This paper describes a three-year study to introduce nine learner-centered instructional techniques into a two-course electrical engineering graduate course sequence in integrated circuits (ICs) targeted to real-world problems in industry, defense, and security. The study measures the student learning in this two-course sequence with the use of a pre-test/post-test teaching methodology and is carried out through a collaboration of Air Force Research Laboratory Sensors Directorate and Air Force Institute of Technology (AFIT), a government institution. The participants in the study were Air Force officers at AFIT.Results presented in this paper demonstrate the effectiveness of a pre-test/post-test teaching methodology even when data is restricted to samples of small size. Results show that a statistically significant improvement was observed in the first course of the two-course sequence when the Diagnostic and Post-Diagnostic evaluation results were compared. Analysis of the final exam results for one course for Year 2 and Year 3 shows that there is statistically significant improvement in performance. This change is attributed to the improved teaching methodology presented in this paper.

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Finite-difference time-domain (FDTD) methods are widely used to model the propagation of electromagnetic radiation in biological tissues. High-performance central processing units (CPUs) can execute FDTD simulations for complex problems using 3-D geometries and heterogeneous tissue material properties. However, when FDTD simulations are employed at terahertz (THz) frequencies excessively long processing times are required to account for finer resolution voxels and larger computational modeling domains. In this study, we developed and tested the performance of 2-D and 3-D FDTD thermal propagation code executed on a graphics processing unit (GPU) device, which was coded using an extension of the C language referred to as CUDA. In order to examine the speedup provided by GPUs, we compared the performance (speed, accuracy) for simulations executed on a GPU (Tesla C2050), a high-performance CPU (Intel Xeon 5504), and supercomputer. Simulations were conducted to model the propagation and thermal deposition of THz radiation in biological materials for several in vitro and in vivo THz exposure scenarios. For both the 2-D and 3-D in vitro simulations, we found that the GPU performed 100 times faster than runs executed on a CPU, and maintained comparable accuracy to that provided by the supercomputer. For the in vivo tissue damage studies, we found that the GPU executed simulations 87x times faster than the CPU. Interestingly, for all exposure duration tested, the CPU, GPU, and supercomputer provided comparable predictions for tissue damage thresholds (ED 50 ). Overall, these results suggest that GPUs can provide performance comparable to a supercomputer and at speeds significantly faster than those possible with a CPU. Therefore, GPUs are an affordable tool for conducting accurate and fast simulations for computationally intensive modeling problems.