Self-modeling in humanoid soccer robots

In this paper we discuss the applicability, potential benefits, open problems and expected contributions that an emerging set of self-modeling techniques might bring on the development of humanoid soccer robots. The idea is that robots might continuously generate, validate and adjust physical models of their sensorimotor interaction with the world. These models are exploited for adapting behavior in simulation, enhancing the learning skills of a robot with the regular transference of controllers developed in simulation to reality. Moreover, these simulations can be used to aid the execution of complex sensorimotor tasks, speed up adaptation and enhance task planning. We present experiments on the generation of behaviors for humanoid soccer robots using the Back-to-Reality algorithm. General motivations are presented, alternative algorithms are discussed and, most importantly, directions of research are proposed.     

Integrating perceptual level of detail with head-pose estimation and its uncertainty

Immersive virtual environments with life-like interaction capabilities can provide a high fidelity view of the virtual world and seamless interaction methods to the user. High demanding requirements, however, raise many challenges in the development of sensing technologies and display systems. The focus of this study is on improving the performance of human–computer interaction by rendering optimizations guided by head pose estimates and their uncertainties. This work is part of a larger study currently being under investigation at NASA Ames, called “Virtual GloveboX” (VGX). VGX is a virtual simulator that aims to provide advanced training and simulation capabilities for astronauts to perform precise biological experiments in a glovebox aboard the International Space Station (ISS). Our objective is to enhance the virtual experience by incorporating information about the user’s viewing direction into the rendering process. In our system, viewing direction is approximated by estimating head orientation using markers placed on a pair of polarized eye-glasses. Using eye-glasses does not pose any constraints in our operational environment since they are an integral part of a stereo display used in VGX. During rendering, perceptual level of detail methods are coupled with head-pose estimation to improve the visual experience. A key contribution of our work is incorporating head pose estimation uncertainties into the level of detail computations to account for head pose estimation errors. Subject tests designed to quantify user satisfaction under different modes of operation indicate that incorporating uncertainty information during rendering improves the visual experience of the user.     

Some (in)sufficient conditions for secure hybrid encryption

In hybrid public key encryption (PKE), first a key encapsulation mechanism (KEM) is used to fix a random session key that is then fed into a highly efficient data encapsulation mechanism (DEM) to encrypt the actual message. A well-known composition theorem states that if both the KEM and the DEM have a high enough level of security (i.e., security against chosen-ciphertext attacks), then so does the hybrid PKE scheme. It is not known if these strong security requirements on the KEM and DEM are also necessary, nor if such general composition theorems exist for weaker levels of security.Using six different security notions for KEMs, 10 for DEMs, and six for PKE schemes, we completely characterize in this work which combinations lead to a secure hybrid PKE scheme (by proving a composition theorem) and which do not (by providing counterexamples). Furthermore, as an independent result, we revisit and extend prior work on the relations among security notions for KEMs and DEMs.     

Methods for the combination of kernel matrices within a support vector framework

The problem of combining different sources of information arises in several situations, for instance, the classification of data with asymmetric similarity matrices or the construction of an optimal classifier from a collection of kernels. Often, each source of information can be expressed as a similarity matrix. In this paper we propose a new class of methods in order to produce, for classification purposes, a single kernel matrix from a collection of kernel (similarity) matrices. Then, the constructed kernel matrix is used to train a Support Vector Machine (SVM). The key ideas within the kernel construction are twofold: the quantification, relative to the classification labels, of the difference of information among the similarities; and the extension of the concept of linear combination of similarity matrices to the concept of functional combination of similarity matrices. The proposed methods have been successfully evaluated and compared with other powerful classifiers and kernel combination techniques on a variety of artificial and real classification problems.     

Mesh Topological Optimization for Improving Piecewise-Linear Image Registration

This paper presents a mutual-information based optimization algorithm for improving piecewise-linear (PWL) image registration. PWL-registration techniques, which are well-suited for registering images of the same scene with relative local distortions, divide the images in conjugate triangular patches that are individually mapped through affine transformations. For this process to be accurate, each pair of corresponding image triangles must be the projections of a planar surface in space; otherwise, the registration incurs in errors that appear in the resultant registered image as local distortions (distorted shapes, broken lines, etc.). Given an initial triangular mesh onto the images, we propose an optimization algorithm that, by swapping edges, modifies the mesh topology looking for an improvement in the registration. For detecting the edges to be swapped we employ a cost function based on the mutual information (MI), a metric for registration consistency more robust to image radiometric differences than other well-known metrics such as normalized cross correlation (NCC). The proposed method has been successfully tested with different sets of test images, both synthetic and real, acquired from different angles and lighting conditions.     

Ubimedia based on readable and writable memory tags

Ubimedia is a concept where media files are embedded in everyday objects and the environment. We propose an approach where the user can read and write these files with his/her personal mobile phone simply by touching the physical objects. This facilitates easy access and storage of, e.g. video and audio files related to the physical object in question. This paper describes our work in developing a technical solution for ubimedia and studying user acceptance of forthcoming ubimedia services. Our technical development of the ubimedia concept has been focused on a mobile phone platform with a tag reader/writer, memory tags with large storage capacity, and the communication between the phone and the tags. Currently, the technical design is in test and evaluation phase. The preliminary results show that the concept works and it can be implemented technically. In parallel with the technical development, we have studied usage possibilities for ubimedia and user acceptance of future ubimedia services. User acceptance has been studied in a web survey and in user evaluations of proofs-of-concept. In addition, an ethical assessment has been carried out. The users appreciated especially the simplicity, speed, low cost and reliability of ubimedia. Ethical concerns were related to control over the download with regard to viruses and other unwanted content.     

On the stabilization parameter in the subgrid scale approximation of scalar convection–diffusion–reaction equations on distorted meshes

In this paper we revisit the definition of the stabilization parameter in the finite element approximation of the convection–diffusion–reaction equation. The starting point is the decomposition of the unknown into its finite element component and a subgrid scale that needs to be approximated. In order to incorporate the distortion of the mesh into this approximation, we transform the equation for the subgrid scale within each element to the shape-regular reference domain. The expression for the subgrid scale arises from an approximate Fourier analysis and the identification of the wave number direction where instabilities are most likely to occur. The final outcome is an expression for the stabilization parameter that accounts for anisotropy and the dominance of either convection or reaction terms in the equation.     

A review of log-polar imaging for visual perception in robotics

Log-polar imaging consists of a type of methods that represent visual information with a space-variant resolution inspired by the visual system of mammals. It has been studied for about three decades and has surpassed conventional approaches in robotics applications, mainly the ones where real-time constraints make it necessary to utilize resource-economic image representations and processing methodologies. This paper surveys the application of log-polar imaging in robotic vision, particularly in visual attention, target tracking, egomotion estimation, and 3D perception. The concise yet comprehensive review offered in this paper is intended to provide novel and experienced roboticists with a quick and gentle overview of log-polar vision and to motivate vision researchers to investigate the many open problems that still need solving. To help readers identify promising research directions, a possible research agenda is outlined. Finally, since log-polar vision is not restricted to robotics, a couple of other areas of application are discussed.     

Spanish Enigma: A History of the Enigma in Spain

Abstract

The presentation of a brief study of the use of Enigma Machines in Spain, during the Spanish Civil War (1936–1939) and later, until their final replacement.