[go: up one dir, main page]

Sheu et al., 1995 - Google Patents

The application of Fourier deconvolution to reaction time data: A cautionary note.

Sheu et al., 1995

View PDF
Document ID
1385442340097016520
Author
Sheu C
Ratcliff R
Publication year
Publication venue
Psychological Bulletin

External Links

Snippet

The Fourier transform method in conjunction with frequency domain smoothing techniques has been suggested as a powerful tool for examining components in a serial, additive reaction time model (PL Smith, 1990). Robustness and sensitivity to violations of the …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
    • G06F17/141—Discrete Fourier transforms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00496—Recognising patterns in signals and combinations thereof
    • G06K9/00503—Preprocessing, e.g. filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00496—Recognising patterns in signals and combinations thereof
    • G06K9/00536—Classification; Matching
    • G06K9/0055—Classification; Matching by matching signal segments
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6228—Selecting the most significant subset of features
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image

Similar Documents

Publication Publication Date Title
US7054792B2 (en) Method, computer program, and system for intrinsic timescale decomposition, filtering, and automated analysis of signals of arbitrary origin or timescale
Huang et al. An orthogonal Hilbert-Huang transform and its application in the spectral representation of earthquake accelerograms
Maiorino et al. Data-driven detrending of nonstationary fractal time series with echo state networks
Kay Model-based probability density function estimation
Aburakhia et al. On the intersection of signal processing and machine learning: A use case-driven analysis approach
Zozor et al. Mixing Bandt-Pompe and Lempel-Ziv approaches: another way to analyze the complexity of continuous-state sequences
Khalil et al. Detection and classification of multiple events in piecewise stationary signals: comparison between autoregressive and multiscale approaches
Sheu et al. The application of Fourier deconvolution to reaction time data: A cautionary note.
Hargreaves et al. Wavelet spectral testing: Application to nonstationary circadian rhythms
Zunino et al. Quantifying the diversity of multiple time series with an ordinal symbolic approach
Berezowski et al. Identification of fast-changing signals by means of adaptive chaotic transformations
Tseng et al. Entropic interpretation of empirical mode decomposition and its applications in signal processing
Shcherban et al. A search method for unknown high-frequency oscillators in noisy signals based on the continuous wavelet transform
Smith et al. An optimized de-noising scheme based on the null hypothesis of intrinsic mode functions
Tokhmpash et al. On analysis of fractional order system identification
Damaševičius et al. IMF remixing for mode demixing in EMD and application for jitter analysis
Al-Badrawi Statistical Properties and Applications of Empirical Mode Decomposition
Hemanthkumar et al. Cognitive load detection on drivers by pupillary analysis
Germán-Salló Empirical Mode Decomposition in Discrete Time Signals Denoising
Alexiev et al. Self-similar decomposition of digital signals
Paluš Chaotic Measures and Real-World Systems: Does the Lyapunov Exponent Always Measure Chaos?
Oakley Genetic programming, the reflection of chaos, and the bootstrap: Towards a useful test for chaos
Pałczyńska Fast harmonics identification based on a compressive sensing approach
Carter et al. Parameter estimation from an Ornstein-Uhlenbeck process with measurement noise
Gu et al. Exploration of the dendritic cell algorithm using the duration calculus