QUEUE ESTIMATION ALGORITHM FOR REAL-TIME CONTROL POLICY USING DETECTOR DATA
Introduction+to+MIMO+Systems
frmLen = 100; % frame lengthnumPackets = 1000; % number of packetsEbNo = 0:2:20; % Eb/No varying to 20 dBN = 2; % maximum number of Tx antennasM = 2; % maximum number of Rx antennasand set up the simulation.% Seed states for repeatabilityseed = [98765 12345]; randn('state', seed(1)); rand('state', seed(2));% Create BPSK mod-demod objectsP = 2; % modulation orderbpskmod = modem.pskmod('M', P, 'SymbolOrder', 'Gray');bpskdemod = modem.pskdemod(bpskmod);% Pre-allocate variables for speedtx2 = zeros(frmLen, N); H = zeros(frmLen, N, M);r21 = zeros(frmLen, 1); r12 = zeros(frmLen, 2);z21 = zeros(frmLen, 1); z21_1 = zeros(frmLen/N, 1); z21_2 = z21_1;z12 = zeros(frmLen, M);error11 = zeros(1, numPackets); BER11 = zeros(1, length(EbNo));error21 = error11; BER21 = BER11; error12 = error11; BER12 = BER11;% Set up a figure for visualizing BER resultsh = gcf; grid on; hold on;set(gca, 'yscale', 'log', 'xlim', [EbNo(1), EbNo(end)], 'ylim', [1e-5 1]); xlabel('Eb/No (dB)'); ylabel('BER'); set(h,'NumberTitle','off');set(h, 'renderer', 'zbuffer'); set(h,'Name','Transmit vs. Receive Diversity'); title('Transmit vs. Receive Diversity');% Loop over several EbNo pointsfor idx = 1:length(EbNo)% Loop over the number of packetsfor packetIdx = 1:numPacketsdata = randint(frmLen, 1, P); % data vector per user per channel tx = modulate(bpskmod, data); % BPSK modulation% Alamouti Space-Time Block Encoder, G2, full rate% G2 = [s1 s2; -s2* s1*]s1 = tx(1:N:end); s2 = tx(2:N:end);tx2(1:N:end, :) = [s1 s2];tx2(2:N:end, :) = [-conj(s2) conj(s1)];% Create the Rayleigh distributed channel response matrix% for two transmit and two receive antennasH(1:N:end, :, :) = (randn(frmLen/2, N, M) + ...j*randn(frmLen/2, N, M))/sqrt(2);% assume held constant for 2 symbol periodsH(2:N:end, :, :) = H(1:N:end, :, :);% Received signals% for uncoded 1x1 systemr11 = awgn(H(:, 1, 1).*tx, EbNo(idx));% for G2-coded 2x1 system - with normalized Tx power, i.e., the% total transmitted power is assumed constantr21 = awgn(sum(H(:, :, 1).*tx2, 2)/sqrt(N), EbNo(idx));% for Maximal-ratio combined 1x2 systemfor i = 1:Mr12(:, i) = awgn(H(:, 1, i).*tx, EbNo(idx));end% Front-end Combiners - assume channel response known at Rx% for G2-coded 2x1 systemhidx = 1:N:length(H);z21_1 = r21(1:N:end).* conj(H(hidx, 1, 1)) + ...conj(r21(2:N:end)).* H(hidx, 2, 1);z21_2 = r21(1:N:end).* conj(H(hidx, 2, 1)) - ...conj(r21(2:N:end)).* H(hidx, 1, 1);z21(1:N:end) = z21_1; z21(2:N:end) = z21_2;% for Maximal-ratio combined 1x2 systemfor i = 1:Mz12(:, i) = r12(:, i).* conj(H(:, 1, i));end% ML Detector (minimum Euclidean distance)demod11 = demodulate(bpskdemod, r11.*conj(H(:, 1, 1)));demod21 = demodulate(bpskdemod, z21);demod12 = demodulate(bpskdemod, sum(z12, 2));% Determine errorserror11(packetIdx) = biterr(demod11, data);error21(packetIdx) = biterr(demod21, data);error12(packetIdx) = biterr(demod12, data);end% end of FOR loop for numPackets% Calculate BER for current idx% for uncoded 1x1 systemBER11(idx) = sum(error11)/(numPackets*frmLen);% for G2 coded 2x1 systemBER21(idx) = sum(error21)/(numPackets*frmLen);% for Maximal-ratio combined 1x2 systemBER12(idx) = sum(error12)/(numPackets*frmLen);% Plot resultssemilogy(EbNo(1:idx), BER11(1:idx), 'r*', ...EbNo(1:idx), BER21(1:idx), 'go',...EbNo(1:idx), BER12(1:idx), 'bs');legend('No Diversity (1Tx, 1Rx)', 'Alamouti (2Tx, 1Rx)',...'Maximal-Ratio Combining (1Tx, 2Rx)');drawnow;end% end of for loop for EbNo% Perform curve fitting and replot the resultsfitBER11 = berfit(EbNo, BER11);fitBER21 = berfit(EbNo, BER21);fitBER12 = berfit(EbNo, BER12);semilogy(EbNo, fitBER11, 'r', EbNo, fitBER21, 'g', EbNo, fitBER12, 'b'); hold off;The transmit diversity system has a computation complexity very similar to that of the receive diversity system.The resulting simulation results show that using two transmit antennas and one receive antenna provides the same diversity order as the maximal-ratio combined (MRC) system of one transmit antenna and two receive antennas.Also observe that transmit diversity has a 3 dB disadvantage when compared to MRC receive diversity. This is because we modelled the total transmitted power to be the same in both cases. If we calibrate the transmitted power such that the received power for these two cases is the same, then the performance would be identical.The accompanying functional scripts, MRC1M.m and OSTBC2M.m aid further exploration for the interested users.PART 2: Space-Time Block Coding with Channel EstimationBuilding on the theory of orthogonal designs, Tarokh et al. [2] generalized Alamouti's transmit diversity scheme to an arbitrary number of transmitter antennas, leading to the concept of Space-Time Block Codes. For complex signal constellations, they showed that Alamouti's scheme is the only full-rate scheme for two transmit antennas.In this section, we study the performance of such a scheme with two receive antennas (i.e., a 2x2 system) with and without channel estimation. In the realistic scenario where the channel state information is not known at the receiver, this has to be extracted from the received signal. We assume that the channel estimator performs this using orthogonal pilot signals that are prepended to every packet [3]. It is assumed that the channel remains unchanged for the length of the packet (i.e.,it undergoes slow fading).A simulation similar to the one described in the previous section is employed here, which leads us to estimate the BER performance for a space-time block codedsystem using two transmit and two receive antennas.Again we start by defining the common simulation parametersfrmLen = 100; % frame lengthmaxNumErrs = 300; % maximum number of errorsmaxNumPackets = 3000; % maximum number of packetsEbNo = 0:2:12; % Eb/No varying to 12 dBN = 2; % number of Tx antennasM = 2; % number of Rx antennaspLen = 8; % number of pilot symbols per frameW = hadamard(pLen);pilots = W(:, 1:N); % orthogonal set per transmit antennaand set up the simulation.% Seed states for repeatabilityseed = [98765 12345]; randn('state', seed(1)); rand('state', seed(2));% Pre-allocate variables for speedtx2 = zeros(frmLen, N); r = zeros(pLen + frmLen, M);H = zeros(pLen + frmLen, N, M); H_e = zeros(frmLen, N, M);z_e = zeros(frmLen, M); z1_e = zeros(frmLen/N, M); z2_e = z1_e;z = z_e; z1 = z1_e; z2 = z2_e;BER22_e = zeros(1, length(EbNo)); BER22 = BER22_e;% Set up a figure for visualizing BER resultsclf(h); grid on; hold on;set(gca,'yscale','log','xlim',[EbNo(1), EbNo(end)],'ylim',[1e-5 1]);xlabel('Eb/No (dB)'); ylabel('BER'); set(h,'NumberTitle','off');set(h,'Name','Orthogonal Space-Time Block Coding');set(h, 'renderer', 'zbuffer'); title('G2-coded 2x2 System');% Loop over several EbNo pointsfor idx = 1:length(EbNo)numPackets = 0; totNumErr22 = 0; totNumErr22_e = 0;% Loop till the number of errors exceed 'maxNumErrs'% or the maximum number of packets have been simulatedwhile (totNumErr22 < maxNumErrs) && (totNumErr22_e < maxNumErrs) && ... (numPackets < maxNumPackets)data = randint(frmLen, 1, P); % data vector per user per channel tx = modulate(bpskmod, data); % BPSK modulation% Alamouti Space-Time Block Encoder, G2, full rate% G2 = [s1 s2; -s2* s1*]s1 = tx(1:N:end); s2 = tx(2:N:end);tx2(1:N:end, :) = [s1 s2];tx2(2:N:end, :) = [-conj(s2) conj(s1)];% Prepend pilot symbols for each frametransmit = [pilots; tx2];% Create the Rayleigh distributed channel response matrixH(1, :, :) = (randn(N, M) + j*randn(N, M))/sqrt(2);% assume held constant for the whole frame and pilot symbolsH = H(ones(pLen + frmLen, 1), :, :);% Received signal for each Rx antenna% with pilot symbols transmittedfor i = 1:M% with normalized Tx powerr(:, i) = awgn(sum(H(:, :, i).*transmit, 2)/sqrt(N), EbNo(idx));end% Channel Estimation% For each link => N*M estimatesfor n = 1:NH_e(1, n, :) = (r(1:pLen, :).' * pilots(:, n))./pLen;end% assume held constant for the whole frameH_e = H_e(ones(frmLen, 1), :, :);% Combiner using estimated channelheidx = 1:N:length(H_e);for i = 1:Mz1_e(:, i) = r(pLen+1:N:end, i).* conj(H_e(heidx, 1, i)) + ... conj(r(pLen+2:N:end, i)).* H_e(heidx, 2, i);z2_e(:, i) = r(pLen+1:N:end, i).* conj(H_e(heidx, 2, i)) - ... conj(r(pLen+2:N:end, i)).* H_e(heidx, 1, i);endz_e(1:N:end, :) = z1_e; z_e(2:N:end, :) = z2_e;% Combiner using known channelhidx = pLen+1:N:length(H);for i = 1:Mz1(:, i) = r(pLen+1:N:end, i).* conj(H(hidx, 1, i)) + ...conj(r(pLen+2:N:end, i)).* H(hidx, 2, i);z2(:, i) = r(pLen+1:N:end, i).* conj(H(hidx, 2, i)) - ...conj(r(pLen+2:N:end, i)).* H(hidx, 1, i);endz(1:N:end, :) = z1; z(2:N:end, :) = z2;% ML Detector (minimum Euclidean distance)demod22_e = demodulate(bpskdemod, sum(z_e, 2)); % estimateddemod22 = demodulate(bpskdemod, sum(z, 2)); % known% Determine errorsnumPackets = numPackets + 1;totNumErr22_e = totNumErr22_e + biterr(demod22_e, data);totNumErr22 = totNumErr22 + biterr(demod22, data);end% end of FOR loop for numPackets% Calculate BER for current idx% for estimated channelBER22_e(idx) = totNumErr22_e/(numPackets*frmLen);% for known channelBER22(idx) = totNumErr22/(numPackets*frmLen);% Plot resultssemilogy(EbNo(1:idx), BER22_e(1:idx), 'ro');semilogy(EbNo(1:idx), BER22(1:idx), 'g*');legend(['Channel estimated with ' num2str(pLen) ' pilot symbols/frame'],...'Known channel');drawnow;end% end of for loop for EbNo% Perform curve fitting and replot the resultsfitBER22_e = berfit(EbNo, BER22_e);fitBER22 = berfit(EbNo, BER22);semilogy(EbNo, fitBER22_e, 'r', EbNo, fitBER22, 'g'); hold off;For the 2x2 simulated system, the diversity order is different than that seen foreither 1x2 or 2x1 systems in the previous section.Note that with 8 pilot symbols for each 100 symbols of data, channel estimationcauses about a 1 dB degradation in performance for the selected Eb/No range. This improves with an increase in the number of pilot symbols per frame but adds to the overhead of the link. In this comparison, we keep the transmitted SNR per symbolto be the same in both cases.The accompanying functional script, OSTBC2M_E.m aids further experimentationfor the interested users.PART 3: Orthogonal Space-Time Block Coding and Further ExplorationsIn this final section, we present some performance results for orthogonal space-timeblock coding using four transmit antennas (4x1 system) using a half-rate code, G4,as per [4].We expect the system to offer a diversity order of 4 and will compare it with 1x4 and2x2 systems, which have the same diversity order also. To allow for a faircomparison, we use quaternary PSK with the half-rate G4 code to achieve the same transmission rate of 1 bit/sec/Hz.Since these results take some time to generate, we load the results from a prior simulation. The functional script OSTBC4M.m is included, which, along withMRC1M.m and OSTBC2M.m, was used to generate these results. The user isurged to use these scripts as a starting point to study other codes and systems.load ostbcRes.mat;% Set up a figure for visualizing BER resultsclf(h); grid on; hold on; set(h, 'renderer', 'zbuffer');set(gca, 'yscale', 'log', 'xlim', [EbNo(1), EbNo(end)], 'ylim', [1e-5 1]);xlabel('Eb/No (dB)'); ylabel('BER'); set(h,'NumberTitle','off');set(h,'Name','Orthogonal Space-Time Block Coding(2)');title('G4-coded 4x1 System and Other Comparisons');% Plot resultssemilogy(EbNo, ber11, 'r*', EbNo, ber41, 'ms', EbNo, ber22, 'c^', ...EbNo, ber14, 'ko');legend('No Diversity (1Tx, 1Rx), BPSK', 'OSTBC (4Tx, 1Rx), QPSK', ...'Alamouti (2Tx, 2Rx), BPSK', 'Maximal-Ratio Combining (1Tx, 4Rx), BPSK'); % Perform curve fittingfitBER11 = berfit(EbNo, ber11);fitBER41 = berfit(EbNo(1:9), ber41(1:9));fitBER22 = berfit(EbNo(1:8), ber22(1:8));fitBER14 = berfit(EbNo(1:7), ber14(1:7));semilogy(EbNo, fitBER11, 'r', EbNo(1:9), fitBER41, 'm', ...EbNo(1:8), fitBER22, 'c', EbNo(1:7), fitBER14, 'k'); hold off;As expected, the similar slopes of the BER curves for the 4x1, 2x2 and 1x4 systems indicate an identical diversity order for each system.Also observe the 3 dB penalty for the 4x1 system that can be attributed to the same total transmitted power assumption made for each of the three systems. If we calibrate the transmitted power such that the received power for each of these systems is the same, then the three systems would perform identically.References:[1] S. M. Alamouti, "A simple transmit diversity technique for wirelesscommunications", IEEE Journal on Selected Areas in Communications,Vol. 16, No. 8, Oct. 1998, pp. 1451-1458.[2] V. Tarokh, H. Jafarkhami, and A.R. Calderbank, "Space-time block codes from orthogonal designs", IEEE Transactions on Information Theory,Vol. 45, No. 5, Jul. 1999, pp. 1456-1467.[3] A.F. Naguib, V. Tarokh, N. Seshadri, and A.R. Calderbank, "Space-time codes for high data rate wireless communication: Mismatch analysis", Proceedings of IEEE International Conf. on Communications,pp. 309-313, June 1997.[4] V. Tarokh, H. Jafarkhami, and A.R. Calderbank, "Space-time block codes for wireless communications: Performance results", IEEE Journal onSelected Areas in Communications, Vol. 17, No. 3, Mar. 1999,pp. 451-460.Copyright 2006 The MathWorks, Inc.Published with MATLAB® 7.4。
-Body [SWW94].-D [GGS01].
![-Body [SWW94].-D [GGS01].](https://img.taocdn.com/s3/m/03fe45c5aa00b52acfc7ca31.png)
A Bibliography of Publications in The InternationalJournal of Supercomputer Applications,The International Journal of Supercomputer Applications and High-Performance Computing,and TheInternational Journal of High PerformanceComputing ApplicationsNelson H.F.BeebeUniversity of UtahDepartment of Mathematics,110LCB155S1400E RM233Salt Lake City,UT84112-0090USATel:+18015815254FAX:+18015814148E-mail:beebe@,beebe@,beebe@(Internet)WWW URL:/~beebe/12April2006Version1.32Title word cross-reference 3[GGS01].d=2[BRT+92].CH+H2 CH∗3 CH2+H[ASW91]. CuO2[SSSW91].K2[CBW95].N[SWW94]. -Body[SWW94].-D[GGS01]./I[CHZ02].0th[RAGW93].100[IHM87].10P[DD89].1917-1991[Mar91].2[DD89].200/VF[DD89].3[THL88].3-D[THL88].3.0[BRM03]. 3090[DD89].3090-200[DD89].3090-200/ VF[DD89].31G*[PUR94].3800[WOG95].125[HRM89].5/SE[KJH96].6[PUR94].6-31G*[PUR94].90[DL97].A&M[Nas92].Access[WHL03]. Accessing[HLP+03].Accurate[TMWS91].Acoustic[GKN+96]. Active[Her91].Ad[BG02].Ada[Kok88]. Adapting[DE03].Adaptive[AH93]. Additive[PR95].Administration[SDA+01].Adsorption[CH94].Advances[KKDV03]. Aerodynamics[YM91].Agents[QWIC02]. agricultural[SH93].Aided[MM90].AIX[Ano01a].Alamos[BBB+91b]. Algebra[GJM88].Algorithm[GJM88]. Algorithms[KL87].All-to-All[BJ92]. Alliant[DD91].Allocation[WPBB01]. alpha[TKSK88].Amdahl[HE01].amines[PUR94].ammonium[PUR94]. Amplitude[BGK+90].analogs[PUR94]. Analysis[MB87,LS90].Analytic[MA89]. Analyze[KKCB98].Analyzers[Ano01a]. Analyzing[WPBB01].Anatomy[YFH+96].Animal[UB95]. animated[LSS93].Animation[SS89]. Aperture[MPG93].API[BH00]. Appendix[Ano01a].Appendixes[Ano01a].AppLeS[SBWS99]. Application[NKR90].Applications[Ano98a].Applied[vL+03]. Applying[Dem90].Approach[FBW87]. Approximate[Cho01].Aqueous[PRT90]. Architectural[Gro03].Architecture[Ish91].architectures[JO92].Area[DFP+96]. ARION[HLP+03].Arising[Ma00]. Arithmetic[BSB89].Army[Aus92].array[JO92].Arrival[Wit92].Aspects[ZOF90].Assessing[ACM88]. Assessment[ZOF90].Assist[BB02]. asynchronous[PH91].Atmosphere[HAF+96].Atmosphere-Ocean[HAF+96]. Atmospheric[ARR99].Atomic[IHM87]. Attributes[Del93].Automata[RE87]. Automatic[Cza03].Automobile[HTSK90].Autonomous[SKB01].Availability[Pra01].Aware[YBA+03]. Axisymmetric[SG91].B[Ano01a].Band[Tho90].Based[Nak99]. Basic[JO92,Don02a].Bay[WLVL+96]. Beamforming[CYT+02].Bearing[FFNP97].Behavior[AK93]. Benchmarking[BRT+92].Benchmarks[BCK89,BBB+91a].Benefits[ACM88].Beowulf[SS99].Best[Lee03].Beyond[SBF90].Binary[DIB00].Biofluid[RKKC90]. Biological[WW92].Biology[SSNM92]. Biomembranes[SABK94].BLAS[DD89]. Blast[Don02a].Block[BS88].Body[TMWS91].Bone[HOPB92]. Boundary[SG91].Bridging[SS99]. Broadcast[BJ92].Builder[DL97]. Building[Wit92].Bulk[DGP+97].Butterfly[Kum89].C[Poz97].C90[ABF+99].Cache[MBW87].Cache-Coherent[Wad99].Cactus[AAF+01].Calculation[ACG+90]. Calculational[ZOF90].calculations[TKSK88].Caltech[Din91]. Caltech/JPL[Din91].Campus[GNTLH97].Campus-Wide[GNTLH97].Can[Pan97]. Cancers[GKB93].Capacity[BL99]. Carcinogens[HB90].Cards[Gro03]. Carlo[MB87].Carolina[LC90].Case[WGI90].CBVE[WLVL+96]. CEBAF[DZDR95].Center[All88,BBW90].Centers[All88]. CFD[GKMT00].CGNR[Man97].3Challenge[Kit90].Changing[MMS88]. Characterization[LPJ98].Chemical[TW87].Chemically[MYC92]. ChemIO[NFK98].Chemistry[EDS95]. Chesapeake[WL VL+96]. Chromodynamics[Liu90].Circular[AEPR92].Circulation[KM95]. CLAS[DZDR95].Classification[Tho90]. Climate[WHL03].Climatic[WBMY90]. Clouds[Tho90].Club[BCK89].Cluster[KT99].Clustering[NRR97]. Clusters[DT99].CM[CC95].CM-2[CC95].CM-5[KJH96].CM-5/SE[KJH96].CM2[CH94].Co[Mat03].Co-reservation[Mat03].Co-scheduling[Mat03].Coarse[BGB+96]. Coarse-Grained[BGB+96].Code[MSK92].Codes[IHM87].Coherent[Wad99].Collaborative[NBB+96].Collaboratory[YFH+96].Collapse[HTSK90].Collections[HLP+03]. Collide[NBB+96].Color[Tho90].Color/ Albedo[Tho90].Combining[Gir02]. Communication[BBDR95]. Communication/Computation[BBDR95].Community[HBSM03].Comparative[MOK00].Comparing[BF01].Comparison[Gen88]. Comparisons[Ma00].Compilers[Ano01a]. Complete[LK01].Complex[GKB93]. Complexity[BGB+96].Component[KBA00].Compositional[KR94,KR95]. Compounds[FWZ91].Compression[DLY+98].Computation[Her88,TR92]. Computational[FBW87].Computations[Duk91].Computer[TW87].Computer-Aided[MM90].Computers[Meu88].Computing[Ewi88,Lee03].Concurrent[MBW87].Conference[KKDV03].Configuration[AEPR92].Confined[ACG+90].Conjugate[Mel87]. Connection[HZ91].Conquer[Cza03]. Constant[MP94].Constrained[NKR90]. Constraints[GSHL03].Contaminant[ABF+99].Context[YBA+03].Context-Aware[YBA+03].Contributors[Ano96b].Control[AK91]. Controlled[DSD+91].convex[SH93]. Coordinate[YRA+02].Coordinated[FP02].CORBA[P´e r03]. Correspondence[IS96].Cortical[WW92]. Coscheduling[BL99].Coupled[HAF+96]. Coupling[P´e r03].CPU[BL99].Crash[HTSK90,CEL+97].CRAY[THL88].CRAY-2[DD89].CRAY-T3E[Ma00].Creutz[BRT+92]. CRPC[CDP+94].Crystal[Cla91]. Crystallography[CTH+93].CUMUL VS[GKP97].CYBER[ABA87]. CYDRA[HRM89].CYDRA-5[HRM89]. D[THL88].DAMPVM[Cza03]. DAMPVM/DAC[Cza03].Data[KBH88]. Data-Intensive[KUE+00].Data-Parallel[HJ96].Dataflow[ACM88]. Datasets[SE92].Davidson[UF89]. Dealing[GSHL03].Debuggers[Ano01a]. Decomposition[Meu88].Decoupled[PH91].Dedicated[GSHL03]. Delay[Rao02].demand[dPIdA03].Dense[Ede93].Department[Kit90]. Deployable[GCL93].Deployment[GCL93].Deposition[MD99]. derivatives[Haj93].Design[GJM88]. Detailed[EDS95].Detector[DZDR95]. Determination[KBH88].Determined[CGB+94].Development[HRM89].device[Lai93]. Devices[RKKC90].Diagrams[FWZ91]. Dielectric[ZOF90].Difference[THL88].4Differential[Meu88].Diffusion[TW87]. Digital[MPG93].dimensional[KS89]. Dimensionality[BFLL99].Dimensions[TW87].Dip[LT90].Direct[CM97].Direction[Mah90]. Directions[Fol90a].Discharge[YW93]. Discovery[AAF+01,AEG+03].Discrete[Ham91].Disk[KNP87]. Disordered[KVY+90].Dissemination[GL97].Dissolution[Cla91].Distance[HME90]. Distributed[MW AR87].Distributed-Memory[MCW+00]. Distributing[CBSB01].Divide[Cza03]. Divide-and-Conquer[Cza03].DNA[HB90].DOE[HBSM03].Domain[Meu88].Domain-Specific[CDH+97b].Double[PRT90].Drive[HE01].Driven[CHZ02].Dual[Ish91].Dual-Level[BBC+00].DV[TKSK88].DV-X[TKSK88].Dynamic[ABA87]. Dynamical[FBW87].Dynamics[Gen88]. e-Science[HWP03].Early[HGD91]. Econometric[Pet87].Economic[NKR90]. Economics[AK91].Eddy[CK01].Editor[dA03].Editorial[Don92]. Education[Mah90].Effective[BCK89].Effects[WBMY90,Haj93].Efficiency[ABA87].Efficient[Mel87]. Eigenvalue[UF89].Eigenvalues[KC92]. Electromagnetic[DGP+97].Electron[KVY+90].Electronic[FWZ91]. Electroweak[BGK+90].Element[KM95]. Eliminating[HME90].Embedded[KK01]. Embedded/Real[KK01].Embedded/ Real-Time[KK01].Enabled[CD97]. Enabling[FKT01].Encoding[DLY+98]. End[Rao02].End-To-End[Rao02]. Endangered[BB02].energies[PUR94]. Energy[IHM87,Kit90].Engineering[MMS88].Enhancement[AAC+97].Enhancements[BDG+95].Entity[BGF02].Entropy[CBW95]. Environment[CCH+88,WL VL+96]. Environmental[DLY+98].Environments[MA89].Equation[Fro91]. Equations[Syz87].Equilibration[NKR90].Equilibrium[NK89].Erratum[KR95]. estimation[SH93].ETA[DD89].ETA-10P[DD89].EuroPVMMPI[KKDV03].Evaluate[WGI90].Evaluating[BBDR95]. Evaluation[BCK89].Event[NRR97]. Events[BG00].Evolution[WJS+90]. Exact[ZK93].Example[NBB+96]. Excited[WLC91].Excited-State[WLC91].Excitement[RAGW93].Expand[GCCC+03].Expect[Pan92]. Experience[HGD91].Experiences[Reu92].Experiment[HME90].Experimental[KL87].Experiments[AK91].Exploration[KPM+96].Exploring[HAF+96].Expression[RS03]. Expressions[BBDR95].Extreme[KC92].F ACOM[IHM87].Factor[DH96]. Factorization[DD89].factorizations[DEKL92].Farming[CKPD99].fast[TKSK88,KNP87].Fault[GKP97]. Faulty[LK01].Feasibility[KR94]. Feature[PTGB02].features[PUR94]. February[Sci92].Feedback[CGB+94]. Feedback-Scaling[CGB+94].Fermions[ZK93].Fernbach[Mar91]. FETI[GCD97].FFT[Wad99].FFT-Based[GGS01].field[PUR94].File[GCCC+03].Film[MD99].Financial[HZ91].Fine[ACM88].Fine-Grain[ACM88].Finite[THL88]. Finite-Element[MS02].First[DQFW90].5Flames[SG91].FLO67[WLB92].Floating[BSB89].Flow[HKK88].Flowfield[MKG90].Flows[MYC92].Fluid[Gen88].Fluid-Structure[KT99]. Fluorinated[DFC90].Fock[KKCB98]. force[PUR94].Forming[CM97].Fortran[KR94].Forum[Don02a]. Forward[THL88].Foundation[Web91]. Four[Tho90].Four-Band[Tho90].Fourier[KNP87].FPS[LT88].Fracture[BG00].Framework[vL+03]. Frankenstein[Wit92].Frontwidth[MBW87].Fueling[Her91]. Fujitsu[Ish91].Full[AEPR92].Fully[YW93].Fun[RAGW93].Function[ZOF90].Fundamental[MR90]. Fusion[ACG+90].Future[BSB89].FX[DD91].FX/80[DD91].Galaxies[Her91].Games[EGMP93].Gap[SS99].Gas[MKG90].Gases[WBMY90].Gauge[Mor89a].Gene[RS03].Generation[DE03].Genetic[RS03].GFLOP[SBF90].Glass[YSN90].Global[WBMY90]. Globalized[GKMT00].Globally[SH93]. Globus[FK97].GloVE[dPIdA03].Glow[YW93].Gluons[BRE+90]. Goodput[BL99].Gradient[Mel87]. Gradient-like[CSV91].GrADS[BCC+01]. Grain[ACM88].Grained[BGB+96]. Grand[Kit90].Graphs[LK01]. Gravitational[SWW94].Gravity[Ham91]. Greenbook’[HBSM03].Greenhouse[WBMY90].Grid[CKPD99,FKT01].Grid-based[LM03].GridLab[A+03]. Grids[DT99,vL+03].Groundwater[MMD98].Growth[Cla91]. Guest[dA03].Guided[F+03].Gyrofluid[KPM+96].Hadron[Liu90].Harbor[BBC+00]. Hartree[KKCB98].Hartree-Fock[KKCB98].Head[GKB93]. Heavy[Reu92].Heavy-Ion[Reu92]. Helium[Fro91].Helix[PRT90]. Helmholtz[BEF+95].Heterogeneous[RAGW93].Hierarchical[GJM88].High[THL88]. High-Level[BCC+01].High-Order[CC95].high-performance[Fer90].High-Pressure[WLC91].High-Wave[BEF+95].Higher[Mah90]. Highly[Sim90].history[Bra91].Hitachi[WOG95].Hoc[CHZ02,BG02]. Homotopy[DZRS99].Hoshen[CBZ97]. Hoshen-Kopelman[CBZ97].Hosted[HBSM03].HPCC[CBB+96].HPF[DL97].HPF-Builder[DL97]. HPVM[CLP+99].HPVM-Based[CLP+99].Hybrid[MS02]. Hyperbolic[FG97].Hypercube[Din91,KL87].Hypercubes[LK01].I-W AY[DFP+96].I/O[PH91].IBM[DD89].Ice[ZOF90].IceT[GS99]. IEH[LK01].II[JP93].IJSGA[Hua03]. ILU[Ma00].Image[AAC+97].Imaging[CBB+96].Immersive[THC+96]. Impact[GJM88,KBH88].Implementation[Mel87]. Implementations[RR96].Implementing[YFH+96].Implications[RE87].Implicit[GKMT00]. Improving[BL99].Incomplete[IIJ93]. Increased[WBMY90].Increasing[WW92].Index[Ano96a]. Industrial[DGP+97].Inequality[NK89]. Infer[RS03].Influence[Ede93]. Information[Ano96b].Information-Driven[CHZ02]. Information-Theoretic[FWSW02]. Infrastructure[FK97].Initial[WLVL+96]. Initio[ASW91].Institute[IHM87]. Instruction[HRM89].6Instrumentation[TM99].Integer[Gro03]. Integrate[BFLL99].Integrated[CFK+94]. Integration[QWIC02].Intel[KL87]. Intensive[Mah90].Inter[FWZ91].Inter-Semiconductor[FWZ91]. Interaction[Liu90].Interactions[TMWS91].Interactive[SS89].Interface[Ano94,SLG95].Interleaving[KNP87].International[Ano98a].Internet[Rao02]. Interpretation[Fei99].Introduction[Nag93].Inverse[Cho01]. Investigation[CK01].Investigations[Mav02].Ion[Reu92].iPSC[HGD91,KR95].iPSC/860[HGD91,KR95].Irregular[Man97]. Ising[BRT+92].Issue[Fol90b].Issues[MBW87].Iterated[RR96]. Iterative[MC90].Japan[IHM87].Jini[Hua03].Jini-based[Hua03].Jumpshot[ZLGS99]. Jupiter[Tho90].Kernel[TM99].Kinetics[ARR99]. knowledge[KT94].Kopelman[CBZ97]. Krylov[GKMT00].Kutta[RR96]. Laboratory[BBB+91b].Laminar[SG91]. Language[Sha88].languages[JO92]. Large[FBW87].Large-Scale[Ewi88]. Lattice[Mor89a].Law[HE01].LBLAS[KJH96,JO92].Learning[AH93]. Legion[GNTLH97].Length[DLY+98]. Level[DD89].Libraries[DMT01].Library[CE00,Poz97].Ligature[KBA00]. like[CSV91].Limited[TW87].Linda[SSNM92].Line[LWOB97].Linear[AGL87].Link[TLG98,Pet87]. Linux[Ano01a].Liquid[DQFW90]. Livermore[WGI90].Local[BRT+92,JO92].Local-Creutz[BRT+92].Localization[CYT+02].Localized[WCE95].Logical[SR98].Long[HRM89].Looking[AK93].Loop[IS96].Loops[WGI90].Loss[ZOF90].LU[DD89].Machine[SS89,LPG88].machines[KS89]. Magnetically[ACG+90]. Magnetohydrodynamic[ACG+90]. making[KT94].Man[Wit92]. Management[HTSK90].Many[TMWS91].Many-Body[TMWS91]. Mappings[PTGB02].Market[NK89]. Market-Based[WPBB01].Markets[IIJ93].Massively[Mon89]. Matching[ZC92].Materials[KVY+90]. Mathematical[Mon89].Matrices[KC92]. Matrix[AGL87].MCell[CBSB01].MCHF[SYF96].Means[BRT+92]. Mechanics[Her88].Mechanism[DZRS99]. Medicine[SSNM92].Meetings[Ano98c]. Member[HTSK90].Memoriam[Mar91]. memories[TKSK88].Memory[MBW87]. Merging[YBA+03].Mesh[WCE95].Mesh-Iterative[MCW+00].Meshes[Ytt97].Meso[GGS01].Meso-Scale[GGS01].Message[Ano94,SLG95].Message-Passing[Ano94,SLG95]. Metacomputing[FK97].Metals[Cla91]. Metascheduling[Mat03].method[TKSK88].Methods[Mel87]. Metric[HE01].MHD[ACG+90]. Microprocessors[WT99].Microscopic[YFH+96].Microtasked[MSK92].Microtasking[HA91].Middleware[CKPD99].Migration[KL87]. MIMD[BOD+91].Mini[Gen88].Mini-Supercomputers[Gen88]. Minimization[Rao02].Minnesota[Aus92].MiPAX[HKK88]. Missions[SKB01].MM2[PUR94].Mobile[FP02].Model[ABA87].7Modeled[WJS+90].Modeling[DD87]. Models[Pet87].Modern[BDG+00].Modified[HB90].Modulo[Gro03]. Molecular[DFC90].Monitoring[L WOB97].Monte[MB87]. Motions[DFC90].Moveout[LT90].MP[LT88].MP/416[THL88].MPI[Ano94].MPI-OpenMP[MS02]. MPI2[MPI98].MPICH[GL97].Much[RAGW93].Multiblock[Ytt97]. Multibody[BGI+99].Multicommodity[NK89].Multicomputer[Man97].Multicomputers[MOK00]. Multidimensional[HL W00]. Multidisciplinary[BGB+96].Multifrontal[MBW87].Multigrid[DMT97].Multilevel[DW97]. Multimodal[FWSW02].Multiparadigm[AS00].Multiphase[ZC92].Multiphysics[MCW+00].Multiple[Mor89b].Multiprocessing[YM91].Multiprocessor[BS88].Multiprocessors[DD91]. Multiprogramming[MA89].Multitasking[THL88].Multiunit[GCL93].NAMD[NHG+96].Nanophase[Nak99]. NAS[BBB+91a].National[BBB+91b,All88].Navier[SBF90].Navier-Stokes[SBF90]. nCube[CL95].NEC[Mor89a].Neck[GKB93].Needs[HBSM03].NERSC[HBSM03].Net[AEG+03]. Netlets[Rao02].NetSolve[CD97]. Network[NZ93].Network-Based[AM00]. Network-Enabled[CD97].Networked[FWSW02].Networks[RE87]. Neural[RE87].Newton[GKMT00]. Newton-Krylov-Schwarz[GKMT00]. Next[DE03].NMR[KBH88].NOE[CGB+94].NOE-Restrained[CGB+94].Non[GSHL03].Non-Dedicated[GSHL03]. Nonequilibrium[YW93].Nonlinear[ABA87].Nonsymmetric[MC90].normal[Haj93]. North[LC90].Northern[UB95].Novel[FWZ91].NSF[Bra91].NT[CLP+99].Nuclear[IHM87].Number[FG97].Numbers[BEF+95]. Numerical[RKKC90].Numerically[Mah90].O[PH91].Oak[HGD91].Object[NHG+96].Object-Oriented[NHG+96].Ocean[KM95,JO90].ODE[BH99].Ohio[BBW90].Oil[KR94].On-Line[L WOB97].Open[LWOB97,AEG+03].Opening[PRT90].OpenMP[BBC+00]. Operating[CW01].Optimal[FG97]. Optimization[LT88].optimizations[PUR94].Optimize[KKCB98].Optimized[MSK92]. Optimizing[Mor89a].Optorsim[B+03]. Order[THL88].Organization[FKT01]. Organized[BGF02].Oriented[NHG+96]. Our[WW92].Overlap[BBDR95]. Overlapping[PR95].Overview[DFP+96]. P4[Mat95].PACE[NKP+00].Pacific[JO90].Package[SYF96].Pair[Fro91].PAM[CEL+97].PAM-CRASH[CEL+97].papers[KKDV03].Paradigm[BGB+96]. Parallel[Syz87].Parallelism[ACM88]. Parallelization[Reu92].parameter[SH93].ParaScope[CCH+88]. Park[UB95].Parkbench[HL00]. Parmetis[LDGR03].Partial[Meu88]. Particle[DD87].Partitioning[Ytt97]. Partitions[WCE95].Passing[Ano94,SLG95].8PASSION[KKCB98].Patching[BH00]. Paths[Rao02].Patterns[GKB93].PC[Ste01].PCs[AWS01].PDEs[Ma00]. 416[THL88].600J[DEKL92].80[DD91]. 860[HGD91,KR95].Albedo[Tho90]. Computation[BBDR95].DAC[Cza03]. JPL[Din91].Logical[Chu99].Real-Time[KK01].VF[DD89]. PERFECT[BCK89].Performance[IHM87].PERMAS[AJL+97].pH[MP94].Phase[YCHH90].Photon[MWAR87]. Physical[SR98].Physical/Logical[Chu99].Physician[Wit92]. Physics[MR90].Pipeline[BFLL99]. Plasma[CDD+90].Plasmas[DD87]. Platforms[BLRR01].Play[Pan97]. POEMS[BBD00].Point[BSB89].Point-SSOR[Ma00].Poisson[GGS01]. Pollution[DFH+96].Polyacetylene[ZOF90].Polyenes[AEPR92].Polymers[DFC90]. Portable[GL97].Portals[BRM03].Power[Dem90].Powerful[Mor89b]. Practical[Cho01].Practice[BR03]. Preconditioned[Mel87].Preconditioner[BBS99].Preconditioners[Ma00].Predict[VS03]. Predicting[WLC91].Prediction[SCB+95].Predictions[RIF01].Preface[DT97]. Preprocessing[DMT97].Preprocessors[Ano01a].Pressure[WLC91].Prime[Sim90]. Principles[DQFW90].Priori[Cho01]. probabilities[Haj93].Problem[UF89]. Problems[FBW87].Procedure[CGB+94]. Process[GCL93].Processing[Mor89b]. Processor[SBF90].Processors[LT88]. Production[MSK92].program[Fer90,Web91].Programming[Syz87].Programs[ACM88].Progress[AGL87]. Project[Wit92,Pet87].Promising[Gir02].Propagation[GKN+96].Properties[ACG+90].prospectus[Bra91]. Protein[KBH88].Prototypical[WLVL+96].Providing[GKP97].Proximal[NZ93]. PVM[BDG+95].PVMGeant[DZDR95]. PVODE[BH99].Qaulity[Mat03].QCD[Din91].QoS[BSCC03].Quadtree[CL95]. Quantized[Ham91].Quantum[FBW87]. Quarks[BRE+90].Quasigeostrophic[KM95].Query[S+03]. Querying[CHZ02].Queuing[Ish91]. Radar[MPG93].Radio[CBB+96]. Randomly[CYT+02].Rare[BB02].Ray[CTH+93].Reacting[MYC92]. Reaction[Koi90].Reactions[TW87]. Ready[Sim90].Real[WLC91].Real-Time[NRR97].Realistic[BR03]. Recognition[RE87].Reconfiguration[LK01].Recovery[BB02].rectangle[Haj93]. Recurrent[Syz87].Reduced[BFLL99]. Reduced-Dimensionality[BFLL99]. Reducing[DLY+98].Reduction[NRR97]. Regional[KM95].Regression[VS03]. Relative[PUR94].Relativity[RIF01]. Remeshing[LDGR03].Remote[BB02]. Replication[B+03].Representations[WW92].Requirements[LPJ98].Research[IHM87,EM89].reservation[Mat03].Reservoir[Ewi88]. Resolution[HB90].Resource[WPBB01]. Response[ZOF90].Restrained[CGB+94]. Results[PUR94].Retrospective[Mar88]. RF[YW93].Ridge[HGD91].Rigid[Nak99].Rigid-Body-Based[Nak99].RISC[Gro03].RISC-Based[Gro03].RNA[SCB+95].Role[Sab91].Roles[MMS88].Rolling[FFNP97].9Routing[MOK00].Run[DLY+98].Runge[RR96].Runge-Kutta[RR96]. Runtime[AJL+97].S[Lai93].S-3800[WOG95].S-MP[Lai93]. SAMCEF[GCD97].SAR[AAC+97]. SARA[SBWS99].SCALA[SFP02]. Scalability[HLW00].Scalable[WLB92]. scalar[KS89].Scale[Pet87].Scaling[CGB+94].Schedule[SBWS99]. Scheduling[CKPD99].Scheme[BG00]. Schemes[BS88].Schr¨o dinger[BFLL99]. Schwarz[PR95].Science[All88]. Sciences[NKR90,DGH+93].Scientific[LS90].SE[KJH96].Sea[LPJ98]. Searches[F+03].Secondary[SCB+95]. Seeing[LPG88].Seismic[CDH+97b]. Select[KKDV03].Selective[RE87].Self[BGF02].Self-Adapting[DE03].Self-Organization[FWSW02].Self-Organized[BGF02].Semantic[FP02].semiconductor[TKSK88]. Semiconductors[Cla91].Sensor[BGF02]. Sensors[FWSW02].Sequence[Jon92]. Serial[NK89].Server[CD97].Service[Mat03].Service-based[HLP+03]. Service-oriented[Hua03].Services[AEG+03].Set[PTGB02]. Severe[WJS+90].Shape[WCDS99]. Shared[MBW87].shared-memory[DEKL92].Shelf[LPJ98]. Should[Pan92].Side[HTSK90].Sidney[Mar91].Sieves[Mon89].Signal[FP02].Simple[SBWS99]. Simulating[BRE+90].Simulation[TW87].Simulations[ABA87]. Simulator[B+03].Simultaneous[ABA87]. Single[BCJ01].Singular[Ber92].Six[WOG95].Skeletonization[DIB00]. small[PUR94].Smart[Gro03].Social[NKR90].Sodium[DQFW90]. Software[Fol90a].Soil[CWHP99].Solaris[Ano01a].Solid[DQFW90].Solution[KBH88].Solutions[Fro91]. Solve[CTH+93].Solved[CSV91].Solver[PR95].Solvers[GGS01].Solving[BS88].Some[Gir02]. Sometimes[RAGW93].Sonic[WW92]. Source[CYT+02].Sowing[GL97].Space[F+03].Spaceborne[SKB01].SPAI[BBS99].Sparce[WT99].Sparse[AGL87].Sparsity[Cho01]. Special[Nag93].Species[BB02].Specific[CDH+97b].Spectral[Tho90]. Spline[Fro91].Splitting[IS96].Spotlight[MPG93].Spread[GKB93]. SSOR[Ma00].Stability[ACG+90]. Standard[Ano94,Poz97].Standards[Pan92].State[WLC91].Static[BLRR01].Statistical[Her88]. Status[MB87].Steering[GKP97].Stefan[CSV91].Stochastic[ABA87]. Stokes[SBF90].Storm[WJS+90]. Strategies[MOK00].Strategy[MCW+00]. Structural[YCHH90].Structure[Liu90]. Structured[Ytt97].Structures[KBH88]. Studies[DQFW90].Study[WJS+90]. Studying[BOD+91].Subdomains[FG97]. Subprograms[Don02a].Subroutines[KJH96,JO92]. Supercomputer[Duk91]. Supercomputers[DD87,Gen88]. Supercomputing[MMS88,All88]. 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通讯业务术语
通讯业务术语BTS 基站收发信台(Base Transceiver Station)BSC 基站操纵器(Base Station Controller)OMC 操作爱护中心（Operation & Maintenance Center）BSS 基站子系统(Base Station Subsystem)MSC 移动业务交换中心(Mobile Switching Center)VLR 拜望位置寄存器(Visiting Location Register)HLR 归属位置寄存器(Home Location Register)AUC 鉴权中心(Authentication Center)EIR 设备身份识别寄存器(Equipment Identity Register)SC 短消息中心(Short Message Center)PLMN 陆地公共移动通信网(Public Land Mobile Network)公共陆地移动网PSTN 公共服务网络（Public Service Telephone Network)ISDN 综合业务数字网俗称“一线通”(Integrated Service Digital NetWork）SCP 业务操纵节点（Service Control Point）SMP 业务治理节点（Service Management Point）SMSC 短消息交换中心(Short Message Switching Center)SSP 业务交换节点（Service Switch Point）STP 信令转接点（Signal Transfer Point）PCF 分组操纵功能PDSN 分组数据服务器GSM 全球移动通信系统(Global System for Mobile communication)GPRS 通用无线分组业务(Gerneral Packer Radio Service)MIN 移动智能网CDMA Code Division Multiple AccessNMS 网络治理系统（Network Management System）NMC 网络治理中心(Network Management Center)FM 故障治理（Fault Management）console 操纵台NE 网元（Network Element）MMT 可治理多层（Managed Multi_Tier）TCH 业务信道BCH 广播信道FCCH 频率校正信道SCH 同步信道BCCH 广播操纵信道CCCH 公共操纵信道SDCCH 独立专用操纵信道PCH 寻呼信道RACH 赶忙接入信道AGCH 准许接入信道DCCH 专用操纵信道SACCH 慢速辅助操纵信道FACCH 快速辅助操纵信道LAPD （D通道链路接续规约）SP （service provide）SP指移动互联网服务内容应用服务的直截了当提供者，负责依照用户的要求开发和提供适合手机用户使用的服务。
ransac经典文章
PROBLEM: Given the set of seven (x,y) pairs shown in the plot, find a best fit line, assuming that no valid datum deviates from this line by more than 0.8 units.
Communications of the ACM June 1981 Volume 24 Number 6
Fig. 1. Failure of Lowing Out The Worst Residual" Heuristic), to Deal with an Erroneous Data Point.
I. Introduction
(RANSAC),for fitting a model to experimental data is
introduced. RANSAC is capable of interpreting/ smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of this paper describes the application of RANSAC to the Location Determination Problem (LDP): Given an image depicting a set of landmarks with known locations, determine that point in space from which the image was obtained. In response to a RANSAC requirement, new results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system that can solve the LDP under difficult viewing
交通工程专业英语词汇表
30th Highest Hourly Volume，30HV 第30 最高小时交通量3-Leg Interchange 三路立体交叉3-Leg Intersection 三路交叉AA.M. Peak Period 早高峰Absolute speed limit 绝对速限Abutting property 邻街建造物Acceleration Lane 加速车道Access 出入口Access Control 出入管制；进出管制Access ramp 出入引道Accessibility 可及性Accident 肇事；事故；意外事件Accident (Crash) Rate 事故率Accident (Crash) Severity 事故严重性Accident Analysis 事故分析；意外分析；肇事分析Accident Assessment 事故鉴定Accident Casualty 事故伤亡Accident Cause 事故原因Accident Characteristics 肇事特性Accident Hazardous Location 易肇祸路段Accident Investigation 事故调查Accident Prone Location 易肇事地点Accuracy 精度Actual travel time 实际行驶时间Adaptive route choice 适应性路线选择Advanced driver information system ADIS 先进驾驶员信息系统Advanced Traffic Management Services ATMS 先进交通管理服务Advanced Traveler Information Services ATIS 先进路人信息服务Advanced vehicle control system 先进车辆控制系统Aerial Map 航测图Aerial perspective 鸟噉图；空中透视Overload, Overloading 超载Air resistance 空气阻力Alignment Design 路线设计；定线设计Algorithm 运算法则All-day Service 全天候服务Alley 巷；道Allowable Bearing Capacity 容许承载量Allowable load 容许载重Alternate Method 替代方法Alternative(s)替代(换)方案American Concrete Institute ACI 美国混凝土学会；美国混凝土研究会American Federal Highway Administration FHWA 美国联邦公路总署American Institute of Transportation Engineers ITE 美国交通工程师学会American Society of Civil Engineers ASCE 美国土木工程师协会Amplification effect 放大效应Amplifier 扩大器Annual Average Daily Traffic, AADT 年平均日交通量Annual budget 年度预算Annual Traffic 年交通量Appropriate measures 适当防制措施Arc 弧线Arrival time 到达时间Arterial 主要干道Asphalt, Asphalt Cement, Asphalt Binder 沥青(美国用语)；沥青胶泥At-Grade Intersection 平面交叉Advanced traffic management system ATMS 先进的交通管理系统；高等交通管理系统Automated toll system 自动化收费系统Automatic Cargo Identification, ACI 自动货物辨识Automatic Vehicle Classification, AVC 自动车辆分类Automatic Vehicle Identification, AVI 自动车辆辨识Automatic Vehicle Location, AVL 自动车辆定位Automatic Vehicle Monitoring, AVM 自动车辆监视Auxiliary Lanes 辅助车道Average Delay Time 平均延滞时间Average Waiting Time 平均等候时间BBalance Cut and Fill 均衡挖填Barrier, Noise barrier, Noise barrier wall 防音墙Birds' eye view 鸟噉图Blast 开炸Bleeding (沥青路面)泛油; (水泥混凝土表面)泛浆Blood alcohol concentration 血液中酒精浓度Bottleneck 瓶颈Bottleneck Road 瓶颈路段Brake failure, Defective brake 煞车失灵Brake light 煞车灯Brake Reaction time 煞车反应时间；制动反应时间Braking Distance 剎车视距(停车视距)Braking system 煞车系统Breakdowns 故障Breath alcohol concentration 呼气酒精含量Brick Pavement 砖铺路面；砖铺面Bridge 桥梁Bridge expansion joint 桥面伸缩缝Bridge inspection 桥梁检测Bridge Management System 桥梁管理系统Bridge span 桥跨Brightness contrast 辉度对照比Brittle fracture 碎裂Gravel Road 碎石路Broken Stone Surface 碎石路面Budget 预算经费Budgetary estimate 经费概算Buffer 缓冲剂；缓冲器Buffer distance 缓冲距离Buffer reach 缓冲段Buffer time 缓冲时间Buffer zone 缓冲带Building Code 建造规则；建造法规Bumper 保险杆Bus Exclusive Lane 公交专用道Bus operation 公交营运Bus Rapid Transit 公交捷运Bus route inquiring system 公交路线查询系统Bus scheduling 公交排班Bus station 公交停靠站Bus Terminal 公交终站；公交总站；公交场站Business District 商业区CCab, Taxi 出租车Capacitated freight distribution 零担货物运输Capacity analysis 容积分析Capacity and level of service analysis 容量与服务水准分析Capacity constraint, Capacity restriction 容量限制Capacity estimation 容量估计Capacity limitation 容量极限值Car accident, Traffic accident 交通事故Car detector, Vehicular detector 车辆侦测器Car following model 跟车模式；自动跟车系统Car navigation system 汽车导向系统Car Ownership 汽车持有；汽车持有权Car Pooling, Carpool 汽车共乘Carbon Dioxide CO2 二氧化碳Casualty 伤亡Caution Light 警告灯Caution Sign 警告标；警告标志Caution Signal 注意信号；警告号志Concrete Pavement 混凝土路面Critical Speed 临界速率Census 普查Center Island 中央岛Centerline 中心线Central Business District CBD 中心商业区Charging system 收费系统Children-only Bus 幼童专用车Circulation 通风；交通Circumferential street (road)外环(环状)道路City Rebuilding 都市重建Classification Count 分类调查Classification of road 道路分类Classification of Soil 土壤分类Clear distance 净距Clear height 净空高Clear Span 净跨距Climate Conditions 气候情况Close System Toll Station 封闭制收费站Closed Loop 封闭环路CO Detector 一氧化碳侦测器Code 规范；数值Coefficient of friction, Friction coefficient, Frictional coefficient 磨擦系数Collision Accident 碰撞事故Collision Warning Systems 碰撞预警系统Commercial Center 商业中心Commercial District 商业区Community Center 杜区中心Community Planning 社区规划Commuter 通勤者Commuter Rail, Commuter Train 通勤火车Commuting Distance 通勤距离Compatibility 兼容性Compensation 征收补偿Complex intersection 复合适交叉路口Composition of Traffic 交通组成Comprehensive Planning 综合性计划Compressibility of Soil 土壤压缩性Computer-Aided Dispatching System 计算机辅助派车系统Concave-convex 凹凸形Concrete barrier (New Jersey) 新泽西(混凝土)护栏Concrete pavement 混凝土铺面Conflicting point 冲突点Congestion degree 拥挤度Congestion pricing 拥挤定价Congestion Time 拥挤时间Congestion toll 拥挤费Construction Sign 施工标志Construction Specification 施工规范Construction/Maintenance Zone 施工维修区Contour Line 等高线Contour Map 等高线图Control of Access 出入管制Convex Function 凸函数Corridor 交通通廊Cost of Service 服务成本Count-down pedestrian signal 行人倒数计时显示器Counter flow 对向车流Country road 乡道Crash 冲撞；碰撞Critical Path 要径Critical Point 临界点Cross road 十字路口；交叉路；十字路Crown 路拱；路冠Crude Oil 原油Curb 缘石；路边石；护角Curve 曲线；曲线板；弯道Cushion material 缓冲材料Cushioning effect 缓冲效应DDaily Rainfall 日降水量Daily variation diagram 日变化图Deceleration 减速度Defective brake 煞车失灵Deformation 变形Defrosting 解冻Degree of Saturation 饱和度Delivery area 卸货区Delivery system 配送系统Delivery time 递送时间；送货时间Demand volume 需求流量Demand-Capacity Control 需求容量控制Demand-supply of parking spaces 停车空间的供需问题Demographic Data 人口资料Density of Traffic 交通密度；车流密度Design Capacity 设计容量Design curve 设计曲线Destination 目的地Destination zone 讫点区Detector 侦测器Deterioration 变质；恶化Diagonal crosswalk lines 班马纹行人穿越道Diesel Fuel 柴油Diffuse 扩散Digital image processing 数字影像处理Digital Map 数字地图Dining area 餐饮区Direction Factor 方向系数Disabled parking lots 残障停车位Dispatching efficiency 调度效率Distance 距离Distance-Measuring Equipment DME 测距仪Distribution center 配运中心Distribution center, Goods distribution center 物流中心Diverging area 分流区；分流区域Diverging point 分流点Dividing Strip 分隔带Domestic 本土的；区域的Door to door service 及门服务；及户服务Double decked bus 双层巴士Double-deck ramp 双层匝道Down Grade 下坡Downstream 下游Downtown street 闹市街道Dozer 推土机Drafting Room 制图室Drain Ditch 排水沟Drain Pipe 排水管Drainage Facilities 排水设施Driver behavior model 驾驶员行为模式Driver Information System 驾驶信息系统Driver Perception Reaction Distance 驾驶员反应距离Driver's License 汽车驾驶执照；汽车驾照Driving Simulator 驾驶仿真器Driving under the influence of alcohol 酒后驾驶Dynamic characteristics 动态特性Dynamic route choice 动态路径选择Dynamic system-optimum control model 动态系统最佳控制模式Dynamic traffic characteristic 动态交通特性Dynamic traffic signal control 动态交通号志控制系统EEarth Embankment 土堤Earth Excavation 挖土Earth Fill 填土Earthquake 地震East-West Expressway 东西向快速公路Economic benefits analysis 经济效益分析Elastic Deformation 弹性变形Elastic equilibrium 弹性平衡Electronic distance measurement instrument 电子测距仪Electronic gate 电子门；电动门Electronic Toll Collection 电子收费Elevated Highway 高架公路Elevation 标高；高程Elevator 电梯E-map of highway 公路电子地图Embankment 路堤Emergency delivery 紧急输送Emergency Escape Ramps 紧急出口匝道Emergency evacuation 紧急疏散Enforcement 执法，执行Engineering Economic Analysis 工程经济分析En-Route Driver Information 途中驾驶员信息En-Route Transit Information 途中运输信息Entrance (entry), ingress 进口路段Entrance exit 出入口Environment factor 环境因素Environmental impact assessment 环境影响评估Environmental sensitive area 环境敏感地带(环境敏感区位) Escalator 电扶梯Excavation Work 挖土工程Excess Fuel Consumption 超额燃油消耗Exclusive bike lane/Bikes only 脚踏车专用道Exclusive lane 专用车道Exit Ramp Closure 出口匝道关闭Exit Ramp Control 出口匝道控制Expansion Factor 膨胀因素；扩展系数Expansion Joint 伸,接缝Explosive 炸药Express slow traffic divider 快慢分隔岛Expressway 快速道路(进出管制或者半进出管制)Glare control 眩光控制Glare screen 防眩设施Glare shield 眩光遮蔽物Global Positioning System GPS 全球定位系统Goods delivery problem 货物配送问题Grade 坡度；纵向坡度Graphical analysis 图解分析法Gravel Road 砾石路Gravity Model 重力模式Greenhouse effect 温室效应Guidance information 导引信息Guide Sign 指示标志HHazardous materials 危（wei）险物品Head light 前灯；车前大灯Head On Collision 车头对撞Heavy weight transportation management 大载重运输管理High beam 远光灯High capacity buses 高容量巴士High Occupancy Vehicle HOV 高乘载车辆High-Occupancy Vehicle Priority Control 高承载率车优先行驶控制High Speed Rail 高速铁路Highway aesthetics 公路美学Highway alignment design 公路线形设计Highway Construction and Maintenance Cost 公路建设维护成本Highway Supervision and Administration 公路监理Histogram 直方统计图Hit-and-run driving 肇事逃逸；闯祸逃逸Holding Line Marker 等候线标记Home interview 家庭访问Horizontal Clearance 侧向净宽Horizontal Curve 平曲线Hourly variation 时变化图Human characteristics 人类特性Human factor 人为因素；人事行为因素Hydrophilic 亲水性Hydrophobic 厌水性IIdeal Condition 理想状况Illegal parking 违规停车Impact 冲击Improving Highway Traffic Order and Safety Projects 道路交通秩序与交通安全改进方案Indemnity of Damage 伤害赔偿Intensity of Rainfall 雨量强度Index system, Indicator system 指标体系Indirect observation 间接观测Individual difference 个人禀性的差异Infrastructure 内部结构；基础建设Inspection of Vehicle 汽车检验Intelligent Transportation System ITS 智能运输系统Intensity and Duration of Rainfall 降雨时间与密度Intercepting Drain 截水管Intercity bus industry 长途客运(业)Intersection design 交叉路口设计Interview technique 访问法；访谈法Intoxicated driving 酒后驾车JJoint Operation of Transport 联运Junction 路口LLag time 延迟时间Landscape design 景观设计Landslide/Slump 坍方Lane, traffic lane 车道Lane Width 车道宽度Latent travel demand 潜在旅次需求Lateral clearance 侧向净距Laws of randomness 随机定理Left turn lane 左转车道Left turn waiting zone 左转待转区Left turning vehicle 左转车辆Length of grade 坡长Level Crossing 平面交叉Level of Service 服务水准License Plate 汽车号牌License Suspension 吊扣驾照License Termination 吊销License Plate Recognition 车牌辨识Light Rail Rapid Transit LRRT 轻轨捷运Load limit 载重限制Loading & unloading zone 上下旅客区段或者装卸货物区段Local Area Network, LAN 局域网络Logical Architecture 逻辑架构Long tunnel 长隧道Longitude 经度Longitudinal Drain 纵向排水Longitudinal Grade 纵坡度Long-Range Planning 长程规划Loop 环道(公路方面)；回路(电路方面)Lost Time 损失时间MMacro or mass analysis 汇总分析；宏观分析Magnetic Levitation Maglev 磁浮运输系统Magnetic loop detector 磁圈侦测器Mainline 主线Management Information System MIS 管理信息系统Manual counts 人工调查法Marking 标线Maximum allowable gradient 最大容许坡度Maximum capacity 最大容量Maximum Density 最大密度Maximum Likelihood Function 最大概似法Maximum Peak Hour Volume 最尖峰小时交通量Measure of Effectiveness MOE 绩效评估指针Mechanical garage 机械式停车楼(间)Merge 合并；并流；进口匝道；并入Merging area 并流区域Merging point 并流点Metropolitan Planning Area 大都会规划区Minimum Grade 最小纵断坡度Minimum sight triangle 最小视界三角形Minimum turning radius 最小转弯半径Mixed flow 混合车队Mixed traffic 混合车队营运Mixed traffic flow 混合车流Monitoring 监测Monorail 单轨铁路Mortality 死亡数Motivation 动机Mountain road 山区道路Multilayer 多层Multileg Interchange 多路立体交叉Multileg Intersection 多路交叉NNational freeway 国道National System Architecture 国家级架构Natural ventilation 自然通风Navigation 引导；导航Net Weight 净重No left turn 不许左转；请勿左转No parking 禁止停车Noise barrier, Sound insulating wall 隔音墙Noise pollution 噪音污染Noise sensitive area 噪音敏感地区Nonhomogeneous flow 不同流向的车流；非均质车流Nonskid Surface Treatment 防滑处理Nonsynchronous controller 异步控制器Novelty 新鲜性Number of Passengers 客运人数Number of Registered Vehicle 车辆登记数Nurture room 育婴室OOccupational Illness 职业病Off parking facilities, Off street parking garage 路外停车场Off Season 运输淡季Off street parking 路外停车One-way arterial street 单向主要干道One-way Street 单行道One-way Ticket 单程票Operating Cost 营运成本Operating Time 营运时间Optimal path 最佳路径Optimal spacing 最适间距Optimum asphalt content 最佳沥青含量Optimum Moisture Content 最佳含水量Ordinance 条例Origin and destination study 起讫点研究Outlet Control 出口控制Overall travel time 全程行驶时间Overburden 超载；覆盖Overloaded vehicle 超载车辆Overloading experiment 超载实验Overpass 天桥；高架道Ozone layer 臭氧层PParameter 参数Parcel distribution industry 包裹配送业Park and ride system 停车转乘系统Parking behavior 停车行为Parking capacity 停车容量Parking demand 停车需求Parking discount 停车折扣Parking facility 停车设施Parking Lot 停车场Parking prohibition 禁止路边停车Parking restriction 停车限制Parking supply 停车供给Passing Sight Distance 超车视距Patrolling 巡逻Pavement aging 铺面老化Pavement Condition 铺面状况Pavement Drainage 路面排水Pavement maintenance 铺面维护Pavement rehabilitation 铺面翻修Pavement roughness 铺面糙度Pavement strength 铺面强度Pavement-width transition marking 路宽渐变段标线Peak Season 运输旺季Pedestrian Crossing 行人穿越道线Pedestrian Signals 行人号志Pedestrian 行人Pedestrian factor 行人因素Pedometer 步测计Perception distance 感识距离Perception Time 认识时间Performance 绩效；功能Permeability Coefficient 透水系数Permeability test 透水试验Photoelectric detector 光电侦测器Platform 平台Pore 孔隙Priority 优先权Private Vehicle 自用车辆Provincial Highway 省道QQualitative 定性Quantification 定量Queue Length 等候线长度Queuing time 等候时间Queuing model 等候模式RRadar meter, speed gun 雷达测速仪Radial street 辐射式道路Radius of curvature 曲率半径Rainfall Frequency 降雨频率Rainfall Intensity 降雨强度Ramp closure 匝道封闭Ramp control 匝道管制；匝道仪控Reaction time 反应时间Real-time 实时Real time scheduling 实时排程Real-time Traffic Information 实时交通信息Rear-end collision 尾撞Reasonable or prudent speed limit 合理速限Reckless driving 驾驶疏忽Reliability 可靠性Remote Area 偏远地区Residential District, Residential Area 住宅区Resistance Value, R-Value 阻力值；R-值Rest Area, Rest Site 歇息区Restricted curb parking 规定时限的路边停车Retail district 零售区Reversible one-way street 调拨式(可变)单行道Revocation 注销驾照Ride sharing 车辆共乘Ride Sharing Program 车辆共乘计划Right of ingress or egress 进出权Road bed, Roadbed, Subgrade 路基Road capacity 道路容量Road closure 道路封闭Road construction 道路建设Road design 道路设计Road Functional Classification 道路功能分类Road geometric factor 道路几何因素Road improvement 道路改善Road landscape, Roadscape 道路景观Road maintenance 道路维护Road pricing 道路定价Road roughness 路面粗糙度Road safety, Traffic safety 道路安全Road surface thickness, Thickness of pavement 路面厚度Road survey 道路测量Road toll 道路收费Road widening 道路拓宽Roadside interview 路旁(边)访问调查Round Trip Ticket 来回票Route choice, Route selection 路线选择Route familiarity 路径熟悉度Route Guidance 路径导引SSafe-passing sight distance 安全超车视距Safe-stopping sight distance 安全停车视距Sample 试样Sample size 抽样大小Sampling 取样Saturation capacity 饱和容量Saturation flow 饱和流量Scale 尺度；比例尺Scanning 扫描Scheduled Service 定时服务班次Scheduled Signal Control 定时号志控制Scheduling 排班School Bus 校车Seat belt (座椅)安全带Semi-actuated signal 半触动号志Semi-actuated Signal Control 半感应号志控制Semicircular 半圆式Semidynamic route guidance 准动态路径导引Sensitivity Analysis 敏感度分析Sensitivity Parameter 敏感度参数Service Area 服务区Sharp Turn 急弯Shear force 剪力Shopping center 购物中心Shortest path 最短路径Shortest path algorithm 最短路径算则Short-Range Planning 短程规划Shoulder 路肩Sidewalk 人行道Sight Triangle 视线三角形Sign 标志Signal 信号；号志Signalized intersection 号志化路口Simulation 仿真Single Journey Ticket 单程票Slope stability analysis 边坡稳定分析Slump 坍方Smart Card 智能卡Soil Stability Analysis 土壤稳定分析Sound barriers 隔音墙Specifications 规范Speed, Velocity 速度Speeding 超速Stability 稳定性Stage construction 分期施工Standard deviation 标准差Static characteristics 静态特性Static Load 静止荷重Stochastic congested network 随机性拥挤路网Strictly Decrease Monotonically 严格单调递减Strictly Increase Monotonically 严格单调递增Subcenter 次中心Superelevation 超高Suspension Bridges 吊桥Suspension from toll 暂停收费Swampy Areas 沼泽区Swerve 偏离正常行车方向；逸出常轨Synchronization 同步Synchronized watch (timer) 同步定时器Synchronous controller 同步控制器System Architecture SA 系统架构TTerminal 场站Time limit 时间限制Time-and-space restriction 时间和空间限制Toll collection station, Toll gate, Toll plaza, Toll station 收费站Tolling equity 收费公平性Topographic maps 地形图Topographic surveys 地形测量Total deformation 总变形Track of vehicle 车辆轨迹Track width 轮距宽度；轨宽Tractive Force 牵引力Trade-off 取舍权衡Traffic Accident 交通事故Traffic accident investigation form 交通事故调查表Traffic administration 交通行政管理Traffic Assignment 交通量指派Traffic Composition 交通组成Traffic congestion, Traffic jam 交通壅塞Traffic control and management 交通控制与管理Traffic Control Center TCC 交通控制中心Traffic corridor 交通走廊Traffic count (survey) 交通量调查Traffic counting program 交通量调查计划Traffic data collection system 交通资料采集系统Traffic demand 交通需求Traffic Demand Management TDM 运输需求管理Traffic Density 车流密度Traffic engineering 交通工程Traffic equilibrium 交通均衡Traffic evacuation 交通疏散Traffic facility, Transportation facility 交通设施Traffic Flow 车流；交通流Traffic impact assessment, Traffic impact evaluation 交通冲击评估Traffic improvement 交通改善Traffic light, Traffic signal 交通号志Traffic Marker 标线Traffic Mitigation Measures 交通疏缓措施Traffic monitoring facility 交通侦测设备Traffic ordinance 交通条例Traffic regulation 交通规则；道路交通安全规则Traffic simulation 交通仿真Traffic Volume/Flow 交通量/流量Transfer station 转运站Transition 渐变段Travel time 行驶时间Traveler Services Information 路人服务信息Trip Generation 旅次发生Trip purpose 旅次目的Truck terminal 货车场站Tunnel 隧道Tunnel Entrance 隧道入口Tunnel excavation 隧道开挖Turning prohibition 禁止转弯运行Turning radius 转弯半径Two lanes 双车道UUnderground Water 地下水Unit price 单价Unrestricted curb parking 未加限制的路边停车Unsignalized intersection 非号志化路口Unstable flow 不稳定车流；不稳定流动状态Uphill way 上坡路段Upstream section 上游段；上流段Urban expressway 都巿快速道路Urban Planning 都市计划VVans 厢式车Vehicle classification 车种分类Vehicle tracing system, Vehicle tracking system 车辆追踪系统Ventilation shaft 通风竖井WWeaving length 交织长度Weaving section 交织区段Weight-in-Motion WIM 行进间测重；动态地磅ZZebra Lines 斑马线。
LTE_Toolbox_datasheet_v3.0
Overview3G Evolution Lab – LTE Library Toolbox v3.0 is a comprehensive physical layersimulation Toolbox for Release 8 of the 3GPP Evolved Universal Terrestrial Radio Access (E-UTRA) standard. The library will accelerate your algorithm and PHY development, supportgolden reference verification and enable test & measurement waveform generation.The Toolbox features functions to perform channel coding/decoding andmodulation/demodulation operations implementing the full physical layer transmit/receiveprocessing chain (FDD and TDD), from transport channels and control information to OFDMand SC-FDMA modulated waveforms. Receive models are offered to recover the transmittedsignals. Channel coding for transport channels and control information is available foruplink and downlink. Test Models and Reference Measurement Channel (RMCs) waveformgenerators are available to easily create reference waveforms.The development roadmap includes tracking compliance from Release 8 into Release 9 andLTE-Advanced.The following transport and physical channels and signals are supported:Downlink Uplink Transport Channels & Control Information Transport Channels & Control InformationDL-SCH HI UL-SCH UCIBCH CFI PRACHDCIPhysical Channels and Signals Physical Channels and SignalsPDSCH PDCCH PUSCH SRSPBCH Referencesignals PUCCHPCFICH PSS - SSS DRS (PUCCH)PHICH DRS(PUSCH)Features•End-to-end conformance simulation test bench•GUI based waveform generators•3GPP Release 8 E-UTRA physical layer implementation conforming to TS36.211, TS36.212 and TS36.213•FDD and TDD duplexing modes •Downlink and uplink support•Complete support for 1, 2 and 4 antenna transmissions including all MIMO layering and precoding options •Full control of all parameters from MATLAB scripts•DCI message creation and control region building and decoding•All physical layer steps available as individual functions/blocks:o Transport channel coding/decodingo Scrambling/descramblingo Symbol Modulation/demappingo Resource element mappingo OFDM and SC-FDMAToolbox functions – Release 3.0Function Functionality Implementationchannel MEXLteBCH BroadcastLteBCHDecode Broadcast channel decoder MEXLteCellRS Cell specific reference signal MEXLteCellRSIndices Cell-specific reference signal indices MEXcoder MEXblockLteCFI CFILteCFIDecode CFI block decoder MEXLteCodeBlkDeseg Code block de-segmentation & code block CRCMEXdecodingsegmentation & code block CRCMEXblockLteCodeBlkSeg CodeattachmentCoding MEXLteConvCode ConvolutionalDecoding MEXLteConvDecode ConvolutionalLteCRC Cyclic redundancy check calculation andMEXappendingLteCRCDecode Cyclic redundancy check decoding and removal MEXLteDCI DCI format structures and bit payloads MEXdecoder MEXLteDCIDecode DCILteDCIDims DCI message dimensions MEXLteDCIEncode DCIencoder MEXestimator M-code LteDLChannelEstimation ChannelLteDLConformanceTestBench DownlinkPDSCH performance test bench M-codeLteDLDeprecoder Deprecoding onto transmission layers MEXestimate using the PSS and SSS M-codetimingLteDLFrameOffset Framechannel estimator for downlink M-codeLteDLPerfectChannelEstimation PerfectLteDLPrecoder Precoding of transmission layers MEXarray M-code LteDLResourceGrid SubframeresourceLteDLResourceGridDims Size of subframe resource array MEXLteDLSCH Downlink shared channel MEXLteDLSCHDecode Downlink shared channel decoder MEXsegmentation information MEXLteDLSCHDims DLSCHLteDuplexDims Dimension information related to duplexing MEXLteEqualizeMIMO MMSE-based joint equalisation and combining M-codeequalization M-code LteEqualizeMMSE MMSELteEqualizeZF Zero forcing MIMO equalization M-codeLteFadingChan Moving propagation conditions MEXLteFreqCorrect Correct for a specified frequency offset M-codeLteFreqOffset Estimate frequency offset using the cyclic prefix M-codeLteHSTChan High speed train propagation conditions MEXLteLayerDemapper Layeronto scrambled and modulatedMEXdemappingcodewordsLteLayerMapper Layer mapping of modulated and scrambledMEXcodewordsLteMovingChan Moving propagation conditions MEXmodulator MEXLteOFDM OFDMdemodulator M-code LteOFDMDemod OFDMM-codeLteOFDMDims Dimension information related to OFDMmodulationLtePBCH Physical broadcast channel MEXLtePBCHDecode Physical broadcast channel decoder M-codeLtePBCHIndices PBCH resource element indices MEXLtePBCHPRBS PBCH pseudo-random scrambling sequence MEXLtePCFICH Physical control format indicator channel MEXcontrol format indicator channelLtePCFICHDecode PhysicalM-codedecodingLtePCFICHDims PCFICH resource dimensions MEX LtePCFICHIndices PCFICH resource element indices MEX LtePCFICHPRBS PCFICH scrambling pseudo-random sequence MEXLtePDCCH Physical downlink control channels MEX LtePDCCHDecode Physical downlink control channel decoding M-code LtePDCCHDeinterleave PDCCH de-interleaving and cyclic shifting MEXdimensions MEXresourceLtePDCCHDims PDCCHLtePDCCHIndices PDCCH resource element indices MEX LtePDCCHInterleave PDCCH interleaving and cyclic shift MEXLtePDCCHPRBS PDCCH scrambling pseudo-random sequence MEX LtePDCCHSearch PDCCH DCI search M-code LtePDCCHSpace PDCCH search space candidates MEXLtePDSCH Physical downlink shared channel MEXshared channel decoding M-code LtePDSCHDecode PhysicaldownlinkLtePDSCHIndices PDSCH Resource Element indices MEXLtePDSCHPRBS PDSCH scrambling pseudo-random sequence MEXLtePHICH Physical hybrid ARQ indicator channels MEX LtePHICHDecode Physical hybrid ARQ indicator channel decoding M-codedeprecoding MEX LtePHICHDeprecoder PHICHresourcedimensions MEX LtePHICHDims PHICHLtePHICHIndices PHICH resource element indices MEXprecoding MEX LtePHICHPrecoder PHICHpseudo-random sequence MEXscramblingLtePHICHPRBS PHICHLtePMIDims Dimensionality related to PDSCH Precoder MatrixM-codeIndication (PMI) reportingM-code LtePMISelection PDSCHPrecoder Matrix Indication (PMI)calculationLtePRACH Physical Random Access Channel MEXRandom Access Channel detector M-code LtePRACHDetect PhysicalLtePRACHDims PRACH resource dimensions MEXMEXLtePRBFromDCI Physicalresource blocks allocated by a DCImessageLtePRBS Pseudo-random binary sequence MEXLtePSS Primary synchronisation signal MEXLtePSSIndices PSS resource element indices MEXLtePUCCH1 Physical Uplink Control Channel Format 1 MEXM-codeLtePUCCH1Decode Physical Uplink Control Channel Format 1decoderLtePUCCH1DRS Physical Uplink Control Channel Format 1MEXDemodulation Reference SignalMEXLtePUCCH1DRSIndices PhysicalUplink Control Channel Format 1 DRSindicesLtePUCCH1Indices Physical Uplink Control Channel Format 1 indices MEXLtePUCCH2 Physical Uplink Control Channel Format 2 MEXLtePUCCH2Decode Physical Uplink Control Channel Format 2M-codedecoderMEXLtePUCCH2DRS Physical Uplink Control Channel Format 2Demodulation Reference SignalM-codeLtePUCCH2DRSDecode Physical Uplink Control Channel Format 2 DRSdecoderMEXLtePUCCH2DRSIndices PhysicalUplink Control Channel Format 2 DRSindicesLtePUCCH2Indices Physical Uplink Control Channel Format 2 indices MEXLtePUCCH2PRBS PUCCH Format 2 scrambling pseudo-random MEXsequenceLtePUSCH Physical Uplink Shared Channel MEXUplink Shared Channel decoder M-codeLtePUSCHDecode PhysicalMEXShared Demodulation ReferenceUplinkLtePUSCHDRS PhysicalSignalLtePUSCHDRSIndices Physical Uplink Shared DRS indices MEXLtePUSCHIndices Physical Uplink Shared channel indices MEXLteRateMatchConv Convolutional Rate Matching MEXLteRateMatchTurbo Turbo Rate Matching MEXLteRateRecoverConv Convolutional Rate Matching Recovery MEXRecovery MEXRateLteRateRecoverTurbo TurboLteRMCDL Downlink reference measurement channelM-codeconfigurationM-codeLteRMCDLTool Downlink reference measurement channelwaveform generatorLteRMCUL Uplink reference measurement channelM-codeconfigurationM-codeLteRMCULTool Uplink reference measurement channel waveformgeneratormodulator MEX LteSCFDMA SC-FDMAdemodulator M-code LteSCFDMADemod SC-FDMALteSRS Uplink Sounding Reference Signal MEXMEXLteSRSDims Dimension information related to SoundingReference SignalLteSRSIndices Uplink Sounding Reference Signal indices MEXLteSCFDMADims Dimension information related to SC-FDMAM-codemodulationLteSSS Secondary synchronisation signal MEXLteSSSIndices SSS resource element indices MEXLteSymbolDemod Constellation demodulation and symbol to bitMEXconversionmodulation MEX LteSymbolMod SymbolLteTestModel Test Model configuration structure M-codeLteTestModelTool Downlink test model waveform generator M-codeCoding MEX LteTurboCode TurboDecoding MEX LteTurboDecode TurboM-code(Orthogonal Space Frequency BlockLteTxDiversityDecode OSFBCCode) decoderLteUCIEncode UCI encoder for PUCCH format 2 transmissions MEXLteUCIDecode UCI decoder for PUCCH format 2 transmissions M-codeMEXsignalreferenceLteUeRS UE-specificLteUeRSIndices UE-specific reference signal indices MEX LteULChannelEstimation Uplink channel estimator for PUSCH M-code LteULChannelEstimationPUCCH1 Uplink channel estimator for PUCCH Format 1 M-code LteULChannelEstimationPUCCH2 Uplink channel estimator for PUCCH Format 2 M-codedeprecoder M-code LteULDeprecoder SC-FDMALteULDescrambler Physical Uplink Shared descrambling M-codeM-codeLteULFrameOffset Uplink frame timing estimate using PUSCH DRSsignalsM-codeframe timing estimate using PUCCH 1LteULFrameOffsetPUCCH1 UplinkDRS signalsframe timing estimate using PUCCH 2M-code LteULFrameOffsetPUCCH2 UplinkDRS signalsprecoder M-code LteULPrecoder SC-FDMALteULResourceGrid Uplink subframe resource matrix M-code LteULResourceGridDims Size of uplink subframe resource matrix MEXLteULSCH Uplink shared channel MEXshared channel decoder M-code LteULSCHDecode Uplinkchannel de-interleaving M-code LteULSCHDeinterleave UL-SCHsegmentation information MEXLteULSCHDims ULSCHLteULSCHInterleave UL-SCH channel interleaving MEXLteULScrambler Physical Uplink Shared scrambling MEXLteVersion Steepest Ascent LTE Toolbox version MEXLteWarning Steepest Ascent LTE Toolbox warning control M-codeSequence MEX LteZadoffChu Zadoff-Chu。
during
A preliminary study of respiratory variations in the photoplethysmogramduring lower body negative pressureSuzanne Wendelken, Stephen Linder, Sue McGrathThayer School of Engineering and the Institute for Security Technology StudiesDartmouth College, Hanover, NH USAsuzanne.wendelken@ABSTRACTPrevious studies have shown that the pho-toplethysmogram (PPG) may be a useful tool forthe noninvasive detection of hypovolemia. The fo-cus has been on determining if frequency analysisof the respiratory induced variations of the PPGcan be used as an indicator of blood volume. Inthis preliminary study, we evaluate these fre-quency analysis techniques for two subjects un-dergoing Lower Body Negative Pressure (LBNP)induced hypovolemia. Using Matlab®-basedsoftware the power of the respiratory componentand heart rate component were calculated usingthe periodogram method for spectral estimation.Consistent with other studies our algorithms wereable to automatically detect changes in the respi-ratory variations. We found a significant increase in the respiratory variations in the PPG during simulated hypovolemia. By taking the ratio of the respiratory power to the heart rate power we consistently detected hypovolemia in subjects corresponding to sequestration of approximately 2 liters of blood (LBNP >70 mm Hg). The in-crease in this ratio occurred before significant change in blood pressure or tachycardia were observed.1. IntroductionEarly detection of hypovolemia is of critical importance for patient care. Standard noninvasive indicators of hypovolemia such as heart rate and blood pressure often do not give enough lead time before rapid decline and cardiovascular col-lapse onsets.Almost twenty years ago, a clinician noticed changes in the pulse oximeter waveform while blood volume decreased during surgery (Partridge 1987). These findings were largely ig-nored and unexplored until the mid to late 1990’s when several research groups began to investi-gate the possibility of using the pulse oximeter waveform for noninvasive blood volume and res-piratory rate assessment.Recent studies have shown that the pho-toplethysmogram (PPG) can be used to detect blood loss in mechanically ventilated, sedated pa-tients (Shamir, Eidelman et al. 1999; Shelley, Stout et al. 1999; Cooke, Ryan et al. 2004; Cannesson, Besnard et al. 2005) and in healthy subjects with paced or spontaneous breathing (Nilsson, Johansson et al. 2003; Gesquiere, Awad et al. 2004; Jablonka, Awad et al. 2004). These studies showed that the respiratory induced varia-tions in the PPG increase as blood volume is lost.In this preliminary study, we analyzed the PPG in two subjects undergoing lower body negative pressure (LBNP). The LBNP device (see Figure 1) safely simulates hypovolemia by se-questering blood into the lower extremities by applying a negative pressure around the legs and abdomen (Cooke, Ryan et al. 2004). This device can simulate severe hemorrhage (approximately 1 liter of blood volume at 40 mm Hg and 2 liters of blood volume at 80 mm Hg).The effects of LBNP conditions on the PPG have not previously been studied. This study uses Figure 1. Subject in the LBNP chamber at the Institute for Surgical Research, Brooks Army Medical Center, San Antonio, Texas.frequency analysis techniques that have previ-ously been shown to be indicators of blood loss to analyze the PPG data to show an increase in the respiratory induced variations of the PPG and a simultaneous decrease in the power of the heart rate signal.2. Methods and MaterialsWith IRB approval and informed consent, two healthy, male individuals participated in the study. Two FDA approved Nonin® pulse oxime-ters were placed on the subjects’ forehead andL B N P = 03.133.233.334T im e (s)P P G i n t e n s i t y5a b s P o w e rFre qu e n cy (H z )L B N P = 803.133.233.334T im e (s)P P G i n t e n s i t yPP G a t -80 m m H g115a b s P o w e rFre qu e ncy (H z )P o w er S pe ctra l D e n sity at 1950 se con d s: -80 mm H gL B N P = 903.13.23.34T im e (s)P P G i n t e n s i t yP P G a t -90 mm H g115a b s P o w e rFre qu e ncy (H z )P o w e r S pe ctra l D e nsity at 2460 secon d s: -90 mm H gfinger. Each sensor was connected to a Nonin OEM III interface module which generated data packets at 75 Hz of filtered 16-bit PPG data. The PPG signal was pre-processed by the OEM III module with high pass and notch filters. A serialRS232 interface allows a personal computer torecord data.A Java-based program was developed to si-multaneously log data from multiple sensors. Theannotated data was saved in text files for lateranalysis.2.1 Experimental ProtocolThree trials were performed using LBNP chamber at ISR, Brooks Army Medical Center. Because there were several research groups in-volved in the study each trial had a slightly dif-ferent protocol. In the first trial there were 3 min-utes at LBNP = 15, 30, 45 and 60 mm Hg. In thesecond trial there were 3 minutes at LBNP = 15,30, 45, 60, 70, 80 and 90 mm Hg. In the third trial the pressure was reduced to LBNP = 80 mm Hgin 60 seconds and then held there for nine min-utes. In this trial, an impedance threshold device (ITD) was used for three minutes during the 80 mm Hg phase.2.2 Data AnalysisUsing Matlab® programming language, we developed signal processing software to analyze the PPG. Our method used a sliding window of2048 samples, with a 700 sample increment (ap-proximately 27 seconds worth of PPG data with 17 seconds of overlapping data). The power spec-tral density was estimated using the periodogram 1method with a Hamming window (length 2048). The respiratory peak power (P resp ) and frequency (f resp ) was found by taking the maximum power between 0.07 and 0.3 Hz. A normal respiratory rate of 12 breaths per minute lies in this fre-quency range. The total power of the respiratory component (P resp_total ) was calculated by integrat-ing the power in the band 0.07 Hz < f < f resp + 0.05 Hz.The heart rate peak power and frequency was found by taking the maximum power (P hr ) at the heart rate frequency (f hr ) as determined by ourfeature extractor algorithm (Linder, Wendelken etal. 2006). This technique was used to find thepeak heart rate because there was occasionallysome high power noise around 1 Hz in laterstages of the trial that did not appear to be related to the actual heart rate signal. The total power inthe heart rate band (P hr_total ) was calculated by in-tegrating the power from the band f hr – 0.2 Hz < f < f hr + 0.2 Hz.1/access/helpdesk/help/tool box/signal/periodogram.html1234567896Time (s)P S DFigure 3. Total power for the heart rate component (red) and respiratory component (blue) during Trial 1 of the LBNP experiment. The triangles mark where the ratio of the respiratory power to the heart rate power is greater than 0.1. The heart rate power decreases and the respiratory power increases as the LBNP decreases pressure, sequestering more blood into the lower extremities.The ratio of the total respiratory power to the to-tal heart rate power was calculated for each win-dow:Ratio resp/h r = P resp_total / P hr_total .A simple threshold detector was implemented us-ing this ratio. From visual inspection, theRatio resp/hr > = 0.1yielded good sensitivity with few false alarms. 3. ResultsAs seen in Figure 2, an increase in the respi-ratory induced variation of the PPG (P resp_total ) and a simultaneous decrease in the power from the heart rate signal (P hr_total ) were observed. This allowed for the detection of simulated hypo-volemia from the forehead sensor while the LBNP pressure was between 70 and 90 mm Hg for both trials. Figure 3 and Figure 4 show the detector output superimposed on a graph of the power ratio. As seen in Figure 4, we observed significant, but transitory, increases in respiratory variations as early as 45 mm Hg in the second trial.However, because of the small number of subjects and the inconsistency between trials we could not at this time fully tune the threshold de-tector and statistically validate the results.4. DiscussionThis preliminary study yielded consistent re-sults with work from other groups regarding the respiratory induced variations.As seen in Figure 2 when the LBNP is in-creased two major morphological changes occur:(1) the amplitude of the cardiac cycle de-creases, and(2) the amplitude of the respiratory increase. We believe that this simultaneous increase in relative power of the respiratory component and decrease in the pulse amplitude is a good indica-tor of hypovolemia. 5. Future workThis study will be supplemented with further LBNP studies on a diverse, healthy population in the summer and fall of 2006. These experiments will allow us to tune the detector and statistically quantify the ratio parameter using ROC curves. 6. AcknowledgementSpecial thanks to Victor Convertino and Gary Muniz at ISR, Brooks Army Medical Center in San Antonio, Texas.This research program is a part of the Insti-tute for Security Technology Studies, supported under Award number 2000-DT-CX-K001 from the U.S. Department of Homeland Security, Sci-ence and Technology Directorate. Points of view in this document are those of the authors and do not necessarily represent the official position of the U.S. Department of Homeland Security or the Science and Technology Directorate.ReferencesCannesson, M., C. Besnard, et al. (2005). "Rela-tion between respiratory variations in pulse oximetry plethysmographic waveform ampli-tude and arterial pulse pressure in ventilated patients." Critical Care 9(5): R562-568. Cooke, W. H., K. L. Ryan, et al. (2004). "Lowerbody negative pressure as a model to study progression to acute hemorrhagic shock in humans." Journal of Applied Physiology 96(4): 1249-61.Gesquiere, M. J., A. A. Awad, et al. (2004). CanEar Plethysmography Detect Moderate Blood Loss in Healthy, Non-Intubated Vol-0.511.522.533.54Time (s)P o w e r r a t i o (r e s p i r a t i o n /H R )Figure 4. The ratio of the respiratory power to the heart rate power increases as the LBNP decreases pressure. The triangles indicate when this ratio exceeds 0.1. This threshold detector consistently detects simulated hypovolemia during LBNP >= 70 mmHg. This data was collected from the fore-head sensor in Trial 2.unteers? ASA Annual Meeting, Las Vegas,Nevada, Anesthesiology.Jablonka, D. H., A. A. Awad, et al. (2004). Ear Plethysmographic Changes during Hemodi-alysis. ASA Annual Meeting, Las Vegas,Nevada, Anesthesiology.Linder, S. P., S. Wendelken, et al. (2006). "Using the Morphology of PhotoplethysmogramPeaks to Detect Changes in Posture." J ClinMonit in press.Nilsson, L., A. Johansson, et al. (2003). "Macro-circulation is not the sole determinant of res-piratory induced variations in the reflectionmode photoplethysmographic signal."Physiol Meas24(4): 925-37.Partridge, B. L. (1987). "Use of pulse oximetry asa noninvasive indicator of intravascular vol-ume status." J Clin Monit3(4): 263-8. Shamir, M., L. A. Eidelman, et al. (1999). "Pulse oximetry plethysmographic waveform dur-ing changes in blood volume." Br J Anaesth82(2): 178-81.Shelley, K. H., R. G. Stout, et al. (1999). The use of joint time frequency analysis of the pulseoximeter waveform to measure the respira-tory rate of ventilated patients. Society forTechnology in Anesthesiology Annual Meet-ing, Anesthesiology.。
Slip-based tire-road friction estimation
Driver Assistance and Local Tra c Management in the Swedish RTI program `91-`94. Main sponsors are The Swedish Board for Industrial and Technical Development, AB Volvo, Saab-Scania AB and The Swedish National Road Administration.
We will here focus on the rst problem and brie y summarize the results of the tests. The basic assumption on a relation between slip and friction is in fact in contradiction to classical tire friction theory, but it has been supported in 5] and it is strongly con rmed in this work and empirical evidence will be presented. However, the di erence in slip on di erent surfaces is quite small, which might be the reason why this relation has not been discovered until recently. The lter used in 5] is based on a time average of one parameter. To compute this, another parameter needs to be estimated, which is done during free-rolling. The proposed lter is based on modern model based signal processing where both these parameters are estimated simultaneously and adaptively and no free-rolling is needed. Furthermore, a change detection algorithm is used for detecting and estimating abrupt changes quickly. Another major contribution here is a systematic test plan including many winter tests on ice and snow, which are two surfaces not tested at all in 5]. An introduction and summary of the project can be found in Section 2. The outline follows the signal ow depicted in Figure 1. The block labeled \Measuring" represents the metrology. The measured quantities are used to compute physical quantities in a straightforward manner as detailed in Appendix A. These quantities are in the next block used to lter out parameters related to the friction. The critical design of the lter is presented in Section 3. In the last block, the ltered parameters are used to classify what the friction is. To design a classi er a large number of tests is required, and the results of these are summarized in Section 4. Finally, conclusions are found in Section 5. Readers only interested in parameter estimation for this application can skip Section 4, while readers not familiar with estimation theory may skip Section 3, since Section 2 provides the context in both cases. A short version of this work was presented in 6].
一种利用历史数据构造虚拟标签的定位算法
计算机应用研究
Application Research of Computers
Vol.29 பைடு நூலகம்o.1 Jan. 2011
一种利用历史数据构造虚拟标签的定位算法
赵劲 1，高强 1，杨凯文 2，刘述 3
（1. 北京航空航天大学电子信息工程学院，北京，100191； 2. 华北计算机系统工程研究所，北京，100096； 3. 工业和信息化部电信研究院，北京，100045） 摘 要:在 RFID 定位算法中， 利用接收信号强度统计模型进行直接定位的精确度不高， 而利用实际参考标签定位存在信号易 碰撞、外出部署不便等问题，因此本文提出一种基于虚拟标签的 RFID 定位算法 VIREH 以克服以上缺点。该算法利用历史数 据构建虚拟参考标签，然后利用虚拟参考标签代替实际参考标签进行定位。依据 VIREH 算法，开发基于 Android 移动设备的 RFID 定位系统，在系统中使用 VIREH 算法进行定位，对定位误差进行了统计以测试算法性能。测试结果表明 VIREH 算法的 定位精度较直接定位有显著提高，较使用实际参考标签没有明显降低，有助于提高 RFID 定位精度。 关键词: 射频识别；虚拟参考标签；安卓定位系统 文献标识码: A 中图分类号: TN925.93 文章编号：
是平均值为 0 的高斯分布随机变数，即信号穿过障碍物产生 的随机衰减，其标准差范围为 4~10。利用该模型，通过带入 依据经验值得到的一组 Pr (d0 ) 与 d 0 ，直接计算得出目标标 签的位置信息，但这种定位方法由于使用单纯的理论模型， 受到的局限很大， 对于穿墙、 无线信号环境复杂等实际情况， 定位精度不高。 在使用实际参考标签进行定位的 LANDMARC 定位算法中 [2] ，利用离目标标签最近的一组实际参考标签进行定位。通 过比较读写器测得的目标标签场强值与实际参考标签场强 值的相对大小来对实际参考标签进行选取，这样的方法可以 有效的抵消环境因素的影响，特别是非视距的定位中，能够 有效地克服障碍物造成信号衰减不规律的问题。但是该方法 存在无法有效处理的、严重的多径效应，若想提高精度，则 需要增加参考标签部署的密度，这样容易带来标签之间发射 信号碰撞、干扰等问题。同时，在定位环境中部署参考标签， 会增加定位设备的成本，在定位环境改变后（如监狱人员外 出劳动等） ，重新部署参考标签会带来不便。 为了克服使用实际参考标签遇到的困难， Zhao Yiyang 等学者提出了通过建立虚拟标签来提高定位精度的定位算 [4][5] 法 。算法通过线性插值法等方法在相邻的两个实际的参 考标签之间来构造若干个虚拟参考标签，然后不同位置部署 的读写器可以利用虚拟参考标签求解出待定位标签的位置。
S501γ谱分析软件 Genie 2k
Genie 2000 Gamma Analysis Software■Peak Correlation – shows normalized peak correla-tion coefficient data resulting from a shape correlation analysis of the measured spectrum. A large correlation indicates a peak.■Peak Residuals – shows the difference in counts between the current peak area results and the measured spectrum.■Nuclide Residuals – shows the difference in counts between the current nuclide results for each peak and the measured spectrum.The residuals in each channel are scaled by the uncer-tainty in the spectrum at that channel:Efficiency CorrectionThe efficiency correction calculation involves calculat-ing a peak efficiency and its uncertainty for each of the found peaks. Genie 2000 supports three separate ef-ficiency calibration models (Figure 2): Dual, Linear, and Empirical. The coefficients of all three models are calcu-lated automatically at efficiency calibration time. A fourth interpolated plot method is also available which simply connects the various calibration spectrum data points to create a calibration curve. For efficiency correction in a sample, one has only to select which of the above mod-els to use; or the proper model can be included as part of an analysis sequence for standard analyses.(Measured Value – Theoretical Value / ( √(Measured Value) )Nuclide Identification and QuantificationThe nuclide identification algorithms take into account all energy lines of a nuclide entered into the analysis library with their proper branching ratios, as well as the half-life of the nuclide. For a positive identification, anuclide must have at least one gamma energy within the user-selected energy tolerance of an observed peak in the spectrum. Furthermore, a sufficient number of the other energies of the nuclide (if there are other energies) must also have matching peaks in the spectrum. The number of peaks that must be seen is determined by the sum of the branching ratios of the peaks with matching spectrum peaks as compared to the sum of the branch-ing ratios of the peaks without matching spectrum peaks. Finally, the decay time of the measured spectrum must not be excessive compared to the half-life of the nuclide.Nuclides that pass these tests with a confidence index greater than the user selected threshold will be classi-fied as identified. For all identified nuclides, the algorithm calculates the value of the confidence index and a decay corrected activity per unit volume (or mass) for each en-ergy with a matching peak in the spectrum. If applicable, the decay correction automatically includes a correction for decay during the acquisition as well as for decay from the sample date to the start of the acquisition. An additional decay correction is automatically provided for samples where the sample material is collected or accumulated over a finite period of time. Such samples include air filters, air cartridges and activation samples.Library Correlation NID Peak LocateThis peak locate analysis engine additionally provides tentative nuclide identification and activity estimation results. It is added to the peak locate selections in the Genie 2000 Acquisition and Analysis Analyze menu upon installation of the S501 Gamma Analysis Op-tion and is also available for use in the batch and S560 Programming Libraries environment. Benefits include the ability to accurately locate minor peaks, especially those hidden in multiplets, while also being able to correct cali-bration errors that might otherwise impair later nuclide identification analyses. This preliminary nuclide identifi-cation capability can be useful for applications in which a quick identification of the nuclides present in a sample is more important than a precise activity determination. The capabilities of this peak locate engine are designed for compatibility with the ANSI N42.342 standard.Figure 2 – Efficiency Calibration Curves.Genie 2000 Gamma Analysis SoftwareInterference Correction and Weighted Mean Activity CalculationsAfter standard nuclide identification, the spectrum can be analyzed for interference sets. An interference set is defined as two or more nuclides with at least one com-mon energy; that is, a peak that has not been resolved into separate peaks by the peak locate and area calcula-tion. The activities of such nuclides are calculated as a solution to a linear least squares equation.The algorithm automatically finds the nuclides that have interferences – no special library is required. However, energies for nuclides can be marked in the library for exclusion from weighted mean activity calculations. The activity of a nuclide which is not part of an interference set is automatically calculated as a weighted average of the activities calculated for each of its peaks. The activity of a nuclide with only one peak and no interference from other nuclides is automatically calculated from its peak. Factors taken into account in the weighting of the peak activities include efficiency, area and efficiency uncer-tainty, and the branching ratios of the various peaks. Parent/Daughter Decay CorrectionThe peaks observed in gamma spectra often originate from a nuclide’s ground state and its progeny. When the parent nuclide and its daughters are not in equilibrium, a correction must be applied to report proper nuclide activities. The Genie 2000 Gamma Analysis software in-cludes an algorithm which applies such correction based on sample time, acquisition start time, elapsed acquisi-tion live time and nuclide Parent/Daughter information contained in the nuclide library used for the analysis. Background Subtraction and Reference Peak CorrectionThe background subtract algorithm allows the subtrac-tion of environmental background peaks from sample spectra. The background spectrum must first be analyzed separately for its peak locations and associ-ated areas. At the time of execution, the background subtract algorithm automatically scales its results to be proportional to the acquisition time of the sample. An energy tolerance to match the peaks in the sample and background spectra can be specified by the user.The purpose of a reference peak correction is to use a reference peak in the spectrum (of a known count rate) to normalize the areas of all other peaks in the spectrum. The reference source can be either an electronic pulser or an external stationary source. Both a reference peak correction and an environmental background subtract can be applied to the same sample spectrum if desired.Minimum Detectable Activity (MDA) CalculationsA Minimum Detectable Activity (MDA) can be calculated for both the radionuclides which have and have not been found in the spectrum. The MDA algorithms can perform Currie MDA or KT A MDA (used for German regula-tory compliance) and LLD calculations. If applied to a spectrum collected with a blank sample, or in an empty shield, the MDA calculation is equivalent to a Lower Limit of Detection (LLD) calculation. The MDA confidence fac-tor and constants are user-selectable. Also, the user can elect to apply variable ROI widths and cascade summing corrections to the MDA calculations.True Coincidence (Cascade) Summing Correction CANBERRA’s unique and patented Cascade Summing Correction feature1 corrects against loss or gain of observ-able peak area as a function of nuclide decay scheme and geometry. T rue coincidence summing can cause systematic peak area errors of 30% or more with certain nuclides and geometries (Figure 3). The Genie 2000 Cascade Summing Correction method uses LabSOCS™ technology to pre-cisely describe the sample/detector geometry without the need for expensive and time consuming calibration using radioactive standards. A generic selection of germanium detector characterizations ensures that Cascade Summing Correction can be carried out for the majority of detector sizes without prior LabSOCS/ISOCS characterization. However, these characterizations, including more specific detector data, may be used if available.Figure 3 – Comparison of Cascade Corrected toEmpirical Efficiency.。
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J. Chang; E. Lieberman, P.E.; E. Prassas, Ph.D.
1
ABSTRACT Real-time traffic control policies often need to estimate queue lengths. Since the large installed base of computerized traffic control systems relies almost exclusively on a [generally sparse] system of loop detectors, estimation of queue length on an approach to a signalized intersection must depend upon “point” measures obtained from a detector. This paper describes a queue length estimation algorithm designed for use with a highly-responsive real-time signal control system. The algorithm requires only a single passage detector, set back from the stop-bar; it utilizes detector counts and occupancy, the kinematic properties of vehicles traveling on a signalized approach and knowledge of the [varying] signal state. This algorithm was designed to support the new RT/IMPOST on-line real-time control policy which is designed to responsively adjust signal timing to provide optimized service in congested (i.e. oversaturated) traffic environments. The WATSim microsimulation model was interfaced with the RT/IMPOST control policy containing this queue estimation algorithm to provide a test and evaluation platform. In this context, results obtained with the queue estimation algorithm operating on simulated detector data were compared with “actual” simulated queue lengths. These comparisons of estimated queue lengths with actual simulated queue lengths over a range of conditions are presented. Field deployment of this algorithm is anticipated in a few months in support of a field demonstration of the RT/IMPOST policy.
J. Chang; E. Lieberman, P.E.; E. Prassas, Ph.D.
2
INTRODUCTION Real-time traffic control policies often need to estimate queue lengths. Since the large installed base of computerized traffic control systems relies almost exclusively on a [generally sparse] system of loop detectors, estimation of queue length on an approach to a signalized intersection must depend upon “point” measures obtained from a detector. This paper describes a queue length estimation algorithm designed for use with a highly-responsive real-time signal control system. The algorithm requires only a single passage detector, set back from the stop-bar; it utilizes detector counts and occupancy, the kinematic properties of vehicles traveling on a signalized approach and knowledge of the [varying] signal state. This algorithm was designed to support the new RT/IMPOST on-line real-time control policy which is designed to responsively adjust signal timing to provide optimized service in congested (i.e. oversaturated) traffic environments. The WATSim microsimulation model was interfaced with the RT/IMPOST control policy containing this queue estimation algorithm to provide a test and evaluation platform. In this context, results obtained with the queue estimation algorithm operating on simulated detector data were compared with “actual” simulated queue lengths. These comparisons of estimated queue lengths with actual simulated queue lengths over a range of conditions are presented. Field deployment of this algorithm is anticipated in a few months in support of a field demonstration of the RT/IMPOST policy. Design Concepts The RT/IMPOST signal control policy relies upon accurate estimates of the standing queue length on each approach for each signal cycle. The standing queue is formed on an approach prior to the arrival of the lead vehicle of the primary (usually, through) incoming platoon. This standing queue is extended in length when the incoming platoon joins it to create the longer extended queue. The RT/IMPOST real time traffic control policy achieves its objectives by limiting this extended queue to avoid spill-back into the upstream intersection. When this extended queue discharges, it provides maximum productivity at the downstream intersection. The control policy adjusts signal timing based largely on knowledge of the standing queue length; also, it is able to predict the length of the extended queue only if it is provided an accurate estimate of the standing queue, as defined. The standing queue on an approach is composed of two groups of vehicles. The first group is from the “primary” flow which entered the approach during the green phase which services the through movement on the approach to the upstream intersection; the second group is the “secondary” flow which is turn-in traffic from the cross street approach(es) to the upstream intersection.