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Relevant bibliographies by topics / Random binary sequences

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Academic literature on the topic 'Random binary sequences'

Author: Grafiati

Published: 5 June 2025

Last updated: 24 June 2025

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Journal articles on the topic "Random binary sequences"

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Hertling, Peter. "Nonrandom Sequences between Random Sequences." JUCS - Journal of Universal Computer Science 11, no. (12) (2005): 1970–85. https://doi.org/10.3217/jucs-011-12-1970.

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Let us say that an infinite binary sequence q lies above an infinite binary sequence p if q can be obtained from p by replacing selected 0's in p by 1's. We show that above any infinite binary Martin-Löf random sequence p there exists an infinite binary nonrandom sequence q above which there exists an infinite binary random sequence r. This result is of interest especially in connection with the new randomness notion for sets of natural numbers introduced in [Hertling and Weihrauch 1998, Hertling and Weihrauch 2003] and in connection with its relation to the Martin-Löf randomness notion for infinite binary sequences.

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Šajić, Slavko, Nebojša Maletić, Branislav M. Todorović, and Milan Šunjevarić. "Random Binary Sequences in Telecommunications." Journal of Electrical Engineering 64, no. 4 (2013): 230–37. http://dx.doi.org/10.2478/jee-2013-0033.

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Realization of modern telecommunication systems is inconceivable without use of different binary sequences. In this paper, an overview of random binary sequences used in different telecommunication systems is given. Basic principles of pseudorandom, chaotic, and true random sequence generation are presented, as well as their application in telecommunications in respect to advantages and drawbacks of the same. Moreover, particular scheme for true random binary sequence generation is given, as well as results of randomness assessment obtained by NIST statistical test suite. Finally, short insight into importance of random binary sequence in secure communications is given.

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De Felice, Antonello. "Binary Pseudo-Random Sequences Theory." Journal of Information and Optimization Sciences 12, no. 2 (1991): 325–33. http://dx.doi.org/10.1080/02522667.1991.10699074.

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MERCER, IDRIS DAVID. "Autocorrelations of Random Binary Sequences." Combinatorics, Probability and Computing 15, no. 05 (2006): 663. http://dx.doi.org/10.1017/s0963548306007589.

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NICKERSON, RAYMOND S., and SUSAN F. BUTLER. "On producing random binary sequences." American Journal of Psychology 122, no. 2 (2009): 141–51. http://dx.doi.org/10.2307/27784387.

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Abstract This experiment addressed the opinion prevailing among researchers that people are poor at producing random binary sequences. Participants tried to produce sets of sequences of outcomes of imaginary coin tosses that could not be distinguished statistically from sets expected from actual coin tossing. The results generally support the conclusion that people are not very good at this task, although the distributional properties of the sets of sequences produced are qualitatively similar to those expected of sets produced by a random process. The results do not support the common finding that people consistently produce substantially more alternations and fewer repetitions than would be produced by chance, nor do they provide evidence of the pervasive operation of a gambler’s fallacy manifesting itself in a tendency for an alternation to increase with the length of a preceding run.

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M., Vishwas C. G., and R. Sanjeev Kunte. "Chaotic Map based Random Binary Key Sequence Generation." International Journal of Computer Network and Information Security 16, no. 4 (2024): 102–17. http://dx.doi.org/10.5815/ijcnis.2024.04.07.

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Image encryption is an efficient mechanism by which digital images can be secured during transmission over communication in which key sequence generation plays a vital role. The proposed system consists of stages such as the generation of four chaotic maps, conversion of generated maps to binary vectors, rotation of Linear Feedback Shift Register (LFSR), and selection of generated binary chaotic key sequences from the generated key pool. The novelty of this implementation is to generate binary sequences by selecting from all four chaotic maps viz., Tent, Logistic, Henon, and Arnold Cat map (ACM). LFSR selects chaotic maps to produce random key sequences. Five primitive polynomials of degrees 5, 6, 7, and 8 are considered for the generation of key sequences. Each primitive polynomial generates 61 binary key sequences stored in a binary key pool. All 61 binary key sequences generated are submitted for the NIST and FIPS tests. Performance analysis is carried out of the generated binary key sequences. From the obtained results, it can be concluded that the binary key sequences are random and unpredictable and have a large key space based on the individual and combination of key sequences. Also, the generated binary key sequences can be efficiently utilized for the encryption of digital images.

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Liu, Lingfeng, Suoxia Miao, Mengfan Cheng, and Xiaojing Gao. "Permutation Entropy for Random Binary Sequences." Entropy 17, no. 12 (2015): 8207–16. http://dx.doi.org/10.3390/e17127872.

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Borbély, József, and András Sárközy. "Quasi-Random Graphs, Pseudo-Random Graphs and Pseudorandom Binary Sequences, I. (Quasi-Random Graphs)." Uniform distribution theory 14, no. 2 (2019): 103–26. http://dx.doi.org/10.2478/udt-2019-0017.

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AbstractIn the last decades many results have been proved on pseudo-randomness of binary sequences. In this series our goal is to show that using many of these results one can also construct large families of quasi-random, pseudo-random and strongly pseudo-random graphs. Indeed, it will be proved that if the first row of the adjacency matrix of a circulant graph forms a binary sequence which possesses certain pseudorandom properties (and there are many large families of binary sequences known with these properties), then the graph is quasi-random, pseudo-random or strongly pseudo-random, respectively. In particular, here in Part I we will construct large families of quasi-random graphs along these lines. (In Parts II and III we will present and study constructions for pseudo-random and strongly pseudo-random graphs, respectively.)

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Sanderson, Yasmine B. "Effective generation of subjectively random binary sequences." Advances in Applied Mathematics 43, no. 1 (2009): 1–11. http://dx.doi.org/10.1016/j.aam.2008.11.003.

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Tsuneda, Akio. "Various Auto-Correlation Functions of m-Bit Random Numbers Generated from Chaotic Binary Sequences." Entropy 23, no. 10 (2021): 1295. http://dx.doi.org/10.3390/e23101295.

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This paper discusses the auto-correlation functions of m-bit random numbers obtained from m chaotic binary sequences generated by one-dimensional nonlinear maps. First, we provide the theoretical auto-correlation function of an m-bit sequence obtained by m binary sequences that are assumed to be uncorrelated to each other. The auto-correlation function is expressed by a simple form using the auto-correlation functions of the binary sequences. This implies that the auto-correlation properties of the m-bit sequences can be easily controlled by the auto-correlation functions of the original binary sequences. In numerical experiments using a computer, we generated m-bit random sequences using some chaotic binary sequences with prescribed auto-correlations generated by one-dimensional chaotic maps. The numerical experiments show that the numerical auto-correlation values are almost equal to the corresponding theoretical ones, and we can generate m-bit sequences with a variety of auto-correlation properties. Furthermore, we also show that the distributions of the generated m-bit sequences are uniform if all of the original binary sequences are balanced (i.e., the probability of 1 (or 0) is equal to 1/2) and independent of one another.

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Dissertations / Theses on the topic "Random binary sequences"

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O'Donovan, T. L. "Signal processing and pseudo-random binary sequences." Thesis, Bangor University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307237.

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Savvidis, Charalampos, and Zeyang Geng. "Onboard Impedance Diagnostics Method of Li-ion Traction Batteries using Pseudo-Random Binary Sequence." Thesis, Linköpings universitet, Tekniska fakulteten, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-118970.

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Environmental and economic reasons have lead automotive companies towards the direction of EVs and HEVs. Stricter emission legislations along with the consumer needs for more cost-efficient and environmental friendly vehicles have increased immensely the amount of hybrid and electric vehicles available in the market. It is essential though for Li-ion batteries, the main propulsion force of EVs and HEVs, to be able to read the battery characteristics in a high accuracy manner, predict life expectancy and behaviour and act accordingly. The following thesis constitutes a concept study of a battery diagnostics method. The method is based on the notion of a pseudo-random binary signal used as the current input and from its voltage response, the impedance is used for the estimation of parameters such as the state of charge and more. The feasibility of the PRBS method at a battery cell has been examined through various tests, both in an experimental manner at the lab but also in a simulation manner. The method is compared for validation against the electrochemical impedance spectroscopy method which is being used as a reference. For both the experimental and the simulation examinations, the PRBS method has been validated and proven to work. No matter the change in the parameters of the system, the method behaves in a similar manner as in the reference EIS method. The level of detail in the research and the performed experiments is what makes the significance of the results of high importance. The method in all ways has been proven to work in the concept study and based on the findings, if implemented on an EV’s or HEV’s electric drive line and the same functionality is observed, be used as a diagnostics method of the battery of the vehicle.

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Martinson, Christiaan Adolph. "Characterisation of a PEM electrolyser using the current interrupt method / Christiaan Adolph Martinson." Thesis, North-West University, 2012. http://hdl.handle.net/10394/8741.

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The need to characterise a PEM electrolyser is motivated by a South African hydrogen company. One of two electrochemical characterisation methods, namely the current interrupt method or electrochemical impedance spectroscopy, is investigated to characterise the PEM electrolyser. Various literature sources can be found on the electrochemical characterisation methods. In this study the current interrupt method is used for the electrochemical characterisation of a PEM electrolyser. The current interrupt method is an electrical test method that will be used to obtain an equivalent electric circuit model of the PEM electrolyser. The equivalent electric circuit model relates to various electrochemical characteristics such as the activation losses, the ohmic losses and the concentration losses. Two variants of the current interrupt method, namely the natural voltage response method and the current switching method, are presented. These methods are used to obtain two different equivalent electric circuit models of the PEM electrolyser. The parameters of the first equivalent electric circuit, namely the Randles cell, will be estimated with the natural voltage response method. The parameters of the second equivalent electric circuit, namely the Randles-Warburg cell, will be estimated with the current switching method. Simulation models of the equivalent electric circuits are developed and tested. The simulation models are used to verify and validate the natural voltage response method and the current switching method. The parameters of the Randles cell simulation model is accurately calculated with the natural voltage response method. The parameters of the Randles-Warburg cell simulation model is accurately calculated with the current switching method. The natural voltage response method and the current switching method are also practically implemented. The results is used to indicate the various electrochemical characteristics of the PEM electrolyser. A Nafion 117 type membrane was tested with the current interrupt method. The membrane resistance parameters of Randles cell were estimated with the natural voltage response method. These values are validated with conductivity measurements found in literature. The results of the Randles- Warburg cell is validated with a system identification validation model.<br>Thesis (MIng (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013

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Weng, Chih-Chi, and 翁志齊. "High-Speed Pseudo Random Binary Sequence Generator." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/13756496845238039684.

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Book chapters on the topic "Random binary sequences"

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Schmidt, Kai-Uwe. "On Random Binary Sequences." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30615-0_28.

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Kohda, Tohru. "Sequences of I.I.D. Binary Random Variables Using Chaotic Dynamics." In Sequences and their Applications. Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0551-0_22.

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Tretter, Steven A. "Pseudo-Random Binary Sequences and Data Scramblers." In Communication System Design Using DSP Algorithms. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4757-9763-3_9.

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Tretter, Steven A. "Pseudo-Random Binary Sequences and Data Scramblers." In Communication System Design Using DSP Algorithms. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4613-0229-2_9.

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Borgosz, Jan, and Boguslaw Cyganek. "Pseudo-Random Binary Sequences Synchronizer Based on Neural Networks." In Computational Science and Its Applications – ICCSA 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24767-8_75.

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Chen, Zhixiong, Shengqiang Li, and Guozhen Xiao. "Construction of Pseudo-random Binary Sequences from Elliptic Curves by Using Discrete Logarithm." In Sequences and Their Applications – SETA 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11863854_24.

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Balakirsky, Vladimir B. "Generating Functions Associated with Random Binary Sequences Consisting of Runs of Lengths 1 and 2." In Sequences and Their Applications - SETA 2004. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11423461_24.

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Finko, Oleg, and Sergey Dichenko. "Secure Pseudo-Random Linear Binary Sequences Generators Based on Arithmetic Polynoms." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15147-2_24.

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Wan, Jie, and Jeffrey Zheng. "Permutation and Complementary Algorithm to Generate Random Sequences for Binary Logic." In Variant Construction from Theoretical Foundation to Applications. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2282-2_14.

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Prestwich, Steven. "A Hybrid Search Architecture Applied to Hard Random 3-SAT and Low-Autocorrelation Binary Sequences." In Principles and Practice of Constraint Programming – CP 2000. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45349-0_25.

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Conference papers on the topic "Random binary sequences"

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Wicik, Robert. "Generating and Testing Binary Random Sequences for Classified Information Protection Systems." In 2024 New Trends in Signal Processing (NTSP). IEEE, 2024. http://dx.doi.org/10.23919/ntsp61680.2024.10726293.

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Oumouss, Lahcen, Asimi Younes, Asimi Ahmed, and Arjdal Rguibi. "Cryptographically robust pseudo-random binary sequence generator based on the integration of LFSRs and CAs." In 2024 International Conference on Circuit, Systems and Communication (ICCSC). IEEE, 2024. http://dx.doi.org/10.1109/iccsc62074.2024.10616798.

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Horan, D. M., and R. A. Guinee. "Correlation analysis of random number sequences based on pseudo random binary sequence generation." In IEEE Information Theory Workshop, 2005. IEEE, 2005. http://dx.doi.org/10.1109/itw.2005.1531861.

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Luca, Adrian, Azeem Ilyas, and Adriana Vlad. "Generating random binary sequences using tent map." In 2011 10th International Symposium on Signals, Circuits and Systems (ISSCS). IEEE, 2011. http://dx.doi.org/10.1109/isscs.2011.5978664.

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Yan, Li, Chris Bingham, Samuel Cruz-Manzo, Delong Cui, and Yunrong Lv. "Battery Impedance Measurement Using Pseudo Random Binary Sequences." In 2020 IEEE Student Conference on Electric Machines and Systems (SCEMS). IEEE, 2020. http://dx.doi.org/10.1109/scems48876.2020.9352336.

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Pesoshin, Valery, Artyom Gumirov, Valery Kuznetsov, and Darya Shirshova. "Generators of the Binary Inverse-Segment Pseudo-Random Sequences." In 2018 IEEE East-West Design & Test Symposium (EWDTS). IEEE, 2018. http://dx.doi.org/10.1109/ewdts.2018.8524819.

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Fairweather, A. J., M. P. Foster, and D. A. Stone. "VRLA battery parameter identification using pseudo random binary sequences (PRBS)." In 5th IET International Conference on Power Electronics, Machines and Drives (PEMD 2010). Institution of Engineering and Technology, 2010. http://dx.doi.org/10.1049/cp.2010.0118.

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Guarin, G., M. Gardill, R. Weigel, G. Fischer, and D. Kissinger. "Ultra-wideband compressed sensing radar based on pseudo random binary sequences." In 2015 German Microwave Conference (GeMiC). IEEE, 2015. http://dx.doi.org/10.1109/gemic.2015.7107796.

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Horan, D., and R. Guinee. "A novel keystream generator using pseudo random binary sequences for cryptographic applications." In IET Irish Signals and Systems Conference (ISSC 2006). IEE, 2006. http://dx.doi.org/10.1049/cp:20060478.

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Lukin, Konstantin, and Valery Shcherbakov. "New method for generation of binary pseudo-random sequences with quasiperfect autocorrelation." In 2016 9th International Kharkiv Symposium on Physics and Engineering of Microwaves, Millimeter and Submillimeter Waves (MSMW). IEEE, 2016. http://dx.doi.org/10.1109/msmw.2016.7538163.

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Reports on the topic "Random binary sequences"

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Yang, Yu, Hen-Geul Yeh, and Bryan Aguirre. Fuel Cell System Development for Heavy Duty Vehicles. Mineta Transportation Institute, 2025. https://doi.org/10.31979/mti.2025.2441.

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As California advances its ambitious goals for transportation electrification to combat climate change, hydrogen-powered fuel cells are emerging as a viable solution for overcoming the challenges of heavy-duty vehicles, offering an efficient alternative to lithium-ion batteries because they produce minimal chemical, thermal, and carbon emissions. One type of hydrogen fuel cell technology called proton exchange membrane fuel cells (PEMFCs) has garnered the most attention due to its distinct advantages, including relatively low operating temperatures (60–80 °C) and reliable performance at high current densities. However, despite their promise, PEMFCs face challenges, including in optimizing stack power output and safety concerns. To tackle these issues, accurate modeling and control strategies are essential. This study focuses on using data-driven modeling (specifically using a process known as “closed-loop system identification” under proportional controller and pseudo-random binary sequence excitation methods) to better understand and manage PEMFC systems. Various transfer functions models were analyzed, including first-order, first-order plus time delay, second-order, and second-order plus time delay models. The resulting closed-loop identification approach was applied on the humidifier, cooling, and oxygen supplier subsystems of simulated PEMFC to build their models under controlled operations. The results of this study highlight the potential of closed-loop system identification techniques to improve fuel cell vehicle performance in power supply, water, and heat management, without interrupting PEMFC operations. These findings demonstrate the significance of precise modeling as a cornerstone for advancing PEMFC control strategies and optimizing their application in renewable transportation and a more sustainable future.

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