ECL333 Digital Signal Processing Lab Quiz
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Questions and Answers

What is the purpose of a DFT matrix in signal processing?

  • To generate a time-domain representation of signals
  • To transform signals from the time domain to the frequency domain (correct)
  • To filter out noise from analog signals
  • To simulate pulse shapes
  • Which command is suggested for plotting real and imaginary parts of the DFT matrix in Python?

  • imshow (correct)
  • plot
  • scatter
  • line
  • How can one verify Parseval's theorem for an N-point DFT?

  • By computing the convolution of two signals
  • By controlling LEDs with a function
  • By demonstrating that the total energy in time domain equals the total energy in frequency domain (correct)
  • By designing an FIR filter
  • What type of pulse is generated using the specified function in the content?

    <p>Triangular pulse</p> Signup and view all the answers

    When testing the FIR low pass filter, which cutoff frequency should be used?

    <p>0.1π</p> Signup and view all the answers

    Which programming language is recommended for controlling output LEDs through input switches?

    <p>C</p> Signup and view all the answers

    In terms of DSP experiments, which signal is NOT mentioned in the simulation list?

    <p>Sine wave signal</p> Signup and view all the answers

    Why is the DFT matrix generated for various values of N, such as 16, 64, and 1024?

    <p>To analyze computation time efficiency for different sizes of input data</p> Signup and view all the answers

    What is the focus of the ECL333 Laboratory course?

    <p>Real-time DSP computing with dedicated hardware</p> Signup and view all the answers

    Which of the following is NOT a course outcome of ECL333?

    <p>Develop advanced algorithm processing</p> Signup and view all the answers

    What is the maximum mark for Continuous Internal Evaluation (CIE) in the ECL333 Laboratory?

    <p>50</p> Signup and view all the answers

    Which software skill is a prerequisite for the ECL333 Laboratory?

    <p>C programming</p> Signup and view all the answers

    What is the weightage of performance, result, and inference in the End Semester Examination?

    <p>25 marks</p> Signup and view all the answers

    Which of the following statements correctly describes a course outcome?

    <p>Students will implement real time LTI systems with block convolution.</p> Signup and view all the answers

    How many total marks are allocated for the End Semester Examination?

    <p>100 marks</p> Signup and view all the answers

    What is the weightage for attendance in Continuous Internal Evaluation?

    <p>15 marks</p> Signup and view all the answers

    Which course outcome involves familiarizing with DSP hardware?

    <p>CO3</p> Signup and view all the answers

    What is the weightage for the internal test in Continuous Internal Evaluation?

    <p>30 marks</p> Signup and view all the answers

    What is the primary purpose of applying IFFT on stored FFT values?

    <p>To observe signal reconstruction</p> Signup and view all the answers

    Which window is used in the FIR low pass filter design mentioned?

    <p>Hamming window</p> Signup and view all the answers

    What should be done if the last block of the input signal values is less than the specified length N?

    <p>Pad the block with zeros</p> Signup and view all the answers

    In the context of block convolution, what does the overlap save method primarily involve?

    <p>Segmenting and overlapping signal blocks</p> Signup and view all the answers

    For what kind of input signal is the designed FIR filter intended to be tested?

    <p>A 1 mV sinusoid from a signal generator</p> Signup and view all the answers

    What is the maximum filter size N used in the FIR filter design as mentioned?

    <p>50</p> Signup and view all the answers

    Which of the following is not mentioned as an experiment in the content?

    <p>Wavelet Transform</p> Signup and view all the answers

    Which textbook is specifically cited for digital signal processing using Python?

    <p>Think DSP: Digital Signal Processing using Python</p> Signup and view all the answers

    What is the purpose of the circcon.py function in the context provided?

    <p>To return the circular convolution of two input sequences</p> Signup and view all the answers

    Which of the following correctly describes Parseval's Theorem?

    <p>It states that the total energy of the time domain signal is equal to the total energy of the frequency domain signal.</p> Signup and view all the answers

    What is the initial step in Experiment 3 regarding DSP hardware?

    <p>Familiarization with DSP cross compilers</p> Signup and view all the answers

    During the FFT experiment, what type of signal is applied to the analog port?

    <p>A 1 kHz sinusoidal signal at 1 mV</p> Signup and view all the answers

    What is the primary advantage of using FFT for signal processing as indicated in the context?

    <p>It saves time during computations compared to direct methods.</p> Signup and view all the answers

    What should be documented after connecting a microphone to the DSP board?

    <p>The output electrical signal observed on a DSO</p> Signup and view all the answers

    What is a necessary condition for the circular convolution to be performed effectively?

    <p>The length must be the maximum of the two sequences.</p> Signup and view all the answers

    What must be accomplished in Experiment 4 regarding linear convolution?

    <p>To write a C function for linear convolution of two arrays</p> Signup and view all the answers

    Study Notes

    Course Description

    • ECL333 is a Digital Signal Processing Laboratory course.
    • The course is designed to provide students with real-time DSP computing experience.
    • Students will use dedicated DSP hardware such as TI or Analog Devices development boards to achieve real-time computing.
    • Prerequisites include ECT 303 Digital Signal Processing and EST 102 Programming in C.

    Course Outcomes

    • Students will be able to simulate digital signals.
    • Students will be able to verify the properties of DFT Computationally.
    • Students will be able to familiarize themselves with DSP hardware and its interface to a computer.
    • Students will be able to implement Linear Time-Invariant (LTI) systems with linear convolution.
    • Students will be able to implement Fast Fourier Transform (FFT) and Inverse Fast Fourier Transform (IFFT), and use them on real-time signals.
    • Students will be able to implement Finite Impulse Response (FIR) low pass filters.
    • Students will be able to implement real-time LTI systems with block convolution and FFT.

    Assessment Pattern

    • The course is graded out of a total of 150 marks, 50 for Continuous Internal Evaluation (CIE) and 100 for End Semester Examination (ESE).

    Continuous Internal Evaluation

    • Each experiment is assessed continuously out of 50 credits.
    • The breakdown of the assessment is as follows:
      • Attendance: 15 marks
      • Continuous assessment: 30 marks
      • Internal Test (Immediately before second series test): 30 marks

    End Semester Examination Pattern

    • The ESE is graded based on the following attributes:
      • Preliminary work: 15 marks
      • Implementing the work/conducting the experiment: 10 marks
      • Performance, result, and inference (usage of equipment and troubleshooting): 25 marks
      • Viva voce: 20 marks
      • Record: 5 marks

    Course Level Assessment Questions

    • CO1-Simulation of Signals:
      • Write a function in Python/MATLAB/Scilab to generate a rectangular pulse.
      • Write a function in Python/MATLAB/Scilab to generate a triangular pulse.
    • CO2-Verification of Properties of DFT:
      • Write a function in Python/MATLAB/Scilab to compute the N-point DFT matrix and plot its real and imaginary parts.
      • Write a function in Python/MATLAB/Scilab to verify Parseval’s theorem for N = 1024.
    • CO3-Familiarization of DSP Hardware:
      • Write a C function to control output LEDs with input switches.
      • Write a C function to connect the analog input port to the output port and test with a microphone.
    • CO4-LTI System with Linear Convolution:
      • Write a function to compute linear convolution, download to the hardware target, and test with some signals.
    • CO5-FFT Computation:
      • Write and download a function to compute N-point FFT to the DSP hardware target and test it on a real-time signal.
      • Write a C function to compute IFFT with the FFT function and test it on DSP hardware.
    • CO6-Implementation of FIR Filter:
      • Design and implement an FIR low pass filter for a cutoff frequency of 0.1π and test it with an AF signal generator.
    • CO7-LTI Systems by Block Convolution:
      • Implement an overlap add block convolution for speech signals on the DSP target.

    List of Experiments

    • Experiment 1. Simulation of Signals:

      • Simulate the following signals using Python/Scilab/MATLAB:
        • Unit impulse signal
        • Unit pulse signal
        • Unit ramp signal
        • Bipolar pulse
        • Triangular signal
    • Experiment 2. Verification of the Properties of DFT:

      • Generate and appreciate a DFT matrix.
      • Write a function that returns the N-point DFT matrix VN for a given N.
      • Plot the real and imaginary parts of VN as images using matshow or imshow commands (in Python) for N = 16, N = 64, and N = 1024.
      • Compute the DFTs of 16-point, 64-point, and 1024-point random sequences using the above matrices.
      • Observe the time of computations for N = 2γ for 2 ≤ γ ≤ 18 (You may use the time module in Python).
      • Use some iterations to plot the times of computation against γ. Plot and understand this curve.
      • Plot the times of computation for the fft function over this curve and appreciate the computational saving with FFT.
      • Circular Convolution:
        • Write a python function circcon.py that returns the circular convolution of an N1 point sequence and an N2 point sequence given at the input.
          • The easiest way is to convert a linear convolution into circular convolution with N = max(N1, N2).
      • Parseval’s Theorem:
        • For the complex random sequences x1[n] and x2[n],
          • Generate two random complex sequences of say 5000 values.
          • Prove the theorem for these signals.
    • Experiment 3. Familiarization of DSP Hardware:

      • Familiarization of the code composer studio (in the case of TI hardware) or Visual DSP (in the case of Analog Devices hardware) or any equivalent cross compiler for DSP programming.
      • Familiarization of the analog and digital input and output ports of the DSP board.
      • Generation and cross compilation and execution of the C code to connect the input digital switches to the output LEDs.
      • Generation and cross compilation and execution of the C code to connect the input analog port to the output. Connect a microphone, speak into it, and observe the output electrical signal on a DSO and store it.
      • Document the work.
    • Experiment 4. Linear Convolution:

      • Write a C function for the linear convolution of two arrays.
      • The arrays may be kept in different files and downloaded to the DSP hardware.
      • Store the result as a file and observe the output.
      • Document the work.
    • Experiment 5. FFT of signals:

      • Write a C function for N-point FFT.
      • Connect a precision signal generator and apply 1 mV, 1 kHz sinusoid at the analog port.
      • Apply the FFT on the input signal with appropriate window size and observe the result.
      • Connect a microphone to the analog port and read in real-time speech.
      • Observe and store the FFT values.
      • Document the work.
    • Experiment 6. IFFT with FFT:

      • Use the FFT function in the previous experiment to compute the IFFT of the input signal.
      • Apply IFFT on the stored FFT values from the previous experiments and observe the reconstruction.
      • Document the work.
    • Experiment 7. FIR low pass filter:

      • Use Python/Scilab to implement the FIR filter response h[n] = sin(ωcn)/πn for a filter size N = 50, ωc = 0.1π and ωc = 0.3π.
      • Realize the hamming(wH[n]) and kaiser (wK[n]) windows.
      • Compute h[n]w[n] in both cases and store as a file.
      • Observe the low pass response in the simulator.
      • Download the filter onto the DSP target board and test with a 1 mV sinusoid from a signal generator connected to the analog port.
      • Test the operation of the filters with speech signals.
      • Document the work.
    • Experiment 8. Overlap Save Block Convolution:

      • Use the file of filter coefficients from the previous experiment.
      • Realize the system shown in the diagram for the input speech signal x[n].
      • Segment the signal values into blocks of length N = 2000. Pad the last block with zeros, if necessary.
      • Implement the overlap save block convolution method.
      • Document the work.
    • Experiment 9. Overlap Add Block Convolution:

      • Use the file of filter coefficients from the previous experiment.
      • Realize the system shown in the previous experiment for the input speech signal x[n].
      • Segment the signal values into blocks of length N = 2000. Pad the last block with zeros, if necessary.
      • Implement the overlap add block convolution method.
      • Document the work.

    Schedule of Experiments

    • Each experiment should be completed in three hours.

    Textbooks

    • Vinay K. Ingle, John G. Proakis, “Digital Signal Processing Using MATLAB.”
    • Allen B. Downey, “Think DSP: Digital Signal Processing using Python.”
    • Rulph Chassaing, “DSP Applications Using C and the TMS320C6x DSK (Topics in Digital Signal Processing)”

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    Description

    Test your knowledge and skills in the ECL333 Digital Signal Processing Laboratory course. This quiz covers essential topics such as real-time DSP computing, implementation of LTI systems, and the use of FFT and IFFT with DSP hardware. Prepare to demonstrate your understanding of digital signals and filter implementation.

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