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A simple method is proposed that is capable of predicting antenna array embedded element patterns based on the radiation and scattering characteristics of a single isolated antenna element. The effect of mutual coupling, which causes embedded element patterns to differ, is accounted for through the multi-scattering of electromagnetic fields between array elements. This is a post-processing step and...
One goal of Virtual Analog modeling of audio circuits is to produce digital models whose behavior matches analog prototypes as closely as possible. Discretization methods provide a systematic approach to generate such models but they introduce frequency response error, such as frequency warping for the trapezoidal method. Recent work showed how using different discretization methods for each reactive...
Simulations and Linear/Nonlinear Optimization mathematical methods constitute presently the choice ofpreference in getting improvements for erosion and corrosion simulations/determinations in general tribology, biotribology and tribocorrosion. This criterion is also applicable for engineering-reliability functional efficacy/efficiency during energy-power plants lifetime — with important consequences...
We propose novel resource allocation algorithms that have the objective of finding a good tradeoff between resource reuse and interference avoidance in wireless networks. To this end, we first study properties of functions that relate the resource budget available to network elements to the optimal utility and to the optimal resource efficiency obtained by solving max-min utility optimization problems...
The cooperative co-evolution (CC) framework is one of the most efficient methods to solve large scale optimization problems. The traditional CC framework divides decision variables into several mutually-exclusive groups. In this paper, we propose the overlapped cooperative co-evolution (OCC) framework for large scale optimization problems. In OCC framework, the decision variables that have strong...
When shooting through a glass window, a foreground scene may be reflected in the captured image. The elimination of this reflection is useful for protecting the privacy of surrounding people, improving image recognition accuracy in robot vision, etc. In this paper, we propose a reflection-removal method based on sparse/no-sparse gradient regularizations with gradient constraints. Experimental results...
In this position paper, we define the SISSY challenge and explore what it might mean for the usual expected integrations and for the more difficult unexpected integrations.
The task of Sparse Symmetric Nonnegative Matrix Factorization(SSNMF) is formulated as optimization problem and solved numerically with the method of projected gradients descent. The adjustable sparsity level allows to emphasize the most significant object features. Clustering of the Yale Faces data set shows that SSNMF provides the same level of quality as common clustering approaches.
The high risk of random access collisions leads to huge challenge for the deployment massive Machine-Type Communications (mMTC), which cannot be sufficiently overcome by current solutions in LTE/LTE-A networks such as the extended access barring (EAB) scheme. The recently studied approaches of grouped random access have shown a great potential in simultaneously reducing the collision rate and the...
Mean-variance mapping optimization (MVMO) is an emerging metaheuristic optimization algorithm, whose evolutionary mechanism performs within a normalized search space. The most remarkable aspect of this mechanism resides in the application of a special mapping function to generate new values of the optimization variables based on their statistical significance throughout the search process. This paper...
Thermal architecture choice has a large impact on a products ultimate performance in thermally limited products. Thermal parameters interact, hence Design Of Experiments (DOE), which can include interaction effects, is the preferred explorative strategy. However, DOE has the drawback of potentially resulting in impractically large experimental arrays and correspondingly overly large datasets and large...
A solid and practical approach for designing an optimal secondary distribution network is proposed. The methodology starts by optimally locating and sizing medium voltage/low voltage transformers then finding the optimal path for secondary circuits. The optimal number of transformers and their location are determined by k-means clustering algorithm, and validated using Davies-Bouldin index. In addition,...
Deep structured of Convolutional Neural Networks (CNN) has recently gained intense attention in development due to its good performance in object recognition. One of the crucial components in CNN is the learning mechanism of weight parameters through backpropagation. In this paper, stochastic diagonal Approximate Greatest Descent (SDAGD) is proposed to train weight parameters in CNN. SDAGD adopts...
This paper proposes a new stereo corresponding algorithm which uses local-based. The Sum of Absolute Differences (SAD) algorithm produces accurate results on the disparity map for the textured regions. However, this algorithm is sensitive to low texture areas and high noise on images with high different brightness and contrast of images. To get over these problems, the proposed algorithm utilizes...
Stochastic Diagonal Approximate Greatest Descent (SDAGD) is proposed to manage the optimization in two stages, (a) apply a radial boundary to estimate step length when the weights are far from solution, (b) apply Newton method when the weights are within the solution level set. This is inspired by a multi-stage decision control system where different strategies is used at different conditions. In...
A GVC (Global Value Chain) optimization requires appropriate approach to make it happen, however, a good approach is not enough when the optimization requires a big changing throughout the chain. It is very important to have a methodology of change management during change processes for GVC optimization. This paper describes the principle of change management, and illustrates a case study on how to...
In the modern joint war, the weapon systems portfolio selection is a necessary procedure for military development and research. The research on weapon system portfolio selection has made some achievements. Generally, the multi-objective programming methods and some intelligent algorithms are used to solve the problem. In this paper, the fuzzy clustering analysis and the maximum deviation methods are...
There is a plenty of unorganized data available in various information repositories and examining this data is very necessary for some future analysis. Clustering this kind of data plays a vital role in knowing about formerly unknown and possibly useful data and also the concerns should be widely examined. Here, we are proposing a high level methodology for clustering the data. First of all the proposed...
Saliency detection in images attracts much research attention for its usage in numerous multimedia applications. In this paper, we propose a saliency detection method based on optimization for RGBD images. With RGBD images, our method utilizes the depth channel to enhance the identification of background and foreground regions. We firstly generate new depth image by using non-linear transformation...
In a game it is often the case that there are multiple roles or types of actors with different goals. One possible target for automatic content generation is to create multiple different software agents for these distinct roles. This paper outlines a technique, based on the multiple worlds model, for creating such actors via evolution. The objective function is based on the performance of the actors...
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