Academic literature on the topic 'Sequenze pseudorandom'
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Journal articles on the topic "Sequenze pseudorandom"
Lin, Dai Mao, Xin Li, and Lei Zhang. "Spectrum Analysis of the Stream Cipher." Applied Mechanics and Materials 380-384 (August 2013): 2884–87. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.2884.
Full textGyarmati, Katalin. "Concatenation of Legendre symbol sequences." Studia Scientiarum Mathematicarum Hungarica 48, no. 2 (June 1, 2011): 193–204. http://dx.doi.org/10.1556/sscmath.48.2011.2.1150.
Full textSárközy, András. "A finite pseudorandom binary sequence." Studia Scientiarum Mathematicarum Hungarica 38, no. 1-4 (May 1, 2001): 377–84. http://dx.doi.org/10.1556/sscmath.38.2001.1-4.28.
Full textQi, Yuchan, and Huaning Liu. "Binary sequences and lattices constructed by discrete logarithms." AIMS Mathematics 7, no. 3 (2022): 4655–71. http://dx.doi.org/10.3934/math.2022259.
Full textSarycheva, Anastasia, Alexey Adamov, Sergey S. Poteshin, Sergey S. Lagunov, and Alexey A. Sysoev. "Influence of multiplexing conditions on artefact signal and the signal-to-noise ratio in the decoded data in Hadamard transform ion mobility spectrometry." European Journal of Mass Spectrometry 26, no. 3 (January 24, 2020): 204–12. http://dx.doi.org/10.1177/1469066719900763.
Full textFeng, Yelai, Huaixi Wang, Chao Chang, Hongyi Lu, Fang Yang, and Chenyang Wang. "A Novel Nonlinear Pseudorandom Sequence Generator for the Fractal Function." Fractal and Fractional 6, no. 10 (October 13, 2022): 589. http://dx.doi.org/10.3390/fractalfract6100589.
Full textZaurbek, A., and D. Z. Dzhuruntaev. "DIGITAL OSCILLATOR CIRCUIT WITH AN EXTENDED REPETITION PERIOD OF A PSEUDO-RANDOM PULSE SEQUENCE." BULLETIN Series of Physics & Mathematical Sciences 69, no. 1 (March 10, 2020): 210–14. http://dx.doi.org/10.51889/2020-1.1728-7901.36.
Full textZvoníček, Václav. "A Pseudorandom Sequence Generated over a Finite Field Using The Möbius Function." Journal of the ASB Society 2, no. 1 (December 27, 2021): 36–42. http://dx.doi.org/10.51337/jasb20211227005.
Full textOkazaki, Hiroyuki. "Probability on Finite and Discrete Set and Uniform Distribution." Formalized Mathematics 17, no. 2 (January 1, 2009): 173–78. http://dx.doi.org/10.2478/v10037-009-0020-z.
Full textWang, Chuanfu, Yi Di, Jianyu Tang, Jing Shuai, Yuchen Zhang, and Qi Lu. "The Dynamic Analysis of a Novel Reconfigurable Cubic Chaotic Map and Its Application in Finite Field." Symmetry 13, no. 8 (August 3, 2021): 1420. http://dx.doi.org/10.3390/sym13081420.
Full textDissertations / Theses on the topic "Sequenze pseudorandom"
Gu, Ting. "STATISTICAL PROPERTIES OF PSEUDORANDOM SEQUENCES." UKnowledge, 2016. http://uknowledge.uky.edu/cs_etds/44.
Full textWalker, Wendy Tolle. "Chaotic pseudorandom sequences and radar." Diss., The University of Arizona, 1993. http://hdl.handle.net/10150/186317.
Full textManchiraju, Dinakar. "Evaluation of Pseudorandom Sequences used in 3rd Generation Spread Spectrum Systems." Ohio University / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1081801327.
Full textManchiraju, Dinakar. "Evaluation of pseudorandom sequences used in third generation spread spectrum systems." Ohio : Ohio University, 2003. http://www.ohiolink.edu/etd/view.cgi?ohiou1081801327.
Full textBasran, Jagdeep S. "Application of pseudorandom binary sequences to the absolute position measurement of automated guided vehicles." Thesis, University of Ottawa (Canada), 1989. http://hdl.handle.net/10393/5329.
Full textVergara, Tinoco Alexander. "Improving the performance of micro-machined metal oxide gas sensors: Optimization of the temperatura modulation mode via pseudorandom sequences." Doctoral thesis, Universitat Rovira i Virgili, 2006. http://hdl.handle.net/10803/8456.
Full textUno de los mayores problemas experimentados en los sistemas de detección de gases basados en dispositivos de óxidos metálicos es su falta de reproducibilidad, estabilidad y selectividad. Con el fin de intentar resolver estos problemas, diferentes estrategias han sido desarrolladas en paralelo. Algunas de ellas se relacionan con la mejora de los materiales y otras implican acondicionamiento o pre-tratamiento de las muestras. Otras estrategias ampliamente empleadas consisten en aprovechar que los sensores presentan sensibilidades solapadas para construir matrices de sensores y emplear técnicas de procesamiento de señal o bien utilizar características de la respuesta dinámica de los sensores.En los últimos años, modular la temperatura de trabajo de los sensores de óxidos metálicos se ha convertido en uno de los métodos más utilizados para incrementar su selectividad. Esto se debe a, dado que la respuesta del sensor varía con su propia temperatura de trabajo, entonces, en determinados casos, midiendo la respuesta de un sensor a n temperaturas de trabajo diferentes, es equivalente a tener una matriz de n sensores diferentes. Esto permite obtener información multivariante de cada sensor individualmente y ayuda a mantener baja la dimensionalidad del sistema de medida para resolver una determinada aplicación. A pesar de los buenos resultados que han sido publicados dentro de este ámbito, la selección de las frecuencias empleadas en la modulación de la temperatura de trabajo de los sensores ha consistido, hasta el momento, en un proceso empírico lo que no garantiza la obtención de los mejores resultados para una determinada aplicación.En este contexto, el principal objetivo de esta tesis doctoral ha consistido en desarrollar un método sistemático que permita determinar cuales son las frecuencias de modulación óptimas que podrían emplearse para resolver un determinado problema de análisis de gases. Este método, extraído del campo de identificación de sistemas, ha sido desarrollado e implementado por primera vez dentro del ámbito de los sensores de gases. Éste consiste en estudiar la respuesta de los sensores en presencia de gases mientras la temperatura de trabajo de los sensores es modulada mediante una señal pseudo-aleatoria de longitud máxima. Estas señales comparten algunas propiedades con el ruido blanco, y por tanto pueden ayudar a estimar la respuesta lineal de un sistema con no-linealidades (por ejemplo, la respuesta impulsional de un sistema sensor-gas).El proceso de optimización es llevado a cabo mediante la selección entre las componentes espectrales de las estimaciones de la respuesta impulsional, de aquellas que más ayudan ya sea a discriminar o a cuantificar los gases objetivo dentro de una aplicación de análisis de gases dada. Teniendo en cuenta que las componentes espectrales están directamente relacionadas con las frecuencias de modulación, la selección de unas pocas componentes espectrales resulta en la determinación de las frecuencias optimas de modulación.En los primeres experimentos, señales binarias pseudo-aleatorias fueron utilizadas para modular la temperatura de trabajo de los sensores de gases basados en óxidos metálicos micro-mecanizados en un rango comprendido entre 0 a 112.5 Hz. La frecuencia superior es ligeramente mayor a la frecuencia de corte de las membranas de los sensores. El resultado principal derivado de estos estudios fue que las frecuencias de modulación interesantes se encuentran en un rango comprendido entre 0 y 1 Hz. Esto es comprensible dado que la cinética de las reacciones y de los procesos de adsorción que se producen en la superficie del sensor son lentos y si estos se han de alterar mediante la modulación térmica, se habrá de elaborar señales de modulación a bajas frecuencias. Esto explica por que se han venido empleado señales moduladoras de temperatura en el rango de los mHz, a pesar que las membranas de un dispositivo micro-mecanizado presentan respuestas mucho más rápidas (típicamente en el orden de los 100 Hz).En los experimentos posteriores a los primeros, un método evolucionado para determinar las frecuencias de modulación óptimas de los sensores micro-mecanizados fue implementado, el cual se basa en el uso de secuencias pseudo-aleatorias multi-nivel de longitud máxima (MLPRS). Las señales de tipo multi-nivel fueron consideradas en lugar de las binarias ya que las primeras permiten obtener una mejor estimación que las segundas de la dinámica lineal de un sistema con no linealidades. Y es bien conocido que los sensores de gases basados en óxidos metálicos presentan no-linealidades en su respuesta.Estos estudios sistemáticos fueron completamente validados mediante la síntesis de señales multi-senoidales con las frecuencias previamente identificadas utilizando secuencias pseudo-aleatorias. Cuando la temperatura de trabajo de los sensores fue modulada por una señal, el contenido frecuencial de la cual es el óptimo, los gases y mezclas de gases considerados pudieron ser discriminados perfectamente y se verificó la posibilidad de obtener modelos de calibración precisos para predecir la concentración de los gases. En algunos casos, estos procesos de validación se llevaron a cabo con sensores que no habían sido utilizados durante el proceso de optimización (por ejemplo, una agrupación de sensores diferentes pero del mismo lote de fabricación).En resumen, El nuevo método desarrollado in esta tesis para seleccionar las frecuencias de modulación optimas se a mostrado consistente y efectivo. El método es de aplicación general y podría ser utilizado en cualquier problema de análisis de gases o bien extendido a otro tipo de sensores (por ejemplo sensores poliméricos).Las contribuciones científicas de esta tesis se han recogido en 4 artículos en revistas internacionales y trece actas de conferencias.
One of the major problems in gas sensing systems that use metal oxide devices is the lack of reproducibility, stability and selectivity. In order to tackle these troubles experienced with metal oxide gas sensors, different strategies have been developed in parallel. Some of these are related to the improvement of materials, or the use of sample conditioning and pre-treating methods. Other widely used techniques include taking benefit of the unavoidable partially overlapping sensitivities by using sensor arrays and pattern recognition techniques or the use of dynamic features from the gas sensor response.In the last years, modulating the working temperature of metal oxide gas sensors has been one of the most used methods to enhance sensor selectivity. This occurs because, since, the sensor response is different at different working temperatures, and therefore, measuring the sensor response at n different temperatures is, in some cases, similar to the use of an array comprising n different sensors. This allows for measuring multivariate information from every single sensor and helps in keeping low the dimensionality of the measurement system needed to solve a specific application. Although the good results reported, until now, the selection of the frequencies used to modulate the working temperature remained an empirical process and that is not an accurate method to ensure that the best results are reached for a given application.In view of this context, the principal objective of this doctoral thesis was to develop a systematic method to determine which are the optimal temperature modulation frequencies to solve a given gas analysis problem. This method, which is borrowed from the field of system identification, has been developed and introduced for the first time in the area of gas sensors. It consists of studying the sensor response to gases when the operating temperature is modulated via maximum-length pseudo-random sequences. Such signals share some properties with white noise and, therefore, can be of help to estimate the linear response of a system with non-linearity (e.g., the impulse response of a sensor-gas system).The optimization process is conducted by selecting among the spectral components of the impulse response estimates, the few that better help either discriminating or quantifying the target gases of a given gas analysis application. Since spectral components are directly related to modulating frequencies, the selection of spectral components results in the determination of the optimal temperature modulating frequencies.In the first experiments, pseudo-random binary signals (PRBS) were employed to modulate the working temperature of micro-machined metal oxide gas sensors in a frequency range from 0 up to 112.5 Hz. The upper frequency is slightly higher than the cutoff frequency of the sensor membranes. The outcome of this initial study was that the important modulating frequencies were in the range between 0 and 1 Hz. This is understandable, since the kinetics of reaction and adsorption processes taking place at the sensor surface (i.e., physisorption/chemisorption/ionosorption) are slow and if these are to be altered by the thermal modulation, low frequency modulating signals need to be devised. This explains why low-frequency temperature-modulating signals (i.e. in the mHz range) have been used with micro-hotplate gas sensors, even though the thermal response of their membranes is much faster (typically, near 100 Hz).In the experiments that followed the first ones, an evolved method to determine the optimal temperature modulating frequencies for micro-hotplate gas sensors was introduced, which was based on the use of maximum length multilevel pseudo-random sequences (MLPRS). Multilevel signals were considered instead of the binary ones because the former can provide a better estimate than the latter of the linear dynamics of a process with non-linearity. And it is well known that temperature-modulated metal oxide gas sensors present non-linearity in their response.These systematic studies were fully validated by synthesizing multi-sinusoidal signals at the optimal frequencies previously identified using pseudo-random sequences. When the sensors had their operating temperatures modulated by a signal with a frequency content that corresponded to the optimal, the gases and gas mixtures considered could be perfectly discriminated and the building of accurate calibration models to predict gas concentration was found to be possible. In some cases, the validation process was conducted on sensors that had not been used for optimization purposes (e.g. a different sensor array from the same fabrication batch).Summarizing, the new method developed in this thesis for selecting the optimal modulating frequencies is shown to be consistent and effective. The method applies generally and could be used in any gas analysis problem or extended to other type of sensors (e.g. conducting polymer sensors).The scientific contributions of this thesis are collected in four journal papers and thirteen conference proceedings.
Ramantanis, Petros. "Contribution to the analysis of optical transmission systems using QPSK modulation." Phd thesis, Institut National des Télécommunications, 2011. http://tel.archives-ouvertes.fr/tel-00765380.
Full textNowak, Michael J. "Mixed Modulation for Remote Sensing with Embedded Navigation." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1462295752.
Full textЛогвиненко, C. В. "Інформаційна стегосистема забезпечення конфіденційності інформації на основі псевдовипадкової послідовності." Master's thesis, Сумський державний університет, 2018. http://essuir.sumdu.edu.ua/handle/123456789/72005.
Full textLejsková, Alena. "Modulátor s rozprostřeným spektrem." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-218913.
Full textBooks on the topic "Sequenze pseudorandom"
Bastajian, V. L. Families of sequences constructed from pseudorandom arrays. Manchester: UMIST, 1996.
Find full textI͡Armolik, V. N. Generation and application of pseudorandom sequences for random testing. Chichester [West Sussex]: Wiley, 1988.
Find full textBook chapters on the topic "Sequenze pseudorandom"
Weik, Martin H. "pseudorandom binary sequence." In Computer Science and Communications Dictionary, 1364. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_15019.
Full textWeik, Martin H. "pseudorandom number sequence." In Computer Science and Communications Dictionary, 1364. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_15023.
Full textKneusel, Ronald T. "Random and Pseudorandom Sequences." In Random Numbers and Computers, 1–25. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77697-2_1.
Full textHershey, John E., and R. K. Rao Yarlagadda. "Random and Pseudorandom Sequences." In Data Transportation and Protection, 259–310. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2195-8_8.
Full textGong, Guang, Thomas A. Berson, and Douglas R. Stinson. "Elliptic Curve Pseudorandom Sequence Generators." In Selected Areas in Cryptography, 34–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-46513-8_3.
Full textBeelen, P. H. T., and J. M. Doumen. "Pseudorandom Sequences from Elliptic Curves." In Finite Fields with Applications to Coding Theory, Cryptography and Related Areas, 37–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-59435-9_3.
Full textRivat, J., and András Sárközy. "On Pseudorandom Sequences and Their Application." In Lecture Notes in Computer Science, 343–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11889342_19.
Full textWinterhof, Arne. "Recent Results on Recursive Nonlinear Pseudorandom Number Generators." In Sequences and Their Applications – SETA 2010, 113–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15874-2_9.
Full textPirsic, Gottlieb, and Arne Winterhof. "Boolean Functions Derived from Pseudorandom Binary Sequences." In Lecture Notes in Computer Science, 101–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30615-0_9.
Full textCerecedo, Manuel, Tsutomu Matsumoto, and Hideki Imai. "Non-interactive generation of shared pseudorandom sequences." In Advances in Cryptology — AUSCRYPT '92, 385–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-57220-1_77.
Full textConference papers on the topic "Sequenze pseudorandom"
Cartwright, Steven. "Optical Computer for Pseudorandom Sequence Identification." In Optical Computing. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/optcomp.1985.wb6.
Full textBusireddygari, Prashanth, and Subhash Kak. "Pseudorandom tableau sequences." In 2017 51st Asilomar Conference on Signals, Systems, and Computers. IEEE, 2017. http://dx.doi.org/10.1109/acssc.2017.8335657.
Full textLi, Qi, Junping Gao, and Xiaoqun Zhao. "Pseudorandom Punctured Binary Sequence Pairs." In 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2008. http://dx.doi.org/10.1109/wicom.2008.405.
Full textRevuelta, J., L. Pesquera, and C. R. Mirasso. "Transmission of solitons at 15 GHz and 20 GHz generated by pseudorandom modulation of laser diodes using TDM." In The European Conference on Lasers and Electro-Optics. Washington, D.C.: Optica Publishing Group, 1996. http://dx.doi.org/10.1364/cleo_europe.1996.cthi47.
Full textPoustie, A. J., K. J. Blow, R. J. Manning, and A. E. Kelly. "All-optical pseudorandom bit sequence generator." In The European Conference on Lasers and Electro-Optics. Washington, D.C.: Optica Publishing Group, 1998. http://dx.doi.org/10.1364/cleo_europe.1998.cpd2.2.
Full textDmitriev, Dmitry, Aleksey Sokolovskiy, Andrey Gladyshev, Vasily Ratushniak, and Valery Tyapkin. "Pseudorandom Sequence Generator Using CORDIC Processor." In 2019 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT). IEEE, 2019. http://dx.doi.org/10.1109/usbereit.2019.8736647.
Full textYa-tao, Yang, Wang Zhi-wei, Niu Xin-xin, and Yang Yi-xian. "A New Way to Construct Pseudorandom Sequence." In 2007 2nd IEEE Conference on Industrial Electronics and Applications. IEEE, 2007. http://dx.doi.org/10.1109/iciea.2007.4318863.
Full textChow, Alex, William S. Coates, and David Hopkins. "A Configurable Asynchronous Pseudorandom Bit Sequence Generator." In 13th IEEE International Symposium on Asynchronous Circuits and Systems (ASYNC'07). IEEE, 2007. http://dx.doi.org/10.1109/async.2007.5.
Full textUnkasevic, Tomislav B., Zoran D. Banjac, Milan Milosavljevic, Predrag Milosav, and Hazhar Abid Mustafa Al-Atrooshi. "Generic Pseudorandom Sequence Generator Based on Permutations." In 2019 27th Telecommunications Forum (TELFOR). IEEE, 2019. http://dx.doi.org/10.1109/telfor48224.2019.8971362.
Full textVISAN, Daniel Alexandru, Ioan LITA, Mariana JURIAN, and Mirela GHERGHE. "Pseudorandom Sequence Generator for Spread Spectrum Communications." In 2018 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2018. http://dx.doi.org/10.1109/ecai.2018.8678968.
Full textReports on the topic "Sequenze pseudorandom"
Barry, W. C., J. W. Heefner, G. S. Jones, J. E. Perry, and R. Rossmanith. Beam position measurement in the CEBAF recirculating linacs by use of pseudorandom pulse sequences. Office of Scientific and Technical Information (OSTI), August 1990. http://dx.doi.org/10.2172/6540805.
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