Academic literature on the topic 'Low computational complexity algorithms'
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Journal articles on the topic "Low computational complexity algorithms"
Zhang, Xinhe, Wenbo Lv, and Haoran Tan. "Low-Complexity GSM Detection Based on Maximum Ratio Combining." Future Internet 14, no. 5 (May 23, 2022): 159. http://dx.doi.org/10.3390/fi14050159.
Full textKharlampovich, Olga, and Alina Vdovina. "Low complexity algorithms in knot theory." International Journal of Algebra and Computation 29, no. 02 (March 2019): 245–62. http://dx.doi.org/10.1142/s0218196718500698.
Full textChen, Haihua, Jialiang Hu, Hui Tian, Shibao Li, Jianhang Liu, and Masakiyo Suzuki. "A Low-Complexity GA-WSF Algorithm for Narrow-Band DOA Estimation." International Journal of Antennas and Propagation 2018 (November 4, 2018): 1–6. http://dx.doi.org/10.1155/2018/7175653.
Full textXue, Xiaomei, Zhengquan Li, Yongqiang Man, Song Xing, Yang Liu, Baolong Li, and Qiong Wu. "Improved Massive MIMO RZF Precoding Algorithm Based on Truncated Kapteyn Series Expansion." Information 10, no. 4 (April 11, 2019): 136. http://dx.doi.org/10.3390/info10040136.
Full textManis, George, Md Aktaruzzaman, and Roberto Sassi. "Low Computational Cost for Sample Entropy." Entropy 20, no. 1 (January 13, 2018): 61. http://dx.doi.org/10.3390/e20010061.
Full textZhang, Xinhe, Yuehua Zhang, Chang Liu, and Hanzhong Jia. "Low-Complexity Detection Algorithms for Spatial Modulation MIMO Systems." Journal of Electrical and Computer Engineering 2018 (November 15, 2018): 1–7. http://dx.doi.org/10.1155/2018/4034625.
Full textRampa, Vittorio. "Design and Implementation of a Low Complexity Multiuser Detector for Hybrid CDMA Systems." Journal of Communications Software and Systems 1, no. 1 (April 6, 2017): 42. http://dx.doi.org/10.24138/jcomss.v1i1.316.
Full textWang, Yuhuan, Jianguo Li, Neng Ye, and Xiangyuan Bu. "Novel Low Complexity BP Decoding Algorithms for Polar Codes: Simplifying on Non-Linear Operations." Electronics 11, no. 1 (December 28, 2021): 93. http://dx.doi.org/10.3390/electronics11010093.
Full textOtunniyi, Temidayo O., and Hermanus C. Myburgh. "Low-Complexity Filter for Software-Defined Radio by Modulated Interpolated Coefficient Decimated Filter in a Hybrid Farrow." Sensors 22, no. 3 (February 3, 2022): 1164. http://dx.doi.org/10.3390/s22031164.
Full textChen, Haihua, Haoran Li, Mingyang Yang, Changbo Xiang, and Masakiyo Suzuki. "General Improvements of Heuristic Algorithms for Low Complexity DOA Estimation." International Journal of Antennas and Propagation 2019 (December 11, 2019): 1–9. http://dx.doi.org/10.1155/2019/3858794.
Full textDissertations / Theses on the topic "Low computational complexity algorithms"
Troiani, Chiara. "Vision-Aided Inertial Navigation : low computational complexity algorithms with applications to Micro Aerial Vehicles." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENM021/document.
Full textAccurate egomotion estimation is of utmost importance for any navigation system.Nowadays di_erent sensors are adopted to localize and navigate in unknownenvironments such as GPS, range sensors, cameras, magnetic field sensors, inertialsensors (IMU). In order to have a robust egomotion estimation, the information ofmultiple sensors is fused. Although the improvements of technology in providingmore accurate sensors, and the efforts of the mobile robotics community in thedevelopment of more performant navigation algorithms, there are still openchallenges. Furthermore, the growing interest of the robotics community in microrobots and swarm of robots pushes towards the employment of low weight, low costsensors and low computational complexity algorithms. In this context inertial sensorsand monocular cameras, thanks to their complementary characteristics, low weight,low cost and widespread use, represent an interesting sensor suite.This dissertation represents a contribution in the framework of vision-aided inertialnavigation and tackles the problems of data association and pose estimation aimingfor low computational complexity algorithms applied to MAVs.For what concerns the data association, a novel method to estimate the relative motionbetween two consecutive camera views is proposed. It only requires the observationof a single feature in the scene and the knowledge of the angular rates from an IMU,under the assumption that the local camera motion lies in a plane perpendicular to thegravity vector. Two very efficient algorithms to remove the outliers of the featurematchingprocess are provided under the abovementioned motion assumption. Inorder to generalize the approach to a 6DoF motion, two feature correspondences andgyroscopic data from IMU measurements are necessary. In this case, two algorithmsare provided to remove wrong data associations in the feature-matching process. Inthe case of a monocular camera mounted on a quadrotor vehicle, motion priors fromIMU are used to discard wrong estimations.For what concerns the pose estimation problem, this thesis provides a closed formsolution which gives the system pose from three natural features observed in a singlecamera image, once the roll and the pitch angles are obtained by the inertialmeasurements under the planar ground assumption.In order to tackle the pose estimation problem in dark or featureless environments, asystem equipped with a monocular camera, inertial sensors and a laser pointer isconsidered. The system moves in the surrounding of a planar surface and the laserpointer produces a laser spot on the abovementioned surface. The laser spot isobserved by the monocular camera and represents the only point feature considered.Through an observability analysis it is demonstrated that the physical quantities whichcan be determined by exploiting the measurements provided by the aforementionedsensor suite during a short time interval are: the distance of the system from the planarsurface; the component of the system speed that is orthogonal to the planar surface;the relative orientation of the system with respect to the planar surface; the orientationof the planar surface with respect to the gravity. A simple recursive method toperform the estimation of all the aforementioned observable quantities is provided.All the contributions of this thesis are validated through experimental results usingboth simulated and real data. Thanks to their low computational complexity, theproposed algorithms are very suitable for real time implementation on systems withlimited on-board computation resources. The considered sensor suite is mounted on aquadrotor vehicle but the contributions of this dissertations can be applied to anymobile device
Goussevskaia, Olga. "Computational complexity and scheduling algorithms for wireless networks." Konstanz Hartung-Gorre, 2009. http://d-nb.info/997891122/04.
Full textUdupa, Pramod. "Algorithmes parallèles et architectures évolutives de faible complexité pour systèmes optiques OFDM cohérents temps réel." Thesis, Rennes 1, 2014. http://www.theses.fr/2014REN1S039/document.
Full textIn this thesis, low-complexity algorithms and architectures for CO-OFDM systems are explored. First, low-complexity algorithms for estimation of timing and carrier frequency offset (CFO) in dispersive channel are studied. A novel low-complexity timing synchro- nization algorithm, which can withstand large amount of dispersive delay, is proposed and compared with previous proposals. Then, the problem of realization of low-complexity parallel architecture is studied. A generalized scalable parallel architecture, which can be used to realize any auto-correlation algorithm, is proposed. It is then extended to handle multiple parallel samples from ADC and provide outputs, which can match the input ADC rate. The scalability of the architecture for higher number of parallel outputs and different kinds of auto-correlation algorithms is explored. An algorithm-architecture approach is then applied to the entire CO-OFDM transceiver chain. At the transmitter side, radix-22 algorithm for IFFT is chosen and parallel Mul- tipath Delay Commutator (MDC) Feed-forward (FF) architecture is designed which con- sumes lesser resources compared to MDC FF architectures of radix-2/4. At the receiver side, efficient algorithm for Integer CFO estimation is adopted and efficiently realized with- out the use of complex multipliers. Reduction in complexity is achieved due to efficient architectures for timing synchronization, FFT and Integer CFO estimation. Fixed-point analysis for the entire transceiver chain is done to find fixed-point sensitive blocks, which affect bit error rate (BER) significantly. The algorithms proposed are validated using opti- cal experiments by the help of arbitrary waveform generator (AWG) at the transmitter and digital storage oscilloscope (DSO) and Matlab at the receiver. BER plots are used to show the validity of the system built. Hardware implementation of the proposed synchronization algorithm is validated using real-time FPGA platform
Danjean, Ludovic. "Low-Complexity Iterative Reconstruction Algorithms in Compressed Sensing." International Foundation for Telemetering, 2013. http://hdl.handle.net/10150/579661.
Full textIn this paper we focus on two low-complexity iterative reconstruction algorithms in compressed sensing. These algorithms, called the approximate message-passing algorithm and the interval-passing algorithm, are suitable to recover sparse signals from a small set of measurements. Depending on the type of measurement matrix (sparse or random) used to acquire the samples of the signal, one or the other reconstruction algorithm can be used. We present the reconstruction results of these two reconstruction algorithms in terms of proportion of correct reconstructions in the noise free case. We also report in this paper possible practical applications of compressed sensing where the choice of the measurement matrix and the reconstruction algorithm are often governed by the constraint of the considered application.
Vorhies, John T. "Low-complexity Algorithms for Light Field Image Processing." University of Akron / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=akron1590771210097321.
Full textUppman, Hannes. "On Some Combinatorial Optimization Problems : Algorithms and Complexity." Doctoral thesis, Linköpings universitet, Programvara och system, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-116859.
Full textSmith, Justin N. "Computational complexity, bounded rationality and the theory of games." Thesis, University of Oxford, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365642.
Full textLundqvist, Samuel. "Computational algorithms for algebras." Doctoral thesis, Stockholm : Department of Mathematics, Stockholm University, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-31552.
Full textAt the time of doctoral defence, the following papers were unpublished and had a status as follows: Paper 3: Manuscript. Paper 4: Manuscript. Paper 5: Manuscript. Paper 6: Manuscript. Härtill 6 uppsatser.
Neyer, Gabriele. "Algorithms, complexity, and software engineering in computational geometry : case studies /." [S.l.] : [s.n.], 2000. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=13586.
Full textBrooks, Duncan John. "Adaptive algorithms for low complexity equalizers in mobile communications." Thesis, Imperial College London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312445.
Full textBooks on the topic "Low computational complexity algorithms"
Kronsjö, Lydia. Computational complexity of sequential and parallel algorithms. Chichester: Wiley, 1985.
Find full textKronsjö, Lydia I. Computational complexity of sequential and parallel algorithms. Chichester: Wiley, 1986.
Find full textComputational complexity of sequential and parallel algorithms. Chichester: Wiley, 1987.
Find full textWilf, Herbert S. Algorithms and complexity. Englewood Cliffs, N.J: Prentice-Hall, 1986.
Find full textAlgorithms and complexity. 2nd ed. Natick, Mass: A.K. Peters, 2002.
Find full text1939-, Steiglitz Kenneth, ed. Combinatorial optimization: Algorithms and complexity. Mineola, N.Y: Dover Publications, 1998.
Find full textNeapolitan, Richard E. Foundations of algorithms. 4th ed. Sudbury, Mass: Jones and Bartlett Publishers, 2011.
Find full textKumarss, Naimipour, ed. Foundations of algorithms. 4th ed. Sudbury, Mass: Jones and Bartlett Publishers, 2011.
Find full textAlgorithms: Their complexity and efficiency. 2nd ed. Chichester: Wiley, 1987.
Find full textComplexity theory: Exploring the limits of efficient algorithms. Berlin: Springer, 2005.
Find full textBook chapters on the topic "Low computational complexity algorithms"
Novak, Miroslav. "Algorithm Optimizations: Low Computational Complexity." In Advances in Pattern Recognition, 213–31. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-143-5_10.
Full textYao, Lin, and Kai Yao. "A Low Complexity Intrusion Detection Algorithm." In Computational Science – ICCS 2007, 461–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72588-6_76.
Full textShan, Ying-ying, and Xuan Wang. "A Low Computational Complexity Multiple Description Image Coding Algorithm Based on JPEG Standard." In Novel Algorithms and Techniques in Telecommunications and Networking, 173–76. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-3662-9_29.
Full textKim, Sosun, Nam Su Chang, Chang Han Kim, Young-Ho Park, and Jongin Lim. "A Fast Inversion Algorithm and Low-Complexity Architecture over GF(2 m )." In Computational Intelligence and Security, 1–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11596981_1.
Full textKim, Sanghun, Seokho Yoon, Hyoung-Kee Choi, and Sun Yong Kim. "A Low Complexity and Robust Frequency Offset Estimation Algorithm for OFDM-Based WLAN Systems." In Computational Science – ICCS 2006, 961–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11758525_127.
Full textTurcza, Paweł, Tomasz Zieliński, and Mariusz Duplaga. "Hardware Implementation Aspects of New Low Complexity Image Coding Algorithm for Wireless Capsule Endoscopy." In Computational Science – ICCS 2008, 476–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69384-0_53.
Full textZontone, Pamela, Antonio Affanni, Riccardo Bernardini, Alessandro Piras, and Roberto Rinaldo. "Low-Complexity Classification Algorithm to Identify Drivers’ Stress Using Electrodermal Activity (EDA) Measurements." In Biomedical Engineering and Computational Intelligence, 25–33. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21726-6_3.
Full textYang, Jia-lin. "On an Improved K-Best Algorithm with High Performance and Low Complexity for MIMO Systems." In Smart Innovations in Communication and Computational Sciences, 309–18. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8971-8_28.
Full textRathod, D. P., and R. N. Awale. "An Efficient Genetic Based Algorithm for an Irregular Low Density Parity Check Code with Low Computational Complexity and Low Error Floor." In Communications in Computer and Information Science, 249–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36321-4_23.
Full textBrucker, Peter. "Computational Complexity." In Scheduling Algorithms, 37–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24804-0_3.
Full textConference papers on the topic "Low computational complexity algorithms"
Hull, A. W., and W. K. Jenkins. "Low computational complexity adaptive algorithms for IIR digital filters." In [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1991. http://dx.doi.org/10.1109/icassp.1991.150817.
Full textChew, Li Wern, Li-Minn Ang, and Kah Phooi Seng. "Low-Complexity Line-Based Motion Estimation Algorithm." In 2007 International Conference on Computational Intelligence and Security (CIS 2007). IEEE, 2007. http://dx.doi.org/10.1109/cis.2007.204.
Full textLiu, Fang, Sinong Wang, Swapna Buccapatnam, and Ness Shroff. "UCBoost: A Boosting Approach to Tame Complexity and Optimality for Stochastic Bandits." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/338.
Full textGao Jing, Wang Jinkuan, Xie Zhibin, and Yan Dongmei. "A modified PTS PAPR reduction algorithm with low computational complexity." In IET 2nd International Conference on Wireless, Mobile and Multimedia Networks (ICWMMN 2008). IEE, 2008. http://dx.doi.org/10.1049/cp:20081004.
Full textWeithoffer, Stefan, Rami Klaimi, Charbel Abdel Nour, Norbert Wehn, and Catherine Douillard. "Low-complexity Computational Units for the Local-SOVA Decoding Algorithm." In 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications. IEEE, 2020. http://dx.doi.org/10.1109/pimrc48278.2020.9217318.
Full textZhao, Yanhua, Vladica Sark, Milos Krstic, and Eckhard Grass. "Low Computational Complexity Algorithm for Hand Gesture Recognition using mmWave RADAR." In 2022 International Symposium on Wireless Communication Systems (ISWCS). IEEE, 2022. http://dx.doi.org/10.1109/iswcs56560.2022.9940253.
Full textSoma, Umamaheshwar, Anil Kumar Tipparti, and Srinivasa Rao Kunupalli. "Improved performance of low complexity K-best sphere decoder algorithm." In 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT). IEEE, 2017. http://dx.doi.org/10.1109/icicct.2017.7975248.
Full textJin, Longxu, Jin Li, Min Zhang, and Yinan Wu. "Low-Complexity Multispectral Images Compression Algorithm Based Distributed Compressive Sensing." In 2013 6th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2013. http://dx.doi.org/10.1109/iscid.2013.149.
Full textAnagreh, Mohammad, Peeter Laud, and Eero Vainikko. "Privacy-preserving Parallel Computation of Shortest Path Algorithms with Low Round Complexity." In 8th International Conference on Information Systems Security and Privacy. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0010775700003120.
Full textBae, Seung Chun, Jae Wook Park, and Won Cheol Lee. "Genetic algorithm based efficient positioning error compensation technique with low computational complexity." In TENCON 2009 - 2009 IEEE Region 10 Conference. IEEE, 2009. http://dx.doi.org/10.1109/tencon.2009.5395935.
Full textReports on the topic "Low computational complexity algorithms"
Ariyawansa, K. A. Low-Complexity Interior Point Algorithms for Stochastic Programming: Derivation Analysis and Performance Evaluation. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada418278.
Full textGil, Oliver Fernández, and Anni-Yasmin Turhan. Answering Regular Path Queries Under Approximate Semantics in Lightweight Description Logics. Technische Universität Dresden, 2020. http://dx.doi.org/10.25368/2022.261.
Full textRusso, David, Daniel M. Tartakovsky, and Shlomo P. Neuman. Development of Predictive Tools for Contaminant Transport through Variably-Saturated Heterogeneous Composite Porous Formations. United States Department of Agriculture, December 2012. http://dx.doi.org/10.32747/2012.7592658.bard.
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