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lefaraleting's Projects

digitalcommunications_practical1 icon digitalcommunications_practical1

Develop a simulation platform1 for a BPSK, 4QAM, 8PSK and 16QAM communication system transmitting information over an additive white Gaussian noise (AWGN) channel.

digitalcommunications_practical2 icon digitalcommunications_practical2

Develop a simulation platform1 for a communication system transmitting information over an additive white Gaussian noise (AWGN) multipath channel. Use the simulation platform developed in Practical 1 to develop a simulation platform that includes the effect of multipath in the received signal, where the channel impulse response (CIR) length is L = 3.

digitalcommunications_practical3 icon digitalcommunications_practical3

Develop two similation platforms to encode and decode BPSK-modulated information through and AWGN channel (no multipath). Use the BPSK simulation platform developed in Practical 1 to develop the simulation platforms to generate uncoded data blocks of length Nu = 100.

e-com-front-end icon e-com-front-end

This is a front end design of an e - commerce website with no back end fucntionality

eai320_practical1 icon eai320_practical1

To investigate the effectiveness of two uninformed search techniques, namely depth-first search (DFS) and breadth-first search (BFS). The algorithms will be used to explore different possible sequences in a game of RPS.

eai320_practical2 icon eai320_practical2

This assignment will require students to find a pseudo-optimal strategy for a rock-paper-scissors (RPS) agent by using a GA. The same RPS framework that was used for the previous assignment will be used again [8].

eai320_practical3 icon eai320_practical3

This assignment will require students to implement an ANN with backpropagation. The trained ANN will be used to propose objects for a rock-paper-scissors (RPS) agent to play during a match. The same RPS framework that was used for the previous assignment will be used again.

eai320_practical4 icon eai320_practical4

The goal of this assignment is to implement a naïve Bayes classifier. The naïve Bayes classifier is an algorithm that solves classification problems by making a simple assumption: that all features in the input vector f are conditionally independent given the class. Although this assumption does not always hold, naïve Bayes classifiers have been widely used and have shown to perform well on a variety of tasks

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