Abstract
In this paper the kinematic design of a 6-dof parallel robotic manipulator is analysed. Firstly, the condition number of the inverse kinematic jacobian is considered as the objective function, measuring the manipulator's dexterity and a genetic algorithm is used to solve the optimization problem. In a second approach, a neural network model of the analytical objective function is developed and subsequently used as the objective function in the genetic algorithm optimization search process. It is shown that the neuro-genetic algorithm can find close to optimal solutions for maximum dexterity, significantly reducing the computational burden. The sensitivity of the condition number in the robot's workspace is analysed and used to guide the designer in choosing the best structural configuration. Finally, a global optimization problem is also addressed.

Abstract
Very few developments have been done to provide electric power supply of instrumented hip prosthesis. Actually, vibration-powered generators are the most appropriate mechanisms for this kind of application's environment. This paper describes the first attempt to develop the concept of energy harvesting from multiple energy sources applied in the same hip implant. Exploiting the potential of the three angular movements over the femoral component, namely in the abduction-adduction, flexion-extension and inward-outward rotation axes, three in board vibration-based mechanisms were developed in order to ensure electric power supply from multiple energy sources. A total of 53.7 mu J/s was harvested by a translation movement-based electromagnetic energy generator when a sinusoidal function with an amplitude of 40 mm and a frequency of 4 Hz was applied. A rotation movement-based electromagnetic energy generator has harvested 0.77 mu J/s when a sinusoidal function with an amplitude of 60 degrees and a frequency of 2.5 Hz was used. The piezoelectric energy harvester has achieved 0.6 mu J/s with the application of a sinusoidal function with an amplitude of 200 N and a frequency of 4 Hz. Besides, its ability of being fully autonomous, operating without expiry and maintenance, while offering safety during its entire lifetime are relevant features. This paper should provide the basis for the development of smart hip prosthesis with the ability to fix the aseptic implant loosening problem.

Abstract
We present a nonlinear mathematical model for a low-size and micro-power vibrational energy harvester targeted to power smart prostheses and in particular hip prostheses. Constraints such as available volume, allowable total weight and energy transducer mechanism difficult the development of an optimal generator system, especially considering the low frequencies involved in the human gait. Since non-linear behavior often conducts to better experimental results than those obtained using linear models, a nonlinear model is being pursued to reduce the volume of the generator and maximize performance. By including the non-linear effects of magnetic levitation used to suspend the inertial mass, influence of friction and effects of inductor non-idealities, preliminary results indicate better generator's performance prediction which allows a more effective customization of the energy harvester. (C) 2012 Elsevier Ltd....Selection and/or peer-review under responsibility of the Symposium Cracoviense Sp. z.o.o.

Abstract
This paper describes a multi-purpose energy management system that can harvest energy from a multitude of power sources. In order to suffice power needs of a smart hip prosthesis, a prototype was built considering the use of two types of power sources: energy harvesting micro-power generators for intermittent implant electronics powering; wireless energy, by means of an activation system, responsible for, when needed, continuously powering the implant electronics and also configuring its mode of operation. Intermittently or continuously, it is now possible to energize more power demanding systems as the ones that uses a RF transceiver. The smart hip prosthesis can now become a wireless body sensor network node using LR-WPAN protocols such as Bluetooth low energy as is intended in this work. Preliminary results proved that is possible to energize a Bluetooth low energy module, for over 100 s, solely using the stored energy produced by one of the micro-power generators. (C) 2012 Elsevier Ltd....Selection and/or peer-review under responsibility of the Symposium Cracoviense Sp. z.o.o.

Abstract
A digital image correlation (DIC) algorithm for displacement measurements combining cross-correlation and a differential technique was validated through a set of experimental tests. These tests consisted of in-plane rigid-body translation and rotation tests, a tensile mechanical test, and a mode I fracture test. The fracture mechanical test, in particular, was intended to assess the accuracy of the method when dealing with discontinuous displacement fields, for which subset-based image correlation methods usually give unreliable results. The proposed algorithm was systematically compared with the Aramis (R) DIC-2D commercial code by processing the same set of images. When processing images from rigid-body and tensile tests (associated with continuous displacement fields), the two methods provided equivalent results. When processing images from the fracture mechanical test, however, the proposed method obtained a better qualitative description of the discontinuous displacements. Moreover, the proposed method gave a more reliable estimation of both crack length and crack opening displacement of the fractured specimen.(C) (C) 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.OE.51.4.043602]

Abstract
This paper presents a system which extends the use of the traditional white cane by the blind for navigation purposes in indoor environments. Depth data of the scene in front of the user is acquired using the Microsoft Kinect sensor which is then mapped into a pattern representation. Using neural networks, the proposed system uses this information to extract relevant features from the scene, enabling the detection of possible obstacles along the way. The results show that the neural network is able to correctly classify the type of pattern presented as input.