A Real-Time Multicast Routing Scheme for Multi-Hop Switched Fieldbuses
|
|
- Myles Burke
- 5 years ago
- Views:
Transcription
1 A Real-Tme ultcast Routng Scheme for ult-op Swtched Feldbuses Lxong Chen* Xue Lu Qxn Wang Yufe Wang *Dept. of ECE School of CS cgll Unv. Dept. of Computng The ong Kong Polytechnc Unv. arch
2 Content Demand Background Problem Defnton and Complexty eurstc Algorthm Evaluaton Related Work
3 Feldbus s fundamentally dfferent from the Internet hence reures dfferent solutons. Feldbus: Specalzed networks used n ndustral mnng medcal vehcular avonc envronments ard Real-Tme Perodc Stable Traffc Bounded w/ Global Info The Internet: Best Effort Random Bursty Traffc Unbounded w/o Global Info
4 Feldbus s evolvng from shared medum to mult-hop swtched due to scalablty needs. Concord 1976
5 Feldbus s evolvng from shared medum to mult-hop swtched due to scalablty needs. A Several undreds of computng nodes
6 Feldbus s evolvng from shared medum to mult-hop swtched due to scalablty needs. Tele-robotc underground mnng saves lves > 5000 annual death toll 2000ft (609.6m
7 Feldbus s evolvng from shared medum to mult-hop swtched due to scalablty needs. Telepresence
8 Feldbus s evolvng from shared medum to mult-hop swtched due to scalablty needs. Robotc anufacturng
9 Shared medum mult-hop swtched: real- tme multcast becomes a problem ( ( ( ( ( n n m m m m n n n n n t x K t u t u B t x A t x odern control assumes IO Real-Tme ultcast btw Sensors-Controllers-Actuators/Observers
10 Shared medum mult-hop swtched: real- tme multcast becomes a problem ( ( ( ( ( n n m m m m n n n n n t x K t u t u B t x A t x Shared edum Real-Tme ultcast: Easy ult-op Swtched Real-Tme ultcast:?
11 De facto standard (real-tme feldbus swtch archtecture: crossbar per-flow- TDA
12 De facto standard (real-tme feldbus swtch archtecture: crossbar per-flow- TDA Input Ports I1 I2 I3
13 De facto standard (real-tme feldbus swtch archtecture: crossbar per-flow- TDA Output Ports I1 O1 I2 O2 I3 O3
14 De facto standard (real-tme feldbus swtch archtecture: crossbar per-flow- TDA Per-Flow-Queueng I1 O1 I2 O2 I3 O3
15 De facto standard (real-tme feldbus swtch archtecture: crossbar per-flow- TDA cells I1 O1 I2 O2 I3 O3
16 De facto standard (real-tme feldbus swtch archtecture: crossbar per-flow- TDA I1 O1 I2 cell cell cell O2 cell cell cell I3 O3
17 De facto standard (real-tme feldbus swtch archtecture: crossbar per-flow- TDA Synchronous perodc cell forwardng Cell-Tme I1 O1 I2 O2 I3 O3
18 De facto standard (real-tme feldbus swtch archtecture: crossbar per-flow- TDA atchng I1 O1 I2 O2 I3 O3
19 De facto standard (real-tme feldbus swtch archtecture: crossbar per-flow- TDA Why atchng? An nput/output can only send/receve one cell per cell-tme I1 O1 I2 O2 I3 O3
20 De facto standard (real-tme feldbus swtch archtecture: crossbar per-flow- TDA Internal atchng: f an nput has multple per-flow- for the same output only one s pcked every cell-tme. I1 O1 I2 O2 I3 O3
21 De facto standard (real-tme feldbus swtch archtecture: crossbar per-flow- TDA I1 O1 I2 O2 I3 O3
22 De facto standard (real-tme feldbus swtch archtecture: crossbar per-flow- TDA I1 O1 I2 O2 I3 O3
23 De facto standard (real-tme feldbus swtch archtecture: crossbar per-flow- TDA I1 O1 I2 O2 I3 O3
24 De facto standard (real-tme feldbus swtch archtecture: crossbar per-flow- TDA TDA schedulng frame of cell-tme e.g. = 5 Ft all real-tme flows perods nto frame e.g. (11 3 (5 2.e. (10 4 Demand Cell tme: I1: I2: I3: I4: a cell to send to O1 a cell to send to O2 a cell to send to O3 a cell to send to O4
25 De facto standard (real-tme feldbus swtch archtecture: crossbar per-flow- TDA Theorem 1: If demand matrx every color cell then have confg. tme scheduler wth O(N 4 tme cost [wang10]. Cell tme: I1: I2: I3: Schedule Demand I4: Cell tme: I1: I2: I3: I4: Schedulng Algorthm a cell to send to O1 a cell to send to O2 a cell to send to O3 a cell to send to O4
26 De facto standard (real-tme feldbus swtch archtecture: crossbar per-flow- TDA Support for ultcast I1 O1 I2 O2 c I3 O3
27 De facto standard (real-tme feldbus swtch archtecture: crossbar per-flow- TDA Support for ultcast I1 O1 I2 O2 c c c I3 O3
28 Real-Tme ultcast Schedulng (RTS problem n mult-hop swtched feldbuses G( V E m ( s D w T { m } ( G( V E
29 Real-Tme ultcast Schedulng (RTS problem n mult-hop swtched feldbuses G( V E m ( s D w T { m } ( G( V E
30 Real-Tme ultcast Schedulng (RTS problem n mult-hop swtched feldbuses G( V E m Graph of the swtched real-tme network (feldbus ( s D w T { m } ( G( V E
31 Real-Tme ultcast Schedulng (RTS problem n mult-hop swtched feldbuses G( V E m Graph of the swtched real-tme network (feldbus ( s D w T { m } Swtches (nodes of the network ( G( V E
32 Real-Tme ultcast Schedulng (RTS problem n mult-hop swtched feldbuses G( V E m Graph of the swtched real-tme network (feldbus ( s D w T { m } Swtches (nodes of the network ( G( V E Edges of the network
33 Real-Tme ultcast Schedulng (RTS problem n mult-hop swtched feldbuses G( V E m ( s D w T { m } ( G( V E
34 Real-Tme ultcast Schedulng (RTS problem n mult-hop swtched feldbuses G( V E m ( s D w T { m } A (real-tme multcast group ( G( V E
35 Real-Tme ultcast Schedulng (RTS problem n mult-hop swtched feldbuses G( V E m ( s D w T { m } A (real-tme multcast group Source ( G( V E End
36 Real-Tme ultcast Schedulng (RTS problem n mult-hop swtched feldbuses GEnds ( V E Destnaton m ( s D w T { m } A (real-tme multcast group Source ( G( V E End
37 Real-Tme ultcast Schedulng (RTS problem n mult-hop swtched feldbuses GEnds ( V E Destnaton m ( s D w T A (real-tme multcast group { m } Source ( G( V E End Cells to multcast every perod
38 Real-Tme ultcast Schedulng (RTS problem n mult-hop swtched feldbuses GEnds ( V E Destnaton Perod (unt: cell-tme m ( s D w T A (real-tme multcast group { m } Source ( G( V E End Cells to multcast every perod
39 Real-Tme ultcast Schedulng (RTS problem n mult-hop swtched feldbuses GEnds ( V E Destnaton Perod (unt: cell-tme Deadlne (relatve unt: cell-tme m ( s D w T A (real-tme multcast group { m } Source ( G( V E End Cells to multcast every perod
40 Real-Tme ultcast Schedulng (RTS problem n mult-hop swtched feldbuses G( V E m ( s D w T { m } ( G( V E
41 Real-Tme ultcast Schedulng (RTS problem n mult-hop swtched feldbuses G( V E The set of all real-tme multcast groups m ( s D w T { m } ( G( V E
42 Real-Tme ultcast Schedulng (RTS problem n mult-hop swtched feldbuses G( V E The set of all real-tme multcast groups m ( s D w T The th real-tme multcast group { m } ( G( V E
43 Real-Tme ultcast Schedulng (RTS problem n mult-hop swtched feldbuses G( V E m ( s D w T { m } ( G( V E
44 Real-Tme ultcast Schedulng (RTS problem n mult-hop swtched feldbuses G( V E An nstance of RTS problem m ( s D w T { m } ( G( V E
45 Real-Tme ultcast Schedulng (RTS problem n mult-hop swtched feldbuses G( V E An nstance of RTS problem m ( s D w T The network(feldbus topology { m } ( G( V E
46 Real-Tme ultcast Schedulng (RTS problem n mult-hop swtched feldbuses G( V E An nstance of RTS problem m ( s D w T The network(feldbus topology { m } The set of RT multcast groups that user demands ( G( V E
47 RTS Problem: Gven a how to schedule each swtch s.t. every m meets needs? G( V E m ( s D w T { m } ( G( V E
48 RTS Problem: Gven a how to schedule each swtch s.t. every m meets ts needs? G( V E m Theorem 2: RTS ( s D w T Problem s NP-ard. { m } ( G( V E
49 -slot Perodc RTS: a subset of RTS problems. } { nstance s an RTS Q } ( } { ( ' ' { T T w D s m m G ' and Q Q
50 -slot Perodc RTS: a subset of RTS problems. } { nstance s an RTS Q } ( } { ( ' ' { T T w D s m m G ' and Q Q Proposton 1: -slot Perodc RTS s NP-ard.
51 -slot Perodc RTS: a subset of RTS problems. } { nstance s an RTS Q } ( } { ( ' ' { T T w D s m m G ' and Q Q Proposton 1: -slot Perodc RTS s NP-ard. Search for eurstc Solutons.
52 Transform an -slot Perodc RTS nstance nto a Real-Tme ultcast Routng (RTR nstance max ~ } ~ ( {( { ~ } ~ ~ ( ~ } ( {( } { ' ( ' T w D s m G T w D s m G and where defne where Gven Q
53 Transform an -slot Perodc RTS nstance nto a Real-Tme ultcast Routng (RTR nstance max ~ } ~ ( {( { ~ } ~ ~ ( ~ } ( {( } { ' ( ' T w D s m G T w D s m G and where defne where Gven Q -slot Perodc RTS nstance
54 Transform an -slot Perodc RTS nstance nto a Real-Tme ultcast Routng (RTR nstance max ~ } ~ ( {( { ~ } ~ ~ ( ~ } ( {( } { ' ( ' T w D s m G T w D s m G and where defne where Gven Q -slot Perodc RTS nstance RTR nstance
55 Transform an -slot Perodc RTS nstance nto a Real-Tme ultcast Routng (RTR nstance max ~ } ~ ( {( { ~ } ~ ~ ( ~ } ( {( } { ' ( ' T w D s m G T w D s m G and where defne where Gven Q -slot Perodc RTS nstance ax ultcast Tree eght RTR nstance
56 Transform an -slot Perodc RTS nstance nto a Real-Tme ultcast Routng (RTR nstance max ~ } ~ ( {( { ~ } ~ ~ ( ~ } ( {( } { ' ( ' T w D s m G T w D s m G and where defne where Gven Q. ( ' ~ ( ~ G G a soluton to s A soluton to
57 Transform an -slot Perodc RTS nstance nto a Real-Tme ultcast Routng (RTR nstance max ~ } ~ ( {( { ~ } ~ ~ ( ~ } ( {( } { ' ( ' T w D s m G T w D s m G and where defne where Gven Q. ( ' ~ ( ~ G G a soluton to s A soluton to RTSchedulng problem becomes a Routng problem.
58 eurstc Routng Algorthm Exstng manstream nternet multcast routng algorthms become Djkstra when network s statc and global nfo s avalable Djkstra s short-comng: only cares about # of hops gnores congeston. s s d
59 eurstc Routng Algorthm Exstng manstream nternet multcast routng algorthms become Djkstra when network s statc and global nfo s avalable Djkstra s short-comng: only cares about # of hops gnores congeston. s s d
60 eurstc Routng Algorthm Exstng manstream nternet multcast routng algorthms become Djkstra when network s statc and global nfo s avalable We want a heurstc routng algorthm that consders both (hops and congeston. s s d
61 eurstc Routng Algorthm Exstng manstream nternet multcast routng algorthms become Djkstra when network s statc and global nfo s avalable We want a heurstc routng algorthm that consders both (hops and congeston. s s d
62 eurstc Routng Algorthm Grow the multple trees smultaneously wth multple teratons. In each teraton 1 lnk s added to each tree. When multple tree contends n a same swtch we carry out a job-huntng-lke negotaton to let only 1 contendng tree grow through an output n ths teraton.
63 The job-huntng-lke contenton negotaton s tself an teratve algorthm. I1 I2 I3 O1 O2 O3 dfferent colors for dfferent trees
64 The job-huntng-lke contenton negotaton s tself an teratve algorthm. I1 I2 I3 O1 O2 O3 dfferent colors for dfferent trees Each tree ranks all outputs and only apply to ts favorte output
65 The job-huntng-lke contenton negotaton s tself an teratve algorthm. I1 I2 O1 O2 dfferent colors for dfferent trees I3 O3 Each tree ranks all outputs and only apply to ts favorte output I1 I2 I3 O1 O2 O3 Each output offers job to the most loyal applcant
66 The job-huntng-lke contenton negotaton s tself an teratve algorthm. updated demand matrx I1 O1 dfferent colors for dfferent trees I1 O1 I2 O2 I2 O2 I3 O3 Each tree ranks all outputs and only apply to ts favorte output I1 I2 O1 O2 I3 O3 Accept job by reservng correspondng frame slots. I3 O3 Each output offers job to the most loyal applcant
67 Rankng functon desgn: consders both E2E delay (hops and congeston.
68 Rankng functon desgn: consders both E2E delay (hops and congeston. rank of o to tree t
69 Rankng functon desgn: consders both E2E delay (hops and congeston. rank of o to tree t routng flexblty: slack to reachng max hop (max e2e delay bound
70 Rankng functon desgn: consders both E2E delay (hops and congeston. rank of o to tree t routng flexblty: slack to reachng max hop (max e2e delay bound shortest dstance to target end
71 Rankng functon desgn: consders both E2E delay (hops and congeston. rank of o to tree t congeston routng flexblty: slack to reachng max hop (max e2e delay bound shortest dstance to target end
72 Defnton of favorte a.k.a. loyalty. t s loyalty to o (t1
73 Evaluaton Setup. 4x4 port real-tme swtches 15x15 suare grd network topology Per port capacty: 1Gbps = 2000 (cell/frame 1 cell = 500 bt Trals n each tral: Random number of multcast groups w = 1~20 cell/frame (500K~10bps
74 Evaluaton Setup. 4x4 port real-tme swtches 15x15 suare grd network topology Per port capacty: 1Gbps = 2000 (cell/frame 1 cell = 500 bt Trals n each tral: Random number of multcast groups w = 1~20 cell/frame (500K~10bps
75 Evaluaton Setup. Network Demanded Utlzaton (Applcaton Layer E2E Utlzaton
76 Evaluaton Results: =3
77 Evaluaton Results: =9
78 anstream Internet multcast routng algorthms manly concern about dynamc dstrbuted group management. Reverse Path Broadcastng/ultcastng (RPB/RP [semera97] Truncated Reverse Path Broadcastng (TRPB [semera97] Dstance Vector ultcast Routng Protocol (DVRP [watzman88] ultcast Extenson to Open Shortest Path Frst (OSPF [moy94a][moy94b] Protocol Independent ultcast (PI [fenner06][adams05] Core-Based Tree ultcast Routng (CBT [ballarde97]
79 anstream Internet multcast routng algorthms become Djkstra for statc network wth global nfo. Reverse Path Broadcastng/ultcastng (RPB/RP [semera97] Truncated Reverse Path Broadcastng (TRPB [semera97] Dstance Vector ultcast Routng Protocol (DVRP [watzman88] ultcast Extenson to Open Shortest Path Frst (OSPF [moy94a][moy94b] Protocol Independent ultcast (PI [fenner06][adams05] Core-Based Tree ultcast Routng (CBT [ballarde97]
80 Others P2P and Overlay Network ultcast are concerned wth statstcal performance nstead of hard real-tme E2E delay bound.
81 Thank You!
82 References [adams05] A. Adams J. Ncholas and W. Sadak Protocol Independent ultcast Dense ode (PI-D: Protocol Specfcaton (Revsed RFC 3973 Jan [ballarde97] A. Ballarde Core Based Tees (CBT ultcast Routng Archtectre RF 2201 Spe [fenner06] B. Fenner. handley. olbrook and I. Kouvelas Protocol Independent ultcast Sparse ode (PI-S: Protocol Specfcaton (Revsed RFC 4601 Aug [moy94a] J. oy ultcast Extensons to OSPF RFC 1584 ar [moy94b] J. oy OSPF: Analyss and Experence RFC 1585 ar [semera97] C. Semera and T. aufer Introducton to IP ultcast Routng IETF draft-etf-mboned-ntro-multcast-03.txt Jul [watzman88] D. Watzman C. Partrdge and S. Deerng Dstance Vector ultcast Routng Protocol RFC 1075 Nov [wang10] Q. Wang and S. Gopalakrshnan Adaptng a an-steam Internet Swtch Archtecture for ult-op Real-Tme Industral Networks n IEEE Transactons on Industral Informatcs v6 n3 ay 2010.
83 De facto standard (real-tme feldbus swtch archtecture: crossbar per-flow- TDA
84 De facto standard (real-tme feldbus swtch archtecture: crossbar per-flow- TDA
85 De facto standard (real-tme feldbus swtch archtecture: crossbar per-flow- TDA
86 De facto standard (real-tme feldbus swtch archtecture: crossbar per-flow- TDA
87 De facto standard (real-tme feldbus swtch archtecture: crossbar per-flow- TDA
88 De facto standard (real-tme feldbus swtch archtecture: crossbar per-flow- TDA
Queueing Networks II Network Performance
Queueng Networks II Network Performance Davd Tpper Assocate Professor Graduate Telecommuncatons and Networkng Program Unversty of Pttsburgh Sldes 6 Networks of Queues Many communcaton systems must be modeled
More informationOutline. Communication. Bellman Ford Algorithm. Bellman Ford Example. Bellman Ford Shortest Path [1]
DYNAMIC SHORTEST PATH SEARCH AND SYNCHRONIZED TASK SWITCHING Jay Wagenpfel, Adran Trachte 2 Outlne Shortest Communcaton Path Searchng Bellmann Ford algorthm Algorthm for dynamc case Modfcatons to our algorthm
More informationCalculation of time complexity (3%)
Problem 1. (30%) Calculaton of tme complexty (3%) Gven n ctes, usng exhaust search to see every result takes O(n!). Calculaton of tme needed to solve the problem (2%) 40 ctes:40! dfferent tours 40 add
More informationResource Allocation with a Budget Constraint for Computing Independent Tasks in the Cloud
Resource Allocaton wth a Budget Constrant for Computng Independent Tasks n the Cloud Wemng Sh and Bo Hong School of Electrcal and Computer Engneerng Georga Insttute of Technology, USA 2nd IEEE Internatonal
More informationAnnexes. EC.1. Cycle-base move illustration. EC.2. Problem Instances
ec Annexes Ths Annex frst llustrates a cycle-based move n the dynamc-block generaton tabu search. It then dsplays the characterstcs of the nstance sets, followed by detaled results of the parametercalbraton
More informationIntra-Domain Traffic Engineering
Intra-Doman Traffc Engneerng Traffc Engneerng (TE) MPLS and traffc engneerng (wll go over very brefly) traffc engneerng as networ-wde optmzaton problem TE through ln weght assgnments Traffc Matrx Estmaton
More informationOverhead-Aware Compositional Analysis of Real-Time Systems
Overhead-Aware ompostonal Analyss of Real-Tme Systems Lnh T.X. Phan, Meng Xu, Jaewoo Lee, nsup Lee, Oleg Sokolsky PRESE enter Department of omputer and nformaton Scence Unversty of Pennsylvana ompostonal
More informationComputing Correlated Equilibria in Multi-Player Games
Computng Correlated Equlbra n Mult-Player Games Chrstos H. Papadmtrou Presented by Zhanxang Huang December 7th, 2005 1 The Author Dr. Chrstos H. Papadmtrou CS professor at UC Berkley (taught at Harvard,
More informationConcepts for Wireless Ad Hoc
Bandwdth and Avalable Bandwdth oncepts for Wreless Ad Hoc Networks Marco A. Alzate Unversdad Dstrtal, Bogotá Néstor M. Peña Unversdad de los Andes, Bogotá Mguel A. abrador Unversty of South Florda, Tampa
More informationPricing and Resource Allocation Game Theoretic Models
Prcng and Resource Allocaton Game Theoretc Models Zhy Huang Changbn Lu Q Zhang Computer and Informaton Scence December 8, 2009 Z. Huang, C. Lu, and Q. Zhang (CIS) Game Theoretc Models December 8, 2009
More informationJoint Energy Management and Resource Allocation in Rechargable Sensor Networks
Jont Energy Management and Resource Allocaton n Rechargable Sensor Networks Ren-Shou Lu, Prasun Snha and C. Emre Koksal Department of CSE and ECE The Oho State Unversty Envronmental Energy Harvestng Many
More informationBurst Cloning: A Proactive Scheme to Reduce Data Loss in Optical Burst-Switched Networks
Burst Clonng: A Proactve Scheme to Reduce Data Loss n Optcal Burst-Swtched Networks Xaodong Huang, Vnod M. Vokkarane, and Jason P. Jue Department of Computer Scence, The Unversty of Texas at Dallas, TX
More informationReal-Time Systems. Multiprocessor scheduling. Multiprocessor scheduling. Multiprocessor scheduling
Real-Tme Systems Multprocessor schedulng Specfcaton Implementaton Verfcaton Multprocessor schedulng -- -- Global schedulng How are tasks assgned to processors? Statc assgnment The processor(s) used for
More informationEmbedded Systems. 4. Aperiodic and Periodic Tasks
Embedded Systems 4. Aperodc and Perodc Tasks Lothar Thele 4-1 Contents of Course 1. Embedded Systems Introducton 2. Software Introducton 7. System Components 10. Models 3. Real-Tme Models 4. Perodc/Aperodc
More informationChanging Topology and Communication Delays
Prepared by F.L. Lews Updated: Saturday, February 3, 00 Changng Topology and Communcaton Delays Changng Topology The graph connectvty or topology may change over tme. Let G { G, G,, G M } wth M fnte be
More informationLast Time. Priority-based scheduling. Schedulable utilization Rate monotonic rule: Keep utilization below 69% Static priorities Dynamic priorities
Last Tme Prorty-based schedulng Statc prortes Dynamc prortes Schedulable utlzaton Rate monotonc rule: Keep utlzaton below 69% Today Response tme analyss Blockng terms Prorty nverson And solutons Release
More informationEEL 6266 Power System Operation and Control. Chapter 3 Economic Dispatch Using Dynamic Programming
EEL 6266 Power System Operaton and Control Chapter 3 Economc Dspatch Usng Dynamc Programmng Pecewse Lnear Cost Functons Common practce many utltes prefer to represent ther generator cost functons as sngle-
More informationNP-Completeness : Proofs
NP-Completeness : Proofs Proof Methods A method to show a decson problem Π NP-complete s as follows. (1) Show Π NP. (2) Choose an NP-complete problem Π. (3) Show Π Π. A method to show an optmzaton problem
More informationSimultaneous Optimization of Berth Allocation, Quay Crane Assignment and Quay Crane Scheduling Problems in Container Terminals
Smultaneous Optmzaton of Berth Allocaton, Quay Crane Assgnment and Quay Crane Schedulng Problems n Contaner Termnals Necat Aras, Yavuz Türkoğulları, Z. Caner Taşkın, Kuban Altınel Abstract In ths work,
More informationCollege of Computer & Information Science Fall 2009 Northeastern University 20 October 2009
College of Computer & Informaton Scence Fall 2009 Northeastern Unversty 20 October 2009 CS7880: Algorthmc Power Tools Scrbe: Jan Wen and Laura Poplawsk Lecture Outlne: Prmal-dual schema Network Desgn:
More information18.1 Introduction and Recap
CS787: Advanced Algorthms Scrbe: Pryananda Shenoy and Shjn Kong Lecturer: Shuch Chawla Topc: Streamng Algorthmscontnued) Date: 0/26/2007 We contnue talng about streamng algorthms n ths lecture, ncludng
More informationECE559VV Project Report
ECE559VV Project Report (Supplementary Notes Loc Xuan Bu I. MAX SUM-RATE SCHEDULING: THE UPLINK CASE We have seen (n the presentaton that, for downlnk (broadcast channels, the strategy maxmzng the sum-rate
More informationAn Optimization Model for Routing in Low Earth Orbit Satellite Constellations
An Optmzaton Model for Routng n Low Earth Orbt Satellte Constellatons A. Ferrera J. Galter P. Mahey Inra Inra Inra Afonso.Ferrera@sopha.nra.fr Jerome.Galter@nra.fr Phlppe.Mahey@sma.fr G. Mateus A. Olvera
More informationCS 331 DESIGN AND ANALYSIS OF ALGORITHMS DYNAMIC PROGRAMMING. Dr. Daisy Tang
CS DESIGN ND NLYSIS OF LGORITHMS DYNMIC PROGRMMING Dr. Dasy Tang Dynamc Programmng Idea: Problems can be dvded nto stages Soluton s a sequence o decsons and the decson at the current stage s based on the
More informationMemetic Algorithm for Flexible Job Shop Scheduling with Preemption
Internatonal Journal of Industral Engneerng & Producton Management (2012) March 2012, Volume 22, Number 4 pp. 331-340 http://ijiepm.ust.ac.r/ Memetc Algorthm for Flexble Job Shop Schedulng wth Preempton
More informationDynamic Programming. Preview. Dynamic Programming. Dynamic Programming. Dynamic Programming (Example: Fibonacci Sequence)
/24/27 Prevew Fbonacc Sequence Longest Common Subsequence Dynamc programmng s a method for solvng complex problems by breakng them down nto smpler sub-problems. It s applcable to problems exhbtng the propertes
More informationComparison of the Population Variance Estimators. of 2-Parameter Exponential Distribution Based on. Multiple Criteria Decision Making Method
Appled Mathematcal Scences, Vol. 7, 0, no. 47, 07-0 HIARI Ltd, www.m-hkar.com Comparson of the Populaton Varance Estmators of -Parameter Exponental Dstrbuton Based on Multple Crtera Decson Makng Method
More informationTokyo Institute of Technology Periodic Sequencing Control over Multi Communication Channels with Packet Losses
oyo Insttute of echnology Fujta Laboratory oyo Insttute of echnology erodc Sequencng Control over Mult Communcaton Channels wth acet Losses FL6-7- /8/6 zwrman Gusrald oyo Insttute of echnology Fujta Laboratory
More informationAnalysis of Discrete Time Queues (Section 4.6)
Analyss of Dscrete Tme Queues (Secton 4.6) Copyrght 2002, Sanjay K. Bose Tme axs dvded nto slots slot slot boundares Arrvals can only occur at slot boundares Servce to a job can only start at a slot boundary
More informationAn Integrated OR/CP Method for Planning and Scheduling
An Integrated OR/CP Method for Plannng and Schedulng John Hooer Carnege Mellon Unversty IT Unversty of Copenhagen June 2005 The Problem Allocate tass to facltes. Schedule tass assgned to each faclty. Subect
More informationCoarse-Grain MTCMOS Sleep
Coarse-Gran MTCMOS Sleep Transstor Szng Usng Delay Budgetng Ehsan Pakbazna and Massoud Pedram Unversty of Southern Calforna Dept. of Electrcal Engneerng DATE-08 Munch, Germany Leakage n CMOS Technology
More informationAdaptive RFID Indoor Positioning Technology for Wheelchair Home Health Care Robot. T. C. Kuo
Adaptve RFID Indoor Postonng Technology for Wheelchar Home Health Care Robot Contents Abstract Introducton RFID Indoor Postonng Method Fuzzy Neural Netor System Expermental Result Concluson -- Abstract
More informationComplement of Type-2 Fuzzy Shortest Path Using Possibility Measure
Intern. J. Fuzzy Mathematcal rchve Vol. 5, No., 04, 9-7 ISSN: 30 34 (P, 30 350 (onlne Publshed on 5 November 04 www.researchmathsc.org Internatonal Journal of Complement of Type- Fuzzy Shortest Path Usng
More informationModelling and Constraint Hardness Characterisation of the Unique-Path OSPF Weight Setting Problem
Modellng and Constrant Hardness Charactersaton of the Unque-Path OSPF Weght Settng Problem Changyong Zhang and Robert Rodose IC-Parc, Imperal College London, London SW7 2AZ, Unted Kngdom {cz, r.rodose}@cparc.mperal.ac.u
More informationThe Minimum Universal Cost Flow in an Infeasible Flow Network
Journal of Scences, Islamc Republc of Iran 17(2): 175-180 (2006) Unversty of Tehran, ISSN 1016-1104 http://jscencesutacr The Mnmum Unversal Cost Flow n an Infeasble Flow Network H Saleh Fathabad * M Bagheran
More informationTopology, Capacity and Flow Assignment Computer Communication Networks: Analysis and Design (Klei. Vol. 2, Chap. 5)
Topology, apacty and Flow Assgnment omputer ommuncaton Networks: Analyss and Desgn (Kle. Vol. 2, hap. 5) Orgnal ateral Prepared by: Professor James S. edtch Lecturer:Prof. assmo Tornatore Typesetter: Dr.
More informationSynchronization Protocols. Task Allocation Bin-Packing Heuristics: First-Fit Subtasks assigned in arbitrary order To allocate a new subtask T i,j
End-to-End Schedulng Framework 1. Tak allocaton: bnd tak to proceor 2. Synchronzaton protocol: enforce precedence contrant 3. Subdeadlne agnment 4. Schedulablty analy Tak Allocaton Bn-Packng eurtc: Frt-Ft
More informationProblem Set 9 Solutions
Desgn and Analyss of Algorthms May 4, 2015 Massachusetts Insttute of Technology 6.046J/18.410J Profs. Erk Demane, Srn Devadas, and Nancy Lynch Problem Set 9 Solutons Problem Set 9 Solutons Ths problem
More informationWhy BP Works STAT 232B
Why BP Works STAT 232B Free Energes Helmholz & Gbbs Free Energes 1 Dstance between Probablstc Models - K-L dvergence b{ KL b{ p{ = b{ ln { } p{ Here, p{ s the eact ont prob. b{ s the appromaton, called
More informationPriority Queuing with Finite Buffer Size and Randomized Push-out Mechanism
ICN 00 Prorty Queung wth Fnte Buffer Sze and Randomzed Push-out Mechansm Vladmr Zaborovsy, Oleg Zayats, Vladmr Muluha Polytechncal Unversty, Sant-Petersburg, Russa Arl 4, 00 Content I. Introducton II.
More informationEffects of bursty traffic in service differentiated Optical Packet Switched networks
Effects of bursty traffc n servce dfferentated Optcal Packet Swtched networks Harald Øverby, orvald Stol Department of Telematcs, orwegan Unversty of Scence and Technology, O.S. Bragstadsplass E, -749
More informationJ. Parallel Distrib. Comput.
J. Parallel Dstrb. Comput. 7 (20) 537 555 Contents lsts avalable at ScenceDrect J. Parallel Dstrb. Comput. ournal homepage: www.elsever.com/locate/pdc Game-theoretc statc load balancng for dstrbuted systems
More informationTransfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system
Transfer Functons Convenent representaton of a lnear, dynamc model. A transfer functon (TF) relates one nput and one output: x t X s y t system Y s The followng termnology s used: x y nput output forcng
More informationSingular Value Decomposition: Theory and Applications
Sngular Value Decomposton: Theory and Applcatons Danel Khashab Sprng 2015 Last Update: March 2, 2015 1 Introducton A = UDV where columns of U and V are orthonormal and matrx D s dagonal wth postve real
More informationLecture Randomized Load Balancing strategies and their analysis. Probability concepts include, counting, the union bound, and Chernoff bounds.
U.C. Berkeley CS273: Parallel and Dstrbuted Theory Lecture 1 Professor Satsh Rao August 26, 2010 Lecturer: Satsh Rao Last revsed September 2, 2010 Lecture 1 1 Course Outlne We wll cover a samplng of the
More informationLecture 3 January 31, 2017
CS 224: Advanced Algorthms Sprng 207 Prof. Jelan Nelson Lecture 3 January 3, 207 Scrbe: Saketh Rama Overvew In the last lecture we covered Y-fast tres and Fuson Trees. In ths lecture we start our dscusson
More informationUsing non-preemptive regions and path modification to improve schedulability of real-time traffic over priority-based NoCs
Real-Tme Syst (2017) 53:886 915 DOI 10.1007/s11241-017-9276-5 Usng non-preemptve regons and path modfcaton to mprove schedulablty of real-tme traffc over prorty-based NoCs Meng Lu 1 Matthas Becker 1 Mors
More informationA Simple Inventory System
A Smple Inventory System Lawrence M. Leems and Stephen K. Park, Dscrete-Event Smulaton: A Frst Course, Prentce Hall, 2006 Hu Chen Computer Scence Vrgna State Unversty Petersburg, Vrgna February 8, 2017
More information6.842 Randomness and Computation February 18, Lecture 4
6.842 Randomness and Computaton February 18, 2014 Lecture 4 Lecturer: Rontt Rubnfeld Scrbe: Amartya Shankha Bswas Topcs 2-Pont Samplng Interactve Proofs Publc cons vs Prvate cons 1 Two Pont Samplng 1.1
More informationPlanning and Scheduling to Minimize Makespan & Tardiness. John Hooker Carnegie Mellon University September 2006
Plannng and Schedulng to Mnmze Makespan & ardness John Hooker Carnege Mellon Unversty September 2006 he Problem Gven a set of tasks, each wth a deadlne 2 he Problem Gven a set of tasks, each wth a deadlne
More informationToward Understanding Heterogeneity in Computing
Toward Understandng Heterogenety n Computng Arnold L. Rosenberg Ron C. Chang Department of Electrcal and Computer Engneerng Colorado State Unversty Fort Collns, CO, USA {rsnbrg, ron.chang@colostate.edu}
More informationStructure and Drive Paul A. Jensen Copyright July 20, 2003
Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.
More informationZeros and Zero Dynamics for Linear, Time-delay System
UNIVERSITA POLITECNICA DELLE MARCHE - FACOLTA DI INGEGNERIA Dpartmento d Ingegnerua Informatca, Gestonale e dell Automazone LabMACS Laboratory of Modelng, Analyss and Control of Dynamcal System Zeros and
More informationAnalysis of Queuing Delay in Multimedia Gateway Call Routing
Analyss of Queung Delay n Multmeda ateway Call Routng Qwe Huang UTtarcom Inc, 33 Wood Ave. outh Iseln, NJ 08830, U..A Errol Lloyd Computer Informaton cences Department, Unv. of Delaware, Newark, DE 976,
More informationDynamic Scheduling and Control-Quality Optimization of Self-Triggered Control Applications
Dynamc Schedulng and Control-Qualty Optmzaton of Self-Trggered Control Applcatons Sam, Sohel; Eles, Petru; Peng, Zebo; Tabuada, Paulo; Cervn, Anton 200 Lnk to publcaton Ctaton for publshed verson (APA):
More informationImproved Worst-Case Response-Time Calculations by Upper-Bound Conditions
Improved Worst-Case Response-Tme Calculatons by Upper-Bound Condtons Vctor Pollex, Steffen Kollmann, Karsten Albers and Frank Slomka Ulm Unversty Insttute of Embedded Systems/Real-Tme Systems {frstname.lastname}@un-ulm.de
More information829. An adaptive method for inertia force identification in cantilever under moving mass
89. An adaptve method for nerta force dentfcaton n cantlever under movng mass Qang Chen 1, Mnzhuo Wang, Hao Yan 3, Haonan Ye 4, Guola Yang 5 1,, 3, 4 Department of Control and System Engneerng, Nanng Unversty,
More informationDepartment of Electrical & Electronic Engineeing Imperial College London. E4.20 Digital IC Design. Median Filter Project Specification
Desgn Project Specfcaton Medan Flter Department of Electrcal & Electronc Engneeng Imperal College London E4.20 Dgtal IC Desgn Medan Flter Project Specfcaton A medan flter s used to remove nose from a sampled
More informationDynamic Systems on Graphs
Prepared by F.L. Lews Updated: Saturday, February 06, 200 Dynamc Systems on Graphs Control Graphs and Consensus A network s a set of nodes that collaborates to acheve what each cannot acheve alone. A network,
More informationEfficient Feasibility Analysis for Real-Time Systems with EDF scheduling*
Effcent Feasblty Analyss for Real-Tme Systems wth EF schedulng* Karsten Albers, Frank Slomka epartment of omputer Scence Unversty of Oldenburg Ammerländer Heerstraße 114-118 26111 Oldenburg, Germany {albers,
More information10-701/ Machine Learning, Fall 2005 Homework 3
10-701/15-781 Machne Learnng, Fall 2005 Homework 3 Out: 10/20/05 Due: begnnng of the class 11/01/05 Instructons Contact questons-10701@autonlaborg for queston Problem 1 Regresson and Cross-valdaton [40
More informationLecture 4: November 17, Part 1 Single Buffer Management
Lecturer: Ad Rosén Algorthms for the anagement of Networs Fall 2003-2004 Lecture 4: November 7, 2003 Scrbe: Guy Grebla Part Sngle Buffer anagement In the prevous lecture we taled about the Combned Input
More informationPartitioned Mixed-Criticality Scheduling on Multiprocessor Platforms
Parttoned Mxed-Crtcalty Schedulng on Multprocessor Platforms Chuanca Gu 1, Nan Guan 1,2, Qngxu Deng 1 and Wang Y 1,2 1 Northeastern Unversty, Chna 2 Uppsala Unversty, Sweden Abstract Schedulng mxed-crtcalty
More informationErrors for Linear Systems
Errors for Lnear Systems When we solve a lnear system Ax b we often do not know A and b exactly, but have only approxmatons  and ˆb avalable. Then the best thng we can do s to solve ˆx ˆb exactly whch
More informationOnline Classification: Perceptron and Winnow
E0 370 Statstcal Learnng Theory Lecture 18 Nov 8, 011 Onlne Classfcaton: Perceptron and Wnnow Lecturer: Shvan Agarwal Scrbe: Shvan Agarwal 1 Introducton In ths lecture we wll start to study the onlne learnng
More informationLecture 10: May 6, 2013
TTIC/CMSC 31150 Mathematcal Toolkt Sprng 013 Madhur Tulsan Lecture 10: May 6, 013 Scrbe: Wenje Luo In today s lecture, we manly talked about random walk on graphs and ntroduce the concept of graph expander,
More informationNON-LINEAR CONVOLUTION: A NEW APPROACH FOR THE AURALIZATION OF DISTORTING SYSTEMS
NON-LINEAR CONVOLUTION: A NEW APPROAC FOR TE AURALIZATION OF DISTORTING SYSTEMS Angelo Farna, Alberto Belln and Enrco Armellon Industral Engneerng Dept., Unversty of Parma, Va delle Scenze 8/A Parma, 00
More informationIntegrated Scheduling and Synthesis of Control Applications on Distributed Embedded Systems
Integrated Schedulng and Synthess of Control Applcatons on Dstrbuted Embedded Systems Sam, Sohel; Cervn, Anton; Eles, Petru; Peng, Zebo 9 Lnk to publcaton Ctaton for publshed verson (APA): Sam, S., Cervn,
More informationDESIGN OF CONTROL PROCESSES IN DPS BLOCKSET FOR MATLAB & SIMULINK
DESIGN OF CONTROL PROCESSES IN DPS BLOCKSET FOR MATLAB & SIMULINK C. Belavý, G. Hulkó, M. Mchalečko, V. Ivanov Department of Automaton, Informatcs and Instrumentaton, Faculty of Mechancal Engneerng, Slovak
More informationLecture Notes on Linear Regression
Lecture Notes on Lnear Regresson Feng L fl@sdueducn Shandong Unversty, Chna Lnear Regresson Problem In regresson problem, we am at predct a contnuous target value gven an nput feature vector We assume
More informationMinimizing Energy Consumption of MPI Programs in Realistic Environment
Mnmzng Energy Consumpton of MPI Programs n Realstc Envronment Amna Guermouche, Ncolas Trquenaux, Benoît Pradelle and Wllam Jalby Unversté de Versalles Sant-Quentn-en-Yvelnes arxv:1502.06733v2 [cs.dc] 25
More informationEEE 241: Linear Systems
EEE : Lnear Systems Summary #: Backpropagaton BACKPROPAGATION The perceptron rule as well as the Wdrow Hoff learnng were desgned to tran sngle layer networks. They suffer from the same dsadvantage: they
More informationAppendix B: Resampling Algorithms
407 Appendx B: Resamplng Algorthms A common problem of all partcle flters s the degeneracy of weghts, whch conssts of the unbounded ncrease of the varance of the mportance weghts ω [ ] of the partcles
More informationQuality of Routing Congestion Games in Wireless Sensor Networks
Qualty of Routng ongeston Gaes n Wreless Sensor Networs ostas Busch Lousana State Unversty Rajgoal Kannan Lousana State Unversty Athanasos Vaslaos Unv. of Western Macedona 1 Outlne of Tal Introducton Prce
More informationOutline and Reading. Dynamic Programming. Dynamic Programming revealed. Computing Fibonacci. The General Dynamic Programming Technique
Outlne and Readng Dynamc Programmng The General Technque ( 5.3.2) -1 Knapsac Problem ( 5.3.3) Matrx Chan-Product ( 5.3.1) Dynamc Programmng verson 1.4 1 Dynamc Programmng verson 1.4 2 Dynamc Programmng
More informationHashing. Alexandra Stefan
Hashng Alexandra Stefan 1 Hash tables Tables Drect access table (or key-ndex table): key => ndex Hash table: key => hash value => ndex Man components Hash functon Collson resoluton Dfferent keys mapped
More informationA Bayesian Approach to Arrival Rate Forecasting for Inhomogeneous Poisson Processes for Mobile Calls
A Bayesan Approach to Arrval Rate Forecastng for Inhomogeneous Posson Processes for Moble Calls Mchael N. Nawar Department of Computer Engneerng Caro Unversty Caro, Egypt mchaelnawar@eee.org Amr F. Atya
More informationSection 8.3 Polar Form of Complex Numbers
80 Chapter 8 Secton 8 Polar Form of Complex Numbers From prevous classes, you may have encountered magnary numbers the square roots of negatve numbers and, more generally, complex numbers whch are the
More informationOptimal Static Partition Configuration in ARINC653 System
JOURNAL OF ELECTRONIC SCIENCE AND TECHNOLOGY, VOL. 9, NO. 4, DECEMBER 7 Optmal Statc rtton Confguraton n ARINC6 System Sheng-Ln Gu, Le Luo, Sen-Sen Tang, and Yang Meng Abstract ARINC6 systems, whch have
More informationZhongping Jiang Tengfei Liu
Zhongpng Jang Tengfe Lu 4 F.L. Lews, NAI Moncref-O Donnell Char, UTA Research Insttute (UTARI) The Unversty of Texas at Arlngton, USA and Qan Ren Consultng Professor, State Key Laboratory of Synthetcal
More informationContinued..& Multiplier
CS222: Computer Arthmetc : Adder Contnued..& Multpler Dr. A. Sahu Dept of Comp. Sc. & Engg. Indan Insttute of Technology Guwahat 1 Outlne Adder Unversal Use (N bt addton) RppleCarry Adder, Full Adder,
More informationTwo-Phase Low-Energy N-Modular Redundancy for Hard Real-Time Multi-Core Systems
1 Two-Phase Low-Energy N-Modular Redundancy for Hard Real-Tme Mult-Core Systems Mohammad Saleh, Alreza Ejlal, and Bashr M. Al-Hashm, Fellow, IEEE Abstract Ths paper proposes an N-modular redundancy (NMR)
More informationCOMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS
Avalable onlne at http://sck.org J. Math. Comput. Sc. 3 (3), No., 6-3 ISSN: 97-537 COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS
More informationJoint Scheduling and Power-Allocation for Interference Management in Wireless Networks
Jont Schedulng and Power-Allocaton for Interference Management n Wreless Networks Xn Lu *, Edwn K. P. Chong, and Ness B. Shroff * * School of Electrcal and Computer Engneerng Purdue Unversty West Lafayette,
More informationMDL-Based Unsupervised Attribute Ranking
MDL-Based Unsupervsed Attrbute Rankng Zdravko Markov Computer Scence Department Central Connectcut State Unversty New Brtan, CT 06050, USA http://www.cs.ccsu.edu/~markov/ markovz@ccsu.edu MDL-Based Unsupervsed
More informationFor now, let us focus on a specific model of neurons. These are simplified from reality but can achieve remarkable results.
Neural Networks : Dervaton compled by Alvn Wan from Professor Jtendra Malk s lecture Ths type of computaton s called deep learnng and s the most popular method for many problems, such as computer vson
More informationEnergy Efficient Routing in Ad Hoc Disaster Recovery Networks
Energy Effcent Routng n Ad Hoc Dsaster Recovery Networks Gl Zussman and Adran Segall Department of Electrcal Engneerng Technon Israel Insttute of Technology Hafa 32000, Israel {glz@tx, segall@ee}.technon.ac.l
More informationbiologically-inspired computing lecture 21 Informatics luis rocha 2015 INDIANA UNIVERSITY biologically Inspired computing
lecture 21 -nspred Sectons I485/H400 course outlook Assgnments: 35% Students wll complete 4/5 assgnments based on algorthms presented n class Lab meets n I1 (West) 109 on Lab Wednesdays Lab 0 : January
More informationIntroduction to the Introduction to Artificial Neural Network
Introducton to the Introducton to Artfcal Neural Netork Vuong Le th Hao Tang s sldes Part of the content of the sldes are from the Internet (possbly th modfcatons). The lecturer does not clam any onershp
More informationIntroduction to Algorithms
Introducton to Algorthms 6.046J/8.40J Lecture 7 Prof. Potr Indyk Data Structures Role of data structures: Encapsulate data Support certan operatons (e.g., INSERT, DELETE, SEARCH) Our focus: effcency of
More informationWinter 2008 CS567 Stochastic Linear/Integer Programming Guest Lecturer: Xu, Huan
Wnter 2008 CS567 Stochastc Lnear/Integer Programmng Guest Lecturer: Xu, Huan Class 2: More Modelng Examples 1 Capacty Expanson Capacty expanson models optmal choces of the tmng and levels of nvestments
More informationFormal solvers of the RT equation
Formal solvers of the RT equaton Formal RT solvers Runge- Kutta (reference solver) Pskunov N.: 979, Master Thess Long characterstcs (Feautrer scheme) Cannon C.J.: 970, ApJ 6, 55 Short characterstcs (Hermtan
More informationUsing Traffic Regulation to Meet End-to-End Deadlines in ATM Networks
IEEE TRANSACTIONS ON COMPUTERS, VOL. 48, NO. 9, SEPTEMBER 1999 917 Usng Traffc Regulaton to Meet End-to-End Deadlnes n ATM Networks Amtava Raha, Sanjay Kamat, Xaohua Ja, Member, IEEE Computer Socety, and
More informationELE B7 Power Systems Engineering. Power Flow- Introduction
ELE B7 Power Systems Engneerng Power Flow- Introducton Introducton to Load Flow Analyss The power flow s the backbone of the power system operaton, analyss and desgn. It s necessary for plannng, operaton,
More informationTOPICS MULTIPLIERLESS FILTER DESIGN ELEMENTARY SCHOOL ALGORITHM MULTIPLICATION
1 2 MULTIPLIERLESS FILTER DESIGN Realzaton of flters wthout full-fledged multplers Some sldes based on support materal by W. Wolf for hs book Modern VLSI Desgn, 3 rd edton. Partly based on followng papers:
More informationp 1 c 2 + p 2 c 2 + p 3 c p m c 2
Where to put a faclty? Gven locatons p 1,..., p m n R n of m houses, want to choose a locaton c n R n for the fre staton. Want c to be as close as possble to all the house. We know how to measure dstance
More informationComplex Numbers Alpha, Round 1 Test #123
Complex Numbers Alpha, Round Test #3. Wrte your 6-dgt ID# n the I.D. NUMBER grd, left-justfed, and bubble. Check that each column has only one number darkened.. In the EXAM NO. grd, wrte the 3-dgt Test
More informationParking Demand Forecasting in Airport Ground Transportation System: Case Study in Hongqiao Airport
Internatonal Symposum on Computers & Informatcs (ISCI 25) Parkng Demand Forecastng n Arport Ground Transportaton System: Case Study n Hongqao Arport Ln Chang, a, L Wefeng, b*, Huanh Yan 2, c, Yang Ge,
More information>>> SOLUTIONS <<< Comprehensive Final Exam for Computer Networks (CNT 6215) Fall 2011
Comprehensve Fnal Exam for Computer Networks (CNT 65) Fall >>> SOLUTIONS
More informationThe Two-scale Finite Element Errors Analysis for One Class of Thermoelastic Problem in Periodic Composites
7 Asa-Pacfc Engneerng Technology Conference (APETC 7) ISBN: 978--6595-443- The Two-scale Fnte Element Errors Analyss for One Class of Thermoelastc Problem n Perodc Compostes Xaoun Deng Mngxang Deng ABSTRACT
More information