Preventing data leakage on the mobile client is a crucial security problem. Therefore, additional control and protection should be taken for the confidential data on the mobile clients that leave the boundaries of the organization. This paper presents a novel approach to the security of the corporate mobile clients, in particular when they operate in the offline mode. The presented approach includes the essential conceptualization and the definition of the core methodology to solve the problem of offline mobile security, i.e. the protection of the confidential data in use when the mobile client is not connected to the corporate cloud. The protection of the sensitive data is provided by the combination of cryptographic means and analytics methods to detect malicious user behavior. The proposed security architecture supports the basic mobile client protection principles: minimized traffic load and reduced communication with the cloud; usage of light-weighted operations and an optimized combination of the security methods.

Abstract This paper deals with Data-Driven (DD) control design in a Model Reference (MR) framework. We present a new \{DD\} method for tuning the parameters of a controller with a fixed structure. Because the method originates from embedding the control design problem in the Prediction Error identification of an optimal controller, it is baptized as Optimal Controller Identification (OCI). Incorporating different levels of prior information about the optimal controller leads to different design choices, which allows to shape the bias and variance errors in its estimation. It is shown that the limit case where all available prior information is incorporated is tantamount to model-based design. Thus, this methodology also provides a framework in which model-based design and \{DD\} design can be fairly and objectively compared. This comparison reveals that \{DD\} design essentially outperforms model-based design by providing better bias shaping, except in the full order controller case, in which there is no bias and model-based design provides smaller variance. The practical effectiveness of the design methodology is illustrated with experimental results.

The present study is divided in two constructive ways: conversion of volumetric bodies constituted by polygonal surfaces (quads) into surfaces NURBS and conformation of patterns or other polygonal entities on such volumetric bodies. There are numerous cases where the target topology cannot be converted to a single and unique NURBS surface, a procedure that would make the task of volumetric transposition easier and which would already be solved by the use of ready made parametric modeling tools. To carry out this article's tasks, generative modeling systems and associative parametrization strategies were adopted. The knowledge for the construction of the systems used in the present research can be used in different fields that have, as a paradigm, the design creativity as one of its beacons, such as Visual Arts, Architecture and Design. The present work aims to make use of digital manufacturing through laser cutting, 3D printing or CNC milling technologies, at the same time expanding strategies.

Abstract Identification of an output error model using the prediction error method leads to an optimization problem built on input/output data collected from the system to be identified. It is often hard to find the global solution of this optimization problem because in most cases both the corresponding objective function and the search space are nonconvex. The difficulty in solving the optimization problem depends mainly on the experimental conditions, more specifically on the spectra of the input/output data collected from the system. It is therefore possible to improve the convergence of the algorithms by properly choosing the data prefilters; in this paper we show how to perform this choice. We present the application of the proposed approach to case studies where the standard algorithms tend to fail to converge to the global minimum.