Effect of Location Accuracy and Shadowing on the Probability of Non-Interfering Concurrent Transmissions in Cognitive Ad Hoc Networks

Cognitive radio ad hoc systems can coexist with a primary network in a scanning-free region, which can be dimensioned by location awareness. This coexistence of networks improves system throughput and increases the efficiency of radio spectrum utilization. However, the location accuracy of real positioning systems affects the right dimensioning of the concurrent transmission region. Moreover, an ad hoc connection may not be able to coexist with the primary link due to the shadowing effect. In this paper we investigate the impact of location accuracy on the concurrent transmission probability and analyze the reliability of concurrent transmissions when shadowing is taken into account. A new analytical model is proposed, which allows to estimate the resulting secure region when the localization uncertainty range is known. Computer simulations show the dependency between the location accuracy and the performance of the proposed topology, as well as the reliability of the resulting secure region

A supervised learning approach involving active subspaces for an efficient genetic algorithm in high-dimensional optimization problems

In this work, we present an extension of genetic algorithm (GA) which exploits the supervised learning technique called active subspaces (AS) to evolve the individuals on a lower-dimensional space. In many cases, GA requires in fact more function evaluations than other optimization methods to converge to the global optimum. Thus, complex and high-dimensional functions can end up extremely demanding (from the computational point of view) to be optimized with the standard algorithm. To address this issue, we propose to linearly map the input parameter space of the original function onto its AS before the evolution, performing the mutation and mate processes in a lower-dimensional space. In this contribution, we describe the novel method called ASGA, presenting differences and similarities with the standard GA method. We test the proposed method over n-dimensional benchmark functions-Rosenbrock, Ackley, Bohachevsky, Rastrigin, Schaffer N. 7, and Zakharov-and finally we apply it to an aeronautical shape optimization problem

A dialogue between mathematics education and special education: ethics, inclusion and differentiation for all

International audienceEthical issues play an important role in moulding the philosophy of mathematics education. The present study spells out ethical features of mathematical learning in terms of inclusion. We present the OPEN-MATH project that aims at accomplishing inclusive mathematics learning environments and a teaching learning model based in such a framework

Enhancing CFD predictions in shape design problems by model and parameter space reduction

In this work we present an advanced computational pipeline for the approximation and prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced order method is based on dynamic mode decomposition (DMD) and it is coupled with dynamic active subspaces (DyAS) to enhance the future state prediction of the target function and reduce the parameter space dimensionality. The pipeline is based on high-fidelity simulations carried out by the application of finite volume method for turbulent flows, and automatic mesh morphing through radial basis functions interpolation technique. The proposed pipeline is able to save 1/3 of the overall computational resources thanks to the application of DMD. Moreover exploiting DyAS and performing the regression on a lower dimensional space results in the reduction of the relative error in the approximation of the time-varying lift coefficient by a factor 2 with respect to using only the DMD

Enhancing CFD predictions in shape design problems by model and parameter space reduction

In this work we present an advanced computational pipeline for the approximation and prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced order method is based on dynamic mode decomposition (DMD) and it is coupled with dynamic active subspaces (DyAS) to enhance the future state prediction of the target function and reduce the parameter space dimensionality. The pipeline is based on high-fidelity simulations carried out by the application of finite volume method for turbulent flows, and automatic mesh morphing through radial basis functions interpolation technique. The proposed pipeline is able to save 1/3 of the overall computational resources thanks to the application of DMD. Moreover exploiting DyAS and performing the regression on a lower dimensional space results in the reduction of the relative error in the approximation of the time-varying lift coefficient by a factor 2 with respect to using only the DMD

Reduced order isogeometric analysis approach for PDEs in parametrized domains

In this contribution, we coupled the isogeometric analysis to a reduced order modelling technique in order to provide a computationally efficient solution in parametric domains. In details, we adopt the free-form deformation method to obtain the parametric formulation of the domain and proper orthogonal decomposition with interpolation for the computational reduction of the model. This technique provides a real-time solution for any parameter by combining several solutions, in this case computed using isogeometric analysis on different geometrical configurations of the domain, properly mapped into a reference configuration. We underline that this reduced order model requires only the full-order solutions, making this approach non-intrusive. We present in this work the results of the application of this methodology to a heat conduction problem inside a deformable collector pipe

A Decade of Building the Community-Engaged School of Health and Human Sciences at The University of North Carolina Greensboro

This case example illustrates key opportunities, processes, and outcomes of nearly a decade of intentional efforts to build and support community-engaged faculty culture and institutionalization in the School of Health and Human Sciences at the University of North Carolina Greensboro. Situated within a university-wide, faculty-led movement to institutionalize support for community engagement through policy and practice, we describe the motivation of faculty and administrative leadership to integrate support for community engagement across teaching, research and service roles in the Health and Human Sciences unit at UNCG. We present critical moments of opportunity that were leveraged by faculty and administrative leadership to integrate community engagement into visioning and planning documents, faculty rewards and awards, curricular programming, and trans-disciplinary scholarly work through community-engaged partnerships. Using information collected through faculty focus groups and document analysis, and as part of a multi-institutional research program sponsored by the American Association of State Colleges and Universities and the New England Resource Center for Higher Education, we describe key outcomes of efforts to institutionalize community engagement at the school level and areas in which community engagement is integrated into the School’s strategic plan. Of special importance are areas related to tenure and non-tenure track faculty recruitment, faculty promotion and tenure policies, faculty grants and awards programs, and school-level strategic planning. Finally, we describe various choices made about where to locate various activities, efforts, and resources, whether at the department, school, or university-wide level