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Relevant bibliographies by topics / Large Signal

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Academic literature on the topic 'Large Signal'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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Journal articles on the topic "Large Signal"

1

Verspecht, J. "Large-signal network analysis." IEEE Microwave Magazine 6, no. 4 (December 2005): 82–92. http://dx.doi.org/10.1109/mmw.2005.1580340.

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Chen, Bao Sheng. "Fault Signal Detection Model for Large-Scale Circuit Communication System." Applied Mechanics and Materials 651-653 (September 2014): 432–35. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.432.

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In the process of fault signal detection for large-scale circuit communication systems, with traditional methods to process detection, the fault detection method is more conservative. A fault signal detection for large-scale circuit communication system based on QRS wave group detection method is proposed. The signal to be measured is transformed appropriately in the time domain or frequency domain to strengthen or separate the QRS component, in order to suppress interference from various noise to signals, and the fault point of circuit communication system fault signal is identified, the filter is utilized as representative to process multiscale decomposition for fault signals of circuit communication systems. Experiments show that QRS wave group detection method can determine the occurrence time of the circuit system fault signal, and further estimate the nature of the fault signal, thus, the fault point of communication system fault signal is found to improve the efficiency of detection.

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Bandler, J. W., R. M. Biernacki, S. H. Chen, J. Song, S. Ye, and Q. J. Zhang. "Analytically unified DC/small-signal/large-signal circuit design." IEEE Transactions on Microwave Theory and Techniques 39, no. 7 (July 1991): 1076–82. http://dx.doi.org/10.1109/22.85372.

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Liu, Yunjiang, Fuzhong Wang, Lu Liu, and Yamin Zhu. "Secondary signal-induced large-parameter stochastic resonance for feature extraction of mechanical faults." International Journal of Modern Physics B 33, no. 15 (June 20, 2019): 1950157. http://dx.doi.org/10.1142/s0217979219501571.

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Aiming to solve the problem that it is difficult to extract large parameter signals from a strong noise background, a novel method of large parameter stochastic resonance (SR) induced by a secondary signal is proposed. The SR mechanism of high-frequency signals is expounded by analyzing the density distribution curve. High-frequency signals are converted to low-frequency signals using the scale transformation method, and then large-parameter SR is induced by the secondary signal. Ultimately, the method is applied to the feature extraction of mechanical faults. Simulation and experimental results indicate that (i) the effect of SR induced by the secondary signal is significantly enhanced when the frequency of the secondary signal is twice that of the signals to be detected after the scale transformation; (ii) when the frequency of secondary signal is twice the maximum frequency of the signals to be detected after the scale transformation, choosing an appropriate amplitude of secondary signal can alleviate the problem that the noise energy is excessively concentrated in the low-frequency channel with regard to the extraction of two-frequency or three-frequency high-frequency signals; and (iii) by adding the secondary signal to the engineering example, the fault power spectrum value of system output is 101% higher than that without the secondary signal.

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Wang, Xiao, Xiao-Hong Zhu, Yi Zhang, and Wei Chen. "Large Enhancement of Perfusion Contribution on fMRI Signal." Journal of Cerebral Blood Flow & Metabolism 32, no. 5 (March 7, 2012): 907–18. http://dx.doi.org/10.1038/jcbfm.2012.26.

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The perfusion contribution to the total functional magnetic resonance imaging (fMRI) signal was investigated using a rat model with mild hypercapnia at 9.4 T, and human subjects with visual stimulation at 4 T. It was found that the total fMRI signal change could be approximated as a linear superposition of ‘true’ blood oxygenation level-dependent (BOLD; T2/T2*) effect and the blood flow-related ( T1) effect. The latter effect was significantly enhanced by using short repetition time and large radiofrequency pulse flip angle and became comparable to the ‘true’ BOLD signal in response to a mild hypercapnia in the rat brain, resulting in an improved contrast-to-noise ratio (CNR). Bipolar diffusion gradients suppressed the intravascular signals but had no significant effect on the flow-related signal. Similar results of enhanced fMRI signal were observed in the human study. The overall results suggest that the observed flow-related signal enhancement is likely originated from perfusion, and this enhancement can improve CNR and the spatial specificity for mapping brain activity and physiology changes. The nature of mixed BOLD and perfusion-related contributions in the total fMRI signal also has implication on BOLD quantification, in particular, the BOLD calibration model commonly used to estimate the change of cerebral metabolic rate of oxygen.

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Shu, Na. "Accurate Detection of Fault Signal in Large-Scale Communication." Advanced Materials Research 989-994 (July 2014): 3802–5. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3802.

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Deep data-mining methods of fault signal in large-scale communication system are researched. Although with the characteristic of frequency uniformity as signals distribute in each reaction zone, common method of fault signal detection based on shortwave dispersing is invalid employing in large-scale communication system, which presents the absence or instability of fault signal. For this reason, a method based on particle swarm optimization is proposed for fault signal detection in large-scale communication system. As reaction speed and activity scope within the whole particle swarm are replaced, accurate results are achieved. Taking particle swarm optimization, it is detected that whether there is a fault in communication systems. The experimental results show that proposed method in signal fault detection process can greatly increase accuracy of signal fault detection, as plays a greater role in future.

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Henkels, W. H., and W. Hwang. "Large-signal 2T, 1C DRAM cell: signal and layout analysis." IEEE Journal of Solid-State Circuits 29, no. 7 (July 1994): 829–32. http://dx.doi.org/10.1109/4.303721.

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Srivastava, Vishnu. "2.5-Dimensional Multi-Signal Large-Signal Analysis of Helix TWTs." IETE Journal of Research 49, no. 4 (July 2003): 239–46. http://dx.doi.org/10.1080/03772063.2003.11416342.

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KRIVOY, G. S., and V. A. KOMASHKO. "rf PUMPED SQUID WITH LARGE OUTPUT SIGNAL." Modern Physics Letters B 05, no. 05 (February 28, 1991): 365–73. http://dx.doi.org/10.1142/s0217984991000435.

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The dc SQUID had been presented for use in the rf SQUID instead of a weak link. The new device, referred to as “double SQUID”, possesses a large output signal (hundreds of microvolts) in operating in a hysteretic mode. For making an operating mode a double SQUID is coupled to a circuit traditional for rf SQUIDs containing a tank circuit and an rf current pumping generator. The magnetic flux being measured is recognized by the dc SQUID quantization loop which results in changing its critical current. As a result the height of the flat part of the tank circuit I–V characteristics coupled to a double SQUID is modulated. It is this modulation that is the SQUID output signal. The experimental investigations of the double SQUID showed the validity of the assumptions under consideration. Output signals up to 690 μV, noise spectral density ≈2×10−5ϕ0/ Hz 1/2 (ϕ0 is the flux quantum) and energy resolution ≈1.4×10−29 J/Hz have been obtained.

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Ouyang, Yi, Richard Y. Zhang, Javad Lavaei, and Pravin Varaiya. "Large-Scale Traffic Signal Offset Optimization." IEEE Transactions on Control of Network Systems 7, no. 3 (September 2020): 1176–87. http://dx.doi.org/10.1109/tcns.2020.2966588.

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Dissertations / Theses on the topic "Large Signal"

1

Maki, D. W. "Large signal modelling of mixers." Thesis, University of Leeds, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.355930.

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Garverick, Steven Lee. "Large signal linearity of scaled MOS transistors." Thesis, Massachusetts Institute of Technology, 1986. http://hdl.handle.net/1721.1/14931.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1987.
MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING
Includes bibliographies.
by Steven L. Garverick.
Ph.D.

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Amaeshi, Lawrence Lemchukwu Nnanyelugo. "Large signal characterization of microwave GaAs MESFETs." Thesis, University of Surrey, 1988. http://epubs.surrey.ac.uk/843717/.

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Large Signal modelling of GaAs MESFETs has often been based on the device material and electrical parameters. This approach, while helping in elucidating the physics of the device, does not help the device user very much. There is the problem of modelling and computation complexity, and of simulation time. Furthermore predictions based on such models may not be consistent with practical reality. A real-time large Rf signal characterization of the device, will help in the understanding of the device behaviour under Large Signal drive, and would also yield valuable results/information, useful to the device designer and user, especially in Large Signal applications. A large Rf signal characterization of GaAs MESFETs, employing Large Signal S-parameter (LSSP), and waveform distortion analysis techniques, is carried out. LSSP design is a natural extension of the SSSP approach where the LSSPs are known. And the LSSP design approach is simplified if the LSSPs are determined easily. Waveform distortions affect the device performance. A LSSP measurement system, (also applicable to SSSP measurements), including an uncomplicated, direct deembedding technique is developed. A direct technique of measuring the current and voltage waveforms of the microwave signals, at the device terminals, is also developed. Measurements of the LSSPs show that only the input parameters: S21 and S11 vary with the Rf. The results are explained against reported trends of variation. The non-linear elements are identified, and a subsequent Large Signal Model (LSM) of the DUTs developed and verified. It is demonstrated that LSMs cannot be generalised. However a systematic approach of determining the LSM of a given device is given. An improved model of the transconductance, Gm, in terms of the S-parameters, and a method to determine the LSSP from small signal parameters are developed and verified. The optimum incident Rf to determine the LSSPs at a given bias is given. The flow of forward conduction,IF is known to damage, by burn-out, the DUT. A limiting resistor was included in the gate external circuit to limit this effect, when large enough Rf was employed. The interaction of the IF with this circuit is investigated, and the self-limiting actions explained. The flow of Is. is found to degrade the output performance and device power added efficiency also. The waveform distortions are investigated, the main causes - the nonlinear elements, and the manner in which they affect the distortions are explored. The non-linearity in the Gm is shown to be the main cause of the output waveform distortion, especially before the onset of forward conduction by the gate Schottky diode. While the forward conduction If and the non-linearity in the depletion capacitance are responsible for the input waveform distortion, hence are secondary causes of output waveform distortions. In particular, the flow of If, due to large Vgs > 0, causes the saturation of the drain voltage, hence the output power. But the waveforms, were in particular insensitive to the output conductance. The results reaffirm the LSM developed. Finally future work is discussed.

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Raghavan, Arvind. "Bipolar large-signal modeling and power amplifier design." Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/13294.

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Hofer, Heiko. "Large-Scale Gradual Change Detection." Neubiberg Universitätsbibliothek der Universität der Bundeswehr, 2010. http://d-nb.info/1001920856/34.

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Bordelon, John H. "A large-signal model for the RF power MOSFET." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/15048.

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Srivastava, Y. "Large signal modelling of coupled-cavity travelling wave tubes." Thesis, Lancaster University, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.379740.

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Senadji, Bouchra. "Méthodes évolutives de localisation de sources large bande /." Paris : Ecole nationale supérieure des Télécommunications, 1994. http://catalogue.bnf.fr/ark:/12148/cb35723464r.

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Lei, Chi-un, and 李志遠. "VLSI macromodeling and signal integrity analysis via digital signal processing techniques." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B45700588.

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Bebek, Gürkan. "Analyzing and modeling large biological networks inferring signal transduction pathways /." online version, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=case1157723743.

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Books on the topic "Large Signal"

1

Faust, Frederick Schiller. The black signal. New York: Dodd, Mead, 1986.

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Faust, Frederick Schiller. The black signal. London: Hale, 1989.

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Faust, Frederick Schiller. The black signal. Boston: G.K. Hall, 1987.

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Hill, Grace Livingston. The red signal. Thorndike, Me: G.K. Hall, 1999.

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VLSI signal processing systems. Boston: Kluwer Academic Publishers, 1986.

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Zhang, Weihong. GaAS MESFET large-signal model for switching power amplifiers. Ottawa: National Library of Canada, 1994.

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7

Khatibzadeh, M. Ali. Large-signal modeling of gallium arsenide field-effect transistors. Raleigh, NC: North Carolina State University, 1987.

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VLSI digital signal processing systems: Design and implementation. New York: Wiley, 1999.

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Stanisic, Balsha R. Synthesis of Power Distribution to Manage Signal Integrity in Mixed-Signal ICs. Boston, MA: Springer US, 1996.

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Hsu, Chi-Ping. Signal routing in integrated circuit layout. Ann Arbor, Mich: UMI Research Press, 1986.

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Book chapters on the topic "Large Signal"

1

Trew, Robert J. "Large-Signal Models." In Compound Semiconductor Device Modelling, 170–93. London: Springer London, 1993. http://dx.doi.org/10.1007/978-1-4471-2048-3_9.

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Bonani, Fabrizio, and Giovanni Ghione. "Noise in Large-Signal Operation." In Springer Series in ADVANCED MICROELECTRONICS, 143–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04530-5_5.

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Hill, Geoff. "Large Signal Domain and Model." In Loudspeaker Modelling and Design, 51–54. New York, NY: Routledge, [2019]: Routledge, 2018. http://dx.doi.org/10.4324/9781351116428-13.

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Angelov, Iltcho. "Mesfet and Hemt Large Signal Modeling." In Microwave Physics and Techniques, 35–50. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5540-3_4.

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Garvanov, Ivan, and Vladimir Ivanov. "Jumping Average Filter Parameter Optimization for Pulsar Signal Detection." In Large-Scale Scientific Computing, 518–23. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41032-2_59.

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Kosina, H., M. Nedjalkov, and S. Selberherr. "Monte Carlo Analysis of the Small-Signal Response of Charge Carriers." In Large-Scale Scientific Computing, 175–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45346-6_17.

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Wolff, A. "Large Signal Resonance and Laser Dilatometer Methods." In Piezoelectricity, 445–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-68683-5_19.

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Kasemtaweechok, Chatchai, Nitiporn Sukkerd, and Chatchavin Hathorn. "Large-Scale Instance Selection Using a Heterogeneous Value Difference Matrix." In Sensor Networks and Signal Processing, 465–79. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4917-5_34.

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Embrechts, Mark J., Christopher J. Gatti, Jonathan Linton, and Badrinath Roysam. "Hierarchical Clustering for Large Data Sets." In Advances in Intelligent Signal Processing and Data Mining, 197–233. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-28696-4_8.

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Yu, Jianjun. "Ultra-Large-Capacity Terahertz Signal Wireless Transmission System." In Broadband Terahertz Communication Technologies, 193–233. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3160-3_10.

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Conference papers on the topic "Large Signal"

1

Shams, S., M. Majerus, M. Tutt, I. Lim, and A. Zlotnicka. "Large signal SiGe HBT model validation for 77GHz large signal applications." In 2008 IEEE Bipolar/BiCMOS Circuits and Technology Meeting - BCTM. IEEE, 2008. http://dx.doi.org/10.1109/bipol.2008.4662754.

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Odyniec, Michat. "Large signal S-parameters." In 2006 67th ARFTG Conference. IEEE, 2006. http://dx.doi.org/10.1109/arftg.2006.4734341.

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Kowalczyk, R. D., C. F. Malcolm, M. F. Kirshner, and C. B. Wilsen. "Large Signal Klystron Simulation." In 2006 IEEE International Vacuum Electronics Conference held jointly with 2006 IEEE International Vacuum Electron Sources. IEEE, 2006. http://dx.doi.org/10.1109/ivelec.2006.1666263.

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Verspecht, Jan, Marc vanden Bossche, and Frans Verbeyst. "Characterizing Components Under Large Signal Excitation: Defining Sensible "Large Signal S-Parameters"?!" In 49th ARFTG Conference Digest. IEEE, 1997. http://dx.doi.org/10.1109/arftg.1997.327217.

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Schreurs, D., E. P. Vandamme, and S. Vandenberghe. "Capabilities of Vectorial Large-Signal Measurements to Validate RF Large-Signal Device Models." In 58th ARFTG Conference Digest. IEEE, 2001. http://dx.doi.org/10.1109/arftg.2001.327498.

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Yang, Shihai, Bozhong Gu, and Yulong Zhang. "Research for diagnosing electronic control fault of astronomical telescope’s armature winding by step signal." In Large Mirrors and Telescopes, edited by Myung K. Cho and Bin Fan. SPIE, 2016. http://dx.doi.org/10.1117/12.2244942.

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Rytting, Doug. "Network analyzers from small signal to large signal measurements." In 2006 67th ARFTG Conference. IEEE, 2006. http://dx.doi.org/10.1109/arftg.2006.4734338.

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Shojaei, Hamid, and Masoumeh Sayyaran. "Signal Coverage Computation in Formal Verification." In 2006 IFIP International Conference on Very Large Scale Integration. IEEE, 2006. http://dx.doi.org/10.1109/vlsisoc.2006.313210.

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Verbeyst, Frans, and Ewout Vandamme. "Large-Signal Network Analysis. Overview of the measurement capabilities of a Large-Signal Network Analyzer." In 58th ARFTG Conference Digest. IEEE, 2001. http://dx.doi.org/10.1109/arftg.2001.327495.

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Boric-Lubecke, Olga, Dee-Son Pan, and Tatsuo Itoh. "Large signal quantum-well oscillator design." In 23rd European Microwave Conference, 1993. IEEE, 1993. http://dx.doi.org/10.1109/euma.1993.336715.

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Reports on the topic "Large Signal"

1

Carlsten, B. E., and P. Ferguson. Large-signal klystron simulations using KLSC. Office of Scientific and Technical Information (OSTI), October 1997. http://dx.doi.org/10.2172/537334.

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2

Whitefield, D. S., C. J. Wei, and J. C. Hwang. Large Signal Characterization and Modeling of Heterojunction-Bipolar Transistors. Fort Belvoir, VA: Defense Technical Information Center, May 1992. http://dx.doi.org/10.21236/ada253011.

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Whitefield, D. S., C. J. Wei, and J. C. Hwang. Large Signal Characterization and Modeling of Heterojunction Bipolar Transistors. Fort Belvoir, VA: Defense Technical Information Center, June 1991. http://dx.doi.org/10.21236/ada241461.

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Hwang, Vincent D., and Tatsuo Itoh. Large Signal Modeling and Analysis of the GaAs MESFET. Fort Belvoir, VA: Defense Technical Information Center, July 1986. http://dx.doi.org/10.21236/ada170304.

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Brunasso, Theresa A. A Large-Signal Analysis Program for Helix Traveling-Wave Tubes. Fort Belvoir, VA: Defense Technical Information Center, February 1987. http://dx.doi.org/10.21236/ada181111.

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Janus, Robert S., Mark B. Moffett, and James M. Powers. Large-Signal Characterization of PMN-PT-Ba (90/10/3). Fort Belvoir, VA: Defense Technical Information Center, October 1997. http://dx.doi.org/10.21236/ada640710.

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Fuchs, E., and M. Masoum. Large signal nonlinear model of anisotropic transformers for nonsinusoidal operation. Office of Scientific and Technical Information (OSTI), September 1989. http://dx.doi.org/10.2172/5370275.

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Mahabir, K., G. Verghese, J. Thottuvelil, and A. Heyman. Linear Models for Large Signal Control of High Power Factor AC-DC Converters. Fort Belvoir, VA: Defense Technical Information Center, November 1989. http://dx.doi.org/10.21236/ada458127.

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Ramchandran, Kannan, and Kristofer Pister. Sensor Webs of SmartDust: Distributed Signal Processing/Data Fusion/Inferencing in Large Microsensor Arrays. Fort Belvoir, VA: Defense Technical Information Center, March 2004. http://dx.doi.org/10.21236/ada422190.

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Bilgutay, Nihat M. Computer Facilities for High-Speed Data Acquisition, Signal Processing and Large Scale System Simulation. Fort Belvoir, VA: Defense Technical Information Center, June 1986. http://dx.doi.org/10.21236/ada170935.

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