This letter investigates the problem of distributed channel selection in cognitive radio ad hoc networks (CRAHNs) with heterogeneous spectrum opportunities. Firstly, we formulate this problem as a local congestion game, which is proved to be an exact potential game. Then, we propose a spatial best response dynamic (SBRD) to rapidly achieve Nash equilibrium via local information exchange. Moreover, the potential function of the game reflects the network collision level and can be used to achieve higher throughput.

A method based on covariance differencing for a uniform linear array is proposed to counter the problem of direction finding of narrowband signals under a colored noise environment. By assuming a Hermitian symmetric Toeplitz matrix for the unknown noise, the array covariance matrix is transformed into a centrohermitian matrix in an appropriate way allowing the noise component to be eliminated. The modified covariance differencing algorithm provides accurate direction of arrival (DOA) estimation when the incident signals are uncorrelated or just two of the signals are coherent. If there are more than two coherent signals, the presented method combined with spatial smoothing (SS) scheme can be used. Unlike the original method, the new approach dispenses the need to determine the true angles and the phantom angles. Simulation results demonstrate the effectiveness of presented algorithm.

Text Categorization (TC) is a task of classifying a set of documents into one or more predefined categories. Centroid-based method, a very popular TC method, aims to make classifiers simple and efficient by constructing one prototype vector for each class. It classifies a document into the class that owns the prototype vector nearest to the document. Many studies have been done on constructing prototype vectors. However, the basic philosophies of these methods are quite different from each other. It makes the comparison and selection of centroid-based TC methods very difficult. It also makes the further development of centroid-based TC methods more challenging. In this paper, based on the observation of its general procedure, the centroid-based text classification is treated as a kind of ranking task, and a unified framework for centroid-based TC methods is proposed. The goal of this unified framework is to classify a text via ranking all possible classes by document-class similarities. Prototype vectors are constructed based on various loss functions for ranking classes. Under this framework, three popular centroid-based methods: Rocchio, Hypothesis Margin Centroid and DragPushing are unified and their details are discussed. A novel centroid-based TC method called SLRCM that uses a smoothing ranking loss function is further proposed. Experiments conducted on several standard databases show that the proposed SLRCM method outperforms the compared centroid-based methods and reaches the same performance as the state-of-the-art TC methods.

We present a learning algorithm of the Hopfield neural network for minimizing edge crossings in linear drawings of nonplanar graphs. The proposed algorithm uses the Hopfield neural network to get a local optimal number of edge crossings, and adjusts the balance between terms of the energy function to make the network escape from the local optimal number of edge crossings. The proposed algorithm is tested on a variety of graphs including some "real word" instances of interconnection networks. The proposed learning algorithm is compared with some existing algorithms. The experimental results indicate that the proposed algorithm yields optimal or near-optimal solutions and outperforms the compared algorithms.