Comparative Study on Performance of Discrete Wavelength Transform and Huffman Compression Technique on 2D Signal

Human heart cardiac muscles activities can be represented graphically using electrical impulses. Electrocardiography (ECG) signals are very important for physicians to diagnose heart disease. In this study, Bluetooth device is used to transmit data wirelessly. For this purpose, the generated signal from heart beat sensor is analyzed in MATLAB using Support Vector Machine (SVM) and Discrete Wavelet Transform (DWT) techniques. ECG Simulator compares the actual signal of infants with the reference signal and notify the physician about healthy or unhealthy ECG signals. Additionally, simulating the ECG pattern of different infants before designing and developing any biomedical technology is quite efficient and cost-effective.

The success of internet technology transform the world of technology and fashioned our life such a lot easier. The matter of duplication and unauthorized use of information become a great threat within the field of technology. To beat these problems, techniques like digital watermarking, steganography and cryptography were introduced. The approach of embedding a secret data associated with the digital signal inside the signal itself is digital watermarking. For embedding and detecting the watermark different techniques are used; spatial domain techniques like Least Significant Bit (LSB) and Patch Work Algorithm, then the frequency domain techniques such as Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Discrete Fourier Transform (DFT) are some of them. Combination of DCT & DWT technique for digital watermarking is proposed here. The proposed methodology is implemented in MATLAB 2017a simulator and result is analysed using evaluation parameters like Peak Signal to Noise Ratio (PSNR) & Mean Square Error (MSE). PSNR value obtained for the case of watermarking using DWT is 27.46 and MSE value is 17.45. The value of PSNR for watermarking using DCT is 41.4 and for MSE the value is 0.67. The PSNR and MSE values obtained for watermarking using LSB technique is 50.55 and 0.58. The proposed method improves the watermarked image quality and the MSE & PSNR value obtained is 0.52 and 51.017 respectively.

In this work, we extend our previous research on gray level co-occurrence matrix (GLCM) based watermark embedding in the discrete cosine transform (DCT) domain to the discrete wavelet transform (DWT) domain. The GLCM method incorporated human visual system information into the embedding process making the watermark more transparent. DWT techniques allow for more compression as fewer coefficients are required to reconstruct an image. In addition, DWT methods will not exhibit block artifacts commonly encountered when applying block based DCT methods. The watermark identification is further enhanced using neural networks. In this research, daubechies wavelets are utilized to evaluate the efficiency of the watermark identification while the method is subjected to multiple attacks such as filtering, compression, or rotation. The results are then compared with previously published methods by the authors such as LMS based correlation and adaptive DWT based watermark identification.

Automated classification of remote sensing images using multileveled MobileNetV2 and DWT techniques

This article presents the fault diagnosis of the face milling tool using sound signal. During milling, sound signals of the face milling tool under healthy and fault conditions are acquired. Discrete wavelet transform (DWT) features are extracted from the acquired sound signals. The support vector machine (SVM) technique is used to classify the face milling tool conditions using the extracted DWT features. Also, a comparison of classification efficiencies of different classifiers with respect to different features extraction methods is carried out. It is shown that, all extracted DWT features demonstrate better results than those obtained from selected statistical features and empirical mode decomposition features. The SVM technique is the best classifier as it has given an encouraging result in this study when compared to other classifiers, and it has provided 83% classification accuracy for the given experimental conditions and workpiece of steel alloy 42CrMo4. Hence, the SVM method and DWT technique can be put forward for the applications of condition monitoring of the face milling tool with sound signal.

This paper presents a comparative analysis of Digital Cosine Transformation (DCT) technique and Discrete Wavelet Transformation (DWT) technique in digital image watermarking. We have used standard digital images for analysing of Watermarked Images and applied standard attacks on the watermarked image. An experimental comparison was made using Matlab.

This work gives insight to the design and implementation of an improved extended Kalman filter (EKF) algorithm combined with the discrete wavelet transform (DWT) for a Li-Ion cell. The discharging/charging voltage signal (DCVS) is applied as source data in the DWT-based analysis due to its ability to extract information from the non-stationary and transient phenomena simultaneously in both the time and frequency domain. Through using the multi-resolution analysis (MRA) implementing the wavelet decomposition and reconstruction, two DWT techniques in processing the EKF-based state-of-charge (SOC) estimation have been newly introduced to improve the battery management (BMS) performance. Firstly, irrespective of the measurement noise model and data rejection for compensating the model errors in the existing method, this work requires less computation and satisfies an improved SOC estimation by the use of a low frequency component A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> of the DCVS, as terminal voltage of a cell. Secondly, the denoising technique in processing the noise-riding DCVS dealing with a high frequency component D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> enables us to estimate SOC without any modification of the algorithm. Experimental results indicate the robustness of the proposed approach for the reliable SOC estimation.

The rolling bearings are considered as the heart of rotating machinery and early fault diagnosis is one of the biggest challenges during operation. Due to complicated mechanical assemblies, detection of the advancing fault and faults at the incipient stage is very difficult and tedious. This work presents a fuzzy rule based classification of bearing faults using Fuzzy C-means clustering method using vibration measurements. Experiments were conducted to collect the vibration signals of a normal bearing and bearings with faults in the inner race, outer race and ball fault. Discrete Wavelet Transform (DWT) technique is used to decompose the vibration signals into different frequency bands. In order to detect the early faults in the bearings, various statistical features were extracted from this decomposed signal of each frequency band. Based on the extracted features, Fuzzy C-means clustering method (FCM) is developed to classify the faults using suitable membership functions and fuzzy rule base is developed for each class of the bearing fault using labeled data. The experimental results show that the proposed method is able to classify the condition of the bearing using the extracted features. The proposed FCM based clustering and classification model provides easier interpretation and implementation for monitoring the condition of the rolling bearings at an early stage and it will be helpful to take the preventive action before a large-scale failure.

-- Digital watermarking plays a critical role in ensuring the protection, authenticity, and integrity of multimedia content, particularly in today's data-driven environment. In this paper, we present a solution for the watermarking of grayscale images utilizing the Discrete Wavelet Transform (DWT) technique. Our approach focuses on embedding watermark information into the frequency components of the image to achieve a balance between imperceptibility and robustness against common signal processing attacks. A complete software-based implementation of the watermark embedding and extraction process has been developed and evaluated using standard grayscale images. Additionally, we propose a high-level VLSI architecture for real-time implementation of the DWT-based watermarking system, emphasizing efficient resource utilization, parallelism, and low power design. Extensive simulations demonstrate the effectiveness of the DWT-based watermarking method, achieving strong imperceptibility and resilience. This research contributes to the growing field of digital image security by presenting a practical approach to scalable hardware design and opens new directions for future real-time, hardware-accelerated watermarking applications. Key Words: Digital watermarking, Discrete Wavelet Transform (DWT), Grayscale images, VLSI architecture, Image security, Frequency domain, Robustness, Imperceptibility, Hardware design, Real-time systems.

Coronary Artery Disease (CAD) is one of the hazardous heart disease which results in angina, Myocardial Infarction (MI) and Sudden Cardiac Death (SCD). CAD is a cardiac disorder in which a plague develops in the interior wall of the arteries resulting in blockage of blood reaching to the heart muscles. Electrocardiogram (ECG) is the cardiac signal which represents cardiac depolarisation and repolarisation regulated at the surface of the chest. The minute variations in amplitude and duration in the ECG wave specifies different pathological conditions which are tedious to interpret visually. Hence computer aided diagnostic systems are used to monitor ECG signals. In the present work, automated diagnosis of CAD is done using Discrete Wavelet Transform (DWT) and nonlinear feature extraction techniques like; Multivariate Multi-scale Entropy (MMSE), Tsallis entropy and renyi entropies. The extracted features after DWT are ranked based on t-value and fed to K Nearest Neighbour (KNN), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and Decision Tree (DT) classifiers for automated classification of normal and CAD classes. This technique provided the highest accuracy of 98.67% using KNN classifier. Hence, the proposed system can aid clinicians in faster and accurate diagnosis of CAD and thereby provide sufficient time for proper treatment.

An improved privacy-preserving DWT-based collaborative filtering scheme

Information and communication technology usage in the healthcare sector is not perceptible due to various challenges with increased healthcare needs. With the outburst of COVID-19, when the different countries announced lockdown and social distancing rules, it is crucial to predict a person's symptoms, which will help in the early diagnosis. In such situations, there is a tremendous growth seen in the usage of various technologies, such as remote health monitoring, Wireless Body Area Networks (WBANs), Machine Learning (ML), and Decision Support system (DSS). Hence, the chapter focuses on detecting diseases and associated symptoms using various ML algorithms. A total of 3073 patient data (heartbeat, snore, and body temperature) has been collected. The collected data were preprocessed to remove empty cells and zero values by replacing the mean of the cells. Later, the extracted features were used in Empirical Mode Decomposition (EWD) and Discrete Wavelet Transformation (DWT). Then, the optimized algorithms with the threshold values were identified by consulting doctors for accurate disease prediction. With the testing performance of various ML algorithms, such as Decision Tree Classifier (DTC), K-Nearest Neighbor (KNN), Gradient Descent (SGD), Naive Bayes (NB), Multilayer perceptron (MLP), Support Vector Machine (SVM), and Random Forest (RF), was compared. Performance evaluation parameters are accuracy, precision, F1 score, and recall. The results showed an average of 100% accuracy with SGD and SVM with DWT, whereas EMD, SVM, and MLP outperformed the state-of-the-art algorithms with 99.83% accuracy.

This paper presents the empirical comparison of different neural network models for the classification of brain Magnetic Resonance Images (MRIs). This work comprises of four stages i.e. dataset collection, feature extraction, feature reduction and classification. The two brain MRI datasets i.e. the Glioma and Alzheimer datasets are considered for this work. Discrete wavelet transformation (DWT) technique is used for the extraction of features from brain MRIs. Principal Component Analysis (PCA) technique is used to for feature reduction to get relevant features. For the classification task, two variants of neural networks i.e. Backpropagation Neural Network (BPNN) and Extreme Learning Machine (ELM) are used and the classification performances are compared using different performance measures. The simulation study exhibits that DWT+PCA+ELM model outperformed the other models for the classification of normal and diseased brain for the two datasets.

An Invisible Logo Watermarking Using Arnold Transform

In this research paper, a new modified approach is proposed for brain tumor classification as well as feature extraction from Magnetic Resonance Imaging (MRI) after pre-processing of the images. The discrete wavelet transformation (DWT) technique is used for feature extraction from MRI images and Artificial Neural Network (ANN) is used for the classification of the type of tumor according to extracted features. Mean, Standard deviation, Variance, Entropy, Skewness, Homogeneity, Contrast, Correlation are the main features used to classify the type of tumor. The proposed model can give a better result in comparison with other available techniques in less computational time as well as a high degree of accuracy. The training and testing accuracies of the proposed model are 100% and 98.20% with a 98.70 % degree of precision respectively.