Linearising Calibration Methods for a Generic Embedded Sensor Interface (GESI)
|
|
- Nathaniel Boone
- 6 years ago
- Views:
Transcription
1 1st Iteratioal Coferece o Sesig Techology November 21-23, 2005 Palmersto North, New Zealad Liearisig Calibratio Methods for a Geeric Embedded Sesor Iterface (GESI) Abstract Amra Pašić Work doe i: PEI Techologies, School of Electroic Egieerig, Curret address: Natioal Cetre for Sesor Research, Dubli City Uiversity, Glasevi, Dubli 9, Irelad amra.pasic@dcu.ie Jim Dowlig School of Electroic Egieerig, Dubli City Uiversity, Glasevi, Dubli 9, Irelad jim.dowlig@dcu.ie Various liearisatio ad calibratio techiques for sesor characteristic are ivestigated ad the most promisig techiques, curve fittig ad the progressive polyomial calibratio method, are the examied i greater detail. Results of simulatios are preseted to compare their performace i order to determie the methods most suitable for the implemetatio of a geeric embedded sesor iterface (GESI). The fudametal limitatios ad trade offs ecoutered i the desig ad implemetatio of these techiques usig a microprocessor based systems are studied. Keywords: liearizatio, calibratio, sesors, characteristic, geeric, embedded, iterface, iterpolatio, curve, fittig, progressive, polyomial, GESI, PPC 1 Itroductio The relatioship betwee a sesor s ad iput is, i geeral, o-liear. For may reasos (ready availability of istrumetatio ad use i cotrol systems, for example) it is desirable that the sigal represetig the sesor iput is liearly related to that iput. To achieve this, the actual sesor must i geeral be liearised. It is usual to combie the liearisatio ad calibratio processes as show i Figure 1. If sesor data are to be subsequetly processed digitally ad a sesor is oliear the simplest way to perform liearisatio is i the digital domai. I a digital sigal processig eviromet, a aalogue calibratio of the sesors ad compesatio for temperature effects or oliearities ofte make sese before the sigal is applied to a aalogue to digital coverter (ADC), it is better to perform the liearisatio digitally. Apart from errors i the trasfer characteristic of the sesor, errors from the other fuctio blocks, such as the ADC or some aalogue iterface, are corrected automatically i the calibratio process. Sice a microcotroller affects the cost of the system ad performig liearisatio ad calibratio is power cosumig, it was ecessary to fid the trade off betwee the liearisatio ad calibratio techiques ad processig power. These techiques are chose so that they ca serve a vast variety of sesors rather tha particular sesors (geeric), hece they ca be a part of a GESI. sesor iput x Sesor fs(x) Sesor sigal Iput sigal vraw=fs(x) Zero ad spa correctio Corrected sigal Iput sigal vcorr=f(x) Liearisatio (f iverse) Corrected ad liearised sigal g(x) Iput sigal vli=g(x) Corrected ad liearised sesor vli Liearisig calibratio process Figure 1: Liearisig calibratio process 185
2 1st Iteratioal Coferece o Sesig Techology November 21-23, 2005 Palmersto North, New Zealad 2 Backgroud 2.1 Liearisig Calibratio Methods A sesor trasfer characteristic ca have oe or more of the followig error types [1]: offset; gai, rage or full-scale error; oliearity; cross-sesitivity; hysteresis; drift. A umber of measuremets must be take to reduce/elimiate these errors icludig the determiatio ad correctio of a sesor s oliearity. The umber of measuremets ecessary to reduce the liearity error depeds o the liearisig calibratio (LC) method used ad, to reduce the costs of calibratio, it is importat to miimize the umber of measuremets. Costs refer to the expese of processig power ad time. This is a importat criterio i the selectio of a appropriate liearisig calibratio method for sesor calibratio. All of the followig LC methods [1] are based o the use of calibratio measuremets: Look-up table, Piecewise liear iterpolatio, Piecewise polyomial or splie iterpolatio, Error miimizatio, Sesor characteristic liearisatio, Curve fittig, Progressive polyomial calibratio (PPC). Look-up table, error miimizatio ad piecewise polyomial or splie liearizatio require may calibratio measuremets, large memory for storage, ad advaced computatios. Error miimizatio ad sesor characteristic liearisatio also require a priori kowledge of the trasfer characteristic ad as such are ot suitable for implemetatio o a GESI. Curvefittig ad progressive polyomial calibratio offer the possibility to liearise a sesor trasfer characteristic usig a small umber of calibratio measuremets. Hece, curve fittig ad progressive polyomial calibratio are chose for further ivestigatios due to their advatages, ad discussed i the followig sectios. 2.2 Curve Fittig Curve fittig ca be achieved by either polyomial approximatio or iterpolatio. I calibratio more tha 5 poits will rarely be used so a iterpolatio would produce better results. The oly advatage is if oise is preset (errors i measuremet). I that case approximatio might be better tha matchig the data exactly. Iterpolatio methods are based o polyomials. A polyomial of degree is uiquely determied by +1 poits. The umber of poits used determies the degree of the polyomial used. The geeral form of a polyomial of degree is P( x) = c c 1 x + c 1 x c1x + (1) 0 Whe a sesor is measured for a small set of kow iput sigals a iterpolatio algorithm is the used to compute a sesor trasfer fuctio v = f est (x) usig curve fittig techiques (Figure 2). The iverse trasfer fuctio is the calculated so the iput sigal ca be derived from the sesor (f est ) 1. The other possibility is to fid a curve fit fuctio for the iverse sesor trasfer fuctio straight away x = (f 1 ) est (v). The advatages of curve fittig are that a iverse trasfer fuctio is obtaied for the complete sigal rage ad memory requiremets are low due to a small umber of coefficiets. The disadvatage ca be that sometimes higher order polyomials are ecessary to get the desired accuracy. This ivolves more complex calculatios ad may also require high accuracy computatios (floatig poit arithmetic). I geeral, the degree of the polyomial is kept low. Sesor sigal f(x) Iput sigal ADC Memory (calibratio coefficiets) H curve fit computatio DAC Calibrated sigal g(x) Iput sigal Figure 2: Iverse trasfer fuctio method for liearisig calibratio based o curve fittig. 2.3 Progressive Polyomial Calibratio Progressive polyomial calibratio [1] operates o the priciple that each calibratio measuremet is used directly to calculate oe calibratio coefficiet i the correctio fuctio. This correctio is the applied to the sesor. Each step is idepedet of the previous oe. The first measuremet is used to correct the offset; the secod corrects the gai ad all the rest of the measuremets are used for oliearity correctio. The advatages are that it gives good liearisatio for a miimum umber of poits, has low memory requiremets due to a small umber of coefficiets ad uses a repetitive algorithm (step by step calibratio). The disadvatages are that some kowledge is eeded whe choosig calibratio measuremets. The iput variable to the sesor is idicated as x, the sesor is idicated by y; the sesor trasfer characteristic is deoted as y = f(x); the desired sesor trasfer characteristic is y = g(x) ad it is assumed to be liear fuctio of the iput sigal, g(x) = Kx (Figure 3). x sesor iput Sesor fs(x) v=fs(x) sesor Progressive Polyomial Calibratio (PPC) Figure 3: Block diagram of PPC method. g(x) liearized sesor 186
3 1st Iteratioal Coferece o Sesig Techology November 21-23, 2005 Palmersto North, New Zealad PPC is performed by takig calibratio measuremets for a set of a well-kow iput sigals x, ad comparig the measured sesor f(x ) to the desired. The method [1] calculates a corrected trasfer curve h (x) after each calibratio measuremet. Previously calculated trasfer fuctios h 1 (x) to h -1 (x), the previous calibrated values y 1 to y ad the th calibratio measuremet f(x ) are used i calculatios. The calibratio ca be preseted as a series of ested formulae: h a, h ( x),..., h ( x), y,..., y ) (2) ( where a is referred to the th calibratio coefficiet. At each calibratio step the calibratio coefficiet a has to be calculated i order to obtai the liearised sesor h (x ) = g(x ) = y. The equatios ca be geeralized [1]. The calibratio coefficiet a is calculated as: y y ref a = (3) y h ( x ) y 1 h 1( x) ref i= 1 i while the th calibratio fuctio is give by: hi ( x) yi h ( x) = h 1( x) + a y (4) y 1 ref i= 1 3 Simulatio ad Test o Sesor Characteristic Fuctios Liearisatio methods usig fuctio iterpolatio based o Taylor polyomial iterpolatio (3 rd order), Gaussia elimiatio, Lagrage iterpolatio ad progressive polyomial calibratio (PPC) were tested ad simulated, firstly o chose fuctios ad the o sesor characteristic fuctios. 3.1 Simulatios o Chose Fuctios To determie the coefficiets of the third order Taylor polyomial i.e. the first three derivatives, the umerical fiite differece formulae were applied. Estimatio of derivatives at some poit (x k, y k ) ca be made to the left (forward differeces), to the right (backward differeces) or about the middle poit (cetral differeces). Cetral differeces use the slope of the secat through the poits (x k-1, y k-1 ) ad (x k+1, y k+1 ) istead of the slope of the taget at the poit (x k, y k ), because it is geerally more accurate, due to the symmetrical positio of two poits about the poit of iterest (Figure 4). The step h should be as small as possible, h 0. The disadvatage with usig cetral differeces is that they caot be applied at the ed poits because there are o kow poits from both sides. These derivatives are coefficiets of the Taylor series ad are used i further calculatios for ay other values of from the sesor (Eq. 5). I order to make this method work it is ecessary to have data i ref poits equally spaced (x h, x+h) for the calibratio process. f ( x) secat h h h x k x k+1 x k-1 x k x k+1 a) b) Figure 4: a) Backward differeces, b) Cetral differeces. f ( x + h) f ( x h) 2h secat f ( x + h) 2 f ( x) + f ( x h) f ( x) (5) 2 h f ( x) f ( x + 2h) 2 f ( x + h) + 2 f ( x h) f ( x 2h) 3 2h The error origiates i choosig a step h; for 5 calibratio measuremets o a wide rage, a large step h is ievitable. Thus the approximatio is oly good aroud the approximatig poit while for the rest of the rage it is very poor. Curve fittig usig the piecewise Taylor polyomial iterpolatio was also simulated. However, calculatig the piecewise Taylor series is ot very practical. It also requires a lot of memory to store the coefficiets of each subrage. With Gaussia elimiatio fidig the iverse matrix is a iefficiet approach i computer algorithms. Basic elimiatio without pivotig cosists of 1 elimiatios steps for a system of equatios ad gives a trasformed liear system equivalet to the origial system that is the solved by backward substitutio. For example, iterpolatio with a third order polyomial (Eq. 1) is simply obtaied solvig the system of 4 equatios with 4 ukows (4 coefficiets) with Gaussia elimiatio. Lagrage iterpolatio i geeral showed that equally spaced y k poits (sesor ) give good results whe iterpolatig the iverse sesor trasfer characteristic. The proposed umber of measuremets is 5; more tha 6 is ot advisable as it ofte causes deviatio leadig to large errors associated with the Ruge pheomeo. The choice of calibratio measuremets i PPC is importat but so is the order i which they are subsequetly used. I terms of choosig calibratio measuremets for the fuctios that chage rapidly it is better to squeeze calibratio measuremets o the iterval of the full rage where the error is expected to be large. I most of the cases this should be doe by placig the third ad rest of the measuremets i that iterval, keepig the first ad the secod 187
4 1st Iteratioal Coferece o Sesig Techology November 21-23, 2005 Palmersto North, New Zealad measuremet at both eds of the full rage for offset ad gai correctio. It is also discovered that whe takig the rest of the measuremets oe of them should be at mid-rage. However, this mid-rage measuremet gives better results whe it is take as a fourth measuremet, with the third measuremet poit betwee it ad the last measuremet o the sesitive iterval. Further improvemet ca be achieved takig additioal measuremets ad/or further aalysis of the sesor trasfer fuctio i order to adjust the calibratio measuremets accordigly. The larger the rage the more poits are eeded i order to miimize the oliearity error. 3.2 Test o Sesor Characteristic Fuctios Taylor series, Gaussia elimiatio, Lagrage iterpolatio ad progressive polyomial calibratio are implemeted for two fuctios that represet actual sesor resposes. Fuctio f 1 describes the Humirel HS1100/HS1101 capacitive RH sesor [2] whereas fuctio f 2 describes the Figaro TG832 semicoductor gas sesor [3]. These resposes are plotted i Figure 5. higher degree Lagrage polyomials. Poits are take equally spaced o the rage as this was prove to give less error for the lower order polyomials. The error obtaied (Figure 8) is less tha 6% of FS. Figure 6: Taylor polyomial iterpolatio ad error percetage for the fuctios f1 ad f2. Figure 5: Sesor resposes used as test fuctios described with f 1 = (0.0005x x x 95.6) ad f 2 = x 1.8. The piecewise Taylor series polyomial for both of the fuctios f 1 ad f 2 (as show i Figure 5) were evaluated by the rule derived i [4] (equatio 5.7). The results obtaied demostrate very good approximatio of the origial fuctios with the error less tha 1.5% of full scale (FS), where FS is the differece betwee maximum ad miimum value of the rage ad it chages with respect to the fuctio evaluated (Figure 6). To costruct the third order polyomial usig Gaussia elimiatio, 4 poits are eeded to solve the system of 4 equatios (4 ukow calibratio coefficiets). Poits are take i such a way to get the better fit for the iverse fuctio. The error for the fuctios f 1 ad f 2 is about 11% of the FS (Figure 7). Lagrage iterpolatio for the same fuctios was restricted to 5 poits to avoid commo errors for Figure 7: Iterpolatio with the third order polyomial usig Gaussia elimiatio for calculatig the coefficiets ad error percetage for the iverse trasfer fuctios of the fuctios f 1 ad f 2. Figure 8: Lagrage polyomial iterpolatio ad error percetage for the iverse trasfer fuctios of the fuctios f 1 ad f
5 1st Iteratioal Coferece o Sesig Techology November 21-23, 2005 Palmersto North, New Zealad The measuremets for PPC were chose equally spaced o iput rage. It ca be see that the error is as large as 12% of FS (Figure 9). I order to reduce the error a additioal measuremet is take where the deviatio was the most proouced. It was expected that this would reduce the error but to the cotrary it produced eve greater deviatio ad larger error (i.e. larger tha 12% of FS, Figure 10). Figure 11: Corrected sesor TF h 5 (x) of the sesor TF f 2 usig the PPC method with 5 poits ad the desired sesor TF g(x) plotted i MATLAB (x = {0, 100, 50, 75, 25} - equally spaced poits o x axis). Figure 9: Corrected sesor TF h 5 (x) of the sesor TF f 1 usig the PPC method with 5 poits ad the desired sesor TF g(x) plotted i MATLAB (x = {0, 100, 50, 75, 25}). Figure 10: Corrected sesor TF h 6 (x) of the sesor TF f 1 usig the PPC method with 6 poits ad the desired sesor TF g(x) plotted i MATLAB (x = {0, 100, 50, 75, 25, 10}). Two tests were ru for the fuctio f 2. Oe test was whe calibratio measuremets were take as equally spaced poits o the iput rage ad the other test was for equally spaced poits o the rage. The results are preseted i Figure 11 (equally o x axis) ad Figure 12 (equally o y axis). Progressive polyomial calibratio produced a error less tha 6% of full scale. This amout of error is associated with the deviatio caused by addig the extra poits. Figure 12: Corrected sesor TF h 5 (x) of the sesor TF f 2 usig the PPC method with 5 poits ad the desired sesor TF g(x) plotted i MATLAB (x = {0, 100, 68.04, 85.23, } - equally spaced poits o y axis). Whe extreme cases are ispected (fuctios x 1/2, x 1, log x ad e x ) all methods experieced difficulties ad the errors were much larger. (This will be addressed i future publicatios). I additio to these tests, some other fuctios ad methods were tested ad the results are summarized i Table 1. The methods that are used for testig ad compariso ad are ot explaied are piecewise liear, piecewise quadratic ad least squares quadratic ad cubic approximatio [5][6]. 4 Coclusios The Taylor series was abadoed because of the ature of the method ad its eed for equally spaced poits which i practice is almost impossible to achieve. Eve a small digressio causes a large error. 189
6 1st Iteratioal Coferece o Sesig Techology November 21-23, 2005 Palmersto North, New Zealad Table 1: Summary of differet methods. FUNCTION GAUSS 2d order of iv PW-liear of iv PPC GAUSS 3rd order of iv METHOD ( ERROR [%] ) LS-quadratic of iv PPC PW-quadratic * of iv LS-cubic of iv PPC f1 = (0.0005x^3-0.08^ x-95.6) 12% 15.00% 14.40% 12.00% >12% 12.00% 12.00% 12% 12% f2 = x^(2.5) 20.60% 15.80% 30.00% 15.84% 14.30% 14.00% 15.85% 11.10% 8.00% f3 = x^(1.8) 11% 14.80% 10% 7.75% 8.50% 5.50% 7.75% 5.42% 4% f4 = x^(1/2.5) 2.50% 6% 4.80% 0.20% 2% 1.67% 0.40% 0.18% 0.67% f5 = x^(1/1.8) 1% 7.70% 1.23% 0.20% 0.62% 0.33% 0.25% 0.20% 0.25% NUMBER OF POINTS NUMBER OF COEFFICIENTS Note: * Gaussia elimiatio is used for calculatig the coefficiets i PW-quadratic method. All the fuctios are tested o x = scale. Colour idicate which method yielded the smallest error for the certai umber of the calibratio poits. Gaussia elimiatio, Lagrage iterpolatio ad PPC are more flexible. However, they require some kowledge of the sesor trasfer fuctio ad adjustig the measuremets accordigly durig the calibratio process which ca be a disadvatage sice the sesor trasfer fuctio is usually ukow. They also have the same drawbacks: selectio of the calibratio measuremets i order to get the best approximatio ad avoid the global depedece o local properties. Lagrage iterpolatio ad progressive polyomial calibratio share the commo problem of deviatio. Addig extra calibratio poits after a certai poit is to o avail as it results i a more complicated model ad eve larger deviatio. However, the progressive polyomial method demostrated good results for certai fuctios ad it was tested further together with the Gaussia elimiatio. Out of the umerous mathematical methods available, Gaussia elimiatio ad PPC gave the best liearisatio results ad were deemed the most suitable for the desig of the liearisatio ad calibratio module. Sice oe of the liearisatio methods is good eough for all types of sesor characteristics it is desirable to have two or more liearisatio methods implemeted o the microcotroller or the DSP chip. Alterative methods ca be the selected through the Graphical User Iterface (GUI), as well as the umber of the calibratio poits, by the perso performig the calibratio process. Such a perso would eed some kowledge of the sesor i order to choose the appropriate method. 5 Ackowledgemets I would like to thak Jim Dowlig, my supervisor, for his suggestios ad support durig this research. I am also thakful to PEI Techologies staff for their guidace ad help durig the research. They shared their kowledge ad gave me a friedly ecouragemet. 6 Refereces [1] Va der Hor, G. ad Huijsig J.L., Itegrated Smart Sesors: Desig ad Calibratio, Kluwer Academic Publishers, Netherlads (1998). [2] Relative humidity sesor hs1100/hs1101 datasheet. Humirel Home Page, humirel.com/product/fichier/hs1101- HS1100.pdf, visited o 20/4/2005. [3] TGS 832 datasheet. Figaro Sesor Home Page, f, visited o 20/4/2005. [4] Pasic, A., M.Eg.: Geerilised Sesor Liearisatio ad Calibratio, Dubli City Uiversity (2004). [5] Maro, M.J. ad Lopez, R.J., Numerical Aalysis: a Practical Approach, Wadsworth Publishig, Califoria USA (1991). [6] Press, W.H, Teukolosky, S.A., Vetterlig, W.T., Flaery, B.P., Numerical Recipes i C, 2d Editio, Cambridge Uiversity Press, Cambridge (1992). 190
Numerical Methods Lecture 6 - Curve Fitting Techniques
Numerical Methods Lecture 6 - Curve Fittig Techiques Topics motivatio iterpolatio liear regressio higher order polyomial form expoetial form Curve fittig - motivatio For root fidig, we used a give fuctio
More informationConsider the following population data for the state of California. Year Population
Assigmets for Bradie Fall 2016 for Chapter 5 Assigmet sheet for Sectios 5.1, 5.3, 5.5, 5.6, 5.7, 5.8 Read Pages 341-349 Exercises for Sectio 5.1 Lagrage Iterpolatio #1, #4, #7, #13, #14 For #1 use MATLAB
More informationCivil Engineering Computation
Civil Egieerig Computatio Fidig Roots of No-Liear Equatios March 14, 1945 World War II The R.A.F. first operatioal use of the Grad Slam bomb, Bielefeld, Germay. Cotets 2 Root basics Excel solver Newto-Raphso
More informationMATHEMATICAL METHODS OF ANALYSIS AND EXPERIMENTAL DATA PROCESSING (Or Methods of Curve Fitting)
MATHEMATICAL METHODS OF ANALYSIS AND EXPERIMENTAL DATA PROCESSING (Or Methods of Curve Fittig) I this chapter, we will eamie some methods of aalysis ad data processig; data obtaied as a result of a give
More informationLU Decomposition Method
SOLUTION OF SIMULTANEOUS LINEAR EQUATIONS LU Decompositio Method Jamie Traha, Autar Kaw, Kevi Marti Uiversity of South Florida Uited States of America kaw@eg.usf.edu http://umericalmethods.eg.usf.edu Itroductio
More informationDesigning a learning system
CS 75 Machie Learig Lecture Desigig a learig system Milos Hauskrecht milos@cs.pitt.edu 539 Seott Square, x-5 people.cs.pitt.edu/~milos/courses/cs75/ Admiistrivia No homework assigmet this week Please try
More informationCreating Exact Bezier Representations of CST Shapes. David D. Marshall. California Polytechnic State University, San Luis Obispo, CA , USA
Creatig Exact Bezier Represetatios of CST Shapes David D. Marshall Califoria Polytechic State Uiversity, Sa Luis Obispo, CA 93407-035, USA The paper presets a method of expressig CST shapes pioeered by
More informationPolynomial Functions and Models. Learning Objectives. Polynomials. P (x) = a n x n + a n 1 x n a 1 x + a 0, a n 0
Polyomial Fuctios ad Models 1 Learig Objectives 1. Idetify polyomial fuctios ad their degree 2. Graph polyomial fuctios usig trasformatios 3. Idetify the real zeros of a polyomial fuctio ad their multiplicity
More informationBezier curves. Figure 2 shows cubic Bezier curves for various control points. In a Bezier curve, only
Edited: Yeh-Liag Hsu (998--; recommeded: Yeh-Liag Hsu (--9; last updated: Yeh-Liag Hsu (9--7. Note: This is the course material for ME55 Geometric modelig ad computer graphics, Yua Ze Uiversity. art of
More informationMath Section 2.2 Polynomial Functions
Math 1330 - Sectio. Polyomial Fuctios Our objectives i workig with polyomial fuctios will be, first, to gather iformatio about the graph of the fuctio ad, secod, to use that iformatio to geerate a reasoably
More informationEM375 STATISTICS AND MEASUREMENT UNCERTAINTY LEAST SQUARES LINEAR REGRESSION ANALYSIS
EM375 STATISTICS AND MEASUREMENT UNCERTAINTY LEAST SQUARES LINEAR REGRESSION ANALYSIS I this uit of the course we ivestigate fittig a straight lie to measured (x, y) data pairs. The equatio we wat to fit
More informationLecture Notes 6 Introduction to algorithm analysis CSS 501 Data Structures and Object-Oriented Programming
Lecture Notes 6 Itroductio to algorithm aalysis CSS 501 Data Structures ad Object-Orieted Programmig Readig for this lecture: Carrao, Chapter 10 To be covered i this lecture: Itroductio to algorithm aalysis
More informationChapter 3 Classification of FFT Processor Algorithms
Chapter Classificatio of FFT Processor Algorithms The computatioal complexity of the Discrete Fourier trasform (DFT) is very high. It requires () 2 complex multiplicatios ad () complex additios [5]. As
More informationCubic Polynomial Curves with a Shape Parameter
roceedigs of the th WSEAS Iteratioal Coferece o Robotics Cotrol ad Maufacturig Techology Hagzhou Chia April -8 00 (pp5-70) Cubic olyomial Curves with a Shape arameter MO GUOLIANG ZHAO YANAN Iformatio ad
More informationBASED ON ITERATIVE ERROR-CORRECTION
A COHPARISO OF CRYPTAALYTIC PRICIPLES BASED O ITERATIVE ERROR-CORRECTIO Miodrag J. MihaljeviC ad Jova Dj. GoliC Istitute of Applied Mathematics ad Electroics. Belgrade School of Electrical Egieerig. Uiversity
More informationHarris Corner Detection Algorithm at Sub-pixel Level and Its Application Yuanfeng Han a, Peijiang Chen b * and Tian Meng c
Iteratioal Coferece o Computatioal Sciece ad Egieerig (ICCSE 015) Harris Corer Detectio Algorithm at Sub-pixel Level ad Its Applicatio Yuafeg Ha a, Peijiag Che b * ad Tia Meg c School of Automobile, Liyi
More informationImproving Template Based Spike Detection
Improvig Template Based Spike Detectio Kirk Smith, Member - IEEE Portlad State Uiversity petra@ee.pdx.edu Abstract Template matchig algorithms like SSE, Covolutio ad Maximum Likelihood are well kow for
More informationAN OPTIMIZATION NETWORK FOR MATRIX INVERSION
397 AN OPTIMIZATION NETWORK FOR MATRIX INVERSION Ju-Seog Jag, S~ Youg Lee, ad Sag-Yug Shi Korea Advaced Istitute of Sciece ad Techology, P.O. Box 150, Cheogryag, Seoul, Korea ABSTRACT Iverse matrix calculatio
More informationA New Morphological 3D Shape Decomposition: Grayscale Interframe Interpolation Method
A ew Morphological 3D Shape Decompositio: Grayscale Iterframe Iterpolatio Method D.. Vizireau Politehica Uiversity Bucharest, Romaia ae@comm.pub.ro R. M. Udrea Politehica Uiversity Bucharest, Romaia mihea@comm.pub.ro
More informationImprovement of the Orthogonal Code Convolution Capabilities Using FPGA Implementation
Improvemet of the Orthogoal Code Covolutio Capabilities Usig FPGA Implemetatio Naima Kaabouch, Member, IEEE, Apara Dhirde, Member, IEEE, Saleh Faruque, Member, IEEE Departmet of Electrical Egieerig, Uiversity
More informationDesigning a learning system
CS 75 Itro to Machie Learig Lecture Desigig a learig system Milos Hauskrecht milos@pitt.edu 539 Seott Square, -5 people.cs.pitt.edu/~milos/courses/cs75/ Admiistrivia No homework assigmet this week Please
More informationANN WHICH COVERS MLP AND RBF
ANN WHICH COVERS MLP AND RBF Josef Boští, Jaromír Kual Faculty of Nuclear Scieces ad Physical Egieerig, CTU i Prague Departmet of Software Egieerig Abstract Two basic types of artificial eural etwors Multi
More informationBayesian approach to reliability modelling for a probability of failure on demand parameter
Bayesia approach to reliability modellig for a probability of failure o demad parameter BÖRCSÖK J., SCHAEFER S. Departmet of Computer Architecture ad System Programmig Uiversity Kassel, Wilhelmshöher Allee
More informationLecture 5. Counting Sort / Radix Sort
Lecture 5. Coutig Sort / Radix Sort T. H. Corme, C. E. Leiserso ad R. L. Rivest Itroductio to Algorithms, 3rd Editio, MIT Press, 2009 Sugkyukwa Uiversity Hyuseug Choo choo@skku.edu Copyright 2000-2018
More informationPseudocode ( 1.1) Analysis of Algorithms. Primitive Operations. Pseudocode Details. Running Time ( 1.1) Estimating performance
Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Pseudocode ( 1.1) High-level descriptio of a algorithm More structured
More informationA SOFTWARE MODEL FOR THE MULTILAYER PERCEPTRON
A SOFTWARE MODEL FOR THE MULTILAYER PERCEPTRON Roberto Lopez ad Eugeio Oñate Iteratioal Ceter for Numerical Methods i Egieerig (CIMNE) Edificio C1, Gra Capitá s/, 08034 Barceloa, Spai ABSTRACT I this work
More informationME 261: Numerical Analysis Lecture-9&10: Numerical Interpolation
ME 6: Numerical Aalysis Lecture-9&: Numerical Iterpolatio Md. Taver Hossai Departmet o Mechaical Egieerig BUET http://tatusher.uet.ac.d To estimate itermediate values etwee precise data poits most commoly
More informationOnes Assignment Method for Solving Traveling Salesman Problem
Joural of mathematics ad computer sciece 0 (0), 58-65 Oes Assigmet Method for Solvig Travelig Salesma Problem Hadi Basirzadeh Departmet of Mathematics, Shahid Chamra Uiversity, Ahvaz, Ira Article history:
More informationAccuracy Improvement in Camera Calibration
Accuracy Improvemet i Camera Calibratio FaJie L Qi Zag ad Reihard Klette CITR, Computer Sciece Departmet The Uiversity of Aucklad Tamaki Campus, Aucklad, New Zealad fli006, qza001@ec.aucklad.ac.z r.klette@aucklad.ac.z
More informationBOOLEAN MATHEMATICS: GENERAL THEORY
CHAPTER 3 BOOLEAN MATHEMATICS: GENERAL THEORY 3.1 ISOMORPHIC PROPERTIES The ame Boolea Arithmetic was chose because it was discovered that literal Boolea Algebra could have a isomorphic umerical aspect.
More informationAnalysis Metrics. Intro to Algorithm Analysis. Slides. 12. Alg Analysis. 12. Alg Analysis
Itro to Algorithm Aalysis Aalysis Metrics Slides. Table of Cotets. Aalysis Metrics 3. Exact Aalysis Rules 4. Simple Summatio 5. Summatio Formulas 6. Order of Magitude 7. Big-O otatio 8. Big-O Theorems
More informationState-space feedback 6 challenges of pole placement
State-space feedbac 6 challeges of pole placemet J Rossiter Itroductio The earlier videos itroduced the cocept of state feedbac ad demostrated that it moves the poles. x u x Kx Bu It was show that whe
More information. Written in factored form it is easy to see that the roots are 2, 2, i,
CMPS A Itroductio to Programmig Programmig Assigmet 4 I this assigmet you will write a java program that determies the real roots of a polyomial that lie withi a specified rage. Recall that the roots (or
More informationIdentification of the Swiss Z24 Highway Bridge by Frequency Domain Decomposition Brincker, Rune; Andersen, P.
Aalborg Uiversitet Idetificatio of the Swiss Z24 Highway Bridge by Frequecy Domai Decompositio Bricker, Rue; Aderse, P. Published i: Proceedigs of IMAC 2 Publicatio date: 22 Documet Versio Publisher's
More informationWhat are we going to learn? CSC Data Structures Analysis of Algorithms. Overview. Algorithm, and Inputs
What are we goig to lear? CSC316-003 Data Structures Aalysis of Algorithms Computer Sciece North Carolia State Uiversity Need to say that some algorithms are better tha others Criteria for evaluatio Structure
More information3D Model Retrieval Method Based on Sample Prediction
20 Iteratioal Coferece o Computer Commuicatio ad Maagemet Proc.of CSIT vol.5 (20) (20) IACSIT Press, Sigapore 3D Model Retrieval Method Based o Sample Predictio Qigche Zhag, Ya Tag* School of Computer
More informationRedundancy Allocation for Series Parallel Systems with Multiple Constraints and Sensitivity Analysis
IOSR Joural of Egieerig Redudacy Allocatio for Series Parallel Systems with Multiple Costraits ad Sesitivity Aalysis S. V. Suresh Babu, D.Maheswar 2, G. Ragaath 3 Y.Viaya Kumar d G.Sakaraiah e (Mechaical
More informationIMP: Superposer Integrated Morphometrics Package Superposition Tool
IMP: Superposer Itegrated Morphometrics Package Superpositio Tool Programmig by: David Lieber ( 03) Caisius College 200 Mai St. Buffalo, NY 4208 Cocept by: H. David Sheets, Dept. of Physics, Caisius College
More informationFPGA IMPLEMENTATION OF BASE-N LOGARITHM. Salvador E. Tropea
FPGA IMPLEMENTATION OF BASE-N LOGARITHM Salvador E. Tropea Electróica e Iformática Istituto Nacioal de Tecología Idustrial Bueos Aires, Argetia email: salvador@iti.gov.ar ABSTRACT I this work, we preset
More informationDynamic Programming and Curve Fitting Based Road Boundary Detection
Dyamic Programmig ad Curve Fittig Based Road Boudary Detectio SHYAM PRASAD ADHIKARI, HYONGSUK KIM, Divisio of Electroics ad Iformatio Egieerig Chobuk Natioal Uiversity 664-4 Ga Deokji-Dog Jeoju-City Jeobuk
More informationData Structures and Algorithms. Analysis of Algorithms
Data Structures ad Algorithms Aalysis of Algorithms Outlie Ruig time Pseudo-code Big-oh otatio Big-theta otatio Big-omega otatio Asymptotic algorithm aalysis Aalysis of Algorithms Iput Algorithm Output
More informationOCR Statistics 1. Working with data. Section 3: Measures of spread
Notes ad Eamples OCR Statistics 1 Workig with data Sectio 3: Measures of spread Just as there are several differet measures of cetral tedec (averages), there are a variet of statistical measures of spread.
More informationTHIN LAYER ORIENTED MAGNETOSTATIC CALCULATION MODULE FOR ELMER FEM, BASED ON THE METHOD OF THE MOMENTS. Roman Szewczyk
THIN LAYER ORIENTED MAGNETOSTATIC CALCULATION MODULE FOR ELMER FEM, BASED ON THE METHOD OF THE MOMENTS Roma Szewczyk Istitute of Metrology ad Biomedical Egieerig, Warsaw Uiversity of Techology E-mail:
More informationThe golden search method: Question 1
1. Golde Sectio Search for the Mode of a Fuctio The golde search method: Questio 1 Suppose the last pair of poits at which we have a fuctio evaluatio is x(), y(). The accordig to the method, If f(x())
More informationEffect of control points distribution on the orthorectification accuracy of an Ikonos II image through rational polynomial functions
Effect of cotrol poits distributio o the orthorectificatio accuracy of a Ikoos II image through ratioal polyomial fuctios Marcela do Valle Machado 1, Mauro Homem Atues 1 ad Paula Debiasi 1 1 Federal Rural
More informationLecture 1: Introduction
Lecture 1: Itroductio g Class orgaizatio Istructor cotact Course objectives ad outcomes Lectures outlie Laboratory outlie Gradig system Tetative schedule g Lab schedule g Itelliget sesor systems (ISS)
More informationPattern Recognition Systems Lab 1 Least Mean Squares
Patter Recogitio Systems Lab 1 Least Mea Squares 1. Objectives This laboratory work itroduces the OpeCV-based framework used throughout the course. I this assigmet a lie is fitted to a set of poits usig
More informationA Note on Least-norm Solution of Global WireWarping
A Note o Least-orm Solutio of Global WireWarpig Charlie C. L. Wag Departmet of Mechaical ad Automatio Egieerig The Chiese Uiversity of Hog Kog Shati, N.T., Hog Kog E-mail: cwag@mae.cuhk.edu.hk Abstract
More informationRunning Time. Analysis of Algorithms. Experimental Studies. Limitations of Experiments
Ruig Time Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects. The
More informationSouth Slave Divisional Education Council. Math 10C
South Slave Divisioal Educatio Coucil Math 10C Curriculum Package February 2012 12 Strad: Measuremet Geeral Outcome: Develop spatial sese ad proportioal reasoig It is expected that studets will: 1. Solve
More information9.1. Sequences and Series. Sequences. What you should learn. Why you should learn it. Definition of Sequence
_9.qxd // : AM Page Chapter 9 Sequeces, Series, ad Probability 9. Sequeces ad Series What you should lear Use sequece otatio to write the terms of sequeces. Use factorial otatio. Use summatio otatio to
More informationA Study on the Performance of Cholesky-Factorization using MPI
A Study o the Performace of Cholesky-Factorizatio usig MPI Ha S. Kim Scott B. Bade Departmet of Computer Sciece ad Egieerig Uiversity of Califoria Sa Diego {hskim, bade}@cs.ucsd.edu Abstract Cholesky-factorizatio
More informationCSC 220: Computer Organization Unit 11 Basic Computer Organization and Design
College of Computer ad Iformatio Scieces Departmet of Computer Sciece CSC 220: Computer Orgaizatio Uit 11 Basic Computer Orgaizatio ad Desig 1 For the rest of the semester, we ll focus o computer architecture:
More informationOn Infinite Groups that are Isomorphic to its Proper Infinite Subgroup. Jaymar Talledo Balihon. Abstract
O Ifiite Groups that are Isomorphic to its Proper Ifiite Subgroup Jaymar Talledo Baliho Abstract Two groups are isomorphic if there exists a isomorphism betwee them Lagrage Theorem states that the order
More informationCIS 121 Data Structures and Algorithms with Java Fall Big-Oh Notation Tuesday, September 5 (Make-up Friday, September 8)
CIS 11 Data Structures ad Algorithms with Java Fall 017 Big-Oh Notatio Tuesday, September 5 (Make-up Friday, September 8) Learig Goals Review Big-Oh ad lear big/small omega/theta otatios Practice solvig
More informationRunning Time ( 3.1) Analysis of Algorithms. Experimental Studies. Limitations of Experiments
Ruig Time ( 3.1) Aalysis of Algorithms Iput Algorithm Output A algorithm is a step- by- step procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects.
More informationAnalysis of Algorithms
Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Ruig Time Most algorithms trasform iput objects ito output objects. The
More information( n+1 2 ) , position=(7+1)/2 =4,(median is observation #4) Median=10lb
Chapter 3 Descriptive Measures Measures of Ceter (Cetral Tedecy) These measures will tell us where is the ceter of our data or where most typical value of a data set lies Mode the value that occurs most
More informationMath 10C Long Range Plans
Math 10C Log Rage Plas Uits: Evaluatio: Homework, projects ad assigmets 10% Uit Tests. 70% Fial Examiatio.. 20% Ay Uit Test may be rewritte for a higher mark. If the retest mark is higher, that mark will
More informationFilter design. 1 Design considerations: a framework. 2 Finite impulse response (FIR) filter design
Filter desig Desig cosideratios: a framework C ı p ı p H(f) Aalysis of fiite wordlegth effects: I practice oe should check that the quatisatio used i the implemetatio does ot degrade the performace of
More informationNeuro Fuzzy Model for Human Face Expression Recognition
IOSR Joural of Computer Egieerig (IOSRJCE) ISSN : 2278-0661 Volume 1, Issue 2 (May-Jue 2012), PP 01-06 Neuro Fuzzy Model for Huma Face Expressio Recogitio Mr. Mayur S. Burage 1, Prof. S. V. Dhopte 2 1
More informationEE 435. Lecture 26. Data Converters. Architectures. Characterization
EE 435 Lecture 26 Data Coverters Architectures Characterizatio . Review from last lecture. Data Coverters Types: A/D (Aalog to Digital) Coverts Aalog Iput to a Digital Output D/A (Digital to Aalog) Coverts
More informationThe Closest Line to a Data Set in the Plane. David Gurney Southeastern Louisiana University Hammond, Louisiana
The Closest Lie to a Data Set i the Plae David Gurey Southeaster Louisiaa Uiversity Hammod, Louisiaa ABSTRACT This paper looks at three differet measures of distace betwee a lie ad a data set i the plae:
More informationNeural Networks A Model of Boolean Functions
Neural Networks A Model of Boolea Fuctios Berd Steibach, Roma Kohut Freiberg Uiversity of Miig ad Techology Istitute of Computer Sciece D-09596 Freiberg, Germay e-mails: steib@iformatik.tu-freiberg.de
More informationSAMPLE VERSUS POPULATION. Population - consists of all possible measurements that can be made on a particular item or procedure.
SAMPLE VERSUS POPULATION Populatio - cosists of all possible measuremets that ca be made o a particular item or procedure. Ofte a populatio has a ifiite umber of data elemets Geerally expese to determie
More informationCSC165H1 Worksheet: Tutorial 8 Algorithm analysis (SOLUTIONS)
CSC165H1, Witer 018 Learig Objectives By the ed of this worksheet, you will: Aalyse the ruig time of fuctios cotaiig ested loops. 1. Nested loop variatios. Each of the followig fuctios takes as iput a
More informationANALYSIS OF RATIONAL FUNCTION DEPENDENCY TO THE HEIGHT DISTRIBUTION OF GROUND CONTROL POINTS IN GEOMETRIC CORRECTION OF AERIAL AND SATELLITE IMAGES
ANALSIS OF RATIONAL FUNCTION DEPENDENC TO THE HEIGHT DISTRIBUTION OF GROUND CONTROL POINTS IN GEOMETRIC CORRECTION OF AERIAL AND SATELLITE IMAGES M. Hosseii, Departmet of Geomatics Egieerig, Faculty of
More informationData diverse software fault tolerance techniques
Data diverse software fault tolerace techiques Complemets desig diversity by compesatig for desig diversity s s limitatios Ivolves obtaiig a related set of poits i the program data space, executig the
More informationMobile terminal 3D image reconstruction program development based on Android Lin Qinhua
Iteratioal Coferece o Automatio, Mechaical Cotrol ad Computatioal Egieerig (AMCCE 05) Mobile termial 3D image recostructio program developmet based o Adroid Li Qihua Sichua Iformatio Techology College
More informationAnalysis of Server Resource Consumption of Meteorological Satellite Application System Based on Contour Curve
Advaces i Computer, Sigals ad Systems (2018) 2: 19-25 Clausius Scietific Press, Caada Aalysis of Server Resource Cosumptio of Meteorological Satellite Applicatio System Based o Cotour Curve Xiagag Zhao
More informationx x 2 x Iput layer = quatity of classificatio mode X T = traspositio matrix The core of such coditioal probability estimatig method is calculatig the
COMPARATIVE RESEARCHES ON PROBABILISTIC NEURAL NETWORKS AND MULTI-LAYER PERCEPTRON NETWORKS FOR REMOTE SENSING IMAGE SEGMENTATION Liu Gag a, b, * a School of Electroic Iformatio, Wuha Uiversity, 430079,
More informationUsing a Dynamic Interval Type-2 Fuzzy Interpolation Method to Improve Modeless Robots Calibrations
Joural of Cotrol Sciece ad Egieerig 3 (25) 9-7 doi:.7265/2328-223/25.3. D DAVID PUBLISHING Usig a Dyamic Iterval Type-2 Fuzzy Iterpolatio Method to Improve Modeless Robots Calibratios Yig Bai ad Dali Wag
More information1.2 Binomial Coefficients and Subsets
1.2. BINOMIAL COEFFICIENTS AND SUBSETS 13 1.2 Biomial Coefficiets ad Subsets 1.2-1 The loop below is part of a program to determie the umber of triagles formed by poits i the plae. for i =1 to for j =
More informationThe following algorithms have been tested as a method of converting an I.F. from 16 to 512 MHz to 31 real 16 MHz USB channels:
DBE Memo#1 MARK 5 MEMO #18 MASSACHUSETTS INSTITUTE OF TECHNOLOGY HAYSTACK OBSERVATORY WESTFORD, MASSACHUSETTS 1886 November 19, 24 Telephoe: 978-692-4764 Fax: 781-981-59 To: From: Mark 5 Developmet Group
More informationHow do we evaluate algorithms?
F2 Readig referece: chapter 2 + slides Algorithm complexity Big O ad big Ω To calculate ruig time Aalysis of recursive Algorithms Next time: Litterature: slides mostly The first Algorithm desig methods:
More informationPerformance Plus Software Parameter Definitions
Performace Plus+ Software Parameter Defiitios/ Performace Plus Software Parameter Defiitios Chapma Techical Note-TG-5 paramete.doc ev-0-03 Performace Plus+ Software Parameter Defiitios/2 Backgroud ad Defiitios
More informationReversible Realization of Quaternary Decoder, Multiplexer, and Demultiplexer Circuits
Egieerig Letters, :, EL Reversible Realizatio of Quaterary Decoder, Multiplexer, ad Demultiplexer Circuits Mozammel H.. Kha, Member, ENG bstract quaterary reversible circuit is more compact tha the correspodig
More informationA General Framework for Accurate Statistical Timing Analysis Considering Correlations
A Geeral Framework for Accurate Statistical Timig Aalysis Cosiderig Correlatios 7.4 Vishal Khadelwal Departmet of ECE Uiversity of Marylad-College Park vishalk@glue.umd.edu Akur Srivastava Departmet of
More informationSecond-Order Domain Decomposition Method for Three-Dimensional Hyperbolic Problems
Iteratioal Mathematical Forum, Vol. 8, 013, o. 7, 311-317 Secod-Order Domai Decompositio Method for Three-Dimesioal Hyperbolic Problems Youbae Ju Departmet of Applied Mathematics Kumoh Natioal Istitute
More informationSolving Fuzzy Assignment Problem Using Fourier Elimination Method
Global Joural of Pure ad Applied Mathematics. ISSN 0973-768 Volume 3, Number 2 (207), pp. 453-462 Research Idia Publicatios http://www.ripublicatio.com Solvig Fuzzy Assigmet Problem Usig Fourier Elimiatio
More informationislerp: An Incremental Approach to Slerp
isler: A Icremetal Aroach to Sler Xi Li Comuter Sciece Deartmet Digie Istitute of Techology xli@digie.edu Abstract I this aer, a icremetal uaterio iterolatio algorithm is itroduced. With the assumtio of
More informationComputer Systems - HS
What have we leared so far? Computer Systems High Level ENGG1203 2d Semester, 2017-18 Applicatios Sigals Systems & Cotrol Systems Computer & Embedded Systems Digital Logic Combiatioal Logic Sequetial Logic
More informationParabolic Path to a Best Best-Fit Line:
Studet Activity : Fidig the Least Squares Regressio Lie By Explorig the Relatioship betwee Slope ad Residuals Objective: How does oe determie a best best-fit lie for a set of data? Eyeballig it may be
More informationAnalysis of Algorithms
Aalysis of Algorithms Ruig Time of a algorithm Ruig Time Upper Bouds Lower Bouds Examples Mathematical facts Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite
More informationOptimal Mapped Mesh on the Circle
Koferece ANSYS 009 Optimal Mapped Mesh o the Circle doc. Ig. Jaroslav Štigler, Ph.D. Bro Uiversity of Techology, aculty of Mechaical gieerig, ergy Istitut, Abstract: This paper brigs out some ideas ad
More informationFast Fourier Transform (FFT) Algorithms
Fast Fourier Trasform FFT Algorithms Relatio to the z-trasform elsewhere, ozero, z x z X x [ ] 2 ~ elsewhere,, ~ e j x X x x π j e z z X X π 2 ~ The DFS X represets evely spaced samples of the z- trasform
More informationAn Algorithm of Mobile Robot Node Location Based on Wireless Sensor Network
A Algorithm of Mobile Robot Node Locatio Based o Wireless Sesor Network https://doi.org/0.399/ijoe.v3i05.7044 Peg A Nigbo Uiversity of Techology, Zhejiag, Chia eirxvrp2269@26.com Abstract I the wireless
More informationOn Computing the Fuzzy Weighted Average Using the KM Algorithms
O Computig the Fuzzy Weighted Average Usig the KM Algorithms Feilog iu ad Jerry M Medel Sigal ad Image Processig Istitute, Departmet of Electrical Egieerig Uiversity of Souther Califoria, 3740 McClitock
More informationOutline and Reading. Analysis of Algorithms. Running Time. Experimental Studies. Limitations of Experiments. Theoretical Analysis
Outlie ad Readig Aalysis of Algorithms Iput Algorithm Output Ruig time ( 3.) Pseudo-code ( 3.2) Coutig primitive operatios ( 3.3-3.) Asymptotic otatio ( 3.6) Asymptotic aalysis ( 3.7) Case study Aalysis
More informationIntro to Scientific Computing: Solutions
Itro to Scietific Computig: Solutios Dr. David M. Goulet. How may steps does it take to separate 3 objects ito groups of 4? We start with 5 objects ad apply 3 steps of the algorithm to reduce the pile
More informationDETECTION OF LANDSLIDE BLOCK BOUNDARIES BY MEANS OF AN AFFINE COORDINATE TRANSFORMATION
Proceedigs, 11 th FIG Symposium o Deformatio Measuremets, Satorii, Greece, 2003. DETECTION OF LANDSLIDE BLOCK BOUNDARIES BY MEANS OF AN AFFINE COORDINATE TRANSFORMATION Michaela Haberler, Heribert Kahme
More informationSecurity of Bluetooth: An overview of Bluetooth Security
Versio 2 Security of Bluetooth: A overview of Bluetooth Security Marjaaa Träskbäck Departmet of Electrical ad Commuicatios Egieerig mtraskba@cc.hut.fi 52655H ABSTRACT The purpose of this paper is to give
More informationA Generalized Set Theoretic Approach for Time and Space Complexity Analysis of Algorithms and Functions
Proceedigs of the 10th WSEAS Iteratioal Coferece o APPLIED MATHEMATICS, Dallas, Texas, USA, November 1-3, 2006 316 A Geeralized Set Theoretic Approach for Time ad Space Complexity Aalysis of Algorithms
More informationSD vs. SD + One of the most important uses of sample statistics is to estimate the corresponding population parameters.
SD vs. SD + Oe of the most importat uses of sample statistics is to estimate the correspodig populatio parameters. The mea of a represetative sample is a good estimate of the mea of the populatio that
More informationAlgorithm. Counting Sort Analysis of Algorithms
Algorithm Coutig Sort Aalysis of Algorithms Assumptios: records Coutig sort Each record cotais keys ad data All keys are i the rage of 1 to k Space The usorted list is stored i A, the sorted list will
More informationCOMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. Chapter 4. The Processor. Part A Datapath Design
COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Iterface 5 th Editio Chapter The Processor Part A path Desig Itroductio CPU performace factors Istructio cout Determied by ISA ad compiler. CPI ad
More informationSoftware development of components for complex signal analysis on the example of adaptive recursive estimation methods.
Software developmet of compoets for complex sigal aalysis o the example of adaptive recursive estimatio methods. SIMON BOYMANN, RALPH MASCHOTTA, SILKE LEHMANN, DUNJA STEUER Istitute of Biomedical Egieerig
More informationAlgorithms for Disk Covering Problems with the Most Points
Algorithms for Disk Coverig Problems with the Most Poits Bi Xiao Departmet of Computig Hog Kog Polytechic Uiversity Hug Hom, Kowloo, Hog Kog csbxiao@comp.polyu.edu.hk Qigfeg Zhuge, Yi He, Zili Shao, Edwi
More informationKernel Smoothing Function and Choosing Bandwidth for Non-Parametric Regression Methods 1
Ozea Joural of Applied Scieces (), 009 Ozea Joural of Applied Scieces (), 009 ISSN 943-49 009 Ozea Publicatio Kerel Smoothig Fuctio ad Choosig Badwidth for No-Parametric Regressio Methods Murat Kayri ad
More informationChapter 5. Functions for All Subtasks. Copyright 2015 Pearson Education, Ltd.. All rights reserved.
Chapter 5 Fuctios for All Subtasks Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 5.1 void Fuctios 5.2 Call-By-Referece Parameters 5.3 Usig Procedural Abstractio 5.4 Testig ad Debuggig
More information