Surface reproduction of elastomeric materials: viscosity and groove shape effects

Objective: To evaluate the effect of viscosity and type of grooves on surface detail reproduction of elastomeric impression materials. Methods: Express putty/light-, Impregum medium- and heavy/light-bodied and Aquasil medium- and putty/light-bodied elastomeric impression materials were chosen for this study. Five impressions were made using a cylindrical aluminum reference block with U- and V- shaped grooves and to produce 35 master dies. Each master die was immersed in distilled water at 370 C for 5 minutes prior to the impression making on moist surfaces. Surface topography of the dies and impressions were captured using Alicona Imaging System. The mean difference in depth between the master dies and its corresponding impressions were analysed using two-way ANOVA, p=.01. Results: The lowest mean difference in depth for U- and V-shaped grooves was obtained from the Express putty/light group. The highest mean difference in depth for U- and V-shaped grooves was obtained from Impregum medium, Aquasil medium, Impregum�· heavy/light and Impregum heavy/light groups respectively. Two-way ANOVA indicated that there was a significant difference in the effect of materials (p < .01) and grooves (p < .01). Conclusion: Express putty/light-bodied elastomeric material produced the best surface detail, and U-shaped groove showed better surface detail reproduction than V-shaped groove

Automated Sensor Rig in Detecting Shape of an Object

AbstractIn this paper an infrared sensor rig device is designed and developed. The ability of infrared range finder in measuring distance is applied to the sensor rig to detect lower limb shape model of prosthetic patient. Arduino is used as a microcontroller to control the whole system of this device with help from the stepper motor for the sensor rotation. Object shape is located at the center of the device and the minimum distance between object and sensor is fixed to 5 cm in order to reduce noise during data collecting. Data captured by the sensor is then saved in a text file for post processing using Matlab software. Various shape of objects have been tested and results obtained show that infrared sensor rig is capable to obtain shape data by plotting graph in 3D format

EEG Different Frequency Sound Response Identification using Neural Network and Fuzzy Techniques

Electroencephalographic (EEG) technology has enabled effective measurement of human brain activity, as functional and physiological changes within the brain may be registered by EEG signals. In this paper, electrical activity of human brain due to sound waves of different frequency, i.e. 40 Hz, 500 Hz, 5000 Hz and 15000 Hz, is studied based on EEG signals. Several signal processing techniques, i.e. Principle Component algorithm, Discrete Wavelet Transform and Fast Fourier Transform, are applied onto the raw EEG signal to extract useful information and specific characteristics from the EEG signals. This research has shown that the characteristics of EEG signals differ with respect to different frequency of sound waves, and hence the EEG signal can be identified with suitable characterization algorithm using artificial intelligent techniques, such as Artificial neural network, fuzzy logic and adaptive neuro-fuzzy system

Characterization, antibacterial and in vitro compatibility of zinc-silver doped hydroxyapatite nanoparticles prepared through microwave synthesis

We investigated the possibility of enhancing hydroxyapatite (HA) bioactivity by co-substituting it with zinc and silver. Zn-Ag-HA nanoparticles were synthesized by using the microwave-assisted wet precipitation process, and their phase purity, elemental composition, morphology, and particle size were analyzed by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and energy dispersive X-ray spectroscopy (EDX). FTIR, XRD, and EDX results showed the characteristic peaks of the Zn-Ag-HA structure, while SEM results demonstrated that the nanoparticles were of spherical shape with a particle size of 70-102 nm. Antibacterial tests of the nanoparticles revealed their antibacterial activity against Staphylococcus aureus and Escherichia colt. By using simulated body fluid (SBF), an apatite layer formation was observed at 28 days. In vitro cell adhesion assay confirmed the cell attachment of normal human osteoblast (NHOst) cells to the disc surface. MTT (3(4, 5-dimethylthiazol-2-y1)-2, 5 diphenyhetrazolium bromide assay indicated that the cells were viable, and the cells proliferated faster on the disks than on the control surface due to the presence of metal ions. In conclusion, the novel Zn-Ag-HA nanoparticles were found to be compatible with in vitro experiments and having potential antibacterial properties. Therefore these nanoparticles could be a promising candidate for future biomedical applications. (C) 2013 Elsevier Ltd and Techna Group S.r.l. All rights reserved

Lung disease recognition methods using audio-based analysis with machine learning

The use of computer-based automated approaches and improvements in lung sound recording techniques have made lung sound-based diagnostics even better and devoid of subjectivity errors. Using a computer to evaluate lung sound features more thoroughly with the use of analyzing changes in lung sound behavior, recording measurements, suppressing the presence of noise contaminations, and graphical representations are all made possible by computer-based lung sound analysis. This paper starts with a discussion of the need for this research area, providing an overview of the field and the motivations behind it. Following that, it details the survey methodology used in this work. It presents a discussion on the elements of sound-based lung disease classification using machine learning algorithms. This includes commonly prior considered datasets, feature extraction techniques, pre-processing methods, artifact removal methods, lung-heart sound separation, deep learning algorithms, and wavelet transform of lung audio signals. The study introduces studies that review lung screening including a summary table of these references and discusses the literature gaps in the existing studies. It is concluded that the use of sound-based machine learning in the classification of respiratory diseases has promising results. While we believe this material will prove valuable to physicians and researchers exploring sound-signal-based machine learning, large-scale investigations remain essential to solidify the findings and foster wider adoption within the medical community

Assessment of noise reduction in ultrasound images of common carotid and brachial arteries

The present study assessed the use of filters for noise reduction in ultrasound images of the common carotid artery (CCA) and brachial artery using intima-media thickness, which is a safe and non-invasive technique for determining subclinical atherosclerosis and cardiovascular risk. A new combined speckle reducing anisotropic diffusion (SRAD) filter for noise reduction is then proposed. Ultrasonic examination of both arteries was performed on 30 men (aged 40 ± 5 years). The programme was designed using MATLAB software to extract consecutive images in bit map format from the audio video interleaves. An additional programme was designed in MATLAB to apply the region of interest (ROI) to the thickness of the intima-media of the posterior walls of the arteries. Block-matching techniques were used to estimate arterial motion from ultrasound images of the B-mode CCA and brachial artery. Different noise reduction filters and Canny edge detection were carried out separately in the ROI. The programme measured mean square error (MSE) and peak signal-to-noise ratio (PSNR). The results demonstrated that the new combined SRAD filter with Canny edge detection identified the lowest value for MSE and the highest value for PSNR in 90 consecutive frames (~3 cardiac cycles). The results indicate that MSE and PSNR were better detected by the proposed combined SRAD filter with Canny edge detection than did several commonly used filters with Canny detection for speckle suppression and preservation detail in carotid and brachial arteries ultrasound images. © The Institution of Engineering and Technology