Explore the vast range of titles available.

The described application of granular computing is motivated because cardiovascular disease (CVD) remains a major killer globally. There is increasing evidence that abnormal respiratory patterns might contribute to the development and progression of CVD. Consequently, a method that would support a physician in respiratory pattern evaluation should be developed. Group decision-making, tri-way reasoning, and rough set–based analysis were applied to granular computing. Signal attributes and anthropomorphic parameters were explored to develop prediction models to determine the percentage contribution of periodic-like, intermediate, and normal breathing patterns in the analyzed signals. The proposed methodology was validated employing k-nearest neighbor (k-NN) and UMAP (uniform manifold approximation and projection). The presented approach applied to respiratory pattern evaluation shows that median accuracies in a considerable number of cases exceeded 0.75. Overall, parameters related to signal analysis are indicated as more important than anthropomorphic features. It was also found that obesity characterized by a high WHR (waist-to-hip ratio) and male sex were predisposing factors for the occurrence of periodic-like or intermediate patterns of respiration. It may be among the essential findings derived from this study. Based on classification measures, it may be observed that a physician may use such a methodology as a respiratory pattern evaluation-aided method.

Although there has been a lot of progress in the general area of signal denoising, noise removal remains a very challenging problem in real-world communication systems. Denoising algorithms are typically used during the image preprocessing phase and are chosen based on the type of image, as a specific algorithm may work for a given noise but not for another one. Moreover, an algorithm can sometimes consider crucial information as being noise and eliminate it, hence the importance of careful selection and tuning of denoising algorithms. Denoising algorithms built on discrete wavelet transform decomposes signals into different frequency resolution levels. Thresholding is then applied to higher frequency components which generally correspond to noise to eliminate this one. In this paper, we review wavelet-based denoising methods and compare their performance based on metrics such as peak signal-to-noise ratio (PSNR) and Structural Similarity (SSIM). This work aims to find the best wavelet denoising algorithm using Peak these metrics. The common Matlab images such as cameraman, barbara, coins, and eight are used for our test. From these tests, the BM3DM_DWT method was found to be the simplest and most efficient for denoising.

Video steganography approach enables hiding chunks of secret information inside video sequences. The features of video sequences including high capacity as well as complex structure make them more preferable for choosing as cover media over other media such as image, text, or audio. Video steganography is a prominent as well as the evolving field in the information security domain and significant number of video steganography methods are proposed in recent years. This article provides a comprehensive review of video steganography methods proposed in the literature. This article initially reviews various raw domain-based video steganography methods. In particular, the raw domain-based methods include spatial domain approaches such as least significant bits (LSB), transform domain-based methods such as discrete wavelet transform, discrete cosine transform, etc. Furthermore, the article looks into various compressed domain steganography methods. A critical comparative analysis is included in the article to analyze and contrast the steganography methods proposed in the literature. A brief description of various evaluation matrices for video steganography methods is provided in this article. Moreover, a brief introduction to steganalysis and video steganalysis is provided. The article concludes with a discussion focused on the limitations and challenges of the video steganography methods. Further, a brief insight into future directions in video steganography systems is provided.

Bearing defects have been accepted as one of the major causes of failure in rotating machinery. It is important to identify and diagnose the failure behavior of bearings for the reliable operation of equipment. In this paper, a low-cost non-contact vibration sensor has been developed for detecting the faults in bearings. The supervised learning method, support vector machine (SVM), has been employed as a tool to validate the effectiveness of the developed sensor. Experimental vibration data collected for different bearing defects under various loading and running conditions have been analyzed to develop a system for diagnosing the faults for machine health monitoring. Fault diagnosis has been accomplished using discrete wavelet transform for denoising the signal. Mahalanobis distance criteria has been employed for selecting the strongest feature on the extracted relevant features. Finally, these selected features have been passed to the SVM classifier for identifying and classifying the various bearing defects. The results reveal that the vibration signatures obtained from developed non-contact sensor compare well with the accelerometer data obtained under the same conditions. A developed sensor is a promising tool for detecting the bearing damage and identifying its class. SVM results have established the effectiveness of the developed non-contact sensor as a vibration measuring instrument which makes the developed sensor a cost-effective tool for the condition monitoring of rotating machines.

A developed comparative analysis of metaheuristic optimization algorithms has been used for optimal active control of structures. The linear quadratic regulator (LQR) has ignored the external excitation in solving the Riccati equation with no sufficient optimal results. To enhance the efficiency of LQR and overcome the non-optimality problem, six intelligent optimization methods including BAT, BEE, differential evolution, firefly, harmony search and imperialist competitive algorithm have been discretely added to wavelet-based LQR to seek the attained optimum feedback gains. The proposed approach has not required the solution of Riccati equation enabling the excitation effect in controlling process. Employing this advantage by each of six mentioned algorithms to three-story and eight-story structures under different earthquakes led to define (1) the best solution, (2) convergence rate and (3) computational effort of all methods. The purpose of this research is to study the aforementioned methods besides the superiority of ICA in finding the optimal responses for active control problem. Numerical simulations have confirmed that the proposed controller is enabling to significantly reduce the structural responses using less control energy compared to LQR.

As Europe integrates more renewable energy resources, notably offshore wind power, into its super meshed grid, the demand for reliable long-distance High Voltage Direct Current (HVDC) transmission systems has surged. This paper addresses the intricacies of HVDC systems built upon Modular Multi-Level Converters (MMCs), especially concerning the rapid rise of DC fault currents. We propose a novel fault identification and classification for DC transmission lines only by employing Long Short-Term Memory (LSTM) networks integrated with Discrete Wavelet Transform (DWT) for feature extraction. Our LSTM-based algorithm operates effectively under challenging environmental conditions, ensuring high fault resistance detection. A unique three-level relay system with multiple time windows (1 ms, 1.5 ms, and 2 ms) ensures accurate fault detection over large distances. Bayesian Optimization is employed for hyperparameter tuning, streamlining the model’s training process. The study shows that our proposed framework exhibits 100% resilience against external faults and disturbances, achieving an average recognition accuracy rate of 99.04% in diverse testing scenarios. Unlike traditional schemes that rely on multiple manual thresholds, our approach utilizes a single intelligently tuned model to detect faults up to 480 ohms, enhancing the efficiency and robustness of DC grid protection.

To protect the medical images integrity, digital watermark is embedded into the medical images. A non-blind medical image watermarking scheme based on hybrid transform is propounded. In this paper, fingerprint of the patient is used as watermark for better authentication, identifying the original medical image and privacy of the patients. In this scheme, lifting wavelet transform (LWT) and discrete wavelet transform (DWT) are utilized for amplifying the watermarking algorithm. The scaling and embedding factors are calculated adaptively with the help of Local Binary Pattern values of the host medical image to achieve better imperceptibility and robustness for medical images and fingerprint watermark, respectively. Two-level decomposition is done where for the first level LWT is utilized and for the second level decomposition DWT is utilized. At the extraction side, non-blind recovery of fingerprint watermark is performed which is similar to the embedding process. The propounded design is implemented on various medical images like Chest X-ray, CT scan and so on. The propounded design provides better imperceptibility and robustness with the combination of LWT–DWT. The result analysis proves that the proposed fingerprint watermarking scheme has attained best results in terms of robustness and authentication with different medical image attacks. Peak Signal to Noise Ratio and Normalized Correlation Coefficient metrics are used for evaluating the proposed scheme. Furthermore, superior results are obtained when compared to related medical image watermarking schemes.

The paper presents a comprehensive analysis of the IEEE-16 bus system under different operating conditions. It discusses the selection of suitable decomposition level and wavelet function for analyzing non-stationary signals to enhance power distribution network fault detection. MATLAB/Simulink is used to simulate the system, and transient fault current signals are processed with the MATLAB Wavelet Toolbox. The optimal decomposition level is determined by energy concentration, with the highest energy found in scales D9 (b4), D8 (b5), and D7 (b6), and D8 having the most concentration. Using MATLAB classifier learner, the article evaluates seven common mother wavelets with 53 wavelet functions, and sym3 is found to be the most efficient wavelet function in terms of training time, prediction speed, and accuracy of SVM classifiers. All fault types both symmetrical/unsymmetrical types, and various normal transient conditions such as load/capacitor/DG switching are detected/discriminated with nearly 100% accuracy at the midpoint of line 6–7 with various fault conditions, inception angles (0, 30, 45, 60, 90 and 120°) and a fault resistance of (5,10, 15, and 20 ohms). Additionally, 9 MW wind Farm is integrated at busbar 10, and various fault scenarios are simulated to assess system performance with 100% Accuracy.

This paper presents a new intelligent image watermarking scheme based on discrete wavelet transform (DWT) and singular values decomposition (SVD) using human visual system (HVS) and particle swarm optimization (PSO). The cover image is transformed by one-level (DWT) and subsequently the LL sub-band of (DWT) transformed image is chosen for embedding. To achieve the highest possible visual quality, the embedding regions are selected based on (HVS). After applying (SVD) on the selected regions, every two watermark bits are embedded indirectly into the U and V t components of SVD decomposition of the selected regions, instead of embedding one watermark bit into the U component and compensating on the V t component that results in twice capacity and reasonable imperceptibility. In addition, for increasing the robustness without losing the transparency, the scaling factors are chosen automatically by (PSO) based on the attacks test results and predefined conditions, instead of using fixed or manually set scaling factors for all different cover images. Experimental and comparative results demonstrated the stability and improved performance of the proposed scheme compared to its parents watermarking schemes. Moreover, the proposed scheme is free of false positive detection error.

Digital watermarking has attracted increasing attentions as it has been the current solution to copyright protection and content authentication in today’s digital transformation, which has become an issue to be addressed in multimedia technology. In this paper, we propose an advanced image watermarking system based on the discrete wavelet transform (DWT) in combination with the singular value decomposition (SVD). Firstly, at the sender side, DWT is applied on a grayscale cover image and then eigendecomposition is performed on original HH (high–high) components. Similar operation is done on a grayscale watermark image. Then, two unitary and one diagonal matrices are combined to form a digital watermarked image applying inverse discrete wavelet transform (iDWT). The diagonal component of original image is transmitted through secured channel. At the receiver end, the watermark image is recovered using the watermarked image and diagonal component of the original image. Finally, we compare the original and recovered watermark image and obtained perfect normalized correlation. Simulation consequences indicate that the presented scheme can satisfy the needs of visual imperceptibility and also has high security and strong robustness against many common attacks and signal processing operations. The proposed digital image watermarking system is also compared to state-of-the-art methods to confirm the reliability and supremacy.