Catálogo de publicaciones - libros

Multiple Sequence Alignment is an essential tool in the analysis and comparison of biological sequences. Unfortunately, the complexity of this problem is exponential. Currently feasible methods are, therefore, only approximations. The multiple sequence alignment algorithms are the most widespread among these approximations. Still, the computation speed of typical problems is often not satisfactory. Hence, the well known progressive alignment scheme of has been subject to parallelization to further accelerate the computation. In the course of this action a unique scheme to parallelize sequence alignment in particular and dynamic programming in general was discovered, which yields an average of / 2 parallel calculations for problem size . The scalability of () tasks for problem size can be even maintained for slower networks.

The concept of fast and slow signals interacting by non-linear lateral processing at the inner plexiform layer and its suitable mathematical formulation, allows for a coherent interpretation of simple, complex and colour coding ganglion retinal cells of various representative vertebrates. In its simplest formulation, the processing can be expressed through non-linear spatio-temporal transformations. The formulations result in a coherent unified conceptual frame to interprete signal processing in vertebrates’ retinae.

The design of visual processing systems in very demanding industrial environments is a technical field in which bioinspiration has not been explored as a developing tool. The need of extremely quick, accurate and real time responses needed in industrial applications is not usually seen as compatible with the “messy”, “slow” or “inaccurate” methods and algorithms inspired in the information processing mechanisms underlying neural activity in the visual pathway. We are trying, thus, to explore the practical possibilities of interaction among concepts from both worlds: the “real” vision system designed for a real time quality control of a production line, and the “inspiration” taken from multi-channel biological vision. In previous papers [1,2] a biologically plausible parallel system for visual detection of form, movement, shape and size has been developed. The system, working off-line and skipping real time restrictions, was tested for a variety of situations, yielding very good results in estimating the mentioned visual characteristics of moving objects. Furthermore, a second parallel-computing version was designed introducing the concept of parallel channel processing, e.g., the discrimination of different visual characteristics by mean of multiprocessors and multithread computing. The architecture we present here, which includes certain concepts developed in the previously explained results [3,4], is intended to work in the production line of a beverage canning industry where cans with faulty imprinted use date and lot number have to be immediately discharged from the line.

After having designed control systems for real autonomous cars in an urban environment using straight lines as reference [2], we are now trying to build a fuzzy control system based on clothoids [3], the curve used in roads and train tracks. This paper proposes a method based on soft computing and upgraded using genetic algorithms. Both sets of simulations are shown and compared.

This paper describes a monocular vision-based Adaptive Cruise Control (ACC) System in the framework of Intelligent Transportation Systems (ITS) technologies. The challenge is to use a single camera as input, in order to achieve a low cost final system that meets the requirements needed to undertake serial production.

This paper addresses the problem of automatic parallel parking by a back-wheel drive vehicle, using a biomimetic model based on direct coupling between vehicle perceptions and actions. This problem is solved by means of a bio-inspired approach in which the vehicle controller does not need to know the car kinematics and dynamics, neither does it call for knowledge of the environment map. The key point in the proposed approach is the definition of performance indices that for automatic parking happen to be functions of the strategic orientations to be injected, in real time, to the car-like robot controller. This solution leads to a dynamic multi-objective optimization problem, which is extremely hard to be dealt with analytically. A genetic algorithm is therefore applied, thanks to which we obtain a very simple and efficient solution. The paper ends with the results of computer simulations.

The automatic control of a vehicle’s steering wheel is now one of the most important challenges in the Intelligent Transportation Systems field. In this paper, we present a fuzzy logic-based automatic steering control system for mass-produced electric power steering (EPS) wheel-equipped vehicles that assures human-like behavior. In the literature, we find a lot of theoretical proposals and some simulations, but only a few work teams offer real solutions for this task. One such solution is the work developed by the Autopia Program in which some vehicles have been automated and can perform some maneuvers mimicking human reactions. In this paper, we use the EPS of a Citroën C3 Pluriel to effect the car’s behavior. The actuator is controlled from an onboard computer housing a fuzzy logic-based autonomous steering system. The vehicle’s internal computers generate the input information, which is read by a CAN bus and a high precision GPS. Some experiments using this equipment on a private test circuit are presented, obtaining an human-like behavior in all the maneuvers.

The design, development, construction and testing of an Artificial-Vision controlled Traffic-Light prototype has been carried out to rule and regulate intersections. Methods, algorithms and automatons have been built up with that purpose to provide the analysis of images and decisions making at real time. The aim has been the development of an intelligent traffic-light capable of capturing the presence or absence of vehicles, pedestrians and their particular situations defined by their trajectories. Besides the above mentioned properties we have to point out the adaptation to the precise characteristics of each crossing, as its geometry, the required equipment, etc. The project has been supervised by RACE, world wide known as experts in road safety awareness, endowing the prototype with reliability and trust.

There are several strategies of how to retrieve depth information from a sequence of images, like depth from motion, depth from shading and depth from stereopsis. In this paper, we introduce a new method to retrieve depth based on motion and stereopsis. A motion detection representation helps establishing further correspondences between different motion information. This representation bases in the permanency memories mechanism, where jumps of pixels between grey level bands are computed in a matrix of charge accumulators. For each frame of a video stereovision sequence, the method fixes the right permanency stereo memory, and displaces the left permanency stereo memory by pixel on the epipolar restriction basis over the right one, in order to analyze the disparities of the motion trails calculated. By means of this functionality, for all possible displacements of one permanency memory over the other, the correspondences between motion trails are checked, and the disparities are assigned, providing a way to analyze the depths of the objects present in the scene.

Recently, the focus of safety systems for intelligent vehicles has been on researching and developing Advanced Driver Assistance Systems (ADAS). Most efforts have been concentrated at the driver, not taking into account the protection of the most vulnerable road users. This paper describes a pedestrian detection algorithm based on stereo vision. The use of visual information is a promising approach to cope with the different appearances of pedestrians and changes of illumination in cluttered environments. Active contour models are used to detect and track people from the images taken by an on-board vision system, performing contour extraction in sequential frames.