A Class of Algorithms for Collision Resolution with Multiplicity Estimation

The wireless connectivity in pervasive computing has ephemeral character and can be used for creating ad hoc networks, sensor networks, connection with RFID (Radio Frequency Identification) tags etc. The communication tasks in such wireless networks often involve an inquiry over a shared channel, which can be invoked for: discovery of neighboring devices in ad hoc networks, counting the number of RFID tags that have a certain property, estimating the mean value contained in a group of sensors etc. Such an inquiry solicits replies from possibly large number of terminals n. This necessitates the usage of algorithms for resolving batch collisions (conflicts) with unknown conflict multiplicity n. In this paper we present a novel approach to the problem of collision resolution for batch conflicts. We show how the conventional tree algorithms for collision resolution can be used to obtain progressively accurate estimation of the multiplicity. We use the estimation to propose a more efficient binary tree algorithm, termed Estimating Binary Tree (EBT) algorithm. The EBT algorithm is suited for implementation when the conflicting nodes are passive, such as e.g. RFID tags. We extend the approach to design the Interval Estimation Conflict Resolution (IECR) algorithm. For n→∞ we prove that the efficiency achieved by IECR for batch arrivals is identical with the efficiency that Gallager’s FCFS algorithm achieves for Poisson packet arrivals. For finite n, the simulation results show that IECR is, to the best of our knowledge, the most efficient batch resolution algorithm reported to date.     

Genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons

In this paper, we propose and investigate a new category of neurofuzzy networks—fuzzy polynomial neural networks (FPNN) endowed with fuzzy set-based polynomial neurons (FSPNs) We develop a comprehensive design methodology involving mechanisms of genetic optimization, and genetic algorithms (GAs) in particular. The conventional FPNNs developed so far are based on the mechanisms of self-organization, fuzzy neurocomputing, and evolutionary optimization. The design of the network exploits the FSPNs as well as the extended group method of data handling (GMDH). Let us stress that in the previous development strategies some essential parameters of the networks (such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of the specific subset of input variables) being available within the network are provided by the designer in advance and kept fixed throughout the overall development process. This restriction may hamper a possibility of developing an optimal architecture of the model. The design proposed in this study addresses this issue. The augmented and genetically developed FPNN (gFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNNs. The GA-based design procedure being applied at each layer of the FPNN leads to the selection of the most suitable nodes (or FSPNs) available within the FPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gFPNN is quantified through experimentation in which we use a number of modeling benchmarks—synthetic and experimental data being commonly used in fuzzy or neurofuzzy modeling. The obtained results demonstrate the superiority of the proposed networks over the models existing in the references.     

Algorithms for multiprocessor scheduling with two job lengths and allocation restrictions

A variant of the High Multiplicity Multiprocessor Scheduling Problem with C job lengths is considered, in which jobs can be processed only by machines not greater than a given index. When C=2, polynomial algorithms are proposed, for the feasibility version of the problem and for maximizing the number of scheduled jobs.     

Synthesis of nonsharp distillation sequences via genetic programming

This paper addresses the application of Genetic Programming (GP) to the synthesis of multicomponent product nonsharp distillation sequences. Combined with the domain knowledge of chemical engineering, some evolutionary factors are improved, and a set of special encoding method and solving strategy is proposed to deal with this kind of problem. The system structural variable is optimized by GP and the continuous variable is optimized by the simulated annealing algorithm simultaneously. Because GP has an automatic searching function, the optimal solution can be found including distillation, splitting, blending and bypassing operations automatically without any superstructures of nonsharp distillation sequences. Three illustrative examples are presented to demonstrate the effective computational strategies.     

Evolving Fault-Tolerant Neural Networks

In this paper, genetic algorithm is used to help improve the tolerance of feedforward neural networks against an open fault. The proposed method does not explicitly add any redundancy to the network, nor does it modify the training algorithm. Experiments show that it may profit the fault tolerance as well as the generalisation ability of neural networks.     

Perceptual evaluation of color gamut mapping algorithms

The recommendation of the CIE has been followed as closely as possible to evaluate the accuracy of five color gamut mapping algorithms (GMAs)—two nonspatial and three spatial algorithms—by psychophysical experiments with 20 test images, 20 observers, one test done on paper and a second one on display. Even though the results do not show any overall “winner,” one GMA is definitely perceived as not accurate. The importance of a high number of test images to obtain robust evaluation is underlined by the high variability of the results depending on the test images. Significant correlations between the percentage of out‐of‐gamut pixels, the number of distinguishable pairs of GMAs, and the perceived difficulty to distinguish them have been found. The type of observers is also important. The experts, who prefer a spatial GMA, show a stronger consensus and look especially for a good rendering of details, whereas the nonexperts hardly make a difference between the GMAs. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 470–476, 2008     

遗传算法在双向反射分布函数模型参数优化中的应用

对以大理石为代表的材料进行了双向反射分布函数测量实验，得出了样品的半球反射率，并研究了其随入射、反射天顶角等参数变化的规律。采用遗传算法建立了金刚砂和大理石材料的双向反射分布函数的参数模型，对模型进行参数反演时，通过遗传算法进行优化。拟合后的实验数据曲线表明，利用遗传算法得到的结果具有较好的一致性。     

An efficient parallel strategy for the perfect domination problem on distance-hereditary graphs

A graph is distance-hereditary if the distance stays the same between any of two vertices in every connected induced subgraph containing both. Two well-known classes of graphs, trees and cographs, both belong to distance-hereditary graphs. In this paper, we first show that the perfect domination problem can be solved in sequential linear-time on distance-hereditary graphs. By sketching some regular property of the problem, we also show that it can be easily parallelized on distance-hereditary graphs.     

Tuning fuzzy power-system stabilizers in multi-machine systems by global optimization algorithms based on efficient domain partitions

In this paper, the problem of global tuning of fuzzy power-system stabilizers (FPSSs) present in a multi-machine power system in order to damp the power system oscillations is considered. In particular, it is formulated as a problem of global minimization of a multiextremal black-box function over a multidimensional hyperinterval. A global optimization technique, recently proposed, is used for solving the stated problem: the search hyperinterval is partitioned into smaller hyperintervals and the objective function is evaluated only at two vertices corresponding to the main diagonal of the generated hyperintervals, thus avoiding unnecessary ponderous simulations. Then, the performances of this technique are numerically compared with ones of a genetic algorithm (GA).     

计算几何与并行算法

本文首先介绍了计算几何的基本概念,论述了计算几何的四个基本问题,即几何搜索问题、相交问题、邻接问题及凸壳问题。然后重点分析了凸壳构造问题,介绍了其最佳串行算法、及相应的并行算法。接着对一些计算几何的串行及并行算法进行了分析比较。最后提出了笔者对新一代并行计算机系统上设计计算几何并行算法的看法。