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Добірка наукової літератури з теми "Radio frequency"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 29 липня 2024

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Статті в журналах з теми "Radio frequency"

NECHIBVUTE, Action, Albert CHAWANDA, Nicholas TARUVINGA, and Pearson LUHANGA. "Radio Frequency Energy Harvesting Sources." Acta Electrotechnica et Informatica 17, no. 4 (December 1, 2017): 19–27. http://dx.doi.org/10.15546/aeei-2017-0030.

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Jayati, Ari Endang, Wahyu Minarti, and Sri Heranurweni. "Analisa Teknis Penetapan Kanal Frekuensi Radio Untuk Lembaga Penyiaran Radio Komunitas Wilayah Kabupaten Batang." Jurnal ELTIKOM 5, no. 2 (September 10, 2021): 73–80. http://dx.doi.org/10.31961/eltikom.v5i2.361.

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The radio frequency spectrum constitutes a limited and strategic natural resource with high economic value, so it must be managed effectively and efficiently to obtain optimal benefits by observing national and international legal principles. Radio Community Broadcasting Institution uses limited frequency allocation in three channels, namely, in the frequency channels 202 (107.7 MHz), 203 (107.8 MHz), and 204 (107.9 MHz), with limited transmit power and area coverage. The problem in this research is the frequency overlap with other community radios in an area. The issue raised is whether it is possible to establish a new community radio in the Batang Regency area by paying attention to existing radios that have licenses in districts/cities that are in the area directly adjacent to Batang Regency by considering the limited allocation of radio frequency channels community, without the occurrence of radio frequency interference with other community radios. The purpose of this research is to solve these problems. It is necessary to have a policy in determining radio frequency users to get good quality radio broadcast reception. The method used is to analyze the frequency determination technique based on the interference analysis on other community broadcasters. By using the Radio Mobile Software for frequency repetition simulation, in this research, the results show that Batang FM Community Radio does not allow to get frequency channels for community radio operations. After all, it interferes with the Service Area of Soneta FM Radio in Pekalongan City because it does not meet the requirements for determining the frequency channel = Eu> NF, namely the Nuisance Field (NF) value of 109.7 dB is greater than the Minimum Usable Field strength (Eu) of 66 dB. In comparison, Limpung FM Radio gets radio frequency on channel 203 (frequency 107.8 MHz) because it meets the requirements for determining the frequency channel = Eu> NF, namely the Minimum Usable Field strength (Eu) 66 dB greater than the Nuisance Field (NF) of 55.7 dB.

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Sackenheim, Maureen McDaniel. "Radio Frequency Ablation." Journal of Diagnostic Medical Sonography 19, no. 2 (March 2003): 88–92. http://dx.doi.org/10.1177/8756479303251097.

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Dondelinger, Robert M. "Radio Frequency Identification." Biomedical Instrumentation & Technology 44, no. 1 (January 1, 2010): 44–47. http://dx.doi.org/10.2345/0899-8205-44.1.44.

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Wyld, David C. "Radio Frequency Identification." Cornell Hospitality Quarterly 49, no. 2 (May 2008): 134–44. http://dx.doi.org/10.1177/1938965508316147.

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Scheck, Anne. "Radio Frequency Identification." Emergency Medicine News 28, no. 3 (March 2006): 34–35. http://dx.doi.org/10.1097/01.eem.0000292061.54727.06.

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Ekers, R. D., and J. F. Bell. "Radio Frequency Interference." Symposium - International Astronomical Union 199 (2002): 498–505. http://dx.doi.org/10.1017/s0074180900169669.

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We describe the nature of the interference challenges facing radio astronomy in the next decade. These challenges will not be solved by regulation only, negotiation and mitigation will become vital. There is no silver bullet for mitigating against interference. A successful mitigation approach is most likely to be a hierarchical or progressive approach throughout the telescope and signal conditioning and processing systems. We summarise some of the approaches, including adaptive systems.

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Westra, Bonnie L. "Radio Frequency Identification." AJN, American Journal of Nursing 109, no. 3 (March 2009): 34–36. http://dx.doi.org/10.1097/01.naj.0000346925.67498.a4.

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Rajaraman, V. "Radio frequency identification." Resonance 22, no. 6 (June 2017): 549–75. http://dx.doi.org/10.1007/s12045-017-0498-6.

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., Manishkumar R. Solanki. "RADIO FREQUENCY IDENTIFICATION." International Journal of Research in Engineering and Technology 06, no. 01 (January 25, 2017): 129–33. http://dx.doi.org/10.15623/ijret.2017.0601024.

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Дисертації з теми "Radio frequency"

Ker, Louise Moira. "Radio AGN evolution with low frequency radio surveys." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/7616.

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Supermassive black holes are leading candidates for the regulation of galaxy growth and evolution over cosmic time, via ‘feedback’ processes, whereby outflows from the Active Galactic Nuclei (AGN) halt star formation within the galaxy. AGN feedback is generally thought to occur in two modes, high-excitation (HERG, or ‘quasar-mode’) and low-excitation (LERG or ’radio-mode’) each having a different effect on the host galaxy. LERGs curtail the growth of the most massive galaxies, whereas HERGs are thought to be activated by mergers/interactions, switching off star formation at high redshift. A critical problem in current extragalactic astrophysics lies in understanding the precise physical mechanisms by which these feedback processes operate, and how they evolve over cosmic time. Radio-loud AGN are an essential tool for studying major feedback mechanisms, as they are found within the largest ellipticals, and hence are beacons for the most massive black holes across the bulk of cosmic time. In this thesis I develop and study existing complete radio samples with extensive new multi-wavelength data in the radio, optical and infrared, aiming to investigate the evolution of AGN feedback modes, and methods to locate and study such systems at the very highest redshifts. This will serve to inform further studies of radio-AGN planned with next generation radio instruments such as the LOw Frequency ARray (LOFAR). Very few radio-loud AGN systems are currently known at high redshifts, and the effectiveness of traditional high redshift selection techniques, such as selection based on steep spectral index, have not been well quantified. A purely evidence-based approach to determining the efficiency of various high redshift selection techniques is presented, using nine highly spectroscopically complete radio samples; although weak correlations are confirmed between spectral index and linear size and redshift, selection first of infrared-faint radio sources remains by far the most efficient method of selecting high-z radio galaxies from complete samples. Radio spectral curvature in four of the complete samples is analysed and the effect of radio spectral shape on the measurement of the radio luminosity function (RLF) of steep-spectrum radio sources is investigated. Below z=1, curvature has negligible effect on the measurement of the RLF, however at higher redshifts, where source numbers are low, the shape of the radio spectrum should be taken into account, as individual source luminosities can change up to 0.1-0.2 dex, and this can in some cases introduce errors in space density measurements of up to a factor of 2-3 where source numbers are low. Building upon these samples, the very first independent determinations of the separate RLFs for high and low excitation radio sources across the bulk of cosmic time are made, out to z=1. Here it is shown that HERGs show very clear signs of strong evolution, in line with theoretical predictions. LERGs also show some very weak evolution with redshift, showing increases in space density of typically around a factor of 2. These measurements are also used to estimate the contribution of LERGs, which typically show weak or no emission lines to the ‘missing redshift’ population, which are sources within the complete samples not identifiable spectroscopically. Complementary to this, a pilot study is presented in selecting ‘missing redshift’ sources which are classed as infra-red faint (IFRS), which show no optical or near-IR identification, and are compact in the radio. Follow up spectroscopy on these candidate high z sources detected no line emission. Finally, work carried out towards the testing and commissioning of the new LOFAR telescope is presented. The findings from this thesis will serve to both streamline and inform high redshift radio-AGN searches and studies planned to be carried out with LOFAR and other multi-wavelength complementary surveys in the near future, and help to open up an as yet unexplored epoch in radio-AGN formation and evolution.

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Finlay, Chris. "Radio Frequency Interference: Simulations for Radio Interferometry Arrays." Master's thesis, Faculty of Science, 2021. http://hdl.handle.net/11427/33716.

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Radio Frequency Interference (RFI) is a massive problem for radio observatories around the world. Due to the growth of telecommunications and air travel RFI is increasing exactly when the world's radio telescopes are increasing significantly in sensitivity, making RFI one of the most pressing problems for astronomy in the era of the Square Kilometre Array (SKA). Traditionally RFI is dealt with through simple algorithms that remove unexpected rapid changes but the recent explosion of machine learning and artificial intelligence (AI) provides an exciting opportunity for pushing the state-of-the-art in RFI excision. Unfortunately, due to the lack of training data for which the true RFI contamination is known, it is impossible to reliably train and compare machine learning algorithms for RFI excision on radio telescope arrays currently. To address this stumbling block we present RFIsim, a radio interferometry simulator that includes the telescope properties of the MeerKAT array, a sky model based on previous radio surveys coupled with an RFI model designed to reproduce actual RFI seen at the MeerKAT site. We perform an indepth comparison of the simulator results with real observations using the MeerKAT telescope and show that RFIsim produces visibilities that mimic those produced by real observations very well. Finally, we describe how the data was key in the development of a new state-of-the-art deep learning RFI flagging algorithm in Vafaei et al. (2020.) [69] In particular, this work demonstrates that transfer learning from simulation to real data is an effective way to leverage the power of machine learning for RFI flagging in real-world observatories.

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Shenouda, Hany H. "An agile frequency synthesizer for frequency hopping radio." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0016/MQ49683.pdf.

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Chen, Bing-Hung. "Inductively coupled radio-frequency discharges." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244566.

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Heikkinen, Jouko. "TELEMETRY AND RADIO FREQUENCY IDENTIFICATION." International Foundation for Telemetering, 1999. http://hdl.handle.net/10150/607334.

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International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada
Comparison of short-range telemetry and radio frequency identification (RFID) systems reveals that they are based on very similar operating principles. Combining the identification and measurement functions into one transponder sensor offers added value for both RFID and telemetry systems. The presence of a memory (e.g. FRAM) in the transponder, required for ID information, can also be utilized for storing measurement results. For passive transponders low power consumption is one of the main objectives. Wireless power transfer for passive transponder sensors together with above aspects concerning a combined telemetry and identification system are discussed.

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Andrews, Seth Dixon. "Extensions to Radio Frequency Fingerprinting." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/95952.

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Radio frequency fingerprinting, a type of physical layer identification, allows identifying wireless transmitters based on their unique hardware. Every wireless transmitter has slight manufacturing variations and differences due to the layout of components. These are manifested as differences in the signal emitted by the device. A variety of techniques have been proposed for identifying transmitters, at the physical layer, based on these differences. This has been successfully demonstrated on a large variety of transmitters and other devices. However, some situations still pose challenges: Some types of fingerprinting feature are very dependent on the modulated signal, especially features based on the frequency content of a signal. This means that changes in transmitter configuration such as bandwidth or modulation will prevent wireless fingerprinting. Such changes may occur frequently with cognitive radios, and in dynamic spectrum access networks. A method is proposed to transform features to be invariant with respect to changes in transmitter configuration. With the transformed features it is possible to re-identify devices with a high degree of certainty. Next, improving performance with limited data by identifying devices using observations crowdsourced from multiple receivers is examined. Combinations of three types of observations are defined. These are combinations of fingerprinter output, features extracted from multiple signals, and raw observations of multiple signals. Performance is demonstrated, although the best method is dependent on the feature set. Other considerations are considered, including processing power and the amount of data needed. Finally, drift in fingerprinting features caused by changes in temperature is examined. Drift results from gradual changes in the physical layer behavior of transmitters, and can have a substantial negative impact on fingerprinting. Even small changes in temperature are found to cause drift, with the oscillator as the primary source of this drift (and other variation) in the fingerprints used. Various methods are tested to compensate for these changes. It is shown that frequency based features not dependent on the carrier are unaffected by drift, but are not able to distinguish between devices. Several models are examined which can improve performance when drift is present.
Doctor of Philosophy
Radio frequency fingerprinting allows uniquely identifying a transmitter based on characteristics of the signal it emits. In this dissertation several extensions to current fingerprinting techniques are given. Together, these allow identification of transmitters which have changed the signal sent, identifying using different measurement types, and compensating for variation in a transmitter's behavior due to changes in temperature.

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Viyyure, Uday Kiran Varma. "Frequency Assignments in Radio Networks." Kent State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=kent1209060158.

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Fernandes, Rui Miguel Félix. "Object signature in radio frequency." Master's thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/13708.

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Mestrado em Engenharia Eletrónica e Telecomunicações
The RF signature can be consider as a fingerprint of an object when submitted to electromagnetic radiation. Based on this concept, the initial goal of this work was to elaborate a comparative analysis of the Radio Frequency signature of different materials. Through the design of a prototype based on an adapted Wi-Fi network was developed an innovative system capable of distinguishing materials with the analysis of their interference in the propagated channel. In order to refine this distinction was utilized a signal processing tool, the Wavelet Transform. This technique serve as support tool of the system for a better differentiation of the studied targets. The versatility of this concept was proved through the analysis of signatures of static targets like metal, wood and plastic, as well as moving targets, giving the example of a moving human. Due to the promising results obtained, the initial objective of the work was expanded being also presented in this document the concept of intruder detection through a Wi-Fi network by the analysis of the Wavelet coefficients.
A Assinatura em Rádio Frequência pode ser considerada como a impressão digital que um objeto manifesta quando submetido a radiação eletromagnética. O objetivo inicial deste trabalho era a elaboração de uma análise comparativa das assinaturas em Rádio Frequência de diferentes materiais. Tendo por base uma rede Wi-Fi adaptada, foi desenvolvido um sistema inovador capaz de distinguir materiais pela análise da interferência dos mesmos no canal de propagação. Com vista a melhorar o desempenho do protótipo inicial, o sinal recebido foi processado através da Transformada de Wavelet. Esta técnica serviu como ferramenta de suporte do sistema para a obtenção de uma diferenciação mais clara dos alvos estudados. Demonstrando a versatilidade deste conceito foram avaliadas as assinaturas de alvos estáticos como o metal, madeira e plástico bem como de alvos móveis dando, como exemplo, uma pessoa em movimento. Devido aos resultados promissores obtidos, o objetivo inicial do sistema foi alargado estando também presente neste documento o conceito de deteção de intrusos através de uma rede Wi-Fi pela análise dos coeficientes de Wavelet.

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Matarrese, Vincent D. "Tapered radio frequency transmission lines." PDXScholar, 1992. https://pdxscholar.library.pdx.edu/open_access_etds/4329.

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A transformation used to obtain solutions for the beam parameter equation of fiber optics is applied to the second order differential equation for nonuniform transmission lines. Methods are developed for deriving possible transmission line tapers from known solutions of the transformed equation. This study begins with a comprehensive overview of previous work done to obtain closed-form solutions for the transmission line equations. Limitations of the lumped parameter model are also discussed. As part of this thesis, a tapered transmission line is constructed, based on one of the solutions obtained from the fiber optics studies. A discussion of the design and measurement results are given in the final chapter.

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Blackard, Kenneth Lee. "Measurements and models of radio frequency impulsive noise inside buildings." Thesis, This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-08182009-040318/.

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Книги з теми "Radio frequency"

McAdams, Alison R. Radio frequency identification. New York: Nova Science Publishers, 2011.

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Hutter, Michael, and Jörn-Marc Schmidt, eds. Radio Frequency Identification. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41332-2.

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AIM, ed. Radio frequency identification. Pittsburgh PA: AIM, 1987.

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L, Hutchinson C., and DeMaw Doug, eds. Radio frequency interference. 5th ed. Newington, CT: American Radio Relay League, 1989.

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McAdams, Alison R., and Alison R. McAdams. Radio frequency identification. New York: Nova Science Publishers, 2011.

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Shivanarul, M. Radio frequency synthesizer. London: University of East London, 1995.

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Institute, IIT Research, and United States. Environmental Protection Agency, eds. Radio frequency heating. [Cincinnati, OH]: U.S. Environmental Protection Agency, 1994.

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Davis, W. Alan. Radio Frequency Circuit Design. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470768020.

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Hickman, Ian. Practical radio-frequency handbook. 3rd ed. Oxford: Newnes, 2002.

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Hickman, Ian. Practical radio-frequency handbook. 3rd ed. Oxford: Newnes, 2002.

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Частини книг з теми "Radio frequency"

Gooch, Jan W. "Radio Frequency." In Encyclopedic Dictionary of Polymers, 607. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_9736.

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Weik, Martin H. "radio frequency." In Computer Science and Communications Dictionary, 1401. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_15368.

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Thompson, A. Richard, James M. Moran, and George W. Swenson. "Radio Frequency Interference." In Astronomy and Astrophysics Library, 787–808. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-44431-4_16.

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Rumney, Moray, Takaharu Nakamura, Stefania Sesia, Tony Sayers, and Adrian Payne. "Radio Frequency Aspects." In LTE - The UMTS Long Term Evolution, 457–502. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9780470978504.ch21.

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Svanberg, Sune. "Radio-Frequency Spectroscopy." In Atomic and Molecular Spectroscopy, 187–226. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-98107-4_7.

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Cameron, Neil. "Radio frequency communication." In Electronics Projects with the ESP8266 and ESP32, 399–436. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6336-5_15.

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Cameron, Neil. "Radio Frequency Identification." In Arduino Applied, 203–17. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3960-5_11.

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Hitchcock, R. Timothy. "Radio-Frequency Radiation." In Hamilton & Hardy's Industrial Toxicology, 1029–44. Hoboken, New Jersey: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781118834015.ch98.

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Williams, Geoff. "Radio Frequency Identification." In Technology for Facility Managers, 75–83. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2019. http://dx.doi.org/10.1002/9781119572626.ch5.

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Svanberg, Sune. "Radio-Frequency Spectroscopy." In Atomic and Molecular Spectroscopy, 187–226. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18520-5_7.

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Тези доповідей конференцій з теми "Radio frequency"

Manoharan, P. K., Arun Naidu, B. C. Joshi, Jayashree Roy, G. Kate, Kaiwalya Pethe, Shridhar Galande, Sachin Jamadar, S. P. Mahajan, and R. A. Patil. "Low Frequency Radio Experiment (LORE)." In 2015 IEEE Radio and Antenna Days of the Indian Ocean (RADIO). IEEE, 2015. http://dx.doi.org/10.1109/radio.2015.7323422.

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Wyckoff, Peter S., and Gregory Hellbourg. "Polar excision for radio frequency interference mitigation in radio astronomy." In 2016 Radio Frequency Interference (RFI). IEEE, 2016. http://dx.doi.org/10.1109/rfint.2016.7833547.

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Slattery, Kevin, and Harry Skinner. "Radio frequency interference." In 2008 IEEE International Symposium on Electromagnetic Compatibility - EMC 2008. IEEE, 2008. http://dx.doi.org/10.1109/isemc.2008.4652206.

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Skinner, Harry, and Kevin Slattery. "Radio frequency interference." In 2008 IEEE International Symposium on Electromagnetic Compatibility - EMC 2008. IEEE, 2008. http://dx.doi.org/10.1109/isemc.2008.4652204.

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Bureau, Sylvain, Markus Bick, Selwyn Piramuthu, Yannick Meiller, Wei Zhou, and Samuel Fosso Wamba. "Radio frequency identification." In the 12th International Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1967486.1967638.

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Puglisi, Mario. "Radio-frequency acceleration." In PHYSICS OF PARTICLE ACCELERATORS. AIP, 1989. http://dx.doi.org/10.1063/1.38067.

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Derbenev, Ya S. "Radio-frequency polarimetry." In The seventh international workshop on polarized gas targets and polarized beams. AIP, 1998. http://dx.doi.org/10.1063/1.55020.

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Perez-Neira, Ana. "Radio Frequency Coding." In 2019 International Conference on Electromagnetics in Advanced Applications (ICEAA). IEEE, 2019. http://dx.doi.org/10.1109/iceaa.2019.8879104.

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Mosiane, Olorato, Nadeem Oozeer, and Bruce A. Bassett. "Radio frequency interference detection using machine learning." In 2016 IEEE Radio and Antenna Days of the Indian Ocean (RADIO). IEEE, 2016. http://dx.doi.org/10.1109/radio.2016.7772036.

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Звіти організацій з теми "Radio frequency"

Jacobson, Joseph. Radio Frequency (RF) Biomolecules. Fort Belvoir, VA: Defense Technical Information Center, November 2005. http://dx.doi.org/10.21236/ada441170.

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Logan, Ronald T., Li Jr., and Ruo D. Radio Frequency Photonic Synthesizer. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada374373.

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Derov, John S., Alvin D. Drehman, Everett E. Crisman, Beverly Turchinetz, and Teresa H. O'Donnell. Radio Frequency and Optical Metamaterials. Fort Belvoir, VA: Defense Technical Information Center, March 2013. http://dx.doi.org/10.21236/ada580325.

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Derov, John S., Alvin D. Drehman, Everett E. Crisman, and Beverly Turchinetz. Radio Frequency and Optical Metamaterials. Fort Belvoir, VA: Defense Technical Information Center, October 2011. http://dx.doi.org/10.21236/ada555467.

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Matarrese, Vincent. Tapered radio frequency transmission lines. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6213.

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Carter, M. D. Radio Frequency Current Drive Considerations for Small Aspect Ratio Tori. Office of Scientific and Technical Information (OSTI), January 1998. http://dx.doi.org/10.2172/814823.

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Carter, M. D., E. F. Jaeger, D. J. Strickler, P. M. Ryan, D. W. Swain, and D. B. Batchelor. Radio frequency current drive considerations for small aspect ratio tori. Office of Scientific and Technical Information (OSTI), July 1998. http://dx.doi.org/10.2172/677115.

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Hietala, V. M., G. A. Vawter, T. M. Brennan, B. E. Hammons, and W. J. Meyer. Optical generation of radio-frequency power. Office of Scientific and Technical Information (OSTI), November 1994. http://dx.doi.org/10.2172/10106860.

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Nelson, Glenn K., Michael A. Lombardi, and Dean T. Okayama. NIST time and frequency radio stations :. Gaithersburg, MD: National Institute of Standards and Technology, 2005. http://dx.doi.org/10.6028/nist.sp.250-67.

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Devlin, D. J., R. S. Barbero, and K. N. Siebein. Radio frequency assisted chemical vapor infiltration. Office of Scientific and Technical Information (OSTI), June 1996. http://dx.doi.org/10.2172/244636.

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