Search results for: Harmonic Wavelet Transform
1059 New Wavelet Indices to Assess Muscle Fatigue during Dynamic Contractions
Authors: González-Izal M., Rodríguez-Carreño I, Mallor-Giménez F, Malanda A, Izquierdo M
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The purpose of this study was to evaluate and compare new indices based on the discrete wavelet transform with another spectral parameters proposed in the literature as mean average voltage, median frequency and ratios between spectral moments applied to estimate acute exercise-induced changes in power output, i.e., to assess peripheral muscle fatigue during a dynamic fatiguing protocol. 15 trained subjects performed 5 sets consisting of 10 leg press, with 2 minutes rest between sets. Surface electromyography was recorded from vastus medialis (VM) muscle. Several surface electromyographic parameters were compared to detect peripheral muscle fatigue. These were: mean average voltage (MAV), median spectral frequency (Fmed), Dimitrov spectral index of muscle fatigue (FInsm5), as well as other five parameters obtained from the discrete wavelet transform (DWT) as ratios between different scales. The new wavelet indices achieved the best results in Pearson correlation coefficients with power output changes during acute dynamic contractions. Their regressions were significantly different from MAV and Fmed. On the other hand, they showed the highest robustness in presence of additive white gaussian noise for different signal to noise ratios (SNRs). Therefore, peripheral impairments assessed by sEMG wavelet indices may be a relevant factor involved in the loss of power output after dynamic high-loading fatiguing task.Keywords: Median Frequency, EMG, wavelet transform, muscle fatigue
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18571058 Discrete and Stationary Adaptive Sub-Band Threshold Method for Improving Image Resolution
Authors: P. Joyce Beryl Princess, Y. Harold Robinson
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Image Processing is a structure of Signal Processing for which the input is the image and the output is also an image or parameter of the image. Image Resolution has been frequently referred as an important aspect of an image. In Image Resolution Enhancement, images are being processed in order to obtain more enhanced resolution. To generate highly resoluted image for a low resoluted input image with high PSNR value. Stationary Wavelet Transform is used for Edge Detection and minimize the loss occurs during Downsampling. Inverse Discrete Wavelet Transform is to get highly resoluted image. Highly resoluted output is generated from the Low resolution input with high quality. Noisy input will generate output with low PSNR value. So Noisy resolution enhancement technique has been used for adaptive sub-band thresholding is used. Downsampling in each of the DWT subbands causes information loss in the respective subbands. SWT is employed to minimize this loss. Inverse Discrete wavelet transform (IDWT) is to convert the object which is downsampled using DWT into a highly resoluted object. Used Image denoising and resolution enhancement techniques will generate image with high PSNR value. Our Proposed method will improve Image Resolution and reached the optimized threshold.Keywords: Image Processing, Inverse Discrete wavelet transform, PSNR.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17801057 A Wavelet Based Object Watermarking System for Image and Video
Authors: Abdessamad Essaouabi, Ibnelhaj Elhassane
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Efficient storage, transmission and use of video information are key requirements in many multimedia applications currently being addressed by MPEG-4. To fulfill these requirements, a new approach for representing video information which relies on an object-based representation, has been adopted. Therefore, objectbased watermarking schemes are needed for copyright protection. This paper proposes a novel blind object watermarking scheme for images and video using the in place lifting shape adaptive-discrete wavelet transform (SA-DWT). In order to make the watermark robust and transparent, the watermark is embedded in the average of wavelet blocks using the visual model based on the human visual system. Wavelet coefficients n least significant bits (LSBs) are adjusted in concert with the average. Simulation results shows that the proposed watermarking scheme is perceptually invisible and robust against many attacks such as lossy image/video compression (e.g. JPEG, JPEG2000 and MPEG-4), scaling, adding noise, filtering, etc.
Keywords: Watermark, visual model, robustness, in place lifting shape adaptive-discrete wavelet transform.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18871056 A Robust Wavelet-Based Watermarking Algorithm Using Edge Detection
Authors: John N. Ellinas
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In this paper, a robust watermarking algorithm using the wavelet transform and edge detection is presented. The efficiency of an image watermarking technique depends on the preservation of visually significant information. This is attained by embedding the watermark transparently with the maximum possible strength. The watermark embedding process is carried over the subband coefficients that lie on edges, where distortions are less noticeable, with a subband level dependent strength. Also, the watermark is embedded to selected coefficients around edges, using a different scale factor for watermark strength, that are captured by a morphological dilation operation. The experimental evaluation of the proposed method shows very good results in terms of robustness and transparency to various attacks such as median filtering, Gaussian noise, JPEG compression and geometrical transformations.Keywords: Watermarking, wavelet transform, edge detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23361055 An Automatic Sleep Spindle Detector based on WT, STFT and WMSD
Authors: J. Costa, M. Ortigueira, A. Batista, T. Paiva
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Sleep spindles are the most interesting hallmark of stage 2 sleep EEG. Their accurate identification in a polysomnographic signal is essential for sleep professionals to help them mark Stage 2 sleep. Sleep Spindles are also promising objective indicators for neurodegenerative disorders. Visual spindle scoring however is a tedious workload. In this paper three different approaches are used for the automatic detection of sleep spindles: Short Time Fourier Transform, Wavelet Transform and Wave Morphology for Spindle Detection. In order to improve the results, a combination of the three detectors is presented and comparison with human expert scorers is performed. The best performance is obtained with a combination of the three algorithms which resulted in a sensitivity and specificity of 94% when compared to human expert scorers.Keywords: EEG, Short Time Fourier Transform, Sleep Spindles, Wave Morphology for Spindle Detection, Wavelet Transform.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23661054 Speech Intelligibility Improvement Using Variable Level Decomposition DWT
Authors: Samba Raju, Chiluveru, Manoj Tripathy
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Intelligibility is an essential characteristic of a speech signal, which is used to help in the understanding of information in speech signal. Background noise in the environment can deteriorate the intelligibility of a recorded speech. In this paper, we presented a simple variance subtracted - variable level discrete wavelet transform, which improve the intelligibility of speech. The proposed algorithm does not require an explicit estimation of noise, i.e., prior knowledge of the noise; hence, it is easy to implement, and it reduces the computational burden. The proposed algorithm decides a separate decomposition level for each frame based on signal dominant and dominant noise criteria. The performance of the proposed algorithm is evaluated with speech intelligibility measure (STOI), and results obtained are compared with Universal Discrete Wavelet Transform (DWT) thresholding and Minimum Mean Square Error (MMSE) methods. The experimental results revealed that the proposed scheme outperformed competing methodsKeywords: Discrete Wavelet Transform, speech intelligibility, STOI, standard deviation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 6821053 Design Techniques and Implementation of Low Power High-Throughput Discrete Wavelet Transform Tilters for JPEG 2000 Standard
Authors: Grigorios D. Dimitroulakos, N. D. Zervas, N. Sklavos, Costas E. Goutis
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In this paper, the implementation of low power, high throughput convolutional filters for the one dimensional Discrete Wavelet Transform and its inverse are presented. The analysis filters have already been used for the implementation of a high performance DWT encoder [15] with minimum memory requirements for the JPEG 2000 standard. This paper presents the design techniques and the implementation of the convolutional filters included in the JPEG2000 standard for the forward and inverse DWT for achieving low-power operation, high performance and reduced memory accesses. Moreover, they have the ability of performing progressive computations so as to minimize the buffering between the decomposition and reconstruction phases. The experimental results illustrate the filters- low power high throughput characteristics as well as their memory efficient operation.Keywords: Discrete Wavelet Transform; JPEG2000 standard; VLSI design; Low Power-Throughput-optimized filters
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12711052 Data Hiding in Images in Discrete Wavelet Domain Using PMM
Authors: Souvik Bhattacharyya, Gautam Sanyal
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Over last two decades, due to hostilities of environment over the internet the concerns about confidentiality of information have increased at phenomenal rate. Therefore to safeguard the information from attacks, number of data/information hiding methods have evolved mostly in spatial and transformation domain.In spatial domain data hiding techniques,the information is embedded directly on the image plane itself. In transform domain data hiding techniques the image is first changed from spatial domain to some other domain and then the secret information is embedded so that the secret information remains more secure from any attack. Information hiding algorithms in time domain or spatial domain have high capacity and relatively lower robustness. In contrast, the algorithms in transform domain, such as DCT, DWT have certain robustness against some multimedia processing.In this work the authors propose a novel steganographic method for hiding information in the transform domain of the gray scale image.The proposed approach works by converting the gray level image in transform domain using discrete integer wavelet technique through lifting scheme.This approach performs a 2-D lifting wavelet decomposition through Haar lifted wavelet of the cover image and computes the approximation coefficients matrix CA and detail coefficients matrices CH, CV, and CD.Next step is to apply the PMM technique in those coefficients to form the stego image. The aim of this paper is to propose a high-capacity image steganography technique that uses pixel mapping method in integer wavelet domain with acceptable levels of imperceptibility and distortion in the cover image and high level of overall security. This solution is independent of the nature of the data to be hidden and produces a stego image with minimum degradation.Keywords: Cover Image, Pixel Mapping Method (PMM), StegoImage, Integer Wavelet Tranform.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 28331051 EEG Spikes Detection, Sorting, and Localization
Authors: Mazin Z. Othman, Maan M. Shaker, Mohammed F. Abdullah
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This study introduces a new method for detecting, sorting, and localizing spikes from multiunit EEG recordings. The method combines the wavelet transform, which localizes distinctive spike features, with Super-Paramagnetic Clustering (SPC) algorithm, which allows automatic classification of the data without assumptions such as low variance or Gaussian distributions. Moreover, the method is capable of setting amplitude thresholds for spike detection. The method makes use of several real EEG data sets, and accordingly the spikes are detected, clustered and their times were detected.Keywords: EEG time localizations, EEG spike detection, superparamagnetic algorithm, wavelet transform.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 25421050 A Comparative Study of SVM Classifiers and Artificial Neural Networks Application for Rolling Element Bearing Fault Diagnosis using Wavelet Transform Preprocessing
Authors: Commander Sunil Tyagi
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Effectiveness of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) classifiers for fault diagnosis of rolling element bearings are presented in this paper. The characteristic features of vibration signals of rotating driveline that was run in its normal condition and with faults introduced were used as input to ANN and SVM classifiers. Simple statistical features such as standard deviation, skewness, kurtosis etc. of the time-domain vibration signal segments along with peaks of the signal and peak of power spectral density (PSD) are used as features to input the ANN and SVM classifier. The effect of preprocessing of the vibration signal by Discreet Wavelet Transform (DWT) prior to feature extraction is also studied. It is shown from the experimental results that the performance of SVM classifier in identification of bearing condition is better then ANN and pre-processing of vibration signal by DWT enhances the effectiveness of both ANN and SVM classifierKeywords: ANN, Artificial Intelligence, Fault Diagnosis, Pattern Recognition, Rolling Element Bearing, SVM. Wavelet Transform
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20991049 Hidden State Probabilistic Modeling for Complex Wavelet Based Image Registration
Authors: F. C. Calnegru
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This article presents a computationally tractable probabilistic model for the relation between the complex wavelet coefficients of two images of the same scene. The two images are acquisitioned at distinct moments of times, or from distinct viewpoints, or by distinct sensors. By means of the introduced probabilistic model, we argue that the similarity between the two images is controlled not by the values of the wavelet coefficients, which can be altered by many factors, but by the nature of the wavelet coefficients, that we model with the help of hidden state variables. We integrate this probabilistic framework in the construction of a new image registration algorithm. This algorithm has sub-pixel accuracy and is robust to noise and to other variations like local illumination changes. We present the performance of our algorithm on various image types.
Keywords: Complex wavelet transform, image registration, modeling using hidden state variables, probabilistic similaritymeasure.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13681048 A Multiresolution Approach for Noised Texture Classification based on the Co-occurrence Matrix and First Order Statistics
Authors: M. Ben Othmen, M. Sayadi, F. Fnaiech
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Wavelet transform provides several important characteristics which can be used in a texture analysis and classification. In this work, an efficient texture classification method, which combines concepts from wavelet and co-occurrence matrices, is presented. An Euclidian distance classifier is used to evaluate the various methods of classification. A comparative study is essential to determine the ideal method. Using this conjecture, we developed a novel feature set for texture classification and demonstrate its effectivenessKeywords: Classification, Wavelet, Co-occurrence, Euclidian Distance, Classifier, Texture.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14731047 Speaker Identification Using Admissible Wavelet Packet Based Decomposition
Authors: Mangesh S. Deshpande, Raghunath S. Holambe
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Mel Frequency Cepstral Coefficient (MFCC) features are widely used as acoustic features for speech recognition as well as speaker recognition. In MFCC feature representation, the Mel frequency scale is used to get a high resolution in low frequency region, and a low resolution in high frequency region. This kind of processing is good for obtaining stable phonetic information, but not suitable for speaker features that are located in high frequency regions. The speaker individual information, which is non-uniformly distributed in the high frequencies, is equally important for speaker recognition. Based on this fact we proposed an admissible wavelet packet based filter structure for speaker identification. Multiresolution capabilities of wavelet packet transform are used to derive the new features. The proposed scheme differs from previous wavelet based works, mainly in designing the filter structure. Unlike others, the proposed filter structure does not follow Mel scale. The closed-set speaker identification experiments performed on the TIMIT database shows improved identification performance compared to other commonly used Mel scale based filter structures using wavelets.Keywords: Speaker identification, Wavelet transform, Feature extraction, MFCC, GMM.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19741046 Evaluation of Wavelet Filters for Image Compression
Authors: G. Sadashivappa, K. V. S. AnandaBabu
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The aim of this paper to characterize a larger set of wavelet functions for implementation in a still image compression system using SPIHT algorithm. This paper discusses important features of wavelet functions and filters used in sub band coding to convert image into wavelet coefficients in MATLAB. Image quality is measured objectively using peak signal to noise ratio (PSNR) and its variation with bit rate (bpp). The effect of different parameters is studied on different wavelet functions. Our results provide a good reference for application designers of wavelet based coder.Keywords: Wavelet, image compression, sub band, SPIHT, PSNR.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22131045 Classification of Acoustic Emission Based Partial Discharge in Oil Pressboard Insulation System Using Wavelet Analysis
Authors: Prasanta Kundu, N.K. Kishore, A.K. Sinha
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Insulation used in transformer is mostly oil pressboard insulation. Insulation failure is one of the major causes of catastrophic failure of transformers. It is established that partial discharges (PD) cause insulation degradation and premature failure of insulation. Online monitoring of PDs can reduce the risk of catastrophic failure of transformers. There are different techniques of partial discharge measurement like, electrical, optical, acoustic, opto-acoustic and ultra high frequency (UHF). Being non invasive and non interference prone, acoustic emission technique is advantageous for online PD measurement. Acoustic detection of p.d. is based on the retrieval and analysis of mechanical or pressure signals produced by partial discharges. Partial discharges are classified according to the origin of discharges. Their effects on insulation deterioration are different for different types. This paper reports experimental results and analysis for classification of partial discharges using acoustic emission signal of laboratory simulated partial discharges in oil pressboard insulation system using three different electrode systems. Acoustic emission signal produced by PD are detected by sensors mounted on the experimental tank surface, stored on an oscilloscope and fed to computer for further analysis. The measured AE signals are analyzed using discrete wavelet transform analysis and wavelet packet analysis. Energy distribution in different frequency bands of discrete wavelet decomposed signal and wavelet packet decomposed signal is calculated. These analyses show a distinct feature useful for PD classification. Wavelet packet analysis can sort out any misclassification arising out of DWT in most cases.
Keywords: Acoustic emission, discrete wavelet transform, partial discharge, wavelet packet analysis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 29731044 Discrete Wavelet Transform Decomposition Level Determination Exploiting Sparseness Measurement
Authors: Lei Lei, Chao Wang, Xin Liu
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Discrete wavelet transform (DWT) has been widely adopted in biomedical signal processing for denoising, compression and so on. Choosing a suitable decomposition level (DL) in DWT is of paramount importance to its performance. In this paper, we propose to exploit sparseness of the transformed signals to determine the appropriate DL. Simulation results have shown that the sparseness of transformed signals after DWT increases with the increasing DLs. Additional Monte-Carlo simulation results have verified the effectiveness of sparseness measure in determining the DL.
Keywords: Sparseness, DWT, decomposition level, ECG.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 58581043 ANFIS Approach for Locating Faults in Underground Cables
Authors: Magdy B. Eteiba, Wael Ismael Wahba, Shimaa Barakat
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This paper presents a fault identification, classification and fault location estimation method based on Discrete Wavelet Transform and Adaptive Network Fuzzy Inference System (ANFIS) for medium voltage cable in the distribution system.
Different faults and locations are simulated by ATP/EMTP, and then certain selected features of the wavelet transformed signals are used as an input for a training process on the ANFIS. Then an accurate fault classifier and locator algorithm was designed, trained and tested using current samples only. The results obtained from ANFIS output were compared with the real output. From the results, it was found that the percentage error between ANFIS output and real output is less than three percent. Hence, it can be concluded that the proposed technique is able to offer high accuracy in both of the fault classification and fault location.
Keywords: ANFIS, Fault location, Underground Cable, Wavelet Transform.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 27331042 A Novel Compression Algorithm for Electrocardiogram Signals based on Wavelet Transform and SPIHT
Authors: Sana Ktata, Kaïs Ouni, Noureddine Ellouze
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Electrocardiogram (ECG) data compression algorithm is needed that will reduce the amount of data to be transmitted, stored and analyzed, but without losing the clinical information content. A wavelet ECG data codec based on the Set Partitioning In Hierarchical Trees (SPIHT) compression algorithm is proposed in this paper. The SPIHT algorithm has achieved notable success in still image coding. We modified the algorithm for the one-dimensional (1-D) case and applied it to compression of ECG data. By this compression method, small percent root mean square difference (PRD) and high compression ratio with low implementation complexity are achieved. Experiments on selected records from the MIT-BIH arrhythmia database revealed that the proposed codec is significantly more efficient in compression and in computation than previously proposed ECG compression schemes. Compression ratios of up to 48:1 for ECG signals lead to acceptable results for visual inspection.Keywords: Discrete Wavelet Transform, ECG compression, SPIHT.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21191041 Fingerprint Compression Using Contourlet Transform and Multistage Vector Quantization
Authors: S. Esakkirajan, T. Veerakumar, V. Senthil Murugan, R. Sudhakar
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This paper presents a new fingerprint coding technique based on contourlet transform and multistage vector quantization. Wavelets have shown their ability in representing natural images that contain smooth areas separated with edges. However, wavelets cannot efficiently take advantage of the fact that the edges usually found in fingerprints are smooth curves. This issue is addressed by directional transforms, known as contourlets, which have the property of preserving edges. The contourlet transform is a new extension to the wavelet transform in two dimensions using nonseparable and directional filter banks. The computation and storage requirements are the major difficulty in implementing a vector quantizer. In the full-search algorithm, the computation and storage complexity is an exponential function of the number of bits used in quantizing each frame of spectral information. The storage requirement in multistage vector quantization is less when compared to full search vector quantization. The coefficients of contourlet transform are quantized by multistage vector quantization. The quantized coefficients are encoded by Huffman coding. The results obtained are tabulated and compared with the existing wavelet based ones.Keywords: Contourlet Transform, Directional Filter bank, Laplacian Pyramid, Multistage Vector Quantization
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20031040 Statistical Wavelet Features, PCA, and SVM Based Approach for EEG Signals Classification
Authors: R. K. Chaurasiya, N. D. Londhe, S. Ghosh
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The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. In this paper, we propose an automatic and efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. In the proposed approach, we start with extracting the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the supportvectors using Support Vector Machine (SVM). The experimental are performed on real and standard dataset. A very high level of classification accuracy is obtained in the result of classification.
Keywords: Discrete Wavelet Transform, Electroencephalogram, Pattern Recognition, Principal Component Analysis, Support Vector Machine.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 31021039 Spectral Entropy Employment in Speech Enhancement based on Wavelet Packet
Authors: Talbi Mourad, Salhi Lotfi, Chérif Adnen
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In this work, we are interested in developing a speech denoising tool by using a discrete wavelet packet transform (DWPT). This speech denoising tool will be employed for applications of recognition, coding and synthesis. For noise reduction, instead of applying the classical thresholding technique, some wavelet packet nodes are set to zero and the others are thresholded. To estimate the non stationary noise level, we employ the spectral entropy. A comparison of our proposed technique to classical denoising methods based on thresholding and spectral subtraction is made in order to evaluate our approach. The experimental implementation uses speech signals corrupted by two sorts of noise, white and Volvo noises. The obtained results from listening tests show that our proposed technique is better than spectral subtraction. The obtained results from SNR computation show the superiority of our technique when compared to the classical thresholding method using the modified hard thresholding function based on u-law algorithm.
Keywords: Enhancement, spectral subtraction, SNR, discrete wavelet packet transform, spectral entropy Histogram
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19791038 Digital Cinema Watermarking State of Art and Comparison
Authors: H. Kelkoul, Y. Zaz
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Nowadays, the vigorous popularity of video processing techniques has resulted in an explosive growth of multimedia data illegal use. So, watermarking security has received much more attention. The purpose of this paper is to explore some watermarking techniques in order to observe their specificities and select the finest methods to apply in digital cinema domain against movie piracy by creating an invisible watermark that includes the date, time and the place where the hacking was done. We have studied three principal watermarking techniques in the frequency domain: Spread spectrum, Wavelet transform domain and finally the digital cinema watermarking transform domain. In this paper, a detailed technique is presented where embedding is performed using direct sequence spread spectrum technique in DWT transform domain. Experiment results shows that the algorithm provides high robustness and good imperceptibility.Keywords: Digital cinema, watermarking, wavelet, spread spectrum, JPEG2000.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 11751037 Glass Bottle Inspector Based on Machine Vision
Authors: Huanjun Liu, Yaonan Wang, Feng Duan
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This text studies glass bottle intelligent inspector based machine vision instead of manual inspection. The system structure is illustrated in detail in this paper. The text presents the method based on watershed transform methods to segment the possible defective regions and extract features of bottle wall by rules. Then wavelet transform are used to exact features of bottle finish from images. After extracting features, the fuzzy support vector machine ensemble is putted forward as classifier. For ensuring that the fuzzy support vector machines have good classification ability, the GA based ensemble method is used to combining the several fuzzy support vector machines. The experiments demonstrate that using this inspector to inspect glass bottles, the accuracy rate may reach above 97.5%.Keywords: Intelligent Inspection, Support Vector Machines, Ensemble Methods, watershed transform, Wavelet Transform
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 38761036 A Wavelet-Based Watermarking Method Exploiting the Contrast Sensitivity Function
Authors: John N. Ellinas, Panagiotis Kenterlis
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The efficiency of an image watermarking technique depends on the preservation of visually significant information. This is attained by embedding the watermark transparently with the maximum possible strength. The current paper presents an approach for still image digital watermarking in which the watermark embedding process employs the wavelet transform and incorporates Human Visual System (HVS) characteristics. The sensitivity of a human observer to contrast with respect to spatial frequency is described by the Contrast Sensitivity Function (CSF). The strength of the watermark within the decomposition subbands, which occupy an interval on the spatial frequencies, is adjusted according to this sensitivity. Moreover, the watermark embedding process is carried over the subband coefficients that lie on edges where distortions are less noticeable. The experimental evaluation of the proposed method shows very good results in terms of robustness and transparency.
Keywords: Image watermarking, wavelet transform, human visual system, contrast sensitivity function.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20861035 A New Technique for Multi Resolution Characterization of Epileptic Spikes in EEG
Authors: H. N. Suresh, Dr. V. Udaya Shankara
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A technique proposed for the automatic detection of spikes in electroencephalograms (EEG). A multi-resolution approach and a non-linear energy operator are exploited. The signal on each EEG channel is decomposed into three sub bands using a non-decimated wavelet transform (WT). The WT is a powerful tool for multi-resolution analysis of non-stationary signal as well as for signal compression, recognition and restoration. Each sub band is analyzed by using a non-linear energy operator, in order to detect spikes. A decision rule detects the presence of spikes in the EEG, relying upon the energy of the three sub-bands. The effectiveness of the proposed technique was confirmed by analyzing both test signals and EEG layouts.Keywords: EEG, Spike, SNEO, Wavelet Transform
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13641034 Wavelet Based Identification of Second Order Linear System
Authors: Sudipta Majumdar, Harish Parthasarathy
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In this paper, a wavelet based method is proposed to identify the constant coefficients of a second order linear system and is compared with the least squares method. The proposed method shows improved accuracy of parameter estimation as compared to the least squares method. Additionally, it has the advantage of smaller data requirement and storage requirement as compared to the least squares method.Keywords: Least squares method, linear system, system identification, wavelet transform.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15641033 An Interlacing Technique-Based Blind Video Watermarking Using Wavelet
Authors: B. Sridhar, C. Arun
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The rapid growth of multimedia technology demands the secure and efficient access to information. This fast growing lose the confidence of unauthorized duplication. Henceforth the protection of multimedia content is becoming more important. Watermarking solves the issue of unlawful copy of advanced data. In this paper, blind video watermarking technique has been proposed. A luminance layer of selected frames is interlaced into two even and odd rows of an image, further it is deinterlaced and equalizes the coefficients of the two shares. Color watermark is split into different blocks, and the pieces of block are concealed in one of the share under the wavelet transform. Stack the two images into a single image by introducing interlaced even and odd rows in the two shares. Finally, chrominance bands are concatenated with the watermarked luminance band. The safeguard level of the secret information is high, and it is undetectable. Results show that the quality of the video is not changed also yields the better PSNR values.
Keywords: Authentication, data security, deinterlaced, wavelet transform, watermarking.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24671032 Wavelet and K-L Seperability Based Feature Extraction Method for Functional Data Classification
Authors: Jun Wan, Zehua Chen, Yingwu Chen, Zhidong Bai
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This paper proposes a novel feature extraction method, based on Discrete Wavelet Transform (DWT) and K-L Seperability (KLS), for the classification of Functional Data (FD). This method combines the decorrelation and reduction property of DWT and the additive independence property of KLS, which is helpful to extraction classification features of FD. It is an advanced approach of the popular wavelet based shrinkage method for functional data reduction and classification. A theory analysis is given in the paper to prove the consistent convergence property, and a simulation study is also done to compare the proposed method with the former shrinkage ones. The experiment results show that this method has advantages in improving classification efficiency, precision and robustness.Keywords: classification, functional data, feature extraction, K-Lseperability, wavelet.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14521031 An Advanced Method for Speech Recognition
Authors: Meysam Mohamad pour, Fardad Farokhi
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In this paper in consideration of each available techniques deficiencies for speech recognition, an advanced method is presented that-s able to classify speech signals with the high accuracy (98%) at the minimum time. In the presented method, first, the recorded signal is preprocessed that this section includes denoising with Mels Frequency Cepstral Analysis and feature extraction using discrete wavelet transform (DWT) coefficients; Then these features are fed to Multilayer Perceptron (MLP) network for classification. Finally, after training of neural network effective features are selected with UTA algorithm.Keywords: Multilayer perceptron (MLP) neural network, Discrete Wavelet Transform (DWT) , Mels Scale Frequency Filter , UTA algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23431030 Fault Detection of Pipeline in Water Distribution Network System
Authors: Shin Je Lee, Go Bong Choi, Jeong Cheol Seo, Jong Min Lee, Gibaek Lee
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Water pipe network is installed underground and once equipped, it is difficult to recognize the state of pipes when the leak or burst happens. Accordingly, post management is often delayed after the fault occurs. Therefore, the systematic fault management system of water pipe network is required to prevent the accident and minimize the loss. In this work, we develop online fault detection system of water pipe network using data of pipes such as flow rate or pressure. The transient model describing water flow in pipelines is presented and simulated using MATLAB. The fault situations such as the leak or burst can be also simulated and flow rate or pressure data when the fault happens are collected. Faults are detected using statistical methods of fast Fourier transform and discrete wavelet transform, and they are compared to find which method shows the better fault detection performance.Keywords: fault detection, water pipeline model, fast Fourier transform, discrete wavelet transform.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2330