Journal articles: 'Frequency domain'

1

McKelvey, T. "Frequency Domain Identification." IFAC Proceedings Volumes 33, no. 15 (June 2000): 7–18. http://dx.doi.org/10.1016/s1474-6670(17)39719-7.

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2

Zhang, Xiaohui, Enqing Chen, and Xiaomin Mu. "Single-Carrier Frequency-Domain Equalization Based on Frequency-Domain Oversampling." IEEE Communications Letters 16, no. 1 (January 2012): 24–26. http://dx.doi.org/10.1109/lcomm.2011.111611.110726.

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3

OKAZAKI, A., K. MOTOYOSHI, M. HIGASHINAKA, T. NAGAYASU, H. KUBO, and A. SHIBUYA. "Frequency-Domain Equalization Incorporated with Frequency-Domain Redundancy for OFDM Systems." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E89-A, no. 10 (October 1, 2006): 2549–57. http://dx.doi.org/10.1093/ietfec/e89-a.10.2549.

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4

Bahmani-Oskooee, Mohsen, Tsangyao Chang, and Omid Ranjbar. "Asymmetric causality using frequency domain and time-frequency domain (wavelet) approaches." Economic Modelling 56 (August 2016): 66–78. http://dx.doi.org/10.1016/j.econmod.2016.03.002.

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Stanković, Ljubiša, Jonatan Lerga, Danilo Mandic, Miloš Brajović, Cédric Richard, and Miloš Daković. "From Time–Frequency to Vertex–Frequency and Back." Mathematics 9, no. 12 (June 17, 2021): 1407. http://dx.doi.org/10.3390/math9121407.

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The paper presents an analysis and overview of vertex–frequency analysis, an emerging area in graph signal processing. A strong formal link of this area to classical time–frequency analysis is provided. Vertex–frequency localization-based approaches to analyzing signals on the graph emerged as a response to challenges of analysis of big data on irregular domains. Graph signals are either localized in the vertex domain before the spectral analysis is performed or are localized in the spectral domain prior to the inverse graph Fourier transform is applied. The latter approach is the spectral form of the vertex–frequency analysis, and it will be considered in this paper since the spectral domain for signal localization is well ordered and thus simpler for application to the graph signals. The localized graph Fourier transform is defined based on its counterpart, the short-time Fourier transform, in classical signal analysis. We consider various spectral window forms based on which these transforms can tackle the localized signal behavior. Conditions for the signal reconstruction, known as the overlap-and-add (OLA) and weighted overlap-and-add (WOLA) methods, are also considered. Since the graphs can be very large, the realizations of vertex–frequency representations using polynomial form localization have a particular significance. These forms use only very localized vertex domains, and do not require either the graph Fourier transform or the inverse graph Fourier transform, are computationally efficient. These kinds of implementations are then applied to classical time–frequency analysis since their simplicity can be very attractive for the implementation in the case of large time-domain signals. Spectral varying forms of the localization functions are presented as well. These spectral varying forms are related to the wavelet transform. For completeness, the inversion and signal reconstruction are discussed as well. The presented theory is illustrated and demonstrated on numerical examples.

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Dong, Jun, Zhong-gui Lu, Zhi-hong Sun, Zhi-tao Peng, Yan-wen Xia, Jing-qin Su, Feng Jing, Hao-yu Yuan, Hua Liu, and Jun Tang. "Transforming characteristic of phase-shift from frequency-domain to time-domain in frequency-domain holography." Optics & Laser Technology 44, no. 3 (April 2012): 594–99. http://dx.doi.org/10.1016/j.optlastec.2011.08.027.

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Liu, Guang Yao, Long Li, Jia Qi Han, Hai Xia Liu, Xiao He Gao, Yan Shi, and Tie Jun Cui. "Frequency-Domain and Spatial-Domain Reconfigurable Metasurface." ACS Applied Materials & Interfaces 12, no. 20 (April 27, 2020): 23554–64. http://dx.doi.org/10.1021/acsami.0c02467.

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8

Ferrari, C., G. Salvetti, E. Tognoni, and E. Tombari. "Time-domain and frequency-domain differential calorimetry." Journal of Thermal Analysis 47, no. 1 (July 1996): 75–85. http://dx.doi.org/10.1007/bf01982687.

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9

Chaber, S., H. Helbig, and MA Gamulescu. "Time-domain-OCT versus Frequency-domain-OCT." Der Ophthalmologe 107, no. 1 (June 6, 2009): 36–40. http://dx.doi.org/10.1007/s00347-009-1941-1.

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10

Koh, Soo N., and Costas Xydeas. "Frequency domain speech coding." Journal of the Acoustical Society of America 93, no. 1 (January 1993): 592–93. http://dx.doi.org/10.1121/1.405579.

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11

Manton, J. H., and Y. Hua. "Robust frequency-domain precoders." IEEE Communications Letters 5, no. 2 (2001): 40–42. http://dx.doi.org/10.1109/4234.905929.

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Lin, Jingyu, Yebin Liu, Jinli Suo, and Qionghai Dai. "Frequency-Domain Transient Imaging." IEEE Transactions on Pattern Analysis and Machine Intelligence 39, no. 5 (May 1, 2017): 937–50. http://dx.doi.org/10.1109/tpami.2016.2560814.

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13

Schroder, D. K., J. E. Park, S. E. Tan, B. D. Choi, S. Kishino, and H. Yoshida. "Frequency domain lifetime characterization." IEEE Transactions on Electron Devices 47, no. 8 (2000): 1653–61. http://dx.doi.org/10.1109/16.853044.

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14

LEVY, D. "Cointegration in frequency domain." Journal of Time Series Analysis 23, no. 3 (May 2002): 333–39. http://dx.doi.org/10.1111/1467-9892.00267.

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15

Hartmann, W. M. "The frequency‐domain grating." Journal of the Acoustical Society of America 78, no. 4 (October 1985): 1421–25. http://dx.doi.org/10.1121/1.392859.

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16

Kupče, Ēriks, and Ray Freeman. "Frequency-domain Hadamard spectroscopy." Journal of Magnetic Resonance 162, no. 1 (May 2003): 158–65. http://dx.doi.org/10.1016/s1090-7807(02)00194-5.

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17

Ghafoori-Shiraz, H., and T. Okoshi. "Optical frequency-domain reflectometery." Optical and Quantum Electronics 18, no. 4 (July 1986): 265–72. http://dx.doi.org/10.1007/bf02029871.

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18

McKelvey, Tomas. "Frequency domain identification methods." Circuits, Systems, and Signal Processing 21, no. 1 (January 2002): 39–55. http://dx.doi.org/10.1007/bf01211650.

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19

Middleton, R. H. "Frequency Domain Adaptive Control." IFAC Proceedings Volumes 21, no. 10 (August 1988): 219–24. http://dx.doi.org/10.1016/b978-0-08-036620-3.50042-9.

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20

Eccles, William J. "Pragmatic Circuits: Frequency Domain." Synthesis Lectures on Digital Circuits and Systems 1, no. 1 (January 2006): 1–136. http://dx.doi.org/10.2200/s00032ed1v01y200605dcs003.

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21

Forootaninia, Zahra, and Rahul Narain. "Frequency-domain smoke guiding." ACM Transactions on Graphics 39, no. 6 (November 26, 2020): 1–10. http://dx.doi.org/10.1145/3414685.3417842.

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22

Goberdhansigh, E., L. Wang, and W. R. Cluett. "Robust frequency domain identification." Chemical Engineering Science 47, no. 8 (June 1992): 1989–99. http://dx.doi.org/10.1016/0009-2509(92)80316-5.

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23

Aliasgari, Javad, Mohammadali Forouzandeh, and Nemai Karmakar. "Chipless RFID Readers for Frequency-Coded Tags: Time-Domain or Frequency-Domain?" IEEE Journal of Radio Frequency Identification 4, no. 2 (June 2020): 146–58. http://dx.doi.org/10.1109/jrfid.2020.2982822.

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24

Yin Zhou, Jialu Chen, and Xiaodong Li. "A Time/Frequency-Domain Unified Delayless Partitioned Block Frequency-Domain Adaptive Filter." IEEE Signal Processing Letters 14, no. 12 (December 2007): 976–79. http://dx.doi.org/10.1109/lsp.2007.906627.

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25

Liang, Han, Wang Yunyi, and Li Sifan. "Frequency-domain nonlinear analysis of microwave circuits using frequency-domain diode model." Microwave and Optical Technology Letters 4, no. 7 (June 1991): 266–69. http://dx.doi.org/10.1002/mop.4650040708.

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26

Murrieta-Rico, Fabian N., Vitalii Petranovskii, Oleg Yu Sergiyenko, Daniel Hernandez-Balbuena, Alexey Pestryakov, and Vyra Tyrsa. "Frequency Domain Sensors and Frequency Measurement Techniques." Applied Mechanics and Materials 756 (April 2015): 575–84. http://dx.doi.org/10.4028/www.scientific.net/amm.756.575.

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Getting fast and accurate information is of paramount importance for most monitoring, data acquisition, and monitoring systems; there are sensors that transform their input into a frequency domain output, this kind of sensors are known as Frequency Domain Sensors (FDS); when the value of the output frequency is measured in a FDS, the value of the sensor’s input (measurand) can be calculated, that is why the frequency measurement in the output of FDS is critical for using such sensors. In this work different kinds of resonant sensors (FDS) are reviewed; also frequency measurement techniques are explored; finally a novel frequency measurement method is proposed and analyzed for resolution improvement in frequency domain sensors.

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27

Poměnková, J., and R. Maršálek. " Time and frequency domain in the business cycle structure." Agricultural Economics (Zemědělská ekonomika) 58, No. 7 (July 23, 2012): 332–46. http://dx.doi.org/10.17221/113/2011-agricecon.

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 The presented paper deals with the identification of cyclical behaviour of business cycle from the time and frequency domain perspective. Herewith, methods for obtaining the growth business cycle are investigated – the first order difference, the unobserved component models, the regression curves and filtration using the Baxter-King, Christiano-Fitzgerald and Hodrick-Prescott filter. In the case of the time domain, the analysis identification of cycle lengths is based on the dating process of the growth business cycle. Thus, the right and left variant of the naive techniques and the Bry-Boschan algorithm are applied. In the case of the frequency domain, the analysis of the cyclical structure trough spectrum estimate via the periodogram and the autoregressive process are suggested. Results from both domain approaches are compared. On their bases, recommendations for the cyclical structure identification of the growth business cycle of the Czech Republic are formulated. In the time domain analysis, the evaluation of the unity results of detrending techniques from the identification turning point points of view is attached. The analyses are done on the quarterly data of the GDP, the total industry excluding construction, the gross capital formation in 1996–2008 and on the final consumption expenditure in 1995–2008.    

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28

Zimmermann, K. P. "On frequency-domain and time-domain phase unwrapping." Proceedings of the IEEE 75, no. 4 (1987): 519–20. http://dx.doi.org/10.1109/proc.1987.13759.

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29

Dehler, M., M. Dohlus, and T. Weiland. "Calculating frequency-domain data by time-domain methods." International Journal of Numerical Modelling: Electronic Networks, Devices and Fields 6, no. 1 (February 1993): 19–27. http://dx.doi.org/10.1002/jnm.1660060104.

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30

Divya, Divya, and Mr Pawan Kumar Mishra. "Frequency Domain Digital Image Segmentation based on a Modified kMeans." International Journal of Innovative Research in Computer Science & Technology 5, no. 4 (July 31, 2017): 317–22. http://dx.doi.org/10.21276/ijircst.2017.5.4.4.

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31

Hadianfard, Mohammad Ali, and Soroosh Kamali. "Analysis of Modal Frequencies Estimated from Frequency Domain Decomposition Method." International Journal of Engineering and Technology 12, no. 3 (August 2020): 41–47. http://dx.doi.org/10.7763/ijet.2020.v12.1182.

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Frequency Domain Decomposition (FDD) is an operational modal analysis method by which the dynamic parameters of structures are obtained using response signals recorded on several locations of structures. In recent years, many researchers have employed the method to estimate the modal parameters of several Multi-Degree-of-Freedom (MDOF) mechanical and civil systems. The accuracy of the results depends on recording and signal processing parameters such as sampling frequency, windowing, filtering etc. As a result, investigation on the influence of these parameters on the accuracy of the estimates is a good practice. In this research, the uncertainty of modal frequencies obtained from FDD method is analyzed. In order to achieve this goal, the exact values of modal parameters must be available to be compared with the results from FDD method. For this purpose, synthetic signals with random characteristics the same as the ambient vibrations’ are produced and structures with known dynamic parameters are simulated and loaded. The effect of several parameters are investigated in the accuracy of results and proper values and settings are proposed to minimize the errors.

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32

Kim. "A Robust Frequency-Domain Multi-Reference Narrowband Adaptive Noise Canceller." Journal of the Acoustical Society of Korea 34, no. 2 (2015): 163. http://dx.doi.org/10.7776/ask.2015.34.2.163.

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33

TAKEDA, K., and F. ADACHI. "Frequency-Domain MMSE Channel Estimation for Frequency-Domain Equalization of DS-CDMA Signals." IEICE Transactions on Communications E90-B, no. 7 (July 1, 2007): 1746–53. http://dx.doi.org/10.1093/ietcom/e90-b.7.1746.

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34

Qian, Si Chong, and Yang Xiang. "The Relationship between Frequency Domain Blind Source Separation and Frequency Domain Adaptive Beamformer." Applied Mechanics and Materials 490-491 (January 2014): 654–62. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.654.

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As two important methods of array signal processing, blind source separation and beamforming can extract the target signal and suppress interference by using the received information of the array element. In the case of convolution mixture of sources, frequency domain blind source separation and frequency domain adaptive beamforming have similar signal model. To find the relationship between them, comparison between the minimization of the off-diagonal components in the BSS update equation and the minimization of the mean square error in the ABF had been made from the perspective of mathematical expressions, and find that the unmixing matrix of the BSS and the filter coefficients of the ABF converge to the same solution in the mean square error sense under the condition that the two source signals are ideally independent. With MATLAB, the equivalence in the frequency domain have been verified and the causes affecting separation performance have been analyzed, which was achieved by simulating instantaneous and convolution mixtures and separating mixture speech in frequency-domain blind source separation and frequency domain adaptive beamforming way.

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35

Chen, Xiang, Hao Liu, Mai Hu, Lu Yao, Zhenyu Xu, Hao Deng, and Ruifeng Kan. "Frequency-Domain Detection for Frequency-Division Multiplexing QEPAS." Sensors 22, no. 11 (May 26, 2022): 4030. http://dx.doi.org/10.3390/s22114030.

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To achieve multi-gas measurements of quartz-enhanced photoacoustic spectroscopy (QEPAS) sensors under a frequency-division multiplexing mode with a narrow modulation frequency interval, we report a frequency-domain detection method. A CH4 absorption line at 1653.72 nm and a CO2 absorption line at 2004.02 nm were investigated in this experiment. A modulation frequency interval of as narrow as 0.6 Hz for CH4 and CO2 detection was achieved. Frequency-domain 2f signals were obtained with a resolution of 0.125 Hz using a real-time frequency analyzer. With the multiple linear regressions of the frequency-domain 2f signals of various gas mixtures, small deviations within 2.5% and good linear relationships for gas detection were observed under the frequency-division multiplexing mode. Detection limits of 0.6 ppm for CH4 and 2.9 ppm for CO2 were simultaneously obtained. With the 0.6-Hz interval, the amplitudes of QEPAS signals will increase substantially since the modulation frequencies are closer to the resonant frequency of a QTF. Furthermore, the frequency-domain detection method with a narrow interval can realize precise gas measurements of more species with more lasers operating under the frequency-division multiplexing mode. Additionally, this method, with a narrow interval of modulation frequencies, can also realize frequency-division multiplexing detection for QEPAS sensors under low pressure despite the ultra-narrow bandwidth of the QTF.

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36

Jacobson, Sheldon H., Arnold H. Buss, and Lee W. Schruben. "Driving Frequency Selection for Frequency Domain Simulation Experiments." Operations Research 39, no. 6 (December 1991): 917–24. http://dx.doi.org/10.1287/opre.39.6.917.

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37

Agrez, Dusan. "Dynamics of Frequency Estimation in the Frequency Domain." IEEE Transactions on Instrumentation and Measurement 56, no. 6 (December 2007): 2111–18. http://dx.doi.org/10.1109/tim.2007.908240.

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38

Lee, Joon-Ho, and Hyo-Tae Kim. "Improvement of natural frequency extraction in frequency domain." Electronics Letters 35, no. 3 (1999): 197. http://dx.doi.org/10.1049/el:19990159.

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39

Ernotte, G., P. Lassonde, F. Légaré, and B. E. Schmidt. "Frequency domain tailoring for intra-pulse frequency mixing." Optics Express 24, no. 21 (October 11, 2016): 24225. http://dx.doi.org/10.1364/oe.24.024225.

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40

Jetmundsen, Bjorn, Richard L. Bielawa, and William G. Flannelly. "Generalized Frequency Domain Substructure Synthesis." Journal of the American Helicopter Society 33, no. 1 (January 1, 1988): 55–64. http://dx.doi.org/10.4050/jahs.33.1.55.

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41

Yang Hong, 杨虹, 黄远辉 Huang Yuanhui, 苗少峰 Miao Shaofeng, 宫睿 Gong Rui, 邵晓鹏 Shao Xiaopeng, and 毕祥丽 Bi Xiangli. "Frequency-domain photoacoustic imaging system." Infrared and Laser Engineering 45, no. 4 (2016): 0424001. http://dx.doi.org/10.3788/irla201645.0424001.

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42

Tan-no, Naohiro, Tsutomu Ichimura, Tadayuki Funaba, Naoki Anndo, and Yoshiki Odagiri. "Optical multimode frequency-domain reflectometer." Optics Letters 19, no. 8 (April 15, 1994): 587. http://dx.doi.org/10.1364/ol.19.000587.

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43

Zaugg, E. C., and D. G. Long. "Generalized Frequency-Domain SAR Processing." IEEE Transactions on Geoscience and Remote Sensing 47, no. 11 (November 2009): 3761–73. http://dx.doi.org/10.1109/tgrs.2009.2025372.

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44

Feng, Xu, Ching-Fang Lin, and Norman P. Coleman. "Frequency-Domain Recursive Robust Identification." Journal of Guidance, Control, and Dynamics 23, no. 5 (September 2000): 908–10. http://dx.doi.org/10.2514/2.4628.

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45

Gillberg, Jonas, Fredrik Gustafsson, and Rik Pintelon. "ROBUST FREQUENCY DOMAIN ARMA MODELLING." IFAC Proceedings Volumes 39, no. 1 (2006): 380–85. http://dx.doi.org/10.3182/20060329-3-au-2901.00056.

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46

Vanhamme, H. "High resolution frequency-domain reflectometry." IEEE Transactions on Instrumentation and Measurement 39, no. 2 (April 1990): 369–75. http://dx.doi.org/10.1109/19.52517.

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47

Carbone, P., and D. Petri. "Effective frequency-domain ADC testing." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 47, no. 7 (July 2000): 660–63. http://dx.doi.org/10.1109/82.850425.

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48

Mielke, J. A. "Frequency domain testing of ADCs." IEEE Design & Test of Computers 13, no. 1 (1996): 64–69. http://dx.doi.org/10.1109/54.485784.

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49

Han, Charles, Bo Sun, Ravi Ramamoorthi, and Eitan Grinspun. "Frequency domain normal map filtering." ACM Transactions on Graphics 26, no. 3 (July 29, 2007): 28. http://dx.doi.org/10.1145/1276377.1276412.

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Jiang, Shan, Meiling Guan, Jiamin Wu, Guocheng Fang, Xinzhu Xu, Dayong Jin, Zhen Liu, et al. "Frequency-domain diagonal extension imaging." Advanced Photonics 2, no. 03 (June 2, 2020): 1. http://dx.doi.org/10.1117/1.ap.2.3.036005.

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Journal articles on the topic 'Frequency domain'

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