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Questions and Answers
What is the purpose of a DFT matrix in signal processing?
What is the purpose of a DFT matrix in signal processing?
Which command is suggested for plotting real and imaginary parts of the DFT matrix in Python?
Which command is suggested for plotting real and imaginary parts of the DFT matrix in Python?
How can one verify Parseval's theorem for an N-point DFT?
How can one verify Parseval's theorem for an N-point DFT?
What type of pulse is generated using the specified function in the content?
What type of pulse is generated using the specified function in the content?
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When testing the FIR low pass filter, which cutoff frequency should be used?
When testing the FIR low pass filter, which cutoff frequency should be used?
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Which programming language is recommended for controlling output LEDs through input switches?
Which programming language is recommended for controlling output LEDs through input switches?
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In terms of DSP experiments, which signal is NOT mentioned in the simulation list?
In terms of DSP experiments, which signal is NOT mentioned in the simulation list?
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Why is the DFT matrix generated for various values of N, such as 16, 64, and 1024?
Why is the DFT matrix generated for various values of N, such as 16, 64, and 1024?
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What is the focus of the ECL333 Laboratory course?
What is the focus of the ECL333 Laboratory course?
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Which of the following is NOT a course outcome of ECL333?
Which of the following is NOT a course outcome of ECL333?
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What is the maximum mark for Continuous Internal Evaluation (CIE) in the ECL333 Laboratory?
What is the maximum mark for Continuous Internal Evaluation (CIE) in the ECL333 Laboratory?
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Which software skill is a prerequisite for the ECL333 Laboratory?
Which software skill is a prerequisite for the ECL333 Laboratory?
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What is the weightage of performance, result, and inference in the End Semester Examination?
What is the weightage of performance, result, and inference in the End Semester Examination?
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Which of the following statements correctly describes a course outcome?
Which of the following statements correctly describes a course outcome?
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How many total marks are allocated for the End Semester Examination?
How many total marks are allocated for the End Semester Examination?
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What is the weightage for attendance in Continuous Internal Evaluation?
What is the weightage for attendance in Continuous Internal Evaluation?
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Which course outcome involves familiarizing with DSP hardware?
Which course outcome involves familiarizing with DSP hardware?
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What is the weightage for the internal test in Continuous Internal Evaluation?
What is the weightage for the internal test in Continuous Internal Evaluation?
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What is the primary purpose of applying IFFT on stored FFT values?
What is the primary purpose of applying IFFT on stored FFT values?
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Which window is used in the FIR low pass filter design mentioned?
Which window is used in the FIR low pass filter design mentioned?
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What should be done if the last block of the input signal values is less than the specified length N?
What should be done if the last block of the input signal values is less than the specified length N?
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In the context of block convolution, what does the overlap save method primarily involve?
In the context of block convolution, what does the overlap save method primarily involve?
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For what kind of input signal is the designed FIR filter intended to be tested?
For what kind of input signal is the designed FIR filter intended to be tested?
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What is the maximum filter size N used in the FIR filter design as mentioned?
What is the maximum filter size N used in the FIR filter design as mentioned?
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Which of the following is not mentioned as an experiment in the content?
Which of the following is not mentioned as an experiment in the content?
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Which textbook is specifically cited for digital signal processing using Python?
Which textbook is specifically cited for digital signal processing using Python?
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What is the purpose of the circcon.py function in the context provided?
What is the purpose of the circcon.py function in the context provided?
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Which of the following correctly describes Parseval's Theorem?
Which of the following correctly describes Parseval's Theorem?
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What is the initial step in Experiment 3 regarding DSP hardware?
What is the initial step in Experiment 3 regarding DSP hardware?
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During the FFT experiment, what type of signal is applied to the analog port?
During the FFT experiment, what type of signal is applied to the analog port?
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What is the primary advantage of using FFT for signal processing as indicated in the context?
What is the primary advantage of using FFT for signal processing as indicated in the context?
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What should be documented after connecting a microphone to the DSP board?
What should be documented after connecting a microphone to the DSP board?
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What is a necessary condition for the circular convolution to be performed effectively?
What is a necessary condition for the circular convolution to be performed effectively?
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What must be accomplished in Experiment 4 regarding linear convolution?
What must be accomplished in Experiment 4 regarding linear convolution?
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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
- Simulate the following signals using Python/Scilab/MATLAB:
-
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).
- 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.
- 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.
- For the complex random sequences x1[n] and x2[n],
-
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.