Analysis of Acoustic Signatures from Moving Vehicles UsingTime-Varying Autoregressive Models
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Is early detection of liver and breast cancers from ultrasound scans possible?
Pattern Recognition Letters - Speciqal issue: Ultrasonic image processing and analysis
An adaptive optimal-kernel time-frequency representation
ICASSP '93 Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04
The fan-chirp transform for non-stationary harmonic signals
Signal Processing
Local instantaneous frequency estimation of multi-component signals
Computers and Electrical Engineering
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Digital Signal Processing
IEEE Transactions on Signal Processing
Matched representations and filters for guided waves
IEEE Transactions on Signal Processing
Techniques to obtain good resolution and concentrated time-frequency distributions: a review
EURASIP Journal on Advances in Signal Processing
Spectral entropy of dyslexic erp signal by means of adaptive optimal kernel
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Superposition frames for adaptive time-frequency analysis and fast reconstruction
IEEE Transactions on Signal Processing
Time-frequency representation based on an adaptive short-time Fourier transform
IEEE Transactions on Signal Processing
Adaptive time-frequency analysis based on autoregressive modeling
Signal Processing
Approximating the time-frequency representation of biosignals with chirplets
EURASIP Journal on Advances in Signal Processing - Special issue on applications of time-frequency signal processing in wireless communications and bioengineering
Validity-guided fuzzy clustering evaluation for neural network-based time-frequency reassignment
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Journal of Visual Communication and Image Representation
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Time-frequency representations with fixed windows or kernels figure prominently in many applications, but perform well only for limited classes of signals. Representations with signal-dependent kernels can overcome this limitation. However, while they often perform well, most existing schemes are block-oriented techniques unsuitable for on-line implementation or for tracking signal components with characteristics that change with time. The time-frequency representation developed in the present paper, based on a signal-dependent radially Gaussian kernel that adapts over time, surmounts these difficulties. The method employs a short-time ambiguity function both for kernel optimization and as an intermediate step in computing constant-time slices of the representation. Careful algorithm design provides reasonably efficient computation and allows on-line implementation. Certain enhancements, such as cone-kernel constraints and approximate retention of marginals, are easily incorporated with little additional computation. While somewhat more expensive than fixed kernel representations, this new technique often provides much better performance. Several examples illustrate its behavior on synthetic and real-world signals