ABSTRACT

The conference on network security and communication engineering is meant to serve as a forum for exchanging new developments and research progresss between scholars, scientists and engineers all over the world and providing a unique opportunity to exchange information, to present the latest results as well as to review the relevant issues on

chapter |2 pages

Network and System Security

chapter |6 pages

Permissions abuse detection for android platform based on droidbox

S.Q. Wang, H.T. Ma & X.H. Wu S.Q. Wang
ByS.Q. Wang, H.T. Ma & X.H. Wu

chapter |2 pages

Grid and Cloud Computing

chapter 2|4 pages

) To each classification index subsets transformation. Classification level knowledge, based on the afore mentioned domain is given, where q is better to classification index system. The knowledge was be an odd number so that we can have a middle expressed as production rule with a basic structure level. below: To quantify each indicator in of a certain ad to all categories classified by single in which, A is the production prerequisite, and B indicator is determined. The value of membership is a set of conclusions. Both A and B can generously function can be determined by using expert scoring be expressions composed by text, numbers, and method. So u we can have the single indicator fuzzy logical operators AND, OR, and NOT. matrix . Here in our research, the prerequisite A led by IF The fuzzy weight vector of every indicators of production rule was the classification index of in ,, a ) the corresponding membership function. Before performing the synthesis process, When single indicator was used in classification, generated. Such as the Single Factor in Fig.1, four of (2) its specific indicators were empty, single emotion,

Byits specific indicators were empty, single emotion, single link as well as single keyword, respectively Performing fuzzy transformation to gives meaning nothing, only emotions or links, and , the single level classification result vector meaningless keywords accumulation. These factors can be matched with sub-bases to get different , which is results. With factors above, some of the rules can be described as:

chapter |4 pages

Operational risk measurement based on POT-Copula

Enhemende

chapter |1 pages

Separation and reception of sub-chip multipath components

X.G. Zhang, Y. Guo & Y.J. Sun L.L. Li
ByX.G. Zhang, Y.J. Sun, Y. Guo & L.L. Li

chapter |3 pages

a (t −qT )e =

By(1)

chapter |1 pages

Euclidean distances as well as the polarities of the are recorded as R according to Equation (3). In traces in order to judge the similarity of two addition, do the same operation on d ' and d ' descriptors, we can determine whether the two and we can also get R ' . Assume the angle o f descriptors match or not. Firstly, if they do not  P  P  and  P  P  is  as well as the angle of have the same type of contrast, this feature point is P ' P ' is ' . Then the difference value treated as a false one and it will not be taken into be tw een R and R ' and the difference between consideration. Conversely, if they have the same  and  ' accordin g to (4) are achieved. In case type of contrast, we go on calculating the that the difference absolute value is in the range of Euclidean distance of them respectively according θ and R and R 'would be considered to be the to: same. It is the same with  and  ' .

  For each feature

chapter |3 pages

Euclidean distances as well as the polarities of the are recorded as R according to Equation (3). In traces in order to judge the similarity of two addition, do the same operation on d ' and d ' descriptors, we can determine whether the two and we can also get R ' . Assume the angle o f descriptors match or not. Firstly, if they do not  P  P  and  P  P  is  as well as the angle of have the same type of contrast, this feature point is P ' P ' is ' . Then the difference value treated as a false one and it will not be taken into be tw een R and R ' and the difference between consideration. Conversely, if they have the same  and  ' accordi n g to (4) are achieved. In case type of contrast, we go on calculating the that the difference absolute value is in the range of Euclidean distance of them respectively according θ and R and R to: same. It is the same 'would be considered to be the with  and  ' .

  For each feature point, compute the ratio of the shortest Euclidean distance and the second shortest one. Next, the ratio is compared with the default There is a proposed criteria that if R is equal threshold value (usually 0.7 in experiment). If the is also equal to ' , P is matched ratio is in the range of 0.7, we think that this point

chapter 1|2 pages

/ Q

chapter |3 pages

[50, 30, 0.014465 0.01 75 In the formula, t is the target probability 5 [ 0 5 ] 0, 30, 0.014465 0.01 75 distr Iinbutth io e n , for a m nd ula, y t is is t he th e ac ttauragletpprroobbaabb il iilt it y y [ 530] , 15, 0.016332 0.01 50 d d is is tr tr ib ib u u ti t o io n n . , and y is the actual probability 3 [ 0 3 ] 0, 15, 0.016332 0.01 50 dis T tr hie bu a ti iomn . of network training is to adjust the 30] weig Th testaoim in oifminzee tw th oerkc ro tr sasine in t g ro p is yftuoncatd io ju n s , t an th de2.2.6 The deployment th w e ei wgehitgshttoam dj iunsit mi eznettfhoerm cr uolsas is enatsro fo plylofw un in c g tion, and A2s .2 s . h 6 ow Th neidneFpilgouyrmee5n , t each RBM is connected to get the weight adjustm ent formula is as following th Aessahuo to w -n en icnoFdiegrurnee5u , raelacnhetRwBoM rk. isTchoennw ec etiegdhttsogoeft trhee -tr aau in to in -e g nc woid ll ersen rv eeu ral stnheetw in oirtk ia . lTw he igw ht esigohftsthoef apurteo --t ern ai cnoidnegrwnielulrsael rv n e tw as o r th kesitnoitp ia alrtw ic e ip ig a h te ts in o f th th e e wahuo to le -e nectowdoerrknoefufrianle -n teutnw in o g rk t s rai tnoinpga . rticipate in the Inclu ding whole network of fine-tuning training. Including

chapter |2 pages

Multimedia, Signal and Image Processing

chapter |2 pages

 the point p . The direction of the normal specifies l = (L , L )  =   I(x ,y),...,I(x ,y),...,I(x ,y ),....,I(x ,y)  

) between the x axis in this article. tion vector of light intensity and environment ( Estimate consistency in shadows showed as follows: Shadow constraints can be represented as linear ine- ,..., ,...,l qualities in the plane. Shown in Figure 2 are two line defined by its normal and point . In each case, a shadow constraint is specified by either a pair For the infinite light source images, convert prob- of lines (wedge cast shadow constraint) or a single lems into error function to obtain the minimum val- line (half-plane attached shadow constraint). ue. Therefore, the process of solvi ng infinite light Formally, a half-plane constraint is specified with

chapter |1 pages

to improve the retrieval accuracy of the image. component of H is large, and the colors Experiments show that retrieval method in this paper distinguished by human eyes are very limited. The has higher accuracy and meets the users’ cognitive method to quantize the three components requirements for image better. will be unequally used in this paper. Divide the hue space into eight parts; both saturation S space and 2 THE IMAGE FEATURE RETRIEVAL brightness compose the H quantized to an one-dimensional 2.1 The Color Feature Retrieval vector： Because color is the most intuitionistic feature and

Haralick e In this paper, a fixed divis xtract textu e texture features. e. Then d methods of content-based image to image information. information of color .In this interdepe and so on. The symbiot block ca directio information of (4) color histogram method is propos Eucli nt, entropy, homogeny een e texture deeper, the value of moment of inertia tion. based image retrieva tic m a distance rix by the relationship between pix e is d ividedinto 16 Haralick expre ction symbiotic matrix in 1970s. The method to build the 16×16. Then matrix. Commonl ular informatio tions moment, entropy, homogeny area and non-similarity and so on. The sy on-sim
By2.3 The Synthesis Feature Retrieval

chapter |1 pages

 (8) Calculate the distance of the corresponding block by ,   ， ， image to be retrieved, and image is the image   in stpo ac e e ig ihnt to pa eritgsh ; tbpoa th rt s s ; at buortahtisoan tu asnpd ace and submitted by users. brightne bsrs ig apacsrecepeiadnicitnevotioaedrieegdihgdtihinvtptiaopdraetrshdt ; rsie ; bneob to tphtathhrstarsseta . uetTrupahrtaeirtoitnos. n h sp esapn Tc a h ee ang adn dy scale of an image is divide d into 16 compos ceotm he po bsrbeirgtihhgtehntqenuseassns zsqepsduapactneocteiazraneerdeodntidoveii -v adin one-dim n ensional e d im dedeinitnsoitotnhtarhle ree pa pratrst . s T . T he h n en  vector： vector： co c m om po psoesethte he qu qaunatnitziezdedtotoanano one-d ne-dim ea n e is s o ve vcetco ： r： nitsegaenria nteger and eL  it sH 9 r n 3 g eS  i sV betw ( e1e ) n 0 an d16. (  (10) 71 The 7n1cTah lc eunla nad th e t its range is between 0 and tionietnetgee aenx tr baec te edx tr farcotm ed tfhre om the histogra h m is to ogbr7ta1a7mi1nTeodThbehtne7an2 in ceachdlaaclnuc7dlua2llteae . theaT hdteolteno . gdegTitevhtieadnneandotihnoveen id -e d e -i d m mhey ast mbiao ttri Mma asayn yaixp amsmaeutereemtreserosafcsuacinramenabgobefeietmexextaxrtgtuarecraetcettd ex dtfurfrorem om th t e he image sipmaacgeeihnsihptsoitasoct3goe * rga3irnmatbmoloo3cbo * ktb3sat , iabnialennodedcdkc7sa , 27l2acnuhdlhanatcednaldltechl . eue . la T idfvioeivadfiteduere th sfteh ea ettuoressysqmyumab to nibotiitozitcqeicum an miamttiarzatierg aisam euo mat rege rue each bl eoacckh . b E im laiomcachgkaeg . seusEpbsaap -c cahebcelsionucitnbkot -o 3hb * 3als3 * o3cbtkhlboelhcoakscsask , s t , ahneadnsdcaamc lc elucluianltai 0ettPi hletehd 4ee 0es efsuaesmta 1out 3rmrus 5eras zmte oe a1qr4 aue tni iesz4 ec okfi dae ai egt ets weight. w W ei hgehnte . aecWaahclhcbeunbloalctocikcan . klgc . EutlEhaaceathicnhg su stbuh -b e -b slbiomlcoim la erk ck h it iyn , itia hsastwhteheleasadsma ju emexstepirneiinsts it eaitxlahplere te sPxsPrttuehrdreeedtceoeecfxsetassuonsrroeisrmossafugsamuenm . iCmaoramirgzimeez . deoCdn1ol41ym4umksikoenidnlsdaysreoufso aae uetruers es to to the wei tghhetswoewfiwgeehiegatischghto . t f . sWuWebha -e chbnhelonscuakclbs -im acluc bl iolc arity, we ad sluianltaigtnkign th ugteshitnehregsleitsmvh im aej us inliarclretaeilrteiytv , y a , wnewceneeeardgajdyu , jesuntseetrng tr yeo , xpepxyerp , enrsetsrtsohstpehtyeh , etmetxetohtxmuetrueernemotfoofamonafeninmitimnaegaoregtf . ieaC . Cionmoaenmrd ti moanolnyalynudsuesdedarae re feedbac f k ee mdebtahtchotkehdemswiewnetihetgiohhgdithssstpsionafoptfeheriae . scaphcahpsuesrbu . -b b -l bolcokckusuisnig ng th teherienrleteelveradvneacpnienectneedredneecpneyeen . nredgreygn , y c , y e . netnrtorpoyp , y , th tehemm om om en etnt of o f in ienretritaia an a d nd Suppos Seuipmfpefaoegdseeebda ehitiomhsdoastdghsieenintiomhtihasbigsepeaprpae to e pe . b r. e retrieved in Energy: E n It eirniigstneytr : etdhrIdetpeepm is needntaedhsneucnrycem . y e . oaf su th re eouf ni tfhoe rm uin ty if oorf mity of the im tahgee image d trieved in data SbSu as puepa , optsaoaebsneadisme im , a im gaaeganeg isiism th tiaehsgeiem submitted by users. The 72 handle tihmaegaeigse to tthoc ol iomrahgbeee be irmerteargtgirreeiaveyevdleedvigneirlna . y level E . E ne nregryg : y : ItItisisthtehemm ea esausruereof of th teheu 2nu fiof iyt oth f o submitted by tuhtseheerism . im aTgahegee7d2adtaahtbaanbsdaesl , e , caonaldnodrihmiimsatgaoege ram is is tiosgtrh e im im ag aeg gr garyay le lveevle . l. (5) vector and (where i = 1, 2... 9) of each sub- pd e  stand ard  (5) vector and susbumb (w m it hittetdreedbiyby = usu1es , resr2 . s . . . T . Th9eh ) e7o27f2heahncahdnldesluecboc -l oolrorhihsitsotgorgarm am y E : n E tr notp ro y p : Entr information y is a

Calculate the distance of the corresponding block by the formula (3) and all blocks’ weights distance is where summarized by the formula (4) .Where image is the pply scope. So the retrieval method sub-blo is equal of each Moment of

chapter |4 pages

Current–mode signal processing based OTA design for multimedia data transmission

Jong-Un Kim, Sung-Dae Yeo, Dong-Ho Kim, Gye-Min Lee & Seong-Kweon Kim
ByJ.-U. Kim, S.-D. Yeo, D.-H. Kim, G.-M. Lee & S.-K. Kim

chapter |6 pages

Impact of partially coherent X-ray source on clinical phase contrast imaging

X.L. Zhang, J. Yang & S. Liu X.L. Zhang & T. Xia
ByX.L. Zhang, J. Yang, T. Xia & S. Liu

chapter |2 pages

Data Mining

chapter |1 pages

model based on multivariate conditional normal dis-distribution is greater than the tail of a normal distri-tribution assumptions, finding that Copula model bution, indicating their yield series present "spikes based on extreme value theory is dominant. and fat tail". Meanwhile, JB statistics are greater than the critical value of 5.9915 at 5% significant 3 EMPIRICAL RESEARCH level, refusing the null hypothesis that the yield se-ries obey normal distribution, that is, the two index 3.1 Selection and pre-processing of sample data yield series are not normally distributed, and we cannot use traditional mean - variance model to ana-This paper selects the daily closing price of Shang-log. hai Composite Index (SH) and S&P500 as study sample. The time period is from January 4, 2000 to 3.3 Stationary test May 28, 2008, and there are a total of 2023 and 2111

chapter |4 pages

Manufacturing information model for design for manufacturing

H.J. Liu, Q.M. Fan & B. Chen Q.M. Fan B. Chen
ByH.J. Liu, Q.M. Fan & B. Chen

chapter |4 pages

Experiments and numerical analysis of flow rate in unstably stratified field

Motoo Fumizawa, Yoshiharu Saito & Naoto Uchiyama
ByM. Fumizawa, Y. Saito & N. Uchiyama

chapter |2 pages

requirement of memory is large. So in this paper we propose an ae q p pu i r oe e a ce h bo a sf e de emo ty h es slli nr r d ie e r to o r en n c ni i s - struction of autocorrela (2) Nyquist sam pr u o le so isoe [ ] e [ ] a i , tp p i or oon cs t non w sn u struction p rop f os aaew he oani hh orrfebg ea s t si modnog oy n o ds tnhlceap eg oir fd . o rr - mance in th st t mm e wes i t t] a h ee p ] l , raota io ch nbfausnecdti on th wei th in d s ir uebc -t recon- plexity. In t mc ueie hn s o dr tauhm , Ct pilloA Fs t [ o i o8 a b lt e aw c s tr iicrcs pl a cg gie mu oon - eh d be y ha e constructed al u cty e o rI nNe at hqt oi s n fe u nh m c td p m io nC s , a[ i7Fn dm iisst ew bbn sh iii ge etn ae l detection is gtr e et d ac r ie n oo f en t pio e fa n s hen sig p n re nals, multi-c ae o r re i et t r sc n ig nl sdea la r i ’ sy gto . g n uI onrm be h e rt mio fe nats uf coub , ctC io AnF , ainsd obtain ai gog rr ef f r ac een do yynpp , eeaoo the length o lsy , m l pl dttir et ccef ir x ( er signal’s number ricP ) p g . Ie ian fe s u te C u rP m e se s t if fce su uinw ea aorrfrriiteehrresstaayU ne d rs i t e system model, OFDM signal is cy-

propose an ap ect recon-

chapter |3 pages

In this paper, when Boss map is used, we evaluate BER performance of CDSK system and novel chaos Figure 1 shows PDF of Boss map that was pro-transceiver according to delay time. Also, we find a posed in order to improve the BER performance. We delay time that is possible to have best BER perfor-produced the PDF shown in figure 1 through the mance in CDSK system and novel chaos transceiver control of two factors having an effect on BER per-after evaluation of BER performance according to formance which we have explained in [5]. delay time. x   0.45 0.503 x (1) 2 BOSS MAP y  x 0.3 Equation (1) indicates the chaos equation of Boss In previous studies, we showed the PDF of chaos map. The Equation (1) was changed from Tent map. signal, and analyzed PDF trends by changing the ini-If the initial value is 0.1 and the parameter alpha is tial value, equation and parameter. And, based on 2.5, trajectory of this equation is expressed as Figure

chapter |4 pages

A routing protocol for two-level clustering in Wireless Sensor Networks

Alghanmi Ali Omar, ChunGun Yu, Woon Sagong, Hyunjin Park & ChongGun Kim Mary Wu
ByA. Ali Omar, C.G. Yu, W. Sagong, H. Park, C.G. Kim & M. Wu

chapter |1 pages

Editor Chan

Editor: Kennis Chan

Registered in England & Wales No. 3099067
5 Howick Place | London | SW1P 1WG© 2024 Informa UK Limited