A New Image Watermarking Scheme Using Genetic Algorithm and Residual Numbers with Discrete Wavelet Transform

Transmission of data over the internet has become a critical issue as a result of the advancement in technology, since it is possible for pirates to steal the intellectual property of content owners. This paper presents a new digital watermarking scheme that combines some operators of the Genetic Algorithm (GA) and the Residue Number (RN) System (RNS) to perform encryption on an image, which is embedded into a cover image for the purposes of watermarking. Thus, an image watermarking scheme uses an encrypted image. The secret image is embedded in decomposed frames of the cover image achieved by applying a three-level Discrete Wavelet Transform (DWT). This is to ensure that the secret information is not exposed even when there is a successful attack on the cover information. Content creators can prove ownership of the multimedia content by unveiling the secret information in a court of law. The proposed scheme was tested with sample data using MATLAB2022 and the results of the simulation show a great deal of imperceptibility and robustness as compared to similar existing schemes.

RESEARCH OF PROBLEMS ON REALIZING DIRECT ALGORITHM OF WAVELET TRANSFORM

Direct algorithm of wavelet transform (WT) is the numerical algorithmobtained from the integral formula of WT by directly digitization. Some problems on realizing thealgorithm are studied. Some conclusions on the direct algorithm of discrete wavelet transform (DWT),such as discrete convolution operation formula of wavelet coefficients and wavelet components,sampling principle and technology to wavelets, deciding method for scale range of wavelets, measuresto solve edge effect problem, etc, are obtained. The realization of direct algorithm of continuouswavelet transform (CWT) is also studied. The computing cost of direct algorithm and Mallat algorithmof DWT are still studied, and the computing formulae are obtained. These works are beneficial todeeply understand WT and Mallat algorithm. Examples in the end show that direct algorithm can alsobe applied widely.

Power Quality Disturbance Classification Method Based on Wavelet Transform and SVM Multi-class Algorithms

The accurate identification and classification of various power quality disturbances are keys to ensuring high-quality electrical energy. In this study, the statistical characteristics of the disturbance signal of wavelet transform coefficients and wavelet transform energy distribution constitute feature vectors. These vectors are then trained and tested using SVM multi-class algorithms. Experimental results demonstrate that the SVM multi-class algorithms, which use the Gaussian radial basis function, exponential radial basis function, and hyperbolic tangent function as basis functions, are suitable methods for power quality disturbance classification.

Biomedical Image Processing Using FCM Algorithm Based on the Wavelet Transform

An effective processing method for biomedical images and the Fuzzy C-mean (FCM) algorithm based on the wavelet transform are investigated.By using hierarchical wavelet decomposition, an original image could be decomposed into one lower image and several detail images. The segmentation started at the lowest resolution with the FCM clustering algorithm and the texture feature extracted from various sub-bands. With the improvement of the FCM algorithm, FCM alternation frequency was decreased and the accuracy of segmentation was advanced.

An Orthogonal Wavelet Transform Fractionally Spaced Blind Equalization Algorithm Based on the Optimization of Genetic Algorithm

An orthogonal wavelet transform fractionally spaced blind equalization algorithm based on the optimization of genetic algorithm(WTFSE-GA) is proposed in viewof the lowconvergence rate,large steady-state mean square error and local convergence of traditional constant modulus blind equalization algorithm(CMA).The proposed algorithm can reduce the signal autocorrelation through the orthogonal wavelet transform of input signal of fractionally spaced blind equalizer,and decrease the possibility of CMA local convergence by using the global random search characteristics of genetic algorithm to optimize the equalizer weight vector.The proposed algorithm has the faster convergence rate and smaller mean square error compared with FSE and WT-FSE.The efficiency of the proposed algorithm is proved by computer simulation of underwater acoustic channels.

Audio Watermarking Using Wavelet Transform and Genetic Algorithm for Realizing High Tolerance to MP3 Compression

Recently, several digital watermarking techniques have been proposed for hiding data in the frequency domain of audio signals to protect the copyrights. However, little attention has been given to the optimal position in the frequency domain for embedding watermarks. In general, there is a tradeoff between the quality of the watermarked audio and the tolerance of watermarks to signal processing methods, such as compression. In the present study, a watermarking method developed for a visual image by using a wavelet transform was applied to an audio clip. We also improved the performance of both the quality of the watermarked audio and the extraction of watermarks after compression by the MP3 technique. To accomplish this, we created a multipurpose optimization problem for deciding the positions of watermarks in the frequency domain and obtaining a near-optimum solution. The near-optimum solution is obtained by using a genetic algorithm. The experimental results show that the proposed method generates watermarked audios of good quality and high tolerance to MP3 compression. In addition, the security was improved by using the characteristic secret key to embed and extract the watermark information.

Comparison of fast discrete wavelet transform algorithms

This paper presents an analysis on and experimental comparison of several typical fast algorithms for discrete wavelet transform (DWT) and their implementation in image compression, particularly the Mallat algorithm, FFT-based algorithm, Short- length based algorithm and Lifting algorithm. The principles, structures and computational complexity of these algorithms are explored in details respectively. The results of the experiments for comparison are consistent to those simulated by MATLAB. It is found that there are limitations in the implementation of DWT. Some algorithms are workable only for special wavelet transform, lacking in generality. Above all, the speed of wavelet transform, as the governing element to the speed of image processing, is in fact the retarding factor for real-time image processing.

PARAMETERS OPTIMIZATION OF CONTINUOUS WAVELET TRANSFORM AND ITS APPLICATION IN ACOUSTIC EMISSION SIGNAL ANALYSIS OF ROLLING BEARING

Morlet wavelet is suitable to extract the impulse components of mechanical fault signals. And thus its continuous wavelet transform （CWT） has been successfully used in the field of fault diagnosis. The principle of scale selection in CWT is discussed. Based on genetic algorithm, an optimization strategy for the waveform parameters of the mother wavelet is proposed with wavelet entropy as the optimization target. Based on the optimized waveform parameters, the wavelet scalogram is used to analyze the simulated acoustic emission （AE） signal and real AE signal of rolling bearing. The results indicate that the proposed method is useful and efficient to improve the quality of CWT.

Developing an Evolutionary Algorithm to Search for an Optimal Multi-Mother Wavelet Packets Combination

The wavelet transform is a popular analysis tool for non-stationary data, but in many cases, the choice of the mother wavelet and basis set remains uncertain, particularly when dealing with physiological data. Furthermore, the possibility exists for combining information from numerous mother wavelets so as to exploit different features from the data. However, the combinatorics become daunting given the large number of basis sets that can be utilized. Recent work in evolutionary computation has produced a subset selection genetic algorithm specifically aimed at the discovery of small, high-performance, subsets from among a large pool of candidates. Our aim was to apply this algorithm to the task of locating subsets of packets from multiple mother wavelet decompositions to estimate cardiac output from chest wall motions while avoiding the computational cost of full signal reconstruction. We present experiments which show how a continuous assessment metric can be extracted from the wavelets coefficients, but the dual-objective nature of the algorithm (high accuracy with small feature sets) imposes a need to restrict the sensitivity of the continuous accuracy metric in order to achieve the small subset size desired. A possibly subtle tradeoff seems to be needed to meet the dual objectives.

Method and application of wavelet shrinkage denoising based on genetic algorithm

Genetic algorithm (GA) based on wavelet transform threshold shrinkage (WTS) and translation-invariant threshold shrinkage (TIS) is introduced into the method of noise reduction, where parameters used in WTS and TIS, such as wavelet function, decomposition levels, hard or soft threshold and threshold can be selected automatically. This paper ends by comparing two noise reduction methods on the basis of their denoising performances, computation time, etc. The effectiveness of these methods in-troduced in this paper is validated by the results of analysis of the simulated and real signals.

Very Low Bit-Rate Video Coding by Combining H.264/AVC Standard and 2-D Discrete Wavelet Transform

In this paper, we propose a new method for very low bit-rate video coding that combines H.264/AVC standard and two-dimensional discrete wavelet transform. In this method, first a two dimensional wavelet transform is applied on each video frame independently to extract the low frequency components for each frame and then the low frequency parts of all frames are coded using H.264/AVC codec. On the other hand, the high frequency parts of the video frames are coded by Run Length Coding algorithm, after applying a threshold to neglect the low value coefficients. Experiments show that our proposed method can achieve better rate-distortion performance at very low bit-rate applications below 16 kbits/s compared to applying H.264/AVC standard directly to all frames. Applications of our proposed video coding technique include video telephony, video-conferencing, transmitting or receiving video over half-rate traffic channels of GSM networks.

Moving Target Tracking Based on LogGabor Wavelet and Mean-shift Algorithm

Tracking moving target from image or video sequence is a hot research topic in computer vision. An algorithm based on LogGabor wavelet and Mean-shift has been proposed for moving target tracking under fixed camera setting and complicated environment. Phase coherency of LogGabor wavelet facilitates to extract the edge of moving target and check noise. According to the edge detection,the starting location of Mean-shift can be estimated using the target center coordinate. Eventually,a real-time moving target can be extracted by doing iterative matching pursuit,and experimental results proved the effectiveness of the method proposed.

Impulse Response Identification Based on Varying Scale Orthogonal Wavelet Packet Transform

In this paper, by applying a group of specific orthogonal wavelet packet to Eykho?algorithm, a new impulse response identification algorithm based on varying scale orthogonal WPTis provided. In comparison to Eykho? algorithm, the new algorithm has better practicability andwider application range. Simulation results show that the proposed impulse response identificationalgorithm can be applied to both deterministic and random systems, and is of higher identificationprecision, stronger anti-noise interference ability and better system dynamic tracking property.

Study on Power Transformers Fault Diagnosis Based on Wavelet Neural Network and D-S Evidence Theory

>Transformer faults are quite complicated phenomena and can occur due to a variety of reasons.There have been several methods for transformer fault synthetic diagnosis,but each of them has its own limitations in real fault diagnosis applications.In order to overcome those shortcomings in the existing methods,a new transformer fault diagnosis method based on a wavelet neural network optimized by adaptive genetic algorithm(AGA)and an improved D-S evidence theory fusion technique is proposed in this paper.The proposed method combines the oil chromatogram data and the off-line electrical test data of transformers to carry out fault diagnosis.Based on the fusion mechanism of D-S evidence theory,the comprehensive reliability of evidence is constructed by considering the evidence importance,the outputs of the neural network and the expert experience.The new method increases the objectivity of the basic probability assignment(BPA)and reduces the basic probability assigned for uncertain and unimportant information.The case study results of using the proposed method show that it has a good performance of fault diagnosis for transformers.

非接触电导检测土壤养分离子的谱峰自动识别方法

[目的/意义]电容耦合非接触式电导检测(Capacitively Coupled Contactless Conductivity Detection,C4D)在农业土壤养分离子检测方面发挥着重要作用。对C4D信号中离子特征峰的有效识别,有利于后续对离子特征峰的定性和定量分析,为加强农业土壤养分管理提供依据。然而,C4D信号的特征峰检测仍然存在无法自动精准识别、人工操作复杂、效率低等缺点。[方法]提出一种基于连续小波变换结合粒子群优化(Particle Swarm Optimization,PSO)和最大类间方差法(Otsu)的谱峰自动识别算法,旨在实现准确、高效、自动化的C4D信号峰识别。采用C4D检测样品溶液,得到离子谱图信号,对谱图信号进行连续小波变换,得到小波变换系数矩阵。通过搜索小波系数变换系数矩阵极值,识别出脊线和谷线。将小波系数矩阵转换为灰度图像,结合PSO和Otsu寻找最佳阈值,进一步对灰度图像的背景和目标分割,再结合原始谱图中的脊谷线识别谱图中的特征峰。[结果与讨论]测试含有41、61和102个峰的数据集,以受试者工作特性(Receiver Operating Characteristic,ROC)曲线和度量值作为评估峰值检测算法性能的准则。与其他方法相比,基于连续小波变换结合粒子群优化的最大类间方差法分割图像(Continuous Wavelet Transform C.ombined with Particle Swarm Optimization of Otsu to Segment Image,CWTSPSO)的谱峰自动识别算法的ROC曲线均保持在0.9以上,度量值分别为0.976、0.915和0.969。CWTSPSO能够有效检测出更多弱峰和重叠峰,同时检测出更少的假峰,有利于提升C4D信号的谱峰识别率和精准性。[结论]本研究提出的CWTSPSO能为非接触式电导检测农业土壤养分离子信号分析提供有力支持。

基于空间变换技术的脉冲激光声信号检测

针对目前脉冲激光声信号检测方法极易受到环境噪声的影响,导致检测精确度不理想的问题。提出基于空间变换技术的脉冲激光声信号检测方法。首先,采用小波阈值法去除脉冲激光声信号中的噪声;其次,利用球傅里叶空间变换技术对声信号中的声源展开定位,获取声信号的准确位置;最后,分别提取声信号中的比值特征、比重特征、频率特征三种参数,在此基础上通过SVM算法对其展开分类识别,以此实现脉冲激光声信号的有效检测。实验结果表明,所提方法的声信号检测精度超过96%。

基于调频连续波雷达的人体生命体征检测算法

针对现有雷达非接触生命体征检测精度低、实时性差等问题,提出一种基于调频连续波(FMCW)雷达的人体生命体征检测算法。首先,通过毫米波雷达获取生命体征信号;其次,利用改进的经验小波变换(EWT)算法,实现生命体征信号的自适应分解和重构,通过引入麻雀搜索算法(SSA)和模糊熵(FE)寻找频谱分割线的最优值;最后,通过改进频率插值的估计算法计算心率和呼吸频率。通过与医用重症监护仪进行对比实验验证所提算法的优越性和鲁棒性。实验结果表明,所提算法相较于小波变换(WT)算法、CEEMD(Complementary Ensemble Empirical Mode Decomposition)算法和VMD(Variational Mode Decomposition)算法,均方误差(MSE)分别减小了77.65、27.25和21.05,平均绝对百分比(MAPE)分别减小了7.33、4.33和3.42个百分点,实时性分别提高了0.72 s, 16.74 s和1.87 s。同时,利用所提算法也实现了对心率变异性(HRV)的检测。

基于首行波曲率半径的交直流互联电网交流输电线路单端量保护

交直流互联电网中逆变侧交流输电线路故障特征与纯交流系统具有较大的差异性,传统交流输电线路的保护方案已经不再适用于逆变侧交流输电线路.首先,基于逆变侧交流输电线路发生区内、外故障时拓扑结构的不同,分别推导了两种情况下线模故障分量电流首行波的表达式,理论上明确了交流输电线路在发生区内、外故障时首行波波形特征的差异性.其次,采用小波变换模极大值法和Levenberg-Marquardt算法提取首行波曲率半径,构造了利用首行波曲率半径进行故障区域识别的高可靠性单端量保护方案.最后,在PSCAD/EMTDC中搭建交直流互联电网模型,使用MATLAB对保护方案进行验证.仿真结果表明:所提保护方案快速有效,可靠性高,耐受过渡电阻的能力较强,具有良好的抗噪声能力.

基于CEEMDAN-VSSLMS的滚动轴承故障诊断

针对传统机械轴承故障诊断模型易受系统噪声干扰、特征识别效率低等问题,提出一种基于信号固有模式深度建模分析的轴承故障诊断方法。首先,将采集到的轴承振动信号进行噪声自适应完全经验模态分解(CEEMDAN),获得不同时间尺度的局部特征信号,使用相关系数判别并去除虚假模态分量,再利用可变步长最小均方算法(VSSLMS)对剩余IMF分量降噪并进行重构;然后,将降噪后的振动信号进行离散小波变换(DWT)得到时频谱图,并利用形态学开运算进行特征增强;最后利用改进GoogLeNet网络模型对特征图进行训练,通过Softmax分类器完成特征归类,从而实现轴承故障诊断。将提出的故障诊断方法应用于不同工况下的轴承故障数据集,试验结果表明,所提方法在噪声干扰下具有较高的诊断精度。

基于小波变换与IAGA-BP神经网络的短期风电功率预测

为提高风功率预测精度,减轻输出风能波动性对风电并网不利影响,提出了基于WT-IAGA-BP神经网络的短期风电功率预测方法。利用风速分区、3σ准则及拉格朗日插值法清洗风电场历史数据;其次,依据小波重构误差,选择db4小波分别提取风速、风向、历史风功率的不同频率特征信号,并引入改进自适应遗传算法(IAGA)对各序列BP神经网络的初始权值与阈值寻优,使用Sigmiod函数通过适应度值自适应改变交叉概率与变异概率;构建各序列的WT-IAGA-BP模型对短期风功率组合预测。通过仿真分析,并与ELM、IAGA-BP、WT-ELM及WT-LSSVM方法对比,验证该方法具有更高的预测精度和更好的预测性能。