Complex Wavelet Research Articles

An ASIC Implementation: Wave-Pipelined Circuit for Dual-Tree Complex Wavelet Transformation

In digital systems, the wave-pipelining concepts are used to achieve maximal rate of operation. A high operating frequency are obtained by adjusting the two factors called clock skew, & clock period in order to latch the combinational logic circuit output as stable. In literature, only trial & error, manual procedures are used for the choice of the optimum clock period and clock skew values between input/outputs register of wave-pipelined circuit.The major contribution of this paper is to propose automated ASIC implementation using wave-pipelined circuits.to verify the proposal a dual-tree complex wavelet transformation algorithm is taken into consideration in which scheme is again carried out into 3 types i.e., Pipelining, wave-pipelining, non-pipeliningWith the implementation result survey, it is observed that wave -pipelined circuits are 30% more faster than that of non-pipelining circuits, pipelined circuits are 49% more faster than that of wave-pipelined circuits but occupies 30% more area and 3% more power dissipating than that of wave-pipelined circuits.

Learning a microlocal prior for limited-angle tomography

Abstract Limited-angle tomography is a highly ill-posed linear inverse problem. It arises in many applications, such as digital breast tomosynthesis. Reconstructions from limited-angle data typically suffer from severe stretching of features along the central direction of projections, leading to poor separation between slices perpendicular to the central direction. In this paper, a new method is introduced, based on machine learning and geometry, producing an estimate for interfaces between regions of different X-ray attenuation. The estimate can be presented on top of the reconstruction, indicating more reliably the separation between features. The method uses directional edge detection, implemented using complex wavelets and enhanced with morphological operations. By using convolutional neural networks, the visible part of the singular support is first extracted and then extended to the full domain, filling in the parts of the singular support that would otherwise be hidden due to the lack of measurement directions.

A Dual-Tree–Complex Wavelet Transform-Based Infrared and Visible Image Fusion Technique and Its Application in Tunnel Crack Detection

Computer vision methods have been widely used in recent years for the detection of structural cracks. To address the issues of poor image quality and the inadequate performance of semantic segmentation networks under low-light conditions in tunnels, in this paper, infrared images are used, and a preprocessing method based on image fusion technology is developed. First, the DAISY descriptor and the perspective transform are applied for image alignment. Then, the source image is decomposed into high- and low-frequency components of different scales and directions using DT-CWT, and high- and low-frequency subband fusion rules are designed according to the characteristics of infrared and visible images. Finally, a fused image is reconstructed from the processed coefficients, and the fusion results are evaluated using the improved semantic segmentation network. The results show that using the proposed fusion method to preprocess images leads to a low false alarm rate and low missed detection rate in comparison to those using the source image directly or using the classical fusion algorithm.

Evaluation of WATERMARK Channel for Underwater Communication using Dual Tree Complex Wavelet Transform based Orthogonal Frequency Division Multiplexing Model

Underwater communication is one of the important research areas which involves design and development of communication systems that can demonstrate high data rate and low Bit Error Rate (BER). In this work three different modulation schemes are compared for their performances in terms of BER and Peak to Average Power Ratio (PAPR). The realistic channel model called WATERMARK is used as a benchmark to evaluate channel performances. The mathematical model is developed in MATLAB and channel environments such as Norway Oslo fjord (NOF1), Norway Continental Shelf (NCS1), Brest Commercial Harbour (BCH1), Kauai (KAU1, KAU2) are considered for modelling different underwater channels. The data symbols are modulated using Dual Tree Complex Wavelet Transform (DTCWT) Orthogonal Frequency Division Multiplexing (OFDM) model to generate multi subcarriers and are demodulated at the receiver considering underwater channel environments. The BER results are evaluated for channel depth less than 10m and 10-50m. An improvement of 2x10-2 in terms of BER is observed when compared with Fast Fourier Transform (FFT) based OFDM model.

Classification of lung pathologies in neonates using dual-tree complex wavelet transform

IntroductionUndiagnosed and untreated lung pathologies are among the leading causes of neonatal deaths in developing countries. Lung Ultrasound (LUS) has been widely accepted as a diagnostic tool for neonatal lung pathologies due to its affordability, portability, and safety. However, healthcare institutions in developing countries lack well-trained clinicians to interpret LUS images, which limits the use of LUS, especially in remote areas. An automated point-of-care tool that could screen and capture LUS morphologies associated with neonatal lung pathologies could aid in rapid and accurate diagnosis.MethodsWe propose a framework for classifying the six most common neonatal lung pathologies using spatially localized line and texture patterns extracted via 2D dual-tree complex wavelet transform (DTCWT). We acquired 1550 LUS images from 42 neonates with varying numbers of lung pathologies. Furthermore, we balanced our data set to avoid bias towards a pathology class.ResultsUsing DTCWT and clinical features as inputs to a linear discriminant analysis (LDA), our approach achieved a per-image cross-validated classification accuracy of 74.39% for the imbalanced data set. Our classification accuracy improved to 92.78% after balancing our data set. Moreover, our proposed framework achieved a maximum per-subject cross-validated classification accuracy of 64.97% with an imbalanced data set while using a balanced data set improves its classification accuracy up to 81.53%.ConclusionOur work could aid in automating the diagnosis of lung pathologies among neonates using LUS. Rapid and accurate diagnosis of lung pathologies could help to decrease neonatal deaths in healthcare institutions that lack well-trained clinicians, especially in developing countries.

HEURISTIC-ASSISTED ADAPTIVE HYBRID DEEP LEARNING MODEL WITH FEATURE SELECTION FOR EPILEPSY DETECTION USING EEG SIGNALS

The word epilepsy is related to a neurological disease occurred by abnormalities of brain neurons. Timely detection of epilepsy is helpful for patients to decrease the mortality rate. To detect seizures, the Encephalogram (EEG) signals are analyzed based on monitoring the conditions of patients, and seizures can be detected from the EEG signal at appropriate times. The manual detection from the EEG signal requires more time for detecting the seizures and also it needs domain knowledge. The miss detection is eliminated by improving the classification performance in automatic epilepsy detection. Nowadays, deep learning models have not been greatly harnessed in the detection of epileptic seizures due to inappropriate descriptions of time-domain signals and sub-optimal classifier design. The aforementioned issues are combated by the novel Adaptive Hybrid Deep Learning (AHDL) approaches for epilepsy detection using EEG signals. Initially, the required EEG signal is collected from benchmark datasets. The collected signals are subjected to a signal decomposition phase that is accomplished by five levels of decomposition using Dual-Tree Complex Wavelet Transform (DTCWT), where the parameters are tuned by Improved Probability-based Coyote Optimization Algorithm (IP-COA). Further, the decomposed signal is given for feature extraction, where it divides the signal into two phases. In the first phase, the first feature set is obtained by using One-Dimensional Convolutional Neural Network (1DCNN), whereas in the second phase, the proposed model utilizes Auto Encoder (AE) to provide the second feature set. These resultant features are getting fused and the optimal feature selection process is found, where the features are obtained optimally by the IP-COA. Finally, epilepsy detection is accomplished with the aid of proposed AHDL with both Radial Basis-Recurrent Neural Networks (RB-RNN), where the hyperparameters are optimized using IP-COA. Thus, the experimental results illustrate that the suggested model enhances the detection and classification rate.

Exploring the efficacy of multi-flavored feature extraction with radiomics and deep features for prostate cancer grading on mpMRI

BackgroundThe purpose of this study is to investigate the use of radiomics and deep features obtained from multiparametric magnetic resonance imaging (mpMRI) for grading prostate cancer. We propose a novel approach called multi-flavored feature extraction or tensor, which combines four mpMRI images using eight different fusion techniques to create 52 images or datasets for each patient. We evaluate the effectiveness of this approach in grading prostate cancer and compare it to traditional methods.MethodsWe used the PROSTATEx-2 dataset consisting of 111 patients’ images from T2W-transverse, T2W-sagittal, DWI, and ADC images. We used eight fusion techniques to merge T2W, DWI, and ADC images, namely Laplacian Pyramid, Ratio of the low-pass pyramid, Discrete Wavelet Transform, Dual-Tree Complex Wavelet Transform, Curvelet Transform, Wavelet Fusion, Weighted Fusion, and Principal Component Analysis. Prostate cancer images were manually segmented, and radiomics features were extracted using the Pyradiomics library in Python. We also used an Autoencoder for deep feature extraction. We used five different feature sets to train the classifiers: all radiomics features, all deep features, radiomics features linked with PCA, deep features linked with PCA, and a combination of radiomics and deep features. We processed the data, including balancing, standardization, PCA, correlation, and Least Absolute Shrinkage and Selection Operator (LASSO) regression. Finally, we used nine classifiers to classify different Gleason grades.ResultsOur results show that the SVM classifier with deep features linked with PCA achieved the most promising results, with an AUC of 0.94 and a balanced accuracy of 0.79. Logistic regression performed best when using only the deep features, with an AUC of 0.93 and balanced accuracy of 0.76. Gaussian Naive Bayes had lower performance compared to other classifiers, while KNN achieved high performance using deep features linked with PCA. Random Forest performed well with the combination of deep features and radiomics features, achieving an AUC of 0.94 and balanced accuracy of 0.76. The Voting classifiers showed higher performance when using only the deep features, with Voting 2 achieving the highest performance, with an AUC of 0.95 and balanced accuracy of 0.78.ConclusionOur study concludes that the proposed multi-flavored feature extraction or tensor approach using radiomics and deep features can be an effective method for grading prostate cancer. Our findings suggest that deep features may be more effective than radiomics features alone in accurately classifying prostate cancer.

Denoising Non-Stationary Signals via Dynamic Multivariate Complex Wavelet Thresholding.

Over the past few years, we have seen an increased need to analyze the dynamically changing behaviors of economic and financial time series. These needs have led to significant demand for methods that denoise non-stationary time series across time and for specific investment horizons (scales) and localized windows (blocks) of time. Wavelets have long been known to decompose non-stationary time series into their different components or scale pieces. Recent methods satisfying this demand first decompose the non-stationary time series using wavelet techniques and then apply a thresholding method to separate and capture the signal and noise components of the series. Traditionally, wavelet thresholding methods rely on the discrete wavelet transform (DWT), which is a static thresholding technique that may not capture the time series of the estimated variance in the additive noise process. We introduce a novel continuous wavelet transform (CWT) dynamically optimized multivariate thresholding method (WaveL2E). Applying this method, we are simultaneously able to separate and capture the signal and noise components while estimating the dynamic noise variance. Our method shows improved results when compared to well-known methods, especially for high-frequency signal-rich time series, typically observed in finance.

From desolvation-induced self-organization on the MALDI anchor target chip surfaces to laser-induced self-organization in MALDI techniques: Correlation-spectral analysis and complex wavelet analysis of tesiographic spots on the anchor chips

This article proposes to analyze the formation and “morphogenesis” during desolvation of drops on MALDI targets and target chips using 2D correlation spectral analysis based on the two-dimensional Fourier transform and wavelet spectroscopy methods in the real and imaginary regions. The results of the correlation-spectral and wavelet analysis are shown in the illustrations in the text of the article.

Improving Chest Monitoring through Magnetic Resonance Angiogram Image Contrast Enhancement.

Magnetic resonance angiography is a medical procedure used to offer an image of the blood vessels and organs of the body. Given the worldwide spread of cardiovascular diseases, more and more resources are invested in treating them. One of the most modern treatments involves the acquisition of images of the heart. Sometimes the contrast of these images is not satisfactory. Injecting invasive enhancement substances to obtain a better view of the cardiac route is not advisable. However, software algorithms can solve the problem. This study proposes and tests a local adaptive contrast-adjustment algorithm using the dual-tree complex wavelet transform. The method has been tested with medical data from a public database to allow comparisons to other methods. The selected algorithm further improved the contrast of images. The performances are given for evaluation, both visually (to help doctors make accurate diagnoses) and in parametric form (to show engineers which parts of the algorithm might need improvement). Compared to other contrast enhancement methods, the proposed wavelet algorithm shows good results and greater stability. Thus, we aim to avoid future pointless complications due to unnecessary contrast substances.

Stabilized videos source identification method based on undecimated dual-tree complex wavelet transform and Bayesian adaptive direct search algorithm

Stabilized videos source identification method based on undecimated dual-tree complex wavelet transform and Bayesian adaptive direct search algorithm

RF Signal Feature Extraction in Integrated Sensing and Communication

Because of the open property of information sharing in integrated sensing and communication, it is inevitable to face security problems such as user information being tampered, eavesdropped, and copied. Radio frequency (RF) individual identification technology is an important means to solve its security problems at present. Whether using machine learning methods or current deep learning-based target fingerprint identification, its performance is based on how well the radio frequency features (RFF) are extracted. Since the received signal is affected by various factors, we believe that we should first find the intrinsic features that can describe the properties of the target, which is the key to enhance the RF fingerprint recognition. In this paper, we try to analyze the intrinsic characteristics of the components that influenced the signal by the transmitting source and derive a mathematical formula to describe the RF characteristics. We propose a method using dynamic wavelet transform and wavelet spectrum (DWTWS) to enhance RFF features. The performance of the proposed method was evaluated by experimental data. Using a support vector machine classifier, the recognition accuracy is 99.6% for 10 individuals at a signal-to-noise ratio (SNR) of 10 dB. In comparison with the dual-tree complex wavelet transform (DT-CWT) feature extraction method and the wavelet scattering transform method, the DWTWS method has increased the interclass distance of different individuals and enhanced the recognition accuracy. The DWTWS method is superior at low SNR, with performance improvements of 53.1% and 10.7% at 0 dB.

An efficient watermarking algorithm for digital audio data in security applications

Transform-domain audio watermarking systems are more robust than time-domain systems. However, the main weakness of these systems is their high computational cost, especially for long-duration audio signals. Therefore, they are not desirable for real-time security applications where speed is a critical factor. In this paper, we propose a fast watermarking system for audio signals operating in the hybrid transform domain formed by the fractional Charlier transform (FrCT) and the dual-tree complex wavelet transform (DTCWT). The central idea of the proposed algorithm is to parallelize the intensive and repetitive steps in the audio watermarking system and then implement them simultaneously on the available physical cores on an embedded systems cluster. In order to have a low power consumption and a low-cost cluster with a large number of physical cores, four Raspberry Pis 4B are used where the communication between them is ensured using the Message Passing Interface (MPI). The adopted Raspberry Pi cluster is also characterized by its portability and mobility, which are required in watermarking-based smart city applications. In addition to its resistance to any possible manipulation (intentional or unintentional), high payload capacity, and high imperceptibility, the proposed parallel system presents a temporal improvement of about 70%, 80%, and 90% using 4, 8, and 16 physical cores of the adopted cluster, respectively.

Aircraft Actuator Performance Analysis Based on Dynamic Neural Network

Monitoring the condition of the aircraft actuators in various operating and environmental circumstances, this paper presents a method for measuring the surface roughness of aircraft actuators. The proposed method starts with the current and vibration signal as failure indicators and a dual-tree complex wavelet transformation (DTCWT) to generate the necessary features. Time-delay neural networks (TDNNs) have been developed for real-time performance monitoring to categorize problems and determine their severity. The simulation results show that the suggested method can accurately identify various faults.

UDTCWT-PHFMs Domain Statistical Image Watermarking Using Vector BW-Type R Distribution

For any image watermarking algorithm, how to achieve the trade-off among robustness, imperceptibility, and watermark capacity is a challenging problem because of their mutually constrained relationship. In this paper, we design a statistical-based image watermarking system to solve the trade-off problem. In embedding process, consider the imperceptibility and the robustness, we inventively combine the undecimated dual tree complex Wavelet transform (UDTCWT) and the polar harmonic Fourier moments (PHFMs) to obtain the UDTCWT-PHFMs magnitudes as the watermark carriers, and we embed watermark signals in multiplicative manner. In modeling phase, by analyzing the statistical property and the multiple correlations of the UDTCWT-PHFMs magnitudes, we bound the magnitudes of different directions into the vector groups, and then we model them with the vector Roy type bivariate Weibull (BW-Type R) distribution so that we can capture accurately the marginal characteristics, the inter-scale dependencies, and the inter-orientation dependencies at the same time. Moreover, we obtain the model parameters with the double looped maximum likelihood estimation (double-looped MLE). Benefit from the reliable modeling result, we finally derive a novel specific image watermark decoder to blindly extract the hidden watermarks. Extensive experiment results declare the designed statistical image watermarking system achieves the better balance among imperceptibility, robustness, and payload.

High-order knowledge-based Discriminant features for kinship verification

This research work aims to propose an effective and robust face kinship verification system by leveraging several axes, including advanced learning techniques, deep learning, and CNN networks. The contributions of this work include the use of a preprocessing method known as Multiscale Retinex with Chromaticity Preservation (MSRCP) and the Gradientfaces technique (GRF) to improve image quality and contrast enhancement. Additionally, a novel discriminative handcrafted descriptor called Histograms of dual-tree complex wavelet transform (Hist-DTCWT) is proposed. Moreover, a logistic regression for score-level fusion is used to enhance the matching process. Furthermore, a high-order knowledge-based feature using a tensor subspace model was proposed to combine multiple discriminative features. Tests were conducted out using three datasets, where the proposed method has outperformed the state the art. Typically, verification accuracies of 96.62%, 95.54% and 92.94% have been reached under Cornell KinFace, UB KinFace and TS KinFace datasets, respectively.

Shape estimation of flexible ultrasound arrays using spatial coherence: A preliminary study

A flexible ultrasound array can potentially provide a larger field-of-view, enhanced imaging resolution, and less operator dependency compared to conventional rigid transducer arrays. However, such transducer arrays require information about relative element positions for beamforming and reconstructing geometrically accurate sonograms. In this study, we assess the potential utility of using spatial coherence of backscattered radiofrequency data to estimate transducer array shape (inverse problem). The methodology is evaluated through 1) simulation of flexible arrays and 2) blinded in vivo experiments using commercial rigid transducer arrays on various anatomical targets (shoulder, forearm, scapular, posterior calf muscles, and abdomen) and multi-purpose ultrasound phantoms. The average Euclidean error of shape estimation is below 0.1 wavelengths for simulated arrays and below 1.4 wavelengths (median: 0.58 wavelengths) for real arrays. The complex wavelet structural similarity index between the B-mode images reconstructed with estimated and ground truth array shapes is above 99 % and 96 %, for simulations and experiments, respectively. These findings suggest that optimizing for spatial coherence may be an effective way to estimate the unknown shape of conformal ultrasound arrays.

Gabor's “complex signal” revisited: Complexifying frames and bases

AbstractIn 1946, Dennis Gabor introduced the analytic signal for real‐valued signals f. Here, H is the Hilbert transform. This complexification of functions allows for an analysis of their amplitude and phase information and has ever since given well‐interpretable insight into the properties of the signals over time. The idea of complexification has been reconsidered with regard to many aspects: examples are the dual tree complex wavelet transform, or via the Riesz transform and the monogenic signal, that is, a multi‐dimensional version of the Hilbert transform, which in combination with multi‐resolution approaches leads to Riesz wavelets, and others. In this context, we ask two questions: Which pairs of real orthonormal bases (ONBs), Riesz bases, frames and Parseval frames and can be “rebricked” to complex‐valued ones ? And which real operators A allow for rebricking via the ansatz ? In this short note, we give answers to these questions with regard to a characterization which linear operators A are suitable for rebricking while maintaining the structure of the original real valued family. Surprisingly, the Hilbert transform is not among them.

RiPa-Net: Recognition of Rice Paddy Diseases with Duo-Layers of CNNs Fostered by Feature Transformation and Selection.

Rice paddy diseases significantly reduce the quantity and quality of crops, so it is essential to recognize them quickly and accurately for prevention and control. Deep learning (DL)-based computer-assisted expert systems are encouraging approaches to solving this issue and dealing with the dearth of subject-matter specialists in this area. Nonetheless, a major generalization obstacle is posed by the existence of small discrepancies between various classes of paddy diseases. Numerous studies have used features taken from a single deep layer of an individual complex DL construction with many deep layers and parameters. All of them have relied on spatial knowledge only to learn their recognition models trained with a large number of features. This study suggests a pipeline called "RiPa-Net" based on three lightweight CNNs that can identify and categorize nine paddy diseases as well as healthy paddy. The suggested pipeline gathers features from two different layers of each of the CNNs. Moreover, the suggested method additionally applies the dual-tree complex wavelet transform (DTCWT) to the deep features of the first layer to obtain spectral-temporal information. Additionally, it incorporates the deep features of the first layer of the three CNNs using principal component analysis (PCA) and discrete cosine transform (DCT) transformation methods, which reduce the dimension of the first layer features. The second layer's spatial deep features are then combined with these fused time-frequency deep features. After that, a feature selection process is introduced to reduce the size of the feature vector and choose only those features that have a significant impact on the recognition process, thereby further reducing recognition complexity. According to the results, combining deep features from two layers of different lightweight CNNs can improve recognition accuracy. Performance also improves as a result of the acquired spatial-spectral-temporal information used to learn models. Using 300 features, the cubic support vector machine (SVM) achieves an outstanding accuracy of 97.5%. The competitive ability of the suggested pipeline is confirmed by a comparison of the experimental results with findings from previously conducted research on the recognition of paddy diseases.

Accurate Detection for Zirconium Sheet Surface Scratches Based on Visible Light Images.

Zirconium sheet has been widely used in various fields, e.g., chemistry and aerospace. The surface scratches on the zirconium sheets caused by complex processing environment have a negative impact on the performance, e.g., working life and fatigue fracture resistance. Therefore, it is necessary to detect the defect of zirconium sheets. However, it is difficult to detect such scratch images due to lots of scattered additive noise and complex interlaced structural texture. Hence, we propose a framework for adaptively detecting scratches on the surface images of zirconium sheets, including noise removing and texture suppressing. First, the noise removal algorithm, i.e., an optimized threshold function based on dual-tree complex wavelet transform, uses selected parameters to remove scattered and numerous noise. Second, the texture suppression algorithm, i.e., an optimized relative total variation enhancement model, employs selected parameters to suppress interlaced texture. Finally, by connecting disconnection based on two types of connection algorithms and replacing the Gaussian filter in the standard Canny edge detection algorithm with our proposed framework, we can more robustly detect the scratches. The experimental results show that the proposed framework is of higher accuracy.