Papers by Keyword: Discrete Wavelet Transform

Abstract: Digital distance relaying is implemented in extra high voltage (EHV) transmission network for speedy and reliable fault detection. But it should not activate in case of slow transients known as power swings. However, for occurrence of any type of fault during power swing, the relay should sense the faulty condition and send trip command to concerned circuit breaker. The detection of fault in a transmission line network with TCSC during the power swing condition has become further complex due to transients generated by series capacitor and the metal–oxide varistor (MOV) protecting it. This paper firstly presents effect of TCSC on distance relay operation for varied TCSC degree of compensation and firing angle values. Further, a discrete wavelet transform-based fast acting fault and swing classification algorithm is proposed which can sense all types of faults during slow as well as fast power swing in first decomposition level and within 0.001 sec. The proposed utilizes optimized threshold values both for swing and fault sensing by using Honey Bee Optimization Algorithm (HBOA). The proposed novel algorithm is coded in MATLAB software and the test system comprising of 400kV, 50Hz parallel transmission line network along with TCSC is built using MATLAB Simulink environment with sim power systems toolbox. It is tested for high/low resistance faults, symmetrical/asymmetrical faults and close-in/far end faults by changing TCSC compensation level and firing angle value. The simulation results prove the accuracy of proposed methodology for swing and fault classification.

Abstract: Automatic sleep scoring systems have being much more attention in last decades. Whereas a wide variety of studies have been used in this subject area, the accuracies are still under acceptable limits to apply these methods in real life data. One can find many high accuracy studies in literature using standard database but when it comes to the using real data reaching such a high performances is not straightforward. In this study, five distinct datasets were prepared using 124 persons including 93 unhealthy and 31 healthy persons. These datasets consist of time-, nonlinear-, welch-, discrete wavelet transform-and Hilbert-Huang transform-features. By applying k-NN, Decision Trees, ANN, SVM and Bagged Tree classifiers to these feature sets in various manners by using feature-selection highest classification accuracy was searched. The maximum classification accuracy was detected in case of Bagged Tree classifier as 95.06% with the use of 14 features among a total of 136 features. This accuracy is relatively high compared with literature for a real-data application.

Abstract: The wavelet transform is an accurate, efficient and efficacious method to improve the quality of the myoelectric signal. Classification of the signal from upper limb using Surface Electromyogram (SEMG) signal has been the matter of extensive research. Number of methods and algorithms have been described by researchers to classify biomedical signals. The main aim of this paper to extract the different coefficient values from the given SEMG data by using Discrete Wavelet Transform (DWT). Afterward, random forest machine learning algorithm was used to identify the different shoulder movement of an upper limb amputee. The combination of wavelet coefficients and random forest exhibited the best performance with 99.2% accuracy for the classification of different shoulder motions. It was found that the different motion can be identified accurately and provide the fundamental information to develop an efficient prosthetic device.

Abstract: In this paper, a robust watermarking technique which combines features of Discrete Wavelet Transformation (DWT) and Discrete Cosine Transformation (DCT) is proposed. Firstly the original color image is converted from RGB space to Lab space. In this technique, DWT is used to decompose the luminance coefficients into various frequency and time scale. The block DCT is applied on DWT coefficients of middle frequency to provide high level of robustness. The encrypted watermark is embedded in it by comparison of the intermediate frequency coefficients. Then the color image is converted from Lab space to RGB space to get the watermarked image. Extracting the watermark does not need the original image and the original watermark, it is a blind watermarking method. Finally, the watermarked image is attacked. Peak signal-to-noise ratio (PSNR) value and the normalized correlation (NC) coefficient value are used to evaluate this technique. Simulation experiments show that the watermark is perceptually invisible, this method can achieve the highest possible robustness without losing the transparency.

Abstract: Digital watermarking technology has become an important means of integrity and respect for people's authenticity, as well as users of copyright and intellectual property security and other interests such as the protection of digital works. In this paper, we proposed a watermarking algorithm based on discrete wavelet transform (DWT) of image and singular valued composition (SVD). The original image is decomposed with DWT,the watermarking image is decomposed with SVD after chaotic scrambling,and then the singular values of watermarking are embedded into some coefficients of decomposed original image. In this algorithm, after decomposing the original host image into four bands, we apply the SVD to watermark image,and modify DWT coefficients of the host image with the singular values of the watermark image. The outstanding features of the proposed algorithm are that it provides larger watermarking capacity and is more robust than others.

Abstract: In such a developed day of information communication, communication is an important essential way of interpersonal communication. As a carrier of information, expression is rich in human behavior information. Facial expression recognition is a combination of many fields, but also a new topic in the field of pattern recognition. This paper mainly studied the facial feature extraction based on MATLAB, by MATLAB software, extracting the expression features through a large number of facial expressions, which can be divided into different facial expressions more accurate classification .

Abstract: In order to install the wave power facilities in ocean and coastal area, it is very important to determine the properties of wave data. Discrete wavelet packet transform was applied in this study and was used as a tool to find out the basic properties of waves around Namae coast. Important features of hydrodynamic pressure such as frequency and magnitude were investigated in different observation time. Also the idea of measuring the noise rate was introduced and applied to both stationary and non-stationary time spans for the comparison. These methods would be useful to check the feasibility of wave energy extraction in various types of coasts.

Abstract: In this paper, we propose a new super-resolution algorithm based on wavelet coefficient. The proposed algorithm uses discrete wavelet transform (DWT) to decompose the input low-resolution image sequences into four subband images, including LL, LH, HL, HH. Then the input images have been processed by the 3DSKR (Three Dimensional Steering Kernel Regression) super resolution (SR) algorithm, and the result replaces the LL subband image, while the three high-frequency subband images have been interpolated. Finally, combining all these images to generate a new high-resolution image by using inverse DWT. Proposed method has been verified on Calendar and Foliage by Matlab software platform. The peak signal-to-noise (PSNR), structural similarity (SSIM) and visual results are compared, and show that the computational complexity of the proposed algorithm decline by 30 percent compared with the existing algorithm to obtain the approximate results.

Abstract: An Image Resolution Enhancement Technique based on Interpolation of the high frequency sub-band of colour images obtained by Discrete Wavelet Transform and the input colour image is proposed in this paper. Interpolation determines the intermediate values on the basis of observed values. One of the commonly used interpolation technique is Bicubic Interpolation. The edges are enhanced by introducing an intermediate stage by using Stationary Wavelet Transform. It is designed to overcome the lack of Translation-Invariance of Discrete Wavelet Transform. This is widely used in Signal Denoising and Pattern Recognition. Discrete Wavelet Transform is applied in order to decompose an input colour image into different sub-bands. Then the high frequency sub-bands as well as the input colour image are interpolated separately. The interpolated high frequency sub-bands and the Stationary Wavelet Transform high frequency sub-bands have the same size which means they can be added with each other. The new corrected high frequency sub-bands can be interpolated further for higher enlargement. Then all these sub-bands are combined with interpolated input image for new high resolution image by using Inverse Discrete Wavelet Transform. This has been done by MATLAB. The Peak Signal-Noise Ratio was obtained upto 5dB greater than the conventional and state-of-art image resolution enhancement techniques.

Abstract: In today's social life, communication between people is essential to do anything important way. The facial expressions are rich in human behavior information, it is very important means of communication, as a carrier of information, expression can convey a lot of voice can not convey the information. Expression recognition field of pattern recognition is a new task, is an essential part of intelligent machines. This paper studies the discrete wavelet transform feature extraction and expression using MATLAB software image feature extraction and treatment with an elastic template matching algorithm to do the appropriate test expression recognition.