CS229 Class Project: Fusion arc treatment planning strategy by adaptive learning cost function based beam selection Ho Jin Kim

Size: px
Start display at page:

Download "CS229 Class Project: Fusion arc treatment planning strategy by adaptive learning cost function based beam selection Ho Jin Kim"

Transcription

1 CS229 Class Project: Fuson arc treatment plannng strategy by adaptve learnng cost functon based beam selecton Ho Jn Km Abstract Current therapeutc modaltes n radaton therapy such as statc feld IMRT and rotatonal sngle-arc VMAT are more orented to ether the plan qualty or the delvery effcency. Recently, fuson arc treatment scheme has been proposed by combnng the advantages of two respectve modaltes. The basc structure s to delver the dose wth rotatonal arc to mantan the delvery effcency, whle the addtonal ntensty modulatons based on statc feld treatment are nserted to chosen angles to enhance the plan qualty. Ths work presents how to select the regons that need addtonal ntensty modulatons, based on adaptve learnng cost functons gven the beamlet ntensty map. 1. Introducton External beam radaton therapy s the most wdely used for treatng the tumor patents n these days. Before the actual treatment, how to delver the dose s planned and optmzed, whch s called 'treatment plannng'. The ultmate objectve of the treatment plannng s to optmze the beam shapes (fluence-map structures), such that t maxmzes the dose to the target, whle sparng the dose of radaton to the crtcal organs. Fgure 1 Types of dose delvery n external beam radaton therapy (a) step-and-shoot (statc) delvery (b) rotatonal arc (1-arc) contnuous dose delvery The dose can be delvered to the target volume n two dfferent ways as llustrated n Fgure 1, stepand-shoot and rotatonal arc delvery. Step-and-shoot (statc feld) treatment 1 delvers the dose n dscrete manner at the specfc gantry angles, whch s used for the ntensty modulated radaton therapy (IMRT). On the other hand, rotatonal arc treatment contnuously delvers the dose to the target, whch s appled to the volumetrc modulated arc therapy (VMAT) 2 wth a sngle arc n most cases. Statc feld treatment can provde suffcent ntensty modulatons at approprate drectons, so that t can effectvely preserve the crtcal organs. It, however, sacrfces the delvery effcency due to the feature of statc feld treatment, and 5-10 statc beams do not own large beam angular frequency to cover the entre angles. VMAT plannng guarantees a great deal of delvery effcency wth smple fluence-map structures and ther transtons for contnuous dose delvery wth a sngle arc. The strct constrant of havng a sngle aperture at each control pont, however, does not possess suffcent ntensty modulatons for some gantry angle drectons. Therefore, to overcome the ptfalls of two therapeutc modaltes, we can come up wth combnng IMRT wth VMAT plannng, whch was newly defned as fuson arc dose delvery 3. The new treatment scheme bascally delvers the dose n contnuous fashon, whle t can stop and nsert addtonal ntensty modulatons at selected gantry angles to mprove the plan qualty at small costs of delvery effcency. Ths work presents how to adaptvely choose the gantry angles based on the cost functon wth gven nformaton of the resultant fluence-map.

2 2. Methods 2.1 Fluence-map optmzaton for sngle-aperture rotatonal arc treatment The basc structure for treatment n ths work s the rotatonal arc treatment wth a sngle aperture. To acheve sngle-arc treatment, two factors should be consdered. Frst, the fluence-map should be smplfed to select one aperture at each control pont. If the resultant fluence-map s complcated, t s dffcult to take one aperture and mantan the optmzed plan qualty. Second, the fluence-map transton between two adjacent control ponts should be suffcently small such that the dose s contnuously delvered wth arc treatment. In fact, there s a specfc constrant to be met n the transton of the fluence-map structures for contnuous arc delvery. Eq.(1) shows the basc model for the fluence-map optmzaton to reflect two factors, N f 1 mnmze Dx + { c x x } 1 f u, v, f u, v,( f + 1) f = 1 u, v subject to λ ( A x d ) ε, x 0 2 n where D s 2D-dfference matrx, x R s the fluence-map to be optmzed (the sub-ndces denoted by u,v correspond to the beamlet components of x, whle the sub-ndex f represents m n the feld order ( n = u v f )), R ( m represents the number of voxels) s the dose matrx, A d s the dose dstrbuton, λ s the mportance factor of structure, and the resdue mposed on each structure s denoted by ε. The total-varaton (TV) mnmzaton n the frst term of the objectve s to smplfy the fluence-map varatons for takng a sngle segment, whle the second term n Eq.(1) ncreases the fluence-map smlarty between two neghborng nodes. The coeffcent c controls the fluence-map smlarty to the total-varaton of the fluence-map. In ths work, t s set to be 0.1 to both preserve the plan qualty and mantan the delvery effcency. We bascally used equ-spaced 60 control ponts for the plan optmzaton wth 6 degree angular dstant. The resultant fluence-map acqured by Eq.(1) can stll have a couple of dfferent ntenstes. In most control ponts, only one aperture s taken, whle t s assumed that two apertures are taken n certan drectons that need addtonal ntensty modulatons. The next subsequent secton wll specfy how to choose the drectons, where addtonal statc feld treatment s benefcal for mprovng the plan qualty. 2.2 Fluence-map optmzaton for sngle-aperture rotatonal arc treatment In order to acqure the actual fluence-map, the soluton acqured by Eq.(1) should pass through addtonal process, called 'leaf-sequencng'. The resultant fluence-map s classfed nto dfferent ntensty levels, fnally beng splt nto dfferent apertures. In most gantry angles, t takes only one aperture for sngle-arc dose delvery, whle ths work suggests havng addtonal segment n certan drectons. The drectons that need addtonal segment can be obtaned from the adaptve learnng approach wth the soluton acqured by Eq.(1). Wth that approach, t s mportant to see whch drectons are contrbutng to mprovng the plan qualty. Ths can be measured by summaton of the data fdelty N terms n Eq.(1), λ ( ) 1 A x d = 2. If the resdue decreases, the plan qualty s consdered to be enhanced. More specfcally, we measure the cost functon defned n Eq.(2) mnmze c ( x, ) N new k = λ ( A x new, k d ) 2 (2) where xnew, k s the fluence-map havng an extra segment at k-th statc feld, and c( x new, k ) s a cost functon wth the fluence-map. For nstance, xnew,1 represents that the extra segment s only added to the frst statc feld, whle the remanng statc felds have a sngle aperture. Ths s well descrbed n Fgure 2, where t has two cost functon values when addng an extra segment to the frst and the second statc feld. If the certan drectons have lower costs, then t would be nterpreted that the drectons can get profts by addng the addtonal segment. It selects the 6 statc felds that correspond to the 6 lowest cost functon values. The reason that t chooses the 6 locatons s to balance between = 1 (1) f

3 the plan qualty and the delvery effcency. For those 6 locatons, the extra segment s nserted, whereas the remanng felds contnues to have a sngle segment. Fgure 2 Cost functon values when the extra segment s only added to (a) the frst statc feld, and (b) the second statc feld 2.3 Evaluatons To valdate the proposed algorthm, the prostate data was appled wth the beamlet sze 18x16 (5mm beamlet resoluton). The CT mages obtaned for the treatment s down-sampled twce for plan optmzaton. The plannng target volume (PTV) s located at the center of body, correspondng to the locaton of prostate, whle the crtcal organs to be preserved are set to be bladder, rectum, and femoral heads close to the target volume. At the ntal step, the fluence-map s optmzed wth 60 statc felds by Eq.(1). The sngle segment s taken n most statc feld locatons, whle the 6 locatons chosen from the adaptve beam angle selecton method have two segments assgned. The remanng control ponts are flled wth lnear nterpolatons on two adjacent fluence-map structures, such that the entre control ponts are deployed wth 2 degree angular dstant. The fluence-map optmzaton based on the TV mnmzaton s performed by a large-scale L1-solver, called TFOCS 4. Ths study compares the two separate plans: proposed fuson arc treatment plan and conventonal VMAT plan. The plan qualty of two plans s assessed by varous crtera. The dose volume hstogram (DVH) curves, whch accumulate the dose volume matchng the amount of dose of radaton, and dose dstrbutons are used to see the dose sparng to the crtcal organs. To evaluate the dose conformty to the target, the conformaton number (CN) 5 s used as defned n Eq.(3). Vτ, ref Vτ, ref Conformaton Number( CN ) = (3) V V where V τ s the volume of PTV, V τ,ref represents the target volume recevng the dose greater than or equal to the reference dose, and V ref s the total volume recevng the dose greater than or equal to the reference dose. The frst term s requred to be equal to or greater than 95% (0.95), whle the second term s recommended to be large enough to assure the secure and safe dose delvery to the target. The delvery effcency s quantfed by the estmated dose delvery tme wth reference to two pulcatons 2,6. 3. Results and Dscussons τ ref Fgure 3 Cost functon values at whch extra segment s sequentally added to 60 statc felds

4 Fgure 3 shows the cost functon values of 60 statc felds when the extra segment s sequentally added to a statc feld. As stated, 6 statc feld locatons correspondng to the 6 lowest cost functon values have two segments, whch are 13, 41, 48, 53, 54, and 55th statc felds. The remanng statc felds have a sngle segment assgned, and sngle-arc based plannng s executed by lnear nterpolaton. Fgure 4 (a) and (b) reveals the dose volume hstogram and dose dstrbutons, whch mples the dose sparng to the crtcal structures n two plans acqured by our proposed fuson arc and conventonal VMAT schemes. The proposed treatment scheme has slghtly better dose sparng n femoral heads and rectum structures than that of conventonal VMAT plan. The mprovement s explctly llustrated n the dose dstrbutons, where the dose sparng to the femoral heads enhances through the addton of apertures to approprate drectons accordng to the adaptve learnng cost functon values. Fgure 4 Comparng fuson arc and VMAT (1-arc) plans n (a) DVHs and (b) dose dstrbutons of the proposed plan (b-1), and conventonal VMAT (b-2) (so-dose lnes are correspondng to 30,65, and 100% of the prescrbed dose The addtonal segments newly assgned to 6 statc felds also affect the dose conformty to the target. The proposed plan yelds greater conformaton number (CN) than the conventonal VMAT plan, as specfed n Table 1. CN acqured by sngle-arc VMAT plannng s about 0.82, whle our proposed plan produces about It demonstrates that our proposed fuson arc plannng strategy can contrbute to mprovng the plan qualty n terms of the dose sparng to the crtcal organs and dose conformty to the target. The enhancement n dose the plan qualty, however, should accompany the sacrfce n the delvery effcency, relatve to sngle-arc VMAT plan, whch s lsted n Table 1. The expense n the delvery effcency s not sgnfcant compared wth the plan done by the step-andshoot dose delvery only, whch s about 3-5 mn. Fuson arc VMAT (1 arc) CN T (s) s s Table 1 Dose conformty to the target by CN and estmated dose delvery tme of two dfferent plans 4. Concluson Ths work presents fuson arc treatment plannng strategy. It nserts addtonal segments to a certan statc felds, where sngle-arc based treatment plannng s bascally appled. The angles that need the addtonal segments assgned are determned by the adaptve learnng cost functon values, based on the resultant fluence-map. The proposed plannng scheme overcomes the drawbacks of the conventonal sngle-arc VMAT plannng, and mproves the plan qualty n terms of the dose sparng to the crtcal organs and dose conformty to the target.

5 References [1] G. A. Ezzel, J. M. Galvn, D. Low, J. R. Palta, I. Rosen, M. B. Sharpe, P. Xa, Y. Xao, L. Xng, and C. X. Yu (subcommttee IMRT and commttee AAPMRT) Gudance document on delvery, treatment plannng, and clncal mplementaton of IMRT: report of the IMRT Subcommttee," Med. Phys. 30, (2003). [2] K. Otto, Volumetrc modulated arc therapy: IMRT n a sngle gantry arc, Med. Phys. 35, (2008) [3] M. M. Matuszak, J. M. Steers, T. Long, D. L. McShan, B. A. Fraass, H. E. Romejn, and R. K. T. Haken, "FusonArc optmzaton: A hybrd volumetrc modulated arc therapy (VMAT) and ntensty modulated radaton therapy (IMRT) plannng strategy,"med. Phys. 40, [4] S. Becker, E. J. Candès, and M. Grant, Templates for Convex Cone Problems wth Applcatons to Sparse Sgnal Recovery, Math. Prog. Comp. 3(3): (2011) [5] A. van t Ret, A. C. Mak, M. A. Moerland, L. H. Elders, and W. van der Zee, A conformaton number to quantfy the degree of conformalty n brachytherapy and external beam rradaton: applcaton to the prostate, Int. J. Radaton Oncology Bo. Phys. 37(3), (1997) [6] R. L, and L. Xng, Brdgng the gap between IMRT and VMAT: dense angularly sampled and sparse ntensty modulated radaton therapy (DASSIM-RT), Med. Phys. 38: (2011)

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

Improvement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration

Improvement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration Improvement of Spatal Resoluton Usng BlockMatchng Based Moton Estmaton and Frame Integraton Danya Suga and Takayuk Hamamoto Graduate School of Engneerng, Tokyo Unversty of Scence, 6-3-1, Nuku, Katsuska-ku,

More information

IMRT workflow. Optimization and Inverse planning. Intensity distribution IMRT IMRT. Dose optimization for IMRT. Bram van Asselen

IMRT workflow. Optimization and Inverse planning. Intensity distribution IMRT IMRT. Dose optimization for IMRT. Bram van Asselen IMRT workflow Otmzaton and Inverse lannng 69 Gy Bram van Asselen IMRT Intensty dstrbuton Webb 003: IMRT s the delvery of radaton to the atent va felds that have non-unform radaton fluence Purose: Fnd a

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

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

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

High resolution 3D Tau-p transform by matching pursuit Weiping Cao* and Warren S. Ross, Shearwater GeoServices

High resolution 3D Tau-p transform by matching pursuit Weiping Cao* and Warren S. Ross, Shearwater GeoServices Hgh resoluton 3D Tau-p transform by matchng pursut Wepng Cao* and Warren S. Ross, Shearwater GeoServces Summary The 3D Tau-p transform s of vtal sgnfcance for processng sesmc data acqured wth modern wde

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

Content Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers

Content Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers IOSR Journal of Electroncs and Communcaton Engneerng (IOSR-JECE) e-issn: 78-834,p- ISSN: 78-8735.Volume 9, Issue, Ver. IV (Mar - Apr. 04), PP 0-07 Content Based Image Retreval Usng -D Dscrete Wavelet wth

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

Reducing Frame Rate for Object Tracking

Reducing Frame Rate for Object Tracking Reducng Frame Rate for Object Trackng Pavel Korshunov 1 and We Tsang Oo 2 1 Natonal Unversty of Sngapore, Sngapore 11977, pavelkor@comp.nus.edu.sg 2 Natonal Unversty of Sngapore, Sngapore 11977, oowt@comp.nus.edu.sg

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

Learning the Kernel Parameters in Kernel Minimum Distance Classifier

Learning the Kernel Parameters in Kernel Minimum Distance Classifier Learnng the Kernel Parameters n Kernel Mnmum Dstance Classfer Daoqang Zhang 1,, Songcan Chen and Zh-Hua Zhou 1* 1 Natonal Laboratory for Novel Software Technology Nanjng Unversty, Nanjng 193, Chna Department

More information

FEATURE EXTRACTION. Dr. K.Vijayarekha. Associate Dean School of Electrical and Electronics Engineering SASTRA University, Thanjavur

FEATURE EXTRACTION. Dr. K.Vijayarekha. Associate Dean School of Electrical and Electronics Engineering SASTRA University, Thanjavur FEATURE EXTRACTION Dr. K.Vjayarekha Assocate Dean School of Electrcal and Electroncs Engneerng SASTRA Unversty, Thanjavur613 41 Jont Intatve of IITs and IISc Funded by MHRD Page 1 of 8 Table of Contents

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

Load-Balanced Anycast Routing

Load-Balanced Anycast Routing Load-Balanced Anycast Routng Chng-Yu Ln, Jung-Hua Lo, and Sy-Yen Kuo Department of Electrcal Engneerng atonal Tawan Unversty, Tape, Tawan sykuo@cc.ee.ntu.edu.tw Abstract For fault-tolerance and load-balance

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

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

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

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

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

TN348: Openlab Module - Colocalization

TN348: Openlab Module - Colocalization TN348: Openlab Module - Colocalzaton Topc The Colocalzaton module provdes the faclty to vsualze and quantfy colocalzaton between pars of mages. The Colocalzaton wndow contans a prevew of the two mages

More information

Discriminative Dictionary Learning with Pairwise Constraints

Discriminative Dictionary Learning with Pairwise Constraints Dscrmnatve Dctonary Learnng wth Parwse Constrants Humn Guo Zhuoln Jang LARRY S. DAVIS UNIVERSITY OF MARYLAND Nov. 6 th, Outlne Introducton/motvaton Dctonary Learnng Dscrmnatve Dctonary Learnng wth Parwse

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

CS 534: Computer Vision Model Fitting

CS 534: Computer Vision Model Fitting CS 534: Computer Vson Model Fttng Sprng 004 Ahmed Elgammal Dept of Computer Scence CS 534 Model Fttng - 1 Outlnes Model fttng s mportant Least-squares fttng Maxmum lkelhood estmaton MAP estmaton Robust

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

CHAPTER 2 PROPOSED IMPROVED PARTICLE SWARM OPTIMIZATION

CHAPTER 2 PROPOSED IMPROVED PARTICLE SWARM OPTIMIZATION 24 CHAPTER 2 PROPOSED IMPROVED PARTICLE SWARM OPTIMIZATION The present chapter proposes an IPSO approach for multprocessor task schedulng problem wth two classfcatons, namely, statc ndependent tasks and

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

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

Distance Calculation from Single Optical Image

Distance Calculation from Single Optical Image 17 Internatonal Conference on Mathematcs, Modellng and Smulaton Technologes and Applcatons (MMSTA 17) ISBN: 978-1-6595-53-8 Dstance Calculaton from Sngle Optcal Image Xao-yng DUAN 1,, Yang-je WEI 1,,*

More information

An Image Fusion Approach Based on Segmentation Region

An Image Fusion Approach Based on Segmentation Region Rong Wang, L-Qun Gao, Shu Yang, Yu-Hua Cha, and Yan-Chun Lu An Image Fuson Approach Based On Segmentaton Regon An Image Fuson Approach Based on Segmentaton Regon Rong Wang, L-Qun Gao, Shu Yang 3, Yu-Hua

More information

Simplification of 3D Meshes

Simplification of 3D Meshes Smplfcaton of 3D Meshes Addy Ngan /4/00 Outlne Motvaton Taxonomy of smplfcaton methods Hoppe et al, Mesh optmzaton Hoppe, Progressve meshes Smplfcaton of 3D Meshes 1 Motvaton Hgh detaled meshes becomng

More information

Electrical analysis of light-weight, triangular weave reflector antennas

Electrical analysis of light-weight, triangular weave reflector antennas Electrcal analyss of lght-weght, trangular weave reflector antennas Knud Pontoppdan TICRA Laederstraede 34 DK-121 Copenhagen K Denmark Emal: kp@tcra.com INTRODUCTION The new lght-weght reflector antenna

More information

Unsupervised Learning and Clustering

Unsupervised Learning and Clustering Unsupervsed Learnng and Clusterng Supervsed vs. Unsupervsed Learnng Up to now we consdered supervsed learnng scenaro, where we are gven 1. samples 1,, n 2. class labels for all samples 1,, n Ths s also

More information

Simulation: Solving Dynamic Models ABE 5646 Week 11 Chapter 2, Spring 2010

Simulation: Solving Dynamic Models ABE 5646 Week 11 Chapter 2, Spring 2010 Smulaton: Solvng Dynamc Models ABE 5646 Week Chapter 2, Sprng 200 Week Descrpton Readng Materal Mar 5- Mar 9 Evaluatng [Crop] Models Comparng a model wth data - Graphcal, errors - Measures of agreement

More information

Ecient Computation of the Most Probable Motion from Fuzzy. Moshe Ben-Ezra Shmuel Peleg Michael Werman. The Hebrew University of Jerusalem

Ecient Computation of the Most Probable Motion from Fuzzy. Moshe Ben-Ezra Shmuel Peleg Michael Werman. The Hebrew University of Jerusalem Ecent Computaton of the Most Probable Moton from Fuzzy Correspondences Moshe Ben-Ezra Shmuel Peleg Mchael Werman Insttute of Computer Scence The Hebrew Unversty of Jerusalem 91904 Jerusalem, Israel Emal:

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

A Fast Visual Tracking Algorithm Based on Circle Pixels Matching

A Fast Visual Tracking Algorithm Based on Circle Pixels Matching A Fast Vsual Trackng Algorthm Based on Crcle Pxels Matchng Zhqang Hou hou_zhq@sohu.com Chongzhao Han czhan@mal.xjtu.edu.cn Ln Zheng Abstract: A fast vsual trackng algorthm based on crcle pxels matchng

More information

LECTURE : MANIFOLD LEARNING

LECTURE : MANIFOLD LEARNING LECTURE : MANIFOLD LEARNING Rta Osadchy Some sldes are due to L.Saul, V. C. Raykar, N. Verma Topcs PCA MDS IsoMap LLE EgenMaps Done! Dmensonalty Reducton Data representaton Inputs are real-valued vectors

More information

Feature Reduction and Selection

Feature Reduction and Selection Feature Reducton and Selecton Dr. Shuang LIANG School of Software Engneerng TongJ Unversty Fall, 2012 Today s Topcs Introducton Problems of Dmensonalty Feature Reducton Statstc methods Prncpal Components

More information

MOTION BLUR ESTIMATION AT CORNERS

MOTION BLUR ESTIMATION AT CORNERS Gacomo Boracch and Vncenzo Caglot Dpartmento d Elettronca e Informazone, Poltecnco d Mlano, Va Ponzo, 34/5-20133 MILANO boracch@elet.polm.t, caglot@elet.polm.t Keywords: Abstract: Pont Spread Functon Parameter

More information

Machine Learning: Algorithms and Applications

Machine Learning: Algorithms and Applications 14/05/1 Machne Learnng: Algorthms and Applcatons Florano Zn Free Unversty of Bozen-Bolzano Faculty of Computer Scence Academc Year 011-01 Lecture 10: 14 May 01 Unsupervsed Learnng cont Sldes courtesy of

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

An Image Compression Algorithm based on Wavelet Transform and LZW

An Image Compression Algorithm based on Wavelet Transform and LZW An Image Compresson Algorthm based on Wavelet Transform and LZW Png Luo a, Janyong Yu b School of Chongqng Unversty of Posts and Telecommuncatons, Chongqng, 400065, Chna Abstract a cylpng@63.com, b y27769864@sna.cn

More information

Hybrid Non-Blind Color Image Watermarking

Hybrid Non-Blind Color Image Watermarking Hybrd Non-Blnd Color Image Watermarkng Ms C.N.Sujatha 1, Dr. P. Satyanarayana 2 1 Assocate Professor, Dept. of ECE, SNIST, Yamnampet, Ghatkesar Hyderabad-501301, Telangana 2 Professor, Dept. of ECE, AITS,

More information

Intelligent Information Acquisition for Improved Clustering

Intelligent Information Acquisition for Improved Clustering Intellgent Informaton Acquston for Improved Clusterng Duy Vu Unversty of Texas at Austn duyvu@cs.utexas.edu Mkhal Blenko Mcrosoft Research mblenko@mcrosoft.com Prem Melvlle IBM T.J. Watson Research Center

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

SVM-based Learning for Multiple Model Estimation

SVM-based Learning for Multiple Model Estimation SVM-based Learnng for Multple Model Estmaton Vladmr Cherkassky and Yunqan Ma Department of Electrcal and Computer Engneerng Unversty of Mnnesota Mnneapols, MN 55455 {cherkass,myq}@ece.umn.edu Abstract:

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

Corner-Based Image Alignment using Pyramid Structure with Gradient Vector Similarity

Corner-Based Image Alignment using Pyramid Structure with Gradient Vector Similarity Journal of Sgnal and Informaton Processng, 013, 4, 114-119 do:10.436/jsp.013.43b00 Publshed Onlne August 013 (http://www.scrp.org/journal/jsp) Corner-Based Image Algnment usng Pyramd Structure wth Gradent

More information

Dynamic Voltage Scaling of Supply and Body Bias Exploiting Software Runtime Distribution

Dynamic Voltage Scaling of Supply and Body Bias Exploiting Software Runtime Distribution Dynamc Voltage Scalng of Supply and Body Bas Explotng Software Runtme Dstrbuton Sungpack Hong EE Department Stanford Unversty Sungjoo Yoo, Byeong Bn, Kyu-Myung Cho, Soo-Kwan Eo Samsung Electroncs Taehwan

More information

A New Approach For the Ranking of Fuzzy Sets With Different Heights

A New Approach For the Ranking of Fuzzy Sets With Different Heights New pproach For the ankng of Fuzzy Sets Wth Dfferent Heghts Pushpnder Sngh School of Mathematcs Computer pplcatons Thapar Unversty, Patala-7 00 Inda pushpndersnl@gmalcom STCT ankng of fuzzy sets plays

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

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 15

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 15 CS434a/541a: Pattern Recognton Prof. Olga Veksler Lecture 15 Today New Topc: Unsupervsed Learnng Supervsed vs. unsupervsed learnng Unsupervsed learnng Net Tme: parametrc unsupervsed learnng Today: nonparametrc

More information

Kent State University CS 4/ Design and Analysis of Algorithms. Dept. of Math & Computer Science LECT-16. Dynamic Programming

Kent State University CS 4/ Design and Analysis of Algorithms. Dept. of Math & Computer Science LECT-16. Dynamic Programming CS 4/560 Desgn and Analyss of Algorthms Kent State Unversty Dept. of Math & Computer Scence LECT-6 Dynamc Programmng 2 Dynamc Programmng Dynamc Programmng, lke the dvde-and-conquer method, solves problems

More information

Multi-view 3D Position Estimation of Sports Players

Multi-view 3D Position Estimation of Sports Players Mult-vew 3D Poston Estmaton of Sports Players Robbe Vos and Wlle Brnk Appled Mathematcs Department of Mathematcal Scences Unversty of Stellenbosch, South Afrca Emal: vosrobbe@gmal.com Abstract The problem

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

A Newton-Type Method for Constrained Least-Squares Data-Fitting with Easy-to-Control Rational Curves

A Newton-Type Method for Constrained Least-Squares Data-Fitting with Easy-to-Control Rational Curves A Newton-Type Method for Constraned Least-Squares Data-Fttng wth Easy-to-Control Ratonal Curves G. Cascola a, L. Roman b, a Department of Mathematcs, Unversty of Bologna, P.zza d Porta San Donato 5, 4017

More information

APPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT

APPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT 3. - 5. 5., Brno, Czech Republc, EU APPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT Abstract Josef TOŠENOVSKÝ ) Lenka MONSPORTOVÁ ) Flp TOŠENOVSKÝ

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

NOVEL CONSTRUCTION OF SHORT LENGTH LDPC CODES FOR SIMPLE DECODING

NOVEL CONSTRUCTION OF SHORT LENGTH LDPC CODES FOR SIMPLE DECODING Journal of Theoretcal and Appled Informaton Technology 27 JATIT. All rghts reserved. www.jatt.org NOVEL CONSTRUCTION OF SHORT LENGTH LDPC CODES FOR SIMPLE DECODING Fatma A. Newagy, Yasmne A. Fahmy, and

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

Intra-Parametric Analysis of a Fuzzy MOLP

Intra-Parametric Analysis of a Fuzzy MOLP Intra-Parametrc Analyss of a Fuzzy MOLP a MIAO-LING WANG a Department of Industral Engneerng and Management a Mnghsn Insttute of Technology and Hsnchu Tawan, ROC b HSIAO-FAN WANG b Insttute of Industral

More information

Six-Band HDTV Camera System for Color Reproduction Based on Spectral Information

Six-Band HDTV Camera System for Color Reproduction Based on Spectral Information IS&T's 23 PICS Conference Sx-Band HDTV Camera System for Color Reproducton Based on Spectral Informaton Kenro Ohsawa )4), Hroyuk Fukuda ), Takeyuk Ajto 2),Yasuhro Komya 2), Hdeak Hanesh 3), Masahro Yamaguch

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

Solving Route Planning Using Euler Path Transform

Solving Route Planning Using Euler Path Transform Solvng Route Plannng Usng Euler Path ransform Y-Chong Zeng Insttute of Informaton Scence Academa Snca awan ychongzeng@s.snca.edu.tw Abstract hs paper presents a method to solve route plannng problem n

More information

The Codesign Challenge

The Codesign Challenge 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.

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

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

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

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

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

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

Parallel Branch and Bound Algorithm - A comparison between serial, OpenMP and MPI implementations

Parallel Branch and Bound Algorithm - A comparison between serial, OpenMP and MPI implementations Journal of Physcs: Conference Seres Parallel Branch and Bound Algorthm - A comparson between seral, OpenMP and MPI mplementatons To cte ths artcle: Luco Barreto and Mchael Bauer 2010 J. Phys.: Conf. Ser.

More information

NAG Fortran Library Chapter Introduction. G10 Smoothing in Statistics

NAG Fortran Library Chapter Introduction. G10 Smoothing in Statistics Introducton G10 NAG Fortran Lbrary Chapter Introducton G10 Smoothng n Statstcs Contents 1 Scope of the Chapter... 2 2 Background to the Problems... 2 2.1 Smoothng Methods... 2 2.2 Smoothng Splnes and Regresson

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

Title: A Novel Protocol for Accuracy Assessment in Classification of Very High Resolution Images

Title: A Novel Protocol for Accuracy Assessment in Classification of Very High Resolution Images 2009 IEEE. Personal use of ths materal s permtted. Permsson from IEEE must be obtaned for all other uses, n any current or future meda, ncludng reprntng/republshng ths materal for advertsng or promotonal

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

LECTURE NOTES Duality Theory, Sensitivity Analysis, and Parametric Programming

LECTURE NOTES Duality Theory, Sensitivity Analysis, and Parametric Programming CEE 60 Davd Rosenberg p. LECTURE NOTES Dualty Theory, Senstvty Analyss, and Parametrc Programmng Learnng Objectves. Revew the prmal LP model formulaton 2. Formulate the Dual Problem of an LP problem (TUES)

More information

Suppression for Luminance Difference of Stereo Image-Pair Based on Improved Histogram Equalization

Suppression for Luminance Difference of Stereo Image-Pair Based on Improved Histogram Equalization Suppresson for Lumnance Dfference of Stereo Image-Par Based on Improved Hstogram Equalzaton Zhao Llng,, Zheng Yuhu 3, Sun Quansen, Xa Deshen School of Computer Scence and Technology, NJUST, Nanjng, Chna.School

More information

Review of approximation techniques

Review of approximation techniques CHAPTER 2 Revew of appromaton technques 2. Introducton Optmzaton problems n engneerng desgn are characterzed by the followng assocated features: the objectve functon and constrants are mplct functons evaluated

More information

Key-Selective Patchwork Method for Audio Watermarking

Key-Selective Patchwork Method for Audio Watermarking Internatonal Journal of Dgtal Content Technology and ts Applcatons Volume 4, Number 4, July 2010 Key-Selectve Patchwork Method for Audo Watermarkng 1 Ch-Man Pun, 2 Jng-Jng Jang 1, Frst and Correspondng

More information

Feature-based image registration using the shape context

Feature-based image registration using the shape context Feature-based mage regstraton usng the shape context LEI HUANG *, ZHEN LI Center for Earth Observaton and Dgtal Earth, Chnese Academy of Scences, Bejng, 100012, Chna Graduate Unversty of Chnese Academy

More information

EXTENDED BIC CRITERION FOR MODEL SELECTION

EXTENDED BIC CRITERION FOR MODEL SELECTION IDIAP RESEARCH REPORT EXTEDED BIC CRITERIO FOR ODEL SELECTIO Itshak Lapdot Andrew orrs IDIAP-RR-0-4 Dalle olle Insttute for Perceptual Artfcal Intellgence P.O.Box 59 artgny Valas Swtzerland phone +4 7

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

Performance Evaluation of an ANFIS Based Power System Stabilizer Applied in Multi-Machine Power Systems

Performance Evaluation of an ANFIS Based Power System Stabilizer Applied in Multi-Machine Power Systems Performance Evaluaton of an ANFIS Based Power System Stablzer Appled n Mult-Machne Power Systems A. A GHARAVEISI 1,2 A.DARABI 3 M. MONADI 4 A. KHAJEH-ZADEH 5 M. RASHIDI-NEJAD 1,2,5 1. Shahd Bahonar Unversty

More information

Lecture 5: Multilayer Perceptrons

Lecture 5: Multilayer Perceptrons Lecture 5: Multlayer Perceptrons Roger Grosse 1 Introducton So far, we ve only talked about lnear models: lnear regresson and lnear bnary classfers. We noted that there are functons that can t be represented

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

2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements

2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements Module 3: Element Propertes Lecture : Lagrange and Serendpty Elements 5 In last lecture note, the nterpolaton functons are derved on the bass of assumed polynomal from Pascal s trangle for the fled varable.

More information

Multicriteria Decision Making

Multicriteria Decision Making Multcrtera Decson Makng Andrés Ramos (Andres.Ramos@comllas.edu) Pedro Sánchez (Pedro.Sanchez@comllas.edu) Sonja Wogrn (Sonja.Wogrn@comllas.edu) Contents 1. Basc concepts 2. Contnuous methods 3. Dscrete

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

A new segmentation algorithm for medical volume image based on K-means clustering

A new segmentation algorithm for medical volume image based on K-means clustering Avalable onlne www.jocpr.com Journal of Chemcal and harmaceutcal Research, 2013, 5(12):113-117 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCRC5 A new segmentaton algorthm for medcal volume mage based

More information

BioTechnology. An Indian Journal FULL PAPER. Trade Science Inc.

BioTechnology. An Indian Journal FULL PAPER. Trade Science Inc. [Type text] [Type text] [Type text] ISSN : 0974-74 Volume 0 Issue BoTechnology 04 An Indan Journal FULL PAPER BTAIJ 0() 04 [684-689] Revew on Chna s sports ndustry fnancng market based on market -orented

More information

Helsinki University Of Technology, Systems Analysis Laboratory Mat Independent research projects in applied mathematics (3 cr)

Helsinki University Of Technology, Systems Analysis Laboratory Mat Independent research projects in applied mathematics (3 cr) Helsnk Unversty Of Technology, Systems Analyss Laboratory Mat-2.08 Independent research projects n appled mathematcs (3 cr) "! #$&% Antt Laukkanen 506 R ajlaukka@cc.hut.f 2 Introducton...3 2 Multattrbute

More information

AUTOMATED personal identification using biometrics

AUTOMATED personal identification using biometrics A 3D Feature Descrptor Recovered from a Sngle 2D Palmprnt Image Qan Zheng,2, Ajay Kumar, and Gang Pan 2 Abstract Desgn and development of effcent and accurate feature descrptors s crtcal for the success

More information

Sequential search. Building Java Programs Chapter 13. Sequential search. Sequential search

Sequential search. Building Java Programs Chapter 13. Sequential search. Sequential search Sequental search Buldng Java Programs Chapter 13 Searchng and Sortng sequental search: Locates a target value n an array/lst by examnng each element from start to fnsh. How many elements wll t need to

More information

Optimal Scheduling of Capture Times in a Multiple Capture Imaging System

Optimal Scheduling of Capture Times in a Multiple Capture Imaging System Optmal Schedulng of Capture Tmes n a Multple Capture Imagng System Tng Chen and Abbas El Gamal Informaton Systems Laboratory Department of Electrcal Engneerng Stanford Unversty Stanford, Calforna 9435,

More information

An efficient method to build panoramic image mosaics

An efficient method to build panoramic image mosaics An effcent method to buld panoramc mage mosacs Pattern Recognton Letters vol. 4 003 Dae-Hyun Km Yong-In Yoon Jong-Soo Cho School of Electrcal Engneerng and Computer Scence Kyungpook Natonal Unv. Abstract

More information

CSCI 104 Sorting Algorithms. Mark Redekopp David Kempe

CSCI 104 Sorting Algorithms. Mark Redekopp David Kempe CSCI 104 Sortng Algorthms Mark Redekopp Davd Kempe Algorthm Effcency SORTING 2 Sortng If we have an unordered lst, sequental search becomes our only choce If we wll perform a lot of searches t may be benefcal

More information

Outline. Self-Organizing Maps (SOM) US Hebbian Learning, Cntd. The learning rule is Hebbian like:

Outline. Self-Organizing Maps (SOM) US Hebbian Learning, Cntd. The learning rule is Hebbian like: Self-Organzng Maps (SOM) Turgay İBRİKÇİ, PhD. Outlne Introducton Structures of SOM SOM Archtecture Neghborhoods SOM Algorthm Examples Summary 1 2 Unsupervsed Hebban Learnng US Hebban Learnng, Cntd 3 A

More information