Journal articles: 'Count-algorithm'

1

Swarnkar, Mayank. "Count based Secured Hash Algorithm." IOSR Journal of Computer Engineering 6, no. 6 (2012): 49–51. http://dx.doi.org/10.9790/0661-0664951.

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Gosset, David, Vadym Kliuchnikov, Michele Mosca, and Vincent Russo. "An algorithm for the T-countAn algorithm for the T-count." Quantum Information and Computation 14, no. 15&16 (November 2014): 1261–76. http://dx.doi.org/10.26421/qic14.15-16-1.

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We consider quantum circuits composed of Clifford and $T$ gates. In this context the $T$ gate has a special status since it confers universal computation when added to the (classically simulable) Clifford gates. However it can be very expensive to implement fault-tolerantly. We therefore view this gate as a resource which should be used only when necessary. Given an $n$-qubit unitary $U$ we are interested in computing a circuit that implements it using the minimum possible number of $T$ gates (called the $T$-count of $U$). A related task is to decide if the $T$-count of $U$ is less than or equal to $m$; we consider this problem as a function of $N=2^n$ and $m$. We provide a classical algorithm which solves it using time and space both upper bounded as $\mathcal{O}(N^m \text{poly}(m,N))$. We implemented our algorithm and used it to show that any Clifford+T circuit for the Toffoli or the Fredkin gate requires at least 7 $T$ gates. This implies that the known 7 $T$ gate circuits for these gates are $T$-optimal. We also provide a simple expression for the $T$-count of single-qubit unitaries.

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Zhang, Haining, and Huanbo Gao. "Large Crowd Count Based on Improved SURF Algorithm." TELKOMNIKA (Telecommunication Computing Electronics and Control) 12, no. 4 (December 1, 2014): 865. http://dx.doi.org/10.12928/telkomnika.v12i4.362.

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4

McLachlan, G. J. "On the EM algorithm for overdispersed count data." Statistical Methods in Medical Research 6, no. 1 (February 1997): 76–98. http://dx.doi.org/10.1177/096228029700600106.

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McLachlan, G. "On the EM algorithm for overdispersed count data." Statistical Methods in Medical Research 6, no. 1 (January 1, 1997): 76–98. http://dx.doi.org/10.1191/096228097666670466.

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Yusifli, Elmar, Reda Yahiaoui, Isabelle Lajoie, and Salahaddin Yusifov. "Embedded Cell Count Algorithm for Cultured Tissue Characterization." IFAC-PapersOnLine 51, no. 30 (2018): 683–87. http://dx.doi.org/10.1016/j.ifacol.2018.11.221.

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7

Arandjelović, V. Dj, A. M. Koturović, and R. B. Vukanović. "Preset count moving average digital algorithm with faster response to an increasing count rate." Review of Scientific Instruments 70, no. 9 (September 1999): 3765–69. http://dx.doi.org/10.1063/1.1149990.

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8

Freire, Paulo C. C., and Alessandro Ridolfi. "An algorithm for determining the rotation count of pulsars." Monthly Notices of the Royal Astronomical Society 476, no. 4 (February 27, 2018): 4794–805. http://dx.doi.org/10.1093/mnras/sty524.

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9

Klinger-Strobel, Mareike, Herbert Suesse, Dagmar Fischer, Mathias W. Pletz, and Oliwia Makarewicz. "A Novel Computerized Cell Count Algorithm for Biofilm Analysis." PLOS ONE 11, no. 5 (May 5, 2016): e0154937. http://dx.doi.org/10.1371/journal.pone.0154937.

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10

Arandjelovic, Vojislav, and Ðordje Saponjic. "The error estimate for the modified preset count ratemeter algorithm." Facta universitatis - series: Electronics and Energetics 18, no. 3 (2005): 421–29. http://dx.doi.org/10.2298/fuee0503421a.

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Analogously to the traditional preset count rate meter algorithm, the error estimate of the modified preset count ratemeter algorithm has been defined as the range around measured mean count rate containing 2/3 of the measurement results. The coefficients determining this range have been calculated for a set of values of the modification parameter (m) and preset count (NT). The calculations cover the ranges of these parameters which are of practical interest (m 0 25 1 5 NT 12 150). By applying the piecewise-linear interpolation scheme it is possible to calculate measurement error for any pair m NT within the ranges of these parameters covered by the present calculations.

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11

Zigic, Aleksandar. "Preset time count rate meter using adaptive digital signal processing." Nuclear Technology and Radiation Protection 20, no. 1 (2005): 64–73. http://dx.doi.org/10.2298/ntrp0501064z.

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Two presented methods were developed to improve classical preset time count rate meters by using adapt able signal processing tools. An optimized detection algorithm that senses the change of mean count rate was implemented in both methods. Three low-pass filters of various structures with adaptable parameters to implement the control of the mean count rate error by suppressing the fluctuations in a controllable way, were considered and one of them implemented in both methods. An adaptation algorithm for preset time interval calculation executed after the low-pass filter was devised and implemented in the first method. This adaptation algorithm makes it possible to obtain shorter preset time intervals for higher stationary mean count rate. The adaptation algorithm for preset time interval calculation executed before the low-pass filter was devised and implemented in the second method. That adaptation algorithm enables sensing of a rapid change of the mean count rate before fluctuations suppression is carried out. Some parameters were fixed to their optimum values after appropriate optimization procedure. Low-pass filters have variable number of stationary coefficients depending on the specified error and the mean count rate. They implement control of the mean count rate error by suppressing fluctuations in a controllable way. The simulated and realized methods, using the developed algorithms, guarantee that the response time shall not exceed 2 s for the mean count rate higher than 2 s-1 and that controllable mean count rate error shall be within the range of ?4% to ?10%.

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12

Logeshwari, P. "Extraction of Subset-Count in Data Streams using EMDMICA Algorithm." International Journal for Research in Applied Science and Engineering Technology 7, no. 6 (June 30, 2019): 2548–52. http://dx.doi.org/10.22214/ijraset.2019.6431.

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13

Chen, Pan-Pan, Wei-Dang Zhang, and Zhong-Rui Li. "On The Lash Bar Count Algorithm Based on Image Recognition." ITM Web of Conferences 7 (2016): 02009. http://dx.doi.org/10.1051/itmconf/20160702009.

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14

Bangari, Keerthi, Sujitha Meduri, and CY Rao. "Implementation of Word Count- Hadoop Framework with Map Reduce Algorithm." International Journal of Computer Trends and Technology 49, no. 3 (July 25, 2017): 179–82. http://dx.doi.org/10.14445/22312803/ijctt-v49p128.

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15

Fong, Daniel Y. T., and Paul Yip. "An EM algorithm for a mixture model of count data." Statistics & Probability Letters 17, no. 1 (May 1993): 53–60. http://dx.doi.org/10.1016/0167-7152(93)90195-o.

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16

Wanxie, Zhong, F. W. Williams, and P. N. Bennett. "Extension of the Wittrick-Williams Algorithm to Mixed Variable Systems." Journal of Vibration and Acoustics 119, no. 3 (July 1, 1997): 334–40. http://dx.doi.org/10.1115/1.2889728.

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A precise integration algorithm has recently been proposed by Zhong (1994) for dynamic stiffness matrix computations, but he did not give a corresponding eigenvalue count method. The Wittrick-Williams algorithm gives an eigenvalue count method for pure displacement formulations, but the precise integration method uses a mixed variable formulation. Therefore the Wittrick-Williams method is extended in this paper to give the eigenvalue count needed by the precise integration method and by other methods involving mixed variable formulations. A simple Timoshenko beam example is included.

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17

Dybus, Aleksandra, Lorna Paul, Sally Wyke, Stephen Brewster, Jason M. R. Gill, Andrew Ramsay, and Evan Campbell. "Validation of smartphone step count algorithm used in STARFISH smartphone application." Technology and Health Care 25, no. 6 (December 4, 2017): 1157–62. http://dx.doi.org/10.3233/thc-170970.

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18

JAMBHULKAR, NITIPRASAD, and LOTAN KUMAR BOSE. "New algorithm for multiple pattern matching using least count of pattern." INTERNATIONAL JOURNAL OF AGRICULTURAL SCIENCES 11, no. 2 (June 15, 2015): 330–36. http://dx.doi.org/10.15740/has/ijas/11.2/330-336.

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19

Kendig, R. P. "An algorithm to count fatigue cycles in an arbitrary loading waveform." Journal of the Acoustical Society of America 101, no. 5 (May 1997): 3086. http://dx.doi.org/10.1121/1.418809.

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20

Zhao, Lu, Zhimin Liu, Sasha F. Levy, and Song Wu. "Bartender: a fast and accurate clustering algorithm to count barcode reads." Bioinformatics 34, no. 5 (October 23, 2017): 739–47. http://dx.doi.org/10.1093/bioinformatics/btx655.

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21

Choubey, Siddharth, and G. R. Sinha. "License Plate Localization Using Novel Recursive Algorithm And Pixel Count Method." i-manager's Journal on Embedded Systems 2, no. 2 (July 15, 2013): 6–16. http://dx.doi.org/10.26634/jes.2.2.2371.

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22

Cho, Kang-Hong, and Nam-Hoon Kim. "LSU Message Count Controlled Link State Update Algorithm in QoS Routin." Journal of the Korea Society of Computer and Information 17, no. 6 (June 30, 2012): 75–81. http://dx.doi.org/10.9708/jksci.2012.17.6.075.

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23

PETROSKA, D., M. PAJAUJIS, G. LEVINAITE, R. RIAUKA, and R. ASOKLIS. "Digital analysis of the trabecular pigmentation using Positive Pixel Count algorithm." Acta Ophthalmologica 89, s248 (September 2011): 0. http://dx.doi.org/10.1111/j.1755-3768.2011.235.x.

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24

Huang, Hsuan-Ming, Chih-Chieh Liu, Meei-Ling Jan, and Ming-Wei Lee. "A low-count reconstruction algorithm for Compton-based prompt gamma imaging." Physics in Medicine & Biology 63, no. 8 (April 19, 2018): 085013. http://dx.doi.org/10.1088/1361-6560/aab737.

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25

Chang, Yuhsin, and Chyong-Hua Chen. "Effective algorithm for high-channel-count multichannel fiber Bragg grating designs." Applied Optics 51, no. 25 (August 22, 2012): 5952. http://dx.doi.org/10.1364/ao.51.005952.

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26

C, Hema, Sharmila Sankar, and Sandhya M. "Tag count based priority scheduling algorithm for mitigating the RFID collisions." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 1 (April 1, 2020): 434. http://dx.doi.org/10.11591/ijeecs.v18.i1.pp434-442.

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<p><span>RFID (Radio Frequency IDentification) is a developing technology that employs electromagnetic signals to send the data among reader and tags to identify categorize and track the moveable items. The objective of this paper is to mitigate the reader collision problem by scheduling the readers in RFID system. Mobile readers in the RFID network send the same frequency signal to the air to read the data from the tags. While these two signals interfere each other and Tags are unable to backscatter signal to the reader. This causes a reader collision problem. The Reader collision problem reduces the lifetime of the RFID network and generates redundant data in the RFID Network. Tag Count based Priority Scheduling algorithm is proposed, that enhances the throughput of the readers and mitigates the reader collision problem. In the cluster based RFID network, The Dragonfly algorithm performed the Cluster Head reader election and cluster construction process and then allotting the mobile readers in the cluster. This algorithm improves the energy efficiency and diminishes the reader collision problem, thereby alleviating the tag information loss and expanding the mobile RFID network life time, while compared with Priority Clustering Protocol and the Graph Coloring based TDMA algorithm.</span></p>

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27

Gamess, Eric, and Manuel Contreras. "A proposal for an algorithm to count nodes using wireless technologies." International Journal of High Performance Computing and Networking 8, no. 4 (2015): 345. http://dx.doi.org/10.1504/ijhpcn.2015.072786.

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28

Gurung, Sarita, A. K. M. Mahtab Hossain, and Kanchana Kanchanasut. "A hop-count based positioning algorithm for wireless ad-hoc networks." Wireless Networks 20, no. 6 (January 10, 2014): 1431–44. http://dx.doi.org/10.1007/s11276-013-0685-7.

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29

Orouskhani, Maysam, Mohammad Teshnehlab, and Mohammad Ali Nekoui. "Evolutionary dynamic multi-objective optimization algorithm based on Borda count method." International Journal of Machine Learning and Cybernetics 10, no. 8 (May 23, 2017): 1931–59. http://dx.doi.org/10.1007/s13042-017-0695-3.

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30

Chakraborty, Soubhik, and Kiran Kumar Sundararajan. "Winograd’s algorithm statistically revisited: It pays to weigh than to count!" Applied Mathematics and Computation 190, no. 1 (July 2007): 15–20. http://dx.doi.org/10.1016/j.amc.2007.01.018.

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31

Hammad, M. E., H. Kasban, R. M. Fikry, Moawad I. Dessoky, O. Zahran, Sayed M. S. Elaraby, and Fathi E. Abd El-Samie. "Pile-up correction algorithm for high count rate gamma ray spectroscopy." Applied Radiation and Isotopes 151 (September 2019): 196–206. http://dx.doi.org/10.1016/j.apradiso.2019.06.003.

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32

Oliveira, Luciano Teixeira de, Luis Marcelo Tavares de Carvalho, Maria Zélia Ferreira, Thomaz Chaves de Andrade Oliveira, and Fausto Weimar Acerbi Junior. "Application of LIDAR to forest inventory for tree count in stands of Eucalyptus sp." CERNE 18, no. 2 (June 2012): 175–84. http://dx.doi.org/10.1590/s0104-77602012000200001.

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Light Detection and Ranging, or LIDAR, has become an effective ancillary tool to extract forest inventory data and for use in other forest studies. This work was aimed at establishing an effective methodology for using LIDAR for tree count in a stand of Eucalyptus sp. located in southern Bahia state. Information provided includes in-flight gross data processing to final tree count. Intermediate processing steps are of critical importance to the quality of results and include the following stages: organizing point clouds, creating a canopy surface model (CSM) through TIN and IDW interpolation and final automated tree count with a local maximum algorithm with 5 x 5 and 3 x 3 windows. Results were checked against manual tree count using Quickbird images, for verification of accuracy. Tree count using IDW interpolation with a 5x5 window for the count algorithm was found to be accurate to 97.36%. This result demonstrates the effectiveness of the methodology and its use potential for future applications.

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33

Zhang, Ying, Yi Wang, and Ying Ze Ye. "Wireless Sensor Network Localization Algorithm Based on Hop-Count and Distributed Learning." Advanced Materials Research 940 (June 2014): 457–60. http://dx.doi.org/10.4028/www.scientific.net/amr.940.457.

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The wireless sensor network localization algorithm in this paper combines hop-count information and distributed learning. The network is classified into many classes based on sensors’ location, and then the class that each sensor falls into is specified. There are a certain number of beacon nodes with position coordinate in network, and they use their own locations as training data in performing above classification. This positioning method merely uses the partial hop-count information between target sensor and reference node in specifying the class of each node. The final simulation experiment will analyze the excellent performance of this method under different system parameters.

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Kaushal, Puneet Kumar, and Rajeev Sobti. "Breaching Security of Full Round Tiny Encryption Algorithm." International Journal of Information Security and Privacy 12, no. 1 (January 2018): 89–98. http://dx.doi.org/10.4018/ijisp.2018010108.

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Tiny encryption algorithm is a 64-bit block cipher designed by Wheeler and Needham in 1994 and attracted much of its attention due to its capability of reducing the hardware cost. In this paper, we introduced coincidence count attack at bit level, a kind of known-plaintext attack and evaluated the resistance of TEA to withstand with it. We also examined confrontation of full round TEA against bit sum attack. Furthermore, we introduced a modest algorithm based on coincidence count and bit sum concept that makes it easy to find relevant plaintext corresponding to an arbitrary cipher text with a probability of 0.93. We also presented how cipher text originated from tiny encryption algorithm can be distinguished from a random permutation of binary sequence.

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Ummels, Darcy, Wouter Bijnens, Jos Aarts, Kenneth Meijer, Anna J. Beurskens, and Emmylou Beekman. "The Validation of a Pocket Worn Activity Tracker for Step Count and Physical Behavior in Older Adults during Simulated Activities of Daily Living." Gerontology and Geriatric Medicine 6 (January 2020): 233372142095173. http://dx.doi.org/10.1177/2333721420951732.

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Purpose: The purpose of this study was to validate optimized algorithm parameter settings for step count and physical behavior for a pocket worn activity tracker in older adults during ADL. Secondly, for a more relevant interpretation of the results, the performance of the optimized algorithm was compared to three reference applications Methods: In a cross-sectional validation study, 20 older adults performed an activity protocol based on ADL with MOXMissActivity versus MOXAnnegarn, activPAL, and Fitbit. The protocol was video recorded and analyzed for step count and dynamic, standing, and sedentary time. Validity was assessed by percentage error (PE), absolute percentage error (APE), Bland-Altman plots and correlation coefficients. Results: For step count, the optimized algorithm had a mean APE of 9.3% and a correlation coefficient of 0.88. The mean APE values of dynamic, standing, and sedentary time were 15.9%, 19.9%, and 9.6%, respectively. The correlation coefficients were 0.55, 0.91, and 0.92, respectively. Three reference applications showed higher errors and lower correlations for all outcome variables. Conclusion: This study showed that the optimized algorithm parameter settings can more validly estimate step count and physical behavior in older adults wearing an activity tracker in the trouser pocket during ADL compared to reference applications.

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Rivera, Redden Rose, and Allan Soriano. "Prediction of Density and Speed of Sound of Binary Ionic Liquid and Ketone Mixtures Using Artificial Neural Network." E3S Web of Conferences 120 (2019): 01003. http://dx.doi.org/10.1051/e3sconf/201912001003.

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The applications of ionic liquids solve a lot of major problems regarding green energy production and environment. Ionic liquids are solvents used as alternative to unfriendly traditional and hazardous solvents which reduces the negative impact to environment to a great extent. This study produced models to predict two of the basic physical properties of binary ionic liquid and ketone mixtures: density and speed of sound. The artificial neural network algorithm was used to predict these properties by varying the temperature, mole fraction, atom count in cation, methyl group count in cation, atom count in anion, hydrogen atom count in anion of ionic liquid and atom count in ketone. Total experimental data points of 2517 for density and 947 for speed of sound were used to train the algorithm and to test the network obtained. The optimum neural network structure determined for density and speed of sound of binary ionic liquid and ketone mixtures were 7-9-9-1 and 7-7-4-1 respectively; overall average percentage error of 2.45% and 2.17% respectively; and mean absolute error of 28.21 kg/m3 and 33.91 m/s respectively. The said algorithm was found applicable for the prediction of density and speed of sound of binary ionic liquid and ketone mixtures.

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37

Kobayashi, Atsushi, Masaki Sekine, Toshiyo Tamura, Hiroaki Miyoshi, Seiji Yorita, and Keitarou Hanada. "Development of new step count algorithm without positioning of the step counter." Journal of Life Support Engineering 20, Supplement (2008): 128. http://dx.doi.org/10.5136/lifesupport.20.supplement_128.

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38

Contreras, Manuel, and Eric Gamess. "An Algorithm to Count Nodes in Wireless Networks Using their Actual Position." International Journal of Information Technology and Computer Science 8, no. 7 (July 8, 2016): 43–52. http://dx.doi.org/10.5815/ijitcs.2016.07.07.

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39

Lim, In-Taek. "Weight Decision Scheme based on Slot-Count in Gen-2 Q-Algorithm." Journal of information and communication convergence engineering 9, no. 2 (April 30, 2011): 172–76. http://dx.doi.org/10.6109/jicce.2011.9.2.172.

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40

Tiwari, Mosmi, Amrita Tiwari, and Deepak Sukheja. "Modified Hop Count Analysis Algorithm (MHCAA) for Preventing Wormhole Attack in WSN." Communications on Applied Electronics 3, no. 3 (October 23, 2015): 6–10. http://dx.doi.org/10.5120/cae2015651896.

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41

Koor, M., A. Vassiljev, and T. Koppel. "Optimal Pump Count Prediction Algorithm for Identical Pumps Working in Parallel Mode." Procedia Engineering 70 (2014): 951–58. http://dx.doi.org/10.1016/j.proeng.2014.02.106.

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42

Contreras, Manuel. "A Multi-interface Multi-channel Algorithm to Count Nodes Using Wireless Technology." American Journal of Networks and Communications 6, no. 1 (2017): 1. http://dx.doi.org/10.11648/j.ajnc.20170601.11.

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43

Atmaja, Ratri Dwi, Junartho Halomoan, Muhammad Ary Murti, and Fiky Yosef Suratman. "AN ALGORITHM TO COUNT A PASSING OBJECT BASED ON VIDEO SIGNAL PROCESSING." Far East Journal of Electronics and Communications 17, no. 1 (March 7, 2017): 141–46. http://dx.doi.org/10.17654/ec017010141.

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44

Park, Min Ho, Jin Seek Choi, and Jeonghoon Mo. "A dynamic hop count shifting RWA algorithm for wavelength routed optical networks." Photonic Network Communications 16, no. 1 (March 4, 2008): 1–9. http://dx.doi.org/10.1007/s11107-008-0112-7.

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Torres-Jimenez, Jose, Idelfonso Izquierdo-Marquez, Daniel Ramirez-Acuna, and Rene Peralta. "Near-optimal algorithm to count occurrences of subsequences of a given length." Discrete Mathematics, Algorithms and Applications 09, no. 03 (May 29, 2017): 1750042. http://dx.doi.org/10.1142/s1793830917500422.

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For [Formula: see text], define [Formula: see text] as the set of integers [Formula: see text]. Given an integer [Formula: see text] and a string [Formula: see text] of length [Formula: see text] over [Formula: see text], we count the number of times that each one of the [Formula: see text] distinct strings of length [Formula: see text] over [Formula: see text] occurs as a subsequence of [Formula: see text]. Our algorithm makes only one scan of [Formula: see text] and solves the problem in time complexity [Formula: see text] and space complexity [Formula: see text]. These are very close to best possible.

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Liu, B., M. Kalinowski, and T. Muramoto. "Enhancements on the decision threshold algorithm of the net count calculation method." Applied Radiation and Isotopes 159 (May 2020): 109084. http://dx.doi.org/10.1016/j.apradiso.2020.109084.

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Stavrov, Andrey. "New Algorithm for Upgrading of Portal Radiation Monitors with Plastic Detectors." ANRI, no. 1 (March 4, 2021): 3–12. http://dx.doi.org/10.37414/2075-1338-2021-104-1-3-12.

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The new algorithm for portal radiation monitors with plastic detectors is discussed in the paper. It is based on optimization of the value of smoothing for count rate profiles, alarming thresholds and so called energy windows. Implementation of this algorithm provides increasing of probability of detection of the radioactive sources including those which are masked by NORM under suppression of natural background. The verification of radioactive sources and NORM on the base of analysis of count rate profiles for point and distributed sources is discussed in the first part.

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Sharma, Prerna, Moolchand Sharma, Divij Gupta, and Nimisha Mittal. "Detection of white blood cells using optimized qGWO." Intelligent Decision Technologies 15, no. 1 (March 24, 2021): 141–49. http://dx.doi.org/10.3233/idt-200055.

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This paper presents an optimized quantum Grey Wolf Optimization algorithm (qGWO), which is an enhanced version of the Grey Wolf optimization algorithm for feature selection of blood cells, which can further used for the detection of WBCs. White blood cells count in the human body determines the immune system of the human body. A deviation in the count of WBCs from the normal cell count in the human body may indicate an abnormal condition. The proposed model uses a quantum grey wolf optimization algorithm for the detection of White Blood cells among the dataset of various types of blood cells. Quantum Grey Wolf algorithm is used to find the minimal set of optimal features from the set of available features to detect the White Blood Cells optimally. As the ordinary Grey Wolf Optimization algorithm also used to find the minimal set of optimal features, but the features selected by qGWO are better in terms of computational time. Further, several classification algorithms such as Support Vector Machine (SVM), Random Forest algorithm, K Nearest Neighbor(KNN) algorithm are applied to the model to predict its accuracy for the selected subset of features after feature selection. The performance of several classifiers is compared, and the model attained the maximum accuracy of 97.8% using KNN with minimum computational time. The result obtained shows that the algorithm proposed is capable of finding an optimal subset of features and maximizing the accuracy.

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49

Selinger, Peter. "Efficient Clifford+T approximation of single-qubit operators." Quantum Information and Computation 15, no. 1&2 (January 2015): 159–80. http://dx.doi.org/10.26421/qic15.1-2-10.

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We give an efficient randomized algorithm for approximating an arbitrary element of $\SU(2)$ by a product of Clifford+$T$ operators, up to any given error threshold $\epsilon>0$. Under a mild hypothesis on the distribution of primes, the algorithm's expected runtime is polynomial in $\log(1/\epsilon)$. If the operator to be approximated is a $z$-rotation, the resulting gate sequence has $T$-count $K+4\log_2(1/\epsilon)$, where $K$ is approximately equal to $10$. We also prove a worst-case lower bound of $K+4\log_2(1/\epsilon)$, where $K=-9$, so that our algorithm is within an additive constant of optimal for certain $z$-rotations. For an arbitrary member of $\SU(2)$, we achieve approximations with $T$-count $K+12\log_2(1/\epsilon)$. By contrast, the Solovay-Kitaev algorithm achieves $T$-count $O(\log^c(1/\epsilon))$, where $c$ is approximately $3.97$.

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Vettoretti, Martina, and Barbara Di Camillo. "A Variable Ranking Method for Machine Learning Models with Correlated Features: In-Silico Validation and Application for Diabetes Prediction." Applied Sciences 11, no. 16 (August 23, 2021): 7740. http://dx.doi.org/10.3390/app11167740.

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When building a predictive model for predicting a clinical outcome using machine learning techniques, the model developers are often interested in ranking the features according to their predictive ability. A commonly used approach to obtain a robust variable ranking is to apply recursive feature elimination (RFE) on multiple resamplings of the training set and then to aggregate the ranking results using the Borda count method. However, the presence of highly correlated features in the training set can deteriorate the ranking performance. In this work, we propose a variant of the method based on RFE and Borda count that takes into account the correlation between variables during the ranking procedure in order to improve the ranking performance in the presence of highly correlated features. The proposed algorithm is tested on simulated datasets in which the true variable importance is known and compared to the standard RFE-Borda count method. According to the root mean square error between the estimated rank and the true (i.e., simulated) feature importance, the proposed algorithm overcomes the standard RFE-Borda count method. Finally, the proposed algorithm is applied to a case study related to the development of a predictive model of type 2 diabetes onset.

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Journal articles on the topic 'Count-algorithm'

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