Skeleton computation of orthogonal polyhedra

Skeletons are powerful geometric abstractions that provide useful representations for a number of geometric operations. The straight skeleton has a lower combinatorial complexity compared with the medial axis. Moreover, while the medial axis of a polyhedron is composed of quadric surfaces the straight skeleton just consist of planar faces. Although there exist several methods to compute the straight skeleton of a polygon, the straight skeleton of polyhedra has been paid much less attention. We require to compute the skeleton of very large datasets storing orthogonal polyhedra. Furthermore, we need to treat geometric degeneracies that usually arise when dealing with orthogonal polyhedra. We present a new approach so as to robustly compute the straight skeleton of orthogonal polyhedra. We follow a geometric technique that works directly with the boundary of an orthogonal polyhedron. Our approach is output sensitive with respect to the number of vertices of the skeleton and solves geometric degeneracies. Unlike the existing straight skeleton algorithms that shrink the object boundary to obtain the skeleton, our algorithm relies on the plane sweep paradigm. The resulting skeleton is only composed of axis‐aligned and 45° rotated planar faces and edges.     

A model of cerebral cortex formation during fetal development using reaction-diffusion-convection equations with Turing space parameters

The cerebral cortex is a gray lamina formed by bodies of neurons covering the cerebral hemispheres, varying in thickness from 1.25 mm in the occipital lobe to 4 mm in the anterior lobe. The brain's surface is about 30 times greater that of the skull because of its many folds; such folds form the gyri, sulci and fissures and mark out areas having specific functions, divided into five lobes. Convolution formation may vary between individuals and is an important feature of brain formation; such patterns can be mathematically represented as Turing patterns. This article describes how a phenomenological model was developed by describing the formation pattern for the gyri occurring in the cerebral cortex by reaction diffusion equations with Turing space parameters. Numerical examples for simplified geometries of a brain were solved to study pattern formation. The finite element method was used for the numerical solution, in conjunction with the Newton–Raphson method. The numerical examples showed that the model can represent cerebral cortex fold formation and reproduce pathologies related to gyri formation, such as polymicrogyria and lissencephaly.     

Multi‐agent system for knowledge‐based event recognition and composition

This work presents a multi‐agent system for knowledge‐based high‐level event composition, which interprets activities, behaviour and situations semantically in a scenario with multi‐sensory monitoring. A perception agent (plurisensory agent and visual agent)‐based structure is presented. The agents process the sensor information and identify (agent decision system) significant changes in the monitored signals, which they send as simple events to the composition agent that searches for and identifies pre‐defined patterns as higher‐level semantic composed events. The structure has a methodology and a set of tools that facilitate its development and application to different fields without having to start from scratch. This creates an environment to develop knowledge‐based systems generally for event composition. The application task of our work is surveillance, and event composition/inference examples are shown which characterize an alarming situation in the scene and resolve identification and tracking problems of people in the scenario being monitored.     

On spiking neural P systems and partially blind counter machines

A k-output spiking neural P system (SNP) with output neurons, , generates a tuple of positive integers if, starting from the initial configuration, there is a sequence of steps such that during the computation, each O _i generates exactly two spikes aa (the times the pair aa are generated may be different for different output neurons) and the time interval between the first a and the second a is n _i . After the output neurons generate their pairs of spikes, the system eventually halts. We give characterizations of sets definable by partially blind multicounter machines in terms of k-output SNPs operating in a sequential mode. Slight variations of the models make them universal.     

Simple multichannel system for the measurement of the net water flux across biological tissues

This paper describes the development of a simple system for measurement of net water movement through biological membrane barriers. The system is based on the detection of a water meniscus inside a polyethylene tube, which reflects the water movement inside one hemichamber of a modified Ussing chamber containing a membrane barrier. The detection device consists of a commercial computer-controlled flat bed scanner and specifically developed software. This system allows one to perform a relatively high number of individual experiments per physical unit. It is a flexible and affordable device, which allows comparatively more information per unit to be obtained than previously described methods.     

Quantum statistical foundation to the fermi liquid model and Ginzburg-Landau wave function

An energy eigenvalue equation for a quasi-particle is derived, starting with the Heisenberg equation of motion for an annihilation operator. An elementary derivation of the Fermi liquid model having a sharply defined Fermi surface in thek-space is given, starting with a realistic model of a metal including the Coulomb interaction amongand between electrons and lattice-ions. The Ginzburg-Landau wave function (r), where represents the superconducting pairon (Cooper-pair) state, is shown to be connected with the one-pairon density operatorn by (r) = r¦_n ^1/2¦. A close analogy between supercurrent and laser is indicated.On sabbatical leave from Department of Physics and Astronomy, State University of New York at Buffalo, Buffalo, New York.     

A note on the complexity of program evaluation

A simple problem concerning evaluation of programs is shown to be nonelementary recursive. The problem is the following: Given an input-free programP (i.e. all variables are initially 0) without nested loops using only instructions of the formx ← 1, x ← x + y, \(x \leftarrow x\dot - y\) ,do x... end, doesP output 0? This problem has time complexity \(2^{2^{ {\mathinner{\mkern2mu\raise1pt\hbox{.}\mkern2mu \raise4pt\hbox{.}\mkern2mu\raise7pt\hbox{.}\mkern1mu}} ^2 } } \) }cn-levels for some constantc. Other results are presented which show how the complexity of the 0-evaluation problem changes when the nonlooping instructions are varied. For example, it is shown that 0-evaluation is PSPACE-complete even for the case when the nonlooping instructions are onlyx ← x + 1,if x = 0then y ←y \(y \leftarrow y\dot - 1\) .     

On restricted one-counter machines

Let be the class of real-time nondeterministic one-counter machines whose counters make at mostone reversal. Let ₁ (respectively, ₂) be the subclass consisting of machines whose only nondeterministic move is in the choice of when to reverse the counter (respectively, when to start using the counter). ₁ and ₂ are among the simplest known classes of machines for which the universe problem has been shown undecidable. (The universe problem for a class of machines is the problem of deciding if an arbitrary machine in the class accepts all its inputs.) Here, we show that the classes of languages accepted by machines in ₁ and ₂ are incomparable. Moreover, the union of the language classes is properly contained in the class defined by . We also, briefly, look at the closure properties of these machines.This research was supported in part by NSF Grant MCS78-01736.     

IMPLEMENTING ENVIRONMENTAL FLOWS IN COMPLEX WATER RESOURCES SYSTEMS – CASE STUDY: THE DUERO RIVER BASIN,SPAIN

European river basin authorities are responsible for the implementation of the new river basin management plans in accordance with the European Water Framework Directive. This paper presents a new methodology framework and approach to define and evaluate environmental flow regimes in the realistic complexities that exist with multiple water resource needs at a basin scale. This approach links river basin simulation models and habitat time series analysis to generate ranges of environmental flows (e‐flows), which are evaluated by using habitat, hydropower production and reliability of water supply criteria to produce best possible alternatives. With the use of these tools, the effects of the proposed e‐flows have been assessed to help in the consultation process. The possible effects analysed are impacts on water supply reliability, hydropower production and aquatic habitat. After public agreements, a heuristic optimization process was applied to maximize e‐flows and habitat indicators, while maintaining a legal level of reliability for water resource demands. The final optimal e‐flows were considered for the river basin management plans of the Duero river basin. This paper demonstrates the importance of considering quantitative hydrologic and ecological aspects of e‐flows at the basin scale in addressing complex water resource systems. This approach merges standard methods such as physical habitat simulations and time series analyses for evaluating alternatives, with recent methods to simulate and optimize water management alternatives in river networks. It can be integrated with or used to complement other frameworks for e‐flow assessments such as the In‐stream Flow Incremental Methodology and Ecological Limits of Hydrologic Alteration. Copyright © 2011 John Wiley & Sons, Ltd.     

Learning Deformable Shape Manifolds

We propose an approach to shape detection of highly deformable shapes in images via manifold learning with regression. Our method does not require shape key points be defined at high contrast image regions, nor do we need an initial estimate of the shape. We only require sufficient representative training data and a rough initial estimate of the object position and scale. We demonstrate the method for face shape learning, and provide a comparison to nonlinear Active Appearance Model. Our method is extremely accurate, to nearly pixel precision and is capable of accurately detecting the shape of faces undergoing extreme expression changes. The technique is robust to occlusions such as glasses and gives reasonable results for extremely degraded image resolutions.