International audienceThe modular exponentiation on large numbers is computationally intensive. An effective way for performing this operation consists in using Montgomery exponentiation in the Residue Number System (RNS). This paper presents an algorithmic and architectural study of such exponentiation approach. From the algorithmic point of view, new and state-of-the-art opportunities that come from the reorganization of operations and precomputations are considered. From the architectural perspective, the design opportunities offered by well-known computer arithmetic techniques are studied, with the aim of developing an efficient arithmetic cell architecture. Furthermore, since the use of efficient RNS bases with a low Hamming weight are being considered with ever more interest, four additional cell architectures specifically tailored to these bases are developed and the tradeoff between benefits and drawbacks is carefully explored. An overall comparison among all the considered algorithmic approaches and cell architectures is presented, with the aim of providing the reader with an extensive overview of the Montgomery exponentiation opportunities in RNS

Augmented reality (AR) is a well-known technology that can be exploited to provide mass-market users an effective and customizable support in a large spectrum of personal applications, by overlapping computer-generated hints to the real world. Mobile devices, such as smartphones and tablets, are playing a key role in the exponential growth of this kind of solutions. Nonetheless, there exists some application domains that just started to take advantage from the AR systems. Maintenance, repair, and assembly have been considered as strategic fields for the application of the AR technology from the 1990s, but often only specialists using ad hoc hardware were involved in limited experimental tests. Nowadays, AR-based maintenance and repair procedures are available also for end-users on consumer electronics devices. This paper aims to explore new challenges and opportunities of this technology, by also presenting the software framework that is being developed in the EASE-R 3 project by exploiting reconfigurable AR procedures and tele-assistance to overcome some of the limitations of current solutions.

The evolution of input device technologies led to identification of the natural user interface (NUI) as the clear evolution of the human-machine interaction, following the shift from command-line interfaces (CLI) to graphical user interfaces (GUI). The design of user interfaces requires a careful mapping of complex user "actions" in order to make the human-computer interaction (HCI) more intuitive, usable, and receptive to the user's needs: in other words, more user-friendly and, why not, fun. NUIs constitute a direct expression of mental concepts and the naturalness and variety of gestures, compared with traditional interaction paradigms, can offer unique opportunities also for new and attracting forms of human-machine interaction. In this paper, a kinectbased NUI is presented; in particular, the proposed NUI is used to control the Ar.Drone quadrotor.

This paper studies the opportunities coming from the use of consumer devices like smartphones and tablets to perform maintenance and assembly procedures with Augmented Reality (AR). Pros and cons are evaluated by comparing completion times and errors made while executing a maintenance procedure with an AR-based tool and paper-based instructions.

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