THE METHOD OF LINES SOLUTION OF THE DISCRETE ORDINATES METHOD FOR RADIATIVE HEAT TRANSFER IN ENCLOSURES

A radiation code based on the method of lines (MOL) solution of the discrete ordinates method (DOM) for transient three-dimensional radiative heat transfer in rectangular enclosures for use in conjunction with a computational fluid dynamics (CFD) code based on the same approach was developed. Assessment of the predictive accuracy of the code by benchmarking its steady-state solutions against exact solutions on one- and three-dimensional test problems shows that the MOL solution of the DOM provides accurate and computationally efficient solutions for radiative heat fluxes and source terms and can be used with confidence in conjunction with CFD codes for transient problems.     

Signal detection based on empirical mode decomposition and Teager–Kaiser energy operator and its application to P and S wave arrival time detection in seismic signal analysis

Neural Computing and Applications - Determining P and S wave arrival times while minimizing noise is a major problem in seismic signal analysis. Precise determination of earthquake onset arrival...     

Vessel extraction in medical images by wave-propagation and traceback

This paper presents an approach for the extraction of vasculature from angiography images by using a wave propagation and traceback mechanism. We discuss both the theory and the implementation of the approach. Using a dual-sigmoidal filter, we label each pixel in an angiogram with the likelihood that it is within a vessel. Representing the reciprocal of this likelihood image as an array of refractive indexes, we propagate a digital wave through the image from the base of the vascular tree. This wave "washes" over the vasculature, ignoring local noise perturbations. The extraction of the vasculature becomes that of tracing the wave along the local normals to the waveform. While the approach is inherently single instruction stream multiple data stream (SIMD), we present an efficient sequential algorithm for the wave propagation and discuss the traceback algorithm. We demonstrate the effectiveness of our integer image neighborhood-based algorithm and its robustness to image noise.     

Surface parameterization in volumetric images for curvature-based feature classification

Curvature-based surface features are well suited for use in multimodal medical image registration. The accuracy of such feature-based registration techniques is dependent upon the reliability of the feature computation. The computation of curvature features requires second derivative information that is best obtained from a parametric surface representation. We present a method of explicitly parameterizing surfaces from volumetric data. Surfaces are extracted, without a global thresholding, using active contour models. A monge/spl acute/ basis for each surface patch is estimated and used to transform the patch into local, or parametric, coordinates. Surface patches are fit to a bicubic polynomial in local coordinates using least squares solved by singular value decomposition. We tested our method by reconstructing surfaces from the surface model and analytically computing Gaussian and mean curvatures. The model was tested on analytical and medical data.     

Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction

This paper presents the application of adaptive neuro-fuzzy inference system (ANFIS) model for estimation of vigilance level by using electroencephalogram (EEG) signals recorded during transition from wakefulness to sleep. The developed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. This study comprises of three stages. In the first stage, three types of EEG signals (alert signal, drowsy signal and sleep signal) were obtained from 30 healthy subjects. In the second stage, for feature extraction, obtained EEG signals were separated to its sub-bands using discrete wavelet transform (DWT). Then, entropy of each sub-band was calculated using Shannon entropy algorithm. In the third stage, the ANFIS was trained with the back-propagation gradient descent method in combination with least squares method. The extracted features of three types of EEG signals were used as input patterns of the three ANFIS classifiers. In order to improve estimation accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The performance of the ANFIS model was tested using the EEG data obtained from 12 healthy subjects that have not been used for the training. The results confirmed that the developed ANFIS classifier has potential for estimation of vigilance level by using EEG signals.     

AIM: attentionally based interaction model for the interpretationof vascular angiography

We propose a model to interpret neurovascular X-ray angiogram (XRA) images interactively. This attentionally based interactive model (AIM) exploits human interaction as part of the solution. AIM posits two channels of interaction: context (what to look for) and focus-of-attention (where to look) as the locus of spatial information exchange between the user and the machine. In an AIM system, the user specifies a context (e.g., a carotid vessel) and directs the attentional spotlight to focus machine processing. AIM involves the user with the computer as integral partners and facilitates varying degrees of human intervention in the process. A hierarchy of context abstractions permits the system to function more autonomously (doing high-level tasks like extracting an arterial vessel) in routine interpretation and to require more user intervention (e.g., locating arterial wall boundaries) as the image complexity increases. This is especially important in medical imaging where the medical professional must have ultimate control and confidence in the system. Such technology can have a significant impact on the design of radiological systems