A New Tensorial Framework for Single-Shell High Angular Resolution Diffusion Imaging

Single-shell high angular resolution diffusion imaging data (HARDI) may be decomposed into a sum of eigenpolynomials of the Laplace-Beltrami operator on the unit sphere. The resulting representation combines the strengths hitherto offered by higher order tensor decomposition in a tensorial framework and spherical harmonic expansion in an analytical framework, but removes some of the conceptual weaknesses of either. In particular it admits analytically closed form expressions for Tikhonov regularization schemes and estimation of an orientation distribution function via the Funk-Radon Transform in tensorial form, which previously required recourse to spherical harmonic decomposition. As such it provides a natural point of departure for a Riemann-Finsler extension of the geometric approach towards tractography and connectivity analysis as has been stipulated in the context of diffusion tensor imaging (DTI), while at the same time retaining the natural coarse-to-fine hierarchy intrinsic to spherical harmonic decomposition.     

A metaheuristic approach to fisheries survey route planning

Every autumn, a research vessel carries out a sampling survey tour to estimate the abundance of groundfish species of the Portuguese continental waters. The sampling operations are carried out at predefined geographical locations, the fishing stations, within predefined multiple time windows. The vessel route starts and ends at the port of Lisbon, and must visit all fishing stations. According to a predefined periodicity, the vessel must enter a port to supply food, refuel, and/or change crew. Given the geographical locations of the fishing stations/ports and current weather conditions, the objective is to minimize the total traveled distance and completion time. We present a mixed integer linear program to describe the problem and propose two sequential heuristic approaches that combine genetic algorithms and adaptive large neighborhood search to solve it. Computational experience with real data shows that the proposed heuristics are suitable tools to solve the problem.     

Object Touch by a Humanoid Robot Avatar Induces Haptic Sensation in the Real Hand

Humanoid robot embodiment is a recently developed form of mediated embodiment. In 2 studies, we report and quantify a new haptic (touch) illusion during embodiment of a humanoid robot. Around 60% of the users in our studies reported haptic sensations in their real hand when they observed their robot avatar touching a curtain with its hand. Critically, our study shows for the first time that users can experience haptic sensations from a nonanthropomorphic embodied limb/agent with visual feedback alone (i.e. no haptic feedback provided). The results have important implications for the understanding of the cognitive processes governing mediated embodiment and the design of avatar scenarios.     

HumanTop: a multi-object tracking tabletop

In this paper, a computer vision based interactive multi-touch tabletop system called HumanTop is introduced. HumanTop implements a stereo camera vision subsystem which allows not only an accurate fingertip tracking algorithm but also a precise touch-over-the-working surface detection method. Based on a pair of visible spectra cameras, a novel synchronization circuit makes the camera caption and the image projection independent from each other, providing the minimum basis for the development of computer vision analysis based on visible spectrum cameras without any interference coming from the projector. The assembly of both cameras and the synchronization circuit is not only capable of performing an ad-hoc version of a depth camera, but it also introduces the recognition and tracking of textured planar objects, even when contents are projected over them. On the other hand HumanTop supports the tracking of sheets of paper and ID-code markers. This set of features makes the HumanTop a comprehensive, intuitive and versatile augmented tabletop that provides multitouch interaction with projective augmented reality on any flat surface. As an example to exploit all the capabilities of HumanTop, an educational application has been developed using an augmented book as a launcher to different didactic contents. A pilot study in which 28 fifth graders participated is presented. Results about efficiency, usability/satisfaction and motivation are provided. These results suggest that HumanTop is an interesting platform for the development of educational contents.     

State estimation of (otherwise unobservable) linear mechanical systems through the use of non-smooth impacts: the case of two mating gears

In this paper, we consider a particular mechanism, constituted by a pair of infinitely rigid mating gears subject to backlash, where only the position of the actuated gear is measured, so that the system is unobservable if the impacts between the teeth of the gears are ignored. Under the assumption that there is an infinite sequence of non-smooth impacts (which assumption is generally satisfied during a usual task, e.g., when the mechanism is actuated by a constant torque), it is possible to design an observer that asymptotically estimates also the position and the velocity of the non-actuated gear. Simulation results are included to verify the effectiveness of the proposed observer.     

Fuzzy Adaptive Control Law for Trajectory Tracking Based on a Fuzzy Adaptive Neural PID Controller of a Multi-rotor Unmanned Aerial Vehicle

International Journal of Control, Automation and Systems - This article presents a fuzzy adaptive control law (FACL) designed for tracking the trajectory of a low-scale unmanned aerial vehicle...     

Case‐based decision support for fault diagnosis: Do salient ratings discourage situation analysis?

In the processing industry, frequent faults call for assistance in diagnosis, and case‐based reasoning (CBR) can provide solutions applied by other operators in the past. This study investigated whether salient case ratings promote an uncritical acceptance of solutions. In 2 experiments, subjects diagnosed faults with a simulated CBR system, and ratings were presented in graphical or verbal format. In most trials, the case with the highest rating provided the correct solution, while in catch‐trials, it did not. Graphical ratings were hypothesized to speed up solutions but discourage cross‐checking and lead to errors in catch‐trials. These hypotheses were not confirmed, even though Experiment 2 maximized the incentive of relying on case ratings. While graphical ratings led subjects to start with the most highly rated case, they did not impair situation analysis and accuracy. The results suggest that during fault diagnosis people are not easily misled into overtrusting a CBR system.     

On the differential benchmarking of promotional efficiency with machine learning modelling (II): Practical applications

The assessment of promotional sales with models constructed by machine learning techniques is arousing interest due, among other reasons, to the current economic situation leading to a more complex environment of simultaneous and concurrent promotional activities. An operative model diagnosis procedure was previously proposed in the companion paper, which can be readily used both for agile decision making on the architecture and implementation details of the machine learning algorithms, and for differential benchmarking among models. In this paper, a detailed example of model analysis is presented for two representative databases with different promotional behaviour, namely, a non-seasonal category (milk) and a heavily seasonal category (beer). The performance of four well-known machine learning techniques with increasing complexity is analyzed in detail here. In particular, k-Nearest Neighbours, General Regression Neural Networks, Multilayer Perceptron (MLP), and Support Vector Machines (SVM), are differentially compared. Present paper evaluates these techniques along the experiments described for both categories when applying the methodological findings obtained in the companion paper. We conclude that some elements included in the architecture are not essential for a good performance of the machine learning promotional models, such as the semiparametric nature of the kernel in SVM models, whereas other can be strongly dependent of the database, such as the convenience of multiple output models in MLP regression schemes. Additionally, the specificity of the behaviour of certain categories and product ranges determines the need to establish suitable and specific procedures for a better prediction and feature extraction.