Showing papers on "S transform published in 2014"
TL;DR: In this paper, a differential scheme for microgrid protection using time-frequency transform such as S-transform was proposed to register the fault patterns in the microgrid at grid-connected and islanded mode.
Abstract: The study presents a differential scheme for microgrid protection using time-frequency transform such as S-transform Initially, the current at the respective buses are retrieved and processed through S-transform to generate time-frequency contours Spectral energy content of the time-frequency contours of the fault current signals are calculated and differential energy is computed to register the fault patterns in the microgrid at grid-connected and islanded mode The proposed scheme is tested for different shunt faults (symmetrical and unsymmetrical) and high-impedance faults in the microgrid with radial and loop structure It is observed that a set threshold on the differential energy can issue the tripping signal for effective protection measure within four cycles from the fault inception The results based on extensive study indicate that the differential energy-based protection scheme can reliably protect the microgrid against different fault situations and thus, is a potential candidate for wide area protection
187 citations
TL;DR: The Synchrosqueezing Transform (SST) as discussed by the authors is an extension of the wavelet transform incorporating elements of empirical mode decomposition and frequency reassignment techniques, which produces a well defined time-frequency representation allowing the identification of instantaneous frequencies in seismic signals.
Abstract: Time-frequency representation of seismic signals provides a source of information that is usually hidden in the Fourier spectrum. The short-time Fourier transform and the wavelet transform are the principal approaches to simultaneously decompose a signal into time and frequency components. Known limitations, such as trade-offs between time and frequency resolution, may be overcome by alternative techniques that extract instantaneous modal components. Empirical mode decomposition aims to decompose a signal into components that are well separated in the time-frequency plane allowing the reconstruction of these components. On the other hand, a recently proposed method called the “synchrosqueezing transform” (SST) is an extension of the wavelet transform incorporating elements of empirical mode decomposition and frequency reassignment techniques. This new tool produces a well-defined time-frequency representation allowing the identification of instantaneous frequencies in seismic signals to highlight ...
148 citations
TL;DR: A novel method for QRS detection in electrocardiograms (ECG) based on the S-Transform, a new time frequency representation (TFR), which provides frequency-dependent resolution while maintaining a direct relationship with the Fourier spectrum.
131 citations
TL;DR: A novel parameter estimation method based on keystone transform and Radon-Fourier transform for space moving targets with high-speed maneuvering performance that can overcome the limitation of Doppler frequency ambiguity and correct range curvature for all targets in one processing step, which simplifies the operation procedure.
Abstract: This letter proposes a novel parameter estimation method based on keystone transform (KT) and Radon-Fourier transform (RFT) for space moving targets with high-speed maneuvering performance. In this method, second-order KT is used to correct the range curvature and part of the range walk for all targets simultaneously. Then, fractional Fourier transform is employed to estimate the targets' radial acceleration, followed by the quadric phase term compensation. Finally, RFT and Clean technique are carried out to correct the residual range walk, and the initial range and radial velocity of moving targets are further obtained. The advantage of the proposed method is that it can overcome the limitation of Doppler frequency ambiguity and correct range curvature for all targets in one processing step, which simplifies the operation procedure. Simulation results are presented to demonstrate the validity of the proposed method.
114 citations
TL;DR: The experiments results show that the proposed algorithm based on the fractional Fourier transform (FRFT) is very robust to JPEG compression noise attacks and image manipulation operations, but also can provide protection even under compound attacks.
89 citations
TL;DR: It is proved that the synchrosqueezed curvelet transform is capable of recognizing each component and precisely estimating local wave-vectors, and a discrete analogue of the continuous transform and several clustering models for decomposition are proposed in detail.
Abstract: This paper introduces the synchrosqueezed curvelet transform as an optimal tool for two-dimensional mode decomposition of wavefronts or banded wave-like components. The syn- chrosqueezed curvelet transform consists of a generalized curvelet transform with application depen- dent geometric scaling parameters, and a synchrosqueezing technique for a sharpened phase space representation. In the case of a superposition of banded wave-like components with well-separated wave-vectors, it is proved that the synchrosqueezed curvelet transform is capable of recognizing each component and precisely estimating local wave-vectors. A discrete analogue of the continuous transform and several clustering models for decomposition are proposed in detail. Some numeri- cal examples with synthetic and real data are provided to demonstrate the above properties of the proposed transform.
66 citations
TL;DR: The main objective of this paper is to study the fractional Fourier transform (FrFT) and the generalized continuous wavelet transform and some of their basic properties.
65 citations
TL;DR: An effective method, for extracting features, so-called “integrated approach”, using integration of Discrete Wavelet Transform and Hyperbolic S Transform, and a new efficient feature selection method namely Orthogonal Forward Selection by incorporating Gram Schmidt procedure and forward selection is applied for selection of the best subset features.
51 citations
TL;DR: It is shown that the Transform K-SVD learns operators which are similar both in appearance and performance to the operators learned from the Analysis SVD, while its computational complexity stays much reduced compared to the Analysis K- SVD.
Abstract: Recently there has been increasing attention directed towards the analysis sparsity models. Consequently, there is a quest for learning the operators which would enable analysis sparse representations for signals in hand. Analysis operator learning algorithms such as the Analysis K-SVD have been proposed. Sparsifying transform learning is a paradigm which is similar to the analysis operator learning, but they differ in some subtle points. In this paper, we propose a novel transform operator learning algorithm called as the Transform K-SVD, which brings the transform learning and the K-SVD based analysis dictionary learning approaches together. The proposed Transform K-SVD has the important advantage that the sparse coding step of the Analysis K-SVD gets replaced with the simple thresholding step of the transform learning framework. We show that the Transform K-SVD learns operators which are similar both in appearance and performance to the operators learned from the Analysis K-SVD, while its computational complexity stays much reduced compared to the Analysis K-SVD.
49 citations
TL;DR: The generalized wavelet transform (GWT) as discussed by the authors is a time-frequency transformation tool based on the idea of the linear canonical transform (LCT) and is capable of representing signals in the time-fractional frequency plane.
46 citations
TL;DR: Empirical results show that the proposed structures can yield an automatic online/offline monitoring of PQ with sparser structures and less computational execution time, both in the training and recognition phases, without sacrificing generality of performance.
TL;DR: A novel detection approach of linear FM signals, with single or multiple components, in the time-frequency plane of Teager-Huang (TH) transform is presented and advantages of THH transform over Hough transform of Wigner-Ville distribution (WVD) are 1) cross-terms free detection and estimation, and 2) good time and frequency resolutions.
Abstract: A novel detection approach of linear FM (LFM) signals, with single or multiple components, in the time-frequency plane of Teager-Huang (TH) transform is presented. The detection scheme that combines TH transform and Hough transform is referred to as Teager-Huang-Hough (THH) transform. The input signal is mapped into the time-frequency plane by using TH transform followed by the application of Hough transform to recognize time-frequency components. LFM components are detected and their parameters are estimated from peaks and their locations in the Hough space. Advantages of THH transform over Hough transform of Wigner-Ville distribution (WVD) are: 1) cross-terms free detection and estimation, and 2) good time and frequency resolutions. No assumptions are made about the number of components of the LFM signals and their models. THH transform is illustrated on multicomponent LFM signals in free and noisy environments and the results compared with WVD-Hough and pseudo-WVD-Hough transforms.
TL;DR: In this article, a fast discrete S-transform (ST)-based time-frequency signal analyzer has been proposed for the detection, classification, and monitoring of power quality (PQ) disturbances varying in an electric power system.
Abstract: SUMMARY
In this paper, a new fast discrete S-transform (ST)-based time-frequency signal analyzer has been proposed for the detection, classification, and monitoring of power quality (PQ) disturbances varying in an electric power system. The proposed algorithm is based on the generalized Fourier algorithm that is used to obtain the time-localized spectral characteristics of the time-varying voltage and current signals belonging to PQ events. The fast ST algorithm is realized with different types of frequency scaling, band pass filtering, and interpolation techniques based on Heisenberg's uncertainty principle resulting in a reduced computation cost. In the conventional ST, the window width decreases at higher frequencies with a reduction in frequency resolution and conversely at low frequencies with wider windows. Therefore, the time-varying PQ disturbance signal is down sampled at low frequencies and cropped at high frequencies resulting in the evaluation of a fewer samples. From the time–frequency matrix output, important features are extracted and used with a binary decision tree for an accurate classification of single and simultaneous PQ events. Further, a unified approach is presented to track the time-varying PQ disturbance waveforms like voltage sag, swell, harmonics, and oscillatory transients and produce estimation of their amplitudes and phase angles. Copyright © 2013 John Wiley & Sons, Ltd.
TL;DR: A new algorithm, fast S-transform (FST) is proposed and is used to analyze the time-varying voltage flicker in this paper, which achieves much lower computational complexity in comparison to the S- transform.
Abstract: Voltage flicker analysis is one of the elementary tools of power quality assessment in power systems. A new algorithm, fast S-transform (FST) is proposed and is used to analyze the time-varying voltage flicker in this paper. A voltage flicker waveform envelope, which captures the main flicker characteristic, is obtained by Teager-Kaiser energy operator instantaneously. FST achieves much lower computational complexity in comparison to the S-transform. FST is used to extract both the time and the frequency domain information of each flicker component. The proposed algorithm can be easily implemented with hardware multipliers, making the method a good choice for real-time applications for the time-frequency analysis of flicker. The implementation of the proposed algorithm in the dual-core processor-digital signal processor and advanced RISC machines-based voltage flickermeter is also introduced. The simulation and application results validate the accuracy and efficiency of the proposed algorithm.
TL;DR: A novel measurement method based on null space pursuit and S transform is presented to detect the faults of the bearing vibration signal and several real vibration signals containing bearing fault signature are investigated to verify the effectiveness.
TL;DR: This letter proposes a novel compression algorithm, exploiting the recently developed redundant tree-based wavelet transform, designed to best sparsify the whole set of aligned frontal facial images using a common feature-ordering.
Abstract: Compression of frontal facial images is an appealing and important application. Recent work has shown that specially tailored algorithms for this task can lead to performance far exceeding JPEG2000. This paper proposes a novel such compression algorithm, exploiting our recently developed redundant treebased wavelet transform. Originally meant for functions defined on graphs and cloud of points, this new transform has been shown to be highly effective as an image adaptive redundant and multi-scale decomposition. The key concept behind this method is reordering of the image pixels so as to form a highly smooth 1D signal that can be sparsified by a regular wavelet. In this work we bring this image adaptive transform to the realm of compression of aligned frontal facial images. Given a training set of such images, the transform is designed to best sparsify the whole set using a common feature-ordering. Our compression scheme consists of sparse coding using the transform, followed by entropy coding of the obtained coefficients. The inverse transform and a post-processing stage are used to decode the compressed image. We demonstrate the performance of the proposed scheme and compare it to other competing algorithms.
11 Mar 2014
TL;DR: This paper proposes to employ S-Transform for the feature extraction stage and Support Vector Machines for the pattern recognition problem and finds promising feature vectors based on S-transform are presented with similar or superior performance than the approach based on Wavelet Packet Transform.
Abstract: The electric energy demand is dramatically growing worldwide and demand reduction emerges as an outstanding strategy; it implies detailed information about the electricity consumption, namely load disaggregation. Typical automatic methods for load disaggregation require high hardware efforts to install one sensor per appliance, whereas Non-intrusive Load Monitoring (NILM) systems diminish the hardware efforts through signal processing and mathematical modeling. One approach to NILM systems is to model the load signatures via artificial intelligence. This paper proposes to employ S-Transform for the feature extraction stage and Support Vector Machines for the pattern recognition problem. Several experiments are presented and the results of the feature extraction with S-Transform and Wavelet Packet Transform are compared. Thus promising feature vectors based on S-Transform are presented with similar or superior performance than the approach based on Wavelet Packet Transform.
01 Jan 2014
TL;DR: An object detection scheme using the Scale Invariant Feature Transform (SIFT) and the Support Vector Machine (SVM) is proposed in this paper, which yields promising performance in terms of detection accuracy.
Abstract: An object detection scheme using the Scale Invariant Feature Transform (SIFT) is proposed in this paper. The SIFT extracts distinctive invariant features from images and it is a useful tool for matching between different views of an object. This paper proposes how the SIFT can be used for an object detection problem, especially human detection problem. The Support Vector Machine (SVM) is adopted as the classifier in the proposed scheme. Experiments on INRIA Perdestrian dataset are performed. Preliminary results show that the proposed SIFT-SVM scheme yields promising performance in terms of detection accuracy.
TL;DR: A secure multi-key single-sideband (SSB) modulation scheme is developed and analyzed its performance in noise and sensitivity to security key perturbations and the duality between analyticity and causality concepts is investigated.
TL;DR: This work presents the two-dimensional quaternion Wigner-Ville distribution (QWVD), and discusses some useful properties of various definitions for the QWVD, which are extensions of the classical Wignen Ville distribution properties.
Abstract: We present the two-dimensional quaternion Wigner-Ville distribution (QWVD). The transform is constructed by substituting the Fourier transform kernel with the quaternion Fourier transform (QFT) kernel in the classical Wigner-Ville distribution definition. Based on the properties of quaternions and the QFT kernel we obtain three types of the QWVD. We discuss some useful properties of various definitions for the QWVD, which are extensions of the classical Wigner-Ville distribution properties.
TL;DR: In this article, a new approach is presented to control the unusual false trip of a three-phase power transformer differential protection due to ultra-saturation phenomenon based on Clarke's Transform and Discrete Wavelet Transform (DWT).
TL;DR: A novel method for color image enhancement based on the discrete quaternion Fourier transform that not only provides true color fidelity for poor quality images but also averages the color components to gray value for balancing colors.
Abstract: This paper presents a novel method for color image enhancement based on the discrete quaternion Fourier transform. We choose the quaternion Fourier transform, because it well-suited for color image processing applications, it processes all 3 color components (R,G,B) simultaneously, it capture the inherent correlation between the components, it does not generate color artifacts or blending , finally it does not need an additional color restoration process. Also we introduce a new CEME measure to evaluate the quality of the enhanced color images. Preliminary results show that the α-rooting based on the quaternion Fourier transform enhancement method out-performs other enhancement methods such as the Fourier transform based α-rooting algorithm and the Multi scale Retinex. On top, the new method not only provides true color fidelity for poor quality images but also averages the color components to gray value for balancing colors. It can be used to enhance edge information and sharp features in images, as well as for enhancing even low contrast images. The proposed algorithms are simple to apply and design, which makes them very practical in image enhancement.
TL;DR: Main contribution of this study is the real-time detection and classification of different PQ disturbances which even a layman can understand through visual displays to ascertain the quality of supply, unlike other PQ analysers available commercially that require some technical expertise.
Abstract: The proliferation of power electronic devices in the power sector has intensified the power quality (PQ) issues. Identification of these PQ issues is one of the most important steps for their effective elimination. Hence, development of a real-time (hardware) PQ monitoring system is of great research interest. This study proposes a multifunctional real-time (hardware) PQ monitoring system using Stockwell transform ( S -transform). The proposed system is designed and implemented in LabVIEW environment, which can display the real-time fast Fourier transform plots, root-mean-square values, harmonic components, values of total harmonic distortion and three-dimensional plots of S -transform of the three-phase voltages. Main contribution of this study is the real-time detection and classification of different PQ disturbances which even a layman can understand through visual displays to ascertain the quality of supply, unlike other PQ analysers available commercially that require some technical expertise.
TL;DR: A novel content adaptive transform framework for the H.264/AVC-based video coding by utilizing pixel rearrangement to dynamically adjust the transform kernels to adapt to the video content and a spiral-scanning method is developed to reorder the transform coefficients for better entropy coding.
Abstract: Transform has been widely used to remove spatial redundancy of prediction residuals in the modern video coding standards. However, since the residual blocks exhibit diverse characteristics in a video sequence, conventional transform methods with fixed transform kernels may result in low efficiency. To tackle this problem, we propose a novel content adaptive transform framework for the H.264/AVC-based video coding. The proposed method utilizes pixel rearrangement to dynamically adjust the transform kernels to adapt to the video content. In addition, unlike the traditional adaptive transforms, the proposed method obtains the transform kernels from the reconstructed block, and hence it consumes only one logic indicator for each transform unit. Moreover, a spiral-scanning method is developed to reorder the transform coefficients for better entropy coding. Experimental results on the Key Technical Area (KTA) platform show that the proposed method can achieve an average bitrate reduction of about 7.95% and 7.0% under all-intra and low-delay configurations, respectively.
TL;DR: In this paper, the mean transform of bounded linear operators acting on a complex Hilbert space was introduced and compared with the Aluthge transform, and the mean transformation of weighted shifts was explored.
TL;DR: In this paper, the q-analog of the Laplace transform is considered and some properties of the Q-Laplace transform are investigated, and some interesting formulas are derived.
Abstract: In this paper, we consider the q-analog of the Laplace transform and investigate some properties of the q-Laplace transform. In our investigation, we derive some interesting formulas related to the q-Laplace transform.
TL;DR: The unfolding technique is used to overcome the problem of difficult to realize pipeline that occur in iterative CORDIC algorithms and has a superior performance in terms of hardware complexity, control complexity, throughput, scalability, modularity, and pipelinability.
Abstract: In this paper, CORDIC (coordinate rotation digital computer)-based Cooley-Tukey fast Fourier transform (FFT)-like algorithms for power-of-two point discrete cosine transform/discrete sine transform/inverse discrete cosine transform/inverse discrete sine transform are proposed and their corresponding unified architectures are developed by fully reusing the unique two basic processing elements. The proposed algorithms have some distinguished advantages, such as FFT-like regular data flow, unique post-scaling factor, and arithmetic-sequence rotation angles. The developed unified architectures can compute four different transforms by simple routing the data flow according to the specific transform without feeding different transform coefficients or different transform kernels. The unfolding technique is used to overcome the problem of difficult to realize pipeline that occur in iterative CORDIC algorithms. Compared to existing unified architectures, the proposed architectures have a superior performance in terms of hardware complexity, control complexity, throughput, scalability, modularity, and pipelinability.
TL;DR: In this article, a new technique employing S-transform (ST)-based features, a three-module artificial neural network and a final rule-based classifier is proposed for the identification and classification of power quality disturbances.
Abstract: SUMMARY
A new technique employing S-transform (ST)-based features, a three-module artificial neural network and a final rule-based classifier is proposed in this paper for the identification and classification of power quality disturbances. To validate the proposed method, various short duration disturbances such as voltage sag, voltage swell, momentary interruption, harmonics, oscillatory transient and impulsive transient, defined in terms of their spectral content, duration and magnitude by IEEE 1159–2009 standards, are used. In addition, simultaneous disturbances such as voltage sag with harmonics, voltage swell with harmonics and voltage sag with impulsive transients are also used. Further, the performance of the proposed system is evaluated in the noisy environment with the addition of Gaussian white noise in the aforementioned signals. The proposed three-module ANN structure has less training requirements, higher accuracy and enhanced ability to classify simultaneous disturbances. The distinct capabilities of extracted features from ST, the three-module ANN and final rule base classifier enable the proposed system to identify and classify both single and simultaneous disturbances effectively. Copyright © 2013 John Wiley & Sons, Ltd.
TL;DR: This work proposes several algorithms, which provide optimal windows for a user-selected TF pattern with respect to different concentration criteria, and base their optimization algorithm on lp-norms as measure of TF spreading.
Abstract: Gabor analysis is one of the most common instances of time-frequency signal analysis. Choosing a suitable window for the Gabor transform of a signal is often a challenge for practical applications, in particular in audio signal processing. Many time-frequency (TF) patterns of different shapes may be present in a signal and they can not all be sparsely represented in the same spectrogram. We propose several algorithms, which provide optimal windows for a user-selected TF pattern with respect to different concentration criteria. We base our optimization algorithm on l p -norms as measure of TF spreading. For a given number of sampling points in the TF plane we also propose optimal lattices to be used with the obtained windows. We illustrate the potentiality of the method on selected numerical examples.
TL;DR: The watermark embedding and detecting techniques are proposed and discussed based on the discrete linear canonical transform, and the results show that the watermark cannot be detected when the parameters of thelinear canonical transform used in the detection are not all the same as the parameters in the embedding progress.
Abstract: The linear canonical transform, which can be looked at the generalization of the fractional Fourier transform and the Fourier transform, has received much interest and proved to be one of the most powerful tools in fractional signal processing community.
A novel watermarking method associated with the linear canonical transform is proposed in this paper. Firstly, the watermark
embedding and detecting techniques are proposed and discussed based on the discrete linear canonical transform. Then the Lena
image has been used to test this watermarking technique. The simulation results demonstrate that the proposed schemes are robust
to several signal processing methods, including addition of Gaussian noise and resizing. Furthermore, the sensitivity of the single
and double parameters of the linear canonical transform is also discussed, and the results show that the watermark cannot be
detected when the parameters of the linear canonical transform used in the detection are not all the same as the parameters used
in the embedding progress.