Control of a supernumerary robotic hand by foot: an experimental study in virtual reality.

In the operational theater, the surgical team could highly benefit from a robotic supplementary hand under the surgeon's full control. The surgeon may so become more autonomous; this may reduce communication errors with the assistants and take over difficult tasks such as holding tools without tremor. In this paper, we therefore examine the possibility to control a third robotic hand with one foot's movements. Three experiments in virtual reality were designed to assess the feasibility of this control strategy, the learning curve of the subjects in different tasks and the coordination of foot movements with the two natural hands. Results show that the limbs are moved simultaneously, in parallel rather than serially. Participants' performance improved within a few minutes of practice without any specific difficulty to complete the tasks. Subjective assessment by the subjects indicated that controlling a third hand by foot has been easy and required only negligible physical and mental efforts. The sense of ownership was reported to improve through the experiments. The mental burden was not directly related to the level of motion required by a task, but depended on the type of activity and practice. The most difficult task was moving two hands and foot in opposite directions. These results suggest that a combination of practice and appropriate tasks can enhance the learning process for controlling a robotic hand by foot

In a demanding task, three-handed manipulation is preferred to two-handed manipulation.

Equipped with a third hand under their direct control, surgeons may be able to perform certain surgical interventions alone; this would reduce the need for a human assistant and related coordination difficulties. However, does human performance improve with three hands compared to two hands? To evaluate this possibility, we carried out a behavioural study on the performance of naive adults catching objects with three virtual hands controlled by their two hands and right foot. The subjects could successfully control the virtual hands in a few trials. With this control strategy, the workspace of the hands was inversely correlated with the task velocity. The comparison of performance between the three and two hands control revealed no significant difference of success in catching falling objects and in average effort during the tasks. Subjects preferred the three handed control strategy, found it easier, with less physical and mental burden. Although the coordination of the foot with the natural hands increased trial after trial, about two minutes of practice was not sufficient to develop a sense of ownership towards the third arm

About Designing an Observer Pattern-Based Architecture for a Multi-objective Metaheuristic Optimization Framework

Multi-objective optimization with metaheuristics is an active and popular research field which is supported by the availability of software frameworks providing algorithms, benchmark problems, quality indicators and other related components. Most of these tools follow a monolithic architecture that frequently leads to a lack of flexibility when a user intends to add new features to the included algorithms. In this paper, we explore a different approach by designing a component-based architecture for a multi-objective optimization framework based on the observer pattern. In this architecture, most of the algorithmic components are observable entities that naturally allows to register a number of observers. This way, a metaheuristic is composed of a set of observable and observer elements, which can be easily extended without requiring to modify the algorithm. We have developed a prototype of this architecture and implemented the NSGA-II evolutionary algorithm on top of it as a case study. Our analysis confirms the improvement of flexibility using this architecture, pointing out the requirements it imposes and how performance is affected when adopting it.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Automatic configuration of NSGA-II with jMetal and irace

jMetal is a Java-based framework for multi-objective optimization with metaheuristics providing, among other features, a wide set of algorithms that are representative of the state-of-the-art. Although it has become a widely used tool in the area, it lacks support for automatic tuning of algorithm parameter settings, which can prevent obtaining accurate Pareto front approximations, especially for inexperienced users. In this paper, we present a first approach to combine jMetal and irace, a package for automatic algorithm configuration; the NSGA-II is chosen as the target algorithm to be tuned. The goal is to facilitate the combined use of both tools to jMetal users to avoid wasting time in adjusting manually the parameters of the algorithms. Our proposal involves the definition of a new algorithm template for evolutionary algorithms, which allows the flexible composition of multi-objective evolutionary algorithms from a set of configurable components, as well as the generation of configuration files for adjusting the algorithm parameters with irace. To validate our approach, NSGA-II is tuned with a benchmark problems and compared with the same algorithm using standard settings, resulting in a new variant that shows a competitive behavior.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Piecewise Linear Representation Segmentation as a Multiobjective Optimization Problem

Proceedings of: Forth International Workshop on User-Centric Technologies and applications (CONTEXTS 2010). Valencia, September 7-10, 2010Actual time series exhibit huge amounts of data which require an unaffordable computational load to be processed, leading to approximate representations to aid these processes. Segmentation processes deal with this issue dividing time series into a certain number of segments and approximating those segments with a basic function. Among the most extended segmentation approaches, piecewise linear representation is highlighted due to its simplicity. This work presents an approach based on the formalization of the segmentation process as a multiobjetive optimization problem and the resolution of that problem with an evolutionary algorithm.This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029-C02-02.Publicad

The application of thin-plate spark chambers to high-energy [pi]-p experiments

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/32259/1/0000321.pd

A Bug in the Multiobjective Optimizer IBEA: Salutary Lessons for Code Release and a Performance Re-Assessment

International audienceThe Indicator-Based Evolutionary Algorithm (IBEA) is one of the first indicator-based multiobjective optimization algorithms and due to its wide availability in several algorithm packages is often used as a reference algorithm when benchmarking multiobjective optimizers. The original publication on IBEA proposes to use two specific variants: one based on the ε-indicator and one based on the hypervolume. Several experimental studies concluded that, surprisingly, the IBEA variant with the ε-indicator performs better than the one with the hypervolume—even if the (unary) hypervolume indicator itself is the quality measure used in the performance assessment. Recently, a small bug has been found in the hypervolume variant of IBEA with large implications on its performance. Here, we not only explain the bug in detail and correct it, but also present the (improved) results of the corrected version. Moreover, and probably even more important for the scientific community, we point out that this bug has been transferred to other than the original software package, discuss how this obscured the bug, and argue in favor of some simple, even obvious guidelines how the optimization community should deal with algorithm source codes, documentation, and the (natural) existence of bugs in the future

Pseudo-unitary symmetry and the Gaussian pseudo-unitary ensemble of random matrices

Employing the currently discussed notion of pseudo-Hermiticity, we define a pseudo-unitary group. Further, we develop a random matrix theory which is invariant under such a group and call this ensemble of pseudo-Hermitian random matrices as the pseudo-unitary ensemble. We obtain exact results for the nearest-neighbour level spacing distribution for (2 X 2) PT-symmetric Hamiltonian matrices which has a novel form, s log (1/s) near zero spacing. This shows a level repulsion in marked distinction with an algebraic form in the Wigner surmise. We believe that this paves way for a description of varied phenomena in two-dimensional statistical mechanics, quantum chromodynamics, and so on.Comment: 9 pages, 2 figures, LaTeX, submitted to the Physical Review Letters on August 20, 200