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SUNY Buffalo - EE - 203
SMALL for Big ThingsUniversity at BuffaloSMALL for Big ThingsUniversity at BuffalonanobioSensors & MicroActuators Learning LabThe State University of New YorknanobioSensors & MicroActuators Learning LabThe State University of New YorkThe Effect of
SUNY Buffalo - EE - 203
SMALL for Big ThingsUniversity at BuffaloSMALL for Big ThingsUniversity at BuffalonanobioSensors & MicroActuators Learning LabThe State University of New YorknanobioSensors & MicroActuators Learning LabThe State University of New YorkTwo circuits
SUNY Buffalo - EE - 203
SMALL for Big ThingsUniversity at BuffaloSMALL for Big ThingsUniversity at BuffalonanobioSensors & MicroActuators Learning LabThe State University of New YorknanobioSensors & MicroActuators Learning LabThe State University of New YorkEE 203 Circui
SUNY Buffalo - EE - 203
SMALL for Big ThingsUniversity at BuffaloSMALL for Big ThingsUniversity at BuffalonanobioSensors & MicroActuators Learning LabThe State University of New YorknanobioSensors & MicroActuators Learning LabThe State University of New YorkSeries RLC Ci
SUNY Buffalo - EE - 203
SMALL for Big ThingsUniversity at BuffaloSMALL for Big ThingsUniversity at BuffalonanobioSensors & MicroActuators Learning LabThe State University of New YorknanobioSensors & MicroActuators Learning LabThe State University of New YorkEE 203 Circui
SUNY Buffalo - EE - 203
SMALL for Big ThingsUniversity at BuffaloSMALL for Big ThingsUniversity at BuffalonanobioSensors & MicroActuators Learning LabThe State University of New YorknanobioSensors & MicroActuators Learning LabThe State University of New YorkEE 203 Circui
SUNY Buffalo - EE - 203
SMALL for Big ThingsUniversity at BuffaloSMALL for Big ThingsUniversity at BuffalonanobioSensors & MicroActuators Learning LabThe State University of New YorknanobioSensors & MicroActuators Learning LabThe State University of New YorkEE 203 Circui
SUNY Buffalo - EE - 203
(ExpressProject "Gavendra"(ProjectVersion "19981106")(ProjectType "Analog or A/D Mixed Mode")(Folder "Design Resources"(Folder "Library")(NoModify)(File ".\gavendra.dsn"(Type "Schematic Design")(BuildFileAddedOrDeleted "x")(CompileFileAddedOrDele
SUNY Buffalo - EE - 203
* Profile: "SCHEMATIC1-Gavendra" [ C:\USERS\GAVENDRA\UB BS ENGINEERING\SPRING2008\EE 203\Project\gavendra-SCHEMATIC1-Gavendra.sim ]* Creating circuit file "gavendra-SCHEMATIC1-Gavendra.sim.cir"* WARNING: THIS AUTOMATICALLY GENERATED FILE MAY BE OVERWRI
SUNY Buffalo - EE - 203
Dept. of Electrical Engineering, University at BuffaloEE(203), Spring 2008Project 11. Circuit Schematic using PSpice.10A0VR121 .5 k10AIR221kA M P L IT U D E = 5 6 . 5 6F R E Q U E N C Y = 4 7 7 .5 6T ra n s i e n tA n a lysis0V10A+
SUNY Buffalo - EE - 203
* Sample standard device library** Copyright OrCAD, Inc. 1998 All Rights Reserved.** $Revision:1.36 $* $Author:drocha $* $Date:17 Feb 1999 09:57:54 $**-** This is a reduced version of OrCAD's standard parts model libraries. Some* components f
SUNY Buffalo - EE - 203
Department of Electrical Engineering, University at BuffaloEE 203 : Circuit Analysis (Spring 2008)Project 2 - Modified (Due on 04/28/08)In this project you are asked to perform design of an OP-AMP circuit based on handcalculations and then simulate th
Stanford - CS - 229
Convex Optimization OverviewZico Kolter (updated by Honglak Lee)October 17, 20081IntroductionMany situations arise in machine learning where we would like to optimize the value ofsome function. That is, given a function f : Rn R, we want to nd x Rn
Stanford - CS - 229
Convex Optimization Overview (cntd)Chuong B. DoNovember 29, 2009During last weeks section, we began our study of convex optimization, the study ofmathematical optimization problems of the form,minimize f (x)nx Rsubject to x C.(1)In a convex opti
Stanford - CS - 229
S = cfw_s1 , s2 , .s|S | z STcfw_sun, cloud, rain|S | = 3ssun , z2 = scloud , z3 = scloud , z4 = srain , z5 = scloud T =5sjt1ttt1P (zt |zt1 , zt2 , ., z1 ) = P (zt |zt1 )S=cfw_z1 =t1P (zt |zt1 ) = P (z2 |z1 ); t 2.Tz0 s00s0z1P (zt |zt
Stanford - CS - 229
Linear Algebra Review and ReferenceZico Kolter (updated by Chuong Do)October 7, 2008Contents1 Basic Concepts and Notation1.1 Basic Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .222 Matrix Multiplication2.1 Vector-Vec
Stanford - CS - 229
CS229 Lecture notesAndrew NgSupervised learningLets start by talking about a few examples of supervised learning problems.Suppose we have a dataset giving the living areas and prices of 47 housesfrom Portland, Oregon:Living area (feet2 )210416002
Stanford - CS - 229
CS229 Lecture notesAndrew NgPart IVGenerative Learning algorithmsSo far, weve mainly been talking about learning algorithms that modelp(y |x; ), the conditional distribution of y given x. For instance, logisticregression modeled p(y |x; ) as h (x) =
Stanford - CS - 229
CS229 Lecture notesAndrew NgPart VSupport Vector MachinesThis set of notes presents the Support Vector Machine (SVM) learning algorithm. SVMs are among the best (and many believe is indeed the best)o-the-shelf supervised learning algorithm. To tell t
Stanford - CS - 229
CS229 Lecture notesAndrew NgPart VILearning Theory1Bias/variance tradeoWhen talking about linear regression, we discussed the problem of whetherto t a simple model such as the linear y = 0 + 1 x, or a more complexmodel such as the polynomial y = 0
Stanford - CS - 229
CS229 Lecture notesAndrew NgPart VIRegularization and modelselectionSuppose we are trying select among several dierent models for a learningproblem. For instance, we might be using a polynomial regression modelh (x) = g (0 + 1 x + 2 x2 + + k xk ),
Stanford - CS - 229
CS229 Lecture notesAndrew Ng1The p erceptron and large margin classiersIn this nal set of notes on learning theory, we will introduce a dierentmodel of machine learning. Specically, we have so far been consideringbatch learning settings in which we
Stanford - CS - 229
CS229 Lecture notesAndrew NgThe k -means clustering algorithmIn the clustering problem, we are given a training set cfw_x(1) , . . . , x(m) , andwant to group the data into a few cohesive clusters. Here, x(i) Rnas usual; but no labels y (i) are given
Stanford - CS - 229
CS229 Lecture notesAndrew NgMixtures of Gaussians and the EM algorithmIn this set of notes, we discuss the EM (Expectation-Maximization) for density estimation.Suppose that we are given a training set cfw_x(1) , . . . , x(m) as usual. Sincewe are in
Stanford - CS - 229
CS229 Lecture notesAndrew NgPart IXThe EM algorithmIn the previous set of notes, we talked about the EM algorithm as applied totting a mixture of Gaussians. In this set of notes, we give a broader viewof the EM algorithm, and show how it can be appl
Stanford - CS - 229
CS229 Lecture notesAndrew NgPart XFactor analysisWhen we have data x(i) Rn that comes from a mixture of several Gaussians,the EM algorithm can be applied to t a mixture model. In this setting,we usually imagine problems were the we have sucient data
Stanford - CS - 229
CS229 Lecture notesAndrew NgPart XIPrincipal components analysisIn our discussion of factor analysis, we gave a way to model data x Rn asapproximately lying in some k -dimension subspace, where kn. Specif(i)ically, we imagined that each point x was
Stanford - CS - 229
CS229 Lecture notesAndrew NgPart XIIIndependent ComponentsAnalysisOur next topic is Independent Components Analysis (ICA). Similar to PCA,this will nd a new basis in which to represent our data. However, the goalis very dierent.As a motivating exa
Stanford - CS - 229
CS229 Lecture notesAndrew NgPart XIIIReinforcement Learning andControlWe now begin our study of reinforcement learning and adaptive control.In supervised learning, we saw algorithms that tried to make their outputsmimic the labels y given in the tr
Stanford - CS - 229
Review of Probability TheoryArian Maleki and Tom DoStanford UniversityProbability theory is the study of uncertainty. Through this class, we will be relying on conceptsfrom probability theory for deriving machine learning algorithms. These notes attem
UCF - CHM - 2210
1CHEMICALCHEMICALBONDINGBONDINGChemical Bonding2How is a molecule orpolyatomic ion heldtogether?Why are atoms distributed atstrange angles?Why are molecules not flat?Can we predict the structure?How is structure related tochemical and physi
UCF - CHM - 2210
Quantum or Wave MechanicsQuantum or Wave Mechanicsde Broglie (1924) proposedde Broglie 1924) proposedthat all moving objectsthat all moving objectshave wave properties.have wave properties.For light: E = mc 22For light: E = mcE = h = hc / E = h
UCF - CHM - 2210
Welcome to theWorld ofChemistryThe Language of Chemistry CHEMICAL ELEMENTS- pure substances that cannot be decomposed byordinary means to other substances.The Language of Chemistry The elements,their names, andsymbols are givenon thePERIODIC
UCF - CHM - 2210
ATOMS AND ELEMENTSTOMSRadioactivityATOMIC COMPOSITION Protons One of the pieces of evidence forthe fact that atoms are made ofsmaller particles came from thework of Marie Curie (1876workMarie1934). She discovered radioactivity ,Sheradioactivi
UCF - CHM - 2210
Empirical and MolecularFormulasA pure compound always consists of thesame elements combined in the sameproportions by weight.Therefore, we can express molecularcomposition as PERCENT BYcompositionPERCENTPercent CompositionPercent CompositionCon
UCF - CHM - 2210
Properties of Ionic CompoundsForming NaCl from Na and Cl2NaCl from Na and 2Electrostatic ForcesCOULOMBS LAW A metal atom cantransfer anelectron to anonmetal. The resultingcation and anionare attracted toeach other byForce of attraction =(cha
UCF - CHM - 2210
MOLECULAR WEIGHTAND MOLAR MASSMolecular weight is the sum of theatomic weights of all atoms in themolecule.Molar mass = molecular weight ingramsWhat is the molar mass ofethanol, C 2H6O?TylenolTylenol1 mol contains2 mol C (12.01 g C/1 mol) = 24
UCF - CHM - 2210
Counting AtomsMg burns in air (O2) toproduce whitemagnesium oxide, MgO.Counting AtomsParticles in a MoleAvogadros Number Chemistry is a quantitativesciencewe need a counting unit.Amedeo Avogadro1776-1856 The MOLEThe MOLE 1 mole is the amount
UCF - CHM - 2210
CHEMICAL REACTIONSCHEMICALChapter 4Chemical EquationsDepict the kind of reactants andDepictreactants andproducts and their relative amountsandin a reaction.4 Al(s) + 3 O2(g) -> 2 Al 2O3 (s)The numbers in the front are calledstoichiometric coef
UCF - CHM - 2210
STOICHIOMETRYSTOICHIOMETRYIt rests on the principle of the conservationItconservationof matter.- the study of thequantitativeaspects ofchemicalreactions.PROBLEM: If 454 g of NH 44NO33PROBLEM: If 454 g of NH NOdecomposes, how much N 22O andde
UCF - CHM - 2210
Reactions Involving aLIMITING REACTANTLIMITING REACTANTS In a given reaction, there is notenough of one reagent to use upthe other reagent completely.LIMITING REACTANTS The reagent in short supplyLIMITS the quantity of productthat can be formed.
UCF - CHM - 2210
Using Stoichiometry toDetermine a FormulaBurn 0.115 g of a hydrocarbon, C xHy, andproduce 0.379 g of CO 2 and 0.1035 g ofH2O. What is the empirical formula ofCxHy?CxHy + some oxygen ->0.379 g CO 2 + 0.1035 g H 2O0.379Using Stoichiometry toDeterm
UCF - CHM - 2210
CHEMICAL REACTIONS INWATERSection 4.6CD-ROM Ch. 4We will look atEXCHANGEREACTIONSAX + B YThe driving force is the formation of aninsoluble compound a precipitate.Pb(NO3)2(aq) + 2 KI( aq) ->2 KNO3(aq) + PbI2(s)AY + B XThe anionsTheexchange pl
UCF - CHM - 2210
Oxidation-Reduction ReactionsEXCHANGE: Precipitation ReactionsSection 4.10Thermite reactionFe2O3(s) + 2 Al(s)->OxidationEXCHANGEGas-FormingReactionsEXCHANGEAcid-BaseReactionsREACTIONSCu(s) + 2 Ag+(aq)-> Cu2+(aq) + 2 Ag(s)Mg(s) + 2 HCl (aq
UCF - CHM - 2210
REACTIONS INSOLUTIONSection 5.5PROBLEM: Dissolve 5.00 g of NiCl 226 H22O inPROBLEM: Dissolve 5.00 g of NiCl 6 H O inenough water to make 250 mL of solution.enough water to make 250 mL of solution.Calculate molarity.Calculate molarity.Terminology
UCF - CHM - 2210
SOLUTION STOICHIOMETRYSection 5.9Zinc reacts withacids to produceH2 gas. If youhave 10.0 g of Zn,what volume of2.50 M HCl isneeded to convertthe Zncompletely?Zinc reacts with acids to produce H22gas. If youZinc reacts with acids to produce H g
UCF - CHM - 2210
Bond Properties12 What is the effect of bonding andstructure on molecular properties?Double bond3Bond OrderBond OrderBond Order: # of bonds between a pair of atomsFractional bond orders occur in moleculesFractionaloccurwith resonance structur
UCF - CHM - 2210
1Using Bond Energies2Using Bond EnergiesEstimate the energy of the reactionHH + Cl Cl -> 2 H ClNet energy = Hrxn =Net= energy required to break bondsenergy- energy evolved when bonds are madeenergy-> O=O + 2 HOHEnergy required to break bonds:
UCF - CHM - 2210
1CHEMICALCHEMICALBONDINGBONDING4IonicIonicBondsBondsof 1 or more electrons fromone atom to another Covalentsomevalence electrons sharedbetween atoms Most bonds are somewherein between.The bond arises from the mutualattraction of 2 nuclei
UCF - CHM - 2210
Bond PolarityBond PolarityMolecular PolarityHCl is POLAR because itPOLARhas a positive end anda negative end.+Why are water molecules attracted to aballoon that has a static electriccharge?+HBond PolarityDue to the bond polarity, theHCl bon
UCF - CHM - 2210
CHEMICALBONDING1The bond arises from the mutual attractionof 2 nuclei for the same electrons.Electron sharing results. (Screen 9.5)results.HA + HBHAHBBond is a balance of attractive and repulsiveforces.41 or more electrons from oneatom to an
UCF - CHM - 2210
Advanced Theories ofChemical Bonding1Chapter 10Atomic Orbitals MOLECULARORBITAL THEORY Robert Mullikan (18961986) valence electrons aredelocalized valence electrons arein orbitals (calledmolecular orbitals)spread over entiremolecule.Molecul
UCF - CHM - 2210
1ORGANIC CHEMISTRY2IsomerismTypes of Organic Compounds3 Isomers have identical composition butIsomers havedifferent structures Two forms of isomerism Vast majority of over 20 million knowncompounds are based on C: organicorganiccompounds. Ge
UCF - CHM - 2210
12Functional Groups3AlcoholsStructures of AlcoholsStructuresAlcohols Characterized by OH group Name: add ol to name of hydrocarbonC3H5OH: how many structural isomers?HMethanolButanolCHHHOHOH HCCCHHHHH2-propanolNaming: Add -ol
UCF - CHM - 2210
BEHAVIOR OF GASES1Chapter 1223Hot Air Balloons How Do They Work?Importanceof Gases Airbags fill with N 2 gas in an accident. Gas is generated by the decomposition ofsodium azide, NaN3. 2 NaN3 -> 2 Na + 3 N2THREESTATESOFMATTER4General Pro
UCF - CHM - 2210
KINETIC MOLECULAR THEORY(KMT)1Theory used to explain gas laws. KMTassumptions are Gases consist of molecules inconstant, random motion. P arises from collisions with containerwalls. No attractive or repulsive forcesbetween molecules. Collisions
UCF - CHM - 2210
WHY?INTERMOLECULAR FORCESChap. 13 Why is water usually aliquid and not a gas? Why does liquid water boilat such a high temperaturefor such a small molecule? Why does ice float onwater? Why do snowflakes have 6sides? Why is I2 a solid whereasC
UCF - CHM - 2210
1LiquidsLiquidsIn a liquidLiquidsSecctoon 13.3Se ti i n 13 3The two key properties we need to describeare EVAPORATION and itsare EVAPORATION andEVAPORATIONoppositeCONDENSATIONoppositeCONDENSATIONTo evaporate, molecules must havesufficient en
UCF - CHM - 2210
Types of SolidsTypes of SolidsMetallic and Ionic SolidsMetallic and Ionic SolidsTablle 13.6Tab e 13 6Sectton 13.4Sec iion 13 4TYPETYPEIonicIonicEXAMPLEEXAMPLENaCl, CaF 2, ZnSNaCl CaF 2 ZnSaCl,MetallicMetallic Na, FeNa, FeMolecular Ice,
UCF - CHM - 2210
Types of SolidsTypes of SolidsMetallic and Ionic SolidsMetallic and Ionic SolidsTablle 13.6Tab e 13 6Sectton 13.4Sec iion 13 4TYPETYPEIonicIonicEXAMPLEEXAMPLENaCl, CaF 2, ZnSNaCl CaF 2 ZnSaCl,MetallicMetallic Na, FeNa, FeMolecular Ice,
UCF - CHM - 2210
Chemical KineticsChemical Kinetics1Chapter 15Chapter 15Chemical KineticsChemical Kinetics2 But this gives us no info on HOW FAST But this gives us no info on HOWreaction goes from reactants to products.reaction goes from reactants to products.