A bio-inspired predictive sensory-motor coordination scheme for robot reaching and preshaping

This paper presents a sensory-motor coordination scheme for a robot hand-arm-head system that provides the robot with the capability to reach an object while pre-shaping the fingers to the required grasp configuration and while predicting the tactile image that will be perceived after grasping. A model for sensory-motor coordination derived from studies in humans inspired the development of this scheme. A peculiar feature of this model is the prediction of the tactile image. The implementation of the proposed scheme is based on a neuro-fuzzy module that, after a learning phase, starting from visual data, calculates the position and orientation of the hand for reaching, selects the best-suited hand configuration, and predicts the tactile feedback. The implementation of the scheme on a humanoid robot allowed experimental validation of its effectiveness in robotics and provided perspectives on applications of sensory predictions in robot motor control.     

Cyclic Nanoindentation for Local High Cycle Fatigue Investigations: A Methodological Approach Accounting for Thermal Drift

Cyclic nanoindentation allows characterizing the influence of single phases and their interactions on fatigue mechanisms. Herein, a method for high cycle fatigue testing by nanoindentation is presented. By combining high- and low-frequency indentation modes, high cycle numbers are achieved while obtaining sufficient data points to reconstruct force–displacement hysteresis loops. A challenge is the stochastic course of thermal drift which is addressed by measuring drift rate in regular low-force holding segments. Drift rates are used to correct the displacement values, yielding reproducible cyclic deformation data as it is shown for two very different materials, a ductile metal and a brittle ceramic.     

Simulations of time series of atmospherically distorted wave fronts

A comparatively simple way to generate time series of atmospherically distorted wave fronts is described and tested. Temporal power spectra of Zernike aberrations, extracted from the time series, agree very well with theoretical predictions. A method for generation of longer time series is also shown to give results in accordance with theory, except for the lowest temporal frequencies modeled. A way to superimpose several time series of wave fronts to generate a multilayer model of the atmosphere is briefly discussed.     

Poly-bagging predictors for classification modelling for credit scoring

Credit scoring modelling comprises one of the leading formal tools for supporting the granting of credit. Its core objective consists of the generation of a score by means of which potential clients can be listed in the order of the probability of default. A critical factor is whether a credit scoring model is accurate enough in order to provide correct classification of the client as a good or bad payer. In this context the concept of bootstraping aggregating (bagging) arises. The basic idea is to generate multiple classifiers by obtaining the predicted values from the fitted models to several replicated datasets and then combining them into a single predictive classification in order to improve the classification accuracy. In this paper we propose a new bagging-type variant procedure, which we call poly-bagging, consisting of combining predictors over a succession of resamplings. The study is derived by credit scoring modelling. The proposed poly-bagging procedure was applied to some different artificial datasets and to a real granting of credit dataset up to three successions of resamplings. We observed better classification accuracy for the two-bagged and the three-bagged models for all considered setups. These results lead to a strong indication that the poly-bagging approach may promote improvement on the modelling performance measures, while keeping a flexible and straightforward bagging-type structure easy to implement.     

Input-output properties of the Page-Hinkley detector

We consider the stochastic input-output properties of a simple non-linear dynamical system, the so-called Page-Hinkley detector, playing a key role in change detection, and also in queuing theory. We show that for L-mixing inputs with negative expectation the output process of this system is L-mixing. The result is applied to get an upper bound for the false alarm rate. The proof is then adapted to get a similar result for the case of random i.i.d. inputs. Possible extensions and open problems are given in the discussion.     

A platform for the development of patient applications in the domain of personalized health

Personalized health (p-health) systems can contribute significantly to the sustainability of healthcare systems, though their feasibility is yet to be proven. One of the problems related to their development is the lack of well-established development tools for this domain. As the p-health paradigm is focused on patient self-management, big challenges arise around the design and implementation of patient systems. This paper presents a reference platform created for the development of these applications, and shows the advantages of its adoption in a complex project dealing with cardio-vascular diseases.     

The effect of ionising radiation on photosynthetic oxygenic microorganisms for survival in space flight revealed by automatic photosystem II-based biosensors

Photosynthetic microorganisms are expected to be useful to maintain an oxygenic atmosphere and to provide biomass for astronauts in the International Space Station as well as in future long-term space flights. However, fluxes of complex ionizing radiation of various intensities and energies make space an extreme environment for the microorganisms, affecting their photosynthetic efficiency. To automatically monitor the photosynthetic Photosystem II (PSII) activity of microorganisms under space conditions an optical biosensor, which utilizes chlorophyll fluorescence as biological transduction system, was built; the PSII activity was monitored by the biosensor during balloon flights at stratospheric altitudes of about 40 km. The effect of space stress on quantum yield of PSII varied among the tested species depending on the growth light conditions at which they were exposed during the flights.     

Recognition of shapes by attributed skeletal graphs

In this paper, we propose a framework to address the problem of generic 2-D shape recognition. The aim is mainly on using the potential strength of skeleton of discrete objects in computer vision and pattern recognition where features of objects are needed for classification. We propose to represent the medial axis characteristic points as an attributed skeletal graph to model the shape. The information about the object shape and its topology is totally embedded in them and this allows the comparison of different objects by graph matching algorithms. The experimental results demonstrate the correctness in detecting its characteristic points and in computing a more regular and effective representation for a perceptual indexing. The matching process, based on a revised graduated assignment algorithm, has produced encouraging results, showing the potential of the developed method in a variety of computer vision and pattern recognition domains. The results demonstrate its robustness in the presence of scale, reflection and rotation transformations and prove the ability to handle noise and occlusions.