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This paper presents a structured dictionary-based model for hyperspectral data that incorporates both spectral and contextual characteristics of a spectral sample, with the goal of hyperspectral image classification. The idea is to partition the pixels of a hyperspectral image into a number of spatial neighborhoods called contextual groups and to model each pixel with a linear combination of a few dictionary elements learned from the data. Since pixels inside a contextual group are often made up of the same materials, their linear combinations are constrained to use common elements from the dictionary. To this end, dictionary learning is carried out with a joint sparse regularizer to induce a common sparsity pattern in the sparse coefficients of each contextual group. The sparse coefficients are then used for classification using a linear SVM. Experimental results on a number of real hyperspectral images confirm the effectiveness of the proposed representation for hyperspectral image classification. Moreover, experiments with simulated multispectral data show that the proposed model is capable of finding representations that may effectively be used for classification of multispectral-resolution samples.Index Terms-Classification, hyperspectral imagery, dictionary learning, probabilistic joint sparse model, linear support vector machines.

Abstract-Aggregation services play an important role in the domain of Wireless Sensor Networks (WSNs) because they significantly reduce the number of required data transmissions, and improve energy efficiency on those networks. In most of the existing aggregation methods that have been developed based on the mathematical models or functions, the user of the WSN has not access to the original observations. In this paper, we propose an algorithm which let the base station access the observations by introducing a distributed method for computing the Principal Component Analysis (PCA). The proposed algorithm is based on transmission workload of the intermediate nodes. By using PCA, we aggregate the incoming packets of an intermediate node into one packet and as a result, an intermediate node merely sends a packet instead of relaying all the incoming packets. Consequently, we can achieve considerable reduction in data transmission. We have analyzed the performance of the proposed algorithm through numerical simulations. The experimental results show that our algorithm performs better than the existing state of the art PCA-based aggregation algorithms such as PCAg in terms of accuracy and efficiency.

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A Mobile Ad hoc Network (MANET) is a collection of wireless mobile nodes forming a self-configuring network without using any existing infrastructure. Since MANETs are not currently deployed on a large scale, research in this area is mostly simulation based. Among other simulation parameters, the mobility model plays a very important role in determining the protocol performance in MANET. Thus, it is essential to study and analyze various mobility models and their effect on MANET protocols. In this paper we introduce a new framework for simulation of mobility models in mobile Ad-Hoc networks. This simulator can generate mobility traces in various mobility models. The mobility traces can be customized for different network simulators using XML and text output formats. User friendly graphical interface and batch processing ability makes our simulator one of the most efficient and useful mobility simulators in this field of research. We also propose some new features and parameters in mobility models to make the behavior of our simulator supported mobility models more similar to real world mobile node motions and fix some problems in last proposed methods to generate mobility models.

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