Статті в журналах: "Bit-complexity"

1

Fagervik, K., and T. G. Jeans. "Low complexity bit by bit soft output demodulator." Electronics Letters 32, no. 11 (1996): 985. http://dx.doi.org/10.1049/el:19960666.

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2

Elser, Veit. "The Complexity of Bit Retrieval." IEEE Transactions on Information Theory 64, no. 1 (January 2018): 412–28. http://dx.doi.org/10.1109/tit.2017.2754485.

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3

Lingas, Andrzej. "Bit complexity of matrix products." Information Processing Letters 38, no. 5 (June 1991): 237–42. http://dx.doi.org/10.1016/0020-0190(91)90065-p.

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4

Paˇtraşcu, Mihai, and Corina E. Tarniţaˇ. "On dynamic bit-probe complexity." Theoretical Computer Science 380, no. 1-2 (June 2007): 127–42. http://dx.doi.org/10.1016/j.tcs.2007.02.058.

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5

Gerstel, O., and S. Zaks. "The Bit Complexity of Distributed Sorting." Algorithmica 18, no. 3 (July 1997): 405–16. http://dx.doi.org/10.1007/pl00009163.

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6

Azmi, Elnaz, Uwe Ehret, Steven V. Weijs, Benjamin L. Ruddell, and Rui A. P. Perdigão. "Technical note: “Bit by bit”: a practical and general approach for evaluating model computational complexity vs. model performance." Hydrology and Earth System Sciences 25, no. 2 (March 3, 2021): 1103–15. http://dx.doi.org/10.5194/hess-25-1103-2021.

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Abstract. One of the main objectives of the scientific enterprise is the development of well-performing yet parsimonious models for all natural phenomena and systems. In the 21st century, scientists usually represent their models, hypotheses, and experimental observations using digital computers. Measuring performance and parsimony of computer models is therefore a key theoretical and practical challenge for 21st century science. “Performance” here refers to a model's ability to reduce predictive uncertainty about an object of interest. “Parsimony” (or complexity) comprises two aspects: descriptive complexity – the size of the model itself which can be measured by the disk space it occupies – and computational complexity – the model's effort to provide output. Descriptive complexity is related to inference quality and generality; computational complexity is often a practical and economic concern for limited computing resources. In this context, this paper has two distinct but related goals. The first is to propose a practical method of measuring computational complexity by utility software “Strace”, which counts the total number of memory visits while running a model on a computer. The second goal is to propose the “bit by bit” method, which combines measuring computational complexity by “Strace” and measuring model performance by information loss relative to observations, both in bit. For demonstration, we apply the “bit by bit” method to watershed models representing a wide diversity of modelling strategies (artificial neural network, auto-regressive, process-based, and others). We demonstrate that computational complexity as measured by “Strace” is sensitive to all aspects of a model, such as the size of the model itself, the input data it reads, its numerical scheme, and time stepping. We further demonstrate that for each model, the bit counts for computational complexity exceed those for performance by several orders of magnitude and that the differences among the models for both computational complexity and performance can be explained by their setup and are in accordance with expectations. We conclude that measuring computational complexity by “Strace” is practical, and it is also general in the sense that it can be applied to any model that can be run on a digital computer. We further conclude that the “bit by bit” approach is general in the sense that it measures two key aspects of a model in the single unit of bit. We suggest that it can be enhanced by additionally measuring a model's descriptive complexity – also in bit.

7

Mallikarachchi, Thanuja, Dumidu Talagala, Hemantha Kodikara Arachchi, Chaminda Hewage, and Anil Fernando. "A Decoding-Complexity and Rate-Controlled Video-Coding Algorithm for HEVC." Future Internet 12, no. 7 (July 16, 2020): 120. http://dx.doi.org/10.3390/fi12070120.

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Video playback on mobile consumer electronic (CE) devices is plagued by fluctuations in the network bandwidth and by limitations in processing and energy availability at the individual devices. Seen as a potential solution, the state-of-the-art adaptive streaming mechanisms address the first aspect, yet the efficient control of the decoding-complexity and the energy use when decoding the video remain unaddressed. The quality of experience (QoE) of the end-users’ experiences, however, depends on the capability to adapt the bit streams to both these constraints (i.e., network bandwidth and device’s energy availability). As a solution, this paper proposes an encoding framework that is capable of generating video bit streams with arbitrary bit rates and decoding-complexity levels using a decoding-complexity–rate–distortion model. The proposed algorithm allocates rate and decoding-complexity levels across frames and coding tree units (CTUs) and adaptively derives the CTU-level coding parameters to achieve their imposed targets with minimal distortion. The experimental results reveal that the proposed algorithm can achieve the target bit rate and the decoding-complexity with 0.4% and 1.78% average errors, respectively, for multiple bit rate and decoding-complexity levels. The proposed algorithm also demonstrates a stable frame-wise rate and decoding-complexity control capability when achieving a decoding-complexity reduction of 10.11 (%/dB). The resultant decoding-complexity reduction translates into an overall energy-consumption reduction of up to 10.52 (%/dB) for a 1 dB peak signal-to-noise ratio (PSNR) quality loss compared to the HM 16.0 encoded bit streams.

8

Tabeshnezhad, Azadeh, A. Lee Swindlehurst, and Tommy Svensson. "Reduced Complexity Precoding for One-Bit Signaling." IEEE Transactions on Vehicular Technology 70, no. 2 (February 2021): 1967–71. http://dx.doi.org/10.1109/tvt.2021.3052113.

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9

Park, Sang-Hyun. "Frame Complexity-Based Adaptive Bit Rate Normalization." Journal of the Korea institute of electronic communication sciences 10, no. 12 (December 31, 2015): 1329–36. http://dx.doi.org/10.13067/jkiecs.2015.10.12.1329.

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10

González, C. M., H. A. Larrondo, and O. A. Rosso. "Statistical complexity measure of pseudorandom bit generators." Physica A: Statistical Mechanics and its Applications 354 (August 2005): 281–300. http://dx.doi.org/10.1016/j.physa.2005.02.054.

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11

Afek, Y., M. Cohen, and E. Haalman. "The bit complexity of the predecessor problem." Information Processing Letters 63, no. 2 (July 1997): 109–12. http://dx.doi.org/10.1016/s0020-0190(97)00094-x.

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12

Dinitz, Yefim, and Noam Solomon. "Two absolute bounds for distributed bit complexity." Theoretical Computer Science 384, no. 2-3 (October 2007): 168–83. http://dx.doi.org/10.1016/j.tcs.2007.04.027.

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13

Kovásznai, Gergely, Andreas Fröhlich, and Armin Biere. "Complexity of Fixed-Size Bit-Vector Logics." Theory of Computing Systems 59, no. 2 (September 7, 2015): 323–76. http://dx.doi.org/10.1007/s00224-015-9653-1.

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14

Akl, Selim G., and Henk Meijer. "On the bit complexity of parallel computations." Integration 6, no. 2 (July 1988): 201–12. http://dx.doi.org/10.1016/0167-9260(88)90039-9.

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15

Sadiq, B. J. S., V. Yu Tsviatkou, and M. N. Bobov. "Combined coding of bit planes of images." «System analysis and applied information science», no. 4 (December 30, 2019): 32–37. http://dx.doi.org/10.21122/2309-4923-2019-4-32-37.

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The aim of this work is to reduce the computational complexity of lossless compression in the spatial domain due to the combined coding (arithmetic and Run-Length Encoding) of a series of bits of bit planes. Known effective compression encoders separately encode the bit planes of the image or transform coefficients, which leads to an increase in computational complexity due to multiple processing of each pixel. The paper proposes the rules for combined coding and combined encoders for bit planes of pixel differences of images with a tunable and constant structure, which have lower computational complexity and the same compression ratio as compared to an arithmetic encoder of bit planes.

16

Chandra Sekhar, M., S. K. Chandini, V. Sai Rohith, V. Jhansi Lakshmi, and M. Pavan Kumar. "Data hiding using bit plane complexity segmentation steganography." International Journal of Engineering & Technology 7, no. 2.20 (April 18, 2018): 33. http://dx.doi.org/10.14419/ijet.v7i2.20.11740.

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Steganography is the basis of information covering the puzzle in some other data (we call it the ship), leaving no obvious evidence of data change. Most conventional steganographic strategies is limited data that hide the limit. They can cover up just 10% (or less) of the information measures of the vessel. This is on the grounds that the standard of those procedures was either to supplant an uncommon piece of the recurrence parts of the vessel picture, or to supplant all the slightest critical image bits with a secret multi-valued data. Our new Steganography uses the image as vessel information, and we enter the data in the bit-plane mystery vessel. This strategy makes use of human attributes, through which people cannot see the structure of any form of data exclusively damned pair, for example. We can replace most of the «Commotion like" regions in the bit-planes of the puzzle vessel image data without deteriorating the quality of the photos. We called our steganography "BPCS-steganography," which remains a Bit-Plane Complexity Segmentation steganography.

17

Ababneh, Ahmad. "Low-Complexity Bit Allocation for RSS Target Localization." IEEE Sensors Journal 19, no. 17 (September 1, 2019): 7733–43. http://dx.doi.org/10.1109/jsen.2019.2914347.

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18

Siahaan, Andysah Putera Utama. "Vernam Conjugated Manipulation of Bit-plane Complexity Segmentation." International Journal of Security and Its Applications 11, no. 9 (September 30, 2017): 1–12. http://dx.doi.org/10.14257/ijsia.2017.11.9.01.

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19

Ferragina, Paolo, Igor Nitto, and Rossano Venturini. "On the Bit-Complexity of Lempel--Ziv Compression." SIAM Journal on Computing 42, no. 4 (January 2013): 1521–41. http://dx.doi.org/10.1137/120869511.

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20

Giménez, Nardo, and Guillermo Matera. "On the bit complexity of polynomial system solving." Journal of Complexity 51 (April 2019): 20–67. http://dx.doi.org/10.1016/j.jco.2018.09.005.

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21

Bramas, Quentin, and Sébastien Tixeuil. "The Random Bit Complexity of Mobile Robots Scattering." International Journal of Foundations of Computer Science 28, no. 02 (February 2017): 111–33. http://dx.doi.org/10.1142/s0129054117500083.

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We consider the problem of scattering n robots in a two dimensional continuous space. As this problem is impossible to solve in a deterministic manner, all solutions must be probabilistic. We investigate the amount of randomness (that is, the number of random bits used by the robots) that is required to achieve scattering. We first prove that n log n random bits are necessary to scatter n robots in any setting. Also, we give a sufficient condition for a scattering algorithm to be random bit optimal. As it turns out that previous solutions for scattering satisfy our condition, they are hence proved random bit optimal for the scattering problem. Then, we investigate the time complexity of scattering when strong multiplicity detection is not available. We prove that such algorithms cannot converge in constant time in the general case and in o(log log n) rounds for random bits optimal scattering algorithms. However, we present a family of scattering algorithms that converge as fast as needed without using multiplicity detection. Also, we put forward a specific protocol of this family that is random bit optimal (O(n log n) random bits are used) and time optimal (O(log log n) rounds are used). This improves the time complexity of previous results in the same setting by a log n factor. Aside from characterizing the random bit complexity of mobile robot scattering, our study also closes the time complexity gap with and without strong multiplicity detection (that is, O(1) time complexity is only achievable when strong multiplicity detection is available, and it is possible to approach a constant value as desired otherwise).

22

Lee, Jae Hack, and Myung Hoon Sunwoo. "Low-Complexity High-Throughput Bit-Wise LDPC Decoder." Journal of Signal Processing Systems 91, no. 8 (July 30, 2018): 855–62. http://dx.doi.org/10.1007/s11265-018-1398-z.

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23

Métivier, Y., J. M. Robson, N. Saheb-Djahromi, and A. Zemmari. "An optimal bit complexity randomized distributed MIS algorithm." Distributed Computing 23, no. 5-6 (November 27, 2010): 331–40. http://dx.doi.org/10.1007/s00446-010-0121-5.

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24

Yu, Yang, Sha Xuejun, and Zhang Zhonghua. "Low complexity bit loading algorithm for OFDM system." Journal of Systems Engineering and Electronics 17, no. 1 (March 2006): 71–74. http://dx.doi.org/10.1016/s1004-4132(06)60013-3.

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25

Lee, Changwook, and Gi J. Jeon. "Low complexity bit allocation algorithm for OFDM systems." International Journal of Communication Systems 21, no. 11 (November 2008): 1171–79. http://dx.doi.org/10.1002/dac.945.

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26

Zamarrud and Muhammed Izharuddin. "8-Bit Quantizer for Chaotic Generator With Reduced Hardware Complexity." International Journal of Rough Sets and Data Analysis 5, no. 3 (July 2018): 55–70. http://dx.doi.org/10.4018/ijrsda.2018070104.

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This article describes how nowadays, data is widely transmitted over the internet in the real time. Wherever the transmission or storage is required, security is needed. High speed processing hardware machine with reduced complexity are used for the security of the data, that are transmitted in real time. The information which is to be secure are encoded by pseudorandom key. Chaotic numbers are used in place of a pseudorandom key. The generated chaotic values are analogous in nature, these analog values are digitized to generate encryption key like 8-bit, 16-bit, 32-bit. To generate an 8-bit key, an 8-bit quantizer is required. The design of 8-bit quantizer requires 256 levels which needs lot of complex hardware to implement. In this article, an 8-bit quantizer is designed with reduced complexity, where hardware requirement is reduced by more than 12 times. Without compromising the randomness of the sequence generated. To increase the randomness and confusion timed hop random selection is used. The randomness of the sequence generated by the chaotic generators is analyzed by NIST test suite, to test for its randomness.

27

Dogan, M. Levent, Alperen A. Ergür, and Elias Tsigaridas. "On the Complexity of Chow and Hurwitz Forms." ACM Communications in Computer Algebra 57, no. 4 (December 2023): 167–99. http://dx.doi.org/10.1145/3653002.3653003.

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We consider the bit complexity of computing Chow forms of projective varieties defined over integers and their generalization to multiprojective spaces. We develop a deterministic algorithm using resultants and obtain a single exponential complexity upper bound. Earlier computational results for Chow forms were in the arithmetic complexity model; thus, our result represents the first bit complexity bound. We also extend our algorithm to Hurwitz forms in projective space and we explore connections between multiprojective Hurwitz forms and matroid theory. The motivation for our work comes from incidence geometry where intriguing computational algebra problems remain open.

28

Perić, Zoran H., and Milan R. Dinčić. "Optimization of the 24-Bit Fixed-Point Format for the Laplacian Source." Mathematics 11, no. 3 (January 21, 2023): 568. http://dx.doi.org/10.3390/math11030568.

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The 32-bit floating-point (FP32) binary format, commonly used for data representation in computers, introduces high complexity, requiring powerful and expensive hardware for data processing and high energy consumption, hence being unsuitable for implementation on sensor nodes, edge devices, and other devices with limited hardware resources. Therefore, it is often necessary to use binary formats of lower complexity than FP32. This paper proposes the usage of the 24-bit fixed-point format that will reduce the complexity in two ways, by decreasing the number of bits and by the fact that the fixed-point format has significantly less complexity than the floating-point format. The paper optimizes the 24-bit fixed-point format and examines its performance for data with the Laplacian distribution, exploiting the analogy between fixed-point binary representation and uniform quantization. Firstly, the optimization of the 24-bit uniform quantizer is performed by deriving two new closed-form formulas for a very accurate calculation of its maximal amplitude. Then, the 24-bit fixed-point format is optimized by optimization of its key parameter and by proposing two adaptation procedures, with the aim to obtain the same performance as of the optimal uniform quantizer in a wide range of variance of input data. It is shown that the proposed 24-bit fixed-point format achieves for 18.425 dB higher performance than the floating-point format with the same number of bits while being less complex.

29

Zhang, Jinwang, Fangxu Lv, Jianjun Shi, Zixiang Tang, and Dongbin Lv. "A High Phase Detection Density and Low Space Complexity Mueller-Muller Phase Detector for DB PAM-4 Wireline Receiver." Electronics 11, no. 19 (October 9, 2022): 3246. http://dx.doi.org/10.3390/electronics11193246.

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A Mueller-Muller Phase Detector (MM PD) technology based on duo-binary four-level pulse amplitude modulation (DB PAM-4) with low complexity and high phase-detection density is presented. The proposed low complexity includes low phase-detection complexity and low space complexity of data processing. The waveform sifting technology simplifies 175 specific waveform changes into five fuzzy waveform change trends, reducing the complexity of subsequent phase detection. By making the data sample before the waveform sifting, the data bit width is reduced from 8 bit to 3 bit, which realizes data dimensionality reduction, greatly reduces the scale of subsequent auxiliary data, reduces the number of basic devices by 13.7%, and reduces the spatial complexity of data processing. The coherent coding of DB PAM-4 combined with waveform sifting increases the phase-detection density from 50% to 65% and improves both phase-detection density and phase-detection gain by 30%, and improves the jitter tolerance. Through the simulation of the clock and data recovery (CDR) model built by Cadence, the fast locking capability of CDR is verified.

30

Crook, David, and John Fulcher. "A Comparison of Bit Serial and Bit Parallel DCT Designs." VLSI Design 3, no. 1 (January 1, 1995): 59–65. http://dx.doi.org/10.1155/1995/30583.

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Bit parallel and bit serial VLSI designs for performing 1-dimensional Discrete Cosine Transforms are compared, in terms of size, complexity and throughput. It is concluded that the bit serial approach is more suited to this application, given the limitations of not only the available VLSI design tools, but also the available silicon real estate allocated for final chip fabrication.

31

Zhang, Yu Long, Guo Chu Shou, Yi Hong Hu, and Zhi Gang Guo. "Low Complexity GF(2^m) Multiplier Based on Iterative Karatsuba Algorithm." Advanced Materials Research 546-547 (July 2012): 1409–14. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.1409.

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The complexity is one important index for Galois Field multiplier. This paper presents one low complexity GF(2m) multiplier based on iterative Karatsuba algorithm. The multiplication is replaced iteratively by three ones of half-length operands which are performed in parallel. The operands are divided into different width such as 64-bit, 32-bit, 16-bit and so on. For the 2m*2mmultiplier, we take 128 bit-widthGF(2128) multipliers as an example. We implement them on FPGA and count the number of the used LUTs and the used registers. Through analyzing the statistic, we find that, when the width of the two multiplication operands is divided to 8 bit, the multiplier consumes the least resources. Compared with the FPGA implementation of the other previous multiplier, this optimum multiplier can save 50% resources in LUTs and the registers.

32

Sun, Shuliang. "A New Information Hiding Method Based on Improved BPCS Steganography." Advances in Multimedia 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/698492.

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Bit-plane complexity segmentation (BPCS) steganography is advantageous in its capacity and imperceptibility. The important step of BPCS steganography is how to locate noisy regions in a cover image exactly. The regular method, black-and-white border complexity, is a simple and easy way, but it is not always useful, especially for periodical patterns. Run-length irregularity and border noisiness are introduced in this paper to work out this problem. Canonical Cray coding (CGC) is also used to replace pure binary coding (PBC), because CGC makes use of characteristic of human vision system. Conjugation operation is applied to convert simple blocks into complex ones. In order to contradict BPCS steganalysis, improved BPCS steganography algorithm adopted different bit-planes with different complexity. The higher the bit-plane is, the smaller the complexity is. It is proven that the improved BPCS steganography is superior to BPCS steganography by experiment.

33

Ge, Shaodi, Chongyi Fan, Jian Wang, and Xiaotao Huang. "Low-Complexity One-Bit DOA Estimation for Massive ULA with a Single Snapshot." Remote Sensing 14, no. 14 (July 17, 2022): 3436. http://dx.doi.org/10.3390/rs14143436.

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Existing one-bit direction of arrival (DOA) estimate methods based on sparse recovery or subspace have issues when used for massive uniform linear arrays (MULAs), such as high computing cost, estimation accuracy depending on grid size, or high snapshot-number requirements. This paper considers the low-complexity one-bit DOA estimation problems for MULA with a single snapshot. Theoretical study and simulation results demonstrate that discrete Fourier transform (DFT) can be applied to MULA for reliable initial DOA estimation even when the received data are quantized by one-bit methods. A precise estimate is then obtained by searching within a tiny area. The resulting method is called one-bit DFT. This method is straightforward and simple to implement. High-precision DOA estimates of MULA can be obtained with a single snapshot, and the computational complexity is significantly less than that of existing one-bit DOA estimation methods. Moreover, the suggested method is easily extensible to multiple snapshot scenarios, and increasing the number of snapshots can further improve estimation precision. Simulation results show the effectiveness of the one-bit DFT method.

34

Xu, Guoliang, and Daowen Qiu. "Partial Boolean Functions With Exact Quantum Query Complexity One." Entropy 23, no. 2 (February 3, 2021): 189. http://dx.doi.org/10.3390/e23020189.

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We provide two sufficient and necessary conditions to characterize any n-bit partial Boolean function with exact quantum query complexity 1. Using the first characterization, we present all n-bit partial Boolean functions that depend on n bits and can be computed exactly by a 1-query quantum algorithm. Due to the second characterization, we construct a function F that maps any n-bit partial Boolean function to some integer, and if an n-bit partial Boolean function f depends on k bits and can be computed exactly by a 1-query quantum algorithm, then F(f) is non-positive. In addition, we show that the number of all n-bit partial Boolean functions that depend on k bits and can be computed exactly by a 1-query quantum algorithm is not bigger than an upper bound depending on n and k. Most importantly, the upper bound is far less than the number of all n-bit partial Boolean functions for all efficiently big n.

35

IBHARALU, F. T., M. O. FALOWO, and A. T. AKINWALE. "RSA ENCRYPTION ALGORITHM AUGUMENTED WITH BIT-STUFFING TECHNIQUE FOR DATA SECURITY." Journal of Natural Sciences Engineering and Technology 16, no. 1 (June 29, 2017): 19–34. http://dx.doi.org/10.51406/jnset.v16i1.1800.

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Data transmission through the internet applications is growing very fast, and this continuous growth demands for new network bandwidth and data security. Encryption plays a major role in security of information systems and internet based applications. In this study, the RSA algorithm was modified with bit-stuffing technique to improve the protection and security of confidential data while in transits or in storage. Our modified algorithm, RSA Bit-stuffed, was implemented and compared with the modified Ron Divest Code4 and the modified RSA in MATLAB using time complexity and avalanche effect as performance metrics. The experimental results showed that our augmented bit-insertion technique increased the time complexity against different attacks, boost the randomness of encrypted messages, and also improve security of encryption keys with bit-length lower than that of the standard RSA.

36

Chung-Chin Lu and Sy-Hann Huang. "On bit-level trellis complexity of Reed-Muller codes." IEEE Transactions on Information Theory 41, no. 6 (1995): 2061–64. http://dx.doi.org/10.1109/18.476337.

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37

Choi, Changryoul, and Jechang Jeong. "Constrained two-bit transform for low-complexity motion estimation." Optical Engineering 53, no. 6 (June 23, 2014): 063104. http://dx.doi.org/10.1117/1.oe.53.6.063104.

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38

Vernon, Stephen Decker. "Reduced computational complexity of bit allocation for perceptual coding." Journal of the Acoustical Society of America 125, no. 1 (2009): 586. http://dx.doi.org/10.1121/1.3074487.

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39

Tao Li, Wai Ho Mow, and K. B. Letaief. "Low complexity iterative decoding for bit-interleaved coded modulation." IEEE Transactions on Wireless Communications 5, no. 8 (August 2006): 1966–70. http://dx.doi.org/10.1109/twc.2006.1687705.

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40

Lee, Chiou-Yng. "Low-complexity bit-parallel systolic multipliers over GF(2 )." Integration 41, no. 1 (January 2008): 106–12. http://dx.doi.org/10.1016/j.vlsi.2007.05.001.

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41

Putera, Andysah. "High Complexity Bit-Plane Security Enchancement in BPCS Steganography." International Journal of Computer Applications 148, no. 3 (August 16, 2016): 17–22. http://dx.doi.org/10.5120/ijca2016911069.

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42

Wang, Qi, Qiuliang Xie, Zhaocheng Wang, Sheng Chen, and Lajos Hanzo. "A Universal Low-Complexity Symbol-to-Bit Soft Demapper." IEEE Transactions on Vehicular Technology 63, no. 1 (January 2014): 119–30. http://dx.doi.org/10.1109/tvt.2013.2272640.

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43

Zhao, Chun'e, Wenping Ma, Tongjiang Yan, and Yuhua Sun. "Linear Complexity of Least Significant Bit of Polynomial Quotients." Chinese Journal of Electronics 26, no. 3 (May 1, 2017): 573–78. http://dx.doi.org/10.1049/cje.2016.10.008.

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44

Chang, Nam Su, Eun Sook Kang, and Seokhie Hong. "Low complexity bit-parallel multiplier forF2ndefined by repeated polynomials." Discrete Applied Mathematics 241 (May 2018): 2–12. http://dx.doi.org/10.1016/j.dam.2016.07.014.

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45

Vozna, N., A. Davletova, Ya Nykolaychuk, and V. Gryga. "Improvement of multi-digital multiplicating devices structures in different theoretical and numerical bases." Computer systems and network 3, no. 1 (December 10, 2021): 7–19. http://dx.doi.org/10.23939/csn2021.01.007.

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The article proposes methods for improving the structures of multi-bit multipliers, which are characterized by increased speed, reduced structural complexity of the device and reduced structural complexity of inputs and outputs depending on the bit multipliers (512-2048 bits), respectively (1024- 4096) times, compared with known multipliers based on classic single-digit full adders. Optimization of structures of multi-bit multipliers is offered. Comparative estimates of structural, functional and relative functional and structural complexities of their circuit implementations are given. The use of optimized circuit solutions of multipliers allows to significantly improve the system characteristics of complex computing devices with a large number of such components in the crystals of microelectronic technologies.

46

Wang, Zongyue, Xiaoyang Dong, Keting Jia, and Jingyuan Zhao. "Differential Fault Attack on KASUMI Cipher Used in GSM Telephony." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/251853.

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The confidentiality of GSM cellular telephony depends on the security of A5 family of cryptosystems. As an algorithm in this family survived from cryptanalysis, A5/3 is based on the block cipher KASUMI. This paper describes a novel differential fault attack on KAUSMI with a 64-bit key. Taking advantage of some mathematical observations on the FL, FO functions, and key schedule, only one 16-bit word fault is required to recover all information of the 64-bit key. The time complexity is only 232encryptions. We have practically simulated the attack on a PC which takes only a few minutes to recover all the key bits. The simulation also experimentally verifies the correctness and complexity.

47

Beeharry, Yogesh, Tulsi Pawan Fowdur, and Krishnaraj M. S. Soyjaudah. "SYMBOL LEVEL DECODING FOR DUO-BINARY TURBO CODES." IIUM Engineering Journal 18, no. 1 (May 30, 2017): 111–31. http://dx.doi.org/10.31436/iiumej.v18i1.661.

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This paper investigates the performance of three different symbol level decoding algorithms for Duo-Binary Turbo codes. Explicit details of the computations involved in the three decoding techniques, and a computational complexity analysis are given. Simulation results with different couple lengths, code-rates, and QPSK modulation reveal that the symbol level decoding with bit-level information outperforms the symbol level decoding by 0.1 dB on average in the error floor region. Moreover, a complexity analysis reveals that symbol level decoding with bit-level information reduces the decoding complexity by 19.6 % in terms of the total number of computations required for each half-iteration as compared to symbol level decoding.

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Chen, Qunlin, Derong Chen, Jiulu Gong, and Jie Ruan. "Low-Complexity Rate-Distortion Optimization of Sampling Rate and Bit-Depth for Compressed Sensing of Images." Entropy 22, no. 1 (January 20, 2020): 125. http://dx.doi.org/10.3390/e22010125.

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Compressed sensing (CS) offers a framework for image acquisition, which has excellent potential in image sampling and compression applications due to the sub-Nyquist sampling rate and low complexity. In engineering practices, the resulting CS samples are quantized by finite bits for transmission. In circumstances where the bit budget for image transmission is constrained, knowing how to choose the sampling rate and the number of bits per measurement (bit-depth) is essential for the quality of CS reconstruction. In this paper, we first present a bit-rate model that considers the compression performance of CS, quantification, and entropy coder. The bit-rate model reveals the relationship between bit rate, sampling rate, and bit-depth. Then, we propose a relative peak signal-to-noise ratio (PSNR) model for evaluating distortion, which reveals the relationship between relative PSNR, sampling rate, and bit-depth. Finally, the optimal sampling rate and bit-depth are determined based on the rate-distortion (RD) criteria with the bit-rate model and the relative PSNR model. The experimental results show that the actual bit rate obtained by the optimized sampling rate and bit-depth is very close to the target bit rate. Compared with the traditional CS coding method with a fixed sampling rate, the proposed method provides better rate-distortion performance, and the additional calculation amount amounts to less than 1%.

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Kuper, Rob. "Effects of Flowering, Foliation, and Autumn Colors on Preference and Restorative Potential for Designed Digital Landscape Models." Environment and Behavior 52, no. 5 (November 12, 2018): 544–76. http://dx.doi.org/10.1177/0013916518811424.

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We investigated whether the vegetative state depicted in stimuli and the date of evaluation were related to restorative potential (RP) and preference ratings. Stimuli depicted flowering, foliated, or autumn-colored plants in scattershot, clustered, or formal arrangements that represented complexity values equal to zero, 2, or 4 bits of Shannon’s information entropy. Analyses included 77 and 105 respondents’ RP and preference ratings from September and January, respectively, along with 106 respondents’ fascination and complexity estimations from September. Evaluation dates did not relate to respondents’ RP and preference ratings. Respondents rated flowering and autumn-colored views significantly higher in RP and preference than foliated, 4-bit views higher than 2-bit, and 2-bit views higher than zero-bit. Flowering and autumn-colored foliage, view area, and plant region quantities indirectly affected RP ratings through preference. Important landscape design and practice implications follow: Flowering plants and red or yellow autumn-colored foliage may increase users’ preference and RP.

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Sadiq, B. J. S., V. Yu Tsviatkou та М. N. Bobov. "Аdaptive combined image coding with prediction of arithmetic code volume". Doklady BGUIR 19, № 2 (27 березня 2021): 31–39. http://dx.doi.org/10.35596/1729-7648-2021-19-2-31-39.

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The problem of increasing the efficiency of coding of halftone images in the space of bit planes of differences in pixel values obtained using differential coding (DPCM – Differential pulse-code modulation) is considered. For a compact representation of DPCM pixel values, it is proposed to use a combined compression encoder that implements arithmetic coding and run-length coding. An arithmetic encoder provides high compression ratios, but has high computational complexity and significant encoding overhead. This makes it effective primarily for compressing the mean-value bit-planes of DPCM pixel values. Run-length coding is extremely simple and outperforms arithmetic coding in compressing long sequences of repetitive symbols that often occur in the upper bit planes of DPCM pixel values. For DPCM bit planes of pixel values of any image, a combination of simple run length coders and complex arithmetic coders can be selected that provides the maximum compression ratio for each bit plane and all planes in general with the least computational complexity. As a result, each image has its own effective combined encoder structure, which depends on the distribution of bits in the bit planes of the DPCM pixel values. To adapt the structure of the combined encoder to the distribution of bits in the bit planes of DPCM pixel values, the article proposes to use prediction of the volume of arithmetic code based on entropy and comparison of the obtained predicted value with the volume of run length code. The entropy is calculated based on the values of the number of repetitions of ones and zero symbols, which are obtained as intermediate results of the run length encoding. This does not require additional computational costs. It was found that in comparison with the adaptation of the combined encoder structure using direct determination of the arithmetic code volume of each bit plane of DPCM pixel values, the proposed encoder structure provides a significant reduction in computational complexity while maintaining high image compression ratios.

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