A Review on Meshing Techniques in Biomedicine

Engineering has a wide range of applications where more detailed and reliable data are needed, one of which is biomedicine. One of the aims of meshing is to use the Finite Element Approach to solve the problem. By analysing and segmenting raw medical imaging data, meshing aids in a better and more precise understanding of the organs and structures of human body. The main goal of this paper is to collect and review the various available methods in meshing. Also, a comparison study of different meshing techniques that are available in biomedicine is performed.

A Two Stage Task Scheduler for Effective Load Optimization in Cloud – FoG Architectures

In recent times, computing technologies have moved over to a new dimension with the advent of cloud platforms which provide seamless rendering of required services to consumers either in static or dynamic state. In addition, the nature of data being handled in today’s scenario has also become sophisticated as mostly real time data acquisition systems equipped with High-Definition capture (HD) have become common. Lately, cloud systems have also become prone to computing overheads owing to huge volume of data being imparted on them especially in real time applications. To assist and simplify the computational complexity of cloud systems, FoG platforms are being integrated into cloud interfaces to streamline and provide computing at the edge nodes rather at the cloud core processors, thus accounting for reduction of load overhead on cloud core processors. This research paper proposes a Two Stage Load Optimizer (TSLO) implemented as a double stage optimizer with one being deployed at FoG level and the other at the Cloud level. The computational complexity analysis is extensively done and compared with existing benchmark methods and superior performance of the suggested method is observed and reported.

Comparative Analysis of Temperature Measurement Methods based on Degree of Agreement

Many sports have a high risk of climatic ailments, such as hypothermia, hyperthermia, and heatstroke. The measurement of a sportsperson's body core temperature (Tc) may have an impact on their performances and it assists them to avoid injuries as well. To avoid complications like electrolyte imbalances or infections, it's essential to precisely measure the core body temperature during targeted temperature control when spontaneous circulation has returned. Previous approaches on the other hand, are intrusive and difficult to use. The usual technique, an oesophageal thermometer, was compared to a disposable non-invasive temperature sensor that used the heat flux methodology. This research indicates that, non-invasive disposable sensors used to measure core body temperature are very reliable when used for targeted temperature control after overcoming a cardiac arrest successfully. The non-invasive method of temperature measurement has somewhat greater accuracy than the invasive approach. The results of this study must be confirmed by more clinical research with various sensor types to figure out if the bounds of agreement could be increased. This will ensure that the findings are accurate based on core temperature.

Transistor Sizing using Hybrid Reinforcement Learning and Graph Convolution Neural Network Algorithm

Transistor sizing is one the developing field in VLSI. Many researches have been conducted to achieve automatic transistor sizing which is a complex task due to its large design area and communication gap between different node and topology. In this paper, automatic transistor sizing is implemented using a combinational methods of Graph Convolutional Neural Network (GCN) and Reinforcement Learning (RL). In the graphical structure the transistor are represented as apexes and the wires are represented as boundaries. Reinforcement learning techniques acts a communication bridge between every node and topology of all circuit. This brings proper communication and understanding among the circuit design. Thus the Figure of Merit (FOM) is increased and the experimental results are compared with different topologies. It is proved that the circuit with prior knowledge about the system, performs well.

Identification of Covid’19 Vaccinator by Deep Learning Approach Using Contactless Palmprints

The invention of the first vaccine has also raised several anti-vaccination views among people. Vaccine reluctance may be exacerbated by the growing reliance on social media, which is considered as a source of health information. During this COVID'19 scenario, the verification of non-vaccinators via the use of biometric characteristics has received greater attention, especially in areas such as vaccination monitoring and other emergency medical services, among other things. The traditional digital camera utilizes the middle-resolution images for commercial applications in a regulated or contact-based environment with user participation, while the latter uses high-resolution latent palmprints. This research study attempts to utilize convolutional neural networks (CNN) for the first time to perform contactless recognition. To identify the COVID '19 vaccine using the CNN technique, this research work has used the contactless palmprint method. Further, this research study utilizes the PalmNet structure of convolutional neural network to resolve the issue. First, the ROI region of the palmprint was extracted from the input picture based on the geometric form of the print. After image registration, the ROI region is sent into a convolutional neural network as an input. The softmax activation function is then used to train the network so that it can choose the optimal learning rate and super parameters for the given learning scenario. The neural networks of the deep learning platform were then compared and summarized.

Quantitative Analysis of Knee Radiography

The most common orthopedic illness in the worldwide, osteoarthritis (OA), affects mainly hand, hip, and knee joints. OA invariably leads to surgical intervention, which is a huge burden on both the individual and the society. There are numerous risk factors that contribute to OA, although the pathogenesis of OA and the molecular basis of through such are unknown at this time. OA is presently identified with an analyses were used to examine and, if required, corroborated through imaging - a radiography study. These traditional methods, on the other hand, are not susceptible to sense the beginning phases of OA, making the creation of precautionary interventions for specific disease problematic. As a result, other approaches which might permit for the timely identification of OA are needed. As a result, computerized perception algorithms give measurable indicators that may be used to determine the severity of OA from photographs in an automated and systematic manner. The study of Knee radiography and its quantitative analysis is analyzed in this paper.

Design of Adaptive Estimator for Nonlinear control system in Noisy Domain

In the recent research studies, adaptive control has been used to monitor a reference control system that has been selected by the researcher. The acceptable reference system can be executed with the presence of nonlinearity and undesirable switches, as they have been disabled. This research study examines the challenges in adapting the state and time-varying parameters, when noise and state disturbances are present in a nonlinear control system. Additionally, this adaptive controller points out the best or most accurate option based on recursive least squares calculations by using the sensor data. This convergence has a rate of arrival with a set time of origin that serves as an estimate for error estimation. In addition, this research work has evaluated the amount of variance in the estimate, which has been caused by external disturbances using adaptive estimators from the reference value present in the noisy domain. The findings obtained via simulation demonstrate the performance of the adaptive estimator through the difference obtained between the reference value and observed value.

Nakagami-m Fading Detection with Eigen Value Spectrum Algorithms

One of the most crucial roles of the cognitive radio (CR) is detection of spectrum ‘holes’. The ‘no a-priori knowledge required’ prospective of blind detection techniques has attracted the attention of researchers and industries, using simple Eigen values. Over the years, a number of study and research has been carried out to determine the impact of thermal noise in the performance of the detector. However, there has not been much work on the impact of man-made noise, which also hinders the performance of the detector. As a result, both man-made impulse noise and thermal Gaussian noise are examined in this proposed study to determine the performance of blind Eigen value-based spectrum sensing. Many studies have been conducted over long sample length by oversampling or increasing the duration of sensing. As a result, a research progress has been made on shorter sample lengths by using a novel algorithm. The proposed system utilizes three algorithms; they are contra-harmonic-mean minimum Eigen value, contra-harmonic mean Maximum Eigen value and maximum Eigenvalue harmonic mean. For smaller sample lengths, there is a substantial rise in the number of cooperative secondary users, as well as a low signal-to-noise ratio when employing the maximum Eigen value Harmonic mean. The experimental analysis of the proposed work with respect to impulse noise and Gaussian signal using Nakagami-m fading channel is observed and the results identified are tabulated.