Complex Wavelet Transform Research Articles

Balancing composite motion optimization using R-ERNN with plant disease

Soybean is a crop with a long cultivation history that plays a significant role on agricultural field. Early diagnosis of Soybean Mosaic Virus Disease (SMVD) is must require, because it causes a fast reduction and significant losses in soybean yields. Therefore, this paper proposes a Balancing Composite Motion optimization utilizing Recalling-Enhanced Recurrent Neural Network with Plant Disease for separating its severity into 0, 1 and 2 grades. Initially, hyper spectral image data was taken from Spec View software. Then, the spectral imageries of soybean leaves are processed to remove the redundancy in frequency bands that emerges owing to hyper spectral camera does not present any important information are filtered by Dual Tree Complex Wavelet Transform (DTCWT). In this, hyper spectral image features are extracted using Ternary pattern and discrete wavelet transform (TP-DWT) method. After completing the process of feature extraction, the feature extracted imageries are given to Recalling-Enhanced Recurrent Neural Network (RE-RNN) and it is used to classify the Soybean mosaic disease. Here, Balancing Composite Motion Optimization (BCMO) algorithm is employed for tuning the RE-RNN hyper parameters. The proposed PDI-RE-RNN-BCMO-HSI method provides 27.03%, 28.94% and 39.04% higher precision compared with existing method like grading model of soybean mosaic infection depending upon hyper spectral imaging technology (PDI-CNN-SVM-HSI), recognization of soybean varieties under hyper spectral imaging technology and one-dimensional CNN (PDI-1D-CNN-HSI) and hyper spectral imaging technology combined with multivariate models to recognize soybean disease (PDI-SNV-ELM-HSI) respectively.

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.

Single-channel Speech Separation Based on Double-density Dual-tree CWT and SNMF

Speech is essential to human communication; therefore, distinguishing it from noise is crucial. Speech separation becomes challenging in real-world circumstances with background noise and overlapping speech. Moreover, the speech separation using short-term Fourier transform (STFT) and discrete wavelet transform (DWT) addresses time and frequency resolution and time-variation issues, respectively. To solve the above issues, a new speech separation technique is presented based on the double-density dual-tree complex wavelet transform (DDDTCWT) and sparse non-negative matrix factorization (SNMF). The signal is separated into high-pass and low-pass frequency components using DDDTCWT wavelet decomposition. For this analysis, we only considered the low-pass frequency components and zeroed out the high-pass ones. Subsequently, the STFT is then applied to each sub-band signal to generate a complex spectrogram. Therefore, we have used SNMF to factorize the joint form of magnitude and the absolute value of real and imaginary (RI) components that decompose the basis and weight matrices. Most researchers enhance the magnitude spectra only, ignore the phase spectra, and estimate the separated speech using noisy phase. As a result, some noise components are present in the estimated speech results. We are dealing with the signal's magnitude as well as the RI components and estimating the phase of the RI parts. Finally, separated speech signals can be achieved using the inverse STFT (ISTFT) and the inverse DDDTCWT (IDDDTCWT). Separation performance is improved for estimating the phase component and the shift-invariant, better direction selectivity, and scheme freedom properties of DDDTCWT. The speech separation efficiency of the proposed algorithm outperforms performance by 6.53–8.17 dB SDR gain, 7.37-9.87 dB SAR gain, and 14.92–17.21 dB SIR gain compared to the NMF method with masking on the TIMIT dataset.

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.

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.

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.

Non-redundant shift-invariant complex wavelet transform and fractional gorilla troops optimization-based deep convolutional neural network for video watermarking

With the development of multimedia processing approaches along with the Internet leads to various issues, like data tampering, data piracy, and illicit dissemination. The quick progression of fast communication networks for digital video transmission has formulated the prerequisite copyright protection for these media. On the other hand, the development of Internet technologies has directed the accessibility of images, audio, and videos in different structures. Moreover, unlicensed users manipulate the utilization of multimedia by broadcasting them on numerous internet sites to get money fraudulently without the involvement of the original copyright holder. Besides, watermarking is a process utilized to hide the watermark sign present in the multimedia statistics that are not perceptible to an intruder for manipulating any information. This paper devised a novel video watermarking technique which uses an optimized deep learning technique. Here, the binary pixel map generation is performed using DCNN for self-embedding in input video. In addition, weights as well as bias of DCNN are trained by designed Fractional Gorilla Troops Optimization-Deep Convolutional Neural Network algorithm, thus FGTO_DCNN algorithm is developed. Accordingly, the FGTO technique is newly introduced by integrating FC along with GTO algorithm. Moreover, non-redundant Shift Invariant Complex Wavelet Transform (SICWT) is employed for extracted key frame and thus watermark image is fed into embedding process. After that, inverse non-redundant SICWT is applied for embedding video, which is further passed to key frame extraction in extraction stage. Finally, non-redundant SICWT is utilized for obtaining extracted watermark image. The developed FGTO-driven DCNN model attained improved performance than other existing video watermarking techniques with Mean Square Error (MSE) of 0.0437, Normalized Correlation Coefficient (NCC) of 0.9998, high Peak Signal-to-Noise Ratio (PSNR) value of 51.93 dB, and high Weighted Signal-to-Noise Ratio (WSNR) of 49.86 dB.

Real-time Classifier of Multilingual Font Styles based on ResNet, SwordNet, Logistic Regression and Random Forest Algorithms

Different languages have different characters. At the same time, each character has a lot of font styles. This makes it difficult for humans to recognize different font styles for different characters. However, being able to detect and identify these font styles quickly and accurately has many important application use cases in different fields. At the same time, a large number of Internet users use web pages to query font styles. Therefore, I choose to make this real-time multilingual font style recognition algorithm. In this paper, I propose an algorithm that recognizes the input text and pictures in real time to judge the language and style of the text. It includes ResNet, SwordNet, logistic regression and random forest algorithms. The whole algorithm also calls pytesseract and Google Tesseract to realize text recognition and text positioning. I used Font Datasets used in "Font and Calligraphy Style Recognition Using Complex Wavelet Transform" for training. At the same time, I also built an image text recognition algorithm and generated various font styles as a data source. Based on this data, we adjusted the parameters and finally achieved an accuracy rate higher than 90%.

Supervised Single Channel Speech Enhancement Method Using UNET

This paper proposes an innovative single-channel supervised speech enhancement (SE) method based on UNET, a convolutional neural network (CNN) architecture that expands on a few changes in the basic CNN architecture. In the training phase, short-time Fourier transform (STFT) is exploited on the noisy time domain signal to build a noisy time-frequency domain signal which is called a complex noisy matrix. We take the real and imaginary parts of the complex noisy matrix and concatenate both of them to form the noisy concatenated matrix. We apply UNET to the noisy concatenated matrix for extracting speech components and train the CNN model. In the testing phase, the same procedure is applied to the noisy time-domain signal as in the training phase in order to construct another noisy concatenated matrix that can be tested using a pre-trained or saved model in order to construct an enhanced concatenated matrix. Finally, from the enhanced concatenated matrix, we separate both the imaginary and real parts to form an enhanced complex matrix. Magnitude and phase are then extracted from the newly created enhanced complex matrix. By using that magnitude and phase, the inverse STFT (ISTFT) can generate the enhanced speech signal. Utilizing the IEEE databases and various types of noise, including stationary and non-stationary noise, the proposed method is evaluated. Comparing the exploratory results of the proposed algorithm to the other five methods of STFT, sparse non-negative matrix factorization (SNMF), dual-tree complex wavelet transform (DTCWT)-SNMF, DTCWT-STFT-SNMF, STFT-convolutional denoising auto encoder (CDAE) and casual multi-head attention mechanism (CMAM) for speech enhancement, we determine that the proposed algorithm generally improves speech quality and intelligibility at all considered signal-to-noise ratios (SNRs). The suggested approach performs better than the other five competing algorithms in every evaluation metric.

Exploring multivariate generalized gamma manifold for color texture retrieval

This work proposes a novel method for color-textured image retrieval on a Multivariate Generalized Gamma Distribution manifold (MGΓD). Thanks to the Gaussian copula theory, we define the expression of MGΓD, which efficiently models the statistical dependence structure between dual-tree complex wavelet transform (DTCWT) of the color components. The major contribution of this paper is to provide a geometric perspective to the MGΓD by treating it as a Riemannian manifold while proposing the geodesic distance (GD) as a measure of Riemannian similarity on it. Based on information geometry tools, we conduct a geometrical study of the MGΓD manifold, allowing us to derive two suitable approximations of the GD. The experiments are performed on five well-known color texture databases, considering the content-based image retrieval (CBIR) framework and using the RGB color space. The obtained results demonstrate the efficiency of the geometric interpretation through the proposed GD as a natural and intuitive similarity measure on the studied statistical manifold.

Optimal Deep Learning Architecture for Automated Segmentation of Cysts in OCT Images Using X-Let Transforms.

The retina is a thin, light-sensitive membrane with a multilayered structure found in the back of the eyeball. There are many types of retinal disorders. The two most prevalent retinal illnesses are Age-Related Macular Degeneration (AMD) and Diabetic Macular Edema (DME). Optical Coherence Tomography (OCT) is a vital retinal imaging technology. X-lets (such as curvelet, DTCWT, contourlet, etc.) have several benefits in image processing and analysis. They can capture both local and non-local features of an image simultaneously. The aim of this paper is to propose an optimal deep learning architecture based on sparse basis functions for the automated segmentation of cystic areas in OCT images. Different X-let transforms were used to produce different network inputs, including curvelet, Dual-Tree Complex Wavelet Transform (DTCWT), circlet, and contourlet. Additionally, three different combinations of these transforms are suggested to achieve more accurate segmentation results. Various metrics, including Dice coefficient, sensitivity, false positive ratio, Jaccard index, and qualitative results, were evaluated to find the optimal networks and combinations of the X-let's sub-bands. The proposed network was tested on both original and noisy datasets. The results show the following facts: (1) contourlet achieves the optimal results between different combinations; (2) the five-channel decomposition using high-pass sub-bands of contourlet transform achieves the best performance; and (3) the five-channel decomposition using high-pass sub-bands formations out-performs the state-of-the-art methods, especially in the noisy dataset. The proposed method has the potential to improve the accuracy and speed of the segmentation process in clinical settings, facilitating the diagnosis and treatment of retinal diseases.

Two stage multi-modal medical image fusion with marine predator algorithm-based cascaded optimal DTCWT and NSST with deep learning

Multimodal medical image fusion is highly essential to minimize the redundancy rate at the time of getting the required information through the input images by diverse medical imaging sensors. The main intention is to get an individual fused image that is highly informative for supporting the clinical evaluation. In this research work, Two Stage Multi-modal Medical Image Fusion is presented via the Cascaded Optimal Dual-Tree Complex Wavelet Transform (O-DTCWT) and the Non-Sub-sampled Shearlet Transform with two different medical image modalities. In the first stage, the collected image 1 and image 2 are separately given to the Optimal DTCWT for signal decomposition, which divides the high and low-frequency components. The high-frequency components are fused via fuzzy logic, whereas the Maximum rule is used to fuse the low frequency components. Then, the fused images are given to the inverse DTCWT for image reconstruction. In image fusion process, the DTCWT method is suitable for shift variance and multi-dimensionality. After, the reconstructed image is fed to the second stage of image decomposition. The NSST transformation technique decomposes the image into low and high-frequency components. Here, the high-frequency components are fused using the Optimized Deep Neural Network (ODNN), and the low-frequency components are fused with the help of averaging rule. The parameters in DTCWT and DNN are optimized by the Enhanced Marine Predator algorithm (EMPO). Finally, the two multi-modal medical images are fused. The simulations are made to evaluate the effectiveness of the developed TSMMIF model to improve image fusion quality.

Deep versus Handcrafted Tensor Radiomics Features: Prediction of Survival in Head and Neck Cancer Using Machine Learning and Fusion Techniques.

Although handcrafted radiomics features (RF) are commonly extracted via radiomics software, employing deep features (DF) extracted from deep learning (DL) algorithms merits significant investigation. Moreover, a "tensor'' radiomics paradigm where various flavours of a given feature are generated and explored can provide added value. We aimed to employ conventional and tensor DFs, and compare their outcome prediction performance to conventional and tensor RFs. 408 patients with head and neck cancer were selected from TCIA. PET images were first registered to CT, enhanced, normalized, and cropped. We employed 15 image-level fusion techniques (e.g., dual tree complex wavelet transform (DTCWT)) to combine PET and CT images. Subsequently, 215 RFs were extracted from each tumor in 17 images (or flavours) including CT only, PET only, and 15 fused PET-CT images through the standardized-SERA radiomics software. Furthermore, a 3 dimensional autoencoder was used to extract DFs. To predict the binary progression-free-survival-outcome, first, an end-to-end CNN algorithm was employed. Subsequently, we applied conventional and tensor DFs vs. RFs as extracted from each image to three sole classifiers, namely multilayer perceptron (MLP), random-forest, and logistic regression (LR), linked with dimension reduction algorithms. DTCWT fusion linked with CNN resulted in accuracies of 75.6 ± 7.0% and 63.4 ± 6.7% in five-fold cross-validation and external-nested-testing, respectively. For the tensor RF-framework, polynomial transform algorithms + analysis of variance feature selector (ANOVA) + LR enabled 76.67 ± 3.3% and 70.6 ± 6.7% in the mentioned tests. For the tensor DF framework, PCA + ANOVA + MLP arrived at 87.0 ± 3.5% and 85.3 ± 5.2% in both tests. This study showed that tensor DF combined with proper machine learning approaches enhanced survival prediction performance compared to conventional DF, tensor and conventional RF, and end-to-end CNN frameworks.

ResCoWNet: A deep convolutional neural network with residual learning based on DT-CWT for despeckling Optical Coherence Tomography images

Various retinal illnesses can be diagnosed and treated using the non-invasive diagnostic technology called OCT (Optical Coherence Tomography). However, the processes used to create OCT images generate speckle noise which considerably reduces the quality of the images, and the subsequent disease diagnosis is negatively impacted by such poor-quality images. The despeckling is effected using conventional methods such as spatial/transform domain filtering, dictionary learning, or a combination of these methods. Deep Convolutional Neural Networks (CNN) have improved the ability to exploit spatial correlations and extract data at various resolutions by adopting a hierarchical network structure, thereby making image-denoising methods more reliable. In deep networks, the Discrete Wavelet Transform (DWT) and its inverse are typically used in place of pooling operations. Nevertheless, the Dual-Tree Complex Wavelet Transform (DT-CWT) can provide a significant performance enhancement compared to deep network models for noise reduction based on DWT pooling. Hence to despeckle OCT images, this paper proposes a novel deep convolutional neural network (ResCoWNet) with residual learning based on DT-CWT. The input noisy image is transformed using DT-CWT into various subbands and is made to pass through several convolutional as well as residual blocks at three different levels in ResCoWNet. The denoised versions of OCT images produced by our model outperform the experimental results both quantitatively and subjectively.

DT-CWT and type-2 fuzzy-HSAPF for harmonic compensation in distribution system

Extensive utilization of power electronics in different applications injects harmonics into degrading the power quality. Harmonics can be eliminated in distribution systems using low-cost passive filters, but, they become bulky and complex in design with the increase in harmonic order. Hence, active power filters (APFs) are proposed as a voltage source converter (VSC) for providing better compensation, but active filtering is costly. To minimize such demerits, this article represents a hybrid shunt active power filter (HSAPF). For reference current calculation, dual-tree complex wavelet transform (DT-CWT) is used here and conventional proportional–integral (CPI), type-1 and type-2 fuzzy logic controllers (T1FLC and T2FLC) are employed for parameter optimization of HSAPF, to enhance the harmonic compensation ability and power factor. The case studies are performed in both simulations as well as real-time experimental setup using dSPACE, and it is analyzed that the proposed DT-CWT and type-2 fuzzy-based HSAPF provide better compensation performance in comparison to other controllers under different operating conditions.