Multilevel Discrete Wavelet Transform Research Articles

Lattice 123 pattern for automated Alzheimer’s detection using EEG signal

AbstractThis paper presents an innovative feature engineering framework based on lattice structures for the automated identification of Alzheimer's disease (AD) using electroencephalogram (EEG) signals. Inspired by the Shannon information entropy theorem, we apply a probabilistic function to create the novel Lattice123 pattern, generating two directed graphs with minimum and maximum distance-based kernels. Using these graphs and three kernel functions (signum, upper ternary, and lower ternary), we generate six feature vectors for each input signal block to extract textural features. Multilevel discrete wavelet transform (MDWT) was used to generate low-level wavelet subbands. Our proposed model mirrors deep learning approaches, facilitating feature extraction in frequency and spatial domains at various levels. We used iterative neighborhood component analysis to select the most discriminative features from the extracted vectors. An iterative hard majority voting and a greedy algorithm were used to generate voted vectors to select the optimal channel-wise and overall results. Our proposed model yielded a classification accuracy of more than 98% and a geometric mean of more than 96%. Our proposed Lattice123 pattern, dynamic graph generation, and MDWT-based multilevel feature extraction can detect AD accurately as the proposed pattern can extract subtle changes from the EEG signal accurately. Our prototype is ready to be validated using a large and diverse database.

DeepFake detection based on high-frequency enhancement network for highly compressed content

The DeepFake, which generates synthetic content, has sparked a revolution in the fight against deception and forgery. However, most existing DeepFake detection methods mainly focus on improving detection performance with high-quality data while ignoring low-quality synthetic content that suffers from high compression. To address this issue, we propose a novel High-Frequency Enhancement framework, which leverages a learnable adaptive high-frequency enhancement network to enrich weak high-frequency information in compressed content without uncompressed data supervision. The framework consists of three branches, i.e., the Basic branch with RGB domain, the Local High-Frequency Enhancement branch with Block-wise Discrete Cosine Transform, and the Global High-Frequency Enhancement branch with Multi-level Discrete Wavelet Transform. Among them, the local branch utilizes the Discrete Cosine Transform coefficient and channel attention mechanism to indirectly achieve adaptive frequency-aware multi-spatial attention, while the global branch supplements the high-frequency information by extracting coarse-to-fine multi-scale high-frequency cues and cascade-residual-based multi-level fusion by Discrete Wavelet Transform coefficients. In addition, we design a Two-Stage Cross-Fusion module to effectively integrate all information, thereby greatly enhancing weak high-frequency information in low-quality data. Experimental results on FaceForensics++, Celeb-DF, and OpenForensics datasets show that the proposed method outperforms the existing state-of-the-art methods and can effectively improve the detection performance of DeepFakes, especially on low-quality data. The code is available here.11https://github.com/Gina-YUE/HiFE.

Dual-Attention Based Multi-Path Approach for Intensifying Stock Market Forecasting

In light of the existing challenges in capturing short-term fluctuations and achieving accurate predictions in stock market time series data, this research presents the “Dual-Attention MDWT-CVT-LSTM,” a revolutionary financial time series forecasting model. This model makes use of a dual-attention mechanism in conjunction with a Variant Transformation-Gated Long Short-Term Memory (LSTM) network. The approach relies on the Multilevel Discrete Wavelet Transform (MDWT) to separate stock market index sequences into high-frequency and low-frequency components. Furthermore, a transformative gating mechanism is incorporated into the LSTM, featuring fusion gated units to create the VT-LSTM, effectively addressing abrupt short-term information changes. Within the dual-attention network, VT-LSTM is combined with one-dimensional temporal convolution (Conv1D) to extract spatial and temporal features of data at different frequencies. This facilitates the prediction of various sub-sequences, enabling multilevel and multi-path forecasting research. The experimental evaluation uses diverse models on stock market index datasets and individual stock datasets. The results show the superior prediction accuracy of the proposed model compared to alternative methods, underscoring its practical viability in stock market forecasting applications.

Epilepsy detection in 121 patient populations using hypercube pattern from EEG signals

BackgroundEpilepsy is one of the most commonly seen neurologic disorders worldwide and has generally caused seizures. Electroencephalography (EEG) is widely used in seizure diagnosis. To detect epilepsy automatically, various machine learning (ML) models have been introduced in the literature, but the used EEG signal datasets for epilepsy detection are relatively small. Our main objective is to present a large EEG signal dataset and investigate the detection ability of a new hypercube pattern-based framework using the EEG signals. Material and methodThis study collected a large EEG signal dataset (10,356 EEG signals) from 121 participants. We proposed a new information fusion-based feature engineering framework to get high classification performance from this dataset. The dataset consists of 35 channels, and our proposed feature engineering model extracts features from each channel. A new hypercube-based feature extractor has been proposed to generate two feature vectors in the feature extraction phase. Various statistical parameters of the signals have been used to create a feature vector. Multilevel discrete wavelet transform (MDWT) has been applied to develop a multileveled feature extraction function, and seven feature vectors have been extracted. In this work, we have extracted 245 (=35 × 7) feature vectors, and the most valuable features from these vectors have been selected using the neighborhood component analysis (NCA) selector. Finally, these selected features were fed to the k nearest neighbors (kNN) classifier with the leave one subject out (LOSO) cross-validation (CV) strategy. These results have been voted/fused to obtain the highest classification performance. ResultsIn this work, we have attained 87.78% classification accuracy using voting these vectors and 79.07% with LOSO CV with the EEG signals. ConclusionsThe proposed fusion-based feature engineering model achieved satisfactory classification performance using the largest EEG signal datasets for epilepsy detection.

The Use of the Multi-Scale Discrete Wavelet Transform and Deep Neural Networks on ECGs for the Diagnosis of 8 Cardio-Vascular Diseases

Cardiovascular diseases (CVD) continues to be the leading cause of death worldwide, with over 17 million deaths each year. In 2015, approximately 422 million people suffered from cardiovascular disease (CVD). Reading and analyzing electrocardiograms (ECGs) can be time consuming, and the development of decision support tools based on automated systems can facilitate and speed up the diagnosis of ECGs. In this paper, we propose a 12 leads ECG signals classification using Multi-level Discrete Wavelet Transform and ResNet34 Deep Learning algorithm which classifies 8 types of cardiovascular diseases: Atrial fibrillation (AF), 1st degree atrioventricular block (AV), Left bundle branch block (LBBB), Right bundle branch block (RBBB), Premature ventricular contraction (PVC), Premature atrial contraction (PAC), ST segment depression (STD), and ST segment elevation (STE). The ECGs are preprocessed, and different features are extracted using Multi-level Discrete Wavelet Transform. The model is trained on a database of more than 6000 electrocardiograms which includes 9 types of 12-lead ECGs: a normal type and the 8 abnormal ones which correspond to the diseases mentioned above.

Automated EEG sentence classification using novel dynamic-sized binary pattern and multilevel discrete wavelet transform techniques with TSEEG database

Electroencephalography (EEG) signal is an important physiological signal commonly used in machine learning to decode brain activities, including imagined words and sentences. We aimed to develop an automated lightweight EEG signal-based sentence classification model using a novel dynamic-sized binary pattern (DSBP) textural feature extractor and iterative multi-classifiers based majority voting (IMCMV) algorithm for iterative voting of results calculated using different classifiers for multi-channel EEG signal inputs. A new Turkish sentence EEG(TSEEG) was prospectively acquired. It comprised of 15-second 14-channel EEG signals recorded when 40 volunteers (for each dataset, we collected EEG signals from 20 participants) were either shown or read corresponding to demonstration or listening modes, respectively. Hence, 20 standardized commonly used sentences were obtained in their native Turkish language. The developed sentence classification model extracted 5,400 multilevel deep features from each channel EEG signal segment using the novel DSBP, statistical features, and multilevel discrete wavelet transform (MDWT). 512 features were then chosen using the neighborhood component analysis selection function. k-nearest neighbor and support vector machine classifiers were used to calculate two prediction vectors from the selected features using tenfold cross-validation, i.e., 28 vectors were generated for each 14-channel EEG recording. Finally, the best general voted results were determined for increasing numbers of iteratively calculated prediction vectors using the novel IMCMV algorithm. Channel-wise and voted results were found to be excellent for sentence classification for the TSEEG dataset in both demonstration and listening modes. The DSBP-IMCMV-based model attained the best general classification rates of 98.81% and 98.19% in the demonstration and listening modes, respectively.

An accurate valvular heart disorders detection model based on a new dual symmetric tree pattern using stethoscope sounds

Background and purposeValvular heart disease (VHD) is an important cause of morbidity and mortality. Echocardiography is the reference standard for VHD diagnosis but is not universally accessible. Manual cardiac auscultation is inadequate for screening VHD. Many machine learning models using heart sounds acquired with an electronic stethoscope may improve the accuracy of VHD diagnosis. We aimed to develop an accurate sound classification model for VHD diagnosis. Materials and methodsA new large stethoscope sound dataset containing 10,366 heart sounds divided into ten categories (nine VHD and one healthy) were prospectively collected. We developed a handcrafted learning model that comprised multilevel feature extraction based on a dual symmetric tree pattern (DSTP) and multilevel discrete wavelet transform (DWT), feature selection, and classification. The multilevel DWT was used to create subbands to extract features at both high and low levels. Then, iterative neighborhood component analysis was used to select the most discriminative 512 features from among the extracted features in the generated feature vector. In the classification phase, a support vector machine (SVM) was used with 10-fold cross-validation (CV) and leave-one-subject-out (LOSO) CV. ResultsOur proposed DSTP-based model attained 99.58% and 99.84% classification accuracies using SVM classifier with 10-fold CV and LOSO CV, respectively. ConclusionsThe presented DSTP-based classification model attained excellent multiclass classification performance on a large prospective heart sound dataset at a low computational cost.

Defining of the Minimum Wavelet Filter Coefficients Bit-Width for 3D Medical Imaging

Medical imaging uses a variety of modalities to provide visual information about a patient. Various methods are used to process this data. Many of them are based on discrete wavelet transform (DWT). Its use will allow effective denoising and compression of 2D and 3D images. This paper proposes a new approach to linear time-invariant wavelet filtering using quantized filter coefficients when using which the computational errors have different signs and allow to partially compensate each other as a result of which the processed image is of high quality. The analysis of the quantization noise of the direct multilevel DWT filter coefficients is carried out. The derived formulas demonstrate the relationship between the quantization accuracy of these coefficients and the processing quality of digital 3D images. The derived formulas for calculating the minimum accuracy of the wavelet filter coefficients representation in the computing devices memory allow minimizing the effect of quantization noise on the result of 3D images processing. Modelling of 3D medical tomographic images DWT processing showed that a decrease in the ratio of the average voxel brightness to the maximum allowable value with increasing color depth of images leads to faster achievement of high quality compared to the results of theoretical analysis with an increase in the value of the scaling degree of the wavelet filter coefficients. The obtained theoretical and practical results open up the possibility for reducing the computational complexity of software and hardware implementation of wavelet processing of 3D medical visual data on modern microelectronic devices (field-programmable gate arrays, application-specific integrated circuits, etc.).

Two-level security approach combining watermarking and encryption for securing critical colored images

With the widespread of the Internet and the rapid growth of technologies, several challenges face data hiding process. Although watermarking is widely used to hide/embed critical data into a cover image, the watermarked image may be attacked/hacked and the embedded data may be extracted or damaged. This paper presents an efficient two-level security approach to protect colored watermark images. In the first level, the color image is embedded into a grayscale image using a multi-level Discrete Wavelet Transform (DWT), Singular Value Decomposition (SVD), and wavelet fusion. In the second level, an additional encryption process on the watermarked image is done by using two-dimensional logistic chaotic map in a case and Advanced Encryption Standard (AES) in another case. The proposed scheme is evaluated by using several images and different hacks on the transmitted image. The experimental results show that the proposed scheme is efficient and can face many attacks such as Gaussian, Blur, Wrap, and Cropping. In addition, the extracted watermarks are recognized even when the marked images suffered from attacks.

An enhanced technique for digital watermarking using multilevel DWT and error correcting codes

Digital Watermarking has attracted researchers’ attention because of its useful applications and, over the past decades, great efforts have been made to develop digital watermarking techniques and algorithms. Most researches use different transform techniques to enhance the robustness and quality of extracted watermark. This paper presents an enhanced technique for digital image watermarking based on multilevel Discrete Wavelet Transform (DWT) in conjunction with the well-known RS codes over finite fields. To observe and appreciate the significance of using the error correcting codes technique for enhancing the digital watermarking performance against attacks, a series of experiments were conducted. The enhanced methodology, presented and implemented in this research, achieved a very good performance. Regarding the significance of using error correcting codes in conjunction with DWT transform digital image watermarking; it was shown that, in all cases investigated, for all the attacks considered, there was an increase in the robustness of the digital watermark, in terms of the performance measure SSIM values. In some cases, it improves, to almost 27 times, the case without using error correcting codes. Among each class of codes, for all the attacks, Reed-Solomon block codes of length n=255 over the Galois field GF(2^8) with k=135, performs better than all others.

Development of an audio watermarking with decentralization of the watermarks

In this paper, a digital watermarking technique for ensuring security to an audio signal is presented. The proposed technique is developed by using discrete wavelet transform (DWT) and singular value decomposition (SVD). The proposed technique is executed by incorporating multi-level DWT along with the use of multiple images with different sizes as watermarks. The proposed technique is developed by keeping in mind the distribution of watermark signals’ energy throughout the entire host signal i.e. the watermarks are decentralized over the entire region of the host signal. The proposed technique is robust as experimentally the proposed technique offers PSNR (Peak Signal to Noise Ratio) of 196.8105 dB and normalized cross-correlation (NCC) over 0.9.

An efficient and robust watermarking approach based on single value decompression, multi-level DWT, and wavelet fusion with scrambled medical images

This paper offers a medical image watermarking approach based on Wavelet Fusion (WF), Singular Value Decomposition (SVD), and Multi-Level Discrete Wavelet Transform (M-DWT) with scrambling techniques for securing the watermarks images. The proposed approach can be used for providing multi-level security in various applications such as military, copyright protection, and telemedicine systems. The key idea of the projected approach is to first combine two digital watermark images into a single fused watermark to increase the embedded information payload. Then, the fused watermark is scrambled using Arnold and Chaotic algorithms. Finally, the scrambled fused watermark is embedded in the cover image using the SVD and three-level DWT algorithms. The selection of the Arnold and chaotic for watermark encryption is attributed to confirm robustness which resists several types of multimedia attacks and upturn the security level. This paper also presents a comparative study of the proposed approach for different digital images to determine its robustness and stability. Several simulation results reveal that the proposed system improves the capacity and security of embedded medical watermarks without affecting the cover image quality. In conclusion, the proposed approach achieved not only precise acceptable perceptual quality with admired Peak Signal-to-Noise Ratio (PSNR) values but similarly high Correlation Coefficient (Cr) and SSIM values in the existence of severe attacks.

Audio Watermarking with Multiple Images as Watermarks

Nowadays, the security of information has become a burning question. Digital watermarking can be very efficient in providing security of a signal being sent. In this paper, a digital watermarking technique for ensuring security to an audio signal is presented. The proposed technique incorporates the use of discrete wavelet transform (DWT) and singular value decomposition (SVD). In the proposed technique, multi-level DWT along with the use of multiple images as watermarks ensures an increased level of security which will be evident from the experimental part of this paper. The proposed technique is developed by keeping in mind the energy density of both the host and the watermark signals and that is why good results are achieved. The proposed technique is robust as experimentally the proposed technique offers PSNR (Peak Signal to Noise Ratio) of 187.9432 dB and normalized correlation (NC) over 0.99.

Algorithm-based fault tolerance for discrete wavelet transform implemented on GPUs

An important application for discrete wavelet transform (DWT) is the image compression such as the JPEG2000 Standard, and GPU is a good choice for the implementation of the DWT. For the application of image compression in the space environment, this paper has discussed the fault tolerance for the forward DWT (FDWT) implemented on GPUs, to deal with the silent data corruption (SDC) errors caused by single event upsets (SEUs). This paper has analyzed the matrix descriptions for the procedures of the forward DWT in the JPEG2000 Standard. Using the matrix descriptions, the propagation of SDC errors in the multi-level forward DWT has been analyzed, and an algorithm-based fault tolerance (ABFT) method has been proposed, in addition, by analyzing the relationship between the data type and the SDC errors, an on-line fast error detection and correction method has been proposed to suppress the propagation of the logical value overflow errors. Finally, the performance of the program with ABFT has been evaluated and the overhead of memory space caused by the checksum data has been further analyzed.

An Internal Folded Hardware-Efficient Architecture for Lifting-Based Multi-Level 2-D 9/7 DWT

In this paper, a novel internal folded hardware-efficient architecture of multi-level 2-D 9/7 discrete wavelet transform (DWT) is proposed. For multi-level DWT, the unfolded structure is more extensively used compared with the folded structure, because of its low memory consumption and low time delay. However, a set of input data valid every few clock cycles caused the mismatch between clock and data in the unfolded structure. The mismatch usually needs to be solved by multi-clock or complex data adjustment, which increases the consumption of hardware resources and the complexity of the overall system. To solve the above problem of the unfolded structure, we adjust the data input timing by using a single clock domain and folding the DWT architecture of different levels in varying degrees, according to their own clock-to-data ratios. For an image of size of N × N pixels and 3-level DWT, the proposed architecture requires only 6N words temporal memory. For 3-level DWT with an image of size 512 × 512 pixels, the hardware estimation and comparison of the existing architectures show that, the hardware estimation result shows at least 30.6% area-delay-product (ADP) decrease, and at least 22.4% transistor-delay-product (TDP) decrease for S = 8, and 25.77% transistor-delay-product (TDP) decrease for S = 16.

Quantization Noise of Multilevel Discrete Wavelet Transform Filters in Image Processing

The effect of the quantization noise of the coefficients of discrete wavelet transform (DWT) filters on the image processing result is analyzed. A multilevel DWT method is proposed for determining the effective bit-width of DWT filter coefficients at which quantization noise has little effect on the image processing result. The dependence of the peak signal-to-noise ratio (PSNR) in DWT of images on the wavelet used, the effective bit-width of the coefficients, and the number of processing levels is revealed. Formulas are derived for determining the minimum bit-width of the coefficients that provide high quality of the processed image (PSNR ≥ 40 dB) depending on the wavelet used and the number of processing levels. Experimental modeling of a multilevel DWT image confirmed the results obtained. In the proposed method, all data are represented in fixed-point format, making possible its hardwareefficient implementation on modern devices (FPGA, ASIC, etc.).

A High-Precision Low-Area Unified Architecture for Lossy and Lossless 3D Multi-Level Discrete Wavelet Transform

This paper presents a high-precision low area lifting-based architecture for the unified implementation of both lossy and lossless 3D multi-level discrete wavelet transform (DWT) using LeGall 5/3 wavelet and cohen-daubechies-feauveau 9/7 wavelet. The proposed system is parallel-pipelined, and resource is shared between the lossy and lossless modes, producing a throughput of two outputs/clock and achieving a high-speed and low-area solution. The data width of the design is taken as 20 bits to reach a high peak signal-to-noise-ratio value for multi-level 3D DWT. Targeting a portable and real-time solution, the proposed architecture was successfully implemented on Xilinx Virtex-5 series field programmable gate array, achieving a clock speed of 290 MHz with a power consumption of 467 mW at 200 MHz clock frequency. The design has also been implemented in UMC 90 nm CMOS technology, which consumes 329 mW power at 200 MHz clock frequency. The proposed solution may be configured as lossless or lossy compression, in the field of 3D image compression system, according to the necessity of the user.

Fusion Based Watermarking Scheme with Multi Level DWT & SVD

This paper proposes “Fusion based watermarking with multi level DWT & singular value decomposition” has been implemented. In watermarking scheme, maintaining security and robustness is major hurdle. To address this issue we proposed a novel non blind embedding scheme with Discrete Wavelet transform (DWT) and Singular Value Decomposition (SVD) techniques. This paper details the design of the proposed watermarking scheme and analyses its robustness in the presence of various possible security attacks that involves in degrading the quality of watermark. In the beginning, the cover image color component (especially Red Component) is decomposed into LL, LH, HL, HH with 2 level DWT: these LH & HL coefficients are further divided into 8*8 blocks and each block is compared to build a fused coefficient. Apply SVD on fused coefficient of cover image and watermark image to embed the singular values with sigmod scaling factor. Finally the watermarked image is generated, after applying inverse SVD & 2 level DWT. At receiver side the inverse process has been implemented to extract watermark image. The efficiency and performance of the proposed method is verified with   Peak Signal to Noise Ratio (PSNR), Root mean square error (RMSE) and Mean Square error (MSE) and compared with recent works of santhi [12] and harsha [13].  

Efficient and robust frame-synchronized blind audio watermarking by featuring multilevel DWT and DCT

This paper presents a self-synchronizing blind audio watermarking method developed based on the distinctive features of multilevel discrete wavelet transform (MDWT) and discrete cosine transform (DCT). In the proposed design, the repetitive pattern hidden in the 11th approximation subband is used to control frame synchronization, and the wideband signal covering the first to ninth detail subbands is used to hide binary information. Tracing the zero-crossings across the extracted synchronous signal makes it possible to recalibrate frame positions and achieve synchronous watermarking on a rational dither modulation (RDM) framework. We also developed a windowed vector modulation (WVM) scheme suited for the time as well as DCT domain to enhance watermark imperceptivity. The proposed watermarking method currently achieves a payload capacity of 86 bits per second. The results of PEAQ testing confirm that the watermarked audio signal retains quality nearly indistinguishable from the original. Compared with two existing synchronized methods designed specifically to cope with playback speed modification, the proposed MDWT---DCT---RDM---WVM method demonstrates superior robustness to commonly encountered attacks. When encountering desynchronization attacks, such as cropping and resampling time-scale modification, the proposed technique is capable of retrieving the watermark bits with a high degree of accuracy.

Channel State Reconstruction Using Multilevel Discrete Wavelet Transform for Improved Fingerprinting-Based Indoor Localization

Recently, channel state information (CSI) has been adopted as an enhanced wireless channel measurement instead of received signal strength (RSS) for indoor WiFi positioning systems. However, although CSI contains richer location information, a challenging problem is the severe dynamic range and fluctuation among the high-dimensional channels, which may degrade accuracy and cause overfitting problems. This paper proposes a novel algorithm for improved fingerprinting-based indoor localization. The proposed algorithm decomposes the CSI sequence using the multilevel discrete wavelet transform (MDWT) and normalizes the wavelet coefficients by employing histogram equalization. The robust features were then extracted by reconstructing CSI through the inverse MDWT of the normalized coefficients. We demonstrate the effectiveness of the proposed algorithm through experiments. The results show that the proposed algorithm outperforms traditional RSS, CSI, and two CSI-based algorithms, FIFS and MIMO.