Q2/ Select the correct choice of the MCQ
1. Internally, the PLC manages, saves, and calculates the value.
A) Hexadecimal format
B) Decimal format
C) Octal format
D) None of the above
2. In the PLC, the scan time refers to the amount of time in which …
(A) transmitted data communications must finish
(B) timers and counters are indexed by
(C)one "rung" of ladder logic takes to complete
(D) the entire program takes to execute
3. Which of the following is the proper sequence for PLC operation?
A) Self-test, input scan, logic scan, output scan Self-test,
B) logic scan, output scan, input scan Self-test,
C) input scan, output scan, logic scan
D) None of the above

Function Point method is a method used to measure the size of the software, focusing primarily on the data processing component.

This method was introduced by Allan Albrecht in 1979, and it uses five basic elements which are inputs, outputs, files, inquiries, and interfaces.

To compute the function point’s estimate for the project, we will use the following formula:

Unadjusted function point = (total user inputs) + (total user outputs) + (total user inquiries) + (total number of files) + (total number of external interfaces)

= 20 + 30 + 25 + 10 + 15

= 100

Function Point is calculated as

Adjusted Function Point (AFP) = UFP x TCF x ECF

Where,

The number of logical statements can be calculated as follows:Number of Logical statements = Function Point * Productivity Factor = 1195 * 47.5 = 56705 statements

From table 16.2, the productivity factor for C# is 47.5.

The number of staff-months can be calculated as:

Number of staff-months = (Number of logical statements)/Productivity rate = 56705/6000 = 9.45 months

Therefore, the number of staff-months needed to implement the project in C# is 9.45 months.

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To send a message using a chatbox that will only be sent privately, the code should include the following terms:

`PHP`, `sendmessage` table, `senderid`, `receiverid`, `content`.Here's an example code for sending a private message using a chatbox in PHP:``` // Establish a database connection$servername = "localhost";$username = "username";$password = "password";$dbname = "database_name";$conn = new mysqli($servername, $username, $password, $dbname);// Check connectionif ($conn->connect_error) { die("Connection failed: " . $conn->connect_error);}if(isset($_POST['send_message'])) { $sender_id = 1; //ID of the sender (you) $receiver_id = 2; //ID of the receiver (the person you're sending the message to) $message = $_POST['message']; //Message content // Insert message into database $sql = "INSERT INTO sendmessage (senderid, reciverid, content) VALUES ('$sender_id', '$receiver_id', '$message')"; if ($conn->query($sql) === TRUE) { // Send message to receiver echo "Message sent successfully!"; } else { echo "Error: " . $sql . "

" . $conn->error; }}?> ```

In this code, the chatbox form should have a `POST` method that sends the message content to the server when the user clicks on the "Send Message" button. The `sender_id` and `receiver_id` should be predefined according to the IDs of the sender (you) and the receiver respectively.

Finally, the message should be inserted into the `sendmessage` table in the database, with its `senderid`, `reciverid`, and `content` values.

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The correct AND-OR-Invert logic implementation of the given expression, X = (ABC + D+E) is shown in Figure (C) In digital circuits, an AND-OR-Invert gate is a type of logic gate that has two or more inputs and three outputs, one of which is the inverted output.

AND-OR-Invert gates are commonly used in digital integrated circuits to perform logic functions. The given expression X = (ABC + D+E) can be implemented using AND and OR gates.

So, the AND-OR-Invert logic implementation of the given expression, X = (ABC + D+E) is shown in Figure (C).Therefore, the correct option is (C). Figure (C) is the correct AND-OR-Invert logic implementation of the expression X = (ABC + D+E).

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The PCA object using scikit-learn functions and plot the cumulative explained variance ratio is shown below.

1. Import the necessary libraries and load the MNIST dataset:

import numpy as np

import matplotlib.pyplot as plt

from sklearn.datasets import fetch_openml

from sklearn.preprocessing import MinMaxScaler

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

from sklearn.linear_model import LogisticRegression

from sklearn.pipeline import Pipeline

# Load MNIST dataset

mnist = fetch_openml('mnist_784')

X, y = mnist['data'], mnist['target']

2. Determine the number of samples per digit in the training and test sets:

# Count samples per digit in training set

train_counts = np.bincount(y[:60000].astype(int))

for i in range(10):

   print(f"Training Set - Digit {i}: {train_counts[i]} samples")

# Count samples per digit in test set

test_counts = np.bincount(y[60000:].astype(int))

for i in range(10):

   print(f"Test Set - Digit {i}: {test_counts[i]} samples")

3. Visualize at least 9 images for each digit in the training data:

# Display 9 images for each digit

digits_to_display = 9

fig, axes = plt.subplots(10, digits_to_display, figsize=(10, 10))

for i in range(10):

   digit_indices = np.where(y[:60000] == str(i))[0]

   random_indices = np.random.choice(digit_indices, digits_to_display, replace=False)

   for j, idx in enumerate(random_indices):

       image = X[idx].reshape(28, 28)

       axes[i, j].imshow(image, cmap='gray')

       axes[i, j].axis('off')

plt.tight_layout()

plt.show()

4. Scale the data with the MinMaxScaler:

# Scale the data with Min-Max scaler

scaler = MinMaxScaler()

X_scaled = scaler.fit_transform(X)

5. Create a PCA object and plot the cumulative explained variance ratio:

# Create PCA object

pca = PCA(n_components=X_scaled.shape[1])

pca.fit(X_scaled)

# Plot cumulative explained variance ratio

plt.plot(np.cumsum(pca.explained_variance_ratio_))

plt.xlabel('Number of Principal Components')

plt.ylabel('Cumulative Explained Variance Ratio')

plt.title('PCA - Cumulative Explained Variance Ratio')

plt.grid(True)

plt.show()

# Determine the number of PCs to preserve 90% of explained variance

explained_variance_ratio = pca.explained_variance_ratio_

n_components_90 = np.argmax(np.cumsum(explained_variance_ratio) >= 0.9) + 1

print(f"Number of PCs to preserve 90% of explained variance: {n_components_90}")

6. Plot the first 9 principal components:

# Plot the first 9 principal components

fig, axes = plt.subplots(3, 3, figsize=(8, 8))

for i, ax in enumerate(axes.flat):

   pc = pca.components_[i]

   ax.imshow(pc.reshape(28, 28), cmap='gray')

   ax.axis('off')

   ax.set_title(f"PC {i+1}")

plt.tight_layout()

plt.show()

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