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Kumar, A.[Arun],
Bahl, R.[Rajendar],
Estimation of instantaneous frequencies using iterative empirical mode
decomposition,
SIViP(8), No. 5, July 2014, pp. 799-812.
Springer DOI
1407
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
Moreno, Á.[Álvaro],
García-Haro, F.J.[Francisco Javier],
Martínez, B.[Beatriz],
Gilabert, M.A.[María Amparo],
Noise Reduction and Gap Filling of fAPAR Time Series Using an Adapted
Local Regression Filter,
RS(6), No. 9, 2014, pp. 8238-8260.
DOI Link
1410
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
McKilliam, R.G.,
Clarkson, I.V.L.,
Quinn, B.G.,
Fast Sparse Period Estimation,
SPLetters(22), No. 1, January 2015, pp. 62-66.
IEEE DOI
1410
Monte Carlo methods
BibRef
Hansson-Sandsten, M.,
Brynolfsson, J.,
The Scaled Reassigned Spectrogram with Perfect Localization for
Estimation of Gaussian Functions,
SPLetters(22), No. 1, January 2015, pp. 100-104.
IEEE DOI
1410
Gaussian processes
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
So, J.,
Kim, D.,
Lee, Y.,
Sung, Y.,
Pilot Signal Design for Massive MIMO Systems:
A Received Signal-To-Noise-Ratio-Based Approach,
SPLetters(22), No. 5, May 2015, pp. 549-553.
IEEE DOI
1411
Channel estimation
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
Trigano, T.,
Gildin, I.,
Sepulcre, Y.,
Pileup Correction Algorithm using an Iterated Sparse Reconstruction
Method,
SPLetters(22), No. 9, September 2015, pp. 1392-1395.
IEEE DOI
1503
Computational modeling
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
Shahmansoori, A.[Arash],
Consecutive adaptive blind estimation of timing offsets for arbitrary
channel time-interleaved ADCs,
SIViP(9), No. 1, January 2015, pp. 45-55.
Springer DOI
1503
analog-to-digital convertors.
BibRef
Shahmansoori, A.[Arash],
Adaptive blind calibration of timing offsets in a two-channel
time-interleaved analog-to-digital converter through Lagrange
interpolation,
SIViP(9), No. 5, July 2015, pp. 1047-1054.
WWW Link.
1506
BibRef
Jung, A.,
Hannak, G.,
Goertz, N.,
Graphical LASSO based Model Selection for Time Series,
SPLetters(22), No. 10, October 2015, pp. 1781-1785.
IEEE DOI
1506
Algorithm design and analysis
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.,
Efficient Multiple Importance Sampling Estimators,
SPLetters(22), No. 10, October 2015, pp. 1757-1761.
IEEE DOI
1506
computational complexity
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
Yang, P.[Peng],
Liu, Z.[Zheng],
Jiang, W.L.[Wen-Li],
Parameter estimation of multi-component chirp signals based on discrete
chirp Fourier transform and population Monte Carlo,
SIViP(9), No. 5, July 2015, pp. 1137-1149.
WWW Link.
1506
BibRef
Yang, Y.L.[Yan-Li],
Deng, J.[Jiahao],
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
Olsen, P.M.[Patrick M.],
Kolden, C.A.[Crystal A.],
Gadamus, L.[Lily],
Developing Theoretical Marine Habitat Suitability Models from
Remotely-Sensed Data and Traditional Ecological Knowledge,
RS(7), No. 9, 2015, pp. 11863.
DOI Link
1511
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
Ren, H.R.[Huo-Rong],
Ren, A.[An],
Li, Z.[Zhiwu],
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
BibRef
Guerrier, S.,
Molinari, R.,
Stebler, Y.,
Theoretical Limitations of Allan Variance-based Regression for Time
Series Model Estimation,
SPLetters(23), No. 5, May 2016, pp. 597-601.
IEEE DOI
1604
calibration
BibRef
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
Wang, G.[Guinan],
Zhang, H.J.[Hong-Juan],
Yu, S.W.[Shi-Wei],
Ding, S.X.[Shu-Xue],
A family of the subgradient algorithm with several cosparsity
inducing functions to the cosparse recovery problem,
PRL(80), No. 1, 2016, pp. 64-69.
Elsevier DOI
1609
Cosparse analysis model
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
BibRef
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
Göken, Ç.,
Gezici, S.,
Optimal Parameter Encoding Based on Worst Case Fisher Information
Under a Secrecy Constraint,
SPLetters(24), No. 11, November 2017, pp. 1611-1615.
IEEE DOI
1710
encoding, mean square error methods,
linear minimum MSE estimator, low-complexity algorithm,
BibRef
Martínez-Rego, D.[David],
Fontenla-Romero, O.[Oscar],
Alonso-Betanzos, A.[Amparo],
Principe, J.C.[José C.],
Fault detection via recurrence time statistics and one-class
classification,
PRL(84), No. 1, 2016, pp. 8-14.
Elsevier DOI
1612
Vibration analysis
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
Wang, P.,
Orlik, P.V.,
Sadamoto, K.,
Tsujita, W.,
Gini, F.,
Parameter Estimation of Hybrid Sinusoidal FM-Polynomial Phase Signal,
SPLetters(24), No. 1, January 2017, pp. 66-70.
IEEE DOI
1702
frequency modulation
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
Tobar, F.,
Rios, G.,
Valdivia, T.,
Guerrero, P.,
Recovering Latent Signals From a Mixture of Measurements Using a
Gaussian Process Prior,
SPLetters(24), No. 2, February 2017, pp. 231-235.
IEEE DOI
1702
Bayes methods
BibRef
Wang, W.D.[Wen-Dong],
Wang, J.J.[Jian-Jun],
Zhang, Z.L.[Zi-Li],
Robust Signal Recovery With Highly Coherent Measurement Matrices,
SPLetters(24), No. 3, March 2017, pp. 304-308.
IEEE DOI
1702
Approximation algorithms
BibRef
Chen, S.J.[Shao-Jie],
Liu, K.[Kai],
Yang, Y.G.[Yu-Guang],
Xu, Y.T.[Yu-Ting],
Lee, S.[Seonjoo],
Lindquist, M.[Martin],
Caffo, B.S.[Brian S.],
Vogelstein, J.T.[Joshua T.],
An M-estimator for reduced-rank system identification,
PRL(86), No. 1, 2017, pp. 76-81.
Elsevier DOI
1702
High dimensional time-series data.
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
Arriaga-Trejo, I.A.,
Orozco-Lugo, A.G.,
Villanueva-Maldonado, J.,
Flores-Troncoso, J.,
Joint I/Q imbalances estimation using data-dependent superimposed
training,
SIViP(11), No. 4, May 2017, pp. 729-736.
Springer DOI
1704
Joint estimation of the channel impulse response and
frequency-dependent in-phase and quadrature-phase (I/Q) imbalances.
BibRef
Wang, P.,
Orlik, P.V.,
Sadamoto, K.,
Tsujita, W.,
Sawa, Y.,
Cramer-Rao Bounds for a Coupled Mixture of Polynomial Phase and
Sinusoidal FM Signals,
SPLetters(24), No. 6, June 2017, pp. 746-750.
IEEE DOI
1609
polynomials, signal processing, CRB, Crame´r-Rao bounds,
polynomial phase signal, pure PPS case, sinusoidal FM signals,
Doppler radar, Frequency modulation, Indexes, Mixture models,
Parameter estimation.
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
Das, M.[Monidipa],
Ghosh, S.K.[Soumya K.],
semBnet: A semantic Bayesian network for multivariate prediction of
meteorological time series data,
PRL(93), No. 1, 2017, pp. 192-201.
Elsevier DOI
1706
Bayesian, network
BibRef
Das, M.[Monidipa],
Ghosh, S.K.[Soumya K.],
Data-driven approaches for meteorological time series prediction: A
comparative study of the state-of-the-art computational intelligence
techniques,
PRL(105), 2018, pp. 155-164.
Elsevier DOI
1804
Computational intelligence, Data-driven modeling,
Bayesian network, Time series prediction, Meteorology
BibRef
Ahmed, A.,
Compressive Acquisition and Least-Squares Reconstruction of
Correlated Signals,
SPLetters(24), No. 7, July 2017, pp. 933-937.
IEEE DOI
1706
Coherence, Computer architecture, Fourier series,
Frequency modulation, Signal reconstruction, Standards,
Array processing, compressive sampling, correlated signals,
micro-sensor arrays, randomized SVD, sub-Nyquist, sampling
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
BibRef
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, etc.
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
de Mattos Neto, P.S.G.[Paulo S.G.],
Cavalcanti, G.D.C.[George D.C.],
Madeiro, F.[Francisco],
Nonlinear combination method of forecasters applied to PM time series,
PRL(95), No. 1, 2017, pp. 65-72.
Elsevier DOI
1708
Forecasting
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
Sharabiani, A.,
Darabi, H.,
Rezaei, A.,
Harford, S.,
Johnson, H.,
Karim, F.,
Efficient Classification of Long Time Series by 3-D Dynamic Time
Warping,
SMCS(47), No. 10, October 2017, pp. 2688-2703.
IEEE DOI
1709
Cybernetics, Heuristic algorithms, Robot sensing systems,
Robustness, Time measurement, Time series analysis, Training,
Approximation methods, dynamic time warping (DTW), time, series,
classification, (TSC)
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
Morel, M.[Marion],
Achard, C.[Catherine],
Kulpa, R.[Richard],
Dubuisson, S.[Séverine],
Time-series averaging using constrained dynamic time warping with
tolerance,
PR(74), No. 1, 2018, pp. 77-89.
Elsevier DOI
1711
Time-series averaging
BibRef
Schultz, D.[David],
Jain, B.J.[Brijnesh J.],
Nonsmooth analysis and subgradient methods for averaging in dynamic
time warping spaces,
PR(74), No. 1, 2018, pp. 340-358.
Elsevier DOI
1711
Dynamic time warping
BibRef
Jain, B.J.[Brijnesh J.],
Schultz, D.[David],
Asymmetric learning vector quantization for efficient nearest
neighbor classification in dynamic time warping spaces,
PR(76), No. 1, 2018, pp. 349-366.
Elsevier DOI
1801
Learning vector quantization
BibRef
Jain, B.J.[Brijnesh J.],
Making the dynamic time warping distance warping-invariant,
PR(94), 2019, pp. 35-52.
Elsevier DOI
1906
Time series, Dynamic time warping, Semi-metric, Nearest-neighbor rule
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
Deng, S.[Song],
Yuan, C.A.[Chang-An],
Yang, L.C.[Le-Chan],
Zhang, L.P.[Li-Ping],
Distributed electricity load forecasting model mining based on hybrid
gene expression programming and cloud computing,
PRL(109), 2018, pp. 72-80.
Elsevier DOI
1806
Load forecasting, Artificial fish swarm,
Gene expression programming, Cloud computing
BibRef
Folgado, D.[Duarte],
Barandas, M.[Marília],
Matias, R.[Ricardo],
Martins, R.[Rodrigo],
Carvalho, M.[Miguel],
Gamboa, H.[Hugo],
Time Alignment Measurement for Time Series,
PR(81), 2018, pp. 268-279.
Elsevier DOI
1806
Time series, Time warping, Similarity, Distance, Signal alignment
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
Bey, N.Y.[Nourédine Yahya],
Highly accurate frequency estimation of brief duration signals in noise,
SIViP(12), No. 7, October 2018, pp. 1279-1283.
WWW Link.
1809
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
Wan, Z.[Zhong],
Guo, J.[Jie],
Liu, J.J.[Jing-Jing],
Liu, W.Y.[Wei-Yi],
A modified spectral conjugate gradient projection method for signal
recovery,
SIViP(12), No. 8, November 2018, pp. 1455-1462.
Springer DOI
1809
Signal recovery.
BibRef
Perotti, L.C.,
Vrinceanu, D.,
Bessis, D.,
Recovery of the Starting Times of Delayed Signals,
SPLetters(25), No. 10, October 2018, pp. 1455-1459.
IEEE DOI
1810
iterative methods, signal processing, smoothing methods,
starting times, delayed signals, starting point, arbitrary number,
Padé approximant
BibRef
Xie, C.[Christopher],
Bijral, A.[Avleen],
Ferres, J.L.[Juan Lavista],
NonSTOP: A NonSTationary Online Prediction Method for Time Series,
SPLetters(25), No. 10, October 2018, pp. 1545-1549.
IEEE DOI
1810
learning (artificial intelligence), time series,
NonSTationary online prediction method, prediction methods,
online learning
BibRef
Horstmann, S.,
Ramírez, D.,
Schreier, P.J.,
Joint Detection of Almost-Cyclostationary Signals and Estimation of
Their Cycle Period,
SPLetters(25), No. 11, November 2018, pp. 1695-1699.
IEEE DOI
1811
channel bank filters, interpolation, signal detection,
signal sampling, cycle period, wide-sense stationary noise,
sample rate conversion
BibRef
Bokde, N.[Neeraj],
Beck, M.W.[Marcus W.],
Álvarez, F.M.[Francisco Martínez],
Kulat, K.[Kishore],
A novel imputation methodology for time series based on pattern
sequence forecasting,
PRL(116), 2018, pp. 88-96.
Elsevier DOI
1812
Time series, Imputation, Forecasting, Data mining
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
Oregi, I.[Izaskun],
Pérez, A.[Aritz],
Del Ser, J.[Javier],
Lozano, J.A.[Jose A.],
On-line Elastic Similarity Measures for time series,
PR(88), 2019, pp. 506-517.
Elsevier DOI
1901
Time series, Streaming data, Dynamic time warping, Elastic similarity measures
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
Wang, X.,
Li, G.,
Varshney, P.K.,
Distributed Detection of Weak Signals From One-Bit Measurements Under
Observation Model Uncertainties,
SPLetters(26), No. 3, March 2019, pp. 415-419.
IEEE DOI
1903
maximum likelihood estimation, quantisation (signal),
signal detection, wireless sensor networks, one-bit data,
locally most powerful test
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
Duan, J.[Junbo],
Idier, J.[Jérôme],
Wang, Y.P.[Yu-Ping],
Wan, M.X.[Ming-Xi],
A Joint Least Squares and Least Absolute Deviation Model,
SPLetters(26), No. 4, April 2019, pp. 543-547.
IEEE DOI
1903
LASSO: Least absolute shrinkage and selection operator.
least squares approximations, signal restoration, JOLESALAD,
generalized LASSO, constrained LASSO, LASSO models,
ramp signal restoration
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
Mohammadi, E.,
Gohari, A.,
Marvasti, F.,
A Square Root Sampling Law for Signal Recovery,
SPLetters(26), No. 4, April 2019, pp. 562-566.
IEEE DOI
1903
Noise measurement, Distortion, Distortion measurement,
Stochastic processes, Atmospheric measurements,
square root law
BibRef
Dlask, M.[Martin],
Kukal, J.[Jaromir],
Hurst exponent estimation from short time series,
SIViP(13), No. 2, March 2019, pp. 263-269.
Springer DOI
1904
Time series.
BibRef
Khan, N.A.[Nabeel Ali],
Mohammadi, M.[Mokhtar],
Ali, S.[Sadiq],
Instantaneous frequency estimation of intersecting and close
multi-component signals with varying amplitudes,
SIViP(13), No. 3, April 2019, pp. 517-524.
Springer DOI
1904
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
Khan, S.A.,
Saleem, S.,
Hassan, S.A.,
Ilyas, M.U.,
An Improved Data-Aided Linear Estimator of Modulation Index for
Binary CPM Signals,
SPLetters(26), No. 5, May 2019, pp. 780-784.
IEEE DOI
1905
continuous phase modulation, error statistics,
estimation algorithm,
partial response
BibRef
Mehrkam, M.[Mehrrad],
Tinati, M.A.[Mohammad Ali],
Rezaii, T.Y.[Tohid Yousefi],
Reconstruction of low-rank jointly sparse signals from multiple
measurement vectors,
SIViP(13), No. 4, June 2019, pp. 683-691.
Springer DOI
1906
BibRef
Zhang, J.,
Wei, Z.,
Yan, Z.,
Zhou, M.,
Pani, A.,
Online Change-Point Detection in Sparse Time Series With Application
to Online Advertising,
SMCS(49), No. 6, June 2019, pp. 1141-1151.
IEEE DOI
1906
Advertising, Predictive models, Time series analysis, Media,
Data models, Noise measurement, Online advertising,
sparse time series (TS)
BibRef
Nichols, J.M.,
Hutchinson, M.N.,
Menkart, N.,
Cranch, G.A.,
Rohde, G.K.,
Time Delay Estimation Via Wasserstein Distance Minimization,
SPLetters(26), No. 6, June 2019, pp. 908-912.
IEEE DOI
1906
computational complexity, delay estimation, minimisation,
signal processing, linear time, time delay estimation,
Wasserstein distance
BibRef
Delgado, R.A.[Ramón A.],
Middleton, R.H.[Richard H.],
Sparse Representation Using Stepwise Tikhonov Regularization With
Offline Computations,
SPLetters(26), No. 6, June 2019, pp. 873-877.
IEEE DOI
1906
iterative methods, least squares approximations,
signal reconstruction, signal representation,
matching pursuit algorithms
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
Abavisani, M.,
Patel, V.M.,
Deep Sparse Representation-Based Classification,
SPLetters(26), No. 6, June 2019, pp. 948-952.
IEEE DOI
1906
Decoding, Training, Feeds, Encoding, Kernel, Optimization, Testing,
Deep learning, sparse representation-based classification, deep sparse representation-based classification
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
Fosson, S.M.,
Abuabiah, M.,
Recovery of Binary Sparse Signals From Compressed Linear Measurements
via Polynomial Optimization,
SPLetters(26), No. 7, July 2019, pp. 1070-1074.
IEEE DOI
1906
Optimization, Noise measurement, Sparse matrices,
Compressed sensing, Image coding, Sensors, Programming,
sparse signal recovery
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
Romanov, E.,
Ordentlich, O.,
Above the Nyquist Rate, Modulo Folding Does Not Hurt,
SPLetters(26), No. 8, August 2019, pp. 1167-1171.
IEEE DOI
1908
bandlimited signals, information theory, signal reconstruction,
signal sampling, discrete-time signal, finite energy signals,
unlimited sampling
BibRef
Gan, M.,
Chen, X.,
Ding, F.,
Chen, G.,
Chen, C.L.P.,
Adaptive RBF-AR Models Based on Multi-Innovation Least Squares Method,
SPLetters(26), No. 8, August 2019, pp. 1182-1186.
IEEE DOI
1908
autoregressive processes, learning (artificial intelligence),
least mean squares methods, parameter estimation,
time series prediction
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
Zhang, S.M.[Shui-Mei],
Zhang, Y.D.[Yimin D.],
Robust Time-Frequency Analysis of Multiple FM Signals With Burst
Missing Samples,
SPLetters(26), No. 8, August 2019, pp. 1172-1176.
IEEE DOI
1908
Hankel matrices, signal reconstruction, time-frequency analysis,
time-frequency analysis, burst missing samples,
nonstationary signal
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
BibRef
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
Jain, B.[Brijnesh],
Revisiting inaccuracies of time series averaging under dynamic time
warping,
PRL(125), 2019, pp. 418-424.
Elsevier DOI
1909
Time series, Dynamic time warping, Time series averaging, k-means
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
Iwana, B.K.[Brian Kenji],
Uchida, S.[Seiichi],
Time series classification using local distance-based features in
multi-modal fusion networks,
PR(97), 2020, pp. 107024.
Elsevier DOI
1910
Convolutional Neural Network, Time series classification,
Dynamic time warping, Distance features
BibRef
Ramos, M.M.P.[Mery Milagros Paco],
del Alamo, C.L.[Cristian López],
Zapana, R.A.[Reynaldo Alfonte],
Forecasting of Meteorological Weather Time Series Through a Feature
Vector Based on Correlation,
CAIP19(I:542-553).
Springer DOI
1909
BibRef
Wang, Z.Y.[Zi-Yin],
Tsechpenakis, G.[Gavriil],
Stream Clustering with Dynamic Estimation of Emerging Local Densities,
ICPR18(2100-2105)
IEEE DOI
1812
Kernel, Clustering algorithms, Dictionaries,
Approximation algorithms, Estimation, Complexity theory, Testing
BibRef
Tong, Z.Q.[Zhi-Qiang],
Tanaka, G.[Gouhei],
Reservoir Computing with Untrained Convolutional Neural Networks for
Image Recognition,
ICPR18(1289-1294)
IEEE DOI
1812
Reservoirs, Feature extraction, Convolution, Image recognition,
Computational modeling, Training, Convolutional neural networks
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
Singh, P.[Pritpal],
Dhiman, G.[Gaurav],
A Fuzzy-LP Approach in Time Series Forecasting,
PReMI17(243-253).
Springer DOI
1711
BibRef
Chirikjian, G.S.[Gregory S.],
Signal Classification in Quotient Spaces via Globally Optimal
Variational Calculus,
Diff-CVML17(735-743)
IEEE DOI
1709
Computer vision, Pattern recognition, Zinc
BibRef
Jiang, L.[Li],
Zhou, J.[Junni],
Yang, R.L.[Run-Ling],
Liu, L.[Li],
Li, L.[Lin],
Parameter estimation of LFMCW signal using S-Method with adaptive
window,
ICIVC17(875-878)
IEEE DOI
1708
Estimation, Frequency estimation, Frequency modulation,
Signal to noise ratio, Time-frequency analysis, Transforms,
S-Method, adaptive window,
linear frequency modulated continuous wave,
short-time fourier transform, time frequency analysis.
BibRef
Ayech, M.W.[Mohamed Walid],
Ziou, D.[Djemel],
K-Autoregressive Clustering: Application on Terahertz Image Analysis,
ICIAR17(145-152).
Springer DOI
1706
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
Zhao, R.[Rui],
Iqbal, M.R.A.,
Bennett, K.P.,
Ji, Q.A.[Qi-Ang],
Wind turbine fault prediction using soft label SVM,
ICPR16(3192-3197)
IEEE DOI
1705
Economic indicators, Sensors, Support vector machines,
Time series analysis, Training, Wind energy, Wind, turbines
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
Marcacini, R.M.,
Carnevali, J.C.,
Domingos, J.,
On combining Websensors and DTW distance for kNN Time Series
Forecasting,
ICPR16(2521-2525)
IEEE DOI
1705
Biological system modeling, Forecasting, Knowledge engineering,
Mathematical model, Predictive models, Time measurement, Time,
series, analysis
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
Narayanan, S.,
Sahoo, S.K.,
Makur, A.,
Recovery of correlated sparse signals using adaptive backtracking
matching pursuit,
VCIP15(1-4)
IEEE DOI
1605
Correlated sparse signals
BibRef
Ravelomanantsoa, A.,
Rabah, H.,
Rouane, A.,
Fast and efficient signals recovery for deterministic compressive
sensing: Applications to biosignals,
DASIP15(1-6)
IEEE DOI
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compressed sensing
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Sarjanoja, S.,
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BM3D image denoising using heterogeneous computing platforms,
DASIP15(1-8)
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1605
filtering theory
BibRef
Molaei, S.M.,
Keyvanpour, M.R.,
An analytical review for event prediction system on time series,
IPRIA15(1-6)
IEEE DOI
1603
data mining
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Gkoktsi, K.,
Tau Siesakul, B.,
Giaralis, A.,
Multi-channel sub-Nyquist cross-spectral estimation for modal
analysis of vibrating structures,
WSSIP15(287-290)
IEEE DOI
1603
acceleration measurement
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Ogryczak, W.[Wlodzimierz],
Hurkala, J.[Jaroslaw],
Determining OWA Operator Weights by Maximum Deviation Minimization,
PReMI15(335-344).
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1511
OWA: ordered weighted averaging
BibRef
Turner, L.D.[Liam D.],
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Push or Delay? Decomposing Smartphone Notification Response Behaviour,
HBUI15(69-83).
Springer DOI
1511
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Acuña, D.[Diego],
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The Effect of Innovation Assumptions on Asymmetric GARCH Models for
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CIARP15(527-534).
Springer DOI
1511
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Abughali, I.K.A.[Ibrahim K. A.],
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Binarizing Change for Fast Trend Similarity Based Clustering of Time
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PReMI15(257-267).
Springer DOI
1511
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Rodriguez, F.[Fernanda],
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Optimal and Linear F-Measure Classifiers Applied to Non-technical
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CIARP15(83-91).
Springer DOI
1511
power supply companies.
BibRef
Ho, C.C.[Chiung Ching],
Ting, C.Y.[Choo-Yee],
Time Series Analysis and Forecasting of Dengue Using Open Data,
IVIC15(51-63).
Springer DOI
1511
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).
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1506
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Atto, A.M.[Abdourrahmane M.],
Fillatre, L.[Lionel],
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Simulation of image time series from dynamical fractional brownian
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ICIP14(6086-6090)
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1502
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Cabrai, R.[Ricardo],
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Bernardino, A.[Alexandre],
de la Torre, F.[Fernando],
Optimal no-intersection multi-label binary localization for time
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ICIP14(3127-3130)
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1502
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String Kernels for Complex Time-Series:
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ICPR14(4429-4434)
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Bargi, A.[Ava],
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An Infinite Adaptive Online Learning Model for Segmentation and
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ICPR14(3440-3445)
IEEE DOI
1412
Accuracy
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Martinez-Vargas, J.D.,
Castro-Hoyos, C.,
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Recursive Separation of Stationary Components by Subspace Projection
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ICPR14(3469-3474)
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1412
Discriminate between stationary and non-stationary signals.
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Conti, J.C.[Jose C.],
Farial, F.A.[Fabio A.],
Almeida, J.[Jurandy],
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ICPR14(3126-3131)
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1412
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Bad Data Analysis with Sparse Sensors for Leak Localisation in Water
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ICPR14(3642-3647)
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Statistical Anomaly Detection in Mean and Variation of Energy
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ICPR14(3570-3575)
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Ring, M.[Matthias],
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A Two-Stage Regression Using Bioimpedance and Temperature for
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Dosiek, L.,
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CIARP13(I:488-495).
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1311
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TimeExplorer: Similarity Search Time Series by Their Signatures,
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CIARP14(231-238).
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1209
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Sepulveda-Cano, L.M.[Lina Maria],
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CIARP12(691-698).
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1209
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Quaternionic Analytic Signal Using Atomic Functions,
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Jovic, A.[Alan],
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Decision Tree Ensembles in Biomedical Time-series Classification,
DAGM12(408-417).
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1209
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Huerta, R.[Ramón],
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Dynamical SVM for Time Series Classification,
DAGM12(216-225).
Springer DOI
1209
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Sabre, R.[Rachid],
Evolutionary Spectrum for Random Field and Missing Observations,
ICISP12(209-216).
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1208
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Deng, J.Q.[Jin-Qiu],
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Hooke and jeeves algorithm for linear least-square problems in sparse
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IASP11(16-20).
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1112
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Zhou, M.[Ming],
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Low SNR signal time-frequency analyzing method,
IASP11(21-25).
IEEE DOI
1112
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Zhang, R.[Rui],
Yin, Y.S.[Yong-Sheng],
Yang, J.[Jun],
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Dual-ADC based digital calibration of timing skew for a
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IASP11(42-45).
IEEE DOI
1112
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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
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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
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Hernández, S.[Sergio],
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Sentiment-Preserving Reduction for Social Media Analysis,
CIARP11(409-416).
Springer DOI
1111
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Rodriguez, N.[Nibaldo],
Rubio, J.[Jose],
Yañez, E.[Eleuterio],
Wavelet Autoregressive Model for Monthly Sardines Catches Forecasting
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CIARP11(654-663).
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1111
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Ramírez, C.[Cristián],
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Forecasting Cash Demand in ATM Using Neural Networks and Least Square
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CIARP11(515-522).
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1111
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Automatically Detecting Peaks in Terahertz Time-Domain Spectroscopy,
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1008
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Pulse Repetition Interval Modulation Recognition Using Symbolization,
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1012
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Time Series Classification Using Support Vector Machine with Gaussian
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ICPR10(29-32).
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1008
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ICPR10(85-88).
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Modeling of Electromagnetic Waves Using Statistical and Numerical
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IVIC09(686-695).
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0911
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System Design of DPF Data Recorder and Data Analysis,
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0910
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Dong, L.F.[Li-Fang],
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Emission Signal Analysis Based on Conventional and Modified Wavelet
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CISP09(1-4).
IEEE DOI
0910
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Combined Nonlinear Iterative Algorithms for Retrieving the Complex Wave
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0910
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Krishnanand, K.R.,
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Classification of Power Quality Disturbances Using GA Based Optimal
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PReMI09(561-566).
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0912
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Wang, Z.X.[Zhong-Xing],
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Real-Time Power Line Harmonics Suppression from MRS Based on Stacking
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CISP09(1-5).
IEEE DOI
0910
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Jibia, A.U.,
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Khalifa, O.O.,
Elfaki, F.,
Cramer-Rao Lower Bound for Parameter Estimation of Multiexponential
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WSSIP09(1-5).
IEEE DOI
0906
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Xing, H.J.[Hao-Jiang],
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Phase Error Measurement Algorithm for Sampling System in Power Fault
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CISP09(1-5).
IEEE DOI
0910
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Huang, N.[Nantian],
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Liu, X.S.[Xiao-Sheng],
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CISP09(1-5).
IEEE DOI
0910
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Zhao, W.Q.[Wen-Qing],
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Diagnosis for Transformer Faults Based on Combinatorial Bayes Network,
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IEEE DOI
0910
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Chandrakala, S.,
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Classification of Multi-variate Varying Length Time Series Using
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PReMI09(13-18).
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0912
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Hanias, M.P.,
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Non-Linear Analysis and Time Series Prediction of an Electrical
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0906
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Xiang, K.[Kui],
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Characterize System Dynamic of Pseudo Periodic Time Series with
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CISP09(1-5).
IEEE DOI
0910
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Zhu, L.L.[Li-Li],
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Weak Signal Detection in Noisy Chaotic Time Series Using ORBFNN,
CISP09(1-4).
IEEE DOI
0910
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Xu, H.[Hua],
Zhang, D.M.[Dong-Mei],
Sun, G.F.[Gao-Fei],
The Assistant Timing Method for Fractionary Spaced Equalizer for Fading
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CISP09(1-4).
IEEE DOI
0910
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Luo, S.[Sheng'en],
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Detection of an Unknown Frequency Hopping Signal Based on Image
Features,
CISP09(1-4).
IEEE DOI
0910
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Yali, Q.[Qin],
Lin, W.Y.[Wen-Yao],
Zhou, S.L.[Shou-Li],
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Detection of Chirp Signal by Combination of Kurtosis Detection and
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CISP09(1-6).
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0910
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Time Delay Estimation Based on Mutual Information Estimation,
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0910
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Time Delay Estimation Based on the Fractional Fourier Transform in the
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Time-Frequency Analysis for Nonlinear Lamb Wave Signal,
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0910
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Hautamaki, V.[Ville],
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ICPR08(1-4).
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0812
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Venkataramana, K.B.[Kini B.],
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Large margin AR model for time series classification,
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0812
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Batyrshin, I.[Ildar],
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Positive and Negative Local Trend Association Patterns in Analysis of
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MCPR14(92-101).
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1407
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Earlier: A1, Only:
Up and Down Trend Associations in Analysis of Time Series Shape
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MCPR12(246-254).
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1208
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Batyrshin, I.[Ildar],
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Time Series Pattern Recognition Based on MAP Transform and Local Trend
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CIARP06(910-919).
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0611
MAP: Moving Approximation Transform.
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Gerek, Ö.N.[Ömer N.],
Ece, D.G.,
A 2D representation for analysis and coding of power quality events,
ICIP03(III: 561-564).
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0312
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Novak, D.,
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Morphology analysis of physiological signals using hidden Markov models,
ICPR04(III: 754-757).
IEEE DOI
0409
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Nelson, D.,
Loughlin, P.J.,
Cristobal, G.,
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Time-frequency methods for biological signal estimation,
ICPR00(Vol III: 110-114).
IEEE DOI
0403
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Sankur, B.,
Kahya, Y.P.,
Guler, E.C.,
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Feature extraction and classification of nonstationary signals based on
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ICPR94(B:592-595).
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9410
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Chapter on New Unsorted Entries, and Other Miscellaneous Papers continues in
Network Analysis, Wireless, Network Intrusion .