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An Inexpensive Approach for Teaching Adaptive Filters Using Real-Time DSP on a New Hardware Platform

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2013 ASEE Annual Conference & Exposition


Atlanta, Georgia

Publication Date

June 23, 2013

Start Date

June 23, 2013

End Date

June 26, 2013



Conference Session

Computer Hardware and Simulation

Tagged Division

Computers in Education

Page Count


Page Numbers

23.172.1 - 23.172.10



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Paper Authors


Michael G. Morrow University of Wisconsin-Madison

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Michael G. Morrow, M.Eng.E.E., P.E., is a faculty associate in the Electrical and Computer Engineering Department at the University of Wisconsin-Madison. He previously taught at Boise State University and the U.S. Naval Academy. He is the founder and president of Educational DSP (eDSP), LLC, developing affordable DSP education solutions. He is a senior member of the IEEE and a member of the American Society for Engineering Education (ASEE).

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Cameron H. G. Wright P.E. University of Wyoming Orcid 16x16

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Dr. Cameron H. G. Wright, P.E., is an associate professor with the Department of Electrical and Computer Engineering at the University of Wyoming, Laramie, Wyo. He was previously professor and deputy department head in the Department of Electrical Engineering at the U.S. Air Force Academy, and served as an R&D engineering officer in the U.S. Air Force for more than 20 years. He received the B.S.E.E. and graduated summa cum laude from Louisiana Tech University in 1983, the M.S.E.E. from Purdue University in 1988, and the Ph.D. from the University of Texas, Austin in 1996. Dr. Wright’s research interests include signal and image processing, real-time embedded computer systems, biomedical instrumentation, and engineering education. He is a member of ASEE, IEEE, SPIE, BMES, NSPE, Tau Beta Pi, and Eta Kappa Nu. His teaching awards include the University of Wyoming Ellbogen Meritorious Classroom Teaching Award in 2012; the Tau Beta Pi WY-A Undergraduate Teaching Award in 2011; the IEEE Student Branch’s Outstanding Professor of the Year in 2005 and 2008; the Mortar Board "Top Prof" award in 2005 and 2007; the Outstanding Teaching Award from the ASEE Rocky Mountain Section in 2007; the John A. Curtis Lecture Award from the Computers in Education Division of ASEE in 1998, 2005, and 2010; and the Brigadier General R. E. Thomas Award for outstanding contribution to cadet education in both 1992 and 1993 at the U.S. Air Force Academy. He currently serves as associate department head, Department of Electrical and Computer Engineering, at the University of Wyoming.

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Thad B. Welch Boise State University

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Dr. Thad B. Welch, P.E. received the B.E.E., M.S.E.E., E.E., and Ph.D. degrees from the Georgia Institute of Technology, Naval Postgraduate School, Naval Postgraduate School, and the University of Colorado in 1979, 1989, 1989, and 1997, respectively. He was commissioned in the U.S. Navy in 1979 and has been assigned to three submarines and a submarine repair tender. He has deployed in the Atlantic Ocean, Mediterranean Sea, and Arctic Ocean.
From 1994 to 1997 he was an instructor and assistant professor teaching in the Electrical Engineering Department at the U.S. Air Force Academy at Colorado Springs, CO. During 1996 to 1997 he was recognized as the Outstanding Academy Educator for the Electrical Engineering Department.
From 1997 to 2007 he was an assistant professor, associate professor, and permanent military professor teaching in the Electrical Engineering Department at the U.S. Naval Academy, Annapolis, Md. During 2000 to 2001 he was recognized as the Outstanding Academy Educator for the Electrical Engineering Department. During 2001 to 2002 he received the Raouf Outstanding engineering educator award. During 2002 to 2003 he was recognized as the Outstanding Researcher for the Electrical Engineering Department. He was an invited scholar at the University of Wyoming in fall 2004, where he was recognized as an eminent engineer and inducted into Tau Beta Pi. In 2006 he co-authored “Real-time Digital Signal Processing, from MATLAB to C with the TMS320C6x DSK” which was translated into Chinese in 2011. The second edition of this text was published in 2012.
From 2007 to 2010 he was professor and chair of the Electrical and Computer Engineering Department at Boise State University, Boise, ID. From 2011 to 2012 he was appointed as the inaugural Signal Processing Education Network (SPEN) Fellow.
His research interests include real-time digital signal processing (DSP), the implementation of DSP-based systems, communication systems analysis, efficient simulation of communication systems, spread-spectrum techniques, and ultra-wideband systems.

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An Inexpensive Approach for Teaching Adaptive Filters Using Real-Time DSP on a New Hardware PlatformDigital signal processing (DSP) has become one of the “must know” topics that many employersexpect of new electrical and computer engineering graduates. It has been found that a trueunderstanding of many fundamental DSP topics can be more fully realized by a student whenthey attempt to implement various DSP algorithms in real-time (typically in C), when comparedto non-real-time implementations with tools such as MATLAB or LabVIEW [1]. In order tohelp students successfully transition from theory to real-time practice, there needs to be both apedagogical method and an infrastructure in place to support them and target as many modes oflearning as are reasonably possible.The authors have long advocated a three-step method of teaching DSP [1–3]. First, we teach thetheory along with interesting and motivating real-time demonstrations. We then have studentsimplement a particular concept using MATLAB, until they are comfortable with the basic topic.Finally, we have them “de-vectorize” their MATLAB code and convert it to C, in order tocompile and run it in real-time on high-performance DSP hardware.There are several choices of hardware the professor may make if using this approach. For thevery popular Texas Instruments (TI) processors, the Spectrum Digital C6713 DSK, the Logic PDZoom OMAP-L138 Experimenters Kit (ZEK), and the relatively new Texas Instruments OMAP-L138 Low Cost Development Kit (LCDK) are all highly capable platforms currently availablefor real-time DSP and all can be used effectively with the recommended pedagogical method.The LCDK is the newest of the three, having been introduced in the latter part of 2012. Itscapabilities are equal to or greater than the other two platforms, and it is the lowest in cost (only$195 as of this writing). One capability of particular interest is the access to not only “line in”audio but also to “microphone in” audio that can support generic stereo microphones or dualmonaural microphones.One application of this now-available stereo microphone capability, that is useful for teachingDSP to students, is adaptive noise cancelation using an adaptive digital filter [4,5]. The theory isstraightforward. As shown in the figure below, two signals are provided to the system. Theupper signal contains “signal plus noise” while the lower signal contains “correlated noise.” Theadaptive filter (the box labeled with the H k ( z ) transfer function) uses the error signal to adjust itstransfer function in real-time so as to optimally cancel out the noise at the output. signal plus noise + output correlated – noise Hk (z)  error signalIn the scenario we present to our students, we are trying to duplicate the noisy environment of afirefighter at the scene of an emergency. The “signal plus noise” represents the combined signalsfrom the firefighter’s helmet-mounted microphone (where the “signal” is his/her voice and the“noise” is a chainsaw running in the background). The “correlated noise" signal represents justthe chainsaw’s signal, as detected by a second microphone located on the firefighter, butnowhere near his/her mouth. The adaptive filter’s purpose is to enhance the voice signal so thatthe firefighter may communicate effectively, for example, when using a radio.The full paper will include a brief review of the basic concepts of adaptive noise cancelation, theimportant DSP concepts which it reinforces in student minds, and the method used for thisexample to enhance student learning, along with student results. A helpful comparison of thethree currently available real-time DSP platforms using TI processors will also be provided.During our presentation at the Annual ASEE Conference in Atlanta, we will provide a real-timedemonstration of the effectiveness of this example system.[1] author info removed for blind review, “Teaching DSP: Bridging the gap from theory to real-time hardware,” ASEE Comput. Educ. J., pp. 14–26, July- September 2003.[2] author info removed for blind review, “Old Tricks for a New Dog: An Innovative Software Tool for Teaching Real-Time DSP on a New Hardware Platform,” ASEE Comput. Educ. J., pp. 64–69, October-December 2011.[3] author info removed for blind review, Real-time digital signal processing: from MATLAB to C with TMS320C6x DSPs, 2nd ed., CRC Press, 2012.[4] S. D. Stern and R. A. David, Signal Processing Algorithms in MATLAB, Prentice Hall PTR, Upper Saddle River, NJ, 1996.[5] S. Haykin, Adaptive Filter Theory, 3rd ed., Prentice Hall, Upper Saddle River, NJ, 1996.

Morrow, M. G., & Wright, C. H. G., & Welch, T. B. (2013, June), An Inexpensive Approach for Teaching Adaptive Filters Using Real-Time DSP on a New Hardware Platform Paper presented at 2013 ASEE Annual Conference & Exposition, Atlanta, Georgia. 10.18260/1-2--19186

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