Curriculum for Second Year of Computer Engineering (2019 Course) (With Effect from 2020-21)
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Sysml Distilled: a Brief Guide to the Systems Modeling Language
ptg11539604 Praise for SysML Distilled “In keeping with the outstanding tradition of Addison-Wesley’s techni- cal publications, Lenny Delligatti’s SysML Distilled does not disappoint. Lenny has done a masterful job of capturing the spirit of OMG SysML as a practical, standards-based modeling language to help systems engi- neers address growing system complexity. This book is loaded with matter-of-fact insights, starting with basic MBSE concepts to distin- guishing the subtle differences between use cases and scenarios to illu- mination on namespaces and SysML packages, and even speaks to some of the more esoteric SysML semantics such as token flows.” — Jeff Estefan, Principal Engineer, NASA’s Jet Propulsion Laboratory “The power of a modeling language, such as SysML, is that it facilitates communication not only within systems engineering but across disci- plines and across the development life cycle. Many languages have the ptg11539604 potential to increase communication, but without an effective guide, they can fall short of that objective. In SysML Distilled, Lenny Delligatti combines just the right amount of technology with a common-sense approach to utilizing SysML toward achieving that communication. Having worked in systems and software engineering across many do- mains for the last 30 years, and having taught computer languages, UML, and SysML to many organizations and within the college setting, I find Lenny’s book an invaluable resource. He presents the concepts clearly and provides useful and pragmatic examples to get you off the ground quickly and enables you to be an effective modeler.” — Thomas W. Fargnoli, Lead Member of the Engineering Staff, Lockheed Martin “This book provides an excellent introduction to SysML. -
The Convergence of Modeling and Programming
The Convergence of Modeling and Programming: Facilitating the Representation of Attributes and Associations in the Umple Model-Oriented Programming Language by Andrew Forward PhD Thesis Presented to the Faculty of Graduate and Postdoctoral Studies in partial fulfillment of the requirements for the degree Doctor of Philosophy (Computer Science1) Ottawa-Carleton Institute for Computer Science School of Information Technology and Engineering University of Ottawa Ottawa, Ontario, K1N 6N5 Canada © Andrew Forward, 2010 1 The Ph.D. program in Computer Science is a joint program with Carleton University, administered by the Ottawa Carleton Institute for Computer Science Acknowledgements A very special, and well-deserved, thank you to the following: a) Dr. Timothy C. Lethbridge. Tim has been a mentor of mine for several years, first as one of my undergraduate professors, later as my Master’s supervisor. Tim has again helped to shape my approach to software engineering, research and academics during my journey as a PhD candidate. b) The Complexity Reduction in Software Engineering (CRUISE) group and in particular Omar Badreddin and Julie Filion. Our weekly meetings, work with IBM, and the collaboration with the development of Umple were of great help. c) My family and friends. Thank you and much love Ayana; your support during this endeavor was much appreciated despite the occasional teasing about me still being in school. To my mom (and editor) Jayne, my dad Bill, my sister Allison and her husband Dennis. And, to my friends Neil, Roy, Van, Rob, Pat, and Ernesto – your help will be forever recorded in my work. Finally a special note to Ryan Lowe, a fellow Software Engineer that helped to keep my work grounded during our lengthy discussion about software development – I will miss you greatly. -
Simple Balanced Binary Search Trees
Simple Balanced Binary Search Trees Prabhakar Ragde Cheriton School of Computer Science University of Waterloo Waterloo, Ontario, Canada [email protected] Efficient implementations of sets and maps (dictionaries) are important in computer science, and bal- anced binary search trees are the basis of the best practical implementations. Pedagogically, however, they are often quite complicated, especially with respect to deletion. I present complete code (with justification and analysis not previously available in the literature) for a purely-functional implemen- tation based on AA trees, which is the simplest treatment of the subject of which I am aware. 1 Introduction Trees are a fundamental data structure, introduced early in most computer science curricula. They are easily motivated by the need to store data that is naturally tree-structured (family trees, structured doc- uments, arithmetic expressions, and programs). We also expose students to the idea that we can impose tree structure on data that is not naturally so, in order to implement efficient manipulation algorithms. The typical first example is the binary search tree. However, they are problematic. Naive insertion and deletion are easy to present in a first course using a functional language (usually the topic is delayed to a second course if an imperative language is used), but in the worst case, this im- plementation degenerates to a list, with linear running time for all operations. The solution is to balance the tree during operations, so that a tree with n nodes has height O(logn). There are many different ways of doing this, but most are too complicated to present this early in the curriculum, so they are usually deferred to a later course on algorithms and data structures, leaving a frustrating gap. -
Wrapped Kd Trees Handed
CMSC 420: Spring 2021 Programming Assignment 2: Wrapped k-d Trees Handed out: Tue, Apr 6. Due: Wed, Apr 21, 11pm. (Submission via Gradescope.) Overview: In this assignment you will implement a variant of the kd-tree data structure, called a wrapped kd-tree (or WKDTree) to store a set of points in 2-dimensional space. This data structure will support insertion, deletion, and a number of geometric queries, some of which will be used later in Part 3 of the programming assignment. The data structure will be templated with the point type, which is any class that implements the Java interface LabeledPoint2D, as described in the file LabeledPoint2D.java from the provided skeleton code. A labeled point is a 2-dimensional point (Point2D from the skeleton code) that supports an additional function getLabel(). This returns a string associated with the point. In our case, the points will be the airports from our earlier projects, and the labels will be the 3-letter airport codes. The associated point (represented as a Point2D) can be extracted using the function getPoint2D(). The individual coordinates (which are floats) can be extracted directly using the functions getX() and getY(), or get(i), where i = 0 for x and i = 1 for y. Your wrapped kd-tree will be templated with one type, which we will call LPoint (for \labeled point"). For example, your file WKDTree will contain the following public class: public class WKDTree<LPoint extends LabeledPoint2D> { ... } Wrapped kd-Tree: Recall that a kd-tree is a data structure based on a hierarchical decomposition of space, using axis-orthogonal splits. -
Slides-Data-Structures.Pdf
Data structures ● Organize your data to support various queries using little time an space Example: Inventory Want to support SEARCH INSERT DELETE ● Given n elements A[1..n] ● Support SEARCH(A,x) := is x in A? ● Trivial solution: scan A. Takes time Θ(n) ● Best possible given A, x. ● What if we are first given A, are allowed to preprocess it, can we then answer SEARCH queries faster? ● How would you preprocess A? ● Given n elements A[1..n] ● Support SEARCH(A,x) := is x in A? ● Preprocess step: Sort A. Takes time O(n log n), Space O(n) ● SEARCH(A[1..n],x) := /* Binary search */ If n = 1 then return YES if A[1] = x, and NO otherwise else if A[n/2] ≤ x then return SEARCH(A[n/2..n]) else return SEARCH(A[1..n/2]) ● Time T(n) = ? ● Given n elements A[1..n] ● Support SEARCH(A,x) := is x in A? ● Preprocess step: Sort A. Takes time O(n log n), Space O(n) ● SEARCH(A[1..n],x) := /* Binary search */ If n = 1 then return YES if A[1] = x, and NO otherwise else if A[n/2] ≤ x then return SEARCH(A[n/2..n]) else return SEARCH(A[1..n/2]) ● Time T(n) = O(log n). ● Given n elements A[1..n] each ≤ k, can you do faster? ● Support SEARCH(A,x) := is x in A? ● DIRECTADDRESS: Initialize S[1..k] to 0 ● Preprocess step: For (i = 1 to n) S[A[i]] = 1 ● T(n) = O(n), Space O(k) ● SEARCH(A,x) = ? ● Given n elements A[1..n] each ≤ k, can you do faster? ● Support SEARCH(A,x) := is x in A? ● DIRECTADDRESS: Initialize S[1..k] to 0 ● Preprocess step: For (i = 1 to n) S[A[i]] = 1 ● T(n) = O(n), Space O(k) ● SEARCH(A,x) = return S[x] ● T(n) = O(1) ● Dynamic problems: ● Want to support SEARCH, INSERT, DELETE ● Support SEARCH(A,x) := is x in A? ● If numbers are small, ≤ k Preprocess: Initialize S to 0. -
CMSC 420: Lecture 6 2-3, Red-Black, and AA Trees
CMSC 420 Dave Mount CMSC 420: Lecture 6 2-3, Red-black, and AA trees \A rose by any other name": In today's lecture, we consider three closely related search trees. All three have the property that they support find, insert, and delete in time O(log n) for a tree with n nodes. Although the definitions appear at first glance to be different, they are essentially equivalent or very slight variants of each other. These are 2-3 trees, red-black trees, and AA trees. Together, they show that the same idea can be viewed from many different perspectives. 2-3 Trees: An \ideal" binary search tree has n nodes and height roughly lg n. (More precisely, the ideal would be blg nc, where we recall our convention that \lg" means logarithm base 2.) However, it is not possible to efficiently maintain a tree subject to such rigid requirements. AVL trees relax the height restriction by allowing the two subtrees associated with each node to be of similar heights. Another way to relax the requirements is to say that a node may have either two or three children (see Fig.1(a) and (b)). When a node has three children, it stores two keys. Given the two key values b and d, the three subtrees A, C, and E must satisfy the requirement that for all a 2 A, c 2 C, and e 2 E, we have a < b < c < d < e; (The concept of an inorder traversal of such a tree can be generalized, but it involves visiting each 3-node twice, once to visit the first key and again to visit the second key.) These are called 2-nodes and 3-nodes, respectively. -
Advanced Data Structures and Implementation
www.getmyuni.com Advanced Data Structures and Implementation • Top-Down Splay Trees • Red-Black Trees • Top-Down Red Black Trees • Top-Down Deletion • Deterministic Skip Lists • AA-Trees • Treaps • k-d Trees • Pairing Heaps www.getmyuni.com Top-Down Splay Tree • Direct strategy requires traversal from the root down the tree, and then bottom-up traversal to implement the splaying tree. • Can implement by storing parent links, or by storing the access path on a stack. • Both methods require large amount of overhead and must handle many special cases. • Initial rotations on the initial access path uses only O(1) extra space, but retains the O(log N) amortized time bound. www.getmyuni.com www.getmyuni.com Case 1: Zig X L R L R Y X Y XR YL Yr XR YL Yr If Y should become root, then X and its right sub tree are made left children of the smallest value in R, and Y is made root of “center” tree. Y does not have to be a leaf for the Zig case to apply. www.getmyuni.com www.getmyuni.com Case 2: Zig-Zig L X R L R Z Y XR Y X Z ZL Zr YR YR XR ZL Zr The value to be splayed is in the tree rooted at Z. Rotate Y about X and attach as left child of smallest value in R www.getmyuni.com www.getmyuni.com Case 3: Zig-Zag (Simplified) L X R L R Y Y XR X YL YL Z Z XR ZL Zr ZL Zr The value to be splayed is in the tree rooted at Z. -
INTO-CPS Tool Chain User Manual
Grant Agreement: 644047 INtegrated TOol chain for model-based design of CPSs INTO-CPS Tool Chain User Manual Deliverable Number: D4.3a Version: 1.0 Date: December, 2017 Public Document http://into-cps.au.dk D4.3a - INTO-CPS Tool Chain User Manual (Public) Contributors: Victor Bandur, AU Peter Gorm Larsen, AU Kenneth Lausdahl, AU Casper Thule, AU Carl Gamble, UNEW Richard Payne, UNEW Adrian Pop, LIU Etienne Brosse, ST Jörg Brauer, VSI Florian Lapschies, VSI Marcel Groothuis, CLP Tom Bokhove, CLP Christian Kleijn, CLP Luis Diogo Couto, UTRC Editors: Victor Bandur, AU Reviewers: Ken Pierce, UNEW Kangfeng Ye, UY Luis Diogo Couto, UTRC Consortium: Aarhus University AU Newcastle University UNEW University of York UY Linköping University LIU Verified Systems International GmbH VSI Controllab Products CLP ClearSy CLE TWT GmbH TWT Agro Intelligence AI United Technologies UTRC Softeam ST 2 D4.3a - INTO-CPS Tool Chain User Manual (Public) Document History Ver Date Author Description 0.01 11-01-2017 Victor Bandur Initial version. 0.02 30-10-2017 Victor Bandur Updates for internal review. 0.03 30-10-2017 Marcel Groothuis Added 20-sim 4C FMI import/ex- port manual. 0.04 12-12-2017 Marcel Groothuis Address internal review comments 20-sim and 20-sim 4C sections. 1.0 18-12-2017 Victor Bandur Final version. 3 D4.3a - INTO-CPS Tool Chain User Manual (Public) Abstract This deliverable is the user manual for the INTO-CPS tool chain, an update of Deliverable D4.2a [BLL+16]. It is targeted at those wishing to make use of the INTO-CPS technology to design and validate cyber-physical systems. -
INTO-CPS Tool Chain User Manual
Grant Agreement: 644047 INtegrated TOol chain for model-based design of CPSs INTO-CPS Tool Chain User Manual Deliverable Number: D4.2a Version: 1.0 Date: December, 2016 Public Document http://into-cps.au.dk D4.2a - INTO-CPS Tool Chain User Manual (Public) Contributors: Victor Bandur, AU Peter Gorm Larsen, AU Kenneth Lausdahl, AU Casper Thule, AU Anders Franz Terkelsen, AU Carl Gamble, UNEW Adrian Pop, LIU Etienne Brosse, ST Jörg Brauer, VSI Florian Lapschies, VSI Marcel Groothuis, CLP Christian Kleijn, CLP Luis Diogo Couto, UTRC Editors: Victor Bandur, AU Reviewers: Ken Pierce, UNEW Ana Cavalcanti, UY Luis Diogo Couto, UTRC Consortium: Aarhus University AU Newcastle University UNEW University of York UY Linköping University LIU Verified Systems International GmbH VSI Controllab Products CLP ClearSy CLE TWT GmbH TWT Agro Intelligence AI United Technologies UTRC Softeam ST 2 D4.2a - INTO-CPS Tool Chain User Manual (Public) Document History Ver Date Author Description 0.01 01-01-2016 Anders Franz Terkelsen Added initial issue handling. 0.02 27-04-2016 Victor Bandur Added subsection on po- tential pitfalls when gen- erating FMUs from RT- Tester/RTT-MBT. 0.03 01-07-2016 Peter Gorm Larsen Added appendix for related industrial tools. 0.04 25-07-2016 Victor Bandur Split section on code gener- ation for Overture into tool- wrapper and standalone. Deferred discussion on ob- taining the individual tools to distribution website on GitHub. 0.05 30-09-2016 Luis Diogo Couto Completely rewrote app sec- tion to refer to the new ver- sion of the app. 0.06 17-10-2016 Marcel Groothuis Updated all 20-sim related sections; added a few related industrially applied tools. -
Implementation of the Tree Structure in the XML and Relational Database
Proceedings of Informing Science & IT Education Conference (InSITE) 2012 Implementation of the Tree Structure in the XML and Relational Database Aleksandar Bulajic Nenad Filipovic Faculty of Information Faculty of Engineering Technology, Science, Metropolitan University, University of Kragujevac, Belgrade, Serbia Kragujevac, Serbia [email protected] [email protected] Abstract A tree structure is a powerful tool for storing and manipulating all kind of data, without differ- ences is it used for natural language analysis, compiling computer languages or storing and ana- lyzing scientific or business data. Algorithms for searching and browsing tree structures are well known, and for free are available numbers of tools for manipulating tree structures implemented in different computer languages. Even is impossible to avoid these algorithms and tools, primary focus in this paper is XML and XML tools. Storing and retrieving, as well as validation of XML tree structures in relational database can be a challenge. This document describes these challenges and currently available solutions. Keywords: Tree structure, XQuey, XML, XML Parser, XSLT, XML Schema, DOM, SAX, Da- tabase, SQL. Introduction Tree structure in computer science is a data structure used for representing hierarchical data struc- tures. Oxford Dictionary (Oxford Advanced Learner’s Dictionary 2011) describes hierarchy as “a system, especially in a society or an organization, in which people are organized into different levels of importance from highest to lowest” and Wikipedia describes hierarchy as “Greek: hier- archia (ἱεραρχία), from hierarches, "leader of sacred rites") is an arrangement of items (objects, names, values, categories, etc.) in which the items are represented as being "above," "below," or "at the same level as" one another.” These definitions are not sufficient because a Tree structure requires also that each node has only one parent and can have zero or more children, and is an acyclic connected graph. -
CMSC 420 Data Structures1
CMSC 420 Data Structures1 David M. Mount Department of Computer Science University of Maryland Fall 2019 1 Copyright, David M. Mount, 2019, Dept. of Computer Science, University of Maryland, College Park, MD, 20742. These lecture notes were prepared by David Mount for the course CMSC 420, Data Structures, at the University of Maryland. Permission to use, copy, modify, and distribute these notes for educational purposes and without fee is hereby granted, provided that this copyright notice appear in all copies. Lecture Notes 1 CMSC 420 Lecture 1: Course Introduction and Background Algorithms and Data Structures: The study of data structures and the algorithms that ma- nipulate them is among the most fundamental topics in computer science. Most of what computer systems spend their time doing is storing, accessing, and manipulating data in one form or another. Some examples from computer science include: Information Retrieval: List the 10 most informative Web pages on the subject of \how to treat high blood pressure?" Identify possible suspects of a crime based on fingerprints or DNA evidence. Find movies that a Netflix subscriber may like based on the movies that this person has already viewed. Find images on the Internet containing both kangaroos and horses. Geographic Information Systems: How many people in the USA live within 25 miles of the Mississippi River? List the 10 movie theaters that are closest to my current location. If sea levels rise 10 meters, what fraction of Florida will be under water? Compilers: You need to store a set of variable names along with their associated types. Given an assignment between two variables we need to look them up in a symbol table, determine their types, and determine whether it is possible to cast from one type to the other). -
AVL Tree, Red-Black Tree, 2-3 Tree, AA Tree, Scapegoat Tree, Splay Tree, Treap,
CSC263 Week 4 Problem Set 2 is due this Tuesday! Due Tuesday (Oct 13) Other Announcements Ass1 marks available on MarkUS ➔ Re-marking requests accepted until October 14 **YOUR MARK MAY GO UP OR DOWN AS THE RESULT OF A REMARK REQUEST Recap ADT: Dictionary ➔ Search, Insert, Delete Binary Search Tree ➔ TreeSearch, TreeInsert, TreeDelete, … ➔ Worst case running time: O(h) ➔ Worst case height h: O(n) Balanced BST: h is O(log n) Balanced BSTs AVL tree, Red-Black tree, 2-3 tree, AA tree, Scapegoat tree, Splay tree, Treap, ... AVL tree Invented by Georgy Adelson-Velsky and E. M. Landis in 1962. First self-balancing BST to be invented. We use BFs to check the balance of a tree. An extra attribute to each node in a BST -- balance factor hR(x): height of x’s right subtree hL(x): height of x’s left subtree BF(x) = hR(x) - hL(x) x BF(x) = 0: x is balanced BF(x) = 1: x is right-heavy BF(x) = -1: x is left-heavy above 3 cases are considered as “good” hL hR L R BF(x) > 1 or < -1: x is imbalanced (not good) heights of some special trees NIL h = 0 h = -1 h = 1 Note: height is measured by the number of edges. AVL tree: definition An AVL tree is a BST in which every node is balanced, right-heavy or left-heavy. i.e., the BF of every node must be 0, 1 or -1. 0 - -- ++ + - - + 0 + 0 0 0 0 It can be proven that the height of an AVL tree with n nodes satisfies i.e., h is in O(log n) WHY? Operations on AVL trees AVL-Search(root, k) AVL-Insert(root, x) AVL-Delete(root, x) Things to worry about ➔ Before the operation, the BST is a valid AVL tree (precondition) ➔ After the operation, the BST must still be a valid AVL tree (so re-balancing may be needed) ➔ The balance factor attributes of some nodes need to be updated.