Catchment on Barakar River. The clustered regions are compared with well-known PSO, FCM, and k-means methods and also with the ground truth knowledge. The results show the superiority of the new method.

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