AI$-$ML: Fall $2023$

Complex Engineering Problem

To be submitted in Hardcopy at the start of class at on the due date.

Solve yourself with understanding, any shortcut$/$plagiarism will harm you badly.

Scenario: Development of a $5$x$32$ Decoder using Gate$-$Level Logic for Precision

TechDynamics.

Company Name: PrecisionTech Dynamics Industry: High$-$Precision Control Systems

and Robotics

Problem Statement: PrecisionTech Dynamics is aiming to develop a gate$-$level logic$-$

based $532$ decoder to efficiently convert $5-$bit binary inputs into $32$ control signals for

their high$-$precision machinery. They seek a robust and accurate decoding system for

enhanced control and coordination within their systems.

Objective:Create a gate$-$level logic$-$based $532$ decoder that precisely translates $5-$bit

binary inputs $(x1,x2,x3,x4,x5)$ into $32$ output lines, delivering specific control signals

tailored for PrecisionTech Dynamics' machinery components.

Handwritten Logical Expressions:

Logical Expressions for Output Lines:

Let's create logical expressions for a few output lines based on the $5-$bit input

combinations:

Output Line $1$:

$y_1=x_1*x_2\frac{*}{b}ar(x_3)\frac{*}{b}ar(x_4)*x_5$

Output Line $2$:

$y_2\frac{?}{b}=ar(x_1)*x_2\frac{*}{b}ar(x_3)*x_4\frac{*}{b}ar(x_5)$

$...$ and so on for all $32$ output lines

Python Implementation of Logical Expressions:

Comparison:

Handwritten Logical Expressions Vs Python Implementation of Logical Expressions

Note: Please apply Neural Network principles to address the issue by creating a neural

network tailored to the specific requirements.