DSP ELEMENTS IN MAX/MSP
|
|
- Sydney Ray
- 6 years ago
- Views:
Transcription
1 DSP ELEMENTS IN MAX/MSP Maarte va Walstij PBASS Sessio 8: DSP Elemets i Max/MSP Why Physical Modellig PM i Max/MSP? real-time iteractive dyamic parameter cotrol ituitive graphical oject programmig cotrol Max ojects cotrol rate calculatios physical model MSP ojects audio rate calculatios Suited to exploratio of soic/music potetial difficult i Matla PBASS Sessio 8: DSP Elemets i Max/MSP
2 Best Way to Programme Physical Models i Max/MSP: Exteral Ojects! C/C/java/javascript Complete physical models with o-liear iteractio caot e scaled aritrarily to avoid roudig errors; umer precisio doules, floats is critical, Max/MSP is much coarser tha C, java, Matla. Precise cotrol over umerical details is usually required; ofte ot possile withi Max/MSP. Computatioally more efficiet tha withi Max/MSP Allows for more fuctioality such as umer of modes Disadvatage: fairly elaorate to programme! Do-ale withi Max/MSP: Simple ojects, o o-liear iteractio. Advatage: easy to programme, helps uderstadig physical models PBASS Sessio 8: DSP Elemets i Max/MSP 3 Mai MSP Ojects for DWG/SDL Modellig Filters iquad~ Delay-Lies: tapi~ & tapout~ implemets a secodorder digital filter Useful for creatig oscillators or atteuatio filters implemets a iterpolated fractioal delay, allows feedack Useful for creatig delay-lies that simulate wave propagatio PBASS Sessio 8: DSP Elemets i Max/MSP 4
3 PBASS Sessio 8: DSP Elemets i Max/MSP 5 Secod-Order Filter: iquad~ x y 0 a a H 0 y a y a x x x y NOTE: the Max/MSP help file uses a otatio i which the a s ad the s are switched. PBASS Sessio 8: DSP Elemets i Max/MSP 6 Delay that allows Feedack: tapi~ & tapout~ feedack g delay i ms -N FD t x yt xt yt g D N H Feedig the sigal ack i is allowed with tapi~/tapout~, ut ot with delay~. D T N t t t x t y d d
4 Some Other Geerally Useful Ojects MSP: ~, -~, *~, /~ DSP-level arithmetic operators click~ sigle impulse geerator oise~ oise geerator lie~ evelope geerator Max: loadag metro expr output iput ags whe patch is loaded regular ags evaluates a C/Matla-like expressio iput i su-patch output i su-patch PBASS Sessio 8: DSP Elemets i Max/MSP 7 Idepedet Su-Patches iir4~ 4 th -order IIR filter usig iquads i cascade PBASS Sessio 8: DSP Elemets i Max/MSP 8
5 Implemetig Propagatio Use tapi~/tapout~ for delay with feedack Use Hatt~ as atteuatio filter Filter tued to correct decay rate at frequecies: α α 0 ω c propagatio over distace d tapi~ tapout~ td delay d t d c P d td f c alp0 alp Hatt~ atteuatio PBASS Sessio 8: DSP Elemets i Max/MSP 9 Implemetig a Feed-Forward System that Allows a Zero Delay Delay td must e ale to go to ero. Miimum delay etwee tapi & tapout is ~ ms! td tapi~ td tapi~.0.0td tapout~ tapout~ tapout~ ~ ~ Solutio: two tapouts~: differece of td PBASS Sessio 8: DSP Elemets i Max/MSP 0
6 Parameter Traslatio A Max exteral i Java script strigpar is supplied for the traslatio of physical strig parameters to SDL model parameters: physical parameters SDL model parameters Note that oe has to eter js at the start of the max ox, followed y the ame of the Java script. Output is created y chagig ANY of the ilets as opposed to stadard Max ojects that oly react to the left ilet. PBASS Sessio 8: DSP Elemets i Max/MSP Parameter Cotrol STRING PARAMETERS L strig legth rho mass desity Te tesio air dampig iteral dampig re relative excitatio poit rp relative pick-up poit SDL MODEL PARAMETERS f fudametal frequecy c wave speed R characteristic impedace alp0 decay rate at f 0 alp decay rate at ff te delay for excitatio sectio Tsdl delay for SDL sectio tp delay for pick-up sectio slider/other cotrollers: scalig/rage eeds to e addressed. Sed/receive might e hady to avoid messy patches s x wireless r x PBASS Sessio 8: DSP Elemets i Max/MSP
7 Impulsive Hammer/Mallet Excitatio ag hardess > 0 hit~ hard total output eergy is cosistet, idepedet of hardess soft PBASS Sessio 8: DSP Elemets i Max/MSP 3 Remider Rememer to use floats iside arithmetic operatio ojects: -~ 0.0 *~ /.0 otherwise you might ot get out what you expect Max/MSP expectig iput as itegers, ad roudig off to ero PBASS Sessio 8: DSP Elemets i Max/MSP 4
8 Supatches to Dowload \\ \mvawalstij\Sites\pass\patches.html strigpar.js traslates physical strig parameters ito SDL model parameters. atteuatioa.js traslates SDL model parameters ito iquad~ filter coefficiets withi Hatt~. Hatt~ atteuatio filter. hit~ simulates simplified hammer excitatio. Some other example files are availale. PBASS Sessio 8: DSP Elemets i Max/MSP 5
Ones Assignment Method for Solving Traveling Salesman Problem
Joural of mathematics ad computer sciece 0 (0), 58-65 Oes Assigmet Method for Solvig Travelig Salesma Problem Hadi Basirzadeh Departmet of Mathematics, Shahid Chamra Uiversity, Ahvaz, Ira Article history:
More informationChapter 3 Classification of FFT Processor Algorithms
Chapter Classificatio of FFT Processor Algorithms The computatioal complexity of the Discrete Fourier trasform (DFT) is very high. It requires () 2 complex multiplicatios ad () complex additios [5]. As
More informationWhat are we going to learn? CSC Data Structures Analysis of Algorithms. Overview. Algorithm, and Inputs
What are we goig to lear? CSC316-003 Data Structures Aalysis of Algorithms Computer Sciece North Carolia State Uiversity Need to say that some algorithms are better tha others Criteria for evaluatio Structure
More informationOutline and Reading. Analysis of Algorithms. Running Time. Experimental Studies. Limitations of Experiments. Theoretical Analysis
Outlie ad Readig Aalysis of Algorithms Iput Algorithm Output Ruig time ( 3.) Pseudo-code ( 3.2) Coutig primitive operatios ( 3.3-3.) Asymptotic otatio ( 3.6) Asymptotic aalysis ( 3.7) Case study Aalysis
More informationRunning Time ( 3.1) Analysis of Algorithms. Experimental Studies. Limitations of Experiments
Ruig Time ( 3.1) Aalysis of Algorithms Iput Algorithm Output A algorithm is a step- by- step procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects.
More informationAnalysis of Algorithms
Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Ruig Time Most algorithms trasform iput objects ito output objects. The
More informationCS : Programming for Non-Majors, Summer 2007 Programming Project #3: Two Little Calculations Due by 12:00pm (noon) Wednesday June
CS 1313 010: Programmig for No-Majors, Summer 2007 Programmig Project #3: Two Little Calculatios Due by 12:00pm (oo) Wedesday Jue 27 2007 This third assigmet will give you experiece writig programs that
More informationThe golden search method: Question 1
1. Golde Sectio Search for the Mode of a Fuctio The golde search method: Questio 1 Suppose the last pair of poits at which we have a fuctio evaluatio is x(), y(). The accordig to the method, If f(x())
More informationRunning Time. Analysis of Algorithms. Experimental Studies. Limitations of Experiments
Ruig Time Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects. The
More informationChapter 2. C++ Basics. Copyright 2015 Pearson Education, Ltd.. All rights reserved.
Chapter 2 C++ Basics Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 2.1 Variables ad Assigmets 2.2 Iput ad Output 2.3 Data Types ad Expressios 2.4 Simple Flow of Cotrol 2.5 Program
More informationCOP4020 Programming Languages. Functional Programming Prof. Robert van Engelen
COP4020 Programmig Laguages Fuctioal Programmig Prof. Robert va Egele Overview What is fuctioal programmig? Historical origis of fuctioal programmig Fuctioal programmig today Cocepts of fuctioal programmig
More informationcondition w i B i S maximum u i
ecture 10 Dyamic Programmig 10.1 Kapsack Problem November 1, 2004 ecturer: Kamal Jai Notes: Tobias Holgers We are give a set of items U = {a 1, a 2,..., a }. Each item has a weight w i Z + ad a utility
More informationChapter 11. Friends, Overloaded Operators, and Arrays in Classes. Copyright 2014 Pearson Addison-Wesley. All rights reserved.
Chapter 11 Frieds, Overloaded Operators, ad Arrays i Classes Copyright 2014 Pearso Addiso-Wesley. All rights reserved. Overview 11.1 Fried Fuctios 11.2 Overloadig Operators 11.3 Arrays ad Classes 11.4
More informationPseudocode ( 1.1) Analysis of Algorithms. Primitive Operations. Pseudocode Details. Running Time ( 1.1) Estimating performance
Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Pseudocode ( 1.1) High-level descriptio of a algorithm More structured
More informationData Structures and Algorithms. Analysis of Algorithms
Data Structures ad Algorithms Aalysis of Algorithms Outlie Ruig time Pseudo-code Big-oh otatio Big-theta otatio Big-omega otatio Asymptotic algorithm aalysis Aalysis of Algorithms Iput Algorithm Output
More informationA New Morphological 3D Shape Decomposition: Grayscale Interframe Interpolation Method
A ew Morphological 3D Shape Decompositio: Grayscale Iterframe Iterpolatio Method D.. Vizireau Politehica Uiversity Bucharest, Romaia ae@comm.pub.ro R. M. Udrea Politehica Uiversity Bucharest, Romaia mihea@comm.pub.ro
More informationChapter 9. Pointers and Dynamic Arrays. Copyright 2015 Pearson Education, Ltd.. All rights reserved.
Chapter 9 Poiters ad Dyamic Arrays Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 9.1 Poiters 9.2 Dyamic Arrays Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Slide 9-3
More informationLinear Time-Invariant Systems
9/9/00 LIEAR TIE-IVARIAT SYSTES Uit, d Part Liear Time-Ivariat Sstems A importat class of discrete-time sstem cosists of those that are Liear Priciple of superpositio Time-ivariat dela of the iput sequece
More informationAppendix D. Controller Implementation
COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Iterface 5 th Editio Appedix D Cotroller Implemetatio Cotroller Implemetatios Combiatioal logic (sigle-cycle); Fiite state machie (multi-cycle, pipelied);
More informationFrom last week. Lecture 5. Outline. Principles of programming languages
Priciples of programmig laguages From last week Lecture 5 http://few.vu.l/~silvis/ppl/2007 Natalia Silvis-Cividjia e-mail: silvis@few.vu.l ML has o assigmet. Explai how to access a old bidig? Is & for
More informationAPPLICATION NOTE PACE1750AE BUILT-IN FUNCTIONS
APPLICATION NOTE PACE175AE BUILT-IN UNCTIONS About This Note This applicatio brief is iteded to explai ad demostrate the use of the special fuctios that are built ito the PACE175AE processor. These powerful
More informationComputer Systems - HS
What have we leared so far? Computer Systems High Level ENGG1203 2d Semester, 2017-18 Applicatios Sigals Systems & Cotrol Systems Computer & Embedded Systems Digital Logic Combiatioal Logic Sequetial Logic
More informationCS200: Hash Tables. Prichard Ch CS200 - Hash Tables 1
CS200: Hash Tables Prichard Ch. 13.2 CS200 - Hash Tables 1 Table Implemetatios: average cases Search Add Remove Sorted array-based Usorted array-based Balaced Search Trees O(log ) O() O() O() O(1) O()
More informationCoherent effects of flow- and pressure hull of a generic submarine on target scattering in an active sonar performance model
Coheret effects of flow- ad pressure hull of a geeric submarie o target scatterig i a active soar performace model P. Schippers TNO-D&V-Uderwater Techology, Oude Waalsdorperweg 63, Post Box 96864, 2509
More informationPython Programming: An Introduction to Computer Science
Pytho Programmig: A Itroductio to Computer Sciece Chapter 1 Computers ad Programs 1 Objectives To uderstad the respective roles of hardware ad software i a computig system. To lear what computer scietists
More informationFilter design. 1 Design considerations: a framework. 2 Finite impulse response (FIR) filter design
Filter desig Desig cosideratios: a framework C ı p ı p H(f) Aalysis of fiite wordlegth effects: I practice oe should check that the quatisatio used i the implemetatio does ot degrade the performace of
More informationAnalysis of Algorithms
Aalysis of Algorithms Ruig Time of a algorithm Ruig Time Upper Bouds Lower Bouds Examples Mathematical facts Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite
More informationThe isoperimetric problem on the hypercube
The isoperimetric problem o the hypercube Prepared by: Steve Butler November 2, 2005 1 The isoperimetric problem We will cosider the -dimesioal hypercube Q Recall that the hypercube Q is a graph whose
More information12-5A. Equivalent Fractions and Decimals. 1 Daily Common Core Review. Common Core. Lesson. Lesson Overview. Math Background
Lesso -A Equivalet Fractios ad Decimals Commo Core Lesso Overview Domai Number ad Operatios Fractios Cluster Uderstad decimal otatio for fractios, ad compare decimal fractios. Stadards.NF. Use decimal
More informationCMSC Computer Architecture Lecture 12: Virtual Memory. Prof. Yanjing Li University of Chicago
CMSC 22200 Computer Architecture Lecture 12: Virtual Memory Prof. Yajig Li Uiversity of Chicago A System with Physical Memory Oly Examples: most Cray machies early PCs Memory early all embedded systems
More informationCOP4020 Programming Languages. Compilers and Interpreters Prof. Robert van Engelen
COP4020 mig Laguages Compilers ad Iterpreters Prof. Robert va Egele Overview Commo compiler ad iterpreter cofiguratios Virtual machies Itegrated developmet eviromets Compiler phases Lexical aalysis Sytax
More informationComputer Science Foundation Exam. August 12, Computer Science. Section 1A. No Calculators! KEY. Solutions and Grading Criteria.
Computer Sciece Foudatio Exam August, 005 Computer Sciece Sectio A No Calculators! Name: SSN: KEY Solutios ad Gradig Criteria Score: 50 I this sectio of the exam, there are four (4) problems. You must
More informationPolynomial Functions and Models. Learning Objectives. Polynomials. P (x) = a n x n + a n 1 x n a 1 x + a 0, a n 0
Polyomial Fuctios ad Models 1 Learig Objectives 1. Idetify polyomial fuctios ad their degree 2. Graph polyomial fuctios usig trasformatios 3. Idetify the real zeros of a polyomial fuctio ad their multiplicity
More informationOrientation. Orientation 10/28/15
Orietatio Orietatio We will defie orietatio to mea a object s istataeous rotatioal cofiguratio Thik of it as the rotatioal equivalet of positio 1 Represetig Positios Cartesia coordiates (x,y,z) are a easy
More informationChapter 3. Floating Point Arithmetic
COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Iterface 5 th Editio Chapter 3 Floatig Poit Arithmetic Review - Multiplicatio 0 1 1 0 = 6 multiplicad 32-bit ALU shift product right multiplier add
More informationΤεχνολογία Λογισμικού
ΕΘΝΙΚΟ ΜΕΤΣΟΒΙΟ ΠΟΛΥΤΕΧΝΕΙΟ Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών Τεχνολογία Λογισμικού, 7ο/9ο εξάμηνο 2018-2019 Τεχνολογία Λογισμικού Ν.Παπασπύρου, Αν.Καθ. ΣΗΜΜΥ, ickie@softlab.tua,gr
More informationEE123 Digital Signal Processing
Last Time EE Digital Sigal Processig Lecture 7 Block Covolutio, Overlap ad Add, FFT Discrete Fourier Trasform Properties of the Liear covolutio through circular Today Liear covolutio with Overlap ad add
More informationAppendix A. Use of Operators in ARPS
A Appedix A. Use of Operators i ARPS The methodology for solvig the equatios of hydrodyamics i either differetial or itegral form usig grid-poit techiques (fiite differece, fiite volume, fiite elemet)
More informationChapter 4 The Datapath
The Ageda Chapter 4 The Datapath Based o slides McGraw-Hill Additioal material 24/25/26 Lewis/Marti Additioal material 28 Roth Additioal material 2 Taylor Additioal material 2 Farmer Tae the elemets that
More information9.1. Sequences and Series. Sequences. What you should learn. Why you should learn it. Definition of Sequence
_9.qxd // : AM Page Chapter 9 Sequeces, Series, ad Probability 9. Sequeces ad Series What you should lear Use sequece otatio to write the terms of sequeces. Use factorial otatio. Use summatio otatio to
More information9 x and g(x) = 4. x. Find (x) 3.6. I. Combining Functions. A. From Equations. Example: Let f(x) = and its domain. Example: Let f(x) = and g(x) = x x 4
1 3.6 I. Combiig Fuctios A. From Equatios Example: Let f(x) = 9 x ad g(x) = 4 f x. Fid (x) g ad its domai. 4 Example: Let f(x) = ad g(x) = x x 4. Fid (f-g)(x) B. From Graphs: Graphical Additio. Example:
More information1.2 Binomial Coefficients and Subsets
1.2. BINOMIAL COEFFICIENTS AND SUBSETS 13 1.2 Biomial Coefficiets ad Subsets 1.2-1 The loop below is part of a program to determie the umber of triagles formed by poits i the plae. for i =1 to for j =
More informationCubic Polynomial Curves with a Shape Parameter
roceedigs of the th WSEAS Iteratioal Coferece o Robotics Cotrol ad Maufacturig Techology Hagzhou Chia April -8 00 (pp5-70) Cubic olyomial Curves with a Shape arameter MO GUOLIANG ZHAO YANAN Iformatio ad
More informationIntroduction to GAMIT/GLOBK Applications of GLOBK. Lecture 11 OVERVIEW
Itroductio to GAMIT/GLOBK Applicatios of GLOBK Lecture 11 GAMIT/GLOBK Lec11 1 OVERVIEW o I this lecture we cover: o Basic types of aalyses with globk l Velocity ad repeatability rus o GLOBK acillary programs
More informationCSC 220: Computer Organization Unit 11 Basic Computer Organization and Design
College of Computer ad Iformatio Scieces Departmet of Computer Sciece CSC 220: Computer Orgaizatio Uit 11 Basic Computer Orgaizatio ad Desig 1 For the rest of the semester, we ll focus o computer architecture:
More informationPython Programming: An Introduction to Computer Science
Pytho Programmig: A Itroductio to Computer Sciece Chapter 6 Defiig Fuctios Pytho Programmig, 2/e 1 Objectives To uderstad why programmers divide programs up ito sets of cooperatig fuctios. To be able to
More informationComputer Graphics Hardware An Overview
Computer Graphics Hardware A Overview Graphics System Moitor Iput devices CPU/Memory GPU Raster Graphics System Raster: A array of picture elemets Based o raster-sca TV techology The scree (ad a picture)
More informationPattern Recognition Systems Lab 1 Least Mean Squares
Patter Recogitio Systems Lab 1 Least Mea Squares 1. Objectives This laboratory work itroduces the OpeCV-based framework used throughout the course. I this assigmet a lie is fitted to a set of poits usig
More informationChapter 8. Strings and Vectors. Copyright 2014 Pearson Addison-Wesley. All rights reserved.
Chapter 8 Strigs ad Vectors Overview 8.1 A Array Type for Strigs 8.2 The Stadard strig Class 8.3 Vectors Slide 8-3 8.1 A Array Type for Strigs A Array Type for Strigs C-strigs ca be used to represet strigs
More informationSwitching Hardware. Spring 2018 CS 438 Staff, University of Illinois 1
Switchig Hardware Sprig 208 CS 438 Staff, Uiversity of Illiois Where are we? Uderstad Differet ways to move through a etwork (forwardig) Read sigs at each switch (datagram) Follow a kow path (virtual circuit)
More informationChapter 8. Strings and Vectors. Copyright 2015 Pearson Education, Ltd.. All rights reserved.
Chapter 8 Strigs ad Vectors Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 8.1 A Array Type for Strigs 8.2 The Stadard strig Class 8.3 Vectors Copyright 2015 Pearso Educatio, Ltd..
More informationForce Network Analysis using Complementary Energy
orce Network Aalysis usig Complemetary Eergy Adrew BORGART Assistat Professor Delft Uiversity of Techology Delft, The Netherlads A.Borgart@tudelft.l Yaick LIEM Studet Delft Uiversity of Techology Delft,
More informationAnalysis of Algorithms
Presetatio for use with the textbook, Algorithm Desig ad Applicatios, by M. T. Goodrich ad R. Tamassia, Wiley, 2015 Aalysis of Algorithms Iput 2015 Goodrich ad Tamassia Algorithm Aalysis of Algorithms
More informationSAMPLE VERSUS POPULATION. Population - consists of all possible measurements that can be made on a particular item or procedure.
SAMPLE VERSUS POPULATION Populatio - cosists of all possible measuremets that ca be made o a particular item or procedure. Ofte a populatio has a ifiite umber of data elemets Geerally expese to determie
More informationCIS 121 Data Structures and Algorithms with Java Spring Stacks, Queues, and Heaps Monday, February 18 / Tuesday, February 19
CIS Data Structures ad Algorithms with Java Sprig 09 Stacks, Queues, ad Heaps Moday, February 8 / Tuesday, February 9 Stacks ad Queues Recall the stack ad queue ADTs (abstract data types from lecture.
More informationTHIN LAYER ORIENTED MAGNETOSTATIC CALCULATION MODULE FOR ELMER FEM, BASED ON THE METHOD OF THE MOMENTS. Roman Szewczyk
THIN LAYER ORIENTED MAGNETOSTATIC CALCULATION MODULE FOR ELMER FEM, BASED ON THE METHOD OF THE MOMENTS Roma Szewczyk Istitute of Metrology ad Biomedical Egieerig, Warsaw Uiversity of Techology E-mail:
More informationBezier curves. Figure 2 shows cubic Bezier curves for various control points. In a Bezier curve, only
Edited: Yeh-Liag Hsu (998--; recommeded: Yeh-Liag Hsu (--9; last updated: Yeh-Liag Hsu (9--7. Note: This is the course material for ME55 Geometric modelig ad computer graphics, Yua Ze Uiversity. art of
More informationChapter 4. Procedural Abstraction and Functions That Return a Value. Copyright 2015 Pearson Education, Ltd.. All rights reserved.
Chapter 4 Procedural Abstractio ad Fuctios That Retur a Value Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 4.1 Top-Dow Desig 4.2 Predefied Fuctios 4.3 Programmer-Defied Fuctios 4.4
More informationFast Fourier Transform (FFT) Algorithms
Fast Fourier Trasform FFT Algorithms Relatio to the z-trasform elsewhere, ozero, z x z X x [ ] 2 ~ elsewhere,, ~ e j x X x x π j e z z X X π 2 ~ The DFS X represets evely spaced samples of the z- trasform
More informationExact Minimum Lower Bound Algorithm for Traveling Salesman Problem
Exact Miimum Lower Boud Algorithm for Travelig Salesma Problem Mohamed Eleiche GeoTiba Systems mohamed.eleiche@gmail.com Abstract The miimum-travel-cost algorithm is a dyamic programmig algorithm to compute
More informationLecture Notes 6 Introduction to algorithm analysis CSS 501 Data Structures and Object-Oriented Programming
Lecture Notes 6 Itroductio to algorithm aalysis CSS 501 Data Structures ad Object-Orieted Programmig Readig for this lecture: Carrao, Chapter 10 To be covered i this lecture: Itroductio to algorithm aalysis
More informationCopyright 2016 Ramez Elmasri and Shamkant B. Navathe
Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe CHAPTER 18 Strategies for Query Processig Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe Itroductio DBMS techiques to process a query Scaer idetifies
More informationAlpha Individual Solutions MAΘ National Convention 2013
Alpha Idividual Solutios MAΘ Natioal Covetio 0 Aswers:. D. A. C 4. D 5. C 6. B 7. A 8. C 9. D 0. B. B. A. D 4. C 5. A 6. C 7. B 8. A 9. A 0. C. E. B. D 4. C 5. A 6. D 7. B 8. C 9. D 0. B TB. 570 TB. 5
More informationAnalysis Metrics. Intro to Algorithm Analysis. Slides. 12. Alg Analysis. 12. Alg Analysis
Itro to Algorithm Aalysis Aalysis Metrics Slides. Table of Cotets. Aalysis Metrics 3. Exact Aalysis Rules 4. Simple Summatio 5. Summatio Formulas 6. Order of Magitude 7. Big-O otatio 8. Big-O Theorems
More information. Written in factored form it is easy to see that the roots are 2, 2, i,
CMPS A Itroductio to Programmig Programmig Assigmet 4 I this assigmet you will write a java program that determies the real roots of a polyomial that lie withi a specified rage. Recall that the roots (or
More informationENGR 132. Fall Exam 1
ENGR 3 Fall 03 Exam INSTRUCTIONS: Duratio: 60 miutes Keep your eyes o your ow work. Keep your work covered at all times.. Each studet is resposible for followig directios. Read carefully.. MATLAB ad Excel
More informationData diverse software fault tolerance techniques
Data diverse software fault tolerace techiques Complemets desig diversity by compesatig for desig diversity s s limitatios Ivolves obtaiig a related set of poits i the program data space, executig the
More informationIsn t It Time You Got Faster, Quicker?
Is t It Time You Got Faster, Quicker? AltiVec Techology At-a-Glace OVERVIEW Motorola s advaced AltiVec techology is desiged to eable host processors compatible with the PowerPC istructio-set architecture
More informationCMPT 125 Assignment 2 Solutions
CMPT 25 Assigmet 2 Solutios Questio (20 marks total) a) Let s cosider a iteger array of size 0. (0 marks, each part is 2 marks) it a[0]; I. How would you assig a poiter, called pa, to store the address
More informationThe following algorithms have been tested as a method of converting an I.F. from 16 to 512 MHz to 31 real 16 MHz USB channels:
DBE Memo#1 MARK 5 MEMO #18 MASSACHUSETTS INSTITUTE OF TECHNOLOGY HAYSTACK OBSERVATORY WESTFORD, MASSACHUSETTS 1886 November 19, 24 Telephoe: 978-692-4764 Fax: 781-981-59 To: From: Mark 5 Developmet Group
More informationLecture 2. RTL Design Methodology. Transition from Pseudocode & Interface to a Corresponding Block Diagram
Lecture 2 RTL Desig Methodology Trasitio from Pseudocode & Iterface to a Correspodig Block Diagram Structure of a Typical Digital Data Iputs Datapath (Executio Uit) Data Outputs System Cotrol Sigals Status
More informationCSE 417: Algorithms and Computational Complexity
Time CSE 47: Algorithms ad Computatioal Readig assigmet Read Chapter of The ALGORITHM Desig Maual Aalysis & Sortig Autum 00 Paul Beame aalysis Problem size Worst-case complexity: max # steps algorithm
More informationIMP: Superposer Integrated Morphometrics Package Superposition Tool
IMP: Superposer Itegrated Morphometrics Package Superpositio Tool Programmig by: David Lieber ( 03) Caisius College 200 Mai St. Buffalo, NY 4208 Cocept by: H. David Sheets, Dept. of Physics, Caisius College
More informationCOSC 1P03. Ch 7 Recursion. Introduction to Data Structures 8.1
COSC 1P03 Ch 7 Recursio Itroductio to Data Structures 8.1 COSC 1P03 Recursio Recursio I Mathematics factorial Fiboacci umbers defie ifiite set with fiite defiitio I Computer Sciece sytax rules fiite defiitio,
More informationExercise 6 (Week 42) For the foreign students only.
These are the last exercises of the course. Please, remember that to pass exercises, the sum of the poits gathered by solvig the questios ad attedig the exercise groups must be at least 4% ( poits) of
More informationEE University of Minnesota. Midterm Exam #1. Prof. Matthew O'Keefe TA: Eric Seppanen. Department of Electrical and Computer Engineering
EE 4363 1 Uiversity of Miesota Midterm Exam #1 Prof. Matthew O'Keefe TA: Eric Seppae Departmet of Electrical ad Computer Egieerig Uiversity of Miesota Twi Cities Campus EE 4363 Itroductio to Microprocessors
More informationOutline. Applications of FFT in Communications. Fundamental FFT Algorithms. FFT Circuit Design Architectures. Conclusions
FFT Circuit Desig Outlie Applicatios of FFT i Commuicatios Fudametal FFT Algorithms FFT Circuit Desig Architectures Coclusios DAB Receiver Tuer OFDM Demodulator Chael Decoder Mpeg Audio Decoder 56/5/ 4/48
More informationCMSC Computer Architecture Lecture 11: More Caches. Prof. Yanjing Li University of Chicago
CMSC 22200 Computer Architecture Lecture 11: More Caches Prof. Yajig Li Uiversity of Chicago Lecture Outlie Caches 2 Review Memory hierarchy Cache basics Locality priciples Spatial ad temporal How to access
More informationNON-LINEAR MODELLING OF A GEOTHERMAL STEAM PIPE
14thNew Zealad Workshop 1992 NON-LNEAR MODELLNG OF A GEOTHERMAL STEAM PPE Y. Huag ad D. H. Freesto Geothermal stitute, Uiversity of Aucklad SUMMARY Recet work o developig a o-liear model for a geothermal
More informationCAEN Tools for Discovery
Applicatio Note AN2086 Sychroizatio of CAEN Digitizers i Multiple Board Acquisitio Systems Viareggio 9 May 2013 Itroductio High speed digitizers fid applicatios i several fields ragig from the idustry
More informationSURVEYING INSTRUMENTS SDR33 SOKKIA ELECTR ONIC FIELD BOOKS NOW EVEN MORE RUGGED PERFORMANCE. from The World Leader in Data Collection
SURVEYING INSTRUMENTS TM SOKKIA SDR33 ELECTR ONIC FIELD BOOKS ELECTRONIC NOW EVEN MORE RUGGED PERFORMANCE from The World Leader i Data Collectio PUT RUGGED, DEPENDABLE POWER IN THE PALM OF YOUR HAND You
More informationtop() Applications of Stacks
CS22 Algorithms ad Data Structures MW :00 am - 2: pm, MSEC 0 Istructor: Xiao Qi Lecture 6: Stacks ad Queues Aoucemets Quiz results Homework 2 is available Due o September 29 th, 2004 www.cs.mt.edu~xqicoursescs22
More informationAN OPTIMIZATION NETWORK FOR MATRIX INVERSION
397 AN OPTIMIZATION NETWORK FOR MATRIX INVERSION Ju-Seog Jag, S~ Youg Lee, ad Sag-Yug Shi Korea Advaced Istitute of Sciece ad Techology, P.O. Box 150, Cheogryag, Seoul, Korea ABSTRACT Iverse matrix calculatio
More informationHow do we evaluate algorithms?
F2 Readig referece: chapter 2 + slides Algorithm complexity Big O ad big Ω To calculate ruig time Aalysis of recursive Algorithms Next time: Litterature: slides mostly The first Algorithm desig methods:
More informationReversible Realization of Quaternary Decoder, Multiplexer, and Demultiplexer Circuits
Egieerig Letters, :, EL Reversible Realizatio of Quaterary Decoder, Multiplexer, ad Demultiplexer Circuits Mozammel H.. Kha, Member, ENG bstract quaterary reversible circuit is more compact tha the correspodig
More informationEE 459/500 HDL Based Digital Design with Programmable Logic. Lecture 13 Control and Sequencing: Hardwired and Microprogrammed Control
EE 459/500 HDL Based Digital Desig with Programmable Logic Lecture 13 Cotrol ad Sequecig: Hardwired ad Microprogrammed Cotrol Refereces: Chapter s 4,5 from textbook Chapter 7 of M.M. Mao ad C.R. Kime,
More informationA Generalized Set Theoretic Approach for Time and Space Complexity Analysis of Algorithms and Functions
Proceedigs of the 10th WSEAS Iteratioal Coferece o APPLIED MATHEMATICS, Dallas, Texas, USA, November 1-3, 2006 316 A Geeralized Set Theoretic Approach for Time ad Space Complexity Aalysis of Algorithms
More informationData Structures and Algorithms Part 1.4
1 Data Structures ad Algorithms Part 1.4 Werer Nutt 2 DSA, Part 1: Itroductio, syllabus, orgaisatio Algorithms Recursio (priciple, trace, factorial, Fiboacci) Sortig (bubble, isertio, selectio) 3 Sortig
More informationEE260: Digital Design, Spring /16/18. n Example: m 0 (=x 1 x 2 ) is adjacent to m 1 (=x 1 x 2 ) and m 2 (=x 1 x 2 ) but NOT m 3 (=x 1 x 2 )
EE26: Digital Desig, Sprig 28 3/6/8 EE 26: Itroductio to Digital Desig Combiatioal Datapath Yao Zheg Departmet of Electrical Egieerig Uiversity of Hawaiʻi at Māoa Combiatioal Logic Blocks Multiplexer Ecoders/Decoders
More informationInstruction and Data Streams
Advaced Architectures Master Iformatics Eg. 2017/18 A.J.Proeça Data Parallelism 1 (vector & SIMD extesios) (most slides are borrowed) AJProeça, Advaced Architectures, MiEI, UMiho, 2017/18 1 Istructio ad
More informationCode Review Defects. Authors: Mika V. Mäntylä and Casper Lassenius Original version: 4 Sep, 2007 Made available online: 24 April, 2013
Code Review s Authors: Mika V. Mätylä ad Casper Lasseius Origial versio: 4 Sep, 2007 Made available olie: 24 April, 2013 This documet cotais further details of the code review defects preseted i [1]. of
More informationImplementing Dynamic Programming Recurrences in Constraint Handling Rules with Rule Priorities
Implemetig Dyamic Programmig Recurreces i Costrait Hadlig Rules with Rule Priorities Ahmed Magdy 1, Frak Raiser, ad Thom Frühwirth 1 Computer Sciece ad Egieerig, Germa Uiversity i Cairo ahmed.mabrouk@studet.guc.edu.eg
More informationSpeeding-up dynamic programming in sequence alignment
Departmet of Computer Sciece Aarhus Uiversity Demark Speedig-up dyamic programmig i sequece aligmet Master s Thesis Dug My Hoa - 443 December, Supervisor: Christia Nørgaard Storm Pederse Implemetatio code
More informationAnnouncements. Reading. Project #4 is on the web. Homework #1. Midterm #2. Chapter 4 ( ) Note policy about project #3 missing components
Aoucemets Readig Chapter 4 (4.1-4.2) Project #4 is o the web ote policy about project #3 missig compoets Homework #1 Due 11/6/01 Chapter 6: 4, 12, 24, 37 Midterm #2 11/8/01 i class 1 Project #4 otes IPv6Iit,
More information1 Graph Sparsfication
CME 305: Discrete Mathematics ad Algorithms 1 Graph Sparsficatio I this sectio we discuss the approximatio of a graph G(V, E) by a sparse graph H(V, F ) o the same vertex set. I particular, we cosider
More informationΤεχνολογία Λογισμικού
ΕΘΝΙΚΟ ΜΕΤΣΟΒΙΟ ΠΟΛΥΤΕΧΝΕΙΟ Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών Τεχνολογία Λογισμικού, 7ο/9ο εξάμηνο 2018-2019 Τεχνολογία Λογισμικού Ν.Παπασπύρου, Αν.Καθ. ΣΗΜΜΥ, ickie@softlab.tua,gr
More informationOCR Statistics 1. Working with data. Section 3: Measures of spread
Notes ad Eamples OCR Statistics 1 Workig with data Sectio 3: Measures of spread Just as there are several differet measures of cetral tedec (averages), there are a variet of statistical measures of spread.
More informationHash Tables. Presentation for use with the textbook Algorithm Design and Applications, by M. T. Goodrich and R. Tamassia, Wiley, 2015.
Presetatio for use with the textbook Algorithm Desig ad Applicatios, by M. T. Goodrich ad R. Tamassia, Wiley, 2015 Hash Tables xkcd. http://xkcd.com/221/. Radom Number. Used with permissio uder Creative
More informationSecond-Order Domain Decomposition Method for Three-Dimensional Hyperbolic Problems
Iteratioal Mathematical Forum, Vol. 8, 013, o. 7, 311-317 Secod-Order Domai Decompositio Method for Three-Dimesioal Hyperbolic Problems Youbae Ju Departmet of Applied Mathematics Kumoh Natioal Istitute
More informationLecture 1: Introduction and Strassen s Algorithm
5-750: Graduate Algorithms Jauary 7, 08 Lecture : Itroductio ad Strasse s Algorithm Lecturer: Gary Miller Scribe: Robert Parker Itroductio Machie models I this class, we will primarily use the Radom Access
More information