Abstract
Each year thousands of people suffer from lower limb amputation, mainly due to three causes: wars, accidents and vascular diseases. The development of lower limb prosthesis is crucial to restore people’s mobility, improving the quality of life of millions of people. This contribution presents a gait events detector algorithm capable of detecting all the events of the human walking. Results show the correct transition between phases during several gait cycles for a human model walking in flat terrain. © 2018 by World Scientific Publishing Co. Pte. Ltd.

Abstract
SLODAR (SLOpe Detection And Ranging) methods recover the atmospheric turbulence profile from cross-correlations of wavefront sensor (WFS) measurements, based on known turbulence models. Our work grows out of several experiments showing that turbulence statistics can deviate significantly from the classical Kolmogorov/ von Kármán models, especially close to the ground. We present a novel SLODAR-type method which simultaneously recovers both the turbulence profile in the atmosphere and the turbulence statistics at the ground layer - namely the slope of the spatial frequency power law. We consider its application to outer scale (L0)- reconstruction and investigate the limits of the joint estimation of such parameters.

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Smart grids technologies are enablers of new business models for domestic consumers with local flexibility (generation, loads, storage) and where access to data is a key requirement in the value stream. However, legislation on personal data privacy and protection imposes the need to develop local models for flexibility modeling and forecasting and exchange models instead of personal data. This paper describes the functional architecture of an home energy management system (HEMS) and its optimization functions. A set of data-driven models, embedded in the HEMS, are discussed for improving renewable energy forecasting skill and modeling multi-period flexibility of distributed energy resources.

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In this paper, a methodology to offer new potential of DR in real-time is presented. Since customers likely have extra possibilities for demand response (DR) participation in real-time, in addition to their scheduled potential in day-ahead, this method helps to provide balance in real-time market via DR aggregators. It can be vital once the stochastic variables of the network such as wind power generators (WPG) do not follow the forecasted production in real-time and have some distortions. Stochastic two-stage programming is applied to manage DR options, including load curtailment (LC), load shifting (LS), and load recovery (LR) in both day-ahead and real-time market. DR options in real-time are scheduled based on possible scenarios that reflect the behavior of wind power generation and are generated through Monte-Carlo simulation method. The merits of the method are demonstrated in a 6-bus case study, which shows a reduction in total operation cost.

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