3:30, SPTM-P10.1
IF ESTIMATION OF HIGHER-ORDER POLYNOMIAL FM SIGNALS CORRUPTED BY MULTIPLICATIVE AND ADDITIVE NOISE
B. BARKAT
In this paper, we propose the peak of the polynomial Wigner-Ville distribution as an instantaneous frequency estimator for polynomial frequency modulated signals in the presence of multiplicative and additive complex Gaussian processes. We show that this estimator is unbiased and we derive an analytic expression of its asymptotic variance. Simulation results, based on Monte-Carlo realisations, are presented in order to show the validity of the theoretical derivations.

3:30, SPTM-P10.2
TIME-FREQUENCY ANALYSIS OF NEAR-FIELD OPTICAL DATA FOR EXTRACTING LOCAL ATTRIBUTES
D. BARCHIESI, C. RICHARD
Near-field microscopy has been developed to characterize optical properties of materials below the diffraction limit. It consists of scanning a probe, which can be of atomic dimensions, a few nanometers above a material surface, and detecting electromagnetic interaction. The resulting near-field optical images are conventionally analyzed by means of Fourier based methods although these data are nonstationary. This observation suggests that time-frequency analysis is potentially a powerful tool for extracting attributes such as local resolution of near-field optical microscopes. In this paper, we use bilinear time-frequency distributions and their optimized version by the AOK procedure to analyze experimental near-field optical and magneto-optical raw images. We show that this approach allows local characterization of optical resolution and separation of relevant optical information from artifacts caused by the scanning probe recording process.

3:30, SPTM-P10.3
A NEW IMPROVED FLEXIBLE SEGMENTATION ALGORITHM USING LOCAL COSINE TRANSFORM
E. DONG, G. LIU, Y. ZHOU
To the problem of no overall optimal merger for one-way merger in the segmentation algorithm proposed by Wang Yongzhong, et al., in this paper, we propose a method of overall optimal search and merger. At the same time, to the unreasonable problem of merging a segment which has non-value (value-segment) and a segment which values are zeros entirely (zeros-segment) to a large segment in Wang's method, we also propose a corresponding method to solve the problem. The main techniques are incarnated in local cosine transform (LCT) algorithm for a single small segment, rather than folding processing using its original neighboring data, instead of making zero-extension, and then fold the each zero-extension segment. A great deal of numerical simulations validate that this new improved technique solves several problems of the binary-based segment algorithm and Wang's segment algorithm, it not only obtains adapted effective segmentation result, but also there are not much more redundancy segmentations.

3:30, SPTM-P10.4
A QUANTITATIVE SNR ANALYSIS OF LINEAR CHIRPS IN THE CONTINUOUS-TIME SHORT-TIME FOURIER TRANSFORM DOMAIN WITH GAUSSIAN WINDOWS
X. XIA, G. WANG, V. CHEN
In this paper, we present a quantitative signal-to-noise ratio (SNR) analysis of linear chirps in the continuous-time short-time Fourier transform (STFT) domain using Gaussian windows, the $3$dB SNR definition is used. It is compared with the SNRs in the time and the Fourier transform domains. Some numerical examples are shown to illustrate the theory.

3:30, SPTM-P10.5
BILINEAR SIGNAL SYNTHESIS IN ARRAY PROCESSING
W. MU, Y. ZHANG, M. AMIN
Multiple source signals impinging on an antenna array can be separated by time-frequency synthesis techniques. Averaging of the time-frequency distributions of the data across the array permits the spatial signatures of sources to play a fundamental role in improving the synthesis performance. This improvement is achieved independent of the temporal characteristics of the source signals and without causing any smearing of the signal terms. Unlike the recently devised blind source separation methods using spatial time-frequency distributions, the proposed method does not require whitening or retrieval of the source directional matrix.

3:30, SPTM-P10.6
ANALYSIS OF NON-STATIONARY MODE COUPLING BY MEANS OF WAVELET-BICOHERENCE
Y. LARSEN, A. HANSSEN, H. PÉCSELI
We present a definition of wavelet-bicoherence based on wavelet-polyspectra. We propose a simple estimator for wavelet-bicoherence, and discuss its statistical properties. In particular it is shown that wavelet-bicoherence estimates has a larger number of effective degrees of freedom than traditional Fourier-based bicoherence estimates. The proposed estimator is applied to detection of coherent couplings in rocket measurements from the ionospheric E-region. It is concluded that wavelet-bicoherence is a well suited tool for analysis of non-stationary mode coupling.

3:30, SPTM-P10.7
DESIGN OF A TIME-FREQUENCY DOMAIN MATCHED FILTER FOR DETECTION OF NON-STATIONARY SIGNALS
E. POWERS, Y. SHIN, S. NAM, C. AN
In this paper, a practical and effective approach is proposed to detect a transient or nonstationary signal component of interest from a composite signal waveform. The detection problem has been re-formulated in terms of time-frequency analysis, and, thus, the conventional 1-D (i.e., time-domain) matched filter approach is extended to the 2-D (here, time-frequency domain) optimal filtering. For that purpose, the reduced interference distribution (RID) algorithm, the outer product expansion of the time-frequency distribution, the singular value decomposition (SVD), and a priori available time-frequency information of a signal part of interest are employed to derive a time-frequency domain matched filter by utilizing the singular values of the sampled time-frequency distribution and the corresponding fractions of signal energy. Finally, one real problem of detecting the snare drum sound event from a measured musical signal is considered to demonstrate the performance of the proposed approach.

3:30, SPTM-P10.8
JOINT USE OF DYNAMICAL CLASSIFIERS AND AMBIGUITY PLANE FEATURES
M. OSTENDORF, L. ATLAS, O. CETIN, R. FISH, S. SUKITTANON, G. BERNARD
This paper argues for using ambiguity plane features within dynamic statistical models for classification problems. The relative contribution of the two model components are investigated in the context of acoustically monitoring cutter wear during milling of titanium, an application where it is known that standard static classification techniques work poorly. Experiments show that explicit modeling of long-term context via a hidden Markov model state improves performance, but mainly by using this to augment sparsely labeled training data. An additional performance gain is achieved by using the shorter-term context of ambiguity plane features.

3:30, SPTM-P10.9
DETECTING FAULTS IN STRUCTURES USING TIME-FREQUENCY TECHNIQUES
A. PAPANDREOU-SUPPAPPOLA, S. PON VARMA, S. SUPPAPPOLA
In this paper, we investigate various methods of classifying time-varying signals. In particular, we are interested in detecting acoustic emissions that may occur in concrete structures during imminent failure. This important classification problem will result in detecting and separating the distress signal from other natural or man made acoustic signals. Due to the time-varying nature of the signals, we employ several time-frequency based classification methods proposed in the literature. We also propose a new automatic classification method that is based on the matching pursuit algorithm, and we demonstrate its superior performance using real data.

3:30, SPTM-P10.10
SCATTERING FUNCTION AND TIME-FREQUENCY SIGNAL PROCESSING
L. NGUYEN, B. SENADJI, B. BOASHASH
The estimation of the scattering function in time-frequency selective fading mobile environment is considered. The scattering function explicitly reveals the time-frequency selective behavior of the fading channel under the well-known WSSUS assumption. We propose two classes of estimators based on a time-frequency framework that generalize the existing estimators while giving an extra freedom according to different criteria wanted to be achieved in the estimation of the scattering function. Instead of using Woodward ambiguity function or symmetric ambiguity function, we use the generalized ambiguity function which comes from the general class of quadratic time-frequency distributions.