This paper describes research exploring the problems associated with interpreting regional blood flow measurements in the brain. We investigate a method for separating normals from those with cerebral diseases, where the disease is caused by, or has resulted in, altered cerebral haemodynamics. Cerebral perfusion maps are divided into 10 vascular territories. The variance scaled mean values from...

We present a Bayesian approach to the problem of inferring the number of inversions and translocations separating two species. The main reason for developing this method is that it will allow us to test hypotheses about the underlying mechanisms, such as the distribution of inversion track lengths or rate constancy among lineages. Here, we apply these methods to comparative maps of eggplant and...

We study the topological dynamics by iterations of a piecewise continuous, non linear and locally contractive map in a real finite dimensional compact ball. We consider those maps satisfying the “separation property”: different continuity pieces have disjoint images. The continuity pieces act as stable topological manifolds while the points in the discontinuity lines, separating different conti...

A new learning method, the fuzzy kernel perceptron (FKP), in which the fuzzy perceptron (FP) and the Mercer kernels are incorporated, is proposed in this paper. The proposed method first maps the input data into a high-dimensional feature space using some implicit mapping functions. Then, the FP is adopted to find a linear separating hyperplane in the high-dimensional feature space. Compared wi...

This work presents an algorithm that is able to achieve novel spatialization effects on multitrack audio signals. It relies on a crossadaptive framework that dynamically maps the azimuth positions of each track’s time-frequency bins with the goal of reducing masking between source signals by dynamically separating them across space. The outputs of this system are compared to traditional panning...

We present a classifier algorithm that approximates the decision surface of labeled data by a patchwork of separating hyperplanes. The hyperplanes are arranged in a way inspired by how Self-Organizing Maps are trained. We take advantage of the fact that the boundaries can often be approximated by linear ones connected by a low-dimensional nonlinear manifold. The resulting classifier allows for ...

Distillation Residue Curve Maps (D-RCM’s) have proven to be an extremely useful graphical tool in the design and operation of both batch and continuous distillation processes. Recently submitted work by the authors has investigated the applications of Residue Curve Maps for membrane permeation systems. Such maps are referred to as M-RCM’s. This paper aims to develop a graphical tool to provide ...