SIGNAL AND DATA PROCESSING
Year:2015 | Volume:- | Issue:2 (SERIAL 22)|Papers Count:9

One of the most important capabilities of information security management systems, which must be implemented in all organizations according to their requirements, is information security risk management. The application of information security risk management is so important that it can be named as the heart of information security management systems. Information security risk rating is considered as the key part of the risk assessment phase in the process of this management. This article presents an applied method by combining two MADM methods of AHP and TOPSIS in a fuzzy environment in order to improve information security risk rating. The results of comparison between the implementation of the combined FAHP-TOPSIS and the FAHP indicated that the weights presented by the proposed FAHP-TOPSIS model have lower variation coefficients and higher mean compared to the FAHP model. As a result, it provides more accurate results with less percentage error.

Parkinson’s disease (PD) is a progressive neurological disorder characterized by tremor, rigidity, and slowness of movement. Recent studies on the brain function show that there are spontaneous fluctuations between separated regions at rest, known as resting state network, affected in various disorders. In this paper, we used amplitude of low frequency fluctuation (ALFF) approach for study of intra-regional characteristics and cross-correlation analysis (CCA) for quantifying inter-regional relationship between anatomical regions. We presented functional connectivity networks of the healthy and PD groups based on the results of CCA. By comparing two networks, we conclude some points. Firstly, the activity of cerebellum and basal ganglia areas had a significant negative correlation in PD patients, while this relationship is weak and non-significant in the healthy. We also used mean values of ALFF and ReHo as intra-region biomarkers. These features together with inter-region characteristics are used in discriminative analysis for classification PD and healthy. The result showed 85% accuracy in clustering. In addition, the score index is 89% and Jaccard coefficient of this clustering is 75%. We found that inter-regional features (CCA) were more significant than intra-regional features (ALFF) and functional connectivity between left cerebellum and left putamen was the best discriminator between PD and control.

This paper addresses a novel control method adapted with varying time delay to improve NCS performance. A well-known challenge with NCSs is the stochastic time delay. Conventional controllers such as PID type controllers which are just tuned with a constant time delay could not be a solution for these systems. Fuzzy logic controllers due to their nonlinear characteristic which is compatible with these systems are potentially a wise option for their control purpose. Fuzzy logic controller could become adaptive by means of neural networks and beneficial to deal with the varying time delay problem. This novel method suggests an adaptive fuzzy logic controller which has been controlled and adapted through the neural network. The rule-based table of designed fuzzy logic controller rotates in relation to estimated time delay. The amount of rotation is obtained from neural network. The proposed method follows the input easily, despite classical methods which result in an unstable system especially over the large time delays as large as 600 ms.

In content based image retrieval systems, the suitable visual features are extracted from images and stored in the feature database Then the feature database are searched to find the most similar images to the query image. In this paper, three types of visual features by 270 components were used for image indexing. Here, we use a weighted distance for similarity measurement between two images. This paper presents a new relevance feedback approach based on similarity refinement. In the proposed approach, weight correction of feature’s components is done by a proposed rule set using the mean and standard deviation of feature vectors of related (positive) and non-related (negative) images. Also, the weight of each type of features is adjusted according to the related images’ rank in the retrieval with this type of feature. To evaluate the performance of the proposed method, a set of comparative experiments on a general image database containing 10000 images of 82 different semantic groups are performed. The results confirm the efficiency of the proposed method comparing by well-known conventional methods.

Unmixing of remote-sensing data using nonnegative matrix factorization has been considered recently. To improve performance, additional constraints are added to the cost function. The main challenge is to introduce constraints that lead to better results for unmixing.Correlation between bands of Hyperspectral images is the problem that is paid less attention to it in the unmixing algorithms. In this paper, we have proposed a new method for unmixing of Hyperspectral data using semi-nonnegative matrix factorization and principal component analysis. In the proposed method, spectral and spatial unmixing is performed simultaneously. Physical constraints applied based on Linear Mixing Model. In addition to physical constraints, characteristics of Hyperspectral data have been exploited in the unmixing process. Sparseness of the abundance is one of the important features of Hyperspectral data, which is applied using the nsNMF matrix. In the proposed method update rules is derived using the ALS algorithm. In the final section of this paper, real and synthetic Hyperspectral data is used to verify the effectiveness of the proposed algorithm. Obtained results show the superiority of the proposed algorithm in comparison with some unmixing algorithms.

Data-driven systems can be adapted to different languages and domains easily. Using this trend in dependency parsing was lead to introduce data-driven approaches. Existence of appreciate corpora that contain sentences and theirs associated dependency trees are the only pre-requirement in data-driven approaches. Despite obtaining high accurate results for dependency parsing task in English language, for many of other languages with high free-word order and rich morphology, most applying algorithms lead to drop in accuracy compared to English language. Therefore, data-driven systems require careful selection of features and tuning of parameters to reach optimal performance.A dependency corpus for Persian language introduced recently. Persian language has high free-word order and rich morphology. In this paper we try to find detect effective factors for decreasing parsing accuracy and we present solutions to improve the accuracy.

The current study analyses the dynamics of the heart rate signals during specific psychological states in order to obtain a detailed understanding of the heart rate patterns during meditation. In the proposed approach, we used heart rate time series available in Physionet database. The dynamics of the signals are then analyzed before and during meditation by examining the recurrence quantification analysis. The results show that the measures of recurrence plots are increased significantly during meditation (p<0.05), which indicates that the dimension of signals are decreased during meditation. In general, the results reveal that the heart rate signals of experienced meditators transit from a chaotic, highly-complex behavior before meditation to a low dimensional chaotic (and quasi-periodic) motion during meditation. This can be due to decreased nonlinear interaction of variables in meditation states and may be related to increased parasympathetic activity and increase of relaxation state.

It is conventional to use multi-dimensional indexing structures to accelerate search operations in content-based image retrieval systems. In most practical applications, high-dimensional feature vectors are required, but current multi-dimensional indexing structures lose their efficiency with growth of dimensions. Increase in dimensions of data space leads to exponentially growth of the search space and increase of the number of nodes in multi-dimensional indexing structure, as well as increase in overlap between nodes in multi-dimensional indexing structures. These problems lead to increase in cost of search through indexing structure. The goal of this research is to propose a divisive hierarchical clustering-based multi-dimensional indexing structure in order to manage high-dimensional feature vectors extracted from images, which also prevents overlapping in its structure. Various tests on high-dimensional datasets indicate the performance of our proposed method in comparison with others.

In classification problems, we often encounter datasets with different percentage of patterns (i.e. classes with a high pattern percentage and classes with a low pattern percentage). These problems are called “classification Problems with imbalanced data-sets”. Fuzzy rule based classification systems are the most popular fuzzy modeling systems used in pattern classification problems. Rule weights have been usually used to improve the classification accuracy and fuzzy versions of confidence and support merits have been widely used for rules weighting in fuzzy rule based classifiers. In this paper, we propose an evolutionary approach based on genetic programming to generate weighting expressions. For producing expressions confidence, support, lift and recall merits are used as terminals of genetic programming. Experiments are performed over 20 imbalanced KEEL's datasets and the results are analyzed using statistical tests. The results show that the proposed method improves the classification accuracy of FRBCS.