Visualization of level-of-detail meshes on the GPU

Extensive research has been carried out in multiresolution models for many decades. The tendency in recent years has been to harness the potential of GPUs to perform the level-of-detail extraction on graphics hardware. The aim of this work is to present a new level-of-detail scheme based on triangles which is both simple and efficient. In this approach, the extraction process updates vertices instead of indices, thus providing a perfect framework for adapting the algorithms to work completely on GPU shaders. One of the key aspects of our proposal is the need for just a single rendering pass in order to obtain the desired geometry. Moreover, coherence among the different approximations is maximized by means of a symmetric extraction algorithm, which performs the same process when refining and coarsening the mesh. Lastly, we also introduce different uses of the scheme to offer continuous and view-dependent resolution.     

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.     

Using OGRO and CertiVeR to improve OCSP validation for Grids

Authentication and authorization in many distributed systems rely on the use of cryptographic credentials that in most of the cases have a defined lifetime. This feature mandates the use of mechanisms able to determine whether a particular credential can be trusted at a given moment. This process is commonly named validation. Among available validation mechanisms, the Online Certificate Status Protocol (OCSP) stands out due to its ability to carry near real time certificate status information. Despite its importance for security, OCSP faces considerable challenges in the computational Grid (i.e. Proxy Certificate’s validation) that are being studied at the Global Grid Forum’s CA Operations Work Group (CAOPS-WG). As members of this group, we have implemented an OCSP validation infrastructure for the Globus Toolkit 4, composed of the CertiVeR Validation Service and our Open GRid Ocsp (OGRO) client library, which introduced the Grid Validation Policy. This paper summarizes our experiences on that work and the results obtained up to now. Furthermore we introduce the prevalidation concept, a mechanism analogous to the Authorization Push-Model, capable of improving OCSP validation performance in Grids. This paper also reports the results obtained with OGRO’s prevalidation rules for Grid Services as a proof of concept.

Distance preserving flattening of surface sections

Curved cross-sections extracted from medical volume images are useful for analyzing nonplanar anatomic structures such as the aorta arch or the pelvis. For visualization and for performing distance measurements, extracted surface sections need to be adequately flattened. We present two different distance preserving surface flattening methods which preserve distances according to a user-specified center of interest and according to user-specified orientations. The first method flattens surface sections by preserving distances along surface curves located within planes having a user specified constant orientation. The second method flattens surfaces along curves located within radial planes crossing the center of interest. We study and compare the properties of the two flattening methods by analyzing their distortion maps. Thanks to a multiresolution approach, we provide surface flattening at interactive rates, allowing users to displace their focus point while visualizing the resulting flattened surface. These distance preserving flattening methods provide new means of inspecting curved cross-sections extracted from medical images.     

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.     

Inflating examples to obtain rules

A new machine learning system is presented in this article. It is called INNER and induces classification rules from a set of training examples. The process followed by this system starts with the random selection of a subset of examples that are iteratively inflated in order to cover the surroundings provided that they are inhabited by examples of the same class, thus becoming rules that will be applied by means of a partial matching mechanism. The rules so obtained can be seen as clusters of examples and represent clear evidence to support explanations about their future classifications and may be used to build intelligent advisors. The whole algorithm can be seen as a set of elastic transformations of examples and rules and produces concise, accurate rule sets, as is experimentally demonstrated in the final section of the article. © 2003 Wiley Periodicals, Inc.     

2‐D/3‐D switchable displays

Abstract— In this paper, the design of a lenticular‐based 2‐D/3‐D display for mobile applications is described. This display combines look‐around capability with good 3‐D resolution. In order to allow high‐resolution datagraphic applications, a concept based on actively switched lenses has been developed. A very noticeable problem for such displays is the occurrence of dark bands. Despite slanting the lenticular and defocusing the lens, banding becomes unacceptable when the display is viewed from an angle. As a solution, fractional viewing systems to reduce the banding intensity by almost two orders of magnitude is introduced. The resulting 3‐D display can be viewed from any horizontal direction without banding.     

Distributed algorithms for barrier coverage using relocatable sensors

We study the barrier coverage problem using relocatable sensor nodes. We assume each sensor can sense an intruder or event inside its sensing range. Sensors are initially located at arbitrary positions on the barrier and can move along the barrier. The goal is to find final positions for sensors so that the entire barrier is covered. In recent years, the problem has been studied extensively in the centralized setting. In this paper, we study a barrier coverage problem in the distributed and discrete setting. We assume that we have n identical sensors located at grid positions on the barrier, and that each sensor repeatedly executes a Look-Compute-Move cycle: based on what it sees in its vicinity, it makes a decision on where to move, and moves to its next position. We make two strong but realistic restrictions on the capabilities of sensors: they have a constant visibility range and can move only a constant distance in every cycle. In this model, we give the first two distributed algorithms that achieve barrier coverage for a line segment barrier when there are enough nodes in the network to cover the entire barrier. Our algorithms are synchronous, and local in the sense that sensors make their decisions independently based only on what they see within their constant visibility range. One of our algorithms is oblivious whereas the other uses two bits of memory at each sensor to store the type of move made in the previous step. We show that our oblivious algorithm terminates within \(\varTheta (n^2)\) steps with the barrier fully covered, while the constant-memory algorithm is shown to take \(\varTheta (n)\) steps to terminate in the worst case. Since any algorithm in which a sensor can only move a constant distance in one step requires \(\varOmega (n)\) steps on some inputs, our second algorithm is asymptotically optimal.