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Journal articles on the topic 'Time-frequency'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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1

Richman, M. S., T. W. Parks, and R. G. Shenoy. "Discrete-time, discrete-frequency, time-frequency analysis." IEEE Transactions on Signal Processing 46, no. 6 (June 1998): 1517–27. http://dx.doi.org/10.1109/78.678465.

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2

Ke Zhang, Ke Zhang, Decai Zou Ke Zhang, Pei Wang Decai Zou, and Wenfang Jing Pei Wang. "A New Device for Two-Way Time-Frequency Real-Time Synchronization." 網際網路技術學刊 24, no. 3 (May 2023): 817–24. http://dx.doi.org/10.53106/160792642023052403024.

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<p>The netted wireless sensor nodes or coherent accumulation processing in multistatic radar imaging requires high accuracy time synchronization. Although GNSS timing can also be used as a time synchronization method to serve the applications above, its timing accuracy will be limited. In this context, we present the hardware implementation for Two-Way Time-Frequency Real-Time Synchronization (TWTFRTS) with an automatic adaptive jitter elimination algorithm based on Kalman and PID, which is implemented in a real-time, low-cost, portable Xilinx ZYNQ device. A short (2 km) baseline TWTFRTS experiment was done with a pair of devices composed of a master device and a slave device. The result shows a high precision of time synchronization performance with the standard deviation (1 σ) better than 1 ns.</p> <p> </p>

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3

Filipsky, Yu K., А. R. Аgadzhanyan, and I. V. Svyryd. "Application of time-frequency spectral analysis methods." Odes’kyi Politechnichnyi Universytet. Pratsi, no. 1 (March 31, 2015): 141–45. http://dx.doi.org/10.15276/opu.1.45.2015.23.

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4

Hlawatsch, F., and W. Kozek. "Time-frequency projection filters and time-frequency signal expansions." IEEE Transactions on Signal Processing 42, no. 12 (1994): 3321–34. http://dx.doi.org/10.1109/78.340770.

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5

Stanković, Ljubiša, Miloš Daković, and Thayananthan Thayaparan. "A real-time time-frequency based instantaneous frequency estimator." Signal Processing 93, no. 5 (May 2013): 1392–97. http://dx.doi.org/10.1016/j.sigpro.2012.11.005.

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6

COHEN, LEON. "Time-Frequency Spatial-Spatial Frequency Representations." Annals of the New York Academy of Sciences 808, no. 1 Nonlinear Sig (January 1997): 97–115. http://dx.doi.org/10.1111/j.1749-6632.1997.tb51655.x.

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7

Hall, Matt. "Time-frequency decomposition." Leading Edge 37, no. 6 (June 2018): 468–70. http://dx.doi.org/10.1190/tle37060468.1.

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8

Popescu, Theodor D. "Time-frequency analysis." Control Engineering Practice 5, no. 2 (February 1997): 292–94. http://dx.doi.org/10.1016/s0967-0661(97)90028-9.

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9

Belouchrani, A., and M. G. Amin. "Time-frequency MUSIC." IEEE Signal Processing Letters 6, no. 5 (May 1999): 109–10. http://dx.doi.org/10.1109/97.755429.

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10

Balan, Radu V., H. Vincent Poor, Scott T. Rickard, and Sergio Verdú. "Canonical time-frequency, time-scale, and frequency-scale representations of time-varying channels." Communications in Information and Systems 5, no. 2 (2005): 197–226. http://dx.doi.org/10.4310/cis.2005.v5.n2.a3.

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11

Richard, C. "Time-frequency-based detection using discrete-time discrete-frequency Wigner distributions." IEEE Transactions on Signal Processing 50, no. 9 (September 2002): 2170–76. http://dx.doi.org/10.1109/tsp.2002.801927.

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12

VELASCO, G. A. M., and M. DÖRFLER. "Sampling time-frequency localized functions and constructing localized time-frequency frames." European Journal of Applied Mathematics 28, no. 5 (December 19, 2016): 854–76. http://dx.doi.org/10.1017/s095679251600053x.

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We study functions whose time-frequency content are concentrated in a compact region in phase space using time-frequency localization operators as a main tool. We obtain approximation inequalities for such functions using a finite linear combination of eigenfunctions of these operators, as well as a local Gabor system covering the region of interest. These would allow the construction of modified time-frequency dictionaries concentrated in the region.

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13

Dalianis, S. A., and J. K. Hammond. "TIME–FREQUENCY SPECTRA FOR FREQUENCY-MODULATED PROCESSES." Mechanical Systems and Signal Processing 11, no. 4 (July 1997): 621–35. http://dx.doi.org/10.1006/mssp.1997.0100.

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14

Zhang, Ran, Xingxing Liu, Yongjun Zheng, Haotun Lv, Baosheng Li, Shenghui Yang, and Yu Tan. "Time‐frequency synchroextracting transform." IET Signal Processing 16, no. 2 (November 9, 2021): 117–31. http://dx.doi.org/10.1049/sil2.12073.

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15

Donchenko, S. I. "Time and frequency measurements." Measurement Techniques 47, no. 10 (October 2004): 985–90. http://dx.doi.org/10.1007/pl00022016.

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16

Mallat, Stephane G. "Adaptive time-frequency decompositions." Optical Engineering 33, no. 7 (July 1, 1994): 2183. http://dx.doi.org/10.1117/12.173207.

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17

DeBrunner, V., M. Ozaydin, and T. Przebinda. "Resolution in time-frequency." IEEE Transactions on Signal Processing 47, no. 3 (March 1999): 783–88. http://dx.doi.org/10.1109/78.747783.

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18

Shie Qian and Dapang Chen. "Joint time-frequency analysis." IEEE Signal Processing Magazine 16, no. 2 (March 1999): 52–67. http://dx.doi.org/10.1109/79.752051.

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19

Ibrahim, Mostafa, Ali Fatih Demir, and Huseyin Arslan. "Time–Frequency Warped Waveforms." IEEE Communications Letters 23, no. 1 (January 2019): 36–39. http://dx.doi.org/10.1109/lcomm.2018.2882498.

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20

Gardner, T. J., and M. O. Magnasco. "Sparse time-frequency representations." Proceedings of the National Academy of Sciences 103, no. 16 (April 6, 2006): 6094–99. http://dx.doi.org/10.1073/pnas.0601707103.

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21

Feng Zhang, Guoan Bi, and Yan Qiu Chen. "Tomography time-frequency transform." IEEE Transactions on Signal Processing 50, no. 6 (June 2002): 1289–97. http://dx.doi.org/10.1109/tsp.2002.1003054.

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22

Nilsen, G. K. "Recursive Time-Frequency Reassignment." IEEE Transactions on Signal Processing 57, no. 8 (August 2009): 3283–87. http://dx.doi.org/10.1109/tsp.2009.2020355.

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23

Hellwig, H. "Time and frequency applications." IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control 40, no. 5 (September 1993): 538–43. http://dx.doi.org/10.1109/58.238107.

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24

Honeine, Paul, Cdric Richard, and Patrick Flandrin. "Time-Frequency Learning Machines." IEEE Transactions on Signal Processing 55, no. 7 (July 2007): 3930–36. http://dx.doi.org/10.1109/tsp.2007.894252.

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25

Anden, Joakim, Vincent Lostanlen, and Stephane Mallat. "Joint Time–Frequency Scattering." IEEE Transactions on Signal Processing 67, no. 14 (July 15, 2019): 3704–18. http://dx.doi.org/10.1109/tsp.2019.2918992.

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26

Donchenko, S. I. "Time and frequency measurements." Measurement Techniques 47, no. 10 (October 2004): 985–90. http://dx.doi.org/10.1007/s11018-005-0007-2.

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27

Dai, L., and Z. Wang. "Time-frequency training OFDM." Electronics Letters 47, no. 20 (2011): 1128. http://dx.doi.org/10.1049/el.2011.2643.

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28

Pei, Soo Chang, and Er Jung Tsai. "New Time-Frequency Distribution." Circuits, Systems, and Signal Processing 14, no. 4 (July 1995): 539–53. http://dx.doi.org/10.1007/bf01260336.

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29

Hinrichs, Maren, and Gerd Wechsung. "Time Bounded Frequency Computations." Information and Computation 139, no. 2 (December 1997): 234–57. http://dx.doi.org/10.1006/inco.1997.2666.

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30

Liu, Naihao, Jinghuai Gao, Xiudi Jiang, Zhuosheng Zhang, and Qian Wang. "Seismic Time–Frequency Analysis via STFT-Based Concentration of Frequency and Time." IEEE Geoscience and Remote Sensing Letters 14, no. 1 (January 2017): 127–31. http://dx.doi.org/10.1109/lgrs.2016.2630734.

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31

Hlawatsch, F., A. H. Costa, and W. Krattenthaler. "Time-frequency signal synthesis with time-frequency extrapolation and don't-care regions." IEEE Transactions on Signal Processing 42, no. 9 (1994): 2513–20. http://dx.doi.org/10.1109/78.317876.

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32

Saraswathy, J., M. Hariharan, Wan Khairunizam, J. Sarojini, N. Thiyagar, Y. Sazali, and Shafriza Nisha. "Time–frequency analysis in infant cry classification using quadratic time frequency distributions." Biocybernetics and Biomedical Engineering 38, no. 3 (2018): 634–45. http://dx.doi.org/10.1016/j.bbe.2018.05.002.

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33

Dvornikov, S. "Generalized Hybrid Scale-Frequency-Time Distributions in Time-Frequency Space: Continued Review." Proceedings of Telecommunication Universities 4, no. 4 (2018): 20–35. http://dx.doi.org/10.31854/1813-324x-2018-4-4-20-35.

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34

Xu, Kui, Youyun Xu, Wenfeng Ma, Wei Xie, and Dongmei Zhang. "Time and Frequency Synchronization for Multicarrier Transmission on Hexagonal Time-Frequency Lattice." IEEE Transactions on Signal Processing 61, no. 24 (December 2013): 6204–19. http://dx.doi.org/10.1109/tsp.2013.2284153.

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35

Colominas, Marcelo A., Sylvain Meignen, and Duong-Hung Pham. "Time-Frequency Filtering Based on Model Fitting in the Time-Frequency Plane." IEEE Signal Processing Letters 26, no. 5 (May 2019): 660–64. http://dx.doi.org/10.1109/lsp.2019.2904148.

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36

WAN, Pengcheng, Weike FENG, Ningning TONG, and Wei WEI. "A time-frequency feature prediction network for time-varying radio frequency interference." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 41, no. 3 (June 2023): 587–94. http://dx.doi.org/10.1051/jnwpu/20234130587.

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The time-varying radio frequency interference has strong nonlinear dynamic characteristics, which is difficult to be predicted by linear method effectively, making the anti-interference decision without sufficient information support. To solve this problem, a recurrent neural network for spectrum prediction based on time-frequency correlation features is proposed. A sliding window is used to characterize the two-dimensional correlation of time-frequency series, and the spectrum prediction problem is transformed into a problem similar to spatiotemporal sequence prediction. A gradient bridge structure across time frames is added to reduce the attenuation of the gradient in the long time and multi-level network propagation. The training efficiency and network performance are improved by the loss function with better matching. Simulation and experimental results verify the validity of the network prediction results.

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37

Lili Wang, Lili Wang, Zhaoshuo Tian Zhaoshuo Tian, Yanchao Zhang Yanchao Zhang, Jing Wang Jing Wang, Shiyou Fu Shiyou Fu, Jianfeng Sun Jianfeng Sun, and Qi Wang Qi Wang. "Frequency stabilization of pulsed CO2 laser using setup-time method." Chinese Optics Letters 10, no. 1 (2012): 011402–11404. http://dx.doi.org/10.3788/col201210.011402.

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38

SUN, Shuping, Zhongwei JIANG, and Haibin WANG. "1204 Heart Sound Clustering Method Using Time-Frequency Distribution Energy." Proceedings of Conference of Chugoku-Shikoku Branch 2010.48 (2010): 365–66. http://dx.doi.org/10.1299/jsmecs.2010.48.365.

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39

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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40

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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41

Nachane, D. M. "Time-Frequency Analysis for Nonstationary Time Series." Journal of Quantitative Economics 2, no. 2 (July 2004): 41–57. http://dx.doi.org/10.1007/bf03404608.

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42

ISHII, K. "Space-Time-Frequency Turbo Code over Time-Varying and Frequency-Selective Fading Channel." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E88-A, no. 10 (October 1, 2005): 2885–95. http://dx.doi.org/10.1093/ietfec/e88-a.10.2885.

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43

Samantaray, Leena, Madhumita Dash, and Rutuparna Panda. "A Review on Time-frequency, Time-scale and Scale-frequency Domain Signal Analysis." IETE Journal of Research 51, no. 4 (July 2005): 287–93. http://dx.doi.org/10.1080/03772063.2005.11416406.

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44

Zeng, Hongjun, and Ran Lu. "High-frequency volatility connectedness and time-frequency correlation among Chinese stock and major commodity markets around COVID-19." Investment Management and Financial Innovations 19, no. 2 (June 23, 2022): 260–73. http://dx.doi.org/10.21511/imfi.19(2).2022.23.

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This study examines the connectedness and time-frequency correlation of price volatility across the Chinese stock market and major commodity markets. This paper applies a DCC-GARCH-based volatility connectedness model and the cross-wavelet transform to examine the transmission of risk patterns in these markets before and during the COVID-19 outbreak, as well as the leading lag relationship and synergistic movements between different time domains. First, the findings of the DCC-GARCH connectedness model show dynamic total spillovers are stronger after the COVID-19 outbreak. Chinese stocks and corn have been net spillovers in the system throughout the sample period, but the Chinese market plays the role of a net receiver of volatility relative to other markets (net pairwise directional connectedness) in the system as a whole. In terms of wavelet results, there is some connection to the connectedness results, with all commodity markets, except soybeans and wheat, showing significant dependence on Chinese equities in the medium/long term following the COVID-19 outbreak. Secondly, the medium-to long-term frequency of the crude oil market and copper market are highly dependent on the Chinese stock market, especially after the COVID-19 outbreak. Meanwhile, the copper market is the main source of risk for the Chinese stock market, while the wheat market sends the least shocks to the Chinese stock market. The findings of this paper will have a direct impact on a number of important decisions made by investors and policymakers.

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45

Mu, Tong, and Yaoliang Song. "Time reversal imaging based on joint space–frequency and frequency–frequency data." International Journal of Microwave and Wireless Technologies 11, no. 3 (January 14, 2019): 207–14. http://dx.doi.org/10.1017/s1759078718001691.

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AbstractA new time reversal (TR) method for target imaging is proposed in this paper. Through single measurement by the antenna array, the received signals are utilized to form the space–frequency–frequency multistatic data matrix (MDM). Singular value decomposition is applied to the matrix to obtain the left singular vectors which span the signal subspace. The obtained vectors are divided into multiple subvectors by two different schemes and used to provide target signatures in the form of coarse frequency dependence and relative phase shifts that can be exploited to construct the imaging function. The performance of the proposed method is investigated through numerical simulations for both single and multiple targets, and the results are compared with the traditional TR method using the frequency–frequency MDM. It turned out that the proposed method is able to achieve high resolution with limited array aperture and shows satisfactory robustness in noise environment. Furthermore, experimental results are provided to show the availability of the method in practical applications.

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46

BO, Lin. "Extraction of Instantaneous Frequency Characteristic Using Time-frequency Ridges." Chinese Journal of Mechanical Engineering 44, no. 10 (2008): 222. http://dx.doi.org/10.3901/jme.2008.10.222.

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47

Lovell, B. C., R. C. Williamson, and B. Boashash. "The relationship between instantaneous frequency and time-frequency representations." IEEE Transactions on Signal Processing 41, no. 3 (March 1993): 1458–61. http://dx.doi.org/10.1109/78.205756.

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48

Khan, Waseem, and Ijaz Mansoor Qureshi. "Frequency Diverse Array Radar With Time-Dependent Frequency Offset." IEEE Antennas and Wireless Propagation Letters 13 (2014): 758–61. http://dx.doi.org/10.1109/lawp.2014.2315215.

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49

Wang, Chenshu, and Moeness G. Amin. "Time–frequency distribution spectral polynomials for instantanous frequency estimation." Signal Processing 76, no. 2 (July 1999): 211–17. http://dx.doi.org/10.1016/s0165-1684(99)00009-2.

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50

Roke, Sylvie, Aart W. Kleyn, and Mischa Bonn. "Time- vs. frequency-domain femtosecond surface sum frequency generation." Chemical Physics Letters 370, no. 1-2 (March 2003): 227–32. http://dx.doi.org/10.1016/s0009-2614(03)00085-x.

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