16081168
Sample dependence correction for order selection in fMRI analysis
Multivariate analysis methods such as independent component analysis (ICA) have been applied to the analysis of functional magnetic resonance imaging (fMRI) data to study the brain function. The selection of the proper number of signals of interest is an important step in the analysis to reduce the risk of over/underfilling. The inherent sample dependence in the spatial or temporal dimension of the fMRI data violates the assumption of independent and identically distributed (i.i.d.) samples and limits the usefulness of the practical formulations of information-theoretic order selection criteria. We propose a novel method using an entropy rate matching principle to mitigate the effects of such sample dependence in order selection. We perform order selection experiments on the simulated fMRI data and show that the incorporation of the proposed method significantly improves the accuracy of the order selection by different criteria. We also use the proposed method to estimate the number of latent sources in fMRI data acquired from multiple subjects performing a visuomotor paradigm. We show that the proposed method improves the order selection by alleviating the over-estimation due to the intrinsic smoothness and the effect of smooth preprocessing on the fMRI data
15872257
Independent component analysis of complex-valued functional magnetic resonance imaging data by complex nonlinearities
Independent component analysis (ICA) for separating complex-valued sources is needed for convolutive source-separation in the frequency domain, or for performing source separation on complex-valued data. Functional magnetic resonance imaging (fMRI) is a technique that produces complex-valued data; however the vast majority of fMRI analyses utilize only magnitude images due in large part to the difficulty of developing a temporal phase model. We have successfully applied ICA to complex fMRI data but there is a need to further optimize the complex ICA. We recently proposed a number of complex nonlinear functions for ICA of complex valued data. We apply two of these functions to fMRI data and examine the properties of these nonlinearities and their efficiency in generating the higher order statistics needed for ICA. We show that the complex infomax using these efficient nonlinearities demonstrates superior performance compared to analysis of the magnitude data with either ICA or linear regression. Complex ICA thus provides a potentially powerful method for the analysis of fMRI data.
45088529
Chaining watermarks for detecting malicious modifications to streaming data
Abstract Authenticating streaming data is a very important research area due to its wide range of applications. Previous technologies mainly focused on authenticating data packets at the IP layer and ensuring the robustness of the verification. These schemes usually incur large communications overhead, which is not desirable in applications with limited bandwidth. In this paper, we propose a novel fragile watermarking algorithm which verifies the integrity of streaming data at the application layer. The data are divided into groups based on synchronization points, so each group can be synchronized and any modifications made to one group only affect up to two groups. A unique watermark is embedded directly into each group to save communications bandwidth. The embedded watermark can detect as well as locate any modifications made to a data stream. To ensure the completeness of the data stream, watermarks are chained across groups so that no matter how many data are deleted, these deletions can be correctly detected. Security analysis and experimental results show that the proposed scheme can efficiently detect and locate modifications and ensure the completeness of data streams.
121595784
Face recognition using shading-based curvature attributes
In this paper, we apply shape-from-shading to face images to extract fields of surface normals and make estimates of surface curvature attributes. The attributes studied include minimum and maximum curvature, mean and Gaussian curvature, and, curvedness and shape-index. The curvature attributes are encoded as histograms and are used to perform recognition using a number of distance and similarity measures including the Euclidean distance, the Shannon entropy, the Renyi entropy and the Tsallis entropy. We compare the results obtained using the different curvature attributes and the different entropy measures. Using precision-recall curves, we find that the best performance is delivered when the Shannon entropy is applied to histograms of shape index and curvedness.
29006676
A Remote Resource Monitoring Approach in Active Networks
Monitoring the resource status in active network nodes is important both for the network administrator to maintain and deploy active networks and for the normal end-users to acquire better performance. Hence, a method that can monitor the resource status at a remote node is needed. Moreover, due to the complexity of the resource usage in active networks, the corresponding method should be able to monitor the status of various resources at remote node dynamically and in a user-specified way. This paper presents the design and implementation of such a resource monitoring method. The method adopts the active application technique to obtain the desired resource status information at a remote network node and transfer them back to the user in the user-specified way. It also allows the user to control the content, the procedure and the precise of the monitoring.
8036895
The case for multipath multimedia transport over wireless ad hoc networks
Real-time multimedia transport has stringent bandwidth, delay, and loss requirements. Supporting this application in current wireless ad hoc networks is a challenge. Such networks are characterized with frequent link failures, as well as congestion. Consequently, data packets are dropped when a link fails or congestion occurs, resulting in low received quality. In addition, a realtime multimedia service may be unavailable when a particular server is unreachable. In this article, we make the case for using multipath transport for realtime multimedia services in wireless ad hoc networks, which provides a unified solution to the above problems. We review existing work on multipath multimedia transport, and discuss the advantages, as well as related issues, of using multipath transport for realtime multimedia transport.
15368149
Hierarchical fuzzy filter method for unsupervised feature selection
The problem of feature selection has long been an active research topic within statistics and pattern recognition. So far, most methods of feature selection focus on supervised data where class information is available. For unsupervised data, the related methods of feature selection are few. The presented article demonstrates a way of unsupervised feature selection, which is a two-level filter model removing the redundant and irrelevant features, respectively. The redundant features are eliminated using any clustering algorithm, and a new method is proposed to remove the irrelevant features: first rank the features according to their relevance to cluster and then a subset of relevant features is selected using the Fuzzy Feature Evaluation Index (FFEI) with some changes and extensions. The experimental results have shown the effectiveness of the proposed method for high-dimensional data. Our major contributions are: (1) to present a new hierarchical filter method for unsupervised feature selection; (2) to propose a new algorithm for removing the irrelevant features; (3) to extend the FFEI, and present a method for calculating the approximate weight of feature in FFEI, which improves the efficiency and robustness of the method.