Characteristics Analysis of Plasticized Polyvinyl Chloride Gel-Based Microlens at Different Temperatures.

Temperature plays a crucial role in the preparation of polyvinyl chloride (PVC) gels for optical applications. Incorrect temperature selection can lead to various issues such as poor surface roughness, inadequate light transmission, and insufficient solution for optical devices. To address this challenge, this study focuses on the preparation of PVC gel samples by combining PVC powder (n = 3000), eco-friendly dibutyl adipate, and tetrahydrofuran at different stirring temperatures ranging from 40 to 70 °C. The PVC gel preparation process is categorized into four groups (T40, T50, T60, and T70) based on the mixing temperatures, employing a controlled test method with specific temperature conditions. The prepared PVC gel samples are then subjected to analysis to evaluate various properties including surface morphology, tensile strength, light transmittance, and electrical response time. Among the samples, the PVC gel prepared at 60 °C (referred to as T60) exhibits excellent optical properties, with a transmittance of 91.2% and a tensile strength of 2.07 MPa. These results indicate that 60 °C is an optimal reaction temperature. Notably, the PVC gel microlenses produced at this temperature achieve their maximum focal length (ranging from -8 to -20 mm) within approximately 60 s, and they recover their initial state within around 80 s after the power is switched off. This focal length achievement is twice as fast as reported in previous studies on microlenses. It is observed that the reaction temperature significantly influences the solubility of the resin-based raw materials and the homogeneity of the gel. Consequently, these findings open up possibilities for utilizing PVC gel microlenses in novel commercial optics applications, thanks to their desirable properties.

Enhancing fine retinal vessel segmentation: Morphological reconstruction and double thresholds filtering strategy.

Eye diseases such as diabetic retinopathy are progressive with various changes in the retinal vessels, and it is difficult to analyze the disease for future treatment. There are many computerized algorithms implemented for retinal vessel segmentation, but the tiny vessels drop off, impacting the performance of the overall algorithms. This research work contains the new image processing techniques such as enhancement filters, coherence filters and binary thresholding techniques to handle the different color retinal fundus image problems to achieve a vessel image that is well-segmented, and the proposed algorithm has improved performance over existing work. Our developed technique incorporates morphological techniques to address the center light reflex issue. Additionally, to effectively resolve the problem of insufficient and varying contrast, our developed technique employs homomorphic methods and Wiener filtering. Coherent filters are used to address the coherence issue of the retina vessels, and then a double thresholding technique is applied with image reconstruction to achieve a correctly segmented vessel image. The results of our developed technique were evaluated using the STARE and DRIVE datasets and it achieves an accuracy of about 0.96 and a sensitivity of 0.81. The performance obtained from our proposed method proved the capability of the method which can be used by ophthalmology experts to diagnose ocular abnormalities and recommended for further treatment.

Analysis of the Performance of a Gel Actuator Made of Plasticized Polyvinyl Chloride/Carboxylated Cellulose Nanocrystals.

Recently, polyvinyl chloride (PVC) gel materials appeared promising for developing actuators, artificial muscles, and sensors. However, their energized response time and recovery limitations restrict their broader applications. Herein, a novel soft composite gel was prepared by mixing functionalized carboxylated cellulose nanocrystals (CCNs) and plasticized PVC. The surface morphology of the plasticized PVC/CCNs composite gel was characterized by scanning electronic microscopy (SEM). The prepared PVC/CCNs gel composites have increased polarity and electrical actuation with a fast response time. Experimental results demonstrated good response characteristics within the actuator model with a multilayer electrode structure when stimulated with a specified DC voltage (1000 V), with deformation of approximately 36.7%. Moreover, this PVC/CCNs gel has excellent tensile elongation, and the elongation at break of the PVC/CCNs gel is greater than the elongation at break of the pure PVC gel under the same thickness conditions. However, these PVC/CCNs composite gels showed excellent properties and development potential and are directed for broad applications in actuators, soft-robotics, and biomedical applications.

Image Segmentation for MR Brain Tumor Detection Using Machine Learning: A Review.

Magnetic Resonance Imaging (MRI) has commonly been used to detect and diagnose brain disease and monitor treatment as non-invasive imaging technology. MRI produces three-dimensional images that help neurologists to identify anomalies from brain images precisely. However, this is a time-consuming and labor-intensive process. The improvement in machine learning and efficient computation provides a computer-aid solution to analyze MRI images and identify the abnormality quickly and accurately. Image segmentation has become a hot and research-oriented area in the medical image analysis community. The computer-aid system for brain abnormalities identification provides the possibility for quickly classifying the disease for early treatment. This article presents a review of the research papers (from 1998 to 2020) on brain tumors segmentation from MRI images. We examined the core segmentation algorithms of each research paper in detail. This article provides readers with a complete overview of the topic and new dimensions of how numerous machine learning and image segmentation approaches are applied to identify brain tumors. By comparing the state-of-the-art and new cutting-edge methods, the deep learning methods are more effective for the segmentation of the tumor from MRI images of the brain.

A Single-Channel Wireless EEG Headset Enabled Neural Activities Analysis for Mental Healthcare Applications.

Electroencephalography (EEG) is a technique of Electrophysiology used in a wide variety of scientific studies and applications. Inadequately, many commercial devices that are available and used worldwide for EEG monitoring are expensive that costs up to thousands of dollars. Over the past few years, because of advancements in technology, different cost-effective EEG recording devices have been made. One such device is a non-invasive single electrode commercial EEG headset called MindWave 002 (MW2), created by NeuroSky Inc that cost less than 100 USD. This work contributes in four distinct ways, first, how mental states such as a focused and relaxed can be identified based on EEG signals recorded by inexpensive MW2 is demonstrated for accurate information extraction. Second, MW2 is considered because apart from cost, the user's comfort level is enhanced due to non-invasive operation, low power consumption, portable small size, and a minimal number of detecting locations of MW2. Third, 2 situations were created to stimulate focus and relaxation states. Prior to analysis, the acquired brain signals were pre-processed to discard artefacts and noise, and band-pass filtering was performed for delta, theta, alpha, beta, and gamma wave extraction. Fourth, analysis of the shapes and nature of extracted waves was performed with power spectral density (PSD), mean amplitude values, and other parameters in LabVIEW. Finally, with comprehensive experiments, the mean values of the focused and relaxed signal EEG signals were found to be 30.23 µV and 15.330 µV respectively. Similarly, average PSD values showed an increase in theta wave value and a decrease in beta wave value related to the focus and relaxed state, respectively. We also analyzed the involuntary and intentional number of blinks recorded by the MW2 device. Our study can be used to check mental health wellness and could provide psychological treatment effects by training the mind to quickly enter a relaxed state and improve the person's ability to focus. In addition, this study can open new avenues for neurofeedback and brain control applications. Supplementary Information: The online version contains supplementary material available at 10.1007/s11277-022-09731-w.

Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19): a detailed review with direction for future research.

Since early 2020, the whole world has been facing the deadly and highly contagious disease named coronavirus disease (COVID-19) and the World Health Organization declared the pandemic on 11 March 2020. Over 23 million positive cases of COVID-19 have been reported till late August 2020. Medical images such as chest X-rays and Computed Tomography scans are becoming one of the main leading clinical diagnosis tools in fighting against COVID-19, underpinned by Artificial Intelligence based techniques, resulting in rapid decision-making in saving lives. This article provides an extensive review of AI-based methods to assist medical practitioners with comprehensive knowledge of the efficient AI-based methods for efficient COVID-19 diagnosis. Nearly all the reported methods so far along with their pros and cons as well as recommendations for improvements are discussed, including image acquisition, segmentation, classification, and follow-up diagnosis phases developed between 2019 and 2020. AI and machine learning technologies have boosted the accuracy of Covid-19 diagnosis, and most of the widely used deep learning methods have been implemented and worked well with a small amount of data for COVID-19 diagnosis. This review presents a detailed mythological analysis for the evaluation of AI-based methods used in the process of detecting COVID-19 from medical images. However, due to the quick outbreak of Covid-19, there are not many ground-truth datasets available for the communities. It is necessary to combine clinical experts' observations and information from images to have a reliable and efficient COVID-19 diagnosis. This paper suggests that future research may focus on multi-modality based models as well as how to select the best model architecture where AI can introduce more intelligence to medical systems to capture the characteristics of diseases by learning from multi-modality data to obtain reliable results for COVID-19 diagnosis for timely treatment .