Penn State College of Communications

About: Penn State College of Communications is a based out in . It is known for research contribution in the topics: Relay & Cognitive radio. The organization has 2106 authors who have published 2119 publications receiving 24693 citations.

From Research to Practice: Towards an Integrated and Comprehensive Faculty Development Program

Abstract: This article describes the design and development of a professional development program based upon research on the competencies necessary for online teaching success [1] conducted at Penn State University in 2009-2010. The article highlights how the results of this research are being aligned with various professional development courses comprising the certificate program for online faculty at Penn State’s World Campus. This article describes the process of research design and implementation to the direct application for professional development.

Win–Win-Driven D2D Content Sharing

Abstract: Win–win cooperation has been broadly treated as one of the most promising goals for device-to-device (D2D) content sharing, especially for the ultrareliable low-latency communications (URLLC) scenario. Unfortunately, the exiting solutions are built on several seemly unpractical conditions: 1) abundant prior information on network and user; 2) optimization variables limited to 1-D space; and 3) a preset order on users’ decision making. In this work, we propose a win–win-driven D2D content-sharing scheme by exploring the blind matching theory. Specifically, we first derive the closed-form expressions of latency and reliability performance for D2D content-sharing scenarios. Accordingly, the URLLC-oriented joint optimization problem for provider–demander pairing and power control of potential providers is formulated as a two-sided one-to-one context-free matching game, which involves a collection of agreement functions of potential providers and demanders’ aspiration levels and exploits a modified notion of pairwise stability as the solution concept. Then, we design a distributed algorithm by utilizing the market and information decentralization characteristics of the blind matching algorithm. Both theoretical analysis and numerical results validate the performance properties, including convergence, optimality, and complexity.

Research and application of combined kernel SVM in dynamic voiceprint password authentication system

Abstract: The kernel function plays an important role in the classification of support vector machines (SVM). In order to solve the problem that a single SVM kernel function can not achieve optimal learning ability and generalization ability in recognition classification at the same time, here we present a new combined kernel function by analyzing and comparing the characteristics of various kernel functions. The new combined kernel function, which is a weighted fusion of radial basis function and polynomial kernel function, has the advantages of both global kernel function and local kernel function. Moreover, we compare the evaluation results of the combined kernel SVM and the SVM using traditional kernels in dynamic voiceprint password authentication system (DVPAS). The experimental results show that the newly constructed multicore SVM classifier obviously superior to linear kernel function, polynomial kernel function and radial basis function in DVPAS.