Analysis of Finite Word-Length Effects

Size: px
Start display at page:

Download "Analysis of Finite Word-Length Effects"

Transcription

1 T-6.46 Digital Signal Pocessing and Filteing Intoduction Analysis of Finite Wod-Length Effects Finite wodlength effects ae caused by: Quantization of the filte coefficients ounding / tuncation of multilication esults Quantization of the inut signal Dynamic ange constaints of the imlementation 4 Olli Simula T-6.46 / Mita: Chate 9 Analysis of Finite Wodlength Effects Ideally, the system aametes along with the signal vaiables have infinite ecision taking any value between and In actice, they can take only discete values within a secified ange since the egistes of the digital machine whee they ae stoed ae of finite length The discetization ocess esults in nonlinea diffeence equations chaacteizing the discete-time systems 4 Olli Simula T-6.46 / Mita: Chate 9 3 Coyight, S. K. Mita Analysis of Finite Wodlength Effects These nonlinea equations, in incile, ae almost imossible to analyze and deal with exactly Howeve, if the quantization amounts ae small comaed to the values of signal vaiables and filte aametes, a simle aoximate theoy based on a statistical model can be alied 4 Olli Simula T-6.46 / Mita: Chate 9 4 Coyight, S. K. Mita Analysis of Finite Wodlength Effects Using the statistical model, it is ossible to deive the effects of discetization and develo esults that can be veified exeimentally Souces of eos - ( Filte coefficient quantization ( A/D convesion (3 Quantization of aithmetic oeations (4 Limit cycles 4 Olli Simula T-6.46 / Mita: Chate 9 5 Coyight, S. K. Mita e [ n ] e j [ Analysis of Noise Poeties and Dynamic ange Constaints Σ Σ * G G * j z ( x [ y[ * F H ( F * i v * [ n ] v * i n [ ] 4 Olli Simula T-6.46 / Mita: Chate 9 6 Σ Mita: Chate 9 / Coyight Olli Simula

2 T-6.46 Digital Signal Pocessing and Filteing Examle: Fist Ode II Filte y [ αy[ n ] + x[ z αz z α H Quantization of coefficients α: H '( α' z Quantization of inut x[: x '[ x[ + e[ ounding/tuncation of v[: v' [ v[ + e [ Outut y[ with finite wodlength: y' [ y[ + η[ α The Quantization Pocess and Eos Factional numbes (sign bit + factional at The quantization ocess model Eo : ounding / Tuncation: ε Q( x x 4 Olli Simula T-6.46 / Mita: Chate Olli Simula T-6.46 / Mita: Chate 9 8 The Quantization Eos Quantization Eo ε ounding b β ( b β ( Two s comlement tuncation b β ( ε t Sign-magnitude and one s comlement tuncation b β ( ε t fo x > b β εt ( fo x < 4 Olli Simula T-6.46 / Mita: Chate Olli Simula T-6.46 / Mita: Chate 9 Quantization of Floating-Point Numbes Only mantissa is quantized; the elative eo is elevant! E E x M, Q( x Q( M Eo : Q( x x Q( M x e x x Analysis of Coefficient Quantization Effects The tansfe function Hˆ of the digital filte imlemented with quantized coefficients is diffeent fom the desied tansfe function H( Main effect of coefficient quantization is to move the oles and zeos to diffeent locations fom the oiginal desied locations 4 Olli Simula T-6.46 / Mita: Chate 9 4 Olli Simula T-6.46 / Mita: Chate 9 Coyight, S. K. Mita Mita: Chate 9 / Coyight Olli Simula

3 T-6.46 Digital Signal Pocessing and Filteing Analysis of Coefficient Quantization Effects The actual fequency esonse ˆ H ( e is thus diffeent fom the desied fequency esonse H ( e In some cases, the oles may move outside the unit cicle causing the imlemented digital filte to become unstable even though the oiginal tansfe function H( is stable Analysis of Coefficient Quantization Effects Diect fom ealizations ae moe sensitive to coefficient quantization than cascade o aallel foms The sensitivity inceases with inceasing filte ode Usually second ode blocks in cascade o aallel ae used 4 Olli Simula T-6.46 / Mita: Chate 9 3 Coyight, S. K. Mita 4 Olli Simula T-6.46 / Mita: Chate 9 4 Coyight, S. K. Mita Coefficient Quantization Effects On a Diect Fom II Filte Gain esonses of a 5-th ode ellitic lowass filte with unquantized and quantized coefficients Fullband Gain esonse Passband Details oiginal - solid line, quantized - dashed line oiginal - solid line, quantized - dashed line ω/ ω/ 4 Olli Simula T-6.46 / Mita: Chate 9 5 Gain, db Gain, db Coyight, S. K. Mita Coefficient Quantization Effects On a Diect Fom II Filte Pole and zeo locations of the filte with quantized coefficients (denoted by x and o and those of the filte with unquantized coefficients (denoted by + and * eal Pat 4 Olli Simula T-6.46 / Mita: Chate 9 6 Coyight, S. K. Mita Imaginay Pat Coefficient Quantization Effects On a Cascade Fom II Filte Gain esonses of a 5-th ode ellitic lowass filte imlemented in a cascade fom Gain, db with unquantized and quantized coefficients Fullband Gain esonse Passband Details - -4 oiginal - solid line, quantized - dashed line oiginal - solid line, quantized - dashed line ω/ ω/ 4 Olli Simula T-6.46 / Mita: Chate 9 7 Gain, db Coyight, S. K. Mita Coefficient Quantization Effects On A Diect Fom FI Filte Gain esonses of a 39-th ode equiile lowass FI filte with unquantized and quantized coefficients Gain, db - -4 Fullband Gain esonse oiginal - solid line, quantized - dashed line oiginal - solid line, quantized - dashed line ω/ ω/ 4 Olli Simula T-6.46 / Mita: Chate 9 8 Gain, db - - Passband details Coyight, S. K. Mita Mita: Chate 9 / Coyight Olli Simula 3

4 T-6.46 Digital Signal Pocessing and Filteing Examle of Coefficient Quantization in 6 th Ode Diect Fom ealization Examle of Coefficient Quantization in 6 th Ode Cascade Fom ealization Amlitude esonses Pole-zeo locations Amlitude esonses Pole-zeo locations 4 Olli Simula T-6.46 / Mita: Chate Olli Simula T-6.46 / Mita: Chate 9 Examle: 6 th ode bandsto filte with unquantized coefficients Cascade fom with coefficients quantized to 6 bits Paallel fom with coefficients quantized to 6 bits 4 Olli Simula T-6.46 / Mita: Chate 9 Coefficient Quantization in FI Filtes Conside an (M-th ode FI tansfe function H M n h[ z Quantization of the filte coefficients esults in a new tansfe function M M n n H ' h'[ z ( h[ + e[ z H( n n 4 Olli Simula T-6.46 / Mita: Chate 9 n H '( H + E( E( Linea hase: h[ + h[n-- Symmety of the imulse esonse not affected by quantization + A/D Convesion Noise Analysis Quantization Noise Model Two s comlement eesentation Analog inut Inut analog samle x[ Quantized inut samle Binay equivalent of quantized inut Quantization of the inut signal intoduces eo at the inut of the filte This eo is oagated though the filte togethe with the inut signal Affects the signal-to-noise atio of the system 4 Olli Simula T-6.46 / Mita: Chate 9 3 ˆ [ < Inut signal is assumed to be scaled to be in the ange of + by dividing its amlitude by FS / 4 Olli Simula T-6.46 / Mita: Chate 9 4 x eq xˆ[ xˆ eq[ FS δ + b FS Mita: Chate 9 / Coyight Olli Simula 4

5 T-6.46 Digital Signal Pocessing and Filteing Quantization Eo The quantization eo e[: e[ Q( x[ x[ xˆ[ x[ Fo two s comlement ounding: δ δ < e[ Outside FS the eo inceases linealy; e[ is called the satuation eo o the oveload noise The outut value is clied to the maximum value 4 Olli Simula T-6.46 / Mita: Chate 9 5 e[ is called ganula noise Model of the Quantization Eo x[ + x ˆ[ x[ + e[ e[ Assumtions: The eo sequence {e[} is a samle sequence of a widesense stationay (WSS white noise ocess, with each samle e[ being unifomly distibuted ove the quantization eo The eo sequence is uncoelated with its coesonding inut sequence {x[} 3 The inut sequence is a samle sequence of a stationay andom ocess The assumtions hold in most actical situations with aidly changing inut signals 4 Olli Simula T-6.46 / Mita: Chate 9 6 Quantization Eo Distibutions (a ounding (b Two s comlement tuncation m e ( δ / ( δ / (( δ / ( δ / δ e m e δ δ ( δ δ e Signal-to-Noise atio Additive quantization noise e[ on the signal x[ Signal-to-quantization noise atio in db is defined as log db x SN e whee x is the signal vaiance (owe and e is the noise vaiance (owe The vaiance eesents the noise owe 4 Olli Simula T-6.46 / Mita: Chate Olli Simula T-6.46 / Mita: Chate 9 8 Signal-to-Noise atio A/D convesion: (b+l bits: δ -(b+ FS, whee FS is the full-scale ange ( b+ b FS FS e δ / log 6.b 6.8- log db x FS SN + A D b FS x Thus, SN inceases 6 db fo each added bit in the wodlength 4 Olli Simula T-6.46 / Mita: Chate 9 9 Effect of Inut Scaling on SN Let the inut scaling facto be A with A> The vaiance of the scaled inut Ax[ is A x The SN changes to SN A/ D FS 6.b log A x 6.b log ( K + log ( A whee FS K x ( x is the MS value of the signal Scaling down the inut signal (A< deceases the SN Scaling u the inut signal (A> inceases the ossibility to exceed the full-scale ange FS esulting in cliing SN 4 Olli Simula T-6.46 / Mita: Chate 9 3 Mita: Chate 9 / Coyight Olli Simula 5

6 T-6.46 Digital Signal Pocessing and Filteing Poagation of Inut Quantization Noise to Digital Filte Outut Due to lineaity of H( and the assumtion that x[ and e[ ae uncoelated the outut can be exessed as a linea combination (sum of two sequences: yˆ [ h[ xˆ[ The outut noise is: [ x[ + e[ ] h[ x[ + h[ e[ ] h[ n m v [ e[ m] h[ n m] 4 Olli Simula T-6.46 / Mita: Chate 9 3 Poagation of Inut Quantization Noise to Digital Filte Outut The mean and vaiance of v[ chaacteize the outut noise j The mean m v is: m v meh ( e The noise vaiance v is: e v The outut noise owe sectum is: vv 4 Olli Simula T-6.46 / Mita: Chate 9 3 e H ( e P ( ω H ( e j ω dω Poagation of Inut Quantization Noise to Digital Filte Outut The nomalized outut noise vaiance is given by ω H e j, ( v v n e which can be witten as: dω Analysis of Aithmetic ound-off Eos j C v, n H H ( z z An equivalent exession is: v, n h[ n 4 Olli Simula T-6.46 / Mita: Chate 9 33 dz Quantization of Multilication esults Assumtions: The eo sequence {e α [} is a samle sequence of a stationay white noise ocess, with each samle e α [ being unifomly distibuted The quantization eo sequence {e α [} is uncoelated with the signal {v[}, the inut sequence {x[} to the filte, and all othe quantization eos The assumtion of {e α [} being uncoelated with {v[} holds fo ounding and two s comlement tuncation 4 Olli Simula T-6.46 / Mita: Chate 9 35 Quantization of Multilication esults The quantization model can be used to analyze the quantization effects at the filte outut Quantization befoe summation The numbe of multilications k l at adde inuts The th banch node with signal value u [ needs to be scaled to event oveflow 4 Olli Simula T-6.46 / Mita: Chate 9 36 Mita: Chate 9 / Coyight Olli Simula 6

7 T-6.46 Digital Signal Pocessing and Filteing Quantization of Multilication esults Statistical model of the filte: f [ Imulse esonse fom filte inut to banch node g l [ Imulse esonse fom inut of lth adde to filte outut 4 Olli Simula T-6.46 / Mita: Chate 9 37 Quantization of Multilication esults Banch nodes to be scaled u [ α lead to multilies and ae v l [ + oututs of summations: Scaling tansfe function: F ( Noise tansfe function: G l ( Let be the vaiance of each individual noise souce; then k l is the noise vaiance of e l [ The outut noise vaiance is: [ ( ] kl G l ( z z dz k G e dω j l ( C 4 Olli Simula T-6.46 / Mita: Chate 9 38 Quantization of Multilication esults The total outut noise vaiance: γ L kl l ( ( z z d j whee L is the numbe of summation nodes to which noise souces ae connected The noise vaiance can also be witten as γ C L l n kl g [ 4 Olli Simula T-6.46 / Mita: Chate 9 39 l The Outut Quantization Noise The amount of noise deends on the imlementation Quantization of multilication esults afte summation educes the numbe of noise souces to one The vaiance of the noise souce e l [ is now DSP ocesso cay out multily-accumulate oeation using double ecision aithmetic 4 Olli Simula T-6.46 / Mita: Chate 9 4 Dynamic ange Scaling Digital filte The th node value u [ has to be scaled Assume that the inut sequence is bounded by unity, i.e., x[ < fo all values of n The objective of scaling is toensue that u [ < fo all and all values of n Dynamic ange Scaling Thee diffeent conditions to ensue that u [ satisfies the conditions: An absolute bound L infinity -bound 3 L -bound Diffeent bounds ae alicable unde cetain inut signal conditions 4 Olli Simula T-6.46 / Mita: Chate Olli Simula T-6.46 / Mita: Chate 9 4 Mita: Chate 9 / Coyight Olli Simula 7

8 T-6.46 Digital Signal Pocessing and Filteing An Absolute Bound f [ Digital filte F ( is the scaling tansfe function The node value u [ is detemined by the convolution f k u [ [ k] x[ n k] 4 Olli Simula T-6.46 / Mita: Chate 9 43 An Absolute Bound Assuming that x[ satisfies the dynamic ange constaint x[ < k 4 Olli Simula T-6.46 / Mita: Chate 9 44 u [ f [ k] x[ n k] f [ k] k The node value u [ now satisfies the dynamic ange constaint, i.e., u [ < if k f [ k] fo all This is both necessay and sufficient condition to guaantee that thee will be no oveflow Scaling with the Absolute Bound If the dynamic ange constaint is not satisfied the filte inut has to be scaled with the multilie K K max f[ k k] The scaling ule based on the absolute bound is too essimistic and educes the SN significantly Moe actical and easy to use scaling ules can be deived in the fequency domain if some infomation about the inut signal is known a ioi 4 Olli Simula T-6.46 / Mita: Chate 9 45 Scaling Noms Definethe L -nom of a Fouie tansfom F(e as j ω F F( e d ω L -nom, F, is the oot-mean-squae (MS value of F(e, and L -nom, F, is the mean absolute value of F(e ove ω Moeove, lim -> F exists fo a continuous F(e and is given by its eak F max F( e ω 4 Olli Simula T-6.46 / Mita: Chate 9 46 Scaling Noms: L -Bound U ( e F ( e An invese Fouie tansfom X ( e n [ F ( e X ( e e u u [ F ( e F ( e F ( e X ( e X ( e X ( e 4 Olli Simula T-6.46 / Mita: Chate 9 47 dω dω dω Scaling Noms: L -Bound If X <, then the dynamic ange constaints satisfied if F If the mean absolute value of the inut sectum is bounded by unity, then thee will be no adde oveflow if the eak gains fom the filte inut to all adde outut nodes ae scaled satisfying the above bound The scaling ule is aely used since with most inut signals encounteed in actice X < does not hold 4 Olli Simula T-6.46 / Mita: Chate 9 48 Mita: Chate 9 / Coyight Olli Simula 8

9 T-6.46 Digital Signal Pocessing and Filteing Scaling Noms: L -Bound n u [ F ( e X ( e e dω Alying Schwaz inequality u [ F ( e dω o equivalently u [ F ( e If the filte inut has finite enegy bounded by unity, i.e., X <, then the adde oveflow can be evented by scaling the filte such that the MS value of the scaling tansfe functions ae bounded by unity: F,,,..., 4 Olli Simula T-6.46 / Mita: Chate 9 49 X ( e X ( e dω A Geneal Scaling ule A moe geneal scaling ule is obtained using Holde s inequality u [ F ( e X ( e q ( ( + q fo all,q >, with Afte the scaling the tansfe functions become F and the scaling constants should be chosen such that F',,,..., In many stuctues the scaling multilies can be absobed to the existing feedfowad multilies 4 Olli Simula T-6.46 / Mita: Chate 9 5 Scaling of a Cascade Fom II Filte The nodes ( * need to be scaled Bi + b iz + bi z H K H i, whee Hi i Ai + a iz + aiz K' Scaling tansfe functions: F H ' l,,,..., A l F ( can be exessed by oles and zeos of the oiginal H( 4 Olli Simula T-6.46 / Mita: Chate 9 5 Scaling - Back-Scaling α FILTE The effect of inut scaling is comensated by back-scaling at the outut of the filte Scaling block-by-block in cascade ealization foms H ( H ( H ( α α α α α Each second ode block is scaled individually The scaling coefficients between the blocks contain the backscaling of the evious block and the scaling of the the next block 4 Olli Simula T-6.46 / Mita: Chate 9 5 α α Scaled Cascade Fom II Filte Stuctue H K l H l, whee + b z Hl + a Scaling tansfe functions: K F Hl, A l,,..., i l z + b z + a l l z The scaled stuctue has new values of the coefficients in the feed-fowad banches Only one citical banch node in each second ode block has to be checked fo oveflow 4 Olli Simula T-6.46 / Mita: Chate 9 53 Otimum Section Odeing and Pole-Zeo Paiing of a Cascade Fom II Digital Filte Odeing of second-ode sections as well as aiing of oles and zeos affects the outut noise owe of the filte Mita: Chate 9 / Coyight Olli Simula 9

10 T-6.46 Digital Signal Pocessing and Filteing Noise Tansfe Functions The noise tansfe functions can be exessed using the tansfe functions of the cascaded second-ode blocks The scaled noise tansfe functions ae given by K Hi βi, l,,..., ; and G+ i l i l 4 Olli Simula T-6.46 / Mita: Chate 9 55 Noise Tansfe Functions The outut noise owe sectum due to oduct ound-off is given by + Pyy ( ω kl ( e l and outut noise vaiance is + + y kl ( e dω kl l l whee the integal in the aenthesis is the squae of the L -nom of the noise tansfe function 4 Olli Simula T-6.46 / Mita: Chate 9 56 Noise Model of Second-Ode Blocks The noise model intoduces noise souces to the inut/outut summation of each block The numbe of elementay noise souces, k l, has diffeent values deending on the location of ounding (befoe o afte the summation and deending on the block (fist, intemediate, last Let k l be the total numbe multilies connected to the l th adde ounding befoe summation: k k + 3, k l 5, fo l, 3,..., ounding afte summation: k l, fo l, Noise Tansfe Functions The scaling coefficients ae The outut noise owe sectum of the scaled filte is + + Pyy ( ω k + H k l Fl H l and outut noise vaiance is ( e + + y k + H kl Fl G l H l,...,+ 4 Olli Simula T-6.46 / Mita: Chate Olli Simula T-6.46 / Mita: Chate 9 58 i l α βi α l + F l H Minimizing the Outut ound-off Noise The scaling tansfe function F l ( contains sections H i (, i,,..., l- The noise tansfe function G l ( contains sections H i (, i l, l+,..., Evey tem in the sum fo the noise owe o the noise vaiance includes the tansfe function of all sections in the cascade ealization To minimize the outut noise owe the noms of H i ( should be minimized fo all values of i by aoiately aiing the oles and zeos 4 Olli Simula T-6.46 / Mita: Chate 9 59 Paiing the Poles and Zeos Poles close to unit cicle intoduce gain and zeos (on the unit cicle intoduce attenuation Fist, the oles closest to the unit cicle should be aied with the neaest zeos Next, the oles closest to the evious set of oles should be aied with the next closest zeos 3 This ocess is continued until all oles and zeos ae aied 4 Olli Simula T-6.46 / Mita: Chate 9 6 Mita: Chate 9 / Coyight Olli Simula

11 T-6.46 Digital Signal Pocessing and Filteing Section Odeing A section in the font at of the cascade has its tansfe function H i ( aeaing moe fequently in the scaling tansfe functions A section nea the outut end of the cascade has its tansfe function H i ( aeaing moe fequently in the noise tansfe function exessions > The best location fo H i ( deends on the tye of noms being alied to the scaling and noise tansfe functions 4 Olli Simula T-6.46 / Mita: Chate 9 6 Section Odeing L scaling: The odeing of aied sections does not influence too much the outut noise owe since all noms in the exessions ae L -noms L scaling: The sections with oles closest to the unit cicle exhibit a eaking magnitude esonse and should be laced close to the outut end > The odeing should be fom least-eaked to most-eaked On the othe hand, the odeing scheme is exactly oosite if the objective is to minimize the eak noise P yy (ω and L - scaling is used The odeing has no effect on the eak noise with L -scaling 4 Olli Simula T-6.46 / Mita: Chate 9 6 Eo Sectum Shaing Quantization eo can be comensated using the so called eofeedback (o eo sectum shaing The filteed eo signal is added to the signal banch befoe quantization (Q[.]. 4 Olli Simula T-6.46 / Mita: Chate 9 63 Eo Sectum Shaing Without eo-feedback the eo signal e[ is the ue quantization eo, i.e., e[ y[ - x[ In the comensated stuctue the eo signal is the diffeence between the outut y[ and the comensated inut signal 4 Olli Simula T-6.46 / Mita: Chate 9 64 Eo Sectum Shaing w[ x[ + ae[ n ] + be[ n ] e[ y[ w[ Substitutingw[: e[ y[ x[ ae[ n ] be[ n ] Total eo between outut and inut is still: e[ y[ x[ Eo Sectum Shaing Solving y[ - x[: y[ x[ e[ + ae[ n ] + be[ n ] Taking the z-tansfom: Y X E( + az E( + bz E( ( + az + bz E( G( E( whee G( is the eo shaing tansfe function 4 Olli Simula T-6.46 / Mita: Chate Olli Simula T-6.46 / Mita: Chate 9 66 Mita: Chate 9 / Coyight Olli Simula

12 T-6.46 Digital Signal Pocessing and Filteing Eo Sectum Shaing Examle: a- and b G( ( + az + bz ( z + z ( z Double zeo is at z Noise sectum is modified by attenuating noise at low fequencies 4 Olli Simula T-6.46 / Mita: Chate 9 67 Mita: Chate 9 / Coyight Olli Simula

Analysis of Arithmetic. Analysis of Arithmetic. Analysis of Arithmetic Round-Off Errors. Analysis of Arithmetic. Analysis of Arithmetic

Analysis of Arithmetic. Analysis of Arithmetic. Analysis of Arithmetic Round-Off Errors. Analysis of Arithmetic. Analysis of Arithmetic In the fixed-oint imlementation of a digital filte only the esult of the multilication oeation is quantied The eesentation of a actical multilie with the quantie at its outut is shown below u v Q ^v The

More information

c( 1) c(0) c(1) Note z 1 represents a unit interval delay Figure 85 3 Transmit equalizer functional model

c( 1) c(0) c(1) Note z 1 represents a unit interval delay Figure 85 3 Transmit equalizer functional model Relace 85.8.3.2 with the following: 85.8.3.2 Tansmitted outut wavefom The 40GBASE-CR4 and 100GBASE-CR10 tansmit function includes ogammable equalization to comensate fo the fequency-deendent loss of the

More information

Absolute Specifications: A typical absolute specification of a lowpass filter is shown in figure 1 where:

Absolute Specifications: A typical absolute specification of a lowpass filter is shown in figure 1 where: FIR FILTER DESIGN The design of an digital filte is caied out in thee steps: ) Specification: Befoe we can design a filte we must have some specifications. These ae detemined by the application. ) Appoximations

More information

Online-routing on the butterfly network: probabilistic analysis

Online-routing on the butterfly network: probabilistic analysis Online-outing on the buttefly netwok: obabilistic analysis Andey Gubichev 19.09.008 Contents 1 Intoduction: definitions 1 Aveage case behavio of the geedy algoithm 3.1 Bounds on congestion................................

More information

Political Science 552

Political Science 552 Political Science 55 Facto and Pincial Comonents Path : Wight s Rules 4 v 4 4 4u R u R v 4. Path may ass though any vaiable only once on a single tavese. Path may go backwads, but not afte going fowad.

More information

ME 3600 Control Systems Frequency Domain Analysis

ME 3600 Control Systems Frequency Domain Analysis ME 3600 Contol Systems Fequency Domain Analysis The fequency esponse of a system is defined as the steady-state esponse of the system to a sinusoidal (hamonic) input. Fo linea systems, the esulting steady-state

More information

Cross section dependence on ski pole sti ness

Cross section dependence on ski pole sti ness Coss section deendence on ski ole sti ness Johan Bystöm and Leonid Kuzmin Abstact Ski equiment oduce SWIX has ecently esented a new ai of ski oles, called SWIX Tiac, which di es fom conventional (ound)

More information

Stanford University CS259Q: Quantum Computing Handout 8 Luca Trevisan October 18, 2012

Stanford University CS259Q: Quantum Computing Handout 8 Luca Trevisan October 18, 2012 Stanfod Univesity CS59Q: Quantum Computing Handout 8 Luca Tevisan Octobe 8, 0 Lectue 8 In which we use the quantum Fouie tansfom to solve the peiod-finding poblem. The Peiod Finding Poblem Let f : {0,...,

More information

H5 Gas meter calibration

H5 Gas meter calibration H5 Gas mete calibation Calibation: detemination of the elation between the hysical aamete to be detemined and the signal of a measuement device. Duing the calibation ocess the measuement equiment is comaed

More information

EEO 401 Digital Signal Processing Prof. Mark Fowler

EEO 401 Digital Signal Processing Prof. Mark Fowler EEO 41 Digital Signal Pocessing Pof. Mak Fowle Note Set #31 Linea Phase FIR Design Optimum Equiipple (Paks-McClellan) Reading: Sect. 1.2.4 1.2.6 of Poakis & Manolakis 1/2 Motivation The window method and

More information

Numerical Integration

Numerical Integration MCEN 473/573 Chapte 0 Numeical Integation Fall, 2006 Textbook, 0.4 and 0.5 Isopaametic Fomula Numeical Integation [] e [ ] T k = h B [ D][ B] e B Jdsdt In pactice, the element stiffness is calculated numeically.

More information

Numerical solution of the first order linear fuzzy differential equations using He0s variational iteration method

Numerical solution of the first order linear fuzzy differential equations using He0s variational iteration method Malaya Jounal of Matematik, Vol. 6, No. 1, 80-84, 2018 htts://doi.og/16637/mjm0601/0012 Numeical solution of the fist ode linea fuzzy diffeential equations using He0s vaiational iteation method M. Ramachandan1

More information

A Deep Convolutional Neural Network Based on Nested Residue Number System

A Deep Convolutional Neural Network Based on Nested Residue Number System A Deep Convolutional Neual Netwok Based on Nested Residue Numbe System Hioki Nakahaa Ehime Univesity, Japan Tsutomu Sasao Meiji Univesity, Japan Abstact A pe-tained deep convolutional neual netwok (DCNN)

More information

3.1 Random variables

3.1 Random variables 3 Chapte III Random Vaiables 3 Random vaiables A sample space S may be difficult to descibe if the elements of S ae not numbes discuss how we can use a ule by which an element s of S may be associated

More information

556: MATHEMATICAL STATISTICS I

556: MATHEMATICAL STATISTICS I 556: MATHEMATICAL STATISTICS I CHAPTER 5: STOCHASTIC CONVERGENCE The following efinitions ae state in tems of scala anom vaiables, but exten natually to vecto anom vaiables efine on the same obability

More information

ON INDEPENDENT SETS IN PURELY ATOMIC PROBABILITY SPACES WITH GEOMETRIC DISTRIBUTION. 1. Introduction. 1 r r. r k for every set E A, E \ {0},

ON INDEPENDENT SETS IN PURELY ATOMIC PROBABILITY SPACES WITH GEOMETRIC DISTRIBUTION. 1. Introduction. 1 r r. r k for every set E A, E \ {0}, ON INDEPENDENT SETS IN PURELY ATOMIC PROBABILITY SPACES WITH GEOMETRIC DISTRIBUTION E. J. IONASCU and A. A. STANCU Abstact. We ae inteested in constucting concete independent events in puely atomic pobability

More information

Probabilistic number theory : A report on work done. What is the probability that a randomly chosen integer has no square factors?

Probabilistic number theory : A report on work done. What is the probability that a randomly chosen integer has no square factors? Pobabilistic numbe theoy : A eot on wo done What is the obability that a andomly chosen intege has no squae factos? We can constuct an initial fomula to give us this value as follows: If a numbe is to

More information

4/18/2005. Statistical Learning Theory

4/18/2005. Statistical Learning Theory Statistical Leaning Theoy Statistical Leaning Theoy A model of supevised leaning consists of: a Envionment - Supplying a vecto x with a fixed but unknown pdf F x (x b Teache. It povides a desied esponse

More information

CMSC 425: Lecture 5 More on Geometry and Geometric Programming

CMSC 425: Lecture 5 More on Geometry and Geometric Programming CMSC 425: Lectue 5 Moe on Geomety and Geometic Pogamming Moe Geometic Pogamming: In this lectue we continue the discussion of basic geometic ogamming fom the eious lectue. We will discuss coodinate systems

More information

Chapter 3 Optical Systems with Annular Pupils

Chapter 3 Optical Systems with Annular Pupils Chapte 3 Optical Systems with Annula Pupils 3 INTRODUCTION In this chapte, we discuss the imaging popeties of a system with an annula pupil in a manne simila to those fo a system with a cicula pupil The

More information

Kepler s problem gravitational attraction

Kepler s problem gravitational attraction Kele s oblem gavitational attaction Summay of fomulas deived fo two-body motion Let the two masses be m and m. The total mass is M = m + m, the educed mass is µ = m m /(m + m ). The gavitational otential

More information

k. s k=1 Part of the significance of the Riemann zeta-function stems from Theorem 9.2. If s > 1 then 1 p s

k. s k=1 Part of the significance of the Riemann zeta-function stems from Theorem 9.2. If s > 1 then 1 p s 9 Pimes in aithmetic ogession Definition 9 The Riemann zeta-function ζs) is the function which assigns to a eal numbe s > the convegent seies k s k Pat of the significance of the Riemann zeta-function

More information

Quantum Fourier Transform

Quantum Fourier Transform Chapte 5 Quantum Fouie Tansfom Many poblems in physics and mathematics ae solved by tansfoming a poblem into some othe poblem with a known solution. Some notable examples ae Laplace tansfom, Legende tansfom,

More information

Lot-sizing for inventory systems with product recovery

Lot-sizing for inventory systems with product recovery Lot-sizing fo inventoy systems with oduct ecovey Ruud Teunte August 29, 2003 Econometic Institute Reot EI2003-28 Abstact We study inventoy systems with oduct ecovey. Recoveed items ae as-good-as-new and

More information

Physics 161 Fall 2011 Extra Credit 2 Investigating Black Holes - Solutions The Following is Worth 50 Points!!!

Physics 161 Fall 2011 Extra Credit 2 Investigating Black Holes - Solutions The Following is Worth 50 Points!!! Physics 161 Fall 011 Exta Cedit Investigating Black Holes - olutions The Following is Woth 50 Points!!! This exta cedit assignment will investigate vaious popeties of black holes that we didn t have time

More information

Basic Gray Level Transformations (2) Negative

Basic Gray Level Transformations (2) Negative Gonzalez & Woods, 22 Basic Gay Level Tansfomations (2) Negative 23 Basic Gay Level Tansfomations (3) Log Tansfomation (Example fo Fouie Tansfom) Fouie spectum values ~1 6 bightest pixels dominant display

More information

C/CS/Phys C191 Shor s order (period) finding algorithm and factoring 11/12/14 Fall 2014 Lecture 22

C/CS/Phys C191 Shor s order (period) finding algorithm and factoring 11/12/14 Fall 2014 Lecture 22 C/CS/Phys C9 Sho s ode (peiod) finding algoithm and factoing /2/4 Fall 204 Lectue 22 With a fast algoithm fo the uantum Fouie Tansfom in hand, it is clea that many useful applications should be possible.

More information

Random Variables and Probability Distribution Random Variable

Random Variables and Probability Distribution Random Variable Random Vaiables and Pobability Distibution Random Vaiable Random vaiable: If S is the sample space P(S) is the powe set of the sample space, P is the pobability of the function then (S, P(S), P) is called

More information

Math 124B February 02, 2012

Math 124B February 02, 2012 Math 24B Febuay 02, 202 Vikto Gigoyan 8 Laplace s equation: popeties We have aleady encounteed Laplace s equation in the context of stationay heat conduction and wave phenomena. Recall that in two spatial

More information

New problems in universal algebraic geometry illustrated by boolean equations

New problems in universal algebraic geometry illustrated by boolean equations New poblems in univesal algebaic geomety illustated by boolean equations axiv:1611.00152v2 [math.ra] 25 Nov 2016 Atem N. Shevlyakov Novembe 28, 2016 Abstact We discuss new poblems in univesal algebaic

More information

Classical Worm algorithms (WA)

Classical Worm algorithms (WA) Classical Wom algoithms (WA) WA was oiginally intoduced fo quantum statistical models by Pokof ev, Svistunov and Tupitsyn (997), and late genealized to classical models by Pokof ev and Svistunov (200).

More information

PROBLEM SET #1 SOLUTIONS by Robert A. DiStasio Jr.

PROBLEM SET #1 SOLUTIONS by Robert A. DiStasio Jr. POBLM S # SOLUIONS by obet A. DiStasio J. Q. he Bon-Oppenheime appoximation is the standad way of appoximating the gound state of a molecula system. Wite down the conditions that detemine the tonic and

More information

Is the general form of Renyi s entropy a contrast for source separation?

Is the general form of Renyi s entropy a contrast for source separation? Is the geneal fom of Renyi s entoy a contast fo souce seaation? Fédéic Vins 1, Dinh-Tuan Pham 2, and Michel Veleysen 1 1 Machine Leaning Gou Univesité catholique de Louvain Louvain-la-Neuve, Belgium {vins,veleysen}@dice.ucl.ac.be

More information

An Exact Solution of Navier Stokes Equation

An Exact Solution of Navier Stokes Equation An Exact Solution of Navie Stokes Equation A. Salih Depatment of Aeospace Engineeing Indian Institute of Space Science and Technology, Thiuvananthapuam, Keala, India. July 20 The pincipal difficulty in

More information

arxiv: v1 [math.nt] 12 May 2017

arxiv: v1 [math.nt] 12 May 2017 SEQUENCES OF CONSECUTIVE HAPPY NUMBERS IN NEGATIVE BASES HELEN G. GRUNDMAN AND PAMELA E. HARRIS axiv:1705.04648v1 [math.nt] 12 May 2017 ABSTRACT. Fo b 2 and e 2, let S e,b : Z Z 0 be the function taking

More information

Macro Theory B. The Permanent Income Hypothesis

Macro Theory B. The Permanent Income Hypothesis Maco Theoy B The Pemanent Income Hypothesis Ofe Setty The Eitan Beglas School of Economics - Tel Aviv Univesity May 15, 2015 1 1 Motivation 1.1 An econometic check We want to build an empiical model with

More information

LC transfer of energy between the driving source and the circuit will be a maximum.

LC transfer of energy between the driving source and the circuit will be a maximum. The Q of oscillatos efeences: L.. Fotney Pinciples of Electonics: Analog and Digital, Hacout Bace Jovanovich 987, Chapte (AC Cicuits) H. J. Pain The Physics of Vibations and Waves, 5 th edition, Wiley

More information

Chapter 8 Sampling. Contents. Dr. Norrarat Wattanamongkhol. Lecturer. Department of Electrical Engineering, Engineering Faculty, sampling

Chapter 8 Sampling. Contents. Dr. Norrarat Wattanamongkhol. Lecturer. Department of Electrical Engineering, Engineering Faculty, sampling Content Chate 8 Samling Lectue D Noaat Wattanamongkhol Samling Theoem Samling of Continuou-Time Signal 3 Poceing Continuou-Time Signal 4 Samling of Dicete-Time Signal 5 Multi-ate Samling Deatment of Electical

More information

HOW TO TEACH THE FUNDAMENTALS OF INFORMATION SCIENCE, CODING, DECODING AND NUMBER SYSTEMS?

HOW TO TEACH THE FUNDAMENTALS OF INFORMATION SCIENCE, CODING, DECODING AND NUMBER SYSTEMS? 6th INTERNATIONAL MULTIDISCIPLINARY CONFERENCE HOW TO TEACH THE FUNDAMENTALS OF INFORMATION SCIENCE, CODING, DECODING AND NUMBER SYSTEMS? Cecília Sitkuné Göömbei College of Nyíegyháza Hungay Abstact: The

More information

Polar Coordinates. a) (2; 30 ) b) (5; 120 ) c) (6; 270 ) d) (9; 330 ) e) (4; 45 )

Polar Coordinates. a) (2; 30 ) b) (5; 120 ) c) (6; 270 ) d) (9; 330 ) e) (4; 45 ) Pola Coodinates We now intoduce anothe method of labelling oints in a lane. We stat by xing a oint in the lane. It is called the ole. A standad choice fo the ole is the oigin (0; 0) fo the Catezian coodinate

More information

(A) 2log( tan cot ) [ ], 2 MATHEMATICS. 1. Which of the following is correct?

(A) 2log( tan cot ) [ ], 2 MATHEMATICS. 1. Which of the following is correct? MATHEMATICS. Which of the following is coect? A L.P.P always has unique solution Evey L.P.P has an optimal solution A L.P.P admits two optimal solutions If a L.P.P admits two optimal solutions then it

More information

EELE 3331 Electromagnetic I Chapter 4. Electrostatic fields. Islamic University of Gaza Electrical Engineering Department Dr.

EELE 3331 Electromagnetic I Chapter 4. Electrostatic fields. Islamic University of Gaza Electrical Engineering Department Dr. EELE 3331 Electomagnetic I Chapte 4 Electostatic fields Islamic Univesity of Gaza Electical Engineeing Depatment D. Talal Skaik 212 1 Electic Potential The Gavitational Analogy Moving an object upwad against

More information

Elementary Statistics and Inference. Elementary Statistics and Inference. 11. Regression (cont.) 22S:025 or 7P:025. Lecture 14.

Elementary Statistics and Inference. Elementary Statistics and Inference. 11. Regression (cont.) 22S:025 or 7P:025. Lecture 14. Elementay tatistics and Infeence :05 o 7P:05 Lectue 14 1 Elementay tatistics and Infeence :05 o 7P:05 Chapte 10 (cont.) D. Two Regession Lines uppose two vaiables, and ae obtained on 100 students, with

More information

MAGNETIC FIELD AROUND TWO SEPARATED MAGNETIZING COILS

MAGNETIC FIELD AROUND TWO SEPARATED MAGNETIZING COILS The 8 th Intenational Confeence of the Slovenian Society fo Non-Destuctive Testing»pplication of Contempoay Non-Destuctive Testing in Engineeing«Septembe 1-3, 5, Potoož, Slovenia, pp. 17-1 MGNETIC FIELD

More information

Rigid Body Dynamics 2. CSE169: Computer Animation Instructor: Steve Rotenberg UCSD, Winter 2018

Rigid Body Dynamics 2. CSE169: Computer Animation Instructor: Steve Rotenberg UCSD, Winter 2018 Rigid Body Dynamics 2 CSE169: Compute Animation nstucto: Steve Rotenbeg UCSD, Winte 2018 Coss Poduct & Hat Opeato Deivative of a Rotating Vecto Let s say that vecto is otating aound the oigin, maintaining

More information

Berkeley Math Circle AIME Preparation March 5, 2013

Berkeley Math Circle AIME Preparation March 5, 2013 Algeba Toolkit Rules of Thumb. Make sue that you can pove all fomulas you use. This is even bette than memoizing the fomulas. Although it is best to memoize, as well. Stive fo elegant, economical methods.

More information

Phys-272 Lecture 17. Motional Electromotive Force (emf) Induced Electric Fields Displacement Currents Maxwell s Equations

Phys-272 Lecture 17. Motional Electromotive Force (emf) Induced Electric Fields Displacement Currents Maxwell s Equations Phys-7 Lectue 17 Motional Electomotive Foce (emf) Induced Electic Fields Displacement Cuents Maxwell s Equations Fom Faaday's Law to Displacement Cuent AC geneato Magnetic Levitation Tain Review of Souces

More information

Errata for Edition 1 of Coding the Matrix, January 13, 2017

Errata for Edition 1 of Coding the Matrix, January 13, 2017 Eata fo Edition of Coding the Matix, Januay 3, 07 You coy might not contain some of these eos. Most do not occu in the coies cuently being sold as Ail 05. Section 0.3:... the inut is a e-image of the inut...

More information

MEASURES OF BLOCK DESIGN EFFICIENCY RECOVERING INTERBLOCK INFORMATION

MEASURES OF BLOCK DESIGN EFFICIENCY RECOVERING INTERBLOCK INFORMATION MEASURES OF BLOCK DESIGN EFFICIENCY RECOVERING INTERBLOCK INFORMATION Walte T. Fedee 337 Waen Hall, Biometics Unit Conell Univesity Ithaca, NY 4853 and Tey P. Speed Division of Mathematics & Statistics,

More information

Information Retrieval Advanced IR models. Luca Bondi

Information Retrieval Advanced IR models. Luca Bondi Advanced IR models Luca Bondi Advanced IR models 2 (LSI) Pobabilistic Latent Semantic Analysis (plsa) Vecto Space Model 3 Stating point: Vecto Space Model Documents and queies epesented as vectos in the

More information

( ) [ ] [ ] [ ] δf φ = F φ+δφ F. xdx.

( ) [ ] [ ] [ ] δf φ = F φ+δφ F. xdx. 9. LAGRANGIAN OF THE ELECTROMAGNETIC FIELD In the pevious section the Lagangian and Hamiltonian of an ensemble of point paticles was developed. This appoach is based on a qt. This discete fomulation can

More information

Computational Methods of Solid Mechanics. Project report

Computational Methods of Solid Mechanics. Project report Computational Methods of Solid Mechanics Poject epot Due on Dec. 6, 25 Pof. Allan F. Bowe Weilin Deng Simulation of adhesive contact with molecula potential Poject desciption In the poject, we will investigate

More information

Computing Electromagnetic Fields in Inhomogeneous Media Using Lattice Gas Automata. I. Introduction

Computing Electromagnetic Fields in Inhomogeneous Media Using Lattice Gas Automata. I. Introduction Comuting Electomagnetic Fields in Inhomogeneous Media Using Lattice Gas Automata M.Zhang, D. Cule, L. Shafai, G. Bidges and N.Simons Deatment of Electical and Comute Engineeing Univesity of Manitoba Winnieg,

More information

Contact impedance of grounded and capacitive electrodes

Contact impedance of grounded and capacitive electrodes Abstact Contact impedance of gounded and capacitive electodes Andeas Hödt Institut fü Geophysik und extateestische Physik, TU Baunschweig The contact impedance of electodes detemines how much cuent can

More information

Basic Bridge Circuits

Basic Bridge Circuits AN7 Datafoth Copoation Page of 6 DID YOU KNOW? Samuel Hunte Chistie (784-865) was bon in London the son of James Chistie, who founded Chistie's Fine At Auctionees. Samuel studied mathematics at Tinity

More information

Transverse Wakefield in a Dielectric Tube with Frequency Dependent Dielectric Constant

Transverse Wakefield in a Dielectric Tube with Frequency Dependent Dielectric Constant ARDB-378 Bob Siemann & Alex Chao /4/5 Page of 8 Tansvese Wakefield in a Dielectic Tube with Fequency Dependent Dielectic Constant This note is a continuation of ARDB-368 that is now extended to the tansvese

More information

1) Consider an object of a parabolic shape with rotational symmetry z

1) Consider an object of a parabolic shape with rotational symmetry z Umeå Univesitet, Fysik 1 Vitaly Bychkov Pov i teknisk fysik, Fluid Mechanics (Stömningsläa), 01-06-01, kl 9.00-15.00 jälpmedel: Students may use any book including the tetbook Lectues on Fluid Dynamics.

More information

Inverse Square Law and Polarization

Inverse Square Law and Polarization Invese Squae Law and Polaization Objectives: To show that light intensity is invesely popotional to the squae of the distance fom a point light souce and to show that the intensity of the light tansmitted

More information

9.1 The multiplicative group of a finite field. Theorem 9.1. The multiplicative group F of a finite field is cyclic.

9.1 The multiplicative group of a finite field. Theorem 9.1. The multiplicative group F of a finite field is cyclic. Chapte 9 Pimitive Roots 9.1 The multiplicative goup of a finite fld Theoem 9.1. The multiplicative goup F of a finite fld is cyclic. Remak: In paticula, if p is a pime then (Z/p) is cyclic. In fact, this

More information

Chapter 5 Linear Equations: Basic Theory and Practice

Chapter 5 Linear Equations: Basic Theory and Practice Chapte 5 inea Equations: Basic Theoy and actice In this chapte and the next, we ae inteested in the linea algebaic equation AX = b, (5-1) whee A is an m n matix, X is an n 1 vecto to be solved fo, and

More information

Stress Intensity Factor

Stress Intensity Factor S 47 Factue Mechanics http://imechanicaog/node/7448 Zhigang Suo Stess Intensity Facto We have modeled a body by using the linea elastic theoy We have modeled a cack in the body by a flat plane, and the

More information

Math Section 4.2 Radians, Arc Length, and Area of a Sector

Math Section 4.2 Radians, Arc Length, and Area of a Sector Math 1330 - Section 4. Radians, Ac Length, and Aea of a Secto The wod tigonomety comes fom two Geek oots, tigonon, meaning having thee sides, and mete, meaning measue. We have aleady defined the six basic

More information

Nuclear Medicine Physics 02 Oct. 2007

Nuclear Medicine Physics 02 Oct. 2007 Nuclea Medicine Physics Oct. 7 Counting Statistics and Eo Popagation Nuclea Medicine Physics Lectues Imaging Reseach Laboatoy, Radiology Dept. Lay MacDonald 1//7 Statistics (Summaized in One Slide) Type

More information

OSCILLATIONS AND GRAVITATION

OSCILLATIONS AND GRAVITATION 1. SIMPLE HARMONIC MOTION Simple hamonic motion is any motion that is equivalent to a single component of unifom cicula motion. In this situation the velocity is always geatest in the middle of the motion,

More information

Do Managers Do Good With Other People s Money? Online Appendix

Do Managers Do Good With Other People s Money? Online Appendix Do Manages Do Good With Othe People s Money? Online Appendix Ing-Haw Cheng Haison Hong Kelly Shue Abstact This is the Online Appendix fo Cheng, Hong and Shue 2013) containing details of the model. Datmouth

More information

( ) F α. a. Sketch! r as a function of r for fixed θ. For the sketch, assume that θ is roughly the same ( )

( ) F α. a. Sketch! r as a function of r for fixed θ. For the sketch, assume that θ is roughly the same ( ) . An acoustic a eflecting off a wav bounda (such as the sea suface) will see onl that pat of the bounda inclined towad the a. Conside a a with inclination to the hoizontal θ (whee θ is necessail positive,

More information

Revision of Lecture Eight

Revision of Lecture Eight Revision of Lectue Eight Baseband equivalent system and equiements of optimal tansmit and eceive filteing: (1) achieve zeo ISI, and () maximise the eceive SNR Thee detection schemes: Theshold detection

More information

Fast DCT-based image convolution algorithms and application to image resampling and hologram reconstruction

Fast DCT-based image convolution algorithms and application to image resampling and hologram reconstruction Fast DCT-based image convolution algoithms and application to image esampling and hologam econstuction Leonid Bilevich* a and Leonid Yaoslavsy** a a Depatment of Physical Electonics, Faculty of Engineeing,

More information

A Multivariate Normal Law for Turing s Formulae

A Multivariate Normal Law for Turing s Formulae A Multivaiate Nomal Law fo Tuing s Fomulae Zhiyi Zhang Depatment of Mathematics and Statistics Univesity of Noth Caolina at Chalotte Chalotte, NC 28223 Abstact This pape establishes a sufficient condition

More information

6 PROBABILITY GENERATING FUNCTIONS

6 PROBABILITY GENERATING FUNCTIONS 6 PROBABILITY GENERATING FUNCTIONS Cetain deivations pesented in this couse have been somewhat heavy on algeba. Fo example, detemining the expectation of the Binomial distibution (page 5.1 tuned out to

More information

THE INFLUENCE OF THE MAGNETIC NON-LINEARITY ON THE MAGNETOSTATIC SHIELDS DESIGN

THE INFLUENCE OF THE MAGNETIC NON-LINEARITY ON THE MAGNETOSTATIC SHIELDS DESIGN THE INFLUENCE OF THE MAGNETIC NON-LINEARITY ON THE MAGNETOSTATIC SHIELDS DESIGN LIVIU NEAMŢ 1, ALINA NEAMŢ, MIRCEA HORGOŞ 1 Key wods: Magnetostatic shields, Magnetic non-lineaity, Finite element method.

More information

2. Electrostatics. Dr. Rakhesh Singh Kshetrimayum 8/11/ Electromagnetic Field Theory by R. S. Kshetrimayum

2. Electrostatics. Dr. Rakhesh Singh Kshetrimayum 8/11/ Electromagnetic Field Theory by R. S. Kshetrimayum 2. Electostatics D. Rakhesh Singh Kshetimayum 1 2.1 Intoduction In this chapte, we will study how to find the electostatic fields fo vaious cases? fo symmetic known chage distibution fo un-symmetic known

More information

$ i. !((( dv vol. Physics 8.02 Quiz One Equations Fall q 1 q 2 r 2 C = 2 C! V 2 = Q 2 2C F = 4!" or. r ˆ = points from source q to observer

$ i. !((( dv vol. Physics 8.02 Quiz One Equations Fall q 1 q 2 r 2 C = 2 C! V 2 = Q 2 2C F = 4! or. r ˆ = points from source q to observer Physics 8.0 Quiz One Equations Fall 006 F = 1 4" o q 1 q = q q ˆ 3 4" o = E 4" o ˆ = points fom souce q to obseve 1 dq E = # ˆ 4" 0 V "## E "d A = Q inside closed suface o d A points fom inside to V =

More information

ELECTROSTATICS::BHSEC MCQ 1. A. B. C. D.

ELECTROSTATICS::BHSEC MCQ 1. A. B. C. D. ELETROSTATIS::BHSE 9-4 MQ. A moving electic chage poduces A. electic field only. B. magnetic field only.. both electic field and magnetic field. D. neithe of these two fields.. both electic field and magnetic

More information

EM Boundary Value Problems

EM Boundary Value Problems EM Bounday Value Poblems 10/ 9 11/ By Ilekta chistidi & Lee, Seung-Hyun A. Geneal Desciption : Maxwell Equations & Loentz Foce We want to find the equations of motion of chaged paticles. The way to do

More information

A New Approach to General Relativity

A New Approach to General Relativity Apeion, Vol. 14, No. 3, July 7 7 A New Appoach to Geneal Relativity Ali Rıza Şahin Gaziosmanpaşa, Istanbul Tukey E-mail: aizasahin@gmail.com Hee we pesent a new point of view fo geneal elativity and/o

More information

Perturbation to Symmetries and Adiabatic Invariants of Nonholonomic Dynamical System of Relative Motion

Perturbation to Symmetries and Adiabatic Invariants of Nonholonomic Dynamical System of Relative Motion Commun. Theo. Phys. Beijing, China) 43 25) pp. 577 581 c Intenational Academic Publishes Vol. 43, No. 4, Apil 15, 25 Petubation to Symmeties and Adiabatic Invaiants of Nonholonomic Dynamical System of

More information

Question Bank. Section A. is skew-hermitian matrix. is diagonalizable. (, ) , Evaluate (, ) 12 about = 1 and = Find, if

Question Bank. Section A. is skew-hermitian matrix. is diagonalizable. (, ) , Evaluate (, ) 12 about = 1 and = Find, if Subject: Mathematics-I Question Bank Section A T T. Find the value of fo which the matix A = T T has ank one. T T i. Is the matix A = i is skew-hemitian matix. i. alculate the invese of the matix = 5 7

More information

763620SS STATISTICAL PHYSICS Solutions 2 Autumn 2012

763620SS STATISTICAL PHYSICS Solutions 2 Autumn 2012 763620SS STATISTICAL PHYSICS Solutions 2 Autumn 2012 1. Continuous Random Walk Conside a continuous one-dimensional andom walk. Let w(s i ds i be the pobability that the length of the i th displacement

More information

EM-2. 1 Coulomb s law, electric field, potential field, superposition q. Electric field of a point charge (1)

EM-2. 1 Coulomb s law, electric field, potential field, superposition q. Electric field of a point charge (1) EM- Coulomb s law, electic field, potential field, supeposition q ' Electic field of a point chage ( ') E( ) kq, whee k / 4 () ' Foce of q on a test chage e at position is ee( ) Electic potential O kq

More information

Psychometric Methods: Theory into Practice Larry R. Price

Psychometric Methods: Theory into Practice Larry R. Price ERRATA Psychometic Methods: Theoy into Pactice Lay R. Pice Eos wee made in Equations 3.5a and 3.5b, Figue 3., equations and text on pages 76 80, and Table 9.1. Vesions of the elevant pages that include

More information

DOING PHYSICS WITH MATLAB COMPUTATIONAL OPTICS

DOING PHYSICS WITH MATLAB COMPUTATIONAL OPTICS DOING PHYIC WITH MTLB COMPUTTIONL OPTIC FOUNDTION OF CLR DIFFRCTION THEORY Ian Coope chool of Physics, Univesity of ydney ian.coope@sydney.edu.au DOWNLOD DIRECTORY FOR MTLB CRIPT View document: Numeical

More information

Waves and Polarization in General

Waves and Polarization in General Waves and Polaization in Geneal Wave means a distubance in a medium that tavels. Fo light, the medium is the electomagnetic field, which can exist in vacuum. The tavel pat defines a diection. The distubance

More information

TUTORIAL 9. Static magnetic field

TUTORIAL 9. Static magnetic field TUTOIAL 9 Static magnetic field Vecto magnetic potential Null Identity % & %$ A # Fist postulation # " B such that: Vecto magnetic potential Vecto Poisson s equation The solution is: " Substitute it into

More information

Homework 7 Solutions

Homework 7 Solutions Homewok 7 olutions Phys 4 Octobe 3, 208. Let s talk about a space monkey. As the space monkey is oiginally obiting in a cicula obit and is massive, its tajectoy satisfies m mon 2 G m mon + L 2 2m mon 2

More information

COUPLED MODELS OF ROLLING, SLIDING AND WHIRLING FRICTION

COUPLED MODELS OF ROLLING, SLIDING AND WHIRLING FRICTION ENOC 008 Saint Petesbug Russia June 30-July 4 008 COUPLED MODELS OF ROLLING SLIDING AND WHIRLING FRICTION Alexey Kieenkov Ins ti tu te fo P ob le ms in Me ch an ic s Ru ss ia n Ac ad em y of Sc ie nc es

More information

Hopefully Helpful Hints for Gauss s Law

Hopefully Helpful Hints for Gauss s Law Hopefully Helpful Hints fo Gauss s Law As befoe, thee ae things you need to know about Gauss s Law. In no paticula ode, they ae: a.) In the context of Gauss s Law, at a diffeential level, the electic flux

More information

Chapter 3: Theory of Modular Arithmetic 38

Chapter 3: Theory of Modular Arithmetic 38 Chapte 3: Theoy of Modula Aithmetic 38 Section D Chinese Remainde Theoem By the end of this section you will be able to pove the Chinese Remainde Theoem apply this theoem to solve simultaneous linea conguences

More information

Sinusoidal Oscillators

Sinusoidal Oscillators Sinuoidal Ocillato Signal geneato: inuoidal, ectangula, tiangula, aw-tooth, etc. Obtaining a ine wave: tiangle functional tanf. ine ine wave geneation: fequency elective netwok in a feedback loo of a PF

More information

Pulse Neutron Neutron (PNN) tool logging for porosity Some theoretical aspects

Pulse Neutron Neutron (PNN) tool logging for porosity Some theoretical aspects Pulse Neuton Neuton (PNN) tool logging fo poosity Some theoetical aspects Intoduction Pehaps the most citicism of Pulse Neuton Neuon (PNN) logging methods has been chage that PNN is to sensitive to the

More information

1 Spherical multipole moments

1 Spherical multipole moments Jackson notes 9 Spheical multipole moments Suppose we have a chage distibution ρ (x) wheeallofthechageiscontained within a spheical egion of adius R, as shown in the diagam. Then thee is no chage in the

More information

Physics 2212 GH Quiz #2 Solutions Spring 2016

Physics 2212 GH Quiz #2 Solutions Spring 2016 Physics 2212 GH Quiz #2 Solutions Sping 216 I. 17 points) Thee point chages, each caying a chage Q = +6. nc, ae placed on an equilateal tiangle of side length = 3. mm. An additional point chage, caying

More information

KCET 2015 TEST PAPER WITH ANSWER KEY (HELD ON TUESDAY 12 th MAY, 2015) MATHEMATICS ALLEN Y (0, 14) (4) 14x + 5y ³ 70 y ³ 14and x - y ³ 5 (2) (3) (4)

KCET 2015 TEST PAPER WITH ANSWER KEY (HELD ON TUESDAY 12 th MAY, 2015) MATHEMATICS ALLEN Y (0, 14) (4) 14x + 5y ³ 70 y ³ 14and x - y ³ 5 (2) (3) (4) KET 0 TEST PAPER WITH ANSWER KEY (HELD ON TUESDAY th MAY, 0). If a and b ae the oots of a + b = 0, then a +b is equal to a b () a b a b () a + b Ans:. If the nd and th tems of G.P. ae and esectively, then

More information

Amplitude and Phase Fluctuations for Gravitational Waves Propagating through Inhomogeneous Mass Distribution in the Universe

Amplitude and Phase Fluctuations for Gravitational Waves Propagating through Inhomogeneous Mass Distribution in the Universe Amplitude and Phase Fluctuations fo Gavitational Waves Popagating though Inhomogeneous Mass Distibution in the Univese Ryuichi Takahashi Nagoya Univ. PD RT ApJ 644 80 006 RT, uyama & Michikoshi A&A 438

More information

Chapter 3 Problem Solutions

Chapter 3 Problem Solutions Chate Poblem Solutions Poblem A Equation (5) gives P P G G log h log h L 4 log d t t t sys Substituting gives P log 6 log log 4 log 875 m B The wavelength is given by 8 The fee-sace ath loss is then 9

More information

Temporal-Difference Learning

Temporal-Difference Learning .997 Decision-Making in Lage-Scale Systems Mach 17 MIT, Sping 004 Handout #17 Lectue Note 13 1 Tempoal-Diffeence Leaning We now conside the poblem of computing an appopiate paamete, so that, given an appoximation

More information

BEST CONSTANTS FOR UNCENTERED MAXIMAL FUNCTIONS. Loukas Grafakos and Stephen Montgomery-Smith University of Missouri, Columbia

BEST CONSTANTS FOR UNCENTERED MAXIMAL FUNCTIONS. Loukas Grafakos and Stephen Montgomery-Smith University of Missouri, Columbia BEST CONSTANTS FOR UNCENTERED MAXIMAL FUNCTIONS Loukas Gafakos and Stehen Montgomey-Smith Univesity of Missoui, Columbia Abstact. We ecisely evaluate the oeato nom of the uncenteed Hady-Littlewood maximal

More information

Weighted Norms of Ambiguity Functions and Wigner Distributions

Weighted Norms of Ambiguity Functions and Wigner Distributions Weighted Noms of Ambiguity Functions and Wigne Distibutions Pete Jung Faunhofe Geman-Sino Lab fo Mobile Communications (MCI) and the Heinich-Hetz Institute jung@hhi.faunhofe.de axiv:cs/06007v4 [cs.it]

More information

15.081J/6.251J Introduction to Mathematical Programming. Lecture 6: The Simplex Method II

15.081J/6.251J Introduction to Mathematical Programming. Lecture 6: The Simplex Method II 15081J/6251J Intoduction to Mathematical Pogamming ectue 6: The Simplex Method II 1 Outline Revised Simplex method Slide 1 The full tableau implementation Anticycling 2 Revised Simplex Initial data: A,

More information

On the Quasi-inverse of a Non-square Matrix: An Infinite Solution

On the Quasi-inverse of a Non-square Matrix: An Infinite Solution Applied Mathematical Sciences, Vol 11, 2017, no 27, 1337-1351 HIKARI Ltd, wwwm-hikaicom https://doiog/1012988/ams20177273 On the Quasi-invese of a Non-squae Matix: An Infinite Solution Ruben D Codeo J

More information