Journal articles: 'Z Algorithm'

1

Munakomi, Sunil, and Karuna Tamrakar. "Introducing ′A-Z′ algorithm for extubation." International Journal of Students� Research 4, no. 2 (2014): 56. http://dx.doi.org/10.4103/2230-7095.149784.

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2

Er, M. C. "Remark on “Algorithm 246: Graycode [Z]”." ACM Transactions on Mathematical Software 11, no. 4 (December 1985): 441–43. http://dx.doi.org/10.1145/6187.356154.

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3

Vassiliadis, S., M. Putrino, and E. Swartz. "Direct two's-complement algorithm forxy±z." Electronics Letters 23, no. 10 (May 7, 1987): 538–40. http://dx.doi.org/10.1049/el:19870388.

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4

Biswas, Siddhartha. "Z-Dijkstra’s Algorithm to solve Shortest Path Problem in a Z-Graph." Oriental journal of computer science and technology 10, no. 1 (March 23, 2017): 180–86. http://dx.doi.org/10.13005/ojcst/10.01.24.

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In this paper the author introduces the notion of Z-weighted graph or Z-graph in Graph Theory, considers the Shortest Path Problem (SPP) in a Z-graph. The classical Dijkstra’s algorithm to find the shortest path in graphs is not applicable to Z-graphs. Consequently the author proposes a new algorithm called by Z-Dijkstra's Algorithm with the philosophy of the classical Dijkstra's Algorithm to solve the SPP in a Z-graph.

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5

Benyahia, Ismahane, Abdesselam Bassou, Chems El Houda Allaoui, and Mohammed Beladgham. "Modified spiht algorithm for quincunx wavelet image coding." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 1 (October 1, 2019): 230. http://dx.doi.org/10.11591/ijeecs.v16.i1.pp230-242.

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<span lang="EN-US">In this paper, an image compression method based on the Quincunx algorithm coupled with the modified SPIHT encoder (called SPIHT-Z) is presented. The SPIHT-Z encoder (coupled with quincunx transform) provides better compression results compared with two other algorithms: conventional wavelet and quincunx both coupled with the SPIHT encoder. The obtained results, using the algorithm that applies (Quincunx with SPIHT-Z) are evaluated by image quality evaluation parameters (PSNR, MSSIM, and VIF). The compression results on twenty test images showed that the proposed algorithm achieved better levels of the image evaluation parameters at low bit rates.</span>

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Guo, Sha-sha, Jie-sheng Wang, and Meng-wei Guo. "Z-Shaped Transfer Functions for Binary Particle Swarm Optimization Algorithm." Computational Intelligence and Neuroscience 2020 (June 8, 2020): 1–21. http://dx.doi.org/10.1155/2020/6502807.

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Particle swarm optimization (PSO) algorithm is a swarm intelligent searching algorithm based on population that simulates the social behavior of birds, bees, or fish groups. The discrete binary particle swarm optimization (BPSO) algorithm maps the continuous search space to a binary space through a new transfer function, and the update process is designed to switch the position of the particles between 0 and 1 in the binary search space. Aiming at the existed BPSO algorithms which are easy to fall into the local optimum, a new Z-shaped probability transfer function is proposed to map the continuous search space to a binary space. By adopting nine typical benchmark functions, the proposed Z-probability transfer function and the V-shaped and S-shaped transfer functions are used to carry out the performance simulation experiments. The results show that the proposed Z-shaped probability transfer function improves the convergence speed and optimization accuracy of the BPSO algorithm.

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7

Renaud, C. "Fast Local and Global Illuminations Through a Simd Z-Buffer." International Journal of Pattern Recognition and Artificial Intelligence 11, no. 07 (November 1997): 1095–112. http://dx.doi.org/10.1142/s0218001497000500.

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The z-buffer is a well-known hidden-part removal technique commonly used by local illumination algorithms. Some global illumination approaches use this technique too, in order to approximate energy exchanges. In this paper we propose a massively parallel implementation of the z-buffer on the MP-1 machine. Efficiency is achieved by precisely studying the different stages of the algorithm, and by taking care in correctly using the SIMD control of the architecture. Local illumination models are then applied to the z-buffer algorithm, by using Gouraud and Phong's interpolations. Finally, the parallel z-buffer is used in a massively parallel radiosity algorithm. The results obtained allow to provide quickly illuminated images by decreasing dramatically the computation time required for global illumination.

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8

Aude, J. C., and A. Louis. "An incremental algorithm for Z-value computations." Computers & Chemistry 26, no. 5 (July 2002): 402–10. http://dx.doi.org/10.1016/s0097-8485(02)00003-7.

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9

Dear, R. G., and Y. S. Sherif. "Z-Basic algorithm for collision avoidance system." IEEE Transactions on Systems, Man, and Cybernetics 21, no. 4 (1991): 915–21. http://dx.doi.org/10.1109/21.108309.

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10

Boodoo, Sudesh, David Hudak, Alexander Ryzhkov, Pengfei Zhang, Norman Donaldson, David Sills, and Janti Reid. "Quantitative Precipitation Estimation from a C-Band Dual-Polarized Radar for the 8 July 2013 Flood in Toronto, Canada." Journal of Hydrometeorology 16, no. 5 (October 1, 2015): 2027–44. http://dx.doi.org/10.1175/jhm-d-15-0003.1.

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Abstract A heavy rainfall event over a 2-h period on 8 July 2013 caused significant flash flooding in the city of Toronto and produced 126 mm of rain accumulation at a gauge located near the Toronto Pearson International Airport. This paper evaluates the quantitative precipitation estimates from the nearby King City C-band dual-polarized radar (WKR). Horizontal reflectivity Z and differential reflectivity ZDR were corrected for attenuation using a modified ZPHI rain profiling algorithm, and rain rates R were calculated from R(Z) and R(Z, ZDR) algorithms. Specific differential phase KDP was used to compute rain rates from three R(KDP) algorithms, one modified to use positive and negative KDP, and an R(KDP, ZDR) algorithm. Additionally, specific attenuation at horizontal polarization A was used to calculate rates from the R(A) algorithm. High-temporal-resolution rain gauge data at 44 locations measured the surface rainfall every 5 min and produced total rainfall accumulations over the affected area. The nearby NEXRAD S-band dual-polarized radar at Buffalo, New York, provided rain-rate and storm accumulation estimates from R(Z) and S-band dual-polarimetric algorithm. These two datasets were used as references to evaluate the C-band estimates. Significant radome attenuation at WKR overshadowed the attenuation correction techniques and resulted in poor rainfall estimates from the R(Z) and R(Z, ZDR) algorithms. Rainfall estimation from the Brandes et al. R(KDP) and R(A) algorithms were superior to the other methods, and the derived storm total accumulation gave biases of 2.1 and −6.1 mm, respectively, with correlations of 0.94. The C-band estimates from the Brandes et al. R(KDP) and R(A) algorithms were comparable to the NEXRAD S-band estimates.

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11

Razim, Oleksandra, Stefano Cavuoti, Massimo Brescia, Giuseppe Riccio, Mara Salvato, and Giuseppe Longo. "Improving the reliability of photometric redshift with machine learning." Monthly Notices of the Royal Astronomical Society 507, no. 4 (August 13, 2021): 5034–52. http://dx.doi.org/10.1093/mnras/stab2334.

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ABSTRACT In order to answer the open questions of modern cosmology and galaxy evolution theory, robust algorithms for calculating photometric redshifts (photo-z) for very large samples of galaxies are needed. Correct estimation of the various photo-z algorithms’ performance requires attention to both the performance metrics and the data used for the estimation. In this work, we use the supervised machine learning algorithm MLPQNA (Multi-Layer Perceptron with Quasi-Newton Algorithm) to calculate photometric redshifts for the galaxies in the COSMOS2015 catalogue and the unsupervised Self-Organizing Maps (SOM) to determine the reliability of the resulting estimates. We find that for zspec < 1.2, MLPQNA photo-z predictions are on the same level of quality as spectral energy distribution fitting photo-z. We show that the SOM successfully detects unreliable zspec that cause biases in the estimation of the photo-z algorithms’ performance. Additionally, we use SOM to select the objects with reliable photo-z predictions. Our cleaning procedures allow us to extract the subset of objects for which the quality of the final photo-z catalogues is improved by a factor of 2, compared to the overall statistics.

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12

Baran, Mirosław. "On Rational Functions Related to Algorithms for a Computation of Roots. I." Science, Technology and Innovation 7, no. 4 (December 31, 2019): 17–25. http://dx.doi.org/10.5604/01.3001.0013.7274.

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We discuss a less known but surprising fact: a very old algorithm for computing square root known as the Bhaskara-Brouncker algorithm contains another and faster algorithms. A similar approach was obtained earlier by A.K. Yeyios [8] in 1992. By the way, we shall present a few useful facts as an essential completion of [8]. In particular, we present a direct proof that k-th Yeyios iterative algorithm is of order k. We also observe that Chebyshev polynomials Tn and Un are a special case of a more general construction. The most valuable idea followed this paper is contained in applications of a simple rational function Φ(w; z) = z-w/z+w.

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13

Futa, Yuichi, Hiroyuki Okazaki, Kazuhisa Nakasho, and Yasunari Shidama. "Torsion Z-module and Torsion-free Z-module." Formalized Mathematics 22, no. 4 (December 1, 2014): 277–89. http://dx.doi.org/10.2478/forma-2014-0028.

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Summary In this article, we formalize a torsion Z-module and a torsionfree Z-module. Especially, we prove formally that finitely generated torsion-free Z-modules are finite rank free. We also formalize properties related to rank of finite rank free Z-modules. The notion of Z-module is necessary for solving lattice problems, LLL (Lenstra, Lenstra, and Lov´asz) base reduction algorithm [20], cryptographic systems with lattice [21], and coding theory [11].

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14

Baran, Mirosław. "On Rational Functions Related to Algorithms for a Computation of Roots. II." Science, Technology and Innovation 7, no. 4 (December 31, 2019): 26–29. http://dx.doi.org/10.5604/01.3001.0013.7275.

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We discuss a nice composition properties related to algorithms for computation of N-roots. Our approach gives direct proof that a version of Newton's iterative algorithm is of order 2. A basic tool used in this note are properties of rational function Φ(w; z) = z-w/(z+w), which was used earlier in [1] in the case of algorithms for computations of square roots.

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15

Yang, Wei, and Chun Jin Wang. "Research on the Normal Error of Large Aspheric Mirror Employed in Bonnet Polishing." Advanced Materials Research 690-693 (May 2013): 3321–24. http://dx.doi.org/10.4028/www.scientific.net/amr.690-693.3321.

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The influence functions action orientation of the Bonnet Polishing (BP) process is perpendicular to the contact zone. So the normal error of the aspheric surface should be used as the residual error to calculate the dwell time. But the Z axis direction error is generally adopted as the residual error and few papers on the normal error have been reported hitherto. Its necessary to pay attention to this issue. In this paper, two algorithms which are Asphericity Subtraction (AS) algorithm and Z Axis Direction Error Transformation (ZADET) algorithm are presented to calculate the normal error of the large aspheric surface. Simulations in three different cases are organized to utilize these two algorithms, together with the comparison of them. And the comparison of the normal error and the Z axis direction error is also organized. Its found that there exists difference between AS algorithm and ZADET algorithm. Both of them can be used to calculate the normal error of the aspheric surface when the ratio value of the width to radius is small. And the difference between the normal error and Z axis direction error is considerable. So the normal error should be used as the residual error to calculate the dwell time in BP process.

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16

Feng, Fei, and Peng Zhang. "Implicit $\text{Z}_\text{bus}$ Gauss Algorithm Revisited." IEEE Transactions on Power Systems 35, no. 5 (September 2020): 4108–11. http://dx.doi.org/10.1109/tpwrs.2020.3000658.

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17

Schiff, J. L., T. J. Surendonk, and W. J. Walker. "An algorithm for computing the inverse Z transform." IEEE Transactions on Signal Processing 40, no. 9 (1992): 2194–98. http://dx.doi.org/10.1109/78.157219.

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18

EREN, MURAT, and WALTER T. HIGGINS. "A new algorithm for computing modified z-transforms." IMA Journal of Mathematical Control and Information 9, no. 2 (1992): 113–29. http://dx.doi.org/10.1093/imamci/9.2.113.

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19

Seo, Jin Keun, Kiwan Jeon, Chang-Ock Lee, and Eung Je Woo. "Non-iterative harmonic B z algorithm in MREIT." Inverse Problems 27, no. 8 (July 11, 2011): 085003. http://dx.doi.org/10.1088/0266-5611/27/8/085003.

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20

Inaba, H., M. Morii, and M. Kasahara. "Decoding algorithm of cyclic codes on Z-channel." Electronics Letters 25, no. 17 (1989): 1119. http://dx.doi.org/10.1049/el:19890751.

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21

Dyer, Scott, and Scott Whitman. "A Vectorized Scan-Line Z-Buffer Rendering Algorithm." IEEE Computer Graphics and Applications 7, no. 7 (July 1987): 34–45. http://dx.doi.org/10.1109/mcg.1987.277012.

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22

Vassiliadis, S., M. Putrino, and E. Schwarz. "Erratum: Direct two's-complement algorithm for XY ± Z." Electronics Letters 23, no. 14 (1987): 763. http://dx.doi.org/10.1049/el:19870540.

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23

Jana, R., A. K. Das, and A. Dutta. "On hidden Z-matrix and interior point algorithm." OPSEARCH 56, no. 4 (September 18, 2019): 1108–16. http://dx.doi.org/10.1007/s12597-019-00412-0.

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24

Chowdhury and, Anirban Narayan, and Rolando D. Somma. "Quantum algorithms for Gibbs sampling and hitting-time estimation." Quantum Information and Computation 17, no. 1&2 (January 2017): 41–64. http://dx.doi.org/10.26421/qic17.1-2-3.

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We present quantum algorithms for solving two problems regarding stochastic processes. The first algorithm prepares the thermal Gibbs state of a quantum system and runs in time almost linear in p Nβ/Z and polynomial in log(1/epsilon), where N is the Hilbert space dimension, β is the inverse temperature, Z is the partition function, and epsilon is the desired precision of the output state. Our quantum algorithm exponentially improves the complexity dependence on 1/epsilon and polynomially improves the dependence on β of known quantum algorithms for this problem. The second algorithm estimates the hitting time of a Markov chain. For a sparse stochastic matrix P, it runs in time almost linear in 1/(epsilon ∆3/2 ), where epsilon is the absolute precision in the estimation and ∆ is a parameter determined by P, and whose inverse is an upper bound of the hitting time. Our quantum algorithm quadratically improves the complexity dependence on 1/epsilon and 1/∆ of the analog classical algorithm for hitting-time estimation. Both algorithms use tools recently developed in the context of Hamiltonian simulation, spectral gap amplification, and solving linear systems of equations.

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25

Joglekar, Prafulla, Q. B. Chung, and Madjid Tavana. "Note on a comparative evaluation of nine well-known algorithms for solving the cell formation problem in group technology." Journal of Applied Mathematics and Decision Sciences 5, no. 4 (January 1, 2001): 253–68. http://dx.doi.org/10.1155/s1173912601000189.

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Over the last three decades, numerous algorithms have been proposed to solve the work-cell formation problem. For practicing manufacturing managers it would be nice to know as to which algorithm would be most effective and efficient for their specific situation. While several studies have attempted to fulfill this need, most have not resulted in any definitive recommendations and a better methodology of evaluation of cell formation algorithms is urgently needed. Prima facie, the methodology underlying Miltenburg and Zhang's (M&Z) (1991) evaluation of nine well-known cell formation algorithms seems very promising. The primary performance measure proposed by M&Z effectively captures the objectives of a good solution to a cell formation problem and is worthy of use in future studies. Unfortunately, a critical review of M&Z's methodology also reveals certain important flaws in M&Z's methodology. For example, M&Z may not have duplicated each algorithm precisely as the developer(s) of that algorithm intended. Second, M&Z's misrepresent Chandrasekharan and Rajagopalan's [C&R's] (1986) grouping efficiency measure. Third, M&Z's secondary performance measures lead them to unnecessarily ambivalent results. Fourth, several of M&Z's empirical conclusions can be theoretically deduced. It is hoped that future evaluations of cell formation algorithms will benefit from both the strengths and weaknesses of M&Z's work.

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26

Ouardi, Aissa, Ali Djebbari, and Boubakar Seddik Bouazza. "Optimal M-BCJR Turbo Decoding: The Z-MAP Algorithm." Wireless Engineering and Technology 02, no. 04 (2011): 230–34. http://dx.doi.org/10.4236/wet.2011.24031.

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27

Thelukuntla, Chandra Shekar, and Veerachary Mummadi. "Adaptive tuning algorithm for single‐phase Z‐source inverters." IET Power Electronics 10, no. 3 (March 2017): 302–12. http://dx.doi.org/10.1049/iet-pel.2015.0129.

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28

Rohrich, Rod J., and Ross I. S. Zbar. "A Simplified Algorithm for the Use of Z-Plasty." Plastic and Reconstructive Surgery 103, no. 5 (April 1999): 1513–17. http://dx.doi.org/10.1097/00006534-199904020-00024.

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Rohrich, Rod J., and Ross I. S. Zbar. "A Simplified Algorithm for the Use of Z-Plasty." Plastic and Reconstructive Surgery 103, no. 5 (April 1999): 1518. http://dx.doi.org/10.1097/00006534-199904020-00025.

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Rohrich, Rod J., and Ross I. S. Zbar. "A Simplified Algorithm for the Use of Z-Plasty." Plastic & Reconstructive Surgery 103, no. 5 (April 1999): 1513–18. http://dx.doi.org/10.1097/00006534-199904050-00024.

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31

Wise, D. Miller. "A SIMPLIFIED ALGORITHM FOR THE USE OF Z-PLASTY." Plastic and Reconstructive Surgery 104, no. 7 (December 1999): 2339. http://dx.doi.org/10.1097/00006534-199912000-00090.

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Rohrich, Rod J., and Ross I. S. Zbar. "A SIMPLIFIED ALGORITHM FOR THE USE OF Z-PLASTY." Plastic and Reconstructive Surgery 104, no. 7 (December 1999): 2339. http://dx.doi.org/10.1097/00006534-199912000-00091.

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33

Rabiner, L. "The chirp z-transform algorithm-a lesson in serendipity." IEEE Signal Processing Magazine 21, no. 2 (March 2004): 118–19. http://dx.doi.org/10.1109/msp.2004.1276120.

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34

Booth, Hilary S., John H. Maindonald, Susan R. Wilson, and Jill E. Gready. "An Efficient Z-Score Algorithm for Assessing Sequence Alignments." Journal of Computational Biology 11, no. 4 (August 2004): 616–25. http://dx.doi.org/10.1089/cmb.2004.11.616.

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35

Chen, Sandy H. L., William Y. C. Chen, Amy M. Fu, and Wenston J. T. Zang. "The Algorithm Z and Ramanujan’s 1 ψ 1 summation." Ramanujan Journal 25, no. 1 (May 1, 2010): 37–47. http://dx.doi.org/10.1007/s11139-010-9233-6.

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36

Barkmeijer, Jan. "The speed interval: a rotation algorithm for endomorphisms of the circle." Ergodic Theory and Dynamical Systems 8, no. 1 (March 1988): 17–33. http://dx.doi.org/10.1017/s0143385700004296.

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AbstractLetfbe a continuous map of the circle into itself of degree one. We introduce the notion of rotation algorithms. One of these algorithms associates eachz∈S1with an interval, the so-called speed intervalS(z,f), which is contained in the rotation interval ρ(f) off. In contrast with the rotation set ρ(z,f), the intervalS(z,f) sometimes allows us to ascertain that ρ(f) is non-degenerate, by using only finitely many elements of {fn(z) |n≥ 0}. We further show that all choices for ρ(z,f) andS(z,f) occur, for certainz∈S1provided that ρ(z,f) ⊂S(z,f) ⊂ ρ(f).

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37

Ross, Neil J., and Peter Selinger. "Optimal ancilla-free Clifford+T approximation of z-rotations." Quantum Information and Computation 16, no. 11&12 (September 2016): 901–53. http://dx.doi.org/10.26421/qic16.11-12-1.

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We consider the problem of approximating arbitrary single-qubit z-rotations by ancillafree Clifford+T circuits, up to given epsilon. We present a fast new probabilistic algorithm for solving this problem optimally, i.e., for finding the shortest possible circuit whatsoever for the given problem instance. The algorithm requires a factoring oracle (such as a quantum computer). Even in the absence of a factoring oracle, the algorithm is still near-optimal under a mild number-theoretic hypothesis. In this case, the algorithm finds a solution of T-count m + O(log(log(1/ε))), where m is the T-count of the second-to-optimal solution. In the typical case, this yields circuit approximations of Tcount 3 log2 (1/ε) + O(log(log(1/ε))). Our algorithm is efficient in practice, and provably efficient under the above-mentioned number-theoretic hypothesis, in the sense that its expected runtime is O(polylog(1/ε)).

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Devi, R. Manjula, P. Keerthika, P. Suresh, and M. Sangeetha. "A Z-Score Fuzzy Exponential Adaptive Skipping Training (Z-Feast) Algorithm for Efficient Pattern Classification." Asian Journal of Research in Social Sciences and Humanities 6, no. 11 (2016): 531. http://dx.doi.org/10.5958/2249-7315.2016.01211.9.

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Zhao, Xinchao, and Xiao-Shan Gao. "Affinity genetic algorithm." Journal of Heuristics 13, no. 2 (January 30, 2007): 133–50. http://dx.doi.org/10.1007/s10732-006-9005-z.

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40

Letichevsky, A. A., A. V. Lyaletski, and M. K. Morokhovets. "Glushkov’s evidence algorithm." Cybernetics and Systems Analysis 49, no. 4 (July 2013): 489–500. http://dx.doi.org/10.1007/s10559-013-9534-z.

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Lee, Wookey, Justin JongSu Song, Charles Cheolgi Lee, Tae-Chang Jo, and James J. H. Lee. "Graph threshold algorithm." Journal of Supercomputing 77, no. 9 (February 19, 2021): 9827–47. http://dx.doi.org/10.1007/s11227-021-03665-z.

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Brociek, Rafał, Agata Chmielowska, and Damian Słota. "Comparison of the Probabilistic Ant Colony Optimization Algorithm and Some Iteration Method in Application for Solving the Inverse Problem on Model With the Caputo Type Fractional Derivative." Entropy 22, no. 5 (May 15, 2020): 555. http://dx.doi.org/10.3390/e22050555.

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This paper presents the algorithms for solving the inverse problems on models with the fractional derivative. The presented algorithm is based on the Real Ant Colony Optimization algorithm. In this paper, the examples of the algorithm application for the inverse heat conduction problem on the model with the fractional derivative of the Caputo type is also presented. Based on those examples, the authors are comparing the proposed algorithm with the iteration method presented in the paper: Zhang, Z. An undetermined coefficient problem for a fractional diffusion equation. Inverse Probl. 2016, 32.

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Ding, Shifei, Chunyang Su, and Junzhao Yu. "An optimizing BP neural network algorithm based on genetic algorithm." Artificial Intelligence Review 36, no. 2 (February 18, 2011): 153–62. http://dx.doi.org/10.1007/s10462-011-9208-z.

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Frazier, David T. "A SIMPLE ITERATIVE Z-ESTIMATOR FOR SEMIPARAMETRIC MODELS." Econometric Theory 35, no. 1 (April 12, 2018): 111–41. http://dx.doi.org/10.1017/s0266466618000063.

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We propose a new iterative estimation algorithm for use in semiparametric models where calculation of Z-estimators by conventional means is difficult or impossible. Unlike a Newton–Raphson approach, which makes use of the entire Hessian, this approach only uses curvature information associated with portions of the Hessian that are relatively easy to calculate. Consistency and asymptotic normality of estimators obtained from this algorithm are established under regularity conditions and an information dominance condition. Two specific examples, a quantile regression model with missing covariates and a GARCH-in-mean model with conditional mean of unknown functional form, demonstrate the applicability of the algorithm. This new approach can be interpreted as an extension of the maximization by parts estimation approach to semiparametric models.

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Ross, Neil J. "Optimal ancilla-free Clifford+V approximation of z-rotations." Quantum Information and Computation 15, no. 11&12 (September 2015): 932–50. http://dx.doi.org/10.26421/qic15.11-12-4.

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We describe a new efficient algorithm to approximate $z$-rotations by ancilla-free Clifford+$V$ circuits, up to a given precision $\epsilon$. Our algorithm is optimal in the presence of an oracle for integer factoring: it outputs the shortest Clifford+$V$ circuit solving the given problem instance. In the absence of such an oracle, our algorithm is still near-optimal, producing circuits of $V$\!-count $m + O(\log(\log(1/\epsilon)))$, where $m$ is the $V$\!-count of the third-to-optimal solution. A restricted version of the algorithm approximates $z$-rotations in the Pauli+$V$ gate set. Our method is based on previous work by the author and Selinger on the optimal ancilla-free approximation of $z$-rotations using Clifford+$T$ gates and on previous work by Bocharov, Gurevich, and Svore on the asymptotically optimal ancilla-free approximation of $z$-rotations using Clifford+$V$ gates.

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Wu, Qing, Xudong Shen, Yuanzhe Jin, Zeyu Chen, Shuai Li, Ameer Hamza Khan, and Dechao Chen. "Intelligent Beetle Antennae Search for UAV Sensing and Avoidance of Obstacles." Sensors 19, no. 8 (April 12, 2019): 1758. http://dx.doi.org/10.3390/s19081758.

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Based on a bio-heuristic algorithm, this paper proposes a novel path planner called obstacle avoidance beetle antennae search (OABAS) algorithm, which is applied to the global path planning of unmanned aerial vehicles (UAVs). Compared with the previous bio-heuristic algorithms, the algorithm proposed in this paper has advantages of a wide search range and breakneck search speed, which resolves the contradictory requirements of the high computational complexity of the bio-heuristic algorithm and real-time path planning of UAVs. Besides, the constraints used by the proposed algorithm satisfy various characteristics of the path, such as shorter path length, maximum allowed turning angle, and obstacle avoidance. Ignoring the z-axis optimization by combining with the minimum threat surface (MTS), the resultant path meets the requirements of efficiency and safety. The effectiveness of the algorithm is substantiated by applying the proposed path planning algorithm on the UAVs. Moreover, comparisons with other existing algorithms further demonstrate the superiority of the proposed OABAS algorithm.

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47

van der Hoeven, Joris, and Michael Monagan. "Computing one billion roots using the tangent Graeffe method." ACM Communications in Computer Algebra 54, no. 3 (September 2020): 65–85. http://dx.doi.org/10.1145/3457341.3457342.

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Let p be a prime of the form p = σ2 k + 1 with σ small and let F p denote the finite field with p elements. Let P ( z ) be a polynomial of degree d in F p [ z ] with d distinct roots in F p . For p =5 · 2 55 + 1 we can compute the roots of such polynomials of degree 10 9 . We believe we are the first to factor such polynomials of size one billion. We used a multi-core computer with two 10 core Intel Xeon E5 2680 v2 CPUs and 128 gigabytes of RAM. The factorization takes just under 4,000 seconds on 10 cores and uses 121 gigabytes of RAM. We used the tangent Graeffe root finding algorithm from [27, 19] which is a factor of O (log d ) faster than the Cantor-Zassenhaus algorithm. We implemented the tangent Graeffe algorithm in C using our own library of 64 bit integer FFT based in-place polynomial algorithms then parallelized the FFT and main steps using Cilk C. In this article we discuss the steps of the tangent Graeffe algorithm, the sub-algorithms that we used, how we parallelized them, and how we organized the memory so we could factor a polynomial of degree 10 9 . We give both a theoretical and practical comparison of the tangent Graeffe algorithm with the Cantor-Zassenhaus algorithm for root finding. We improve the complexity of the tangent Graeffe algorithm by a factor of 2. We present a new in-place product tree multiplication algorithm that is fully parallelizable. We present some timings comparing our software with Magma's polynomial factorization command. Polynomial root finding over smooth finite fields is a key ingredient for algorithms for sparse polynomial interpolation that are based on geometric sequences. This application was also one of our main motivations for the present work.

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48

Wang, Wenxiu, Shuguo Pan, Jiawei Peng, Jian Shen, Min Zhang, Wang Gao, and Chenglin Xia. "Z-ADALINE based high-precision wide-frequency signal measurement algorithm for power electronic power grid." E3S Web of Conferences 185 (2020): 01041. http://dx.doi.org/10.1051/e3sconf/202018501041.

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Intermittent wind power, photovoltaic and other renewable energies have been paralleled, which makes the phenomena of high-order harmonics and simple harmonics more and more serious in the power system, showing a wide-frequency trend. The existing measurement algorithms mainly aim at signals in midfrequency and low-frequency. Besides, they are lack of a uniform high-precision algorithm for widefrequency measurement. To solve this problem, we propose a high-precision algorithm based on Z-ADALINE. Firstly, Zoom FFT algorithm is used to analyze original sampled signals. This step enables the refinement of its frequency spectrum, and obtains accurate frequency measurement results. At this time, the number of frequencies can also be determined. Secondly, the result of Zoom FFT is used as the input of the adaptive linear neural network(ADALINE). ADALINE can estimate amplitude and phase with high precision. The simulation results show that the proposed algorithm can realize high-precision measurement of frequency, amplitude and phase of wide-frequency signal effectively. Among them, the frequency resolution can be up to 0.3 Hz. The amplitude error is within 1V. Phase error is less than 0.6°. The results may provide some significant references for practical wide-frequency signal measurement in power electronic power grid.

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Grgić, Ivan, Dinko Vukadinović, Mateo Bašić, and Matija Bubalo. "Calculation of Semiconductor Power Losses of a Three-Phase Quasi-Z-Source Inverter." Electronics 9, no. 10 (October 6, 2020): 1642. http://dx.doi.org/10.3390/electronics9101642.

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This paper presents two novel algorithms for the calculation of semiconductor losses of a three-phase quasi-Z-source inverter (qZSI). The conduction and switching losses are calculated based on the output current-voltage characteristics and switching characteristics, respectively, which are provided by the semiconductor device manufacturer. The considered inverter has been operated in a stand-alone operation mode, whereby the sinusoidal pulse width modulation (SPWM) with injected 3rd harmonic has been implemented. The proposed algorithms calculate the losses of the insulated gate bipolar transistors (IGBTs) and the free-wheeling diodes in the inverter bridge, as well as the losses of the impedance network diode. The first considered algorithm requires the mean value of the inverter input voltage, the mean value of the impedance network inductor current, the peak value of the phase current, the modulation index, the duty cycle, and the phase angle between the fundamental output phase current and voltage. Its implementation is feasible only for the Z-source-related topologies with the SPWM. The second considered algorithm requires the instantaneous values of the inverter input voltage, the impedance network diode current, the impedance network inductor current, the phase current, and the duty cycle. However, it does not impose any limitations regarding the inverter topology or switching modulation strategy. The semiconductor losses calculated by the proposed algorithms were compared with the experimentally determined losses. Based on the comparison, the correction factor for the IGBT switching energies was determined so the errors of both the algorithms were reduced to less than 12%.

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50

Yang, Jian, Chang Liu, and Yanfei Wang. "Real-time DBS imaging algorithm based on chirp z-transform." IEICE Electronics Express 9, no. 21 (2012): 1660–65. http://dx.doi.org/10.1587/elex.9.1660.

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Journal articles on the topic 'Z Algorithm'

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