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ECET 350 Full Course DeVry

ECET 350 Full Course DeVry<br>Just Click on Below Link To Download This Course:<br>https://www.coursetutor.us/product/ecet-350-full-course-devry/<br>ECET 350 Full Course DeVry<br> <br>ECET 350 Topic 1 Analog Active Filter Design Sallen-Key and Multifeedback Circuits<br>ECET 350 Topic 1 Discussion<br>WEEK 1: ACTIVE FILTER DESIGN PARAMETERS<br>What are the features you would consider essential if you were designing your perfect amplifier? Define the values for parameters, such as input resistance, output resistance, and voltage gain.<br>

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ECET 350 Full Course DeVry

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  1. ECET 350 Full Course DeVry Just Click on Below Link To Download This Course: https://www.coursetutor.us/product/ecet-350-full-course-devry/ Or Email us help@coursetutor.us ECET 350 Full Course DeVry ECET 350 Topic 1 Analog Active Filter Design Sallen-Key and Multifeedback Circuits ECET 350 Topic 1 Discussion WEEK 1: ACTIVE FILTER DESIGN PARAMETERS What are the features you would consider essential if you were designing your perfect amplifier? Define the values for parameters, such as input resistance, output resistance, and voltage gain. WEEK 1: ACTIVE FILTER COMPONENT CHOICES Give a thorough discussion about why you chose a certain value for your perfect amplifier parameter (one of input resistance, output resistance, and voltage gain only). ECET 350 Topic 1 Lab 1 Sallen-Key Active Filter Design Objectives Design and simulate a Butterworth, low-pass Sallen-Key active filter Tools Needed  Multisim Software Introduction  Active filters are key elements in both analog and digital signal processing. In this lab, you will first design, and using Multisim, simulate a Butterworth type, Sallen-Key low-pass filter comparing the design specifications against the simulation. Next, you are to actually construct the designed

  2. Butterworth and test its response measured against the design specifications, noting any differences between the simulated filter and the actual filter. Deliverables Answer all questions, complete all tables, and paste all figures and graphs in the Week 1 Lab Cover Sheet here (Links to an external site.) . Submit your Week 1 Lab Cover Sheet. You can also download the Week 1 Cover Sheet for the Week 1 Lab in the Files section of the Course Menu. Required Software Multisim and Excel Access the software at https://lab.devry.edu (Links to an external site.). Lab Steps STEP 1: Butterworth, Low-Pass Sallen-Key Active Filter Design In this part, you will design an active low-pass filter with the following specifications. Second-order low-pass filter, 3-dB ripple at the cut-off frequency of 3 kHz, type: Butterworth, circuit topology (VCVS): low-pass Sallen-Key circuit The second-order, low-pass prototype for Butterworth type is given as HP(s)=Hos2+1.4142s+1HP(s)=Hos2+1.4142s+1where HoHois DC gain to be determined, and the cut-off frequency ωC=2π.3000ωC=2π.3000rad/s. 1. Determine the transfer function using low-pass to low-pass transformation: s=sωCs=sωC. Include your answer in the Lab cover report and from the transform function, identify the b0 and b1 coefficients. H(s)=H(s)=bo=bo= b1=b1=Choose the second-order, Sallen-Key low-pass filter shown in Figure 1.

  3. Figure 1: Second-Order, Sallen-Key Low-Pass Filter Based on circuit analysis, the circuit transfer function of the Sallen-Key low-pass filter shown in Figure 1 is given below. G(s)=VoutVin=Gbos2+b1s+boG(s)=VoutVin=Gbos2+b1s+boWhere G=1+R4R3,bo=1R1R2C1C2G=1+R4R3,bo=1R1R2C1C2b1=1R1C2+1R2C 2−R4R2R3C1b1=1R1C2+1R2C2−R4R2R3C1To solve for circuit parameters, one of the solutions could be determined using the following conditions. C1=C2=0.01μFC1=C2=0.01μFand R1=R2R1=R2. 2. By matching coefficients of the Butterworth filter transfer function, H(s),H(s),with the Sallen-Key circuit transfer function ,G(s),G(s), the design formulas are found below. Calculate values for the circuit parameters, and include your answers in the Lab cover report. R1=R2=√1boC1C2=R1=R2=1boC1C2=For a Butterworth response, the ratio of R4R3R4R3may be set at 0.586. If R3 is selected to be 10 kΩ, calculate the value for R4 and copy all calculation and values for R1, R2, R3, and R4 in the Week 1 Lab cover report.

  4. R1=R2=R3=R4=R1=R2=R3=R4=3. Calculate the theoretical filter gains, and complete the calculated entries in Table 1 in the Lab cover report for verification. Note: The pass band ripple, Ap, for this type of Butterworth filter you may assume is approximately 3 dB. Ho=G=1+R4R3=G(dB)=20logG=Ho=G=1+R4R3=G(dB)=20logG=ϵ2=10Ap( dB)10−1=ϵ2=10Ap(dB)10−1=MC=Ho√1+ϵ2=MC(dB)=20logMC=MC=Ho1+ϵ 2=MC(dB)=20logMC=Roll-off rate: RR≅−20N=RR≅−20N=(dB/decade) where N is the order of the filter. Where G(dB)G(dB): the filter DC gain MC(dB)MC(dB): the gain at ωCωC(radians/sec) ωCωC: the cut-off frequency APAP: the passband ripple (dB) HoHo: the filter passband gain RRRR: the roll-off rate (dB/decade) 4. Use MultiSim to simulate the designed Sallen-Key circuit and verify DC gain, gain at the cutoff frequency, and roll-off rate from the Bode plotter. Copy the Multisim schematic of your filter, and paste it into the Week 1 Lab cover report. Next, copy the steady state frequency response from the Bode-plotter, and paste it into the Week 1 Lab cover report, as well. Complete the measured entries in Table 1 in the Week 1 Lab cover report. Note that for the low-pass filters, the estimated roll-off rate is RR(dbdecde)=G1(dB)−G2(dB)logf1−logf2=G1(dB)−G2(dB)log(f1f2)RR(dbd ecde)=G1(dB)−G2(dB)logf1−logf2=G1(dB)−G2(dB)log(f1f2)where G1G1at frequency f1f1and G2G2at frequency f2f2are two measured gains beyond the cutoff frequency. You may also want to modify the Bode output window to record measurements over a wider range of frequencies and magnitudes.

  5. Figure 2: Multisim Example of Filter Simulation—Values Are Not Correct for This Lab ECET 350 Topic 1 Course Project TEAM FORMATION The class will be organized into teams with 3-4 students per team. In Week 1, you will work on team formation and selection of the focus of the course project. Week by week, you will work as a team to build out the final course project deliverable due in week 7. Note! All teams must be approved by the instructor. See the Course Project Overview in Introduction and Resources.

  6. ECET 350 Topic 2 Sampling and Reconstruction of Signals ECET 350 Topic 2 Discussion WEEK 2: NYQUIST SAMPLING REQUIREMENTS You are given an input signal with a maximum input frequency of 1 kHz. Also, given that the Nyquist criteria of a minimum sampling frequency is two times the highest input frequency, are there any potential problems of sampling the input signal at a sampling rate of exactly 2 kHz? WEEK 2: SAMPLING FREQUENCY AND BIT RESOLUTION Given a real-time digital signal processing system, how do the sampling frequency and the number of bits used in performing the analog-to-digital conversion of an analog input signal impact the design and performance of the system? What features of the system are affected by these two factors? ECET 350 Topic 2 Lab 2 Signal Sampling and Reconstruction Objectives Use principles of signal sampling and reconstruction to construct an electronic circuit to  sample, hold, and reconstruct the signal. Apply the antialiasing and anti-imaging filters to perform proper simulation of signal  sampling and reconstruction. Software Multisim  Introduction Signal sampling is usually performed by sampling an analog signal at appropriate rates according to the Nyquist theorem and then holding the sampled voltage during the time required for the ADC to convert the voltage level to a binary code (digital value). The analog signal should be band- limited so that the sampling frequency can be chosen according to the Nyquist theorem, namely , in which is the maximum frequency or the upper band of the analog signal. To ensure that the signal is band-limited, an antialiasing filter (restricting low-pass filter) is deployed as the first block in the path of the input signal.

  7. The digital value, the output of ADC, could be processed using a DSP algorithm mainly composed of a digital filter. After digitally processing the signal, it has to be reconstructed and delivered back to the analog world, which is the binary code, and the result of DSP operation is converted back to a sample and hold voltage level. The converted voltage levels are further fed to the anti-image filter (smoothing low-pass filter) to reconstruct the analog signal. Figure 1A shows the complete signal sampling and reconstruction system. To investigate signal sampling and reconstruction in this lab experiment, a simplified system that omits the DSP section is shown in Figure 1B. Figures 1A and 1B: Signal Sampling and Reconstruction If the sampling condition is violated, the aliasing would occur. This effect will cause undesired frequencies known as alias frequencies within the information frequency band. To avoid aliasing, Figure 2A shows the sampling and reconstruction using an antialiasing filter. Figure 2B shows the simplified system that omits the DSP section and will be used in this lab experiment for simulation.

  8. Figures 2A and 2B: Signal Sampling and Reconstruction With an Antialiasing Filter Deliverables Answer all questions, complete all tables, and paste all figures and graphs in the Week 2 Lab Cover Sheet here (Links to an external site.) . Submit your Week 2 Lab assignment. You can also download the cover sheet for Week 2 Lab in the Files section of the Course Menu. Required Software Multisim Access the software at https://lab.devry.edu (Links to an external site.). All Steps Lab Steps STEP 1: Antialiasing and Anti-imaging Filter Specifications Using MultiSim, construct the circuit shown as Figure 3. Set the sampling rectangular pulses (sampling clock) as the following. Vp (pulse value) = -5 volts Period: 0.125 ms Pulse width 0.02 ms Set the sinusoidal voltage source as the following. Frequency = 1000 Hz

  9. Vp (amplitude)=1 volts=0.707 rms, DC offset = 1 volt Explanation of the circuit: Two opamps on the top are the buffer amplifiers before and after the sampler. Sampler is a JFET used as analog switch; its gate is driven by the narrow pulse train as specified above. There are two identical active low-pass filters used for antialiasing and anti- imaging with second order, Sallen-Key topology. Before simulation, address the following questions and include your answers in the Lab cover report. 1. Determine the cutoff frequency of the antialiasing and anti-imaging active filters used in the circuit. 2. Frequency of the signal to be sampled 3. Sampling period 4. Sampling frequency 5. Is the sampling theorem satisfied? Justify your answer. 6. Predict the frequencies and estimated voltage amplitude of each frequency in the range from 0 Hz to 10 kHz of the sampled signals according to the sampling theorem.

  10. Figure 3: Sampling and Reconstruction Circuit As Built in MultiSim STEP 2: Antialiasing and Anti-imaging Filter Simulation Open the first spectrum analyzer by left double clicking on the icon. In the frequency section, set start to 0 Hz and end to 10 kHz. Then click on Enter. Set the amplitude range to 0.25 V/Div and Lin(Linear) display. Set the frequency resolution to 100 Hz. Start the simulation by clicking on the power switch in the top right hand corner of the window. Copy the screen display on the spectrum analyzer to include in your report, use Alt+Print Scrn buttons to capture the spectrum analyzer view only when it is selected, and paste it in your Lab cover report in the section marked antialiasing and anti-imaging spectrum analyzer screen capture.

  11. Using the mouse, move the cursor so that it overlays the center of the spectral signal on the simulator. Use the cursor to measure the frequency and RMS voltage for each peak from 0 to 10 kHz, and record your measurements in Table 1 in your Lab cover report. STEP 3: Signal Reconstruction Simulation The original signal can be fully recovered by low-pass filtering (anti-image filtering) the sampled signal if the sampling condition is satisfied. Left double click on the second spectrum analyzer attached to the low-pass filter. Run the simulation using the same setting of the spectrum analyzer. Copy the screen display on the spectrum analyzer to include in your Lab cover report in the space provided, and label the graph. Use the spectrum analyzer to answer the questions at the end of the lab. Include your answers in the Week 2 Lab cover report in the space provided. STEP 4: Antialiasing Simulation Now disconnect the input sinusoidal source from the antialiasing filter, and connect it directly to the buffer preceding the sampler (see Figure 4). Set the sinusoidal function as the following. Frequency = 7000 Hz Vp (amplitude) =1 volts=0.707 rms, DC offset = 1 volt

  12. Figure 4: Sampling and Reconstruction Circuit While Skipping Antialiasing Filter Left double click on the first spectrum analyzer attached to the second buffer amplifier before the anti-imaging filter. Run the simulation using the same setting of the spectrum analyzer. Copy the screen display on the spectrum analyzer to include in your Lab cover report. Label the graph. The original signal cannot be fully recovered by anti-image filtering the sampled signal if the sampling condition is not satisfied. Left double click on the second spectrum analyzer attached to the anti-imaging filter. Run the simulation using the same setting of the spectrum analyzer.

  13. Copy the screen display on the spectrum analyzer to include in your Lab cover report. Label the graph. STEP 5: Signal Reconstruction Simulation Now, use the same setting for the sinusoidal function as the following. Frequency = 7000 Hz Vp (amplitude) =1 volts=0.707 rms, DC offset = 1 volt Connect the sinusoidal function output to the input of the antialiasing filter as in Figure 3. Run the simulation using the same setting for both of the spectrum analyzers. Copy the screen display on the spectrum analyzer 2 on the output of the anti-imaging filter to include in your Week 2 Lab cover report and paste it in the space provided. Graded Questions From the first spectrum analyzer captured in Step 3: 1. What is the expected frequency after signal reconstruction? 2. What is the frequency measured from the spectrum? 3. Did you fully recover the original signal? From the first spectrum analyzer captured in Step 4: 4. Frequency of the signal to be sampled 5. Sampling frequency 6. Is the sampling theorem satisfied? 7. List frequencies of the sampled signals in the range from 0 to 10 kHz. From the second spectrum analyzer captured in Step 4: 8. Did you fully recover the original signal? 9. List the aliasing frequencies, if any. From the spectrum analyzer captured in Step 5: 10. Frequency of the signal to be sampled 11. Sampling frequency

  14. 12. Is the sampling theorem satisfied? 13. Can you find frequencies of the sampled signals for the range from 0 to 10 kHz? ECET 350 Topic 2 HOMEWORK Chapter 2, pages 49 – 56, problems 2a, 2b, 5, 6, 8, 14, 16, 28 Don’t forget to submit your assignment. ECET 350 Topic 2 COURSE PROJECT PRELIMINARY PROJECT DESIGN & PARTS ORDERING The teams must start to research the design and find a low power op amp. Don’t forget to order parts that are needed for the design. See the Course Project Overview in Introduction and Resources. Nothing is to be submitted this week. ECET 350 Topic 3 Difference Equations, Convolution, and Moving Filters ECET 350 Topic 3 Discussion WEEK 3: MOVING AVERAGE FILTERS This stock market uses moving average filters sometimes to determine the trend analysis of a stock’s performance. Discuss how you think this filtering of a stock’s value compares to filtering an input signal. Why do you think this is used for stocks? Can you think of any other applications of a moving average filter and why it would be useful? WEEK 3: RECURSIVE AND NONRECURSIVE FILTERS This week, both infinite (recursive) and finite (nonrecursive) filters and their responses, along with impulse and step responses, were discussed. List and discuss examples that might be seen in your daily life that might be modeled by either of these filters, and tell why you think they are either recursive or nonrecursive. Would an input to this example be a step or impulse function? For example, if you are in a large, empty room, such as a gymnasium, and someone yells out hey, what kind of input is the shout, and what is the effect or response of the gymnasium?

  15. ECET 350 Topic 3 Lab 3: Digital Filtering and Spectral Effect Objectives To learn how to determine the difference equation given FIR (Finite-impulse response) or  IIR (Infinite Impulse Response) system coefficients. To learn how to determine the FIR transfer function based on the given difference  equation, and learn how to calculate and display frequency responses of the FIR system and perform digital filtering. To learn how to determine the IIR transfer function based on the given difference  equation, and learn how to calculate and display frequency responses of the IIR systems and perform digital filtering. To use Simulink FIR design tool to design various types of filters and demonstrate the  filtering process by composite input signals. Software MATLAB software Please download the lab instructions (Links to an external site.). Deliverables Submit your Week 3 Lab for grading. ECET 350 Topic 3 Homework Chapter 3 Homework Problems: page 80, 1.a, 1.b, 1.c, 1.d, 2.a, 2.b, 7.a, 7b, 8.a, 8.b, 10. 13, 14, 16.a, 16.b, Chapter 6 Homework Problems: Page 208 2.a, 9, 10.a, 10.b, 21, 25.a, 25.b Don’t forget to submit your assignment. ECET 350 Topic 4 Introduction to Finite Impulse Response Filter Design and Implementation ECET 350 Topic 4 Discussion WEEK 4: COURSE PROJECT (VIDEO POST/PEER REVIEW)

  16. ASSEMBLY OF DESIGN & VIDEO POST 1 The team should have a preliminary design and parts should be assembled. You should have your first video post this week and you should also comment on other team discussion post about their design. See the Course Project Overview in Introduction and Resources. Remember to submit your video. ECET 350 Topic 4 Lab 4 IIR and FIR Notch and DC_Blocker Digital Filter Design Objectives To learn how to determine the difference equation given FIR (Finite-impulse response) or  IIR (Infinite Impulse Response) system coefficients. To learn how to determine the FIR transfer function based on the given difference  equation, and learn how to calculate and display frequency responses of the FIR system and perform digital filtering using Matlab.\ To learn how to determine the IIR transfer function based on the given difference  equation, and learn how to calculate and display frequency responses of the IIR systems and perform digital filtering using Matl  Software Needed MATLAB  Instructions Please download the lab instructions (Links to an external site.). ECET 350 Topic 4 Homework Chapter 7 Homework Problems: Page 290 1.a, 1.b, 7, 28,

  17. ECET 350 Topic 5 Introduction to Band Pass, High-Pass, and Band Stop Finite Impulse Response Filter Design and Implementation ECET 350 Topic 5 Discussion WEEK 5: COURSE PROJECT INITIAL DESIGN TESTING & VIDEO POST 2 The team should have a design assembled and ready for testing. You should have your second video post this week and you should also comment on other team discussion post about their design. You should have started your research report and oral presentation. See the Course Project Overview in Introduction and Resources. Remember to submit your assignment. ECET 350 Topic 5 Lab 5 Matlab Designed Band Pass Finite Impulse Response Filters Objectives Design a high order, FIR band pass using Matlab, and analyze the performance of that  filter Software Needed Lab 5 Band Pass FIR, available in the Files section of the Course Menu  Excel  Matlab with Signal Processing Toolbox  Deliverables Answer all questions, complete all tables, and paste all figures and graphs in the Week 5 Lab Cover Sheet (Links to an external site.). Submit your Week 5 Lab Cover Sheet for grading. You can also download the Week 5 Lab Cover Sheet for the Week 5 Lab in the Files section of the Course Menu. Lab Steps STEP 1: Introduction This lab uses the Parks-McClellan FIR design algorithm within Matlab to create a relatively high- order bandpass FIR filter that is to be designed according to specifications given in this handout.

  18. You are to design a band pass FIR filter using the Sptools Matlab toolbox according to the following specification. Unlike the windowed, impulse response method discussed in the textbook and implemented in the previous lab, Matlab uses a completely different algorithm called the Parks- McClellan filter design algorithm. This algorithm is an iterative algorithm, meaning it performs the filter coefficient calculations repeatedly, comparing the design results with a predetermined error factor until the design results are below the error factor. The Parks-McClellan algorithm is very efficient, usually obtaining the desired design criteria with 10 to 12 iterations of the design loop and error process. STEP 2: Band Pass Filter Design The specification for the filter is shown below. Please note that the specifications correspond directly to parameters that are to be entered in the Matlab Sptool graphical filter design program. Filter Type: Equiripple Band pass Filter Order: Minimum Order Frequency Specification Units: Hertz Fs (Sampling Frequency): 2000 Fstop1: 380 Fpass1: 400 Fpass2: 600

  19. Fstop2: 620 Magnitude Specifications Units: dB Astop1(stop band attenuation below Fstop1): 40 Apass(pass band ripple): 1 Astop2(stop band attenuation above Fstop2): 40 Once you have opened Matlab, you will need to open the signal processing toolbox. To do so, from the Matlab command window, type Sptool, and then press enter. The Sptool session window, shown in Figure 1, should now open. In the filters column, click on New. This should now open the filter designer window as shown in Figure 2.

  20. Figure 1: Sptool Session Start-Up Window Figure 2: Filter Designer Window Reopen the Sptool session window and click on file | save session as and save your filter design to a location of your choice for later reference. Please name your filter design session, but please note that Matlab does not accept long file names or file names with spaces in them. Next, while still in the Sptool window, move the cursor to the filters field and click on filt1[design]. Next, click on edit|name and select filt1. Enter a descriptive name for your filter. Again, please note that Matlab is fussy about file names so no spaces or special characters may be used, and it is recommended you use the same name as your saved session name.

  21. Reopen the filter designer window and using the parameters provided in the previous section of this lab, enter the band pass filter design parameters in the appropriate boxes as shown in Figure 2. Please check your entries, and once you have verified all parameters are correct, click the Design Filter button at the bottom of the window. Figure 3: Filter Designer Band Pass Filter Results The filter designer window should now change to a graph of your designed filter’s response as shown in Figure 3. Leave the filter designer window open, but click on the Sptool session window and select the name of your filter in the Filters column.

  22. Click on View, and a new window as shown in Figure 4 should appear with a new magnitude plot of your filter. Click on Edit in the menu bar, and then edit the title of the graph to ECET350 Lab 5 Band Pass Magnitude Response (dB). Copy and paste this graph into your Week 5 Lab Cover Sheet— Graph 1: FIR Band Pass Filter Frequency Magnitude Response Graph. Figure 4: FIR Band Pass Magnitude Response View Next, click on analysis and select phase response on the drop-down menu. A new window with a plot of the phase response of your filter should appear as shown in Figure 5. Edit the title of this graph as well to state ECET350 Lab 5 Band Pass Phase Response. Copy and paste this graph into your Week 5 Lab Cover Sheet—Graph 2: FIR Band Pass Filter Phase Response Graph.

  23. To obtain the actual filter coefficients generated by the filter designer, reopen the Sptool session window. Make sure that your filter is selected and highlighted in the filters column and then click on the file menu bar item. Once the menu opens, select the export function as shown in Figure 6. Once the export window opens, make sure that ONLY your filter is highlighted in the export list, as shown in Figure 7. Also, make sure that the export filters as TF objects is unchecked . Once you have verified this, click on the export to workspace button to export the filter coefficients into the Matlab workspace window. Figure 5: FIR Band Pass Phase Response View

  24. Figure 6: Sptool Session Export Menu Figure 7: Export Filter to Workspace Window Once the export is complete, open the Matlab workspace and type the name of your filter appended with .tf.num. For example, if your filter design name was MyBPFIR, then enter MyBPFIR.tf.num and

  25. then press enter. A listing of your filter coefficients should scroll by on the screen as shown in Figure 8. Figure 8: Exported Filter Coefficients—Floating Point You should note that all of the filters are listed as floating point numbers. However, for this high of an order digital filter, there is a good possibility that using floating point emulation on the Tower system may take too long and corrupt the filter output. The next steps then involve converting the coefficients to a fixed-point representation and then exporting the coefficients to a text file, which could be copied into your CodeWarrior source code and filter coefficients array. To convert the floating point coefficients to fixed point, enter the following command in the Matlab command window, substituting your filter name for filt1. coefs = round(filt1.tf.num * 2^15) <ENTER> This command takes the floating point coefficients, which are all less than 1.0, and multiplies them by 2 raised to the 15 thpower, which performs the proper integer scaling for the 9S12 fixed-point

  26. math operations. The command then rounds them to the nearest integer value and saves them in the new vector called coefs. After you have entered this command, the new integer values should appear in the Matlab workspace window. Before you can save your coefficients to a file, you will need to change the output directory of Matlab. At the top of the Matlab workspace, change the current directory by clicking the browse (three dots …) button to change the directory to a location of your choice, such as your flash drive. After you have done this, save the filter coefficients in a comma-separated, variable (csv) text file by entering the following command. csvwrite (‘filtcoef.txt’, coefs) <ENTER> Examine your file save location, and you should now see the file filtcoef.txt. Open the file with a text editor, such as Notepad, and then copy and paste the filter coefficients into your Week 5 Lab Cover Sheet—Listing 1: FIR Band Pass Filter Coefficients. ECET 350 Topic 5 Homework Chapter 7 Homework Problems: Page 290 2.a, 2.b, 4.a, 4.b, 8, 9, 10, 29, 30, ECET 350 Topic 6 Introduction to Infinite Impulse Response Filters and Design Methodologies ECET 350 Topic 6 Discussion WEEK 6: COURSE PROJECT (VIDEO/PEER REVIEW) FINAL DESIGN TESTING & VIDEO POST 3 The team should have a final design assembled and tested. You should have your third video post this week and you should also comment on other team discussion post about their design. You should also be fine tuning your research report and oral presentation. Next week is show time!! See the Course Project Overview in Introduction and Resources. Remember to submit your assignment.

  27. ECET 350 Topic 6 Lab 6 IIR Digital Filter Design using Bilinear Transformation Objectives To learn about how to design IIR filters using Matlab’s bilinear transform command. Software Needed  MATLAB  Deliverables Submit your Week 6 Lab for grading. Lab Steps STEP 1:IIR Digital Filter Design using Bilinear Transformation Please download the lab instructions (Links to an external site.). ECET 350 Topic 6 Homework Chapter 8 Homework Problems: Page 392 1, 3, 6, 9 ECET 350 Topic 7 Advanced Infinite Impulse Response Filters and Design Methodologies ECET 350 Topic 7 Discussion WEEK 7: COMPARISON OF BUTTERWORTH AND CHEBYSHEV TYPE I FILTERS Various comparisons are stated in the text and the lecture material between Butterworth and Chebyshev Type I IIR filters. What sort of criteria do you think should be used in deciding which type of filter to use? WEEK 7: COEFFICIENT QUANTIZATION IN IIR FILTERS Coefficient quantization has been demonstrated to have a significant effect on the processing of digital IIR filters. How do you think the introduction of feedback terms in IIR filters is affected by quantization, and is it more or less critical than the effect upon FIR filters? Include in your answer any differences or comparisons you have noticed between the aky[n-k] filter coefficients and the bkx[n-k] filter coefficients.

  28. ECET 350 Topic 7 Lab 7 Infinite Impulse Response Band Pass Filters Objectives Design an IIR band pass using Matlab and analyze Software Needed  Lab 7 Band Pass FIR, available in the Files section of the Course Menu  Excel  Matlab with Signal Processing Toolbox  Deliverables Answer all questions, complete all tables, and paste all figures and graphs in the Week 7 Lab Cover Sheet (Links to an external site.). Submit your Week 7 Lab Cover Sheet for grading. You can also download the Week 7 Lab Cover Sheet for the Week 7 Lab in the Files section of the Course Menu. Lab Steps STEP 1: Introduction This lab uses the Sptool box and filter design and analysis tool within Matlab to design a Chebyshev Type I band pass IIR filter using specifications given in this handout. You are to design a band pass IIR filter using the Sptool s Matlab toolbox according to the provided specification. This algorithm is an iterative algorithm, meaning it performs the filter coefficient calculations repeatedly, comparing the design results with a predetermined error factor until the design results are below the error factor. The algorithm is very efficient, usually obtaining the desired design criteria with 10- to 12-iterations of the design loop and error process. High-Order Pass Band Pass Filter Design The specification for the filter is shown below. Please note that the specifications correspond directly to parameters that are to be entered in the Matlab Sptool graphical filter design program. Filter Type: Band pass Design Method: IIR Chebyshev Type I

  29. Filter Order: Minimum Order Frequency Specification Units: Hertz Fs (Sampling Frequency): 2,000 Fstop1: 380 Fpass1: 400 Fpass2: 600 Fstop2: 620 Magnitude Specifications Units: dB Astop1 (stop band attenuation below Fstop1): 40 Apass (pass band ripple): 1 Astop2 (stop band attenuation above Fstop2): 40

  30. Once you have opened Matlab, you will need to open the signal processing toolbox. To do so, from the Matlab command window, type Sptool and then press enter. The Sptool session window, shown in Figure 1, should now open. In the filters column, click on new. This should now open the filter designer window, as shown in Figure 2. Figure 1: Sptool Session Startup Window

  31. Figure 2: Filter designer Window Reopen the Sptool session window and click on file | save session as, and save your filter design to a location of your choice for later reference. Please name your filter design session, but please note that Matlab does not accept long file names or file names with spaces in them. Next, while still in the Sptool window, move the cursor to the filters field and click on filt1[design]. Next, click on edit|name and select filt1. Enter a descriptive name for your filter. Again, please note that Matlab is fussy about file names so no spaces or special characters may be used, and it is recommended you use the same name as your saved session name. Reopen the filter designer window and, using the parameters provided in the previous section of this lab, enter the band pass filter design parameters in the appropriate boxes as shown in Figure 2.

  32. Please check your entries, and once you have verified all parameters are correct, click the design filter button at the bottom of the window. Figure 3: Filter Designer Band Pass Filter Results The filter designer window should now change to a graph of your designed filter’s response as shown in Figure 3 and that the magnitude response matches that of the desired filter specifications.

  33. Notice that in the top left-hand box, it states that the order of the filter is 20 and that the sections are 10 and that the structure is direct-form II, second order sections. This means that the filter coefficients that the filter design software has calculated are in 10, second-order polynomials. In order to implement this in software, we will need to modify the filter implementation software to calculate the product of 10, second-order polynomials. A new issue that we are facing with IIR filters that was not so large of a problem with FIR filters is that of the coefficient precision and dynamic range. To see this, go to the filter designer menu bar, click on analysis, and then filter coefficients as shown in Figure 4.

  34. Figure 4: IIR Band Pass Filter Coefficient Analysis Menu What should appear next is shown in Figure 5. This is a listing of all of the numerator and denominator coefficients of the 10, second-order polynomials that would be used to implement the filter that has been designed. You can scroll up and down to view the coefficients. This may be done as a product of polynomials, which would require a significant modification to the software that was used in earlier labs that implemented the FIR filters.

  35. Figure 5: IIR Band Pass Filter Coefficients View The Matlab filter design software is capable of modifying the design so that the coefficients are implemented as a single-stage polynomial. To do this, click on edit and then convert to single section as shown in Figure 6.

  36. Figure 6: Convert IIR Filter to Single Section After this is done, the coefficients may be viewed by going to the filter designer menu bar, click on analysis, and then filter coefficients as was shown earlier in Figure 4. Figure 7 now shows the single stage (or section) filter coefficients, which is now in the form of a tenth-order polynomial.

  37. Figure 7: IIR Single-Section Filter Coefficients Scroll up and down in the filter coefficients window and inspect the coefficients. One thing that should be instantly recognized is the very large dynamic range from smallest coefficient in the

  38. numerator to the largest coefficient in the denominator. Ignoring any coefficients with more than seven leading zeros (which would be set to zero in the software) yields the following coefficients. Smallest numerator coefficient: 0.000000029286166789081516 Largest denominator coefficient: 141.81011580196366 STEP 3: Single Section IIR Band Pass Filter Design While it is possible to implement the 10, second-order stages, this lab will stay with a single stage implementation approach by reducing the filter performance specification, and thereby, simplifying the filter design and order. Reopen the filter design and analysis tool window, and modify the filter design to reflect the following new specifications. Filter Type: Band pass Design Method: IIR Chebyshev Type I Filter Order: Minimum Order Frequency Specification Units: Hertz Fs (Sampling Frequency): 2,000 Fstop1: 200 Fpass1: 400

  39. Fpass2: 600 Fstop2: 800 Magnitude Specifications Units: dB Astop1 (stop band attenuation below Fstop1): 40 Apass (pass band ripple): 1 Astop2 (stop band attenuation above Fstop2): 40 Once you have entered these specifications, click on the design filter button, and you should now see what is shown in Figure 8.

  40. Figure 8: Single Section IIR Band Pass Filter Design Convert the design to a single section design as you did before, and you should now see the results as shown in Figure 9. This filter is easily implemented on the Tower system.

  41. Figure 9: Single Section IIR Filter Coefficients For your Lab Cover Sheet, you will need to provide three documents from the filter design and analysis tool. 1. IIR Band Pass Filter Frequency Magnitude Response Graph 2. IIR Band Pass Filter Phase Response Graph 3. IIR Band Pass Filter Single Section Filter Coefficients Graph 1: These may all be obtained by opening the Sptool startup window as shown in Figure 1. Click on view and a new window should appear with a new magnitude plot of your filter. Click on edit in the menu bar, and then edit the title of the graph to “ECET 350 Lab 7 IIR Band Pass Magnitude Response (dB).” Copy and paste this graph into your Week 7 Lab Cover Sheet. Graph 2: Next, click on analysis and select phase response on the drop down menu. A new window with a plot of the phase response of your filter should appear. Edit the title of this graph as well to state “ECET 350 Lab 7 IIR Band Pass Phase Response.” Copy and paste this graph into your Week 7 Lab Cover Sheet.

  42. Graph 3: Lastly, but not least, click on analysis and select filter coefficients on the drop down menu. A new window with a listing of your single structure filter coefficients will open. Copy and paste this graph into your Week 7 Lab Cover Sheet. ECET 350 Topic 7 Homework Chapter 8 Homework Problems: Page 394 11, 12, 14, 15 ECET 350 Topic 7 Course Project Course Project written report and project presentation due. See the Course Project Overview in Introduction and Resources. Remember to submit your assignment. ECET 350 Topic 8 FINAL EXAM ECET 350 Topic 8 Discussion WEEK 8: LOOKING AHEAD Class, looking back over the Course Objectives for this course, what are you looking forward to learning more about throughout your education and career? WEEK 8: FINAL EXAM INSTRUCTIONS Here is some information about the Final Exam. This Final Exam covers COs 1, 2, 3, 4, 5, 6, and 7 and Chapters 2, 3, 4, 5, 9, and 10.  This Final Exam is worth 240 total points and includes  o14 multiple choice questions worth 5 points each; ofive short answer questions worth 10 points each; and osix essay questions worth 20 points each. You have 3 hours and 30 minutes to finish the Final Exam.  When the time limit is reached you will be exited from the exam.  Good luck! By submitting this work, I am attesting that it abides by the Student Honor Code.

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