The Codesign Challenge

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "The Codesign Challenge"

Transcription

1 ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn The Codesgn Challenge Objectves In the codesgn challenge, your task s to accelerate a gven software reference mplementaton as fast as possble. You can use any of the prevously dscussed technques to accelerate the mplementaton: use software optmzaton, buld a coprocessor, optmze the hardware/software communcaton. The constrants of your mplementaton are 1. that t must be completed by 11/26/2007 at 5:00PM. 2. that t must run correctly on the Spartan 3E starter kt. 3. that t follows the gven testng procedure to demonstrate the performance of your mplementaton. The qualty of your desgn wll be evaluated usng the followng ctera: 1. the resultng clock cycle count of your mplementaton, wth a clock cycle correspondng to one tck of an OPB Tmer module clocked at 50MHz.. 2. the area of your desgn, expressed n slces of the Spartan3E FPGA. 3. the tme when you turned n the soluton (before the deadlne, but earler s better). The clock cycle count s a frst-order crterum, the area s a second-order crterum, the desgn tme s a thrd order crterum. Faster (but correct) desgns wll always wn. For clock cycle counts that le wthn 1% of each other, area wll be used as a dstnctve factor. For example, gven four desgns A, B, C, and D as shown below, the rankng would be as follows, from best to worst: D, B, C, A. In case the area as well as the cycle count are wthn 1% of each other, then the tme of postng the soluton wll be used to resolve the rankng of the two desgns. Area (Slces) D C < 1% of n B A n Cycle Count Thus, all desgns wll be strctly ranked accordng to these crtera. It s n your nterest to try and fnd the hghest possble performance that can stll be accommodated on a Spartan3 board, and to fnd that soluton as quckly as possble. P. Schaumont, Vrgna Tech

2 ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn Assgnment: Coordnate Rotaton Dgtal Computer (CORDIC) The task s to mplement a CORDIC algorthm as effcently as possble. CORDIC s often used n dgtal hardware to mplement trgonometrc functons. The CORDIC kernel mplements a vector rotaton operaton. In a two-dmensonal space, a vector rotaton starts from a vector (X,Y) and rotates t over an angle ph as follows: x' = x cos( φ) y sn( φ) y' = y cos( φ) + xsn( φ) Ths can be rearranged to: x' = cos( φ)[ x y tan( φ)] y' = cos( φ)[ y + x tan( φ)] An effcent mplementaton of ths formula s possble be restrctng the rotaton to amounts of angles for whch tan(φ ) = ± 2. Thus, we should ensure that the tangent of the angle s a power of two. Under that condton, the above rotaton formulas requre only shft-operatons to mplement the multplcaton wth tan( φ ). We call the rotaton over such an angle an elementary rotaton. An arbtrary angle can now be approxmated as a sequence of elementary rotatons, much n the same way as the ndvdual bts n a btvector can express weghts to approxmate an nteger number. Ths dea s llustrated n the fgure above. We need to mplement a rotaton over angle β. We start wth an ntal vector v0 at (1,0). The frst elementary rotaton s over an angle tan 1 (0.5). Ths rotates v0 counter-clockwse to v1, usng the rotaton formulas gven

3 ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn above. The next elementary rotaton would be over an angle tan 1 (0.25). Agan, ths would be a counter-clockwse rotaton, such that we decrease the error between the desred rotaton angle β and the approxmatons n terms of elementary rotatons. v1 now moves to the poston v2. The next rotaton, over tan 1 (0.125), would be clock wse, snce v2 has moved beyond the desred rotaton β. By usng ncreasngly smaller elementary rotatons, we would obtan an ncreasngly better approxmaton. Therefore, we can express the rotaton formulas above usng a set of dfference equatons. x + 1 = K [ x y+ 1 = K[ y wth K d = ± 1 = cos(tan y. d.2 + x. d ) = ] ] At each teraton, a smaller rotaton angle s selected, and a decson to rotate forward or backward s made ( d = ± 1 ) such that we obtan a better approxmaton of the actual rotaton angle n terms of elementary rotatons. Note that the constants n these formulas only depend on elementary rotatons, and as such they can be evaluated upfront and stored as constants. In CORDIC mplementatons, the K factors are not appled at each rotaton, but rather they are collected nto a sngle scalng factor A. For a large number of (ncreasngly smaller) elementary rotatons, A converges to and s gven by A = lm To fnd how well the target rotaton angle s approxmated by elementary rotatons, we can also nclude an angle-accumulator nto the teratons, defned by z + 1 = z d tan 1 (2 ) Ths angle accumulator expresses the dfference between the target angle and the seres of elementary rotatons.

4 ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn CORDIC algorthms are used n two possble modes of operaton. In the rotaton mode, we start wth a desred rotaton angle and rotate a gven vector over that angle. At each teraton, the decson to rotate counter-clockwse or clockwse s made based on the sgn of the angle accumulator. The objectve s to drve the angle accumulator to zero. The result of the rotaton mode s a gven vector rotated over a gven angle. In the vector mode, we start wth a gven vector and rotate that vector untl the vector s algned wth the X axs. At each teraton, the decson to rotate counterclockwse or clockwse s made by the sgn of the Y component of the vector. The objectve s to drve the Y component to zero. The result of the vector mode s the angle of a gven vector. CORDIC mplementaton on Spartan 3E Starter Kt The codesgn challenge s descrbed by the followng ntal archtecture. DDR Ram target_angle[65536] result_x[65536] result_y[65536] McroBlaze DDR Controller OPB Tmer In a DDR Ram, three 64 KWord arrays are stored. The objectve s to rotate a unt vector (1,0) over all the angles expressed n target_angle[ ], and store the result of each rotaton n result_x[ ] and result_y[ ]. The performance of your desgn s measured as the tme t takes to complete ths set of rotatons (ncludng readng from/wrtng to DDR). To accelerate the desgn, you can modfy the hardware as needed (add coprocessors, develop effcent data transfer technques, etc).

5 ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn start prepare_angle() tmer_on reference cycles reference_cordc() golden_cordc() tmer_off tmer_on cordc cycles your_cordc() tmer_off Speedup = reference cycles cordc cycles check_result() golden_cordc() prnt cycles prnt errors You desgn wll be tested usng a test program (runnng on Mcroblaze) as descrbed above. Intally, the mcroblaze wll generate 64K random target angles. Next, t wll collect the executon tmng for 64K rotatons on two cordc functons. The frst s a reference mplementaton n software (reference_cordc). The rato of the two cycle counts determnes the relatve speedup obtaned by your mplementaton. Note that ths method of speedup measurement s relatvely ndependent of the compler optmzaton level, snce the -O2 flag wll beneft the reference mplementaton as well. Fnally, your desgn results are verfed aganst the golden reference. For a vald soluton, zero errors are requred (.e. f your soluton shows a sngle error, t s automatcally moved to lowest rank of all desgns returned by the class). The CORDIC reference algorthm s mplemented usng fxed-pont arthmetc and s expressed usng ntegers. A fxed-pont data type <32,28> s used. In ths data type, the value 1 s expressed as (1 << 28). The scalng factor allows expresson of fractonal values. For example, 0.75 s expressed as: 0.75 = = (1 << 27) <32,28> + (1 << 26) <32,28> = 671,088,640 <32,28> For the verfcaton process descrbed above to succeed, your accelerated CORDIC mplementaton must have the same bt-accuracy as the reference CORDIC mplementaton.

6 ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn How to start On Blackboard, download the baselne reference mplementaton. Ths desgn wll run drectly on your Spartan kt. Start by studyng the reference mplementaton software. Ths reference mplementaton uses calls to golden_cordc n order to mplement the your_cordc functon. Eventually, you need to accelerate your_cordc as fast as possble. It s hghly recommended to construct a cosmulaton model of your desgn usng GEZEL. Whle you can develop coprocessor hardware drectly n VHDL, t wll requre you to take care of many detals at once. Gong through cosmulaton frst enables you to test your dea before takng t to the board. Also, when developng hardware, ntally test your deas on small desgns, such as 100 rotatons (rather then 64K). When the low level components work fne, next verfy how well t scales up to 64K rotatons. Also, carefully consder tradeoffs. You can move part of the golden_cordc functon to hardware, or move the complete golden_cordc to hardware. You can use a memory-mapped nterface, or use an FSL nterface. You can wrte VHDL or GEZEL code (If HDL are unfamlar to you, please stck to GEZEL). You can mplement the golden_cordc n hardware as a completely unrolled functon, or desgn t n hardware as an FSMD, usng multple control steps. You can send arguments serally or n parallel. You can provde arguments wth a processor (Mcroblaze) or through DMA. There are obvously more mplementaton alternatves than the allocated desgn tme. Thus, you wll have to thnk before you mplement, and experment to fnd the largest acceleraton as quckly as possble. Always focus on the bottleneck n the overall system. Remember the earler examples we dscussed. Hardware parallelsm s useless unless the datappes nto that hardware has suffcent bandwdth. Also, make use of your homework assgnments/solutons to see examples how a memory-mapped nterface or an FSL nterface can be created.

7 ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn What to turn n By the deadlne, post the followng nformaton on Blackboard. A short report (no more than 4 pages) that summarzes the man characterstcs of your desgn. Your report must at least contan the followng table. Area of the baselne desgn (slces) Performance of the baselne desgn (cycles) Area of the optmzed desgn (slces) Performance of the optmzed desgn (cycles) In addton, you are encouraged to dscuss trade-offs you made, to provde a blockdagram of the resultng system, to descrbe the archtectural features of the hardware coprocessor you made, and so on. Also nclude a screenshot of the desgn as t executes, such as shown below. If you developed a cosmulaton model n GEZEL, also provde the cosmulaton model (C drver and FDL fle). The optmzed mplementaton n XPS. Before postng the desgn on Blackboard, make sure you run Project->Clean All Generated Fles. Then, zp the project drectory and post t on Blackboard.

8 ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn Gradng Your desgn wll be graded based on the numbers you report, n combnaton wth the cosmulaton model and the XPS project you wll turn n. The cosmulaton model, and the XPS project may be run to verfy the correctness of the statements you make n the report. The rankng crtera descrbed above wll be used. Havng a workng soluton s not suffcent to obtan a full grade. Havng a speed mprovement of, for example, 3 tmes, s not suffcent to obtan a full grade. The full grade wll go to the desgn wth the hghest performance. All other desgns wll be strctly ranked accordng n relaton to the best one. Ths strct rankng rule s ntroduced based on the observaton that, under free market condtons, better desgns have a better chance to make t nto a product. However, don t let ths rule spol the fun. Ths s your chance to explore new deas and to try out what you have learned n ths class! We wll dscuss the desgn n detal n the class of November 12, and partly n the class of November 14.

Programming in Fortran 90 : 2017/2018

Programming in Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Exercse 1 : Evaluaton of functon dependng on nput Wrte a program who evaluate the functon f (x,y) for any two user specfed values

More information

Complex Numbers. Now we also saw that if a and b were both positive then ab = a b. For a second let s forget that restriction and do the following.

Complex Numbers. Now we also saw that if a and b were both positive then ab = a b. For a second let s forget that restriction and do the following. Complex Numbers The last topc n ths secton s not really related to most of what we ve done n ths chapter, although t s somewhat related to the radcals secton as we wll see. We also won t need the materal

More information

Compiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz

Compiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz Compler Desgn Sprng 2014 Regster Allocaton Sample Exercses and Solutons Prof. Pedro C. Dnz USC / Informaton Scences Insttute 4676 Admralty Way, Sute 1001 Marna del Rey, Calforna 90292 pedro@s.edu Regster

More information

Some material adapted from Mohamed Younis, UMBC CMSC 611 Spr 2003 course slides Some material adapted from Hennessy & Patterson / 2003 Elsevier

Some material adapted from Mohamed Younis, UMBC CMSC 611 Spr 2003 course slides Some material adapted from Hennessy & Patterson / 2003 Elsevier Some materal adapted from Mohamed Youns, UMBC CMSC 611 Spr 2003 course sldes Some materal adapted from Hennessy & Patterson / 2003 Elsever Scence Performance = 1 Executon tme Speedup = Performance (B)

More information

6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour

6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour 6.854 Advanced Algorthms Petar Maymounkov Problem Set 11 (November 23, 2005) Wth: Benjamn Rossman, Oren Wemann, and Pouya Kheradpour Problem 1. We reduce vertex cover to MAX-SAT wth weghts, such that the

More information

Parallelism for Nested Loops with Non-uniform and Flow Dependences

Parallelism for Nested Loops with Non-uniform and Flow Dependences Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr

More information

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009.

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009. Farrukh Jabeen Algorthms 51 Assgnment #2 Due Date: June 15, 29. Assgnment # 2 Chapter 3 Dscrete Fourer Transforms Implement the FFT for the DFT. Descrbed n sectons 3.1 and 3.2. Delverables: 1. Concse descrpton

More information

R s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes

R s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes SPH3UW Unt 7.3 Sphercal Concave Mrrors Page 1 of 1 Notes Physcs Tool box Concave Mrror If the reflectng surface takes place on the nner surface of the sphercal shape so that the centre of the mrror bulges

More information

The Greedy Method. Outline and Reading. Change Money Problem. Greedy Algorithms. Applications of the Greedy Strategy. The Greedy Method Technique

The Greedy Method. Outline and Reading. Change Money Problem. Greedy Algorithms. Applications of the Greedy Strategy. The Greedy Method Technique //00 :0 AM Outlne and Readng The Greedy Method The Greedy Method Technque (secton.) Fractonal Knapsack Problem (secton..) Task Schedulng (secton..) Mnmum Spannng Trees (secton.) Change Money Problem Greedy

More information

Optimizing Document Scoring for Query Retrieval

Optimizing Document Scoring for Query Retrieval Optmzng Document Scorng for Query Retreval Brent Ellwen baellwe@cs.stanford.edu Abstract The goal of ths project was to automate the process of tunng a document query engne. Specfcally, I used machne learnng

More information

Mathematics 256 a course in differential equations for engineering students

Mathematics 256 a course in differential equations for engineering students Mathematcs 56 a course n dfferental equatons for engneerng students Chapter 5. More effcent methods of numercal soluton Euler s method s qute neffcent. Because the error s essentally proportonal to the

More information

Lecture 3: Computer Arithmetic: Multiplication and Division

Lecture 3: Computer Arithmetic: Multiplication and Division 8-447 Lecture 3: Computer Arthmetc: Multplcaton and Dvson James C. Hoe Dept of ECE, CMU January 26, 29 S 9 L3- Announcements: Handout survey due Lab partner?? Read P&H Ch 3 Read IEEE 754-985 Handouts:

More information

A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS

A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS Proceedngs of the Wnter Smulaton Conference M E Kuhl, N M Steger, F B Armstrong, and J A Jones, eds A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS Mark W Brantley Chun-Hung

More information

Support Vector Machines

Support Vector Machines /9/207 MIST.6060 Busness Intellgence and Data Mnng What are Support Vector Machnes? Support Vector Machnes Support Vector Machnes (SVMs) are supervsed learnng technques that analyze data and recognze patterns.

More information

A Binarization Algorithm specialized on Document Images and Photos

A Binarization Algorithm specialized on Document Images and Photos A Bnarzaton Algorthm specalzed on Document mages and Photos Ergna Kavalleratou Dept. of nformaton and Communcaton Systems Engneerng Unversty of the Aegean kavalleratou@aegean.gr Abstract n ths paper, a

More information

GSLM Operations Research II Fall 13/14

GSLM Operations Research II Fall 13/14 GSLM 58 Operatons Research II Fall /4 6. Separable Programmng Consder a general NLP mn f(x) s.t. g j (x) b j j =. m. Defnton 6.. The NLP s a separable program f ts objectve functon and all constrants are

More information

For instance, ; the five basic number-sets are increasingly more n A B & B A A = B (1)

For instance, ; the five basic number-sets are increasingly more n A B & B A A = B (1) Secton 1.2 Subsets and the Boolean operatons on sets If every element of the set A s an element of the set B, we say that A s a subset of B, or that A s contaned n B, or that B contans A, and we wrte A

More information

A mathematical programming approach to the analysis, design and scheduling of offshore oilfields

A mathematical programming approach to the analysis, design and scheduling of offshore oilfields 17 th European Symposum on Computer Aded Process Engneerng ESCAPE17 V. Plesu and P.S. Agach (Edtors) 2007 Elsever B.V. All rghts reserved. 1 A mathematcal programmng approach to the analyss, desgn and

More information

Brave New World Pseudocode Reference

Brave New World Pseudocode Reference Brave New World Pseudocode Reference Pseudocode s a way to descrbe how to accomplsh tasks usng basc steps lke those a computer mght perform. In ths week s lab, you'll see how a form of pseudocode can be

More information

Problem Definitions and Evaluation Criteria for Computational Expensive Optimization

Problem Definitions and Evaluation Criteria for Computational Expensive Optimization Problem efntons and Evaluaton Crtera for Computatonal Expensve Optmzaton B. Lu 1, Q. Chen and Q. Zhang 3, J. J. Lang 4, P. N. Suganthan, B. Y. Qu 6 1 epartment of Computng, Glyndwr Unversty, UK Faclty

More information

CS246: Mining Massive Datasets Jure Leskovec, Stanford University

CS246: Mining Massive Datasets Jure Leskovec, Stanford University CS46: Mnng Massve Datasets Jure Leskovec, Stanford Unversty http://cs46.stanford.edu /19/013 Jure Leskovec, Stanford CS46: Mnng Massve Datasets, http://cs46.stanford.edu Perceptron: y = sgn( x Ho to fnd

More information

Cluster Analysis of Electrical Behavior

Cluster Analysis of Electrical Behavior Journal of Computer and Communcatons, 205, 3, 88-93 Publshed Onlne May 205 n ScRes. http://www.scrp.org/ournal/cc http://dx.do.org/0.4236/cc.205.350 Cluster Analyss of Electrcal Behavor Ln Lu Ln Lu, School

More information

Problem Set 3 Solutions

Problem Set 3 Solutions Introducton to Algorthms October 4, 2002 Massachusetts Insttute of Technology 6046J/18410J Professors Erk Demane and Shaf Goldwasser Handout 14 Problem Set 3 Solutons (Exercses were not to be turned n,

More information

User Authentication Based On Behavioral Mouse Dynamics Biometrics

User Authentication Based On Behavioral Mouse Dynamics Biometrics User Authentcaton Based On Behavoral Mouse Dynamcs Bometrcs Chee-Hyung Yoon Danel Donghyun Km Department of Computer Scence Department of Computer Scence Stanford Unversty Stanford Unversty Stanford, CA

More information

Wishing you all a Total Quality New Year!

Wishing you all a Total Quality New Year! Total Qualty Management and Sx Sgma Post Graduate Program 214-15 Sesson 4 Vnay Kumar Kalakband Assstant Professor Operatons & Systems Area 1 Wshng you all a Total Qualty New Year! Hope you acheve Sx sgma

More information

Module Management Tool in Software Development Organizations

Module Management Tool in Software Development Organizations Journal of Computer Scence (5): 8-, 7 ISSN 59-66 7 Scence Publcatons Management Tool n Software Development Organzatons Ahmad A. Al-Rababah and Mohammad A. Al-Rababah Faculty of IT, Al-Ahlyyah Amman Unversty,

More information

S1 Note. Basis functions.

S1 Note. Basis functions. S1 Note. Bass functons. Contents Types of bass functons...1 The Fourer bass...2 B-splne bass...3 Power and type I error rates wth dfferent numbers of bass functons...4 Table S1. Smulaton results of type

More information

Subspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points;

Subspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points; Subspace clusterng Clusterng Fundamental to all clusterng technques s the choce of dstance measure between data ponts; D q ( ) ( ) 2 x x = x x, j k = 1 k jk Squared Eucldean dstance Assumpton: All features

More information

Parallel matrix-vector multiplication

Parallel matrix-vector multiplication Appendx A Parallel matrx-vector multplcaton The reduced transton matrx of the three-dmensonal cage model for gel electrophoress, descrbed n secton 3.2, becomes excessvely large for polymer lengths more

More information

Decision Strategies for Rating Objects in Knowledge-Shared Research Networks

Decision Strategies for Rating Objects in Knowledge-Shared Research Networks Decson Strateges for Ratng Objects n Knowledge-Shared Research etwors ALEXADRA GRACHAROVA *, HAS-JOACHM ER **, HASSA OUR ELD ** OM SUUROE ***, HARR ARAKSE *** * nsttute of Control and System Research,

More information

Exercises (Part 4) Introduction to R UCLA/CCPR. John Fox, February 2005

Exercises (Part 4) Introduction to R UCLA/CCPR. John Fox, February 2005 Exercses (Part 4) Introducton to R UCLA/CCPR John Fox, February 2005 1. A challengng problem: Iterated weghted least squares (IWLS) s a standard method of fttng generalzed lnear models to data. As descrbed

More information

Steps for Computing the Dissimilarity, Entropy, Herfindahl-Hirschman and. Accessibility (Gravity with Competition) Indices

Steps for Computing the Dissimilarity, Entropy, Herfindahl-Hirschman and. Accessibility (Gravity with Competition) Indices Steps for Computng the Dssmlarty, Entropy, Herfndahl-Hrschman and Accessblty (Gravty wth Competton) Indces I. Dssmlarty Index Measurement: The followng formula can be used to measure the evenness between

More information

Algorithm To Convert A Decimal To A Fraction

Algorithm To Convert A Decimal To A Fraction Algorthm To Convert A ecmal To A Fracton by John Kennedy Mathematcs epartment Santa Monca College 1900 Pco Blvd. Santa Monca, CA 90405 jrkennedy6@gmal.com Except for ths comment explanng that t s blank

More information

Array transposition in CUDA shared memory

Array transposition in CUDA shared memory Array transposton n CUDA shared memory Mke Gles February 19, 2014 Abstract Ths short note s nspred by some code wrtten by Jeremy Appleyard for the transposton of data through shared memory. I had some

More information

Range images. Range image registration. Examples of sampling patterns. Range images and range surfaces

Range images. Range image registration. Examples of sampling patterns. Range images and range surfaces Range mages For many structured lght scanners, the range data forms a hghly regular pattern known as a range mage. he samplng pattern s determned by the specfc scanner. Range mage regstraton 1 Examples

More information

Memory Modeling in ESL-RTL Equivalence Checking

Memory Modeling in ESL-RTL Equivalence Checking 11.4 Memory Modelng n ESL-RTL Equvalence Checkng Alfred Koelbl 2025 NW Cornelus Pass Rd. Hllsboro, OR 97124 koelbl@synopsys.com Jerry R. Burch 2025 NW Cornelus Pass Rd. Hllsboro, OR 97124 burch@synopsys.com

More information

Nachos Project 3. Speaker: Sheng-Wei Cheng 2010/12/16

Nachos Project 3. Speaker: Sheng-Wei Cheng 2010/12/16 Nachos Project Speaker: Sheng-We Cheng //6 Agenda Motvaton User Programs n Nachos Related Nachos Code for User Programs Project Assgnment Bonus Submsson Agenda Motvaton User Programs n Nachos Related Nachos

More information

THE low-density parity-check (LDPC) code is getting

THE low-density parity-check (LDPC) code is getting Implementng the NASA Deep Space LDPC Codes for Defense Applcatons Wley H. Zhao, Jeffrey P. Long 1 Abstract Selected codes from, and extended from, the NASA s deep space low-densty party-check (LDPC) codes

More information

3D vector computer graphics

3D vector computer graphics 3D vector computer graphcs Paolo Varagnolo: freelance engneer Padova Aprl 2016 Prvate Practce ----------------------------------- 1. Introducton Vector 3D model representaton n computer graphcs requres

More information

CMPS 10 Introduction to Computer Science Lecture Notes

CMPS 10 Introduction to Computer Science Lecture Notes CPS 0 Introducton to Computer Scence Lecture Notes Chapter : Algorthm Desgn How should we present algorthms? Natural languages lke Englsh, Spansh, or French whch are rch n nterpretaton and meanng are not

More information

LOOP ANALYSIS. The second systematic technique to determine all currents and voltages in a circuit

LOOP ANALYSIS. The second systematic technique to determine all currents and voltages in a circuit LOOP ANALYSS The second systematic technique to determine all currents and voltages in a circuit T S DUAL TO NODE ANALYSS - T FRST DETERMNES ALL CURRENTS N A CRCUT AND THEN T USES OHM S LAW TO COMPUTE

More information

An Optimal Algorithm for Prufer Codes *

An Optimal Algorithm for Prufer Codes * J. Software Engneerng & Applcatons, 2009, 2: 111-115 do:10.4236/jsea.2009.22016 Publshed Onlne July 2009 (www.scrp.org/journal/jsea) An Optmal Algorthm for Prufer Codes * Xaodong Wang 1, 2, Le Wang 3,

More information

Inverse Kinematics (part 2) CSE169: Computer Animation Instructor: Steve Rotenberg UCSD, Spring 2016

Inverse Kinematics (part 2) CSE169: Computer Animation Instructor: Steve Rotenberg UCSD, Spring 2016 Inverse Knematcs (part 2) CSE169: Computer Anmaton Instructor: Steve Rotenberg UCSD, Sprng 2016 Forward Knematcs We wll use the vector: Φ... 1 2 M to represent the array of M jont DOF values We wll also

More information

11. HARMS How To: CSV Import

11. HARMS How To: CSV Import and Rsk System 11. How To: CSV Import Preparng the spreadsheet for CSV Import Refer to the spreadsheet template to ad algnng spreadsheet columns wth Data Felds. The spreadsheet s shown n the Appendx, an

More information

Esc101 Lecture 1 st April, 2008 Generating Permutation

Esc101 Lecture 1 st April, 2008 Generating Permutation Esc101 Lecture 1 Aprl, 2008 Generatng Permutaton In ths class we wll look at a problem to wrte a program that takes as nput 1,2,...,N and prnts out all possble permutatons of the numbers 1,2,...,N. For

More information

Classifying Acoustic Transient Signals Using Artificial Intelligence

Classifying Acoustic Transient Signals Using Artificial Intelligence Classfyng Acoustc Transent Sgnals Usng Artfcal Intellgence Steve Sutton, Unversty of North Carolna At Wlmngton (suttons@charter.net) Greg Huff, Unversty of North Carolna At Wlmngton (jgh7476@uncwl.edu)

More information

Random Kernel Perceptron on ATTiny2313 Microcontroller

Random Kernel Perceptron on ATTiny2313 Microcontroller Random Kernel Perceptron on ATTny233 Mcrocontroller Nemanja Djurc Department of Computer and Informaton Scences, Temple Unversty Phladelpha, PA 922, USA nemanja.djurc@temple.edu Slobodan Vucetc Department

More information

SLAM Summer School 2006 Practical 2: SLAM using Monocular Vision

SLAM Summer School 2006 Practical 2: SLAM using Monocular Vision SLAM Summer School 2006 Practcal 2: SLAM usng Monocular Vson Javer Cvera, Unversty of Zaragoza Andrew J. Davson, Imperal College London J.M.M Montel, Unversty of Zaragoza. josemar@unzar.es, jcvera@unzar.es,

More information

Chapter 6 Programmng the fnte element method Inow turn to the man subject of ths book: The mplementaton of the fnte element algorthm n computer programs. In order to make my dscusson as straghtforward

More information

Accounting for the Use of Different Length Scale Factors in x, y and z Directions

Accounting for the Use of Different Length Scale Factors in x, y and z Directions 1 Accountng for the Use of Dfferent Length Scale Factors n x, y and z Drectons Taha Soch (taha.soch@kcl.ac.uk) Imagng Scences & Bomedcal Engneerng, Kng s College London, The Rayne Insttute, St Thomas Hosptal,

More information

Data Representation in Digital Design, a Single Conversion Equation and a Formal Languages Approach

Data Representation in Digital Design, a Single Conversion Equation and a Formal Languages Approach Data Representaton n Dgtal Desgn, a Sngle Converson Equaton and a Formal Languages Approach Hassan Farhat Unversty of Nebraska at Omaha Abstract- In the study of data representaton n dgtal desgn and computer

More information

Vectorization of Image Outlines Using Rational Spline and Genetic Algorithm

Vectorization of Image Outlines Using Rational Spline and Genetic Algorithm 01 Internatonal Conference on Image, Vson and Computng (ICIVC 01) IPCSIT vol. 50 (01) (01) IACSIT Press, Sngapore DOI: 10.776/IPCSIT.01.V50.4 Vectorzaton of Image Outlnes Usng Ratonal Splne and Genetc

More information

3. CR parameters and Multi-Objective Fitness Function

3. CR parameters and Multi-Objective Fitness Function 3 CR parameters and Mult-objectve Ftness Functon 41 3. CR parameters and Mult-Objectve Ftness Functon 3.1. Introducton Cogntve rados dynamcally confgure the wreless communcaton system, whch takes beneft

More information

Fast Computation of Shortest Path for Visiting Segments in the Plane

Fast Computation of Shortest Path for Visiting Segments in the Plane Send Orders for Reprnts to reprnts@benthamscence.ae 4 The Open Cybernetcs & Systemcs Journal, 04, 8, 4-9 Open Access Fast Computaton of Shortest Path for Vstng Segments n the Plane Ljuan Wang,, Bo Jang

More information

AADL : about scheduling analysis

AADL : about scheduling analysis AADL : about schedulng analyss Schedulng analyss, what s t? Embedded real-tme crtcal systems have temporal constrants to meet (e.g. deadlne). Many systems are bult wth operatng systems provdng multtaskng

More information

Optimization Methods: Integer Programming Integer Linear Programming 1. Module 7 Lecture Notes 1. Integer Linear Programming

Optimization Methods: Integer Programming Integer Linear Programming 1. Module 7 Lecture Notes 1. Integer Linear Programming Optzaton Methods: Integer Prograng Integer Lnear Prograng Module Lecture Notes Integer Lnear Prograng Introducton In all the prevous lectures n lnear prograng dscussed so far, the desgn varables consdered

More information

Communication-Minimal Partitioning and Data Alignment for Af"ne Nested Loops

Communication-Minimal Partitioning and Data Alignment for Afne Nested Loops Communcaton-Mnmal Parttonng and Data Algnment for Af"ne Nested Loops HYUK-JAE LEE 1 AND JOSÉ A. B. FORTES 2 1 Department of Computer Scence, Lousana Tech Unversty, Ruston, LA 71272, USA 2 School of Electrcal

More information

Conditional Speculative Decimal Addition*

Conditional Speculative Decimal Addition* Condtonal Speculatve Decmal Addton Alvaro Vazquez and Elsardo Antelo Dep. of Electronc and Computer Engneerng Unv. of Santago de Compostela, Span Ths work was supported n part by Xunta de Galca under grant

More information

Storage Binding in RTL synthesis

Storage Binding in RTL synthesis Storage Bndng n RTL synthess Pe Zhang Danel D. Gajsk Techncal Report ICS-0-37 August 0th, 200 Center for Embedded Computer Systems Department of Informaton and Computer Scence Unersty of Calforna, Irne

More information

Simulation Based Analysis of FAST TCP using OMNET++

Simulation Based Analysis of FAST TCP using OMNET++ Smulaton Based Analyss of FAST TCP usng OMNET++ Umar ul Hassan 04030038@lums.edu.pk Md Term Report CS678 Topcs n Internet Research Sprng, 2006 Introducton Internet traffc s doublng roughly every 3 months

More information

An Iterative Solution Approach to Process Plant Layout using Mixed Integer Optimisation

An Iterative Solution Approach to Process Plant Layout using Mixed Integer Optimisation 17 th European Symposum on Computer Aded Process Engneerng ESCAPE17 V. Plesu and P.S. Agach (Edtors) 2007 Elsever B.V. All rghts reserved. 1 An Iteratve Soluton Approach to Process Plant Layout usng Mxed

More information

Machine Learning 9. week

Machine Learning 9. week Machne Learnng 9. week Mappng Concept Radal Bass Functons (RBF) RBF Networks 1 Mappng It s probably the best scenaro for the classfcaton of two dataset s to separate them lnearly. As you see n the below

More information

RADIX-10 PARALLEL DECIMAL MULTIPLIER

RADIX-10 PARALLEL DECIMAL MULTIPLIER RADIX-10 PARALLEL DECIMAL MULTIPLIER 1 MRUNALINI E. INGLE & 2 TEJASWINI PANSE 1&2 Electroncs Engneerng, Yeshwantrao Chavan College of Engneerng, Nagpur, Inda E-mal : mrunalngle@gmal.com, tejaswn.deshmukh@gmal.com

More information

Smoothing Spline ANOVA for variable screening

Smoothing Spline ANOVA for variable screening Smoothng Splne ANOVA for varable screenng a useful tool for metamodels tranng and mult-objectve optmzaton L. Rcco, E. Rgon, A. Turco Outlne RSM Introducton Possble couplng Test case MOO MOO wth Game Theory

More information

BIN XIA et al: AN IMPROVED K-MEANS ALGORITHM BASED ON CLOUD PLATFORM FOR DATA MINING

BIN XIA et al: AN IMPROVED K-MEANS ALGORITHM BASED ON CLOUD PLATFORM FOR DATA MINING An Improved K-means Algorthm based on Cloud Platform for Data Mnng Bn Xa *, Yan Lu 2. School of nformaton and management scence, Henan Agrcultural Unversty, Zhengzhou, Henan 450002, P.R. Chna 2. College

More information

NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS

NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS ARPN Journal of Engneerng and Appled Scences 006-017 Asan Research Publshng Network (ARPN). All rghts reserved. NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS Igor Grgoryev, Svetlana

More information

Structure from Motion

Structure from Motion Structure from Moton Structure from Moton For now, statc scene and movng camera Equvalentl, rgdl movng scene and statc camera Lmtng case of stereo wth man cameras Lmtng case of multvew camera calbraton

More information

Motivation. EE 457 Unit 4. Throughput vs. Latency. Performance Depends on View Point?! Computer System Performance. An individual user wants to:

Motivation. EE 457 Unit 4. Throughput vs. Latency. Performance Depends on View Point?! Computer System Performance. An individual user wants to: 4.1 4.2 Motvaton EE 457 Unt 4 Computer System Performance An ndvdual user wants to: Mnmze sngle program executon tme A datacenter owner wants to: Maxmze number of Mnmze ( ) http://e-tellgentnternetmarketng.com/webste/frustrated-computer-user-2/

More information

Assembler. Building a Modern Computer From First Principles.

Assembler. Building a Modern Computer From First Principles. Assembler Buldng a Modern Computer From Frst Prncples www.nand2tetrs.org Elements of Computng Systems, Nsan & Schocken, MIT Press, www.nand2tetrs.org, Chapter 6: Assembler slde Where we are at: Human Thought

More information

Sum of Linear and Fractional Multiobjective Programming Problem under Fuzzy Rules Constraints

Sum of Linear and Fractional Multiobjective Programming Problem under Fuzzy Rules Constraints Australan Journal of Basc and Appled Scences, 2(4): 1204-1208, 2008 ISSN 1991-8178 Sum of Lnear and Fractonal Multobjectve Programmng Problem under Fuzzy Rules Constrants 1 2 Sanjay Jan and Kalash Lachhwan

More information

Related-Mode Attacks on CTR Encryption Mode

Related-Mode Attacks on CTR Encryption Mode Internatonal Journal of Network Securty, Vol.4, No.3, PP.282 287, May 2007 282 Related-Mode Attacks on CTR Encrypton Mode Dayn Wang, Dongda Ln, and Wenlng Wu (Correspondng author: Dayn Wang) Key Laboratory

More information

Outline. Digital Systems. C.2: Gates, Truth Tables and Logic Equations. Truth Tables. Logic Gates 9/8/2011

Outline. Digital Systems. C.2: Gates, Truth Tables and Logic Equations. Truth Tables. Logic Gates 9/8/2011 9/8/2 2 Outlne Appendx C: The Bascs of Logc Desgn TDT4255 Computer Desgn Case Study: TDT4255 Communcaton Module Lecture 2 Magnus Jahre 3 4 Dgtal Systems C.2: Gates, Truth Tables and Logc Equatons All sgnals

More information

Virtual Memory. Background. No. 10. Virtual Memory: concept. Logical Memory Space (review) Demand Paging(1) Virtual Memory

Virtual Memory. Background. No. 10. Virtual Memory: concept. Logical Memory Space (review) Demand Paging(1) Virtual Memory Background EECS. Operatng System Fundamentals No. Vrtual Memory Prof. Hu Jang Department of Electrcal Engneerng and Computer Scence, York Unversty Memory-management methods normally requres the entre process

More information

Parallel Inverse Halftoning by Look-Up Table (LUT) Partitioning

Parallel Inverse Halftoning by Look-Up Table (LUT) Partitioning Parallel Inverse Halftonng by Look-Up Table (LUT) Parttonng Umar F. Sddq and Sadq M. Sat umar@ccse.kfupm.edu.sa, sadq@kfupm.edu.sa KFUPM Box: Department of Computer Engneerng, Kng Fahd Unversty of Petroleum

More information

A Fast Content-Based Multimedia Retrieval Technique Using Compressed Data

A Fast Content-Based Multimedia Retrieval Technique Using Compressed Data A Fast Content-Based Multmeda Retreval Technque Usng Compressed Data Borko Furht and Pornvt Saksobhavvat NSF Multmeda Laboratory Florda Atlantc Unversty, Boca Raton, Florda 3343 ABSTRACT In ths paper,

More information

A Unified Framework for Semantics and Feature Based Relevance Feedback in Image Retrieval Systems

A Unified Framework for Semantics and Feature Based Relevance Feedback in Image Retrieval Systems A Unfed Framework for Semantcs and Feature Based Relevance Feedback n Image Retreval Systems Ye Lu *, Chunhu Hu 2, Xngquan Zhu 3*, HongJang Zhang 2, Qang Yang * School of Computng Scence Smon Fraser Unversty

More information

Computer Animation and Visualisation. Lecture 4. Rigging / Skinning

Computer Animation and Visualisation. Lecture 4. Rigging / Skinning Computer Anmaton and Vsualsaton Lecture 4. Rggng / Sknnng Taku Komura Overvew Sknnng / Rggng Background knowledge Lnear Blendng How to decde weghts? Example-based Method Anatomcal models Sknnng Assume

More information

Quality Improvement Algorithm for Tetrahedral Mesh Based on Optimal Delaunay Triangulation

Quality Improvement Algorithm for Tetrahedral Mesh Based on Optimal Delaunay Triangulation Intellgent Informaton Management, 013, 5, 191-195 Publshed Onlne November 013 (http://www.scrp.org/journal/m) http://dx.do.org/10.36/m.013.5601 Qualty Improvement Algorthm for Tetrahedral Mesh Based on

More information

Meta-heuristics for Multidimensional Knapsack Problems

Meta-heuristics for Multidimensional Knapsack Problems 2012 4th Internatonal Conference on Computer Research and Development IPCSIT vol.39 (2012) (2012) IACSIT Press, Sngapore Meta-heurstcs for Multdmensonal Knapsack Problems Zhbao Man + Computer Scence Department,

More information

Some Advanced SPC Tools 1. Cumulative Sum Control (Cusum) Chart For the data shown in Table 9-1, the x chart can be generated.

Some Advanced SPC Tools 1. Cumulative Sum Control (Cusum) Chart For the data shown in Table 9-1, the x chart can be generated. Some Advanced SP Tools 1. umulatve Sum ontrol (usum) hart For the data shown n Table 9-1, the x chart can be generated. However, the shft taken place at sample #21 s not apparent. 92 For ths set samples,

More information

Biostatistics 615/815

Biostatistics 615/815 The E-M Algorthm Bostatstcs 615/815 Lecture 17 Last Lecture: The Smplex Method General method for optmzaton Makes few assumptons about functon Crawls towards mnmum Some recommendatons Multple startng ponts

More information

Improving High Level Synthesis Optimization Opportunity Through Polyhedral Transformations

Improving High Level Synthesis Optimization Opportunity Through Polyhedral Transformations Improvng Hgh Level Synthess Optmzaton Opportunty Through Polyhedral Transformatons We Zuo 2,5, Yun Lang 1, Peng L 1, Kyle Rupnow 3, Demng Chen 2,3 and Jason Cong 1,4 1 Center for Energy-Effcent Computng

More information

FPGA Implementation of CORDIC Algorithms for Sine and Cosine Generator

FPGA Implementation of CORDIC Algorithms for Sine and Cosine Generator The 5th Internatonal Conference on Electrcal Engneerng and Informatcs 25 August -, 25, Bal, Indonesa FPGA Implementaton of CORDIC Algorthms for Sne and Cosne Generator Antonus P. Renardy, Nur Ahmad, Ashbr

More information

An Entropy-Based Approach to Integrated Information Needs Assessment

An Entropy-Based Approach to Integrated Information Needs Assessment Dstrbuton Statement A: Approved for publc release; dstrbuton s unlmted. An Entropy-Based Approach to ntegrated nformaton Needs Assessment June 8, 2004 Wllam J. Farrell Lockheed Martn Advanced Technology

More information

Improving Low Density Parity Check Codes Over the Erasure Channel. The Nelder Mead Downhill Simplex Method. Scott Stransky

Improving Low Density Parity Check Codes Over the Erasure Channel. The Nelder Mead Downhill Simplex Method. Scott Stransky Improvng Low Densty Party Check Codes Over the Erasure Channel The Nelder Mead Downhll Smplex Method Scott Stransky Programmng n conjuncton wth: Bors Cukalovc 18.413 Fnal Project Sprng 2004 Page 1 Abstract

More information

Fast Color Space Transformation for Embedded Controller by SA-C Recofigurable Computing

Fast Color Space Transformation for Embedded Controller by SA-C Recofigurable Computing Internatonal Journal of Informaton and Electroncs Engneerng, Vol., No., July Fast Color Space Transformaton for Embedded Controller by SA-C Recofgurable Computng Jan-Long Kuo Abstract Ths paper proposes

More information

Hermite Splines in Lie Groups as Products of Geodesics

Hermite Splines in Lie Groups as Products of Geodesics Hermte Splnes n Le Groups as Products of Geodescs Ethan Eade Updated May 28, 2017 1 Introducton 1.1 Goal Ths document defnes a curve n the Le group G parametrzed by tme and by structural parameters n the

More information

A Facet Generation Procedure. for solving 0/1 integer programs

A Facet Generation Procedure. for solving 0/1 integer programs A Facet Generaton Procedure for solvng 0/ nteger programs by Gyana R. Parja IBM Corporaton, Poughkeepse, NY 260 Radu Gaddov Emery Worldwde Arlnes, Vandala, Oho 45377 and Wlbert E. Wlhelm Teas A&M Unversty,

More information

X- Chart Using ANOM Approach

X- Chart Using ANOM Approach ISSN 1684-8403 Journal of Statstcs Volume 17, 010, pp. 3-3 Abstract X- Chart Usng ANOM Approach Gullapall Chakravarth 1 and Chaluvad Venkateswara Rao Control lmts for ndvdual measurements (X) chart are

More information

Analysis of Continuous Beams in General

Analysis of Continuous Beams in General Analyss of Contnuous Beams n General Contnuous beams consdered here are prsmatc, rgdly connected to each beam segment and supported at varous ponts along the beam. onts are selected at ponts of support,

More information

Circuit Analysis I (ENGR 2405) Chapter 3 Method of Analysis Nodal(KCL) and Mesh(KVL)

Circuit Analysis I (ENGR 2405) Chapter 3 Method of Analysis Nodal(KCL) and Mesh(KVL) Crcut Analyss I (ENG 405) Chapter Method of Analyss Nodal(KCL) and Mesh(KVL) Nodal Analyss If nstead of focusng on the oltages of the crcut elements, one looks at the oltages at the nodes of the crcut,

More information

Loop Transformations, Dependences, and Parallelization

Loop Transformations, Dependences, and Parallelization Loop Transformatons, Dependences, and Parallelzaton Announcements Mdterm s Frday from 3-4:15 n ths room Today Semester long project Data dependence recap Parallelsm and storage tradeoff Scalar expanson

More information

Learning to Project in Multi-Objective Binary Linear Programming

Learning to Project in Multi-Objective Binary Linear Programming Learnng to Project n Mult-Objectve Bnary Lnear Programmng Alvaro Serra-Altamranda Department of Industral and Management System Engneerng, Unversty of South Florda, Tampa, FL, 33620 USA, amserra@mal.usf.edu,

More information

High-Boost Mesh Filtering for 3-D Shape Enhancement

High-Boost Mesh Filtering for 3-D Shape Enhancement Hgh-Boost Mesh Flterng for 3-D Shape Enhancement Hrokazu Yagou Λ Alexander Belyaev y Damng We z Λ y z ; ; Shape Modelng Laboratory, Unversty of Azu, Azu-Wakamatsu 965-8580 Japan y Computer Graphcs Group,

More information

y and the total sum of

y and the total sum of Lnear regresson Testng for non-lnearty In analytcal chemstry, lnear regresson s commonly used n the constructon of calbraton functons requred for analytcal technques such as gas chromatography, atomc absorpton

More information

Life Tables (Times) Summary. Sample StatFolio: lifetable times.sgp

Life Tables (Times) Summary. Sample StatFolio: lifetable times.sgp Lfe Tables (Tmes) Summary... 1 Data Input... 2 Analyss Summary... 3 Survval Functon... 5 Log Survval Functon... 6 Cumulatve Hazard Functon... 7 Percentles... 7 Group Comparsons... 8 Summary The Lfe Tables

More information

Curve Representation for Outlines of Planar Images using Multilevel Coordinate Search

Curve Representation for Outlines of Planar Images using Multilevel Coordinate Search Curve Representaton for Outlnes of Planar Images usng Multlevel Coordnate Search MHAMMAD SARFRAZ and NAELAH AL-DABBOUS Department of Informaton Scence Kuwat Unversty Adalya Campus, P.O. Box 5969, Safat

More information

New Extensions of the 3-Simplex for Exterior Orientation

New Extensions of the 3-Simplex for Exterior Orientation New Extensons of the 3-Smplex for Exteror Orentaton John M. Stenbs Tyrone L. Vncent Wllam A. Hoff Colorado School of Mnes jstenbs@gmal.com tvncent@mnes.edu whoff@mnes.edu Abstract Object pose may be determned

More information

Classifier Selection Based on Data Complexity Measures *

Classifier Selection Based on Data Complexity Measures * Classfer Selecton Based on Data Complexty Measures * Edth Hernández-Reyes, J.A. Carrasco-Ochoa, and J.Fco. Martínez-Trndad Natonal Insttute for Astrophyscs, Optcs and Electroncs, Lus Enrque Erro No.1 Sta.

More information

Outline. Type of Machine Learning. Examples of Application. Unsupervised Learning

Outline. Type of Machine Learning. Examples of Application. Unsupervised Learning Outlne Artfcal Intellgence and ts applcatons Lecture 8 Unsupervsed Learnng Professor Danel Yeung danyeung@eee.org Dr. Patrck Chan patrckchan@eee.org South Chna Unversty of Technology, Chna Introducton

More information