ECE 2300 Digital Logic & Computer Organization

Size: px
Start display at page:

Download "ECE 2300 Digital Logic & Computer Organization"

Transcription

1 ECE 23 Digital Logic & Computer Organization Spring 28 Combinational Logic Minimization Lecture 3:

2 Announcements Lab 2 is on CMS Tutorial B: CAD tool installation HW will be released tonight Rescheduled lecture: Monday Feb 7:5pm, PHL Video replay on Thursday Feb 8 during the regular lecture time at Thurston 23 Lecture 3: 2

3 Boolean Function Representations Y = if and only if A B (B +C) Step : Lay out the truth table Step 2: Derive canonical forms Canonical sum Y = A B C + A B C + A B C + A B C + A B C + A B C = A,B,C (,,3,4,5,6) Canonical product Y = (A+B +C)(A +B +C ) = A,B,C (2,7) ABC Y Step 3: Simplification (this lecture) Lecture 3: 3

4 Combinational Logic Outputs depend only on current inputs Example: Detect if the input is an odd number Can be represented in two-level or multi-level forms In contrast, sequential logic has memory or state Example: Detect if the last two inputs are odd We ll cover sequential logic later Lecture 3: 4

5 Two-Level Logic: Sum-of-Products Sum of product terms (SOP) e.g., A B + A C + A B C Circuits look something like this A bubble indicates the signal is inverted wire inputs output AND-OR Lecture 3: 5

6 Two-Level Logic: Product-of-Sums Product of sum terms (POS) e.g., (A +C ) (B +C ) (A+B+C) Circuits look something like this OR-AND Combinational logic can be expressed as SOP or POS Lecture 3: 6

7 Algebraic Simplification Apply theorems to canonical sum (product) to reduce () the number of terms, and (2) the number of literals in each term Results in a more compact expression and lower cost digital logic implementation We focus on minimizing two-level logic in this lecture Lecture 3: 7

8 Lecture 3: 8 A B Cin Cout S Cout = A' B Cin + A B' Cin + A B Cin' + A B Cin S = A' B' Cin + A' B Cin' + A B' Cin' + A B Cin Cout S Cin B A Cout S Cin B A S Cin B A S Cin B A Algebraic Simplification Example Binary adder inputs: A, B, Carry-in (Cin) outputs: Sum, Carry-out (Cout) Truth Table à Canonical sum Cout

9 Algebraic Simplification Example Cout = A' B Cin + A B' Cin + A B Cin' + A B Cin = A' B Cin + A B' Cin + A B Cin' + A B Cin + A B Cin (idempotency) = B Cin + A B' Cin + A B Cin' + A B Cin (combining) = B Cin + A B' Cin + A B Cin' + A B Cin + A B Cin (idempotency) = B Cin + A Cin + A B Cin' + A B Cin (combining) = B Cin + A Cin + A B (combining) We can apply these theorems in a more intuitive fashion using a Karnaugh Map Lecture 3: 9

10 Reduction in Hardware Cost Cout = A' B Cin + A B' Cin + A B Cin' + A B Cin = B Cin + A Cin + A B 3 inverters 4 three-input ANDs four-input OR à 3 two-input ANDs three-input OR Lecture 3:

11 Karnaugh Map (K-Map) Idea: Use combining and idempotency theorems visually to simplify canonical forms (into twolevel SOP or POS) Multidimensional representation of a truth table Adjacent cells represent minterms (or maxterms) that differ by exactly one literal Cyclic encoding along each dimension At most two variables per dimension Lecture 3:

12 Lecture 3: K-Map for Cout AB Cin Cout S Cin B A Cout S Cin B A Cout S Cin B A Cout S Cin B A 2

13 Some K-Map Definitions -cell: Minterm of a canonical sum Implicant: Set of adjacent -cells A rectangle in K-Map Number of cells contained must be a power of 2 Prime Implicant: Implicant that cannot be contained in a larger implicant AB Cin A prime implicant Lecture 3: 3

14 Minimization Using K-Map Goal: Cover all -cells with the minimum number of prime implicants Procedure Plot s corresponding to minterms of function Circle largest possible rectangular sets of s Must be power of 2 Including wrap-around sets Repeat until all minterms are covered OR product terms derived from each circle Minimizes number of gates and gate inputs Lecture 3: 4

15 Simplifying Cout Using a K-Map Cout = A' B Cin + A B' Cin + A B Cin' + A B Cin = A' B Cin + A B' Cin + A B Cin' + A B Cin + A B Cin (idempotency) = B Cin + A B' Cin + A B Cin' + A B Cin (combining) = B Cin + A B' Cin + A B Cin' + A B Cin + A B Cin (idempotency) = B Cin + A Cin + A B Cin' + A B Cin (combining) = B Cin + A Cin + A B (combining) AB Cin Ovals result from applying combining theorem Idempotency theorem allows ovals to overlap Lecture 3: 5

16 3 Variable K-Map Example F=Σ A,B,C (4,5,6,7) C AB F = A Lecture 3: 6

17 Another Example F=Σ A,B,C (,,2,4,5,7) C AB A C B A C F = A C + B + A C Lecture 3: 7

18 4 Variable K-Map CD AB Lecture 3: 8

19 Combining Theorem in Action CD AB A BC D + A BCD + ABC D + ABCD A BD + BD ABD Lecture 3: 9

20 Combining Theorem in Action CD AB A B C D + A B CD + AB C D + AB CD A B D + AB D B D Lecture 3: 2

21 4 Variable Karnaugh Map Example F=Σ A,B,C,D (5,7,2,3,4,5) CD AB F = B D + A B Lecture 3: 2

22 Another Example Detect all even digits from to 5 except 6 F=Σ A,B,C,D (,2,4,8,,2,4) CD AB F = B D + AD + C D Lecture 3: 22

23 Minimizing Product-of-Sums Procedure Plot s corresponding to maxterms of function Circle largest possible rectangular sets of s Must be power of 2 Including wrap-around sets Repeat until all maxterms are covered AND sum terms derived from each circle Lecture 3: 23

24 Product-of-Sums Example F=π A,B,C,D (, 2, 6, 7, 8, ) CD AB Corners: B+D Other: A+B +C F=(B+D) (A+B +C ) Lecture 3: 24

25 H&H.7, 2.8 Before Next Class Next Time CMOS Logic Lecture 3: 25

ECE 2300 Digital Logic & Computer Organization

ECE 2300 Digital Logic & Computer Organization ECE 23 Digital Logic & Computer Organization Spring 27 Combinational Logic Minimization Lecture 3: Announcements Lab 2 is on CMS Tutorial B: CAD tool installation HW will be released tonight Rescheduled

More information

Test Equipment Depot Washington Street Melrose, MA TestEquipmentDepot.com

Test Equipment Depot Washington Street Melrose, MA TestEquipmentDepot.com Test Equipment Depot - 800.517.8431-99 Washington Street Melrose, MA 02176 TestEquipmentDepot.com Table of Contents Table of Contents... iii Preface... viii Getting Started... 9 0.1 Breadboard... 9 0.2

More information

Lecture (09) Karnaugh Maps 2

Lecture (09) Karnaugh Maps 2 Lecture (09) Karnaugh Maps 2 By: Dr. Ahmed ElShafee ١ Dr. Ahmed ElShafee, ACU : Spring 207, Logic Design I Determination of Minimum Expansions Using Essential Prime Implicants Cover: A switching function

More information

Lecture (05) Boolean Algebra

Lecture (05) Boolean Algebra Lecture (5) Boolean Algebra By: Dr. Ahmed ElShafee Dr. Ahmed ElShafee, ACU : Spring 29 CSE22 Logic Design I Introduction George Boole developed Boolean algebra in 847 and used it to solve problems in mathematical

More information

Part I. Boolean modelling exercises

Part I. Boolean modelling exercises Part I. Boolean modelling exercises. Glucose repression of Icl in yeast In yeast Saccharomyces cerevisiae, expression of enzyme Icl (isocitrate lyase-, involved in the gluconeogenesis pathway) is important

More information

VLSI Design, Fall Design of Adders Design of Adders. Jacob Abraham

VLSI Design, Fall Design of Adders Design of Adders. Jacob Abraham 8. Design of Adders 1 8. Design of Adders Jacob Abraham Department of Electrical and Computer Engineering The University of Texas at Austin VLSI Design Fall 2017 September 27, 2017 ECE Department, University

More information

CPSC 121 Some Sample Questions for the Final Exam

CPSC 121 Some Sample Questions for the Final Exam CPSC 121 Some Sample Questions for the Final Exam [0] 1. Tautologies and Contradictions: Determine whether the following statements are tautologies (definitely true), contradictions (definitely false),

More information

A Logic Simplification Based on Expert System Application for TBC Diagnosis

A Logic Simplification Based on Expert System Application for TBC Diagnosis International ICST Conference on Wireless Mobile Communication and Healthcare MOBIHEALTH 2010, Ayia Napa, Cyprus A Logic Simplification Based on Expert System Application for TBC Diagnosis Harun SÜMBÜL,

More information

Chapter 3 Software Packages to Install How to Set Up Python Eclipse How to Set Up Eclipse... 42

Chapter 3 Software Packages to Install How to Set Up Python Eclipse How to Set Up Eclipse... 42 Table of Contents Preface..... 21 About the Authors... 23 Acknowledgments... 24 How This Book is Organized... 24 Who Should Buy This Book?... 24 Where to Find Answers to Review Questions and Exercises...

More information

Review Questions in Introductory Knowledge... 37

Review Questions in Introductory Knowledge... 37 Table of Contents Preface..... 17 About the Authors... 19 How This Book is Organized... 20 Who Should Buy This Book?... 20 Where to Find Answers to Review Questions and Exercises... 20 How to Report Errata...

More information

Chapter 8 Multioperand Addition

Chapter 8 Multioperand Addition Chapter 8 Multioperand Addition Using Two-Operand Adders Carry-Save Adders Wallace and Dadda Trees Parallel Counters Generalized Parallel Counters Adding Multiple Signed Numbers Ch 8. Multioperand Addition

More information

KENDRIYA VIDYALAYA SANGATHAN (PATNA REGION)

KENDRIYA VIDYALAYA SANGATHAN (PATNA REGION) KENDRIYA VIDYALAYA SANGATHAN (PATNA REGION) SUMMATIVE ASSESSMENT -2 (,2015-2016) CLASS VII MATHEMATICS BLUEPRINT CHAPTER/TOPIC M C Q (1) V S A (2) S A (3) L A (4) OTBA TOTAL CONGRUENCE OF TRIANGLES 2(2)

More information

Content Scope & Sequence

Content Scope & Sequence Content Scope & Sequence GRADE 2 scottforesman.com (800) 552-2259 Copyright Pearson Education, Inc. 0606443 1 Counting, Coins, and Combinations Counting, Coins, and Combinations (Addition, Subtraction,

More information

4. Model evaluation & selection

4. Model evaluation & selection Foundations of Machine Learning CentraleSupélec Fall 2017 4. Model evaluation & selection Chloé-Agathe Azencot Centre for Computational Biology, Mines ParisTech chloe-agathe.azencott@mines-paristech.fr

More information

Assignment Question Paper I. 1. What is the external and internal commands and write 5 commands for each with result?

Assignment Question Paper I. 1. What is the external and internal commands and write 5 commands for each with result? Subject:- INFORMATION TECHNOLOGY Max Marks -30 kvksa esa gy djuk vfuok;z gsa 1. What is the external and internal commands and write 5 commands for each with result? 2. Writes the features of UNIX Operating

More information

MIDTERM EXAM. Total 150. Problem Points Grade. STAT 541 Introduction to Biostatistics. Name. Spring 2008 Ismor Fischer

MIDTERM EXAM. Total 150. Problem Points Grade. STAT 541 Introduction to Biostatistics. Name. Spring 2008 Ismor Fischer STAT 541 Introduction to Biostatistics Spring 2008 Ismor Fischer Name MIDTERM EXAM Instructions: This exam is worth 150 points ( 1/3 of your course grade). Answer Problem 1, and any two of the remaining

More information

PLC Fundamentals. Module 4: Programming with Ladder Logic. Academic Services Unit PREPARED BY. January 2013

PLC Fundamentals. Module 4: Programming with Ladder Logic. Academic Services Unit PREPARED BY. January 2013 PLC Fundamentals Module 4: Programming with Ladder Logic PREPARED BY Academic Services Unit January 2013 Applied Technology High Schools, 2013 Module 4: Programming with Ladder Logic Module Objectives

More information

Sample Math 71B Final Exam #1. Answer Key

Sample Math 71B Final Exam #1. Answer Key Sample Math 71B Final Exam #1 Answer Key 1. (2 points) Graph the equation. Be sure to plot the points on the graph at. 2. Solve for. 3. Given that, find and simplify. 4. Suppose and a. (1 point) Find.

More information

Grade 6 Math Circles Winter February 6/7 Number Theory

Grade 6 Math Circles Winter February 6/7 Number Theory Faculty of Mathematics Waterloo, Ontario N2L 3G1 Centre for Education in Mathematics and Computing Grade 6 Math Circles Winter 2018 - February 6/7 Number Theory Warm-up! What is special about the following

More information

Mental Processes -- How the Mind Arises from the Brain. Roger Ellman

Mental Processes -- How the Mind Arises from the Brain. Roger Ellman Mental Processes -- How the Mind Arises from the Brain Roger Ellman Abstract Cognition is best understood by examining a model of a cognitive system. Such a model is presented in the following paper. Most

More information

3. Model evaluation & selection

3. Model evaluation & selection Foundations of Machine Learning CentraleSupélec Fall 2016 3. Model evaluation & selection Chloé-Agathe Azencot Centre for Computational Biology, Mines ParisTech chloe-agathe.azencott@mines-paristech.fr

More information

Exploring the Functional Significance of Dendritic Inhibition In Cortical Pyramidal Cells

Exploring the Functional Significance of Dendritic Inhibition In Cortical Pyramidal Cells Neurocomputing, 5-5:389 95, 003. Exploring the Functional Significance of Dendritic Inhibition In Cortical Pyramidal Cells M. W. Spratling and M. H. Johnson Centre for Brain and Cognitive Development,

More information

Cayley Graphs. Ryan Jensen. March 26, University of Tennessee. Cayley Graphs. Ryan Jensen. Groups. Free Groups. Graphs.

Cayley Graphs. Ryan Jensen. March 26, University of Tennessee. Cayley Graphs. Ryan Jensen. Groups. Free Groups. Graphs. Cayley Forming Free Cayley Cayley University of Tennessee March 26, 2014 Group Cayley Forming Free Cayley Definition A group is a nonempty set G with a binary operation which satisfies the following: (i)

More information

EECS150 - Digital Design Lecture 7 - Boolean Algebra II

EECS150 - Digital Design Lecture 7 - Boolean Algebra II EECS5 Digitl Design Leture 7 Boolen Alger II Ferury 2, 22 John Wwrzynek Spring 22 EECS5 Le7Bool2 Pge Cnonil Forms Outline They give us metho to go from TT to Boolen Equtions Twolevel Logi Simplifition

More information

1 (vi) maths Multiple Choice Questions (MCQs) CLASS: VI SUBJECT: MATHS. Chapter - 1

1 (vi) maths Multiple Choice Questions (MCQs) CLASS: VI SUBJECT: MATHS. Chapter - 1 1 (vi) maths Multiple Choice Questions (MCQs) CLASS: VI SUBJECT: MATHS Chapter - 1 The smallest two digit number is (a) 10 (b) 90 (c) 99 (d) none Greatest three digit number using three distinct digit

More information

Bayesian Tailored Testing and the Influence

Bayesian Tailored Testing and the Influence Bayesian Tailored Testing and the Influence of Item Bank Characteristics Carl J. Jensema Gallaudet College Owen s (1969) Bayesian tailored testing method is introduced along with a brief review of its

More information

COGS 1: FALL Section E

COGS 1: FALL Section E COGS 1: FALL 2018 Section E Professor Boyle mboyle@ucsd.edu Monday, 2-3:50pm CSB 130 Zoe tzcheng@ucsd.edu Monday, 12-12:50pm CSB 223 Lauren lcurley@ucsd.edu Monday, 2:-250pm CSB 225 Subathra suraj@ucsd.edu

More information

The Guardian Euro Poll CATI Fieldwork: 24th Februuary - 8th March 2011

The Guardian Euro Poll CATI Fieldwork: 24th Februuary - 8th March 2011 Table 1 Q.1 Over the next twelve months, do you expect your country's economic situation to generally get better, stay about the same or get worse? Page 1 Get a lot better (+2) 159 6 45 4%ac 23 a 31 a

More information

Assignment Question Paper I

Assignment Question Paper I Subject : - Discrete Mathematics Maximum Marks : 30 1. Define Harmonic Mean (H.M.) of two given numbers relation between A.M.,G.M. &H.M.? 2. How we can represent the set & notation, define types of sets?

More information

Cognitive Neuroscience History of Neural Networks in Artificial Intelligence The concept of neural network in artificial intelligence

Cognitive Neuroscience History of Neural Networks in Artificial Intelligence The concept of neural network in artificial intelligence Cognitive Neuroscience History of Neural Networks in Artificial Intelligence The concept of neural network in artificial intelligence To understand the network paradigm also requires examining the history

More information

Grade 6 Math Circles Winter February 6/7 Number Theory - Solutions Warm-up! What is special about the following groups of numbers?

Grade 6 Math Circles Winter February 6/7 Number Theory - Solutions Warm-up! What is special about the following groups of numbers? Faculty of Mathematics Waterloo, Ontario N2L 3G1 Grade 6 Math Circles Winter 2018 - February 6/7 Number Theory - Solutions Warm-up! What is special about the following groups of numbers? Centre for Education

More information

Protecting Patient Privacy in Genomic Analysis

Protecting Patient Privacy in Genomic Analysis Protecting Patient Privacy in Genomic Analysis David Wu Stanford University based on joint works with: Gill Bejerano, Bonnie Berger, Johannes A. Birgmeier, Dan Boneh, Hyunghoon Cho, and Karthik A. Jagadeesh

More information

KINGS COLLEGE OF ENGINEERING DEPARTMENT OF MECHANICAL ENGINEERING QUESTION BANK. Subject Name: ELECTRONICS AND MICRIPROCESSORS UNIT I

KINGS COLLEGE OF ENGINEERING DEPARTMENT OF MECHANICAL ENGINEERING QUESTION BANK. Subject Name: ELECTRONICS AND MICRIPROCESSORS UNIT I KINGS COLLEGE OF ENGINEERING DEPARTMENT OF MECHANICAL ENGINEERING QUESTION BANK Subject Name: ELECTRONICS AND MICRIPROCESSORS UNIT I Year/Sem:II / IV PART-A(2 MARKS) SEMICONDUCTORS AND RECTIFIERS 1. What

More information

CS Information Visualization Sep. 10, 2012 John Stasko. General representation techniques for multivariate (>3) variables per data case

CS Information Visualization Sep. 10, 2012 John Stasko. General representation techniques for multivariate (>3) variables per data case Topic Notes Multivariate Visual Representations 1 CS 7450 - Information Visualization Sep. 10, 2012 John Stasko Agenda General representation techniques for multivariate (>3) variables per data case But

More information

CS 182 Midterm review

CS 182 Midterm review CS 182 Midterm review Near far Image schemas What is a schema? a pattern capturing structure in the world What is an image schema? a schema about interaction with the world Can you name some? Containment

More information

PLL. The system blocks and their input and output signals are as follows:

PLL. The system blocks and their input and output signals are as follows: PLL This is the most complicated of the three optional labs - but is definitely the coolest system and if you can get it working, you should be able to learn a lot. The top level block diagram of the PLL

More information

Sets, Logic, and Probability As Used in Decision Support Systems

Sets, Logic, and Probability As Used in Decision Support Systems Sets, Logic, and Probability As Used in Decision Support Systems HAP 752 Advanced Health Informa6on Systems Janusz Wojtusiak, PhD George Mason University Spring 2014 Part of the inhumanity of the computer

More information

MITOCW ocw f99-lec20_300k

MITOCW ocw f99-lec20_300k MITOCW ocw-18.06-f99-lec20_300k OK, this is lecture twenty. And this is the final lecture on determinants. And it's about the applications. So we worked hard in the last two lectures to get a formula for

More information

Visual Perception. Agenda. Visual perception. CS Information Visualization January 20, 2011 John Stasko. Pre-attentive processing Color Etc.

Visual Perception. Agenda. Visual perception. CS Information Visualization January 20, 2011 John Stasko. Pre-attentive processing Color Etc. Topic Notes Visual Perception CS 7450 - Information Visualization January 20, 2011 John Stasko Agenda Visual perception Pre-attentive processing Color Etc. Spring 2011 CS 7450 2 1 Semiotics The study of

More information

Speaker Notes: Qualitative Comparative Analysis (QCA) in Implementation Studies

Speaker Notes: Qualitative Comparative Analysis (QCA) in Implementation Studies Speaker Notes: Qualitative Comparative Analysis (QCA) in Implementation Studies PART 1: OVERVIEW Slide 1: Overview Welcome to Qualitative Comparative Analysis in Implementation Studies. This narrated powerpoint

More information

Simulation of associative learning with the replaced elements model

Simulation of associative learning with the replaced elements model Behavior Research Methods 7, 39 (4), 993- Simulation of associative learning with the replaced elements model STEVEN GLAUTIER University of Southampton, Southampton, England Associative learning theories

More information

Convolutional Coding: Fundamentals and Applications. L. H. Charles Lee. Artech House Boston London

Convolutional Coding: Fundamentals and Applications. L. H. Charles Lee. Artech House Boston London Convolutional Coding: Fundamentals and Applications L. H. Charles Lee Artech House Boston London Contents Preface xi Chapter 1 Introduction of Coded Digital Communication Systems 1 1.1 Introduction 1 1.2

More information

Loop Dependence and Parallelism

Loop Dependence and Parallelism Loop Dependence and Parallelism Announcements Reading assignments for next week due Midterm is Monday March 6th, example midterms posted Assignment 2 is due the week after that. Start now! Today Data dependencies

More information

Revised 2016 GED Test Performance Level Descriptors: Level 1 (Below Passing: )

Revised 2016 GED Test Performance Level Descriptors: Level 1 (Below Passing: ) Revised 2016 GED Test Performance Level Descriptors: Level 1 (Below Passing: 100-144) Test-takers who score at the Below Passing level are typically able to comprehend and analyze simple passages similar

More information

Protecting Patient Privacy in Genomic Analysis

Protecting Patient Privacy in Genomic Analysis Protecting Patient Privacy in Genomic Analysis David Wu Stanford University based on joint works with: Gill Bejerano, Bonnie Berger, Johannes A. Birgmeier, Dan Boneh, Hyunghoon Cho, and Karthik A. Jagadeesh

More information

Low-Power 1-bit CMOS Full Adder Using Subthreshold Conduction Region

Low-Power 1-bit CMOS Full Adder Using Subthreshold Conduction Region International Journal of Scientific & Engineering Research Volume 2, Issue 6, June-2011 1 Low-Power 1-bit CMOS Full Adder Using Subthreshold Conduction Region Vishal Sharma, Sanjay Kumar Abstract In balancing

More information

Analysis of in-vivo extracellular recordings. Ryan Morrill Bootcamp 9/10/2014

Analysis of in-vivo extracellular recordings. Ryan Morrill Bootcamp 9/10/2014 Analysis of in-vivo extracellular recordings Ryan Morrill Bootcamp 9/10/2014 Goals for the lecture Be able to: Conceptually understand some of the analysis and jargon encountered in a typical (sensory)

More information

Memory by Temporal Sampling

Memory by Temporal Sampling Memory by Temporal Sampling Caroline Morin Collaborators: Trevor Penney (Dept. Psychology, National University of Singapore) Stephen Lewandowsky (Dept. Psychology, University of Western Australia) Free

More information

19th AWCBR (Australian Winter Conference on Brain Research), 2001, Queenstown, AU

19th AWCBR (Australian Winter Conference on Brain Research), 2001, Queenstown, AU 19th AWCBR (Australian Winter Conference on Brain Research), 21, Queenstown, AU https://www.otago.ac.nz/awcbr/proceedings/otago394614.pdf Do local modification rules allow efficient learning about distributed

More information

Math HL Chapter 12 Probability

Math HL Chapter 12 Probability Math HL Chapter 12 Probability Name: Read the notes and fill in any blanks. Work through the ALL of the examples. Self-Check your own progress by rating where you are. # Learning Targets Lesson I have

More information

Multi-band LC VCO GHz PMCC_VCOMB12G

Multi-band LC VCO GHz PMCC_VCOMB12G Multi-band LC VCO 8-11.3GHz PMCC_VCOMB12G IP MACRO Datasheet Rev 1.2 Process: 65nm CMOS DESCRIPTION PMCC_VCOMB12G is a low noise multi-band differential LC voltage controlled oscillator (VCO). The IP block

More information

ERA: Architectures for Inference

ERA: Architectures for Inference ERA: Architectures for Inference Dan Hammerstrom Electrical And Computer Engineering 7/28/09 1 Intelligent Computing In spite of the transistor bounty of Moore s law, there is a large class of problems

More information

Artificial Intelligence Programming Probability

Artificial Intelligence Programming Probability Artificial Intelligence Programming Probability Chris Brooks Department of Computer Science University of San Francisco Department of Computer Science University of San Francisco p.1/25 17-0: Uncertainty

More information

Belief Propagation and Wiring Length Optimization as Organizing Principles for Cortical Microcircuits.

Belief Propagation and Wiring Length Optimization as Organizing Principles for Cortical Microcircuits. Belief Propagation and Wiring Length Optimization as Organizing Principles for Cortical Microcircuits. Dileep George Electrical Engineering, Stanford University & Numenta Inc., Menlo Park, CA 94025 dil@stanford.edu

More information

Lung Cancer Detection using Morphological Segmentation and Gabor Filtration Approaches

Lung Cancer Detection using Morphological Segmentation and Gabor Filtration Approaches Lung Cancer Detection using Morphological Segmentation and Gabor Filtration Approaches Mokhled S. Al-Tarawneh, Suha Al-Habashneh, Norah Shaker, Weam Tarawneh and Sajedah Tarawneh Computer Engineering Department,

More information

Application of Phased Array Radar Theory to Ultrasonic Linear Array Medical Imaging System

Application of Phased Array Radar Theory to Ultrasonic Linear Array Medical Imaging System Application of Phased Array Radar Theory to Ultrasonic Linear Array Medical Imaging System R. K. Saha, S. Karmakar, S. Saha, M. Roy, S. Sarkar and S.K. Sen Microelectronics Division, Saha Institute of

More information

Sparse Coding in Sparse Winner Networks

Sparse Coding in Sparse Winner Networks Sparse Coding in Sparse Winner Networks Janusz A. Starzyk 1, Yinyin Liu 1, David Vogel 2 1 School of Electrical Engineering & Computer Science Ohio University, Athens, OH 45701 {starzyk, yliu}@bobcat.ent.ohiou.edu

More information

Shannon Expansion Based Supply-Gated Logic for Improved Power and Testability

Shannon Expansion Based Supply-Gated Logic for Improved Power and Testability Shannon Expansion Based Supply-Gated Logic for Improved Power and Testability S. Ghosh, S. Bhunia, and K. Roy School of Electrical and Computer Engineering, Purdue University, IN {ghosh3, bhunias, kaushik}@ecn.purdue.edu

More information

Lecture 10: Efficient Design of Current-Starved VCO

Lecture 10: Efficient Design of Current-Starved VCO Lecture 10: Efficient Design of Current-Starved VCO CSCE 6933/5933 Instructor: Saraju P. Mohanty, Ph. D. NOTE: The figures, text etc included in slides are borrowed from various books, websites, authors

More information

Rolls,E.T. (2016) Cerebral Cortex: Principles of Operation. Oxford University Press.

Rolls,E.T. (2016) Cerebral Cortex: Principles of Operation. Oxford University Press. Digital Signal Processing and the Brain Is the brain a digital signal processor? Digital vs continuous signals Digital signals involve streams of binary encoded numbers The brain uses digital, all or none,

More information

Contents. Just Classifier? Rules. Rules: example. Classification Rule Generation for Bioinformatics. Rule Extraction from a trained network

Contents. Just Classifier? Rules. Rules: example. Classification Rule Generation for Bioinformatics. Rule Extraction from a trained network Contents Classification Rule Generation for Bioinformatics Hyeoncheol Kim Rule Extraction from Neural Networks Algorithm Ex] Promoter Domain Hybrid Model of Knowledge and Learning Knowledge refinement

More information

Outline. EECS150 - Digital Design Lecture 5 - Boolean Algebra II. Canonical Forms. Sum of Products (cont.)

Outline. EECS150 - Digital Design Lecture 5 - Boolean Algebra II. Canonical Forms. Sum of Products (cont.) EECS5 Digitl Design Leture 5 Boolen Alger II Septemer, 22 John Wwrznek Outline Cnonil Forms The give us metho to go rom TT to Boolen Equtions Twolevel Logi Simpliition Kmp metho Multilevel Logi NAND/NOR

More information

VISUAL PERCEPTION & COGNITIVE PROCESSES

VISUAL PERCEPTION & COGNITIVE PROCESSES VISUAL PERCEPTION & COGNITIVE PROCESSES Prof. Rahul C. Basole CS4460 > March 31, 2016 How Are Graphics Used? Larkin & Simon (1987) investigated usefulness of graphical displays Graphical visualization

More information

Design of Low-Power CMOS Cell Structures Using Subthreshold Conduction Region

Design of Low-Power CMOS Cell Structures Using Subthreshold Conduction Region International Journal of Scientific & Engineering Research, Volume 2, Issue 2, February-2011 1 Design of Low-Power CMOS Cell Structures Using Subthreshold Conduction Region Vishal Sharma, Sanjay Kumar

More information

EECS150 - Digital Design Lecture 5 - Boolean Algebra II

EECS150 - Digital Design Lecture 5 - Boolean Algebra II EECS5 Digitl Design Leture 5 Boolen Alger II Septemer, 22 John Wwrzynek Fll 22 EECS5 Le5Bool2 Pge Cnonil Forms Outline They give us metho to go from TT to Boolen Equtions Twolevel Logi Simplifition Kmp

More information

Curriculum structure of B.Tech ECE Programme (III Semester onwards)

Curriculum structure of B.Tech ECE Programme (III Semester onwards) 1.Range for total credits in III, IV, V and VI semester is 22-28 2. A course on Management (PC), will be run in VII semester for four branches and the rest will have it in VIII semester 3. Course credit

More information

QUALITY MONITORING OF INSTANT WHOLE MILK POWDER USING VARIOUS CONTROL METHODS

QUALITY MONITORING OF INSTANT WHOLE MILK POWDER USING VARIOUS CONTROL METHODS QUALITY MONITORING OF INSTANT WHOLE MILK POWDER USING VARIOUS CONTROL METHODS Mansi Gupta 1, Ayush Garg 2 1Department of Food Science, The University of Auckland, Auckland, New Zealand 2Department of Chemical

More information

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Math 24 Chapter 24-Study Guide Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Solve the equation. ) -0.333x - 4 = 0.667x A) {-8} B) {-4} C)

More information

AUTOMATIONWORX. User Manual. UM EN IBS SRE 1A Order No.: INTERBUS Register Expansion Chip IBS SRE 1A

AUTOMATIONWORX. User Manual. UM EN IBS SRE 1A Order No.: INTERBUS Register Expansion Chip IBS SRE 1A AUTOMATIONWORX User Manual UM EN IBS SRE 1A Order No.: 2888741 INTERBUS Register Expansion Chip IBS SRE 1A AUTOMATIONWORX User Manual INTERBUS Register Expansion Chip IBS SRE 1A 06/2006 Designation: Revision:

More information

CS-E Deep Learning Session 4: Convolutional Networks

CS-E Deep Learning Session 4: Convolutional Networks CS-E4050 - Deep Learning Session 4: Convolutional Networks Jyri Kivinen Aalto University 23 September 2015 Credits: Thanks to Tapani Raiko for slides material. CS-E4050 - Deep Learning Session 4: Convolutional

More information

Separating Cue Encoding From Target Processing in the Explicit Task- Cuing Procedure: Are There True Task Switch Effects?

Separating Cue Encoding From Target Processing in the Explicit Task- Cuing Procedure: Are There True Task Switch Effects? Journal of Experimental Psychology: Learning, Memory, and Cognition 2007, Vol. 33, No. 3, 484 502 Copyright 2007 by the American Psychological Association 0278-7393/07/$12.00 DOI: 10.1037/0278-7393.33.3.484

More information

SNJB College of Engineering Department of Computer Engineering

SNJB College of Engineering Department of Computer Engineering 1. Intel s programmable device (8253) facilitates the generation of accurate time delays. a) counter b) timer c) both a & b d) none of these 2. The programmable timer device (8253) contains three independent

More information

Organizing Data. Types of Distributions. Uniform distribution All ranges or categories have nearly the same value a.k.a. rectangular distribution

Organizing Data. Types of Distributions. Uniform distribution All ranges or categories have nearly the same value a.k.a. rectangular distribution Organizing Data Frequency How many of the data are in a category or range Just count up how many there are Notation x = number in one category n = total number in sample (all categories combined) Relative

More information

SUPPLEMENTARY INFORMATION

SUPPLEMENTARY INFORMATION doi:1.138/nature1216 Supplementary Methods M.1 Definition and properties of dimensionality Consider an experiment with a discrete set of c different experimental conditions, corresponding to all distinct

More information

Frequency Distributions

Frequency Distributions Frequency Distributions In this section, we look at ways to organize data in order to make it more user friendly. It is difficult to obtain any meaningful information from the data as presented in the

More information

A Correlation of. to the. Common Core State Standards for Mathematics Grade 1

A Correlation of. to the. Common Core State Standards for Mathematics Grade 1 A Correlation of 2012 to the Introduction This document demonstrates how in Number, Data, and Space 2012 meets the indicators of the,. Correlation references are to the unit number and are cited at the

More information

Signet 2850 Conductivity/Resistivity Sensor Electronics and Integral Systems

Signet 2850 Conductivity/Resistivity Sensor Electronics and Integral Systems Signet 2850 Conductivity/Resistivity Sensor Electronics and Integral Systems Features Certificate of calibration supplied with all sensors. Universal Mount Custom cell constant programmed into the electronics.

More information

MOCKTIME.COM ONLINE TEST SERIES CORRESPONDENCE COURSE

MOCKTIME.COM ONLINE TEST SERIES CORRESPONDENCE COURSE Simplification 1. Simplify : 1 + (a) 28/17 (b) 15/7 (c) 23/17 (d) 38/17 2. Evaluate: [ ] (a) 9/10 (b) -(8/17 ) (c) - (16/19 ) (d) 4/7 3. 45 [4- {3 - (3 3 6)}] is equal to : (a) 10 (b) 6 (c) 4 (d) 0 4.

More information

Administrative notes. Computational Thinking ct.cs.ubc.ca

Administrative notes. Computational Thinking ct.cs.ubc.ca Administrative notes March 14: Midterm 2: this will cover all lectures, labs and readings between Tue Jan 31 and Thu Mar 9 inclusive Practice Midterm 2 is on Exercises webpage: http://www.ugrad.cs.ubc.ca/~cs100/2016w2/

More information

Introduction to Qualitative Comparative Analysis (QCA)

Introduction to Qualitative Comparative Analysis (QCA) Introduction to Qualitative Comparative Analysis (QCA) Vaidas Morkevičius Policy and Public Administration Institute Kaunas University of Technology 2012 November 11, Riga Lecture 1 Basic introduction

More information

Introduction to Computational Neuroscience

Introduction to Computational Neuroscience Introduction to Computational Neuroscience Lecture 6: Single neuron models Lesson Title 1 Introduction 2 Structure and Function of the NS 3 Windows to the Brain 4 Data analysis I 5 Data analysis II 6 Single

More information

CRITERIA FOR USE. A GRAPHICAL EXPLANATION OF BI-VARIATE (2 VARIABLE) REGRESSION ANALYSISSys

CRITERIA FOR USE. A GRAPHICAL EXPLANATION OF BI-VARIATE (2 VARIABLE) REGRESSION ANALYSISSys Multiple Regression Analysis 1 CRITERIA FOR USE Multiple regression analysis is used to test the effects of n independent (predictor) variables on a single dependent (criterion) variable. Regression tests

More information

Engineering Development of the Valley VRI Package Agricultural Equipment Technology Conference

Engineering Development of the Valley VRI Package Agricultural Equipment Technology Conference Engineering Development of the Valley VRI Package 2013 Agricultural Equipment Technology Conference Discuss development of VRI Background Design Process Design Solutions Prescriptions Testing 2 The Ag

More information

AND BIOMEDICAL SYSTEMS Rahul Sarpeshkar

AND BIOMEDICAL SYSTEMS Rahul Sarpeshkar ULTRA-LOW-POWER LOW BIO-INSPIRED INSPIRED AND BIOMEDICAL SYSTEMS Rahul Sarpeshkar Research Lab of Electronics Massachusetts Institute of Technology Electrical Engineering and Computer Science FOE Talk

More information

Agents and Environments

Agents and Environments Agents and Environments Berlin Chen 2004 Reference: 1. S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Chapter 2 AI 2004 Berlin Chen 1 What is an Agent An agent interacts with its

More information

An Introduction to the CBS Health Cognitive Assessment

An Introduction to the CBS Health Cognitive Assessment An Introduction to the CBS Health Cognitive Assessment CBS Health is an online brain health assessment service used by leading healthcare practitioners to quantify and objectively 1 of 9 assess, monitor,

More information

Solve It Math A Regents Problems

Solve It Math A Regents Problems IMP Unit: Solve It Solve It Math A Regents Problems #4 / 2 pts 2 Which epression is a factor of + 2 15? (1) ( 3) (3) ( +15) (2) ( + 3) (4) ( 5) #11 / 2 pts If 9 + 2a = 3a 4, then equals (1) a (3) (2) a

More information

Common Core Standards: The New US Intended Curriculum

Common Core Standards: The New US Intended Curriculum Common Core Standards: The New US Intended Curriculum Andy Porter Jennifer McMaken Jun Hwang Rui Yang University of Pennsylvania Presented at the Brown Center on Education Policy at Brookings October 28,

More information

Signet 2850 Conductivity/Resistivity Sensor Electronics and Integral Systems with PVDF Sensor

Signet 2850 Conductivity/Resistivity Sensor Electronics and Integral Systems with PVDF Sensor Signet 2850 Conductivity/Resistivity Sensor Electronics and Integral Systems with PVDF Sensor Features Test certificate supplied with all sensors Universal Mount Custom cell constant programmed into the

More information

Question 1 Multiple Choice (8 marks)

Question 1 Multiple Choice (8 marks) Philadelphia University Student Name: Faculty of Engineering Student Number: Dept. of Computer Engineering First Exam, First Semester: 2015/2016 Course Title: Neural Networks and Fuzzy Logic Date: 19/11/2015

More information

The Do s and Don ts of Pressure Transducers

The Do s and Don ts of Pressure Transducers The Do s and Don ts of Pressure Transducers ABSTRACT When specifying a pressure transducer for a process measurement, a number of items have to be considered. Some of the more important ones are discussed

More information

IJREAS Volume 2, Issue 2 (February 2012) ISSN: LUNG CANCER DETECTION USING DIGITAL IMAGE PROCESSING ABSTRACT

IJREAS Volume 2, Issue 2 (February 2012) ISSN: LUNG CANCER DETECTION USING DIGITAL IMAGE PROCESSING ABSTRACT LUNG CANCER DETECTION USING DIGITAL IMAGE PROCESSING Anita Chaudhary* Sonit Sukhraj Singh* ABSTRACT In recent years the image processing mechanisms are used widely in several medical areas for improving

More information

Investigations in Number, Data, and Space, Grade 1, 2nd Edition 2008 Correlated to: Washington Mathematics Standards for Grade 1

Investigations in Number, Data, and Space, Grade 1, 2nd Edition 2008 Correlated to: Washington Mathematics Standards for Grade 1 Grade 1 Investigations in Number, Data, and Space, Grade 1, 2nd Edition 2008 1.1. Core Content: Whole number relationships (Numbers, Operations) 1.1.A Count by ones forward and backward from 1 to 120,

More information

CHAPTER 9 SUMMARY AND CONCLUSION

CHAPTER 9 SUMMARY AND CONCLUSION CHAPTER 9 SUMMARY AND CONCLUSION 9.1 SUMMARY In this thesis, the CAD system for early detection and classification of ischemic stroke in CT image, hemorrhage and hematoma in brain CT image and brain tumor

More information

Neural Coding. Computing and the Brain. How Is Information Coded in Networks of Spiking Neurons?

Neural Coding. Computing and the Brain. How Is Information Coded in Networks of Spiking Neurons? Neural Coding Computing and the Brain How Is Information Coded in Networks of Spiking Neurons? Coding in spike (AP) sequences from individual neurons Coding in activity of a population of neurons Spring

More information

Hoare Logic and Model Checking. LTL and CTL: a perspective. Learning outcomes. Model Checking Lecture 12: Loose ends

Hoare Logic and Model Checking. LTL and CTL: a perspective. Learning outcomes. Model Checking Lecture 12: Loose ends Learning outcomes Hoare Logic and Model Checking Model Checking Lecture 12: Loose ends Dominic Mulligan Based on previous slides by Alan Mycroft and Mike Gordon Programming, Logic, and Semantics Group

More information

SLHS 267 Spring 2014

SLHS 267 Spring 2014 SLHS 267 Spring 2014 Title of Course: ACOUSTICS FOR THE SPEECH AND HEARING SCIENCES Day & Time M,W,F, @ 9-9:50 a.m. Room & Building: Instructor: Room: Chemistry 134 James Dean, AuD Office SLHS 523 Office

More information

BIOL 458 BIOMETRY Lab 7 Multi-Factor ANOVA

BIOL 458 BIOMETRY Lab 7 Multi-Factor ANOVA BIOL 458 BIOMETRY Lab 7 Multi-Factor ANOVA PART 1: Introduction to Factorial ANOVA ingle factor or One - Way Analysis of Variance can be used to test the null hypothesis that k or more treatment or group

More information

CSE 599E Lecture 2: Basic Neuroscience

CSE 599E Lecture 2: Basic Neuroscience CSE 599E Lecture 2: Basic Neuroscience 1 Today s Roadmap The neuron doctrine (or dogma) Neuronal signaling The electrochemical dance of ions Action Potentials (= spikes) Synapses and Synaptic Plasticity

More information