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

Size: px
Start display at page:

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

Transcription

1 Implementng the NASA Deep Space LDPC Codes for Defense Applcatons Wley H. Zhao, Jeffrey P. Long 1 Abstract Selected codes from, and extended from, the NASA s deep space low-densty party-check (LDPC) codes are mplemented for hgh speed defense applcatons. Ths s part of an effort to buld Government reference waveform mplementatons to assst defense acquston programs and to promote waveform re-use. Detals of the decoder mplementaton, ncludng memory layout, parallelzaton archtecture, layered-decodng schedulng, and feld programmable gate array (FPGA) resource utlzaton are presented. Index Terms Forward Error Correcton, Low Densty Party Check Codes, FPGA I. INTRODUCTION THE low-densty party-check (LDPC) code s gettng utlzed for hgh speed hgh performance applcatons due to the ease of ts parallel mplementaton n hardware and close to capacty performance. Waveforms employng LDPC codes nclude DVB-S2 [1] and IEEE [2] and lately an ncreasng number of defense applcatons have begun to ncorporate LDPC codes n ther waveform desgns, such as the Global tonng Systems (GPS) [3] among others [4], [5]. As waveforms n defense applcatons are desgned or upgraded, one can expect LDPC codes to fnd wder applcaton n order to take the advantage of the hgh speed mplementaton and capacty approachng performance. In ths paper, we present the mplementaton of four LDPC codes based on the NASA deep space LDPC codes [6]. The work s a part of an effort to buld Government reference waveform mplementatons to help reduce rsk and promote waveform re-use across related defense acquston programs. The codes are mplemented n Very hgh speed ntegrated crcut Hardware Descrpton Language (VHDL) wth the ntent of employng Feld Programmable Gate Arrays (FPGA) to offer a re-programmable rado soluton. The paper begns wth a bref descrpton of the LDPC codes mplemented along wth the decodng algorthm used (Sec. II) and contnues wth a descrpton of the archtectural approach utlzed (Sec III). Ths dscusson wll nclude detals on layered processng and proper schedulng as a technque to ncrease throughput by ppe-lnng multple layers smultaneously n the decodng engne. In addton, the memory utlzaton and layout wll be explaned n the context of the layered processng approach. The paper wll conclude wth FPGA resource utlzaton for the mplementaton as well as the throughput achevable and performance curves (Sec. IV). Wley H. Zhao s wth the MITRE Corporaton, Bedford, MA, e-mal: wzhao@mtre.org. Jeffrey P. Long s wth the MITRE Corporaton, Bedford, MA, e-mal: jplong@mtre.org. Although not presented here t should be mentoned that a hgh speed parallel encoder was also developed as a complement to the decoder dscussed n ths work. II. THE IMPLEMENTED CODES AND DECODING ALGORITHM DESCRIPTION Four LDPC codes based on NASA s deep space LDPC codes are mplemented n ths effort. Two of them are rate 1/2 wth the coded block sze of 2048 and bts and the other two are rate 7/8 wth the coded block sze of 4096 and bts. These code rates and block szes accommodate both power-lmted, bandwdth-lmted and short, long delay scenaros as the applcaton requres. Among these codes, rate 1/ bt code s drectly from [6]. The other three are extensons from [6]. NASA s deep space LDPC codes are Accumulate Repeat- 4 Jagged-Accumulate (AR4JA) codes [7]. These are structured LDPC codes desgned by lftng up a protograph party check matrx nto a larger party check matrx consstng of crculants. Party check matrces constructed wth crculants are crtcal n mplementng parallel decodng algorthms for hgh speed applcatons. The DVB-S2 codes and the IEEE codes are of ths type. However, unlke DVB-S2 codes, whch consst of supermposed crculants, the NASA deep space LDPC codes consst of only smple crculants. Ths sgnfcantly smplfes the layered decoder mplementaton[8], [9]. The layered decodng algorthm has been the preferred decodng algorthm because of ts faster convergence compared to the floodng algorthm [10]. For structured LDPC codes consstng of smple crculants, each row of crculants n the party check matrx naturally becomes a layer. The actual rows wthn a layer (.e., check nodes, c-nodes) can be processed n parallel because they update dfferent varable nodes (columns n the party check matrx, v-nodes) wthout memory access conflcts. The decodng proceeds as follows: Step 0: ntalze the v-node Log-lkelhood-rato (LLR) to the channel LLR and c-node to v-node messages to zero at the begnnng of a code block LQ = LLR(x ) (1) Lr j = 0 (2) Step 1, 2, and 3 are appled to each layer at frst, then repeated for each decodng teraton Step 1: compute the v-node to c-node messages Lq j, for each c-node j n a layer Lq j = LQ Lr j (3)

2 2 Step 2: compute the c-node to v-node messages Lr j = sgn(lq j) f f ( Lq j ) (4) V j\ Step 3: update v-node LLRs V j\ LQ = Lq j + Lr j (5) where LQ s the v-node LLR; LLR(x ) s the receved channel LLR for bt ; Lr j s the message from c-node j to v-node ; Lq j s the message from v-node to c-node j; V j represents the set of v-nodes connected to c-node j; V j \ represents V j excludng v-node. The functon f s defned as f(x) = log ex + 1 ( x ) e x 1 = log tan (6) 2 and t satsfes a specal addton rule ( ) f f(β ) β (7) where the parwse addton s β 1 β2 = mn(β 1, β 2 ) + log (1 + e (β1+β2)) ( log 1 + e β1 β2 ). (8) In ths mplementaton, (4) s computed usng the λ-mn algorthm [11] wth λ = 3,.e., among the v-nodes n V j \, the smallest three are used to compute the c-node to v-node messages usng (7) and (8). Equaton (8) s mplemented by a small lookup table for the log functon. III. HARDWARE ARCHITECTURE Hardware mplementaton of a LDPC decoder can be very resource ntensve. A well thought out archtecture approach s the key to developng a decoder that s both hgh throughput and small n sze. In addton extra care must be taken to talor the mplementaton for an FPGA target. The hardware archtecture approach taken here attempts to maxmze throughput and mnmze hardware complexty by utlzng a parallel layered processng scheme. Ths takes advantage of the structured nature of these NASA LDPC codes as well as the use of crculants n buldng the party check matrx. A. Decoder memory layout and parallelzaton scheme For LDPC codes wth party check matrces made of crculants of sze M (.e., each crculant s a M M rotated dentty matrx), (3), (4), and (5) can be mplemented n parallel by a factor up to M. For example, M LQ s and M Lr s are accessed n parallel to update M Lq s smultaneously. Ths s closely related to the memory layout of LQ and Lr. In the followng, we follow a smlar approach used for DVB-S2 codes [12]. Fg.1 llustrates how LQ and Lr s lad out n FPGA block RAM (BRAM). LQ s (ntally populated wth nput LLRs) are parttoned nto multple rows, each wth M values. Correspondngly, Lr s are parttoned nto rows wth M values each. Each memory access reads or wrtes one row of LQ or Lr wth M values. The parallel decodng processng s equvalent to processng crculant by crculant. Each vald par of LQ row and Lr row corresponds to a crculant n the party check matrx. Snce rotatons n each crculant are dfferent, t s necessary to algn LQ and Lr to a common reference so that the 1 to M element of a Lr row corresponds to the 1 to M element of the correspondng LQ row. Ths s acheved by crcularly left rotatng a LQ row by the amount ndcated by the correspondng crculant rotaton. After the rotaton, the frst element n the rotated LQ corresponds to the frst element of the correspondng Lr row as ndcated n Fg.1. Fgure 1. An example decoder memory layout of Lr and LQ for a hypothetcal code wth crculant sze of M = 128. Each map from a row of Lr to a row of LQ corresponds to a crculant n the party check matrx. The relaton of the crculant rotaton and the left rotaton of a LQ row to algn wth the Lr row s ndcated. The mappng of Lr rows to LQ rows and the correspondng crculant rotatons are the code parameters saved n the decoder. For the four mplemented LDPC codes, the crculant szes are 128 (for 2048 rate 1/2), 1024 (for rate 1/2), 64 (for 4096 rate 7/8), and 256 (for rate 7/8). Full parallelzaton by M would requre too much FPGA resources for large M (e.g., 1024). Further more, the four codes need to share the same decoder archtecture so that they can be swtch from one another wthout a complete FPGA reload. In the followng, a scheme of parallelzaton by L, wth L beng a factor of M, s descrbed followng a smlar approach n [13]. Wth ths scheme, L = 64 s the maxmum parallelzaton supported by all four codes mplemented. To create a parallel by L decoder, each row of LQ and Lr n Fg.1 s converted nto z = M/L rows by takng every other z th element nto a new row wth wdth L. We call these smaller rows sub-rows. After the re-arrangement, the same memory access scheme now accesses sub-rows, L elements at a tme. The layered decodng algorthm now works on sublayers. The crcular left rotatons that algn sub-rows of LQ and Lr, and the sub-row processng order are related to the orgnal

3 3 crculant rotaton by the followng t 1 = R M /z (9) t 2 = R M % z (10) A s = sub + t 2 ) % z (k { (11) R s = (t 1 + 1) % L A s < t 2 t 1 A s t 2 (12) where R M [0, M 1] s the rotaton of the crculant; k sub [0, z 1] s the sub-row processng order ndex; A s [0, z 1] s the ndex of the sub-row correspondng to the processng order ndex k sub ; R s [0, L 1] s the rotaton of the LQ sub-row needed to algn sub-rows of LQ and Lr. The symbol takes the nteger porton of a dvson and % s the remander operator. Fg.2 shows an example of ths operaton wth a crculant of sze M = 9 and a parallelzaton by L = 3. elements are actually repeated n the hardware 64 tmes. The memory s constructed of FPGA block RAMs arranged n parallel to effectvely buld a sngle very wde word memory. The number of block RAM requred depends on what the FPGA vendors offer n terms of the maxmum word wdth per block RAM. Snce the decoder s parallel by 64 each word n RAM s 64 tmes the wdth of each element. Detals on the choce of metrc wdth s gven n Sec. IV. The majorty of the RAM utlzaton s contaned n the Lr and LQ RAM. The LQ RAM s ntally loaded wth the nput LLRs as n step 0 and then contnually updated each teraton. The multplexer shown facltes that. The shfter s a smple 64 element wde barrel shfter ppe-lned to mprove speed. Snce the v-node to c-node calculaton of step 1 s needed for the LQ updates calculated n step 3 a smple FIFO s utlzed to temporarly store the step 1 results for step 3. A full memory s not usually needed here but the FIFO s stll requred to support the very wde word wdth. LQ RAM Lr RAM Shfter Parallel Input S l c e Parallel Output Check Node Calculaton FIFO These Elements are Repeated n Parallel Fgure 3. Decodng Core Fgure 2. The example llustrates how to convert from the full parallelzaton by the crculant sze M = 9 to a parallelzaton by L = 3. Dfferent colored elements n a row of LQ are converted to z = M/L = 3 sub-rows. If the crculant assocated wth ths row of LQ and a certan row of Lr (not shown) has a rotaton value of 2, the left rotated LQ row for parallelzaton by M s shown at the bottom left. The rotaton value of 2 s dstrbuted among z = 3 sub-rows for parallelzaton by L = 3, the frst two sub-rows are left rotated by 1 and the thrd sub-row s not rotated. The thrd sub-row s the frst to be processed. The c-node to v-node calculaton or check node calculaton (Fg. 4) of step 2 utlzes a λ-mn algorthm wth λ = 3 as mentoned n Sec. II. Ths s mplemented usng a mnmum sort routne followed by a calculaton whch performs the operaton n (4). The operaton denoted as mn* performs β 1 β 2 as n (8). The complete detals of the mn* operaton are shown n Fg. 5 As mentoned the log functon s mplemented va a small look-up table. Ths table has only a few small values so t s hard-coded and syntheszes to combnatoral logc. B. Decodng Core and Check Node Calculaton The decoder core shown n Fg. 3 s responsble for mplementng the algorthm explaned n Sec. II. Snce the decodng s done n parallel, the dagram s color coded to show whch C. Layer Schedulng The decoder processes multple layers n each teraton. As a decodng teraton s essentally a process where you read memory, calculate some results and wrte back to memory, n theory t s possble to ppelne the operaton to process

4 4 reg Fll Input RAM Read Input RAM Lq Sgn Sgn Array Read Output RAM DECODE Wrte Output RAM mn2 Mn Sort mn1 mn0 Mn* Mn* Lr Fgure 6. Decoder Processng Approach Top Level Control Fgure 4. Check Node Calculaton Rdy Rdy Data Vald Input Ram LDPC 64 Wde 64 Wde Core Output Ram Data Vald LLR Data (Seral) Decoded Data(1 Bt) β 1 β 2 Mn Fgure 5. Mn* Calculaton layers back to back and ncrease throughput. In practce there s latency n the calculaton step and f a memory read requres data that s stll processng through the ppelne then there wll be an error. In our decoder mplementaton, layers are reordered to avod such memory conflcts so that multple layers can be ppe-lned to ncrease throughput. For codes that cannot avod such memory conflcts, wat tmes are nserted n the ppelne flow so that the problem layer s completed and wrtten nto memory before the next layer s started. D. Overall Archtecture As was shown n Fg. 3 the decodng core processes parallel sets of LQ and Lr n a sngle clock cycle. Ths creates a very effcent processng approach but does not lend tself to a very practcal external nterface. To facltate data transfer n and out of the decoder core, two sets of memory are utlzed. Ths memory serves several functons. It converts the seral stream of LLR data comng nto the decoder nto the wde parallel word and performs the reverse wth the output data bts. It also performs the packng and unpackng of the ncomng and outgong data nto the partcular memory arrangement as mentoned n Sec. III-A. Lastly t acts smlar to a double buffer arrangement to allow data to be contnually transferred n and out whle decodng. Fg. 6 shows how decodng and data transfer occurs. Snce the decodng process requres the most amount of tme, nput data can be slowly read n and slowly read out whle the decodng completes. We then take advantage of the hgh degree of parallelzaton and quckly transfer the contents n and out of the RAMs wthout sgnfcantly mpactng throughput. A complete component (Fg. 7) ncludes ths RAM at the nput and output wth addtonal control to synchronze the decodng and transfer functons. LUT LUT y Fgure 7. Decoder Top Level Dagram IV. FPGA RESOURCE UTILIZATION, THROUGHPUT AND PERFORMANCE Ths FPGA mplementaton was targeted at a Xlnx FPGA n the Vrtex 6 famly. Whle not the latest technology offered by Xlnx ths FPGA s consdered the stable choce for many of the FPGA processng cards currently offered on the market. As the results n Tab. I show the desgn does not exceed the sze constrants of modern FPGAs and t would be possble to utlze multple cores wthn a sngle FPGA n order to accommodate very hgh data rates. Table I FPGA RESOURCE UTILIZATION Xlnx Vrtex 6 LX 130 Slce Regsters Slce LUTS Block RAM (36Kb) 36 The desgn metrc wdths shown n Tab. II are perhaps the most crtcal n terms of determnng overall sze. They drectly mpact the number of memory elements requred as well as the logc and regster usage. Obvously the goal would be to make these metrcs as small as possble but you can quckly encounter a loss n performance and may need to trade off sze for performance. Table II IMPLEMENTATION DETAILS LLR Input Metrc Wdth 5 Internal LQ Metrc Wdth 8 Lr Metrc Wdth 6 The decoder throughput s gven n Tab. III. In the example gven, the decoder s asked to do 30 teratons. The desgn as targeted for a Xlnx V6 s able to acheve a 175 Mhz clock rate. The throughput of each code s then calculated as shown. Obvously wth fewer teratons the throughput could be ncreased so agan a balance needs to be found tradng throughput aganst performance. Of note s the row enttled Extra Processng Delay. Ths s the extra wat tme n clock

5 5 Table III DECODER THROUGHPUT Rate 1/2 2K Rate 1/2 16K Rate 7/8 4K Rate 7/8 16K Clock Speed Acheved 175 MHz 175 MHz 175 MHz 175 MHz M L Rows/Iteraton Iteratons Extra Processng Delay Uncoded Throughput 31 Mbps 50 Mbps 52 Mbps 75 Mbps cycles mentoned n Sec. III-C. Ths drectly mpacts the throughput and needs to be mnmzed as much as possble utlzng careful schedulng of the layers. Fg.8 shows the smulated Bt Error Rate (BER) and the block, here referred as Frame, Error Rate (FER) for the four mplemented codes usng a fxed pont decoder model runnng at 30 teratons. Fgure 8. Smulated BER and FER curves for the four mplemented codes usng a fxed pont decoder model runnng at 30 teratons. V. CONCLUSIONS In ths work we have successfully mplemented an extenson to the NASA codes developed for Deep Space applcatons wth potental use n varous hgh-speed hgh-throughput defense applcatons. In addton we have shown how t s possble to acheve hgh throughput wth few logc resources and stll mantan decodng performance utlzng layered parallel processng. It s felt that the results presented here wll be crtcal to helpng LDPC codes fnd more applcaton n Defense applcatons wthout the mpact to Sze Weght and Power (SWaP) as s tradtonally thought. In addton the utlzaton of FPGAs ensures use n re-programmable rado applcatons. Future work wll focus on further enhancements and optmzatons to ncrease throughput and reduce sze whle mantanng the performance shown. REFERENCES [1] Dgtal vdeo broadcastng (DVB); Second generaton framng structure, channel codng and modulaton systems for broadcastng, nteractve servces, news gatherng and other broad-band satellte applcatons, ETSI Std. EN V1.2.1, [2] IEEE Standard for Local and metropoltan area networks Part 16: Ar Interface for Fxed and Moble Broadband Wreless Access Systems, IEEE Std e, [3] Navstar GPS Space Segment/User Segment L1C Interfaces, GLOBAL POSITIONING SYSTEMS WING (GPSW) SYSTEM ENGINEERING and INTEGRATION, Verson 5.4, 15 October [4] Hgh Data Rate - Rado Frequency Ground Modem, Phase 3, Modem Performance Specfcaton, The MITRE Corporaton, Draft, 08 June [5] Jont Internet Protocol Modem Performance Specfcaton, Defense Informaton Systems Agency and Defense Communcatons and Army Transmsson Systems, IS-GPS-800, 04 Sep [6] Low Densty Party Check Codes for Use n Near-Earth and Deep Space Applcatons, Orange Book, Issue 2, Consulatve Commttee for Space Data Systems (CCSDS) Expermental Specfcaton O-2, [7] D. Dvsalar, C. Jones, S. Dolnar, and J. Thorpe, Protograph based ldpc codes wth mnmum dstance lnearly growng wth block sze, n Global Telecommuncatons Conference, GLOBECOM 05. IEEE, vol. 3, 2005, pp. 5 pp.. [8] M. Mansour and N. Shanbhag, Hgh-throughput ldpc decoders, Very Large Scale Integraton (VLSI) Systems, IEEE Transactons on, vol. 11, no. 6, pp , [9] D. E. Hocevar, A reduced complexty decoder archtecture va layered decodng of ldpc codes, n IEEE Workshop on Sgnal Processng Systems, 2004, p [10] R. G. Gallager, Low-Densty Party-Check Codes. Cambrdge, MA: M.I.T. Press, [11] F. Gulloud, E. Boutllon, and J.-L. Danger, Lambda-Mn decodng algorthm of regular and rregular LDPC codes, n 3rd Internatonal Symposum on Turbo Codes and related topcs, 1-5 september, Brest, France, [12] M. Eroz, F.-W. Sun, and L.-N. Lee, An nnovatve low-densty partycheck code desgn wth near-shannon-lmt performance and smple mplementaton, Communcatons, IEEE Transactons on, vol. 54, no. 1, pp , [13] M. Gomes, G. Falcao, V. Slva, V. Ferrera, A. Sengo, and M. Falcao, Flexble parallel archtecture for dvb-s2 ldpc decoders, n Global Telecommuncatons Conference, GLOBECOM 07. IEEE, 2007, pp

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

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

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

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

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

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

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

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

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

More information

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

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

More information

RADIX-10 PARALLEL DECIMAL MULTIPLIER

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

More information

3. CR parameters and Multi-Objective Fitness Function

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

More information

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

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

More information

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

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

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

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

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

More information

DESIGNING TRANSMISSION SCHEDULES FOR WIRELESS AD HOC NETWORKS TO MAXIMIZE NETWORK THROUGHPUT

DESIGNING TRANSMISSION SCHEDULES FOR WIRELESS AD HOC NETWORKS TO MAXIMIZE NETWORK THROUGHPUT DESIGNING TRANSMISSION SCHEDULES FOR WIRELESS AD HOC NETWORKS TO MAXIMIZE NETWORK THROUGHPUT Bran J. Wolf, Joseph L. Hammond, and Harlan B. Russell Dept. of Electrcal and Computer Engneerng, Clemson Unversty,

More information

Efficient Distributed File System (EDFS)

Efficient Distributed File System (EDFS) Effcent Dstrbuted Fle System (EDFS) (Sem-Centralzed) Debessay(Debsh) Fesehaye, Rahul Malk & Klara Naherstedt Unversty of Illnos-Urbana Champagn Contents Problem Statement, Related Work, EDFS Desgn Rate

More information

Analysis of Min Sum Iterative Decoder using Buffer Insertion

Analysis of Min Sum Iterative Decoder using Buffer Insertion Analyss of Mn Sum Iteratve ecoder usng Buffer Inserton Saravanan Swapna M.E II year, ept of ECE SSN College of Engneerng M. Anbuselv Assstant Professor, ept of ECE SSN College of Engneerng S.Salvahanan

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

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

CACHE MEMORY DESIGN FOR INTERNET PROCESSORS

CACHE MEMORY DESIGN FOR INTERNET PROCESSORS CACHE MEMORY DESIGN FOR INTERNET PROCESSORS WE EVALUATE A SERIES OF THREE PROGRESSIVELY MORE AGGRESSIVE ROUTING-TABLE CACHE DESIGNS AND DEMONSTRATE THAT THE INCORPORATION OF HARDWARE CACHES INTO INTERNET

More information

Convolutional interleaver for unequal error protection of turbo codes

Convolutional interleaver for unequal error protection of turbo codes Convolutonal nterleaver for unequal error protecton of turbo codes Sna Vaf, Tadeusz Wysock, Ian Burnett Unversty of Wollongong, SW 2522, Australa E-mal:{sv39,wysock,an_burnett}@uow.edu.au Abstract: Ths

More information

Fibre-Optic AWG-based Real-Time Networks

Fibre-Optic AWG-based Real-Time Networks Fbre-Optc AWG-based Real-Tme Networks Krstna Kunert, Annette Böhm, Magnus Jonsson, School of Informaton Scence, Computer and Electrcal Engneerng, Halmstad Unversty {Magnus.Jonsson, Krstna.Kunert}@de.hh.se

More information

GSLM Operations Research II Fall 13/14

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

More information

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

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

Improving High Level Synthesis Optimization Opportunity Through Polyhedral Transformations

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

More information

CMPS 10 Introduction to Computer Science Lecture Notes

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

More information

Assembler. Building a Modern Computer From First Principles.

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

More information

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

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

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

More information

VRT012 User s guide V0.1. Address: Žirmūnų g. 27, Vilnius LT-09105, Phone: (370-5) , Fax: (370-5) ,

VRT012 User s guide V0.1. Address: Žirmūnų g. 27, Vilnius LT-09105, Phone: (370-5) , Fax: (370-5) , VRT012 User s gude V0.1 Thank you for purchasng our product. We hope ths user-frendly devce wll be helpful n realsng your deas and brngng comfort to your lfe. Please take few mnutes to read ths manual

More information

A RECONFIGURABLE ARCHITECTURE FOR MULTI-GIGABIT SPEED CONTENT-BASED ROUTING. James Moscola, Young H. Cho, John W. Lockwood

A RECONFIGURABLE ARCHITECTURE FOR MULTI-GIGABIT SPEED CONTENT-BASED ROUTING. James Moscola, Young H. Cho, John W. Lockwood A RECONFIGURABLE ARCHITECTURE FOR MULTI-GIGABIT SPEED CONTENT-BASED ROUTING James Moscola, Young H. Cho, John W. Lockwood Dept. of Computer Scence and Engneerng Washngton Unversty, St. Lous, MO {jmm5,

More information

Cache Performance 3/28/17. Agenda. Cache Abstraction and Metrics. Direct-Mapped Cache: Placement and Access

Cache Performance 3/28/17. Agenda. Cache Abstraction and Metrics. Direct-Mapped Cache: Placement and Access Agenda Cache Performance Samra Khan March 28, 217 Revew from last lecture Cache access Assocatvty Replacement Cache Performance Cache Abstracton and Metrcs Address Tag Store (s the address n the cache?

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

ELEC 377 Operating Systems. Week 6 Class 3

ELEC 377 Operating Systems. Week 6 Class 3 ELEC 377 Operatng Systems Week 6 Class 3 Last Class Memory Management Memory Pagng Pagng Structure ELEC 377 Operatng Systems Today Pagng Szes Vrtual Memory Concept Demand Pagng ELEC 377 Operatng Systems

More information

AADL : about scheduling analysis

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

More information

FAHP and Modified GRA Based Network Selection in Heterogeneous Wireless Networks

FAHP and Modified GRA Based Network Selection in Heterogeneous Wireless Networks 2017 2nd Internatonal Semnar on Appled Physcs, Optoelectroncs and Photoncs (APOP 2017) ISBN: 978-1-60595-522-3 FAHP and Modfed GRA Based Network Selecton n Heterogeneous Wreless Networks Xaohan DU, Zhqng

More information

Load Balancing for Hex-Cell Interconnection Network

Load Balancing for Hex-Cell Interconnection Network Int. J. Communcatons, Network and System Scences,,, - Publshed Onlne Aprl n ScRes. http://www.scrp.org/journal/jcns http://dx.do.org/./jcns.. Load Balancng for Hex-Cell Interconnecton Network Saher Manaseer,

More information

Mallathahally, Bangalore, India 1 2

Mallathahally, Bangalore, India 1 2 7 IMPLEMENTATION OF HIGH PERFORMANCE BINARY SQUARER PRADEEP M C, RAMESH S, Department of Electroncs and Communcaton Engneerng, Dr. Ambedkar Insttute of Technology, Mallathahally, Bangalore, Inda pradeepmc@gmal.com,

More information

Positive Semi-definite Programming Localization in Wireless Sensor Networks

Positive Semi-definite Programming Localization in Wireless Sensor Networks Postve Sem-defnte Programmng Localzaton n Wreless Sensor etworks Shengdong Xe 1,, Jn Wang, Aqun Hu 1, Yunl Gu, Jang Xu, 1 School of Informaton Scence and Engneerng, Southeast Unversty, 10096, anjng Computer

More information

Priority-Based Scheduling Algorithm for Downlink Traffics in IEEE Networks

Priority-Based Scheduling Algorithm for Downlink Traffics in IEEE Networks Prorty-Based Schedulng Algorthm for Downlnk Traffcs n IEEE 80.6 Networks Ja-Mng Lang, Jen-Jee Chen, You-Chun Wang, Yu-Chee Tseng, and Bao-Shuh P. Ln Department of Computer Scence Natonal Chao-Tung Unversty,

More information

Video Proxy System for a Large-scale VOD System (DINA)

Video Proxy System for a Large-scale VOD System (DINA) Vdeo Proxy System for a Large-scale VOD System (DINA) KWUN-CHUNG CHAN #, KWOK-WAI CHEUNG *# #Department of Informaton Engneerng *Centre of Innovaton and Technology The Chnese Unversty of Hong Kong SHATIN,

More information

u Delay Delay x p Data, u x p1 Encoder 1 Puncturer Interleaver p2 p2 Encoder 2

u Delay Delay x p Data, u x p1 Encoder 1 Puncturer Interleaver p2 p2 Encoder 2 Implementaton of Near Shannon Lmt Error-Correctng Codes Usng Recongurable Hardware Benjamn Levne, R. Reed Taylor, and Herman Schmt Abstract Error correctng codes (ECCs) are wdely used n dgtal communcatons.

More information

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

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

More information

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

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

More information

Mathematics 256 a course in differential equations for engineering students

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

More information

Real-Time Guarantees. Traffic Characteristics. Flow Control

Real-Time Guarantees. Traffic Characteristics. Flow Control Real-Tme Guarantees Requrements on RT communcaton protocols: delay (response s) small jtter small throughput hgh error detecton at recever (and sender) small error detecton latency no thrashng under peak

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

A New Memory Reduced Radix-4 CORDIC Processor For FFT Operation

A New Memory Reduced Radix-4 CORDIC Processor For FFT Operation IOSR Journal of VLSI and Sgnal Processng (IOSR-JVSP) Volume, Issue 5 (May. Jun. 013), PP 09-16 e-issn: 319 400, p-issn No. : 319 4197 www.osrjournals.org A New Memory Reduced Radx-4 CORDIC Processor For

More information

Term Weighting Classification System Using the Chi-square Statistic for the Classification Subtask at NTCIR-6 Patent Retrieval Task

Term Weighting Classification System Using the Chi-square Statistic for the Classification Subtask at NTCIR-6 Patent Retrieval Task Proceedngs of NTCIR-6 Workshop Meetng, May 15-18, 2007, Tokyo, Japan Term Weghtng Classfcaton System Usng the Ch-square Statstc for the Classfcaton Subtask at NTCIR-6 Patent Retreval Task Kotaro Hashmoto

More information

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

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

More information

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

Conditional Speculative Decimal Addition*

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

More information

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

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

More information

Module Management Tool in Software Development Organizations

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

More information

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

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

More information

Overview. Basic Setup [9] Motivation and Tasks. Modularization 2008/2/20 IMPROVED COVERAGE CONTROL USING ONLY LOCAL INFORMATION

Overview. Basic Setup [9] Motivation and Tasks. Modularization 2008/2/20 IMPROVED COVERAGE CONTROL USING ONLY LOCAL INFORMATION Overvew 2 IMPROVED COVERAGE CONTROL USING ONLY LOCAL INFORMATION Introducton Mult- Smulator MASIM Theoretcal Work and Smulaton Results Concluson Jay Wagenpfel, Adran Trachte Motvaton and Tasks Basc Setup

More information

Network Coding as a Dynamical System

Network Coding as a Dynamical System Network Codng as a Dynamcal System Narayan B. Mandayam IEEE Dstngushed Lecture (jont work wth Dan Zhang and a Su) Department of Electrcal and Computer Engneerng Rutgers Unversty Outlne. Introducton 2.

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

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

The stream cipher MICKEY-128 (version 1) Algorithm specification issue 1.0

The stream cipher MICKEY-128 (version 1) Algorithm specification issue 1.0 The stream cpher MICKEY-128 (verson 1 Algorthm specfcaton ssue 1. Steve Babbage Vodafone Group R&D, Newbury, UK steve.babbage@vodafone.com Matthew Dodd Independent consultant matthew@mdodd.net www.mdodd.net

More information

A Saturation Binary Neural Network for Crossbar Switching Problem

A Saturation Binary Neural Network for Crossbar Switching Problem A Saturaton Bnary Neural Network for Crossbar Swtchng Problem Cu Zhang 1, L-Qng Zhao 2, and Rong-Long Wang 2 1 Department of Autocontrol, Laonng Insttute of Scence and Technology, Benx, Chna bxlkyzhangcu@163.com

More information

ARTICLE IN PRESS. Signal Processing: Image Communication

ARTICLE IN PRESS. Signal Processing: Image Communication Sgnal Processng: Image Communcaton 23 (2008) 754 768 Contents lsts avalable at ScenceDrect Sgnal Processng: Image Communcaton journal homepage: www.elsever.com/locate/mage Dstrbuted meda rate allocaton

More information

A Flexible Architecture for Creating Scheduling Algorithms as used in STK Scheduler

A Flexible Architecture for Creating Scheduling Algorithms as used in STK Scheduler A Flexble Archtecture for Creatng Schedulng Algorthms as used n STK Scheduler W. A. Fsher and Ella Herz Optwse Corporaton and Orbt Logc Incorporated fsher@optwse.com and ella.herz@orbtlogc.com Abstract

More information

Random Kernel Perceptron on ATTiny2313 Microcontroller

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

More information

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

Lecture 3: Computer Arithmetic: Multiplication and Division

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

More information

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

Design and Analysis of Algorithms

Design and Analysis of Algorithms Desgn and Analyss of Algorthms Heaps and Heapsort Reference: CLRS Chapter 6 Topcs: Heaps Heapsort Prorty queue Huo Hongwe Recap and overvew The story so far... Inserton sort runnng tme of Θ(n 2 ); sorts

More information

Evaluation of an Enhanced Scheme for High-level Nested Network Mobility

Evaluation of an Enhanced Scheme for High-level Nested Network Mobility IJCSNS Internatonal Journal of Computer Scence and Network Securty, VOL.15 No.10, October 2015 1 Evaluaton of an Enhanced Scheme for Hgh-level Nested Network Moblty Mohammed Babker Al Mohammed, Asha Hassan.

More information

Spatially Coupled Repeat-Accumulate Coded Cooperation

Spatially Coupled Repeat-Accumulate Coded Cooperation Spatally Coupled Repeat-Accumulate Coded Cooperaton Naok Takesh and Ko Ishbash Advanced Wreless Communcaton Research Center (AWCC) The Unversty of Electro-Communcatons, 1-5-1 Chofugaoka, Chofu-sh, Tokyo

More information

Routing in Degree-constrained FSO Mesh Networks

Routing in Degree-constrained FSO Mesh Networks Internatonal Journal of Hybrd Informaton Technology Vol., No., Aprl, 009 Routng n Degree-constraned FSO Mesh Networks Zpng Hu, Pramode Verma, and James Sluss Jr. School of Electrcal & Computer Engneerng

More information

5 The Primal-Dual Method

5 The Primal-Dual Method 5 The Prmal-Dual Method Orgnally desgned as a method for solvng lnear programs, where t reduces weghted optmzaton problems to smpler combnatoral ones, the prmal-dual method (PDM) has receved much attenton

More information

Programming in Fortran 90 : 2017/2018

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

More information

Efficient Content Distribution in Wireless P2P Networks

Efficient Content Distribution in Wireless P2P Networks Effcent Content Dstrbuton n Wreless P2P Networs Qong Sun, Vctor O. K. L, and Ka-Cheong Leung Department of Electrcal and Electronc Engneerng The Unversty of Hong Kong Pofulam Road, Hong Kong, Chna {oansun,

More information

Greedy Technique - Definition

Greedy Technique - Definition Greedy Technque Greedy Technque - Defnton The greedy method s a general algorthm desgn paradgm, bult on the follong elements: confguratons: dfferent choces, collectons, or values to fnd objectve functon:

More information

Solving two-person zero-sum game by Matlab

Solving two-person zero-sum game by Matlab Appled Mechancs and Materals Onlne: 2011-02-02 ISSN: 1662-7482, Vols. 50-51, pp 262-265 do:10.4028/www.scentfc.net/amm.50-51.262 2011 Trans Tech Publcatons, Swtzerland Solvng two-person zero-sum game by

More information

Machine Learning. Support Vector Machines. (contains material adapted from talks by Constantin F. Aliferis & Ioannis Tsamardinos, and Martin Law)

Machine Learning. Support Vector Machines. (contains material adapted from talks by Constantin F. Aliferis & Ioannis Tsamardinos, and Martin Law) Machne Learnng Support Vector Machnes (contans materal adapted from talks by Constantn F. Alfers & Ioanns Tsamardnos, and Martn Law) Bryan Pardo, Machne Learnng: EECS 349 Fall 2014 Support Vector Machnes

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

Today s Outline. Sorting: The Big Picture. Why Sort? Selection Sort: Idea. Insertion Sort: Idea. Sorting Chapter 7 in Weiss.

Today s Outline. Sorting: The Big Picture. Why Sort? Selection Sort: Idea. Insertion Sort: Idea. Sorting Chapter 7 in Weiss. Today s Outlne Sortng Chapter 7 n Wess CSE 26 Data Structures Ruth Anderson Announcements Wrtten Homework #6 due Frday 2/26 at the begnnng of lecture Proect Code due Mon March 1 by 11pm Today s Topcs:

More information

An Application of the Dulmage-Mendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices

An Application of the Dulmage-Mendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices Internatonal Mathematcal Forum, Vol 7, 2012, no 52, 2549-2554 An Applcaton of the Dulmage-Mendelsohn Decomposton to Sparse Null Space Bases of Full Row Rank Matrces Mostafa Khorramzadeh Department of Mathematcal

More information

Connection-information-based connection rerouting for connection-oriented mobile communication networks

Connection-information-based connection rerouting for connection-oriented mobile communication networks Dstrb. Syst. Engng 5 (1998) 47 65. Prnted n the UK PII: S0967-1846(98)90513-7 Connecton-nformaton-based connecton reroutng for connecton-orented moble communcaton networks Mnho Song, Yanghee Cho and Chongsang

More information

Storage Binding in RTL synthesis

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

More information

The Impact of Delayed Acknowledgement on E-TCP Performance In Wireless networks

The Impact of Delayed Acknowledgement on E-TCP Performance In Wireless networks The mpact of Delayed Acknoledgement on E-TCP Performance n Wreless netorks Deddy Chandra and Rchard J. Harrs School of Electrcal and Computer System Engneerng Royal Melbourne nsttute of Technology Melbourne,

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

TPL-Aware Displacement-driven Detailed Placement Refinement with Coloring Constraints

TPL-Aware Displacement-driven Detailed Placement Refinement with Coloring Constraints TPL-ware Dsplacement-drven Detaled Placement Refnement wth Colorng Constrants Tao Ln Iowa State Unversty tln@astate.edu Chrs Chu Iowa State Unversty cnchu@astate.edu BSTRCT To mnmze the effect of process

More information

SAO: A Stream Index for Answering Linear Optimization Queries

SAO: A Stream Index for Answering Linear Optimization Queries SAO: A Stream Index for Answerng near Optmzaton Queres Gang uo Kun-ung Wu Phlp S. Yu IBM T.J. Watson Research Center {luog, klwu, psyu}@us.bm.com Abstract near optmzaton queres retreve the top-k tuples

More information

Using Delayed Addition Techniques to Accelerate Integer and Floating-Point Calculations in Configurable Hardware

Using Delayed Addition Techniques to Accelerate Integer and Floating-Point Calculations in Configurable Hardware Draft submtted for publcaton. Please do not dstrbute Usng Delayed Addton echnques to Accelerate Integer and Floatng-Pont Calculatons n Confgurable Hardware Zhen Luo, Nonmember and Margaret Martonos, Member,

More information

CS 268: Lecture 8 Router Support for Congestion Control

CS 268: Lecture 8 Router Support for Congestion Control CS 268: Lecture 8 Router Support for Congeston Control Ion Stoca Computer Scence Dvson Department of Electrcal Engneerng and Computer Scences Unversty of Calforna, Berkeley Berkeley, CA 9472-1776 Router

More information

3D vector computer graphics

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

More information

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

Esc101 Lecture 1 st April, 2008 Generating Permutation

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

More information

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

Analysis of Collaborative Distributed Admission Control in x Networks

Analysis of Collaborative Distributed Admission Control in x Networks 1 Analyss of Collaboratve Dstrbuted Admsson Control n 82.11x Networks Thnh Nguyen, Member, IEEE, Ken Nguyen, Member, IEEE, Lnha He, Member, IEEE, Abstract Wth the recent surge of wreless home networks,

More information

A Distributed Dynamic Bandwidth Allocation Algorithm in EPON

A Distributed Dynamic Bandwidth Allocation Algorithm in EPON www.ccsenet.org/mas Modern Appled Scence Vol. 4, o. 7; July 2010 A Dstrbuted Dynamc Bandwdth Allocaton Algorthm n EPO Feng Cao, Demng Lu, Mnmng Zhang, Kang Yang & Ynbo Qan School of Optoelectronc Scence

More information

Evaluation of TCP Variants and Bandwidth on Demand over Next Generation Satellite Network

Evaluation of TCP Variants and Bandwidth on Demand over Next Generation Satellite Network Evaluaton of TCP Varants and Bandwdth on Demand over Next Generaton Satellte Network Songrth Kttperachol, Zhl Sun and Hatham Cruckshank Centre for Communcaton Systems Research Unversty of Surrey Guldford,

More information

An Efficient Genetic Algorithm with Fuzzy c-means Clustering for Traveling Salesman Problem

An Efficient Genetic Algorithm with Fuzzy c-means Clustering for Traveling Salesman Problem An Effcent Genetc Algorthm wth Fuzzy c-means Clusterng for Travelng Salesman Problem Jong-Won Yoon and Sung-Bae Cho Dept. of Computer Scence Yonse Unversty Seoul, Korea jwyoon@sclab.yonse.ac.r, sbcho@cs.yonse.ac.r

More information

Research Article. ISSN (Print) s k and. d k rate of k -th flow, source node and

Research Article. ISSN (Print) s k and. d k rate of k -th flow, source node and Scholars Journal of Engneerng and Technology (SJET) Sch. J. Eng. Tech., 2015; 3(4A):343-350 Scholars Academc and Scentfc Publsher (An Internatonal Publsher for Academc and Scentfc Resources) www.saspublsher.com

More information