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?

Triangular pulse Signup and view all the answers

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

0.1π Signup and view all the answers

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

C Signup and view all the answers

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

Sine wave signal Signup and view all the answers

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

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

What is the focus of the ECL333 Laboratory course?

Real-time DSP computing with dedicated hardware Signup and view all the answers

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

Develop advanced algorithm processing Signup and view all the answers

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

50 Signup and view all the answers

Which software skill is a prerequisite for the ECL333 Laboratory?

C programming Signup and view all the answers

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

25 marks Signup and view all the answers

Which of the following statements correctly describes a course outcome?

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

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

100 marks Signup and view all the answers

What is the weightage for attendance in Continuous Internal Evaluation?

15 marks Signup and view all the answers

Which course outcome involves familiarizing with DSP hardware?

CO3 Signup and view all the answers

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

30 marks Signup and view all the answers

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

To observe signal reconstruction Signup and view all the answers

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

Hamming window 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?

Pad the block with zeros Signup and view all the answers

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

Segmenting and overlapping signal blocks Signup and view all the answers

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

A 1 mV sinusoid from a signal generator Signup and view all the answers

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

50 Signup and view all the answers

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

Wavelet Transform Signup and view all the answers

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

Think DSP: Digital Signal Processing using Python Signup and view all the answers

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

To return the circular convolution of two input sequences Signup and view all the answers

Which of the following correctly describes Parseval's Theorem?

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

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

Familiarization with DSP cross compilers Signup and view all the answers

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

A 1 kHz sinusoidal signal at 1 mV Signup and view all the answers

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

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

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

The output electrical signal observed on a DSO Signup and view all the answers

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

The length must be the maximum of the two sequences. Signup and view all the answers

What must be accomplished in Experiment 4 regarding linear convolution?

To write a C function for linear convolution of two arrays 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.