11796157
Functional Brain Image Analysis Using Joint Function-Structure Priors
We propose a new method for context-driven analysis of functional magnetic resonance images (fMRI) that incorporates spatial relationships between functional parameter clusters and anatomical structure directly for the first time. We design a parametric scheme that relates functional and structural spatially-compact regions in a single unified manner. Our method is motivated by the fact that the fMRI and anatomical MRI (aMRI) have consistent relations that provide configurations and context that aid in fMRI analysis. We develop a statistical decision-making strategy to estimate new fMRI parameter images (based on a General Linear Model-GLM) and spatially-clustered zones within these images. The analysis is based on the time-series data and contextual information related to appropriate spatial grouping of parameters in the functional data and the relationship of this grouping to relevant gray matter structure from the anatomical data. We introduce a representation for the joint prior of the functional and structural information, and define a joint probability distribution over the variations of functional clusters and the related structure contained in a set of training images. We estimate the Maximum A Posteriori (MAP) functional parameters, formulating the function-structure model in terms of level set functions. Results from 3D fMRI and aMRI show that this context-driven analysis potentially extracts more meaningful information than the standard GLM approach.
2345378
Segmentation of 3D deformable objects with level set based prior models
We propose a level set based deformable model for the segmentation of multiple objects from 3D medical images using shape prior constraints. As an extension to the level set distribution model of object shape presented in previous papers, this paper evaluates the performance of the level set representation of the object shape by comparing it with the point distribution model(PDM) using the Chi-square test. We define a maximum a posteriori (MAP) estimation model using level set based prior information to realize the segmentation of the multiple objects. To achieve this , only one level set function is employed as the representation of the multiple objects of interest within the image. We then define the probability distribution over the variations of objects contained in a set of training images. We found the algorithm to be computationally efficient, robust to noise, able to handle multidimensional data, and avoids the need for explicit point correspondences during the training phase. Results and validation from various experiments on 2D/3D medical images are demonstrated.
2866284
BLINKS: ranked keyword searches on graphs
Query processing over graph-structured data is enjoying a growing number of applications. A top-k keyword search query on a graph finds the top k answers according to some ranking criteria, where each answer is a substructure of the graph containing all query keywords. Current techniques for supporting such queries on general graphs suffer from several drawbacks, e.g., poor worst-case performance, not taking full advantage of indexes, and high memory requirements. To address these problems, we propose BLINKS, a bi-level indexing and query processing scheme for top-k keyword search on graphs. BLINKS follows a search strategy with provable performance bounds, while additionally exploiting a bi-level index for pruning and accelerating the search. To reduce the index space, BLINKS partitions a data graph into blocks: The bi-level index stores summary information at the block level to initiate and guide search among blocks, and more detailed information for each block to accelerate search within blocks. Our experiments show that BLINKS offers orders-of-magnitude performance improvement over existing approaches.
26719731
Recursive discrete Fourier transform with unified IIR filter structures
In this paper, we propose two unified IIR filters to recursively compute the discrete Fourier transform (DFT). With the advantage of local connection, regularity and modularity, the proposed methods can compute all DFT coefficients with the same filter structure. Based on stability behavior, the proposed IIR filter structures with optimal coefficients can achieve more accurate results than the traditional ones whose filter coefficients should be changed for computing the different DFT coefficients. Simulation results verify the aforementioned theoretical observations.
19364030
The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees
Abstract Multiple attribute decision analysis (MADA) problems having both quantitative and qualitative attributes under uncertainty can be modeled using the evidential reasoning (ER) approach. Several types of uncertainties such as ignorance and fuzziness can be modeled in the ER framework. In this paper, the ER approach will be extended to model new types of uncertainties including interval belief degrees and interval data that could be incurred in decision situations such as group decision making. The Dempster–Shafer (D–S) theory of evidence is first extended, which is one of the bases of the ER approach. The analytical ER algorithm is used to combine all evidence simultaneously. Two pairs of nonlinear optimization models are constructed to estimate the upper and lower bounds of the combined belief degrees and to compute the maximum and the minimum expected utilities of each alternative, respectively. Interval data are equivalently transformed to interval belief degrees and are incorporated into the nonlinear optimization models. A cargo ship selection problem is examined to show the implementation process of the proposed approach.
8918930
Model-Driven Dynamic Control of Embedded Wireless Sensor Networks
Next-generation wireless sensor networks may revolutionize understanding of environmental change by assimilating heterogeneous data, assessing the relative value and costs of data collection, and scheduling activities accordingly. Thus, they are dynamic, data-driven distributed systems that integrate sensing with modeling and prediction in an adaptive framework. Integration of a range of technologies will allow estimation of the value of future data in terms of its contribution to understanding and cost. This balance is especially important for environmental data, where sampling intervals will range from meters and seconds to landscapes and years. In this paper, we first describe a general framework for dynamic data-driven wireless network control that combines modeling of the sensor network and its embedding environment, both in and out of the network. We then describe a range of challenges that must be addressed, and an integrated suite of solutions for the design of dynamic sensor networks.
6709166
Harmonium Models for Semantic Video Representation and Classification
Accurate and efficient video classification demands the fusion of multimodal information and the use of intermediate representations. Combining the two ideas into the same framework, we propose a probabilistic approach for video classification using intermediate semantic representations derived from the multi-modal features. Based on a class of bipartite undirected graphical models named harmonium, our approach represents video data as latent semantic topics derived by jointly modeling the transcript keywords and color-histogram features, and perform classification using these latent topics under a unified framework. We show satisfactory classification performance of our approach on a benchmark dataset, and some interesting insights of the data provided by this approach.