The Informational Foundation of the Human Act

This book is the result of a collective research effort performed during many years in both Sweden and Spain. It is the result of attempting to develop a new field of research that could we denominate «human act informatics.» The goal has been to use the technologies of information to the study of the human act in general, including embodied acts and disembodied acts. The book presents a theory of the quantification of the informational value of human acts as order, opposing the living order against entropy. We present acting as a set of decisions and choices aimed to create order and to impose Modernity. Karl Popper’s frequency theory of probability is applied to characterize human acts regarding their degree of freedom and to set up a scale of order in human decisions. The traditional theory of economics and social science characterize the human act as rational, utilitarian and ethical. Our results emphasize that the unique significance of an act lies in its capacity to generate order. An adequate methodology is then presented to defend such hypothesis according to which, the rationality respective irrationality of acting, is in fact only a function of the act’s organizational capacity. From this perspective, it has been necessary to define «order» respective «disorder» as operative concepts that allowed the comparison of the organizational differences generated by each kind of act. According to the presented conclusions, the spontaneity of living, as unconscious thinking, dreaming, loving, etc. and the mainstream of the human acts, are utilitarian, but in an irrational way; they are rooted in unconscious drifts and therefore must be considered irrational-utility acts

Vehicle detection and tracking using homography-based plane rectification and particle filtering

This paper presents a full system for vehicle detection and tracking in non-stationary settings based on computer vision. The method proposed for vehicle detection exploits the geometrical relations between the elements in the scene so that moving objects (i.e., vehicles) can be detected by analyzing motion parallax. Namely, the homography of the road plane between successive images is computed. Most remarkably, a novel probabilistic framework based on Kalman filtering is presented for reliable and accurate homography estimation. The estimated homography is used for image alignment, which in turn allows to detect the moving vehicles in the image. Tracking of vehicles is performed on the basis of a multidimensional particle filter, which also manages the exit and entries of objects. The filter involves a mixture likelihood model that allows a better adaptation of the particles to the observed measurements. The system is specially designed for highway environments, where it has been proven to yield excellent results

Multiple object tracking using an automatic veriable-dimension particle filter

Object tracking through particle filtering has been widely addressed in recent years. However, most works assume a constant number of objects or utilize an external detector that monitors the entry or exit of objects in the scene. In this work, a novel tracking method based on particle filtering that is able to automatically track a variable number of objects is presented. As opposed to classical prior data assignment approaches, adaptation of tracks to the measurements is managed globally. Additionally, the designed particle filter is able to generate hypotheses on the presence of new objects in the scene, and to confirm or dismiss them by gradually adapting to the global observation. The method is especially suited for environments where traditional object detectors render noisy measurements and frequent artifacts, such as that given by a camera mounted on a vehicle, where it is proven to yield excellent results

Video analysis based vehicle detection and tracking using an MCMC sampling framework

This article presents a probabilistic method for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted camera. The method is designed to address the main shortcomings of traditional particle filtering approaches, namely Bayesian methods based on importance sampling, for use in traffic environments. These methods do not scale well when the dimensionality of the feature space grows, which creates significant limitations when tracking multiple objects. Alternatively, the proposed method is based on a Markov chain Monte Carlo (MCMC) approach, which allows efficient sampling of the feature space. The method involves important contributions in both the motion and the observation models of the tracker. Indeed, as opposed to particle filter-based tracking methods in the literature, which typically resort to observation models based on appearance or template matching, in this study a likelihood model that combines appearance analysis with information from motion parallax is introduced. Regarding the motion model, a new interaction treatment is defined based on Markov random fields (MRF) that allows for the handling of possible inter-dependencies in vehicle trajectories. As for vehicle detection, the method relies on a supervised classification stage using support vector machines (SVM). The contribution in this field is twofold. First, a new descriptor based on the analysis of gradient orientations in concentric rectangles is dened. This descriptor involves a much smaller feature space compared to traditional descriptors, which are too costly for real-time applications. Second, a new vehicle image database is generated to train the SVM and made public. The proposed vehicle detection and tracking method is proven to outperform existing methods and to successfully handle challenging situations in the test sequences

Use of tire derived aggregate in tunnel cut-and-cover

A case-history is reported in which tire derived aggregate (TDA) was successfully applied to reduce the weight of fill upon a cut-and-cover railway tunnel. Subsequent 3D numerical analyses are used to explore the effect of different assumptions about the constitutive model of the TDA material. Alternative dispositions of TDA around the tunnel section are also examined. Reductions of up to 60% in lining bending moment may be achieved. For the case analyzed the elastic description of the TDA has little influence on tunnel lining loads, although is important for fill settlement estimates.Peer ReviewedPostprint (author's final draft

Distributed Correlation-Based Feature Selection in Spark

CFS (Correlation-Based Feature Selection) is an FS algorithm that has been successfully applied to classification problems in many domains. We describe Distributed CFS (DiCFS) as a completely redesigned, scalable, parallel and distributed version of the CFS algorithm, capable of dealing with the large volumes of data typical of big data applications. Two versions of the algorithm were implemented and compared using the Apache Spark cluster computing model, currently gaining popularity due to its much faster processing times than Hadoop's MapReduce model. We tested our algorithms on four publicly available datasets, each consisting of a large number of instances and two also consisting of a large number of features. The results show that our algorithms were superior in terms of both time-efficiency and scalability. In leveraging a computer cluster, they were able to handle larger datasets than the non-distributed WEKA version while maintaining the quality of the results, i.e., exactly the same features were returned by our algorithms when compared to the original algorithm available in WEKA.Comment: 25 pages, 5 figure

Drought risk and vulnerability in water supply systems.

This paper provides an overview of the challenges presented to the managers of water supply systems by drought and water scarcity. Risk assessment is an essential tool for the diagnostic of water scarcity in this type of systems. The evaluation of the risk of water shortage is performed with the use of complex mathematical models. Different alternatives to address the problem are presented, covering a range of methodological approaches. The actions adopted to prevent or mitigate the effects of water scarcity should be properly organized in drought management plan. The process of development and implementation of drought management plans is briefly described presenting several examples taken from the Mediterranean region

Robust Multiple Lane Road Modeling Based on Perspective Analysis

Road modeling is the first step towards environment perception within driver assistance video-based systems. Typically, lane modeling allows applications such as lane departure warning or lane invasion by other vehicles. In this paper, a new monocular image processing strategy that achieves a robust multiple lane model is proposed. The identification of multiple lanes is done by firstly detecting the own lane and estimating its geometry under perspective distortion. The perspective analysis and curve fitting allows to hypothesize adjacent lanes assuming some a priori knowledge about the road. The verification of these hypotheses is carried out by a confidence level analysis. Several types of sequences have been tested, with different illumination conditions, presence of shadows and significant curvature, all performing in realtime. Results show the robustness of the system, delivering accurate multiple lane road models in most situations