Why signal processing analyzes data patterns in MATLAB - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a simple signal vector.

MATLAB

signal = [1, 2, 3, 4, [1]];

Drag options to blanks, or click blank then click option'

A7

B5

C6

D8

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a number that breaks the sequence.

The signal vector ends with 5 to complete the sequence.

2fill in blank

medium

Complete the code to calculate the mean of the signal.

MATLAB

average = mean([1]);

Drag options to blanks, or click blank then click option'

Asignal

Bdata

Cvalues

Dinput

Attempts:

3 left

💡 Hint

Common Mistakes

Using undefined variables like 'data' or 'input'.

The variable 'signal' holds the data to find the mean.

3fill in blank

hard

Fix the error in the code to plot the signal correctly.

MATLAB

plot([1]);

Drag options to blanks, or click blank then click option'

Adata

By

Csignal

Dx

Attempts:

3 left

💡 Hint

Common Mistakes

Plotting undefined variables like 'data' or 'x'.

The 'plot' function needs the signal vector to display the data.

4fill in blank

hard

Fill both blanks to create a filtered signal using a moving average.

MATLAB

filtered_signal = movmean([1], [2]);

Drag options to blanks, or click blank then click option'

Asignal

Bdata

C3

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong variable names or window sizes.

Use 'signal' as input and window size 3 for the moving average.

5fill in blank

hard

Fill all three blanks to compute and plot the frequency spectrum of the signal.

MATLAB

Y = fft([1]);
N = length([2]);
f = (0:N-1)*([3]/N);
plot(f, abs(Y));

Drag options to blanks, or click blank then click option'

Asignal

C1

Dlength(signal)

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variables or wrong sampling frequency.

Use 'signal' for FFT and length, and 1 as sampling frequency.

Practice

1. Why does signal processing analyze data patterns in MATLAB?

easy

A. To extract useful information from signals

B. To create random noise in data

C. To slow down data processing

D. To delete all signal data

Solution

1. Step 1: Understand the purpose of signal processing

   Signal processing aims to find meaningful information hidden in data patterns.

2. Step 2: Relate to MATLAB's role

   MATLAB helps analyze these patterns to extract useful information, not to destroy or slow data.

3. Final Answer:

   To extract useful information from signals -> Option A

4. Quick Check:

   Signal processing = extract useful info [OK]

Hint: Signal processing finds useful info in data patterns [OK]

Common Mistakes:

• Thinking signal processing adds noise
• Believing it slows down data
• Assuming it deletes data

2. Which MATLAB command is used to plot a signal pattern for analysis?

easy

A. save(signal)

B. print(signal)

C. read(signal)

D. plot(signal)

Solution

1. Step 1: Identify MATLAB plotting function

   The function plot() is used to draw graphs of data like signals.

2. Step 2: Check other options

   print() prints to printer, read() reads files, save() saves data, none plot signals.

3. Final Answer:

   plot(signal) -> Option D

4. Quick Check:

   Plotting = plot() function [OK]

Hint: Use plot() to see signal shapes in MATLAB [OK]

Common Mistakes:

• Using print() to plot
• Confusing read() with plot()
• Trying save() to display data

3. What is the output of this MATLAB code?

signal = [1 2 3 4 5];
filtered = signal > 3;
disp(filtered);

medium

A. [false false false true true]

B. [1 2 3 4 5]

C. [0 0 0 1 1]

D. [3 4 5]

Solution

1. Step 1: Understand the comparison operation

   The expression signal > 3 checks each element if it is greater than 3, returning logical true or false.

2. Step 2: Interpret logical array output

   In MATLAB, disp() on a logical array displays it as numeric 0s and 1s: [0 0 0 1 1] since elements 4 and 5 are greater than 3.

3. Final Answer:

   [0 0 0 1 1] -> Option C

4. Quick Check:

   Comparison > 3 = logical array displayed as [0 0 0 1 1] [OK]

Hint: Logical comparisons return arrays displayed as 0/1 by disp() in MATLAB [OK]

Common Mistakes:

• Expecting textual 'false'/'true' instead of numeric 0/1
• Thinking it filters values automatically
• Confusing output with original signal

4. Find the error in this MATLAB code for filtering signal values greater than 2:

signal = [1 2 3 4];
filtered = signal > 2;
result = signal(filtered);
disp(result);

medium

A. Use parentheses: result = signal(filtered == 1);

B. No error, code works correctly

C. filtered should be signal >= 2

D. disp() cannot display arrays

Solution

1. Step 1: Check logical indexing usage

   The variable filtered is a logical array marking elements > 2.

2. Step 2: Verify indexing and display

   Using signal(filtered) selects elements where filtered is true. disp() can display arrays.

3. Final Answer:

   No error, code works correctly -> Option B

4. Quick Check:

   Logical indexing with disp() works [OK]

Hint: Logical indexing selects elements directly in MATLAB [OK]

Common Mistakes:

• Thinking disp() can't show arrays
• Confusing logical indexing syntax
• Changing comparison operator unnecessarily

5. You want to extract only positive peaks from a noisy signal in MATLAB. Which approach best uses signal processing to analyze data patterns?

hard

A. Use findpeaks() to locate peaks and filter positive values

B. Use sort() to order all values and pick the largest

C. Use mean() to average the signal and ignore peaks

D. Use fft() to convert signal to frequency and ignore time peaks

Solution

1. Step 1: Identify function for peak detection

   findpeaks() is designed to find local maxima (peaks) in signals.

2. Step 2: Filter positive peaks

   After finding peaks, filtering positive values extracts only positive peaks, matching the goal.

3. Final Answer:

   Use findpeaks() to locate peaks and filter positive values -> Option A

4. Quick Check:

   Peak detection = findpeaks() + filter positive [OK]

Hint: Use findpeaks() to detect and filter positive peaks [OK]

Common Mistakes:

• Using sort() which ignores peak positions
• Using mean() which smooths out peaks
• Using fft() which changes domain, not direct peak detection