Lookahead Geographic Routing for Sensor Networks

 Octavia Thomas
 6 months ago
 Views:
Transcription
1 1 Lookahead Geographic Routig for Sesor Networks Jiaxi You 1, Domiik Lieckfeldt 1, Qi Ha, Jakob Salzma 1, ad Dirk Timmerma 1 1 Uiversity of Rostock, Germay 1 {jiaxi.you, domiik.lieckfeldt, jakob.salzma, Colorado School of Mies, US bstract s sesor etworks are deployed over various terrais, the complexity of their topology cotiues to grow. Voids i etworks ofte cause existig geographic routig algorithms to fail. I this paper, we propose a ovel geographic routig algorithm called Forwardig with VIrtual Positio (ViP). We itroduce virtual positio as the middle positio of all direct eighbors of a ode. Istead of comparig odes real geographic positios, ViP employs virtual positios whe selectig the ext hop. Such virtual positio reflects the eighborhood of a sesor ode, as well as the tedecy of further forwardig. For sparselydeployed etworks, ViP icreases success rate of packet routig without itroducig sigificat overhead. Furthermore, multiple levels of virtual positio ca be obtaied with localized iteratio. We propose the Forwardig with Multilevel Virtual Positio (MVP) algorithm ad the Forwardig with Hierarchical Virtual Positio (HVP) algorithm. Differet levels of virtual positios ad their combiatios are used alteratively to icrease success rate of packet routig i sesor etworks. Key words geographic routig, greedy forwardig, routig hole, wireless sesor etworks. I. INTRODUCTION Wireless Sesor Networks (WSNs) are composed of may sesor odes, which are capable of sesig physical parameters, processig data ad commuicatig wirelessly [1]. Sesor odes are usually battery powered ad left uatteded after deploymet. Sice radio commuicatio amog sesor odes is the mai drai of eergy i WSNs, eergy efficiecy remais a critical desig issue for WSNs. mog various routig algorithms, odemad routig algorithms such as ODV [] are popular, while their floodigbased route discovery ofte lead to high cotrol overhead [3]. I cotrast, siglepath geographic routig with Forwardig (GF) is attractive for WSNs [1]. I a basic GF algorithm, a ode commuicates oly with its direct eighbors (1hop). The eighborig ode that further miimizes the remaiig distace of a packet to its destiatio will be selected as the ext hop. Such localized approach is This work is partially fiaced by the Germa Research Foudatio (DFG) (post graduate program MuSM, GRK 144). Fig. 1. example of ad MFR geographical routig. packet is set from ode S to ode D. The algorithm selects ode as the exthop, sice is the eighbor that is the closest to the destiatio. The MFR algorithm selects ode B as the ext hop, sice B makes the greatest progress alog the directio of packet forwardig (SD). Both ad MFR requires the ext hop to be closer to the destiatio tha the curret ode. effective ad ca be dyamically adapted to chages, which oly require positio iformatio of sesor odes [1]. The algorithm [4] ad the Most Forwardig progress withi Radius (MFR) algorithm [5] are two fudametal approaches of GF based o distace. Fig. 1 illustrates the priciple of the algorithm ad the MFR algorithm. s moder WSNs are becomig popular i various applicatios, their topology is becomig complicated. Due to limited precisio of deploymet, voids ca cause routig holes i the etwork, where ofte lead traditioal GF algorithms to fail [6]. The reaso is the local miimum pheomeo illustrated i Fig.. I siglepath GF routig algorithm (e.g. or MFR), forwardig of packets towards the sik ca fail at ode, sice there is o direct eighbor closer to the destiatio tha ode itself. Besides, ueve power cosumptio o sesor odes ca also lead to ew routig holes i the later lifetime of WSNs. The objective of our work is to improve the success rate of GF for sparse WSNs or WSNs with small routig holes. To this ed, we preset a ovel geographic routig algorithm sik hole Fig.. example of local miimum.
2 amed Forwardig with VIrtual Positio (ViP)", as well as its improved versios called the Forwardig with Multilevel Virtual Positio (MVP)" algorithm ad the " Forwardig with Hierarchical Virtual Positio (HVP)" algorithm. Each proposed algorithm has two variats usig the ad MFR priciple respectively. The mai advatage of our approaches is that the algorithms simply employ GF throughout the routig process, ad iheretly results i high routig efficiecy as the basic GF algorithms. I the mea time, the amout of cotrol overhead of the proposed algorithms is strictly limited. The basic assumptio of our approaches is that each ode i the etwork is aware of its ow geographic positio as well as the positios of its direct eighbors. II. RELTED WORK Various geographic routig algorithms [6] have bee proposed i recet years to address the local miimum problem of GF. Most of the routig algorithms are composed of two phases. Such algorithms start with a basic GF algorithm, ad recover from local miimum with their complemetary holebypassig phases. The Perimeter Stateless Routig (GPSR) is oe of the fudametal algorithms based o plaar graphs [7]. plaar graph is a subgraph with ocrossig edges, which represets the same coectivity as the origial etwork. GPSR uses GF ad switches to its perimeter mode whe a local miimum is met. The righthad rule is employed i the perimeter mode, where packets are routed couterclockwise alog the edge o the face of a plaar graph. Bose et al. proposed the FCE1 ad FCE [8] algorithms that use the perimeter of the plaar graph formed at each ode. Such approaches have high success rate of packet routig, but also high cotrol overhead due to the plaarizatio processes ad the maiteace of the plaar graph iformatio o every sesor ode. I the abovemetioed phase algorithms, the performace durig the GF phase is much better tha durig their complemetary phases [9]. To icrease the ratio of GF durig routig, Liu et al. preseted the idea of liged Virtual Coordiate System (VCS) [9]. Virtual coordiates are based o iteger umber of hops to the referece odes (achor odes) ad represet a coarse approximatio of ode locatio. The success rate of greedy routig is improved with VCS, while floodig of cotrol message from achor odes to the whole etwork causes large cotrol overhead durig the settig up of VCS. I [10], Stojmeovic et al. proposed to use GF with iformatio of hop eighbors (hop GF). The success rate of the GF algorithms is improved with each ode aware of its direct eighbors as well as its hop eighbors (eighbors of its eighbors). mog all direct ad hop eighbors, packets are set to the ode which is the closest to the destiatio via 1hop or hop forwardig. Sice farther eighbors are available for the selectio of exthop, small routig holes ca be avoided efficietly. The authors also proved that such GFbased methods are iheretly loopfree. The method i [10] solely employs the basic idea of GF durig routig. Such a method limits cotrol overhead sice it oly requires local kowledge of sesor odes (iformatio of 1hop ad hop eighbors) ad avoids sophisticated complemetary phases to recover from local miimums. III. LGORITHM DESIGN s metioed earlier, hop GF uses extra kowledge of  hop eighbors i GF. Such iformatio o farther eighbors improves the success rate of GF i a straightforward maer. However, hop GF is ot strictly localized to the direct eighbors, therefore, it is ot scalable for further extesio. For example, whe further improvemet of success rate is demaded, odes eed to have iformatio of eighbors withi N hops (N>=3). For a D wireless etwork, the umber of eighbors stored o each sesor odes icreases proportioally to N. Similarly is the cotrol overhead for the maiteace of eighbor iformatio withi N hops.. Forwardig with Virtual Positio (ViP) We itroduce the virtual positio of a ode as the middle poit of all its direct eighbors. If a ode (x,y ) has direct eighbors V,1 (x,1,y,1 ), V, (x,,y, ),, V, (x,,y, ), the virtual positio of ode is: Fig. 3. example of usig virtual positios i GF. The packet is routed from ode 10 to ode 13. Whe usig the geographic positios of odes, ode 1 becomes a local miimum for both ad MFR, as show i (a). With virtual positios, a path ca be foud by MFRViP, as show i (b). With d level virtual positio, both MVP (level) ad MFR MVP (level) fid paths aroud the hole, as show i (c).
3 3 ( x ', y ') = (( x, 1 + x, + K+ x, ) /,( y,1 + y, + K+ y, ) / ) Each ode calculates its virtual positio accordig to (1), ad broadcasts its virtual positio to its direct eighbors. The iformatio of virtual positio is stored o odes themselves ad their direct eighbors. I other words, each ode has the kowledge of its ow virtual positio, ad the virtual positios of its direct eighbors. Fig. 3(a) shows the geographic positios of odes. Fig. 3(b) shows the calculated virtual positios of odes. The virtual positio of ode 11 is the same as its real geographic positio, sice its direct eighbors (ode 6, 7, 8, 10, 1, 14, 15, 16) are located uiformly i the trasmissio circle cetered at the geographic positio of ode 11. I cotrast, the virtual positio of ode 1 (Fig. 3(b)) locates to the left of the geographic positio of ode 1 (Fig. 3(a)), because the positios of its direct eighbors (ode 7, 8, 11, 15, 16) are leftbiased i the trasmissio circle of ode 1. The virtual positio of a ode provides a idicatio of how the direct eighbors are located aroud the ode o average, hece it is a suitable metric to demostrate the tedecy of further forwardig durig geographic routig. Cosider the example i Fig. 3, for a packet that eeds to be set from ode 10 to ode 13, both ad MFR get stuck at ode 1, sice there is o eighbor that ca make further progress towards the destiatio. I cotrast, the virtual positio of ode 1 is strogly leftbiased due to the void o the right side of ode 1. s a result, MFRViP fids a path ( ) aroud the hole usig virtual positios of odes. We propose Forwardig with Virtual Positio (ViP), a lookahead geographic routig algorithm based o the coordiate system of virtual positios. Istead of usig farther eighbors as i hop GF, ViP uses the virtual positios of odes to ivolve farther eighbors i the lookahead routig process. Our algorithm is strictly localized, where a ode oly iteracts with its direct eighbors. ViP has two variats called ad MFRViP, which are based o the priciple of the ad MFR algorithm, respectively. s a example for a D WSN, whe a packet with destiatio D(x D,y D ) arrives at ode (x,y ),  ViP evaluates its ow virtual positio (x,y ) ad the virtual positios of its direct eighbors. The eighbor has the virtual positio that is the closest to ode D is selected as the ext hop. ccordig to the algorithm, the virtual positio of the selected eighbor must be also closer to the destiatio tha the virtual positio of curret ode. Namely, the exthop ode V,i of meets the followig two coditios: arg mi( ( x ' x ) + ( y ' y ) ) () ad: V, i, i D, i D (1) (x,i ' x D ) + (y,i ' y D ) < ( (x ' x D ) + (y ' y D ) (3) Similarly, MFRViP selects the eighbor that makes the greatest progress towards the destiatio of a packet. Give the vectors D ad DV,i, the exthop ode V,i of MFRViP is the oe that has the miimal dot product of the two vectors: arg mi( D DV ) (4) V, i ad also satisfies equatio (3). ad MFRViP termiate whe there is o eighbor that has the virtual positio to make further progress towards the destiatio of a packet. With this idea, odes oly eed to iteract with their direct eighbors to obtai iformatio of virtual positio. Therefore, settig up virtual positios is strictly localized. The message overhead i the worst case is O(M), where M is the total umber of odes i a etwork. B. Forwardig with Multilevel Virtual Positio (MVP) To further improve the success rate of GF, we itroduce the cocept of multilevel virtual positio that cosiders farther odes (eighbors of KHop, K>=1). If we refer to the virtual positio derived usig Equatio (1) as the 1 st level virtual positio, the the K th level virtual positio (K>=1) of ode ca be doe with K iteratios of local broadcast betwee odes ad their direct eighbors V,1 (x,1,y,1 ), V, (x,,y, ),, V, (x,,y, ), as follows. ( x K, y K ) = (( x,1 ( y,1 + x, + y,, i + K + x + K + y,, ) /, ) / ) Such K th level virtual positios of odes idicate how the K hop eighbors are located o average, also the tedecy of further forwardig durig geographic routig. Usig this cocept, we itroduce the Forwardig with Multilevel Virtual Positio (MVP)" algorithm based o K th level virtual positio (K>=1), where each ode kows its ow K th  level virtual positio, ad the K th level virtual positios of its direct eighbors. MVP equals ViP whe K is 1. The variats MVP ad MFRMVP evaluate the K th level virtual positios accordig to the priciple of the ad MFR algorithm, respectively. Comparig to ViP, MVP employs a higher level of virtual positios to further improve the success rate of GF. Fig. 3(c) is a example of MVP usig d level virtual positio, which demostrates that usig virtual positio of higher level idicates better forwardig tedecy i geographic routig, sice it takes farther eighbors ito cosideratio. MVP ad MFRMVP stop whe there is o eighbor that has the K th level virtual positio that makes further progress towards the destiatio of a packet. C. Forwardig with Hierarchical Virtual Positio (HVP) Usig K th level virtual positio (K>=1) ca itroduce ew local miimums durig routig. For istace, i Fig. 4, a packet eeds to be set from ode 5 to ode 4. Whe usig the geographic positios of odes, the packet ca be successfully delivered (path i Fig. 4(a)). While usig 1 st level virtual positio, ode 5 has o eighbor to make further progress towards ode 4, thus becomes a local miimum of  (5)
4 4 D. lgorithm Properties I a GF algorithm, a ode eeds to evaluate all the etries of eighbors stored o the odes, ad select the ext hop based o a certai criterio. Therefore, the average storage ad computatioal overhead is proportioal to the umber of eighbor etries o odes. This metric also reflects the cotrol Fig. 4. example of routig holes itroduced by usig virtual positios. ViP ad MFRViP. To address this problem, we itroduce the Forwardig with Hierarchical Virtual Positio (HVP) algorithm (Table. 1 shows the pseudo code of HVP). HVP uses the combiatio of all Klevel virtual positios (K>=1) ad the geographic positios of odes i a dowhill fashio. Namely, whe a local miimum is met usig K th level virtual positio, HVP will lower the level of virtual positio from K th  level to (K1) th level (K>=), or from 1 st level virtual positio to real geographic positio. HVP requires odes to store the geographic positios, as well as all the Klevel virtual positios of itself ad its direct eighbors. flag flag_level is added to the packets to idicate the curret level of virtual positio (usig geographic positio whe K=0). I our example (Fig. 4(b)), the packet stuck (usig 1 st level virtual positio) at ode 5 ca set its flag_level to be 0, ad reach its destiatio via the path by usig the geographic positios of odes. The two variats HVP ad MFR HVP start with K th level virtual positio (K>=1) accordig to the priciple of the ad MFR algorithm, respectively. To esure that HVP is loopfree, such dowhill process is uidirectioal. Namely, for a packet iitialized with K th level virtual positio ad adjusted to (K1) th level virtual positio after meetig a local miimum, the packet stays o (K1) th  level ad ca oly be further adjusted to the lower levels (or from 1 st level virtual positio to geographic positio). PROCEDURE hadle_packet TBLE 1 PSEUDO CODE OF GFHVP ON NODE N WHILE there is local miimum ND packet.flag_level>=0 { packet.flag_level; } IF packet.flag_level>=0 { forward packet usig virtual positio of level packet.flag_level idicated by the packet (usig geographic positio whe packet.flag_level is 0); } ELSE { routig failed; } lgorithm GF, GFViP, GFMVP TBLE LGORITHM OVERHED Overhead GFHVP starts with K th level virtual positio Nhop GF R: trasmissio rage of odes. D: average umber of odes i a uit area O(π R D) O(π R D (K+1)) O(π (N R) D) message overhead to maitai the iformatio of virtual positios. Table shows cotrol overhead of related algorithms. Here we assume that odes are deployed with the same desity i a D etwork. The proposed ViP, MVP ad HVP algorithms are strictly localized, ad ca termiate based o local kowledge, i.e., they are loopfree. Lemma 3.1. For ay static etwork, ViP is loopfree. Proof: s proved i [10], siglepath routig algorithms based o the algorithm or the MFR algorithm is iheretly loopfree. ViP employs the virtual positio coordiatio system which is the 1to1 mappig of the origial geographic coordiate system of a etwork. Istead of the geographic positios, ViP evaluates the correspodig virtual positios of odes based o the coordiate system of virtual positio. Thus, ad MFRViP are also iheretly loopfree. Namly, ViP is loopfree. Lemma 3.. For ay static etwork, MVP is loopfree. Proof: ssume that MVP of K th level virtual positio (K>=1) is loop free. Sice MVP of (K+1) th level virtual positio employs the 1to1 mappig of the K th level virtual positio coordiate system. Therefore, MVP of (K+1) th level virtual positio is loopfree. s proved i Lemma3.1, MVP of 1 st level virtual positio is loop free. Thus, MVP of K th level virtual positio is loop free for ay K>=1. Lemma 3.3. For ay static etwork, HVP is loopfree. Proof: HVP starts with K th level virtual positio (K>=1) ad adjust the flag_level of packets uidirectioally util 0, which refers to the coordiate system of geographic positio. s proved i Lemma3., MVP of K th level virtual positio is loop free. Therefore, each step i the dowhill scalig of HVP is loopfree. Thus, HVP starts with K th level virtual positio is loop free for ay K>=1.
5 5 IV. PERFORMNCE EVLUTION We implemeted the proposed algorithms usig Matlab. The simulated etwork was a 1000 m 1000 m square plae, where 500 sesor odes were radomly deployed. Packets were geerated with radom pairs of sourcedestiatio addresses. Differet commuicatio rages of sesors are used. We used a simple disccommuicatio model: sesor odes i the trasmissio rage ca receive sigals from a trasmitter without loss. For the same deploymet desity, smaller commuicatio rage implies sparser layout of a etwork ad bigger chace to ecouter routig holes durig packet forwardig. The objective of our experimets is to demostrate the performace of our algorithm i sparselydeployed WSNs, or WSNs with small voids due to ouiform deploymets. We show oly the results of the variat, MVP ad HVP. The performace of MFRViP, MFRMVP ad MFRHVP are similar from our simulatio. The results were compared with the Hop algorithm [10] ad its extesio, the 3Hop algorithm. We use the followig metrics to compare the performace of the simulated routig algorithms: Success rate of GF: This is defied as the percetage of packet delivery with radom packet forwardig. 500 packets with radom sourcedestiatio pairs were geerated ad forwarded i the simulated etwork. Whe the trasmissio rage of odes is small, voids appear frequetly i the etwork. This metric illustrates the ability of avoidig routig hole of the simulated algorithms. Delay of GF: This is defied as the average hop couts of packets from their source to the destiatio. Give the same success rate, small packet delay (i hops) represets better performace i terms of routig efficiecy ad eergy cosumptio. lgorithm Overhead: The umber of eighbor etries stored o sesor odes implies the storage overhead, as well as the computatioal overhead by choosig of exthop. Furthermore, the cotrol message overhead of maitaiig eighbor iformatio is also proportioal to the umber of etries. Experimetal Results: Fig. 5 shows the average percetage of successful packet forwardig from our simulatio. Fig. 5(a) compares the performace of the algorithm, Hop,  ViP ad MVP (usig d level virtual positios). Whe the commuicatio rage of sesor odes is short, odes have relatively few eighbors, which leads to low success rates of the simulated algorithms. The Hop ad cosider the hop eighbors of odes. s the commuicatio rage icreases, Hop ad demostrate similar success rates, which are higher comparig to. MVP usig d level virtual positio results i eve better success rate sice it cosiders 3hop eighbors. ll the simulated algorithms reach full delivery of packets i deselydeployed etworks (whe trasmissio rage reaches 10m i verage Success Rate verage Success Rate 100% 90% 80% 70% 60% 50% 40% 30% 0% 10% Hop % 90% 80% 70% 60% 50% 40% (a) 30% 0% HVP (1 level) 10% HVP ( level) 0 (b) Fig. 5. verage success rate of the algorithms with differet commuicatio rages Fig. 5(a)). Fig. 5(b) illustrates the performace of the dowhill scalig i the HVP algorithms start with 1 st level ad d level virtual positios. Comparig to the correspodig MVP algorithms usig the same level of virtual positios, HVP has higher success rate due to its additioal search o virtual positios of lower levels ad the geographic positios of odes. Fig. 6 shows the average delay of the successfully delivered packets from our simulatio. Whe the etwork is sparselydeployed (trasmissio rage < 70m i Fig. 6(a)), the legth of routig paths icreases with the icreasig of the commuicatio rage. The reaso is that more packets with distat source ad destiatio ca be delivered whe the coectivity of a etwork icreases. Hop,  ViP ad MVP (usig d level virtual positio) have bigger average delay, sice additioal packet with sourcedestiatio pair of loger distace ca also be delivered with the lookahead algorithms. s the trasmissio rage reaches 70m (Fig. 6(a)), the hop cout starts to fall due to the high success rates ad the icreasig stepsize of forwardig. We observe that the results of the simulated algorithms are similar whe the success rates reach 100%. Therefore, the lookahead routig algorithms (Hop, ad  MVP) have similar routig efficiecy as the algorithm. Fig. 7 illustrates the cotrol overhead of the simulated
6 6 verage Delay [hop cout] verage Delay [hop cout] (a) Hop HVP (1 level) HVP ( level) 1 (b) Fig. 6. verage delay of the algorithms with differet commuicatio rages algorithms idicated by the umber of eighbor etries o odes. With similar performace i terms of success rate, results i lower storage ad computatioal overhead compared to the Hop algorithm. s a extesio of the Hop algorithm, the rapidly icreasig overhead of the 3Hop algorithm shows that the NHop idea is ot scalable. I cotrast, the umber of eighbor etries of our ad  MVP algorithms stays low as the algorithm. The storage ad computatioal overhead of HVP icreases liearly alog with the level of virtual positio it starts with. V. CONCLUSION ND FUTURE WORK I this paper, we preset a ovel geographic routig algorithm amed Forwardig with Virtual Positio (ViP)", as well as its two extesios called the Forwardig with Multilevel Virtual Positio (MVP)" algorithm ad the Forwardig with Hierarchical Virtual Positio (HVP) algorithm. Each of the three algorithms has two variats based o the priciple of the algorithm ad the Most Forwardig progress withi Radius (MFR) algorithm, respectively. Simulatio results demostrate that our proposed algorithms improve the success rate of geographic routig for sparselydeployed WSNs ad Etries of Neighbors ,, MVP Hop 3Hop HVP (1 level) HVP ( level) 0 Fig. 7. Number of eighbor etries with differet commuicatio rages WSNs with small routig holes. The proposed algorithms solely employ Forwardig (GF) throughout the routig processes, ad iheretly results i high routig efficiecy as the basic GF algorithms. Furthermore, the cotrol overhead of our proposed algorithms is strictly limited. For future work, the tradeoff betwee the overhead caused by usig higher levels of virtual positios ad the resulted performace will be formally aalyzed. Cotext iformatio such as remaiig eergy will be cosidered as the routig parameter for our algorithms. REFERENCES [1] K. kkaya ad M. Youis, " survey of routig protocols for wireless sesor etworks", Elsevier d Hoc Network Joural, 005, Vol. 3/3, pp [] C.E. Perkis ad E.M. BeldigRoyer, "d hoc odemad distace vector (ODV) routig", i IEEE Workshop o Mobile Computig Systems ad pplicatios, Feb [3] Pham, N. N., You, J., ad Wo, C. " Compariso of Wireless Sesor Network Routig Protocols o a Experimetal Testbed", I Proceedigs of the IEEE iteratioal Coferece o Sesor Networks, Ubiquitous, ad Trustworthy Computig  Vol  Workshops  Volume 0 (Jue 0507, 006). SUTC. IEEE Computer Society, Washigto, DC, [4] G. G. Fi. "Routig ad addressig problems i large metropolitascale iteretworks", Techical Report ISI/RR , Iformatio Scieces Istitute, Mars [5] H. Takagi ad L. Kleirock, "Optimal Trasmissio Rages for Radomly Distributed Packet Radio Termials", IEEE Tras. Comm., vol. 3, o. 3, pp , [6] Nadeem hmed, Salil S. Kahere, Sajay Jha, "The holes problem i wireless sesor etworks: a survey", CM SIGMOBILE Mobile Computig ad Commuicatios Review, v.9., pril 005. [7] Brad Karp ad H. T. Kug, "GPSR: perimeter stateless routig for wireless etworks", i Proceedigs of the CM/IEEE MobiCom '000, pages 4354, 000. [8] P. Bose, P. Mori, I. Stojmeovic, ad J. Urrutia, "Routig with guarateed delivery i ad hoc wireless etworks", Wireless Networks, 7: , 001. [9] Ke Liu, Nael bughazaleh, "liged Virtual Coordiates for Routig i WSNs", Mobile dhoc ad Sesor Systems (MSS), 006 IEEE Iteratioal Coferece o, vol., o., pp , Oct [10] Stojmeovic, I. ad Li, X, "LoopFree Hybrid SiglePath/Floodig Routig lgorithms with Guarateed Delivery for Wireless Networks", IEEE Tras. Parallel Distrib. Syst. 1, 10 (Oct. 001),
Adaptive Resource Allocation for Electric Environmental Pollution through the Control Network
Available olie at www.sciecedirect.com Eergy Procedia 6 (202) 60 64 202 Iteratioal Coferece o Future Eergy, Eviromet, ad Materials Adaptive Resource Allocatio for Electric Evirometal Pollutio through the
More informationStone Images Retrieval Based on Color Histogram
Stoe Images Retrieval Based o Color Histogram Qiag Zhao, Jie Yag, Jigyi Yag, Hogxig Liu School of Iformatio Egieerig, Wuha Uiversity of Techology Wuha, Chia Abstract Stoe images color features are chose
More information. Written in factored form it is easy to see that the roots are 2, 2, i,
CMPS A Itroductio to Programmig Programmig Assigmet 4 I this assigmet you will write a java program that determies the real roots of a polyomial that lie withi a specified rage. Recall that the roots (or
More informationCS200: Hash Tables. Prichard Ch CS200  Hash Tables 1
CS200: Hash Tables Prichard Ch. 13.2 CS200  Hash Tables 1 Table Implemetatios: average cases Search Add Remove Sorted arraybased Usorted arraybased Balaced Search Trees O(log ) O() O() O() O(1) O()
More informationOnes Assignment Method for Solving Traveling Salesman Problem
Joural of mathematics ad computer sciece 0 (0), 5865 Oes Assigmet Method for Solvig Travelig Salesma Problem Hadi Basirzadeh Departmet of Mathematics, Shahid Chamra Uiversity, Ahvaz, Ira Article history:
More informationThe PentaS: A Scalable Crossbar Network for Distributed Shared Memory Multiprocessor Systems
The PetaS: A Scalable Crossbar Network for Distributed Shared Memory Multiprocessor Systems Abdulkarim Ayyad Departmet of Computer Egieerig, AlQuds Uiversity, Jerusalem, P.O. Box 20002 Tel: 022797024,
More informationIMP: Superposer Integrated Morphometrics Package Superposition Tool
IMP: Superposer Itegrated Morphometrics Package Superpositio Tool Programmig by: David Lieber ( 03) Caisius College 200 Mai St. Buffalo, NY 4208 Cocept by: H. David Sheets, Dept. of Physics, Caisius College
More informationSocialP2P: An Online Social Network Based P2P File Sharing System
1.119/TPDS.214.23592, IEEE Trasactios o Parallel ad Distributed Systems 1 : A Olie Social Network Based P2P File Sharig System Haiyig She*, Seior Member, IEEE, Ze Li, Studet Member, IEEE, Kag Che Abstract
More informationThe golden search method: Question 1
1. Golde Sectio Search for the Mode of a Fuctio The golde search method: Questio 1 Suppose the last pair of poits at which we have a fuctio evaluatio is x(), y(). The accordig to the method, If f(x())
More informationBGP Attributes and Path Selection. ISP Training Workshops
BGP Attributes ad Path Selectio ISP Traiig Workshops 1 BGP Attributes The tools available for the job 2 What Is a Attribute?... Next Hop AS Path MED...... p Part of a BGP Update p Describes the characteristics
More informationSD vs. SD + One of the most important uses of sample statistics is to estimate the corresponding population parameters.
SD vs. SD + Oe of the most importat uses of sample statistics is to estimate the correspodig populatio parameters. The mea of a represetative sample is a good estimate of the mea of the populatio that
More informationMałgorzata Sterna. Mateusz Cicheński, Mateusz Jarus, Michał Miszkiewicz, Jarosław Szymczak
Małgorzata Stera Mateusz Cicheński, Mateusz Jarus, Michał Miszkiewicz, Jarosław Szymczak Istitute of Computig Sciece Pozań Uiversity of Techology Pozań  Polad Scope of the Talk Problem defiitio MP Formulatio
More informationThroughputDelay Tradeoffs in LargeScale MANETs with Network Coding
ThroughputDelay Tradeoffs i LargeScale MANETs with Network Codig Chi Zhag ad Yuguag Fag Departmet of Electrical ad Computer Egieerig Uiversity of Florida, Gaiesville, FL 326 Email: {zhagchi@, fag@ece.}ufl.edu
More informationRouting with Guaranteed Delivery in ad hoc Wireless Networks*
Routig with Guarateed Delivery i ad hoc Wireless Networks* Prosejit Bose School of Computer Sciece Carleto Uiversity jit@scs.carleto.ca Pat Mori School of Computer Sciece Carleto Uiversity mori@scs.carleto.ca
More information1&1 Next Level Hosting
1&1 Next Level Hostig Performace Level: Performace that grows with your requiremets Copyright 1&1 Iteret SE 2017 1ad1.com 2 1&1 NEXT LEVEL HOSTING 3 Fast page loadig ad short respose times play importat
More informationIntroduction to OSPF. ISP Training Workshops
Itroductio to OSPF ISP Traiig Workshops 1 OSPF p Ope Shortest Path First p Lik state or SPF techology p Developed by OSPF workig group of IETF (RFC 1247) p OSPFv2 stadard described i RFC2328 p Desiged
More information( n+1 2 ) , position=(7+1)/2 =4,(median is observation #4) Median=10lb
Chapter 3 Descriptive Measures Measures of Ceter (Cetral Tedecy) These measures will tell us where is the ceter of our data or where most typical value of a data set lies Mode the value that occurs most
More informationTANT: A NatureInspired Data Gathering Protocol for Wireless Sensor Networks 1
22 JOURNAL OF COMMUNICATIONS, VOL. 1, NO. 2, MAY 26 TANT: A NatureIspired Data Gatherig Protocol for Wireless Sesor Networks 1 S. Selvakeedy School of Iformatio Techologies, Uiversity of Sydey, NSW 26,
More informationInvestigating methods for improving Bagged knn classifiers
Ivestigatig methods for improvig Bagged knn classifiers Fuad M. Alkoot Telecommuicatio & Navigatio Istitute, P.A.A.E.T. P.O.Box 4575, Alsalmia, 22046 Kuwait Abstract We experimet with baggig knn classifiers
More informationCopyright 2016 Ramez Elmasri and Shamkant B. Navathe
Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe CHAPTER 22 Database Recovery Techiques Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe Itroductio Recovery algorithms Recovery cocepts Writeahead
More informationGPUMP: a MultiplePrecision Integer Library for GPUs
GPUMP: a MultiplePrecisio Iteger Library for GPUs Kaiyog Zhao ad Xiaowe Chu Departmet of Computer Sciece, Hog Kog Baptist Uiversity Hog Kog, P. R. Chia Email: {kyzhao, chxw}@comp.hkbu.edu.hk Abstract
More informationEvaluation of Distributed and Replicated HLR for Location Management in PCS Network
JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 9, 850 (2003) Evaluatio of Distributed ad Replicated HLR for Locatio Maagemet i PCS Network Departmet of Computer Sciece ad Iformatio Egieerig Natioal Chiao
More informationOptimization of Priority based CPU Scheduling Algorithms to Minimize Starvation of Processes using an Efficiency Factor
Iteratioal Joural of Computer Applicatios (97 8887) Volume 132 No.11, December21 Optimizatio of based CPU Schedulig Algorithms to Miimize Starvatio of Processes usig a Efficiecy Factor Muhammad A. Mustapha
More informationA Key Distribution method for Reducing Storage and Supporting High Level Security in the Largescale WSN
Iteratioal Joural of Digital Cotet Techology ad its Applicatios Vol. 2 No 1, March 2008 A Key Distributio method for Reducig Storage ad Supportig High Level Security i the Largescale WSN YooSu Jeog *1,
More informationService discovery in ad hoc networks
Service discovery i ad hoc etworks Mohammed Haddad ad Hamamache Kheddouci Laboratoire PRISMa  Uiversité Claude Berard Lyo, Bât. 7, 843, Bd. du ov. 98, 69622 Villeurbae Cedex Abstract. Various researches
More informationCIS 121 Data Structures and Algorithms with Java Fall BigOh Notation Tuesday, September 5 (Makeup Friday, September 8)
CIS 11 Data Structures ad Algorithms with Java Fall 017 BigOh Notatio Tuesday, September 5 (Makeup Friday, September 8) Learig Goals Review BigOh ad lear big/small omega/theta otatios Practice solvig
More informationSpeedingup dynamic programming in sequence alignment
Departmet of Computer Sciece Aarhus Uiversity Demark Speedigup dyamic programmig i sequece aligmet Master s Thesis Dug My Hoa  443 December, Supervisor: Christia Nørgaard Storm Pederse Implemetatio code
More informationOctahedral Graph Scaling
Octahedral Graph Scalig Peter Russell Jauary 1, 2015 Abstract There is presetly o strog iterpretatio for the otio of vertex graph scalig. This paper presets a ew defiitio for the term i the cotext of
More informationCOMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. Chapter 4. The Processor. Part A Datapath Design
COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Iterface 5 th Editio Chapter The Processor Part A path Desig Itroductio CPU performace factors Istructio cout Determied by ISA ad compiler. CPI ad
More informationOptimization on Retrieving Containers Based on Multiphase Hybrid Dynamic Programming
Available olie at www.sciecedirect.com ScieceDirect Procedia  Social ad Behavioral Scie ce s 96 ( 2013 ) 844 855 Abstract 13th COTA Iteratioal Coferece of Trasportatio Professioals (CICTP 2013) Optimizatio
More informationk (check node degree) and j (variable node degree)
A Parallel Turbo Decodig Message Passig Architecture for Array LDPC Codes Kira Guam, Pakaj Bhagawat, Weihuag Wag, Gwa Choi, Mark Yeary * Dept. of Electrical Egieerig, Texas A&M Uiversity, College Statio,
More informationPseudocode ( 1.1) Analysis of Algorithms. Primitive Operations. Pseudocode Details. Running Time ( 1.1) Estimating performance
Aalysis of Algorithms Iput Algorithm Output A algorithm is a stepbystep procedure for solvig a problem i a fiite amout of time. Pseudocode ( 1.1) Highlevel descriptio of a algorithm More structured
More informationLearning to Shoot a Goal Lecture 8: Learning Models and Skills
Learig to Shoot a Goal Lecture 8: Learig Models ad Skills How do we acquire skill at shootig goals? CS 344R/393R: Robotics Bejami Kuipers Learig to Shoot a Goal The robot eeds to shoot the ball i the goal.
More informationImproving Information Retrieval System Security via an Optimal Maximal Coding Scheme
Improvig Iformatio Retrieval System Security via a Optimal Maximal Codig Scheme Dogyag Log Departmet of Computer Sciece, City Uiversity of Hog Kog, 8 Tat Chee Aveue Kowloo, Hog Kog SAR, PRC dylog@cs.cityu.edu.hk
More informationAN EFFICIENT GROUP KEY MANAGEMENT USING CODE FOR KEY CALCULATION FOR SIMULTANEOUS JOIN/LEAVE: CKCS
Iteratioal Joural of Computer Networks & Commuicatios (IJCNC) Vol.4, No.4, July 01 AN EFFICIENT GROUP KEY MANAGEMENT USING CODE FOR KEY CALCULATION FOR SIMULTANEOUS JOIN/LEAVE: CKCS Melisa Hajyvahabzadeh
More informationDesign of efficient, virtual nonblocking optical switches
Desig of efficiet, virtual oblockig otical switches Larry F. Lid, Michael Sratt Mobile Systems ad Services Laboratory HP Laboratories Bristol HPL200239 March 3 th, 2002* otical switchig, switch desig
More informationProperties and Embeddings of Interconnection Networks Based on the Hexcube
JOURNAL OF INFORMATION PROPERTIES SCIENCE AND AND ENGINEERING EMBEDDINGS OF 16, THE 8195 HEXCUBE (2000) 81 Short Paper Properties ad Embeddigs of Itercoectio Networks Based o the Hexcube JUNGSING JWO,
More informationFuzzy Rule Selection by Data Mining Criteria and Genetic Algorithms
Fuzzy Rule Selectio by Data Miig Criteria ad Geetic Algorithms Hisao Ishibuchi Dept. of Idustrial Egieerig Osaka Prefecture Uiversity 11 Gakuecho, Sakai, Osaka 5998531, JAPAN Email: hisaoi@ie.osakafuu.ac.jp
More informationExamples and Applications of Binary Search
Toy Gog ITEE Uiersity of Queeslad I the secod lecture last week we studied the biary search algorithm that soles the problem of determiig if a particular alue appears i a sorted list of iteger or ot. We
More informationSoftware development of components for complex signal analysis on the example of adaptive recursive estimation methods.
Software developmet of compoets for complex sigal aalysis o the example of adaptive recursive estimatio methods. SIMON BOYMANN, RALPH MASCHOTTA, SILKE LEHMANN, DUNJA STEUER Istitute of Biomedical Egieerig
More informationA Dynamic Separator Algorithm
A Dyamic Separator Algorithm Degait Armo Joh Reif Departmet of Computer Sciece, Duke Uiversity Durham, NC 277080129 Abstract Our work is based o the pioeerig work i sphere separators doe by Miller, Teg,
More informationHomework 1 Solutions MA 522 Fall 2017
Homework 1 Solutios MA 5 Fall 017 1. Cosider the searchig problem: Iput A sequece of umbers A = [a 1,..., a ] ad a value v. Output A idex i such that v = A[i] or the special value NIL if v does ot appear
More informationA GroupBased Architecture for Wireless Sensor Networks
A GroupBased Architecture for Wireless Sesor Networs Jaime Lloret, Miguel Garcia ad Jesus Tomas Departmet of Commuicatios Polytechic Uiversity of Valecia Valecia, Spai jlloret@dcom.upv.es, migarpi@teleco.upv.es,
More informationR 5 N: Randomized Recursive Routing for RestrictedRoute Networks
R 5 N: Radomized Recursive Routig for RestrictedRoute Networks Natha S. Evas Techische Uiversität Müche Muich, Germay Email: evas@et.i.tum.de Christia Grothoff Techische Uiversität Müche Muich, Germay
More informationFAST BITREVERSALS ON UNIPROCESSORS AND SHAREDMEMORY MULTIPROCESSORS
SIAM J. SCI. COMPUT. Vol. 22, No. 6, pp. 2113 2134 c 21 Society for Idustrial ad Applied Mathematics FAST BITREVERSALS ON UNIPROCESSORS AND SHAREDMEMORY MULTIPROCESSORS ZHAO ZHANG AND XIAODONG ZHANG
More informationA Very Simple Approach for 3D to 2D Mapping
A Very Simple Approach for D to D appig Sadipa Dey (1 Ajith Abraham ( Sugata Sayal ( Sadipa Dey (1 Ashi Software Private Limited INFINITY Tower II 10 th Floor Plot No.  4. Block GP Salt Lake Electroics
More informationConstantTime Distributed Dominating Set Approximation*
CostatTime Distributed Domiatig Set Approximatio* Fabia Kuh Departmet of Computer Sciece ETH Zurich 8092 Zurich, Switzerlad kuh @ if.ethz.ch Roger Wattehofer Departmet of Computer Sciece ETH Zurich 8092
More informationHADOOP: A NEW APPROACH FOR DOCUMENT CLUSTERING
Y.K. Patil* Iteratioal Joural of Advaced Research i ISSN: 22786244 IT ad Egieerig Impact Factor: 4.54 HADOOP: A NEW APPROACH FOR DOCUMENT CLUSTERING Prof. V.S. Nadedkar** Abstract: Documet clusterig is
More informationAnalysis of Documents Clustering Using Sampled Agglomerative Technique
Aalysis of Documets Clusterig Usig Sampled Agglomerative Techique Omar H. Karam, Ahmed M. Hamad, ad Sheri M. Moussa Abstract I this paper a clusterig algorithm for documets is proposed that adapts a sampligbased
More informationChapter 3 MATHEMATICAL MODELING OF TOLERANCE ALLOCATION AND OVERVIEW OF EVOLUTIONARY ALGORITHMS
28 Chapter 3 MATHEMATICAL MODELING OF TOLERANCE ALLOCATION AND OVERVIEW OF EVOLUTIONARY ALGORITHMS Tolerace sythesis deals with the allocatio of tolerace values to various dimesios of idividual compoets
More informationStrong Complementary Acyclic Domination of a Graph
Aals of Pure ad Applied Mathematics Vol 8, No, 04, 8389 ISSN: 79087X (P), 790888(olie) Published o 7 December 04 wwwresearchmathsciorg Aals of Strog Complemetary Acyclic Domiatio of a Graph NSaradha
More informationHistory Based Probabilistic Backoff Algorithm
America Joural of Egieerig ad Applied Scieces, 2012, 5 (3), 230236 ISSN: 19417020 2014 Rajagopala ad Mala, This ope access article is distributed uder a Creative Commos Attributio (CCBY) 3.0 licese
More informationBGP with an Adaptive Minimal Route Advertisement Interval
with a Adaptive Miimal Route Advertisemet Iterval Nead Lasković ad Ljiljaa Trajković Simo Fraser Uiversity Vacouver, British Columbia, Caada {laskovi, ljilja}@cs.sfu.ca Abstract The duratio of the Miimal
More informationGlobal Support Guide. Verizon WIreless. For the BlackBerry 8830 World Edition Smartphone and the Motorola Z6c
Verizo WIreless Global Support Guide For the BlackBerry 8830 World Editio Smartphoe ad the Motorola Z6c For complete iformatio o global services, please refer to verizowireless.com/vzglobal. Whether i
More information1 Enterprise Modeler
1 Eterprise Modeler Itroductio I BaaERP, a Busiess Cotrol Model ad a Eterprise Structure Model for multisite cofiguratios are itroduced. Eterprise Structure Model Busiess Cotrol Models Busiess Fuctio
More informationMultiThreading. Hyper, Multi, and Simultaneous Thread Execution
MultiThreadig Hyper, Multi, ad Simultaeous Thread Executio 1 Performace To Date Icreasig processor performace Pipeliig. Brach predictio. Superscalar executio. Outoforder executio. Caches. HyperThreadig
More informationClubsbased Particle Swarm Optimization
Clubsbased Particle Swarm Optimizatio Wesam Elshamy, Hassa M. Emara ad A. Bahgat Departmet of Electrical Power ad Machies, Faculty of Egieerig, Cairo Uiversity, Egypt {wesamelshamy, hmrashad, ahmed.bahgat}@ieee.org
More informationReversible Realization of Quaternary Decoder, Multiplexer, and Demultiplexer Circuits
Egieerig Letters, :, EL Reversible Realizatio of Quaterary Decoder, Multiplexer, ad Demultiplexer Circuits Mozammel H.. Kha, Member, ENG bstract quaterary reversible circuit is more compact tha the correspodig
More informationIdentification of the Swiss Z24 Highway Bridge by Frequency Domain Decomposition Brincker, Rune; Andersen, P.
Aalborg Uiversitet Idetificatio of the Swiss Z24 Highway Bridge by Frequecy Domai Decompositio Bricker, Rue; Aderse, P. Published i: Proceedigs of IMAC 2 Publicatio date: 22 Documet Versio Publisher's
More informationEnhancing Efficiency of Software Fault Tolerance Techniques in Satellite Motion System
Joural of Iformatio Systems ad Telecommuicatio, Vol. 2, No. 3, JulySeptember 2014 173 Ehacig Efficiecy of Software Fault Tolerace Techiques i Satellite Motio System Hoda Baki Departmet of Electrical ad
More information9.1. Sequences and Series. Sequences. What you should learn. Why you should learn it. Definition of Sequence
_9.qxd // : AM Page Chapter 9 Sequeces, Series, ad Probability 9. Sequeces ad Series What you should lear Use sequece otatio to write the terms of sequeces. Use factorial otatio. Use summatio otatio to
More informationQuorum Based Data Replication in Grid Environment
Quorum Based Data Replicatio i Grid Eviromet Rohaya Latip, Hamidah Ibrahim, Mohamed Othma, Md Nasir Sulaima, ad Azizol Abdullah Faculty of Computer Sciece ad Iformatio Techology, Uiversiti Putra Malaysia
More informationGuide to Applying Online
Guide to Applyig Olie Itroductio Respodig to requests for additioal iformatio Reportig: submittig your moitorig or ed of grat Pledges: submittig your Itroductio This guide is to help charities submit their
More informationClustering Sparse Graphs
Clusterig Sparse Graphs Yudog Che Departmet of Electrical ad Computer Egieerig The Uiversity of Texas at Austi Austi, TX 7872 ydche@utexas.edu Sujay Saghavi Departmet of Electrical ad Computer Egieerig
More informationA Kernel Density Based Approach for Large Scale Image Retrieval
A Kerel Desity Based Approach for Large Scale Image Retrieval Wei Tog Departmet of Computer Sciece ad Egieerig Michiga State Uiversity East Lasig, MI, USA togwei@cse.msu.edu Rog Ji Departmet of Computer
More informationEnd Semester Examination CSE, III Yr. (I Sem), 30002: Computer Organization
Ed Semester Examiatio 201314 CSE, III Yr. (I Sem), 30002: Computer Orgaizatio Istructios: GROUP A 1. Write the questio paper group (A, B, C, D), o frot page top of aswer book, as per what is metioed
More informationAn Algorithm to Solve Fuzzy Trapezoidal Transshipment Problem
Iteratioal Joural of Systems Sciece ad Applied Mathematics 206; (4): 5862 http://www.sciecepublishiggroup.com/j/ssam doi: 0.648/j.ssam.206004.4 A Algorithm to Solve Fuzzy Trapezoidal Trasshipmet Problem
More informationl1 text string ( l characters : 2lbytes) pointer table the ith word table of coincidence number of prex characters. pointer table the ith word
A New Method of Ngram Statistics for Large Number of ad Automatic Extractio of Words ad Phrases from Large Text Data of Japaese Makoto Nagao, Shisuke Mori Departmet of Electrical Egieerig Kyoto Uiversity
More informationData management in distributed data bases*
Data maagemet i distributed data bases* by C. V. RAMAMOORTHY ad BENJAMN W. WAH Uiversity of Califoria Berkeley, Califoria NTRODUCTON The recet advaces i largescale itegrated logic ad memory techology,
More informationMR2010I %MktBSize Macro 989. %MktBSize Macro
MR2010I %MktBSize Macro 989 %MktBSize Macro The %MktBSize autocall macro suggests sizes for balaced icomplete block desigs (BIBDs). The sizes that it reports are sizes that meet ecessary but ot sufficiet
More informationProceedings  5th IMA Conference on Mathematics in Defence 23 November 2017, The Royal Military Academy Sandhurst, Camberley, UK
3 November 7, The Royal Military Academy Sadhurst, Camberley, UK Eteral Fake Costraits Iterpolatio: the ed of Ruge pheomeo with high degree polyomials relyig o equispaced odes Applicatio to aerial robotics
More informationOur second algorithm. Comp 135 Machine Learning Computer Science Tufts University. Decision Trees. Decision Trees. Decision Trees.
Comp 135 Machie Learig Computer Sciece Tufts Uiversity Fall 2017 Roi Khardo Some of these slides were adapted from previous slides by Carla Brodley Our secod algorithm Let s look at a simple dataset for
More informationAbstract With the evolution of the Internet, multicast communications seem particularly well adapted for large scale
(IJCSIS) Iteratioal Joural of Computer Sciece ad Iformatio Security, Aalysis of the various key maagemet algorithms ad ew proposal i the secure multicast commuicatios Joe Prathap P M. Departmet of Iformatio
More informationDesign Optimization of the Parameters of Cycling Equipment Based on Ergonomics
Desig Optimizatio of the Parameters of Cyclig Equipmet Based o Ergoomics Shu Qiao 1*, Jiagfeg Lua, Da Dig 1 Liaoig Shihua Uiversity, Liaoig, Fushu,113001,Chia Physical fitess testig ceterliaoig shihua
More informationVariance as a Stopping Criterion for Genetic Algorithms with Elitist Model
Fudameta Iformaticae 120 (2012) 145 164 145 DOI 10.3233/FI2012754 IOS Press Variace as a Stoppig Criterio for Geetic Algorithms with Elitist Model Diabadhu Bhadari, C. A. Murthy, Sakar K. Pal Ceter for
More informationImage based Cats and Possums Identification for Intelligent Trapping Systems
Volume 159 No, February 017 Image based Cats ad Possums Idetificatio for Itelliget Trappig Systems T. A. S. Achala Perera School of Egieerig Aucklad Uiversity of Techology New Zealad Joh Collis School
More informationEnhancing Cloud Computing Scheduling based on Queuing Models
Ehacig Cloud Computig Schedulig based o Queuig Models Mohamed Eisa Computer Sciece Departmet, Port Said Uiversity, 42526 Port Said, Egypt E. I. Esedimy Computer Sciece Departmet, Masoura Uiversity, Masoura,
More informationSheared Interpolation and Gradient Estimation. for RealTime Volume Rendering. Hanspeter Pster, Frank Wessels, and Arie Kaufman
Sheared Iterpolatio ad Gradiet Estimatio for RealTime Volume Rederig Haspeter Pster, Frak Wessels, ad Arie Kaufma Departmet of Computer Sciece State Uiversity of New York at Stoy Brook Stoy Brook, NY
More informationcdominating Sets for Families of Graphs
cdomiatig Sets for Families of Graphs Kelsie Syder Mathematics Uiversity of Mary Washigto April 6, 011 1 Abstract The topic of domiatio i graphs has a rich history, begiig with chess ethusiasts i the
More information15 UNSUPERVISED LEARNING
15 UNSUPERVISED LEARNING [My father] advised me to sit every few moths i my readig chair for a etire eveig, close my eyes ad try to thik of ew problems to solve. I took his advice very seriously ad have
More informationof types having a total order and a predecessor and a successor function. In the
J. Fuctioal Programmig 1 (1): 1{000, Jauary 1993 c 1993 Cambridge Uiversity Press 1 F U N C T I O N A L P E A R L S Diets for Fat Sets Marti Erwig FerUiversitat Hage, Praktische Iformatik IV 58084 Hage,
More informationDigital System Design
July, 22 9:55 vra235_ch Sheet umber Page umber 65 black chapter Digital System Desig a b c d e f g h 8 7 6 5 4 3 2. Bd3 g6+, Ke8 d8 65 July, 22 9:55 vra235_ch Sheet umber 2 Page umber 66 black 66 CHAPTER
More informationRecursion. Recursion. Mathematical induction: example. Recursion. The sum of the first n odd numbers is n 2 : Informal proof: Principle:
Recursio Recursio Jordi Cortadella Departmet of Computer Sciece Priciple: Reduce a complex problem ito a simpler istace of the same problem Recursio Itroductio to Programmig Dept. CS, UPC 2 Mathematical
More informationOrcad Layout. Autoplacement User s Guide
Orcad Layout Autoplacemet User s Guide Copyright 19852000 Cadece Desig Systems, Ic. All rights reserved. Trademarks Allegro, Ambit, BuildGates, Cadece, Cadece logo, Cocept, Diva, Dracula, Gate Esemble,
More informationRunning Time ( 3.1) Analysis of Algorithms. Experimental Studies. Limitations of Experiments
Ruig Time ( 3.1) Aalysis of Algorithms Iput Algorithm Output A algorithm is a step by step procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects.
More informationModule Instantiation. Finite State Machines. Two Types of FSMs. Finite State Machines. Given submodule mux32two: Instantiation of mux32two
Give submodule mux32two: 2to MUX module mux32two (iput [3:] i,i, iput sel, output [3:] out); Module Istatiatio Fiite Machies esig methodology for sequetial logic  idetify distict s  create trasitio
More informationEnergyefficient HTTP Adaptive Streaming for High Quality Video over HetNets
5 IEEE 6th Iteratioal Symposium o Persoal Idoor ad Mobile Radio Commuicatios  (PIMRC): Services Eergyefficiet HTTP Adaptive Streamig for High Quality Video over HetNets Yumi Go Oh Cha Kwo ad Hwaju Sog
More informationFramework for Adaptive Swarms Simulation and Optimization using MapReduce. Sergi Canyameres 1, Doina Logofătu 2
Framework for Adaptive Swarms Simulatio ad Optimizatio usig MapReduce Sergi Cayameres, Doia Logofătu 2 2 Computer Sciece Departmet of Frakfurt am Mai Uiversity of Applied Scieces, Nibelugeplatz 6038, Frakfurt
More informationMOTIF XF Extension Owner s Manual
MOTIF XF Extesio Ower s Maual Table of Cotets About MOTIF XF Extesio...2 What Extesio ca do...2 Auto settig of Audio Driver... 2 Auto settigs of Remote Device... 2 Project templates with Iput/ Output Bus
More informationEmpirical Validate C&K Suite for Predict FaultProneness of ObjectOriented Classes Developed Using Fuzzy Logic.
Empirical Validate C&K Suite for Predict FaultProeess of ObjectOrieted Classes Developed Usig Fuzzy Logic. Mohammad Amro 1, Moataz Ahmed 1, Kaaa Faisal 2 1 Iformatio ad Computer Sciece Departmet, Kig
More informationPolymorph: Morphing Among Multiple Images
Feature Article Polymorph: Morphig Amog Multiple Images Image metamorphosis has prove to be a powerful visual effects tool. May breathtakig examples ow appear i film ad televisio, depictig the fluid trasformatio
More informationImplementation of a Timestamping Service for SunSPOT Sensors
Available olie at www.sciecedirect.com Procedia Techology 7 ( 2013 ) 4 10 The 2013 Iberoamerica Coferece o Electroics Egieerig ad Computer Sciece Implemetatio of a Timestampig Service for SuSPOT Sesors
More informationSorting 9/15/2009. Sorting Problem. Insertion Sort: Soundness. Insertion Sort. Insertion Sort: Running Time. Insertion Sort: Soundness
9/5/009 Algorithms Sortig 3 Sortig Sortig Problem The Sortig Problem Istace: A sequece of umbers Objective: A permutatio (reorderig) such that a ' K a' a, K,a a ', K, a' of the iput sequece The umbers
More informationBAAN IVb/c. Structure, master data, and configuration of BEMIS
BAAN IVb/c Structure, master data, ad cofiguratio of BEMIS A publicatio of: Baa Developmet BV POBox 143 3770 AC Bareveld The Netherlads Prited i the Netherlads Baa Developmet BV 1998 All rights reserved
More informationExtending The Sleuth Kit and its Underlying Model for Pooled Storage File System Forensic Analysis
Extedig The Sleuth Kit ad its Uderlyig Model for Pooled File System Foresic Aalysis Frauhofer Istitute for Commuicatio, Iformatio Processig ad Ergoomics JaNiclas Hilgert* Marti Lambertz Daiel Plohma jaiclas.hilgert@fkie.frauhofer.de
More informationOn the Throughput Capacity of InformationCentric Networks
O the Throughput Capacity of IformatioCetric Networks Bita Azimdoost, Cedric estphal, ad Hamid R. Sadjadpour Departmet of Electrical Egieerig ad Computer Egieerig Uiversity of Califoria Sata Cruz, Sata
More informationApproximating Betweenness Centrality
Approximatig Betweeess Cetrality David A. Bader, Shiva Kitali, Kamesh Madduri, ad Milea Mihail College of Computig Georgia Istitute of Techology {bader,kitali,kamesh,mihail}@cc.gatech.edu Abstract. Betweeess
More informationAllocation of copies of a file in an information network
Allocatio of copies of a file i a iformatio etwork by R. G. CASEY IBM Research Laboratory Sa Jose, Califoria INTRODUCTION We cosider a mathematical model of a iformatio etwork of odes, some of which cotai
More information