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School: Stanford
Course: The Fourier Transform And Its Applications
EE261 Raj Bhatnagar Summer 2009-2010 EE 261 The Fourier Transform and its Applications Midterm Examination 19 July 2010 (a) This exam consists of 4 questions with 12 total subparts for a total of 50 points. (b) The questions dier in length and diculty. Do
School: Stanford
EE 284 F. Tobagi Autumn 2010-2011 EE284 Homework Assignment No. 1 Topic: Switching Techniques, Network Topologies Handed out: September 21, 2010 Due: September 30, 2010 in class (Previously September 28 but now extended by 2 days) Total Points: 45 ALL WOR
School: Stanford
Course: Introduction To Digital Communication
EE279 Introduction to Digital Communication Handout 13 Solutions to Homework 5 Stanford University Due February 19, 2014 Problem 1. (Average Energy of PAM). (2,2,2 marks) m Solution 1. (a) The pdf of S can be written as fS (s) = i2 m +1 (s (2i 1)a) while
School: Stanford
EE116 Spr13 Summary of Apr. 30th and May 2nd Lectures Yang Liu In these two classes, we studied the following topics: PN Junction Formation: A PN junction is formed by contacted p-type and n-type regions. To understand its band-diagram, we may consider a
School: Stanford
Course: Convex Optimization
function s = cvx_where %CVX_WHERE Returns the location of the CVX system. % CVX_WHERE returns a string containing the base directory of the CVX % modeling framework. Within that directory are some useful % subdirectories and files: % functions/ new functi
School: Stanford
Course: Convex Optimization
function [ sout, slist ] = cvx_solver( sname ) %CVX_SOLVER CVX solver selection. % CVX_SOLVER <solver_name> or CVX_SOLVER('<solver_name>') % selects the named solver the CVX uses to solve models. The solver name % is case-insensitive; so, for example, bot
School: Stanford
Course: Convex Optimization
function sout = cvx_solver_settings( varargin ) %CVX_SOLVER_SETTINGS CVX solver settings adjustment. % CVX_SOLVER_SETTINGS is used to adjust the advaned settings of the % current solver being used by CVX. Before using this function, please % read the IM
School: Stanford
Course: Convex Optimization
function cvx_save_prefs( in_setup ) %CVX_SAVE_PREFS Saves current CVX settings for future MATLAB sessions. % CVX_SAVE_PREFS saves the the current global CVX settings to a special % prefences file (stored in the "prefdir" directory). This enables CVX to %
School: Stanford
Course: Convex Optimization
function sout = cvx_quiet( flag ) %CVX_QUIET CVX output control. % CVX_QUIET(TRUE) suppresses all text output from CVX (except for error and % warning messages). Specifically, solver progress is not printed. % % CVX_QUIET(FALSE) restores full text output.
School: Stanford
Course: Convex Optimization
function sout = cvx_profile( flag ) % CVX_PROFILE CVX-specific profiler control. % This is a function used for internal CVX development to help determine % performance limits within the CVX code itself, by turning off the profiler % when the solver is be
School: Stanford
Course: Principles And Models Of Semiconductor Devices
Esquire Magazine, December 1983, pp. 346-374. America is today in the midst of a great technological revolution. With the advent of the silicon chip, information processing, communications, and the national economy have been strikingly altered. The new te
School: Stanford
Course: On Achievability Via Random Binning
1 On Achievability via Random Binning Ritesh Kolte, Kartik Venkat cfw_rkolte, kvenkat@stanford.edu AbstractIn [1], the authors present a novel tool to establish achievability results in network information theoretic problems. The main idea is to study a s
School: Stanford
Course: Semiconductor Optoelectronic Devices
1/10/12 EE243 Semiconductor Optoelectronic Devices ! Prof. James Harris! Room 328, Paul Allen Center for Integrated Systems (CISX)! ! Harris@snow.stanford.edu! Web Page - http:/ee.stanford.edu/~harris! (650) 723-9775, (650) 723-4659 fax! Ofce Hours 2: 05
School: Stanford
Course: Analog Integrated Circuit Design
Lecture 6 Design Example 2 Extrinsic Capacitance Boris Murmann Stanford University murmann@stanford.edu Copyright 2004 by Boris Murmann B. Murmann EE 214 Lecture 6 (HO#9) 1 Overview Reading 1.6.7 (Parasitic Elements) 7.1, 7.2.0, 7.2.1 (Mille
School: Stanford
Course: Analog Integrated Circuit Design
Lecture 24 kT/C Noise Boris Murmann Stanford University murmann@stanford.edu Copyright 2004 by Boris Murmann B. Murmann EE 214 Lecture 24 (HO#32) 1 Overview Introduction Having established the basic noise mechanisms in MOSFETS, today's lectur
School: Stanford
Course: Principles And Models Of Semiconductor Devices
EE 216: Principles and Models of Semiconductor Devices Lecture 16 Silicon bonding Si (14) - 1s2 2s2 2p6 3s23p2 1s2 2s2 2p6 3s13px13py13pz1 4 valence electrons, covalent bonding between atoms 1 More than two atoms Interactions of valence orbitals produce
School: Stanford
Course: Principles And Models Of Semiconductor Devices
EE 216: Principles and Models of Semiconductor Devices Lecture 15 Junctions and devices under study Metal-semiconductor junction & diode PN junction & diode Bipolar junction transistor Photonic devices: LED, solar cell, photodiode MOS junction and capacit
School: Stanford
Course: Principles And Models Of Semiconductor Devices
EE 216: Principles and Models of Semiconductor Devices Lecture 14 Reading Pierret, pg. 691 710 Dennard Scaling Paper (optional) Junctions and devices under study Metal-semiconductor junction & diode PN junction & diode Bipolar junction transistor Photonic
School: Stanford
Course: The Fourier Transform And Its Applications
EE261 Raj Bhatnagar Summer 2009-2010 EE 261 The Fourier Transform and its Applications Midterm Examination 19 July 2010 (a) This exam consists of 4 questions with 12 total subparts for a total of 50 points. (b) The questions dier in length and diculty. Do
School: Stanford
Course: Principles And Models Of Semiconductor Devices
EE 216 FINAL EXAM Duration: 3 hours Fall 2008 Total Score: 200; #Problems = 8 Make sure to STATE ALL ASSUMPTIONS you make. The following values may be helpful: Ge EG = 0.66 eV at T = 300K Si EG = 1.12 eV at T = 300K NC (Si) = 3 x 1019 cm-3 NV (Si) = 2 x 1
School: Stanford
Course: Principles And Models Of Semiconductor Devices
c hv.jz d u e I+"1- e lec<i. cfw_ ra/ - v o l t e-19 f de '77 = *r" tr = erLlpJX J e=-# o( V = - leax ("/ q<o.bJic- fu) q I 'lea uo, l " P " 6 r^x v lN lr"u p-tL Q"wJ- conv,cts (q) tlr Qa @e Fy'h,-r. " .^*oo b/u Sr X AI , ^,.,- ^ r. lr, + h-rn "- " o", t
School: Stanford
Course: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2011 Solutions to Midterm Exam 1 1. (10 points) Multiplying periodic functions Let f (t) and g (t) be periodic functions with period 1 and Fourier series expansions given by n= an ei2nt , f (t) = n= n
School: Stanford
EE263 Dec. 56 or Dec. 67, 2008. Prof. S. Boyd Final exam This is a 24 hour take-home nal exam. Please turn it in at Bytes Cafe in the Packard building, 24 hours after you pick it up. Please read the following instructions carefully. You may use any books,
School: Stanford
EE 284 F. Tobagi Autumn 2010-2011 EE284 Homework Assignment No. 1 Topic: Switching Techniques, Network Topologies Handed out: September 21, 2010 Due: September 30, 2010 in class (Previously September 28 but now extended by 2 days) Total Points: 45 ALL WOR
School: Stanford
Course: Introduction To Digital Communication
EE279 Introduction to Digital Communication Handout 13 Solutions to Homework 5 Stanford University Due February 19, 2014 Problem 1. (Average Energy of PAM). (2,2,2 marks) m Solution 1. (a) The pdf of S can be written as fS (s) = i2 m +1 (s (2i 1)a) while
School: Stanford
Course: Digital MOS Integrated Circuits
EE313 Winter 2009-10 J. Kim & M. Horowitz page 1 of 8 SOLUTIONS TO HOMEWORK #2 1. Logical Effort simulations (20 points) The spice deck and Virtuoso schematics /usr/class/ee313/HW2/sol. for this problem can be found in: Delay is measured as the average of
School: Stanford
Course: Introduction To Digital Communication
EE279 Introduction to Digital Communication Handout Solutions to Homework 3 Stanford University Due January 29, 2014 Problem 1. (Artifacts of Suboptimality) Let H take on the values 0 and 1 equiprobably. Conditional on H = 0, the observation Y is N (1, 2
School: Stanford
Course: Circuits I
EE101A/Winter 2013 Prof. Simon Wong Homework #2 (Due Wednesday, 1/23/13) 1. Determine the equivalent resistance measured between the two terminals if all resistors are 1K. (This is a 2D hexagon, NOT a 3D cube.) R =? 2. Use Nodal Analysis to determine the
School: Stanford
Course: EE - Digital CMOS Integrated Circuits
Custom WaveView User Guide Version F-2011.09-SP1, December 2011 Copyright Notice and Proprietary Information Copyright 2011 Synopsys, Inc. All rights reserved. This software and documentation contain confidential and proprietary information that is the pr
School: Stanford
Course: EE - Digital CMOS Integrated Circuits
EE213 Winter 2014-15 M. Horowitz page 1 of 15 VIRTUOSO TUTORIAL Introduction Before starting on this tutorial, please read the first few paragraphs of HW#2 which provide instructions on creating the proper working directory and sourcing the correct files.
School: Stanford
Course: EE - Digital CMOS Integrated Circuits
HSPICE Toolbox for MATLAB Michael Perrott (perrott@mtl.mit.edu) Copyright 1999 by Silicon Laboratories, Inc. 7 October 1999 The Hspice toolbox for Matlab is a collection of Matlab routines that allow you to manipulate and view signals generated by Hspice
School: Stanford
Course: Advanced Analog Integrated Circuit Design
CAD BASICS STANFORD UNIVERSITY Department of Electrical Engineering EE114/EE214A & EE214B Revised: January 2015 1 About This Handout This tutorial is composed of two parts. The first part is a quick start in which you will go through all the steps you nee
School: Stanford
Course: Advanced Analog Integrated Circuit Design
EE214B Winter 2014-15 B. Murmann Page 1 of 7 DESIGN PROJECT Part I due on Monday, March 2, 2015, 5pm Part II due on Wednesday, March 11, 2015, noon Overview In this project you will work on the design of the wideband transimpedance amplifier shown in Figu
School: Stanford
PRELAB 3 MORE OP-AMP CIRCUITS! If you cant fix it, make it a feature. Anonymous OBJECTIVES (Why am I doing this prelab?) To gain insight into op-amp application circuits beyond those considered in Lab 2. To understand the basics of analog filters. To u
School: Stanford
Course: Solid State Physics II
TutorialonPC1D MohitMehta ProgramDescription PC1Dsolvesthefullycouplednonlinearequationsforthe quasi1dtransportofelectrons&holesincrystalline semiconductordevices,withemphasisonphotovoltaic devices. OnlyfilerequiredtoruntheprogramisPC1D.exe. PC1D.hlppro
School: Stanford
Course: Fundamentals Of Analog Integrated Circuit Design
EE114/ 214A Review Session 2 Simon Basilico and Yaoyu Tao Stanford University taoyaoyu@stanford.edu basilico@stanford.edu A. Arbabian, R. Dutton, B. Murmann EE 114/214A 1 Important Announcements Start HW2 as soon as possible as it requires HSpice setup a
School: Stanford
Course: Fundamentals Of Analog Integrated Circuit Design
EE114/ 214A Review Session 1 Jayant Charthad Stanford University jayantc@stanford.edu A. Arbabian, R. Dutton, B. Murmann EE 114/214A 1 Important Announcements Please make sure you are enrolled on the course website and you are getting course announcement
School: Stanford
Course: Introduction To Computer Networks
EE 284 F. Tobagi Autumn 2014-2015 EE284 Review Session #7 Topics: Reservation ALOHA, Slotted ALOHA, Performance, CSMA/CD November 7, 2014 1 Reservation ALOHA In Reservation ALOHA. Packets belonging to the same message do not contend for the channel on the
School: Stanford
Course: Introduction To Computer Networks
EE 284 F. Tobagi Autumn 2014-2015 EE284 Review Session No. 10 Topic: TCP December 5, 2014 Problem 1: TCP Consider two hosts A and B that have data to be exchanged using the Transmission Control Protocol (TCP). In this problem, we will assume that the prop
School: Stanford
Course: Introduction To Computer Networks
EE 284 F. Tobagi Autumn 2014-2015 EE284 Review Session No. 9 Topic: Internetworking between Bridges and Routers, Virtual circuit routing November 21, 2014 Problem 1: Internetworking between Bridges and Routers Segment 3 D C 2 ROUTER 171.1.2.12 AA:BB:CC:DD
School: Stanford
Course: Probabilistic System Analysis
EE178 Introductory lecture Monday, September 26, 2011 Outline EE178 Probability Goals Topics Administrative stuff Monday, September 26, 2011 what is EE178/278A? probability + statistics + EE examples ~ Stat 116, Math 151 important background for E
School: Stanford
Course: Introduction To Computer Networks
Course Administration EE284 Introduction to Computer Networks Instructor: Professor Fouad Tobagi Gates 339 Telephone: 650-723-1708 E-mail: tobagi@stanford.edu Office hours: TBD Teaching Assistant: Bhrugurajsinh Chudasama E-mail: bhrugu@stanford.edu EE2
School: Stanford
Course: Optical Micro- And Nano-cavities
EE340: Optical micro- and nano-cavities Instructor: Jelena Vuckovic Spring 2012 Syllabus (tentative) Part 1 Introduction to optical resonators Lossless hollow rectangular resonator Losses in a resonator. Quality (Q) factor of a resonator Finesse, free-
School: Stanford
Course: Optical Micro- And Nano-cavities
EE340: Optical micro- and nano-cavities Instructor: Jelena Vuckovic Spring 2011 Mon Wed Fri 10 - 10:50 am Classroom: Y2E2 111 Class web-site http:/www.stanford.edu/class/ee340 (lecture notes and assignments are posted on the coursework portion of the clas
School: Stanford
Handout #2 March 28, 2011 CS103 Robert Plummer CS103 Syllabus Date Day Lecture # Topic PS Due Reading I. Logic, Sets, Relations, and Functions (8 lectures) 3/28 M 1 Intro, propositional logic, truth tables equivalences, De Morgan's Laws 3/30 W 2 Predicate
School: Stanford
Course: The Fourier Transform And Its Applications
EE261 Raj Bhatnagar Summer 2009-2010 EE 261 The Fourier Transform and its Applications Midterm Examination 19 July 2010 (a) This exam consists of 4 questions with 12 total subparts for a total of 50 points. (b) The questions dier in length and diculty. Do
School: Stanford
EE 284 F. Tobagi Autumn 2010-2011 EE284 Homework Assignment No. 1 Topic: Switching Techniques, Network Topologies Handed out: September 21, 2010 Due: September 30, 2010 in class (Previously September 28 but now extended by 2 days) Total Points: 45 ALL WOR
School: Stanford
Course: Introduction To Digital Communication
EE279 Introduction to Digital Communication Handout 13 Solutions to Homework 5 Stanford University Due February 19, 2014 Problem 1. (Average Energy of PAM). (2,2,2 marks) m Solution 1. (a) The pdf of S can be written as fS (s) = i2 m +1 (s (2i 1)a) while
School: Stanford
EE116 Spr13 Summary of Apr. 30th and May 2nd Lectures Yang Liu In these two classes, we studied the following topics: PN Junction Formation: A PN junction is formed by contacted p-type and n-type regions. To understand its band-diagram, we may consider a
School: Stanford
Course: Principles And Models Of Semiconductor Devices
EE 216 FINAL EXAM Duration: 3 hours Fall 2008 Total Score: 200; #Problems = 8 Make sure to STATE ALL ASSUMPTIONS you make. The following values may be helpful: Ge EG = 0.66 eV at T = 300K Si EG = 1.12 eV at T = 300K NC (Si) = 3 x 1019 cm-3 NV (Si) = 2 x 1
School: Stanford
Course: Principles And Models Of Semiconductor Devices
c hv.jz d u e I+"1- e lec<i. cfw_ ra/ - v o l t e-19 f de '77 = *r" tr = erLlpJX J e=-# o( V = - leax ("/ q<o.bJic- fu) q I 'lea uo, l " P " 6 r^x v lN lr"u p-tL Q"wJ- conv,cts (q) tlr Qa @e Fy'h,-r. " .^*oo b/u Sr X AI , ^,.,- ^ r. lr, + h-rn "- " o", t
School: Stanford
Course: Digital MOS Integrated Circuits
EE313 Winter 2009-10 J. Kim & M. Horowitz page 1 of 8 SOLUTIONS TO HOMEWORK #2 1. Logical Effort simulations (20 points) The spice deck and Virtuoso schematics /usr/class/ee313/HW2/sol. for this problem can be found in: Delay is measured as the average of
School: Stanford
Course: Introduction To Digital Communication
EE279 Introduction to Digital Communication Handout Solutions to Homework 3 Stanford University Due January 29, 2014 Problem 1. (Artifacts of Suboptimality) Let H take on the values 0 and 1 equiprobably. Conditional on H = 0, the observation Y is N (1, 2
School: Stanford
Course: Circuits I
EE101A/Winter 2013 Prof. Simon Wong Homework #2 (Due Wednesday, 1/23/13) 1. Determine the equivalent resistance measured between the two terminals if all resistors are 1K. (This is a 2D hexagon, NOT a 3D cube.) R =? 2. Use Nodal Analysis to determine the
School: Stanford
Course: Introduction To VLSI Systems
EE271 Introduction to VLSI Design Subhasish Mitra Computer Systems Laboratory Stanford University subh@stanford.edu Copyright 2011 by Subhasish Mitra, With significant contributions from Mark Horowitz, Don Stark, and Azita Emami SM EE271 Lecture 1 Notes o
School: Stanford
Course: Principles And Models Of Semiconductor Devices
2-The FundamentalsEnergy Bands in Semiconductors ! I. Crystal Structures Solids can be classied as: A. Crystalline - three dimensional long range order of atoms; repeating "unit cell". Examples: Si wafer, diamond, GaAs, ZnSe. B. Polyc
School: Stanford
Course: Integrated Circuit Fabrication Processes
EE 212 FALL 09-10 HOMEWORK ASSIGNMENT #3 ASSIGNED: THURSDAY OCT. 15 DUE: THURSDAY OCT. 22 SOLUTION SHEET #1. An experimental DUV resist has a contrast of 5. It is being used with a projection imaging system that produces the aerial image shown below. Will
School: Stanford
Course: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2012 Solutions to Problem Set Four 1. (10 points) Solving the wave equation An innite string is stretched along the x-axis and is given an initial displacement described by a function f (x). It is the
School: Stanford
Course: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2012 Problem Set Eight Due Wednesday, November 28 1. (20 points) A True Story : Professor Osgood and a graduate student were working on a discrete form of the sampling theorem. This included looking a
School: Stanford
Course: Stochastic Control
EE365, Spring 2011-12 Professors S. Boyd, S. Lall, and B. Van Roy EE365 / MS&E251 Homework 5 Solutions 1. A rened inventory model. We consider an inventory model that is more rened than the one youve seen in the lectures. The amount of inventory at time t
School: Stanford
Course: Stochastic Control
EE365, Spring 2011-12 Professors S. Boyd, S. Lall, and B. Van Roy EE365 / MS&E251 Homework 5 1. A rened inventory model. We consider an inventory model that is more rened than the one youve seen in the lectures. The amount of inventory at time t is denote
School: Stanford
Course: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2011 Solutions to Midterm Exam 1 1. (10 points) Multiplying periodic functions Let f (t) and g (t) be periodic functions with period 1 and Fourier series expansions given by n= an ei2nt , f (t) = n= n
School: Stanford
Course: Digital MOS Integrated Circuits
EE313 Winter 09/10 J. Kim & M. Horowitz Handout # Page 1 of 6 HOMEWORK #2 (Due: Wednesday Jan. 27th, 2010; in class) 1. Logical Effort simulations In this problem, you will use HSPICE to measure the logical effort of a few different kinds of gates. Accord
School: Stanford
EE263 Dec. 56 or Dec. 67, 2008. Prof. S. Boyd Final exam This is a 24 hour take-home nal exam. Please turn it in at Bytes Cafe in the Packard building, 24 hours after you pick it up. Please read the following instructions carefully. You may use any books,
School: Stanford
Course: Introduction To Statistical Signal Processing
EE 278B Statistical Signal Processing October 20, 2011 Handout #6 Homework #4 Due Thursday, October 27 1. Coloring and whitening. Let 210 = 1 2 1 . 012 a. Find the coloring and whitening matrices of using the eigenvalue method discussed in lecture slides
School: Stanford
Course: Basic Physics For Solid State Electronics
1. Semiconductor carrier statistics (40 points) Consider a semiconductor with a face-centered cubic lattice and with cubic symmetry. The valence band has a maximum at with an energy E = 0 and with an effective mass m0 = me. (me is the mass of a free elect
School: Stanford
Course: Introduction To Statistical Signal Processing
EE 278 Statistical Signal Processing Homework #8 Due: Wednesday, December 2 November 18, 2009 Handout #18 1. Discrete-time Wiener process. Let cfw_Zn : n 0 be a discrete-time white Gaussian noise process; that is, Z1 , Z2 , Z3 , . . . are i.i.d. N (0, 1).
School: Stanford
Course: Digital MOS Integrated Circuits
EE313 Winter 13/14 M. Horowitz Page 1 of 19 EE313 - PROJECT PART 1 (Due: Wed, Feb 26th, 2014; in class) In the first part of the project, you will design and implement the basic components of the memory system in Spice: - the decoder, the sense amp, and t
School: Stanford
Course: Integrated Circuit Fabrication Processes
EE 212 FALL 09-10 HOMEWORK ASSIGNMENT #2 ASSIGNED: THURSDAY OCT. 1 DUE: THURSDAY OCT. 8 SOLUTION SHEET Reading Assignment: Chapters 3 and 4 in the text. #1. Spend 30 min or so scanning the information in the 2007 ITRS Front End Processes (on the class web
School: Stanford
Course: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2012 Problem Set Four Due Wednesday, October 24 1. (10 points) Solving the wave equation An innite string is stretched along the x-axis and is given an initial displacement described by a function f (
School: Stanford
Course: RF Integrated Circuit Design
EE314: CMOS RF Integrated Circuit Design Introduction to Electrical Oscillators Stanford University Shwetabh Verma Hamid Rategh S. Verma/H. Rategh Stanford University Oscillators 1 What Are Oscillators (good for) ? Convert energy at DC to RF. Feedback sys
School: Stanford
Course: Introduction To Statistical Signal Processing
EE 278B Statistical Signal Processing October 25, 2011 Handout #7 Homework #3 Solutions 1. (15 points) Estimation vs. detection. a. We can easily nd the piecewise constant density of Y 1 |y | 1 4 1 fY (y ) = 8 1 < |y | 3 0 otherwise The conditional proba
School: Stanford
Course: Introduction To Statistical Signal Processing
EE 278B Statistical Signal Processing October 18, 2011 Handout #5 Homework #2 Solutions 1. (5 points) First available teller. The tellers service times are exponentially distributed, hence memoryless. Thus the service time distribution does not depend on
School: Stanford
Course: Semiconductor Optoelectronic Devices
1/10/12 EE243 Semiconductor Optoelectronic Devices ! Prof. James Harris! Room 328, Paul Allen Center for Integrated Systems (CISX)! ! Harris@snow.stanford.edu! Web Page - http:/ee.stanford.edu/~harris! (650) 723-9775, (650) 723-4659 fax! Ofce Hours 2: 05
School: Stanford
EE 284 F. Tobagi Autumn 2010-2011 EE284 Homework Assignment No. 1 SOLUTIONS Total Points: 45 Problem 1 (Answer, 10 points): The number of packets needed to send the message of M bits equals: k= M P k - 1 packets have P bits of data and the last one contai
School: Stanford
Course: Introduction To Digital Image Processing
EE 168 Introduction to Digital Image Processing Handout #32 March 7, 2012 HOMEWORK 7 SOLUTIONS Problem 1: Color Wheels We can represent an N x N color image by a three-dimensional array such that the first two dimensions are of size N each, and the third
School: Stanford
Fall 2012 EE 292L Nanomanufacturing Problem Set 3 Solution (135 Points + 20 Bonus) Multiple-Choice Questions (2 points x 20 = 40 points) You may need to choose more than one option. 1. A , D 2. A 3. B 4. B, C 5. B 6. D 7. B 8. B 9. B 10. A, B, C, D 11. D
School: Stanford
Course: The Fourier Transform And Its Applications
EE 261 The Fourier Transform and its Applications Fall 2009 Solutions to Problem Set One 1. Some practice with geometric sums and complex exponentials (5 points each) Well make much use of formulas for the sum of a geometric series, especially in combinat
School: Stanford
Course: EE314
EE214B Bipolar Junction Transistors Handout #3 B. Murmann Stanford University Winter 2012-13 Textbook Sections: 8.18.4, 8.6 History Bardeen, Brattain, and Shockley, 1947 W. Brinkman, D. Haggan, and W. Troutman, A history of the invention of the transistor
School: Stanford
Course: Introduction To Statistical Signal Processing
EE 278B Statistical Signal Processing Thursday, November 17, 2011 Handout #16 Homework #7 Due Thursday, December 1 1. Autocorrelation functions. Find the autocorrelation functions of a. the process X (t) = At + B of problem 2 in homework 6. b. the process
School: Stanford
Course: INTRODUCTION TO LINEAR DYNAMIC SYSTEM
EE 261 The Fourier Transform and its Applications Fall 2011 Solutions to Problem Set Three 1. (5 points) Equivalent width: Still another reciprocal relationship The equivalent width of a signal f (t), with f (0) = 0, is the width of a rectangle having hei
School: Stanford
Course: The Fourier Transform And Its Applications
EE 261 The Fourier Transform and its Applications Fall 2009 Solutions to Problem Set Two 1. (10 points) A famous sum You cannot go through life knowing about Fourier series and not know the application to evaluating a very famous sum. Let S (t) be the saw
School: Stanford
Course: Introduction To Linear Dynamical Systems
EE263 Prof. S. Boyd EE263 homework 8 additional exercise 1. Some simple matrix inequality counter-examples. (a) Find a (square) matrix A, which has all eigenvalues real and positive, but there is a vector x for which xT Ax < 0. (Give A and x, and the eige
School: Stanford
EE243 Winter 2014 Homework 7 J.S.Harris EE 243 Homework 7 Due Thursday, March 6, 2014, at 5 pm, CISX 329 1. Reflective modulator (30 points) We are designing a reflective quantum well modulator, desiring to achieve the maximum contrast ratio with the foll
School: Stanford
Course: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2012 Problem Set Eight Due Wednesday, November 28 1. (20 points) A True Story : Professor Osgood and a graduate student were working on a discrete form of the sampling theorem. This included looking a
School: Stanford
Course: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2012 Problem Set Three Due Wednesday, October 17, 2012 1. (5 points) Equivalent width: Still another reciprocal relationship The equivalent width of a signal f (t), with f (0) = 0, is the width of a r
School: Stanford
Course: EE314
EE214B Feedback Circuits Part I Handout #8 B. Murmann Stanford University Winter 2012-13 Textbook Sections: 5.1, 5.2, 5.3, 5.4 Discrete Feedback Circuits Using General Purpose OpAmps B. Murmann EE214B Winter 2012-13 HO8 2 Properties of General Purpose OpA
School: Stanford
Course: The Fourier Transform And Its Applications
EE261 Raj Bhatnagar Summer 2010-2011 EE 261 The Fourier Transform and its Applications Problem Set 1 Due Wednesday, June 29 1. (10 points) Some practice with complex numbers (a) Express the following numbers in polar form: (i) (ii) (iii) (iv) (b) For (i)
School: Stanford
Course: Introduction To Statistical Signal Processing
EE 278 Statistical Signal Processing Homework #7 Solutions November 20, 2009 Handout #19 1. Convergence examples. Consider the following sequences of random variables dened on the probability space (, F , P), where = cfw_0, 1, . . . , m 1, F is the collec
School: Stanford
Course: Information Theory
EE376A: Homeworks #6 Solutions 1. Cascaded BSCs. Consider the two discrete memoryless channels (X , p1 (y |x), Y ) and (Y , p2 (z |y ), Z ). Let p1 (y |x) and p2 (z |y ) be binary symmetric channels with crossover probabilities 1 and 2 respectively. 1 1 0
School: Stanford
Course: Introduction To Communication Systems
EE 279 Professor Cox Solution to Final 1. (12pt) a) ii) b) i) iii) c) i) iv) d) vi) 2. (35pt) t Winter 2005-2006 HO # In phase-acceleration modulation we have: f (t ) = f c + K ! x(" )d" . Therefore to recover the signal we should extract the phase
School: Stanford
Course: Analog Integrated Circuit Design
EE214 Winter 04/05 Page 1 of 1 HOMEWORK #2 Solutions (Due: Monday, October 11, 2004, noon PT) 1. Use Spice to simulate gm/ID vs. VOV, (e.g. as shown on slides 3 and 4 of lecture 4). a) Generate a plot of gm/ID for EE214 NMOS devices with L=0.35m and
School: Stanford
Course: Stochastic Control
EE365, Spring 2011-12 Professors S. Boyd, S. Lall, and B. Van Roy EE365 / MS&E251 Homework 3 Solutions 1. Total-variation distance. The total variation distance between distributions and is given by dTV (, ) = max Prob(E ) Prob(E ) , E X i.e., the maximu
School: Stanford
Course: EE314
EE214B Feedback Circuits Part II Handout #9 B. Murmann Stanford University Winter 2012-13 Textbook Sections: 5.4, 9.4.4 Motivating Example: TIA for High-Speed Optical Networks Transimpedance gain 1800 Bandwidth 34 GHz Input noise 25 Maximum input 1.3 mAp
School: Stanford
Course: Digital MOS Integrated Circuits
EE313 Winter 09/10 J. Kim & M. Horowitz Handout # Page 1 of 5 HOMEWORK #3 (Due: Wednesday, Feb. 4, 2009: in class) 1. HSPICE Simulation for Velocity Saturated Model In the lectures, we learned many short channel effects in MOS transistors. In this problem
School: Stanford
EE 261 Fourier Transform and Applications March 17, 2011 Handout #21 Final Examination Solutions 1. (15 points) Fourier series. A function f (t) with period 1 has the Fourier series coecients n 1 n<0 2 cn = 0 n=0 1n 2 n>0 These Fourier series coecients
School: Stanford
Course: The Fourier Transform And Its Applications
EE261 Raj Bhatnagar Summer 2010-2011 EE 261 The Fourier Transform and its Applications Problem Set 3 Due Wednesday 13 July 1. (15 points) Convolution and cross-correlation The cross-correlation (sometimes just called correlation) of two real-valued signal
School: Stanford
Course: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2011 Final Exam December 15, 2011 Notes: There are eight questions for a total of 140 points Be sure to write your name (neatly) on your exam booklet(s) Write all your answers in your exam booklets Wh
School: Stanford
Course: VLSI Signal Conditioning Circuits
Lecture 7 Switched Capacitor Circuit Examples and Analysis Corrections: 5/4: Slide 32: Typo in last equation 6/13: Slide 31: beta/gm -> 1/(gm*beta) Boris Murmann Stanford University murmann@stanford.edu Copyright 2006 by Boris Murmann B. Murmann EE 315 Le
School: Stanford
Course: Convex Optimization I
EE364a, Winter 2011-12 Prof. S. Boyd EE364a Homework 4 solutions 5.27 Equality constrained least-squares. Consider the equality constrained least-squares problem minimize Ax b 2 2 subject to Gx = h where A Rmn with rank A = n, and G Rpn with rank G = p. G
School: Stanford
Course: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2012 Midterm Exam October 31, 2012 There are ve questions for a total of 85 points. Please write your answers in the exam booklet provided, and make sure that your answers stand out. Dont forget to
School: Stanford
Course: Introduction To Statistical Signal Processing
EE 278B Statistical Signal Processing October 13, 2011 Handout #4 Homework #3 Due Thursday, October 20 1. Estimation vs. detection. Signal X and noise Z are independent random variables, where X= +1 with probability 1 with probability 1 2 1 , 2 and Z U[2,
School: Stanford
Course: INTRO TO ANALOG DESIGN
Lecture 5 EE 114/214A Lecture 5 Gain and Biasing Considerations Finite Output Resistance R. Dutton, B. Murmann Stanford University R. Dutton, B. Murmann EE114/214A 1 Common Source Amplifier Revisited Interesting question How much voltage gain can we get
School: Stanford
Course: Convex Optimization
function s = cvx_where %CVX_WHERE Returns the location of the CVX system. % CVX_WHERE returns a string containing the base directory of the CVX % modeling framework. Within that directory are some useful % subdirectories and files: % functions/ new functi
School: Stanford
Course: Convex Optimization
function [ sout, slist ] = cvx_solver( sname ) %CVX_SOLVER CVX solver selection. % CVX_SOLVER <solver_name> or CVX_SOLVER('<solver_name>') % selects the named solver the CVX uses to solve models. The solver name % is case-insensitive; so, for example, bot
School: Stanford
Course: Convex Optimization
function sout = cvx_solver_settings( varargin ) %CVX_SOLVER_SETTINGS CVX solver settings adjustment. % CVX_SOLVER_SETTINGS is used to adjust the advaned settings of the % current solver being used by CVX. Before using this function, please % read the IM
School: Stanford
Course: Convex Optimization
function cvx_save_prefs( in_setup ) %CVX_SAVE_PREFS Saves current CVX settings for future MATLAB sessions. % CVX_SAVE_PREFS saves the the current global CVX settings to a special % prefences file (stored in the "prefdir" directory). This enables CVX to %
School: Stanford
Course: Convex Optimization
function sout = cvx_quiet( flag ) %CVX_QUIET CVX output control. % CVX_QUIET(TRUE) suppresses all text output from CVX (except for error and % warning messages). Specifically, solver progress is not printed. % % CVX_QUIET(FALSE) restores full text output.
School: Stanford
Course: Convex Optimization
function sout = cvx_profile( flag ) % CVX_PROFILE CVX-specific profiler control. % This is a function used for internal CVX development to help determine % performance limits within the CVX code itself, by turning off the profiler % when the solver is be
School: Stanford
Course: Convex Optimization
function sout = cvx_precision( flag ) %CVX_PRECISION Controls CVX solver precision. % The CVX_PRECISION command controls the precision-related stopping criteria % for the numerical solver. Up to 3 precision levels can be specified: % 0 <= PBEST <= PHIGH <
School: Stanford
Course: Convex Optimization
function sout = cvx_power_warning( flag ) %CVX_POWER_WARNING Controls the CVX warning message for x.^p expressions. % CVX converts power functions like x.^p, for variable x and fixed p, into % solvable form using an SOCP transformation. For quadratics x.^
School: Stanford
Course: Convex Optimization
function sout = cvx_pause( flag ) %CVX_PAUSE Pauses the processing of CVX models. % CVX_PAUSE(TRUE) instructs CVX to pause and wait for user keypress before % and after proceeding with the numerical solution of a model. The pauses % occur within the CVX_E
School: Stanford
Course: Convex Optimization
function cvx_expert( flag ) %CVX_EXPERT CVX expert mode. % CVX_EXPERT(TRUE) enables certain feature of CVX that have not yet been % announced to the general audience due to insufficient testing. % Specifically, CVX_EXPERT(TRUE) enables the use of successi
School: Stanford
Course: Convex Optimization
function cvx_end %CVX_END Completes a cvx specification. % CVX_BEGIN marks the end of a new cvx model, and instructs cvx to % complete its processing. For standard, complete models, cvx will send % a transformed version of the problem to a solver to obtai
School: Stanford
Course: Convex Optimization
% CVX_CLEAR Clears all active CVX data. % CVX_CLEAR clears the current CVX model in progress. This is useful if, for % example, you have made an error typing in your model and wish to start % over. Typing this before entering another CVX_BEGIN again avoi
School: Stanford
Course: Convex Optimization
function cvx_begin( varargin ) %CVX_BEGIN Starts a new CVX specification. % CVX_BEGIN marks the beginning of a new CVX model. Following this command % may be variable declarations, objective functions, and constraints, and % a CVX_END to mark the completi
School: Stanford
Course: Convex Optimization
% CVX: Top-level commands to control CVX. % % cvx_begin - Starts a new CVX specification. % cvx_clear - Clears all active CVX data. % cvx_end - Completes a cvx specification. % cvx_pause - Pauses the processing of CVX models. % cvx_power_warning - Control
School: Stanford
Course: Convex Optimization
Convex Optimization EE364a: Review Session 9 Final review Stanford University Winter Quarter, 3/12/2013 1 Outline Announcements Review of course material Convex sets Convex functions Duality Non-convex optimization problems Selected problems from past exa
School: Stanford
Course: Convex Optimization
The CVX Users Guide Release 2.0 (beta) Michael C. Grant, Stephen P. Boyd CVX Research, Inc. January 18, 2013 CONTENTS 1 2 3 4 5 Introduction 1.1 What is CVX? . . . . . . . . . . . . . . . 1.2 What is disciplined convex programming? 1.3 What CVX is not . .
School: Stanford
Course: Convex Optimization
builtins/ builtins/@cvx/ builtins/@cvxcnst/ commands/ commands/@cvx/ functions/ functions/@cvx/ functions/square_/ functions/vec_/ gurobi/ gurobi/maci64/ keywords/ lib/ lib/@cell/ lib/@cvx/ lib/@cvxcnst/ lib/@cvxdual/ lib/@cvxin/ lib/@cvxobj/ lib/@cvxprob
School: Stanford
Course: Convex Optimization
CVX: A system for disciplined convex programming Copyright 2005-2014 CVX Research, Inc. Professional package - Thank you for your interest in CVX! INSTALLATION - For full installation instructions, please see the Installation section of the users' guide,
School: Stanford
Course: Convex Optimization
GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. <http:/fsf.org/> Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble Th
School: Stanford
Course: Convex Optimization
function varargout = cvx_version( varargin ) % CVX_VERSION Returns version and environment information for CVX. % % When called with no arguments, CVX_VERSION prints out version and % platform information that is needed when submitting CVX bug reports. %
School: Stanford
Course: Convex Optimization
CVX: A system for disciplined convex programming 2005-2014 CVX Research, Inc., Austin, TX. http:/cvxr.com info@cvxr.com Thank you for using CVX! The files contained in the various CVX distributions come from various sources and are covered under a varie
School: Stanford
Course: Convex Optimization
function prevpath = cvx_startup( quiet ) %CVX_STARTUP Quietly add CVX to your MATLAB path (for startup). % Running CVX_STARTUP upon startup ensures that CVX is properly included % in your MATLAB path. % % On Mac and PC systems, this function is not necess
School: Stanford
Course: Convex Optimization
function success = cvx_grbgetkey( kcode, overwrite ) % CVX_GRBGETKEY Retrieves and saves a Gurobi/CVX license. % % This function is used to install Gurobi license keys for use in CVX. It % is called with your Gurobi license code as a string argument; e.g.
School: Stanford
Course: Convex Optimization
function cvx_setup( varargin ) % CVX_SETUP Sets up and tests the cvx distribution. % This function is to be called any time CVX is installed on a new machine, % to insure that the paths are set properly and the MEX files are compiled. global cvx_ try cv
School: Stanford
Course: Convex Optimization
% CVX: A system for disciplined convex programming. % CVX is a modeling framework for building, constructing, and solving % disciplined convex programs. % % cvx_setup - Sets up and tests the cvx distribution. % cvx_error - Formats text for inclusion in e
School: Stanford
Course: Convex Optimization
function lines = cvx_error( errmsg, widths, useline, prefix, chop ) % CVX_ERROR Formats text for inclusion in error messages. % This is an internal function used by CVX. It needed to be in the CVX % home directory so that it's available during a fresh ins
School: Stanford
Course: Convex Optimization
GUROBI OPTIMIZATION, INC. END-USER LICENSE AGREEMENT (Agreement) Please read the terms and conditions of this license agreement carefully. By installing and enabling the Gurobi Product(s) you are accepting the terms of this agreement. (The Product(s) will
School: Stanford
Course: Convex Optimization
GUROBI OPTIMIZATION, INC. END-USER LICENSE AGREEMENT (Agreement) Please read the terms and conditions of this license agreement carefully. By installing and enabling the Gurobi Product(s) you are accepting the terms of this agreement. (The Product(s) will
School: Stanford
Course: Convex Optimization
Copying tests/vm/page-parallel to scratch partition. Copying tests/vm/child-linear to scratch partition. qemu -hda /tmp/99xGObpaCp.dsk -m 4 -net none -nographic -monitor null PiLo hda1 Loading. Kernel command line: -q -f extract run page-parallel Pintos b
School: Stanford
Course: Convex Optimization
Copying tests/vm/page-parallel to scratch partition. Copying tests/vm/child-linear to scratch partition. qemu -hda /tmp/COW_t9HXcG.dsk -m 4 -net none -nographic -monitor null PiLo hda1 Loading. Kernel command line: -q -f extract run page-parallel Pintos b
School: Stanford
Course: Convex Optimization
Additional Exercises for Convex Optimization Stephen Boyd Lieven Vandenberghe December 26, 2009 This is a collection of additional exercises, meant to supplement those found in the book Convex Optimization, by Stephen Boyd and Lieven Vandenberghe. These e
School: Stanford
Course: Computer System Architecture
EE282 Review Session 3 Programming Assignment 2 Mingyu Gao http:/ee282.stanford.edu EE282 05/16/2014 Review Session 1 Announcements n HW2 is due on Monday n Work on PA2 and HW3 n Want to see your grades online? n https:/ www.stanford.edu/class/ee282/cgi-b
School: Stanford
Course: Computer System Architecture
EE282 Review Session 2 Programming Assignment 1 Mingyu Gao http:/ee282.stanford.edu EE282 04/18/2014 Review Session 1 Announcements n HW1 and PA1 are both out n n n Work with your teammates Start early! Still work alone? n Contact us ASAP 2 Todays Agenda
School: Stanford
Course: Computer System Architecture
EE282 Review Session 1 Quick Review for EE108B Mingyu Gao http:/ee282.stanford.edu Acks for slides: Christos Kozyrakis, Christina Delimitrou EE282 04/04/2014 Review Session 1 Announcements n Check out TAs office hours on the website n n Come to us with an
School: Stanford
Course: Computer System Architecture
EE282 Computer Systems Architecture Lecture Schedule Christos Kozyrakis Spring 2014/15 EE282 Tentative Lecture Schedule http:/ee282.stanford.edu Notes: Check the online schedule frequently for the latest updates on lectures and review sessions. Readings m
School: Stanford
Course: Computer System Architecture
EE282 Project Assignment #2 Spring 2015 Datacenter Design Study Due Date: Monday, June 1st, 2015 at 5PM PDT For this assignment, you will explore performance, power, and cost trade-offs in the design of a large-s
School: Stanford
Course: Computer System Architecture
EE282 Project Assignment #2 Spring 2015 Datacenter Design Study Due Date: Monday, June 1st, 2015 at 5PM PDT For this assignment, you will explore performance, power, and cost trade-offs in the design of a large-scale datacenter. We provide you with a queu
School: Stanford
Course: Computer System Architecture
EE282 Project Assignment #2 Spring 2015 Datacenter Design Studies Due Date: Monday, June 1st, 2015 at 5PM PDT For this assignment, you will explore performance, power and cost tradeoffs in the design of a large-scale datacenter. We provide you with a queu
School: Stanford
Course: Computer System Architecture
EE282 Project Assignment #1 Spring 2015 Multi-core Design Exploration Due Date: Monday, May 4th, 2015 at 5PM PDT For this assignment, you will explore performance, energy, and area trade-offs in the design of a
School: Stanford
Course: Computer System Architecture
EE282 Project Assignment #1 Spring 2015 Multi-core Design Exploration Due Date: Monday, May 4th, 2015 at 5PM PDT For this assignment, you will explore performance, energy, and area trade-offs in the design of a multi-core chip. We provide you with a multi
School: Stanford
Course: Computer System Architecture
EE282 Lecture 12 Datacenter Hardware Christos Kozyrakis http:/ee282.stanford.edu EE282 Spring 2015 Lecture 12 1 Announcements n HW2 is due on 5/11 n PA2 available on Monday 2 Reminder: What is a Datacenter (DC) compute infrastructure for internetn The sca
School: Stanford
Course: Computer System Architecture
EE282 Spring 2015 Prof. Kozyrakis Homework Assignment #2 Due: Monday, 05/27/2015, 5pm @ Gates Hall 303 Please work in groups of 2 students Instructions: Please print out and submit the homework to the box outside Gates 303 by the due date above. Show your
School: Stanford
Course: Computer System Architecture
Stanford EE282 (Guest Talk): Computer Systems Architecture for Mobile Ofer Shacham, PhD shacham@google.com 1 The Brief History of. Me (or who gave me the right to give this talk ) Long ago: Spent five years in the Navy; Worked for IBM R&D Started Stanford
School: Stanford
Course: Computer System Architecture
EE282 Computer Systems Architecture Spring 2015 Homework Assignment #2 Due: Monday, 05/11/2015, 5pm @ Gates Hall 303 Please work in groups of 2 students Instructions: Please print out and submit the
School: Stanford
Course: Computer System Architecture
EE282 Lecture 13 Datacenter Management Christos Kozyrakis http:/ee282.stanford.edu EE282 Spring 2015 Lecture 13 1 Announcements n HW2 is due today n PA2 is available today n Guest lecture on Wednesday n n Data Center Computers: Modern challenges in CPU de
School: Stanford
Course: Computer System Architecture
EE282 Computer Systems Architecture Spring 2015 Homework Assignment #1 Due: Wednesday, 04/22/2015, 5pm @ Gates Hall 303 Please work in groups of 2 students Instructions: Please print out and submit t
School: Stanford
Course: Computer System Architecture
EE282 Lecture 14 Power Management Christos Kozyrakis http:/ee282.stanford.edu EE282 Spring 2014 Lecture 14 1 Announcements n HW3 is available today n Start on project 2 early 2 Reminder: Datacenter Management n Main issues n n Static partitioning n n Simp
School: Stanford
Course: Computer System Architecture
EE282 Lecture 11 Virtual Machines+ Datacenter Introduction Christos Kozyrakis http:/ee282.stanford.edu EE282 Spring 2015 Lecture 11 1 Announcements n Project 1 is due today n HW2 is due on 5/11 2 Reminder: Virtual Machines Virtual Machines (VMs) VMM (a.k.
School: Stanford
Course: Computer System Architecture
EE282 Lecture 10 I/O and Virtualization Christos Kozyrakis http:/ee282.stanford.edu EE282 Spring 2015 Lecture 10 1 Announcements n Project 1 is due on 5/4th n HW2 will be available today n n Work in groups of 3 Help the TAs design the review session for F
School: Stanford
Course: Computer System Architecture
EE282 Lecture 8 Advanced I/O Christos Kozyrakis http:/ee282.stanford.edu EE282 Spring 2015 Lecture 08 1 Announcements n HW1 is due today n Project 1 is due on 5/4th n Reading material for Flash n n n Flash Storage Memory, A. Leventhal, Communications of A
School: Stanford
Course: Computer System Architecture
EE282 Computer Systems Architecture Christos Kozyrakis http:/ee282.stanford.edu EE282 Spring 2015 Lecture 01 1 A Few Words About Christos n n n ATLAS ATM Switch Associate professor of EE & CS PhD from Berkeley, BSc from U. of Crete Research n Making syste
School: Stanford
Course: Computer System Architecture
EE282 Lecture 7 Flash Christos Kozyrakis http:/ee282.stanford.edu EE282 Spring 2015 Lecture 07 1 Announcements n HW1 is due on 4/22nd n Project 1 is due on 5/4th n Reading material for DRAM (lecture 6) n n n B. Jacob, The Memory System, Synthesis Lecture
School: Stanford
Course: Computer System Architecture
EE282 Lecture 3 Advanced Caching Christos Kozyrakis http:/ee282.stanford.edu EE282 Spring 2015 Lecture 03 1 Announcements n Find HW & project partners n n n Use the webpage forum if needed Groups of 2 Make sure you catch up with EE180 material n Use EE180
School: Stanford
Course: Computer System Architecture
EE282 Lecture 5 Performance Engineering Christina Delimitrou http:/ee282.stanford.edu EE282 Spring 2015 Lecture 05 1 Announcements n Assignments n n n HW1 is out PA1 out later today HW & PA partners (groups of 2) n n If you still dont have a partner, cont
School: Stanford
Course: Computer System Architecture
EE282 Lecture 2 Processor Core Technology Christos Kozyrakis http:/ee282.stanford.edu EE282 Spring 2015 Lecture 02 1 Announcements n Check the class schedule for assignment deadlines n n Register on Axess, webpage, and forum n n Make sure you can complete
School: Stanford
Course: Computer System Architecture
EE282 Lecture 6 Main Memory Christos Kozyrakis http:/ee282.stanford.edu EE282 Spring 2015 Lecture 06 1 Announcements n HW1 is available n 4.15 5.50pm in Huang 18 n Project 1 is available n Review session on Friday n Optional reading for this lecture (pdf
School: Stanford
Course: Computer System Architecture
EE282 Lecture 4 Advanced Caching (2) Christos Kozyrakis http:/ee282.stanford.edu EE282 Spring 2015 Lecture 04 1 Announcements n Upcoming assignments n n Find HW & PA partners (groups of 2) n n PA1 and HW1 out on Monday Use the webpage forums if needed Nee
School: Stanford
Course: Computer System Architecture
EE282 Computer Systems Architecture Course Information Christos Kozyrakis Spring 2014-15 EE282 Computer Systems Architecture Course Information http:/ee282.stanford.edu Course description: EE282 focuses on advanced, system-level architecture techniques fo
School: Stanford
Course: Solid State Physics II
EE 237 : Solar Energy Conversion Logistics tanford University Books EE 237 : Aneesh Nainani Syllabus & Grading 1 Logistics Class Meets : M and W : 4:15-5:30pm Meets Where ? : Instructors : Aneesh Nainani (Applied Materials/Stanford) nainani@stanford.edu
School: Stanford
Course: Solid State Physics II
EE237: Lecture 2 LCOE Solar Radiation Solar Tracking EE 237 : Aneesh Nainani Food for thought Time shift PV by two time zones to match Peak Capacity. Food for thought Time shift PV by two time zones to match Peak Capacity. How many panels does Aneesh need
School: Stanford
Course: Principles And Models Of Semiconductor Devices
Esquire Magazine, December 1983, pp. 346-374. America is today in the midst of a great technological revolution. With the advent of the silicon chip, information processing, communications, and the national economy have been strikingly altered. The new te
School: Stanford
Course: On Achievability Via Random Binning
1 On Achievability via Random Binning Ritesh Kolte, Kartik Venkat cfw_rkolte, kvenkat@stanford.edu AbstractIn [1], the authors present a novel tool to establish achievability results in network information theoretic problems. The main idea is to study a s
School: Stanford
Course: Semiconductor Optoelectronic Devices
1/10/12 EE243 Semiconductor Optoelectronic Devices ! Prof. James Harris! Room 328, Paul Allen Center for Integrated Systems (CISX)! ! Harris@snow.stanford.edu! Web Page - http:/ee.stanford.edu/~harris! (650) 723-9775, (650) 723-4659 fax! Ofce Hours 2: 05
School: Stanford
Course: Analog Integrated Circuit Design
Lecture 6 Design Example 2 Extrinsic Capacitance Boris Murmann Stanford University murmann@stanford.edu Copyright 2004 by Boris Murmann B. Murmann EE 214 Lecture 6 (HO#9) 1 Overview Reading 1.6.7 (Parasitic Elements) 7.1, 7.2.0, 7.2.1 (Mille
School: Stanford
Course: Analog Integrated Circuit Design
Lecture 24 kT/C Noise Boris Murmann Stanford University murmann@stanford.edu Copyright 2004 by Boris Murmann B. Murmann EE 214 Lecture 24 (HO#32) 1 Overview Introduction Having established the basic noise mechanisms in MOSFETS, today's lectur
School: Stanford
Course: Principles And Models Of Semiconductor Devices
EE 216: Principles and Models of Semiconductor Devices Lecture 16 Silicon bonding Si (14) - 1s2 2s2 2p6 3s23p2 1s2 2s2 2p6 3s13px13py13pz1 4 valence electrons, covalent bonding between atoms 1 More than two atoms Interactions of valence orbitals produce
School: Stanford
Course: Principles And Models Of Semiconductor Devices
EE 216: Principles and Models of Semiconductor Devices Lecture 15 Junctions and devices under study Metal-semiconductor junction & diode PN junction & diode Bipolar junction transistor Photonic devices: LED, solar cell, photodiode MOS junction and capacit
School: Stanford
Course: Principles And Models Of Semiconductor Devices
EE 216: Principles and Models of Semiconductor Devices Lecture 14 Reading Pierret, pg. 691 710 Dennard Scaling Paper (optional) Junctions and devices under study Metal-semiconductor junction & diode PN junction & diode Bipolar junction transistor Photonic
School: Stanford
Course: Principles And Models Of Semiconductor Devices
EE 216: Principles and Models of Semiconductor Devices Lecture 11 Reading Pierret, pg. 347 368 Junctions and devices under study Metal-semiconductor junction & diode PN junction & diode Bipolar junction transistor Photonic devices: LED, solar cell, photod
School: Stanford
Course: Principles And Models Of Semiconductor Devices
EE 216: Principles and Models of Semiconductor Devices Lecture 13 Reading Pierret, pg. 611 637 Pierret, pg. 645 671 Junctions and devices under study Metal-semiconductor junction & diode PN junction & diode Bipolar junction transistor Photonic devices: LE
School: Stanford
Course: Principles And Models Of Semiconductor Devices
EE 216: Principles and Models of Semiconductor Devices Lecture 12 Reading Pierret, pg. 563 599 Junctions and devices under study Metal-semiconductor junction & diode PN junction & diode Bipolar junction transistor Photonic devices: LED, solar cell, photod
School: Stanford
Course: Principles And Models Of Semiconductor Devices
EE 216: Principles and Models of Semiconductor Devices Lecture 8 Reading Pierret, pg. 235 281 Pierret, pg. 301 324 Junctions and devices under study Metal-semiconductor junction & diode PN junction & diode Bipolar junction transistor Photonic devices: LED
School: Stanford
Course: Principles And Models Of Semiconductor Devices
EE 216: Principles and Models of Semiconductor Devices Lecture 9 Reading Pierret, pg. 371 400 Pierret, pg. 429 433 Junctions and devices under study Metal-semiconductor junction & diode PN junction & diode Bipolar junction transistor Photonic devices: LED
School: Stanford
Course: Principles And Models Of Semiconductor Devices
EE 216: Principles and Models of Semiconductor Devices Lecture 7 Reading Pierret, pg. 195 227 Junctions and devices under study Metal-semiconductor junction & diode PN junction & diode Bipolar junction transistor Photonic devices: LED, solar cell, photodi
School: Stanford
Course: Computer System Architecture
A Few Words About Christos ! ! ! ATLAS(ATM(Switch( Associate(professor(of(EE(&(CS( PhD(from(Berkeley,(BSc(from(U.(of(Crete( Research( ! Making(systems(faster,(greener,(cheaper( ! Hardware(and(system(soKware( Raksha(Security(System( ! Resource(ecient(cloud
School: Stanford
Course: Computer System Architecture
Applications Channel API ISA Link What Computer Architects Do Interfaces IR Regs Technology Software Requirements Component & System Organization Computer Architect Measurement & Analysis 37
School: Stanford
Course: Computer System Architecture
Textbooks ! Required: The Datacenter as a Computer, 2nd ed. ! ! ! Optional: Computer Architecture: A Quantitative Approach, 5th ed. ! ! ! By L. Barroso, J. Clidaras, and U. Holzle Dont buy; PDF available to all Stanford students By J. Hennessy & D. Patter
School: Stanford
Course: Computer System Architecture
Rules (in addition to Honor Code) ! All deadlines are final, no extensions, no exceptions ! ! If you cannot make it, dont take the class Alternative exam days/time ! Only for legitimate conflicts with other Stanford class or documented health problems, fa
School: Stanford
Course: Computer System Architecture
Prerequisites ! EE180 or EE108b or equivalent in other institution ! ! ! Programming in C, C+, or similar language ! ! You should know very well: ISA & assembly code, basic pipelining & caching, basic I/O concepts, virtual memory Refresh your memory asap
School: Stanford
Course: Computer System Architecture
Class Load ! One exams ! ! ! See exam date/time on schedule handout Local SCPD students must come to campus 3 homework sets ! ! 2 project assignments ! ! Work in groups of 2 students Work in groups of 2 students Tentative grading ! Exams 45%, projects 40%
School: Stanford
Course: Computer System Architecture
The Class Forum (Piazza) ! The preferred way to ask class-related questions ! ! We promise to check & answer very often, especially close to deadlines The rules ! Before posting a new question, check if its already been asked ! ! Choose an appropriate sub
School: Stanford
Course: Computer System Architecture
Class Basics ! Lectures: Mo & Wed, 9.30 10.45pm, Packard 101 ! Also on web (SCPD), but come to class & ask questions! ! There will also be a few review sessions on Fridays ! ! Time & location to be announced Web page: http:/ee282.stanford.edu ! ! ! Announ
School: Stanford
Course: Computer System Architecture
The EE282 Team ! Instructor: Christos Kozyrakis ! ! Plus a few guest lectuerers Teaching assistants ! James Hegarty, grad student, CS ! Song Han, grad student, EE ! Administrative support: Sue George ! Check webpage for contact info, office hours, etc 3
School: Stanford
Course: Computer System Architecture
EE282 Computer Systems Architecture Christos(Kozyrakis ( ( h.p:/ee282.stanford.edu ( EE282$Spring$2015$Lecture$01$
School: Stanford
Course: Computer System Architecture
Useful Application Properties ! Parallelism ! ! ! ! ! Locality in memory and I/O references ! ! ! Temporal or spatial Predictability ! ! Data-level: same operation on sequences of elements Instruction-level: independent ops within sequential code Request-
School: Stanford
Course: Computer System Architecture
Discussion ! Caching ! ! ! Indirection ! ! ! ! How can you amortize the high cost of memory accesses? Other examples? Redundancy ! ! ! How do you typically dial your doctors phone number? Examples in system design? Amortization ! ! Why do caches reduce me
School: Stanford
Course: Computer System Architecture
Applications Channel API ISA Link What is a Computer Architect? Interfaces IR Regs Technology Constraints & Requirements Component & System Organization Computer Architect Measurement & Analysis 30
School: Stanford
Course: Computer System Architecture
Discussion ! Pipelining ! ! ! Parallel processing ! ! ! What makes it possible? What should you be aware of? Out-of-order execution ! ! What is it and why does it work? Does it improve latency or bandwidth? In what order should you execute the tasks in a
School: Stanford
Course: Computer System Architecture
Reminder: Cost of Semiconductor Chips Cost per wafer Cost per die = Dies per wafer Yield Dies per wafer = Wafer area Die area Wafer yield Yield = (1+ Defects per area Die area) N ! Main Roughly N>10 point: bigger chips are more expensive! 35
School: Stanford
Course: Computer System Architecture
Key Tools for System Architects Pipelining ! Parallelism ! Out-of-order execution ! Prediction (or speculation) ! Locality & caching ! Indirection ! Amortization ! Redundancy ! Specialization ! Focus on the common case ! 38
School: Stanford
Course: Computer System Architecture
Other Contributors to Cost ! Testing cost ! ! IC packaging ! ! <2W no heat-sink, <10W no fan, >100+W liquid/spray cooling Other ! ! ! Depends on die size, number of pins, and power consumption Cost of cooling system ! ! cost/die = (cost/hour x test time)
School: Stanford
Course: Computer System Architecture
Major Classes of Systems Traditional PCs ! High perf, bang-for-the-buck Notebooks ! Portability, energy constraints Smartphones ! Integration, energy constraints Embedded systems ! Cost/size/energy constraints, real-time Datacenters ! Ultra scale, through
School: Stanford
Course: Computer System Architecture
Reminder: Program Execution Time ! Latency metric: program execution time in seconds CPUtime = = Seconds Cycles Seconds = Pr ogram Pr ogram Cycle Instructions Cycles Seconds Pr ogram Instruction Cycle = IC CPI CCT ! Your system architecture can affect all
School: Stanford
Course: Computer System Architecture
Reminder: Amdahls Law ! ! Speedup = CPUtimeold / CPUtimenew Given an optimization x that accelerates fraction fx of program by a factor of Sx, how much is the overall speedup? Speedup = ! CPUTimeold CPUTimeold 1 = = CPUTimenew CPUTime [(1 f ) + f x ] (1 f
School: Stanford
Course: Computer System Architecture
Performance Metrics Revisited ! Latency or Execution Time or Response Time ! ! Wall-clock time to complete a task Bandwidth or Throughput or Execution Rate ! ! Number of tasks completed per unit of time Metric is independent of exact number of tasks execu
School: Stanford
Course: Computer System Architecture
Metrics of Efficiency ! Notebooks ! Cost, performance (latency), battery life, size ! Smartphones ! Size, performance (latency), battery life, cost ! Datacenter ! Performance (throughput and latency), quality of service, reliability, total cost of ownersh
School: Stanford
Course: Computer System Architecture
Historical Perspective: Tech Changes lead to Changes in Architecture ! 1970s ! ! ! ! ! Multi-chip CPUs Semiconductor memory very expensive Complex instruction sets (code density) Microcoded control 1980s ! ! ! ! ! ! ! 1990s ! ! ! ! 5K 500 K transistors Si
School: Stanford
Course: Computer System Architecture
Applications Channel API ISA Link What Computer Architects Do Interfaces IR Regs Technology Constraints & Requirements Component & System Organization Computer Architect Measurement & Analysis 22
School: Stanford
Course: Computer System Architecture
Technology Scaling: Past Chip Power 3.0 1.4x frequency 2.5 2.0 0.7x capacitance 2x transistors 0.7x voltage 1.5 2.8x capability, same power 1.0 ! 1.5 2.0 Chip Capability 2.5 3.0 Moores law (more transistors) + Dennard scaling (lower Vdd) ! 2.8x in chip ca
School: Stanford
Course: Computer System Architecture
Technology Scaling: The Present Chip Power 3.0 2.5 2.0 2x transistors 1.5 1.0 ! 2.0 Chip Capability 2.5 3.0 Moores Law without Dennard scaling ! ! 1.5 0.7x capacitance 32x gap per decade 1.4x in chip capability per generation at constant power Implication
School: Stanford
Course: Computer System Architecture
Technology Scaling Voltage & Power Power*=*C*Vdd2*F0"1*+*Vdd*Ileakage* Data courtesy S. Borkar/Intel 2011 ! Implications? 26
School: Stanford
Course: Computer System Architecture
Bandwidth vs Latency ! ! Why is this happening? Implications? 24
School: Stanford
Course: Computer System Architecture
Applications Channel API ISA Link What Computer Architects Do Interfaces IR Regs Technology Constraints & Requirements Component & System Organization Computer Architect Measurement & Analysis 18
School: Stanford
Course: Computer System Architecture
Technology Scaling Transistors, Frequency, Cores, Power 25
School: Stanford
Course: Computer System Architecture
Typical Technology Trends ! Semiconductors (processor & memory chips) ! Moores Law: # of devices per chip doubles every ~2 years ! ! ! ! Device speed improves by <15% per year Long on-chip wires get worse; voltages have stopped scaling Storage (hard disks
School: Stanford
Course: Computer System Architecture
Avoid: Jack of All Trades ! Proofs or counter arguments? Cellphone + screen/keyboard/extra battery? ! Cellphones + cloud computing? ! 21
School: Stanford
Course: Computer System Architecture
Avoid: Jack of All Trades ! Assume 2 app domains and 3 system designs Applica'on* Domain* System*1* Eciency* System*2* Eciency* System*3* Eciency* A( 10( 100( 30( B( 100( 10( 30( 55( 55( 30( Average( ! On the average, computer 3 looks best ! But it will p
School: Stanford
Course: Computer System Architecture
Applications ! Personal computers ! ! Smartphones ! ! ? Datacenters ! ! Browser/email, productivity tools, graphics-intensive games, engineering tools, ? Implications? 19
School: Stanford
Course: Computer System Architecture
Tentative Class Schedule ! ! ! ! ! ! ! Processor technology Memory hierarchies (x2) Main memory Performance engineering Non-volatile storage Advanced I/O System-on-a-chip ! Datacenter introduction Datacenter hardware Resource management Reliability Power
School: Stanford
Course: Computer System Architecture
Projects ! PA1: Multi-core design space study Match the Hardware to the Software ! Get personal with area, energy, power constraints ! ! PA2: Datacenter design space study Match the Software to the Hardware ! Sounds simple, but ! ! Both involve large des
School: Stanford
Course: Computer System Architecture
System Architecture vs. Processor Architecture ! EE282 focuses more on ! System rather than processor architecture ! ! ! ! Few of you will build processor cores Many will build systems of various sizes, all will use systems State-of-the-art technology rat
School: Stanford
Course: Computer System Architecture
Overall System Architecture Multiple interacting layers ! Application Libraries Operating System Drivers VM SW Scheduler This class focuses on ! Hardware architecture ! Processor ! VM HW System Bus ! Controller Main Graphics Memory HW Term architecture us
School: Stanford
Course: Computer System Architecture
EE282 Goals ! Understand the hardware and system software technology in modern computing systems ! How and why they are organized the way they are ! Study common issues and advanced solutions ! Interactions with technology and applications ! Interactions
School: Stanford
Course: Computer System Architecture
EE282 Lecture 1 Introduction Christos(Kozyrakis ( ( h.p:/ee282.stanford.edu ( EE282$Spring$201$Lecture$01$
School: Stanford
Course: EE - Digital CMOS Integrated Circuits
Problem #1: Domino Logic Sizing Dgate Clk A Clk stage1 stage2 Dgate ? ? 8*Lmin max 8*lmin max stage3 Dgate stage11 Dgate Y Dgate Dgate Dgate For the 11 stage circuit above, size via simulation the output inverter, and the NMOS pulldown of the DGATE such t
School: Stanford
Course: Advanced Analog Integrated Circuit Design
EE214B Advanced Analog Integrated Circuit Design - Winter 2015 Boris Murmann Stanford University murmann@stanford.edu Table of Contents Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 Chapter 11 Chapter
School: Stanford
Course: Advanced Analog Integrated Circuit Design
ESE319 Introduction to Microelectronics BJT Biasing Cont. & Small Signal Model Bias Design Example using 1/3, 1/3, 1/3 Rule Small Signal BJT Models Small Signal Analysis Kenneth R. Laker, updated 18Sep13 KRL 1 ESE319 Introduction to Microelectronics Emi
School: Stanford
Course: Advanced Analog Integrated Circuit Design
4/18/2011 section 5_8 BJT Internal Capacitances 1/2 5.8 BJT Internal Capacitances Reading Assignment: 485-490 BJTs exhibit capacitance between each of its terminals (i.e., base, emitter, collector). These capacitances ultimately limit amplifier bandwidth.
School: Stanford
Course: Digital Systems I
EE108a Section 3 Handout Number representation Fixed point We can represent numbers that have fractional digits in binary the same way we do in decimal: 0 0 1 1 0 1 . 0 1 1 13.375 = ! ! ! 25=
School: Stanford
Course: Digital Systems I
EE108a Section 4 Handout Sequential logic Stateful circuits A flipflop is a unit of memory. When designing sequential logic, figure out what signals your circuit needs to remember about the task its doing, and make a flipflop for each of them. Never conne
School: Stanford
Course: Digital Systems I
EE108a Section 2 Handout More Verilog Parameters Parameters can be declared in the module header: module module_name #( parameter name1 = default1, parameter name2 = default2, ) (
School: Stanford
Course: The Fourier Transform And Its Applications
EE261 Raj Bhatnagar Summer 2009-2010 EE 261 The Fourier Transform and its Applications Midterm Examination 19 July 2010 (a) This exam consists of 4 questions with 12 total subparts for a total of 50 points. (b) The questions dier in length and diculty. Do
School: Stanford
Course: Principles And Models Of Semiconductor Devices
EE 216 FINAL EXAM Duration: 3 hours Fall 2008 Total Score: 200; #Problems = 8 Make sure to STATE ALL ASSUMPTIONS you make. The following values may be helpful: Ge EG = 0.66 eV at T = 300K Si EG = 1.12 eV at T = 300K NC (Si) = 3 x 1019 cm-3 NV (Si) = 2 x 1
School: Stanford
Course: Principles And Models Of Semiconductor Devices
c hv.jz d u e I+"1- e lec<i. cfw_ ra/ - v o l t e-19 f de '77 = *r" tr = erLlpJX J e=-# o( V = - leax ("/ q<o.bJic- fu) q I 'lea uo, l " P " 6 r^x v lN lr"u p-tL Q"wJ- conv,cts (q) tlr Qa @e Fy'h,-r. " .^*oo b/u Sr X AI , ^,.,- ^ r. lr, + h-rn "- " o", t
School: Stanford
Course: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2011 Solutions to Midterm Exam 1 1. (10 points) Multiplying periodic functions Let f (t) and g (t) be periodic functions with period 1 and Fourier series expansions given by n= an ei2nt , f (t) = n= n
School: Stanford
EE263 Dec. 56 or Dec. 67, 2008. Prof. S. Boyd Final exam This is a 24 hour take-home nal exam. Please turn it in at Bytes Cafe in the Packard building, 24 hours after you pick it up. Please read the following instructions carefully. You may use any books,
School: Stanford
Course: Basic Physics For Solid State Electronics
1. Semiconductor carrier statistics (40 points) Consider a semiconductor with a face-centered cubic lattice and with cubic symmetry. The valence band has a maximum at with an energy E = 0 and with an effective mass m0 = me. (me is the mass of a free elect
School: Stanford
Course: Introduction To Communication Systems
EE 279 Professor Cox Solution to Final 1. (12pt) a) ii) b) i) iii) c) i) iv) d) vi) 2. (35pt) t Winter 2005-2006 HO # In phase-acceleration modulation we have: f (t ) = f c + K ! x(" )d" . Therefore to recover the signal we should extract the phase
School: Stanford
EE 261 Fourier Transform and Applications March 17, 2011 Handout #21 Final Examination Solutions 1. (15 points) Fourier series. A function f (t) with period 1 has the Fourier series coecients n 1 n<0 2 cn = 0 n=0 1n 2 n>0 These Fourier series coecients
School: Stanford
Course: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2012 Midterm Exam October 31, 2012 There are ve questions for a total of 85 points. Please write your answers in the exam booklet provided, and make sure that your answers stand out. Dont forget to
School: Stanford
Course: Circuits I
E EI O I A FINAL WINTER0 9 NAME I.D.N UMBER SIGNATURE TIME : 3 H OURS OPENB OOKS,O PENN OTES NO P C o TW IRELESSC OMMUNICATION D EVICE STATE Y OUR A SSUMPTIONS ND R EASONING A NO C REDIT F OR A NSWERS ITHOUT R EASONING W (1) (2) ( 3) (4) n6 n6 n2 n6 130 (
School: Stanford
Course: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2012 Final Exam Solutions 1. (15 points)Finding Fourier transforms: The following two questions are independent. (a) (5) In communications theory the analytic signal fa (t) of a signal f (t) is dened,
School: Stanford
EE364a Convex Optimization I March 1415 or March 1516, 2008. Prof. S. Boyd Final exam solutions You may use any books, notes, or computer programs (e.g., Matlab, cvx), but you may not discuss the exam with anyone until March 18, after everyone has taken t
School: Stanford
Course: Convex Optimization
Additional Exercises for Convex Optimization Stephen Boyd Lieven Vandenberghe August 30, 2012 This is a collection of additional exercises, meant to supplement those found in the book Convex Optimization, by Stephen Boyd and Lieven Vandenberghe. These exe
School: Stanford
Course: Convex Optimization
EE364a Convex Optimization I June 23 or June 34, 2010 Prof. S. Boyd Final exam This is a 24 hour take-home nal. Please turn it in to one of the TAs, at Bytes Cafe in the Packard building, 24 hours after you pick it up. You may use any books, notes, or com
School: Stanford
Course: Convex Optimization
EE364a Convex Optimization I March 1112 or March 1213, 2011 Prof. S. Boyd Final exam solutions This is a 24 hour take-home nal. Please turn it in to one of the TAs, at Bytes Cafe in the Packard building, 24 hours after you pick it up. You may use any book
School: Stanford
Course: Convex Optimization
EE364a Convex Optimization I March 1617 or 1718, 2012 Prof. S. Boyd Final exam solutions This is a 24 hour take-home nal. Please turn it in at Bytes Cafe in the Packard building, 24 hours after you pick it up. You may use any books, notes, or computer pro
School: Stanford
Course: Convex Optimization
EE364a Convex Optimization I March 1617 or 1718, 2012 Prof. S. Boyd Final exam This is a 24 hour take-home nal. Please turn it in at Bytes Cafe in the Packard building, 24 hours after you pick it up. You may use any books, notes, or computer programs (e.g
School: Stanford
Course: Convex Optimization
EE364a Convex Optimization I March 1112 or March 1213, 2011 Prof. S. Boyd Final exam This is a 24 hour take-home nal. Please turn it in to one of the TAs, at Bytes Cafe in the Packard building, 24 hours after you pick it up. You may use any books, notes,
School: Stanford
Course: Principles And Models Of Semiconductor Devices
ENEE 313, Spr 09 Midterm II Solution PART IDRIFT AND DIFFUSION, 30 pts 1. We have a silicon sample with non-uniform doping. The sample is 200 m long: In the gure, L = 200 m= 0.02 cm. At the x = 0 edge, the sample is doped at an acceptor density of NA (0)
School: Stanford
Course: Digital Systems I
Lecture 5 Numbers and Arithmetic Subhasish Mitra Stanford University subh@stanford.edu Copyright 2013 by Subhasish Mitra With Major contributions from Bill Dally 1 Announcements Homework 1 is graded and will be returned in class. Homework 2 due today at
School: Stanford
Course: Introduction To VLSI Systems
EE271 Autumn 14-15 Midterm Igor Markov Page 1 of 13 EE271 Introduction to VLSI Systems Midterm Examination November 3, 2014 12:50-2:05pm Room: Hewlett ? The exam is closed-book, but you may prepare and bring one standard-size sheet (two pages) of notes th
School: Stanford
Course: INTRODUCTION TO LINEAR DYNAMICAL SYSTEMS
EE263 Midterm Exam Solutions This is a 24 hour take-home midterm. Please turn it in at Bytes Cafe in the Packard building, 24 hours after you pick it up. You may use any books, notes, or computer programs (e.g., matlab), but you may not discuss the exam
School: Stanford
Course: CONVEX OPTIMIZATION I
Stanford University A. Emami E105 Summer 2013 7/25/13 6:00-7:30 PM HO #: Midterm Midterm Exam (Open Book & Notes; Stanford Honor Code Observed) [15] 1. Referring to Figure 1, relate the closed-loop transfer function poles () and zeros () for the five case
School: Stanford
Course: CONVEX OPTIMIZATION I
E105: Midterm Solutions 5/1/2014 E105: Midterm Solutions 5/1/2014 1. Answer the following questions as True or False. A. A Type I system always exhibits a non-zero o-set to a step reference input. FALSE, a Type I system by denition has zero steady-state e
School: Stanford
Course: CONVEX OPTIMIZATION I
E105: Midterm Solutions 7/25/2013 E105: Midterm Solutions Prof. A. Emami-Naeini & Jun Kyu Lee 7/25/2013 1. Referring to Figure 1, relate the closed-loop transfer function poles ( ) and zeros ( ) for the ve cases shown in the left hand column, to the corre
School: Stanford
Course: Convex Optimization
1. OptimalStucture: Structurevolume=188.55 a=crosssetion UniformCrosssection: Structurevolume=492 a_unif=5.7709 Code: lightest_struct_data; %Construct A A = zeros(m,n); for i=1:m for j=1:n if(i=r(j) A(i,j) = 1; elseif(i=s(j) A(i,j) = -1; end end end %Opti
School: Stanford
Course: Convex Optimization
% affine policy. m = 20; n = 10; p = 5; randn('state',0); rand('state',0); A = randn(m,n); c = rand(n,1); b0 = ones(m,1); B = .15*sprandn(m,p,.3);
School: Stanford
Course: Convex Optimization
% Minimum time speed profile along a road. N = 50; % number of intervals m = 1500; % mass of vehicle d = 200; % distance between knot points h = (100*sin(1:N+1)/(N+1)*5*pi/2+pi/4) + . [zeros(1,10) -10*(1:10) +6*(1:31)-100])'; % elevation at knot points e
School: Stanford
Course: Convex Optimization
% Fitting a generalized additive regression model. % Seed random number generator to get the same result every time randn('state',0); rand('state',0); N=2^8; n=9; %X=normrnd(0,6,N,n); X=6*randn(N,n); lambda=0.0391; K=15; p=-7:1:7; %Define the functions. (
School: Stanford
Course: Convex Optimization
% Least-cost road grading. n = 100; e = 5*sin(1:n)/n*3*pi)'+sin(1:n)/n*10*pi)';% elevation of the road d = 1; % distance between points D1 = .08; % the road grade should never be greater than 8% D2 = .025; % the road grade should never change faster than
School: Stanford
Course: Convex Optimization
clear; road_grading_data; cvx_begin variable h(n) minimize sum( alpha_fill*pow_pos(max(h-e,0),2) ) + beta_fill * n + sum( alpha_cut*pow_pos(max(e-h,0),2) ) + beta_cut * n subject to abs(h(2:n)-h(1:n-1) <= D1*d abs(h(3:n)-2*h(2:n-1)+h(1:n-2) <= D2*d^2
School: Stanford
Course: Convex Optimization
clear; k = 4; l = [-.6:0.01:-.3 0.7:0.01:1.8]'; poly = [l l.^2 l.^3 l.^4 l.^5]; cvx_begin variables c(k+1) t minimize t subject to -t <= poly*c -1<= t cvx_end
School: Stanford
Course: Convex Optimization
clear; min_time_speed_data; % min time cvx_begin variables q(N+1) s(N+1) f(N+1) minimize log_sum_exp(log(d)-log(s(1:N) subject to m/2*(q(2:N+1)-(1-2*d*C_D/m)*q(1:N) = m*g*(h(1:N)-h(2:N+1) + eta*f(2:N+1) m/2*q(1) = eta*f(1) q >= 0 s >= 0 f >= 0 fo
School: Stanford
Course: Convex Optimization
clear; min_time_speed_data; % min time cvx_begin quiet variables q(N+1) s(N+1) f(N+1) minimize log_sum_exp(log(d)-log(s(1:N) subject to m/2*(q(2:N+1)-(1-2*d*C_D/m)*q(1:N) = m*g*(h(1:N)-h(2:N+1) + eta*f(2:N+1) m/2*q(1) = eta*f(1) q >= 0 s >= 0 f >=
School: Stanford
Course: Convex Optimization
clear; gen_add_reg_data; % find region for the X I = zeros(N,n); for i = 1:N for j = 1:n idx = min(find(X(i,j)<=p); if isempty(idx) idx = K+1; end I(i,j) = idx; end end % Additive Regression dim = 0:n-1; dim = (K+1)*dim; prep = repmat(p',1,n); v =
School: Stanford
Course: Convex Optimization
clear; eta = 0.7; alpha = 0.5; Tideal = 65; lambda = 0.5; t = 1:24; Tout = 77 + 10*sin(2*pi/22*t + 2); Pt = 5+0.3*sin(2*pi/24*t); v = 0; cvx_begin variable x(24) for i=1:24 v = v + Pt(i)*quad_over_lin(x(i)-Tout(i),x(i); end minimize alpha/eta*sum(v)+
School: Stanford
Course: Convex Optimization
clear; gen_add_reg_data; % find region for the X I = zeros(N,n); for i = 1:N for j = 1:n idx = min(find(X(i,j)<=p); if isempty(idx) idx = K+1; end I(i,j) = idx; end end % Additive Regression dim = 0:n-1; dim = (K+1)*dim; prep = repmat(p',1,n); v =
School: Stanford
Course: Convex Optimization
clear; eta = 0.7; alpha = 0.5; Tideal = 65; lambda = 0.5; t = 1:24; Tout = 77 + 10*sin(2*pi/20*t + 2); Pt = 2+sin(2*pi/24*t); v = 0; cvx_begin variable x(24) for i=1:24 v = v + Pt(i)*quad_over_lin(x(i)-Tout(i),x(i); end minimize alpha/eta*sum(v)+lamb
School: Stanford
Course: Convex Optimization
clear; affine_pol_data; % Affine Approx cvx_begin quiet variables x0(n) K(n,p) minimize (c'*x0) subject to norms(A*K -B,1,2) <= b0 - A*x0 cvx_end % Optimal Case Nsamp = 100; u_samp = 2*rand(p,Nsamp)-1; opt_cx = zeros(Nsamp,1); for i = 1:Nsamp cvx_beg
School: Stanford
Course: Convex Optimization
clear; road_grading_data; cvx_begin variable h(n) minimize sum( alpha_fill*pow_pos(max(h-e,0),2) + beta_fill * pos(h-e) ) + sum( alpha_cut*pow_pos(max(e-h,0),2) + beta_cut * pos(e-h) subject to abs(h(2:n)-h(1:n-1) <= D1*d abs(h(3:n)-2*h(2:n-1)+h(1:n-
School: Stanford
Course: Convex Optimization
A3.9 suboptimalsolution. c) Code: t = [-3:6/200:3]'; y = exp(t); tsqr = t.^2; one = ones(201,1); basis = [one,t,tsqr]; Code: m=30; n=100; A1 = randn(m,n); A2 = randn(m,n); b1 = randn(m,1); b2 = randn(m,1); A = [A1,-A2;A2,A1]; b = [b1;b2]; epsilon = .001;
School: Stanford
Course: Convex Optimization I
EE364a Convex Optimization I June 34 or June 45, 2009. Prof. S. Boyd Final exam You may use any books, notes, or computer programs (e.g., Matlab, CVX), but you may not discuss the exam with anyone until June 12, after everyone has taken the exam. The only
School: Stanford
Course: Fundamentals Of Analog Integrated Circuit Design
Problem 1 (gm2*vo1 + (vo1-vo2)/ro2)*ro3 gm2(ro2 | ro3) gm2(ro2 | ro3) Problem 4 Problem 6 1 = 2 = 1 2 1 = 0 2 = 1 8
School: Stanford
Course: Fundamentals Of Analog Integrated Circuit Design
Problem 1 Problem 2 Problem 3 Problem 5 It is also possible to use Rz to place the zero on top of the non-dominant pole and cancel it! (I would suggest pushing the zero to infinity for practicality purposes, however.) Problem 6
School: Stanford
Course: Fundamentals Of Analog Integrated Circuit Design
Problem 1 *Problem 1 C * ee114 device models .include /usr/class/ee114/hspice/ee114_hspice.sp .model my_nmos nmos kp=50u vto=0.5 + lambda=0.1 cox=2.3e-3 capop=1 * D G SB mn1 vout 0 vs vs my_nmos w=50u l=1u vdd vdd 0 dc=2.5 vss vss 0 dc=-2.5 Ib vs vss dc=2
School: Stanford
Course: Fundamentals Of Analog Integrated Circuit Design
EE114/214A Autumn 14-15 A. Arbabian Page 1 of 7 Homework #5 (Due: Wednesday, October 29, 2014, 4pm PT) Total points for this HW = 90 points, bonus points = 32 points Suggested reading: 1) This HW covers principles taught in lecture notes 10, 13, 16 of thi
School: Stanford
Course: Fundamentals Of Analog Integrated Circuit Design
HW2 Solutions Problem 1 This plot shows Id vs Vds for MN1 for various values of Vgs Problem 2 Spice codes: .include /usr/class/ee114/hspice/ee114_hspice.sp .param r1=10k .param r2=2Meg .param r3=3Meg .param r4=2k .param r5=300 .param r6=3k .param c1=1 .pa
School: Stanford
Course: Fundamentals Of Analog Integrated Circuit Design
EE114/214A Autumn 14-15 A. Arbabian Page 1 of 5 Homework #7 (Ungraded HW; do not submit; solutions will be posted on Wednesday, 12/3/2014) Suggested reading: 1) This HW covers principles taught in lecture notes 21-27 of this course. 2) For further referen
School: Stanford
Course: Fundamentals Of Analog Integrated Circuit Design
Problem 1 Problem2 / 1 2 1 1 / 2 / 2 1+ Problem 3 clear all; close all; R=1E3; C=1E-12; A0=2; F=1; H0 = tf(A0^3, conv(conv([R*C, 1],[R*C, 1]), [R*C, 1]); H_CL = feedback(H0, F, -1); figure; bode(H_CL); grid on; Bode Diagram 300 Magnitude (dB) 200 100 0
School: Stanford
Course: Fundamentals Of Analog Integrated Circuit Design
EE114/214A Autumn 14-15 A. Arbabian Page 1 of 4 Homework #6 (Due: Friday, November 21, 2014, 4pm PT) Total points for this HW = 100 points Suggested reading: 1) This HW covers principles taught in lecture notes 21-23 of this course. 2) For further referen
School: Stanford
Course: Fundamentals Of Analog Integrated Circuit Design
EE114/214A Autumn 14-15 A. Arbabian Page 1 of 6 Homework #2 (Due: Wednesday, October 08, 2014, 4pm PT) Heads up: Please get started on this HW as soon as possible and dont leave it for the last few days because it also involves some HSpice simulations whi
School: Stanford
Course: Fundamentals Of Analog Integrated Circuit Design
EE114/214A Autumn 14-15 A. Arbabian Page 1 of 5 Homework #4 (Due: Wednesday, October 22, 2014, 4pm PT) Total points for this HW = 95 points, bonus points = 25 points Suggested reading: 1) This HW covers principles taught in lecture notes 10, 11, 13 and 16
School: Stanford
Course: Fundamentals Of Analog Integrated Circuit Design
( . - 1 ^ 1 1 0 t H 5 ) . - . H @ ) : r 1 n . f ; - . ^ . . . X - . 5 ( ^ . ^
School: Stanford
Course: Fundamentals Of Analog Integrated Circuit Design
EE114/214A Autumn 14-15 A. Arbabian Page 1 of 8 DESIGN PROJECT Check-point #1: Wednesday, November 19, 2014, 4pm PT Check-point #2: Monday, December 01, 2014, 4pm PT Final Deadline: Thursday, December 04, 2014, 4pm PT 1. Overview and Specifications The fi
School: Stanford
Course: Introduction To VLSI Systems
EE271 Markov - Fall 2014 EE271 Problem Set 1 Solution 1. Chip Power (5) = !" ! ! ! = ! = !" 86 = 1.2 16.6 3.6 Hence, Vdd must be less than 1.2V to meet the power budget. 2. Transistor Logic (4*5=20) (a) A*B = ( (A*B) = ( A + B) (b) NAND gate EE27
School: Stanford
Course: Introduction To VLSI Systems
EE271 Markov - Fall 2014 EE271 Problem Set 2 Solution 1. Short Answers a) We run DRC on layout to ensure that all of the design rules of the processing technology are met. Design rules ensure that the picture we drew can be printed. If, for exampl
School: Stanford
Course: Introduction To VLSI Systems
Lecture Testing and Design for Testability Subhasish Mitra Stanford University subh@stanford.edu Copyright 2014 by Subhasish Mitra SM EE271 1 Overview Introduction The fabrication process is one of the most precise manufacturing methods we know of, but i
School: Stanford
Course: Introduction To VLSI Systems
M4-M4 Short SM EE271 1 Source: [Spirakis ETW 2002] 3 Metal 1 Shelving M4 Void Formations Metal2 extrusion/ ILD2 crack EE271 Poly stringer Silicon damage Manufacturing Process Isn t Perfect Void under anchor SM Copyright 2014 by Subhasish Mitra Subhasish M
School: Stanford
EE 284 F. Tobagi Autumn 2010-2011 EE284 Homework Assignment No. 1 Topic: Switching Techniques, Network Topologies Handed out: September 21, 2010 Due: September 30, 2010 in class (Previously September 28 but now extended by 2 days) Total Points: 45 ALL WOR
School: Stanford
Course: Introduction To Digital Communication
EE279 Introduction to Digital Communication Handout 13 Solutions to Homework 5 Stanford University Due February 19, 2014 Problem 1. (Average Energy of PAM). (2,2,2 marks) m Solution 1. (a) The pdf of S can be written as fS (s) = i2 m +1 (s (2i 1)a) while
School: Stanford
Course: Digital MOS Integrated Circuits
EE313 Winter 2009-10 J. Kim & M. Horowitz page 1 of 8 SOLUTIONS TO HOMEWORK #2 1. Logical Effort simulations (20 points) The spice deck and Virtuoso schematics /usr/class/ee313/HW2/sol. for this problem can be found in: Delay is measured as the average of
School: Stanford
Course: Introduction To Digital Communication
EE279 Introduction to Digital Communication Handout Solutions to Homework 3 Stanford University Due January 29, 2014 Problem 1. (Artifacts of Suboptimality) Let H take on the values 0 and 1 equiprobably. Conditional on H = 0, the observation Y is N (1, 2
School: Stanford
Course: Circuits I
EE101A/Winter 2013 Prof. Simon Wong Homework #2 (Due Wednesday, 1/23/13) 1. Determine the equivalent resistance measured between the two terminals if all resistors are 1K. (This is a 2D hexagon, NOT a 3D cube.) R =? 2. Use Nodal Analysis to determine the
School: Stanford
Course: Integrated Circuit Fabrication Processes
EE 212 FALL 09-10 HOMEWORK ASSIGNMENT #3 ASSIGNED: THURSDAY OCT. 15 DUE: THURSDAY OCT. 22 SOLUTION SHEET #1. An experimental DUV resist has a contrast of 5. It is being used with a projection imaging system that produces the aerial image shown below. Will
School: Stanford
Course: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2012 Solutions to Problem Set Four 1. (10 points) Solving the wave equation An innite string is stretched along the x-axis and is given an initial displacement described by a function f (x). It is the
School: Stanford
Course: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2012 Problem Set Eight Due Wednesday, November 28 1. (20 points) A True Story : Professor Osgood and a graduate student were working on a discrete form of the sampling theorem. This included looking a
School: Stanford
Course: Stochastic Control
EE365, Spring 2011-12 Professors S. Boyd, S. Lall, and B. Van Roy EE365 / MS&E251 Homework 5 Solutions 1. A rened inventory model. We consider an inventory model that is more rened than the one youve seen in the lectures. The amount of inventory at time t
School: Stanford
Course: Stochastic Control
EE365, Spring 2011-12 Professors S. Boyd, S. Lall, and B. Van Roy EE365 / MS&E251 Homework 5 1. A rened inventory model. We consider an inventory model that is more rened than the one youve seen in the lectures. The amount of inventory at time t is denote
School: Stanford
Course: Digital MOS Integrated Circuits
EE313 Winter 09/10 J. Kim & M. Horowitz Handout # Page 1 of 6 HOMEWORK #2 (Due: Wednesday Jan. 27th, 2010; in class) 1. Logical Effort simulations In this problem, you will use HSPICE to measure the logical effort of a few different kinds of gates. Accord
School: Stanford
Course: Introduction To Statistical Signal Processing
EE 278B Statistical Signal Processing October 20, 2011 Handout #6 Homework #4 Due Thursday, October 27 1. Coloring and whitening. Let 210 = 1 2 1 . 012 a. Find the coloring and whitening matrices of using the eigenvalue method discussed in lecture slides
School: Stanford
Course: Introduction To Statistical Signal Processing
EE 278 Statistical Signal Processing Homework #8 Due: Wednesday, December 2 November 18, 2009 Handout #18 1. Discrete-time Wiener process. Let cfw_Zn : n 0 be a discrete-time white Gaussian noise process; that is, Z1 , Z2 , Z3 , . . . are i.i.d. N (0, 1).
School: Stanford
Course: Integrated Circuit Fabrication Processes
EE 212 FALL 09-10 HOMEWORK ASSIGNMENT #2 ASSIGNED: THURSDAY OCT. 1 DUE: THURSDAY OCT. 8 SOLUTION SHEET Reading Assignment: Chapters 3 and 4 in the text. #1. Spend 30 min or so scanning the information in the 2007 ITRS Front End Processes (on the class web
School: Stanford
Course: Introduction To Statistical Signal Processing
EE 278B Statistical Signal Processing October 25, 2011 Handout #7 Homework #3 Solutions 1. (15 points) Estimation vs. detection. a. We can easily nd the piecewise constant density of Y 1 |y | 1 4 1 fY (y ) = 8 1 < |y | 3 0 otherwise The conditional proba
School: Stanford
Course: Introduction To Statistical Signal Processing
EE 278B Statistical Signal Processing October 18, 2011 Handout #5 Homework #2 Solutions 1. (5 points) First available teller. The tellers service times are exponentially distributed, hence memoryless. Thus the service time distribution does not depend on
School: Stanford
EE 284 F. Tobagi Autumn 2010-2011 EE284 Homework Assignment No. 1 SOLUTIONS Total Points: 45 Problem 1 (Answer, 10 points): The number of packets needed to send the message of M bits equals: k= M P k - 1 packets have P bits of data and the last one contai
School: Stanford
Course: Introduction To Digital Image Processing
EE 168 Introduction to Digital Image Processing Handout #32 March 7, 2012 HOMEWORK 7 SOLUTIONS Problem 1: Color Wheels We can represent an N x N color image by a three-dimensional array such that the first two dimensions are of size N each, and the third
School: Stanford
Course: The Fourier Transform And Its Applications
EE 261 The Fourier Transform and its Applications Fall 2009 Solutions to Problem Set One 1. Some practice with geometric sums and complex exponentials (5 points each) Well make much use of formulas for the sum of a geometric series, especially in combinat
School: Stanford
Course: Introduction To Statistical Signal Processing
EE 278B Statistical Signal Processing Thursday, November 17, 2011 Handout #16 Homework #7 Due Thursday, December 1 1. Autocorrelation functions. Find the autocorrelation functions of a. the process X (t) = At + B of problem 2 in homework 6. b. the process
School: Stanford
Course: The Fourier Transform And Its Applications
EE 261 The Fourier Transform and its Applications Fall 2009 Solutions to Problem Set Two 1. (10 points) A famous sum You cannot go through life knowing about Fourier series and not know the application to evaluating a very famous sum. Let S (t) be the saw
School: Stanford
Course: Introduction To Linear Dynamical Systems
EE263 Prof. S. Boyd EE263 homework 8 additional exercise 1. Some simple matrix inequality counter-examples. (a) Find a (square) matrix A, which has all eigenvalues real and positive, but there is a vector x for which xT Ax < 0. (Give A and x, and the eige
School: Stanford
EE243 Winter 2014 Homework 7 J.S.Harris EE 243 Homework 7 Due Thursday, March 6, 2014, at 5 pm, CISX 329 1. Reflective modulator (30 points) We are designing a reflective quantum well modulator, desiring to achieve the maximum contrast ratio with the foll
School: Stanford
Course: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2012 Problem Set Three Due Wednesday, October 17, 2012 1. (5 points) Equivalent width: Still another reciprocal relationship The equivalent width of a signal f (t), with f (0) = 0, is the width of a r
School: Stanford
Course: The Fourier Transform And Its Applications
EE261 Raj Bhatnagar Summer 2010-2011 EE 261 The Fourier Transform and its Applications Problem Set 1 Due Wednesday, June 29 1. (10 points) Some practice with complex numbers (a) Express the following numbers in polar form: (i) (ii) (iii) (iv) (b) For (i)
School: Stanford
Course: Introduction To Statistical Signal Processing
EE 278 Statistical Signal Processing Homework #7 Solutions November 20, 2009 Handout #19 1. Convergence examples. Consider the following sequences of random variables dened on the probability space (, F , P), where = cfw_0, 1, . . . , m 1, F is the collec
School: Stanford
Course: Information Theory
EE376A: Homeworks #6 Solutions 1. Cascaded BSCs. Consider the two discrete memoryless channels (X , p1 (y |x), Y ) and (Y , p2 (z |y ), Z ). Let p1 (y |x) and p2 (z |y ) be binary symmetric channels with crossover probabilities 1 and 2 respectively. 1 1 0
School: Stanford
Course: Analog Integrated Circuit Design
EE214 Winter 04/05 Page 1 of 1 HOMEWORK #2 Solutions (Due: Monday, October 11, 2004, noon PT) 1. Use Spice to simulate gm/ID vs. VOV, (e.g. as shown on slides 3 and 4 of lecture 4). a) Generate a plot of gm/ID for EE214 NMOS devices with L=0.35m and
School: Stanford
Course: Stochastic Control
EE365, Spring 2011-12 Professors S. Boyd, S. Lall, and B. Van Roy EE365 / MS&E251 Homework 3 Solutions 1. Total-variation distance. The total variation distance between distributions and is given by dTV (, ) = max Prob(E ) Prob(E ) , E X i.e., the maximu
School: Stanford
Course: Digital MOS Integrated Circuits
EE313 Winter 09/10 J. Kim & M. Horowitz Handout # Page 1 of 5 HOMEWORK #3 (Due: Wednesday, Feb. 4, 2009: in class) 1. HSPICE Simulation for Velocity Saturated Model In the lectures, we learned many short channel effects in MOS transistors. In this problem
School: Stanford
Course: The Fourier Transform And Its Applications
EE261 Raj Bhatnagar Summer 2010-2011 EE 261 The Fourier Transform and its Applications Problem Set 3 Due Wednesday 13 July 1. (15 points) Convolution and cross-correlation The cross-correlation (sometimes just called correlation) of two real-valued signal
School: Stanford
Course: Convex Optimization I
EE364a, Winter 2011-12 Prof. S. Boyd EE364a Homework 4 solutions 5.27 Equality constrained least-squares. Consider the equality constrained least-squares problem minimize Ax b 2 2 subject to Gx = h where A Rmn with rank A = n, and G Rpn with rank G = p. G
School: Stanford
Course: Introduction To Statistical Signal Processing
EE 278B Statistical Signal Processing October 13, 2011 Handout #4 Homework #3 Due Thursday, October 20 1. Estimation vs. detection. Signal X and noise Z are independent random variables, where X= +1 with probability 1 with probability 1 2 1 , 2 and Z U[2,
School: Stanford
Course: Introduction To Statistical Signal Processing
EE 278B Statistical Signal Processing October 29, 2011 Handout #9 Homework #4 Solutions 1. (10 points) Coloring and whitening. a. We denote the eigenvalue and eigenvector matrices of as and U , respectively. After using linear algebra methods (or Matlab,
School: Stanford
Course: Digital MOS Integrated Circuits
EE313 Winter 2009-10 J. Kim & M. Horowitz Handout #8 page 1 of 10 SOLUTIONS TO HOMEWORK #0 Problem # 1 (1.1) Run HSPICE on etude1.sp. (1.2) Use CScope to look at the DC transfer characteristic curves. Notice that inverters with different ratios have diffe
School: Stanford
Course: INTRODUCTION TO LINEAR DYNAMICAL SYSTEMS
EE263, Autumn 2013-2014 Professor S. Lall EE263 Homework 2 Solutions 1. Some linear functions associated with a convolution system. Suppose that u and y are scalar-valued discrete-time signals (i.e., sequences) related via convolution: y (k ) = j hj u (k
School: Stanford
Course: Introduction To Statistical Signal Processing
EE 278B Statistical Signal Processing Tuesday, December 6, 2011 Handout #19 Homework #7 Solutions 1. (20 points) Autocorrelation functions. a. The mean function is X (t) = E[At + B ] = E[A]t + E[B ] = 0. The autocorrelation function is RX (t1 , t2 ) = E[(
School: Stanford
Course: Digital MOS Integrated Circuits
EE313 Winter 09/10 J. Kim & M. Horowitz Handout # Page 1 of 16 HOMEWORK #3 SOLUTIONS 1. HSPICE Simulation for Velocity Saturated Model (25pts) In the lecture, we learned many short channel effects in MOS transistors. In this problem, you need to run HSPIC
School: Stanford
EE 261 Fourier Transform and Applications February 16, 2011 Handout #13 Homework #5 Due Friday, February 25 1. Exercises on distributions. a. Let g (t) be a Schwartz function. Show that g (t) (t) = g (0) (t) g (0) (t) . b. Let Tf be the distribution induc
School: Stanford
Course: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2012 Solutions to Problem Set One 1. Some practice combining simple signals. (5 points each) The scaled triangle function with a parameter a > 0 is 1 1 a |t| , 0, a (t) = (t/a) = |t| a |t| > a The gra
School: Stanford
Course: VLSI Signal Conditioning Circuits
EE315A Spring 2009 B. Murmann Page 1 of 3 HOMEWORK #5 (Due: Tuesday, May 12, 2009, 1pm PT) 1. Consider the idealized single-stage OTA feedback circuit shown below. The OTA is described by the "OTA1" behavioral model discussed in class and has the followin
School: Stanford
Course: Analog Integrated Circuit Design
EE214 Winter 04/05 B. Murmann Handout #4 Page 1 of 2 HOMEWORK #1 (Due: Monday, October 4, 2004, noon PT) You will not need (and should not use) Spice for any part of this problem set. Use simple long channel MOS models in all problems and ignore fi
School: Stanford
Course: Digital Systems I
EE108B Spring 2003-2004 Prof. Kozyrakis EE108b - Problem Set #1 Solutions (Total 100 points) This homework assignment helps you to be familiar with MIPS assembly language. A full reference guide for MIPS instructions is available in section A.10 (Appendix
School: Stanford
EE263 Prof. S. Boyd EE263 homework 1 additional exercise 1. Ane functions. A function f : Rn Rm is called ane if for any x, y Rn and any , R with + = 1, we have f (x + y ) = f (x) + f (y ). (Without the restriction + = 1, this would be the denition of lin
School: Stanford
Course: The Fourier Transform And Its Applications
EE 261 The Fourier Transform and its Applications Fall 2009 Problem Set Eight Due Wednesday, November 18 1. (10 points) Dierent denitions for the DFT This is an alternate version, in one respect, to Section 6.9 in the notes, on dierent denitions of the DF
School: Stanford
Course: VLSI Signal Conditioning Circuits
EE315A Spring 2009 B. Murmann Page 1 of 1 HOMEWORK #1 (Due: Thursday, April 9, 2009, 1pm PT) 1. Cadence warm-up. Work through the "Virtuoso Tutorial" handout available on the course website under "CAD". Submit a printout of the circuit schematic and phase
School: Stanford
Course: INTRODUCTION TO LINEAR DYNAMIC SYSTEM
EE263 Autumn 2011-12 Prof. S. Lall EE263 homework problems 1. A simple power control algorithm for a wireless network. First some background. We consider a network of n transmitter/receiver pairs. Transmitter i transmits at power level pi (which is positi
School: Stanford
Course: Probability
EE 178 Probabilistic Systems Analysis Homework #2 Due Thursday, January 24, 2008 Handout #2 January 17, 2008 1. Catching the train. The probability that Riddley Walker goes for a run in the morning before work is 2/5. If he runs then the probabilit
School: Stanford
Course: Linear Dynamical Systems
EE263 Autumn 2012-13 Prof. S. Boyd EE263 homework 3 solutions 2.15 Gradient of some common functions. Recall that the gradient of a dierentiable function f : Rn R, at a point x Rn , is dened as the vector f x1 . . . f (x) = f xn , where the partial deriv
School: Stanford
Course: INTRODUCTION TO LINEAR DYNAMICAL SYSTEMS
EE263, Autumn 2013-2014 Professor S. Lall EE263 Homework 1 Solutions 1. Some standard time-series models. A time series is just a discrete-time signal, i.e., a function from Z+ into R. We think of u(k ) as the value of the signal or quantity u at time (or
School: Stanford
Course: Stochastic Control
EE365, Spring 2011-12 Professors S. Boyd, S. Lall, and B. Van Roy EE365 Homework 1 solutions 1.1 Optimal disposition of a stock. You must sell a total amount B > 0 of a stock in two rounds. In each round you can sell any nonnegative amount of the stock; b
School: Stanford
Course: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2012 Problem Set Nine Due Friday, December 7 1. (20 points) 2D Fourier Transforms Find the 2D Fourier Transforms of: (a) sin 2ax1 sin 2bx2 Solution: Because the function is separable we have F (sin 2a
School: Stanford
EE364a, Winter 2007-08 Prof. S. Boyd EE364a Homework 4 solutions 4.11 Problems involving 1 - and -norms. Formulate the following problems as LPs. Explain in detail the relation between the optimal solution of each problem and the solution of its equivalen
School: Stanford
Course: Circuits I
EE101A / Winter 2013 Prof. Simon Wong Homework #7 (Due March 6, 2013) You can use equations already derived in lecture notes or textbook. Please write your Name and Lab Section time on the front page. 1. Sedra & Smith, p. 341, Problem 5.79. The figure sho
School: Stanford
Course: VLSI Signal Conditioning Circuits
EE315A Spring 2009 B. Murmann Page 1 of 2 HOMEWORK #2 (Due: Thursday, April 16, 2009, 1pm PT) 1. Design a 4th order Butterworth lowpass filter with 0.3 dB maximum attenuation (worst case) in the passband (0 Hz to 500 kHz) and a nominal gain of 1. Implemen
School: Stanford
Homework #1 EE 282 Autumn 2008 Professor Kozyrakis Homework Set 1 Due: Wednesday, 10/15/2008, 5pm Please work in groups of 3 students Instructions: Submit to the box outside Gates 310 by the due date above. Show your work, state your assumptions, and just
School: Stanford
Course: Analog Integrated Circuit Design
6) c) The derivation below makes no assumptions, other than that the above-calculated small signal voltage gain accurately predicts the voltage swings at Vo1 and Vo2 and that the quiescent points do not shift in presence of the signal. The first stag
School: Stanford
Course: Linear Dynamical Systems
EE263 Autumn 2012-13 Prof. S. Boyd EE263 homework 2 solutions 2.21 Express the following statements in matrix language. You can assume that all matrices mentioned have appropriate dimensions. Here is an example: Every column of C is a linear combination o
School: Stanford
Course: Integrated Circuit Fabrication Processes
EE 212 FALL 09-010 HOMEWORK ASSIGNMENT #1 ASSIGNED: THURSDAY SEPT. 24 DUE: THURSDAY OCT. 1 ANSWER SHEET Reading Assignment: Chapters 1 and 2 in the text. #1. Spend 30 min or so scanning the information in the 2007 ITRS Executive Summary (on the class webs
School: Stanford
Course: EE - Digital CMOS Integrated Circuits
Custom WaveView User Guide Version F-2011.09-SP1, December 2011 Copyright Notice and Proprietary Information Copyright 2011 Synopsys, Inc. All rights reserved. This software and documentation contain confidential and proprietary information that is the pr
School: Stanford
Course: EE - Digital CMOS Integrated Circuits
EE213 Winter 2014-15 M. Horowitz page 1 of 15 VIRTUOSO TUTORIAL Introduction Before starting on this tutorial, please read the first few paragraphs of HW#2 which provide instructions on creating the proper working directory and sourcing the correct files.
School: Stanford
Course: EE - Digital CMOS Integrated Circuits
HSPICE Toolbox for MATLAB Michael Perrott (perrott@mtl.mit.edu) Copyright 1999 by Silicon Laboratories, Inc. 7 October 1999 The Hspice toolbox for Matlab is a collection of Matlab routines that allow you to manipulate and view signals generated by Hspice
School: Stanford
Course: Advanced Analog Integrated Circuit Design
CAD BASICS STANFORD UNIVERSITY Department of Electrical Engineering EE114/EE214A & EE214B Revised: January 2015 1 About This Handout This tutorial is composed of two parts. The first part is a quick start in which you will go through all the steps you nee
School: Stanford
Course: Advanced Analog Integrated Circuit Design
EE214B Winter 2014-15 B. Murmann Page 1 of 7 DESIGN PROJECT Part I due on Monday, March 2, 2015, 5pm Part II due on Wednesday, March 11, 2015, noon Overview In this project you will work on the design of the wideband transimpedance amplifier shown in Figu
School: Stanford
PRELAB 3 MORE OP-AMP CIRCUITS! If you cant fix it, make it a feature. Anonymous OBJECTIVES (Why am I doing this prelab?) To gain insight into op-amp application circuits beyond those considered in Lab 2. To understand the basics of analog filters. To u
School: Stanford
PRELAB 6 ADDITIONAL CIRCUIT CONCEPTS If you dont know where youre going, any path will take you there. Unknown OBJECTIVES (Why am I doing this prelab?) To learn about oscillators and how to simulate them in Spice. By Professor Gregory Kovacs Edited and U
School: Stanford
PRELAB 5 OPTOELECTRONIC CIRCUITS Its o.k. if we lose money on the product, well just make it up in volume! Harvard MBA Graduate OBJECTIVES (Why am I doing this prelab?) To learn about interfaces between the optical world and the electronic world. WHERES
School: Stanford
PRELAB 4 INTERFACE CIRCUITS AAAAAAAHHHHH. ZZZZZZ. FTHFPHTHTF. AAAAAHHHH! EE122 Student Who Tests Circuits with Wet Fingertips OBJECTIVES (Why am I doing this prelab?) To investigate some of the ways we interface electronics to the real world. WHERES MY P
School: Stanford
PRELAB 1 PHYSICAL & VIRTUAL INSTRUMENTS FOR ELECTRONICS The Future Begins Tomorrow! Motto of YoyoDyne Engineering in the movie Buckaroo Banzai OBJECTIVES (Why am I doing this prelab?) Review of basic instruments (physical and virtual). Review of electroni
School: Stanford
PRELAB 5 OPTOELECTRONIC CIRCUITS Its o.k. if we lose money on the product, well just make it up in volume! Harvard MBA Graduate OBJECTIVES (Why am I doing this prelab?) To learn about interfaces between the optical world and the electronic world. WHERES
School: Stanford
Chapter 5 Build a Photovoltaic Controller Photovoltaic cells are a great source of renewable energy. With the sun directly overhead, there is about 1kW of solar energy (energetic photons) per square meter of area. A photovoltaic panel converts this solar
School: Stanford
EE152 Lab 2 Revision 1, 30 Sep 2013 1 Energy Meter In this lab, youll build and program a meter that measures voltage, current, power, and energy at DC and AC. Assigned: October 1, 2013. Signoffs: Week of October 7, 2013. 1 New Code Download the code from
School: Stanford
EE152 Lab 4 Revision 1, 21 Oct 2013 1 Motor Control Part II Signoffs: Week of October 21, 2013. 1 Introduction In this lab you will implement the speed controller that you designed in the previous lab with real hardware and demonstrate that the motor can
School: Stanford
EE152 Lab 3 Revision 2, 17 Oct 2013 1 Motor Control Part I 1 Introduction This lab has two parts. For the rst week, you will characterize a brushed DC motor, build a mathematical model of it, and design a speed controller for it. In the second week, you w
School: Stanford
EE152 Lab 1 Revision 3, 30 Sep 2013 1 Lab 1: The Beginning This lab is an introduction to developing for an AVR microcontroller and the tools we will use for the rest of this course. Assigned: September 24, 2013. Signoffs: Week of September 30, 2013. 1 He
School: Stanford
Course: Circuits I
EE 101A / Winter 2013 Lab #7 (For week of 3/11) Lab 7: Switching Voltage Regulator 1. Motivation: Improving the Efficiency of Voltage Regulator In the AC-DC converter that you have built, we use a potentiometer with a source follower in the last stage to
School: Stanford
Course: Circuits I
EE 101A / Winter 2013 Lab #6 (For weeks of 2/25 and 3/4) Lab 6: Output Stages & the Source Follower 1. Motivation: Making our converter a better voltage source Weve spent several weeks building an AC/DC voltage converter to use as a power supply, which is
School: Stanford
Course: Circuits I
EE 101A / Winter 13 Lab #5 (For week of 2/18) Lab 5: Enabling Variable Output Voltage 1. Motivation: Now that we have a nice DC signal, lets add versatility! After adding the Zener diode in the last lab, we have a DC output around 15 V. We could stop here
School: Stanford
Course: Circuits I
EE 101A / Winter 13 Lab #4 (Week of 2/4/13) Lab 4: Voltage Regulation Sedra & Smith, Chapter 4.5 1. Motivation: How can we get rid of those ripples? As you measured in the previous lab, the rectifier output is single polarity, but still half a sine wave.
School: Stanford
Course: Circuits I
EE 101A / Winter 13 Lab #3 (Week of 1/28/13) Lab 3: Full Wave Rectifier Background Reading : Sedra & Smith, Chapter 4.5 1. Motivation: Output voltage is not even close to DC! The role of measurement equipment, such as the oscilloscope, is pretty obvious e
School: Stanford
Course: Circuits I
EE 101A / Winter 13 Lab #2 (Week of 1/21/13) Lab 2: Diode Characterization Background Reading : Sedra & Smith, Sections 4.1 4.4 1. Motivation: We will use silicon diodes to convert AC voltage to DC voltage in next Lab. In this lab, we will characterize th
School: Stanford
Course: Circuits I
EE 101A / Winter 2013 Lab #1 (Week of 1/14) EE 101A Lab Introduction Welcome to EE101A! The lab component of the course is intended to complement the material you learn from lecture by having you analyze and build a very useful circuit using basic electro
School: Stanford
Course: Circuits I
EE 101A / Winter 2013 Optional Lab #0 Open Lab (Packard 064, Wed 1/9 and Thur 1/10, 7pm-9pm) EE101A - LABORATORY FAMILIARIZATION OBJECTIVES To provide an introduction to the electronics laboratory environment and test equipment. The instruments available
School: Stanford
Course: Digital Systems II
EE108B Fall 2012-13 Prof. Olukotun EE 108B Lab Assignment #4 Caches Due: Thursday, December 6, 2012 1. Introduction At this point you have created a single-cycle microprocessor and added pipelining to it. Up to now we have modeled memory accesses simply a
School: Stanford
Course: Digital Systems II
./._lab3# #000755 #000765 #000024 #00000000445 12045332372 012456# 0# #ustar#00chris#staff# #000000 #000000 # # #Mac OS X # #2# %#ATTR# %### %com.apple.metadata:kMDItemWhereFroms#bplist00#_#Nsftp:/corn.stanford.edu/afs /ir.stanford.edu/users/c/h/chrisnc/e
School: Stanford
Course: Digital Systems II
EE108B Fall 2012-13 Prof. Olukotun EE 108B Lab Assignment #3 Pipelining Due: Tuesday, November 13, 2012 1. Introduction Pipelining has introduced huge performance gains to the processor. With these performance gains, there has been additional complexity i
School: Stanford
Course: Digital Systems II
irom changed to combinational logic why pc+8
School: Stanford
Course: Digital Systems II
EE108B Fall 2012-13 Prof. Olukotun EE108B Lab 2 Processor Datapath Design Due: Thursday, November 1st Introduction: Now that you have seen some of the benefits of the software approach to problems, we will spend the next three labs building a processor th
School: Stanford
Course: Digital Systems II
EE108B Prof. Olukotun Fall 2012 EE 108B Lab Assignment #1 MIPS Assembly Programming Due Tuesday, October 16, 2012 1. Introduction Throughout these labs, you will be designing a processor that can execute programs written in MIPS assembly. Once th
School: Stanford
Course: Digital Design Laboratory
EE 121 Digital Design Laboratory October 3, 2002 Handout #6 Laboratory Assignment #2 Laboratory Familiarization: the Real (Analog) World Due date: to be completed in lab from October 711, 2002 The following is an introduction to using the equipment in the
School: Stanford
Course: Circuits I
EE 101A / Winter 10 Lab #7 Lab 7: SPICE (For week of 3/8) You can use the PSpice simulator in the Lab, or the PSpice CD in the back cover of your Sedra & Smith textbook. 1. Motivation: The role of circuit simulation Another important tool for a circuit de
School: Stanford
Course: Circuits I
EE 101A / Winter 2010 Lab #6 (For weeks of 2/22 and 3/1) Lab 6: Output Stages & the Source Follower 1. Motivation: Making our converter a better voltage source Weve spent several weeks building an AC/DC voltage converter to use as a power supply, which is
School: Stanford
Course: Circuits I
EE 101A / Winter 10 Lab #5 (For week of 2/15) Lab 5: Enabling Variable Output Voltage 1. Motivation: Now that we have a nice DC signal, lets add versatility! After adding the Zener diode in the last lab, we have a DC output around 15 V. We could stop here
School: Stanford
Course: Circuits I
EE 101A / Winter 10 Lab #4 (Week of 2/1) Lab 4: Voltage Regulation Sedra & Smith, Chapter 3.5 1. Motivation: How can we get rid of those ripples? dr As you measured in the previous lab, the rectifier output is single polarity, but still half a sine wave.
School: Stanford
Course: Circuits I
EE 101A / Winter 10 Lab #3 (Week of 1/25/10) Lab 3: Full Wave Rectifier Background Reading : Sedra & Smith, Chapter 3.5 1. Motivation: Output voltage is not even close to DC! The role of measurement equipment, such as the oscilloscope, is pretty obvious e
School: Stanford
Course: Circuits I
EE 101A / Winter 10 Lab #2 (Week of 1/18/10) Revised Lab 2: Diode Characterization Background Reading : Sedra & Smith, Sections 3.1 3.4 1. Motivation: We will use silicon diodes to convert AC voltage to DC voltage in next Lab. In this lab, we will charact
School: Stanford
Course: Circuits I
0.8 SolarCellModel withoutillumination Rleakage diode ID withoutillumination subtractingleakage 0 .6 0.4 0.2 0 leakage VD 0 0.5 underillumination 1 1 0.5 0 .2 0.4 SolarCellModel underillumination Iillumination diode
School: Stanford
Course: Circuits I
AC Power P=VI, P =I2R two 110V AC, 180o out of phase (two phases) Sint ( Sint ) = 2 Sint Si hot hot neutral hot Electrical Appliance Hot Neutral Metal Chassis to Ground Power Grid Transformers Power Sub-Station 100-500KV to 7200V Transformer Drum 7200V to
School: Stanford
Course: Circuits I
EE 101A / Winter 2010 Lab #1 EE 101A Lab Introduction Welcome to EE101A! The lab component of the course is intended to complement the material you learn from lecture by having you analyze and build a very useful circuit using basic electronic components.
School: Stanford
Course: Circuits I
EE 101A / Winter 2010 Optional Lab #0 Open Lab (Packard 064, Wed 1/6 and Thur 1/7, 7pm-9pm) EE101A - LABORATORY FAMILIARIZATION OBJECTIVES To provide an introduction to the electronics laboratory environment and test equipment. The instruments available
School: Stanford
EE 121 Digital Design Laboratory October 10, 2002 Handout #10 Laboratory Assignment #3 Floating Point Conversion Due date: Friday, October 18. Prelab due: Tuesday, October 15 For this laboratory assignment, you will use Xilinx Foundation software t
School: Stanford
Course: Solid State Physics II
TutorialonPC1D MohitMehta ProgramDescription PC1Dsolvesthefullycouplednonlinearequationsforthe quasi1dtransportofelectrons&holesincrystalline semiconductordevices,withemphasisonphotovoltaic devices. OnlyfilerequiredtoruntheprogramisPC1D.exe. PC1D.hlppro
School: Stanford
Course: Fundamentals Of Analog Integrated Circuit Design
EE114/ 214A Review Session 2 Simon Basilico and Yaoyu Tao Stanford University taoyaoyu@stanford.edu basilico@stanford.edu A. Arbabian, R. Dutton, B. Murmann EE 114/214A 1 Important Announcements Start HW2 as soon as possible as it requires HSpice setup a
School: Stanford
Course: Fundamentals Of Analog Integrated Circuit Design
EE114/ 214A Review Session 1 Jayant Charthad Stanford University jayantc@stanford.edu A. Arbabian, R. Dutton, B. Murmann EE 114/214A 1 Important Announcements Please make sure you are enrolled on the course website and you are getting course announcement
School: Stanford
Course: Introduction To Computer Networks
EE 284 F. Tobagi Autumn 2014-2015 EE284 Review Session #7 Topics: Reservation ALOHA, Slotted ALOHA, Performance, CSMA/CD November 7, 2014 1 Reservation ALOHA In Reservation ALOHA. Packets belonging to the same message do not contend for the channel on the
School: Stanford
Course: Introduction To Computer Networks
EE 284 F. Tobagi Autumn 2014-2015 EE284 Review Session No. 10 Topic: TCP December 5, 2014 Problem 1: TCP Consider two hosts A and B that have data to be exchanged using the Transmission Control Protocol (TCP). In this problem, we will assume that the prop
School: Stanford
Course: Introduction To Computer Networks
EE 284 F. Tobagi Autumn 2014-2015 EE284 Review Session No. 9 Topic: Internetworking between Bridges and Routers, Virtual circuit routing November 21, 2014 Problem 1: Internetworking between Bridges and Routers Segment 3 D C 2 ROUTER 171.1.2.12 AA:BB:CC:DD
School: Stanford
Course: Introduction To Computer Networks
EE 284 F. Tobagi Autumn 2014-2015 EE284 Review Session No. 8 Topic: Bridging - performance, Bridging scenario November 14, 2014 Problem 1: Bridging - Performance Consider K LAN segments that equally divide the LAN, such that the amount of trac generated p
School: Stanford
Course: Introduction To Computer Networks
EE 284 F. Tobagi Autumn 2014-2015 EE284 Review Session #6 Topics: Probability Distributions October 31, 2014 1 The Poisson Distribution The Poisson distribution is a discrete probability distribution that gives the probability that a certain number of eve
School: Stanford
Course: Introduction To Computer Networks
EE 284 F. Tobagi Autumn 2014-2015 EE284 Review session 3 Topics: CRC 1 CRC a. Given the message 100100100100100 and generator sequence 10011, what then is the checksummed message? (Show your computation.) b. In a CRC detection, if a generator polynomial i
School: Stanford
Course: Introduction To Computer Networks
EE 284 F. Tobagi Autumn 2014-2015 EE284 Review Session #4 Part #2 Topics: Sliding Window Control October 23, 2014 Review of Sliding Window Flow Control Mechanism: 1) Acknowledgement - ACK(N): Ackowledges the error-free receipt of all frames upto and inclu
School: Stanford
Course: Probabilistic System Analysis
LECTURE NOTES Course 6.041-6.431 M.I.T. FALL 2000 Introduction to Probability Dimitri P. Bertsekas and John N. Tsitsiklis Professors of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge, Massachusetts These notes
School: Stanford
Course: Probabilistic System Analysis
EE178 Introductory lecture Monday, September 26, 2011 Outline EE178 Probability Goals Topics Administrative stuff Monday, September 26, 2011 what is EE178/278A? probability + statistics + EE examples ~ Stat 116, Math 151 important background for E
School: Stanford
Course: Introduction To Computer Networks
Course Administration EE284 Introduction to Computer Networks Instructor: Professor Fouad Tobagi Gates 339 Telephone: 650-723-1708 E-mail: tobagi@stanford.edu Office hours: TBD Teaching Assistant: Bhrugurajsinh Chudasama E-mail: bhrugu@stanford.edu EE2
School: Stanford
Course: Optical Micro- And Nano-cavities
EE340: Optical micro- and nano-cavities Instructor: Jelena Vuckovic Spring 2012 Syllabus (tentative) Part 1 Introduction to optical resonators Lossless hollow rectangular resonator Losses in a resonator. Quality (Q) factor of a resonator Finesse, free-
School: Stanford
Course: Optical Micro- And Nano-cavities
EE340: Optical micro- and nano-cavities Instructor: Jelena Vuckovic Spring 2011 Mon Wed Fri 10 - 10:50 am Classroom: Y2E2 111 Class web-site http:/www.stanford.edu/class/ee340 (lecture notes and assignments are posted on the coursework portion of the clas
School: Stanford
Handout #2 March 28, 2011 CS103 Robert Plummer CS103 Syllabus Date Day Lecture # Topic PS Due Reading I. Logic, Sets, Relations, and Functions (8 lectures) 3/28 M 1 Intro, propositional logic, truth tables equivalences, De Morgan's Laws 3/30 W 2 Predicate