Discrete Wavelet Transforms

An automatic Uni-or Multi-modal Inteligent Seizure Acquisition (UISA/MISA) system is highly applicable for onset detection of epileptic seizures based on motion data. The modalities used are surface electromyography (sEMG), acceleration (ACC) and angular velocity (ANG). The new proposed automatic algorithm on motion data is extracting features as "log-sum" measures of discrete wavelet components. Classification into the two groups "seizure" versus "nonseizure" is made based on the support vector machine (SVM) algorithm. The algorithm performs with a sensitivity of 91-100%, a median latency of 1 second and a specificity of 100% on multi-modal data from five healthy subjects simulating seizures. The uni-modal algorithm based on sEMG data from the subjects and patients performs satisfactorily in some cases. As expected, our results clearly show superiority of the multimodal approach, as compared with the uni-modal one.

An adaptive two step paradigm for the super-resolution of optical images is developed in this paper. The procedure locally projects image samples onto a family of kernels that are learned from image data. First, an unsupervised feature extraction is performed on local neighborhood information from a training image. These features are then used to cluster the neighborhoods into disjoint sets for which an optimal mapping relating homologous neighborhoods across scales can be learned in a supervised manner. A superresolved image is obtained through the convolution of a low resolution test image with the established family of kernels. Results demonstrate the effectiveness of the approach.

This paper investigates the application of a new concept called the "Overcomplete Discrete Wavelet Transform"(0DWT) for image sequences compression. It has been demonstrated that: "The translated function with any integer multiple of the sampling period is completely represented in the wavelet space by one of the ODWT members". This theoretical result leads to a new motion estimation and motion compensation scheme working in the wavelet transform domain. Our simulation experiments, performed on real image sequences, show high quality and low bit rate performances. By performing the motion estimation in the wavelet space, a quite modest computational complexity is ensured.

An evolutionary-based procedure for designing adaptive filters based on second-generation wavelet (lifting scheme) packet decomposition for industrial fault detection is presented. The proposed procedure is validated by an experimental case study for induction motor fault diagnosis in an elevator system. Preliminary results on two typologies of faults, broken rotor bars and static air gap eccentricity, are discussed by showing encouraging performance.

A novel robust watermarking scheme based on wavelet transform is presented in this paper. The watermark is embedded into significant triplet of wavelet coefficients in the three lowest detail subband images. The embedding algorithm only needs to modify a small amount of the wavelet coefficients, which leads to very small distortion and guarantees the visual quality of the watermarked image. The performance of the proposed algorithm is tested on a series of normal attacks in image processing, such as JPEG compression, Gaussian noise addition, median filter, etc. The corresponding experimental results show that the proposed watermarking scheme has better performance.

This work represents a comparative study for the methods of registration for dental panoramic X-ray images. The study tried to find the best registration technique used to register images of the jaw. Three image registration techniques are implemented and applied to dental panoramic X-ray images in both spatial domain and wavelet domain. The first technique is based on cross-correlation. The second one depends on selecting control points from both the reference and the input images. The last technique is based on maximization of mutual information between the two images. In the wavelet domain, discrete wavelet transform (DWT) has been performed to the reference image and the test image before the three techniques have been applied. Then the registration methods have been applied using approximation coefficients in the registration procedure. After finishing the registration steps, inverse discrete wavelet transform (IDWT) has been applied in order to obtain the registered image. The...

Actually, hand vein biometrics is a recent technology that offers system for identification /authentication, it ranks among the best biometric modality by the results developed. Just like any recognition system this has four steps: the acquisition, enhancement, feature extraction and classification. This paper present the enhancement's step of the SAB11 Data Base followed by new adaptive feature extraction method for the dorsal hand vein biometrics; which is the discrete wavelet transform.

The use of two dimensional (2-D) continuous wavelet analysis has not been extensive for image processing using wavelets. It has been overshadowed by the 2-D discrete dyadic wavelet transform (DWT) due to its compactness and excellent performance in coding, data compression, image reconstruction, etc. However, the 2-D DWT has some restrictions on the scale and position parameters, and it does not detect all the features of an image unless properly tuned. The 2-D continuous wavelet transform (CWT), on the other hand, is more flexible and provides complete control over the scale and position parameters; and thus it is capable of extracting various features of an image, which cannot be accomplished by the DWT.

A progressive improvement in gait following knee arthroplasty surgery can be observed during walking and transitional activities such as sitting/standing. Accurate assessment of such changes traditionally requires the use of a gait lab, which is often impractical, expensive, and labour intensive. Quantifying gait impairment following knee arthroplasty by employing wearable sensors allows for continuous monitoring of recovery. This study employed a recognised protocol of activities both pre-operatively, and at regular intervals up to twenty-four weeks post-total knee arthroplasty. The results suggest that a wearable miniaturised ear-worn sensor is potentially useful in monitoring post-operative recovery, and in identifying patients who fail to improve as expected, thus facilitating early clinical review and intervention.

In the recent years, Internet multimedia applications have become very popular. Valuable multimedia content such as digital images, however, is vulnerable to unauthorized access while in storage and during transmission over a network. Streaming digital images also require high network bandwidth for transmission. For effective image transmission over Internet; therefore, both security and bandwidth issues must be considered. In this paper, we present a novel scheme, which combines Discrete Wavelet Transform (DWT) for image compression and block cipher Data Encryption Standard (DES) for image encryption. The simulation results indicate thaf our proposed method enhances the security for image transmission over the Internet as well as improves the transmission rate.

In this letter, a new satellite image contrast enhancement technique based on the discrete wavelet transform (DWT) and singular value decomposition has been proposed. The technique decomposes the input image into the four frequency subbands by using DWT and estimates the singular value matrix of the low-low subband image, and, then, it reconstructs the enhanced image by applying inverse DWT. The technique is compared with conventional image equalization techniques such as standard general histogram equalization and local histogram equalization, as well as state-of-the-art techniques such as brightness preserving dynamic histogram equalization and singular value equalization. The experimental results show the superiority of the proposed method over conventional and state-of-the-art techniques.

The wavelet transform is an efficient technique for multi-resolution analysis of non-stationary and fast transient signals. For this reason, wavelet transform has been widely applied in signal analysis through processing, encoding, denoising and encrypting. The objective of this paper is to represent the development process of VHSIC (Very High Speed Integrated

In this paper, we propose to extract the texture features of currency note images. To extract the features, first the Discrete Wavelet Transform (DWT) in particular Daubechies 1 (DB1) is utilized on a currency note and the approximate coefficient matrix of the transformed image is obtained. A set of coefficient statistical moments are then extracted from the approximate efficient matrix. The extracted features are stored in a feature vector. The extracted features can be used for recognition, classification and retrieval of currency notes.

In this paper, a novel multi-resolution filter for on-line decomposition and analysis of partial discharge (PD) signals based on discrete wavelet transform (DWT) is developed. By applying this filter, as soon as a new sample of the original signal is drawn, the new elements of detail and approximate components at any desired level is derived, and the successive procedure of signal processing in basic DWT is eliminated. Using this filter, the PD or any non-stationary (transient) signal is decomposed to different frequency band components, simultaneously. Therefore, the decomposition components of PD signals in all frequency bandwidths are simultaneously available. The frequency characteristic of this filter is verified, then in order to make the proposed filter more suitable for on-line applications, its order is reduced without losing the accuracy of the results. Results, for simulation and field test data, have shown the appropriateness of the proposed filter for on-line PD detection and measurement.

In this paper, we propose a pipelined real-time architecture for forward/inverse wavelet transforms that take into account the filter group delays. The required resources and the reconstruction error of this architecture were evaluated and compared to those of the conventional one. These architectures were implemented on FPGA using Xilinx System Generator and XUP Virtex-II Pro development board.

The detection and localisation of partial discharge (PD) activities is gaining importance in maintaining operational transformers and diagnosing those which are faulty. Ultra-high-frequency (UHF) techniques have been applied successfully to detection of PD activities in HV transformers because of their sensitivity to fast transient pulses. When an UHF PD monitoring system is applied, one of the crucial pieces of information

feature extraction tool to analyze sub-band frequency of Abstract--The purpose of feature extraction technique in wavelet transform. The sub-band frequencies were used as an image processing is to represent the image in its compact and input to the expert model network. The common technique unique form of single values or matrix vector. Low level feature used for feature extraction of DWT coefficient is by using extraction involves automatic extraction of features from an neural network [6], [7], [8]. In this study, wavelets will be image without doing any processing method. In this paper, we used in the analysis of narrow and broad weed images since consider the use of high level feature extraction technique to the harcteisti ofnarow nd boadwee by DWT allows decomposition of the image into different levels investigate theof resolution. By doing so, we introduce a new feature set that implementing the 2 Dimensional Discrete Wavelet Transform is reduon Bysis of we oducen t. sehat Pahang,

In this paper, the performance of traditional variance-based method for detection of epileptic seizures in EEG signals are compared with various methods based on nonlinear time series analysis, entropies, logistic regression, discrete wavelet transform and time frequency distributions. We noted that variance-based method in compare to the mentioned methods had the best result (100%) applied on the same database.

In the recent years, Internet multimedia applications have become very popular. Valuable multimedia content such as digital images, however, is vulnerable to unauthorized access while in storage and during transmission over a network. Streaming digital images also require high network bandwidth for transmission. For effective image transmission over Internet; therefore, both security and bandwidth issues must be considered. In this paper, we present a novel scheme, which combines Discrete Wavelet Transform (DWT) for image compression and block cipher Data Encryption Standard (DES) for image encryption. The simulation results indicate thaf our proposed method enhances the security for image transmission over the Internet as well as improves the transmission rate.

Induction machines are widely used in industrial applications. Safety, reliability, efficiency and performance are major concerns that direct the research activities in the field of electrical machines. Even though the induction machine is very reliable, many failures can occur such as bearing faults, air-gap eccentricity and broken rotor bars. The challenge is therefore to detect them at an early stage in order to prevent breakdowns.

Transmission of wavelet-compressed still images using FFT-OFDM over multipath power line communication (PLC) channels under additive white Gaussian noise (AWGN) and impulsive noise is addressed. Only the low-subbands coefficients were chosen to be transmitted from each decomposition level for compression, rather than using a global threshold for the compression of the wavelet coefficients. A study of the number of paths and length of link have been carried out to observe their effect on the quality of image via structural similarity quality assessment.

This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the Electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained devices, thus the power consumption and complexity of the processing algorithms should remain at a minimum level. Under this context, we choose to employ the Discrete Wavelet Transform (DWT) with the Haar function being the mother wavelet, as our principal analysis method. From the modulus-maxima analysis on the DWT coefficients, an approximation of the ECG fiducial points is extracted. These initial findings are complimented with a refinement stage, based on the time-domain morphological properties of the ECG, which alleviates the decreased temporal resolution of the DWT. The resulting algorithm is a hybrid scheme of time and frequency domain signal processing. Feature extraction results fr...

Reverberation and multi-path reflection artifacts are a common problem in ultrasound imaging. We propose a novel method to remove these artifacts. Regions adversely affected by these artifacts are replaced with textures that resemble the underlying object(s), which were originally obscured. Our proposed method incorporates optimally soft thresholding the 2D discrete wavelet transform of the artifact regions to produce a near optimal estimate of the reflectivity values due only to the reverberation and multi-path reflection artifacts. Simply subtracting this estimate from the original reflectivity values, we attain a near optimal estimate of the artifact free reflectivity values. We provide simulated and B mode images to substantiate the benefits of this method in producing an artifact removed image.

Pixel-level image fusion (PLIF) performance assessment includes information theory, feature-based, structural similarity, and perception-based objective metrics. However, to relate these metrics to human understanding requires subjective metrics. This paper proposes to use statistical analyses to assess PLIF performance over objective and subjective metrics. Nonparametric tests are applied to the subjective and objective assessment data from three multi-resolution image fusion methods using visual and infrared images. The tests can offer the performance information about the fusion algorithm at a designated significance level. Statistical analysis of PLIF facilitates the establishment of a baseline for the research in image fusion and serves as a statistical validation for proposing, comparing, and adopting a new PLIF algorithm.

. This paper presents the development of a segmentation algorithm for ECG signals, using simultaneously discrete and continuous wavelet transform. Here, it has been proposed the use of the discrete transform in order to make the pre-processing, signal filtering and detection of QRS complex; also the continuous transform to correct R and S wave detection as well as for the T wave, enabling the characteristics components identification in ECG signal. The algorithm was implemented in MATLAB® software and the QT Database was used for validation, considering the detection and delineation in signals with small or negative polarity QRS complex; and inverted T waves or with depression or elevation in the ST segment. For QRS complex it was found a sensibility Se=99,8% and a positive predictivity of P+=99,8%; and for the T wave a sensibility of Se=97,6 and a positive predictivity of P+=97,4%. The results show that the algorithm can be applied in signals with different morphologies even with low Signal to noise ratio and baseline.

Nowadays industry pays much attention to prevent failures that may interrupt production with severe consequences in cost, product quality, and safety. The most-analyzed parameters for monitoring dynamic characteristics and ensuring correct functioning of systems are electric current, voltage, and vibrations. System-on-chip (SoC) design is an approach to increase performance and overcome costs during equipment monitoring. This work presents the design and implementation of a low-cost SoC design that utilizes reconfigurable hardware and a customized embedded processor for time-frequency analysis on industrial equipment through short-time Fourier transform and discrete wavelet transform. Three study cases (electric current supply to an induction motor during startup transient, voltage supply to an induction motor through a variable speed drive, and vibration signals from industrial-robot links) show the suitability of the proposed monitoring system for time-frequency analysis of different signals in distinct industrial applications, and early diagnosis and prognosis of abnormalities in monitored systems.

In this paper, a design of a dual symbol processor for arithmetic coder architecture implemented on FPGA is proposed. Usually, the regular operation of the MQ-Coder is sequential, which can process only one symbol at a time. However, the bit-plane coding can generate more than one symbol per clock cycle. Consequently, the coding speed will be limited and bottlenecked at the interface between the output of the bit-plane coding and the input of the MQ-Coder. Therefore, the proposed MQ-Coder architecture is designed to process two symbols for each clock cycle. The success comes from the proposed prediction process of the upper bound value and the index value. As a result, the proposed arithmetic coder architecture can process with the speed greater than 100 MHz with the throughput greater than 60 Msymbols/sec.

In this paper, a new segmentation technique for multivalued images is elaborated. The technique accesses multiscale edge information of a multivalued image by a concept, called multiscale fundamental form. At different scales, an edge map of the multivalued images is obtained, at which a watershed-based algorithm is applied. The multiscale behavior of the obtained watershed regions is employed to conduct a region merging procedure. In order to remove noise or local texture, prior to segmentation an anisotropic diffusion filter is applied, also making use of the multiscale fundamental forms. In this way, the entire procedure is applied using multivalued processing.

DNA micro arrays are useful to identify the function and regulation of a large number of genes in a single experiment, even whole genomes. In this work, we analyze the relationship between DNA micro array image histograms and the compression performance of loss less JPEG2000. Also, a reversible transform based on histogram swapping is proposed. Intensive experimental results using different coding parameters are discussed. Results suggest that this transform improves previous loss less JPEG2000 results on all DNA micro array image sets.

Sudden Cardiac Death (SCD) is an unexpected death caused by loss of heart function when the electrical impulses fired from the ventricles become irregular. Most common SCDs are caused by cardiac arrhythmias and coronary heart disease. They are mainly due to Acute Myocardial Infarction (AMI), myocardial ischaemia and cardiac arrhythmia. This paper aims at automating the recognition of ST-segment deviations and transient ST episodes which helps in the diagnosis of myocardial ischaemia and also classifying major cardiac arrhythmia. Our approach is based on the application of signal processing and artificial intelligence to the heart signal known as the ECG (Electrocardiogram). We propose an improved morphological feature vector including ST-segment information for heart beat classification by supervised learning using the support vector machine approach. Our system has been tested and yielded an accuracy of 93.33% for the ST episode detection on the European ST-T Database and 96.35% on MIT-BIH Arrhythmia Database for classifying six major groups, i.e. Normal, Ventricular, Atrial, Fusion, Right Bundle and Left Bundle Branch Block beats.

Two wavelet transformations are used for speckle reduction and enhancement of synthetic aperture radar (SAR) images. First, a discrete wavelet transformation (DWT) based on oversampled filter banks is used. The oversampled DWT is called a double-density DWT (DD-DWT) and is based on a single-scaling function (low pass) and two distinct wavelet functions (high pass). Second, a discrete wavelet transformation based

In this paper we present a new approach to ltering and reconstruction of periodic signals. The tool that proves to handle these tasks very e ciently is the discrete Zak transform. The discrete Zak transform can be viewed as the discrete Fourier transform performed on the signal blocks. It also can be considered the polyphase representation of periodic signals. Fast ltering-decimation-interpolation-reconstruction algorithms are developed in the Zak Transform domain both for the undersampling and critical sampling cases. The technique of nding the optimal biorthogonal lter banks, i.e. those that would provide the best reconstruction even in the undersampling case, is presented. An algorithm for orthogonalization of non-orthogonal lters is developed. The condition for perfect reconstruction for the periodic signals is derived. The generalizations are made for the non-periodic sequences and several ways to apply the developed technique to the nonperiodic sequences are considered. Finally the developed technique is applied to recursive lter banks and the discrete wavelet decomposition.

The information security has become one of the most significant problems in data communication. So it becomes an inseparable part of data communication. In order to address this problem, cryptography and steganography can be combined. This paper proposes a secure communication system. It employs cryptographic algorithm together with steganography. The jointing of these techniques provides a robust and strong communication system that able to withstand against attackers. In this paper, the filter bank cipher is used to encrypt the secret text message, it provide high level of security, scalability and speed. After that, a discrete wavelet transforms (DWT) based steganography is employed to hide the encrypted message in the cover image by modifying the wavelet coefficients. The performance of the proposed system is evaluated using peak signal to noise ratio (PSNR) and histogram analysis. The simulation results show that, the proposed system provides high level of security.

Index Terms -De-noising, ensemble empirical mode decomposition (EEMD), high voltage (HV) equipment, mathematical morphology, partial discharge (PD), wavelet transform (WT). worked for a number of projects on machine fault diagnosis and energy conservation for 3 years. His research interests include signal processing, partial discharge, and condition monitoring of power systems.

In this paper, we propose an architecture that performs the forward and inverse discrete wavelet transform (DWT) using a lifting-based scheme for the set of seven filters proposed in JPEG2000. The architecture consists of two row processors, two column processors, and two memory modules. Each processor contains two adders, one multiplier, and one shifter. The precision of the multipliers and adders has been determined using extensive simulation. Each memory module consists of four banks in order to support the high computational bandwidth. The architecture has been designed to generate an output every cycle for the JPEG2000 default filters. The schedules have been generated by hand and the corresponding timings listed. Finally, the architecture has been implemented in behavioral VHDL. The estimated area of the proposed architecture in 0.18-technology is 2.8 mm square, and the estimated frequency of operation is 200 Mhz.

A filter-bank modulation technique called filtered multitone (FMT) and its application to data transmission for very high-speed digital subscriber line technology are described. The proposed scheme leads to significantly lower spectral overlapping between adjacent subchannels than for known multicarrier techniques such as discrete multitone (DMT) or discrete wavelet multitone. FMT modulation mitigates interference due to echo and near-end crosstalk signals, and increases the system throughput and reach. Signal equalization in an FMT receiver is accomplished in the form of per-subchannel symbol-spaced or fractionally spaced linear or decision-feedback equalization. The problem of channel coding for this type of modulation is also addressed, and an approach that allows combined removal of intersymbol-interference via precoding and trellis coding is described. Furthermore, practical design aspects regarding filter-bank realization, initial transceiver training, adaptive equalization, and timing recovery are discussed. Finally, simulation results of the performance achieved by FMT modulation for very high-speed digital subscriber line systems, where upstream and downstream signals are separated by frequency-division duplexing, are presented and compared with DMT modulation

Application of wavelet transform technique to high impedance arcing fault detection in distribution networks is presented. Phase displacement between discrete wavelet coefficients calculated for zero sequence voltage and current signals at natural network frequency is tracked. The developed wavelet based HIF detector has been tested with EMTP-ATP generated signals, proving better performance than traditionally used algorithms and methods. The protection method proposed is efficient, robust and may be used for HIF detection independently of the network neutral point grounding mode.

Partial discharge (PD) source classification aims to identify the types of defects causing discharges in high voltage (HV) equipment. This paper presents a comprehensive study of applying pattern recognition techniques to automatic PD source classification. Three challenging issues are investigated in this paper. The first issue is the feature extraction for obtaining representative attributes from the original PD measurement data. Several approaches including stochastic neighbour embedding (SNE), principle component analysis (PCA), kernel principle component analysis (KPCA), discrete wavelet transform (DWT), and conventional statistic operators are adopted for feature extraction. The second issue is the pattern recognition algorithms for identifying various types of PD sources. A novel fuzzy support vector machine (FSVM) and a variety of artificial neural networks (ANNs) are applied in the paper. The third issue is the identification of multiple PD sources, which may occur in HV equipment simultaneously. Two approaches are proposed to address this issue. To evaluate the performance of various algorithms in this paper, extensive laboratory experiments on a number of artificial PD models are conducted. The classification results reveal that FSVM significantly outperforms a number of ANN algorithms. The practical PD sources classification for HV equipment is a considerable complicated problem. Therefore, this paper also discusses some issues of meaningful application of the above proposed pattern recognition techniques for practical PD sources classification of HV equipment. Index Terms -Partial discharge (PD), PD source classification, artificial neural network (ANN), fuzzy support vector machine (FSVM), and pattern recognition. Hui Ma (M'95) received his B.Eng. and M.Eng. degrees from Xi'an Jiaotong University, China in 1991 and 1994, M.Eng.