Session 12b6 A Digital Signal Processing Laboratory with ... - CiteSeerX

Session 12b6 A Digital Signal Processing Laboratory with Style Douglas E. Melton, Ph.D., Cynthia J. Finelli, Ph.D., and Laura M. Rust, Ph.D. Department of Electrical and Computer Engineering Kettering University (formerly GMI Engineering & Management Institute) 1700 West Third Avenue, Flint, Michigan, 48504 Abstract - A digital signal processing (DSP) laboratory is under development at Kettering University. The laboratory materials and procedures are designed to accommodate varied student learning styles. The implications of this learning-style based approach to undergraduate DSP laboratory development are presented, including descriptions of experimental procedures.*

Introduction A digital signal processing (DSP) laboratory is a typical component of present-day electrical engineering curricula. The need for such a lab at Kettering University motivated the effort to develop DSP laboratory exercises with sections that accommodate a variety of learning styles. As a result, each laboratory experiment is developed within the context of specific DSP applications, including musical synthesis, room acoustics and electrocardiogram processing. The underlying theory and fundamental concepts to be investigated in each laboratory experiment are presented in small components, called mini-lab exercises, that complement and build on one another. These mini-lab exercises are completed by students in an open laboratory setting where an instructor is not necessarily present.

A well-recognized model of learning [6] suggests there are three pieces of information needed to acquire scientific knowledge: an application that motivates concept comprehension, a level of abstraction or modeling, and an understanding of the links between the two. The importance of an application to motivate learning is historically recorded with the most familiar examples appearing in mathematics and science. It is not unusual, or by any means novel, to reintroduce motivating applications. However, we wish to emphasize that the engineering curriculum has, at times, diminished the importance of the applications in an attempt to provide a foundational, mathematically-based toolset. In an effort to modularize topics in engineering education, the application to motivate learning is often deleted from the teaching materials and presentation, even in the laboratory. The complexity of the DSP subject matter suggests the need for the sequential presentation of ideas that build to the larger global concepts the student must master. This progressive exploration of ideas leading to the mastery of specific concepts has resulted in the format for laboratory exercises being developed for the Kettering University DSP laboratory.

Addressing Multiple Learning Styles in a DSP Laboratory

Motivating Learning with DSP Applications The variety and number of texts being published for undergraduate DSP education clearly illustrates the variation in presentation styles. Recent texts include those specific to software tools [1], [2], those oriented toward the freshmen or sophomore level student [3], [4], and those focusing on practice and application [5]. Any one of these texts provides the student with an excellent resource. The use and application of the relevant algorithms and mathematics given in the texts is easily extended and conceptually solidified through experimentation. Many authors recognize the value of experimentation in mastering signal processing concepts and include software experiments in their publications. Such software experiments are included in the labs described here and then are extended to hardware experiments to be completed using a variety of readily-available DSP hardware tools. *

This work has been supported, in part, by an Instrumentation and Laboratory Improvement grant from the National Science Foundation and Texas Instruments, Inc. through the TI University Program.

In a 1988 publication, Felder and Silverman [7] summarized many models of learning styles, including their own, and concluded that the most effective teaching method employs a balance of learning styles. With this in mind, the laboratory experiments presented in this paper were developed to provide students with a variety of activities addressing multiple learning styles. For example, since students may be visual or verbal learners, information regarding laboratory procedures is presented both pictorially and in written form. A potential disadvantage of presenting information in both forms is voluminous laboratory handouts. By using the internet to provide access to the laboratory materials, the problem of distributing and using bulky handouts is eliminated. During the laboratory, students can identify their own preferred learning style and access information most suited to that style. Table 1 shows how the Felder-Silverman model categorizes learning tendencies. Each of the five contrasting tendencies -- sensing versus intuitive, visual versus verbal, inductive versus deductive, active versus reflective, and

0-7803-5643-8/99/$10.00 © 1999 IEEE November 10 - 13, 1999 San Juan, Puerto Rico 29th ASEE/IEEE Frontiers in Education Conference 12b6-14

Session 12b6 sequential versus global -- is shown. The teaching technique used to address specific learning needs in the DSP laboratory is shown in the right-hand column. Sensing versus Intuitive Learners Sensing learners prefer concrete facts and procedures, while intuitive learners prefer theory and meaning. Incorporating laboratory experiments with the traditional sequence of lectures is a simple way to accommodate both learning styles. To further motivate both types of learners, the laboratory experiments are closely coupled to the lecture material, and short summaries of the theory presented in lecture are accessible through hyperlinks in the web-based experimental procedures. Visual versus Verbal Learners Visual learners prefer pictures and diagrams, while verbal learners prefer words. The laboratory experiments described in this paper present material in both formats so students can either follow a flow chart outlining the steps or read the procedures. Furthermore, students can review expected results at key points in the experiment. For example, displays from the spectrum analyzer and oscilloscope have been captured and incorporated in the laboratory material to demonstrate the type of output a student should expect to see. These visual milestones can provide a key factor in the success of executing experiments in an open-schedule lab, where no formal lecture session precedes the laboratory and a laboratory instructor is not necessarily present. In addition to providing visual prompts, audio cues in the form of *.wav files are also provided for some signal processing experiments. For example, during the experiment on synthesizing realistic instrument sounds, students investigate harmonic structure and exponential decay. While determining various rates and parameters for a particular musical instrument, students can listen to an example of the original instrument to be simulated, thus preventing the need to provide an oboe and organ at every laboratory station. Inductive versus Deductive Learners Inductive learners organize specific ideas into larger, general concepts, while deductive learners prefer to be presented with a general concept that is then followed by supporting examples. To accommodate these two learning styles, the laboratory experiments developed consist of several mini-lab exercises. For example, the bearing wear experiment introduces students to the use of the FFT to sense bearing wear in a rotating machine and is designed to illustrate the use of FFT-based frequency analysis. This laboratory is composed of four mini-lab exercises that accommodate both

inductive and deductive learners. Early questions regarding time versus frequency resolution and the uncertainty principle favor the deductive learner, while later questions that require the student to draw conclusions about the limitations of data length favor the inductive learner. Active versus Reflective Learners Active learners are characterized as experimentalists who often prefer group work, while reflective learners prefer to work alone. Since traditional laboratory experiments are often completed in groups, they primarily accommodate the active learner. However, the use of web-delivered laboratories allows some portions of the experiments to be conducted individually, thereby accommodating both learning styles. In the bearing wear example, the mini-lab exercises require individual responses from students. The remaining elements are conducted in a group of typically three students. Each student is assigned a responsibility, such as wiring the hardware, programming the DSP hardware, or recording the results of the laboratory, and the responsibilities are rotated to different group members as the course progresses. Sequential versus Global Learners Sequential learners understand small, closely-linked ideas while global learners understand large complex concepts but may not be able to break those concepts into smaller ideas. In an effort to provide a balance between these two learning styles, both the mini-lab exercises for each experiment and the entire set of laboratory experiments have been organized into a logical sequence. Describing the application at the beginning of the laboratory encourages global learners. Also, extra-credit questions that require an understanding of multiple concepts favor global learners. For example, one extra-credit question associated with the preceding example follows: How would your implementation of a bearingfailure detection algorithm be affected by an 8bit analog-to-digital converter instead of the supplied 16-bit converter? This requires students to recall, from previous experiments, the effects of lower quantization resolution including limited dynamic range and additional quantization noise. A global learner would be inclined to evaluate the effect of the quantization noise spectra on the final implementation of their algorithm.


Session 12b6 Table 1: Learning Styles and DSP Laboratory Teaching Techniques Used to Address the Different Styles Style

Laboratory teaching techniques for addressing the learning style

Sensing

Laboratory experiments expose students to material ranging from mathematics to software and hardware DSP implementation. Each lab is oriented toward a physical, realistic application.

Intuitive

Each laboratory is closely coupled with the classroom lectures. Students can review the theory by accessing hyperlinks in the web-based laboratory procedures.

Visual

The laboratory procedures are presented as a block diagram or flowchart, showing (in pictures) experiment milestones and key points at which to record information.

Verbal

The laboratory procedures are presented verbally and in written form, describing both the laboratory procedures and the expected experimental results.

Inductive

Each laboratory experiment includes several mini-lab exercises that collectively emphasize an overall concept presented in class.

Deductive

Students are asked to extrapolate results of each mini-lab exercise to a subsequent one. Students also write about the use of the concept in new or similar applications.

Active

Each laboratory has a group component requiring members to adopt specific responsibilities. The responsibilities vary throughout the term.

Reflective

Each laboratory has a component that is completed individually during an open lab time. Students may work at an individual pace.

Sequential

The mini-lab exercises are organized in a sequential fashion, building from simple ideas to more sophisticated implementations.

Global

The overall concept is repeatedly reinforced through the experiment by executing the mini-lab exercises.

Laboratory Stations and Experiments At Kettering University, the elective DSP course is taught twice per year with typical total enrollment of about 20 undergraduate seniors per term. These students have completed the traditional sequence of circuits courses and an introductory programming course. In the laboratory, ten stations include equipment for DSP experiments. The equipment was acquired, in part, through a Laboratory Initiation and Improvement grant from the National Science Foundation. For analysis and test-signal generation, each station includes a two-channel Stanford Research Systems SR-785 spectrum analyzer, a Hewlett-Packard signal generator, and a Tektronix digital storage oscilloscope. Each station also includes computing equipment for MathWorks MATLAB, and DaDiSP. Six stations house Texas Instruments TMS320C30 (donated to Kettering University through the Texas Instruments' University Program) and Analog-Devices EZ-Kit SHARC evaluation modules. The remaining four workstations are dedicated to simulation and software experiments using NeXT computers. Other specialty equipment in the laboratory includes MiniDisc recorders, biological signal amplifiers and telephone equipment.

Table 2 outlines the experiments to be conducted in the DSP laboratory.While the focus of each experiment is an application, the key theoretical concepts are readily identified.

Experimental Procedures An overview of mini-laboratory procedures is provided for each experiment to further illustrate the style of experiments being developed at Kettering University. These laboratory experiments are made available to the students on a web browser to reduce clutter and provide links to extra visual and audio information that may be helpful to the student. All mini-lab exercises are listed here in ''boxed" form following the description of each laboratory. In the laboratory setting, the mini-labs are hyper-linked to procedures that conform to the methodology of accommodating a variety of learning styles. Each exercise requires qualitative and quantitative information. Completion of the experiment requires instructor approval. Students submit written reports, including answers to questions and experimental results obtained from the oscilloscope and spectrum analyzer displays.


Session 12b6 Table 2: Description of Experiments #

Application

Key concepts

1

Musical Synthesis

Mathematical description of signals, wave-tables, familiarization with DSP hardware

2

Digital Audio Recording

Quantization noise, companding, aliasing, effect of anti-aliasing filters

3

Machinery Bearing Wear

Frequency analysis, FFT, resolution, use of windowing

4

Composite Video Signal Processing

FIR filters, comb filters, inverse-comb filters, pole-zero structures

5

Modeling the Human Ear

IIR filters, pole-zero matching, bilinear transform, deriving filter coefficients

6

Simulating Room Acoustics

FIR and IIR filtering, modeling resonances, modeling delayed echoes, filter structures/topologies

7

Analyzing the Electrocardiogram

Morphology, frequency estimation, filtering to improve SNR

Experiment #1 - Musical Synthesis This experiment, which includes a set of six mini-lab exercises, requires the student to investigate the synthesis of sounds historically produced by musical instruments such as the piano, oboe, and organ. Here, these sounds will be synthesized using DSP hardware and software. The goal of these mini-lab exercises is to realistically synthesize notes, so that they are indiscernible from notes played on the original instrument. This experiment introduces students to a discrete-time mathematical description of signals. Periodic signals are constructed from wave-tables as a practical means of generating signals with harmonic content. Finally, students are introduced to DSP hardware in this laboratory session.

Experiment #2 - Digital Audio Recording This experiment, which includes a set of four mini-lab exercises, requires the student to investigate the quality of digital audio recording for speech and music. An audio source is digitized and replayed to quantitatively and qualitatively determine the detrimental effects of quantization and aliasing. The goal of these mini-lab exercises is to determine minimum acceptable sampling and quantization requirements for a variety of audio applications. This experiment introduces the use of a spectrum analyzer. Recordings of aliasing are provided for reference. Students can listen to the output of recording with inadequate antialiasing filters. As tone sweeps, the anti-aliasing is audible. 1.

1. 2. 3. 4. 5.

6.

Create signals using mathematical expressions in Goldwave, an audio waveform editor. Create discrete-time signals in MATLAB. Create a wave-table generated sound in MATLAB. Experiment with model-generated sounds in ResoLab on the NeXT workstations. Determine harmonic decay rates from spectral waterfall plots of recorded instrument sounds on the NeXT workstations. Create wave-table generated sounds on a SHARC DSP processor.

2.

3. 4.

Create a quantization simulation using MATLAB and analyze quantization noise in time and frequency. Create an ''echo" program on the Texas Instruments DSP processor with variable masking of leastsignificant bits. Rate the quality of audio for various input sources while the masking (quantization) is varied. Rate the quality of audio for various input sources while the sampling rate is varied. Experiment #3 - Machinery Bearing Wear

This experiment, which includes a set of four mini-lab exercises,requires the student to investigate frequency analysis and the development of a failure prediction 0-7803-5643-8/99/$10.00 © 1999 IEEE November 10 - 13, 1999 San Juan, Puerto Rico 29th ASEE/IEEE Frontiers in Education Conference 12b6-17

Session 12b6 algorithm. Recorded accelerometer signals from several rotating machines are available for analysis. The goal of these mini-lab exercises is to develop a simple algorithm suitable for predicting bearing failure. This experiment introduces the use of FFT analysis and the effect of FFT length and windowing on the resolution. 1. 2.

3.

4.

Predict and verify the frequency content in short, simple signals using MATLAB. Using the NeXT stations, perform frequency analysis, FFT, and spectrogram of your recorded speech or whistle using a variety of settings for FFT lengths and windows. Determine the salient features of a failing bearing from recorded accelerometer signals using MATLAB. Using MATLAB, create an algorithm capable of predicting bearing failure. Test the algorithm on the database of recorded signals.

digital IIR filters to model physical systems. The primary bending mode of the resonant cilia of the inner ear can be represented as a series of second-order resonant filters [8]. The goal of this experiment is to model the response of the inner ear using an IIR filter. This filter is implemented twice, once using a standard recursive linear difference equation and once as a parametric filter based upon the bilinear transform. 1.

2.

3.

Predict the second-order frequency response function of a cilia using bending equations and MATLAB. From the Laplace-domain transfer function, create a parametric digital filter. Parameters include cilia length and a damping factor. Using the SHARC DSP processor, implement multiple second-order transfer functions which provide band selection throughout the audible range, simulating the cilia responses.

Experiment #4 - Composite Video Signal Processing

Experiment #6 - Simulating Room Acoustics

This experiment, which includes a set of three mini-lab exercises, requires the student to investigate the development of a filter suitable for separating chrominance and luminance signals in a composite video signal. Both chrominance (color) and luminance (brightness) signals are quasi-periodic and thus have a harmonic structure. Television signals are designed such that the combined chrominance and luminance signals are not overlapping harmonics, but intermingled in an alternating fashion. A comb filter is suitable to remove one set of harmonics from the composite signal. The goal of these mini-lab exercises is to determine the requirements for implementing a comb filter suitable to separate composite television signals. This experiment introduces FIR filters and frequency analysis of filters described by linear difference equations.

This experiment, which includes a set of five mini-lab exercises, requires the student to simulate room acoustics using a high-order IIR filter. Room acoustics are complex but may be approximated by a combination of delays, which represent primary acoustic reflections, and resonances, which approximate the rich modal response of a typical room. Schroeder's method of generating reverberation [9], [10] is implemented in real-time. This experiment introduces the digital modeling of a complex system using experimental and curve fitting procedures. 1.

2. 1.

2.

3.

Predict and verify the frequency response of simple, non-recursive difference equations using MATLAB. Implement a comb filter for a low-frequency recorded simulation of chrominance and luminance filters. Test the system using a spectrum and transfer function analyzer. From the specification for commercial broadcast television signals, determine the required sampling rate and order of a comb filter capable of separating chrominance and luminance signals. Experiment #5 - Modeling the Human Ear

This experiment, which includes a set of three mini-lab exercises, requires the student to investigate the use of

3. 4.

5.

Measure the room response at a point by using a loudspeaker and microphone. Capture the data and export to MATLAB for estimation of the transfer function. Using MATLAB, find a least-squares fit to approximate the transfer function. Using the SHARC DSP processor, implement the digital filter and test the frequency response. Investigate the frequency response of an inverse comb filter. Determine the zero/pole structure and observe the audible effect of using a difference equation with near unity recursion using MATLAB. Using the SHARC DSP processor, implement Schroeder's reverberation algorithm.

Experiment #7 - Analyzing the Electrocardiogram This experiment, which includes a set of two mini-lab exercises, requires the student to analyze a biological signal. Students acquire their own electrocardiogram using a biological amplifier and appropriate electrodes, and they


Session 12b6 determine their heart rate under various physiologic conditions. Students also analyze recorded electrocardiograms which represent cardiac arrhythmias, and they develop algorithms based on waveform morphology, frequency, or a combination of the two to identify the irregularities [11]. Students are exposed to typical algorithms for arrhythmia detection used in commercial pacemakers and defibrillators. This final experiment is open-ended, allowing the student to use any digital signal processing techniques they desire. 1.

2.

Record electrocardiogram signals. Using the Texas Instruments C30 DSP processors, develop and implement an algorithm, complete with any necessary pre-filter, to determine instantaneous heart rate in real-time. Given three data passages (one normal, one abnormal, and one unidentified), develop an algorithm to determine the type of third passage. Use a template beat of the normal passage for shape comparison with both passages, in combination with analysis of the frequency content of each passage.

Addison-Wesley Publishing Company, Menlo Park, California, 1996. [5] S. W. Smith, The Scientist and Engineer's Guide to Digital Signal Processing, California Technical Publishing, 1997. [6] R. M. Felder, J. E. Stice, and R. Brent, “National effective teaching institute”, in Annual Conference of the American Society for Engineering Education, 1997. [7] R. M. Felder and L. K. Silverman, “Learning and teaching styles in engineering education”, Journal of Engineering Education, vol. 7, no. 78, 1988. [8] A. J. Hudspeth and V. S. Markin, “The ear's gears: Mechanoelectrical transduction by hair”, Physics Today, vol. 47, no. 2, February 1994. [9] M. R. Schroeder, “Natural sounding articial reverberation”, Journal of Audio Engineering Society, vol. 10, no. 1, 1962. [10] S. J. Orfanidis, Introduction to Digital Signal Processing, Prentice-Hall, Inc., New Jersey, 1996. [11] J. L. Willems et. al., “The diagnostic performance of computer programs for interpretation of electrocardiograms”, The New England Journal of Medicine, vol. 325, no. 25, December 1991.

Conclusion The DSP laboratory development described here focuses on application-based laboratory experiments that intentionally include components targeted to specific learning styles. The lab experiments consist of several mini-lab exercises that sequentially expose students to a thought process needed to master complex DSP concepts. The web-based presentation of materials and simultaneous use of both symbolic and pictorial delivery reaches a broad cross-section of the student body. Based on the level of interest in the class currently, enrollment is expected to increase as upper-class students taking the course discuss it with other students.

References [1] V. K. Ingle and J. G. Proakis, Digital Signal Processing, Using MATLAB Version 4, PWS Publishing Company, Boston, Massachussets, 1997. [2] J. H. McClellan, C. S. Burrus, A. V. Oppenheim, T. W.Parks, R. W. Schafer, and H. W. Schuessler, Computer-Based Exercises for Signal Processing Using MATLAB Version 5, Prentice-Hall, Inc., New Jersey, 1998. [3] J. H. McClellan, R. W. Schafer, and M. A. Yoder, DSP First: A Multimedia Approach, Prentice-Hall, Inc., New Jersey, 1998. [4] K. Steiglitz, A Digital Signal Processing Primer with Applications to Digital Audio and Computer Music,
