Application of Wavelet & Multi-Resolution Analysis to Detection of the Time of PN Code Acquisition

In the field of CDMA system, DS-SS technology has been used widely. Thereby, a great deal research on acquisition method of PN code is based on DS-SS. In the traditional way, the power detection method of judgment is used widely. Based on the characteristic of PN code acquired signal (namely BPSK signal or QPSK signal) and characteristic of un acquired signal (namely white Gaussian noise), this paper introduces the wavelet detection method of PN code acquisition time. Meanwhile, the performance of wavelet threshold is also studied. In the end, the statistics of parameters in this detection method is made. The result indicates that the wavelet & multi resolution has practical value in the signal processing.     

Audio blind watermarking scheme combined DWT DCT QR

In order to protect the copyright of digital audio and video in Internet, we propose a novel audio blind watermarking scheme combined discrete wavelets transform, discrete cosine transform, QR decomposition and audio characteristics. In this algorithm, the audio are split into blocks, and each block are decomposed on two dimensional discrete wavelet transform (DWT), then the approximate sub band coefficients are decomposed on discrete cosine transform (DCT), and the first quarter of the DCT coefficients are decomposed on QR decomposition and get a triangle matrix. At last, the watermarking information is embedded into the triangle matrix. The experiments show that the algorithm can get better balance between transparency and robustness of watermark, and it has strong robustness against the common audio signal processing such as additive white Gaussian noise, re sampling, re quantization, low pass filter, MP3 compression and cutting replacement.     

QRS complexes detection using quadratic b-spline wavelet transform algorithm

Aiming at the low accuracy and low adaptability of wave detection, a QRS complexes detection algorithm is proposed based on quadratic b-spline wavelet, while combined with binary search algorithm and arc approximating curve algorithm. The signal is decomposed with quadratic b-spline wavelet through Mallat algorithm and the R wave is detected by adjusting the threshold with binary search and modulus maximumizing. The T wave and P wave are detected by using arc approximating curve algorithm based on the least square. This algorithm is certified with the ECG signals from MIT-BIH database and is demonstrated that the algorithm enhanced the adaptability of R wave detection and improved the accuracy of T wave and P wave detection. The simulation experiment shows that the improved algorithm can effectively improve the automatic detection capabilities of ECG signals.     

A floating threshold quantization algorithm to suppressmixing interferences in partial discharge

Due to the obvious difference of energy distribution frequencies from partial discharge (PD) signal and its mixing interferences (white noise and narrow brand), we uses the characteristic that node decomposition coefficients of wavelet packet transform can effectively show the energy change of signals to build up a floating threshold quantization algorithm (FTQA) varying with the noise energy of PD decomposition coefficients. It makes the node thresholds under the optimal base various with the noise strength in decomposition coefficients to self adaptively reality the choice of optimal threshold to finely partition PD decomposition coefficients. For simulated and real PD signals with mixing interferences, the conditional global threshold quantization algorithm (GTQA) and the proposed floating threshold quantization algorithm are employed to suppress the mixing interferences in PD signals and compared, and the results show that the proposed algorithm has the stronger suppression ability to mixing interference on PD signal and keeps perfect PD waveform via suppression.     

On line tool breakage monitoring method based on power information and cross-correlation algorithm

In order to reliably monitor unexpected tool failure and prevent workpiece or machine tool from possible damages in batch machining, a tool breakage on-line monitoring method based on power information and cross-correlation algorithm is proposed. In this method, wavelet coefficients of spindle-power signal are used as the characteristic vector of machining information, and then the vector sequence extracted from a normal machining process via Mallat wavelet is defined as the reference template for monitoring cutting tool condition. In batch machining, real-time characteristic vector of the workpiece in machining process is extracted via an improved real-time wavelet algorithm. The correlation of two vector sub-sequences within a sampling time window, which is described by generalized cross-correlation coefficient, decreases apparently when the tool is broken. The generalized cross-correlation coefficient is defined as tool condition index (TCI), and tool breakage can be detected by monitoring the TCI with a threshold value. Experiments show that the method can accurately identify tool breakage failures in normal machining condition, and thus it is practical.     

Wavelet Packet Decomposition Based on Entropy Threshold for White-noise Rejection in PD Signal

This paper analyzes the different characteristics of white-noise interference in the signals of partial discharge (PD) after wavelet transform. There is high value in lots of scales for PD and white-noise interference is to zero with increasing scale. The threshold is set for wavelet coefficient in all scales. If the coefficient of signal is higher than the threshold, it is PD signal. Otherwise it is noise interference. A threshold-based wavelet packet transform (WPT) algorithm is put forward to suppress white noise interference in PD signals. The results testifies that it has a favorable adaptability to extract PD signals using WPT.     

The Signals Analysis System Based on Fast Wavelet Transform

The fast wavelet transform (FWT) algorithm in wavelet analysis was introduced in the paper. With quadrature mirror filters (QMF) associated with popular wavelet bases, the fast wavelet decomposition and reconstruction for signals were implemented. Combined with virtual instrument technique, the FWT analysis system for signals was successfully developed. The system can break up signal not only into approximations, which are the high-scale and low-frequency components of the signal, but also into details which are the low-scale and high-frequency components. Especially it can identify singularity signal, which contain some important message of equipment condition and fault, and refine signal from noisy signal, which is corrupted by noise.     

Watermarking algorithm based on statistical property

A novel robust blind watermarking algorithm in DCT domain was proposed. The DCT coefficients are arranged in a specific way, and then the watermark is embedded based on odd even difference for optimal embedding. This algorithm works in a new pattern other than block division and search of the DCT coefficients in the middle frequencies in the traditional pattern, which can overcome the problem in searching suitable coefficients in the middle frequencies for watermark embedding and the small embedding capacity. Experimental results show that the algorithm is simple and good at perceptual transparency as well as robustness against noise and JPEG compression.     

EEG Signal Denoising Based on Wavelet Transform

The article focuses on the method of noise cancellation for EEG signal. The method of notch filter is discussed. According to the frequency of noise and the principle of notch filter, the design result of the notch filter and the denoised signal are presented. Then, the analysis of EEG signal are proposed based on wavelet transform (WT) and noise cancellation using WT. Wavelet transform is a multi-resolution time-frequency analysis method. It can decompose mixed signal into signals at different frequency bands. The EEG signal is analyzed and denoised using WT, then the results are presented respectively. Comparing the experiment results shows that WT can detect and process noise in the EEG signal effectively.     

Detection Method for R-Wave of ECG Signal With Wavelet Transform

The R-wave of ECG signal represents the electrical activation of the ventricles, which initiates ventricle contraction, and the typical peak value singular signal, so the R-wave of ECG signal is localized precisely and analyzed accurately using the wavelet transform. The principium of the precise detection method for R-wave in ECG signal is researched. The special properties of Mexican hat wavelet in time-domain are analyzed, too. This wavelet has every order continuity, symmetry, exponential attenuation and one vanishing moment. For this reason, the mexican hat wavelet basis has the excellent localization and analyzing precision. Using the MIT/BIH (Massachusetts Institute of Technology / Boston's Beth Israel Hospital) Arrhythmia Database and the applications in clinic, the precise detection method can detect accurately and localize precisely to the R-wave in ECG signal in the serious noise signal. This method has the quite high locating precision (its error is not more than one sampling point and the points of the R-wave in ECG signal about 80 percent are localized precisely) and analyzing accuracy (no accumulative error). The real-time of the method is excellent, and the real-time detection to the R-wave of ECG signal can achieve using this method.     

Detection of Edges within the Cross Sectionfrom Projections of Parallel Beams Via the Wavelet Method

The wavelet analysis are used in the detection of edges within the cross sectionfrom projections of parallel beams. This method can be applied in computerized tomography (CT) inorder to reduce the noise and the number of projections.     

Application of Wavelet Denoising in Partial Discharge Online Monitoring of Transformer

Because of the strong interferences, such as discrete spectrum interferences (DSI), white noise and pulse-shaped interferences, it is still a difficult work to extract the partial discharge (PD) signal for transformer online monitoring techniques. Through deeply study on the automatic selection of threshold value and mother wavelet, the wavelet-based de-noising method for partial discharge signals is proposed. The analyzing results on simulation signal and field-measured signal indicate that the proposed method is fit for de-noising white noise but for DSI the comparably worse de-noising results is acquired. Hence it is predicted that good de-noising results will be achieved with the wavelet-based de-noising method combined with digital filtering method fitting for de-noising DSI.     

A Virtual Wavelet Transform Analyzer for the Signal Analysis Based on the Direct Algorithm

A virtual wavelet transform analyzer for the signal analysis based on the direct algorithm is introduced so that the discrete wavelet transform and continuous wavelet transform is maken to signal in the direct algorithm. The authors first introduce the direct algorithm of the WT, which is numerical algorithm obtained from the original formula of the wavelet transform by directly numericalizing. Then some conclusions are drawn on the direct algorithm. The examples are the sampling principle and technology for the wavelets, the limitation of the scale range of the wavelets and the measures to solve the edge phenomenal in the direct algorithm of the discrete wavelet transform, and some conclusions in the direct algorithm of the continuous wavelet transform. The virtual wavelet transform analyzer for the signal analysis based on the direct algorithm explored based on these studies and combined with virtual instrument technique can make the discrete wavelet transform and continuous wavelet transform to signal with any basic wavelet. It can be applied in studying the property of any basic wavelet and learning the theory on the wavelet transform, and also in making some engineering signal analysis. In the end, the authors give some typical examples for the application of the virtual analyzer. These examples show that the analyzer can be applied in many situations.     

Speedy Recognition of Noise in Real Time Signal Based on Wavelet Transform

It's necessary to process position and gesture of rocket on real time within the flight of rocket. This work should base exact data. But much noise has been found in the data and the noise recognize is regarded as a focus. A speedy online arithmetic recognizing noise is proposed based on wavelet transform. The computing complexity measured by time of this arithmetic is a constant which is greatly reduces the works of calculation of wavelet transform. It can recognize the noise fast when the signal is gathered. The applications in these problems show that the effective arithmetic satisfies the needs of real time and can handle the real time data measured in other yields.     

Filtering Construct of Adaptive Filter Grou Pwith Multi-scale Decomposition

The principle and method of the adaptive filter and the filtering with wavelet transform were analyzed, and the model and method of adaptive filtering with wavelet transforms for the transient signal was established. The separated noise of signal by the multi-scale decomposition of wavelet transforms, was the input signal of adaptive filter, and accordingly the optimal filtering method of signal-noise decomposition was realized. By the adaptive filter grou Pbased on the wavelet transform, the optimal filtering to the multi-noise of signal is achieved at the same time, and the method presented in this paper has the excellent filtering capability. Examples of application demonstrate that this method presented is excellent to realize the optimal estimate to the valuable signal and noise of the transient signal in the same frequency segment.     

Parameter optimized Morlet wavelet de noising method based on cross validation method

A new de noising method based on parameter optimized Morlet wavelet is put forward. The Morlet wavelet is chosen as the mother wavelet because its shape is similar to the mechanical shock signals. The mother Morlet wavelet is improved by adding two parameters which decide the shape of the mother wavelet in time domain. The added parameters and the appropriate scale parameter for the wavelet transformation are designed by the cross validation method. Finally, the useful components of the signal can be obtained by the improved Morlet wavelet de noising method. The gear fault diagnosis experimental result shows that the proposed method has a good de nosing performance and it is effective in fault feature extraction.     

Hilbert-Huang transform and denoising for partial discharge ultrsonic singals of large motor

Empirical mode decomposition (EMD) algorithm is introduded as the core of the Hilbert-huang transform (HHT), and implementation process of EMD is analyzed. Then data compression denoising algorithm based on EMD is proposed, simulation and experimental signals are used for verification of the effect of EMD. In the same data sources, the comparison of data compression denoising approaches based on the EMD, db2 wavelet and db8 wavelet are conducted. In addition, physical experiment of the same analysis and comparisons are conducted on a running motor in a Chongqing electrical plant. Simulation and experimental results show that data compression denoising algorithm based on EMD can achieve the same denoising effect, or even better than based on db2 wavelet, db8 wavelet. The former is more perfect than the latter in the real signal processing, and denoising based on EMD is not loss of the original signal energy.     

The Location Algorithm of QRS Waves Based on Fractal Box Dimensions Detection Methods

We research fractal characteristics of ECG signal, and find that it is logarithmic linear relationship between the boxes number covering signal and box scale. It is shown that ECG signal have some characteristics. Further more, we discover that fractal dimensions at QRS site are higher than others when using a time windows to detect signal. Based on this foundation, we raise a location algorithm of QRS waves which is based on fractal boxes dimension detection methods. The application results of this algorithm show that it can get rid of noise in ECG effectively with high speed, so this algorithm can be used in real time detection of ECG signal.     

An edge detection method based on a good point set genetic algorithm

In order to improve the convergence rate of genetic algorithms based on edge detection, a novel edge detection method based on a good point set genetic algorithm (GGA) was proposed. The proposed method designed the crossover operation with the theory of good point set in which the progeny inherits the common genes of the parents which represent its family so as to improve the convergence rate of the genetic algorithm. Furthermore, before the algorithm was used for edge detection, the feature space of the image grey level was transformed into the feature space of the fuzzy entropy. Dissimilarity enhancement processing next was applied to the image by using a fuzzy entropy theory to filter the non edge pixels so as to reduce the scale of the solution domain. This approach offered another efficient way to improve the convergence rate. Experimental results show the proposed algorithm performs very well in terms of convergence rate. The detected edge image is well localized, thin, and robustly resistant to noise.     

Corner detection based on evolution difference of plannar curves in B spline scale space

In order to improve image corner detection precision and efficiency, a novel corner detection algorithm based on B spline scale space evolution difference is proposed. The norm of DoB is defined as corner response function to evaluate the multi scale evolution difference. The DoB corner detector confluents the image boundary features with different scales, which can not only strengthen the response of the feature points, but also depress the influence of noise. Among all corner detection algorithms based on B Spline, the proposed algorithm is relatively lower for computation complexity and faster. The comparative experiments demonstrate that the proposed algorithm works well for localization, robustness against noise, as well as invariance to rotation and scalability.