Search results for "A* algorithm"

Social Interactions Among Bacteriophages

2020

Although viruses lack many of the social adaptations shown by more complex organisms, different types of social interactions have been unraveled in viruses. Phage research has contributed significantly to the development of this field, called sociovirology, with the discovery of processes such as intracellular and extracellular public good production, prudent host exploitation, cheating, and inter-phage communication. We here review and discuss these processes from a social evolution approach. Similar to other organisms, the origin and maintenance of phage-phage interactions can be explained using kin selection, group selection and game theory approaches. Key determinants of phage social ev…

Datorzinātne un informācijas tehnoloģijas

2011

Discussion of “Soil Water Retention Characteristics of Vertisols and Pedotransfer Functions Based on Nearest Neighbor and Neural Networks Approaches …

2013

Inverse eigenvalue problem for normal J-hamiltonian matrices

2015

[EN] A complex square matrix A is called J-hamiltonian if AT is hermitian where J is a normal real matrix such that J(2) = -I-n. In this paper we solve the problem of finding J-hamiltonian normal solutions for the inverse eigenvalue problem. (C) 2015 Elsevier Ltd. All rights reserved.

SYSTOLIC GENERATION OF k-ARY TREES

1999

The only parallel generating algorithms for k-ary trees are those of Akl and Stojmenović in 1996 and of Vajnovszki and Phillips in 1997. In the first of them, trees are represented by an inversion table and the processor model is a linear aray multicomputer. In the second, trees are represented by bitstrings and the algorithm executes on a shared memory multiprocessor. In this paper we give a parallel generating algorithm for k-ary trees represented by generalized P–sequences for execution on a linear array multicomputer.

Implementation of a new adaptive algorithm using fuzzy cost function and robust to impulsive noise

2012

Adaptive filters are used in a wide range of applications such as noise cancellation, system identification, and prediction. One of the main problems for theses filters is the impulsive noise as it generates algorithm unstability. This work shows the development, simulation and hardware implementation of a new algorithm robust to impulsive noise. Hardware implementation becomes essential in many cases where a real time execution, reduced size, or low power system is needed. An efficient hardware architecture is proposed and different optimizations for size and speed are developed: no need for control state machine, reduced computation requirements due to simplifications, etc. Furthermore, t…

Fast Image Restoration Algorithms Based on PDE Models Using Modified Hopfield Neural Network

2010

Two image restoration algorithms based on modified Hop field neural network and variational partial differential equations (PDE) were proposed in our previous work [1, 2]. But the convergence rate of the proposed algorithms was slow. In this paper, we develop a fast update rule based on modified Hop field neural network (MHNN) of continuous state change and two fast image restoration algorithms. Experimental results show that, when compared with the previous algorithms, our proposed algorithms have better performance both in convergence rate and in image restoration quality.

Automating HAZOP analysis of batch processes

1999

Abstract A support system for the hazard and operabilty studies of batch processes is presented. The search of causes and consequences is automatically performed using similar qualitative models, in form of logic minitrees, for the phases of the operation procedure and the equipment units. More models are considered for the equipment units, one for each subtask in wich they are involved. The search algorithm is integrated by rules for subdividing the plant to be analysed in nodes.

A morphology-based approach to the evaluation of atrial fibrillation organization.

2007

Using the Hermite Regression Algorithm to Improve the Generalization Capability of a Neural Network

1999

In this paper it is shown that the ability of classification and the ability of approximating a function are correlated to the value (in the training points) of the gradient of the output function learned by the network.