Omnidirectional Depth Computation from a Single Image

Omnidirectional cameras offer a much wider field of view than the perspective ones and alleviate the problems due to occlusions. However, both types of cameras suffer from the lack of depth perception. A practical method for obtaining depth in computer vision is to project a known structured light pattern on the scene avoiding the problems and costs involved by stereo vision. This paper is focused on the idea of combining omnidirectional vision and structured light with the aim to provide 3D information about the scene. The resulting sensor is formed by a single catadioptric camera and an omnidirectional light projector. It is also discussed how this sensor can be used in robot navigation applications

On optimal computation of MPLS label binding for multipoint-to-point connections

Most network operators have considered reducing Label Switched Routers (LSR) label spaces (i.e. the number of labels that can be used) as a means of simplifying management of underlaying Virtual Private Networks (VPNs) and, hence, reducing operational expenditure (OPEX). This letter discusses the problem of reducing the label spaces in Multiprotocol Label Switched (MPLS) networks using label merging - better known as MultiPoint-to-Point (MP2P) connections. Because of its origins in IP, MP2P connections have been considered to have tree- shapes with Label Switched Paths (LSP) as branches. Due to this fact, previous works by many authors affirm that the problem of minimizing the label space using MP2P in MPLS - the Merging Problem - cannot be solved optimally with a polynomial algorithm (NP-complete), since it involves a hard- decision problem. However, in this letter, the Merging Problem is analyzed, from the perspective of MPLS, and it is deduced that tree-shapes in MP2P connections are irrelevant. By overriding this tree-shape consideration, it is possible to perform label merging in polynomial time. Based on how MPLS signaling works, this letter proposes an algorithm to compute the minimum number of labels using label merging: the Full Label Merging algorithm. As conclusion, we reclassify the Merging Problem as Polynomial-solvable, instead of NP-complete. In addition, simulation experiments confirm that without the tree-branch selection problem, more labels can be reduced

Estudi comparatiu de l'algorisme basat en una colònia artificial d'abelles

La finalitat d'aquest projecte és la realització d'un estudi comparatiu de l'algoritme basat en una colònia artificial d'abelles, Artificial Bee Colony (ABC), comparat amb un conjunt d'algoritmes fonamentats en el paradigma de la computació evolutiva. S'utilitzarà l'eficàcia a l'hora d'optimitzar diverses funcions com a mesura comparativa. Els algoritmes amb els quals es comparara l'algoritme ABC són: algoritmes genètics, evolució diferencial i optimització amb eixam de partícules.

The spatiotemporal trajectory of a dengue epidemic in a medium-sized city

Understanding the transmission dynamics of infectious diseases is important to allow for improvements of control measures. To investigate the spatiotemporal pattern of an epidemic dengue occurred at a medium-sized city in the Northeast Region of Brazil in 2009, we conducted an ecological study of the notified dengue cases georeferenced according to epidemiological week (EW) and home address. Kernel density estimation and space-time interaction were analysed using the Knox method. The evolution of the epidemic was analysed using an animated projection technique. The dengue incidence was 6.918.7/100,000 inhabitants; the peak of the epidemic occurred from 8 February-1 March, EWs 6-9 (828.7/100,000 inhabitants). There were cases throughout the city and was identified space-time interaction. Three epicenters were responsible for spreading the disease in an expansion and relocation diffusion pattern. If the health services could detect in real time the epicenters and apply nimbly control measures, may possibly reduce the magnitude of dengue epidemics.

Rank estimation of trajectory matrix in motion segmentation

A novel technique for estimating the rank of the trajectory matrix in the local subspace affinity (LSA) motion segmentation framework is presented. This new rank estimation is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built with LSA. The result is an enhanced model selection technique for trajectory matrix rank estimation by which it is possible to automate LSA, without requiring any a priori knowledge, and to improve the final segmentation

Transmission delays in residual computation

The design of control, estimation or diagnosis algorithms most often assumes that all available process variables represent the system state at the same instant of time. However, this is never true in current network systems, because of the unknown deterministic or stochastic transmission delays introduced by the communication network. During the diagnosing stage, this will often generate false alarms. Under nominal operation, the different transmission delays associated with the variables that appear in the computation form produce discrepancies of the residuals from zero. A technique aiming at the minimisation of the resulting false alarms rate, that is based on the explicit modelling of communication delays and on their best-case estimation is proposed

A Monte Carlo method for accelerating the computation of animated radiosity sequences

Realistic rendering animation is known to be an expensive processing task when physically-based global illumination methods are used in order to improve illumination details. This paper presents an acceleration technique to compute animations in radiosity environments. The technique is based on an interpolated approach that exploits temporal coherence in radiosity. A fast global Monte Carlo pre-processing step is introduced to the whole computation of the animated sequence to select important frames. These are fully computed and used as a base for the interpolation of all the sequence. The approach is completely view-independent. Once the illumination is computed, it can be visualized by any animated camera. Results present significant high speed-ups showing that the technique could be an interesting alternative to deterministic methods for computing non-interactive radiosity animations for moderately complex scenarios

Sex differences in the computation of traveling distance

Path integration is known to provide information to keep track of spatial location. Surprisingly, few investigations concerning sex differences in computation of the traveling distance have been done. This work was aimed at analyzing the reproduction of both passive and active linear displacements in women and men. To this end, the displacement of blindfolded subjects was done in a wheelchair, then on foot, three times in each condition for a fixed distance. Copies of passive and active traveling distance, distance estimations and pointing responses towards the starting point were analyzed. In passive condition and comparatively to men, women error was larger. Whereas traveling distance was generally underestimated in women, it was overestimated in men. In active condition, no sex differences were observed. When blindfolded subjects have to estimate the traveling distance, the female error was larger than the male one. But, when subjects were asked to indicate the visual cue corresponding to the traveling distance, the male error was larger than the female one. Finally, pointing to the starting point (0°) after a whole-body rotation showed a larger deviation from 0° in men than in women. These results suggest that sex of the subjects influence brain computation of path integration information.

Role of Particle Shape on the Stress Propagation in Granular Packings

We present an experimental and numerical study on the influence that particle aspect ratio has on the mechanical and structural properties of granular packings. For grains with maximal symmetry (squares), the stress propagation in the packing localizes forming chainlike forces analogous to the ones observed for spherical grains. This scenario can be understood in terms of stochastic models of aggregation and random multiplicative processes. As the grains elongate, the stress propagation is strongly affected. The interparticle normal force distribution tends toward a Gaussian, and, correspondingly, the force chains spread leading to a more uniform stress distribution reminiscent of the hydrostatic profiles known for standard liquids

A hierarchical approach to model parameter optimization for developmental systems.

In the context of Systems Biology, computer simulations of gene regulatory networks provide a powerful tool to validate hypotheses and to explore possible system behaviors. Nevertheless, modeling a system poses some challenges of its own: especially the step of model calibration is often difficult due to insufficient data. For example when considering developmental systems, mostly qualitative data describing the developmental trajectory is available while common calibration techniques rely on high-resolution quantitative data. Focusing on the calibration of differential equation models for developmental systems, this study investigates different approaches to utilize the available data to overcome these difficulties. More specifically, the fact that developmental processes are hierarchically organized is exploited to increase convergence rates of the calibration process as well as to save computation time. Using a gene regulatory network model for stem cell homeostasis in Arabidopsis thaliana the performance of the different investigated approaches is evaluated, documenting considerable gains provided by the proposed hierarchical approach.

Probabilistic evidential assessment of gunshot residue particle evidence (Part II) : Bayesian parameter estimation for experimental count data.

Part I of this series of articles focused on the construction of graphical probabilistic inference procedures, at various levels of detail, for assessing the evidential value of gunshot residue (GSR) particle evidence. The proposed models - in the form of Bayesian networks - address the issues of background presence of GSR particles, analytical performance (i.e., the efficiency of evidence searching and analysis procedures) and contamination. The use and practical implementation of Bayesian networks for case pre-assessment is also discussed. This paper, Part II, concentrates on Bayesian parameter estimation. This topic complements Part I in that it offers means for producing estimates useable for the numerical specification of the proposed probabilistic graphical models. Bayesian estimation procedures are given a primary focus of attention because they allow the scientist to combine (his/her) prior knowledge about the problem of interest with newly acquired experimental data. The present paper also considers further topics such as the sensitivity of the likelihood ratio due to uncertainty in parameters and the study of likelihood ratio values obtained for members of particular populations (e.g., individuals with or without exposure to GSR).

On codes, matroids, and secure multiparty computation from linear secret-sharing schemes

Error-correcting codes and matroids have been widely used in the study of ordinary secret sharing schemes. In this paper, the connections between codes, matroids, and a special class of secret sharing schemes, namely, multiplicative linear secret sharing schemes (LSSSs), are studied. Such schemes are known to enable multiparty computation protocols secure against general (nonthreshold) adversaries.Two open problems related to the complexity of multiplicative LSSSs are considered in this paper. The first one deals with strongly multiplicative LSSSs. As opposed to the case of multiplicative LSSSs, it is not known whether there is an efficient method to transform an LSSS into a strongly multiplicative LSSS for the same access structure with a polynomial increase of the complexity. A property of strongly multiplicative LSSSs that could be useful in solving this problem is proved. Namely, using a suitable generalization of the well-known Berlekamp–Welch decoder, it is shown that all strongly multiplicative LSSSs enable efficient reconstruction of a shared secret in the presence of malicious faults. The second one is to characterize the access structures of ideal multiplicative LSSSs. Specifically, the considered open problem is to determine whether all self-dual vector space access structures are in this situation. By the aforementioned connection, this in fact constitutes an open problem about matroid theory, since it can be restated in terms of representability of identically self-dual matroids by self-dual codes. A new concept is introduced, the flat-partition, that provides a useful classification of identically self-dual matroids. Uniform identically self-dual matroids, which are known to be representable by self-dual codes, form one of the classes. It is proved that this property also holds for the family of matroids that, in a natural way, is the next class in the above classification: the identically self-dual bipartite matroids.

Causal explanations and scientific realism in particle physics

Particle physics studies highly complex processes which cannot be directly observed. Scientific realism claims that we are nevertheless warranted in believing that these processes really occur and that the objects involved in them really exist. This dissertation defends a version of scientific realism, called causal realism, in the context of particle physics. I start by introducing the central theses and arguments in the recent philosophical debate on scientific realism (chapter 1), with a special focus on an important presupposition of the debate, namely common sense realism. Chapter 2 then discusses entity realism, which introduces a crucial element into the debate by emphasizing the importance of experiments in defending scientific realism. Most of the chapter is concerned with Ian Hacking's position, but I also argue that Nancy Cartwright's version of entity realism is ultimately preferable as a basis for further development. In chapter 3,1 take a step back and consider the question whether the realism debate is worth pursuing at all. Arthur Fine has given a negative answer to that question, proposing his natural ontologica! attitude as an alternative to both realism and antirealism. I argue that the debate (in particular the realist side of it) is in fact less vicious than Fine presents it. The second part of my work (chapters 4-6) develops, illustrates and defends causal realism. The key idea is that inference to the best explanation is reliable in some cases, but not in others. Chapter 4 characterizes the difference between these two kinds of cases in terms of three criteria which distinguish causal from theoretical warrant. In order to flesh out this distinction, chapter 5 then applies it to a concrete case from the history of particle physics, the discovery of the neutrino. This case study shows that the distinction between causal and theoretical warrant is crucial for understanding what it means to "directly detect" a new particle. But the distinction is also an effective tool against what I take to be the presently most powerful objection to scientific realism: Kyle Stanford's argument from unconceived alternatives. I respond to this argument in chapter 6, and I illustrate my response with a discussion of Jean Perrin's experimental work concerning the atomic hypothesis. In the final part of the dissertation, I turn to the specific challenges posed to realism by quantum theories. One of these challenges comes from the experimental violations of Bell's inequalities, which indicate a failure of locality in the quantum domain. I show in chapter 7 how causal realism can further our understanding of quantum non-locality by taking account of some recent experimental results. Another challenge to realism in quantum mechanics comes from delayed-choice experiments, which seem to imply that certain aspects of what happens in an experiment can be influenced by later choices of the experimenter. Chapter 8 analyzes these experiments and argues that they do not warrant the antirealist conclusions which some commentators draw from them. It pays particular attention to the case of delayed-choice entanglement swapping and the corresponding question whether entanglement is a real physical relation. In chapter 9,1 finally address relativistic quantum theories. It is often claimed that these theories are incompatible with a particle ontology, and this calls into question causal realism's commitment to localizable and countable entities. I defend the commitments of causal realism against these objections, and I conclude with some remarks connecting the interpretation of quantum field theory to more general metaphysical issues confronting causal realism.