In this paper, we propose two kinds of modifications in speaker recognition. First, the correlations between frequency channels are of prime importance for speaker recognition. Some of these correlations are lost when...In this paper, we propose two kinds of modifications in speaker recognition. First, the correlations between frequency channels are of prime importance for speaker recognition. Some of these correlations are lost when the frequency domain is divided into sub-bands. Consequently we propose a particularly redundant parallel architecture for which most of the correlations are kept. Second, generally a log transformation used to modify the power spectrum is done after the filter-bank in the classical spectrum calculation. We will see that performing this transformation before the filter bank is more interesting in our case. In the processing of recognition, the Gaussian mixture model （GMM） recognition arithmetic is adopted. Experiments on speech corrupted by noise show a better adaptability of this approach in noisy environments, comoared with a conventional device, esoeciallv when oruning of some recognizers is performed.展开更多
This paper presents the application of dual-number matrix to the formulation of Jacobian equations of robot with redundancy, the analytical technique that is based on the dual-number matrices, a 3 × 3 matrix with...This paper presents the application of dual-number matrix to the formulation of Jacobian equations of robot with redundancy, the analytical technique that is based on the dual-number matrices, a 3 × 3 matrix with dualnumber elements, and the dual-number transformation method. Dual-number matrices make possible a concise representation of joint parameters. In particular, the method can effectively be used for direct determination of Jacobian matrices. It is shown that the proposed procedure contributes a simplified approach to the formulation of robotic kinematics, dynamics and control system modelling.展开更多
Ensuring digital media security through robust image watermarking is essential to prevent unauthorized distribution,tampering,and copyright infringement.This study introduces a novel hybrid watermarking framework that...Ensuring digital media security through robust image watermarking is essential to prevent unauthorized distribution,tampering,and copyright infringement.This study introduces a novel hybrid watermarking framework that integrates Discrete Wavelet Transform(DWT),Redundant Discrete Wavelet Transform(RDWT),and Möbius Transformations(MT),with optimization of transformation parameters achieved via a Genetic Algorithm(GA).By combining frequency and spatial domain techniques,the proposed method significantly enhances both the imper-ceptibility and robustness of watermark embedding.The approach leverages DWT and RDWT for multi-resolution decomposition,enabling watermark insertion in frequency subbands that balance visibility and resistance to attacks.RDWT,in particular,offers shift-invariance,which improves performance under geometric transformations.Möbius transformations are employed for spatial manipulation,providing conformal mapping and spatial dispersion that fortify watermark resilience against rotation,scaling,and translation.The GA dynamically optimizes the Möbius parameters,selecting configurations that maximize robustness metrics such as Peak Signal-to-Noise Ratio(PSNR),Structural Similarity Index Measure(SSIM),Bit Error Rate(BER),and Normalized Cross-Correlation(NCC).Extensive experiments conducted on medical and standard benchmark images demonstrate the efficacy of the proposed RDWT-MT scheme.Results show that PSNR exceeds 68 dB,SSIM approaches 1.0,and BER remains at 0.0000,indicating excellent imperceptibility and perfect watermark recovery.Moreover,the method exhibits exceptional resilience to a wide range of image processing attacks,including Gaussian noise,JPEG compression,histogram equalization,and cropping,achieving NCC values close to or equal to 1.0.Comparative evaluations with state-of-the-art watermarking techniques highlight the superiority of the proposed method in terms of robustness,fidelity,and computational efficiency.The hybrid framework ensures secure,adaptive watermark embedding,making it highly suitable for applications in digital rights management,content authentication,and medical image protection.The integration of spatial and frequency domain features with evolutionary optimization presents a promising direction for future watermarking technologies.展开更多
The transform base function method is one of the most commonly used techniques for seismic denoising, which achieves the purpose of removing noise by utilizing the sparseness and separateness of seismic data in the tr...The transform base function method is one of the most commonly used techniques for seismic denoising, which achieves the purpose of removing noise by utilizing the sparseness and separateness of seismic data in the transform base function domain. However, the effect is not satisfactory because it needs to pre-select a set of fixed transform-base functions and process the corresponding transform. In order to find a new approach, we introduce learning-type overcomplete dictionaries, i.e., optimally sparse data representation is achieved through learning and training driven by seismic modeling data, instead of using a single set of fixed transform bases. In this paper, we combine dictionary learning with total variation （TV） minimization to suppress pseudo-Gibbs artifacts and describe the effects of non-uniform dictionary sub-block scale on removing noises. Taking the discrete cosine transform and random noise as an example, we made comparisons between a single transform base, non-learning-type, overcomplete dictionary and a learning-type overcomplete dictionary and also compare the results with uniform and nonuniform size dictionary atoms. The results show that, when seismic data is represented sparsely using the learning-type overcomplete dictionary, noise is also removed and visibility and signal to noise ratio is markedly increased. We also compare the results with uniform and nonuniform size dictionary atoms, which demonstrate that a nonuniform dictionary atom is more suitable for seismic denoising.展开更多
In the time-frequency analysis of seismic signals, the matching pursuit algorithm is an effective tool for non-stationary signals, and has high time-frequency resolution and a transient structure with local self-adapt...In the time-frequency analysis of seismic signals, the matching pursuit algorithm is an effective tool for non-stationary signals, and has high time-frequency resolution and a transient structure with local self-adaption. We expand the time-frequency dictionary library with Ricker, Morlet, and mixed phase seismic wavelets, to make the method more suitable for seismic signal time-frequency decomposition. In this paper, we demonstrated the algorithm theory using synthetic seismic data, and tested the method using synthetic data with 25% noise. We compared the matching pursuit results of the time-frequency dictionaries. The results indicated that the dictionary which matched the signal characteristics better would obtain better results, and can reflect the information of seismic data effectively.展开更多
Because of various complicated factors in seismic data collection,the random noise of seismic data is too difficult to avoid.This random noise reduces the quality of seismic data and increases the difficulty of seismi...Because of various complicated factors in seismic data collection,the random noise of seismic data is too difficult to avoid.This random noise reduces the quality of seismic data and increases the difficulty of seismic data processing and interpretation.Improving the denoising technology is significant.In order to improve seismic data denoising result,a novel method named data-driven tight frame(DDTF)is introduced in this paper.First,we get the sparse coefficients of seismic data with noise by DDTF.Then we remove the smaller sparse coefficient by using the hard threshold function.Finally,we get the denoised seismic data by inverse transform.Furthermore,the DDTF is compared with curvelet transform in the stimulation and practical seismic data experiments to validate its performance.DDTF can raise the signal-to-noise ratio of seismic data denoising and protect the effective signal well.展开更多
Decomposing a signal based upon redundan dictionaries is a new method for data representation on sig- nal processing. It approximates a signal with an overcom- plete system instead of an orthonormal basis to provide a...Decomposing a signal based upon redundan dictionaries is a new method for data representation on sig- nal processing. It approximates a signal with an overcom- plete system instead of an orthonormal basis to provide a sufficient choice for adaptive sparse decompositions. Re- placing the original data with a sparse approximation can result in not only a higher compression ratio, but also greater flexibility in capturing the inherent structure of the natura signals with the redundancy of dictionaries. This paper gives an overview of a series of recent results in this field, and deals with the relationship between sparsity of signal de- composition and incoherence of dictionaries with BP and MP algorithms. The current and future challenges of the dic- tionary construction are discussed.展开更多
To address the issue of low estimation performance of the traditional off-grid sparse Bayesian learning algorithm in the complex shallow-water localization environment for acoustic target direction estimation,this pap...To address the issue of low estimation performance of the traditional off-grid sparse Bayesian learning algorithm in the complex shallow-water localization environment for acoustic target direction estimation,this paper proposes a real-domain out-of-state sparse Bayesian learning algorithm that combines dictionary learning and unitary transformation for direction estimation.The algorithm employs the K-means singular value decomposition dictionary learning method to represent the actual received signal of a uniform linear array using a small number of linear combinations of basic received signals,thereby achieving noise reduction for the original signal.The denoised signal matrix is then constructed into a processing matrix that satisfies the central Hermitian property.By applying a unitary transformation,the signal data is converted from complex-domain operations to real-domain operations,which reduces computational complexity.Finally,singular value decomposition and outlier sparse Bayesian learning algorithms are used for iterative processing to achieve target direction estimation.Simulation analysis and sea trial data results demonstrate that compared with the off-grid sparse Bayesian learning algorithm,under conditions of low signal-to-noise ratio and low frame rate,the proposed algorithm has improved azimuth estimation accuracy and algorithm robustness,and is less complex.展开更多
A novel robust diagnostic system based on a linear fractional transform(LFT)representation combined with a static redundancy approach is proposed to design a residual generator for fault detection and localization in ...A novel robust diagnostic system based on a linear fractional transform(LFT)representation combined with a static redundancy approach is proposed to design a residual generator for fault detection and localization in a wind system using the doubly fed induction generator(DFIG).As a result,faults in DFIG-based grid-connected wind systems can be grouped into three classes of faults,namely,model uncertainty-related faults(FLMU),set point disturbance-related faults(FLDS)and parameter uncertainty-related faults(FLPU).Based on the parity-space residual generations,an artificial neural network(ANN)structure has been combined with the classification to enable the assessment of hidden,indistinguishable or small amplitude faults.The training validation with two data sizes of 1278*4 and 1278*1 respectively at the inputs and outputs of the proposed ANN,presents better performance for a mean squared error value(MSE=3.0532e 9),and a good correlation between outputs and targets for a regression value(R=1).It emerges that the proposed robust and complete diagnostic system for the optimal and sustainable integration of wind turbines into the grid,offers very great advan-tages,particularly with regard to the precise and rapid detection of faults,and the assessment of hidden faults and/or ambiguous fault states in the wind system based on DFIG.In addition,the proposed approach allows the use of a reduced number of data,sensors and actuators required.Consequently,the system maintenance difficulties,complexity and cost of the diagnostic system are reduced.展开更多
基金the National Natural Science Foundation of China (No. 60171043, 60371046)
文摘In this paper, we propose two kinds of modifications in speaker recognition. First, the correlations between frequency channels are of prime importance for speaker recognition. Some of these correlations are lost when the frequency domain is divided into sub-bands. Consequently we propose a particularly redundant parallel architecture for which most of the correlations are kept. Second, generally a log transformation used to modify the power spectrum is done after the filter-bank in the classical spectrum calculation. We will see that performing this transformation before the filter bank is more interesting in our case. In the processing of recognition, the Gaussian mixture model （GMM） recognition arithmetic is adopted. Experiments on speech corrupted by noise show a better adaptability of this approach in noisy environments, comoared with a conventional device, esoeciallv when oruning of some recognizers is performed.
文摘This paper presents the application of dual-number matrix to the formulation of Jacobian equations of robot with redundancy, the analytical technique that is based on the dual-number matrices, a 3 × 3 matrix with dualnumber elements, and the dual-number transformation method. Dual-number matrices make possible a concise representation of joint parameters. In particular, the method can effectively be used for direct determination of Jacobian matrices. It is shown that the proposed procedure contributes a simplified approach to the formulation of robotic kinematics, dynamics and control system modelling.
文摘Ensuring digital media security through robust image watermarking is essential to prevent unauthorized distribution,tampering,and copyright infringement.This study introduces a novel hybrid watermarking framework that integrates Discrete Wavelet Transform(DWT),Redundant Discrete Wavelet Transform(RDWT),and Möbius Transformations(MT),with optimization of transformation parameters achieved via a Genetic Algorithm(GA).By combining frequency and spatial domain techniques,the proposed method significantly enhances both the imper-ceptibility and robustness of watermark embedding.The approach leverages DWT and RDWT for multi-resolution decomposition,enabling watermark insertion in frequency subbands that balance visibility and resistance to attacks.RDWT,in particular,offers shift-invariance,which improves performance under geometric transformations.Möbius transformations are employed for spatial manipulation,providing conformal mapping and spatial dispersion that fortify watermark resilience against rotation,scaling,and translation.The GA dynamically optimizes the Möbius parameters,selecting configurations that maximize robustness metrics such as Peak Signal-to-Noise Ratio(PSNR),Structural Similarity Index Measure(SSIM),Bit Error Rate(BER),and Normalized Cross-Correlation(NCC).Extensive experiments conducted on medical and standard benchmark images demonstrate the efficacy of the proposed RDWT-MT scheme.Results show that PSNR exceeds 68 dB,SSIM approaches 1.0,and BER remains at 0.0000,indicating excellent imperceptibility and perfect watermark recovery.Moreover,the method exhibits exceptional resilience to a wide range of image processing attacks,including Gaussian noise,JPEG compression,histogram equalization,and cropping,achieving NCC values close to or equal to 1.0.Comparative evaluations with state-of-the-art watermarking techniques highlight the superiority of the proposed method in terms of robustness,fidelity,and computational efficiency.The hybrid framework ensures secure,adaptive watermark embedding,making it highly suitable for applications in digital rights management,content authentication,and medical image protection.The integration of spatial and frequency domain features with evolutionary optimization presents a promising direction for future watermarking technologies.
基金supported by The National 973 program (No. 2007 CB209505)Basic Research Project of PetroChina's 12th Five Year Plan (No. 2011A-3601)RIPED Youth Innovation Foundation (No. 2010-A-26-01)
文摘The transform base function method is one of the most commonly used techniques for seismic denoising, which achieves the purpose of removing noise by utilizing the sparseness and separateness of seismic data in the transform base function domain. However, the effect is not satisfactory because it needs to pre-select a set of fixed transform-base functions and process the corresponding transform. In order to find a new approach, we introduce learning-type overcomplete dictionaries, i.e., optimally sparse data representation is achieved through learning and training driven by seismic modeling data, instead of using a single set of fixed transform bases. In this paper, we combine dictionary learning with total variation （TV） minimization to suppress pseudo-Gibbs artifacts and describe the effects of non-uniform dictionary sub-block scale on removing noises. Taking the discrete cosine transform and random noise as an example, we made comparisons between a single transform base, non-learning-type, overcomplete dictionary and a learning-type overcomplete dictionary and also compare the results with uniform and nonuniform size dictionary atoms. The results show that, when seismic data is represented sparsely using the learning-type overcomplete dictionary, noise is also removed and visibility and signal to noise ratio is markedly increased. We also compare the results with uniform and nonuniform size dictionary atoms, which demonstrate that a nonuniform dictionary atom is more suitable for seismic denoising.
文摘In the time-frequency analysis of seismic signals, the matching pursuit algorithm is an effective tool for non-stationary signals, and has high time-frequency resolution and a transient structure with local self-adaption. We expand the time-frequency dictionary library with Ricker, Morlet, and mixed phase seismic wavelets, to make the method more suitable for seismic signal time-frequency decomposition. In this paper, we demonstrated the algorithm theory using synthetic seismic data, and tested the method using synthetic data with 25% noise. We compared the matching pursuit results of the time-frequency dictionaries. The results indicated that the dictionary which matched the signal characteristics better would obtain better results, and can reflect the information of seismic data effectively.
文摘Because of various complicated factors in seismic data collection,the random noise of seismic data is too difficult to avoid.This random noise reduces the quality of seismic data and increases the difficulty of seismic data processing and interpretation.Improving the denoising technology is significant.In order to improve seismic data denoising result,a novel method named data-driven tight frame(DDTF)is introduced in this paper.First,we get the sparse coefficients of seismic data with noise by DDTF.Then we remove the smaller sparse coefficient by using the hard threshold function.Finally,we get the denoised seismic data by inverse transform.Furthermore,the DDTF is compared with curvelet transform in the stimulation and practical seismic data experiments to validate its performance.DDTF can raise the signal-to-noise ratio of seismic data denoising and protect the effective signal well.
基金This work was supported in part by the National Committee for Nationalities,China Scholarship Council and Education Department of China.
文摘Decomposing a signal based upon redundan dictionaries is a new method for data representation on sig- nal processing. It approximates a signal with an overcom- plete system instead of an orthonormal basis to provide a sufficient choice for adaptive sparse decompositions. Re- placing the original data with a sparse approximation can result in not only a higher compression ratio, but also greater flexibility in capturing the inherent structure of the natura signals with the redundancy of dictionaries. This paper gives an overview of a series of recent results in this field, and deals with the relationship between sparsity of signal de- composition and incoherence of dictionaries with BP and MP algorithms. The current and future challenges of the dic- tionary construction are discussed.
基金supported by the National Natural Science Foundation of China(61761048)the Basic Research Special General project of Yunnan Province(202101AT070132)the Yunnan Minzu University Graduate Research Innovation Fund Project(2024SKY122).
文摘To address the issue of low estimation performance of the traditional off-grid sparse Bayesian learning algorithm in the complex shallow-water localization environment for acoustic target direction estimation,this paper proposes a real-domain out-of-state sparse Bayesian learning algorithm that combines dictionary learning and unitary transformation for direction estimation.The algorithm employs the K-means singular value decomposition dictionary learning method to represent the actual received signal of a uniform linear array using a small number of linear combinations of basic received signals,thereby achieving noise reduction for the original signal.The denoised signal matrix is then constructed into a processing matrix that satisfies the central Hermitian property.By applying a unitary transformation,the signal data is converted from complex-domain operations to real-domain operations,which reduces computational complexity.Finally,singular value decomposition and outlier sparse Bayesian learning algorithms are used for iterative processing to achieve target direction estimation.Simulation analysis and sea trial data results demonstrate that compared with the off-grid sparse Bayesian learning algorithm,under conditions of low signal-to-noise ratio and low frame rate,the proposed algorithm has improved azimuth estimation accuracy and algorithm robustness,and is less complex.
文摘A novel robust diagnostic system based on a linear fractional transform(LFT)representation combined with a static redundancy approach is proposed to design a residual generator for fault detection and localization in a wind system using the doubly fed induction generator(DFIG).As a result,faults in DFIG-based grid-connected wind systems can be grouped into three classes of faults,namely,model uncertainty-related faults(FLMU),set point disturbance-related faults(FLDS)and parameter uncertainty-related faults(FLPU).Based on the parity-space residual generations,an artificial neural network(ANN)structure has been combined with the classification to enable the assessment of hidden,indistinguishable or small amplitude faults.The training validation with two data sizes of 1278*4 and 1278*1 respectively at the inputs and outputs of the proposed ANN,presents better performance for a mean squared error value(MSE=3.0532e 9),and a good correlation between outputs and targets for a regression value(R=1).It emerges that the proposed robust and complete diagnostic system for the optimal and sustainable integration of wind turbines into the grid,offers very great advan-tages,particularly with regard to the precise and rapid detection of faults,and the assessment of hidden faults and/or ambiguous fault states in the wind system based on DFIG.In addition,the proposed approach allows the use of a reduced number of data,sensors and actuators required.Consequently,the system maintenance difficulties,complexity and cost of the diagnostic system are reduced.
