In this paper we present the design and technical implementation of a web based Multi-Modal User Interface (MMUI) tailored for elderly users of the robotic services developed by the EU FP7 Large-Scale Integration Project Robot-Era. The project partners are working to significantly enhance the performance and acceptability of technological services for ageing well by delivering a fully realized system based on the cooperation of multiple heterogeneous robots and with the support of an Ambient Assisted Living environment. To this end, elderly users were involved in the definition of the services and in the design of the hardware and software of the robotic platforms from the first stages of the development process and in real experimentation in two test sites. In particular, here we detail the interface software system for multi-modal elderly-robot interaction. The MMUI is designed to run on any device including touch-screen mobiles and tablets that are preferred by the elderly. This is obtained by integrating web based solutions with the Robot-Era middlewares and planner. Finally we present some preliminary results of ongoing experiments to show the successful evaluation of usability by potential users and to discuss the future directions to improve the proposed MMUI software system.

Web services are rapidly changing the landscape of software engineering. One of the most interesting challenges introduced by web services is represented by Quality Of Service (QoS)--aware composition and late--binding. This allows to bind, at run--time, a service--oriented system with a set of services that, among those providing the required features, meet some non--functional constraints, and optimize criteria such as the overall cost or response time. In other words, QoS--aware composition can be modeled as an optimization problem.We propose to adopt Genetic Algorithms to this aim. Genetic Algorithms, while being slower than integer programming, represent a more scalable choice, and are more suitable to handle generic QoS attributes. The paper describes our approach and its applicability, advantages and weaknesses, discussing results of some numerical simulations.

Run-time service discovery and late-binding constitute some of the most challenging issues of service-oriented software engineering. For late-binding to be effective in the case of composite services, a QoS-aware composition mechanism is needed. This means determining the set of services that, once composed, not only will perform the required functionality, but also will best contribute to achieve the level of QoS promised in service level agreements (SLAs). However, QoS-aware composition relies on estimated QoS values and workflow execution paths previously obtained using a monitoring mechanism. At run-time, the actual QoS values may deviate from the estimations, or the execution path may not be the one foreseen. These changes could increase the risk of breaking SLAs and obtaining a poor QoS. Such a risk could be avoided by replanning the service bindings of the workflow slice still to be executed. This paper proposes an approach to trigger and perform composite service replanning during execution. An evaluation has been performed simulating execution and replanning on a set of composite service workflows.

This paper describes in detail the criteria for the design of a mobile laboratory for the characterization and measurement of manmade electromagnetic noise in the urban, suburban, and rural environment. A significant difference from previous similar measurements is the bandwidth of the receiving system, which is here of the order of 1 MHz. A simple model is proposed to describe the impulsive noise together with a series of experiments to validate the model and determine the significant necessary parameters. The detailed requirements for each of the subsystems of the mobile laboratory are discussed and some typical results obtained in trial runs are presented.

A detailed study is presented of some statistical properties of the stochastic process, that consists of the sum of two sine waves of unknown relative phase and a normal process. Since none of the statistics investigated seem to yield a closed-form expression, all the derivations are cast in a form that is particularly suitable for machine computation. Specifically, results are presented for the probability density function (pdf) of the envelope and the instantaneous value, the moments of these distributions, and the relative cumulative density function (cdf). The analysis hinges on expanding the functions of interest in a way that allows computation by means of recursive relations. Specifically, all the expansions are expressed in terms of sums of products of Gaussian hypergeometric functions and Laguerre polynomials. Computer results obtained on a CDC 6600 are presented. Ifaandbare the amplitudes of the two sine waves, normalized to the rms noise level, the expansions presented are useful up to values ofa,bof about 17 dB, in double precision on the CDC 6600. A different approximation is also given for the case of very high SNR.

The problem of designing an optimum probing signal for discriminating between two known linear filters in the presence of noise is considered. It is shown that in the general case of colored noise, the optimum signal is the solution of two simultaneous integral equations. An expression for the probability of error is given, and some illustrative examples are presented.

The problem of simultaneous detection and estimation of signals in noise is formulated in the language of statistical decision theory. Optimum structures and corresponding general measures of system performance are derived under the Bayes criterion of minimum average risk for the detectors and estimators appropriate to this type of joint operation: It is shown that whereas the structures of the resulting optimum detectors require a class of modified likelihood ratios, the structures of the optimum estimators, which act on the data when there is uncertainty as to the presence of a signal[p(H_{1}) < 1 ], have a common canonical form for a wide variety of operating strategies. This form is identical with that obtained for estimation alone[p(H_{1}) < 1 ], even though there is generally mutual coupling between detector and extractor. A simple structure is obtained for the estimation of amplitude and waveform in the case of a quadratic cost function (least mean-square error), where it is found that the estimator which is optimum here[p(H_{1}) < 1 ]is the product of the corresponding Bayes estimator in the "classical" case [P(H1) = 1 ] and a simple algebraic function of the generalized likelihood ratio. In this case, one can also show that estimators that are unbiased in the classical sense remain unbiased. In parallel with the classical theory, a generalized version of unconditional maximum likelihood estimation is obtained for the "simple" cost function whenp(H_{1} < 1. It is found that estimators that are linear in the classical case[p(H_{1}) = 1 ]are nonlinear in the more general situation[p(H_{1})<1], where increased structural complexity is always the rule for both detectors and estimators. A specific example involving the coherent estimation of signal amplitude illustrates the approach. Analogous extensions to prediction and filtering are formulated, making it evident that a broad area for further generalizations of classical Bayes detection and extraction theory is available for systematic investigation.

The maximum improvement that can be expected from amplitude and/or phase adaptivity in the performance of an optimum receiver is evaluated in detail for a general class of broad- and narrow-band signals subject to slow Rayleigh fading in the "on-off" mode of operation. The pertinent Bayes risks are evaluated and compared for different states of a priori information. It is found that amplitude adaptivity yields an advantage increasing without bounds with the SNR, whereas phase adaptivity is a decreasing function of the SNR ratio. For the interesting region of moderately low error probabilities, and with equal a priori error risks, amplitude adaptivity yields an improvement in performance equivalent to3-6dB in SNR, whereas phase adaptivity yields an improvement of2or1dB depending on whether the amplitude also is known or not. The effect of asymmetry of a priori error risks is discussed. It is also pointed out that the optimum test for deciding on the presence or absence of the signal is uniformly most powerful with respect to the amplitude, so that no advantage can be expected from amplitude adaptivity in the Neyman-Pearson mode of operation.