Hardware-based laboratories have been successfully integrated into individual Digital Signal Processing (DSP) courses at many universities. Typically, most hardware-based DSP laboratory experiences are offered to upper-level students and focus on programming the signal processor. While this approach may be ideal for preparing motivated upper-level students for future careers in signal processing, it is not suitable for students with no prior experience in the field. To address this issue, a hardware-based signal processing laboratory, based on the Texas Instruments 6713 DSP Starter Kit (DSK), suitable for students in an introductory signals and systems course has been developed. After two semesters of implementation, results indicate that student understanding is enhanced and interest levels increase when a DSP hardwarebased laboratory is integrated into an introductory signal processing course.
In order to develop improved remediation techniques for hearing impairment, auditory researchers must gain a greater understanding of the relation between the psychophysics of hearing and the underlying physiology. One approach to studying the auditory system has been to design computational auditory models that predict neurophysiological data such as neural firing rates [15], [1]. To link these physiologically-based models to psychophysics, theoretical bounds on detection performance have been derived using signal detection theory to analyze the simulated data for various psychophysical tasks [20]. Previous efforts, including our own recent work using the Auditory Image Model, have demonstrated the validity of this type of analysis; however, theoretical predictions often continue to exceed experimentally-measured performance [9], [21]. In this paper, we compare predictions of detection performance across several computational auditory models. We also reconcile some of the previously observed discrepancies by incorporating appropriate signal uncertainty into the optimal detector.
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