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CSCI 4/5587 Programming Assignment #1

Problem Description: Given the IRIS dataset (consists of 150 samples, four input features, and three different output classes), train and compute the performances of the following classifiers using 10-fold cross-validations (10 FCV): [5 6 = 30 points] The classifiers are: (a) ETC (Extra Tree Classifier), (b) Bagging, (c) DTC (Decision Tree Classifier), (d) LR (Logistic Regression), (e) SVC…

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ENEE-3587 Microcontroller Project 2

Objective: generate a square wave based on user input. The user will be prompted to enter the frequency and duty cycle of the square signal on the LCD screen.  The user will use a 4×4 keypad to enter the frequency and duty cycle. The user input will be echoed (displayed) on the screen. Invalid keys will be filtered (ie if user enters 123A then the screen will only display 123 and the A is…

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CSC 440 Assignment 5: Dynamic Programming - Seam Carving

Introduction In a 2007 paper, Avidan and Shamir describe a new method for resizing images with minimal visual disruption. The breakthrough is an algorithm for removing the lowest energy vertical or horizontal seam in the image. Both the demonstration video and original paper will be posted to EdStem. Please watch the video so that the terminology used here is clear. You are given an image, and…

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Drone Dispatch! Express Delivery

Scenario Description The following is a text description of the system you are being tasked to develop. The system requirements – explicit and implicit – are included this document, and they need to be identified and reflected in your Enhanced Entity-Relationship Diagram (EERD). You are being asked to design and develop a system to monitor deliveries of grocery products to customers. This system…

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Lab 3: Out-of-Order Pipeline with In-Order Commit

This is an individual assignment. You can discuss this assignment with other classmates but you should code your assignment individually. You are NOT allowed to see the code of (or show your code to) other students or of past submissions that may be available online. We will be using software that detects code similarity. If you think you collaborated closely enough with another student that…

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Lab 2: Dependency Tracking and Forwarding for 5-stage Superscalar Pipeline with Branch Prediction (10 Pts)

This is an individual assignment. You can discuss this assignment with other classmates, but you should code your assignment individually. You are NOT allowed to see the code of (or show your code to) other students. We will be using code similarity detection software. If you think you collaborated closely enough with another student that could lead to more code similarities than would normally…

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Lab 1: Analyzing Benchmark Traces and Computing CPI (5pts)

This is an individual assignment. You can discuss this assignment with your classmates but you should code your assignment individually. You are NOT allowed to see the code of (or show your code to) other students. We will use code plagiarism detection software on all submissions. OBJECTIVE: The objective of the first lab assignment is to test the proficiency of students in doing…

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Project 6: Warehouse Automation

Overview In this lab, your robot will be tasked with supervising a warehouse. The robot will have to map out its environment (using a lot of the same algorithms you have employed throughout the class), while avoiding obstacles (walls). If an obstacle is hit, the run is terminated. Your goal is to visit all of the five landmarks within 6 minutes (360 seconds). All the important files are present…

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LAB 5: PATH PLANNING

The objective of this lab is to implement and test robot path planning capabilities, specifically the RRT algorithm. You will first implement the RRT algorithm, then apply it to drive a 2D robot in the WeBots simulation environment from start to goal configurations. In this lab, we assume that the obstacles and map are known ahead of time. To help you out, the TAs have built a few mazes as WeBots…

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PROJECT 4: PARTICLE FILTER IMPLEMENTATION IN WEBOTS SIMULATOR

In Project 4, you will implement a particle filter in the Webot simulator. It builds upon the foundation established in prior labs, emphasizing on: Coordinate Transformation: To understand and implement coordinate transformations between the world frame, sensor frame, and robot frame to enable accurate localization and perception in robotics tasks. Differntial Drive: To understand and implement…

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LAB 3: PARTICLE FILTER

The objective of Lab 3 is to implement a particle filter (a.k.a. Monte Carlo Localization). In this lab, you will work entirely in simulation to validate your algorithm. Recall that a particle filter repeats these main steps: A motion update step, in which the particles representing the robot’s potential location are updated according to the motion control command sent to the robot A…

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3630 Project 2: Multi-Modal Sensing

Overview The setting for this project is that an e-puck robot is navigating a maze to find a traffic sign. You are provided code that will determine whether the traffic sign is in the camera feed of the robot. Your task will be to determine: The depth (not the distance) and angle to the sign using the 2-cameras method The distance and angle to the sign using camera+lidar method There are two…

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ECE368: Lab 3: Hidden Markov Model

Suppose that a Mars rover is wandering in a region which is modeled as a grid of width 12 and height 8, as shown in Fig 1. We do not know the exact location of the rover over time. Instead, we only get some noisy observations about the rover from a sensor. In this lab, we use hidden Markov model to track the rover’s movement over time. The rover’s position at time i = 0; 1; 2; : : : is modeled as…

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ECE368: Lab 2: Bayesian Linear Regression

In this lab, we use Bayesian regression to t a linear model. Consider a linear model of the form z = a1x + a0 + w; (1) where x is the scale input variable, and a = (a0; a1)T is the vector-valued parameter with unknown entries a0, a1, and w is the additive Gaussian noise: w N (0; 2); (2) where 2 is a known parameter. Suppose that we have access to a training data set containing N samples…

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ECE368 Lab 1: Classification with Multinomial and Gaussian Models

Naive Bayes Classifier for Spam Filtering In the rst part of the lab, we use a Na ve Bayes Classi er to build a spam email lter based on whether and how many times each word in a xed vocabulary occurs in the email. Suppose that we need to classify set of N emails, and each email n is represented by fxn; yng; n = 1; 2; : : : ; N, where yn is the class label which takes the value yn = ( 1 if…

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Project #2: Structured Problem Solving and Planning

Overview In this project, you will gain further experience with three different topics we have discussed in lecture: formal logic, constraint satisfaction problems, and vehicle motion planning. The goals are to: implement an algorithm for logical inference over definite clauses; use local search to solve the classic N-queens constraint satisfaction problem (for N > 100); and implement and…

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Project #1: State-Space Search

Overview In this project, you will implement and analyze a number of uninformed and informed search strategies for different problem domains. The goals are to: experiment with basic uninformed search strategies on an explicit graph; and implement A? for a simple 2D maze domain and analyze the difficulty of random problem instances. The project has two parts, worth a total of 50 points. All…