The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Selin Aviyente - One of the best experts on this subject based on the ideXlab platform.
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Analysis of Event Related Potentials using PCA and Matching Pursuit on the Time-Frequency Plane
2006 International Conference of the IEEE Engineering in Medicine and Biology Society, 2006Co-Authors: Selin Aviyente, Edward M. Bernat, Stephen M. Malone, William G. IaconoAbstract:Joint time-Frequency representations offer a rich representation of event related potentials (ERPs) that cannot be obtained through individual time or Frequency domain analysis. This rich representation, however, comes at the expense of increased data volume and the difficulty of interpreting the resulting representations. Therefore, methods that can reduce the large amount of time-Frequency data to physiologically meaningful components are essential. The method presented in this paper extends principal component analysis to the time-Frequency Plane to reduce a large set of ERPs to a small number of significant components. These components are then characterized using a Gabor dictionary to offer a succinct parametrization of the ERP data. The results show that the principal component analysis is successful at extracting components that can be described as the superposition of a small number of Gabor logons, and that the resulting set of logons succinctly represent physiologically meaningful ERP events.
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EUSIPCO - Information-theoretic signal processing on the time-Frequency Plane and applications
2005Co-Authors: Selin AviyenteAbstract:Time-Frequency analysis is a major tool in representing the energy distribution of time-varying signals. There has been a lot of research on various properties of these representations. However, there is a general lack of quantitative measures in describing the amount of information encoded into a time-Frequency distribution. Recently, information-theoretic measures such as entropy and divergence have been adapted to the time-Frequency Plane to quantify the complexity of individual signals as well as the difference between signals. In this paper, we present a variety of information-theoretic measures and their definitions on the time-Frequency Plane. The properties of these measures and how they can be applied to signal classification problems are discussed in detail. We then present an application of information-theoretic signal processing to the analysis of event- related brain potentials.
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Adaptive minimum entropy decomposition on the time-Frequency Plane
IEEE SP 13th Workshop on Statistical Signal Processing 2005, 2005Co-Authors: Zeyong Shan, Selin AviyenteAbstract:In many applications, such as array processing and sensor networks, it is desirable to extract the source signals that generate the observed output signals. Some common approaches include principal component analysis, which assumes uncorrelated source signals, and independent component analysis, which assumes the independence of the underlying sources. In recent years, there has been efforts to perform source separation in the time-Frequency domain since most real life signals of interest are non-stationary (A. Belouchrani and M.G. Amin, 1998). In this paper, we introduce one such component extraction approach on the time-Frequency Plane. The proposed approach extracts components that are well-concentrated on the time-Frequency Plane. In order to quantify the compactness or the concentration of the extracted components, we use the entropy measure as adapted to the time-Frequency distributions. It has been shown that signals which achieve minimum entropy on the time-Frequency Plane are Gabor logons. Based on this idea, we propose an adaptive Gabor logon extraction method from a given set of observed signals. The proposed method extracts the most significant Gabor logons as the components using an adaptive filtering approach. The method is applied on an example data set to show the effectiveness of the component extraction algorithm
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Toward a theory of information processing on the time-Frequency Plane
Advanced Signal Processing Algorithms Architectures and Implementations XIV, 2004Co-Authors: Selin AviyenteAbstract:Information processing theory aims to quantify how well signals encode information and how well systems process information. Time-Frequency distributions have been used to represent the energy distribution of time-varying signals for the past twenty years. There has been a lot of research on various properties of these representations. However, there is a general lack of quantitative analysis in describing the amount of information encoded into a time-Frequency distribution. This paper aims to quantify how well time-Frequency distributions represent information by using information-theoretic distance measures. Different distance measures, such as Kullback-Leibler distance, R\'{e}nyi distance, will be adapted to the time-Frequency Plane. Their performance in quantifying the information in a given signal will be compared. A sensitivity analysis for different distance measures will be carried out to assess their robustness under perturbation. Different example signals will be considered for illustrating the information processing in time-Frequency distributions.
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Information theoretic signal detection on the time-Frequency Plane
Advanced Signal Processing Algorithms Architectures and Implementations XIII, 2003Co-Authors: Selin AviyenteAbstract:A comprehensive theory for time-Frequency based signal detection has been developed during the past decade. The time-Frequency detectors proposed in literature are linear structures operating on the time-Frequency representation of the signals and are equivalent to quadratic receivers that are defined in the time domain. In this paper, an information theoretic approach for signal detection on the time-Frequency Plane is introduced. In recent years, Renyi entropy has been proposed as an effective measure for quantifying signal complexity on the time-Frequency Plane and some important properties of this measure have been proven. In this paper, a new approach that uses the entropy functional as the test statistic for signal detection is developed. The minimum error detection algorithm is derived and the performance of this new signal detection method is demonstrated through examples.
William J. Williams - One of the best experts on this subject based on the ideXlab platform.
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ICASSP (6) - Entropy based detection on the time-Frequency Plane
2003 IEEE International Conference on Acoustics Speech and Signal Processing 2003. Proceedings. (ICASSP '03)., 1Co-Authors: Selin Aviyente, William J. WilliamsAbstract:A comprehensive theory for time-Frequency based signal detection has been developed during the past decade. The time-Frequency detectors proposed in the literature are linear structures operating on the time-Frequency representation of the signals and are equivalent to quadratic receivers that are defined in the time domain. We introduce the concept of entropy based detection on the time-Frequency Plane. In recent years, Renyi entropy has been proposed as an effective measure for quantifying signal complexity on the time-Frequency Plane and some important properties of this measure have been proven. A new approach that uses the entropy functional as the test statistic for signal detection is developed. A minimum error detection algorithm is derived and the performance of this new signal detection method is demonstrated through examples.
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ICASSP - Information bounds for random signals in time-Frequency Plane
2001 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.01CH37221), 1Co-Authors: Selin Aviyente, William J. WilliamsAbstract:Renyi entropy has been proposed as one of the methods for measuring signal information content and complexity on the time-Frequency Plane. It provides a quantitative measure for the uncertainty of the signal. All of the previous work concerning Renyi entropy in the time-Frequency Plane has focused on determining the number of signal components in a given deterministic signal. We discuss the behaviour of Renyi entropy when the signal is random, more specifically white complex Gaussian noise. We present the bounds on the expected value of Renyi entropy and discuss ways to minimize the uncertainty by deriving conditions on the time-Frequency kernel. The performance of minimum entropy kernels in determining the number of signal elements is demonstrated. Finally, some possible applications of Renyi entropy for signal detection are discussed.
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A new energy distribution on the time-Frequency Plane
Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis (Cat. No.98TH8380), 1Co-Authors: Tzu-hsien Sang, William J. WilliamsAbstract:A new time-Frequency distribution (TFD) is developed along the line of thinking of the concentration issue of TFDs. A new formulation is given to shed new light on this issue for the short-time Fourier transform (STFT). The new insight is then extended to more general energy density functions and a procedure is developed to construct a complex-valued function which plays a similar role as the STFT does and has a superior concentration limit. The magnitude of this new function is used as the energy density function on the T-F Plane. Numerical examples, in certain cases of which the auto terms are preserved while the cross terms are virtually eliminated, justify our intuition.
Caroline Chaux - One of the best experts on this subject based on the ideXlab platform.
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Phase inpainting in time-Frequency Plane
2018Co-Authors: Ama Marina Kreme, Valentin Emiya, Caroline ChauxAbstract:We propose a new problem of missing data reconstruction in the time-Frequency Plane. This problem called phase inpainting, consists in reconstructing a signal from time-Frequency observations where all amplitudes and some phases are known while the remaining phases are missing. A mathematical formulation of this problem is given. We propose three alternatives of existing algorithms. An iterative algorithm: Griffin and Lim and two semidefinite programming optimization algorithms: PhaseLift and PhaseCut. The obtained results show that knowledge of certain phases improves the reconstruction's quality.
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Being low-rank in the time-Frequency Plane
2018Co-Authors: Valentin Emiya, Ronan Hamon, Caroline ChauxAbstract:When solving inverse problems and using optimization methods with matrix variables in signal processing and machine learning, it is customary to assume some low-rank prior on the targeted solution. Nonnegative matrix factorization of spectrograms is a case in point in audio signal processing. However, this low-rank prior is not straightforwardly related to complex matrices obtained from a short-time Fourier – or discrete Gabor – transform (STFT), which is generally defined from and studied based on a modulation operator and a translation operator applied to a so-called window. This paper is a first study of the low-rankness property of time-Frequency matrices. We characterize the set of signals with a rank-r (complex) STFT matrix in the case of a unit hop size and Frequency step with few assumptions on the transform parameters. We discuss the scope of this result and its implications on low-rank approximations of STFT matrices.
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being low rank in the time Frequency Plane
International Conference on Acoustics Speech and Signal Processing, 2018Co-Authors: Valentin Emiya, Ronan Hamon, Caroline ChauxAbstract:When using optimization methods with matrix variables in signal processing and machine learning, it is customary to assume some low-rank prior on the targeted solution. Nonnegative matrix factorization of spectrograms is a case in point in audio signal processing. However, this low-rank prior is not straightforwardly related to complex matrices obtained from a short-time Fourier - or discrete Gabor - transform (STFT), which is generally defined from and studied based on a modulation operator and a translation operator applied to a so-called window. This paper is a first study of the low-rankness property of time-Frequency matrices. We characterize the set of signals with a rank- $r$ (complex) STFT matrix in the case of a unit hop size and Frequency step with few assumptions on the transform parameters. We discuss the scope of this result and its implications on low-rank approximations of STFT matrices.
Jelena Kovacevic - One of the best experts on this subject based on the ideXlab platform.
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Arbitrary tilings of the time-Frequency Plane using local bases
IEEE Transactions on Signal Processing, 1999Co-Authors: Riccardo Bernardini, Jelena KovacevicAbstract:We show how to design filters given a prescribed tiling of the time-Frequency Plane. Moreover, we impose on these filters the structure of local orthogonal bases. These bases were recently constructed as a generalization of the cosine-modulated filter banks in discrete time and local trigonometric bases in continuous time. They have been found to be of considerable practical importance due to their simplicity (all filters are obtained from a single prototype) and low computational complexity. We show examples of design, in particular, that of a critical-band system for use in audio coding.
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ICASSP (3) - Time-varying orthonormal tilings of the time-Frequency Plane
IEEE International Conference on Acoustics Speech and Signal Processing, 1993Co-Authors: Cormac Herley, Jelena Kovacevic, Kannan Ramchandran, Martin VetterliAbstract:Expansions that give arbitrary orthonormal tilings of the time-Frequency Plane are considered. These differ from the short-time Fourier transform, wavelet transform, and wavelet packet tilings in that they change over time. It is shown how this can be achieved using time-varying orthogonal tree structures, which preserve orthogonality, even across transitions. One method is based on lapped orthogonal transforms, which makes it possible to change the number of channels in the transform. A second method is based on the construction of orthogonal boundary filters to construct essentially arbitrary tilings. A double-tree algorithm is presented that, for a given signal, decides on the best binary segmentation and on which tree split to use for each segment. That is, it is a joint optimization of time and Frequency splitting. The algorithm is optimal for additive cost functions (e.g., rate distortion), which gives the best time-varying bases. Results of experiments on test signals are shown. >
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Arbitrary orthogonal tilings of the time-Frequency Plane
[1992] Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis, 1Co-Authors: Cormac Herley, Jelena Kovacevic, Kannan Ramchandran, Martin VetterliAbstract:Expansions which give arbitrarily orthonormal tilings of the time-Frequency Plane are considered. These differ from the short-time Fourier transform, wavelet transform, and wavelet packets tilings in that they change over time. It is shown how orthonormal tilings can be achieved using time-varying orthogonal tree structures, which preserve orthogonality, even across transitions. One method is based on lapped orthogonal transforms, which makes it possible to change the number of channels in the transform. A second method is based on the construction of boundary filters and gives arbitrary tilings. An algorithm is presented which for a given signal decides on the best binary segmentation and which tree split to use for each segment. It is optimal in a rate-distortion sense. The results of experiments on test signals are presented. >
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ICASSP - Local bases yielding arbitrary tilings of the time-Frequency Plane
1996 IEEE International Conference on Acoustics Speech and Signal Processing Conference Proceedings, 1Co-Authors: Riccardo Bernardini, Jelena KovacevicAbstract:We show how to obtain arbitrary tilings of the time-Frequency Plane using local orthogonal bases. These bases were constructed as a generalization of the cosine-modulated filter banks in discrete time, and local trigonometric bases in continuous time. Due to the fact that they use a single prototype window, these bases also lead to time-varying tilings. Moreover, they have a fast implementation algorithm, and allow for multidimensional irreducible basis functions. We show examples of design, in particular, that of a critical-band system for use in audio coding.
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Data Compression Conference - Arbitrary tilings of the time-Frequency Plane using local bases
Proceedings of Data Compression Conference - DCC '96, 1Co-Authors: Riccardo Bernardini, Jelena KovacevicAbstract:We show how to obtain arbitrary tilings of the time-Frequency Plane using local orthogonal bases. These bases were recently constructed as a generalization of the cosine-modulated filter banks in discrete time, and local sine and cosine bases in continuous time. Due to the fact that they use a single prototype window, these bases also lead to time-varying tilings. Moreover, they have a fast implementation algorithm, and allow for multidimensional irreducible basis functions. As an example, we show how to design a critical-band system for use in audio coding.
Kehar Singh - One of the best experts on this subject based on the ideXlab platform.
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double phase image encryption using gyrator transforms and structured phase mask in the Frequency Plane
Optics and Lasers in Engineering, 2015Co-Authors: Hukum Singh, Sunanda Vashisth, Anil Kumar Yadav, Kehar SinghAbstract:Abstract Fully-phase image encryption is considered more secure as compared to an amplitude image encryption. In the present paper, an encryption scheme is proposed for double phase-images. The phase-images are bonded with random phase masks and then gyrator transformed. The two resulting images are then added and subtracted to give intermediate images which are bonded with a structured phase mask (SPM) based on devil’s vortex Fresnel lens (DVFL) in the Frequency Plane. Thereafter, the images are once again transformed using a gyrator transform (GT) to give the corresponding encrypted images. The use of a structured phase mask enhances the key space for encryption and also overcomes the problem of axis alignment associated with an optical set-up. The decryption process is the reverse of encryption. The validity of the proposed scheme is established from the computer simulation results using MATLAB 7.1 platform. The performance of the scheme is evaluated in terms of mean-squared-error (MSE) between the input-, and the decrypted images. In addition, the sensitivity to encryption keys such as SPM parameters, and transform angles of GT is investigated. The technique is likely to provide enhanced security in view of the increased number of encryption parameters. Robustness of the system against occlusion and noise attacks has also been investigated.
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devil s vortex phase structure as Frequency Plane mask for image encryption using the fractional mellin transform
International Journal of Optics, 2014Co-Authors: Sunanda Vashisth, Hukum Singh, Anil Kumar Yadav, Kehar SinghAbstract:A Frequency Plane phase mask based on Devil’s vortex structure has been used for image encryption using the fractional Mellin transform. The phase key for decryption is obtained by an iterative phase retrieval algorithm. The proposed scheme has been validated for grayscale secret target images, by numerical simulation. The efficacy of the scheme has been evaluated by computing mean-squared-error between the secret target image and the decrypted image. Sensitivity analysis of the decryption process to variations in various encryption parameters has been carried out. The proposed encryption scheme has been seen to exhibit reasonable robustness against occlusion attack.
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Devil’s Vortex Phase Structure as Frequency Plane Mask for Image Encryption Using the Fractional Mellin Transform
International Journal of Optics, 2014Co-Authors: Sunanda Vashisth, Hukum Singh, Anil Kumar Yadav, Kehar SinghAbstract:A Frequency Plane phase mask based on Devil’s vortex structure has been used for image encryption using the fractional Mellin transform. The phase key for decryption is obtained by an iterative phase retrieval algorithm. The proposed scheme has been validated for grayscale secret target images, by numerical simulation. The efficacy of the scheme has been evaluated by computing mean-squared-error between the secret target image and the decrypted image. Sensitivity analysis of the decryption process to variations in various encryption parameters has been carried out. The proposed encryption scheme has been seen to exhibit reasonable robustness against occlusion attack.
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A cryptosystem for watermarking based on fractional Fourier transform using a random phase mask in the input Plane and a structured phase mask in the Frequency Plane
2014Co-Authors: Hukum Singh, Sunanda Vashisth, Atul Yadav, Kehar SinghAbstract:A watermarking scheme has been proposed in the fractional Fourier transform (FrFT) domain, using a random phase mask in the input Plane and a phase mask based on the devil’s vortex Fresnel lens (DVFL) in the Frequency Plane. The use of a structured phase mask based on the DVFL provides an advantage of extra encryption parameters, besides overcoming the problem of axis alignment associated with an optical set-up. The encrypted image resulting from the application of FrFT is attenuated by a factor and combined with a host image to provide a watermarked image. The decryption process is the reverse of the encryption. Digital implementation of the proposed scheme has been performed using MATLAB 7.1. The validity of the proposed scheme has been established by comparing the results of decryption with the input images. The performance of the scheme has been evaluated in terms of mean-squared-error (MSE). In addition, the sensitivity to encryption keys such as DVFL parameters and the FrFT orders has been studied. The proposed technique provides enhanced security. © Anita Publications. All rights reserved.