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Relevant bibliographies by topics / Time-frequency

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Academic literature on the topic 'Time-frequency'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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Journal articles on the topic "Time-frequency"

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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Time-frequency"

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Coates, Mark J. "Time frequency modelling." Thesis, University of Cambridge, 1999. https://www.repository.cam.ac.uk/handle/1810/272036.

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Elayouty, Amira Sherif Mohamed. "Time and frequency domain statistical methods for high-frequency time series." Thesis, University of Glasgow, 2017. http://theses.gla.ac.uk/8061/.

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Advances in sensor technology enable environmental monitoring programmes to record and store measurements at high-temporal resolution over long time periods. These large volumes of high-frequency data promote an increasingly comprehensive picture of many environmental processes that would not have been accessible in the past with monthly, fortnightly or even daily sampling. However, benefiting from these increasing amounts of high-frequency data presents various challenges in terms of data processing and statistical modeling using standard methods and software tools. These challenges are attributed to the large volumes of data, the persistent and long memory serial correlation in the data, the signal to noise ratio, and the complex and time-varying dynamics and inter-relationships between the different drivers of the process at different timescales. This thesis aims at using and developing a variety of statistical methods in both the time and frequency domains to effectively explore and analyze high-frequency time series data as well as to reduce their dimensionality, with specific application to a 3 year hydrological time series. Firstly, the thesis investigates the statistical challenges of exploring, modeling and analyzing these large volumes of high-frequency time series. Thereafter, it uses and develops more advanced statistical techniques to: (i) better visualize and identify the different modes of variability and common patterns in such data, and (ii) provide a more adequate dimension reduction representation to the data, which takes into account the persistent serial dependence structure and non-stationarity in the series. Throughout the thesis, a 15-minute resolution time series of excess partial pressure of carbon dioxide (EpCO2) obtained for a small catchment in the River Dee in Scotland has been used as an illustrative data set. Understanding the bio-geochemical and hydrological drivers of EpCO 2 is very important to the assessment of the global carbon budget. Specifically, Chapters 1 and 2 present a range of advanced statistical approaches in both the time and frequency domains, including wavelet analysis and additive models, to visualize and explore temporal variations and relationships between variables for the River Dee data across the different timescales to investigate the statistical challenges posed by such data. In Chapter 3, a functional data analysis approach is employed to identify the common daily patterns of EpCO2 by means of functional principal component analysis and functional cluster analysis. The techniques used in this chapter assume independent functional data. However, in numerous applications, functional observations are serially correlated over time, e.g. where each curve represents a segment of the whole time interval. In this situation, ignoring the temporal dependence may result in an inappropriate dimension reduction of the data and inefficient inference procedures. Subsequently, the dynamic functional principal components, recently developed by Hor mann et al. (2014), are considered in Chapter 4 to account for the temporal correlation using a frequency domain approach. A specific contribution of this thesis is the extension of the methodology of dynamic functional principal components to temporally dependent functional data estimated using any type of basis functions, not only orthogonal basis functions. Based on the scores of the proposed general version of dynamic functional principal components, a novel clustering approach is proposed and used to cluster the daily curves of EpCO2 taking into account the dependence structure in the data. The dynamic functional principal components depend in their construction on the assumption of second-order stationarity, which is not a realistic assumption in most environmental applications. Therefore, in Chapter 5, a second specific contribution of this thesis is the development of a time-varying dynamic functional principal components which allows the components to vary smoothly over time. The performance of these smooth dynamic functional principal components is evaluated empirically using the EpCO2 data and using a simulation study. The simulation study compares the performance of smooth and original dynamic functional principal components under both stationary and non-stationary conditions. The smooth dynamic functional principal components have shown considerable improvement in representing non-stationary dependent functional data in smaller dimensions. Using a bootstrap inference procedure, the smooth dynamic functional principal components have been subsequently employed to investigate whether or not the spectral density and covariance structure of the functional time series under study change over time. To account for the possible changes in the covariance structure, a clustering approach based on the proposed smooth dynamic functional principal components is suggested and the results of application are discussed. Finally, Chapter 6 provides a summary of the work presented within this thesis, discusses the limitations and implications and proposes areas for future research.

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Mai, Cuong. "Frequency Estimation Using Time-Frequency Based Methods." ScholarWorks@UNO, 2007. http://scholarworks.uno.edu/td/571.

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Any periodic signal can be decomposed into a sum of oscillating functions. Traditionally, cosine and sine segments have been used to represent a single period of the periodic signal (Fourier Series). In more general cases, each of these functions can be represented by a set of spectral parameters such as its amplitude, frequency, phase, and the variability of its instantaneous spectral components. The accuracy of these parameters depends on several processing variables such as resolution, noise level, and bias of the algorithm used. This thesis presents some background of existing frequency estimation techniques and proposes a new technique for estimating the instantaneous frequency of signals using short sinusoid-like basis functions. Furthermore, it also shows that the proposed algorithm can be implemented in a popular embedded DSPmicroprocessor for practical use. This algorithm can also be implemented using more complex features on more resourceful processing processors in order to improve estimation accuracy

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Lie, Victor Daniel. "Relational time-frequency analysis." Diss., Restricted to subscribing institutions, 2009. http://proquest.umi.com/pqdweb?did=1997611171&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Ahrabian, Alireza. "Multivariate time-frequency analysis." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/28958.

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Recent advances in time-frequency theory have led to the development of high resolution time-frequency algorithms, such as the empirical mode decomposition (EMD) and the synchrosqueezing transform (SST). These algorithms provide enhanced localization in representing time varying oscillatory components over conventional linear and quadratic time-frequency algorithms. However, with the emergence of low cost multichannel sensor technology, multivariate extensions of time-frequency algorithms are needed in order to exploit the inter-channel dependencies that may arise for multivariate data. Applications of this framework range from filtering to the analysis of oscillatory components. To this end, this thesis first seeks to introduce a multivariate extension of the synchrosqueezing transform, so as to identify a set of oscillations common to the multivariate data. Furthermore, a new framework for multivariate time-frequency representations is developed using the proposed multivariate extension of the SST. The performance of the proposed algorithms are demonstrated on a wide variety of both simulated and real world data sets, such as in phase synchrony spectrograms and multivariate signal denoising. Finally, multivariate extensions of the EMD have been developed that capture the inter-channel dependencies in multivariate data. This is achieved by processing such data directly in higher dimensional spaces where they reside, and by accounting for the power imbalance across multivariate data channels that are recorded from real world sensors, thereby preserving the multivariate structure of the data. These optimized performance of such data driven algorithms when processing multivariate data with power imbalances and inter-channel correlations, and is demonstrated on the real world examples of Doppler radar processing.

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Terwilleger, Erin. "Multidimensional time-frequency analysis /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3052223.

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Abdoush, Yazan <1989&gt. "Time-Frequency Signal Analysis and Adaptive Instantaneous Frequency Estimation." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amsdottorato.unibo.it/9079/1/Thesis.pdf.

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Most of the human-made and physical signals have nonstationary spectra that evolve rapidly with time. To study and characterize such signals, the classic time-domain and frequency-domain representations are inadequate, since they do not provide joint time and frequency information; meaning that, they are signal representations in which the time and frequency variables are mutually exclusive. Time-frequency (TF) signal analysis (TFSA) concerns the processing of signals with time-varying spectral content. It allows for the construction of a signal representation in which the time and frequency variables are not averaged with respect to each other, but rather present together. This doctoral thesis has two main points of focus: TFSA based on a linear TF transform with progressive frequency-dependent resolution in the TF domain, known in the literature as the S-transform (ST), and designing adaptive methods for instantaneous frequency (IF) estimation, which is a fundamental concept in TFSA with numerous practical applications. The main original contributions are: 1- Modifications in the existing discrete definitions for implementing and inverting the ST to ensure exact invertibility and eliminate artifacts in the synthesized signal. 2- Derivation of an algorithm for least-squares signal synthesis form a modified discrete ST. 3- Formulation of a computationally efficient, fully discrete, and exactly invertible ST with a controllable TF sampling scheme, providing frequency resolution that can be varied and made as high as required. 4- Accuracy analysis of IF estimation based on a family of linear TF transforms that use Gaussian observation windows to localize the Fourier oscillatory kernel with arbitrarily defined standard deviations, and derivation of closed-form easily interpreted expressions for the bias and the variance of the estimation error. 5- Design of adaptive methods for IF estimation based on linear and quadratic TF representations.

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Maeser, Anna Marie. "Time-frequency dual and quantization." View electronic thesis (PDF), 2009. http://dl.uncw.edu/etd/2009-1/maesera/annamaeser.pdf.

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Sylvestre, Benoit. "Time-scale modification of speech : a time-frequency approach." Thesis, McGill University, 1991. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=60496.

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Time-scale modification (TSM) is a process whereby signals are compressed or expanded in time in a manner which preserves their original frequency characteristics. This work explores TSM algorithms for sampled speech. A known approach (2) which is based on the short-time Fourier transform (STFT) is first reviewed, then modified to provide high-quality TSM of speech signals at a lower computational cost. The proposed algorithm resembles the sinusoidal speech model (SSM) based approach (9), yet incorporates new phase compensatory measures to prevent excessive structural deterioration of the time-scaled signal. In addition, a novel incremental scheme for modifying polar parameters results in substantial computational savings.

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Khumsat, Phanumas. "Transition frequency integration : technique for high frequency continuous-time filters." Thesis, Imperial College London, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398200.

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Books on the topic "Time-frequency"

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Xiu, Liming, ed. From Frequency to Time-Average-Frequency. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119102175.

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Tolimieri, Richard. Time-frequency representations. Boston: Birkhauser Boston, 1997.

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Hlawatsch, Franz, and Franois Auger, eds. Time-Frequency Analysis. London, UK: ISTE, 2008. http://dx.doi.org/10.1002/9780470611203.

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Tolimieri, Richard, and Myoung An. Time-Frequency Representations. Boston, MA: Birkhäuser Boston, 1996. http://dx.doi.org/10.1007/978-1-4612-4152-2.

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Tolimieri, Richard. Time-Frequency Representations. Boston, MA: Birkhäuser Boston, 1996.

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6

Cohen, Leon. Time-frequency analysis. Englewood Cliffs, N.J: Prentice Hall PTR, 1995.

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7

James, Jespersen, and Hanson D. W, eds. Time and frequency. New York: Institute of Electrical and Electronics Engineers, 1991.

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8

Flandrin, Patrick. Time-frequency/time scale analysis. San Diego: Academic Press, 1999.

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9

Riley, Michael D. Speech Time-Frequency Representations. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4613-1079-2.

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D, Riley Michael. Speech time-frequency representations. Boston (Mass.): Kluwer Academic, 1988.

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Book chapters on the topic "Time-frequency"

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Hlawatsch, Franz. "Time-Frequency Filters and Time-Frequency Expansions." In The Kluwer International Series in Engineering and Computer Science, 105–24. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2815-6_5.

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Tolimieri, Richard, and Myoung An. "Review of algebra." In Time-Frequency Representations, 1–18. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-4152-2_1.

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Tolimieri, Richard, and Myoung An. "Cross-ambiguity function." In Time-Frequency Representations, 141–50. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-4152-2_10.

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Tolimieri, Richard, and Myoung An. "Ambiguity surfaces." In Time-Frequency Representations, 151–53. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-4152-2_11.

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Tolimieri, Richard, and Myoung An. "Orthonormal W-H systems." In Time-Frequency Representations, 155–68. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-4152-2_12.

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Tolimieri, Richard, and Myoung An. "Duality." In Time-Frequency Representations, 169–85. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-4152-2_13.

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Tolimieri, Richard, and Myoung An. "Frames." In Time-Frequency Representations, 187–97. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-4152-2_14.

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Tolimieri, Richard, and Myoung An. "Implementation." In Time-Frequency Representations, 199–218. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-4152-2_15.

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Tolimieri, Richard, and Myoung An. "Algebra of multirate structures." In Time-Frequency Representations, 219–38. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-4152-2_16.

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Tolimieri, Richard, and Myoung An. "Multirate structures." In Time-Frequency Representations, 239–59. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-4152-2_17.

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Conference papers on the topic "Time-frequency"

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Flandrin, P. "On time-frequency localization." In IEE Seminar on Time-Scale and Time-Frequency Analysis and Applications. IEE, 2000. http://dx.doi.org/10.1049/ic:20000550.

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Chanteau, B., O. Lopez, F. Auguste, B. Darquie, C. Chardonnet, A. Amy-Klein, W. Zhang, G. Santarelli, and Y. Le Coq. "Mid-IR frequency measurement using an optical frequency comb and a long-distance remote frequency reference." In 2012 European Frequency and Time Forum (EFTF). IEEE, 2012. http://dx.doi.org/10.1109/eftf.2012.6502424.

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Langer, Martin, Kristof Teichel, Dieter Sibold, and Rainer Bermbach. "Time synchronization performance using the network time security protocol." In 2018 European Frequency and Time Forum (EFTF). IEEE, 2018. http://dx.doi.org/10.1109/eftf.2018.8409017.

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Howe, D. A., A. Hati, and C. W. Nelson. "Ultra-low phase noise frequency synthesis from optical atomic frequency standards." In 2014 European Frequency and Time Forum (EFTF). IEEE, 2014. http://dx.doi.org/10.1109/eftf.2014.7331422.

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Siccardi, M., M. Abgrall, and G. D. Rovera. "About time measurements." In 2012 European Frequency and Time Forum (EFTF). IEEE, 2012. http://dx.doi.org/10.1109/eftf.2012.6502406.

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Dobrogowski, Andrzej, and Michal Kasznia. "Real-time assessment of dynamic Allan deviation and dynamic time deviation." In 2012 European Frequency and Time Forum (EFTF). IEEE, 2012. http://dx.doi.org/10.1109/eftf.2012.6502376.

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Szplet, Ryszard, Pawel Kwiatkowski, Krzysztof Rozyc, Marek Sawicki, and Zbigniew Jachna. "Modular time interval counter." In 2014 European Frequency and Time Forum (EFTF). IEEE, 2014. http://dx.doi.org/10.1109/eftf.2014.7331544.

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Sibold, Dieter, and Kristof Teichel. "Network Time Security specification." In 2016 European Frequency and Time Forum (EFTF). IEEE, 2016. http://dx.doi.org/10.1109/eftf.2016.7477786.

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Schneider, Matthias, and Christoph Ruland. "Verification of time signals." In 2016 European Frequency and Time Forum (EFTF). IEEE, 2016. http://dx.doi.org/10.1109/eftf.2016.7477789.

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Jiang, Z., F. Arias, A. Bauch, S.-Y. Calvin Lin, J. Nawrocki, A. Czubla, P. Uhrich, et al. "Accurate time link calibration for UTC time transfer - Status of the BIPM pilot study on the UTC time link calibration." In 2014 European Frequency and Time Forum (EFTF). IEEE, 2014. http://dx.doi.org/10.1109/eftf.2014.7331541.

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Reports on the topic "Time-frequency"

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Potts, Petrina C. NIST time and frequency bulletin. National Institute of Standards and Technology, January 2014. http://dx.doi.org/10.6028/nist.ir.7980-01.

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Potts, Petrina C. NIST Time and Frequency Bulletin. National Institute of Standards and Technology, February 2014. http://dx.doi.org/10.6028/nist.ir.7980-02.

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Potts, Petrina C. NIST Time and Frequency Bulletin. National Institute of Standards and Technology, April 2014. http://dx.doi.org/10.6028/nist.ir.7980-03.

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Potts, Petrina C. NIST Time and Frequency Bulletin. National Institute of Standards and Technology, April 2014. http://dx.doi.org/10.6028/nist.ir.7980-04.

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Potts, Petrina C. NIST time and frequency bulletin. National Institute of Standards and Technology, May 2014. http://dx.doi.org/10.6028/nist.ir.7980-05.

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Potts, Petrina C. NIST time and frequency bulletin. National Institute of Standards and Technology, June 2014. http://dx.doi.org/10.6028/nist.ir.7980-06.

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Potts, Petrina C. NIST time and frequency bulletin. National Institute of Standards and Technology, July 2014. http://dx.doi.org/10.6028/nist.ir.7980-07.

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Potts, Petrina C. NIST time and frequency bulletin. Gaithersburg, MD: National Institute of Standards and Technology, 2014. http://dx.doi.org/10.6028/nist.ir.7980-08.

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Potts, Petrina C. NIST time and frequency bulletin. Gaithersburg, MD: National Institute of Standards and Technology, 2014. http://dx.doi.org/10.6028/nist.ir.7980-09.

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Potts, Petrina C. NIST time and frequency bulletin. Gaithersburg, MD: National Institute of Standards and Technology, 2014. http://dx.doi.org/10.6028/nist.ir.7980-10.

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