Hideo Minamihara

An evaluation on the peak value distribution using level crossings for non‐Gaussian‐type random signals with wide frequency band

It is extremely difficult to carry out a rigorous theoretical evaluation of the peak value distribution in the maximum-value statistical analysis of random signals because second derivative information is needed in addition to the instantaneous value and the first derivative. On the other hand, the number of level crossings can be evaluated with only the first derivative information, and hence a theoretical evaluation equation that can be applied regardless of the amplitude distribution and the frequency characteristics is explicitly derived. In this paper, a function is derived to evaluate theoretically the relationship between the expected number of signal crossings of a certain level and the number of peaks exceeding this level under the condition of Gaussian distribution. The non-Gaussian nature of the amplitude distribution is reflected in this function and is coupled to a conventional non-Gaussian level-crossing evaluation equation, allowing the practical peak value distribution analysis of broadband non-Gaussian arbitrary random signals. Finally, the validity of the evaluation equation is confirmed by digital simulation.

New Kick Operators in Iterated Local Search Based Metaheuristic for Solving the Node Placement Problem in Multihop Networks

We consider a problem of finding an optimal node placement that minimizes the amount of traffic by reducing the weighted hop distances in multihop networks. The problem is called Node Placement Problem (NPP) and is one of the most important issues in multihop networks. NPP is known to be NP-hard. Therefore, several heuristic and metaheuristic algorithms have been proposed for optimizing NPP. Recently we proposed Iterated k-swap Local Search (IKLS) algorithm, which showed better performance than previous metaheuristic algorithms proposed by other researchers. IKLS simply consists of k-swap local search and a kick (mutation or perturbation) operator called Cross-Kick, a method that aims to escape from local optima. In this paper we focus on the kick operators in order to improve the performance of IKLS for NPP. New kick operators are presented and their effectivities are shown through computational experiments on the benchmark instances of NPP. The results show that IV-Kick with Rhombus is more effective than Cross-Kick and other kick operators, particularly for large-scaled instances.