ENERGY EFFICIENT WIRELESS TRANSMISSION OF MPEG-4 FINE GRANULAR SCALABLE VIDEO

Size: px
Start display at page:

Download "ENERGY EFFICIENT WIRELESS TRANSMISSION OF MPEG-4 FINE GRANULAR SCALABLE VIDEO"

Transcription

1 NGY FFICINT WISS TANSMISSION OF MG-4 FIN GANUA SCAA VIDO Crstna. Costa, Yftach senberg 2, Fan Zha 2, Aggeos K. Katsaggeos 2 DIT, Unversty of Trento -Va Sommarve 4, I-38 Trento (ITAY) - crstna.costa@ng.untn.t 2 Northwestern Unversty, Departement of C, vanston, I 68, USA - {yesenbe, fzha, agg}@ece.northwestern.edu Abstract - Fne granuar scaabty s a codng too, recenty ntroduced n the emergng MG-4 standard, whch enabes the creaton of very fexbe scaabe vdeo btstreams. Ths paper nvestgates the transmsson of fne granuar scaabe (FGS) vdeo over wreess ns, usng power management for unequa error protecton of the btstream. In wreess systems, energy may be a mted resource, and a wse use of t s mportant for system effcency. An agorthm s proposed whch s abe to optmay dstrbute the tota avaabe power for the transmsson of the enhancement ayer, gven a dstorton or energy constrant. xpermenta resuts demonstrate the performance advantage of the proposed agorthm over fxed power schemes and heurstc approaches. INTODUCTION mergng wreess communcaton systems have evoved from a voce based servce to a mutmeda orented one. Ths transton has ncreased the demands and compexty requred from transmttng devces. Wreess networs have unque characterstcs that dstngush them from more tradtona wred networs. These characterstcs must be taen nto account when desgnng mutmeda systems and, when possbe, expoted as an advantage. One of the maor obstaces n wreess ns s the effects of channe osses on the transmtted data. Ths, combned wth the varabty of the channe characterstcs and the mtaton of resources, such as power, motvates the need for further research nto dfferent approaches for data transmsson. When speang about vdeo codng and transmsson, dfferent approaches have been proposed to cope wth these mtatons. Among them, the use of scaabty has been seen as a usefu too and new methods for achevng t have been ncuded n recent standards []. Scaabty s a codng method that produces an encoded sequence easy capabe of accommodatng dfferent btrates. In order to generate a scaabe btstream, the fne granuar scaabty (FGS) encoder produces two btstreams, commony named ase and nhancement ayers. The ase ayer () can be decoded ndependenty from the nhancement ayer (), and produces a ow quaty reconstructon of the vdeo sequence. A hgher quaty reconstructon can then be acheved by decodng both the ase and nhancement ayers together. Currenty, art 2 of the MG-4 standard ncudes FGS as an encodng too [],[2]. The FGS approach dffers from tradtona ayered methods for vdeo scaabty because of ts capabty to acheve a smooth transton between dfferent bt rates. In MG-4 FGS, the ase ayer behaves as a standard basene MG-4 compressed btstream. The dfference between the reconstructed and the orgna vdeo sequence s encoded n the nhancement ayer. rogressve encodng of the s acheved by a bt-pane codng of the DCT of the resdua mage. The DCT transform s performed on a boc bass, as s done for the ase ayer, but the data n the s entropy coded one bt-pane () at tme. The encoded data s then sent startng from the most sgnfcant bt-pane (MS) to the east sgnfcant bt-pane (S) Due to ts structure, the enhancement ayer can be truncated at any pont. In ths way, rate contro can be performed by smpy truncatng the btstream n order to adapt t to varyng channe characterstcs. Due to ts nherent scaabty and fexbty, FGS aso aows compexty scaabty and easy resource adaptaton dependng on the capabtes of vdeo devces. Thus, n addton to vdeo conferencng, FGS s aso sutabe for vdeo mutcast. An nterestng overvew of appcatons enabed by FGS technoogy s gven n [3]. Wth regard to the reated wor, unequa error protecton (U) between and usng dfferent technques, such as AQ (Automatc epeat request) and FC (Forward rror Correcton), have been studed n the past [4]. The appcaton of U wthn the FGS btstream was frst consdered by Schaar et a. n [], where the Fne-Graned oss rotecton (FG) framewor was ntroduced. ased on t, Yang et a. proposed n [6] a degressve protecton agorthm (D) based on FC for optmay assgnng protecton redundancy among bt-panes. In [7], Wang et a. studed the probem of rate-dstorton optmzed U for rogressve FGS (FGS) over wreess channes usng prortzed forward error correcton (FC) for the and. A smar probem was studed n [8] n whch the obectve was to mnmze the processng power for FGS vdeo gven bandwdth and dstorton constrants. In [9], a ont FC and transmsson power aocaton scheme for ayered vdeo transmsson over a mutpe user CDMA networs was proposed. In that wor, scaabty was acheved usng 3D-SIHT (waveet based codng). The obectve was to mnmze the end-to-end dstorton through optma bt aocaton among source ayers and power aocaton among dfferent CDMA channes. The authors n [] consdered onty adaptng the source bt rate and the transmsson power n order to maxmzng the performance of a CDMA system subect to a constrant on the equvaent bandwdth. In that wor, an H.263+ codec was used to generate the ayered bt stream. In ths wor, we consder the transmsson of an MG-4 FGS vdeo sequence over a snge wreess channe for a snge user. We propose an agorthm that aows U of the enhancement ayer pacets through optma transmsson power aocaton. Gven transmsson power and bt rate constrants, our goa s to mnmze the end-to-end dstorton by optmay aocatng transmsson power to the dfferent pacets n the. 2 FGS IN WISS VIDO TANSMISSION When transmttng n an error prone envronment, error protecton technques become of fundamenta mportance. In I Communcatons Socety /4/$. (c) 4 I

2 scaabe codng, the compressed vdeo s coded nto separate btstreams, whch can receve dfferent eves of protecton. Typcay, the ase ayer s heavy protected so that t s receved neary error free, whe a dfferent and ghter protecton scheme s used for the nhancement ayer. Due to the structure of the FGS btstream, t s aso possbe to perform unequa error protecton wthn the, snce the mportance of the data wthn the ayer decreases gong from the MS to the S. Mean t ane Sze (bytes) t ane Number Fg. Average sze of the bt-panes for the Foreman sequence (QCIF). y anayzng the btstream, we can note that typcay the most sgnfcant bt-pane s the smaest n sze (.e., has the smaest number of bts), and bt-pane sze ncreases from the most sgnfcant to the east sgnfcant bt-pane. As an exampe, n Fg. the average sze of the dfferent bt-panes s potted for the Foreman sequence (the was coded wth bt rate set at 4Kbps). Ths behavor s due to severa factors. It s ey that the frst bt pane contans mosty the nformaton reated to those macrobocs that have more error n the base ayer, such as those wth a ot of moton nformaton. Ths means that the frst bt pane contans a greater number of zeros n regons where there are statc macrobocs, such as the bacground or contnuous tone areas. Fewer bts are requred to encode arge regons of zeros. Another reason s due to the fact that the data n the east sgnfcant bt panes s ess correated, and thus more dffcut to compress wth entropy codng. As a consequence, not a the data n the nhancement ayer has the same mportance. Frst, t s not possbe to decode the data of a bt pane wthout decodng the precedng bt panes. Secondy, the data of the MS aso carres more nformaton, wth respect to the S. It s possbe then to expot ths structure for mpementng unequa error protecton (U) of the enhancement ayer data as n []. Another mportant ssue n mobe envronments s power management. nergy s a precous resource, snce batteres have a mted fe, and usng t n a wse way can greaty mprove the overa quaty of the transmsson. Snce the probabty of pacet ost s drecty reated to the power assgned to the transmtted pacet, we can thn of controng the probabty of oss by varyng the transmsson power n order to acheve unequa error protecton of the pacets []. In ths paper, an agorthm s proposed that aows unequa error protecton of the enhancement ayer pacets through optma transmsson power aocaton. 3 OW ASD UNQUA O OTCTION Tang advantage of the structure of FGS codng, t s possbe to mpement a prortzaton of the enhancement ayer pacets: a hgher eve of protecton can be gven to the more sgnfcant bt-panes, mang the protecton smaer as the bt-panes become ess sgnfcant. Assumng that the s aways receved correcty and that a maxmum amount of energy tot for the frame s gven, our goa s to determne how to aocate the avaabe power n such a way that mnmzes the overa dstorton. If we subdvde the btstream nto pacets and transmt these pacets over the channe, then the tota energy used for transmttng the s gven by the foowng formua: tot, () where s the number of bts n the -th pacet, s the power used for transmttng t, and s the channe bt-rate. The mnmzaton probem can be expressed as n the foows: mn { D [ ]}, s. t. tot, s the expected vaue of the dstorton mprovement ntroduced by onty decodng the and a the correcty receved pacets. If we assume that each pacet s successfuy decoded ony f t and a the prevous pacets are where D s the dstorton of the and [ ] correcty receved, we can wrte [ ] [ ] ( ) where as: (2) ρ, (3) ρ s the oss probabty for the -th pacet, and s the dstorton mprovement ntroduced by t. y ntroducng a agrange mutper λ, the souton of the constraned mnmzaton probem can be found by sovng the foowng unconstraned probem: ( ) mn J mn D + ρ λ.(4) The average transmsson power used by a moduaton scheme drecty affects the probabty of pacet oss. We assume that the reatonshp between the probabty of oss for the -th pacet ρ and the power used for transmttng t s nown at the transmtter: ρ g( ). () The functon g can be defned usng an anaytca mode of the wreess channe or determned from emprca measurements. In Secton 4, we use an anaytca mode based on nformaton theoretc resuts. The frst dervatve of the cost functon J wth respect to can be wrtten as: J ρ ( ) ρ + λ. (6) Note that at the optma souton the dervatve n (6) must equa zero. earrangng equaton (6) we can wrte the foowng expressons, for ρ : ( ) λ f (,, ) ρ, (7) from whch we can easy obtan, for,,-2: I Communcatons Socety /4/$. (c) 4 I

3 h ( ) λ f (,, ) ( ρ ) where f ρ, (8) (,, ) h + ρ. The frst dervatve of the cost functon J wth respect to can be wrtten, for ρ, as: J ρ ( ) ( ) ρ ρ + λ.(9) Substtutng the expressons n (8) nto (9), we obtan, after some smpe manpuatons, for <, the foowng reatonshp, for,,-: ρ ( ρ ). () ρ h ( ρ ) ( ) + ρ + h In the fna expresson, the eft sde represents the nformaton reated to the -th pacet whch depends ony on the power of the foowng pacets, (+) to. Ths observaton motvates our recursve power aocaton agorthm descrbed n the foowng sectons. 4 CHANN MOD DFINITION We assume that each transmtted pacet ether arrves error free or s dropped due to a channe fade. If we defne the channe mode as n [2]: e ρ, () we can rearrange () and obtan the foowng expresson: ( ) ( ) e +. (2) + The ast expresson aows us to recursvey cacuate a (,,-) once vaue of {,, } + s nown. In other words, for a gven we can recursvey cacuate n terms of, and therefore use () n order to cacuate the resutng tota energy. The mnmzaton probem can then be soved by fndng the vaue of that satsfes the energy constrant. A cosed form souton to the probem of fndng the optma s dffcut to compute anaytcay. Therefore, a numerca method such as the bsecton method can be used. Fg.2a shows, for a specfc sequence, the pot of tot versus the power of the ast pacet,. As we can see from Fg.2a, for each vaue of tot (.e. for each energy budget) we can fnd two soutons (.e., two ponts n whch the dervatve of the cost functon equas zero): the agorthm must pc the one that gves the mnmum dstorton. Fg.2b shows the pot of the resutng MS versus. Fg. 2 Foreman QCIF sequence, frame, equa sze pacets: tota energy tot and frame dstorton (MS) versus the power of the ast pacet,. In, the dotted ne represents the energy budget. Fg.3 robabty of pacet oss ρ versus for the ast 4 pacets (- 3,..,). If the ne representng the energy constrant does not ntersect wth the energy curve, t means that a souton usng a maxmum number of pacets max does not exst, because a the soutons ead to a tota energy that s arger than the energy constrant. In ths case we have to consder the souton (the ast pacet s not transmtted) and reconsder the probem by cacuatng the curve wth max -. From the obtaned resuts, we can observe that, n the case of equa sze pacets the power assgned to each + pacet decreases wth :. As a proof one can rearrange equaton (2) n order to obtan: ( ) ( ) + ( ) 2 2 e,(3) + from whch we can derve the foowng nequaty: + +. (4) Snce the power assgned to each pacet s reated by () to the probabty of pacet ost, ths means that the probabty of oss ncreases as the pacet number ncreases. The ast pacet (-th) w be the pacet wth the owest power, and maxmum probabty of oss (Fg.3). I Communcatons Socety /4/$. (c) 4 I

4 DUA OM FOMUATION In the dua probem, we want to mnmze the tota energy used for transmttng the enhancement ayer, subect to a dstorton constrant. In ths case, the mnmzaton probem can be expressed n the foowng way: mn, s. t. Dtot D [ ], () where D tot s the maxmum acceptabe dstorton for the frame. The technques used to sove the mnmum dstorton probem (q. (2)) can aso be used to sove the mnmum energy probem (q. ()). Note that for the mnmum energy probem s teratvey adusted unt the dstorton constrant s met. The dua probem presented here s usefu for appcatons n whch a desred eve of vsua quaty must be mantaned usng the east amount of energy. 6 XIMNTA STU AND SUTS In order to verfy the proposed method, experments wth dfferent types of pacetzaton and codng parameters have been performed. The sequences have been coded usng the MG-4 FGS reference software [3]. The ase ayer was coded wth a fxed target btrate, utzng the mpementaton of the TMN rate contro present n the reference software. The motvaton behnd seectng a fxed s that typcay the ase ayer s compressed at a maxmum btrate, such that t can aways be receved wth neggbe probabty of error. The nhancement ayer can then be parttoned n such a way that t can be transmtted whenever the btrate s greater than (and ess than a certan ), fuy utzng the bandwdth avaabe at the tme of transmsson. In ths way, t s possbe to adapt the coded vdeo to the tme-varyng characterstcs of the channe. Once coded, the s pacetzed usng a fxed pacet ength scheme. Tabe summarzes the parameters chosen for these experments. A the experments reported here have been performed for the QCIF format (44x76 pxes) Foreman test sequence. Smar resuts not presented n ths paper due to space mtatons have been obtaned wth other test sequences, such as Carphone, Ayo, and Saesman. Three sets of experments, named A, and C, have been performed for anayzng the behavor of the agorthm, wth dfferent pacet szes and transmsson rates (Tabe 2). Tabe Genera parameter settngs tot for each frame 4 J Frame rate fps Target bt rate for ( ) 4 Kbps Inta Q for I-Frame Inta Q for -Frame 3 Tabe 2 arameter settngs for the three experments acet Sze Target Tota t ate xperment A bytes Kbps xperment bytes 4 Kbps xperment C bytes 4 Kbps In order to compare the resuts obtaned from the proposed agorthm, an equa energy scheme and an emprc mode have been mpemented. The equa energy scheme assgns to each pacet the same amount of energy. The emprca method vares the power per pacet n a heurstc manor, and has been chosen n order to perform near the optma agorthm. In experment A, the emprca method assgns the power per pacet n the foowng way: tot, 2,,,7 > 7 (6) In Fg. 4a and b, we pot, for each frame, respectvey, the power assgned to each pacet by the emprca method and the optma power for experment A. For experment the emprca method assgns the power per pacet as foows: tot, m 9 < m, m m 2 m m 9 (7), > Frame # acet # Frame # 3 4 acet # 2 Frame # acet # Frame # acet # ) Fg.4 xperment A: power per pacet wth the emprca method and optma power per pacet Frame # acet # Fg. xperment : power per pacet wth the emprca method and optma power per pacet Frame # acet # 26 Fg.6 xperment C: power per pacet wth the emprca method and optma power per pacet I Communcatons Socety /4/$. (c) 4 I

5 (c) F.7 SN: experment A, and (c) C In Fg. a and b, we pot for each frame respectvey the power assgned to each pacet by the emprca method and the optma power for experment. Fnay, for experment C, the emprca method assgns power as foows: tot, m 4 < m, m 4 m 2 m m 4 (8), > In Fg. 6a and b, we pot for each frame respectvey the power assgned to each pacet by the emprca method and the optma power for experment C. The SN per frame for the proposed optma power aocaton approach, the equa energy scheme, and the emprca method are shown for experments A,, and C n Fg. 7. As expected, the SN obtaned from the optmzed method s aways hgher than both the equa energy and emprca methods. Fg. 7 demonstrates the potenta of utzng power adaptaton to acheve unequa error protecton n wreess vdeo communcatons usng FGS. As shown n Fg. 7, the optmzed power aocaton approach acheves an average expected SN that s on average.24 d,.24 d, and.9 d hgher than the equa energy approach for experments A,, and C respectvey. The proposed approach s aso abe to acheve sgnfcant gans over the emprca method, whch adapts the energy per pacet n a heurstc manor. For exampe, n Fg. 7c, the emprca method acheves an average expected SN that s on average.48 d ower than the optmzed approach. The performance of the equa power and emprca approaches strongy depends on the parameter settngs as we as on the characterstcs of the vdeo sequence. Therefore, t s dffcut to desgn a generc emprca agorthm that can aways perform near the optma power aocaton. As shown n Fg. 7, an equa power per pacet approach may perform near the optmzed approach for some parameter settng (Fg. 7a), whe the emprca approach may gve a better approxmaton at other operatng ponts (Fg. 7b). The proposed optma power aocaton scheme on the other hand s abe to adaptvey aocate the avaabe energy based on the transmsson parameters and the source characterstcs. 7 CONCUSIONS An agorthm for optmay aocatng the avaabe transmsson energy to the enhancement ayer pacets n an FGS wreess vdeo communcaton system has been presented. Unequa error protecton s acheved through adaptve power aocaton. The agorthm s smpe, and can be used to effcenty cacuate the optma power dstrbuton between the enhancement ayer pacets as we for vadatng faster emprca methods. The methodoogy used s appcabe not ony to FGS vdeo codng but aso other nds of progressve coders, such as waveet based mage or vdeo codng. FNCS [] ISO/IC MG-4, Informaton Technoogy Codng of Audo Vdeo Obect art2: Vsua Amendment 4: Streamng Vdeo rofe, MG /N38, Juy. [2] W., Overvew of fne granuarty scaabty n MG-4 vdeo standard, I Trans. on Crcuts and Systems for Vdeo Technoogy, vo., pp. 3-37, March. [3] M. van der Schaar,. G. oand, and Q., Nove appcatons of fne-granuar-scaabty: Internet & wreess vdeo, scaabe storage, personazed TV, unversa meda codng, roc. of SCI/ISAS - Orando, USA. [4] U. Horn, K. Stuhmüer, M. n, and. Grod, obust Internet vdeo transmsson based on scaabe codng and unequa error protecton, Image Communcaton, vo., no. -2, pp , Sept [] M. van der Schaar and H. adha, Unequa pacet oss resence for Fne-Granuar-Scaabty vdeo, I Trans. on Mutmeda, vo. 3, pp , Dec.. [6] X. K. Yang, C. Zhu, Z. G., G. N. Feng, S. Wu, and N. ng, A degressve error protecton agorthm for MG-4 FGS vdeo streamng, roc. of I ICI, Sept. 2. [7] G. Wang, Q. Zhang, W. Zhu, and Y.-Q. Zhang, Channe-adaptve unequa error protecton for scaabe vdeo transmsson over wreess channe, roc. of SI, Vsua Communcatons and Image rocessng, pp , San Jose, CA, Jan.. [8] Q. Zhang, W. Zhu, and Y.-Q. Zhang, Networ-adaptve scaabe vdeo streamng over 3G wreess networ, roc. I ICI, Thessaon, Greece, Oct.. [9] S. Zhao, Z. Xong, and X. Wang, Jont error contro and power aocaton for vdeo transmsson over CDMA networs wth mutuser detecton, I Trans. Crcuts and Systems for Vdeo Technoogy, vo.2, pp , June 2. [] Y. S. Chan and J. W. Modestno, Transport of scaabe vdeo over CDMA wreess networs: A ont source codng and power contro approach, roc. I ICI, Oct.. [] Y. senberg, C.. una, T.N. appas,. erry, and A.K. Katsaggeos, Jont source codng and transmsson power management for energy effcent wreess vdeo communcatons, I Trans. on Crcuts and Systems for Vdeo Technoogy, vo. 2, pp , June 2. [2]. Ozarow, S. Shama, and A.Wyner, Informaton theoretc consderatons for ceuar mobe rado, I Trans. Vehcuar Tech., vo. 43, Issue 2, pp , May 994. [3] ISO/IC MG-4 FGS eference Software, avaabe va March 3. I Communcatons Socety /4/$. (c) 4 I

Research on Complex Networks Control Based on Fuzzy Integral Sliding Theory

Research on Complex Networks Control Based on Fuzzy Integral Sliding Theory Advanced Scence and Technoogy Letters Vo.83 (ISA 205), pp.60-65 http://dx.do.org/0.4257/ast.205.83.2 Research on Compex etworks Contro Based on Fuzzy Integra Sdng Theory Dongsheng Yang, Bngqng L, 2, He

More information

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:

More information

Interference Alignment and Degrees of Freedom Region of Cellular Sigma Channel

Interference Alignment and Degrees of Freedom Region of Cellular Sigma Channel 2011 IEEE Internatona Symposum on Informaton Theory Proceedngs Interference Agnment and Degrees of Freedom Regon of Ceuar Sgma Channe Huaru Yn 1 Le Ke 2 Zhengdao Wang 2 1 WINLAB Dept of EEIS Unv. of Sc.

More information

MARKOV CHAIN AND HIDDEN MARKOV MODEL

MARKOV CHAIN AND HIDDEN MARKOV MODEL MARKOV CHAIN AND HIDDEN MARKOV MODEL JIAN ZHANG JIANZHAN@STAT.PURDUE.EDU Markov chan and hdden Markov mode are probaby the smpest modes whch can be used to mode sequenta data,.e. data sampes whch are not

More information

Associative Memories

Associative Memories Assocatve Memores We consder now modes for unsupervsed earnng probems, caed auto-assocaton probems. Assocaton s the task of mappng patterns to patterns. In an assocatve memory the stmuus of an ncompete

More information

Lower Bounding Procedures for the Single Allocation Hub Location Problem

Lower Bounding Procedures for the Single Allocation Hub Location Problem Lower Boundng Procedures for the Snge Aocaton Hub Locaton Probem Borzou Rostam 1,2 Chrstoph Buchhem 1,4 Fautät für Mathemat, TU Dortmund, Germany J. Faban Meer 1,3 Uwe Causen 1 Insttute of Transport Logstcs,

More information

VQ widely used in coding speech, image, and video

VQ widely used in coding speech, image, and video at Scalar quantzers are specal cases of vector quantzers (VQ): they are constraned to look at one sample at a tme (memoryless) VQ does not have such constrant better RD perfomance expected Source codng

More information

Neural network-based athletics performance prediction optimization model applied research

Neural network-based athletics performance prediction optimization model applied research Avaabe onne www.jocpr.com Journa of Chemca and Pharmaceutca Research, 04, 6(6):8-5 Research Artce ISSN : 0975-784 CODEN(USA) : JCPRC5 Neura networ-based athetcs performance predcton optmzaton mode apped

More information

Numerical Investigation of Power Tunability in Two-Section QD Superluminescent Diodes

Numerical Investigation of Power Tunability in Two-Section QD Superluminescent Diodes Numerca Investgaton of Power Tunabty n Two-Secton QD Superumnescent Dodes Matta Rossett Paoo Bardea Ivo Montrosset POLITECNICO DI TORINO DELEN Summary 1. A smpfed mode for QD Super Lumnescent Dodes (SLD)

More information

Downlink Power Allocation for CoMP-NOMA in Multi-Cell Networks

Downlink Power Allocation for CoMP-NOMA in Multi-Cell Networks Downn Power Aocaton for CoMP-NOMA n Mut-Ce Networs Md Shpon A, Eram Hossan, Arafat A-Dwe, and Dong In Km arxv:80.0498v [eess.sp] 6 Dec 207 Abstract Ths wor consders the probem of dynamc power aocaton n

More information

Polite Water-filling for Weighted Sum-rate Maximization in MIMO B-MAC Networks under. Multiple Linear Constraints

Polite Water-filling for Weighted Sum-rate Maximization in MIMO B-MAC Networks under. Multiple Linear Constraints 2011 IEEE Internatona Symposum on Informaton Theory Proceedngs Pote Water-fng for Weghted Sum-rate Maxmzaton n MIMO B-MAC Networks under Mutpe near Constrants An u 1, Youjan u 2, Vncent K. N. au 3, Hage

More information

Xin Li Department of Information Systems, College of Business, City University of Hong Kong, Hong Kong, CHINA

Xin Li Department of Information Systems, College of Business, City University of Hong Kong, Hong Kong, CHINA RESEARCH ARTICLE MOELING FIXE OS BETTING FOR FUTURE EVENT PREICTION Weyun Chen eartment of Educatona Informaton Technoogy, Facuty of Educaton, East Chna Norma Unversty, Shangha, CHINA {weyun.chen@qq.com}

More information

Cyclic Codes BCH Codes

Cyclic Codes BCH Codes Cycc Codes BCH Codes Gaos Feds GF m A Gaos fed of m eements can be obtaned usng the symbos 0,, á, and the eements beng 0,, á, á, á 3 m,... so that fed F* s cosed under mutpcaton wth m eements. The operator

More information

A General Column Generation Algorithm Applied to System Reliability Optimization Problems

A General Column Generation Algorithm Applied to System Reliability Optimization Problems A Genera Coumn Generaton Agorthm Apped to System Reabty Optmzaton Probems Lea Za, Davd W. Cot, Department of Industra and Systems Engneerng, Rutgers Unversty, Pscataway, J 08854, USA Abstract A genera

More information

we have E Y x t ( ( xl)) 1 ( xl), e a in I( Λ ) are as follows:

we have E Y x t ( ( xl)) 1 ( xl), e a in I( Λ ) are as follows: APPENDICES Aendx : the roof of Equaton (6 For j m n we have Smary from Equaton ( note that j '( ( ( j E Y x t ( ( x ( x a V ( ( x a ( ( x ( x b V ( ( x b V x e d ( abx ( ( x e a a bx ( x xe b a bx By usng

More information

COXREG. Estimation (1)

COXREG. Estimation (1) COXREG Cox (972) frst suggested the modes n whch factors reated to fetme have a mutpcatve effect on the hazard functon. These modes are caed proportona hazards (PH) modes. Under the proportona hazards

More information

Greyworld White Balancing with Low Computation Cost for On- Board Video Capturing

Greyworld White Balancing with Low Computation Cost for On- Board Video Capturing reyword Whte aancng wth Low Computaton Cost for On- oard Vdeo Capturng Peng Wu Yuxn Zoe) Lu Hewett-Packard Laboratores Hewett-Packard Co. Pao Ato CA 94304 USA Abstract Whte baancng s a process commony

More information

Achieving Optimal Throughput Utility and Low Delay with CSMA-like Algorithms: A Virtual Multi-Channel Approach

Achieving Optimal Throughput Utility and Low Delay with CSMA-like Algorithms: A Virtual Multi-Channel Approach Achevng Optma Throughput Utty and Low Deay wth SMA-ke Agorthms: A Vrtua Mut-hanne Approach Po-Ka Huang, Student Member, IEEE, and Xaojun Ln, Senor Member, IEEE Abstract SMA agorthms have recenty receved

More information

Grover s Algorithm + Quantum Zeno Effect + Vaidman

Grover s Algorithm + Quantum Zeno Effect + Vaidman Grover s Algorthm + Quantum Zeno Effect + Vadman CS 294-2 Bomb 10/12/04 Fall 2004 Lecture 11 Grover s algorthm Recall that Grover s algorthm for searchng over a space of sze wors as follows: consder the

More information

ECE559VV Project Report

ECE559VV 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 information

Boundary Value Problems. Lecture Objectives. Ch. 27

Boundary Value Problems. Lecture Objectives. Ch. 27 Boundar Vaue Probes Ch. 7 Lecture Obectves o understand the dfference between an nta vaue and boundar vaue ODE o be abe to understand when and how to app the shootng ethod and FD ethod. o understand what

More information

GENERATION OF GOLD-SEQUENCES WITH APPLICATIONS TO SPREAD SPECTRUM SYSTEMS

GENERATION OF GOLD-SEQUENCES WITH APPLICATIONS TO SPREAD SPECTRUM SYSTEMS GENERATION OF GOLD-SEQUENCES WITH APPLICATIONS TO SPREAD SPECTRUM SYSTEMS F. Rodríguez Henríquez (1), Member, IEEE, N. Cruz Cortés (1), Member, IEEE, J.M. Rocha-Pérez (2) Member, IEEE. F. Amaro Sánchez

More information

On balancing multiple video streams with distributed QoS control in mobile communications

On balancing multiple video streams with distributed QoS control in mobile communications On balancng multple vdeo streams wth dstrbuted QoS control n moble communcatons Arjen van der Schaaf, José Angel Lso Arellano, and R. (Inald) L. Lagendjk TU Delft, Mekelweg 4, 68 CD Delft, The Netherlands

More information

On Uplink-Downlink Sum-MSE Duality of Multi-hop MIMO Relay Channel

On Uplink-Downlink Sum-MSE Duality of Multi-hop MIMO Relay Channel On Upn-Downn Sum-MSE Duat of Mut-hop MIMO Rea Channe A Cagata Cr, Muhammad R. A. handaer, Yue Rong and Yngbo ua Department of Eectrca Engneerng, Unverst of Caforna Rversde, Rversde, CA, 95 Centre for Wreess

More information

Image Classification Using EM And JE algorithms

Image Classification Using EM And JE algorithms Machne earnng project report Fa, 2 Xaojn Sh, jennfer@soe Image Cassfcaton Usng EM And JE agorthms Xaojn Sh Department of Computer Engneerng, Unversty of Caforna, Santa Cruz, CA, 9564 jennfer@soe.ucsc.edu

More information

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE Analytcal soluton s usually not possble when exctaton vares arbtrarly wth tme or f the system s nonlnear. Such problems can be solved by numercal tmesteppng

More information

Predicting Model of Traffic Volume Based on Grey-Markov

Predicting Model of Traffic Volume Based on Grey-Markov Vo. No. Modern Apped Scence Predctng Mode of Traffc Voume Based on Grey-Marov Ynpeng Zhang Zhengzhou Muncpa Engneerng Desgn & Research Insttute Zhengzhou 5005 Chna Abstract Grey-marov forecastng mode of

More information

Optimum Selection Combining for M-QAM on Fading Channels

Optimum Selection Combining for M-QAM on Fading Channels Optmum Seecton Combnng for M-QAM on Fadng Channes M. Surendra Raju, Ramesh Annavajjaa and A. Chockangam Insca Semconductors Inda Pvt. Ltd, Bangaore-56000, Inda Department of ECE, Unversty of Caforna, San

More information

WAVELET-BASED IMAGE COMPRESSION USING SUPPORT VECTOR MACHINE LEARNING AND ENCODING TECHNIQUES

WAVELET-BASED IMAGE COMPRESSION USING SUPPORT VECTOR MACHINE LEARNING AND ENCODING TECHNIQUES WAVELE-BASED IMAGE COMPRESSION USING SUPPOR VECOR MACHINE LEARNING AND ENCODING ECHNIQUES Rakb Ahmed Gppsand Schoo of Computng and Informaton echnoogy Monash Unversty, Gppsand Campus Austraa. Rakb.Ahmed@nfotech.monash.edu.au

More information

Application of Particle Swarm Optimization to Economic Dispatch Problem: Advantages and Disadvantages

Application of Particle Swarm Optimization to Economic Dispatch Problem: Advantages and Disadvantages Appcaton of Partce Swarm Optmzaton to Economc Dspatch Probem: Advantages and Dsadvantages Kwang Y. Lee, Feow, IEEE, and Jong-Bae Par, Member, IEEE Abstract--Ths paper summarzes the state-of-art partce

More information

LECTURE 21 Mohr s Method for Calculation of General Displacements. 1 The Reciprocal Theorem

LECTURE 21 Mohr s Method for Calculation of General Displacements. 1 The Reciprocal Theorem V. DEMENKO MECHANICS OF MATERIALS 05 LECTURE Mohr s Method for Cacuaton of Genera Dspacements The Recproca Theorem The recproca theorem s one of the genera theorems of strength of materas. It foows drect

More information

DISTRIBUTED PROCESSING OVER ADAPTIVE NETWORKS. Cassio G. Lopes and Ali H. Sayed

DISTRIBUTED PROCESSING OVER ADAPTIVE NETWORKS. Cassio G. Lopes and Ali H. Sayed DISTRIBUTED PROCESSIG OVER ADAPTIVE ETWORKS Casso G Lopes and A H Sayed Department of Eectrca Engneerng Unversty of Caforna Los Angees, CA, 995 Ema: {casso, sayed@eeucaedu ABSTRACT Dstrbuted adaptve agorthms

More information

Single-Source/Sink Network Error Correction Is as Hard as Multiple-Unicast

Single-Source/Sink Network Error Correction Is as Hard as Multiple-Unicast Snge-Source/Snk Network Error Correcton Is as Hard as Mutpe-Uncast Wentao Huang and Tracey Ho Department of Eectrca Engneerng Caforna Insttute of Technoogy Pasadena, CA {whuang,tho}@catech.edu Mchae Langberg

More information

Multispectral Remote Sensing Image Classification Algorithm Based on Rough Set Theory

Multispectral Remote Sensing Image Classification Algorithm Based on Rough Set Theory Proceedngs of the 2009 IEEE Internatona Conference on Systems Man and Cybernetcs San Antono TX USA - October 2009 Mutspectra Remote Sensng Image Cassfcaton Agorthm Based on Rough Set Theory Yng Wang Xaoyun

More information

An Upper Bound on SINR Threshold for Call Admission Control in Multiple-Class CDMA Systems with Imperfect Power-Control

An Upper Bound on SINR Threshold for Call Admission Control in Multiple-Class CDMA Systems with Imperfect Power-Control An Upper Bound on SINR Threshold for Call Admsson Control n Multple-Class CDMA Systems wth Imperfect ower-control Mahmoud El-Sayes MacDonald, Dettwler and Assocates td. (MDA) Toronto, Canada melsayes@hotmal.com

More information

A MIN-MAX REGRET ROBUST OPTIMIZATION APPROACH FOR LARGE SCALE FULL FACTORIAL SCENARIO DESIGN OF DATA UNCERTAINTY

A MIN-MAX REGRET ROBUST OPTIMIZATION APPROACH FOR LARGE SCALE FULL FACTORIAL SCENARIO DESIGN OF DATA UNCERTAINTY A MIN-MAX REGRET ROBST OPTIMIZATION APPROACH FOR ARGE SCAE F FACTORIA SCENARIO DESIGN OF DATA NCERTAINTY Travat Assavapokee Department of Industra Engneerng, nversty of Houston, Houston, Texas 7704-4008,

More information

Lecture 4: November 17, Part 1 Single Buffer Management

Lecture 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 information

Linear Approximation with Regularization and Moving Least Squares

Linear Approximation with Regularization and Moving Least Squares Lnear Approxmaton wth Regularzaton and Movng Least Squares Igor Grešovn May 007 Revson 4.6 (Revson : March 004). 5 4 3 0.5 3 3.5 4 Contents: Lnear Fttng...4. Weghted Least Squares n Functon Approxmaton...

More information

Analysis of Bipartite Graph Codes on the Binary Erasure Channel

Analysis of Bipartite Graph Codes on the Binary Erasure Channel Anayss of Bpartte Graph Codes on the Bnary Erasure Channe Arya Mazumdar Department of ECE Unversty of Maryand, Coege Par ema: arya@umdedu Abstract We derve densty evouton equatons for codes on bpartte

More information

A FAST HEURISTIC FOR TASKS ASSIGNMENT IN MANYCORE SYSTEMS WITH VOLTAGE-FREQUENCY ISLANDS

A FAST HEURISTIC FOR TASKS ASSIGNMENT IN MANYCORE SYSTEMS WITH VOLTAGE-FREQUENCY ISLANDS Shervn Haamn A FAST HEURISTIC FOR TASKS ASSIGNMENT IN MANYCORE SYSTEMS WITH VOLTAGE-FREQUENCY ISLANDS INTRODUCTION Increasng computatons n applcatons has led to faster processng. o Use more cores n a chp

More information

L-Edge Chromatic Number Of A Graph

L-Edge Chromatic Number Of A Graph IJISET - Internatona Journa of Innovatve Scence Engneerng & Technoogy Vo. 3 Issue 3 March 06. ISSN 348 7968 L-Edge Chromatc Number Of A Graph Dr.R.B.Gnana Joth Assocate Professor of Mathematcs V.V.Vannaperuma

More information

A parametric Linear Programming Model Describing Bandwidth Sharing Policies for ABR Traffic

A parametric Linear Programming Model Describing Bandwidth Sharing Policies for ABR Traffic parametrc Lnear Programmng Mode Descrbng Bandwdth Sharng Poces for BR Traffc I. Moschoos, M. Logothets and G. Kokknaks Wre ommuncatons Laboratory, Dept. of Eectrca & omputer Engneerng, Unversty of Patras,

More information

Lossy Compression. Compromise accuracy of reconstruction for increased compression.

Lossy Compression. Compromise accuracy of reconstruction for increased compression. Lossy Compresson Compromse accuracy of reconstructon for ncreased compresson. The reconstructon s usually vsbly ndstngushable from the orgnal mage. Typcally, one can get up to 0:1 compresson wth almost

More information

3. Stress-strain relationships of a composite layer

3. Stress-strain relationships of a composite layer OM PO I O U P U N I V I Y O F W N ompostes ourse 8-9 Unversty of wente ng. &ech... tress-stran reatonshps of a composte ayer - Laurent Warnet & emo Aerman.. tress-stran reatonshps of a composte ayer Introducton

More information

Physics 5153 Classical Mechanics. D Alembert s Principle and The Lagrangian-1

Physics 5153 Classical Mechanics. D Alembert s Principle and The Lagrangian-1 P. Guterrez Physcs 5153 Classcal Mechancs D Alembert s Prncple and The Lagrangan 1 Introducton The prncple of vrtual work provdes a method of solvng problems of statc equlbrum wthout havng to consder the

More information

Flexible Quantization

Flexible Quantization wb 06/02/21 1 Flexble Quantzaton Bastaan Klejn KTH School of Electrcal Engneerng Stocholm wb 06/02/21 2 Overvew Motvaton for codng technologes Basc quantzaton and codng Hgh-rate quantzaton theory wb 06/02/21

More information

Example: Suppose we want to build a classifier that recognizes WebPages of graduate students.

Example: Suppose we want to build a classifier that recognizes WebPages of graduate students. Exampe: Suppose we want to bud a cassfer that recognzes WebPages of graduate students. How can we fnd tranng data? We can browse the web and coect a sampe of WebPages of graduate students of varous unverstes.

More information

SIMULTANEOUS wireless information and power transfer. Joint Optimization of Power and Data Transfer in Multiuser MIMO Systems

SIMULTANEOUS wireless information and power transfer. Joint Optimization of Power and Data Transfer in Multiuser MIMO Systems Jont Optmzaton of Power and Data ransfer n Mutuser MIMO Systems Javer Rubo, Antono Pascua-Iserte, Dane P. Paomar, and Andrea Godsmth Unverstat Potècnca de Cataunya UPC, Barceona, Span ong Kong Unversty

More information

Chapter 7 Channel Capacity and Coding

Chapter 7 Channel Capacity and Coding Wreless Informaton Transmsson System Lab. Chapter 7 Channel Capacty and Codng Insttute of Communcatons Engneerng atonal Sun Yat-sen Unversty Contents 7. Channel models and channel capacty 7.. Channel models

More information

Lecture 3: Shannon s Theorem

Lecture 3: Shannon s Theorem CSE 533: Error-Correctng Codes (Autumn 006 Lecture 3: Shannon s Theorem October 9, 006 Lecturer: Venkatesan Guruswam Scrbe: Wdad Machmouch 1 Communcaton Model The communcaton model we are usng conssts

More information

A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization

A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization Journa of Machne Learnng Research 18 17 1-5 Submtted 9/16; Revsed 1/17; Pubshed 1/17 A Genera Dstrbuted Dua Coordnate Optmzaton Framework for Reguarzed Loss Mnmzaton Shun Zheng Insttute for Interdscpnary

More information

Entropy Coding. A complete entropy codec, which is an encoder/decoder. pair, consists of the process of encoding or

Entropy Coding. A complete entropy codec, which is an encoder/decoder. pair, consists of the process of encoding or Sgnal Compresson Sgnal Compresson Entropy Codng Entropy codng s also known as zero-error codng, data compresson or lossless compresson. Entropy codng s wdely used n vrtually all popular nternatonal multmeda

More information

On the Power Function of the Likelihood Ratio Test for MANOVA

On the Power Function of the Likelihood Ratio Test for MANOVA Journa of Mutvarate Anayss 8, 416 41 (00) do:10.1006/jmva.001.036 On the Power Functon of the Lkehood Rato Test for MANOVA Dua Kumar Bhaumk Unversty of South Aabama and Unversty of Inos at Chcago and Sanat

More information

Lower bounds for the Crossing Number of the Cartesian Product of a Vertex-transitive Graph with a Cycle

Lower bounds for the Crossing Number of the Cartesian Product of a Vertex-transitive Graph with a Cycle Lower bounds for the Crossng Number of the Cartesan Product of a Vertex-transtve Graph wth a Cyce Junho Won MIT-PRIMES December 4, 013 Abstract. The mnmum number of crossngs for a drawngs of a gven graph

More information

Note 2. Ling fong Li. 1 Klein Gordon Equation Probablity interpretation Solutions to Klein-Gordon Equation... 2

Note 2. Ling fong Li. 1 Klein Gordon Equation Probablity interpretation Solutions to Klein-Gordon Equation... 2 Note 2 Lng fong L Contents Ken Gordon Equaton. Probabty nterpretaton......................................2 Soutons to Ken-Gordon Equaton............................... 2 2 Drac Equaton 3 2. Probabty nterpretaton.....................................

More information

Module 2. Random Processes. Version 2 ECE IIT, Kharagpur

Module 2. Random Processes. Version 2 ECE IIT, Kharagpur Module Random Processes Lesson 6 Functons of Random Varables After readng ths lesson, ou wll learn about cdf of functon of a random varable. Formula for determnng the pdf of a random varable. Let, X be

More information

G : Statistical Mechanics

G : Statistical Mechanics G25.2651: Statstca Mechancs Notes for Lecture 11 I. PRINCIPLES OF QUANTUM STATISTICAL MECHANICS The probem of quantum statstca mechancs s the quantum mechanca treatment of an N-partce system. Suppose the

More information

Chapter 7 Channel Capacity and Coding

Chapter 7 Channel Capacity and Coding Chapter 7 Channel Capacty and Codng Contents 7. Channel models and channel capacty 7.. Channel models Bnary symmetrc channel Dscrete memoryless channels Dscrete-nput, contnuous-output channel Waveform

More information

Optimization of JK Flip Flop Layout with Minimal Average Power of Consumption based on ACOR, Fuzzy-ACOR, GA, and Fuzzy-GA

Optimization of JK Flip Flop Layout with Minimal Average Power of Consumption based on ACOR, Fuzzy-ACOR, GA, and Fuzzy-GA Journa of mathematcs and computer Scence 4 (05) - 5 Optmzaton of JK Fp Fop Layout wth Mnma Average Power of Consumpton based on ACOR, Fuzzy-ACOR, GA, and Fuzzy-GA Farshd Kevanan *,, A Yekta *,, Nasser

More information

Resource Allocation with a Budget Constraint for Computing Independent Tasks in the Cloud

Resource 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 information

Nested case-control and case-cohort studies

Nested case-control and case-cohort studies Outne: Nested case-contro and case-cohort studes Ørnuf Borgan Department of Mathematcs Unversty of Oso NORBIS course Unversty of Oso 4-8 December 217 1 Radaton and breast cancer data Nested case contro

More information

A finite difference method for heat equation in the unbounded domain

A finite difference method for heat equation in the unbounded domain Internatona Conerence on Advanced ectronc Scence and Technoogy (AST 6) A nte derence method or heat equaton n the unbounded doman a Quan Zheng and Xn Zhao Coege o Scence North Chna nversty o Technoogy

More information

Supplementary Material: Learning Structured Weight Uncertainty in Bayesian Neural Networks

Supplementary Material: Learning Structured Weight Uncertainty in Bayesian Neural Networks Shengyang Sun, Changyou Chen, Lawrence Carn Suppementary Matera: Learnng Structured Weght Uncertanty n Bayesan Neura Networks Shengyang Sun Changyou Chen Lawrence Carn Tsnghua Unversty Duke Unversty Duke

More information

Achieving Optimal Throughput Utility and Low Delay with CSMA-like Algorithms: A Virtual Multi-Channel Approach

Achieving Optimal Throughput Utility and Low Delay with CSMA-like Algorithms: A Virtual Multi-Channel Approach IEEE/AM TRANSATIONS ON NETWORKING, VOL. X, NO. XX, XXXXXXX 20X Achevng Optma Throughput Utty and Low Deay wth SMA-ke Agorthms: A Vrtua Mut-hanne Approach Po-Ka Huang, Student Member, IEEE, and Xaojun Ln,

More information

MAXIMUM A POSTERIORI TRANSDUCTION

MAXIMUM A POSTERIORI TRANSDUCTION MAXIMUM A POSTERIORI TRANSDUCTION LI-WEI WANG, JU-FU FENG School of Mathematcal Scences, Peng Unversty, Bejng, 0087, Chna Center for Informaton Scences, Peng Unversty, Bejng, 0087, Chna E-MIAL: {wanglw,

More information

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

COMPARISON 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 information

COS 521: Advanced Algorithms Game Theory and Linear Programming

COS 521: Advanced Algorithms Game Theory and Linear Programming COS 521: Advanced Algorthms Game Theory and Lnear Programmng Moses Charkar February 27, 2013 In these notes, we ntroduce some basc concepts n game theory and lnear programmng (LP). We show a connecton

More information

Development of whole CORe Thermal Hydraulic analysis code CORTH Pan JunJie, Tang QiFen, Chai XiaoMing, Lu Wei, Liu Dong

Development of whole CORe Thermal Hydraulic analysis code CORTH Pan JunJie, Tang QiFen, Chai XiaoMing, Lu Wei, Liu Dong Deveopment of whoe CORe Therma Hydrauc anayss code CORTH Pan JunJe, Tang QFen, Cha XaoMng, Lu We, Lu Dong cence and technoogy on reactor system desgn technoogy, Nucear Power Insttute of Chna, Chengdu,

More information

Smooth Extraction of SVC Fine-Granular SNR Scalable Videos with a Virtual-GOP-Based Rate-Distortion Modeling

Smooth Extraction of SVC Fine-Granular SNR Scalable Videos with a Virtual-GOP-Based Rate-Distortion Modeling F Smooth Extracton of SVC Fne-Granular SNR Scalable Vdeos wth a Vrtual-GOP-Based Rate-Dstorton Modelng * Jun a SunF, Wen Gao a, Debn Zhao b a Insttute of Dgtal Meda, EECS, Pekng Unversty, Bejng, 1871,

More information

COMBINING SPATIAL COMPONENTS IN SEISMIC DESIGN

COMBINING SPATIAL COMPONENTS IN SEISMIC DESIGN Transactons, SMRT- COMBINING SPATIAL COMPONENTS IN SEISMIC DESIGN Mchae O Leary, PhD, PE and Kevn Huberty, PE, SE Nucear Power Technooges Dvson, Sargent & Lundy, Chcago, IL 6060 ABSTRACT Accordng to Reguatory

More information

A Class of Distributed Optimization Methods with Event-Triggered Communication

A Class of Distributed Optimization Methods with Event-Triggered Communication A Cass of Dstrbuted Optmzaton Methods wth Event-Trggered Communcaton Martn C. Mene Mchae Ubrch Sebastan Abrecht the date of recept and acceptance shoud be nserted ater Abstract We present a cass of methods

More information

CSci 6974 and ECSE 6966 Math. Tech. for Vision, Graphics and Robotics Lecture 21, April 17, 2006 Estimating A Plane Homography

CSci 6974 and ECSE 6966 Math. Tech. for Vision, Graphics and Robotics Lecture 21, April 17, 2006 Estimating A Plane Homography CSc 6974 and ECSE 6966 Math. Tech. for Vson, Graphcs and Robotcs Lecture 21, Aprl 17, 2006 Estmatng A Plane Homography Overvew We contnue wth a dscusson of the major ssues, usng estmaton of plane projectve

More information

Lecture 10 Support Vector Machines II

Lecture 10 Support Vector Machines II Lecture 10 Support Vector Machnes II 22 February 2016 Taylor B. Arnold Yale Statstcs STAT 365/665 1/28 Notes: Problem 3 s posted and due ths upcomng Frday There was an early bug n the fake-test data; fxed

More information

QUARTERLY OF APPLIED MATHEMATICS

QUARTERLY OF APPLIED MATHEMATICS QUARTERLY OF APPLIED MATHEMATICS Voume XLI October 983 Number 3 DIAKOPTICS OR TEARING-A MATHEMATICAL APPROACH* By P. W. AITCHISON Unversty of Mantoba Abstract. The method of dakoptcs or tearng was ntroduced

More information

Lecture Notes on Linear Regression

Lecture 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 information

Inthem-machine flow shop problem, a set of jobs, each

Inthem-machine flow shop problem, a set of jobs, each THE ASYMPTOTIC OPTIMALITY OF THE SPT RULE FOR THE FLOW SHOP MEAN COMPLETION TIME PROBLEM PHILIP KAMINSKY Industra Engneerng and Operatons Research, Unversty of Caforna, Bereey, Caforna 9470, amnsy@eor.bereey.edu

More information

Supporting Information

Supporting Information Supportng Informaton The neural network f n Eq. 1 s gven by: f x l = ReLU W atom x l + b atom, 2 where ReLU s the element-wse rectfed lnear unt, 21.e., ReLUx = max0, x, W atom R d d s the weght matrx to

More information

Networked Cooperative Distributed Model Predictive Control Based on State Observer

Networked Cooperative Distributed Model Predictive Control Based on State Observer Apped Mathematcs, 6, 7, 48-64 ubshed Onne June 6 n ScRes. http://www.scrp.org/journa/am http://dx.do.org/.436/am.6.73 Networed Cooperatve Dstrbuted Mode redctve Contro Based on State Observer Ba Su, Yanan

More information

Quantum Runge-Lenz Vector and the Hydrogen Atom, the hidden SO(4) symmetry

Quantum Runge-Lenz Vector and the Hydrogen Atom, the hidden SO(4) symmetry Quantum Runge-Lenz ector and the Hydrogen Atom, the hdden SO(4) symmetry Pasca Szrftgser and Edgardo S. Cheb-Terrab () Laboratore PhLAM, UMR CNRS 85, Unversté Le, F-59655, France () Mapesoft Let's consder

More information

EGR 544 Communication Theory

EGR 544 Communication Theory EGR 544 Communcaton Theory. Informaton Sources Z. Alyazcoglu Electrcal and Computer Engneerng Department Cal Poly Pomona Introducton Informaton Source x n Informaton sources Analog sources Dscrete sources

More information

Math1110 (Spring 2009) Prelim 3 - Solutions

Math1110 (Spring 2009) Prelim 3 - Solutions Math 1110 (Sprng 2009) Solutons to Prelm 3 (04/21/2009) 1 Queston 1. (16 ponts) Short answer. Math1110 (Sprng 2009) Prelm 3 - Solutons x a 1 (a) (4 ponts) Please evaluate lm, where a and b are postve numbers.

More information

NUMERICAL DIFFERENTIATION

NUMERICAL DIFFERENTIATION NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the

More information

State Amplification and State Masking for the Binary Energy Harvesting Channel

State Amplification and State Masking for the Binary Energy Harvesting Channel State Amplfcaton and State Maskng for the Bnary Energy Harvestng Channel Kaya Tutuncuoglu, Omur Ozel 2, Ayln Yener, and Sennur Ulukus 2 Department of Electrcal Engneerng, The Pennsylvana State Unversty,

More information

DC-Free Turbo Coding Scheme Using MAP/SOVA Algorithms

DC-Free Turbo Coding Scheme Using MAP/SOVA Algorithms Proceedngs of the 5th WSEAS Internatonal Conference on Telecommuncatons and Informatcs, Istanbul, Turkey, May 27-29, 26 (pp192-197 DC-Free Turbo Codng Scheme Usng MAP/SOVA Algorthms Prof. Dr. M. Amr Mokhtar

More information

Chapter 6 Hidden Markov Models. Chaochun Wei Spring 2018

Chapter 6 Hidden Markov Models. Chaochun Wei Spring 2018 896 920 987 2006 Chapter 6 Hdden Markov Modes Chaochun We Sprng 208 Contents Readng materas Introducton to Hdden Markov Mode Markov chans Hdden Markov Modes Parameter estmaton for HMMs 2 Readng Rabner,

More information

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons

More information

18.1 Introduction and Recap

18.1 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 information

Quantitative Evaluation Method of Each Generation Margin for Power System Planning

Quantitative Evaluation Method of Each Generation Margin for Power System Planning 1 Quanttatve Evauaton Method of Each Generaton Margn for Poer System Pannng SU Su Department of Eectrca Engneerng, Tohoku Unversty, Japan moden25@ececetohokuacjp Abstract Increasng effcency of poer pants

More information

Pulse Coded Modulation

Pulse Coded Modulation Pulse Coded Modulaton PCM (Pulse Coded Modulaton) s a voce codng technque defned by the ITU-T G.711 standard and t s used n dgtal telephony to encode the voce sgnal. The frst step n the analog to dgtal

More information

n α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0

n α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0 MODULE 2 Topcs: Lnear ndependence, bass and dmenson We have seen that f n a set of vectors one vector s a lnear combnaton of the remanng vectors n the set then the span of the set s unchanged f that vector

More information

MAXIMIZING THE THROUGHPUT OF CDMA DATA COMMUNICATIONS THROUGH JOINT ADMISSION AND POWER CONTROL

MAXIMIZING THE THROUGHPUT OF CDMA DATA COMMUNICATIONS THROUGH JOINT ADMISSION AND POWER CONTROL MAXIMIZI THE THROUHPUT OF CDMA DATA COMMUICATIOS THROUH JOIT ADMISSIO AD POWER COTROL Penna Orensten, Davd oodman and Zory Marantz Department of Electrcal and Computer Engneerng Polytechnc Unversty Brooklyn,

More information

n-step cycle inequalities: facets for continuous n-mixing set and strong cuts for multi-module capacitated lot-sizing problem

n-step cycle inequalities: facets for continuous n-mixing set and strong cuts for multi-module capacitated lot-sizing problem n-step cyce nequates: facets for contnuous n-mxng set and strong cuts for mut-modue capactated ot-szng probem Mansh Bansa and Kavash Kanfar Department of Industra and Systems Engneerng, Texas A&M Unversty,

More information

Structure and Drive Paul A. Jensen Copyright July 20, 2003

Structure 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 information

Tracking with Kalman Filter

Tracking with Kalman Filter Trackng wth Kalman Flter Scott T. Acton Vrgna Image and Vdeo Analyss (VIVA), Charles L. Brown Department of Electrcal and Computer Engneerng Department of Bomedcal Engneerng Unversty of Vrgna, Charlottesvlle,

More information

Multicommodity Distribution System Design

Multicommodity Distribution System Design Mummodt strbun stem esgn Consder the probem where ommodtes are produed and shpped from pants through potenta dstrbun enters C usmers. Pants C Cosmers Mummodt strbun stem esgn Consder the probem where ommodtes

More information

Microwave Diversity Imaging Compression Using Bioinspired

Microwave Diversity Imaging Compression Using Bioinspired Mcrowave Dversty Imagng Compresson Usng Bonspred Neural Networks Youwe Yuan 1, Yong L 1, Wele Xu 1, Janghong Yu * 1 School of Computer Scence and Technology, Hangzhou Danz Unversty, Hangzhou, Zhejang,

More information

Quality-of-Service Routing in Heterogeneous Networks with Optimal Buffer and Bandwidth Allocation

Quality-of-Service Routing in Heterogeneous Networks with Optimal Buffer and Bandwidth Allocation Purdue Unversty Purdue e-pubs ECE Technca Reorts Eectrca and Comuter Engneerng -6-007 Quaty-of-Servce Routng n Heterogeneous Networs wth Otma Buffer and Bandwdth Aocaton Waseem Sheh Purdue Unversty, waseem@urdue.edu

More information

Lecture 7: Boltzmann distribution & Thermodynamics of mixing

Lecture 7: Boltzmann distribution & Thermodynamics of mixing Prof. Tbbtt Lecture 7 etworks & Gels Lecture 7: Boltzmann dstrbuton & Thermodynamcs of mxng 1 Suggested readng Prof. Mark W. Tbbtt ETH Zürch 13 März 018 Molecular Drvng Forces Dll and Bromberg: Chapters

More information

Uncertainty in measurements of power and energy on power networks

Uncertainty in measurements of power and energy on power networks Uncertanty n measurements of power and energy on power networks E. Manov, N. Kolev Department of Measurement and Instrumentaton, Techncal Unversty Sofa, bul. Klment Ohrdsk No8, bl., 000 Sofa, Bulgara Tel./fax:

More information

princeton univ. F 17 cos 521: Advanced Algorithm Design Lecture 7: LP Duality Lecturer: Matt Weinberg

princeton univ. F 17 cos 521: Advanced Algorithm Design Lecture 7: LP Duality Lecturer: Matt Weinberg prnceton unv. F 17 cos 521: Advanced Algorthm Desgn Lecture 7: LP Dualty Lecturer: Matt Wenberg Scrbe: LP Dualty s an extremely useful tool for analyzng structural propertes of lnear programs. Whle there

More information