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In this paper, a new iterative algorithm for determination of direction cosine matrix (DCM) is presented, it can be expressed as two forms, matrix exponential and six scalar differential equations respectively, corresponding to each form, related algorithms used for the orthonormalization are derived, although the simulations and algorithm analysis are omitted here, the Van test results verified its...
This paper deals with the advanced and developed methodology know for cancer multi classification using an Extreme Learning Machine (ELM) for microarray gene expression cancer diagnosis, this used for directing multicategory classification problems in the cancer diagnosis area. ELM avoids problems like local minima; improper learning rate and over fitting commonly faced by iterative learning methods...
In this paper an iterative, interference cancelling receive structure for single carrier transmission using superimposed training is studied. We derive the analytical MSE limits for LMMSE channel estimator using perfect channel knowledge and show that the simulated values follow well the analytical ones. Then, we utilise these results to analyse the MSE performance of a combined ML-LMMSE chanel estimator...
The goal of our work is to limit in-band interference in wireless communication systems. Based on a three-regime interference classifier, we propose a novel distributed inter-cell power allocation algorithm where each cell computes by an iterative process its minimum power budget to meet its local quality of service (QoS) constraints. Analytical results show how our proposal permits to notably reduce...
In this paper we study the variational inequalities with separable operators. A projection-type alternating direction method is proposed for such problems, which only need some projections onto simple sets and some function evaluations during each iteration. Convergence of the new method is proved under mild assumptions. Numerical experiments show that the new method is effective.
Algorithms for designing beamforming matrices for interference alignment in multiuser time-invariant multi-input multi-output interference channels are proposed. Based on a new formulation for the necessary and sufficient condition for interference alignment, the proposed algorithm iteratively minimizes overall interference misalignment in a least squares sense. Additional modified algorithms are...
We propose a robust signal to interference and noise ratio (SINR) balancing beamforming technique for a multiple input and multiple output (MIMO) based cognitive radio (CR) network. The aim is to design transmitter and receiver beamformers for multiple secondary users (SUs) while ensuring interference leakage to multiple primary users (PUs) is below a prescribed threshold. We use an iterative approach...
Artificial Neural Networks (ANN) is gaining significant importance for pattern recognition applications particularly in the medical field. A hybrid neural network such as Counter Propagation Neural Network (CPN) is highly desirable since it comprises the advantages of supervised and unsupervised training methodologies. Even though it guarantees high accuracy, the network is computationally non-feasible...
Based on neural network, an improvement scheme that iterative matrix replace secondary derivative has been developed by introduced quasi-Newton algorithm. Profile code based on probability has been used and comparison of window width and learning training has been completed. The experiment results indicate that the prediction for secondary structures of protein obtain a very good effect based on neural...
A new method for the optimal design of the electromagnetic devices is presented. The method utilizes artificial neural networks (ANNs) in a design environment which encompasses numerical computations and expert's input for generating a variety of ANN training data. Results of two implementation examples are provided. The optimal design is obtained quickly (in a matter of milliseconds) once the ANNs...
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