6.0 LEVEL 3 BINNED PRODUCT


 Warren Stokes
 1 years ago
 Views:
Transcription
1 6.0 LEVEL 3 BINNED PRODUCT 6.1 Itroductio Level 3 Bied product data are itegrated from Level 2 GAC data for specified period. Level 3 Bied products are classified ito three products: ocea color, vegetatio ad sea surface temperature. There are products correspodig to the itegratio period of a day, a week, a moth ad a year. Each product is separated header ad each datas. (ote 1) Ocea color product store followig data. Bi data Normalized waterleavig radiace. (412m, 443m, 490m, 520m, 565m), Aerosol radiace at (670m, 765m, 865m), epsilo(670 : 865), ad taua (865m). CZCSlike pigmet cocetratio Chlorophyll a cocetratio Diffuse atteuatio coefficiet at 490m Itegral chlorophyll a Vegetatio idices product stores followig data. Bi data. Vegetatio idices Sea surface temperature product stores followig data Bi data. Sea surface temperature (ote 1) If you try to access to multiple files of level 3Bied product by HDF libraries, all files must be stored curret directory. Note that the data types i throughout this documet are expressed with followigs: Ch: character lie Short: 2 byte iteger (siged) Ushort: 2 byte iteger (usiged) Log: 4 byte iteger (siged) Ulog: 4 byte iteger (usiged) Real: 4 byte real umber Double: 8 byte real umber Byte: 1 byte iteger Biary data is Big Edia, IEEE format. Siged data is described i twos complemet.
2 6.2 Namig Covetio Ocea color product :L3BOC$ (Bi data), L3BOC$.x00 (radiace, epsilo, taua), L3BOC$.x01(CZCSlike pigmet), L3BOC$.x02 (chlorophyll a cocetratio), L3BOC$.x03 (Diffuse atteuatio coefficiet at 490m), L3BOC$.x04 (Itegral chlorophyll a) Vegetatio product :L3BVI$ (Bi data), L3BVI$.x00 (Vegetatio idices) Sea surface temperature :L3BST$ (Bi data), L3BST$.x00 (Sea surface temperature) product * $ is as follows: 'D' shows a day, 'W' shows a week, 'M' shows a moth, 'Y' shows a year.
3 Mai File Subordiate Files Global Attributes chlor_a_k_490 _sum _sum_sq Level3 Bied K_490 class: Plaetary Grid _sum _sum_sq chlor_a SEAGrid class: Geometry _sum _sum_sq registratio straddle bis radius max_orth max_south seam_lo CZCSpigmet 1 records _sum _sum_sq Lw_ tau_865 BiIdex class: Idex _sum _sum_sq  _sum _sum_sq row_um vsize hsize start_um begi extet max records 2160 records BiList class: Mai vegetatio bi_um obs scees time_rec weights flags_set _sum _sum_sq records records SST _sum _sum_sq records Figure 6 Structure of 3Bied product
4 6.3 Global Attributes V group ame (Tag = VG) (refer 6.2) CDF0.0 V group class Missio ad Documetatio [oc] : Ocea Color, [vi] : Vegetatio Idices, [st] : Sea Surface Temperature V data ame Number of data Product Name 1 Ch 7 / 11 (refer 6.2) The ame of the product file (without path) Title 1 Ch 25 "OCTS Level3 Bied " Ceter 1 Ch 31 "NASDA/Earth Observatio Ceter" OCTS Processig Ceter (Natioal Space Developmet Agecy of Japa) Missio 1 Ch 11 "ADEOS OCTS" Missio (satellite ame ad sesor ame) Missio Characteristics 1 Ch 189 "Nomial orbit: icliatio = (Su Sychroous); ode = 10:1510:45 AM (descedig); eccetricity =< ; altitude = 797km; groud speed = 6.6km/sec; revolutios per day = 14+11/41" Sesor 1 Ch 43 "Ocea Color ad Temperature Scaer (OCTS)"
5 V data ame Number of data Sesor Characteristics 1 Ch 123 "Number of bads = 12; umber of detectors per bad = 10; bits per pixel = 10; sca period = 0.905sec; bit rate = 3Mbit/sec" Product Type 1 Ch 4 "day" 5 "week" 6 "moth" 5 "year" Subtype 1 Ch 12 "Ocea Color" 19 "Vegetatio Idices" 24 "Sea Surface Temperature" Replacemet Flag 1 Ch 9 "ORIGINAL" If this is the first versio of this product delivered to the NASDA/EOC. Software ID 1 Ch 4 "X Y" Idetities versio of the operatioal software used to create this product. X : OCTS software versio Y : OCTS database versio Processig Time 1 Ch 22 YYYYMMDD hh:mm:ss.ttt Local time of geeratio of this product. Processig Cotrol 1 Ch over 111 (refer attachmet B) All iput ad processig cotrol parameters used by the callig program to geerate the product. Processig Log 1 Ch 160 (refer attachmet C) Processig status iformatio.
6 V data ame Number of data L2 Flag Usage 1 Ch 139 : [oc] "AEROSOL1,LOWLW1,HIGHTAU1,SOLZEN1,TURBIDW1, List of algorithm ames (each COCCOLITH1,CLDICE1,INCPLTSET1,NEGLW1,COASTZ1, separated by oe comma) for SATZEN1,BRIGHT1,SUNGLINT1,NEARCLOUD1,LAND1, the bits (masks ad flags) i EPSILON1" : [oc] the paret Level2 products. 56 : [vi] "INCPLTSET1,OCEAN1,SCANANG1,OCEANGAIN1, SATURATE1,BRIGHT1" : [vi] 51 : [st] "INCPLTSET1,LAND1,IRCLOUD1,SURFWIND1, EMIANG1,SSTQC1" : [st] L2 Egieerig Quality Usage 24 Byte 24 Flags for showig emergecy telemetry. (For bits i the array that are set (=1), the correspodig bit i the paret Level2 products' "eg_qual" values are used for exclusio durig biig.
7 6.3.2 Time V data ame Number of data Period Start Year 1 Short 2 Year of start of biig period (cf. "Start Year"); used for iterpretig "time_rec" of Vdata "BiList". Period Start Day 1 Short 2 GMT dayof year of start of biig period (cf. "Start Day") used for iterpretig "time_rec" of Vdata "BiList". Period Ed Year 1 Short 2 Year of ed of biig period (cf. "Ed Year"); used for iterpretig "time_rec" of Vdata "BiList". Period Ed Day 1 Short 2 GMT dayof year of ed of biig period (cf. "Ed Day") used for iterpretig "time_rec" of Vdata "BiList". Start Time 1 Ch 22 YYYYMMDD hh:mm:ss.ttt Start GMT of earliest iput product. Ed Time 1 Ch 22 YYYYMMDD hh:mm:ss.ttt Ed GMT of latest iput product. Start Year 1 Short 2 GMT year of data start for earliest iput product. Start Day 1 Short 2 GMT dayofyear of data start for earliest iput product. Start Millisec 1 Log 4 GMT millisecodsofday of data start for latest iput product. Ed Year 1 Short 2 GMT year of data start for latest iput product. Ed Day 1 Short 2 GMT dayofyear of data start for latest iput product. Ed Millisec 1 Log 4 GMT millisecodsofday of data start for latest iput product.
8 6.3.3 Explaatio V data ame Number of data Latitude Uits 1 Ch 14 "degrees North" Uits used for all lat. values i this product. Logitude Uits 1 Ch 13 "degrees East" Uits used for all log. values i this product. Bis 1 Log 4 Number of bis i this product. ( ) Percet Bis 1 Real 4 Percetage of umber of bis i this product by umber of total bis. ( Bis x 100 / )
9 6.4 Level 3 Bied V Group i Mai File The Level 3 Bied product V data listed i each subsectio bellow belog to the V group Level 3 Bied which is of class Plaetary Grid. For OCTS Level 3 Bied products, this V group is spread over multiple HDF files. Ocea color product cosists of a mai file ad 5 subordiate files ad vegetatio product ad sea surface temperature product cosists of a mai file ad oly oe subordiate file. If you try to access to these mai file ad subordiate files by HDF libraries, all files must be stored o curret directory. The mai file cotais the global attributes described above as well as the V data described i this subsectio.
10 V group ame (Tag = VG) Level3 Bied V group class PlaetaryGrid Vdata SEAGrid of Class Geometry This Vdata cotais iformatio eeded for descriptio of the geographic biig scheme to HDF access software ad may ot be useful to most users. V data ame Field ame Dimesio SEAGrid registratio 1 Log 4 5 Locatio of characteristic poit withi bi. [ Geometry ] straddle 1 Log 4 0 (o) Does a latitudial bad straddle the equator? bis 1 Log Number of equatorial bis. radius 1 Double Earth's radius i kilometers. max_orth 1 Double Northermost latitude i grid. max_south 1 Double Southermost latitude i grid. seam_lo 1 Double Logitude of westermost edge of grid
11 6.4.2 Vdata BiIdex of Class Idex Vdata "BiIdex" of class "Idex" cotais oe record of the followig fields for each of the 2,160 latitudial bi rows i the geographic biig scheme. This Vdata cotais iformatio eeded for descriptio of the geographic biig scheme to HDF access software ad may ot be useful to most users. V data ame BiIdex [ Idex ] Field ame Dimesio row_um 2160 Log Idex of row correspodig to each "BiIdex" record. vsize 2160 Double Northsouth extet (degrees latitude) of bis for each (1/12 degree) row. hsize 2160 Double Eastwest extet (degrees logitude) of bis for each row start_um 2160 Log Bi umber of first bi i the grid for each row (cf. "begi"); always the same set of values for the set of rows: 1 for row 0, 4 for row 1,..., for row begi 2160 Log Bi umber of first data cotaiig bi for each row (cf. "start_um"). extet 2160 Log 8640 Number of bis actually stored (i.e., cotaiig data) for each row. max 2160 Log The maximum umber of bis i the grid for each row.
12 6.4.3 Vdata BiList of Class Mai Vdata "BiList" of class "Mai" cotais oe record of the followig fields for each bi i which at least oe pixel was bied. Records for bis i which o pixels were bied ("samps" = 0) are excluded from the product. : umber of bis (value of "extet") V data ame BiList [ Mai ] Field ame Dimesio bi_um Log The idex umber of the bi represeted by this record ad correspodig records i each of the Vdatas of class "Subordiate. obs Short 2 Number of observatios (pixels) bied i this bi. scees Short 2 Number of scees cotributig data (at least oe pixel) to this bi.
13 V data ame BiList [ Mai ] (cotiue) Field ame Dimesio time_rec Short 2 The bit sequece represets the time distributio of the data bied i this bi; the bits represet cosecutive time i the biig period (as defied by the gloval attributes "Period Start Year", "Period Start Day", "Period Ed Year", ad "Period Ed Day"), the lowest bi beig the earliest; for daily products, bits correspod to the relative sequece of orbits bied; for weekly products, each bit represets oe day; for mothly products, each bit represets two days; for yearly products, each bit represets o caledar moth; a bit is set (=1) oly if data for the time correspodig to that bit were bied i this bi. weights Real 4 Sum of the weights of the equivalet bis of the iput products. flags_set Short 2 16 bits i two bytes correspodig to those of the paret Level2 products' "l2_flags" for Ocea Color, "VI" for Vegetatio Idices, or "SST" for Sea Surface Temperature.
14 6.5 Level 3 Bied V group i Subordiate Files The Level 3 bied product V data listed below belog to the V group Level 3 Bied which is of class Plaetary Grid. For OCTS Level 3 bied products, the V group is spread over multiple HDF files: a mai file ad subordiate files. If you try to access to these mai file ad subordiate files by HDF libraries, all files must be stored o curret directory. Each subordiate file cosists of oe or several V data of class Subordiate ad each V data is amed for the geophysical quality beig bied as follows: (1) Ocea Color Product Lw_412 : ormalized waterleavig radiace (mw cm 2 mm 1 sr 1 ) at 412 m Lw_443 : ormalized waterleavig radiace (mw cm 2 mm 1 sr 1 ) at 443 m Lw_490 : ormalized waterleavig radiace (mw cm 2 mm 1 sr 1 ) at 490 m Lw_520 : ormalized waterleavig radiace (mw cm 2 mm 1 sr 1 ) at 520 m Lw_565 : ormalized waterleavig radiace (mw cm 2 mm 1 sr 1 ) at 565 m La_670 : aerosol radiace (mw cm 2 mm 1 sr 1 ) at 670 m La_765 : aerosol radiace (mw cm 2 mm 1 sr 1 ) at 765 m La_865 : aerosol radiace (mw cm 2 mm 1 sr 1 ) at 865 m epsilo : epsilo of aerosol collectio tau_865 : aerosol optical thickess at 865 m Above 10 parameters are stored i oe subordiate file. CZCS_pigmet : CZCSlike pigmet cocetratio (mg m 3 ) chlor_a : chlorophyll a cocetratio (mg m 3 ) K_490 : diffuse atteuatio coefficiet (m 1 ) at 490 m chlor_a_k_490: itegral chlorophyll (mg m 2 ), calculated usig the Level 2 values chlorophyll a divided by K (490). (2) Vegetatio vegetatio : vegetatio idices (3) Sea surface Temperature SST : sea surface temperature (K) For each file cotaiig a Vdata of the class Subordiate, the ame of the mai file (same as cotet of the global attribute Product Name) is writte i ASCII startig with the first byte of the file byte 0. The data records of these V data start at byte 512 i each file. (TBD) Each V data cotais two fields, the ames of which are made up of the ame of the V data itself cocateated with _sum ad _sum_sq, as, for example, Lw_412_sum ad Lw_412_sum_sq:
15 [Blak]
16 V group ame (Tag = VG) Level3 Bied V group class PlaetaryGrid Ocea Color (1) Normalized waterleavig radiace (412m,443m,490m,520m,565m), Aerosol radiace (670m,765m,865m), epsilo of aerosol correctio(670:865), aerosol optical thickess : umber of bis (sum of value of "extet") V data ame Lw_412 [ Subordiate ] Lw_443 [ Subordiate ] Field ame Dimesio Lw_412_sum Real 4 sum of atural logs of bied pixel values for correspodig ormalized waterleavig radiace at 412 m. Lw_412_sum_sq Real 4 sum of squares of atural logs of bied pixel values for correspodig Lw 412 m. Lw_443_sum Real 4 sum of atural logs of bied pixel values for correspodig ormalized waterleavig radiace at 443 m. Lw_443_sum_sq Real 4 sum of squares of atural logs of bied pixel values for correspodig Lw 443 m.
17 V data ame Lw_490 [ Subordiate ] Lw_520 [ Subordiate ] Lw_565 [ Subordiate ] La_670 [ Subordiate ] Field ame Dimesio Lw_490_sum Real 4 sum of atural logs of bied pixel values for correspodig ormalized waterleavig radiace at 490 m. Lw_490_sum_sq Real 4 sum of squares of atural logs of bied pixel values for correspodig Lw 490 m. Lw_520_sum Real 4 sum of atural logs of bied pixel values for correspodig ormalized waterleavig radiace at 520 m. Lw_520_sum_sq Real 4 sum of squares of atural logs of bied pixel values for correspodig Lw 520 m. Lw_565_sum Real 4 sum of atural logs of bied pixel values for correspodig ormalized waterleavig radiace at 565 m. Lw_565_sum_sq Real 4 sum of squares of atural logs of bied pixel values for correspodig Lw 565 m. La_670_sum Real 4 sum of atural logs of bied pixel values for correspodig aerosol radiace at 670 m. La_670_sum_sq Real 4 sum of squares of atural logs of bied pixel values for correspodig La 670 m.
18 V data ame La_765 [ Subordiate ] La_865 [ Subordiate ] eps_68 [ Subordiate ] tau_865 [ Subordiate ] Field ame Dimesio La_765_sum Real 4 sum of atural logs of bied pixel values for correspodig aerosol radiace at 765 m. La_765_sum_sq Real 4 sum of squares of atural logs of bied pixel values for correspodig La 765 m. La_865_sum Real 4 sum of atural logs of bied pixel values for correspodig aerosol radiace at 865 m. La_865_sum_sq Real 4 sum of squares of atural logs of bied pixel values for correspodig La 865 m. eps_68_sum Real 4 sum of atural logs of bied pixel values for correspodig epsilo of aerosol correctio at 670 ad 865 m. eps_68_sum_sq Real 4 sum of squares of atural logs of bied pixel values for correspodig epsilo (670:865) tau_865_sum Real 4 sum of atural logs of bied pixel values for correspodig aerosol optical thickess at 865 m. tau_865_sum_sq Real 4 sum of squares of atural logs of bied pixel values for correspodig tau at 865 m.
19 (2) CZCSlike pigmet cocetratio : umber of bis (sum of value of "extet") V data ame CZCS_pigmet [ Subordiate ] Field ame Dimesio CZCS_pigmet_sum Real 4 sum of atural logs of bied pixel values for correspodig CZCSlike pigmet cocetratio. CZCS_pigmet_sum_sq Real 4 sum of squares of atural logs of bied pixel values for correspodig CZCSlike pigmet.
20 (3) Chlorophyll a cocetratio : umber of bis (sum of value of "extet") V data ame chlor_a [ Subordiate ] Field ame Dimesio chlor_a_sum Real 4 sum of atural logs of bied pixel values for correspodig chlorophyll a cocetratio chlor_a_sum_sq Real 4 sum of squares of atural logs of bied pixel values for correspodig chlorophyll a.
21 (4) Diffuse atteuatio coefficiet at 490 m : umber of bis (sum of value of "extet") V data ame K_490 [ Subordiate ] Field ame Dimesio K_490_sum Real 4 sum of atural logs of bied pixel values for correspodig diffuse atteuatio coefficiet at 490 m. K_490_sum_sq Real 4 sum of squares of atural logs of bied pixel values for correspodig diffuse atteuatio coefficiet.
22 (5) Itegral chlorophyll, calculated usig the Level2 values chlorophyll a divided by K(490) : umber of bis (sum of value of "extet") V data ame chlor_a_k_490 [ Subordiate ] Field ame Dimesio chlor_a_k_490_sum Real 4 sum of atural logs of bied pixel values for correspodig itegral chlorophyll, calculated usig the Level2 values chlorophyll a divided by K(490). chlor_a_k_490_sum_sq Real 4 sum of squares of atural logs of bied pixel values for correspodig itegral chlorophyll a.
23 6.5.2 Vegetatio idices : umber of bis (sum of value of "extet") V data ame vegetatio [ Subordiate ] Field ame Dimesio vegetatio_sum Real 4 sum of bied pixel values for correspodig vegetatio idices. vegetatio_sum_sq Real 4 sum of squares of bied pixel values for correspodig vegetatio idices.
24 6.5.3 Sea Surface Temperature : umber of bis (sum of value of "extet") V data ame SST [ Subordiate ] Field ame Dimesio SST_sum Real 4 sum of bied pixel values for correspodig sea surface temperature. SST_sum_sq Real 4 sum of squares of bied pixel values for correspodig sea surface temperature.
Ocean Level3 Standard Mapped Image Products June 4, 2010
Ocean Level3 Standard Mapped Image Products June 4, 2010 1.0 Introduction This document describes the specifications of Ocean Level3 standard mapped archive products that are produced and distributed
More informationCS100: Introduction to Computer Science
Review: History of Computers CS100: Itroductio to Computer Sciece Maiframes Miicomputers Lecture 2: Data Storage  Bits, their storage ad mai memory Persoal Computers & Workstatios Review: The Role of
More informationEngineering Data Management
BaaERP 5.0c Maufacturig Egieerig Data Maagemet Module Procedure UP128A US Documetiformatio Documet Documet code : UP128A US Documet group : User Documetatio Documet title : Egieerig Data Maagemet Applicatio/Package
More informationBaanERP. BaanERP Windows Client Installation Guide
BaaERP A publicatio of: Baa Developmet B.V. P.O.Box 143 3770 AC Bareveld The Netherlads Prited i the Netherlads Baa Developmet B.V. 1999. All rights reserved. The iformatio i this documet is subject to
More informationPUBLIC RELATIONS PROJECT 2016
PUBLIC RELATIONS PROJECT 2016 The purpose of the Public Relatios Project is to provide a opportuity for the chapter members to demostrate the kowledge ad skills eeded i plaig, orgaizig, implemetig ad evaluatig
More informationYour organization has a Class B IP address of 166.144.0.0 Before you implement subnetting, the Network ID and Host ID are divided as follows:
Subettig Subettig is used to subdivide a sigle class of etwork i to multiple smaller etworks. Example: Your orgaizatio has a Class B IP address of 166.144.0.0 Before you implemet subettig, the Network
More informationBaan Service Master Data Management
Baa Service Master Data Maagemet Module Procedure UP069A US Documetiformatio Documet Documet code : UP069A US Documet group : User Documetatio Documet title : Master Data Maagemet Applicatio/Package :
More informationIn nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008
I ite Sequeces Dr. Philippe B. Laval Keesaw State Uiversity October 9, 2008 Abstract This had out is a itroductio to i ite sequeces. mai de itios ad presets some elemetary results. It gives the I ite Sequeces
More informationCHAPTER 3 DIGITAL CODING OF SIGNALS
CHAPTER 3 DIGITAL CODING OF SIGNALS Computers are ofte used to automate the recordig of measuremets. The trasducers ad sigal coditioig circuits produce a voltage sigal that is proportioal to a quatity
More informationMaximum Likelihood Estimators.
Lecture 2 Maximum Likelihood Estimators. Matlab example. As a motivatio, let us look at oe Matlab example. Let us geerate a radom sample of size 00 from beta distributio Beta(5, 2). We will lear the defiitio
More informationPUBLIC RELATIONS PROJECT 2015
PUBLIC RELATIONS PROJECT 2015 Supported by MARKETING The purpose of the Public Relatios Project is to provide a opportuity for the chapter members to demostrate the kowledge ad skills eeded i plaig, orgaizig,
More informationCooleyTukey. Tukey FFT Algorithms. FFT Algorithms. Cooley
Cooley CooleyTuey Tuey FFT Algorithms FFT Algorithms Cosider a legth sequece x[ with a poit DFT X[ where Represet the idices ad as +, +, Cooley CooleyTuey Tuey FFT Algorithms FFT Algorithms Usig these
More informationCS100: Introduction to Computer Science
Iclass Exercise: CS100: Itroductio to Computer Sciece What is a flipflop? What are the properties of flipflops? Draw a simple flipflop circuit? Lecture 3: Data Storage  Mass storage & represetig
More informationBaanERP 5.0c. EDI User Guide
BaaERP 5.0c A publicatio of: Baa Developmet B.V. P.O.Box 143 3770 AC Bareveld The Netherlads Prited i the Netherlads Baa Developmet B.V. 1999. All rights reserved. The iformatio i this documet is subject
More informationDepartment of Computer Science, University of Otago
Departmet of Computer Sciece, Uiversity of Otago Techical Report OUCS200609 Permutatios Cotaiig May Patters Authors: M.H. Albert Departmet of Computer Sciece, Uiversity of Otago Micah Colema, Rya Fly
More informationINDEPENDENT BUSINESS PLAN EVENT 2016
INDEPENDENT BUSINESS PLAN EVENT 2016 The Idepedet Busiess Pla Evet ivolves the developmet of a comprehesive proposal to start a ew busiess. Ay type of busiess may be used. The Idepedet Busiess Pla Evet
More informationNormal Distribution.
Normal Distributio www.icrf.l Normal distributio I probability theory, the ormal or Gaussia distributio, is a cotiuous probability distributio that is ofte used as a first approimatio to describe realvalued
More informationEscola Federal de Engenharia de Itajubá
Escola Federal de Egeharia de Itajubá Departameto de Egeharia Mecâica PósGraduação em Egeharia Mecâica MPF04 ANÁLISE DE SINAIS E AQUISÇÃO DE DADOS SINAIS E SISTEMAS Trabalho 02 (MATLAB) Prof. Dr. José
More informationEngineering 323 Beautiful Homework Set 3 1 of 7 Kuszmar Problem 2.51
Egieerig 33 eautiful Homewor et 3 of 7 Kuszmar roblem.5.5 large departmet store sells sport shirts i three sizes small, medium, ad large, three patters plaid, prit, ad stripe, ad two sleeve legths log
More informationTHE ARITHMETIC OF INTEGERS.  multiplication, exponentiation, division, addition, and subtraction
THE ARITHMETIC OF INTEGERS  multiplicatio, expoetiatio, divisio, additio, ad subtractio What to do ad what ot to do. THE INTEGERS Recall that a iteger is oe of the whole umbers, which may be either positive,
More informationo to 80ppm range of turbidity.
REAL TIME MONITORING OF TURBID WATER BY USING VIDEO CAMERA PERSONAL COMPUTER AND IMAGE PROCESSING ' Nobuyuki Mizutai.Kazuya Saito,Yoichi Numata ASIA AIR SURVEY CO.,LTD. 36 Tamuracho,Atsugishi.Kaagawake,243,Japa.
More informationMathematical goals. Starting points. Materials required. Time needed
Level A1 of challege: C A1 Mathematical goals Startig poits Materials required Time eeded Iterpretig algebraic expressios To help learers to: traslate betwee words, symbols, tables, ad area represetatios
More informationSolving Inequalities
Solvig Iequalities Say Thaks to the Authors Click http://www.ck12.org/saythaks (No sig i required) To access a customizable versio of this book, as well as other iteractive cotet, visit www.ck12.org CK12
More informationHow to read A Mutual Fund shareholder report
Ivestor BulletI How to read A Mutual Fud shareholder report The SEC s Office of Ivestor Educatio ad Advocacy is issuig this Ivestor Bulleti to educate idividual ivestors about mutual fud shareholder reports.
More informationDesktop Management. Desktop Management Tools
Desktop Maagemet 9 Desktop Maagemet Tools Mac OS X icludes three desktop maagemet tools that you might fid helpful to work more efficietly ad productively: u Stacks puts expadable folders i the Dock. Clickig
More informationDomain 1: Designing a SQL Server Instance and a Database Solution
Maual SQL Server 2008 Desig, Optimize ad Maitai (70450) 18004186789 Domai 1: Desigig a SQL Server Istace ad a Database Solutio Desigig for CPU, Memory ad Storage Capacity Requiremets Whe desigig a
More informationCREATIVE MARKETING PROJECT 2016
CREATIVE MARKETING PROJECT 2016 The Creative Marketig Project is a chapter project that develops i chapter members a aalytical ad creative approach to the marketig process, actively egages chapter members
More informationExploratory Data Analysis
1 Exploratory Data Aalysis Exploratory data aalysis is ofte the rst step i a statistical aalysis, for it helps uderstadig the mai features of the particular sample that a aalyst is usig. Itelliget descriptios
More informationUser manual and preprogrammed spreadsheets for performing revision analysis
User maual ad preprogrammed spreadsheets for performig revisio aalysis This documet describes how to perform revisio aalysis usig preprogrammed template spreadsheets based o data extracted from the OECD
More informationConversion Instructions:
Coversio Istructios: QMS magicolor 2 DeskLaser to QMS magicolor 2 CX 1800502001A Trademarks QMS, the QMS logo, ad magicolor are registered trademarks of QMS, Ic., registered i the Uited States Patet ad
More informationI apply to subscribe for a Stocks & Shares ISA for the tax year 20 /20 and each subsequent year until further notice.
IFSL Brooks Macdoald Fud Stocks & Shares ISA Trasfer Applicatio Form IFSL Brooks Macdoald Fud Stocks & Shares ISA Trasfer Applicatio Form Please complete usig BLOCK CAPITALS ad retur the completed form
More informationBaan Finance Accounts Payable
Baa Fiace Accouts Payable Module Procedure UP035A US Documetiformatio Documet Documet code : UP035A US Documet group : User Documetatio Documet title : Accouts Payable Applicatio/Package : Baa Fiace Editio
More informationA GUIDE TO LEVEL 3 VALUE ADDED IN 2013 SCHOOL AND COLLEGE PERFORMANCE TABLES
A GUIDE TO LEVEL 3 VALUE ADDED IN 2013 SCHOOL AND COLLEGE PERFORMANCE TABLES Cotets Page No. Summary Iterpretig School ad College Value Added Scores 2 What is Value Added? 3 The Learer Achievemet Tracker
More informationOverview on SBox Design Principles
Overview o SBox Desig Priciples Debdeep Mukhopadhyay Assistat Professor Departmet of Computer Sciece ad Egieerig Idia Istitute of Techology Kharagpur INDIA 721302 What is a SBox? SBoxes are Boolea
More information4.1 Sigma Notation and Riemann Sums
0 the itegral. Sigma Notatio ad Riema Sums Oe strategy for calculatig the area of a regio is to cut the regio ito simple shapes, calculate the area of each simple shape, ad the add these smaller areas
More informationI apply to subscribe for a Stocks & Shares NISA for the tax year 2015/2016 and each subsequent year until further notice.
IFSL Brooks Macdoald Fud Stocks & Shares NISA trasfer applicatio form IFSL Brooks Macdoald Fud Stocks & Shares NISA trasfer applicatio form Please complete usig BLOCK CAPITALS ad retur the completed form
More informationConvention Paper 6764
Audio Egieerig Society Covetio Paper 6764 Preseted at the 10th Covetio 006 May 0 3 Paris, Frace This covetio paper has bee reproduced from the author's advace mauscript, without editig, correctios, or
More informationA Combined Continuous/Binary Genetic Algorithm for Microstrip Antenna Design
A Combied Cotiuous/Biary Geetic Algorithm for Microstrip Atea Desig Rady L. Haupt The Pesylvaia State Uiversity Applied Research Laboratory P. O. Box 30 State College, PA 168040030 haupt@ieee.org Abstract:
More informationOur aim is to show that under reasonable assumptions a given 2πperiodic function f can be represented as convergent series
8 Fourier Series Our aim is to show that uder reasoable assumptios a give periodic fuctio f ca be represeted as coverget series f(x) = a + (a cos x + b si x). (8.) By defiitio, the covergece of the series
More information3. Covariance and Correlation
Virtual Laboratories > 3. Expected Value > 1 2 3 4 5 6 3. Covariace ad Correlatio Recall that by takig the expected value of various trasformatios of a radom variable, we ca measure may iterestig characteristics
More informationSoving Recurrence Relations
Sovig Recurrece Relatios Part 1. Homogeeous liear 2d degree relatios with costat coefficiets. Cosider the recurrece relatio ( ) T () + at ( 1) + bt ( 2) = 0 This is called a homogeeous liear 2d degree
More informationNATIONAL SENIOR CERTIFICATE GRADE 12
NATIONAL SENIOR CERTIFICATE GRADE MATHEMATICS P EXEMPLAR 04 MARKS: 50 TIME: 3 hours This questio paper cosists of 8 pages ad iformatio sheet. Please tur over Mathematics/P DBE/04 NSC Grade Eemplar INSTRUCTIONS
More informationEUROCONTROL PRISMIL. EUROCONTROL civilmilitary performance monitoring system
EUROCONTROL PRISMIL EUROCONTROL civilmilitary performace moitorig system Itroductio What is PRISMIL? PRISMIL is a olie civilmilitary performace moitorig system which facilitates the combied performace
More informationA Recursive Formula for Moments of a Binomial Distribution
A Recursive Formula for Momets of a Biomial Distributio Árpád Béyi beyi@mathumassedu, Uiversity of Massachusetts, Amherst, MA 01003 ad Saverio M Maago smmaago@psavymil Naval Postgraduate School, Moterey,
More information5: Introduction to Estimation
5: Itroductio to Estimatio Cotets Acroyms ad symbols... 1 Statistical iferece... Estimatig µ with cofidece... 3 Samplig distributio of the mea... 3 Cofidece Iterval for μ whe σ is kow before had... 4 Sample
More information1 Computing the Standard Deviation of Sample Means
Computig the Stadard Deviatio of Sample Meas Quality cotrol charts are based o sample meas ot o idividual values withi a sample. A sample is a group of items, which are cosidered all together for our aalysis.
More informationHCL Dynamic Spiking Protocol
ELI LILLY AND COMPANY TIPPECANOE LABORATORIES LAFAYETTE, IN Revisio 2.0 TABLE OF CONTENTS REVISION HISTORY... 2. REVISION.0... 2.2 REVISION 2.0... 2 2 OVERVIEW... 3 3 DEFINITIONS... 5 4 EQUIPMENT... 7
More informationBEA elink Adapter for Kenan Arbor/BP. User Guide
BEA elik Adapter for Kea Arbor/BP User Guide BEA elik Adapter for Kea Arbor/BP Versio 1.1 Documet Editio 1.1 April 2000 Copyright Copyright 2000 BEA Systems, Ic. All Rights Reserved. Restricted Rights
More information*The most important feature of MRP as compared with ordinary inventory control analysis is its time phasing feature.
Itegrated Productio ad Ivetory Cotrol System MRP ad MRP II Framework of Maufacturig System Ivetory cotrol, productio schedulig, capacity plaig ad fiacial ad busiess decisios i a productio system are iterrelated.
More information5.3. Generalized Permutations and Combinations
53 GENERALIZED PERMUTATIONS AND COMBINATIONS 73 53 Geeralized Permutatios ad Combiatios 53 Permutatios with Repeated Elemets Assume that we have a alphabet with letters ad we wat to write all possible
More informationBiology 171L Environment and Ecology Lab Lab 2: Descriptive Statistics, Presenting Data and Graphing Relationships
Biology 171L Eviromet ad Ecology Lab Lab : Descriptive Statistics, Presetig Data ad Graphig Relatioships Itroductio Log lists of data are ofte ot very useful for idetifyig geeral treds i the data or the
More information7. Sample Covariance and Correlation
1 of 8 7/16/2009 6:06 AM Virtual Laboratories > 6. Radom Samples > 1 2 3 4 5 6 7 7. Sample Covariace ad Correlatio The Bivariate Model Suppose agai that we have a basic radom experimet, ad that X ad Y
More informationFASHION MERCHANDISING PROMOTION PLAN 2015
FASHION MERCHANDISING PROMOTION PLAN 2015 Sposored by MARKETING The purpose of the Fashio Merchadisig Promotio Pla is to provide a opportuity for the participats to demostrate promotioal competecies ad
More informationODBC. Getting Started With Sage Timberline Office ODBC
ODBC Gettig Started With Sage Timberlie Office ODBC NOTICE This documet ad the Sage Timberlie Office software may be used oly i accordace with the accompayig Sage Timberlie Office Ed User Licese Agreemet.
More informationMultiplexers and Demultiplexers
I this lesso, you will lear about: Multiplexers ad Demultiplexers 1. Multiplexers 2. Combiatioal circuit implemetatio with multiplexers 3. Demultiplexers 4. Some examples Multiplexer A Multiplexer (see
More informationWeek 3 Conditional probabilities, Bayes formula, WEEK 3 page 1 Expected value of a random variable
Week 3 Coditioal probabilities, Bayes formula, WEEK 3 page 1 Expected value of a radom variable We recall our discussio of 5 card poker hads. Example 13 : a) What is the probability of evet A that a 5
More information5 Boolean Decision Trees (February 11)
5 Boolea Decisio Trees (February 11) 5.1 Graph Coectivity Suppose we are give a udirected graph G, represeted as a boolea adjacecy matrix = (a ij ), where a ij = 1 if ad oly if vertices i ad j are coected
More informationSECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES
SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES Read Sectio 1.5 (pages 5 9) Overview I Sectio 1.5 we lear to work with summatio otatio ad formulas. We will also itroduce a brief overview of sequeces,
More informationJavaFX. JavaFX 2.2.5 Installation Guide Release 2.2.5 E2047409. February 2013 Installation instructions by operating system for JavaFX 2.2.
JavaFX JavaFX 2.2.5 Istallatio Guide Release 2.2.5 E2047409 February 2013 Istallatio istructios by operatig system for JavaFX 2.2.5 JavaFX/JavaFX 2.2.5 Istallatio Guide E2047409 Copyright 2008, 2013,
More information! encor e networks TM
! ecor e etworks TM Copyright 2003 Ecore Networks, Ic. All rights reserved. SigalPath 201 (SP201 ) Istallatio Guide Versio C, July 2004 Part Number 15469.1000 SigalPath Software Versio 1100 This Istallatio
More information3.1 Measures of Central Tendency. Introduction 5/28/2013. Data Description. Outline. Objectives. Objectives. Traditional Statistics Average
5/8/013 C H 3A P T E R Outlie 3 1 Measures of Cetral Tedecy 3 Measures of Variatio 3 3 3 Measuresof Positio 3 4 Exploratory Data Aalysis Copyright 013 The McGraw Hill Compaies, Ic. C H 3A P T E R Objectives
More informationFactors of sums of powers of binomial coefficients
ACTA ARITHMETICA LXXXVI.1 (1998) Factors of sums of powers of biomial coefficiets by Neil J. Cali (Clemso, S.C.) Dedicated to the memory of Paul Erdős 1. Itroductio. It is well ow that if ( ) a f,a = the
More informationChatpun Khamyat Department of Industrial Engineering, Kasetsart University, Bangkok, Thailand ocpky@hotmail.com
SOLVING THE OIL DELIVERY TRUCKS ROUTING PROBLEM WITH MODIFY MULTITRAVELING SALESMAN PROBLEM APPROACH CASE STUDY: THE SME'S OIL LOGISTIC COMPANY IN BANGKOK THAILAND Chatpu Khamyat Departmet of Idustrial
More informationThe second difference is the sequence of differences of the first difference sequence, 2
Differece Equatios I differetial equatios, you look for a fuctio that satisfies ad equatio ivolvig derivatives. I differece equatios, istead of a fuctio of a cotiuous variable (such as time), we look for
More information1 Correlation and Regression Analysis
1 Correlatio ad Regressio Aalysis I this sectio we will be ivestigatig the relatioship betwee two cotiuous variable, such as height ad weight, the cocetratio of a ijected drug ad heart rate, or the cosumptio
More informationRecursion and Recurrences
Chapter 5 Recursio ad Recurreces 5.1 Growth Rates of Solutios to Recurreces Divide ad Coquer Algorithms Oe of the most basic ad powerful algorithmic techiques is divide ad coquer. Cosider, for example,
More informationChair for Network Architectures and Services Institute of Informatics TU München Prof. Carle. Network Security. Chapter 2 Basics
Chair for Network Architectures ad Services Istitute of Iformatics TU Müche Prof. Carle Network Security Chapter 2 Basics 2.4 Radom Number Geeratio for Cryptographic Protocols Motivatio It is crucial to
More information, a Wishart distribution with n 1 degrees of freedom and scale matrix.
UMEÅ UNIVERSITET Matematiskstatistiska istitutioe Multivariat dataaalys D MSTD79 PA TENTAMEN 00409 LÖSNINGSFÖRSLAG TILL TENTAMEN I MATEMATISK STATISTIK Multivariat dataaalys D, 5 poäg.. Assume that
More informationFigure 40.1. Figure 40.2
40 Regular Polygos Covex ad Cocave Shapes A plae figure is said to be covex if every lie segmet draw betwee ay two poits iside the figure lies etirely iside the figure. A figure that is ot covex is called
More informationCase Study. Normal and t Distributions. Density Plot. Normal Distributions
Case Study Normal ad t Distributios Bret Halo ad Bret Larget Departmet of Statistics Uiversity of Wiscosi Madiso October 11 13, 2011 Case Study Body temperature varies withi idividuals over time (it ca
More informationLecture 4: Cauchy sequences, BolzanoWeierstrass, and the Squeeze theorem
Lecture 4: Cauchy sequeces, BolzaoWeierstrass, ad the Squeeze theorem The purpose of this lecture is more modest tha the previous oes. It is to state certai coditios uder which we are guarateed that limits
More informationThe Stable Marriage Problem
The Stable Marriage Problem William Hut Lae Departmet of Computer Sciece ad Electrical Egieerig, West Virgiia Uiversity, Morgatow, WV William.Hut@mail.wvu.edu 1 Itroductio Imagie you are a matchmaker,
More informationDomain 1: Configuring Domain Name System (DNS) for Active Directory
Maual Widows Domai 1: Cofigurig Domai Name System (DNS) for Active Directory Cofigure zoes I Domai Name System (DNS), a DNS amespace ca be divided ito zoes. The zoes store ame iformatio about oe or more
More informationFoundations of Operations Research
Foudatios of Operatios Research Master of Sciece i Computer Egieerig Roberto Cordoe roberto.cordoe@uimi.it Tuesday 13.1515.15 Thursday 10.1513.15 http://homes.di.uimi.it/~cordoe/courses/2014for/2014for.html
More informationINTERNATIONAL BUSINESS PLAN EVENT 2015
INTERNATIONAL BUSINESS PLAN EVENT 2015 The Iteratioal Busiess Pla Evet ivolves the developmet of a proposal to start a ew busiess veture i a iteratioal settig, a aalysis of the iteratioal busiess situatio,
More informationFIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. 1. Powers of a matrix
FIBONACCI NUMBERS: AN APPLICATION OF LINEAR ALGEBRA. Powers of a matrix We begi with a propositio which illustrates the usefuless of the diagoalizatio. Recall that a square matrix A is diogaalizable if
More informationSerial ATA PCI Host Adapter AEC6290/6295
Serial ATA PCI Host Adapter AEC6290/6295 User s Maual Versio:1.0 Copyright 2003 ACARD Techology Corp. Release: April 2003 Copyright ad Trademarks The iformatio of the product i this maual is subject to
More informationDivide and Conquer. Maximum/minimum. Integer Multiplication. CS125 Lecture 4 Fall 2015
CS125 Lecture 4 Fall 2015 Divide ad Coquer We have see oe geeral paradigm for fidig algorithms: the greedy approach. We ow cosider aother geeral paradigm, kow as divide ad coquer. We have already see a
More informationModified Line Search Method for Global Optimization
Modified Lie Search Method for Global Optimizatio Cria Grosa ad Ajith Abraham Ceter of Excellece for Quatifiable Quality of Service Norwegia Uiversity of Sciece ad Techology Trodheim, Norway {cria, ajith}@q2s.tu.o
More informationhp calculators HP 12C Statistics  average and standard deviation Average and standard deviation concepts HP12C average and standard deviation
HP 1C Statistics  average ad stadard deviatio Average ad stadard deviatio cocepts HP1C average ad stadard deviatio Practice calculatig averages ad stadard deviatios with oe or two variables HP 1C Statistics
More informationPredictive Modeling Data. in the ACT Electronic Student Record
Predictive Modelig Data i the ACT Electroic Studet Record overview Predictive Modelig Data Added to the ACT Electroic Studet Record With the release of studet records i September 2012, predictive modelig
More informationSemiconductor Devices
emicoductor evices Prof. Zbigiew Lisik epartmet of emicoductor ad Optoelectroics evices room: 116 email: zbigiew.lisik@p.lodz.pl Uipolar devices IFE T&C JFET Trasistor Uipolar evices  Trasistors asic
More informationARIB STDT6325.213 V3.9.0. Spreading and modulation (FDD) (Release 1999)
ARIB STDT635.13 V3.9.0 Spreadig ad modulatio (FDD) (Release 1999) Refer to "Idustrial Property Rights (IPR)" i the preface of ARIB STDT63 for Related Idustrial Property Rights. Refer to "Notice" i the
More informationNow here is the important step
LINEST i Excel The Excel spreadsheet fuctio "liest" is a complete liear least squares curve fittig routie that produces ucertaity estimates for the fit values. There are two ways to access the "liest"
More informationPSYCHOLOGICAL STATISTICS
UNIVERSITY OF CALICUT SCHOOL OF DISTANCE EDUCATION B Sc. Cousellig Psychology (0 Adm.) IV SEMESTER COMPLEMENTARY COURSE PSYCHOLOGICAL STATISTICS QUESTION BANK. Iferetial statistics is the brach of statistics
More informationSection 1.6: Proof by Mathematical Induction
Sectio.6 Proof by Iductio Sectio.6: Proof by Mathematical Iductio Purpose of Sectio: To itroduce the Priciple of Mathematical Iductio, both weak ad the strog versios, ad show how certai types of theorems
More informationConfidence Intervals for One Mean
Chapter 420 Cofidece Itervals for Oe Mea Itroductio This routie calculates the sample size ecessary to achieve a specified distace from the mea to the cofidece limit(s) at a stated cofidece level for a
More informationINVESTMENT PERFORMANCE COUNCIL (IPC)
INVESTMENT PEFOMANCE COUNCIL (IPC) INVITATION TO COMMENT: Global Ivestmet Performace Stadards (GIPS ) Guidace Statemet o Calculatio Methodology The Associatio for Ivestmet Maagemet ad esearch (AIM) seeks
More information.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth
Questio 1: What is a ordiary auity? Let s look at a ordiary auity that is certai ad simple. By this, we mea a auity over a fixed term whose paymet period matches the iterest coversio period. Additioally,
More informationHere are a couple of warnings to my students who may be here to get a copy of what happened on a day that you missed.
This documet was writte ad copyrighted by Paul Dawkis. Use of this documet ad its olie versio is govered by the Terms ad Coditios of Use located at http://tutorial.math.lamar.edu/terms.asp. The olie versio
More informationInfinite Sequences and Series
CHAPTER 4 Ifiite Sequeces ad Series 4.1. Sequeces A sequece is a ifiite ordered list of umbers, for example the sequece of odd positive itegers: 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29...
More informationUniversal coding for classes of sources
Coexios module: m46228 Uiversal codig for classes of sources Dever Greee This work is produced by The Coexios Project ad licesed uder the Creative Commos Attributio Licese We have discussed several parametric
More informationAnalyzing Longitudinal Data from Complex Surveys Using SUDAAN
Aalyzig Logitudial Data from Complex Surveys Usig SUDAAN Darryl Creel Statistics ad Epidemiology, RTI Iteratioal, 312 Trotter Farm Drive, Rockville, MD, 20850 Abstract SUDAAN: Software for the Statistical
More informationGENERATING A FRACTAL SQUARE
GENERATING A FRACTAL SQUARE I 194 the Swedish mathematicia Helge vo Koch(187194 itroduced oe o the earliest ow ractals, amely, the Koch Sowlae. It is a closed cotiuous curve with discotiuities i its derivative
More informationMARTINGALES AND A BASIC APPLICATION
MARTINGALES AND A BASIC APPLICATION TURNER SMITH Abstract. This paper will develop the measuretheoretic approach to probability i order to preset the defiitio of martigales. From there we will apply this
More informationTrigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is
0_0605.qxd /5/05 0:45 AM Page 470 470 Chapter 6 Additioal Topics i Trigoometry 6.5 Trigoometric Form of a Complex Number What you should lear Plot complex umbers i the complex plae ad fid absolute values
More informationPermutations, the Parity Theorem, and Determinants
1 Permutatios, the Parity Theorem, ad Determiats Joh A. Guber Departmet of Electrical ad Computer Egieerig Uiversity of Wiscosi Madiso Cotets 1 What is a Permutatio 1 2 Cycles 2 2.1 Traspositios 4 3 Orbits
More informationLecture 13. Lecturer: Jonathan Kelner Scribe: Jonathan Pines (2009)
18.409 A Algorithmist s Toolkit October 27, 2009 Lecture 13 Lecturer: Joatha Keler Scribe: Joatha Pies (2009) 1 Outlie Last time, we proved the BruMikowski iequality for boxes. Today we ll go over the
More informationA Guide to the Pricing Conventions of SFE Interest Rate Products
A Guide to the Pricig Covetios of SFE Iterest Rate Products SFE 30 Day Iterbak Cash Rate Futures Physical 90 Day Bak Bills SFE 90 Day Bak Bill Futures SFE 90 Day Bak Bill Futures Tick Value Calculatios
More informationProject Deliverables. CS 361, Lecture 28. Outline. Project Deliverables. Administrative. Project Comments
Project Deliverables CS 361, Lecture 28 Jared Saia Uiversity of New Mexico Each Group should tur i oe group project cosistig of: About 612 pages of text (ca be loger with appedix) 612 figures (please
More information