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Time–frequency analysis
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#	Item
101	Transmitter Identification ― Experimental Techniques and Results ― Tsutomu SUGIYAMA, Masaaki SHIBUKI, Ken IWASAKI, and Takayuki HIRANO We delineated the transient response patterns of several different radio transmit Add to Reading List Source URL: www.nict.go.jp Language: English - Date: 2013-11-20 21:00:12 Technology Signal processing Radar Spectrogram Time–frequency analysis Transmitter Amplitude Antenna Noise Waves Physics Acoustics
102	Activity 20 – Spectrogram Plots and Magnetic Storminess Teacher’s Guide: The THEMIS magnetometer produces a second data product called the Spectrogram. Interpreting this data may be beyond the needs and or level of t Add to Reading List Source URL: cse.ssl.berkeley.edu Language: English - Date: 2010-01-11 19:18:34 Geomagnetism Space science Time–frequency analysis Acoustics Signal processing Spectrogram K-index Space weather Magnetometer Electromagnetism Physics Magnetism
103	Centre for Atmospheric Chemistry Hilbert-Huang Transform: application to greenhouse gas time series analysis X. Shi1, R. Macatangay1, D. Griffith1 , D. Striling2 , N. Deutscher3 Add to Reading List Source URL: cawcr.gov.au Language: English - Date: 2011-11-21 01:23:52 Atmospheric chemistry Greenhouse gas Frequency domain Statistics Mathematics Mathematical sciences Signal processing Hilbert–Huang transform Time series
104	Echo Meter User Manual Copyright © 2011-2013Copyright © [removed]Model EM3+ Wildlife Acoustics, Inc. www.wildlifeacoustics.com Add to Reading List Source URL: www.wildlifeacoustics.com Language: English - Date: 2013-05-14 10:29:21 Wildlife Acoustics Zoosemiotics Biology Time–frequency analysis Reason Spectrogram Gamepad Software Application software Acoustics Animal communication Bats
105	I ., IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 41, NO. 3, MARCH[removed] Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2005-03-23 17:23:59 Integral transforms Digital signal processing Time–frequency analysis Hilbert transform Analytic signal Convolution Fourier transform Normal distribution Maximum likelihood Mathematical analysis Fourier analysis Signal processing
106	On the limits of spectral methods for frequency estimation A. G. Rossberg∗ Freiburger Zentrum f¨ ur Datenanalyse und Modellbildung (FDM), Eckerstr. 1, 79104 Freiburg, Germany Add to Reading List Source URL: dsp-book.narod.ru Language: English - Date: 2013-05-06 00:57:02 Signal processing Unitary operators Digital signal processing Time series analysis Spectral density Frequency Periodogram Discrete Fourier transform Fourier transform Mathematical analysis Fourier analysis Mathematics
107	CurveLab Toolbox, Version 2.0 Emmanuel Cand`es, Laurent Demanet, Lexing Ying Applied and Computational Mathematics California Institute of Technology, Pasadena CA[removed] Add to Reading List Source URL: www.curvelet.org Language: English - Date: 2013-08-09 15:51:52 Wavelets Curvelet Time–frequency analysis MATLAB MEX file FFTW Fast Fourier transform Software Array programming languages Image processing
108	IMPLEMENTATION AND ANALYSIS OF SPEECH RECOGNITION FRONT-ENDS V. Mantha, R. Duncan, Y. Wu, and J. Zhao Signal Group Department of Electrical and Computer Engineering Mississippi State University, Mississippi State, Missis Add to Reading List Source URL: dsp-book.narod.ru Language: English - Date: 2013-05-06 00:57:02 Mathematical analysis Signal processing Fourier analysis Time series analysis Cepstrum Mel-frequency cepstrum Speech recognition Linear predictive coding Digital filter Digital signal processing Mathematics Algebra
109	20th International Congress on Modelling and Simulation, Adelaide, Australia, 1–6 December 2013 www.mssanz.org.au/modsim2013 Modelling High-Frequency Volatility with Three-State FIGARCH models Y. Shi a , K.Y-. Ho a Add to Reading List Source URL: www.mssanz.org.au Language: English - Date: 2013-11-19 22:07:31 Mathematical sciences Technical analysis Markov models Autoregressive conditional heteroskedasticity Econometrics Time series analysis Volatility Economic model Time series Statistics Mathematical finance Financial economics
110	Curvelets – A Surprisingly Effective Nonadaptive Representation For Objects with Edges Emmanuel J. Cand`es and David L. Donoho Abstract. It is widely believed that to efficiently represent an otherwise smooth object wi Add to Reading List Source URL: www.curvelet.org Language: English - Date: 2013-08-09 15:51:51 Curvelet Time–frequency analysis Mathematics Functional analysis Wavelet Daubechies wavelet Fourier series Discontinuity Overcompleteness Wavelets Mathematical analysis Image processing

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