Background: Speckle noise removal is the most important step in the processing of echocardiographic images. A speckle-free image produces useful information to diagnose heart-related diseases. Images which contain low noise and sharp edges are more easily analyzed by the clinicians. This noise removal stage is also a preprocessing stage in segmentation techniques.
Methods: A new formulation has been proposed for a well-known nonlinear complex diffusion filter (NCDF). Its diffusion coefficient and the time step size are modified to give fast processing and better results. An investigation has been performed among nine patients suffering from mitral regurgitation. Images have been taken with 2D echo in apical and parasternal views.
Results: The peak signal-to-noise ratio (PSNR), universal quality index (Qi), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) have been calculated, and the results show that the proposed method is much better than the previous filters for echocardiographic images.
Conclusions: The proposed method, modified nonlinear complex diffusion filter (MNCDF), smooths the homogeneous area and enhances the fine details.
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http://dx.doi.org/10.1007/s12574-012-0119-z | DOI Listing |
Sci Rep
January 2025
Department of Physics, Khalifa University of Science and Technology, 127788, Abu Dhabi, United Arab Emirates.
In this study, biopolymer composites based on chitosan (CS) with enhanced optical properties were functionalized using Manganese metal complexes and black tea solution dyes. The results indicate that CS with Mn-complexes can produce polymer hybrids with high absorption, high refractive index and controlled optical band gaps, with a significant reduction from 6.24 eV to 1.
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January 2025
Institute of Theoretical & Applied Informatics, Polish Academy of Sciences (IITiS-PAN), 44-100 Gliwice, Poland.
Edge computing systems must offer low latency at low cost and low power consumption for sensors and other applications, including the IoT, smart vehicles, smart homes, and 6G. Thus, substantial research has been conducted to identify optimum task allocation schemes in this context using non-linear optimization, machine learning, and market-based algorithms. Prior work has mainly focused on two methodologies: (i) formulating non-linear optimizations that lead to NP-hard problems, which are processed via heuristics, and (ii) using AI-based formulations, such as reinforcement learning, that are then tested with simulations.
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January 2025
School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430070, China.
Remaining useful life (RUL) prediction is a cornerstone of Prognostic and Health Management (PHM) for power machinery, playing a crucial role in ensuring the reliability and safety of these critical systems. In recent years, deep learning techniques have shown great promise in RUL prediction, providing more reliable and accurate outcomes. However, existing models often struggle with comprehensive feature extraction, especially in capturing the complex behavior of power machinery, where non-linear degradation patterns arise under varying operational conditions.
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January 2025
College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China.
Aiming at the control challenges faced by unmanned surface vessels (USVs) in complex environments, such as nonlinearities, parameter uncertainties, and environmental perturbations, we propose a non-singular terminal integral sliding mode control strategy based on an extended state observer (ESO). The strategy first employs a third-order linear extended state observer to estimate the total disturbances of the USV system, encompassing both external disturbances and internal nonlinearities. Subsequently, a backstepping sliding mode controller based on the Lyapunov theory is designed to generate the steering torque control commands for the USV.
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January 2025
School of Mathematics and Information Science, Guangxi University, Nanning 530004, China.
In this paper, a novel particle filter based on one-step smoothing is proposed for nonlinear systems with random one-step delay and missing measurements. Such problems are commonly encountered in networked control systems, where random one-step delay and missing measurements significantly increase the difficulty of dynamic state estimation. The particle filter is a nonlinear filtering method based on sequential Monte Carlo sampling.
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