Dual-tree Complex Wavelet Packet Research Articles

Looseness condition feature extraction of viscoelastic sandwich structure using dual-tree complex wavelet packet-based deep autoencoder network

It is significant to perform looseness condition detection of viscoelastic sandwich structures to avoid serious accidents. Due to the multilayer characteristic of the viscoelastic sandwich structure, the vibration response signal of such structures is nonlinear and nonstationary. Furthermore, the looseness condition feature signal contained in the vibration response signal is very puny. Condition feature extraction has become a challenging task in the looseness condition detection of viscoelastic sandwich structures. Therefore, a novel method called dual-tree complex wavelet packet-based deep autoencoder network is proposed for this task. First, the vibration response signal of the viscoelastic sandwich structure is decomposed by dual-tree complex wavelet packet transform and the sub-band signals which contain rich energy are extracted. Then, the energies of the extracted sub-band signals are calculated to form a feature set. Finally, a deep autoencoder network is established to fuse the feature set, and the fused feature is viewed as the detection index to detect the looseness condition of the viscoelastic sandwich structure. The proposed method is applied to the connecting bolt looseness condition detection of the viscoelastic sandwich structure to validate its effectiveness. Compared with the detection method based on dual-tree complex wavelet packet transform and energy and the detection method based on dual-tree complex wavelet packet transform and permutation entropy, the results indicate that the effectiveness of the proposed method in this article is more superior to that of the other two methods.

A novel aging state recognition method of a viscoelastic sandwich structure based on permutation entropy of dual-tree complex wavelet packet transform and generalized Chebyshev support vector machine

A viscoelastic sandwich structure is widely used in mechanical equipment, but therein viscoelastic layers inevitably suffer from aging which changes the dynamic characteristics of the structure and influences the whole performance of the equipment. Hence, accurate and automatic aging state recognition of the viscoelastic sandwich structure is very significant to monitor structural health state and guarantee equipment operating reliably. To fulfill this task, by analyzing the sensor-based vibration response signals, a novel aging state recognition approach of the viscoelastic sandwich structure based on permutation entropy of dual-tree complex wavelet packet transform and generalized Chebyshev support vector machine is proposed in this article. To extract effective aging feature information, the measured nonlinear and non-stationary vibration response signals are processed by dual-tree complex wavelet packet transform, and multiple permutation entropy features are extracted from the frequency-band signals to reflect structural aging states. For accurate and automatic aging state classification, generalized Chebyshev kernel is introduced, and multi-class generalized Chebyshev support vector machine is developed to classify structural aging states. In order to demonstrate the effectiveness of the proposed method, a typical viscoelastic sandwich structure is designed and fabricated, and various structural aging states are created through the hot oxygen–accelerated aging of viscoelastic layers. The testing results show that the proposed method can recognize the different structural aging states accurately and automatically. In addition, the superiority of dual-tree complex wavelet packet transform in processing vibration response signals and the performance of generalized Chebyshev support vector machine in classifying structural aging states are respectively validated by comparing with the commonly used methods.

A Self-Adaptive 1D Convolutional Neural Network for Flight-State Identification.

The vibration of a wing structure in the air reflects coupled aerodynamic–mechanical responses under varying flight states that are defined by the angle of attack and airspeed. It is of great challenge to identify the flight state from the complex vibration signals. In this paper, a novel one-dimension convolutional neural network (CNN) is developed, which is able to automatically extract useful features from the structural vibration of a recently fabricated self-sensing wing through wind-tunnel experiments. The obtained signals are firstly decomposed into various subsignals with different frequency bands via dual-tree complex-wavelet packet transformation. Then, the reconstructed subsignals are selected to form the best combination for multichannel inputs of the CNN. A swarm-based evolutionary algorithm called grey-wolf optimizer is utilized to optimize a set of key parameters of the CNN, which saves considerable human efforts. Two case studies demonstrate the high identification accuracy and robustness of the proposed method over standard deep-learning methods in flight-state identification, thus providing new perspectives in self-awareness toward the next generation of intelligent air vehicles.

Ground Object Information Recovery for Thin Cloud Contaminated Optical Remote Sensing Images

Ground object information on optical remote sensing images is obscured by thin clouds. This paper proposes a ground object information recovery algorithm for thin cloud contaminated optical remote sensing images by combining methods of support vector guidance filtering and transfer learning. Firstly, thin cloud contaminated target images and cloud-free images are decomposed into multidirectional subbands by using multi-directional nonsubsampled dual-tree complex wavelet packet transform (NS-DTCWPT). Then support vector guided filter is applied to remove thin cloud and transfer learning method is used to predict the ground object information on multidirectional subbands. Finally, the processed multidirectional subbands are reconstructed by using inverse multi-directional NS-DTCWPT to obtain the ground object information recovery images. The proposed algorithm combines the advantages of methods of support vector guidance filtering and transfer learning. Experimental results show that the proposed algorithm can effectively remove the thin clouds on the optical remote sensing images and obtain a good recovery effect of ground object information.

Feature Extraction Method for Condition Monitoring of Rolling Element Bearings Based on Dual-Tree Complex Wavelet Packet Transform and VMD

The feature extraction of rolling element bearings vibration signals is one of the key issue for high-speed rotating machinery condition monitoring. A new scheme based on Dual-Tree Complex Wavelet Packet Transform (DTCWPT) and Variational Mode Decomposition (VMD) for extracting vibration condition monitoring feature is proposed. First, DTCWPT is used to reduce noise and pseudo frequency components from vibration signals by the energy ratio. Second, a set of Intrinsic Mode Function components (IMFs) can be got by VMD. Then, the energy ratio between the screening vibration signal and IMFs are calculated. And, the corresponding IMFs are selected according to the energy ratio threshold. Finally, applying the spectrum analysis technology, the condition monitoring feature can be extracted from the reconstructing signal. The experimental results of simulation signals and practical rolling element bearings vibration signals show that the scheme is feasible and effective for extracting the bearings operation state feature.

A fault diagnosis approach for rolling element bearings based on dual-tree complex wavelet packet transform-improved intrinsic time-scale decomposition, singular value decomposition, and online sequential extreme learning machine

The fault diagnosis of rolling element bearings is very important for ensuring the safe operation of rotary machineries. Targeting the nonstationary characteristics of the vibration signals of rolling element bearings, a novel approach based on dual-tree complex wavelet packet transform, improved intrinsic time-scale decomposition, and the online sequential extreme learning machine is proposed in this article for the fault recognition of rolling element bearing. First, the feature extraction method of the measured signal is presented by combining improved intrinsic time-scale decomposition with dual-tree complex wavelet packet transform as preprocessor and two-step screening processes based on the energy ratio, the vibration signal is adaptively decomposed into a set of proper rotation components; second, the matrix formed by different proper rotation components and singular value decomposition is used to obtain singular value as eigenvector; finally, singular values are input to online sequential extreme...

HSV and Dual Tree Complex Wavelet Packet Transform based Image fusion for Satellite Images

HSV and Dual Tree Complex Wavelet Packet Transform based Image fusion for Satellite Images

Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet

Automatic and accurate identification of rolling bearing fault categories, especially for the fault severities and compound faults, is a challenge in rotating machinery fault diagnosis. For this purpose, a novel method called adaptive deep belief network (DBN) with dual-tree complex wavelet packet (DTCWPT) is developed in this paper. DTCWPT is used to preprocess the vibration signals to refine the fault characteristics information, and an original feature set is designed from each frequency-band signal of DTCWPT. An adaptive DBN is constructed to improve the convergence rate and identification accuracy with multiple stacked adaptive restricted Boltzmann machines (RBMs). The proposed method is applied to the fault diagnosis of rolling bearings. The results confirm that the proposed method is more effective than the existing methods.

Speech enhancement via two-stage dual tree complex wavelet packet transform with a speech presence probability estimator.

In this paper, a two-stage dual tree complex wavelet packet transform (DTCWPT) based speech enhancement algorithm has been proposed, in which a speech presence probability (SPP) estimator and a generalized minimum mean squared error (MMSE) estimator are developed. To overcome the drawback of signal distortions caused by down sampling of wavelet packet transform (WPT), a two-stage analytic decomposition concatenating undecimated wavelet packet transform (UWPT) and decimated WPT is employed. An SPP estimator in the DTCWPT domain is derived based on a generalized Gamma distribution of speech, and Gaussian noise assumption. The validation results show that the proposed algorithm can obtain enhanced perceptual evaluation of speech quality (PESQ), and segmental signal-to-noise ratio (SegSNR) at low signal-to-noise ratio (SNR) nonstationary noise, compared with four other state-of-the-art speech enhancement algorithms, including optimally modified log-spectral amplitude (OM-LSA), soft masking using a posteriori SNR uncertainty (SMPO), a posteriori SPP based MMSE estimation (MMSE-SPP), and adaptive Bayesian wavelet thresholding (BWT).

Optimized Denoising scheme via Opposition based Self-adaptive learning PSO algorithm for Wavelet Based ECG Signal Noise Reduction

Electrocardiogram (ECG) signal is significant to diagnose cardiac arrhythmia among various biological signals. The accurate analysis of noisy electrocardiographic (ECG) signal is a very motivating challenge. According to this automated analysis, the noises present in electrocardiogram signal need to be removed for perfect diagnosis. Numerous investigators have been reported different techniques for denoising the electrocardiographic signal in recent years. In this paper, an efficient scheme for denoising electrocardiogram (ECG) signals is proposed based on a wavelet-based threshold mechanism. This scheme is based on an opposition-based self-adaptive learning particle swarm optimisation (OSLPSO) in dual tree complex wavelet packet scheme, in which the OSLPSO is utilised to for threshold optimisation. Different abnormal and normal electrocardiographic signals are tested to evaluate this approach from MIT/BIH arrhythmia database, by artificially adding white Gaussian noises with variation of 5 dB, 10 dB and 15 dB. Simulation results illustrate that the proposed system has good performance in various noise level and obtains better visual quality compared with other methods.

On ultrasonic communication through metal structure for machine embedded sensing

With the development of machine embedded sensing, wireless communication through metal structures is becoming a critical issue in manufacturing equipment, process monitoring and control. To address it, this paper presents a multi-carrier ultrasonic communication scheme to achieve high-bit data rate for machine embedded sensing through metal structures. To extract the coded information from ultrasonic signal and denoise the signal, a new signal processing method, named multi-scale enveloping symbolic analysis (MuSEnSA), is also formulated by integrating dual-tree complex wavelet packet transform and symbolic analysis. Dual-tree complex wavelet packet transform (DT-CWPT) is firstly investigated by separating multi-carrier signals under noise contamination, given its properties of shift-invariance and flexible time frequency partitioning. A new envelope extraction and threshold setting strategy for selected wavelet coefficients is then introduced to retrieve the coded digital information by taking advantage of symbolic analysis. Numerical and experimental studies are performed to evaluate the effectiveness of the developed signal processing method for injection molding monitoring.

Comparison of various texture classification methods using multiresolution analysis and linear regression modelling.

Textures play an important role in image classification. This paper proposes a high performance texture classification method using a combination of multiresolution analysis tool and linear regression modelling by channel elimination. The correlation between different frequency regions has been validated as a sort of effective texture characteristic. This method is motivated by the observation that there exists a distinctive correlation between the image samples belonging to the same kind of texture, at different frequency regions obtained by a wavelet transform. Experimentally, it is observed that this correlation differs across textures. The linear regression modelling is employed to analyze this correlation and extract texture features that characterize the samples. Our method considers not only the frequency regions but also the correlation between these regions. This paper primarily focuses on applying the Dual Tree Complex Wavelet Packet Transform and the Linear Regression model for classification of the obtained texture features. Additionally the paper also presents a comparative assessment of the classification results obtained from the above method with two more types of wavelet transform methods namely the Discrete Wavelet Transform and the Discrete Wavelet Packet Transform.

A Novel Method of Fault Diagnosis for Rolling Bearing Based on Dual Tree Complex Wavelet Packet Transform and Improved Multiscale Permutation Entropy

A novel method of fault diagnosis for rolling bearing, which combines the dual tree complex wavelet packet transform (DTCWPT), the improved multiscale permutation entropy (IMPE), and the linear local tangent space alignment (LLTSA) with the extreme learning machine (ELM), is put forward in this paper. In this method, in order to effectively discover the underlying feature information, DTCWPT, which has the attractive properties as nearly shift invariance and reduced aliasing, is firstly utilized to decompose the original signal into a set of subband signals. Then, IMPE, which is designed to reduce the variability of entropy measures, is applied to characterize the properties of each obtained subband signal at different scales. Furthermore, the feature vectors are constructed by combining IMPE of each subband signal. After the feature vectors construction, LLTSA is employed to compress the high dimensional vectors of the training and the testing samples into the low dimensional vectors with better distinguishability. Finally, the ELM classifier is used to automatically accomplish the condition identification with the low dimensional feature vectors. The experimental data analysis results validate the effectiveness of the presented diagnosis method and demonstrate that this method can be applied to distinguish the different fault types and fault degrees of rolling bearings.

A novel intelligent method for mechanical fault diagnosis based on dual-tree complex wavelet packet transform and multiple classifier fusion

Identifying fault categories, especially for compound faults, is a challenging task in mechanical fault diagnosis. For this task, this paper proposes a novel intelligent method based on dual-tree complex wavelet packet transform (DTCWPT) and multiple classifier fusion. In this method, in order to effectively extract underlying fault characteristic information, DTCWPT, which enjoys such attractive properties as nearly shift-invariance and reduced aliasing, is introduced and performed on vibration signals, and then an original feature set including time-domain and frequency-domain features is extracted from the frequency-band signals of DTCWPT to reveal machine conditions. In order to achieve the desired performance of fault classification, a new framework for multiple classifier fusion is developed on the basis of neighborhood rough set (NRS). The original feature set is firstly granulated into different granularity levels by NRS and different sensitive feature sets (SFSs) are obtained for reflecting the machine conditions from different perspectives. After that, the SFSs are respectively input into different classification algorithms to achieve the complementary advantages and substantive fusion of different classifiers. Finally, the classification results of multiple classifiers based on different classification algorithms and input SFSs are fused using Bayesian belief method to come up with the final diagnosis result. The proposed method is applied to the fault diagnosis of gearbox and locomotive roller bearings. The diagnosis results show that the proposed method is able to reliably identify the different fault categories which include both single fault and compound faults, which has a better classification performance compared to any one of the individual classifiers. Moreover, the validity of feature selection based on NRS and the superiority of feature extraction based on DTCWPT are also demonstrated by the testing results.

Individual discrimination of freely swimming pulse-type electric fish from electrode array recordings

Pulse-type weakly electric fishes communicate through electrical discharges with a stereotyped waveform, varying solely the interval between pulses according to the information being transmitted. This simple codification mechanism is similar to the one found in various known neuronal circuits, which renders these animals as good models for the study of natural communication systems, allowing experiments involving behavioral and neuroethological aspects. Performing analysis of data collected from more than one freely swimming fish is a challenge since the detected electric organ discharge (EOD) patterns are dependent on each animal׳s position and orientation relative to the electrodes. However, since each fish emits a characteristic EOD waveform, computational tools can be employed to match each EOD to the respective fish. In this paper we describe a computational method able to recognize fish EODs from dyads using normalized feature vectors obtained by applying Fourier and dual-tree complex wavelet packet transforms. We employ support vector machines as classifiers, and a continuity constraint algorithm allows us to solve issues caused by overlapping EODs and signal saturation. Extensive validation procedures with Gymnotus sp. showed that EODs can be assigned correctly to each fish with only two errors per million discharges.

Directional dual-tree complex wavelet packet transforms for processing quadrature signals.

Quadrature signals containing in-phase and quadrature-phase components are used in many signal processing applications in every field of science and engineering. Specifically, Doppler ultrasound systems used to evaluate cardiovascular disorders noninvasively also result in quadrature format signals. In order to obtain directional blood flow information, the quadrature outputs have to be preprocessed using methods such as asymmetrical and symmetrical phasing filter techniques. These resultant directional signals can be employed in order to detect asymptomatic embolic signals caused by small emboli, which are indicators of a possible future stroke, in the cerebral circulation. Various transform-based methods such as Fourier and wavelet were frequently used in processing embolic signals. However, most of the times, the Fourier and discrete wavelet transforms are not appropriate for the analysis of embolic signals due to their non-stationary time-frequency behavior. Alternatively, discrete wavelet packet transform can perform an adaptive decomposition of the time-frequency axis. In this study, directional discrete wavelet packet transforms, which have the ability to map directional information while processing quadrature signals and have less computational complexity than the existing wavelet packet-based methods, are introduced. The performances of proposed methods are examined in detail by using single-frequency, synthetic narrow-band, and embolic quadrature signals.

Symmetrical directional dual-tree complex wavelet packet transform.

In this study, a symmetrical directional complex discrete wavelet packet transform, which can be applied directly to the quadrature format signals and has the ability of mapping directional information during decomposition stage, is proposed. With the proposed symmetrical directional complex wavelet packet transform, traditional symmetrical phasing filter technique, which is used for quadrature signal to directional signal conversion, is eliminated and the computational complexity of whole process is reduced. The performance of proposed method is examined in detail using real quadrature embolic signals.

Classification of emotions induced by music videos and correlation with participants’ rating

Emotional experience and preference play a vital role in selection of multimedia content for an individual. Brain electrical activity bears the emotional cues needed for emotion detection, but very modest research has been done to extract those cues. This paper presents a novel machine learning approach using Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) time–frequency features from electroencephalogram (EEG) to detect emotions together with an analysis of brain activity in different emotional states. Firstly, DT-CWPT is used to extract time–frequency emotional features. Then non-redundant and most discriminating emotional features are selected through singular value decomposition (SVD), QR factorization with column pivoting (QRcp) and F-Ratio based feature selection (FS) method. The reduced emotional feature set is used to classify emotion using support vector machine (SVM) and validated by leave-one-out cross-validation scheme. Results confirm the robustness and consistency in classification of emotions from EEG signals and significant correlation between participants’ self assessed ratings with emotional features. It also gives an analysis of activities in brain region during different emotional states.

Fault diagnosis of rolling bearing based on dual-treecomplex wavelet packet transform

Fault diagnosis of rolling bearing based on dual-treecomplex wavelet packet transform

复轮廓波包的构造及其图像去噪应用

Considering the normal contourlet transform only decomposed the low frequency coefficients of the signals, and ignored the high frequency coefficients, a novel complex contourlet packet transform is constructed by combining the analytic dual-tree complex wavelet packet transform and nonsubsampled directional filter banks. Then a complex contourlet packet image denoising algorithm based on neighbouring thresholding classification is proposed. The new transform has good characteristics of multiresolution, localization, directionality and anisotropy, as well as translation invariance. Furthermore, it has more abundant direction components. The experimental result shows that the complex contourlet packet transform can restrain Gibbs-like artificial around edges in the course of denoising, and preserve more details and textures of the images efficiently. The PSNR and the visual quality of this algorithm are also superior to the traditional methods.