Abstract
An indirect downsampling approach for continuous-time input/output system identification is proposed. This modus operandi was introduced to system identification through a sub-space algorithm, where the input/output data set is partitioned into lower rate m subsets. Then, a state-space discrete-time model is identified by fusing the data subsets into a single one. In the present work the identification of the input/output downsampled model is performed by a least squares and a simplified refined instrumental variables (IV) procedures. In this approach, the inter-sample behaviour is preserved by the addition of fictitious inputs, leading to an increase of excitation requirements of the input signal. This over requirement is removed by directly estimating from the data the parameters of the transfer function numerator. The performance of the method is illustrated using the Rao-Garnier test system.

Abstract
Industrial agents technology leverages the benefits of multiagent systems, distributed computing, artificial intelligence techniques and semantics in the field of production, services and infrastructure sectors, providing a new way to design and engineer control solutions based on the decentralization of control over distributed structures. The key drivers for this application are the benefits of agent-based industrial systems, namely in terms of robustness, scalability, reconfigurability and productivity, all of which translate to a greater competitive advantage. This manuscript monitors the chronology of research and development of the industrial applications of multiagent and holonic systems. It provides the comprehensive overview of methodologies, architectures and applications of agents in industrial domain from early nineties up to present. It also gives an outlook of the current trends as well as challenges and possible future application domains of industrial agents.

Abstract

Abstract
This paper presents an approach to infer UI patterns existent in a web application. This reverse engineering process is performed in two steps. First, execution traces are collected from user interactions using the Selenium software. Second, the existing UI patterns within those traces are identified using Machine Learning inference with the Aleph ILP system. The paper describes and illustrates the proposed methodology on a case study over the Amazon web site.

Abstract
In this work it is presented a study of the reflection spectra yielded by a Fiber Bragg Grating sensor embedded into an epoxy glue line between two wood arms, in a double cantilever beam (DCB) Mode I delamination test. The reflection spectra were obtained using a Spectral Analyzer Fibersensing Bragmeter FS2200SA in regular time intervals, as the stress applied to the laminates is continuously increased until fracture occurs. They initially show a typical Bragg grating reflection spectrum, which gradually changes into more complicated, multiple-peak spectra, resulting from a non-homogenous strain distribution along the board line. Based on these results, a model was derived for the variation of the grating effective index which fits the observed spectra when the irregular strain distribution is observed. This model consists of usual cosine description of Bragg grating effective index with linear phase variation, plus a logarithmic phase change along the fiber length, resulting in the increment of the grating wavelength with increasing distance from the load application point. Moreover, from this model the strain distribution along the grating is found, yielding the expected result.

Abstract
The use of images acquired in unconstrained scenarios is giving rise to new challenges in the field of iris recognition. Many works in literature reported excellent results in both iris segmentation and recognition but mostly with images acquired in controlled conditions. The intention to broaden the field of application of iris recognition, such as airport security or personal identification in mobile devices, is therefore hindered by the inherent unconstrained nature under which images are to be acquired. The proposed work focuses on mutual context information from iris centre and iris limbic and pupillary contours to perform robust and accurate iris segmentation in noisy images. The developed algorithm was tested on the MobBIO database with a promising 96% segmentation accuracy for the limbic contour.