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Journal articles on the topic 'Low-complexity'

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Published: 4 June 2021
Last updated: 14 March 2023

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1

Schmidhuber, Jurgen. "Low-Complexity Art." Leonardo 30, no. 2 (1997): 97. http://dx.doi.org/10.2307/1576418.

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2

Lee, Woon-Sang, Jae-Hyun Ro, Hyun-Sun Hwang, and Hyoung-Kyu Song. "Low-Complexity Adaptive Detector in Single-User MIMO System." International Journal of Signal Processing Systems 7, no. 4 (December 2019): 129–32. http://dx.doi.org/10.18178/ijsps.7.4.129-132.

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3

Mier, Pablo, Lisanna Paladin, Stella Tamana, Sophia Petrosian, Borbála Hajdu-Soltész, Annika Urbanek, Aleksandra Gruca, et al. "Disentangling the complexity of low complexity proteins." Briefings in Bioinformatics 21, no. 2 (January 30, 2019): 458–72. http://dx.doi.org/10.1093/bib/bbz007.

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Abstract There are multiple definitions for low complexity regions (LCRs) in protein sequences, with all of them broadly considering LCRs as regions with fewer amino acid types compared to an average composition. Following this view, LCRs can also be defined as regions showing composition bias. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, and more generally the overlaps between different properties related to LCRs, using examples. We argue that statistical measures alone cannot capture all structural aspects of LCRs and recommend the combined usage of a variety of predictive tools and measurements. While the methodologies available to study LCRs are already very advanced, we foresee that a more comprehensive annotation of sequences in the databases will enable the improvement of predictions and a better understanding of the evolution and the connection between structure and function of LCRs. This will require the use of standards for the generation and exchange of data describing all aspects of LCRs. Short abstract There are multiple definitions for low complexity regions (LCRs) in protein sequences. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, plus overlaps between different properties related to LCRs, using examples.
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4

Thiyagarajan, Karthik, Kamal El-Sankary, Yongsheng Wang, and Issam Hammad. "Low Complexity Multimedia Encryption." International Journal of Computer Network and Information Security 8, no. 4 (April 8, 2016): 1–13. http://dx.doi.org/10.5815/ijcnis.2016.04.01.

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5

Jackson, Marcel. "Low Growth Equational Complexity." Proceedings of the Edinburgh Mathematical Society 62, no. 1 (September 25, 2018): 197–210. http://dx.doi.org/10.1017/s0013091518000354.

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AbstractThe equational complexity function $\beta \nu \,:\,{\open N} \to {\open N}$ of an equational class of algebras bounds the size of equation required to determine the membership of n-element algebras in . Known examples of finitely generated varieties with unbounded equational complexity have growth in Ω(nc), usually for c ≥ (1/2). We show that much slower growth is possible, exhibiting $O(\log_{2}^{3}(n))$ growth among varieties of semilattice-ordered inverse semigroups and additive idempotent semirings. We also examine a quasivariety analogue of equational complexity, and show that a finite group has polylogarithmic quasi-equational complexity function, bounded if and only if all Sylow subgroups are abelian.
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6

Mirani, Ali, Erik Agrell, and Magnus Karlsson. "Low-Complexity Geometric Shaping." Journal of Lightwave Technology 39, no. 2 (January 15, 2021): 363–71. http://dx.doi.org/10.1109/jlt.2020.3033031.

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7

Ash, David W., Ian F. Blake, and Scott A. Vanstone. "Low complexity normal bases." Discrete Applied Mathematics 25, no. 3 (November 1989): 191–210. http://dx.doi.org/10.1016/0166-218x(89)90001-2.

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8

Di Fiore, Carmine, Stefano Fanelli, and Paolo Zellini. "Low-complexity minimization algorithms." Numerical Linear Algebra with Applications 12, no. 8 (2005): 755–68. http://dx.doi.org/10.1002/nla.449.

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9

Hajjaj, Moufida, Fadoua Mhiri, and Ridha Bouallegue. "Low-Complexity MMSE Channel Estimator for MB-OFDM UWB Systems." International Journal of Future Computer and Communication 3, no. 4 (2014): 227–31. http://dx.doi.org/10.7763/ijfcc.2014.v3.301.

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10

Balmahoon, Tarika, and Hongjun Xu. "Low-complexity EDAS and low-complexity detection scheme for MPSK spatial modulation." IET Communications 10, no. 14 (September 20, 2016): 1752–57. http://dx.doi.org/10.1049/iet-com.2015.0890.

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11

Mier, Pablo, and Miguel A. Andrade-Navarro. "Assessing the low complexity of protein sequences via the low complexity triangle." PLOS ONE 15, no. 12 (December 30, 2020): e0239154. http://dx.doi.org/10.1371/journal.pone.0239154.

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Background Proteins with low complexity regions (LCRs) have atypical sequence and structural features. Their amino acid composition varies from the expected, determined proteome-wise, and they do not follow the rules of structural folding that prevail in globular regions. One way to characterize these regions is by assessing the repeatability of a sequence, that is, calculating the local propensity of a region to be part of a repeat. Results We combine two local measures of low complexity, repeatability (using the RES algorithm) and fraction of the most frequent amino acid, to evaluate different proteomes, datasets of protein regions with specific features, and individual cases of proteins with extreme compositions. We apply a representation called ‘low complexity triangle’ as a proof-of-concept to represent the low complexity measured values. Results show that proteomes have distinct signatures in the low complexity triangle, and that these signatures are associated to complexity features of the sequences. We developed a web tool called LCT (http://cbdm-01.zdv.uni-mainz.de/~munoz/lct/) to allow users to calculate the low complexity triangle of a given protein or region of interest. Conclusions The low complexity triangle proves to be a suitable procedure to represent the general low complexity of a sequence or protein dataset. Homorepeats, direpeats, compositionally biased regions and globular regions occupy characteristic positions in the triangle. The described pipeline can be used to characterize LCRs and may help in quantifying the content of degenerated tandem repeats in proteins and proteomes.
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12

Bocharova, I. E., A. V. Porov, T. S. Bondarev, and O. V. Finkelshteyn. "Low-complexity lossless image coding." Automatic Control and Computer Sciences 48, no. 5 (September 2014): 303–11. http://dx.doi.org/10.3103/s0146411614050034.

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13

Mainali, P., Qiong Yang, G. Lafruit, L. Van Gool, and R. Lauwereins. "Robust Low Complexity Corner Detector." IEEE Transactions on Circuits and Systems for Video Technology 21, no. 4 (April 2011): 435–45. http://dx.doi.org/10.1109/tcsvt.2011.2125411.

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14

Avgustinovich, Sergey V., Julien Cassaigne, and Anna E. Frid. "Sequences of low arithmetical complexity." RAIRO - Theoretical Informatics and Applications 40, no. 4 (October 2006): 569–82. http://dx.doi.org/10.1051/ita:2006041.

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15

Wei, Ruey-Yi, and Tzu-Yun Lin. "Low-Complexity Differential Spatial Modulation." IEEE Wireless Communications Letters 8, no. 2 (April 2019): 356–59. http://dx.doi.org/10.1109/lwc.2018.2872990.

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16

Mora, Benjamin, and David S. Ebert. "Low-complexity maximum intensity projection." ACM Transactions on Graphics (TOG) 24, no. 4 (October 2005): 1392–416. http://dx.doi.org/10.1145/1095878.1095886.

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17

Rabiee, M., M. A. Attari, and S. Ghaemmaghami. "A Low Complexity NSAF Algorithm." IEEE Signal Processing Letters 19, no. 11 (November 2012): 716–19. http://dx.doi.org/10.1109/lsp.2012.2215321.

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18

Vijayanagar, Krishna Rao, Joohee Kim, Yunsik Lee, and Jong-bok Kim. "Low complexity distributed video coding." Journal of Visual Communication and Image Representation 25, no. 2 (February 2014): 361–72. http://dx.doi.org/10.1016/j.jvcir.2013.12.006.

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19

Correia, Miguel, Nuno Ferreira Neves, Lau Cheuk Lung, and Paulo Ver�ssimo. "Low complexity Byzantine-resilient consensus." Distributed Computing 17, no. 3 (March 2005): 237–49. http://dx.doi.org/10.1007/s00446-004-0110-7.

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20

Darup, M. Schulze, and M. Mönnigmann. "Low complexity suboptimal explicit NMPC." IFAC Proceedings Volumes 45, no. 17 (2012): 406–11. http://dx.doi.org/10.3182/20120823-5-nl-3013.00080.

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21

Tan, B. S. E., and G. J. R. Povey. "Low complexity spread spectrum correlator." Electronics Letters 33, no. 14 (1997): 1204. http://dx.doi.org/10.1049/el:19970852.

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22

Silverman, Joseph H. "Rings of Low Multiplicative Complexity." Finite Fields and Their Applications 6, no. 2 (April 2000): 175–91. http://dx.doi.org/10.1006/ffta.1999.0270.

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23

Renfei Liu and K. K. Parhi. "Low-Latency Low-Complexity Architectures for Viterbi Decoders." IEEE Transactions on Circuits and Systems I: Regular Papers 56, no. 10 (October 2009): 2315–24. http://dx.doi.org/10.1109/tcsi.2008.2012217.

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24

Jun, Liu, Luo Zhongqiang, and Xiong Xingzhong. "Low-Complexity Synchronization Scheme with Low-Resolution ADCs." Information 9, no. 12 (December 7, 2018): 313. http://dx.doi.org/10.3390/info9120313.

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An important function of next-generation (5G) and beyond mobile communication systems is aim to provide thousand-fold capacity growth and to support high-speed data transmission up to several megabits per second. However, the research community and industries have to face a dilemma of power consumption and hardware design to satisfy the increasing communication requirements. For the purpose of improving the system cost, power consumption, and implementation complexity, a novel scheme of symbol timing and frequency offset estimation with low-resolution analog-to-digital converters (ADCs) based on an orthogonal frequency division multiplexing ultra-wideband (OFDM-UWB) system is proposed in this paper. In our work, we first verified the principle that the autocorrelation of the pseudo-noise (PN) sequences was not affected by low-resolution quantization. With the help of this property, the timing synchronization could be strongly implemented against the influence of low-resolution quantization. Then, the transmitted signal structure and low-resolution quantization scheme under the synchronization scheme were designed. Finally, a frequency offset estimation model with one-bit timing synchronization was established. Theoretical analysis and simulation results corroborate that the performance of the proposed scheme not only approximates to that of the full-resolution synchronization scheme, but also has lower power consumption and computational complexity.
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25

Cui, Z., and Z. Wang. "Improved low-complexity low-density parity-check decoding." IET Communications 2, no. 8 (2008): 1061. http://dx.doi.org/10.1049/iet-com:20070570.

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26

Ryan, D. J., I. B. Collings, and I. V. L. Clarkson. "Low-complexity low-PAR transmission for MIMO-DSL." IEEE Communications Letters 9, no. 10 (October 2005): 868–70. http://dx.doi.org/10.1109/lcomm.2005.10022.

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27

Abedi, Ali Asghar, Mohammad Reza Mosavi, and Karim Mohammadi. "Low Computational Complexity in Low-Cost GNSS Receivers." Wireless Personal Communications 112, no. 1 (December 16, 2019): 37–59. http://dx.doi.org/10.1007/s11277-019-07014-5.

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28

Lin, Jin-Fa, Zheng-Jie Hong, Chang-Ming Tsai, Bo-Cheng Wu, and Shao-Wei Yu. "Novel Low-Complexity and Low-Power Flip-Flop Design." Electronics 9, no. 5 (May 10, 2020): 783. http://dx.doi.org/10.3390/electronics9050783.

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In this paper, a compact and low-power true single-phase flip-flop (FF) design with fully static operations is presented. The design is developed by using various circuit-reduction schemes and features a hybrid logic style employing both pass transistor logic (PTL) and static complementary metal-oxide semiconductor (CMOS) logic to reduce circuit complexity. These circuit optimization measures pay off in various aspects, including smaller clock-to-Q (CQ) delay, lower average power, lower leakage power, and smaller layout area; and the transistor-count is only 17. Fabricated in TSMC 180 nm CMOS technology, it reduces by over 29% the chip area compared to the conventional transmission gate FF (TGFF). To further show digital circuit/system level advantages, a multi-mode shift register has been realized. Experimental measurement results at 1.8 V/4 MHz show that, compared with the TGFF design, the proposed design saves 64.7% of power consumption while reducing chip area by 26.2%.
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29

WINGER, LOWELL L., JOHN A. ROBINSON, and M. ED JERNIGAN. "LOW-COMPLEXITY CHARACTER EXTRACTION IN LOW-CONTRAST SCENE IMAGES." International Journal of Pattern Recognition and Artificial Intelligence 14, no. 02 (March 2000): 113–35. http://dx.doi.org/10.1142/s0218001400000106.

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There is wide application for the extraction of textual information from low-contrast, complex natural images. We are particularly interested in segmentation and thresholding algorithms for use in a portable text-to-speech system for the vision impaired. Reading low-contrast LCD displays is the target application. We present a low-complexity method for automatically extracting text of any size, font, and format from images acquired by a video camera that may be poorly focused and aimed, under conditions of inadequate and uneven illumination. The new method consists of fast thresholding that combines a local variance measure with a logical stroke-width method, and with a low-complexity statistical and contextual noise segmentation. The performance of this method compares favorably with more complex methods for the extraction of characters from scene images. Initial results are encouraging for application in a robust portable reader.
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30

Giard, Pascal, Alexios Balatsoukas-Stimming, Gabi Sarkis, Claude Thibeault, and Warren J. Gross. "Fast Low-Complexity Decoders for Low-Rate Polar Codes." Journal of Signal Processing Systems 90, no. 5 (August 23, 2016): 675–85. http://dx.doi.org/10.1007/s11265-016-1173-y.

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31

Kahe, Ghasem, and Farhad Masoumi Ganjgah. "MAKAN: A low‐cost low‐complexity local positioning system." Navigation 66, no. 2 (May 21, 2019): 401–15. http://dx.doi.org/10.1002/navi.308.

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32

So, Minseok, Kyeongwon Park, and Wangrok Oh. "Low-complexity ICI Estimator for SEFDM." IEIE Transactions on Smart Processing & Computing 9, no. 6 (December 31, 2020): 485–90. http://dx.doi.org/10.5573/ieiespc.2020.9.6.485.

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33

HALMI, Mohd Hairi, Mohamad Yusoff ALIAS, and Teong Chee CHUAH. "Low-Complexity Semi-Coherent MIMO Systems." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E93-A, no. 4 (2010): 833–36. http://dx.doi.org/10.1587/transfun.e93.a.833.

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34

Du, Wei-na, Jun Sun, and Miao Sima. "Improved EZBC algorithm with low complexity." IEICE Electronics Express 1, no. 15 (2004): 447–52. http://dx.doi.org/10.1587/elex.1.447.

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35

Lee, Dongeun, Jaesik Choi, and Heonshik Shin. "Low-complexity compressive sensing with downsampling." IEICE Electronics Express 11, no. 3 (2014): 20130947. http://dx.doi.org/10.1587/elex.11.20130947.

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36

Kye, Hyoseon, and Minhae Kwon. "PCA-Based Low-Complexity Anomaly Detection." Journal of Korean Institute of Communications and Information Sciences 46, no. 6 (June 30, 2021): 941–55. http://dx.doi.org/10.7840/kics.2021.46.6.941.

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37

Hadfi, Rafik, and Takayuki Ito. "Low-Complexity Exploration in Utility Hypergraphs." Journal of Information Processing 23, no. 2 (2015): 176–84. http://dx.doi.org/10.2197/ipsjjip.23.176.

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38

Otunniyi, Temidayo, Adedotun O.Owojori, Erastus O. Ogunti, and Akinlolu A. Ponnle. "Low Complexity Farrow Differential Channelizer Algorithm." Communications on Applied Electronics 1, no. 6 (April 25, 2015): 36–42. http://dx.doi.org/10.5120/cae-1571.

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39

Liquan Shen, Zhi Liu, Ping An, Ran Ma, and Zhaoyang Zhang. "Low-Complexity Mode Decision for MVC." IEEE Transactions on Circuits and Systems for Video Technology 21, no. 6 (June 2011): 837–43. http://dx.doi.org/10.1109/tcsvt.2011.2130310.

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40

Tiwari, Shashank, Suvra Sekhar Das, and Vivek Rangamgari. "Low complexity LMMSE Receiver for OTFS." IEEE Communications Letters 23, no. 12 (December 2019): 2205–9. http://dx.doi.org/10.1109/lcomm.2019.2945564.

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41

Kra, Bryna. "Dynamics of Systems with Low Complexity." Notices of the American Mathematical Society 66, no. 01 (January 1, 2019): 1. http://dx.doi.org/10.1090/noti1779.

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42

Fletcher, P., and M. Dean. "Low complexity implementation of LMS algorithm." Electronics Letters 38, no. 15 (2002): 836. http://dx.doi.org/10.1049/el:20020531.

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43

Bengtsson, M., and B. Ottersten. "Low-complexity estimators for distributed sources." IEEE Transactions on Signal Processing 48, no. 8 (2000): 2185–94. http://dx.doi.org/10.1109/78.851999.

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44

Damanik, David. "Schrödinger operators with low-complexity potentials." Ferroelectrics 250, no. 1 (February 2001): 143–49. http://dx.doi.org/10.1080/00150190108225053.

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45

Pandya, N., and B. Honary. "Low-complexity decoding of LDPC codes." Electronics Letters 43, no. 18 (2007): 990. http://dx.doi.org/10.1049/el:20071653.

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46

Liu, J., and Y. Zakharov. "Low complexity dynamically regularised RLS algorithm." Electronics Letters 44, no. 14 (2008): 886. http://dx.doi.org/10.1049/el:20081096.

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47

Barakatain, Masoud, and Frank R. Kschischang. "Low-Complexity Concatenated LDPC-Staircase Codes." Journal of Lightwave Technology 36, no. 12 (June 15, 2018): 2443–49. http://dx.doi.org/10.1109/jlt.2018.2812738.

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48

Insausti, Xabier, Pedro M. Crespo, Jesus Gutierrez-Gutierrez, and Marta Zarraga-Rodriguez. "Low-Complexity Analog Linear Coding Scheme." IEEE Communications Letters 22, no. 9 (September 2018): 1754–57. http://dx.doi.org/10.1109/lcomm.2018.2848941.

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49

Tiwari, Shashank, and Suvra Sekhar Das. "Low-Complexity Joint-MMSE GFDM Receiver." IEEE Transactions on Communications 66, no. 4 (April 2018): 1661–74. http://dx.doi.org/10.1109/tcomm.2017.2780854.

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50

Choong Sang Cho and Sangkeun Lee. "Low-Complexity Topological Derivative-Based Segmentation." IEEE Transactions on Image Processing 24, no. 2 (February 2015): 734–41. http://dx.doi.org/10.1109/tip.2014.2387018.

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