Histogram Processing
|
|
- Corey Parks
- 5 years ago
- Views:
Transcription
1 Hitogam Poceing Lectue 4 (Chapte 3) Hitogam Poceing The hitogam of a digital image with gay level fom to L- i a dicete function h( )=n, whee: i the th gay level n i the numbe of pixel in the image with that gay level =,, 2,, L- Nomalized hitogam: P( )=n / (M N) M N i the total numbe of pixel in the image um of all component = In hitogam opeation we will be woing motly with nomalized hitogam.
2 Hitogam Poceing The hape of the hitogam of an image doe povide ueful info about the poibility fo contat enhancement. Type of poceing: Hitogam equalization Hitogam matching (pecification) JERS- Why ae hitogam impotant? 2
3 Some baic tool: Cumulative ditibution function (CDF): t= i= ( ) ( ) Fx = P x = P t dt P i t= i= P Fx Pobability denity function (PDF): d fx Fx P d = P d An example Coin toing: H (-), T () F x < =.5 < Fx fx
4 Pixel a andom vaiable Conide each pixel a a andom vaiable The pobability of picing a pixel with a gay level i: n ( ) NM P The nomalized hitogam povide the ditibution of the pobabilitie of picing a given gay level What would be the pobability of picing a pixel with a gay level that i within the ange? i= = i= ( ) P ni MN The poblem domain Input image ( β ) Pf Tanfomation T Output image Pg h? h h? h 4
5 The diect poblem Given: P f T ( β ) - the hitogam of the input image - a given pixel tanfomation whee: T i ingle valued and monotonically inceaing ( ) T fo What would be the hitogam P g () of the output image? The diect poblem: Input image Output image Pf ( β ) T Pg h? h 5
6 Finding P g () g dt P P T ( T ) ( ) f = f ( ) = d dt x P dx x= T Finding P g (): an example Given T(x)=x+, find P g (): P g f ( ) ( ) x= T = ( ) P T Pf = = = Pf dt x dx ( ) h h 6
7 The invee poblem Given P f (β) and P g (), what hould be T o that P f (β) will be tanfomed to P g ()? Pf ( β ) T Pg h? h hitogam matching (pecification) Hitogam matching A both ide ae a function of T - () and ince thi function i ingle valued, we could apply T to both ide: ( ) T T ( T ) Pf ( T ) P ( ) ( ) f T ( T ) T Pf Pg = Pg ( T ) = = dt ( x) dt ( x) dt dx dx d x= T x= T ( T ) dt Pg T = d ( ) Pf 7
8 Hitogam matching Integating both ide: ( ) T dt x P T x dx = P T x dt x = P y dy = F T ( ( )) ( ( )) ( ) ( ) y= T ( x) g g g g dx f ( ) = P x dx F f ( ) Hitogam matching (cont.) We theefoe get: g ( ) = f ( ) = F T F ( ) ( ) = g ( f ) F F T F F g g g f ( ) T F F 8
9 An example: the continuou cae ( f ( )) F F x g 2 2x x x x Pg ( x) = Fg ( x) = othewie ( ) othewie 2 2 x x 2x x x Pf ( x) = Ff ( x) = othewie othewie 2 x x x x Fg ( x) = F ( x) = g othewie othewie 2 2x x x = othewie Hitogam matching (pecification): (The dicete cae) The pocedue fo hitogam-pecification baed enhancement i: Find the CDF of the oiginal image uing: = T ( ) = j= n j n n: total numbe of pixel, nj: numbe of pixel with gay level j, L: numbe of dicete gay level 9
10 Hitogam (matching) pecification (The dicete cae) Specify the deied denity function and obtain the tanfomation function G(z): z v = G( z) = P ( w) z i= z ni n p : the deiable PDF fo the output image Apply the invee tanfomation function z=g - () to the level obtained in tep. Hitogam (matching) pecification: (The dicete cae) The new, poceed veion of the oiginal image conit of gay level chaacteized by the pecified denity p z (z). In eence: z = G ( ) z = G [ T ( )]
11 Hitogam matching (pecification): (The dicete cae) T = Fg ( Ff ) Fg Pf ( t ) t= The oiginal CDF Ff Fg The equied CDF T P t dt P t ( ) = f ( ) f ( ) t= F g Finding F g - : the dicete cae Fo each input gay level in the CDF of f Find the element z in the CDF of g fo which (CDF(f)-CDF(g))=min Find the gay level that coepond to CDF(g z ) De-nomalize [ ] Add the pai [ ] to the LUT End
12 An example (uing non-nomalized hitogam) ( ) ( ) _ 4 h f h g gay level LUT in out An example: x f x g
13 An example (cont.) The invee CDF A woed example (cont.) out in
14 An example (cont.) The Taget image The tanfomed image An example (cont.) 2.5 x 4 x The Taget hitogam The tanfomed hitogam 4
15 Hitogam Equalization Given P f (β), what hould be T o that P g () would be unifom? Pf ( β ) T P g = L h? h Hitogam equalization (cont.) ( ) ( ) P T dt x P L P T L P x f g = = = f = f L dt x dx x= T dx x= T ( ) ( ) ( ) A both ide ae a function of T - () and ince thi function i ingle valued, we could ue intead of T - (): dt d integate ( ) ( ) = L P T = L P t dt L P t f f f t= 5
16 Hitogam Equalization (the dicete cae) n T( ) P ( ) j = = = j j= n j= Hitogam equalization(he) eult ae imila to contat tetching but offe the advantage of full automation, ince HE automatically detemine a tanfomation function to poduce a new image with a unifom hitogam. Hitogam equalization: an example of the dicete cae IN n_i Pi Sigma_Pi New_Gay OUT Image Hitogam Nommalized Hitogam Numbe of Pixel n_i Sigma_Pi Gay Level Numbe of Pixel Gay Level 6
17 Hitogam equalization A Compaion Between Hitogam Equalization and Hitogam Matching 7
18 A Compaion Between Hitogam Equalization and Hitogam Matching (cont.) A tanfomation function fo Hitogam Equalization 8
19 Hitogam matching (pecification): The pimay difficulty in applying the hitogam pecification method to image enhancement lie in being able to contuct a meaningful hitogam. So Eithe a paticula pobability denity function (uch a a Gauian denity) i pecified and then a hitogam i fomed by digitizing the given function, O a hitogam hape i pecified on a gaphic device and then i fed into the poceo executing the hitogam pecification algoithm. Hitogam matching (pecification) v. Hitogam equalization Hitogam equalization doe not allow inteactive image enhancement and geneate only one eult: an appoximation to a unifom hitogam. Sometime we need to be able to pecify paticula hitogam hape capable of highlighting cetain gay-level ange. 9
Image Enhancement: Histogram-based methods
Image Enhancement: Hitogam-baed method The hitogam of a digital image with gayvalue, i the dicete function,, L n n # ixel with value Total # ixel image The function eeent the faction of the total numbe
More informationSimulation of Spatially Correlated Large-Scale Parameters and Obtaining Model Parameters from Measurements
Simulation of Spatially Coelated Lage-Scale Paamete and Obtaining Model Paamete fom PER ZETTERBERG Stockholm Septembe 8 TRITA EE 8:49 Simulation of Spatially Coelated Lage-Scale Paamete and Obtaining Model
More informationBasic Gray Level Transformations (2) Negative
Gonzalez & Woods, 22 Basic Gay Level Tansfomations (2) Negative 23 Basic Gay Level Tansfomations (3) Log Tansfomation (Example fo Fouie Tansfom) Fouie spectum values ~1 6 bightest pixels dominant display
More informationLet {X n, n 1} be a sequence of independent and identically distributed random variables with a common cdf F (x) and pdf f(x).
Kangweon-Kyungki Math Jou 2 24, No, pp 5 22 RCURRNC RLATION FOR QUOTINTS OF TH POWR DISTRIBUTION BY RCORD VALUS Min-Young Lee and Se-Kyung Chang Abtact In thi pape we etablih ome ecuence elation atified
More informationFI 2201 Electromagnetism
FI Electomagnetim Aleande A. Ikanda, Ph.D. Phyic of Magnetim and Photonic Reeach Goup ecto Analyi CURILINEAR COORDINAES, DIRAC DELA FUNCION AND HEORY OF ECOR FIELDS Cuvilinea Coodinate Sytem Cateian coodinate:
More informationEstimation and Confidence Intervals: Additional Topics
Chapte 8 Etimation and Confidence Inteval: Additional Topic Thi chapte imply follow the method in Chapte 7 fo foming confidence inteval The text i a bit dioganized hee o hopefully we can implify Etimation:
More informationTheorem 2: Proof: Note 1: Proof: Note 2:
A New 3-Dimenional Polynomial Intepolation Method: An Algoithmic Appoach Amitava Chattejee* and Rupak Bhattachayya** A new 3-dimenional intepolation method i intoduced in thi pape. Coeponding to the method
More informationChapter 19 Webassign Help Problems
Chapte 9 Webaign Help Poblem 4 5 6 7 8 9 0 Poblem 4: The pictue fo thi poblem i a bit mileading. They eally jut give you the pictue fo Pat b. So let fix that. Hee i the pictue fo Pat (a): Pat (a) imply
More informationShrinkage Estimation of Reliability Function for Some Lifetime Distributions
Ameican Jounal of Computational and Applied Mathematic 4, 4(3): 9-96 DOI:.593/j.ajcam.443.4 Shinkage Etimation of eliability Function fo Some Lifetime Ditibution anjita Pandey Depatment of Statitic, niveity
More informationNew On-Line Algorithms for the Page Replication Problem. Susanne Albers y Hisashi Koga z. Abstract
New On-Line Algoithm fo the Page Replication Poblem Suanne Albe y Hiahi Koga z Abtact We peent impoved competitive on-line algoithm fo the page eplication poblem and concentate on impotant netwok topologie
More informationInference for A One Way Factorial Experiment. By Ed Stanek and Elaine Puleo
Infeence fo A One Way Factoial Expeiment By Ed Stanek and Elaine Puleo. Intoduction We develop etimating equation fo Facto Level mean in a completely andomized one way factoial expeiment. Thi development
More informationThen the number of elements of S of weight n is exactly the number of compositions of n into k parts.
Geneating Function In a geneal combinatoial poblem, we have a univee S of object, and we want to count the numbe of object with a cetain popety. Fo example, if S i the et of all gaph, we might want to
More informationTheory. Single Soil Layer. ProShake User s Manual
PoShake Ue Manual Theoy PoShake ue a fequency domain appoach to olve the gound epone poblem. In imple tem, the input motion i epeented a the um of a eie of ine wave of diffeent amplitude, fequencie, and
More information3.1 Random variables
3 Chapte III Random Vaiables 3 Random vaiables A sample space S may be difficult to descibe if the elements of S ae not numbes discuss how we can use a ule by which an element s of S may be associated
More informationone primary direction in which heat transfers (generally the smallest dimension) simple model good representation for solving engineering problems
CHAPTER 3: One-Dimenional Steady-State Conduction one pimay diection in which heat tanfe (geneally the mallet dimenion) imple model good epeentation fo olving engineeing poblem 3. Plane Wall 3.. hot fluid
More informationQuantum Fourier Transform
Chapte 5 Quantum Fouie Tansfom Many poblems in physics and mathematics ae solved by tansfoming a poblem into some othe poblem with a known solution. Some notable examples ae Laplace tansfom, Legende tansfom,
More informationA note on rescalings of the skew-normal distribution
Poyeccione Jounal of Mathematic Vol. 31, N o 3, pp. 197-07, Septembe 01. Univeidad Católica del Note Antofagata - Chile A note on ecaling of the kew-nomal ditibution OSVALDO VENEGAS Univeidad Católica
More informationChapter 8 Sampling. Contents. Dr. Norrarat Wattanamongkhol. Lecturer. Department of Electrical Engineering, Engineering Faculty, sampling
Content Chate 8 Samling Lectue D Noaat Wattanamongkhol Samling Theoem Samling of Continuou-Time Signal 3 Poceing Continuou-Time Signal 4 Samling of Dicete-Time Signal 5 Multi-ate Samling Deatment of Electical
More informationElementary Statistics and Inference. Elementary Statistics and Inference. 11. Regression (cont.) 22S:025 or 7P:025. Lecture 14.
Elementay tatistics and Infeence :05 o 7P:05 Lectue 14 1 Elementay tatistics and Infeence :05 o 7P:05 Chapte 10 (cont.) D. Two Regession Lines uppose two vaiables, and ae obtained on 100 students, with
More informationV V The circumflex (^) tells us this is a unit vector
Vecto Vecto have Diection and Magnitude Mike ailey mjb@c.oegontate.edu Magnitude: V V V V x y z vecto.pptx Vecto Can lo e Defined a the oitional Diffeence etween Two oint 3 Unit Vecto have a Magnitude
More informationBasic propositional and. The fundamentals of deduction
Baic ooitional and edicate logic The fundamental of deduction 1 Logic and it alication Logic i the tudy of the atten of deduction Logic lay two main ole in comutation: Modeling : logical entence ae the
More information1 Explicit Explore or Exploit (E 3 ) Algorithm
2.997 Decision-Making in Lage-Scale Systems Mach 3 MIT, Sping 2004 Handout #2 Lectue Note 9 Explicit Exploe o Exploit (E 3 ) Algoithm Last lectue, we studied the Q-leaning algoithm: [ ] Q t+ (x t, a t
More informationApproximation Techniques for Spatial Data
Appoximation Technique fo Spatial Data Abhinandan Da Conell Univeity ada@c.conell.edu Johanne Gehke Conell Univeity johanne@c.conell.edu Miek Riedewald Conell Univeity miek@c.conell.edu ABSTRACT Spatial
More informationSolutions Practice Test PHYS 211 Exam 2
Solution Pactice Tet PHYS 11 Exam 1A We can plit thi poblem up into two pat, each one dealing with a epaate axi. Fo both the x- and y- axe, we have two foce (one given, one unknown) and we get the following
More informationQUADRATIC DEPENDENCE MEASURE FOR NONLINEAR BLIND SOURCES SEPARATION
QUADRATI DPNDN MASUR FR NNLINAR BLIND SURS SPARATIN Sophie Achad Dinh Tuan Pham Univ. of Genoble Laboatoy of Modeling and omputation IMAG.N.R.S. B.P. 5X 84 Genoble edex Fance Sophie.Achad@imag.f Dinh-Tuan.Pham@imag.f
More informationEddy Currents in Permanent Magnets of a Multi-pole Direct Drive Motor
Acta Technica Jauineni Vol. 6. No. 1. 2013 Eddy Cuent in Pemanent Magnet of a Multi-pole Diect Dive Moto G. Gotovac 1, G. Lampic 1, D. Miljavec 2 Elaphe Ltd. 1, Univeity of Ljubljana, Faculty of Electical
More informationOptimization of Prediction Intervals for Order Statistics Based on Censored Data
Poceeding of the Wold Conge on Engineeing ol I WCE, July 6-8,, London, UK Optimization of Pediction Inteval fo Ode Statitic Baed on Cenoed Data Nichola A Nechval, Membe, IAENG, Mai Pugaili, Kontantin N
More informationMath 151. Rumbos Spring Solutions to Assignment #7
Math. Rumbos Sping 202 Solutions to Assignment #7. Fo each of the following, find the value of the constant c fo which the given function, p(x, is the pobability mass function (pmf of some discete andom
More informationAERODYNAMIC DESIGN METHOD FOR SUPERSONIC SLENDER BODY USING AN INVERSE PROBLEM
AERODYNAMIC DESIGN METHOD FOR SUPERSONIC SLENDER BODY Kia Matuhima*, Ikki Yamamichi**, Naoko Tokugawa*** * Dept. Engineeing, Univeity of Toyama, Gofuku 3190, Toyama 930-8555, JAPAN. Phone: +81-76-445-6796,
More informationApplied Aerodynamics
Applied Aeodynamics Def: Mach Numbe (M), M a atio of flow velocity to the speed of sound Compessibility Effects Def: eynolds Numbe (e), e ρ c µ atio of inetial foces to viscous foces iscous Effects If
More informationFall 2004/05 Solutions to Assignment 5: The Stationary Phase Method Provided by Mustafa Sabri Kilic. I(x) = e ixt e it5 /5 dt (1) Z J(λ) =
8.35 Fall 24/5 Solution to Aignment 5: The Stationay Phae Method Povided by Mutafa Sabi Kilic. Find the leading tem fo each of the integal below fo λ >>. (a) R eiλt3 dt (b) R e iλt2 dt (c) R eiλ co t dt
More informationThis lecture. Transformations in 2D. Where are we at? Why do we need transformations?
Thi lectue Tanfomation in 2D Thoma Sheme Richa (Hao) Zhang Geomet baic Affine pace an affine tanfomation Ue of homogeneou cooinate Concatenation of tanfomation Intouction to Compute Gaphic CMT 36 Lectue
More informationDetailed solution of IES 2014 (ECE) Conventional Paper II. solve I 0 and use same formula again. Saturation region
etailed olution of IS 4 (C) Conventional Pape II qv qv Sol. (a) IC I e Ie K K 4 I =.7 Fo I C = m olve I and ue ame fomula again K IC V ln 5ln 4 q I.7 =.8576 Volt Sol. (b) VGS VS Vupply 5V N MOS channel,
More informationAn invariance principle for maintaining the operating point of a neuron
Netwok: Computation in Neual Sytem Septembe 2008; 19(3): 213 235 An invaiance pinciple fo maintaining the opeating point of a neuon TERRY ELLIOTT, XUTAO KUANG, NIGEL R. SHADBOLT, & KLAUS-PETER ZAUNER Depatment
More informationPOISSON S EQUATION 2 V 0
POISSON S EQUATION We have seen how to solve the equation but geneally we have V V4k We now look at a vey geneal way of attacking this poblem though Geen s Functions. It tuns out that this poblem has applications
More informationGenerating a Random Collection of Discrete Joint Probability Distributions Subject to Partial Information
Methodol Comput Appl Pobab DOI 10.1007/11009-01-99-9 Geneating a Random Collection of Dicete Joint Pobability Ditibution Subject to Patial Infomation Lui V. Montiel J. Eic Bickel Received: 4 Octobe 011
More informationRandom Variables and Probability Distribution Random Variable
Random Vaiables and Pobability Distibution Random Vaiable Random vaiable: If S is the sample space P(S) is the powe set of the sample space, P is the pobability of the function then (S, P(S), P) is called
More informationA Neural Network for the Travelling Salesman Problem with a Well Behaved Energy Function
A Neual Netwok fo the Tavelling Saleman Poblem with a Well Behaved Enegy Function Maco Budinich and Babaa Roaio Dipatimento di Fiica & INFN, Via Valeio, 347 Tiete, Italy E-mail: mbh@tiete.infn.it (Contibuted
More information1) (A B) = A B ( ) 2) A B = A. i) A A = φ i j. ii) Additional Important Properties of Sets. De Morgan s Theorems :
Additional Impotant Popeties of Sets De Mogan s Theoems : A A S S Φ, Φ S _ ( A ) A ) (A B) A B ( ) 2) A B A B Cadinality of A, A, is defined as the numbe of elements in the set A. {a,b,c} 3, { }, while
More informationSupplemental Materials. Advanced Thermoelectrics Governed by Single Parabolic Band Model:
Electonic Supplementay Mateial (ESI) fo Phyical Chemity Chemical Phyic. Thi jounal i The Royal Society of Chemity 04 Supplemental Mateial Advanced Themoelectic Govened by Single Paabolic and Model: Mg
More informationNetwork Capacity Allocation in Service Overlay Networks
Netwo Capacity Allocation in Sevice Ovelay Netwo Ngo Lam 1, Zbigniew Dziong 2, Lone G. Maon 1 1 Depatment of Electical & Compute Engineeing, McGill Univeity, 3480 Univeity Steet, Monteal, Quebec, Canada
More informationExploration of the three-person duel
Exploation of the thee-peson duel Andy Paish 15 August 2006 1 The duel Pictue a duel: two shootes facing one anothe, taking tuns fiing at one anothe, each with a fixed pobability of hitting his opponent.
More informationJuan Aparicio and Jose M. Cordero and Jesús Pastor
MPRA Munich Peonal RePEc Achive The detemination of the leat ditance to the tongly efficient fontie in Data Envelopment Analyi oiented model: modelling and computational apect Juan Apaicio and Joe M. Codeo
More informationPrecision Spectrophotometry
Peciion Spectophotomety Pupoe The pinciple of peciion pectophotomety ae illutated in thi expeiment by the detemination of chomium (III). ppaatu Spectophotomete (B&L Spec 20 D) Cuvette (minimum 2) Pipet:
More informationStanford University CS259Q: Quantum Computing Handout 8 Luca Trevisan October 18, 2012
Stanfod Univesity CS59Q: Quantum Computing Handout 8 Luca Tevisan Octobe 8, 0 Lectue 8 In which we use the quantum Fouie tansfom to solve the peiod-finding poblem. The Peiod Finding Poblem Let f : {0,...,
More informationLecture 17 - Eulerian-Granular Model. Applied Computational Fluid Dynamics
Lectue 7 - Euleian-Ganula Model Applied Computational Fluid Dynamic Intucto: Andé Bakke http://www.bakke.og Andé Bakke (00-006) Fluent Inc. (00) Content Oveview. Deciption of ganula flow. Momentum equation
More informationMathematical Modeling of Metabolic Processes in a Living Organism in Relation to Nutrition
Mathematical Modeling of Metabolic Pocee in a Living Oganim in Relation to Nutition Dimitova N., Makov S. Depatment Biomathematic Intitute of Mathematic and Infomatic Bulgaian Academy of Science 8 Acad.
More informationCourse Outline. ECE 178: Image Processing REVIEW. Relationship between pixels. Connected components. Distance Measures. Linear systems-review
ECE 78: Image Pocessing REVIEW Lectue #2 Mach 3, 23 Couse Outline! Intoduction! Digital Images! Image Tansfoms! Sampling and Quantization! Image Enhancement! Image/Video Coding JPEG MPEG Mach 3, 23 Mach
More informationC/CS/Phys C191 Shor s order (period) finding algorithm and factoring 11/12/14 Fall 2014 Lecture 22
C/CS/Phys C9 Sho s ode (peiod) finding algoithm and factoing /2/4 Fall 204 Lectue 22 With a fast algoithm fo the uantum Fouie Tansfom in hand, it is clea that many useful applications should be possible.
More informationSPH3UW/SPH4U Unit 3.2 Forces in Cetripetal Motion Page 1 of 6. Notes Physics Tool Box
SPH3UW/SPH4U Unit 3. Foce in Cetipetal Motion Page 1 o 6 Note Phyic Tool Box Net Foce: acting on an object in uniom cicula motion act towad the cente o the cicle. Magnitude o Net Foce: combine Newton Second
More informationAbsolute Specifications: A typical absolute specification of a lowpass filter is shown in figure 1 where:
FIR FILTER DESIGN The design of an digital filte is caied out in thee steps: ) Specification: Befoe we can design a filte we must have some specifications. These ae detemined by the application. ) Appoximations
More informationMATERIAL SPREADING AND COMPACTION IN POWDER-BASED SOLID FREEFORM FABRICATION METHODS: MATHEMATICAL MODELING
MATERIAL SPREADING AND COMPACTION IN POWDER-BASED SOLID FREEFORM FABRICATION METHODS: MATHEMATICAL MODELING Yae Shanjani and Ehan Toyekani Depatment of Mechanical and Mechatonic Engineeing, Univeity of
More informationBoise State University Department of Electrical and Computer Engineering ECE470 Electric Machines
Boie State Univeity Depatment of Electical and Compute Engineeing ECE470 Electic Machine Deivation of the Pe-Phae Steady-State Equivalent Cicuit of a hee-phae Induction Machine Nomenclatue θ: oto haft
More informationHonors Classical Physics I
Hono Claical Phyic I PHY141 Lectue 9 Newton Law of Gavity Pleae et you Clicke Channel to 1 9/15/014 Lectue 9 1 Newton Law of Gavity Gavitational attaction i the foce that act between object that have a
More informationProbablistically Checkable Proofs
Lectue 12 Pobablistically Checkable Poofs May 13, 2004 Lectue: Paul Beame Notes: Chis Re 12.1 Pobablisitically Checkable Poofs Oveview We know that IP = PSPACE. This means thee is an inteactive potocol
More informationIntroduction: Vectors and Integrals
Intoduction: Vectos and Integals Vectos a Vectos ae chaacteized by two paametes: length (magnitude) diection a These vectos ae the same Sum of the vectos: a b a a b b a b a b a Vectos Sum of the vectos:
More informationThe geometric construction of Ewald sphere and Bragg condition:
The geometic constuction of Ewald sphee and Bagg condition: The constuction of Ewald sphee must be done such that the Bagg condition is satisfied. This can be done as follows: i) Daw a wave vecto k in
More informationTo Feel a Force Chapter 7 Static equilibrium - torque and friction
To eel a oce Chapte 7 Chapte 7: Static fiction, toque and static equilibium A. Review of foce vectos Between the eath and a small mass, gavitational foces of equal magnitude and opposite diection act on
More informationTwo-Body Problem with Varying Mass in Case. of Isotropic Mass Loss
Adv Theo Appl Mech Vol no 69-8 Two-Body Poblem with Vaying Ma in Cae of Iotopic Ma o W A Rahoma M K Ahmed and I A El-Tohamy Caio Univeity Faculty of Science Dept of Atonomy Caio 6 Egypt FA Abd El-Salam
More informationOn the quadratic support of strongly convex functions
Int. J. Nonlinea Anal. Appl. 7 2016 No. 1, 15-20 ISSN: 2008-6822 electonic http://dx.doi.og/10.22075/ijnaa.2015.273 On the quadatic uppot of tongly convex function S. Abbazadeh a,b,, M. Ehaghi Godji a
More informationPhysical & Interfacial Electrochemistry 2013
Phyical & Intefacial Electochemity 13 Lectue. Electolyte Solution : Ion/olvent inteaction: ionic olvation. Module JS CH334 MoleculaThemodynamic and Kinetic Ionic: the phyical chemity of ionic olution.
More informationUsing DEA and AHP for Multiplicative Aggregation of Hierarchical Indicators
Ameican Jounal of Opeation Reeach, 205, 5, 327-336 Publihed Online Septembe 205 in SciRe. http://www.cip.og/jounal/ajo http://dx.doi.og/0.4236/ajo.205.55026 Uing DEA and AHP fo Multiplicative Aggegation
More informationReview: Electrostatics and Magnetostatics
Review: Electostatics and Magnetostatics In the static egime, electomagnetic quantities do not vay as a function of time. We have two main cases: ELECTROSTATICS The electic chages do not change postion
More informationResponse Time Distributions in Partially-Coherent Quantum Walk Models for Simple Decision Tasks
Repone Time Ditibution in Patially-Coheent Quantum Walk Model fo Simple Deciion Tak Ian G. Fu (ifu@eleceng.adelaide.edu.au) School of Electical and Electonic Engineeing, Univeity of Adelaide, SA 55, Autalia
More information3. Perturbation of Kerr BH
3. Petubation of Ke BH hoizon at Δ = 0 ( = ± ) Unfotunately, it i technically fomidable to deal with the metic petubation of Ke BH becaue of coupling between and θ Nevethele, thee exit a fomalim (Newman-Penoe
More informationRotational Kinetic Energy
Add Impotant Rotational Kinetic Enegy Page: 353 NGSS Standad: N/A Rotational Kinetic Enegy MA Cuiculum Famewok (006):.1,.,.3 AP Phyic 1 Leaning Objective: N/A, but olling poblem have appeaed on peviou
More informationPhysics Spring 2012 Announcements: Mar 07, 2012
Physics 00 - Sping 01 Announcements: Ma 07, 01 HW#6 due date has been extended to the moning of Wed. Ma 1. Test # (i. Ma ) will cove only chaptes 0 and 1 All of chapte will be coveed in Test #4!!! Test
More informationMONTE CARLO SIMULATION OF FLUID FLOW
MONTE CARLO SIMULATION OF FLUID FLOW M. Ragheb 3/7/3 INTRODUCTION We conside the situation of Fee Molecula Collisionless and Reflective Flow. Collisionless flows occu in the field of aefied gas dynamics.
More informationKey Establishment Protocols. Cryptography CS 507 Erkay Savas Sabanci University
Key Establishment Potocols Cyptogaphy CS 507 Ekay Savas Sabanci Univesity ekays@sabanciuniv.edu Key distibution poblem Secuity of the keys Even if the cyptogaphic algoithms & potocols ae cyptogaphically
More informationACCURATE FLOATING-POINT SUMMATION IN CUB
ACCURATE FLOATING-POINT SUMMATION IN CUB URI VERNER Summe inten OUTLINE Who need accuate floating-point ummation?! Round-off eo: ouce and ecovey A new method fo accuate FP ummation on a GPU Added a a function
More informationCOMP Parallel Computing SMM (3) OpenMP Case Study: The Barnes-Hut N-body Algorithm
COMP 633 - Paallel Computing Lectue 8 Septembe 14, 2017 SMM (3) OpenMP Case Study: The Banes-Hut N-body Algoithm Topics Case study: the Banes-Hut algoithm Study an impotant algoithm in scientific computing»
More informationDeconvolution of VLBI Images Based on Compressive Sensing
Deconvolution of VLBI Image Baed on Compeive Sening Andiyan Bayu Sumono School of Electical Engineeing and Infomatic, Intitut Tenologi Bandung, Jl. Ganeha, Bandung, Indoneia umono@ada.ee.itb.ac.id, umono@yahoo.com
More informationConcomitants of Dual Generalized Order Statistics from Farlie Gumbel Morgenstern Type Bivariate Inverse Rayleigh Distribution
J Stat Appl Po 4, No, 53-65 5 53 Jounal of Statitic Application & Pobability An Intenational Jounal http://dxdoiog/785/jap/46 Concomitant of Dual Genealized Ode Statitic fom Falie Gumbel ogenten Type Bivaiate
More informationMaximum Likelihood Logistic Regression With Auxiliary Information
niveity of Wollongong Reeach Online Cente fo Statitical Suvey Methodology Woking Pape Seie Faculty of Engineeing and Infomation Science 2008 Maximum Likelihood Logitic Regeion With Auxiliay Infomation
More informationChapter 5 Force and Motion
Chapte 5 Foce and Motion In chaptes 2 and 4 we have studied kinematics i.e. descibed the motion of objects using paametes such as the position vecto, velocity and acceleation without any insights as to
More informationOn the undulatory theory of positive and negative electrons
Su la théoie ondulatoie de électon poitif and negatif J. Phy. et le Rad. 7 (1936) 347-353. On the undulatoy theoy of poitive and negative electon By AL. PROCA Intitut Heni Poincaé Pai Tanlated by D. H.
More information( ) [ ] [ ] [ ] δf φ = F φ+δφ F. xdx.
9. LAGRANGIAN OF THE ELECTROMAGNETIC FIELD In the pevious section the Lagangian and Hamiltonian of an ensemble of point paticles was developed. This appoach is based on a qt. This discete fomulation can
More information2. Electrostatics. Dr. Rakhesh Singh Kshetrimayum 8/11/ Electromagnetic Field Theory by R. S. Kshetrimayum
2. Electostatics D. Rakhesh Singh Kshetimayum 1 2.1 Intoduction In this chapte, we will study how to find the electostatic fields fo vaious cases? fo symmetic known chage distibution fo un-symmetic known
More informationIncreasing the Strength of Standard Involute Gear Teeth with Novel Circular Root Fillet Design
Ameican Jounal of Applied Science 2 (6): 1058-1064, 2005 ISSN 1546-9239 Science Publication, 2005 Inceaing the Stength of Standad Involute Gea Teeth with Novel Cicula Root Fillet Deign V. Spita, Th. Cotopoulo
More informationarxiv: v1 [cs.gt] 23 Jan 2009
Bid Optimization fo Boad Match Ad Auction Eyal Even Da Yihay Manou Vahab S. Miokni S. Muthukihnan Ui Nadav aiv:0901.3754v1 [c.gt] 23 Jan 2009 ABSTRACT Ad auction in ponoed each uppot boad match that allow
More informationA Markov Decision Approach for the Computation of Testability of RTL Constructs
A Makov Decision Appoach fo the Computation of Testability of RTL Constucts José Miguel Fenandes Abstact In the analysis of digital cicuits, to study testability estimation measues, dissipated powe and
More informationLecture 5 Solving Problems using Green s Theorem. 1. Show how Green s theorem can be used to solve general electrostatic problems 2.
Lectue 5 Solving Poblems using Geen s Theoem Today s topics. Show how Geen s theoem can be used to solve geneal electostatic poblems. Dielectics A well known application of Geen s theoem. Last time we
More informationChapter 6. NEWTON S 2nd LAW AND UNIFORM CIRCULAR MOTION
Chapte 6 NEWTON S nd LAW AND UNIFORM CIRCULAR MOTION Phyic 1 1 3 4 ting Quetion: A ball attached to the end of a ting i whiled in a hoizontal plane. At the point indicated, the ting beak. Looking down
More informationA Fundamental Tradeoff between Computation and Communication in Distributed Computing
1 A Fundamental Tadeoff between Computation and Communication in Ditibuted Computing Songze Li, Student embe, IEEE, ohammad Ali addah-ali, embe, IEEE, Qian Yu, Student embe, IEEE, and A. Salman Avetimeh,
More information1 Notes on Order Statistics
1 Notes on Ode Statistics Fo X a andom vecto in R n with distibution F, and π S n, define X π by and F π by X π (X π(1),..., X π(n) ) F π (x 1,..., x n ) F (x π 1 (1),..., x π 1 (n)); then the distibution
More informationNew problems in universal algebraic geometry illustrated by boolean equations
New poblems in univesal algebaic geomety illustated by boolean equations axiv:1611.00152v2 [math.ra] 25 Nov 2016 Atem N. Shevlyakov Novembe 28, 2016 Abstact We discuss new poblems in univesal algebaic
More information6 PROBABILITY GENERATING FUNCTIONS
6 PROBABILITY GENERATING FUNCTIONS Cetain deivations pesented in this couse have been somewhat heavy on algeba. Fo example, detemining the expectation of the Binomial distibution (page 5.1 tuned out to
More informationTheWaveandHelmholtzEquations
TheWaveandHelmholtzEquations Ramani Duaiswami The Univesity of Mayland, College Pak Febuay 3, 2006 Abstact CMSC828D notes (adapted fom mateial witten with Nail Gumeov). Wok in pogess 1 Acoustic Waves 1.1
More information763620SS STATISTICAL PHYSICS Solutions 2 Autumn 2012
763620SS STATISTICAL PHYSICS Solutions 2 Autumn 2012 1. Continuous Random Walk Conside a continuous one-dimensional andom walk. Let w(s i ds i be the pobability that the length of the i th displacement
More informationLikelihood vs. Information in Aligning Biopolymer Sequences. UCSD Technical Report CS Timothy L. Bailey
Likelihood vs. Infomation in Aligning Biopolyme Sequences UCSD Technical Repot CS93-318 Timothy L. Bailey Depatment of Compute Science and Engineeing Univesity of Califonia, San Diego 1 Febuay, 1993 ABSTRACT:
More informationASTR415: Problem Set #6
ASTR45: Poblem Set #6 Cuan D. Muhlbege Univesity of Mayland (Dated: May 7, 27) Using existing implementations of the leapfog and Runge-Kutta methods fo solving coupled odinay diffeential equations, seveal
More information4/18/2005. Statistical Learning Theory
Statistical Leaning Theoy Statistical Leaning Theoy A model of supevised leaning consists of: a Envionment - Supplying a vecto x with a fixed but unknown pdf F x (x b Teache. It povides a desied esponse
More information( ) F α. a. Sketch! r as a function of r for fixed θ. For the sketch, assume that θ is roughly the same ( )
. An acoustic a eflecting off a wav bounda (such as the sea suface) will see onl that pat of the bounda inclined towad the a. Conside a a with inclination to the hoizontal θ (whee θ is necessail positive,
More informationIntroduction to Mathematical Statistics Robert V. Hogg Joeseph McKean Allen T. Craig Seventh Edition
Intoduction to Mathematical Statistics Robet V. Hogg Joeseph McKean Allen T. Caig Seventh Edition Peason Education Limited Edinbugh Gate Halow Essex CM2 2JE England and Associated Companies thoughout the
More informationFRACTIONAL ORDER SYSTEM IDENTIFICATION BASED ON GENETIC ALGORITHMS
Jounal of Engineeing Science and Technology Vol. 8, No. 6 (2013) 713-722 School of Engineeing, Taylo niveity FRACTIONAL ORDER SSTEM IDENTIFICATION BASED ON GENETIC ALGORITHMS MAZIN Z. OTHMAN*, EMAD A.
More informationSchool Timetabling using Genetic Search
School Timetabling using Genetic Seach Caldeia JP, Rosa AC Laseeb - ISR IST email: acosa@is.ist.utl.pt Abstact In the pape we discuss the implementation of a genetic based algoithm that is used to poduce
More informationEstimates on Invariant Tori near an Elliptic Equilibrium Point of a Hamiltonian System
jounal of diffeential equation 131, 277303 (1996) aticle no. 0165 Etimate on Invaiant Toi nea an Elliptic Equilibium Point of a Hamiltonian Sytem Amadeu Delham* Depatament de Matema tica Aplicada I, Univeitat
More informationBosons and fermions in social and economic systems. Sergey A. Rashkovskiy
Boon and femion in ocial and economic ytem Segey A. Rahkovkiy Ihlinky Intitute fo Poblem in Mechanic of the Ruian Academy of Science, Venadkogo Ave., /, Mocow, 9526, Ruia Tomk State Univeity, 36 Lenina
More informationA hint of renormalization
A hint of enomalization Betand Delamotte a) Laboatoie de Phyique Théoique et Haute Enegie, Univeité Pai VI, Piee et Maie Cuie, Pai VII, Deni Dideot, 2 Place Juieu, 75251 Pai Cedex 05, Fance Received 28
More informationyou of a spring. The potential energy for a spring is given by the parabola U( x)
Small oscillations The theoy of small oscillations is an extemely impotant topic in mechanics. Conside a system that has a potential enegy diagam as below: U B C A x Thee ae thee points of stable equilibium,
More information