26.1.5 Time Series Analysis, One-D Waveform Analysis, One-D Signals

Love, P.L., Simaan, M.,
Automatic recognition of primitive changes in manufacturing process signals,
PR(21), No. 4, 1988, pp. 333-342.
Elsevier DOI 0309
BibRef

Saadani, A., Gelpi, P., Tortelier, P.,
A variable-order Markov-chain-based model for Rayleigh fading and RAKE receiver,
SPLetters(11), No. 3, March 2004, pp. 356-358.
IEEE Abstract. 0404
BibRef

Li, Y., Vucetic, B., Tang, Y., Zhang, Q.,
Space-Time Trellis Codes With Linear Transformation for Fast Fading Channels,
SPLetters(11), No. 11, November 2004, pp. 895-898.
IEEE Abstract. 0411
BibRef

Liao, T.W.[T. Warren],
Clustering of time series data: A survey,
PR(38), No. 11, November 2005, pp. 1857-1874.
Elsevier DOI Award, Pattern Recognition, Honorable Mention. 0509
BibRef

Liao, T.W.[T. Warren],
A clustering procedure for exploratory mining of vector time series,
PR(40), No. 9, September 2007, pp. 2550-2562.
Elsevier DOI 0705
Vector time series; Clustering; Clustering algorithms; Validity index BibRef

Wu, J.K., Liang, Y., Wu, Q., Chen, G.T.,
Frequency tracking techniques of power systems in coloured noises,
VISP(153), No. 6, December 2006, pp. 795-804.
DOI Link 0702
BibRef

Sarkar, M.[Manish],
Ruggedness measures of medical time series using fuzzy-rough sets and fractals,
PRL(27), No. 5, 1 April 2006, pp. 447-454.
Elsevier DOI Characterization; Time series; Fuzzy; Rough; Fuzzy-rough; Hurst exponent and fractal 0604
BibRef

Rajagopalan, V.[Venkatesh], Ray, A.[Asok], Samsi, R.[Rohan], Mayer, J.[Jeffrey],
Pattern identification in dynamical systems via symbolic time series analysis,
PR(40), No. 11, November 2007, pp. 2897-2907.
Elsevier DOI 0707
Time series patterns. Pattern classification; Symbolic time series analysis; Markov modeling BibRef

Yan, K., Jiang, J., Wang, Y.G., Liu, H.T.,
Outage Probability of Selection Cooperation With MRC in Nakagami-m Fading Channels,
SPLetters(16), No. 12, December 2009, pp. 1031-1034.
IEEE DOI 0909
BibRef

Cami, A.[Aurel], Wallstrom, G.L.[Garrick L.], Fowlkes, A.L.[Ashley L.], Panozzo, C.A.[Cathy A.], Hogan, W.R.[William R.],
Mining aggregates of over-the-counter products for syndromic surveillance,
PRL(30), No. 3, 1 February 2009, pp. 255-266.
Elsevier DOI 0804
Biosurveillance; Outbreak detection; Linear regression; Time series aggregation BibRef

Kowalski, M.[Matthieu], Torrésani, B.[Bruno],
Sparsity and persistence: mixed norms provide simple signal models with dependent coefficients,
SIViP(3), No. 3, September 2009, pp. xx-yy.
Springer DOI 0910
BibRef

Jin, X.[Xin], Gupta, S.[Shalabh], Mukherjee, K.[Kushal], Ray, A.[Asok],
Wavelet-based feature extraction using probabilistic finite state automata for pattern classification,
PR(44), No. 7, July 2011, pp. 1343-1356.
Elsevier DOI 1103
Time series analysis; Symbolic dynamics; Feature extraction; Pattern classification; Probabilistic finite state automata BibRef

Yang, C., Le Bouquin Jeannes, R., Faucon, G., Shu, H.,
Extracting Information on Flow Direction in Multivariate Time Series,
SPLetters(18), No. 4, April 2011, pp. 251-254.
IEEE DOI 1103
BibRef

Fidalgo-Merino, R., Nunez, M.,
Self-Adaptive Induction of Regression Trees,
PAMI(33), No. 8, August 2011, pp. 1659-1672.
IEEE DOI 1107
Binary regression tree dealing with data stream. BibRef

Oya, A., Navarro-Moreno, J., Ruiz-Molina, J.C.,
Widely Linear Simulation of Continuous-Time Complex-Valued Random Signals,
SPLetters(18), No. 9, September 2011, pp. 513-516.
IEEE DOI 1108
BibRef

Navarro-Moreno, J., Fernandez-Alcala, R.M., Ruiz-Molina, J.C.,
A Quaternion Widely Linear Series Expansion and Its Applications,
SPLetters(19), No. 12, December 2012, pp. 868-871.
IEEE DOI 1212
BibRef

Jiménez-López, J.D.[José D.], Navarro-Moreno, J.[Jesús], Fernández-Alcalá, R.M.[Rosa M.], Ruiz-Molina, J.C.[Juan C.],
Proper Processing of beta-Quaternion Wide-Sense Markov Signals From Randomly Lost Observations,
SPLetters(31), 2024, pp. 2060-2064.
IEEE DOI 2408
Quaternions, Estimation, Vectors, Algebra, Signal processing algorithms, Nonlinear filters, wide-sense Markov signals BibRef

Sayed-Mouchaweh, M.[Moamar], Messai, N.[Nadhir],
A clustering-based approach for the identification of a class of temporally switched linear systems,
PRL(33), No. 2, 15 January 2012, pp. 144-151.
Elsevier DOI 1112
Classification; Clustering; Identification; Hybrid dynamic systems BibRef

Martin, R.K.,
Using Alpha Shapes to Approximate Signal Strength Based Positioning Performance,
SPLetters(18), No. 12, December 2011, pp. 741-744.
IEEE DOI 1112
BibRef

Li, R.[Rui], Tian, T.P.[Tai-Peng], Sclaroff, S.[Stan],
Divide, Conquer and Coordinate: Globally Coordinated Switching Linear Dynamical System,
PAMI(34), No. 4, April 2012, pp. 654-669.
IEEE DOI 1203
Representation for high-dimensional time series. BibRef

Ward, J.P., Kirshner, H., Unser, M.,
Is Uniqueness Lost for Under-Sampled Continuous-Time Auto-Regressive Processes?,
SPLetters(19), No. 4, April 2012, pp. 183-186.
IEEE DOI 1203
BibRef

Sugavaneswaran, L., Xie, S., Umapathy, K., Krishnan, S.,
Time-Frequency Analysis via Ramanujan Sums,
SPLetters(19), No. 6, June 2012, pp. 352-355.
IEEE DOI 1202
BibRef

Vallejos, R.O.[Ronny O.],
Testing for the absence of correlation between two spatial or temporal sequences,
PRL(33), No. 13, 1 October 2012, pp. 1741-1748.
Elsevier DOI 1208
Codispersion coefficient; Time series; Spatial processes; Hypothesis testing BibRef

Valério, D.[Duarte], da Costa, J.S.[José Sá],
Fractional reset control,
SIViP(6), No. 3, September 2012, pp. 495-501.
WWW Link. 1209
BibRef

Chen, D.[Dali], Chen, Y.Q.[Yang-Quan], Xue, D.Y.[Ding-Yu],
1-D and 2-D digital fractional-order Savitzky-Golay differentiator,
SIViP(6), No. 3, September 2012, pp. 503-511.
WWW Link. 1209
BibRef

Bhalekar, S.[Sachin],
Dynamical analysis of fractional order Uçar prototype delayed system,
SIViP(6), No. 3, September 2012, pp. 513-519.
WWW Link. 1209
BibRef

Bhalekar, S.[Sachin],
On the Uçar prototype model with incommensurate delays,
SIViP(8), No. 4, May 2014, pp. 635-639.
WWW Link. 1404
BibRef

Bhalekar, S.[Sachin],
Stability analysis of Uçar prototype delayed system,
SIViP(10), No. 4, April 2016, pp. 777-781.
WWW Link. 1604
BibRef

Dulf, E.H.[Eva-Henrietta], Pop, C.I.[Cristina-Ioana], Dulf, F.V.[Francisc-Vasile],
Fractional calculus in 13C separation column control,
SIViP(6), No. 3, September 2012, pp. 479-485.
WWW Link. 1209
BibRef

Pop, C.I.[Cristina I.], Ionescu, C.M.[Clara M.], de Keyser, R.[Robain], Dulf, E.H.[Eva H.],
Robustness evaluation of fractional order control for varying time delay processes,
SIViP(6), No. 3, September 2012, pp. 453-461.
WWW Link. 1209
BibRef

Hosseinnia, S.H.[S. Hassan], Tejado, I.[Inés], Vinagre, B.M.[Blas M.], Sierociuk, D.[Dominik],
Boolean-based fractional order SMC for switching systems: Application to a DC-DC buck converter,
SIViP(6), No. 3, September 2012, pp. 445-451.
WWW Link. 1209
BibRef

Melchior, P.[Pierre], Pellet, M.[Mathieu], Petit, J.[Julien], Cabelguen, J.M.[Jean-Marie], Oustaloup, A.[Alain],
Analysis of muscle length effect on an S type motor-unit fractional multi-model,
SIViP(6), No. 3, September 2012, pp. 421-428.
WWW Link. 1209
BibRef

Bensouici, T.[Tahar], Charef, A.[Abdelfatah],
Approximate realization of digital fractional forward operator using digital IIR filter,
SIViP(6), No. 3, September 2012, pp. 411-420.
WWW Link. 1209
BibRef

Maione, G.[Guido],
Thiele's continued fractions in digital implementation of noninteger differintegrators,
SIViP(6), No. 3, September 2012, pp. 401-410.
WWW Link. 1209
BibRef

Malti, R.[Rachid], Melchior, P.[Pierre], Lanusse, P.[Patrick], Oustaloup, A.[Alain],
Object-oriented CRONE toolbox for fractional differential signal processing,
SIViP(6), No. 3, September 2012, pp. 393-400.
WWW Link. 1209
BibRef

Adams, J.L.[Jay L.], Veillette, R.J.[Robert J.], Hartley, T.T.[Tom T.],
Conjugate-order systems for signal processing: Stability, causality, boundedness, compactness,
SIViP(6), No. 3, September 2012, pp. 373-380.
WWW Link. 1209
BibRef

Couceiro, M.S.[Micael S.], Rocha, R.P.[Rui P.], Ferreira, N.M.F.[N. M. Fonseca], Machado, J.A.T.[J. A. Tenreiro],
Introducing the fractional-order Darwinian PSO,
SIViP(6), No. 3, September 2012, pp. 343-350.
WWW Link. 1209
BibRef

Ortigueira, M.D.[Manuel D.], Magin, R.L.[Richard L.], Trujillo, J.J.[Juan J.], Velasco, M.P.[M. Pilar],
A real regularised fractional derivative,
SIViP(6), No. 3, September 2012, pp. 351-358.
WWW Link. 1209
BibRef

Trigeassou, J.C., Maamri, N., Sabatier, J., Oustaloup, A.,
Transients of fractional-order integrator and derivatives,
SIViP(6), No. 3, September 2012, pp. 359-372.
WWW Link. 1209
BibRef

Venkitaraman, A., Seelamantula, C.S.,
A Technique to Compute Smooth Amplitude, Phase, and Frequency Modulations From the Analytic Signal,
SPLetters(19), No. 10, October 2012, pp. 623-626.
IEEE DOI 1209
BibRef

Venkitaraman, A., Seelamantula, C.S.,
On Computing Amplitude, Phase, and Frequency Modulations Using a Vector Interpretation of the Analytic Signal,
SPLetters(20), No. 12, 2013, pp. 1187-1190.
IEEE DOI 1311
Eigenvalues and eigenfunctions BibRef

Venkitaraman, A., Seelamantula, C.S.,
Temporal Envelope Fit of Transient Audio Signals,
SPLetters(20), No. 12, 2013, pp. 1191-1194.
IEEE DOI 1311
Closed-form solutions BibRef

Lee, Y.[Yuni], Sung, Y.C.[Young-Chul],
Generalized Chernoff Information for Mismatched Bayesian Detection and Its Application to Energy Detection,
SPLetters(19), No. 11, November 2012, pp. 753-756.
IEEE DOI 1210
BibRef

Diouf, C., Telescu, M., Cloastre, P., Tanguy, N.,
On the Use of Equality Constraints in the Identification of Volterra-Laguerre Models,
SPLetters(19), No. 12, December 2012, pp. 857-860.
IEEE DOI 1212
BibRef

Sigg, C.D., Dikk, T., Buhmann, J.M.,
Learning Dictionaries With Bounded Self-Coherence,
SPLetters(19), No. 12, December 2012, pp. 861-864.
IEEE DOI 1212
BibRef

Hou, Y., Liu, G., Wang, Q., Xiang, W.,
Performance Optimization of Digital Spectrum Analyzer With Gaussian Input Signal,
SPLetters(20), No. 1, January 2013, pp. 31-34.
IEEE DOI 1212
BibRef

Mohammadi, M.[Mokhtar], Pouyan, A.A.[Ali Akbar], Khan, N.A.[Nabeel Ali],
A highly adaptive directional time-frequency distribution,
SIViP(10), No. 7, October 2016, pp. 1369-1376.
WWW Link. 1609
BibRef

Wu, G.[Gang], Yang, J.W.[Ji-Wen],
A representation of time series based on implicit polynomial curve,
PRL(34), No. 4, 1 March 2013, pp. 361-371.
Elsevier DOI 1302
Implicit polynomial curve; Time series; Similarity measure; Dimension reduction BibRef

Fang, J.[Jun], Liu, Y.M.[Yu-Meng], Li, H.B.[Hong-Bin], Li, S.Q.[Shao-Qian],
One-Bit Quantizer Design for Multisensor GLRT Fusion,
SPLetters(20), No. 3, March 2013, pp. 257-260.
IEEE DOI 1303
BibRef

Rajashekar, R., Hari, K.V.S., Hanzo, L.,
Structured dispersion matrices from division algebra codes for space-time shift keying,
SPLetters(20), No. 4, April 2013, pp. 371-374.
IEEE DOI 1303
BibRef

Rajashekar, R., Hari, K.V.S., Hanzo, L.,
A Reduced-Complexity Partial-Interference-Cancellation Group Decoder for STBCs,
SPLetters(20), No. 10, 2013, pp. 929-932.
IEEE DOI 1309
Toeplitz matrices BibRef

Setlur, P., Devroye, N.,
An Information Theoretic Take on Time Reversal for Nonstationary Channels,
SPLetters(20), No. 4, April 2013, pp. 327-330.
IEEE DOI 1303
BibRef

Li, P.[Po], Wang, D.C.[De-Chun], Wang, L.[Lu],
Separation of micro-Doppler signals based on time frequency filter and Viterbi algorithm,
SIViP(7), No. 3, May 2013, pp. 593-605.
WWW Link. 1305
BibRef

Vemulapalli, P.K.[Pramod K.], Monga, V.[Vishal], Brennan, S.N.[Sean N.],
Robust Extrema Features for Time-Series Data Analysis,
PAMI(35), No. 6, June 2013, pp. 1464-1479.
IEEE DOI 1305
BibRef

Hayashi, A.[Akira], Iwata, K.[Kazunori], Suematsu, N.[Nobuo],
Marginalized Viterbi algorithm for hierarchical hidden Markov models,
PR(46), No. 12, 2013, pp. 3452-3459.
Elsevier DOI 1308
Time series data BibRef

Montillet, J.P., McClusky, S., Yu, K.[Kegen],
Extracting Colored Noise Statistics in Time Series via Negentropy,
SPLetters(20), No. 9, 2013, pp. 857-860.
IEEE DOI 1308
convex programming BibRef

Diversi, R.[Roberto], Guidorzi, R.[Roberto],
Optimal filtering of multivariate noisy AR processes,
SIViP(7), No. 5, September 2013, pp. 873-878.
Springer DOI 1309
BibRef

Baydogan, M.G.[Mustafa Gokce], Runger, G.[George], Tuv, E.[Eugene],
A Bag-of-Features Framework to Classify Time Series,
PAMI(35), No. 11, 2013, pp. 2796-2802.
IEEE DOI 1309
Supervised learning; codebook; feature extraction BibRef

Wang, J., Li, X., Liao, S.S., Hua, Z.,
A Hybrid Approach for Automatic Incident Detection,
ITS(14), No. 3, 2013, pp. 1176-1185.
IEEE DOI 1309
Automatic incident detection (AID) BibRef

Xu, G.L.[Guan-Lei], Wang, X.T.[Xiao-Tong], Wang, L.T.[Long-Tao], Liu, B.[Bo], Su, S.P.[Shi-Peng], Xu, X.G.[Xiao-Gang],
Generalized uncertainty principles associated with Hilbert transform,
SIViP(8), No. 2, February 2014, pp. 279-285.
Springer DOI 1402
BibRef

Çelik, V.[Vedat], Demir, Y.[Yakup],
Chaotic dynamics of the fractional order nonlinear system with time delay,
SIViP(8), No. 1, January 2014, pp. 65-70.
WWW Link. 1402
BibRef

Tan, V.Y.F., Atia, G.K.,
Strong Impossibility Results for Sparse Signal Processing,
SPLetters(21), No. 3, March 2014, pp. 260-264.
IEEE DOI 1403
error statistics BibRef

Kazemi, K.[Kamran], Amirian, M.[Mohammadreza], Dehghani, M.J.[Mohammad Javad],
The S-transform using a new window to improve frequency and time resolutions,
SIViP(8), No. 3, March 2014, pp. 533-541.
Springer DOI 1403
BibRef

Liu, Z.L.[Zi-Long], Parampalli, U., Guan, Y.L.[Yong Liang],
On Even-Period Binary Z-Complementary Pairs with Large ZCZs,
SPLetters(21), No. 3, March 2014, pp. 284-287.
IEEE DOI 1403
Golay codes BibRef

Adhikary, A.R.[Avik Ranjan], Majhi, S.[Sudhan], Liu, Z.L.[Zi-Long], Guan, Y.L.[Yong Liang],
New Sets of Even-Length Binary Z-Complementary Pairs With Asymptotic ZCZ Ratio of 3/4,
SPLetters(25), No. 7, July 2018, pp. 970-973.
IEEE DOI 1807
Golay codes, binary codes, correlation methods, sequences, ZCZ region, ZCZ width, asymptotic ZCZ ratio, zero correlation zone (ZCZ) BibRef

López-Yáñez, I.[Itzamá], Sheremetov, L.[Leonid], Yáñez-Márquez, C.[Cornelio],
A novel associative model for time series data mining,
PRL(41), No. 1, 2014, pp. 23-33.
Elsevier DOI 1403
Time series data mining BibRef

Längkvist, M.[Martin], Karlsson, L.[Lars], Loutfi, A.[Amy],
A review of unsupervised feature learning and deep learning for time-series modeling,
PRL(42), No. 1, 2014, pp. 11-24.
Elsevier DOI 1404
Time-series BibRef

Górecki, T.[Tomasz],
Using derivatives in a longest common subsequence dissimilarity measure for time series classification,
PRL(45), No. 1, 2014, pp. 99-105.
Elsevier DOI 1407
Longest common subsequence BibRef

Boecking, B.[Benedikt], Chalup, S.K.[Stephan K.], Seese, D.[Detlef], Wong, A.S.W.[Aaron S.W.],
Support vector clustering of time series data with alignment kernels,
PRL(45), No. 1, 2014, pp. 129-135.
Elsevier DOI 1407
Support vector clustering BibRef

Zhao, H.[Hui], Wang, R.[Ruyan], Song, D.P.[Dai-Ping], Wu, D.P.[Da-Peng],
Maximally concentrated sequences in both time and linear canonical transform domains,
SIViP(8), No. 5, July 2014, pp. 819-829.
Springer DOI 1407
BibRef

Frasca, M.[Marco], Farina, A.[Alfonso],
Tartaglia-Pascal triangle and Brownian motion in non-euclidean geometries: application to heat and Black-Scholes equations,
SIViP(8), No. 6, September 2014, pp. 1149-1157.
Springer DOI 1408
Financial models. BibRef

Liu, X.[Xiang], Kosakowski, M.,
Max-Log-MAP Soft Demapper with Logarithmic Complexity for M-PAM Signals,
SPLetters(22), No. 1, January 2015, pp. 50-53.
IEEE DOI 1410
computational complexity quadrature amplitude modulation. BibRef

Naraghi-Pour, M., Soltanmohammadi, E.,
Tenor: A Measure of Central Tendency for Distributed Networks,
SPLetters(22), No. 1, January 2015, pp. 58-61.
IEEE DOI 1410
Phase of the first non-zero frequency of the discrete Fourier transform of the pmf. BibRef

Garcia-Trevino, E.S., Barria, J.A.,
Structural Generative Descriptions for Time Series Classification,
Cyber(44), No. 10, October 2014, pp. 1978-1991.
IEEE DOI 1410
data mining BibRef

Wang, Z.X.[Zhi-Xin], Chan, C.F.[Cheung-Fat],
Continuous Function Modeling of Head-Related Impulse Response,
SPLetters(22), No. 3, March 2015, pp. 283-287.
IEEE DOI 1410
Azimuth BibRef

Maymon, S., Eldar, Y.C.,
The Viterbi Algorithm for Subset Selection,
SPLetters(22), No. 5, May 2015, pp. 524-528.
IEEE DOI 1411
Dictionaries BibRef

Gan, M.[Min], Chen, C.L.P., Li, H.X.[Han-Xiong], Chen, L.[Long],
Gradient Radial Basis Function Based Varying-Coefficient Autoregressive Model for Nonlinear and Nonstationary Time Series,
SPLetters(22), No. 7, July 2015, pp. 809-812.
IEEE DOI 1412
autoregressive processes BibRef

Gharehbaghi, A.[Arash], Ask, P.[Per], Babic, A.[Ankica],
A pattern recognition framework for detecting dynamic changes on cyclic time series,
PR(48), No. 3, 2015, pp. 696-708.
Elsevier DOI 1412
Hybrid model BibRef

Trigano, T., Barat, E., Dautremer, T., Montagu, T.,
Fast Digital Filtering of Spectrometric Data for Pile-up Correction,
SPLetters(22), No. 7, July 2015, pp. 973-977.
IEEE DOI 1412
Detectors BibRef

Hensman, J., Rattray, M., Lawrence, N.D.,
Fast Nonparametric Clustering of Structured Time-Series,
PAMI(37), No. 2, February 2015, pp. 383-393.
IEEE DOI 1502
Biological system modeling BibRef

Xu, Z., MacEachern, S., Xu, X.,
Modeling Non-Gaussian Time Series with Nonparametric Bayesian Model,
PAMI(37), No. 2, February 2015, pp. 372-382.
IEEE DOI 1502
Analytical models BibRef

Kumar, M., Stoll, N., Stoll, R., Thurow, K.,
A Stochastic Framework for Robust Fuzzy Filtering and Analysis of Signals: Part I,
Cyber(46), No. 5, May 2016, pp. 1118-1131.
IEEE DOI 1604
Algorithm design and analysis BibRef

Kumar, M., Stoll, N., Stoll, R., Thurow, K.,
A Stochastic Framework for Robust Fuzzy Filtering and Analysis of Signals: Part II,
Cyber(45), No. 3, March 2015, pp. 486-496.
IEEE DOI 1502
Algorithm design and analysis BibRef

Naha, A., Samanta, A.K., Routray, A., Deb, A.K.,
Determining Autocorrelation Matrix Size and Sampling Frequency for MUSIC Algorithm,
SPLetters(22), No. 8, August 2015, pp. 1016-1020.
IEEE DOI 1502
correlation methods BibRef

Pei, S.C.[Soo-Chang], Chang, K.W.[Kuo-Wei],
Perfect Gaussian Integer Sequences of Arbitrary Length,
SPLetters(22), No. 8, August 2015, pp. 1040-1044.
IEEE DOI 1502
Gaussian processes BibRef

Markovsky, I.,
Comparison of Adaptive and Model-Free Methods for Dynamic Measurement,
SPLetters(22), No. 8, August 2015, pp. 1094-1097.
IEEE DOI 1502
adaptive signal processing BibRef

Pei, S.C.[Soo-Chang], Lu, K.S.[Keng-Shih],
Intrinsic Integer-Periodic Functions for Discrete Periodicity Detection,
SPLetters(22), No. 8, August 2015, pp. 1108-1112.
IEEE DOI 1502
signal sampling BibRef

Walker, J.S.[James S.], Jones, M.W.[Mark W.], Laramee, R.S.[Robert S.], Bidder, O.R.[Owen R.], Williams, H.J.[Hannah J.], Scott, R.[Rebecca], Shepard, E.L.C.[Emily L. C.], Wilson, R.P.[Rory P.],
TimeClassifier: a visual analytic system for the classification of multi-dimensional time series data,
VC(31), No. 6-8, June 2015, pp. 1067-1078.
Springer DOI 1506
BibRef

Farokhi, F., Shames, I., Cantoni, M.,
Promoting Truthful Behavior in Participatory-Sensing Mechanisms,
SPLetters(22), No. 10, October 2015, pp. 1538-1542.
IEEE DOI 1506
game theory BibRef

Konar, A., Sidiropoulos, N.D.,
Hidden Convexity in QCQP with Toeplitz-Hermitian Quadratics,
SPLetters(22), No. 10, October 2015, pp. 1623-1627.
IEEE DOI 1506
Hermitian matrices. Quadratically Constrained Quadratic Programming. BibRef

Wei, X.Y.[Xiao-Yao], Dragotti, P.L.[Pier Luigi],
Guaranteed Performance in the FRI Setting,
SPLetters(22), No. 10, October 2015, pp. 1661-1665.
IEEE DOI 1506
noise Finite Rate of Innovation. BibRef

Tenneti, S.V., Vaidyanathan, P.P.,
Arbitrarily Shaped Periods in Multidimensional Discrete Time Periodicity,
SPLetters(22), No. 10, October 2015, pp. 1748-1751.
IEEE DOI 1506
mathematical analysis BibRef

Elvira, V., Martino, L., Luengo, D., Bugallo, M.F.,
Heretical Multiple Importance Sampling,
SPLetters(23), No. 10, October 2016, pp. 1474-1478.
IEEE DOI 1610
importance sampling BibRef

Mousavi, A.[Ali], Monsefi, R.[Reza], Elvira, V.[Víctor],
Hamiltonian Adaptive Importance Sampling,
SPLetters(28), 2021, pp. 713-717.
IEEE DOI 2104
Proposals, Monte Carlo methods, Artificial intelligence, Signal processing algorithms, Markov processes, hamiltonies an monte carlo BibRef

Yang, Y.L.[Yan-Li], Deng, J.H.[Jia-Hao], Kang, D.[Dali],
An improved empirical mode decomposition by using dyadic masking signals,
SIViP(9), No. 6, September 2015, pp. 1259-1263.
Springer DOI 1509
BibRef

Khan, M.[Majid], Shah, T.[Tariq],
An efficient construction of substitution box with fractional chaotic system,
SIViP(9), No. 6, September 2015, pp. 1335-1338.
Springer DOI 1509
BibRef

Luo, W.[Wen], Yu, Z.Y.[Zhao-Yuan], Xiao, S.J.[Sheng-Jun], Zhu, A.X.[A-Xing], Yuan, L.W.[Lin-Wang],
Exploratory Method for Spatio-Temporal Feature Extraction and Clustering: An Integrated Multi-Scale Framework,
IJGI(4), No. 4, 2015, pp. 1870.
DOI Link 1511
E.g. weather data and El Nino. BibRef

Biswas, N., Ray, P., Varshney, P.K.,
Distributed Detection Over Channels with Memory,
SPLetters(22), No. 12, December 2015, pp. 2494-2498.
IEEE DOI 1512
Markov processes BibRef

Khanduri, P., Kailkhura, B., Thiagarajan, J.J., Varshney, P.K.,
Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach,
SPLetters(23), No. 10, October 2016, pp. 1484-1488.
IEEE DOI 1610
compressed sensing BibRef

Cheng, X.C.[Xian-Cheng], Geng, B.C.[Bao-Cheng], Khanduri, P.[Prashant], Chen, B.X.[Bai-Xiao], Varshney, P.K.[Pramod K.],
Joint Collaboration and Compression Design for Random Signal Detection in Wireless Sensor Networks,
SPLetters(28), 2021, pp. 1630-1634.
IEEE DOI 2109
Sensors, Collaboration, Wireless sensor networks, Sparse matrices, Signal detection, Optimization, Estimation, generalized deflection coefficient BibRef

Ren, H.R.[Huo-Rong], Ren, A.[An], Li, Z.W.[Zhi-Wu],
A new strategy for the suppression of cross-terms in pseudo Wigner-Ville distribution,
SIViP(10), No. 1, January 2016, pp. 139-144.
Springer DOI 1601
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Rostaghi, M., Azami, H.,
Dispersion Entropy: A Measure for Time-Series Analysis,
SPLetters(23), No. 5, May 2016, pp. 610-614.
IEEE DOI 1604
Bandwidth BibRef

Mohammadi, H., Steele, C., Chau, T.,
Post-Segmentation Swallowing Accelerometry Signal Trimming and False Positive Reduction,
SPLetters(23), No. 9, September 2016, pp. 1221-1225.
IEEE DOI 1609
accelerometers BibRef

Yuan, W.J.[Wei-Jie], Wu, N.[Nan], Wang, H.[Hua], Kuang, J.M.[Jing-Ming],
Variational Inference-Based Frequency-Domain Equalization for Faster-Than-Nyquist Signaling in Doubly Selective Channels,
SPLetters(23), No. 9, September 2016, pp. 1270-1274.
IEEE DOI 1609
computational complexity BibRef

Pirondini, E., Vybornova, A., Coscia, M., van de Ville, D.,
A Spectral Method for Generating Surrogate Graph Signals,
SPLetters(23), No. 9, September 2016, pp. 1275-1278.
IEEE DOI 1609
Fourier transforms BibRef

Shah, S.F.A.[S. Faisal A.], Wang, L.[Lei], Li, C.D.[Chuan-Dong], Zhang, Z.H.[Zhu-Hong],
Low-Complexity Design of Noninteger Fractionally Spaced Adaptive Equalizers for Coherent Optical Receivers,
SPLetters(23), No. 9, September 2016, pp. 1289-1293.
IEEE DOI 1609
adaptive equalisers BibRef

Sarkar, S.[Soumalya], Chattopdhyay, P.[Pritthi], Ray, A.[Asok],
Symbolization of dynamic data-driven systems for signal representation,
SIViP(10), No. 8, November 2016, pp. 1535-1542.
Springer DOI 1610
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Ferrand, P.,
Mi-Xing Oscillators for Phase Noise Reduction,
SPLetters(23), No. 11, November 2016, pp. 1597-1601.
IEEE DOI 1609
circuit noise BibRef

de Fréin, R., Rickard, S.T.,
Power-Weighted Divergences for Relative Attenuation and Delay Estimation,
SPLetters(23), No. 11, November 2016, pp. 1612-1616.
IEEE DOI 1609
approximation theory BibRef

Bayram, S., Dulek, B., Gezici, S.,
Joint Detection and Decoding in the Presence of Prior Information With Uncertainty,
SPLetters(23), No. 11, November 2016, pp. 1602-1606.
IEEE DOI 1609
decoding BibRef

Asadi, N., Mirzaei, A., Haghshenas, E.,
Creating Discriminative Models for Time Series Classification and Clustering by HMM Ensembles,
Cyber(46), No. 12, December 2016, pp. 2899-2910.
IEEE DOI 1612
Biological system modeling BibRef

Johard, L.[Leonard], Ruffaldi, E.[Emanuele],
Self-organizing trajectories,
PRL(84), No. 1, 2016, pp. 177-184.
Elsevier DOI 1612
Shape averaging BibRef

Adhikary, A.R.[Avik Ranjan], Liu, Z.L.[Zi-Long], Guan, Y.L.[Yong Liang], Majhi, S.[Sudhan], Budishin, S.Z.[Srdjan Z.],
Optimal Binary Periodic Almost-Complementary Pairs,
SPLetters(23), No. 12, December 2016, pp. 1816-1820.
IEEE DOI 1612
binary sequences BibRef

Valsesia, D.[Diego], Magli, E.[Enrico],
Binary Adaptive Embeddings From Order Statistics of Random Projections,
SPLetters(24), No. 1, January 2017, pp. 111-115.
IEEE DOI 1702
signal processing BibRef

Oloo, F.[Francis], Wallentin, G.[Gudrun],
An Adaptive Agent-Based Model of Homing Pigeons: A Genetic Algorithm Approach,
IJGI(6), No. 1, 2017, pp. xx-yy.
DOI Link 1702
Simulate flight routes of homing pigeons. BibRef

Su, B.[Bing], Ding, X.Q.[Xiao-Qing], Liu, C.S.[Chang-Song], Wang, H.[Hao], Wu, Y.[Ying],
Discriminative Transformation for Multi-Dimensional Temporal Sequences,
IP(26), No. 7, July 2017, pp. 3579-3593.
IEEE DOI 1706
Adaptation models, Character recognition, Data models, Hidden Markov models, Training, Transforms, Max-min inter-sequence distance analysis, BibRef

Su, B.[Bing], Ding, X.Q.[Xiao-Qing], Wang, H.[Hao], Wu, Y.[Ying],
Discriminative Dimensionality Reduction for Multi-Dimensional Sequences,
PAMI(40), No. 1, January 2018, pp. 77-91.
IEEE DOI 1712
Analytical models, Computational modeling, Data models, Hidden Markov models, Sequences, sequence classification BibRef

Kasasbeh, H., Viswanathan, R., Cao, L.,
Noise Correlation Effect on Detection: Signals in Equicorrelated or Autoregressive(1) Gaussian,
SPLetters(24), No. 7, July 2017, pp. 1078-1082.
IEEE DOI 1706
Correlation, Covariance matrices, Eigenvalues and eigenfunctions, Gaussian noise, Sensors, Signal design, Signal to noise ratio, Autoregressive, Gaussian noise, correlation, multiple sensors, signal, detection BibRef

Audibert, L.[Lorenzo], Haddar, H.[Houssem],
The Generalized Linear Sampling Method for Limited Aperture Measurements,
SIIMS(10), No. 2, 2017, pp. 845-870.
DOI Link 1708
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Al Sarray, B.[Basad], Chrétien, S.[Stéphane], Clarkson, P.[Paul], Cottez, G.[Guillaume],
Enhancing Prony's method by nuclear norm penalization and extension to missing data,
SIViP(11), No. 6, September 2017, pp. 1089-1096.
Springer DOI 1708
modelling signals using a finite sum of exponential terms. Weather models BibRef

Dumitrescu, B.[Bogdan],
Designing Incoherent Frames With Only Matrix Vector Multiplications,
SPLetters(24), No. 9, September 2017, pp. 1265-1269.
IEEE DOI 1708
least squares approximations, minimax techniques, atom-by-atom optimization strategy, incoherent frames, min-max problem, mutual coherence, shifted power method, signal processing, Eigenvalues and eigenfunctions, Optimization, power method, weighted least squares. BibRef

Tuncel, K.S.[Kerem Sinan], Baydogan, M.G.[Mustafa Gokce],
Autoregressive forests for multivariate time series modeling,
PR(73), No. 1, 2018, pp. 202-215.
Elsevier DOI 1709
Multivariate time series BibRef

Dadkhahi, H., Duarte, M.F., Marlin, B.M.,
Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series,
IP(26), No. 11, November 2017, pp. 5435-5446.
IEEE DOI 1709
Data models, Manifolds, Noise measurement, Principal component analysis, Sensors, Time series analysis, Training, dimensionality reduction, out-of-sample extension. BibRef

Darsena, D., Gelli, G., Iudice, I., Verde, F.,
Second-Order Statistics of One-Sided CPM Signals,
SPLetters(24), No. 10, October 2017, pp. 1512-1516.
IEEE DOI 1710
CPM: continuous phase modulation, correlation methods, higher order statistics, signal representation, BibRef

Mikalsen, K.Ø.[Karl Øyvind], Bianchi, F.M.[Filippo Maria], Soguero-Ruiz, C.[Cristina], Jenssen, R.[Robert],
Time series cluster kernel for learning similarities between multivariate time series with missing data,
PR(76), No. 1, 2018, pp. 569-581.
Elsevier DOI 1801
Multivariate time series BibRef

Bianchi, F.M.[Filippo Maria], Livi, L.[Lorenzo], Mikalsen, K.Ø.[Karl Øyvind], Kampffmeyer, M.[Michael], Jenssen, R.[Robert],
Learning representations of multivariate time series with missing data,
PR(96), 2019, pp. 106973.
Elsevier DOI 1909
Representation learning, Multivariate time series, Autoencoders, Recurrent neural networks, Kernel methods BibRef

Zhu, Z., Karnik, S., Davenport, M.A., Romberg, J., Wakin, M.B.,
The Eigenvalue Distribution of Discrete Periodic Time-Frequency Limiting Operators,
SPLetters(25), No. 1, January 2018, pp. 95-99.
IEEE DOI 1801
bandlimited signals, discrete Fourier transforms, eigenvalues and eigenfunctions, matrix algebra, time-frequency analysis BibRef

Gong, Z., Li, C., Jiang, F.,
AUV-Aided Joint Localization and Time Synchronization for Underwater Acoustic Sensor Networks,
SPLetters(25), No. 4, April 2018, pp. 477-481.
IEEE DOI 1804
autonomous underwater vehicles, error analysis, mobile communication, nonlinear equations, sensor placement, underwater sensor networks BibRef

de Carvalho Pagliosa, L.[Lucas], Fernandes de Mello, R.[Rodrigo],
Semi-supervised time series classification on positive and unlabeled problems using cross-recurrence quantification analysis,
PR(80), 2018, pp. 53-63.
Elsevier DOI 1805
Time series, Semi-supervised classification, Positive and unlabeled, Self-training, Phase space, Cross-recurrence quantification analysis BibRef

Li, C.P., Chang, K.J., Chang, H.H., Chen, Y.M.,
Perfect Sequences of Odd Prime Length,
SPLetters(25), No. 7, July 2018, pp. 966-969.
IEEE DOI 1807
correlation methods, cyclic codes, sequential codes, cyclic group, degree-(K + 1) PS, distinct nonzero elements, periodic autocorrelation function (PACF) BibRef

Jing, Y., Liang, J., Zhou, D., So, H.C.,
Spectrally Constrained Unimodular Sequence Design Without Spectral Level Mask,
SPLetters(25), No. 7, July 2018, pp. 1004-1008.
IEEE DOI 1807
optimisation, sequences, spectral analysis, acceleration scheme, algorithm convergence speed, auxiliary variables, spectral level BibRef

Miao, Y., Sun, H., Qi, J.,
Synchro-Compensating Chirplet Transform,
SPLetters(25), No. 9, September 2018, pp. 1413-1417.
IEEE DOI 1809
demodulation, signal processing, time-frequency analysis, transforms, energy compensation, time-frequency transform, time-frequency representation (TFR) BibRef

Gómez-Chova, L.[Luis], Santos-Rodríguez, R.[Raúl], Camps-Valls, G.[Gustau],
Signal-to-noise ratio in reproducing kernel Hilbert spaces,
PRL(112), 2018, pp. 75-82.
Elsevier DOI 1809
Kernel methods, Noise model, Signal-to-noise ratio, SNR, Heteroscedastic, Feature extraction, Signal classification, Causal inference BibRef

de Souza, D.B.[D. Baptista], Kuhn, E.V., Seara, R.,
A Time-Varying Autoregressive Model for Characterizing Nonstationary Processes,
SPLetters(26), No. 1, January 2019, pp. 134-138.
IEEE DOI 1901
autoregressive processes, covariance matrices, random processes, signal processing, vectors, time-varying autoregressive model, time-varying autoregressive (TVAR) model BibRef

Jiang, G.X.[Gao-Xia], Wang, W.J.[Wen-Jian], Zhang, W.K.[Wen-Kai],
A novel distance measure for time series: Maximum shifting correlation distance,
PRL(117), 2019, pp. 58-65.
Elsevier DOI 1901
Time series, Distance measure, Second distance, Clustering, Classification BibRef

Gong, Z., Chen, H., Yuan, B., Yao, X.,
Multiobjective Learning in the Model Space for Time Series Classification,
Cyber(49), No. 3, March 2019, pp. 918-932.
IEEE DOI 1902
Time series analysis, Hidden Markov models, Kernel, Data models, Time measurement, Heuristic algorithms, Echo state network (ESN), time series classification BibRef

Marandi, M.K.[Mostafa Khalili], Rave, W.[Wolfgang], Fettweis, G.[Gerhard],
Beam Selection Based on Sequential Competition,
SPLetters(26), No. 3, March 2019, pp. 455-459.
IEEE DOI 1903
error statistics, statistical testing, genie knowledge, beam selection, SNR operating point, sequential test adaptively, generalized likelihood ratio test BibRef

Cheng, Y.[Yao], Haardt, M.[Martin],
Enhanced Direct Fitting Algorithms for PARAFAC2 With Algebraic Ingredients,
SPLetters(26), No. 4, April 2019, pp. 533-537.
IEEE DOI 1903
geometry, matrix algebra, matrix decomposition, numerical analysis, search problems, statistical analysis, tensors, direct fitting algorithm BibRef

Shmaliy, Y.S.[Yuriy S.], Zhao, S.[Shunyi], Ahn, C.K.[Choon Ki],
Optimal and Unbiased Filtering With Colored Process Noise Using State Differencing,
SPLetters(26), No. 4, April 2019, pp. 548-551.
IEEE DOI 1903
Mathematical model, Signal processing algorithms, Kalman filters, Noise measurement, Standards, Real-time systems, unbiased FIR filter BibRef

Alrashdi, A.M.[Ayed M.], Ben Atitallah, I.[Ismail], Al-Naffouri, T.Y.[Tareq Y.],
Precise Performance Analysis of the Box-Elastic Net Under Matrix Uncertainties,
SPLetters(26), No. 5, May 2019, pp. 655-659.
IEEE DOI 1905
Gaussian distribution, Gaussian noise, matrix algebra, mean square error methods, signal processing, box-constraint BibRef

Ramírez-Espinosa, P., Morales-Jimenez, D., Cortés, J.A., Paris, J.F., Martos-Naya, E.,
New Approximation to Distribution of Positive RVs Applied to Gaussian Quadratic Forms,
SPLetters(26), No. 6, June 2019, pp. 923-927.
IEEE DOI 1906
Probability density function, Convergence, Random variables, Signal processing, Distribution functions, Closed-form solutions, statistical distributions BibRef

Mozerov, M.G., Yang, F., van de Weijer, J.,
Sparse Data Interpolation Using the Geodesic Distance Affinity Space,
SPLetters(26), No. 6, June 2019, pp. 943-947.
IEEE DOI 1906
Interpolation, Signal processing algorithms, Image edge detection, Optical imaging, Kernel, Pipelines, adaptive filter BibRef

Roa, N.B.[Nathalie Barbosa], Travé-Massuyès, L.[Louise], Grisales-Palacio, V.H.[Victor H.],
DyClee: Dynamic clustering for tracking evolving environments,
PR(94), 2019, pp. 162-186.
Elsevier DOI 1906
Dynamic clustering, Data mining, On-line learning, Time-series, Data streams, Multi-density clustering BibRef

Zhang, Q.[Qin], Wu, J.[Jia], Zhang, P.[Peng], Long, G.D.[Guo-Dong], Zhang, C.Q.[Cheng-Qi],
Salient Subsequence Learning for Time Series Clustering,
PAMI(41), No. 9, Sep. 2019, pp. 2193-2207.
IEEE DOI 1908
Time series analysis, Feature extraction, Spectral analysis, Analytical models, Labeling, Robustness, Data models, Time series, clustering BibRef

Zeng, F.X.[Fan-Xin], He, X.P.[Xi-Ping], Xuan, G.X.[Gui-Xin], Zhang, Z.Y.[Zhen-Yu], Peng, Y.N.[Yan-Ni], Yan, L.[Li],
Perfect Gaussian Integer Sequences Embedding Pre-Given Gaussian Integers,
SPLetters(26), No. 8, August 2019, pp. 1122-1126.
IEEE DOI 1908
Gaussian processes, sequences, pre-given Gaussian integer, perfect GI sequences, PGIS design, arbitrary pre-given GI, perfect Gaussian integer sequences BibRef

Tian, N.[Nili], Wang, X.L.[Xiao-Ling], Ling, B.W.K.[Bingo Wing-Kuen], Sakalli, M.[Mustafa],
Properties of approximated empirical mode decomposition and optimal design of its system kernel matrix for signal decomposition,
SIViP(13), No. 6, September 2019, pp. 1173-1181.
Springer DOI 1908
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Zhang, Z.H.[Zhi-Hong], Zhang, G.Z.[Gen-Zhou], Zhang, Z.H.[Zhong-Hao], Chen, G.[Guo], Zeng, Y.B.[Yang-Bin], Wang, B.Z.[Bei-Zhan], Hancock, E.R.[Edwin R.],
Structural network inference from time-series data using a generative model and transfer entropy,
PRL(125), 2019, pp. 357-363.
Elsevier DOI 1909
Transfer entropy, Supergraph, Time series, Network inference, Expectation maximization algorithm BibRef

ur Rehman, N.[Naveed], Khan, B.[Bushra], Naveed, K.[Khuram],
Data-Driven Multivariate Signal Denoising Using Mahalanobis Distance,
SPLetters(26), No. 9, September 2019, pp. 1408-1412.
IEEE DOI 1909
Noise reduction, Covariance matrices, Correlation, Signal processing algorithms, Gaussian noise, multivariate empirical mode decomposition BibRef

Mello, C.E.[Carlos E.], Carvalho, A.S.T.[André S.T.], Lyra, A.[Adria], Pedreira, C.E.[Carlos E.],
Time series classification via divergence measures between probability density functions,
PRL(125), 2019, pp. 42-48.
Elsevier DOI 1909
Time series classification, Kernel methods, Time delay embedding, Kernel density estimation BibRef

Baptista de Souza, D.[Douglas], Chanussot, J.[Jocelyn], Favre, A.C.[Anne-Catherine], Borgnat, P.[Pierre],
An Improved Stationarity Test Based on Surrogates,
SPLetters(26), No. 10, October 2019, pp. 1431-1435.
IEEE DOI 1909
Time-frequency analysis, Testing, Feature extraction, Reactive power, Probability density function, Guidelines, surrogates BibRef

Saatci, E.[Esra], Saatci, E.[Ertugrul],
Period Determination in Cyclo-Stationary Signals by Autocorrelation and Ramanujan Subspaces,
SPLetters(27), 2020, pp. 266-270.
IEEE DOI 2002
Period determination, cyclo-stationary signals, time-varying cyclic autocorrelation, Ramanujan sums BibRef

Siyou Fotso, V.S.[Vanel Steve], Mephu Nguifo, E.[Engelbert], Vaslin, P.[Philippe],
Frobenius correlation based u-shapelets discovery for time series clustering,
PR(103), 2020, pp. 107301.
Elsevier DOI 2005
Clustering, UShapelet, Correlation, Time series BibRef

Mahjoub, C.[Chahira], Bellanger, J.J.[Jean-Jacques], Kachouri, A.[Abdennaceur], Jeannès, R.L.[Régine Le_Bouquin],
On the performance of temporal Granger causality measurements on time series: a comparative study,
SIViP(14), No. 5, July 2020, pp. 945-953.
Springer DOI 2006
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Bayram, I.[Ilker],
An Exploratory Method for Smooth/Transient Decomposition,
SPLetters(27), 2020, pp. 890-894.
IEEE DOI 2006
Constrained filtering, gaussian process, signal decomposition, empirical mode decomposition, vital signs monitoring, radar BibRef

Carmona-Poyato, Á.[Ángel], Fernández-García, N.L.[Nicolás Luis], Madrid-Cuevas, F.J.[Francisco José], Durán-Rosal, A.M.[Antonio Manuel],
A new approach for optimal time-series segmentation,
PRL(135), 2020, pp. 153-159.
Elsevier DOI 2006
Data representation, Data compression, Optimal time series segmentation, Time series size reduction BibRef

Carmona-Poyato, Á.[Ángel], Fernández-Garcia, N.L.[Nicolás Luis], Madrid-Cuevas, F.J.[Francisco José], Durán-Rosal, A.M.[Antonio Manuel],
A new approach for optimal offline time-series segmentation with error bound guarantee,
PR(115), 2021, pp. 107917.
Elsevier DOI 2104
Data representation, Optimal time series segmentation, Error bound guarantee, -norm BibRef

Shin, D., Cho, H., Yang, I.,
Power-Law Processor Over Segmentation for Variable Length Transients Detection,
SPLetters(27), 2020, pp. 1065-1069.
IEEE DOI 2007
Detectors, Transient analysis, Signal to noise ratio, Frequency-domain analysis, Time-domain analysis, segmentation BibRef

Clarkson, I.V.L., Sirianunpiboon, S., Howard, S.D.,
Frequency Estimation in Coherent, Periodic Pulse Trains,
SPLetters(27), 2020, pp. 1415-1419.
IEEE DOI 2009
Frequency estimation, Shape, Signal to noise ratio, Coherence, Frequency modulation, Estimation, Transmitters, Doppler radar, radar measurements BibRef

Zhou, Y., Ding, F.,
Modeling Nonlinear Processes Using the Radial Basis Function-Based State-Dependent Autoregressive Models,
SPLetters(27), 2020, pp. 1600-1604.
IEEE DOI 2009
Signal processing algorithms, Parameter estimation, Technological innovation, Mathematical model, data saturation BibRef

Türkmen, A.C.[Ali Caner], Çapan, G.[Gökhan], Cemgil, A.T.[Ali Taylan],
Clustering Event Streams With Low Rank Hawkes Processes,
SPLetters(27), 2020, pp. 1575-1579.
IEEE DOI 2009
e.g. neural spikes. Clustering algorithms, Numerical stability, Machine learning algorithms, Signal processing algorithms, clustering BibRef

Wang, W.H.[Wei-Han],
MAGAN: A masked autoencoder generative adversarial network for processing missing IoT sequence data,
PRL(138), 2020, pp. 211-216.
Elsevier DOI 2010
Missing data, Time series data, Sensor data, GAN, Deep learning BibRef

Lim, H.K.[Hyun-Ki], Choi, H.[Heeseung], Choi, Y.[Yeji], Kim, I.J.[Ig-Jae],
Memetic algorithm for multivariate time-series segmentation,
PRL(138), 2020, pp. 60-67.
Elsevier DOI 2010
Time series segmentation, Multivariate data, Memetic algorithm BibRef

Dimitrov, M., Baitcheva, T., Nikolov, N.,
On the Generation of Long Binary Sequences With Record-Breaking PSL Values,
SPLetters(27), 2020, pp. 1904-1908.
IEEE DOI 2011
Signal processing algorithms, Complexity theory, Heuristic algorithms, Correlation, Radar, Optimization, Art, peak sidelobe level (PSL) BibRef

Okhrin, Y., Schmid, W., Semeniuk, I.,
New Approaches for Monitoring Image Data,
IP(30), 2021, pp. 921-933.
IEEE DOI 2012
Control charts, Process control, Monitoring, Feature extraction, Image analysis, Digital images, Time series analysis, statistical process control BibRef

Lin, X., Huang, Y., Ma, W.K.,
Robust Downlink Transmit Optimization Under Quantized Channel Feedback via the Strong Duality for QCQP,
SPLetters(28), 2021, pp. 1-5.
IEEE DOI 2101
Array signal processing, Channel estimation, Signal to noise ratio, Interference, Optimization, Downlink, the strong duality of QCQP BibRef

Chi, K., Shen, J., Li, Y., Li, Y., Wang, S.,
Multi-Function Radar Signal Sorting Based on Complex Network,
SPLetters(28), 2021, pp. 91-95.
IEEE DOI 2101
Radar, Sorting, Complex networks, Clustering algorithms, Task analysis, Radar detection, Time series analysis, signal sorting BibRef

Prasanna, D., Sriram, C., Murthy, C.R.,
On the Identifiability of Sparse Vectors From Modulo Compressed Sensing Measurements,
SPLetters(28), 2021, pp. 131-134.
IEEE DOI 2101
Dynamic range, Optimization, Signal processing algorithms, Indexes, Heuristic algorithms, Compressed sensing, Sparse matrices, modulo compressed sensing BibRef

Li, Q.Z.[Qing-Zhe], Zhao, L.[Liang], Lee, Y.C.[Yi-Ching], Sassan, A.[Avesta], Lin, J.[Jessica],
CPM: A general feature dependency pattern mining framework for contrast multivariate time series,
PR(112), 2021, pp. 107711.
Elsevier DOI 2102
Contrast pattern, Feature dependency, Controlled experiment, Driving behavior, Multivariate time series BibRef

Cao, Z., Dai, J., Xu, W., Chang, C.,
Fast Variational Bayesian Inference for Temporally Correlated Sparse Signal Recovery,
SPLetters(28), 2021, pp. 214-218.
IEEE DOI 2102
Bayes methods, Correlation, Signal processing algorithms, Sparse matrices, Biomedical measurement, Time measurement, variational Bayesian inference (VBI) BibRef

Moreno-Muñoz, P.[Pablo], Ramírez, D.[David], Artés-Rodríguez, A.[Antonio],
Change-point detection in hierarchical circadian models,
PR(113), 2021, pp. 107820.
Elsevier DOI 2103
Change-point detection, Circadian models, Heterogeneous data, Latent variable models, Non-stationary periodic covariance functions BibRef

Song, Q., Ma, X.,
High-Resolution Time Delay Estimation Algorithms Through Cross-Correlation Post-Processing,
SPLetters(28), 2021, pp. 479-483.
IEEE DOI 2103
Delay effects, Signal processing algorithms, Estimation, Deconvolution, Signal to noise ratio, Correlation, weighted L1 norm minimizing BibRef

Li, H.L.[Hai-Lin], Liu, Z.C.[Ze-Chen],
Multivariate time series clustering based on complex network,
PR(115), 2021, pp. 107919.
Elsevier DOI 2104
Multivariate time series, Data mining, Clustering analysis, Complex network BibRef

Gu, X.B.[Xiao-Bo], Li, J.Z.[Jian-Zhong], Zhou, G.X.[Guo-Xu], Xie, S.L.[Sheng-Li],
Improved Clock Parameters Tracking and Ranging Method Based on Two-Way Timing Stamps Exchange Mechanism,
SPLetters(28), 2021, pp. 598-602.
IEEE DOI 2104
Clocks, Timing, Mathematical model, Propagation delay, Estimation, Wireless sensor networks, Signal processing algorithms, wireless sensor network BibRef

Wang, W.Y.[Wen-Yuan], Dogancay, K.[Kutluyil],
Convergence Issues in Sequential Partial-Update LMS for Cyclostationary White Gaussian Input Signals,
SPLetters(28), 2021, pp. 967-971.
IEEE DOI 2106
Convergence, Signal processing algorithms, Field programmable gate arrays, Computational complexity, convergence difficulty BibRef

Li, X.Q.[Xian-Qing], Duan, Z.S.[Zhan-Sheng],
Joint Cramér-Rao Lower Bound for Nonlinear Parametric Systems With Cross-Correlated Noises,
SPLetters(28), 2021, pp. 977-981.
IEEE DOI 2106
Noise measurement, Gaussian noise, Additives, Nonlinear systems, Time measurement, Parameter estimation, Estimation, JCRLB, JSPE, gaussian noises BibRef

Lim, Y.C.[Yong Ching], Saramäki, T.[Tapio], Diniz, P.S.R.[Paulo S. R.], Liu, Q.L.[Qing-Lai],
A Method for Scaling Window Sidelobe Magnitude,
SPLetters(28), 2021, pp. 1056-1059.
IEEE DOI 2106
Frequency response, Iterative methods, Chebyshev approximation, Frequency conversion, Signal processing algorithms, Prototypes, main lobe sidelobe tradeoff BibRef

Lim, Y.C.[Yong Ching], Saramäki, T.[Tapio], Diniz, P.S.R.[Paulo S. R.], Liu, Q.L.[Qing-Lai],
Fast Convergence Method for Scaling Window Sidelobe Magnitude,
SPLetters(28), 2021, pp. 2078-2081.
IEEE DOI 2112
Signal processing algorithms, Convergence, Shape, Chebyshev approximation, Signal processing, Laboratories, Indexes, main lobe sidelobe tradeoff BibRef

Lim, Y.C.[Yong Ching], Wu, Z.Y.[Zhi-You], Liu, Q.L.[Qing-Lai], Diniz, P.S.R.[Paulo S. R.], Saramäki, T.[Tapio],
An Efficient Method With Guaranteed Convergence for Window Sidelobe Magnitude Reduction,
SPLetters(31), 2024, pp. 1516-1519.
IEEE DOI 2406
Convergence, Signal processing algorithms, Chebyshev approximation, Array signal processing, Polynomials, window BibRef

Görgülü, B.[Berk], Baydogan, M.G.[Mustafa Gökçe],
Randomized trees for time series representation and similarity,
PR(120), 2021, pp. 108097.
Elsevier DOI 2109
Time series, Representation learning, Random trees, Classification BibRef

Masiliunas, D.[Dainius], Tsendbazar, N.E.[Nandin-Erdene], Herold, M.[Martin], Verbesselt, J.[Jan],
BFAST Lite: A Lightweight Break Detection Method for Time Series Analysis,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
Time series change detection. BibRef

Fang, D.[Dianwu], Wang, L.[Lizhen], Wang, J.[Jialong], Wang, M.[Meijiao],
High Influencing Pattern Discovery over Time Series Data,
IJGI(10), No. 10, 2021, pp. xx-yy.
DOI Link 2110
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Zhang, Y.[Ye], Hou, Y.[Yi], OuYang, K.W.[Ke-Wei], Zhou, S.L.[Shi-Lin],
Multi-scale signed recurrence plot based time series classification using inception architectural networks,
PR(123), 2022, pp. 108385.
Elsevier DOI 2112
Time series classification, Multi-scale, Signed, Recurrence plots, Inception network BibRef

Guijo-Rubio, D.[David], Durán-Rosal, A.M.[Antonio Manuel], Gutiérrez, P.A.[Pedro Antonio], Troncoso, A.[Alicia], Hervás-Martínez, C.[César],
Time-Series Clustering Based on the Characterization of Segment Typologies,
Cyber(51), No. 11, November 2021, pp. 5409-5422.
IEEE DOI 2112
Time series analysis, Hidden Markov models, Clustering algorithms, Time measurement, time-series clustering BibRef

Yan, W.H.[Wen-He], Dong, M.[Ming], Li, S.F.[Shi-Feng], Yang, C.Z.[Chao-Zhong], Yuan, J.B.[Jiang-Bin], Hu, Z.P.[Zhao-Peng], Hua, Y.[Yu],
An eLoran Signal Cycle Identification Method Based on Joint Time-Frequency Domain,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Mukherjee, S.[Saptarshi], Dowling, K.[Karen], Dong, Y.C.[Yi-Cong], Li, K.[Kexin], Conway, A.[Adam], Rakheja, S.[Shaloo], Voss, L.[Lars],
A Prony-Based Curve-Fitting Method for Characterization of RF Pulses From Optoelectronic Devices,
SPLetters(29), 2022, pp. 364-368.
IEEE DOI 2202
Fitting, Semiconductor device measurement, Radio frequency, Pulse measurements, Time measurement, Optoelectronic devices, signal analysis BibRef

Wu, X.J.[Xiao-Jing],
Identification of Co-Clusters with Coherent Trends in Geo-Referenced Time Series,
IJGI(11), No. 2, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Ma, Q.L.[Qian-Li], Li, S.[Sen], Cottrell, G.W.[Garrison W.],
Adversarial Joint-Learning Recurrent Neural Network for Incomplete Time Series Classification,
PAMI(44), No. 4, April 2022, pp. 1765-1776.
IEEE DOI 2203
Time series analysis, Recurrent neural networks, Data models, Training, Analytical models, Sensors, exploding error BibRef

Péalat, C.[Clément], Bouleux, G.[Guillaume], Cheutet, V.[Vincent],
Improved time series clustering based on new geometric frameworks,
PR(124), 2022, pp. 108423.
Elsevier DOI 2203
Clustering, Time series, Delayed coordinate embedding, Embedding, Stiefel manifold, UMAP, HDBSCAN BibRef

Peng, K.[Kun], Shang, P.J.[Peng-Jian],
Characterizing ordinal network of time series based on complexity-entropy curve,
PR(124), 2022, pp. 108464.
Elsevier DOI 2203
Ordinal network, Signal processing, Symbolic patterns, Tsallis -entropy, Novelty detection BibRef

Wickstrøm, K.[Kristoffer], Kampffmeyer, M.[Michael], Mikalsen, K.Ø.[Karl Øyvind], Jenssen, R.[Robert],
Mixing up contrastive learning: Self-supervised representation learning for time series,
PRL(155), 2022, pp. 54-61.
Elsevier DOI 2203
Time series, Self-supervised learning, Contrastive learning, Mixup, Transfer learning BibRef

Velasco-Forero, S.[Santiago], Pagès, R., Angulo, J.[Jesus],
Learnable Empirical Mode Decomposition based on Mathematical Morphology,
SIIMS(15), No. 1, 2022, pp. 23-44.
DOI Link 2204
BibRef

Combettes, P.L.[Patrick L.], Woodstock, Z.C.[Zev C.],
A Variational Inequality Model for the Construction of Signals from Inconsistent Nonlinear Equations,
SIIMS(15), No. 1, 2022, pp. 84-109.
DOI Link 2204
BibRef

He, Z.[Zihao], He, H.Y.[Hong-Yu], Liu, X.L.[Xiao-Li], Wen, J.M.[Jin-Ming],
An Improved Sufficient Condition for Sparse Signal Recovery With Minimization of L1-L2,
SPLetters(29), 2022, pp. 907-911.
IEEE DOI 2205
Coherence, Upper bound, Minimization, Matching pursuit algorithms, Information science, Noise measurement, Wireless communication, L_1-L_2-minimization BibRef

Abanda, A., Mori, U., Lozano, J.A.[Jose A.],
Time series classifier recommendation by a meta-learning approach,
PR(128), 2022, pp. 108671.
Elsevier DOI 2205
Time series classification, Meta-learning, Landmarkers, Hierarchical inference, Meta-targets BibRef

Abdu-Aguye, M.G.[Mubarak G.], Gomaa, W.[Walid], Makihara, Y.S.[Yasu-Shi], Yagi, Y.S.[Yasu-Shi],
Investigating strategies towards adversarially robust time series classification,
PRL(156), 2022, pp. 104-111.
Elsevier DOI 2205
Time series, Adversarial, Shapelets BibRef

Fu, Z.J.[Zhao-Ji], Wang, C.[Can], Wei, G.D.[Guo-Dong], Zhang, W.R.[Wen-Rui], Du, S.[Shaofu], Hong, S.[Shenda],
HITS: Binarizing physiological time series with deep hashing neural network,
PRL(156), 2022, pp. 23-28.
Elsevier DOI 2205
Deep neural network, Physiological time series, Deep hashing BibRef

Fatima, G.[Ghania], Arora, A.[Aakash], Babu, P.[Prabhu], Stoica, P.[Petre],
Learning Sparse Graphs via Majorization-Minimization for Smooth Node Signals,
SPLetters(29), 2022, pp. 1022-1026.
IEEE DOI 2205
Signal processing algorithms, Convergence, Laplace equations, Sparse matrices, Signal processing, Numerical simulation, smooth signals BibRef

Xu, H.[Huan], Ding, F.[Feng], Champagne, B.[Benoit],
Joint Parameter and Time-Delay Estimation for a Class of Nonlinear Time-Series Models,
SPLetters(29), 2022, pp. 947-951.
IEEE DOI 2205
Estimation, Signal processing algorithms, Mathematical models, Technological innovation, Stochastic processes, redundant rule BibRef

Wang, Y.H.[Yung-Hung], Cheng, S.H.[Shao-Ho],
Boundary Effects for EMD-Based Algorithms,
SPLetters(29), 2022, pp. 1032-1036.
IEEE DOI 2205
Empirical mode decomposition. Signal processing algorithms, Mathematical models, Indexes, Sufficient conditions, Splines (mathematics), Real-time systems, error BibRef

Liu, X.Q.[Xiao-Qian], Chi, E.C.[Eric C.],
Revisiting convexity-preserving signal recovery with the linearly involved GMC penalty,
PRL(156), 2022, pp. 60-66.
Elsevier DOI 2205
Convexity-preserving nonconvex strategy, Generalized minimax concave penalty, Saddle-point problem BibRef

Li, J.[Jimei], Ding, F.[Feng],
Fitting Nonlinear Signal Models Using the Increasing-Data Criterion,
SPLetters(29), 2022, pp. 1302-1306.
IEEE DOI 2206
Signal processing algorithms, Estimation, Parameter estimation, Approximation algorithms, Stability criteria, hierarchical identification BibRef

Wang, H.[Heshan], Liu, Y.X.[Yu-Xi], Wang, D.S.[Dong-Shu], Luo, Y.[Yong], Tong, C.D.[Chu-Dong], Lv, Z.M.[Zhao-Min],
Discriminative and regularized echo state network for time series classification,
PR(130), 2022, pp. 108811.
Elsevier DOI 2206
Echo state network, Recurrent neural networks, Discriminative feature extraction, Outlier-robust weights BibRef

Liu, J.[Jixue], Li, J.[Jiuyong], Liu, L.[Lin],
FastOPM: A practical method for partial match of time series,
PR(130), 2022, pp. 108808.
Elsevier DOI 2206
Time series, Query processing, Global optimization, Partial match BibRef

Jiang, L.[Lei], Zhang, H.[Haijian], Yu, L.[Lei], Hua, G.[Guang],
A Data-Driven High-Resolution Time-Frequency Distribution,
SPLetters(29), 2022, pp. 1512-1516.
IEEE DOI 2208
Kernel, Convolution, Signal resolution, Feature extraction, Signal to noise ratio, Training, Time-frequency analysis, high-resolution time-frequency distribution BibRef

Ferrari, A.[André], Richard, C.[Cédric], Bourrier, A.[Anthony], Bouchikhi, I.[Ikram],
Online change-point detection with kernels,
PR(133), 2023, pp. 109022.
Elsevier DOI 2210
Non-parametric change-point detection, Reproducing kernel Hilbert space, Convergence analysis BibRef

Yao, Y.Y.[Yue-Yue], Ma, J.H.[Jiang-Hong], Ye, Y.M.[Yun-Ming],
Regularizing autoencoders with wavelet transform for sequence anomaly detection,
PR(134), 2023, pp. 109084.
Elsevier DOI 2212
Sequence anomaly detection, Autoencoder, Discrete wavelet transform, Frequency domain regularization, Sample-adaptive regularization weight BibRef

Romero-Medrano, L.[Lorena], Artés-Rodríguez, A.[Antonio],
Multi-Source Change-Point Detection over Local Observation Models,
PR(134), 2023, pp. 109116.
Elsevier DOI 2212
E.g. changes in medical data. Change-point detection, Multi-source data, Heterogeneous data, Latent variable models BibRef

Thakur, D.[Dipanwita], Biswas, S.[Suparna],
Online Change Point Detection in Application With Transition-Aware Activity Recognition,
HMS(52), No. 6, December 2022, pp. 1176-1185.
IEEE DOI 2212
Sensors, Feature extraction, Smart phones, Activity recognition, Monitoring, Accelerometers, Hidden Markov models, transition-aware activity recognition BibRef

El-Jaroudi, A.[Amro], Loughlin, P.[Patrick],
Identifying Resonant Poles by Visual Inspection of Pole-Zero Plots,
SPLetters(29), 2022, pp. 2363-2366.
IEEE DOI 2212
Linear systems, Damping, Resonant frequency, Visualization, Inspection, Frequency response, Discrete-time systems, Resonance, z-transform BibRef

Babu, P.[Prabhu], Stoica, P.[Petre],
Multiple Hypothesis Testing-Based Cepstrum Thresholding for Nonparametric Spectral Estimation,
SPLetters(29), 2022, pp. 2367-2371.
IEEE DOI 2212
Testing, Cepstrum, Estimation, Standards, Spectral analysis, Smoothing methods, Random variables, multiple hypothesis testing BibRef

Yildiz, A.Y.[A. Yarkin], Koç, E.[Emirhan], Koç, A.[Aykut],
Multivariate Time Series Imputation With Transformers,
SPLetters(29), 2022, pp. 2517-2521.
IEEE DOI 2301
Transformers, Time series analysis, Training, Decoding, Data models, Medical services, Computational modeling, Deep learning, unsupervised learning BibRef

Roques, A.[Axel], Zhao, A.[Anne],
Association Rules Discovery of Deviant Events in Multivariate Time Series: An Analysis and Implementation of the SAX-ARM Algorithm,
IPOL(12), 2022, pp. 604-624.
DOI Link 2301
Code, Time Series. Tims Series analysis. BibRef

Xie, L.J.[Lie-Jun],
Improved RIC Bounds in Terms of delta _2s for Hard Thresholding-Based Algorithms,
SPLetters(30), 2023, pp. 21-25.
IEEE DOI 2302
Signal processing algorithms, Sensors, Indexes, Partitioning algorithms, Linear systems, Iterative algorithms, sparse recovery algorithm BibRef

Herrmann, M.[Matthieu], Webb, G.I.[Geoffrey I.],
Amercing: An intuitive and effective constraint for dynamic time warping,
PR(137), 2023, pp. 109333.
Elsevier DOI 2302
Time series, Dynamic time warping, Elastic distance BibRef

Zhang, N.[Nan], Sun, S.L.[Shi-Liang],
Multiview Unsupervised Shapelet Learning for Multivariate Time Series Clustering,
PAMI(45), No. 4, April 2023, pp. 4981-4996.
IEEE DOI 2303
Time series analysis, Adaptation models, Task analysis, Learning systems, Sun, Representation learning, Correlation, adaptive neighbor BibRef

Umatani, R.[Ryohei], Imai, T.[Takashi], Kawamoto, K.[Kaoru], Kunimasa, S.[Shutaro],
Time series clustering with an EM algorithm for mixtures of linear Gaussian state space models,
PR(138), 2023, pp. 109375.
Elsevier DOI 2303
Time series clustering, Model-based clustering, State space model, EM algorithm, Mixture model BibRef

Huang, P.H.[Po-Hsun], Hsiao, T.C.[Tzu-Chien],
Intrinsic Entropy: A Novel Adaptive Method for Measuring the Instantaneous Complexity of Time Series,
SPLetters(30), 2023, pp. 160-164.
IEEE DOI 2303
Entropy, Complexity theory, White noise, Standards, Time series analysis, Time measurement, Size measurement, signal regularity BibRef

Maunu, T.[Tyler], Lerman, G.[Gilad],
Depth Descent Synchronization in SO(D),
IJCV(131), No. 1, January 2023, pp. 968-986.
Springer DOI 2303
BibRef

Xu, H.[Han], Li, Z.Q.[Zi-Qi], Guan, A.[Anqi], Xu, M.H.[Ming-Hua], Wang, B.[Bang],
Opinion-Climate-Based Hegselmann-Krause dynamics,
PRL(167), 2023, pp. 9-17.
Elsevier DOI 2303
Opinion dynamics, Hegselmann-Krause model, Opinion pattern recognition, Spiral of silence, Cyber-physical-social services BibRef

Huska, M.[Martin], Cicone, A.[Antonio], Kang, S.H.[Sung Ha], Morigi, S.[Serena],
A Two-stage Signal Decomposition into Jump, Oscillation and Trend using ADMM,
IPOL(13), 2023, pp. 153-166.
DOI Link 2306
BibRef

Wang, Y.J.[Yu-Jing], Yang, Y.M.[Ya-Ming], Li, Z.[Zhuo], Bai, J.G.[Jian-Gang], Zhang, M.L.[Ming-Liang], Li, X.T.[Xiang-Tai], Yu, J.[Jing], Zhang, C.[Ce], Huang, G.[Gao], Tong, Y.H.[Yun-Hai],
Convolution-Enhanced Evolving Attention Networks,
PAMI(45), No. 7, July 2023, pp. 8176-8192.
IEEE DOI 2306
Transformers, Task analysis, Convolution, Neural networks, Machine translation, Time series analysis, time series BibRef

Dulek, B.[Berkan], Isik, S.[Selin],
Sequence Detection with Dependent Observations under Parameter Uncertainty,
SPLetters(30), 2023, pp. 603-607.
IEEE DOI 2306
Sensors, Measurement, Markov processes, Sensor fusion, Signal processing algorithms, Random variables, temporal and spatial dependence BibRef

Xiang, X.W.[Xiao-Wei], Liu, Y.[Yang], Fang, G.[Gaoyun], Liu, J.[Jing], Zhao, M.Y.[Meng-Yang],
Two-Stage Alignments Framework for Unsupervised Domain Adaptation on Time Series Data,
SPLetters(30), 2023, pp. 698-702.
IEEE DOI 2307
Feature extraction, Training, Task analysis, MIMICs, Time series analysis, Data mining, Adaptation models, time series BibRef

Zhang, H.W.[Hong-Wei], Wang, H.Y.[Hai-Yan], Liang, X.M.[Xuan-Ming], Yan, Y.S.[Yong-Sheng], Shen, X.H.[Xiao-Hong],
Weighted Undirected Similarity Network Construction and Application for Nonlinear Time Series Detection,
SPLetters(30), 2023, pp. 728-732.
IEEE DOI 2307
Time series analysis, Covariance matrices, White noise, Signal to noise ratio, Matrix converters, Sea measurements, Oceans, weighted undirected similarity network BibRef

Yang, Y.[Yang], Cheng, Y.Q.[Yong-Qiang], Wu, H.[Hao], Yang, Z.[Zheng], Wang, H.Q.[Hong-Qiang],
Parametric Instantaneous Frequency Estimation via PWSR with Adaptive QFM Dictionary,
SPLetters(30), 2023, pp. 738-742.
IEEE DOI 2307
Estimation, Frequency modulation, Dictionaries, Vibrations, Adaptation models, Frequency estimation, Trajectory, time-frequency distribution BibRef

Dhake, H.[Harshal], Kashyap, Y.[Yashwant], Kosmopoulos, P.[Panagiotis],
Algorithms for Hyperparameter Tuning of LSTMs for Time Series Forecasting,
RS(15), No. 8, 2023, pp. 2076.
DOI Link 2305
BibRef

Li, B.[Bing], Cui, W.[Wei], Zhang, L.[Le], Zhu, C.[Ce], Wang, W.[Wei], Tsang, I.W.[Ivor W.], Zhou, J.T.Y.[Joey Tian-Yi],
DifFormer: Multi-Resolutional Differencing Transformer With Dynamic Ranging for Time Series Analysis,
PAMI(45), No. 11, November 2023, pp. 13586-13598.
IEEE DOI 2310
BibRef

Xu, C.[Chen], Xie, Y.[Yao],
Conformal Prediction for Time Series,
PAMI(45), No. 10, October 2023, pp. 11575-11587.
IEEE DOI 2310
BibRef

El Amouri, H.[Hussein], Lampert, T.[Thomas], Gançarski, P.[Pierre], Mallet, C.[Clément],
Constrained DTW preserving shapelets for explainable time-series clustering,
PR(143), 2023, pp. 109804.
Elsevier DOI 2310
Dynamic Time Warping. Shapelets, Semi-supervised learning, Constrained clustering, Time-series, Representation learning BibRef

Giannoulis, M.[Michail], Harris, A.[Andrew], Barra, V.[Vincent],
DITAN: A deep-learning domain agnostic framework for detection and interpretation of temporally-based multivariate ANomalies,
PR(143), 2023, pp. 109814.
Elsevier DOI 2310
Multivariate time series, Anomaly detection, Neural networks, Generic normality feature learning, Predictability modeling BibRef

Li, W.[Wei], He, R.[Ruliang], Liang, B.B.[Bin-Bin], Yang, F.[Fan], Han, S.C.[Song-Chen],
Similarity Measure of Time Series With Different Sampling Frequencies Based on Context Density Consistency and Dynamic Time Warping,
SPLetters(30), 2023, pp. 1417-1421.
IEEE DOI 2310
BibRef

Zhan, F.[Fei], Zhou, X.F.[Xiao-Feng], Li, S.[Shuai], Jia, D.[Dongni], Song, H.[Hong],
Learning Latent ODEs With Graph RNN for Multi-Channel Time Series Forecasting,
SPLetters(30), 2023, pp. 1432-1436.
IEEE DOI 2310
BibRef

Ping, X.J.[Xiao-Jing], Luan, X.L.[Xiao-Li], Zhao, S.[Shunyi], Ding, F.[Feng], Liu, F.[Fei],
Parameters-Transfer Identification for Dynamic Systems and Recursive Form,
SPLetters(30), 2023, pp. 1302-1306.
IEEE DOI 2310
BibRef

Sim, S.[Sunghyun], Kim, D.[Dohee], Bae, H.[Hyerim],
Correlation Recurrent Units: A Novel Neural Architecture for Improving the Predictive Performance of Time-Series Data,
PAMI(45), No. 12, December 2023, pp. 14266-14283.
IEEE DOI 2311
BibRef

Eldele, E.[Emadeldeen], Ragab, M.[Mohamed], Chen, Z.H.[Zheng-Hua], Wu, M.[Min], Kwoh, C.K.[Chee-Keong], Li, X.L.[Xiao-Li], Guan, C.T.[Cun-Tai],
Self-Supervised Contrastive Representation Learning for Semi-Supervised Time-Series Classification,
PAMI(45), No. 12, December 2023, pp. 15604-15618.
IEEE DOI 2311
BibRef

Mbouopda, M.F.[Michael Franklin], Mephu-Nguifo, E.[Engelbert],
Scalable and accurate subsequence transform for time series classification,
PR(147), 2024, pp. 110121.
Elsevier DOI 2312
Time series, Classification, Shapelet, Scalability, Interpretability BibRef

Wang, Z.[Zheng], Ran, H.W.[Hao-Wei], Ren, J.C.[Jin-Chang], Sun, M.J.[Mei-Jun],
PWDformer: Deformable transformer for long-term series forecasting,
PR(147), 2024, pp. 110118.
Elsevier DOI 2312
Long-term forecasting, Time series forecasting, Deep learning, Transformer BibRef

Sun, C.X.[Chen-Xi], Li, H.Y.[Hong-Yan], Song, M.[Moxian], Cai, D.[Derun], Zhang, B.F.[Bao-Feng], Hong, S.[Shenda],
Time pattern reconstruction for classification of irregularly sampled time series,
PR(147), 2024, pp. 110075.
Elsevier DOI 2312
Classification of irregularly sampled time series, Time pattern, Deep learning, Healthcare and medical application BibRef

Cao, Z.X.[Zhen-Xiang], Seeuws, N.[Nick], de Vos, M.[Maarten], Bertrand, A.[Alexander],
A Novel Loss for Change Point Detection Models With Time-Invariant Representations,
SPLetters(30), 2023, pp. 1737-1741.
IEEE DOI 2312
BibRef

Yu, H.Q.[Hong-Qing], Wang, Z.[Ziyi], Qiao, H.[Heng],
On Variational Block Sparse Recovery With Unknown Partition and L_0-Norm Constraint,
SPLetters(31), 2024, pp. 96-100.
IEEE DOI 2401
BibRef

Rupniewski, M.W.[Marek W.],
Reconstruction of Recurring Pulses From Distribution of Short Sequences of Samples,
SPLetters(31), 2024, pp. 396-400.
IEEE DOI 2402
Probability distribution, Streams, Signal processing algorithms, Estimation, Approximation algorithms, Shape, nonuniform sampling BibRef

Li, C.J.Y.[Carol Jing-Yi], Rademacher, R.[Richard], Boland, D.[David], Jin, C.T.[Craig T.], Spooner, C.M.[Chad M.], Leong, P.H.W.[Philip H.W.],
S^3CA: A Sparse Strip Spectral Correlation Analyzer,
SPLetters(31), 2024, pp. 646-650.
IEEE DOI 2403
Correlation, Strips, Time-frequency analysis, Fast Fourier transforms, Signal processing algorithms, spectral correlation density BibRef

Paim, A.M.[Aldo M.], Enembreck, F.[Fabrício],
Adaptive regularized ensemble for evolving data stream classification,
PRL(180), 2024, pp. 55-61.
Elsevier DOI 2404
Data stream mining, Regularized ensemble, Ensemble learning, Concept drift, Random subspaces BibRef

Younis, R.[Raneen], Hakmeh, A.[Abdul], Ahmadi, Z.[Zahra],
MTS2Graph: Interpretable multivariate time series classification with temporal evolving graphs,
PR(152), 2024, pp. 110486.
Elsevier DOI Code:
WWW Link. 2405
Multivariate time series, Interpretability, Neural networks, Classification BibRef

Li, Z.[Zhen], Gao, Z.Q.[Zhao-Qi], Sun, F.Y.[Feng-Yuan], Gao, J.H.[Jing-Huai], Zhang, W.[Wei],
Instantaneous Frequency Extraction for Nonstationary Signals via a Squeezing Operator with a Fixed-Point Iteration Method,
RS(16), No. 8, 2024, pp. 1412.
DOI Link 2405
BibRef

Choi, G.[Guebin], Oh, H.S.[Hee-Seok],
Decomposition via elastic-band transform,
PRL(182), 2024, pp. 76-82.
Elsevier DOI 2405
Decomposition, Elastic-band transform, Multiscale method BibRef

Pang, J.[Jie], Gao, B.[Bo], Wang, N.[Ning],
Asymptotic Spectral Distribution of a Second-Order Progressive Scattering Channel,
SPLetters(31), 2024, pp. 1404-1408.
IEEE DOI 2405
Eigenvalues and eigenfunctions, Scattering, Vectors, Transmission line matrix methods, Mathematical models, probability density function BibRef

Velásquez-Sanmartín, F.[Francisco], Insausti, X.[Xabier], Zárraga-Rodríguez, M.[Marta], Gutiérrez-Gutiérrez, J.[Jesús],
A Mathematical Model for the Analysis of Jet Engine Fuel Consumption During Aircraft Climb and Descent,
ITS(25), No. 5, May 2024, pp. 3605-3614.
IEEE DOI 2405
Aircraft, Fuels, Aircraft propulsion, Mathematical models, Atmospheric modeling, Aircraft manufacture, Air traffic control, pollutant gas emissions BibRef

Štumpf, M.[Martin], Nordebo, S.[Sven],
Physical Bounds on the Time-Domain Response of a Linear Time-Invariant System,
SPLetters(31), 2024, pp. 1324-1328.
IEEE DOI 2405
Time-domain analysis, Laplace equations, Integral equations, Impedance, Voltage, Power transmission lines, Frequency response, physical bounds BibRef

Said, K.A.[Karim A.], Beex, A.A., Liu, L.J.[Ling-Jia],
Maximally Concentrated Sequences After Half-Sample Shifts,
SPLetters(31), 2024, pp. 1354-1358.
IEEE DOI 2405
Indexes, Delays, Tail, Interpolation, Eigenvalues and eigenfunctions, Upper bound, Frequency-domain analysis, Band-limit, index-limit BibRef

Peng, X.P.[Xiu-Ping], Wu, C.Y.[Cong-Ying], Lin, H.B.[Hong-Bin],
Multiple SNC-ZCZ Sequence Sets With Optimal Correlations Based on Zak Transforms,
SPLetters(31), 2024, pp. 1464-1468.
IEEE DOI 2406
Spectrally-null-constrained. zero correlation zone. Vectors, Transforms, Indexes, Interference, Task analysis, Sparse approximation, Radar signal processing, finite Zak transform (FZT) BibRef

Chen, J.Q.[Jun-Qi], Tan, X.[Xu], Rahardja, S.[Sylwan], Yang, J.W.[Jia-Wei], Rahardja, S.[Susanto],
Joint Selective State Space Model and Detrending for Robust Time Series Anomaly Detection,
SPLetters(31), 2024, pp. 2050-2054.
IEEE DOI 2408
Market research, Time series analysis, Data models, Context modeling, Kernel, Information filters, Training, time series detrending BibRef

Lee, D.[Daesoo], Malacarne, S.[Sara], Aune, E.[Erlend],
Explainable time series anomaly detection using masked latent generative modeling,
PR(156), 2024, pp. 110826.
Elsevier DOI Code:
WWW Link. 2408
Time series anomaly detection (TSAD), TimeVQVAE-AD, TimeVQVAE, Masked generative modeling, Explainable AI (XAI), Explainable anomaly detection BibRef

Ripani, B.[Barbara], Modenini, A.[Andrea], Montorsi, G.[Guido],
Digital PLLs for Phase Noise Channels: A Concept Based on the Tikhonov Distribution,
SPLetters(31), 2024, pp. 2040-2044.
IEEE DOI 2408
Phase locked loops, Kalman filters, Gaussian distribution, Vectors, Filtering, Channel estimation, Steady-state, sum product algorithm BibRef

Freire-Obregón, D.[David], Lorenzo-Navarro, J.[Javier], Santana, O.J.[Oliverio J.], Hernández-Sosa, D.[Daniel], Castrillón-Santana, M.[Modesto],
A Large-scale Analysis of Athletes' Cumulative Race Time in Running Events,
CIAP23(I:282-292).
Springer DOI 2312
BibRef

Lv, S.[Suhuan], Wang, Z.L.[Zhuo-Lin], Ye, O.[Ou], Liu, Y.[Ying],
Abnormal Signal Detection Method Based on Bimodal Fusion,
ICIVC22(895-900)
IEEE DOI 2301
Costs, Fuses, Simulation, Frequency-domain analysis, Interference, Feature extraction, Autonomous aerial vehicles, bimodal fusion BibRef

Han, J.H.[Jia-Heng], Li, H.G.[Hong-Gai], Cui, J.S.[Jin-Shi], Lan, Q.[Qili], Wang, L.[Li],
Psychology-Inspired Interaction Process Analysis based on Time Series,
ICPR22(1004-1011)
IEEE DOI 2212
Correlation, Microscopy, Time series analysis, Semantics, Psychology, Machine learning, Feature extraction BibRef

Oba, D.[Daisuke], Matsuo, S.[Shinnosuke], Iwana, B.K.[Brian Kenji],
Dynamic Data Augmentation with Gating Networks for Time Series Recognition,
ICPR22(3034-3040)
IEEE DOI 2212
Histograms, Analytical models, Time series analysis, Neural networks, Radar, Machine learning BibRef

Himeur, Y.[Yassine], Alsalemi, A.[Abdullah], Bensaali, F.[Faycal], Amira, A.[Abbes],
Appliance identification using a histogram post-processing of 2D local binary patterns for smart grid applications,
ICPR21(5744-5751)
IEEE DOI 2105
Performance evaluation, Histograms, Home appliances, Feature extraction, Real-time systems, Encoding, Eigenvalues and eigenfunctions BibRef

Pealat, C.[Clément], Bouleux, G.[Guillaume], Cheutet, V.[Vincent],
Improved Time-Series Clustering with UMAP dimension reduction method,
ICPR21(5658-5665)
IEEE DOI 2105
Geometry, Dimensionality reduction, Manifolds, Databases, Time series analysis, Clustering algorithms, Finance BibRef

Schreiber, J.[Jens], Sick, B.[Bernhard],
Emerging Relation Network and Task Embedding for Multi-Task Regression Problems,
ICPR21(2663-2670)
IEEE DOI 2105
Time series analysis,Natural language processing, Task analysis, Power generation BibRef

Wang, J.J.[Jian-Jia], Wu, H.[Hui], Hancock, E.R.[Edwin R.],
Thermal Characterisation of Unweighted and Weighted Networks,
ICPR21(1641-1648)
IEEE DOI 2105
Heating systems, Thermodynamics, Temperature, Fluctuations, Statistical analysis, Time series analysis, Sociology BibRef

Garg, Y.[Yash], Candan, K.S.[K. Selçuk],
SDMA: Saliency-Driven Mutual Cross Attention for Multi-Variate Time Series,
ICPR21(7242-7249)
IEEE DOI 2105
Time series analysis, Gesture recognition, Data models, Multiaccess communication, Noise measurement, Optimization BibRef

Forest, F.[Florent], Mourer, A.[Alex], Lebbah, M.[Mustapha], Azzag, H.[Hanane], Lacaille, J.[Jéróme],
An Invariance-guided Stability Criterion for Time Series Clustering Validation,
ICPR21(9296-9303)
IEEE DOI 2105
Measurement, Adaptation models, Perturbation methods, Time series analysis, Stability criteria, Diversity reception, Clustering algorithms BibRef

Akodad, S., Bombrun, L., Berthoumieu, Y., Germain, C.,
Cluster Kernel For Learning Similarities Between Symmetric Positive Definite Matrix Time Series,
ICIP20(3304-3308)
IEEE DOI 2011
Time series analysis, Kernel, Covariance matrices, Training, Symmetric matrices, Earth, Brain modeling, remote sensing. BibRef

Xie, Z.C.[Ze-Cheng], Huang, Y.X.[Yao-Xiong], Zhu, Y.Z.[Yuan-Zhi], Jin, L.W.[Lian-Wen], Liu, Y.L.[Yu-Liang], Xie, L.[Lele],
Aggregation Cross-Entropy for Sequence Recognition,
CVPR19(6531-6540).
IEEE DOI 2002
Code:
WWW Link. BibRef

Tanisaro, P., Heidemann, G.,
A very concise feature representation for time series classification understanding,
MVA19(1-6)
DOI Link 1911
data analysis, feature extraction, pattern classification, random forests, recurrent neural nets, time series, Feature extraction BibRef

Boubrahimi, S.F., Ma, R., Aydin, B., Hamdi, S.M., Angryk, R.,
Scalable kNN Search Approximation for Time Series Data,
ICPR18(970-975)
IEEE DOI 1812
Univariate Time Series classification, Scalable Nearest Neighbor Search, Density-Based Clustering BibRef

Han, Y., Zhang, S., Geng, Z.,
Multi-Frequency Decomposition with Fully Convolutional Neural Network for Time Series Classification,
ICPR18(284-289)
IEEE DOI 1812
Feature extraction, Time series analysis, Convolution, Neural networks, Discrete Fourier transforms, Kernel, Multi-Frequency decomposition BibRef

Zhao, R., Schalk, G., Ji, Q.,
Temporal Pattern Localization using Mixed Integer Linear Programming,
ICPR18(1361-1365)
IEEE DOI 1812
Time series analysis, Hidden Markov models, Data collection, Optimization, Probabilistic logic, Pattern recognition BibRef

Caglar, I.[Ibrahim], Hancock, E.R.[Edwin R.],
Graph Time Series Analysis Using Transfer Entropy,
SSSPR18(217-226).
Springer DOI 1810
BibRef

González-Vanegas, W.[Wilson], Alvarez-Meza, A.[Andrés], Orozco-Gutierrez, Á.[Álvaro],
Sparse Hilbert Embedding-Based Statistical Inference of Stochastic Ecological Systems,
CIARP17(255-262).
Springer DOI 1802
BibRef

Chirikjian, G.S.[Gregory S.],
Signal Classification in Quotient Spaces via Globally Optimal Variational Calculus,
Diff-CVML17(735-743)
IEEE DOI 1709
Pattern recognition BibRef

Zhao, R.[Rui], Schalk, G.[Gerwin], Ji, Q.[Qiang],
Robust signal identification for dynamic pattern classification,
ICPR16(3910-3915)
IEEE DOI 1705
Computational modeling, Data models, Hidden Markov models, Motion segmentation, Robustness, Testing, Time, series, analysis BibRef

Dupont, M., Marteau, P.F., Ghouaiel, N.,
Detecting low-quality reference time series in stream recognition,
ICPR16(2556-2561)
IEEE DOI 1705
Big Data, Measurement, Pattern recognition, Sensors, Testing, Time series analysis, Training BibRef

Ye, C., Wilson, R.C., Hancock, E.R.[Edwin R.],
Analyzing graph time series using a generative model,
ICPR16(3338-3343)
IEEE DOI 1705
Analytical models, Complexity theory, Computational modeling, Data models, Entropy, Probabilistic logic, Probability, distribution BibRef

Ridi, A., Gisler, C., Hennebert, J.,
Aggregation procedure of Gaussian Mixture Models for additive features,
ICPR16(2544-2549)
IEEE DOI 1705
Additives, Biological system modeling, Computational modeling, Covariance matrices, Hidden Markov models, Time series analysis, Training BibRef

Kulczycki, P.[Piotr], Charytanowicz, M.[Malgorzata], Kowalski, P.A.[Piotr A.], Lukasik, S.[Szymon],
Atypical (Rare) Elements Detection: A Conditional Nonparametric Approach,
CompIMAGE16(56-64).
Springer DOI 1704
BibRef

Saidane, Y.[Yosra], Ben Jebara, S.[Sofia],
EMG signal analysis for comprehension of genders differences behavior during pre-motor activity,
ISIVC16(325-330)
IEEE DOI 1704
Correlation BibRef

Lo Presti, L.[Liliana], La Cascia, M.[Marco],
A Novel Time Series Kernel for Sequences Generated by LTI Systems,
ACCV16(III: 433-451).
Springer DOI 1704
BibRef

Guo, L., Liew, A.W.C.,
Online-Offline Extreme Learning Machine with Concept Drift Tracking for Time Series Data,
DICTA16(1-6)
IEEE DOI 1701
Data models BibRef

Seversky, L.M.[Lee M.], Davis, S.[Shelby], Berger, M.[Matthew],
On Time-Series Topological Data Analysis: New Data and Opportunities,
DIFF-CV16(1014-1022)
IEEE DOI 1612
BibRef

Bascol, K.[Kevin], Emonet, R.[Rémi], Fromont, E.[Elisa], Odobez, J.M.[Jean-Marc],
Unsupervised Interpretable Pattern Discovery in Time Series Using Autoencoders,
SSSPR16(427-438).
Springer DOI 1611
BibRef

Vuksanovic, B., Pota, H.,
Resonant modes analysis in power systems algorithms and Matlab GUI,
ICIVC16(129-134)
IEEE DOI 1610
graphical user interfaces BibRef

Ogryczak, W.[Wlodzimierz], Hurkala, J.[Jaroslaw],
Determining OWA Operator Weights by Maximum Deviation Minimization,
PReMI15(335-344).
Springer DOI 1511
OWA: ordered weighted averaging BibRef

Turner, L.D.[Liam D.], Allen, S.M.[Stuart M.], Whitaker, R.M.[Roger M.],
Push or Delay? Decomposing Smartphone Notification Response Behaviour,
HBUI15(69-83).
Springer DOI 1511
BibRef

Acuña, D.[Diego], Allende-Cid, H.[Héctor], Allende, H.[Héctor],
The Effect of Innovation Assumptions on Asymmetric GARCH Models for Volatility Forecasting,
CIARP15(527-534).
Springer DOI 1511
BibRef

Abughali, I.K.A.[Ibrahim K. A.], Minz, S.[Sonajharia],
Binarizing Change for Fast Trend Similarity Based Clustering of Time Series Data,
PReMI15(257-267).
Springer DOI 1511
BibRef

Rodriguez, F.[Fernanda], di Martino, M.[Matías], Kosut, J.P.[Juan Pablo], Santomauro, F.[Fernando], Lecumberry, F.[Federico], Fernández, A.[Alicia],
Optimal and Linear F-Measure Classifiers Applied to Non-technical Losses Detection,
CIARP15(83-91).
Springer DOI 1511
power supply companies. BibRef

Martínez-Vargas, J.D., Castro-Hoyos, C., Espinosa-Oviedo, J.J., Álvarez-Mesa, A.M., Castellanos-Dominguez, G.,
Single-Channel Separation Between Stationary and Non-stationary Signals Using Relevant Information,
IbPRIA15(452-459).
Springer DOI 1506
BibRef

Atto, A.M.[Abdourrahmane M.], Fillatre, L.[Lionel], Antonini, M.[Marc], Nikiforov, I.[Igor],
Simulation of image time series from dynamical fractional brownian fields,
ICIP14(6086-6090)
IEEE DOI 1502
BibRef

Cabrai, R.[Ricardo], Costeira, J.P.[Joao P.], Bernardino, A.[Alexandre], de la Torre, F.[Fernando],
Optimal no-intersection multi-label binary localization for time series using totally unimodular linear programming,
ICIP14(3127-3130)
IEEE DOI 1502
Computer vision BibRef

Damoulas, T.[Theodoros], He, J.[Jin], Bernstein, R.[Rich], Gomes, C.P.[Carla P.], Arora, A.[Anish],
String Kernels for Complex Time-Series: Counting Targets from Sensed Movement,
ICPR14(4429-4434)
IEEE DOI 1412
Approximation methods BibRef

Bargi, A.[Ava], Xu, R.Y.D.[Richard Yi Da], Piccardi, M.[Massimo],
An Infinite Adaptive Online Learning Model for Segmentation and Classification of Streaming Data,
ICPR14(3440-3445)
IEEE DOI 1412
Accuracy BibRef

Martinez-Vargas, J.D., Castro-Hoyos, C., Alvarez-Meza, A.M., Acosta-Medina, C.D., Castellanos-Domínguez, C.G.[Cesar German],
Recursive Separation of Stationary Components by Subspace Projection and Stochastic Constraints,
ICPR14(3469-3474)
IEEE DOI 1412
Discriminate between stationary and non-stationary signals. BibRef

Conti, J.C.[Jose C.], Farial, F.A.[Fabio A.], Almeida, J.[Jurandy], Alberton, B.[Bruna], Morellato, L.P.C.[Leonor P.C.], Camolesi, L.[Luiz], da Silva Torres, R.[Ricardo],
Evaluation of Time Series Distance Functions in the Task of Detecting Remote Phenology Patterns,
ICPR14(3126-3131)
IEEE DOI 1412
Accuracy BibRef

de Sousa, C.A.R.[Celso A.R.], Souza, V.M.A.[Vinicius M.A.], Batista, G.E.A.P.A.[Gustavo E.A.P.A.],
Time Series Transductive Classification on Imbalanced Data Sets: An Experimental Study,
ICPR14(3780-3785)
IEEE DOI 1412
Algorithm design and analysis BibRef

Souza, V.M.A.[Vinicius M.A.], Silva, D.F.[Diego F.], Batista, G.E.A.P.A.[Gustavo E.A.P.A.],
Extracting Texture Features for Time Series Classification,
ICPR14(1425-1430)
IEEE DOI 1412
Accuracy BibRef

Fusco, F.[Francesco], Eck, B.[Bradley], McKenna, S.[Sean],
Bad Data Analysis with Sparse Sensors for Leak Localisation in Water Distribution Networks,
ICPR14(3642-3647)
IEEE DOI 1412
Clustering algorithms BibRef

Chen, B.[Bei], Sinn, M.[Mathieu], Ploennigs, J.[Joern], Schumann, A.[Anika],
Statistical Anomaly Detection in Mean and Variation of Energy Consumption,
ICPR14(3570-3575)
IEEE DOI 1412
Buildings BibRef

Ring, M.[Matthias], Lohmueller, C.[Clemens], Rauh, M.[Manfred], Eskofier, B.M.[Bjoern M.],
A Two-Stage Regression Using Bioimpedance and Temperature for Hydration Assessment During Sports,
ICPR14(4519-4524)
IEEE DOI 1412
Bioimpedance BibRef

Dosiek, L.,
Extracting Electrical Network Frequency From Digital Recordings Using Frequency Demodulation,
SPLetters(22), No. 6, June 2015, pp. 691-695.
IEEE DOI 1411
Digital recording BibRef

Kiefel, M.[Martin], Schuler, C.[Christian], Hennig, P.[Philipp],
Probabilistic Progress Bars,
GCPR14(331-341).
Springer DOI 1411
BibRef

Siedhoff, D.[Dominic], Fichtenberger, H.[Hendrik], Libuschewski, P.[Pascal], Weichert, F.[Frank], Sohler, C.[Christian], Müller, H.[Heinrich],
Signal/Background Classification of Time Series for Biological Virus Detection,
GCPR14(388-398).
Springer DOI 1411
BibRef

Reittu, H.[Hannu], Bazsó, F.[Fülöp], Weiss, R.[Robert],
Regular Decomposition of Multivariate Time Series and Other Matrices,
SSSPR14(424-433).
Springer DOI 1408
BibRef

Fojtu, Š.[Šimon], Zimmermann, K.[Karel], Pajdla, T.[Tomáš], Hlavác, V.[Václav],
Domain Adaptation for Sequential Detection,
SCIA13(215-224).
Springer DOI 1311
BibRef

Iglesias Martínez, M.E.[Miguel Enrique], Hernández Montero, F.E.[Fidel Ernesto],
Detection of Periodic Signals in Noise Based on Higher-Order Statistics Joined to Convolution Process and Spectral Analysis,
CIARP13(I:488-495).
Springer DOI 1311
BibRef

Dang, T.N.[Tuan Nhon], Wilkinson, L.[Leland],
TimeExplorer: Similarity Search Time Series by Their Signatures,
ISVC13(I:280-289).
Springer DOI 1310
BibRef

Bergel, I., Leshem, A.,
The Performance of Zero Forcing DSL Systems,
SPLetters(20), No. 5, May 2013, pp. 527-530.
IEEE DOI 1304
BibRef

Fusco, F.[Francesco], Wurst, M.[Michael], Yoon, J.[Ji_Won],
Mining residential household information from low-resolution smart meter data,
ICPR12(3545-3548).
WWW Link. 1302
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Yoon, J.W.[Ji Won], Fusco, F.[Francesco], Wurst, M.[Michael],
Bayesian separation of wind power generation signals,
ICPR12(2660-2663).
WWW Link. 1302
BibRef

Liu, R.Q.[Ruo-Qian], Xu, S.[Shen], Fang, C.[Chen], Liu, Y.W.[Yung-Wen], Murphey, Y.L.[Yi L.], Kochhar, D.S.[Dev S.],
Statistical modeling and signal selection in multivariate time series pattern classification,
ICPR12(2853-2856).
WWW Link. 1302
BibRef

Suematsu, N.[Nobuo], Hayashi, A.[Akira],
Time series alignment with Gaussian processes,
ICPR12(2355-2358).
WWW Link. 1302
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Guerra-Filho, G.[Gutemberg],
String Features: Geodesic Sweeping Detection and Quasi-invariant Time-Series Description,
AVSS12(392-397).
IEEE DOI 1211
BibRef

Cardona-Morales, O.[Oscar], Castellanos-Dominguez, G.[German],
Order Tracking by Square-Root Cubature Kalman Filter with Constraints,
MCPR16(104-114).
Springer DOI 1608
BibRef

Sierra-Alonso, E.F.[Edgar F.], Cardona-Morales, O.[Oscar], Acosta-Medina, C.D.[Carlos D.], Castellanos-Dominguez, G.[German],
Spectral Correlation Measure for Selecting Intrinsic Mode Functions,
CIARP14(231-238).
Springer DOI 1411
BibRef

Cárdenas-Peña, D.[David], Martínez-Vargas, J.D.[Juan David], Castellanos-Domínguez, C.G.[Cesar Germán],
Extraction of Stationary Spectral Components Using Stochastic Variability,
CIARP12(765-772).
Springer DOI 1209
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Sepulveda-Cano, L.M.[Lina Maria], Acosta-Medina, C.D.[Carlos Daniel], Castellanos-Dominguez, C.G.[Cesar Germán],
Finite Rank Series Modeling for Discrimination of Non-stationary Signals,
CIARP12(691-698).
Springer DOI 1209
BibRef

Moya-Sánchez, E.U.[E. Ulises], Bayro-Corrochano, E.[Eduardo],
Quaternionic Analytic Signal Using Atomic Functions,
CIARP12(699-706).
Springer DOI 1209
BibRef

Jovic, A.[Alan], Brkic, K.[Karla], Bogunovic, N.[Nikola],
Decision Tree Ensembles in Biomedical Time-series Classification,
DAGM12(408-417).
Springer DOI 1209
BibRef

Huerta, R.[Ramón], Vembu, S.[Shankar], Muezzinoglu, M.K.[Mehmet K.], Vergara, A.[Alexander],
Dynamical SVM for Time Series Classification,
DAGM12(216-225).
Springer DOI 1209
BibRef

Sabre, R.[Rachid],
Evolutionary Spectrum for Random Field and Missing Observations,
ICISP12(209-216).
Springer DOI 1208
BibRef

Zhou, M.[Ming], Yang, H.B.[Hua-Bing], Zhu, J.L.[Jing-Li], Shi, J.T.[Jun-Tao],
Low SNR signal time-frequency analyzing method,
IASP11(21-25).
IEEE DOI 1112
BibRef

Zhang, R.[Rui], Yin, Y.S.[Yong-Sheng], Yang, J.[Jun], Gao, M.L.[Ming-Lun],
Dual-ADC based digital calibration of timing skew for a time-interleaved ADC,
IASP11(42-45).
IEEE DOI 1112
BibRef

Zhu, W.J.[Wei-Jie], Wu, W.[Wei],
Design of wide-band array with frequency invariant beam pattern by using adaptive synthesis method,
IASP11(688-693).
IEEE DOI 1112
BibRef

Shen, C.J.[Chuan-Jun], Wang, Y.M.[Yue-Min], Zhou, F.J.[Fang-Jun], Sun, F.R.[Feng-Rui],
Guided wave signal recognition by matching pursuit based on evolutionary programming algorithm,
IASP11(519-523).
IEEE DOI 1112
BibRef

Hernández, S.[Sergio], Sallis, P.[Philip],
Sentiment-Preserving Reduction for Social Media Analysis,
CIARP11(409-416).
Springer DOI 1111
BibRef

Song, K.H.[Kyu-Ha], Lee, D.W.[Dong-Weon], Han, J.W.[Jin-Woo], Park, B.K.[Byung-Koo],
Pulse Repetition Interval Modulation Recognition Using Symbolization,
DICTA10(540-545).
IEEE DOI 1012
BibRef

Zhang, D.Y.[Dong-Yu], Zuo, W.M.[Wang-Meng], Zhang, D.[David], Zhang, H.Z.[Hong-Zhi],
Time Series Classification Using Support Vector Machine with Gaussian Elastic Metric Kernel,
ICPR10(29-32).
IEEE DOI 1008
BibRef

Panagiotakis, C.[Costas],
Simultaneous Segmentation and Modelling of Signals Based on an Equipartition Principle,
ICPR10(85-88).
IEEE DOI 1008
BibRef

Daud, H.[Hanita], Sagayan, V.[Vijanth], Yahya, N.[Noorhana], Najwati, W.[Wan],
Modeling of Electromagnetic Waves Using Statistical and Numerical Techniques,
IVIC09(686-695).
Springer DOI 0911
BibRef

Li, Q.A.[Qi-Ang], Zhang, F.C.[Fa-Chao], Zhang, R.F.[Rui-Feng],
System Design of DPF Data Recorder and Data Analysis,
CISP09(1-4).
IEEE DOI 0910
BibRef

Dong, L.F.[Li-Fang], Yue, H.[Han], Yang, Y.J.[Yu-Jie], Xiao, H.[Hong], Wang, S.A.[Shu-Ai],
Emission Signal Analysis Based on Conventional and Modified Wavelet Cross-Correlation,
CISP09(1-4).
IEEE DOI 0910
BibRef

Zheng, S.L.[Shi-Ling], Xue, B.D.[Bin-Dang], Jiang, Z.G.[Zhi-Guo],
Combined Nonlinear Iterative Algorithms for Retrieving the Complex Wave Field,
CISP09(1-4).
IEEE DOI 0910
BibRef

Chandrakala, S., Sekhar, C.C.[C. Chandra],
Classification of Multi-variate Varying Length Time Series Using Descriptive Statistical Features,
PReMI09(13-18).
Springer DOI 0912
BibRef

Xiang, K.[Kui], Chen, J.[Jing],
Characterize System Dynamic of Pseudo Periodic Time Series with Evolution Networks,
CISP09(1-5).
IEEE DOI 0910
BibRef

Zhu, L.L.[Li-Li], Zhao, Y.[Ye],
Weak Signal Detection in Noisy Chaotic Time Series Using ORBFNN,
CISP09(1-4).
IEEE DOI 0910
BibRef

Xu, H.[Hua], Zhang, D.M.[Dong-Mei], Sun, G.F.[Gao-Fei],
The Assistant Timing Method for Fractionary Spaced Equalizer for Fading Channel,
CISP09(1-4).
IEEE DOI 0910
BibRef

Luo, S.[Sheng'en], Luo, L.Y.[Lai-Yuan],
Detection of an Unknown Frequency Hopping Signal Based on Image Features,
CISP09(1-4).
IEEE DOI 0910
BibRef

Yali, Q.[Qin], Lin, W.Y.[Wen-Yao], Zhou, S.L.[Shou-Li], Hu, H.R.[Hai-Rong],
Detection of Chirp Signal by Combination of Kurtosis Detection and Filtering in Fractional Fourier Domain,
CISP09(1-6).
IEEE DOI 0910
BibRef

Xu, H.H.[Han-Hui], Xu, C.G.[Chun-Guang], Zhou, S.Y.[Shi-Yuan], Hu, Y.[Yong],
Time-Frequency Analysis for Nonlinear Lamb Wave Signal,
CISP09(1-6).
IEEE DOI 0910
BibRef

Hautamaki, V.[Ville], Nykanen, P.[Pekka], Franti, P.[Pasi],
Time-series clustering by approximate prototypes,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Venkataramana, K.B.[Kini B.], Sekhar, C.C.[C. Chandra],
Large margin AR model for time series classification,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Batyrshin, I.[Ildar], Solovyev, V.[Valery],
Positive and Negative Local Trend Association Patterns in Analysis of Associations between Time Series,
MCPR14(92-101).
Springer DOI 1407
BibRef
Earlier: A1, Only:
Up and Down Trend Associations in Analysis of Time Series Shape Association Patterns,
MCPR12(246-254).
Springer DOI 1208
BibRef

Batyrshin, I.[Ildar], Sheremetov, L.[Leonid],
Time Series Pattern Recognition Based on MAP Transform and Local Trend Associations,
CIARP06(910-919).
Springer DOI 0611
MAP: Moving Approximation Transform. BibRef

Sankur, B., Kahya, Y.P., Guler, E.C., Engin, T.,
Feature extraction and classification of nonstationary signals based on the multiresolution signal decomposition,
ICPR94(B:592-595).
IEEE DOI 9410
BibRef

Chapter on New Unsorted Entries, and Other Miscellaneous Papers continues in
Time Series Warping, Time Warping .