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1stlecture_overviewjan23

Course: ENGR 160, Fall 2009
School: Wisconsin
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160: InterEgr Introduction to Engineering Design Spring 2008 Eman Zaki & John Murphy Course Coordinators InterEgr 160: Introduction to Engineering Design Eman Zaki zaki@engr.wisc.edu (608) 265-8268 1150 Engineering Hall John Murphy jmurphy@engr.wisc.edu (608) 265-4186 147 Engineering Research Building InterEgr 160: Introduction to Engineering Design Course Structure Guest lecturers {technical and...

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160: InterEgr Introduction to Engineering Design Spring 2008 Eman Zaki & John Murphy Course Coordinators InterEgr 160: Introduction to Engineering Design Eman Zaki zaki@engr.wisc.edu (608) 265-8268 1150 Engineering Hall John Murphy jmurphy@engr.wisc.edu (608) 265-4186 147 Engineering Research Building InterEgr 160: Introduction to Engineering Design Course Structure Guest lecturers {technical and human issues} Lab instructors SAs {student assistants} Assigned specifically to a lab Lead SAs {consulting lab sections, free floating} Extra seminars on welding, fabrication, electronics, etc. Lab Section Schedule on Web Page {seating chart} With instructor and SA names Questions of the Week {QOW} Starting week #2 {lab instructors will explain use} Syllabus {on web page} Syllabus is fluid, schedule may change Set up a CAE account {should have email accounts} http://www.engr.wisc.edu/interegr/courses/interegr160.html InterEgr 160: Introduction to Engineering Design http://www.engr.wisc.edu/interegr/courses/interegr160.html From College of Engineering Web Page http://www.engr.wisc.edu Course Homepages Interdisciplinary Courses (Engineering) (INTEREGR) INTEREGR 160: Introduction to Engineering _Course Homepage_ Instructor Pages InterEgr 160: Introduction to Engineering Design Lecture 2 days a week {seminar format} Lab sections once a week {3 hours} "Real World" engineering project with client 1st time through in small teams {4 students} 2nd time through for real {end product} Required behavior Stay focused in lecture and lab Outstanding speakers as guest lecturers NO newspapers, NO laptops Keep notes in journal for grading InterEgr 160: Introduction to Engineering Design Must purchase a lab/lecture journal Computation Notebook {43-648} Or something similar NO spiral notebooks Tape information {i.e. lectures} in journals Journals will be graded by instructors Purchase journals {ASME 1800 EH} Or UW Bookstore NO textbook {except Al Gomez's section} InterEgr 160: Introduction to Engineering Design Grading Instructors determine grades Generally 50% from team components Meaning shared a grade with team members {no separation} 50% from individual work Lab/lecture journals {note taking & participation} QOW Small team presentations Weekly emails Peer reviews Success in this class Do the little things {instructor will explain} Communication, communication, communication {3 Xs enough} This means listening Work well in a team environment Have fun {enthusiasm increases value of work and product} InterEgr 160: Introduction to Engineering Design Project for client {discussed in lab} Final requirements {in place of final exam} Provide product {that works} Presentation {PP} to client {professional} Design report InterEgr 160: Introduction to Engineering Design Next week's lectures Design process overview John Murphy Bring your "A" game Meat & Potatoes of the class Thorough notes {available on web} Design Process Overview What is design? process of going from specific needs to a product that meets those needs Design Process Overview Important aspects of design must be useful (customer needs) be safe Measures of product/design cost, quality and time (lab tower building) customer and management want it cheaper, better and faster Final designs may be radically different, but "roadmap" for process t...

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Wisconsin - ENGR - 160
InterEgr 160: Final LectureEman Zaki & John Murphy Course Coordinatorszaki@engr.wisc.edu (608) 265-8268 jmurphy@engr.wisc.edu (608) 265-4186InterEgr 160 Class expectations {seminar format} Worked Lab sections Real World engineering project
Wisconsin - ENGR - 160
InterEgr 160 Fall 2007 FINAL PRESENTATIONS PRACTICE SCHEDULE(This is the official schedule. You are welcome to make other arrangements on your own.)SA's select one half hour for your team.Thursday, December 6 3:00-7:30 1800 E Hall 10:00-10:30 1
Wisconsin - ENGR - 160
Name Jennifer Binzley Dan Goeser Jennifer Gerschke John Murphy Eman Zaki Dave Huth Sarah Treu Alan Gomez Tim Knapp Adam Steen Nick Hichon Hallie Kreitlow Amit Nimunkar Joshua Kundert Silas Bernardoni Jon Christian Ken Walz Ramsey Kropp , , , kropp@wi
Wisconsin - ME - 349
The Cutting EdgeUpdate PresentationME349 Nick Hoel 3 Jeff Neis 1 Scott Neis 2 Brad Schaller 4 April 14, 2004Project ReviewCombine functions of a string trimmer and a riding lawnmower deckDesign Goals Compatible with variety of mower decks Won
Carnegie Mellon - DP - 21
Flynn Jones 4/25/2006 15-394 Assignment 6NYC Webcam Report Part 3AbstractLow resolution camera already installed throughout the United States can be used as a way to better resolve irregular traffic flow within a given area. This could possibly b
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Verifying Patterns from Stopped Traffic at IntersectionsCiera Christopher Institute for Software Research, International Carnegie Mellon University cchristo@cs.cmu.edu Abstract This paper examine where a driver stops relative to the white stop line.
Carnegie Mellon - TR - 814
Uniform Distributions on the Natural NumbersOliver SchirokauerDepartment of Mathematics Oberlin College Oberlin, OH 44074 U.S.AJoseph B. KadaneDepartment of Statistics Carnegie Mellon University Pittsburgh, PA 15213 U.S.AAbstract. We compare
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Calculating RemittitursJoseph B. Kadane1Abstract This note reviews several methods proposed for calculating the amount of an appropriate remittitur using a sample of "comparable" cases. It recommends a simple quantile estimate.A remittitur is a p
Carnegie Mellon - STAT - 36
, '~"~ J@aing and Inference 4 (19$@45-54). @aN6ktii~kIb~MdAlb?is~ir$ Company -5.' ._c',(>' ' a+/ _ :-loF *?iixbiz- NUMBIER 6x, ,~`~,_,~; `c;RdlpHI v) II," ,`(3 _I &;:,ry"&)-.r .: I&3pmmeti, , ^,i' , .;. > ,."A , s\ JI i ' ./1":. ^c%f
Carnegie Mellon - STAT - 217
Handout 3 Supplements material in Sections 2.1 and 2.2 in L-GQuick Review of Set Theory Let be a set and denote the elements in by .The empty set contains no elements and is a subset of every set, . A subset of is a collection of elements of , and
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Golden Redux-1. Changes in the gridpoints of Golden when the endpoints of the search interval are changed.2. Remaking the Golden program to more efficiently use memory, with pointers.--1. Changes in the gridpoints of Golden when the en
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Gre The tical I de I n Com r S nce at ore as pute cie John Laffe rty Le cture24 Nove be 16, 2006 m r C 15-251 S Fall 2006 C gieMe Unive arne llon rsityGradeS chool Re visite d: How To Multiply Two Num rs be2X2= 5Thebe way is st ofte far from n o
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Great Theoretical Ideas In Computer Science Anupam Gupta Lecture 16 Oct 19, 2006 CS 15-251 Fall 2006 Carnegie Mellon UniversityPolynomials, Secret Sharing, And Error-Correcting CodesP(X) = X3 +X2 +X1 +Polynomials in one variable over the re
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Great Theoretical Ideas In Computer Science Anupam Gupta Lecture 1 Aug 29th, 2OO6 CS 15-251 Fall 2006 Carnegie Mellon UniversityPancakes With A Problem!Fall 06 Course StaffProfs: Anupam Gupta John Lafferty Albert Sheu Andreas Krause Todd Phillip
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Great Theoretical Ideas In Computer Science Anupam Gupta Lecture 3 Sept 5, 2006 CS 15-251 Fall 2006 Carnegie Mellon UniversityInduction II: Inductive PicturesInductive Proof: "Standard" Induction "Least Counter-example" "All Previous" Induction a
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AREA FORECAST DISCUSSIONNATIONAL WEATHER SERVICE MILWAUKEE/SULLIVAN WI309 PM CST SUN FEB 23 2003FCST FOCUS ON DVLPG LGT SNOW TNGT.THEN TEMPS ESPCLY MON NGT.HAVE BEEN WATCHING LGT SNOW DVLP OVR IOWA AND SRN MN AND PROGS INGNRL AGRMNT WITH MOVG
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FXUS63 KMKX 240928AFDMKEAREA FORECAST DISCUSSIONNATIONAL WEATHER SERVICE MILWAUKEE/SULLIVAN WI328 AM CST MON FEB 24 2003FCST FOCUS ON END OF LGT SNOW TODAY.THEN EFFECTS OF ARCTIC AIRMASSSETTLING IN OVER REGION.LGT SNOW TAPERING OFF FROM WES
Wisconsin - CS - 701
Finding Additional Independent Instructions for Parallel IssueWe can extend the capabilities of processors: Out of order execution allows a processor to "search ahead" for independent instructions to launch.Reading AssignmentRead pp 367-386 o
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'Fault Finding and Fixing' Combinations and Chance Tasks - Set #2 (solutions)Malcolm Swan Mathematics Education University of Nottingham Malcolm.Swan@nottingham.ac.uk Jim Ridgway School of Education University of Durham Jim.Ridgway@durham.ac.uk Comb
Wisconsin - CL - 1
'Fault Finding and Fixing' Percentages Tasks - Set #1Malcolm Swan Mathematics Education University of Nottingham Malcolm.Swan@nottingham.ac.uk Jim Ridgway School of Education University of Durham Jim.Ridgway@durham.ac.uk Percentages are a common sou
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MS&E 371 Problem Solving II VBA Naming Conventions Variables Data Type Boolean Byte Integer Long Single Double Currency Decimal Date String Variant Object Controls Control Form Command Button Label Text Box Option Button Check Box List Box Combo Box
Carnegie Mellon - PS - 70208
70208 Regression Analysis Dan LiHomework 2Due May. 31, 05 Total points: 200 Important: You should turn in the project proposal together with this homework. Guideline for this homework:Empirical part of the problem set can be done in either R or e
Carnegie Mellon - V - 30
INSTALLATIONHow to install Mobal 3.0b (Beta release) for SunOS 4.1.* and X11 1. Select a directory that is to become Mobal's home directory.This should be an empty new directory in which the different Mobalsubdirectories will go.2. Set the env
Carnegie Mellon - OC - 1
Package: Name: Summary: Version: Description:areas/learning/systems/oc1/ OC1 Oblique Classifier 1OC1 (Oblique Classifier 1) is a multivariate decision tree induction system designed for applications where the instances have numeric feature values
Carnegie Mellon - AQ - 1
Package: Name: Summary: Version: Description:areas/learning/systems/learn_pl/aq1/ AQ-Prolog Reimplementation of Michalski's AQ for attribute vectors 14-JAN-94This directory contains Jeffrey M. Becker's AQ-PROLOG, a reimplementation of Michalski's
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Package: Name: Summary: Version: Description:areas/learning/systems/learn_pl/arch1/ ARCH1 Winston's incremental learning procedure. 14-JAN-94This directory contains ARCH1, Stefan Wrobel's implementation of Winston's incremental learning procedure
Carnegie Mellon - FOIL - 2
/*FOIL.2.1-Summary of changes from FOIL.1:-* arbitrary number of constants* arbitrary constant names* incorporation of types and keys* modified calculation of encoding length* option to prohibit negated literals* some more risk-fr
Carnegie Mellon - OC - 1
- ANNOUNCEMENT OC1 is now available via Anonymous FTP.- OC1 (Oblique Classifier 1) is a multivariate decision treeinduction system designed for applications where the instances havenumeric feature values. O
Wisconsin - A - 636
O212.956MeUnknown A08 in CDCl3 Athena (Bruker-AC 250) 13C spectrum200198.123O CH3 O158.233150133.25210077.682 77.258 76.834 76.591 75.821CDCl3 ref'ed to 77.25ppm57.05850 PPM48.88737.518 35.031 33.232 28.339 27.893 25.997
Wisconsin - C - 636
Sample F41 (F:C-01), Spring 2003 Unknowns24 Mar 2003 D. R. Mowrey Name Code Solvent(s) Operator(s) Notes Acquisition Time (sec) File Name Nucleus Sweep Width (Hz) F41 CDCl3 Brian Popp None Class Spring 2003 Mass Spec. Synthesized by FW 215.0 [+23 Na
Wisconsin - N - 636
Unknown N06 in CDCl3 Athena (Bruker-AC 300) 1H spectrumCH3O C Ph O CH2CH2 CH23.63.43.23.02.82.6PPMCH2CH3all expansions but 12.6ppm at 20Hz/cmN CH31.2 1.1 1.0 0.9 0.8 0.7 PPM1.85 1.80 1.75 1.70 1.65PPM1.96 3.265.50
Wisconsin - A - 11
Unknown A11 in CDCl3 INOVA-600 HSQCAD 1H-13C 1-bond hsqc spectrum1200x400 FT'd to 2048x1024 gaussian apodization How does one make the 2 and 3 assignments without this spectrum (and then confirm the chirality of 1 without the NOE data)?11 12 10 9 8
Wisconsin - P - 60
Unknown P60 in CDCl3 Athena (Bruker-AC 300) 1H spectrum10Hz/cm 10Hz/cmH3C NCH3+3.75 3.70 3.65 3.60 3.55 3.50 3.45 3.40 PPM 2.72 2.70 2.68 2.66 PPMHCH220Hz/cm 10Hz/cm2.12.01.91.81.7PPM5.047.357.307.257.207.15PPM
Wisconsin - B - 14
030124_21 59 (1.091) Cm (57:78-200:220) 100361.1B14 in methanol LCT S. Przybylinski699.1TOF MS ES+ 2.77e4%700.2 362.1 701.202002503003504004505005506006507007508008509009501000m/z030124_21 59 (1.091)
Wisconsin - A - 11
Unknown A11 in CDCl3 INOVA-600 gcosy 1H COSY spectrumF2 (ppm) 1.2 1.47a 7b-OH 10b 12b 6 12a 10a 11a 3a 11b15 3b 2a 2b161024x256 FT'd to 1024x1024 sinebell-squared apodization symmetrized11 12 10 9 8 7 61.6 1.8 2.0 2.2 2.4 2.6 2.8H3C3
Wisconsin - E - 636
E03 (E:A-03) in CDCl3 orginally labelled E05 Athena Automation run Jan 16, 2003 D. Mowrey8.07.87.67.47.27.06.8PPMExpansions are 20 Hz/cmCH3OCH3HOO2.22.12.01.91.8PPM4.40 1.994.34PPM 3.00 3.011.981.98 2
Wisconsin - C - 13
Unknown O13 in CDCl3 Athena (Bruker-AC 300) 1H spectrumO O CH3all expansions at 20Hz/cmOCH33.43.33.23.1PPM2.952.012.020.990.981.00 1.001.962.018.07.97.87.77.67.57.44.36 4.32 PPM 4.38 4.34 4.30 PPM2.
Wisconsin - C - 636
Unknown O13 in CDCl3 Athena (Bruker-AC 300) 1H spectrumO O CH3all expansions at 20Hz/cmOCH33.43.33.23.1PPM2.952.012.020.990.981.00 1.001.962.018.07.97.87.77.67.57.44.36 4.32 PPM 4.38 4.34 4.30 PPM2.
Wisconsin - C - 42
Unknown O42 in CDCl3 Athena (Bruker-AC 300) 1H spectrumCH3 O OO2.25 2.20 2.15 2.10 2.05 2.00 1.95 1.90 PPM3.5 8.1 8.0 7.93.063.42.993.33.23.1PPM7.87.77.6PPM3.002.00all expansions at 20Hz/cm1.07 1.051.041.041.00
Wisconsin - C - 636
Unknown O42 in CDCl3 Athena (Bruker-AC 300) 1H spectrumCH3 O OO2.25 2.20 2.15 2.10 2.05 2.00 1.95 1.90 PPM3.5 8.1 8.0 7.93.063.42.993.33.23.1PPM7.87.77.6PPM3.002.00all expansions at 20Hz/cm1.07 1.051.041.041.00
Wisconsin - C - 10
Unknown O10 in CDCl3 Athena (Bruker-AC 300) 13C spectrum180 160169.844CH2O O C140CH3120 100 80148.982 132.341 130.753 130.696 130.498 130.047 129.923 128.757 128.165 128.005 127.490 126.866 126.611 126.553 123.311 121.414 121.297 115.
Wisconsin - C - 636
Unknown O10 in CDCl3 Athena (Bruker-AC 300) 13C spectrum180 160169.844CH2O O C140CH3120 100 80148.982 132.341 130.753 130.696 130.498 130.047 129.923 128.757 128.165 128.005 127.490 126.866 126.611 126.553 123.311 121.414 121.297 115.
Wisconsin - C - 636
Unknown H82 (H:E-02) in CDCl3 Athena Automation 300MHz cgfryinteg=.75 exp=200Hz/cmall other expansions 20Hz/cm2.9 13.0 12.5 12.0 11.5 11.0 10.5 10.0 PPM2.82.72.62.52.42.32.22.1PPM3.55 3.50 3.45 3.40 3.35 3.30 3.25 PPM8.0
Wisconsin - C - 82
Unknown H82 (H:E-02) in CDCl3 Athena Automation 300MHz cgfryinteg=.75 exp=200Hz/cmall other expansions 20Hz/cm2.9 13.0 12.5 12.0 11.5 11.0 10.5 10.0 PPM2.82.72.62.52.42.32.22.1PPM3.55 3.50 3.45 3.40 3.35 3.30 3.25 PPM8.0
Wisconsin - C - 636
Unknown B05 NOESY1D data from INOVA-600, H15 selectedmix=1s, d1=10s, nt=16H1320 1H9H8 7H12 11 16 152 3 4 18 1710 5 6O191714H H14HHH158.07.87.67.47.27.06.86.66.4ppm3.63.43.2
Wisconsin - B - 636
Unknown B05 NOESY1D data from INOVA-600, H15 selectedmix=1s, d1=10s, nt=16H1320 1H9H8 7H12 11 16 152 3 4 18 1710 5 6O191714H H14HHH158.07.87.67.47.27.06.86.66.4ppm3.63.43.2
Wisconsin - C - 26
Unknown Y26 in CDCl3 Hermes (Mercury-300) 1H spectrumdataset: ta_Y26_146_17_02HN+Cl-2.82.62.42.22.01.8PPMall expansions at 20 Hz/cm1.41.31.21.11.00.9PPM7.75 7.70 7.65 7.60 7.55 7.50 7.45 7.40PPM2.902.9
Wisconsin - C - 636
Unknown Y26 in CDCl3 Hermes (Mercury-300) 1H spectrumdataset: ta_Y26_146_17_02HN+Cl-2.82.62.42.22.01.8PPMall expansions at 20 Hz/cm1.41.31.21.11.00.9PPM7.75 7.70 7.65 7.60 7.55 7.50 7.45 7.40PPM2.902.9
Wisconsin - C - 24
Unknown L24 in CDCl3 Athena (Bruker-AC 300) 1H COSY spectrum512x128 FT'd to 512x512 sinebell-squared apodization symmetrizedO OC2H5PPM F2H3C HC H3C O6NO NO 258765 PPM F14321874321
Wisconsin - A - 636
201.857Unknown A07 in CDCl3 Athena (Bruker-AC 250) 13C spectrum200O OEt COOH174.521 173.128150140.929 135.660 131.499 128.917 128.255 127.195 126.824 124.899 124.030 123.79810093.132CDCl3 refed to 77.25ppm77.760 77.250 76.74364.29
Wisconsin - A - 636
77.673 77.250 76.82642.82736.357179.065Unknown A09 in CDCl3 Athena (Bruker-AC 250) 13C spectrumH3C CH CH2COOH145.653128.796 126.928 126.738CDCl3 ref'ed to 77.25ppm2001501005022.0700PPM
Wisconsin - A - 10
Unknown A10 in CDCl3 Athena (Bruker-AC 250) 13C spectrum180MeO172.260160 140159.793141.532 137.744CH3O126.656 126.481 126.301 124.710 119.295120 100 80 60 40 20 PPMCH2 C O OCH3CDCl3 ref'ed to 77.25ppm107.16577.673 77.250 76
Wisconsin - C - 636
030124_20 60 (1.100) Cm (60:74-177:196) 239.1 100F80 in methanol LCT S. PrzybylinskiTOF MS ES1.55e4%240.1 237.1 479.20150200250300350400450500550600650700750m/z030124_20 60 (1.100) Cm (60:74-177:196) 100239.1
Wisconsin - C - 82
030124_18 54 (0.990) Cm (49:71-124:152) 297.0 100H82 in methanol LCT S. PrzybylinskiTOF MS ES8.36e4%298.1595.1596.2 655.40200225250275300325350375400425450475500525550575600625650675700725750
Carnegie Mellon - COMMALG - 05
CA LECTURE 8SCRIBE: MATTHEW WRIGHTNew topic: Localisation (we see the geometrical reason behind this word later in the course). Recall the "field of fractions" construction for an ID R. We let X be the set of pairs (a, b) where a, b R with b = 0
Carnegie Mellon - COMMALG - 05
COMMUTATIVE ALGEBRA HW 2 SOLUTIONSJCDue in class Mon 5 September. (1) A prime ideal J is minimal i for all prime I J we have I = J. Show that every prime ideal contains a minimal prime ideal. Note that any family of sets is partially ordered by .
Carnegie Mellon - WEEK - 10
Announcements for March 21 Announcements:1. The URL of the page should be:http:/www.andrew.cmu.edu/course/88120/2. 2nd exam on Thursday 1. Paper outlines due the week we return from breakReason, Passion, & Social CognitionWeek 10, Part 1
Carnegie Mellon - WEEK - 15
Agenda for May 21. Logistics for class dinner party What to bring Where to meet Guest 2. Finish emotional control Review class data Review "Big Questions" 3. General review for exam 4. Return papersReason, Passion, & Social CognitionWeek 15
Carnegie Mellon - WEEK - 9
AnnouncementsforMarch16GeneralAnnouncements1. Notesonpapertopics Narrowtheindependentanddependentvariables Donotincludedissertations,ifpossible1. Thisweeksdiscussants:Liz,Margo,Justin 2. Responsestomidtermevaluations Websitewillbeupsoon Mov
Wisconsin - BIRTHTO - 3
FBAWhat Are Children Trying to Tell Us?: Assessing the Function of Their Behavior by Lise Fox and Michelle DudaReprinted from the What Works Briefs from the Center on the So