D. Boto-Giralda

Wavelet‐Based Denoising for Traffic Volume Time Series Forecasting with Self‐Organizing Neural Networks

In their goal to effectively manage the use of existing infrastructures, intelligent transportation systems require precise forecasting of near-term traffic volumes to feed real-time analytical models and traffic surveillance tools that alert of network links reaching their capacity. This article proposes a new methodological approach for short-term predictions of time series of volume data at isolated cross sections. The originality in the computational modeling stems from the fit of threshold values used in the stationary wavelet-based denoising process applied on the time series, and from the determination of patterns that characterize the evolution of its samples over a fixed prediction horizon. A self-organizing fuzzy neural network is optimized in its configuration parameters for learning and recognition of these patterns. Four real-world data sets from 3 interstate roads are considered for evaluating the performance of the proposed model. A quantitative comparison made with the results obtained by 4 other relevant prediction models shows a favorable outcome.

Recognition of coloured and textured images through a multi-scale neural architecture with orientational filtering and chromatic diffusion

The aim of this paper is to outline a multiple scale neural model to recognise colour images of textured scenes. This model combines colour and textural information in order to recognise colour texture images through the operation of two main components: a segmentation component composed of the colour opponent system (COS) and the chromatic segmentation system (CSS); and a recognition component formed by an ARTMAP-based neural network with scale and orientation-invariance properties. Segmentation is achieved by perceptual contour extraction and diffusion processes on the colour opponent channels based on the human psychophysical theory of colour perception. This colour regions enhancement along with their local textural features constitutes the recognition pattern to be sent to the supervised neural classifier. The CSS accomplishes the colour region enhancement through a multiple scale loop of oriented filters and competition-cooperation mechanisms. Afterwards, the neural architecture performs an attentive recognition of the scene using those oriented filters responses and the chromatic diffusions. Some comparative tests with other models are included in order to prove the recognition capabilities of this neural architecture and how the use of colour information encourages the texture classification and the accuracy of the boundary detection.

Neural Network Model Based on Fuzzy ARTMAP for Forecasting of Highway Traffic Data

In this chapter, a neural network model is presented for forecasting the average speed values at highway traffic detectors locations using the Fuzzy ARTMAP theory. The performance of the model is measured by the deviation between the speed values provided by the loop detectors and the predicted speed values. Different Fuzzy ARTMAP configuration cases are analysed in their training and testing phases. Some ad-hoc mechanisms added to the basic Fuzzy ARTMAP structure are also described to improve the entire model performance. The achieved results make this model suitable for being implemented on advanced traffic management systems (ATMS) and advanced traveller information system (ATIS).

Real-time hands, face and facial features detection and tracking: Application to cognitive rehabilitation tests monitoring

In this paper, a marker-free computer vision system for cognitive rehabilitation tests monitoring is presented. The system monitors and analyzes the correct and incorrect realization of a set of psicomotricity exercises in which a hand has to touch a facial feature. The monitoring requires different human body parts detection and tracking. Detection of face, eyes, nose, and hands is achieved with a set of classifiers built independently based on the AdaBoost algorithm. Comparisons with other detection approaches, regarding performance and applicability to the monitoring system, are presented. Face and hands tracking is accomplished through the CAMShift algorithm with independent and adaptive two-dimensional histograms of the chromaticity components of the TSL color space for the pixels inside these three regions. The TSL color space was selected after a study of five color spaces regarding skin color characterization. The system is easily implemented with a consumer-grade computer and a camera, unconstrained background and illumination and runs at more than 23 frames per second. The system was tested and achieved a successful monitoring percentage of 97.62%. The automation of the human body parts motion monitoring, its analysis in relation to the psicomotricity exercise indicated to the patient and the storage of the result of the realization of a set of exercises free the rehabilitation experts of doing such demanding tasks. The vision-based system is potentially applicable to other human-computer interface tasks with minor changes.

Computer vision architecture for real-time face and hand detection and tracking

In this paper we present a computer vision architecture to detect and track the face and hands of a human being in real time from a video sequence captured by a webcam. The architecture has a first preprocessing stage, including a color filtering module, a motion filtering module, a color-based segmentation, a processing channels merge module and, finally, a contour search and discrimination module. The aim of the first stage is to discard the image regions which are highly unlikely to correspond with skin. Thus, the second stage of the architecture is a previously trained Fuzzy ARTMAP multiscale neural network module which only processes those image regions selected by the preprocessing stage, which are fully expected to be skin. The neural networks make the last decision about face and hand detection. After that, the architecture tracks the trajectories which face and hands follow.

Analysis of the benefits and constraints for the implementation of cloud computing over an EHRs system

The Cloud Computing paradigm means a radical change over the IT technologies. This transform offers us many benefits in terms of e-services. Cloud Computing offers us a new solution for the implementation of electronic management system in a huge variety of fields. So the e-health is included on these solutions. Despite the fact that Cloud Computing is under development there are a lot of opportunities of implementation of Cloud Computing over e-health services. So in this paper we are going to discuss the viability of the implementation of this new model over an Electronic Health Records (EHRs) system. To find an answer of this issue we are going to analyze the benefits and constraints that can be given in this kind of systems.

Texture Classification of the Entire Brodatz Database through an Orientational-Invariant Neural Architecture

This paper presents a supervised neural architecture, called SOON, for texture classification. Multi-scale Gabor filtering is used to extract the textural features which shape the input to a neural classifier with orientation invariance properties in order to accomplish the classification. Three increasing complexity tests over the well-known Brodatz database are performed to quantify its behavior. The test simulations, including the entire texture album classification, show the stability and robustness of the SOON response.

Adapting the Telecommunication Engineering curriculum to the EEES: A project based learning tied to several subjects

This paper describes the adaptation process to the European Credit Transfer System requirements of several subjects aiming at the Information and Communication Technologies (ICT) learning. Specifically, these subjects are sited at the Telecommunications Engineering studies lectured in the University of Valladolid. In a first step two first grade subjects have been established, while in a second and final step, coinciding with the new degrees beginning, it will be extended to five subjects placed in consecutive semesters. The global programming has been divided into several subprojects of growing complexity, developed into subjects sited in different and successive semesters of the degree, following a pathway leading to the development of a global project throughout four years. The whole learning process is ICT-supported, as tools for overcoming distance and scheduling barriers are offered. In particular, Moodle platform is used, which has been enhanced with self-evaluation and co-evaluation tools developed by the teaching group. Main innovation regarding to the classical approach consists of a computer programming subject focused on the student learning and based on the detailed specification of the activity the students have to perform in and out of the classroom in order to achieve the educational objectives of each of the subjects. The educational strategies used to accomplish these objectives are based on the cooperative learning, on the teamwork developing a programming project (Project Based Learning, PBL), and on the discovery learning.

Computer Vision-Based Eyelid Closure Detection: A Comparison of MLP and SVM Classifiers

In this paper, a vision-based system to detect the eyelid closure for driver alertness monitoring is presented. Similarity measures with three eye templates (open, nearly close, and close) were calculated from many different features, such as 1-D and 2-D histograms and horizontal and vertical projections, of a big set of rectangular eyes images. Two classifiers, Multi-Layer Perceptron and Support Vector Machine, were intensively studied to select the best with the sequential forward feature selection. The system is based on the selected Multi-Layer Perceptron classifier, which is used to measure PERCLOS (percentage of time eyelids are close). The monitoring system is implemented with a consumer-grade computer and a webcam with passive illumination, runs at 55 fps, and achieved an overall accuracy of 95.75% with videos with different users, environments and illumination. The system can be used to monitor driver alertness robustly in real time.

Real-time nose detection and tracking based on AdaBoost and optical flow algorithms

In this paper we present a fast and robust nose detection and tracking application which runs on a consumer-grade computer with video input from an inexpensive Universal Serial Bus camera. Nose detection is based on the AdaBoost algorithm with Haar-like features. A detailed study was developed to select the positive and negative training samples and the parameters of the detector. Pyramidal Lucas-Kanade optical flow tracking algorithm is applied to the nostrils from a previous nose detection in a frame of a video sequence. Tracking takes 2 ms and is robust to different face positions, backgrounds and illumination. The nose detection and tracking application can be used alone or integrated in a hand-free vision-based Human-Computer Interface.