Browsing by Author "Department of Computer Information and Graphics Technology, School of Engineering and Technology"

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    Distributed Consensus-based Weight Design for Cooperative Spectrum Sensing
    (IEEE, 2015-01) Zhang, Wenlin; Guo, Yi; Liu, Hongbo; Chen, Yingying; Wang, Zheng; Mitola, Joseph III; Department of Computer Information and Graphics Technology, School of Engineering and Technology
    In this paper, we study the distributed spectrum sensing in cognitive radio networks. Existing distributed consensus-based fusion algorithms only ensure equal gain combining of local measurements, whose performance may be incomparable to various centralized soft combining schemes. Motivated by this fact, we consider practical channel conditions and link failures, and develop new weighted soft measurement combining without a centralized fusion center. Following the measurement by its energy detector, each secondary user exchanges its own measurement statistics with its local one-hop neighbors, and chooses the information exchanging rate according to the measurement channel condition, e.g., the signal-to-noise ratio (SNR). We rigorously prove the convergence of the new consensus algorithm, and show all secondary users hold the same global decision statistics from the weighted soft measurement combining throughout the network. We also provide distributed optimal weight design under uncorrelated measurement channels. The convergence rate of the consensus iteration is given under the assumption that each communication link has an independent probability to fail, and the upper bound of the iteration number of the $ \epsilon$ -convergence is explicitly given as a function of system parameters. Simulation results show significant improvement of the sensing performance compared to existing consensus-based approaches, and the performance of the distributed weighted design is comparable to the centralized weighted combining scheme.
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    Do Students Like the Flipped Classroom? An Investigation of Student Reaction to a Flipped Undergraduate IT Course
    (IEEE, 2014-10) Elliott, Rob; Department of Computer Information and Graphics Technology, School of Engineering and Technology
    The flipped classroom pedagogy has achieved significant mention in academic circles in recent years. "Flipping" involves the reinvention of a traditional course so that students engage with learning materials via recorded lectures and interactive exercises prior to attending class and then use class time for more interactive activities. Proper implementation of a flipped classroom is difficult to gauge, but combines successful techniques for distance education with constructivist learning theory in the classroom. While flipped classrooms are not a novel concept, technological advances and increased comfort with distance learning have made the tools to produce and consume course materials more pervasive. Flipped classroom experiments have had both positive and less-positive results and are generally measured by a significant improvement in learning outcomes. This study, however, analyzes the opinions of students in a flipped sophomore-level information technology course by using a combination of surveys and reflective statements. The author demonstrates that at the outset students are new - and somewhat receptive - to the concept of the flipped classroom. By the conclusion of the course satisfaction with the pedagogy is significant. Finally, student feedback is provided in an effort to inform instructors in the development of their own flipped classrooms.
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    Supplementary Material to “Distributed Consensus-based Weight Design for Cooperative Spectrum Sensing”
    (IEEE, 2015-01) Zhang, Wenlin; Guo, Yi; Liu, Hongbo; Chen, Yingying; Wang, Zheng; Mitola, Joseph III; Department of Computer Information and Graphics Technology, School of Engineering and Technology
    Abstract—This material is a supplement to the paper “Distributed Consensus-based Weight Design for Cooperative Spectrum Sensing”. Section 1 offers related literature review on cooperative spectrum sensing and consensus algorithms. Section 2 presents related notations and models of the consensus-based graph theory. Section 3 offers further analysis of the proposed spectrum sensing scheme including detection threshold settings and convergence properties in terms of detection performance. Section 4 presents the proofs for the convergence of the proposed consensus algorithm, and discusses the convergence of the proposed algorithm under random link failure network models. Section 5 shows additional simulation results.