VOL. 5, NO. 12, December 2015 ISSN ARPN Journal of Science and Technology All rights reserved.

Size: px
Start display at page:

Download "VOL. 5, NO. 12, December 2015 ISSN ARPN Journal of Science and Technology All rights reserved."

Transcription

1 Moded Thomson and Tau based Imulse Nose Detecto G. Maagatham S. Md Mansoo Room 3 A. Sasthadev Assstant Pofesso Deatment of Electoncs and Communcaton Unvesty College of Engneeng Dndgul Inda Assstant Pofesso Deatment of Electoncs and Communcaton Thagaaa College of Engneeng GST Road Thuaankundam Madua Taml Nadu Inda 3 Reseach Schola Deatment of Electoncs and Communcaton Thagaaa College of Engneeng GST Road Thuaankundam Madua Taml Nadu Inda gmaagathamganesan@gmal.com smmoom@tce.edu ABSTRACT Pesevng edges and detals n the ocess of mulsve nose flteng s an motant oblem. To avod mage smoothng only couted xels must be flteed. Many flteng technques exst fo mulse nose emoval wth vaety of nose detecton methods. An analyss of such detecton methods wth vaous levels of nose shows the need of a new mulse nose detecto. In ode to denty the couted xels a new mulse detecto based on a moded Thomson Tau technque s oosed. Exemental esults show that the oosed method woks at all levels of nose couton. Keywods: Detecto mulse nose gaussan nose fltes thomson and tau estmaton. INTRODUCTION The couton by mulse nose s a fequently encounteed oblem n mage acquston and tansmsson. Attenuaton of nose and esevaton of detals ae usually two contadctoy asects of mage ocessng. Nevetheless both of them ae motant to subsequent ocessng stages []. Medan flte and ts vaants [-3] ae the ealest oosed methods fo mulse emoval. Howeve snce such fltes teat all xels of an mage n the same way they tend to mody xels that ae undstubed by nose. Theefoe late methods [-8] ae of usually two stage wth an mulse detecton stage at whch mulses ae located and an mulse flteng stage at whch only the located mulses ae flteed thus the modcaton of good xels ae avoded. The efomance of such two-staged fltes geatly deends on the effcency of the mulse detecto. An deal mulse detecto has to exactly ma the oston of all mulses wthout any msdetecton. The comutaton tme of an mulse detecto s also an motant facto of concen snce t s ust the ntal stage n flteng. Though vaous Imulse detectos avalable n the lteatue detect mulses most often they ethe msdetect non mulsve xels as mulses o comutatonally exensve. In ths ae an mulse detecto based on the moded Thomson and Tau technque [9] has been oosed along wth the efomance analyss of vaous mulse detectos. The oosed mulse detecto s comaed wth the exstng mulse detecton schemes n tems of level of exact detecton and msdetecton. The ae s oganzed as follows: Nose n an mage descbes the nose model consdeed. Some of the exstng mulse detectos ae befed n mulse detectos - an ovevew. The oosed mulse detecto s exlaned n oosed methodology. nally analyss and conclusons based on smulated exemental esults ae esented n exemental analyss.. Nose n an Image Consde an mage I of sze obsevaton mage of same sze. M N and an I () Whee s mulse nose wth andom values. The nose s assumed to be unomly dstbuted wth a obablty N I Wth Wth Pobablty () Whee Pobablty =..M and = N and z / M N. o 8 bt mages a xel s couted t s elaced by ostve o negatve mulse values. Conventonally mulse nose assumes fo a negatve mulse and 55 fo a ostve mulse. In geneal the amltude of mulses ae not fxed but ae allowed to vay between the ostve and negatve eaks. But hee fxed amltude and andom dstbuton of mulses s assumed n the obseved couted mage.. IMPULSE DETECTORS-AN OVERVIEW In ode to descbe how the mulse detectos wok on a couted mage an ovevew of mulse detectos s esented. Consde a wndow W of sze (K+)(K+) centeed aound the xel x of concen ostoned at () such that x... x... x } (3) { k k k k The ank of evey xel R s defned as the oston the xel occues when the elements of the wndow ae soted n ascendng ode. Let the medan of the wndow be gven by 67

2 m medan () The absolute devaton of evey xel fom the medan s denoted as d x m (5) Each mulse detecto makes use of few of these aametes n mang the oston of mulses.. Detecto based on Rank Ode Ctea Ths detecto s called as Dfeental Rank Imulse Detecto (DRID) []. Ths detecto makes decsons based on both the ank of the xel and the absolute devaton of the xel fom the medan of the wndow as M t (3). Detecto based on Pogessve Swtchng Medan lte The mulse detecto used n ogessve swtchng medan flte [] s based on the absolute devaton of evey xel fom the medan of the set. The flag s set the absolute devaton s geate than a secc theshold t as gven below. M t (6). Detecto based on PWMAD The mulse detecto usng Pxel Wse Medan Absolute Devaton (PWMAD) [6] of the wndow W s gven as PWMAD medan d ) medan( x m ) (7) ( A xel n the couted mage s as an mulse d n PWMAD n t (8).3 Detecto based on ROAD Statstcs Rank odeed absolute dfeences (ROAD) [] statstc s defned as ROAD m x m x whee <m<7 n a 3x3 neghbohood and (9) th ( x) smallest d fo y W () xy whee x y () d xy s the dfeence n the xel values of xels x and y. The outut of the mulse detecto s dctated by a theshold t as.5 Detecto based on Imoved Medan lte In ths algothm [3] the nut mage s fst convolved usng fou kenels K = to and can be defned as K = K 3 = K = K = A aamete s obseved such that = mn { K 3 } () Whee s the convoluton oeato? The s comaed wth the theshold to detect the mulse as show below. t (5).6 Accuate Nose Detecto fo Imulse Nose Accuate nose detecto conssts of two systems. st system detemnes the locaton of mulsve nose by usng two medan fltes wth dfeent szes of wndows accodng to a new flag mage. The second system vees the weathe each nose xel detemned by the fst system s an mulse o not. 3. PROPOSED METHODOLOGY In an mage couted by fxed mulse nose a statstcal ocedue fo elmnatng mulsve data fom a samle data of wndow of tycally 7 7 s nvestgated. ROAD t () The couted mage s consdeed block by block athe xel by xel. Blocks ae allowed to ovela atally fo bette efomance. The mean and standad 68

3 devaton of the samle and S ae calculated and then the mulses ae located based on Moded Thomson and Tau Technque. o the atcula wndow W the mean and the standad devaton ae calculated as mean W S N Whee N s the numbe of elements n the set? (6) (7) The devaton ( ) of evey xel ( ) fom mean s then detemned a (8) These devatons ae standadzed as (9) S S nally the lagest s comaed wth that of the tabulated tau values n Table. If t s geate than the tabulated value then the xel s maed as an mulse. Now the mean and devaton of the set elmnatng the xel coesondng to the mulse s ecalculated and the ocedue s eeated teatvely untl all the values ae less than the tabulated tau value. Evey teaton checks fo the esence of an mulse Snce t consdes nut mage as atally ovelang blocks the comutatonal comlexty s O ((N-m) ^) whee m s the ovela allowed. When m= the comutatons become O (N^) whch s same as that of othe detectos. Ths bnay flag matx s used fo estmatng the level of couton and also n flteng of mulses. 3. Poosed Imulse Nose Level Estmaton Technque In a couted mage the level of mulse nose couton s to be estmated. Cetan aametes of fltes lke the wndow o kenel sze theshold values etc ae deendent on the nose level. Hence a new nose level estmaton scheme fo mulse nose couted mages s oosed. The oosed nose level estmaton technque s based on the flag matx obtaned fom the mulse detecton stage. The accuacy of estmaton hghly deends on the elablty of flag matx. Geneally the mulse detectos efom well at hghe nose levels whee the numbe of msdetectons deceases. Hence the obseved mage s futhe couted by mulse nose as Only one mulse could be detected at a tme. The seach fo an mulse could be caed ove block by block. o bette efomance the mage s consdeed as ovelang block of data by whch the msdetectons ae educed. Hence the oosed technque eques elatvely less comutatonal tme and calculatons. The oosed mulse detecton technque well suts n dentyng the mulses n the nosy envonment. The comutatonal comlexty of the oosed mulse detecto s low when comaed to othe state of at fltes. Table : Standad Tau values N Tau N Tau ( ) wth obablty Y( ) wth obablty () Whee s the nose fom a contolled souce? The oston of these addtve mulses s eesented by a bnay flag matx f n whch eesents the esence of an mulse. Ths stochastc nose added mage s gven as nut to the mulse detecto a flag matx f s obtaned. Ths flag matx s efned so as to exclude the mulses that ae ntentonally added to the obseved mage and s gven as f () Whee s the bnay flag matx coesondng to the oston of mulses n the obseved mage? When the addtve mulses ovela wth the actual mulses the accuacy of the estmate s deceased. 69

4 Hence the ocess of addng nose and detectng the ostons s eeated fo k teatons the outut of each teaton beng eesented as k. The Comason of Imulse Detectos fnal flag matx s obtaned fom the bnay summaton of all these k flag matces. ^ f ^ ^ ^ f f... f k ^ f k () Pecentage of coect detecton 8 6 Rank Ode lte PSM PWMAD Unvesal Imulse Detecto Imoved Medan lte Accuate Nose Detecto Poosed Detecto A facto called Nose Count (NC) whch s the numbe of mulses n the couted mage s defned. It s deved fom the flag matx and s gven as S S NC (3) Nose Pecentage gue : Comason of mulse detectos-n mages couted wth mulse nose alone The estmate fo nose level N s based on the NC. The level s calculated as NC N () S S whee S and S eesent the dmensons of the mage.. EPERIMENTAL ANALYSIS The oosed mulse detecto based on moded Thomson and Tau technque s tested fo mages couted wth only mulse nose and fo mages couted wth mxed nose also. The efomance of the oosed mulse detecto n comason wth othe detectos on mages couted wth only mulse nose s lotted n gue-. The oosed technque faly detects the mulses wth the least comutatonal tme. The Nose estmaton scheme s also tested fo mulse nose couted mages and mxed nose couted mages. Table- and gue- show the dfeence between actual and estmated nose level. The mulse nose level estmaton s consstent nste of nose levels and mage detals as nfeed fom Table 3. gue-3 lots the estmaton eo ecentage aganst the numbe of teatons emloyed n estmatng the nose vaance. It could be seen that at least 3 teatons s necessay fo exact estmaton of nose vaance. The mulse nose level calculaton s exemented on mages couted only wth mulse nose and on mages couted wth mxed nose. The esults valdate that the estmaton s elable untl the mulse detecto s elable gue : Nose estmaton at vaous nose levels Table : Eo n nose estmaton fo vaous mages at vaous nose levels Inut Nose Level Images Eo n Estmaton Tle lowes Ccut Eght Pout Pond Temle Mandll Test Llly

5 Eo n Estmaton gue 3: Eo n nose estmaton fo vaous mages at vaous nose levels Table 3: Nose estmaton fo dfeent mages Image Actual Nose level Estmated Nose Level Lena 5 % 9.85 % Cameaman 5 % 9.93 % Bdge 5 % 5.38 % Pees 5 % 9.85 % Mandll 5 % 5.8 % % Eo Inut Nose Level Te lowes Ccut Eght Pout Pond Temle Pefomance Analyss of Estmaton Stategy No. of Iteattons gue : Pefomance of estmaton stategy vesus teatons nvolved 5. CONCLUSION Ths eseach wok oosed a Moded Thomson and Tau Technque to detect the mulse nose esecally when the mage s affected at hgh densty. The ablty of the Moded Thomson and Tau Technque towads detectng mulse nose s demonstated by comang vaous detectos. REERENCES [] Rafael C. Gonzalez and Rchad E. Woods Dgtal mage ocessng nd Edton Pentce- Hall. [] N. C. Gallaghe J and G.W. Wse A theoetcal analyss of the oetes of medan fltes IEEE Tans. Acoust. Seech Sgnal Pocessng vol [3] S.J.Ko and Y.H Lee Cente Weghted Medan ltes and the Alcaton to Image Enhancement IEEE Tans. Ccuts Syst. vol 38 no.9 Set. 99. [] E. Abeu and S. K. Mzta A Sgnal-Deendent Rank Odeed Mean (SD-ROM) lte A New Aoach fo Removal of Imulses fom Hghly Couted Images IEEE 995. [5] Davd Zhang and Zhou Wang Image Infomaton Restoaton Based on Long-Range Coelaton IEEE Tansactons on Ccuts and Systems fo Vdeo Technology vol. no. 5 May. [6] Vladm Cnoevc Von Senk and Želen Tovsk Advanced Imulse Detecton based on Pxel-Wse MAD IEEE Sgnal Pocessng Lettes vol. no. 7 July [7] Raymond H. Chan Chung-Wa Ho and Mla Nkolova Salt-and-Pee Nose Removal by Medan-Tye Nose Detectos and Detal- Pesevng Regulazaton IEEE Tansactons on Image Pocessng vol. No. Octobe 5 [8] P.Badulescu and R. Zacn() A two-state swtched-medan flte CAS Poceedngs vol [9] Alan C. Acock A Gentle Intoducton to Stata Second Edton A Stata Pess Publcaton Texas. [] Z. Wang and D. Zhang Pogessve swtchng medan flte fo the emoval of mulse nose fom hghly couted mages IEEE Tans. Ccuts Syst. vol Jan [] Roman Ganett Tmothy Huegech Chales Chu Wene He A Unvesal Nose Removal Algothm wth an Imulse Detecto IEEE Tansactons on Image Pocessng vol. No. Novembe 5. [] Igo Azenbeg and Constantne Butakoff Effectve Imulse Detecto Based on Rank-Ode Ctea IEEE Sgnal Pocessng Lettes vol. no. 3 Mach. [3] Son Zocan Imoved Medan lte fo Imulse Nose Removal TEELSIKS Octobe 3. 65

INTERVAL ESTIMATION FOR THE QUANTILE OF A TWO-PARAMETER EXPONENTIAL DISTRIBUTION

INTERVAL ESTIMATION FOR THE QUANTILE OF A TWO-PARAMETER EXPONENTIAL DISTRIBUTION Intenatonal Jounal of Innovatve Management, Infomaton & Poducton ISME Intenatonalc0 ISSN 85-5439 Volume, Numbe, June 0 PP. 78-8 INTERVAL ESTIMATION FOR THE QUANTILE OF A TWO-PARAMETER EXPONENTIAL DISTRIBUTION

More information

Parameter Estimation Method in Ridge Regression

Parameter Estimation Method in Ridge Regression Paamete Estmaton Method n dge egesson Dougade.V. Det. of tatstcs, hvaj Unvesty Kolhau-46004. nda. adougade@edff.com Kashd D.N. Det. of tatstcs, hvaj Unvesty Kolhau-46004. nda. dnkashd_n@yahoo.com bstact

More information

Multistage Median Ranked Set Sampling for Estimating the Population Median

Multistage Median Ranked Set Sampling for Estimating the Population Median Jounal of Mathematcs and Statstcs 3 (: 58-64 007 ISSN 549-3644 007 Scence Publcatons Multstage Medan Ranked Set Samplng fo Estmatng the Populaton Medan Abdul Azz Jeman Ame Al-Oma and Kamaulzaman Ibahm

More information

Machine Learning. Spectral Clustering. Lecture 23, April 14, Reading: Eric Xing 1

Machine Learning. Spectral Clustering. Lecture 23, April 14, Reading: Eric Xing 1 Machne Leanng -7/5 7/5-78, 78, Spng 8 Spectal Clusteng Ec Xng Lectue 3, pl 4, 8 Readng: Ec Xng Data Clusteng wo dffeent ctea Compactness, e.g., k-means, mxtue models Connectvty, e.g., spectal clusteng

More information

CS649 Sensor Networks IP Track Lecture 3: Target/Source Localization in Sensor Networks

CS649 Sensor Networks IP Track Lecture 3: Target/Source Localization in Sensor Networks C649 enso etwoks IP Tack Lectue 3: Taget/ouce Localaton n enso etwoks I-Jeng Wang http://hng.cs.jhu.edu/wsn06/ png 006 C 649 Taget/ouce Localaton n Weless enso etwoks Basc Poblem tatement: Collaboatve

More information

Tian Zheng Department of Statistics Columbia University

Tian Zheng Department of Statistics Columbia University Haplotype Tansmsson Assocaton (HTA) An "Impotance" Measue fo Selectng Genetc Makes Tan Zheng Depatment of Statstcs Columba Unvesty Ths s a jont wok wth Pofesso Shaw-Hwa Lo n the Depatment of Statstcs at

More information

Analysis of the chemical equilibrium of combustion at constant volume

Analysis of the chemical equilibrium of combustion at constant volume Analyss of the chemcal equlbum of combuston at constant volume Maus BEBENEL* *Coesondng autho LIEHNICA Unvesty of Buchaest Faculty of Aeosace Engneeng h. olzu Steet -5 6 Buchaest omana mausbeb@yahoo.com

More information

P 365. r r r )...(1 365

P 365. r r r )...(1 365 SCIENCE WORLD JOURNAL VOL (NO4) 008 www.scecncewoldounal.og ISSN 597-64 SHORT COMMUNICATION ANALYSING THE APPROXIMATION MODEL TO BIRTHDAY PROBLEM *CHOJI, D.N. & DEME, A.C. Depatment of Mathematcs Unvesty

More information

On Maneuvering Target Tracking with Online Observed Colored Glint Noise Parameter Estimation

On Maneuvering Target Tracking with Online Observed Colored Glint Noise Parameter Estimation Wold Academy of Scence, Engneeng and Technology 6 7 On Maneuveng Taget Tacng wth Onlne Obseved Coloed Glnt Nose Paamete Estmaton M. A. Masnad-Sha, and S. A. Banan Abstact In ths pape a compehensve algothm

More information

PARAMETRIC FAULT LOCATION OF ELECTRICAL CIRCUIT USING SUPPORT VECTOR MACHINE

PARAMETRIC FAULT LOCATION OF ELECTRICAL CIRCUIT USING SUPPORT VECTOR MACHINE XVIII IMEKO WORLD CONGRESS Metology fo a Sustanable Develoment Setembe, 7 22, 2006, Ro de Janeo, Bazl PARAMETRIC FAULT LOCATION OF ELECTRICAL CIRCUIT USING SUPPORT VECTOR MACHINE S. Osowsk,2, T. Makewcz,

More information

Stochastic Orders Comparisons of Negative Binomial Distribution with Negative Binomial Lindley Distribution

Stochastic Orders Comparisons of Negative Binomial Distribution with Negative Binomial Lindley Distribution Oen Jounal of Statcs 8- htt://dxdoog/46/os5 Publshed Onlne Al (htt://wwwscrpog/ounal/os) Stochac Odes Comasons of Negatve Bnomal Dbuton wth Negatve Bnomal Lndley Dbuton Chooat Pudommaat Wna Bodhsuwan Deatment

More information

Distinct 8-QAM+ Perfect Arrays Fanxin Zeng 1, a, Zhenyu Zhang 2,1, b, Linjie Qian 1, c

Distinct 8-QAM+ Perfect Arrays Fanxin Zeng 1, a, Zhenyu Zhang 2,1, b, Linjie Qian 1, c nd Intenatonal Confeence on Electcal Compute Engneeng and Electoncs (ICECEE 15) Dstnct 8-QAM+ Pefect Aays Fanxn Zeng 1 a Zhenyu Zhang 1 b Lnje Qan 1 c 1 Chongqng Key Laboatoy of Emegency Communcaton Chongqng

More information

OPTIMISED PERMUTATION FILTER

OPTIMISED PERMUTATION FILTER 50 Acta Electotechnca et Infomatca o, Vol, 200 OPTIMISED PERMUTATIO FILTER * Rastslav LUKÁČ, ** Ján LIZÁK * Depatment of Electoncs and Multmeda Communcatons, Techncal Unvesty of Košce, Pak Komenského 3,

More information

Correspondence Analysis & Related Methods

Correspondence Analysis & Related Methods Coespondence Analyss & Related Methods Ineta contbutons n weghted PCA PCA s a method of data vsualzaton whch epesents the tue postons of ponts n a map whch comes closest to all the ponts, closest n sense

More information

A. Thicknesses and Densities

A. Thicknesses and Densities 10 Lab0 The Eath s Shells A. Thcknesses and Denstes Any theoy of the nteo of the Eath must be consstent wth the fact that ts aggegate densty s 5.5 g/cm (ecall we calculated ths densty last tme). In othe

More information

Khintchine-Type Inequalities and Their Applications in Optimization

Khintchine-Type Inequalities and Their Applications in Optimization Khntchne-Type Inequaltes and The Applcatons n Optmzaton Anthony Man-Cho So Depatment of Systems Engneeng & Engneeng Management The Chnese Unvesty of Hong Kong ISDS-Kolloquum Unvestaet Wen 29 June 2009

More information

an application to HRQoL

an application to HRQoL AlmaMate Studoum Unvesty of Bologna A flexle IRT Model fo health questonnae: an applcaton to HRQoL Seena Boccol Gula Cavn Depatment of Statstcal Scence, Unvesty of Bologna 9 th Intenatonal Confeence on

More information

Box-Particle Labeled Multi-Bernoulli Filter for Multiple Extended Target Tracking

Box-Particle Labeled Multi-Bernoulli Filter for Multiple Extended Target Tracking RADIOENGINEERING, VOL. 5, NO. 3, SEPTEBER 06 57 Bo-Patcle Labeled ult-benoull Flte fo ultle Etended Taget Tacng ao LI, Zang LIN, We AN, Yyu ZHOU ollege of Electonc Scence and Engneeng, Natonal Unvesty

More information

3. A Review of Some Existing AW (BT, CT) Algorithms

3. A Review of Some Existing AW (BT, CT) Algorithms 3. A Revew of Some Exstng AW (BT, CT) Algothms In ths secton, some typcal ant-wndp algothms wll be descbed. As the soltons fo bmpless and condtoned tansfe ae smla to those fo ant-wndp, the pesented algothms

More information

19 The Born-Oppenheimer Approximation

19 The Born-Oppenheimer Approximation 9 The Bon-Oppenheme Appoxmaton The full nonelatvstc Hamltonan fo a molecule s gven by (n a.u.) Ĥ = A M A A A, Z A + A + >j j (883) Lets ewte the Hamltonan to emphasze the goal as Ĥ = + A A A, >j j M A

More information

iclicker Quiz a) True b) False Theoretical physics: the eternal quest for a missing minus sign and/or a factor of two. Which will be an issue today?

iclicker Quiz a) True b) False Theoretical physics: the eternal quest for a missing minus sign and/or a factor of two. Which will be an issue today? Clce Quz I egsteed my quz tansmtte va the couse webste (not on the clce.com webste. I ealze that untl I do so, my quz scoes wll not be ecoded. a Tue b False Theoetcal hyscs: the etenal quest fo a mssng

More information

Exact Simplification of Support Vector Solutions

Exact Simplification of Support Vector Solutions Jounal of Machne Leanng Reseach 2 (200) 293-297 Submtted 3/0; Publshed 2/0 Exact Smplfcaton of Suppot Vecto Solutons Tom Downs TD@ITEE.UQ.EDU.AU School of Infomaton Technology and Electcal Engneeng Unvesty

More information

State Estimation. Ali Abur Northeastern University, USA. Nov. 01, 2017 Fall 2017 CURENT Course Lecture Notes

State Estimation. Ali Abur Northeastern University, USA. Nov. 01, 2017 Fall 2017 CURENT Course Lecture Notes State Estmaton Al Abu Notheasten Unvesty, USA Nov. 0, 07 Fall 07 CURENT Couse Lectue Notes Opeatng States of a Powe System Al Abu NORMAL STATE SECURE o INSECURE RESTORATIVE STATE EMERGENCY STATE PARTIAL

More information

On the Distribution of the Weighted Sum of L Independent Rician and Nakagami Envelopes in the Presence of AWGN

On the Distribution of the Weighted Sum of L Independent Rician and Nakagami Envelopes in the Presence of AWGN On the Dstbuton of the Weghted Sum of L Indeendent Rcan and Naagam Enveloes n the Pesence of AWN eoge K Kaagannds and Stavos A Kotsooulos Abstact: An altenatve, unfed, sem-analytcal aoach fo the evaluaton

More information

Event Shape Update. T. Doyle S. Hanlon I. Skillicorn. A. Everett A. Savin. Event Shapes, A. Everett, U. Wisconsin ZEUS Meeting, October 15,

Event Shape Update. T. Doyle S. Hanlon I. Skillicorn. A. Everett A. Savin. Event Shapes, A. Everett, U. Wisconsin ZEUS Meeting, October 15, Event Shape Update A. Eveett A. Savn T. Doyle S. Hanlon I. Skllcon Event Shapes, A. Eveett, U. Wsconsn ZEUS Meetng, Octobe 15, 2003-1 Outlne Pogess of Event Shapes n DIS Smla to publshed pape: Powe Coecton

More information

International Journal of Statistika and Mathematika, ISSN: E-ISSN: , Volume 9, Issue 1, 2014 pp 34-39

International Journal of Statistika and Mathematika, ISSN: E-ISSN: , Volume 9, Issue 1, 2014 pp 34-39 Intenatonal Jounal of Statstka and Mathematka, ISSN: 2277-2790 E-ISSN: 2249-8605, Volume 9, Issue 1, 2014 34-39 Desgnng of Genealzed Two Plan System wth Reettve Defeed Samlng Plan as Refeence Plan Usng

More information

Closed-loop adaptive optics using a CMOS image quality metric sensor

Closed-loop adaptive optics using a CMOS image quality metric sensor Closed-loop adaptve optcs usng a CMOS mage qualty metc senso Chueh Tng, Mchael Gles, Adtya Rayankula, and Pual Futh Klpsch School of Electcal and Compute Engneeng ew Mexco State Unvesty Las Cuces, ew Mexco

More information

The Greatest Deviation Correlation Coefficient and its Geometrical Interpretation

The Greatest Deviation Correlation Coefficient and its Geometrical Interpretation By Rudy A. Gdeon The Unvesty of Montana The Geatest Devaton Coelaton Coeffcent and ts Geometcal Intepetaton The Geatest Devaton Coelaton Coeffcent (GDCC) was ntoduced by Gdeon and Hollste (987). The GDCC

More information

N = N t ; t 0. N is the number of claims paid by the

N = N t ; t 0. N is the number of claims paid by the Iulan MICEA, Ph Mhaela COVIG, Ph Canddate epatment of Mathematcs The Buchaest Academy of Economc Studes an CECHIN-CISTA Uncedt Tac Bank, Lugoj SOME APPOXIMATIONS USE IN THE ISK POCESS OF INSUANCE COMPANY

More information

Energy in Closed Systems

Energy in Closed Systems Enegy n Closed Systems Anamta Palt palt.anamta@gmal.com Abstact The wtng ndcates a beakdown of the classcal laws. We consde consevaton of enegy wth a many body system n elaton to the nvese squae law and

More information

4 Recursive Linear Predictor

4 Recursive Linear Predictor 4 Recusve Lnea Pedcto The man objectve of ths chapte s to desgn a lnea pedcto wthout havng a po knowledge about the coelaton popetes of the nput sgnal. In the conventonal lnea pedcto the known coelaton

More information

Impulse Noise Removal Technique Based on Fuzzy Logic

Impulse Noise Removal Technique Based on Fuzzy Logic Impulse Nose Removal Technque Based on Fuzzy Logc 1 Mthlesh Atulkar, 2 A.S. Zadgaonkar and 3 Sanjay Kumar C V Raman Unversty, Kota, Blaspur, Inda 1 m.atulkar@gmal.com, 2 arunzad28@hotmal.com, 3 sanrapur@redffmal.com

More information

ON A PROBLEM OF SPATIAL ARRANGEMENT OF SERVICE STATIONS. Alexander Andronov, Andrey Kashurin

ON A PROBLEM OF SPATIAL ARRANGEMENT OF SERVICE STATIONS. Alexander Andronov, Andrey Kashurin Pat I Pobobabystc Modes Comute Moden and New Technooes 007 Vo No 3-37 Tansot and Teecommuncaton Insttute Lomonosova Ra LV-09 Latva ON A PROBLEM OF SPATIAL ARRANGEMENT OF SERVICE STATIONS Aeande Andonov

More information

A NOVEL DWELLING TIME DESIGN METHOD FOR LOW PROBABILITY OF INTERCEPT IN A COMPLEX RADAR NETWORK

A NOVEL DWELLING TIME DESIGN METHOD FOR LOW PROBABILITY OF INTERCEPT IN A COMPLEX RADAR NETWORK Z. Zhang et al., Int. J. of Desgn & Natue and Ecodynamcs. Vol. 0, No. 4 (205) 30 39 A NOVEL DWELLING TIME DESIGN METHOD FOR LOW PROBABILITY OF INTERCEPT IN A COMPLEX RADAR NETWORK Z. ZHANG,2,3, J. ZHU

More information

Generalized Loss Variance Bounds

Generalized Loss Variance Bounds Int. J. Contem. ath. Scences Vol. 7 0 no. 3 559-567 Genealzed Loss Vaance Bounds Wene Hülmann FRSGlobal Swtzeland Seefeldstasse 69 CH-8008 Züch Swtzeland wene.huelmann@fsglobal.com whulmann@bluewn.ch Abstact

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

Minimal Detectable Biases of GPS observations for a weighted ionosphere

Minimal Detectable Biases of GPS observations for a weighted ionosphere LETTER Eath Planets Space, 52, 857 862, 2000 Mnmal Detectable Bases of GPS obsevatons fo a weghted onosphee K. de Jong and P. J. G. Teunssen Depatment of Mathematcal Geodesy and Postonng, Delft Unvesty

More information

A Method of Reliability Target Setting for Electric Power Distribution Systems Using Data Envelopment Analysis

A Method of Reliability Target Setting for Electric Power Distribution Systems Using Data Envelopment Analysis 27 กก ก 9 2-3 2554 ก ก ก A Method of Relablty aget Settng fo Electc Powe Dstbuton Systems Usng Data Envelopment Analyss ก 2 ก ก ก ก ก 0900 2 ก ก ก ก ก 0900 E-mal: penjan262@hotmal.com Penjan Sng-o Psut

More information

A Branch and Bound Method for Sum of Completion Permutation Flow Shop

A Branch and Bound Method for Sum of Completion Permutation Flow Shop UNLV Theses, Dssetatons, Pofessonal Paes, and Castones 5--04 A Banch and Bound ethod fo Sum of Comleton Pemutaton Flow Sho Swana Kodmala Unvesty of Nevada, Las Vegas, swanakodmala@gmal.com Follow ths and

More information

Optimal System for Warm Standby Components in the Presence of Standby Switching Failures, Two Types of Failures and General Repair Time

Optimal System for Warm Standby Components in the Presence of Standby Switching Failures, Two Types of Failures and General Repair Time Intenatonal Jounal of ompute Applcatons (5 ) Volume 44 No, Apl Optmal System fo Wam Standby omponents n the esence of Standby Swtchng Falues, Two Types of Falues and Geneal Repa Tme Mohamed Salah EL-Shebeny

More information

Vibration Input Identification using Dynamic Strain Measurement

Vibration Input Identification using Dynamic Strain Measurement Vbaton Input Identfcaton usng Dynamc Stan Measuement Takum ITOFUJI 1 ;TakuyaYOSHIMURA ; 1, Tokyo Metopoltan Unvesty, Japan ABSTRACT Tansfe Path Analyss (TPA) has been conducted n ode to mpove the nose

More information

8 Baire Category Theorem and Uniform Boundedness

8 Baire Category Theorem and Uniform Boundedness 8 Bae Categoy Theoem and Unfom Boundedness Pncple 8.1 Bae s Categoy Theoem Valdty of many esults n analyss depends on the completeness popety. Ths popety addesses the nadequacy of the system of atonal

More information

Scalars and Vectors Scalar

Scalars and Vectors Scalar Scalas and ectos Scala A phscal quantt that s completel chaacteed b a eal numbe (o b ts numecal value) s called a scala. In othe wods a scala possesses onl a magntude. Mass denst volume tempeatue tme eneg

More information

UNIT10 PLANE OF REGRESSION

UNIT10 PLANE OF REGRESSION UIT0 PLAE OF REGRESSIO Plane of Regesson Stuctue 0. Intoducton Ojectves 0. Yule s otaton 0. Plane of Regesson fo thee Vaales 0.4 Popetes of Resduals 0.5 Vaance of the Resduals 0.6 Summay 0.7 Solutons /

More information

WORKING PAPER SERIES

WORKING PAPER SERIES College of Busness Admnstaton Unvesty of Rhode Island Wllam A. Ome WORKING PAPER SERIES encouagng ceatve eseach 8/9 No. 6 Ths wokng ae sees s ntended to facltate dscusson and encouage the exchange of deas.

More information

IMAGE DENOISING USING NEW ADAPTIVE BASED MEDIAN FILTER

IMAGE DENOISING USING NEW ADAPTIVE BASED MEDIAN FILTER Sgnal & Image Processng : An Internatonal Journal (SIPIJ) Vol.5, No.4, August 2014 IMAGE DENOISING USING NEW ADAPTIVE BASED MEDIAN FILTER Suman Shrestha 1, 2 1 Unversty of Massachusetts Medcal School,

More information

APPLICATIONS OF SEMIGENERALIZED -CLOSED SETS

APPLICATIONS OF SEMIGENERALIZED -CLOSED SETS Intenatonal Jounal of Mathematcal Engneeng Scence ISSN : 22776982 Volume Issue 4 (Apl 202) http://www.mes.com/ https://stes.google.com/ste/mesounal/ APPLICATIONS OF SEMIGENERALIZED CLOSED SETS G.SHANMUGAM,

More information

Efficiency of the principal component Liu-type estimator in logistic

Efficiency of the principal component Liu-type estimator in logistic Effcency of the pncpal component Lu-type estmato n logstc egesson model Jbo Wu and Yasn Asa 2 School of Mathematcs and Fnance, Chongqng Unvesty of Ats and Scences, Chongqng, Chna 2 Depatment of Mathematcs-Compute

More information

EE 5337 Computational Electromagnetics (CEM)

EE 5337 Computational Electromagnetics (CEM) 7//28 Instucto D. Raymond Rumpf (95) 747 6958 cumpf@utep.edu EE 5337 Computatonal Electomagnetcs (CEM) Lectue #6 TMM Extas Lectue 6These notes may contan copyghted mateal obtaned unde fa use ules. Dstbuton

More information

Cooperative and Active Sensing in Mobile Sensor Networks for Scalar Field Mapping

Cooperative and Active Sensing in Mobile Sensor Networks for Scalar Field Mapping 3 IEEE Intenatonal Confeence on Automaton Scence and Engneeng (CASE) TuBT. Coopeatve and Actve Sensng n Moble Senso Netwos fo Scala Feld Mappng Hung Manh La, Wehua Sheng and Jmng Chen Abstact Scala feld

More information

Physics 2A Chapter 11 - Universal Gravitation Fall 2017

Physics 2A Chapter 11 - Universal Gravitation Fall 2017 Physcs A Chapte - Unvesal Gavtaton Fall 07 hese notes ae ve pages. A quck summay: he text boxes n the notes contan the esults that wll compse the toolbox o Chapte. hee ae thee sectons: the law o gavtaton,

More information

On the Latency Bound of Deficit Round Robin

On the Latency Bound of Deficit Round Robin Poceedngs of the Intenatonal Confeence on Compute Communcatons and Netwoks Mam, Floda, USA, Octobe 4 6, 22 On the Latency Bound of Defct Round Robn Sall S. Kanhee and Hash Sethu Depatment of ECE, Dexel

More information

Thermodynamics of solids 4. Statistical thermodynamics and the 3 rd law. Kwangheon Park Kyung Hee University Department of Nuclear Engineering

Thermodynamics of solids 4. Statistical thermodynamics and the 3 rd law. Kwangheon Park Kyung Hee University Department of Nuclear Engineering Themodynamcs of solds 4. Statstcal themodynamcs and the 3 d law Kwangheon Pak Kyung Hee Unvesty Depatment of Nuclea Engneeng 4.1. Intoducton to statstcal themodynamcs Classcal themodynamcs Statstcal themodynamcs

More information

A. P. Sakis Meliopoulos Power System Modeling, Analysis and Control. Chapter 7 3 Operating State Estimation 3

A. P. Sakis Meliopoulos Power System Modeling, Analysis and Control. Chapter 7 3 Operating State Estimation 3 DRAF and INCOMPLEE able of Contents fom A. P. Saks Melopoulos Powe System Modelng, Analyss and Contol Chapte 7 3 Opeatng State Estmaton 3 7. Intoducton 3 7. SCADA System 4 7.3 System Netwok Confguato 7

More information

AN ALGORITHM FOR CALCULATING THE CYCLETIME AND GREENTIMES FOR A SIGNALIZED INTERSECTION

AN ALGORITHM FOR CALCULATING THE CYCLETIME AND GREENTIMES FOR A SIGNALIZED INTERSECTION AN AGORITHM OR CACUATING THE CYCETIME AND GREENTIMES OR A SIGNAIZED INTERSECTION Henk Taale 1. Intoducton o a snalzed ntesecton wth a fedte contol state the cclete and eentes ae the vaables that nfluence

More information

Switching Median Filter Based on Iterative Clustering Noise Detection

Switching Median Filter Based on Iterative Clustering Noise Detection Swtchng Medan Flter Based on Iteratve Clusterng Nose Detecton Chngakham Neeta Dev 1, Kesham Prtamdas 2 1 Deartment of Comuter Scence and Engneerng, Natonal Insttute of Technology, Manur, Inda 2 Deartment

More information

Anomalies detection on spatially inhomogeneous polyzonal images

Anomalies detection on spatially inhomogeneous polyzonal images Anomales detecton on spatally nhomogeneous polyzonal mages N.A. Andyanov 1 K.K. Vaslev 1 V.E. Dementev 1 1 Ulyanovs State Techncal Unvesty Seveny Venets steet 3 437 Ulyanovs Russa Abstact The tet deals

More information

Parallel Algorithms for Residue Scaling and Error Correction in Residue Arithmetic

Parallel Algorithms for Residue Scaling and Error Correction in Residue Arithmetic Weless Engneeng Technology 8- htt://ddoog/6/wet Publshed Onlne Octobe (htt://wwwscog/ounal/wet) Paallel Algoths fo Resdue Scalng Eo Coecton n Resdue Athetc Hao-Yung Lo Tng-We Ln Deatent of Electcal Engneeng

More information

Concept of Game Equilibrium. Game theory. Normal- Form Representation. Game definition. Lecture Notes II-1 Static Games of Complete Information

Concept of Game Equilibrium. Game theory. Normal- Form Representation. Game definition. Lecture Notes II-1 Static Games of Complete Information Game theoy he study of multeson decsons Fou tyes of games Statc games of comlete nfomaton ynamc games of comlete nfomaton Statc games of ncomlete nfomaton ynamc games of ncomlete nfomaton Statc v. dynamc

More information

LASER ABLATION ICP-MS: DATA REDUCTION

LASER ABLATION ICP-MS: DATA REDUCTION Lee, C-T A Lase Ablaton Data educton 2006 LASE ABLATON CP-MS: DATA EDUCTON Cn-Ty A. Lee 24 Septembe 2006 Analyss and calculaton of concentatons Lase ablaton analyses ae done n tme-esolved mode. A ~30 s

More information

A Brief Guide to Recognizing and Coping With Failures of the Classical Regression Assumptions

A Brief Guide to Recognizing and Coping With Failures of the Classical Regression Assumptions A Bef Gude to Recognzng and Copng Wth Falues of the Classcal Regesson Assumptons Model: Y 1 k X 1 X fxed n epeated samples IID 0, I. Specfcaton Poblems A. Unnecessay explanatoy vaables 1. OLS s no longe

More information

Approximate Abundance Histograms and Their Use for Genome Size Estimation

Approximate Abundance Histograms and Their Use for Genome Size Estimation J. Hlaváčová (Ed.): ITAT 2017 Poceedngs, pp. 27 34 CEUR Wokshop Poceedngs Vol. 1885, ISSN 1613-0073, c 2017 M. Lpovský, T. Vnař, B. Bejová Appoxmate Abundance Hstogams and The Use fo Genome Sze Estmaton

More information

Amplifier Constant Gain and Noise

Amplifier Constant Gain and Noise Amplfe Constant Gan and ose by Manfed Thumm and Wene Wesbeck Foschungszentum Kalsuhe n de Helmholtz - Gemenschaft Unvestät Kalsuhe (TH) Reseach Unvesty founded 85 Ccles of Constant Gan (I) If s taken to

More information

Detection and Estimation Theory

Detection and Estimation Theory ESE 54 Detecton and Etmaton Theoy Joeph A. O Sullvan Samuel C. Sach Pofeo Electonc Sytem and Sgnal Reeach Laboatoy Electcal and Sytem Engneeng Wahngton Unvety 411 Jolley Hall 314-935-4173 (Lnda anwe) jao@wutl.edu

More information

ADAPTIVE IMAGE FILTERING

ADAPTIVE IMAGE FILTERING Why adaptve? ADAPTIVE IMAGE FILTERING average detals and contours are aected Averagng should not be appled n contour / detals regons. Adaptaton Adaptaton = modyng the parameters o a prrocessng block accordng

More information

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Analyss of Varance and Desgn of Exerments-I MODULE III LECTURE - 2 EXPERIMENTAL DESIGN MODELS Dr. Shalabh Deartment of Mathematcs and Statstcs Indan Insttute of Technology Kanur 2 We consder the models

More information

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

Dirichlet Mixture Priors: Inference and Adjustment

Dirichlet Mixture Priors: Inference and Adjustment Dchlet Mxtue Pos: Infeence and Adustment Xugang Ye (Wokng wth Stephen Altschul and Y Kuo Yu) Natonal Cante fo Botechnology Infomaton Motvaton Real-wold obects Independent obsevatons Categocal data () (2)

More information

Homework 10 Stat 547. Problem ) Z D!

Homework 10 Stat 547. Problem ) Z D! Homework 0 Stat 547 Problem 74 Notaton: h s the hazard rate for the aneulod grou, h s the hazard rate for the dlod grou (a Log-rank test s erformed: H 0 : h (t = h (t Sgnfcance level α = 005 Test statstc

More information

ORBIT uncertainty propagation plays an important role in

ORBIT uncertainty propagation plays an important role in JOURNAL OF GUIDANCE, CONTROL, AND DYNAMICS Vol. 3, No. 6, Novembe Decembe 27 Nonlnea Sem-Analytc Metods fo Tajectoy Estmaton Ryan S. Pa and Danel J. Sceees Unvesty of Mcgan, Ann Abo, Mcgan 489 DOI:.254/.296

More information

STRUCTURE IN LEGISLATIVE BARGAINING

STRUCTURE IN LEGISLATIVE BARGAINING NOT FOR PUBICATION ONINE APPENDICES FOR STRUCTURE IN EGISATIVE BARGAINING Adan de Goot Ruz Roald Rame Athu Scham APPENDIX A: PROOF FOR PROPOSITION FOR HIGHY STRUCTURED GAME APPENDIX B: PROOFS FOR PROPOSITIONS

More information

Contact, information, consultations

Contact, information, consultations ontact, nfomaton, consultatons hemsty A Bldg; oom 07 phone: 058-347-769 cellula: 664 66 97 E-mal: wojtek_c@pg.gda.pl Offce hous: Fday, 9-0 a.m. A quote of the week (o camel of the week): hee s no expedence

More information

University of Bath DOI: /S Publication date: Document Version Peer reviewed version. Link to publication

University of Bath DOI: /S Publication date: Document Version Peer reviewed version. Link to publication Ctaton fo ublshed veson: Has, D, Havey, DI, Leyboune, S & Sakkas,, 'Local asymtotc owe of the Im-Pesaan-Shn anel unt oot test and the mact of ntal obsevatons' Econometc Theoy, vol. 6, no.,. -4. htts://do.og/.7/s66466699768

More information

Generating Functions, Weighted and Non-Weighted Sums for Powers of Second-Order Recurrence Sequences

Generating Functions, Weighted and Non-Weighted Sums for Powers of Second-Order Recurrence Sequences Geneatng Functons, Weghted and Non-Weghted Sums fo Powes of Second-Ode Recuence Sequences Pantelmon Stăncă Aubun Unvesty Montgomey, Depatment of Mathematcs Montgomey, AL 3614-403, USA e-mal: stanca@studel.aum.edu

More information

Physics 207 Lecture 16

Physics 207 Lecture 16 Physcs 07 Lectue 6 Goals: Lectue 6 Chapte Extend the patcle odel to gd-bodes Undestand the equlbu of an extended object. Analyze ollng oton Undestand otaton about a fxed axs. Eploy consevaton of angula

More information

Pattern Analyses (EOF Analysis) Introduction Definition of EOFs Estimation of EOFs Inference Rotated EOFs

Pattern Analyses (EOF Analysis) Introduction Definition of EOFs Estimation of EOFs Inference Rotated EOFs Patten Analyses (EOF Analyss) Intoducton Defnton of EOFs Estmaton of EOFs Infeence Rotated EOFs . Patten Analyses Intoducton: What s t about? Patten analyses ae technques used to dentfy pattens of the

More information

(8) Gain Stage and Simple Output Stage

(8) Gain Stage and Simple Output Stage EEEB23 Electoncs Analyss & Desgn (8) Gan Stage and Smple Output Stage Leanng Outcome Able to: Analyze an example of a gan stage and output stage of a multstage amplfe. efeence: Neamen, Chapte 11 8.0) ntoducton

More information

Rigid Bodies: Equivalent Systems of Forces

Rigid Bodies: Equivalent Systems of Forces Engneeng Statcs, ENGR 2301 Chapte 3 Rgd Bodes: Equvalent Sstems of oces Intoducton Teatment of a bod as a sngle patcle s not alwas possble. In geneal, the se of the bod and the specfc ponts of applcaton

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

Variance estimation in multi-phase calibration

Variance estimation in multi-phase calibration Catalogue no. -00-X ISSN 49-09 Suvey Methodology Vaance estmaton n mult-hase calbaton by Noam Cohen, Dan Ben-Hu and Lusa Buck Release date: June, 07 How to obtan moe nfomaton Fo nfomaton about ths oduct

More information

CSE-571 Robotics. Ball Tracking in RoboCup. Tracking Techniques. Rao-Blackwelized Particle Filters for State Estimation

CSE-571 Robotics. Ball Tracking in RoboCup. Tracking Techniques. Rao-Blackwelized Particle Filters for State Estimation CSE-571 Rootcs Rao-Blacwelzed Patcle Fltes fo State Estaton Ball Tacng n RooCup Exteely nosy nonlnea oton of oseve Inaccuate sensng lted pocessng powe Inteactons etween taget and Goal: envonent Unfed faewo

More information

Another generalization of the gcd-sum function

Another generalization of the gcd-sum function Aab J Math 2013 2:313 320 DOI 10.1007/s40065-013-0077-y László Tóth Anothe genealzaton of the gcd-sum functon Receved: 12 Octobe 2012 / Acceted: 22 May 2013 / Publshed onlne: 5 June 2013 The Authos 2013.

More information

Scaling Growth in Heat Transfer Surfaces and Its Thermohydraulic Effect Upon the Performance of Cooling Systems

Scaling Growth in Heat Transfer Surfaces and Its Thermohydraulic Effect Upon the Performance of Cooling Systems 799 A publcaton of CHEMICAL ENGINEERING TRANSACTIONS VOL. 61, 017 Guest Edtos: Peta S Vabanov, Rongxn Su, Hon Loong Lam, Xa Lu, Jří J Klemeš Copyght 017, AIDIC Sevz S..l. ISBN 978-88-95608-51-8; ISSN 83-916

More information

Experimental study on parameter choices in norm-r support vector regression machines with noisy input

Experimental study on parameter choices in norm-r support vector regression machines with noisy input Soft Comput 006) 0: 9 3 DOI 0.007/s00500-005-0474-z ORIGINAL PAPER S. Wang J. Zhu F. L. Chung Hu Dewen Expemental study on paamete choces n nom- suppot vecto egesson machnes wth nosy nput Publshed onlne:

More information

THE REGRESSION MODEL OF TRANSMISSION LINE ICING BASED ON NEURAL NETWORKS

THE REGRESSION MODEL OF TRANSMISSION LINE ICING BASED ON NEURAL NETWORKS The 4th Intenatonal Wokshop on Atmosphec Icng of Stuctues, Chongqng, Chna, May 8 - May 3, 20 THE REGRESSION MODEL OF TRANSMISSION LINE ICING BASED ON NEURAL NETWORKS Sun Muxa, Da Dong*, Hao Yanpeng, Huang

More information

Set of square-integrable function 2 L : function space F

Set of square-integrable function 2 L : function space F Set of squae-ntegable functon L : functon space F Motvaton: In ou pevous dscussons we have seen that fo fee patcles wave equatons (Helmholt o Schödnge) can be expessed n tems of egenvalue equatons. H E,

More information

Learning the structure of Bayesian belief networks

Learning the structure of Bayesian belief networks Lectue 17 Leanng the stuctue of Bayesan belef netwoks Mlos Hauskecht mlos@cs.ptt.edu 5329 Sennott Squae Leanng of BBN Leanng. Leanng of paametes of condtonal pobabltes Leanng of the netwok stuctue Vaables:

More information

PARAMETER ESTIMATION FOR TWO WEIBULL POPULATIONS UNDER JOINT TYPE II CENSORED SCHEME

PARAMETER ESTIMATION FOR TWO WEIBULL POPULATIONS UNDER JOINT TYPE II CENSORED SCHEME Sept 04 Vol 5 No 04 Intenatonal Jounal of Engneeng Appled Scences 0-04 EAAS & ARF All ghts eseed wwweaas-ounalog ISSN305-869 PARAMETER ESTIMATION FOR TWO WEIBULL POPULATIONS UNDER JOINT TYPE II CENSORED

More information

Sliding mode multiple observer for fault detection and isolation

Sliding mode multiple observer for fault detection and isolation Sldng mode multle obseve fo fault detecton and solaton bdelkade khenak, Mohammed Chadl, Dde Maqun, José Ragot o cte ths veson: bdelkade khenak, Mohammed Chadl, Dde Maqun, José Ragot. Sldng mode multle

More information

A Study about One-Dimensional Steady State. Heat Transfer in Cylindrical and. Spherical Coordinates

A Study about One-Dimensional Steady State. Heat Transfer in Cylindrical and. Spherical Coordinates Appled Mathematcal Scences, Vol. 7, 03, no. 5, 67-633 HIKARI Ltd, www.m-hka.com http://dx.do.og/0.988/ams.03.38448 A Study about One-Dmensonal Steady State Heat ansfe n ylndcal and Sphecal oodnates Lesson

More information

Monte Carlo comparison of back-propagation, conjugate-gradient, and finite-difference training algorithms for multilayer perceptrons

Monte Carlo comparison of back-propagation, conjugate-gradient, and finite-difference training algorithms for multilayer perceptrons Rocheste Insttute of Technology RIT Schola Woks Theses Thess/Dssetaton Collectons 20 Monte Calo compason of back-popagaton, conugate-gadent, and fnte-dffeence tanng algothms fo multlaye peceptons Stephen

More information

VParC: A Compression Scheme for Numeric Data in Column-Oriented Databases

VParC: A Compression Scheme for Numeric Data in Column-Oriented Databases The Intenatonal Aab Jounal of Infomaton Technology VPaC: A Compesson Scheme fo Numec Data n Column-Oented Databases Ke Yan, Hong Zhu, and Kevn Lü School of Compute Scence and Technology, Huazhong Unvesty

More information

L-MOMENTS EVALUATION FOR IDENTICALLY AND NONIDENTICALLY WEIBULL DISTRIBUTED RANDOM VARIABLES

L-MOMENTS EVALUATION FOR IDENTICALLY AND NONIDENTICALLY WEIBULL DISTRIBUTED RANDOM VARIABLES THE PUBLISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Sees A, OF THE ROMANIAN ACADEMY Volume 8, Numbe 3/27,. - L-MOMENTS EVALUATION FOR IDENTICALLY AND NONIDENTICALLY WEIBULL DISTRIBUTED RANDOM VARIABLES

More information

A Queuing Model for an Automated Workstation Receiving Jobs from an Automated Workstation

A Queuing Model for an Automated Workstation Receiving Jobs from an Automated Workstation Intenatonal Jounal of Opeatons Reseach Intenatonal Jounal of Opeatons Reseach Vol. 7, o. 4, 918 (1 A Queung Model fo an Automated Wokstaton Recevng Jobs fom an Automated Wokstaton Davd S. Km School of

More information

Physics 11b Lecture #2. Electric Field Electric Flux Gauss s Law

Physics 11b Lecture #2. Electric Field Electric Flux Gauss s Law Physcs 11b Lectue # Electc Feld Electc Flux Gauss s Law What We Dd Last Tme Electc chage = How object esponds to electc foce Comes n postve and negatve flavos Conseved Electc foce Coulomb s Law F Same

More information

CFAR BI DETECTOR IN BINOMIAL DISTRIBUTION PULSE JAMMING 1. I. Garvanov. (Submitted by Academician Ivan Popchev on June 23, 2003)

CFAR BI DETECTOR IN BINOMIAL DISTRIBUTION PULSE JAMMING 1. I. Garvanov. (Submitted by Academician Ivan Popchev on June 23, 2003) FA BI EEO I BIOMIAL ISIBUIO PULSE JAMMIG I. Gavanov (Submtted by Academcan Ivan Popchev on June 3, 3) Abtact: In many pactcal tuaton, howeve, the envonment peence of tong pule ammng (PJ) wth hgh ntenty;

More information

Mining Data Streams-Estimating Frequency Moment

Mining Data Streams-Estimating Frequency Moment Mnng Data Streams-Estmatng Frequency Moment Barna Saha October 26, 2017 Frequency Moment Computng moments nvolves dstrbuton of frequences of dfferent elements n the stream. Frequency Moment Computng moments

More information

Fourier Transform. Additive noise. Fourier Tansform. I = S + N. Noise doesn t depend on signal. We ll consider:

Fourier Transform. Additive noise. Fourier Tansform. I = S + N. Noise doesn t depend on signal. We ll consider: Flterng Announcements HW2 wll be posted later today Constructng a mosac by warpng mages. CSE252A Lecture 10a Flterng Exampel: Smoothng by Averagng Kernel: (From Bll Freeman) m=2 I Kernel sze s m+1 by m+1

More information

Backward Haplotype Transmission Association (BHTA) Algorithm. Tian Zheng Department of Statistics Columbia University. February 5 th, 2002

Backward Haplotype Transmission Association (BHTA) Algorithm. Tian Zheng Department of Statistics Columbia University. February 5 th, 2002 Backwad Haplotype Tansmsson Assocaton (BHTA) Algothm A Fast ult-pont Sceenng ethod fo Complex Tats Tan Zheng Depatment of Statstcs Columba Unvesty Febuay 5 th, 2002 Ths s a jont wok wth Pofesso Shaw-Hwa

More information

Particle Swarm Optimization Algorithm for Designing BP and BS IIR Digital Filter

Particle Swarm Optimization Algorithm for Designing BP and BS IIR Digital Filter Patcle Swam Optmzaton Algothm fo Desgnng BP and BS IIR Dgtal Flte Rant Kau 1, Damanpeet Sngh 1Depatment of Electoncs & Communcaton 1 Punab Unvesty, Patala Depatment of Compute Scence SantLongowal Insttute

More information