Quantitative and comparative analysis of effectivity and robustness for enhanced and optimized non-local mean filter combining pixel and patch information on MR images of musculoskeletal system

In the area of musculoskeletal MR images analysis, the image denoising plays an important role in enhancing the spatial image area for further processing. Recent studies have shown that non-local means (NLM) methods appear to be more effective and robust when compared with conventional local statistical filters, including median or average filters, when Rician noise is presented. A significant limitation of NLM is the fact that thy have the tendency to suppress tiny objects, which may represent clinically important information. For this reason, we provide an extensive quantitative and objective analysis of a novel NLM algorithm, taking advantage of pixel and patch similarity information with the optimization procedure for optimal filter parameters selection to demonstrate a higher robustness and effectivity, when comparing with NLM and conventional local means methods, including average and median filters. We provide extensive testing on variable noise generators with dynamical noise intensity to objectively demonstrate the robustness of the method in a noisy environment, which simulates relevant, variable and real conditions. This work also objectively evaluates the potential and benefits of the application of NLM filters in contrast to conventional local-mean filters. The final part of the analysis is focused on the segmentation performance when an NLM filter is applied. This analysis demonstrates a better performance of tissue identification with the application of smoothing procedure under worsening image conditions.Web of Science2112art. no. 416

Improved Denoising of Structural Vibration Data Employing Bilateral Filtering

With the continuous advancement of data acquisition and signal processing, sensors, and wireless communication, copious research work has been done using vibration response signals for structural damage detection. However, in actual projects, vibration signals are often subject to noise interference during acquisition and transmission, thereby reducing the accuracy of damage identification. In order to effectively remove the noise interference, bilateral filtering, a filtering method commonly used in the field of image processing for improving data signal-to-noise ratio was introduced. Based on the Gaussian filter, the method constructs a bilateral filtering kernel function by multiplying the spatial proximity Gaussian kernel function and the numerical similarity Gaussian kernel function and replaces the current data with the data obtained by weighting the neighborhood data, thereby implementing filtering. By processing the simulated data and experimental data, introducing a time-frequency analysis method and a method for calculating the time-frequency spectrum energy, the denoising abilities of median filtering, wavelet denoising and bilateral filtering were compared. The results show that the bilateral filtering method can better preserve the details of the effective signal while suppressing the noise interference and effectively improve the data quality for structural damage detection. The effectiveness and feasibility of the bilateral filtering method applied to the noise suppression of vibration signals is verified

Improved Denoising of Structural Vibration Data Employing Bilateral Filtering

With the continuous advancement of data acquisition and signal processing, sensors, and wireless communication, copious research work has been done using vibration response signals for structural damage detection. However, in actual projects, vibration signals are often subject to noise interference during acquisition and transmission, thereby reducing the accuracy of damage identification. In order to effectively remove the noise interference, bilateral filtering, a filtering method commonly used in the field of image processing for improving data signal-to-noise ratio was introduced. Based on the Gaussian filter, the method constructs a bilateral filtering kernel function by multiplying the spatial proximity Gaussian kernel function and the numerical similarity Gaussian kernel function and replaces the current data with the data obtained by weighting the neighborhood data, thereby implementing filtering. By processing the simulated data and experimental data, introducing a time-frequency analysis method and a method for calculating the time-frequency spectrum energy, the denoising abilities of median filtering, wavelet denoising and bilateral filtering were compared. The results show that the bilateral filtering method can better preserve the details of the effective signal while suppressing the noise interference and effectively improve the data quality for structural damage detection. The effectiveness and feasibility of the bilateral filtering method applied to the noise suppression of vibration signals is verified

Multi-point static dexterous posture manipulation for the stiffness identification of serial kinematic end-effectors.

Masters Degree. University of KwaZulu-Natal, Durban.The low stiffness inherent in serial robots hinders its application to perform advanced operations due to its reduced accuracy imparted through deformations within the links and joints. The high repeatability, extended workspace, and speed of serial manipulators make them appealing to perform precision operations as opposed to its alternative, the CNC machine. However, due to the serial arrangement of the linkages of the system, they lack the accuracy to meet present-day demands. To address the low stiffness problem, this research provided a low-cost dexterous posture identification method. The study investigated the joint stiffness of a Fanuc M10-iA 6 Degree of Freedom (DOF) serial manipulator. The investigation involved a multivariable analysis that focused on the robot’s workspace, kinematic singularity, and dexterity to locate high stiffness areas and postures. The joint stiffness modelling applied the Virtual Joint Method (VJM), which replaced the complicated mechanical robot joints with one-dimensional (1-D) springs. The effects of stress and deflection are linearly related; the highest stress in a robot’s structure is distributed to the higher load-bearing elements such as the robot joints, end-effector, and tool. Therefore, by locating optimal postures, the induced stresses can be better regulated throughout the robot’s structure, thereby reducing resonant vibrations of the system and improving process accuracy and repeatability. These aspects are quantifiably pitched in terms of the magnitude differences in the end-effector deflection. The unique combination of the dexterity and the stiffness analyses aimed to provide roboticists and manufacturers with an easy and systematic solution to improve the stiffness, accuracy, and repeatability of their serial robots. A simple, user-friendly and cost-effective alternative to deflection measurements using accelerometers is provided, which offers an alternative to laser tracking devices that are commonly used for studies of this nature. The first investigation focused on identifying the overall workspace of the Fanuc M-10iA robot. The reachable workspace was investigated to understand the functionality and potential of the Fanuc robot. Most robotic studies stem from analysing the workspace since the workspace is a governing factor of the manipulator and end-effector placement, and its operations, in a manufacturing setting. The second investigation looked at identifying non-reachable areas and points surrounding the robot. This analysis, along with the workspace examination, provided a conclusive testing platform to test the dexterity and stiffness methodologies. Although the research focused on fixing the end-effector at a point (static case), the testing platform was structured precisely to cater for all robotic manufacturing tasks that are subjected to high applied forces and vibrations. Such tasks include, but are not limited to, drilling, tapping, fastening, or welding, and some dynamic and hybrid manufacturing operations. The third investigation was the application of a dexterous study that applied an Inverse Kinematic (IK) method to localise multiple robot configurations about a user-defined point in space. This process was necessary since the study is based on a multi-point dexterous posture identification technique to improve the stiffness of Serial Kinematic Machines (SKMs). The stiffness at various points and configurations were tested, which provided a series of stiff and non-stiff areas and postures within the robot’s workspace. MATLAB®, a technical computing software, was used to model the workspace and singularity of the robot. The dexterity and stiffness analyses were numerically evaluated using Wolfram Mathematica. The multivariable analyses served to improve the accuracy of serial robots and promote their functionality towards high force application manufacturing tasks. Apart from the improved stiffness performance offered, the future benefit of the method could advance the longevity of the robot as well as minimise the regular robot maintenance that is often required due to excessive loading, stress, and strain on the robot motors, joints, and links

Mathematical Modeling and Simulation in Mechanics and Dynamic Systems

The present book contains the 16 papers accepted and published in the Special Issue “Mathematical Modeling and Simulation in Mechanics and Dynamic Systems” of the MDPI “Mathematics” journal, which cover a wide range of topics connected to the theory and applications of Modeling and Simulation of Dynamic Systems in different field. These topics include, among others, methods to model and simulate mechanical system in real engineering. It is hopped that the book will find interest and be useful for those working in the area of Modeling and Simulation of the Dynamic Systems, as well as for those with the proper mathematical background and willing to become familiar with recent advances in Dynamic Systems, which has nowadays entered almost all sectors of human life and activity