Catálogo de publicaciones - libros

Web based computerised auctions are increasingly present in the Internet, and we can imagine that in the future automated buyer and seller agents will conduct automated transactions in this manner. The purpose of this paper is to model automated bidders and sellers which interact through a computerised auction. We model bidding process using random processes with discrete state-space. We obtain analytical solutions for a variety of single auction models, including English and Vickrey auctions, and relate the income per unit time to the other parameters including the rate of arrival of bids, the seller’s decision time, the value of the good, and the “rest time” of the seller between successive auctions. We examine how the seller’s “decision time” impacts the expected income per unit time received by the seller, and illustrate its effect via numerical examples.

We present a system that exploits advanced Mixed and Virtual Reality technologies to create a surveillance and security system that could be also extended to define emergency prevention plans in crowdy environments. Surveillance cameras are carried by a mini Blimp which is tele-operated using an innovative Virtual Reality interface with haptic feedback. An interactive control room (CAVE) receives multiple video streams from airborne and fixed cameras. Eye tracking technology allows for turning the user’s gaze into the main interaction mechanism; the user in charge can examine, zoom and select specific views by looking at them. Video streams selected at the control room can be redirected to agents equipped with a PDA. On-field agents can examine the video sent by the control center and locate the actual position of the airborne cameras in a GPS-driven map. The aerial video would be augmented with real-time 3D crowd to create more realist risk and emergency prevention plans. The prototype we present shows the added value of integrating AR/VR technologies into a complex application and opens up several research directions in the areas of tele-operation, Multimodal Interfaces, simulation, risk and emergency prevention plans, etc.

Adequate software functionality and quality is a crucial issue in a society that vitally depends on software systems. The rising expectations of software users, the distribution of software over networks, size and complexity of today’s software systems bring our engineering abilities to limits. Functionality, the cost and the quality of software critically depend on an adequate requirements engineering. We argue in favor of systematic requirements engineering that is model-based, targeting comprehensive system architectures and deeply integrated into software life cycle models.

Interventional MRI scanners now allow neurosurgeons to make images throughout the course of surgery. While these machines are still relatively rare today, they are bound to become a key instrument in the operating theatres of major medical centers. The successful use of such machines requires close collaboration between surgeons and engineers. This presentation describes the problem of neurosurgical navigation and discusses some of its algorithmic challenges, such as the joint use of multiple imaging modalities (CT, MRI, PET, etc), image registration, field-artifact removal, multi-modality image segmentation, biomechanical models of the brain, finite-element models (FEM) for tracking tissue deformation, and a generalization of FEM, known as XFEM, to handle the cuts, retractions, and resections occuring during surgery.

After a broad overview of European support mechanisms to collaborative research, the presentation will give a summary of presently running EU projects within the IST Programme as part of the sixth Framework Programme (FP6). It will then focus on current activities that relate to Computer and Information Sciences including examples of key projects.

We describe a new decision list induction algorithm called the Greedy Prepend Algorithm (GPA). GPA improves on other decision list algorithms by introducing a new objective function for rule selection and a set of novel search algorithms that allow application to large scale real world problems. GPA achieves state-of-the-art classification accuracy on the protein secondary structure prediction problem in bioinformatics and the English part of speech tagging problem in computational linguistics. For both domains GPA produces a rule set that human experts find easy to interpret, a marked advantage in decision support environments. In addition, we compare GPA to other decision list induction algorithms as well as support vector machines, C4.5, naive Bayes, and a nearest neighbor method on a number of standard data sets from the UCI machine learning repository.

The exponentiation problem is computing x ^ n for positive integer exponents n where the quality is measured by number of multiplications it requires. However, finding minimum number of multiplications is an NP-complete problem. This problem is very important for many applications such as RSA encryption and ElGamal decryption. Solving minimum Brauer chain problem is a way to solve the exponentiation problem. In this paper, five heuristics for approximating minimum length Brauer chain for a given number n is discussed. These heuristics are based on some greedy approaches and dynamic programming. As a result, we empirically get 1.1-approximation for the problem.

In this paper, we propose a model and algorithms to solve a load balancing problem on a printed circuit board assembly line. On this line of two identical machines, the problem of allocation of component types to machines is analyzed in detail. Twenty eight dispatching rules are developed and extensive computational experimentation is performed. It has been observed that the imbalance per board increases as a function of the number of board types. Furthermore, the greedy dispatching rules perform better than the construction dispatching rules.

Crew scheduling is a crux of running a regional airline. The regional airline is characterized as a crew dependant airline due to the fact that the flight operation of the airline is rather strictly constrained by crew availability and qualifications. Furthermore, the airline must accommodate frequent changes in flight crew requirements due to seasonal or monthly flight demand fluctuation. In this paper, a heuristic approach is proposed to schedule the airline’s crew members. Additionally, an integer linear programming formulation is suggested to verify the feasibility of the heuristic approach. Evaluation purposes, the crew schedules of the operation of the regional airline were generated to check the computational advantages of the approach. The proposed heuristic approach finds a good crew schedule with computational efficiency for the airline.

The segmentation of the liver structure from a computed tomography (CT) image is an important function of the software designed to assist liver diagnostics, because it allows for the elimination of excess information from the diagnostic process. In this paper, the task of segmentation has been implemented through first finding the contour of the liver which is made up of a finite number of joint polylines approximating individual fragments of the liver boundary in the CT image. Next, the field outside the contour is divided into two polygons and eliminated from the image. The initial reference point for the calculations is the lumbar section of the spine which is a central point of any CT image of the liver. The automatic method of segmentation is to be used in a dedicated computer system designed to diagnose liver patients.