Showing papers on "S transform published in 2010"

Seislet transform and seislet frame

Abstract: We introduce a digital waveletlike transform, which is tailored specifically for representing seismic data. The transform provides a multiscale orthogonal basis with basis functions aligned along seismic events in the input data. It is defined with the help of the wavelet-lifting scheme combined with local plane-wave destruction. In the 1D case, the seislet transform is designed to follow locally sinusoidal components. In the 2D case, it is designed to follow local plane-wave components with smoothly variable slopes. If more than one component is present, the transform turns into an overcomplete representation or a tight frame. In these terms, the classic digital wavelet transform is simply a seislet transform for a zero frequency (in one dimension) or zero slope (in two dimensions). The main objective of the new transform is an effective seismic-data compression for designing efficient data-analysis algorithms. Traditional signal-processing tasks such as noise attenuation and trace interpolation become simply defined in the seislet domain. When applied in the offset direction on common-midpoint or common-image-point gathers, the seislet transform finds an additional application in optimal stacking of seismic records.

Medical image enhancement algorithm based on wavelet transform

Abstract: Low contrast and poor quality are main problems in the production of medical images. By using the wavelet transform and Haar transform, a novel image enhancement approach is proposed. First, a medical image was decomposed with wavelet transform. Secondly, all high-frequency sub-images were decomposed with Haar transform. Thirdly, noise in the frequency field was reduced by the soft-threshold method. Fourthly, high-frequency coefficients were enhanced by different weight values in different sub-images. Then, the enhanced image was obtained through the inverse wavelet transform and inverse Haar transform. Lastly, the image's histogram was stretched by nonlinear histogram equalisation. Experiments showed that this method can not only enhance an image's details but can also preserve its edge features effectively.

Towards jointly optimal spatial prediction and adaptive transform in video/image coding

Abstract: This paper proposes a new approach to combined spatial (Intra) prediction and adaptive transform coding in block-based video and image compression. Context-adaptive spatial prediction from available, previously decoded boundaries of the block, is followed by optimal transform coding of the prediction residual. The derivation of both the prediction and the adaptive transform for the prediction error, assumes a separable first-order Gauss-Markov model for the image signal. The resulting optimal transform is shown to be a close relative of the sine transform with phase and frequencies such that basis vectors tend to vanish at known boundaries and maximize energy at unknown boundaries. The overall scheme switches between the above sine-like transform and discrete cosine transform (per direction, horizontal or vertical) depending on the prediction and boundary information. It is implemented within the H.264/AVC intra mode, is shown in experiments to significantly outperform the standard intra mode, and achieve significant reduction of the blocking effect.

Power quality time series data mining using S-transform and fuzzy expert system

Abstract: This paper presents a new approach for power quality time series data mining using S-transform based fuzzy expert system (FES). Initially the power signal time series disturbance data are pre-processed through an advanced signal processing tool such as S-transform and various statistical features are extracted, which are used as inputs to the fuzzy expert system for power quality event detection. The proposed expert system uses a data mining approach for assigning a certainty factor for each classification rule, thereby providing robustness to the rule in the presence of noise. Further to provide a very high degree of accuracy in pattern classification, both the Gaussian and trapezoidal membership functions of the concerned fuzzy sets are optimized using a fuzzy logic based adaptive particle swarm optimization (PSO) technique. The proposed hybrid PSO-fuzzy expert system (PSOFES) provides accurate classification rates even under noisy conditions compared to the existing techniques, which show the efficacy and robustness of the proposed algorithm for power quality time series data mining.

Gabor Transform, SPWVD, Gabor-Wigner Transform and Wavelet Transform - Tools For Power Quality Monitoring

Abstract: The one-dimension frequency analysis based on DFT (Discrete FT ) is sufficient in many cases in detecting power disturbances and evaluating power quality (PQ). To illustrate in a more comprehensive manner the character of the signal, time-frequency analyses are p erformed. The most common known time-frequency representations (TFR) are spectrogram (SPEC) and Gabor Transform (GT). However, the method has a relatively low time-frequency resolution. The other TF R: Discreet Dyadic Wavelet Transform (DDWT), Smoothed Pseudo Wigner-Ville Distribution (SPWVD) and new Gabor-Wigner Transform (GWT) are described in the paper. The main features of the transforms, on the basis of testing signals, are presented.

Review: Choice of the wavelet analyzing in the phonocardiogram signal analysis using the discrete and the packet wavelet transform

Abstract: The phonocardiogram signal (PCG) can be utilized more efficiently by medical doctors when they are displayed visually, rather through a conventional stethoscope. This signal provides clinician with valuable diagnostic and prognostic information. Although the PCG signal analysis by auscultation is convenient as clinical tool, heart sound signals are so complex and non-stationary that they have a great difficulty to analyze in time or frequency domain. We have studied the extraction of features out of heart sounds in time-frequency (TF) domain for recognition of heart sounds through TF analysis. This article highlights the importance of the choice of wavelet analyzing wavelet and its order in the phonocardiogram signal analysis using the two versions of the wavelet transform: the discrete wavelet transform (DWT) and the packet wavelet transform (PWT). This analysis is based on the application of a large number of orthogonal and bi-orthogonal wavelets and whenever you measure the value of the average difference (in absolute value) between the original signal and the synthesis signal obtained by multiresolution analysis (AM). The performance of the discrete wavelet transform (DWT) and the packet wavelet transform (PWT) in the PCG signal analysis are evaluated and discussed in this paper. The results we obtain show the clinical usefulness of our extraction methods for recognition of heart sounds (or PCG signal).

Double-image encryption based on the affine transform and the gyrator transform

Abstract: We propose a kind of double-image-encryption algorithm by using the affine transform in the gyrator transform domain. Two original images are converted into the real part and the imaginary part of a complex function by employing the affine transform. And then the complex function is encoded and transformed into the gyrator domain. The affine transform, the encoding and the gyrator transform are performed twice in this encryption method. The parameters in the affine transform and the gyrator transform are regarded as the key for the encryption algorithm. Some numerical simulations have validated the feasibility of the proposed image encryption scheme.

A novel transform for image compression

Abstract: In this paper, we propose an orthogonal multiplication-free transform of order that is an integral power of two by an appropriate extension of the well-known fourthorder integer discrete cosine transform. Moreover, we develop an efficient algorithm for its fast computation. It is shown that the computational and structural complexities of the algorithm are similar to that of the Hadamard transform. By applying the proposed transform to image compression, we show that it outperforms the existing transforms having complexities similar to that of the proposed one.

On the Shiftability of Dual-Tree Complex Wavelet Transforms

Abstract: The dual-tree complex wavelet transform (DT-\BBCWT) is known to exhibit better shift-invariance than the conventional discrete wavelet transform. We propose an amplitude-phase representation of the DT-IBBC WT which, among other things, offers a direct explanation for the improvement in the shift-invariance. The representation is based on the shifting action of the group of fractional Hilbert transform (fHT) operators, which extends the notion of arbitrary phase-shifts from sinusoids to finite-energy signals (wavelets in particular). In particular, we characterize the shift ability of the DT-\BBCWT in terms of the shifting property of the fHTs. At the heart of the representation are certain fundamental invariances of the fHT group, namely that of translation, dilation, and norm, which play a decisive role in establishing the key properties of the transform. It turns out that these fundamental invariances are exclusive to this group. Next, by introducing a generalization of the Bedrosian theorem for the fHT operator, we derive an explicitly understanding of the shifting action of the fHT for the particular family of wavelets obtained through the modulation of lowpass functions (e.g., the Shannon and Gabor wavelet). This, in effect, links the corresponding dual-tree transform with the framework of windowed-Fourier analysis. Finally, we extend these ideas to the multidimensional setting by introducing a directional extension of the fHT, the fractional directional Hilbert transform. In particular, we derive a signal representation involving the superposition of direction-selective wavelets with appropriate phase-shifts, which helps explain the improved shift-invariance of the transform along certain preferential directions.

Reduced Time Complexity for Detection of Copy-Move Forgery Using Discrete Wavelet Transform

Abstract: As result of powerful image processing tools, digital image forgeries have already become a serious social problem. In this paper we describe an effective method to detect Copy-Move forgery in digital images. Our technique works by first applying DWT (Discrete Wavelet Transform) to the input image to yield a reduced dimensional representation [1]. Then the compressed image is divided into overlapping blocks. These blocks are then sorted and duplicated blocks are identified using Phase Correlation as similarity criterion. Due to DWT usage, detection is first carried out on lowest level image representation. This approach drastically reduces the time needed for the detection process and increases accuracy of detection process. General Terms Image processing, Digital image forgery

OC-seislet: Seislet transform construction with differential offset continuation

Abstract: Many of the geophysical data-analysis problems such as signal-noise separation and data regularization are conveniently formulated in a transform domain, in which the signal appears sparse. Classic transforms such as the Fourier transform or the digital wavelet transform (DWT) fail occasionally in processing complex seismic wavefields because of the nonstationarity of seismic data in time and space dimensions. We present a sparse multiscale transform domain specifically tailored to seismic reflection data. The new wavelet-like transform — the OC-seislet transform — uses a differential offset-continuation (OC) operator that predicts prestack reflection data in offset, midpoint, and time coordinates. It provides a high compression of reflection events. Its compression properties indicate the potential of OC seislets for applications such as seismic data regularization or noise attenuation. Results of applying the method to synthetic and field data examples demonstrate that the OC-seislet transform can recon...

An efficient technique for OFDM system using discrete wavelet transform

Abstract: With the rapid expand of wireless digital communications, demand for wireless systems that are reliable and have a high spectral efficiency have increased too Orthogonal Frequency Division Multiplexing (OFDM) has been recognized for its good performance to achieve high data rates Fast Fourier Transforms (FFT) has been used to produce the orthogonal sub-carriers Due to the drawbacks of OFDM-FFT based system which are the high peak-to-average ratio (PAR) and the synchronization, many works have replaced the Fourier transform part by wavelet transform In this paper, an efficient technique for the OFDM system using wavelet transform is proposed This system shows a superior performance when compared with traditional OFDM-FFT systems through an Additive White Gaussian Noise (AWGN) channel The system performance is described in Bit Error Rate (BER) as a function of Signal to Noise Ratio (SNR) and the peak-to-average ratio (PAR) Furthermore, the proposed system gives nearly a perfect reconstruction for the input signal in the presence of Gaussian noise.

Performance Comparison of various levels of Fusion of Multi-focused Images using Wavelet Transform

Abstract: The fast development of digital image processing leads to the growth of feature extraction of images which leads to the development of Image fusion. Image fusion is defined as the process of combining two or more different images into a new single image retaining important features from each image with extended information content. There are two approaches to image fusion, namely Spatial Fusion and Transform fusion. In Spatial fusion, the pixel values from the source images are directly summed up and taken average to form the pixel of the composite image at that location. Transform fusion uses transform for representing the source images at multi scale. The most common widely used transform for image fusion at multi scale is Wavelet Transform since it minimizes structural distortions. But, wavelet transform suffers from lack of shift invariance & poor directionality and these disadvantages are overcome by Stationary Wavelet Transform and Dual Tree Wavelet Transform. The conventional convolution-based implementation of the discrete wavelet transform has high computational and memory requirements. Lifting Wavelets has been developed to overcome these drawbacks. The Multi-Wavelet Transform of image signals produces a non-redundant image representation, which provides better spatial and spectral localization of image formation than discrete wavelet transform. And there are three levels of image fusion namely Pixel level, Area level and region level. This paper evaluates the performance of all levels of multi focused image fusion of using Discrete Wavelet Transform, Stationary Wavelet Transform, Lifting Wavelet Transform, Multi Wavelet Transform, Dual Tree Discrete Wavelet Transform and Dual Tree Complex Wavelet transform in terms of various performance measures.

Local Fractional Fourier's Transform Based on the Local Fractional Calculus

Abstract: A new modeling for the local fractional Fourier's transform containing the local fractional calculus is investigated in fractional space. The properties of the local fractional Fourier's transform are obtained and two examples for the local fractional systems are investigated in detail.

Gabor Transform, Gabor-Wigner Transform and SPWVD as a time-frequency analysis of power quality

Abstract: The measurement algorithms applied in power quality measurement systems are based on Fourier Transformation (FT). The discrete versions of Fourier transformation - DFT (Discrete FT) and FFT (Fast Fourier Transformation) are most commonly used. That one-dimension frequency analysis is sufficient in many cases. However, to illustrate the character of the signal in a more comprehensive manner, it is crucial to represent the investigated signal on time-frequency plane. There are a lot of time-frequency representations (TFR) for presenting measured signal. The most common known are spectrogram (SPEC) and Gabor Transform (GT), which are based on direct DFT results. However, the method has relatively low time-frequency resolution. The other TFR representing Cohen class: Wigner-Ville Distribution (WVD) and its variants: Pseudo Wigner-Ville Distribution (PWVD), Smoothed Pseudo Wigner-Ville Distribution (SPWVD) and Gabor-Wigner Transform (GWT) are described in the paper. The main features of the transforms, on the basis of testing signals, were presented.

Comparison of fourier & wavelet transform methods for transmission line fault classification

Abstract: Nowadays, power supply has become a business asset. The quality and reliability of power system needs to be maintained in order to obtain optimum performance. Therefore, it is extremely important that transmission line faults from various sources to be identified accurately, reliably and be corrected as soon as possible. In this paper, a new technique is discussed by which avoiding noise in fault detection in high voltage transmission lines is achieved. Later, a comparative study of the performance of Fourier transform and wavelet transform based methods combined with protective relaying pattern classifier algorithm Neural Network for classification of faults is presented. A new classification method is proposed for decreasing training time and dimensions of NN. The proposed algorithms are based on Fourier transform analysis of fundamental frequency of current signals in the event of a short circuit. Similar analysis is performed on transient current signals using multi-resolution Haar wavelet transform, and comparative characteristics of the two methods are discussed.

Image retrieval by Kekre's transform applied on each row of Walsh transformed VQ codebook

Abstract: The new technique for image retrieval using the color-texture features extracted from images based on vector quantization with Kekre's fast codebook generation is proposed. This gives better discrimination capability for CBIR. Here the database image is divided into 2x2 pixel windows to obtain 12 color descriptors per window (Red, Green and Blue per pixel) to form a vector. Collection of all such vectors is a training set. Then the Kekre's Fast Codebook Generation (KFCG) is applied on this set to get 16 codevectors. The Walsh transform is applied on each column of the codebook, followed by Kekre's transform applied on each row of the Walsh transformed codebook. This transform vector then is used as the image signature (feature vector) for image retrieval. The method takes lesser computations as compared to conventional Walsh applied on complete image. The method gives the color-texture features of the image database at reduced feature set size. Proposed method gives better precision and recall as compared to full Walsh based CBIR. Proposed method avoids resizing of images which is required for any transform based feature extraction method.

Focus measure based on the energy of high-frequency components in the S transform

Abstract: Focus measure plays a fundamental role in the shape from focus technique. In this Letter, we suggest a focus measure in the S-transform domain that is based on the energy of high-frequency components. A localized spectrum by using variable window size provides a more accurate method of measuring image sharpness as compared to other focus measures proposed in spectral domains. An optimal focus measure is obtained by selecting an appropriate frequency-dependent window width. The performance of the proposed focus measure is compared with those of existing focus measures in terms of three-dimensional shape recovery. Experimental results demonstrate the effectiveness of the proposed focus measure.

Method for intelligent analysis of transient power quality disturbance based on networking

Abstract: The invention relates to a method for the intelligent analysis of transient power quality disturbance based on networking, which comprises the following steps of: combining disturbance signals of a plurality of monitoring points in an electric network, and performing disturbance detection to determine the starting time and the ending time of the transient power quality disturbance by using form non-sampling wavelet analysis; performing disturbance identification to perform feature extraction and encoding on power quality disturbance signals by combining window Fourier transform and S transform, and comparing the features of the power quality disturbance signals with a binary threshold matrix to identify the type of the disturbance; and during disturbance positioning, switching disturbance to a capacitor, taking the disturbance energy and the power spectrum of the disturbance signals as characteristics, combining a support vector machine to determine the switching position of the capacitor, and for voltage sag disturbance, judging the change polarity of current components at the time of voltage sag to position the relative position of a disturbance source so as to position the disturbance source. The method uses transient signals of a plurality of the monitoring points in the electric network, provides a comprehensive intelligent analysis result of the transient power quality disturbance, and can provide comprehensive and accurate data support for evaluating and controlling the problems of the transient power quality disturbance.

S-transform based cumulative sum detector (CUSUM ) for islanding detection in Distributed Generations

Abstract: The proposed technique uses time-frequency transform such as S-transform for islanding detection in Distributed Generations. The S-transform is an invertible time-frequency spectral localization technique that provides the time-frequency contours of the voltage and current signals retrieved at target DG location. A cumulative sum detector (CUSUM) is devised based on the spectral energy content of the negative sequence component of the current and voltage signals. The technique has been tested for islanding detection at different target DG location and provides accurate results. Thus the CUSUM based technique is found to be highly effective for islanding detection under wide range of operating conditions in the power distribution network including multiple DGs.

Features extraction based on wavelet packet transform for B-mode ultrasound liver images

Abstract: This paper concentrates on the analysis of features extracted through the application of wavelet packet transform for B-mode ultrasound liver images. For years, the scientific community has been trying to figure out an effective method of feature extraction for revealing texture details and the succeeding classifications. Methods of feature extraction can be roughly classified into three categories: spatial-domain based, frequency-domain based and model based. Comparative study of typical methodologies within each category has proved that frequency-domain based algorithms, especially the wavelet transform are more effective in texture characterization and the succeeding classification. In our recent study, experiment result demonstrates wavelet packet transform is more advantageous than wavelet transform, intuitively because wavelet packet transform not only decompose the approximation of an image but also the details, thus can better describe texture surface. Single feature extracted will all go through feature selection procedure and finally the discriminative capacity of feature vector can be compared and verified by feature vector selection criteria and classification correct ratio of support vector machine.

S-transform based islanding detection in grid-connected distributed generation based power system

Abstract: S-transform and wavelet transform based approach for islanding detection is proposed in distributed generation (DG) based hybrid system. The hybrid system consisting of photovoltaic, fuel cell and wind systems connected to utility grid are considered. The negative sequence voltage signal is used in islanding detection of these resources from the grid. The energy content and standard deviation (SD) of S-transform contour and wavelet transform signal is also reported for the study which clearly reflects the advantages of S-transform in localizing and detecting the islanding events.

Ripplet-II transform for feature extraction

Abstract: Current image representation schemes have limited capability of representing 2D singularities (e.g., edges in an image). Wavelet transform has better performance in representing 1D singularities than Fourier transform. Recently invented ridgelet and curvelet transform achieve better performance in resolving 2D singularities than wavelet transform. To further improve the capability of representing 2D singularities, this paper proposes a new transform called ripplet transform Type II (ripplet-II). The new transform is able to capture 2D singularities along a family of curves in images. In fact, ridgelet transform is a special case of ripplet-II transform with degree 1. Ripplet-II transform can be used for feature extraction due to its efficiency in representing edges and textures. Experiments in texture classification and image retrieval demonstrate that the ripplet-II transform based scheme outperforms wavelet and ridgelet transform based approaches.

A New Approach Based on S-transform for Discrimination and Classification of Inrush Current from Internal Fault Currents Using Probabilistic Neural Network

Abstract: This article presents a new approach for differential protection of power transformers. The proposed method uses S-transform and a probabilistic neural network to discriminate internal faults from inrush current. S-transform is utilized to extract some useful features of non-stationary signal analysis, giving the information of transient currents both in time and frequency domains. The features extracted using S-transform are applied to train probabilistic neural network classifiers. This approach has been realized through two different stages. In the first stage, discrimination of inrush current and fault current has been done; in the second stage, different types of fault current will be recognized in four steps. The performance of this algorithm is demonstrated by simulation of different faults and switching conditions on a power transformer using PSCAD/EMTDC software (Manitoba HVDC Research Center, Winnipeg, Manitoba, Canada). The simulation results show that the combination of S-transform an...