A CNN based hybrid approach towards automatic image registration

Image registration is a key component of spatial analyses that involve different data sets of the same area. Automatic approaches in this domain have witnessed the application of several intelligent methodologies over the past decade; however accuracy of these approaches have been limited due to the inability to properly model shape as well as contextual information. In this paper, we investigate the possibility of an evolutionary computing based framework towards automatic image registration. Cellular Neural Network has been found to be effective in improving feature matching as well as resampling stages of registration, and complexity of the approach has been considerably reduced using corset optimization. CNN-prolog based approach has been adopted to dynamically use spectral and spatial information for representing contextual knowledge. The salient features of this work are feature point optimisation, adaptive resampling and intelligent object modelling. Investigations over various satellite images revealed that considerable success has been achieved with the procedure. Methodology also illustrated to be effective in providing intelligent interpretation and adaptive resampling

Investigation Of Reactively Sputtered Silicon Carbon Boron Nitride (sicbn) Thin Films For High Temperature Applications

The increasing demand for efficient energy systems in the last decade has brought about the development of advanced sensor systems that utilize advance detection methods to help in preventive maintenance of these essential systems. These usually are needed in hard to access environments where conditions are extreme and unfit for human interaction. Thin film based sensors deposited directly on the surfaces exposed to harsh environments can serve as ideal means of measuring the temperature of the component during operation. They provide the basic advantage of proximity to the surface and hence accurate measurement of the surface temperature. The low mass size ratio provides the additional advantage of least interference to system operation. The four elements consisting of Si, C, B, and N can be used to form binary, ternary and quaternary compounds like carbides, nitrides, which are chemically and thermally stable with extreme hardness, thermal conductivity and can be doped n- and p-type. Hence these compounds can be potential candidates for high temperature applications. This research is focused on studying sputtering as a candidate to obtain thin SiCBN films. The deposition and characterization of amorphous thin films of silicon boron carbon nitride (SiCBN) is reported. The SiCBN thin films were deposited in a radio frequency (rf) magnetron sputtering system using reactive co-sputtering of silicon carbide (SiC) and boron nitride (BN) targets. Films of different compositions were deposited by varying the ratios of argon and nitrogen gas in the sputtering ambient. Investigation of the oxidation kinetics of these materials was performed to study high temperature compatibility of the material. Surface characterization of the deposited films was performed using X-ray photoelectron spectroscopy and optical profilometry. Studies reveal that the chemical state of the films is highly sensitive to nitrogen flow ratios during sputtering. Surface analysis shows that smooth and uniform SiCBN films can be produced using this technique. Carbon and nitrogen content in the films seem to be sensitive to annealing temperatures. However depth profile studies reveal certain stoichiometric compositions to be stable after high temperature anneal up to 900ºC. Electrical and optical characteristics are also investigated with interesting results. Finally a metal semiconductor metal structure based optoelectronic device is demonstrated with excellent performance improvement over standard silicon based devices under higher temperature conditions

Oral Hygiene Practices: Ancient Historical Review

The ancient history of the world’s fascination with oral health is a long and illustrious one. Numerous dental epidemiological studies indicate that people are keeping their teeth longer than over before in this century. Neolithic age and prehistoric age people used agents and devices that have evolved, by custom, myth, beliefs and by research, to enable people, withprofessional assistance, to maintain good oral health. The first mentions of teeth and dental hygiene were found in inscriptions from Mesopotamian clay tablets, so called ‘oral hygieneproducts’ including toothpicks, chewing sticks, tooth powders and mouthwashes, dating back to 5,000 years ago. The Egyptians, Mesopotamians’, Greco-Romans, Hindus and Chinese discovered variety of dental treatments and intricate surgical operations. The profession has met the challenge by developing and perfecting a myriad of devices and agents tothwart these pathogenic factors since ages. We certainly eat well, speak well, look fine and ‘smell fresh’–but we also have plaque, gingivitis and dental caries. The reader can determinehow much our ancestors thought, invented and practiced oral hygiene long long ago and which gave raise to later inventions

Spatial analysis in public health domain: an NLP approach

Remote sensing products are effectively used as a tool for decision making in various fields, especially in medical research and health care analyses. GIS is particularly well suited in this context because of its spatial analysis and display capabilities. The integration of RS techniques in public health has been categorised as continuous and discrete strategies where latter is preferred. We have investigated the integration of these approaches through linguistic interpretation of images. In this paper, we propose a framework for direct natural language interpretation of satellite images using probabilistic grammar rules in conjunction with evolutionary computing techniques. Spectral and spatial information has been dynamically combined using adaptive kernel strategy for effective representation of the contextual knowledge. The developed methodology has been evaluated in different querying contexts and investigations revealed that considerable success has been achieved with the procedure. The methodology has also demonstrated to be effective in intelligent interpolation, automatic interpretation as well as attribute, topology, proximity, and semantic analyses

Analyzing the effects of exercise prescribed based on health-related fitness assessment among different somatotypes

Introduction: Human body types (somatotypes) are classified into ectomorph, mesomorph, and endomorph. The ectomorphs are physically weak and usually tall. Mesomorphs were characterized as muscular, thick skinned with good upright posture. Endomorphs characterized as fat, heavy, and usually short. Methods: This study is an interventional study, in which a total number of 45 healthy male volunteers between the age group of 22 and 28 years were observed. Written consent was obtained from the patients after a detailed explanation of the study. Exercises were prescribed and executed based on the American College of Sports Medicine (ACSM) guidelines for exercise testing and prescription. Body composition, cardiorespiratory endurance, muscular strength, muscular endurance, and flexibility were assessed. Results: The statistical analyses were done using the SPSS software version 16 executed at a 95% confidence interval. Mean and standard deviations were calculated by descriptive statistics. A paired t-test was done to find the effectiveness of the intervention. The level of significance in all tests was set to p < 0.05. Positive changes were observed in health-related fitness among the three groups. Conclusion: This study reports about finding the somatotypes, and exercising based on that will provide the best results in health-related fitness components designed by the ACSM

A vector machine based approach towards object oriented classification of remotely sensed imagery

Remote sensing techniques are widely used for land cover classification and related analyses; however the availability of high resolution images have limited the accuracy of pixel based approaches. In this paper, we have analyzed the feasibility of incorporating contextual information to a support machine and have evaluated its performances with reference to the traditional approaches. We have adopted certain automatic approaches based on advanced techniques such as Cellular Automata and Genetic Algorithm for improving effective overlap between classes. Proposed methodology has been evaluated in comparison with the conventional approaches with reference to the study area using relevant statistical parameters. Accuracy improvement of the proposed approach may be attributed to the effectiveness in combining spatial and spectral information

Experimental Investigation of Wire Wicked and Mesh Wicked Heat Pipe

An experimental investigation is carried out for determining heat pipes heat transfer rate involving wire wick and mesh wick. The investigation is conducted in order to examine the efficiency of wire wicked heat pipe. The wire wick is investigated for heat transfer rate and efficiency by comparing it with the widely used economic and efficient mesh wick structure. The investigation function involved detecting the heat transfer at different angle of inclinations. Both the heat pipes considered have the same thickness of wick layers and same working fluid tested under ideal situations.

ConVision Benchmark: A Contemporary Framework to Benchmark CNN and ViT Models

Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have shown remarkable performance in computer vision tasks, including object detection and image recognition. These models have evolved significantly in architecture, efficiency, and versatility. Concurrently, deep-learning frameworks have diversified, with versions that often complicate reproducibility and unified benchmarking. We propose ConVision Benchmark, a comprehensive framework in PyTorch, to standardize the implementation and evaluation of state-of-the-art CNN and ViT models. This framework addresses common challenges such as version mismatches and inconsistent validation metrics. As a proof of concept, we performed an extensive benchmark analysis on a COVID-19 dataset, encompassing nearly 200 CNN and ViT models in which DenseNet-161 and MaxViT-Tiny achieved exceptional accuracy with a peak performance of around 95%. Although we primarily used the COVID-19 dataset for image classification, the framework is adaptable to a variety of datasets, enhancing its applicability across different domains. Our methodology includes rigorous performance evaluations, highlighting metrics such as accuracy, precision, recall, F1 score, and computational efficiency (FLOPs, MACs, CPU, and GPU latency). The ConVision Benchmark facilitates a comprehensive understanding of model efficacy, aiding researchers in deploying high-performance models for diverse applications.This article is published as Bangalore Vijayakumar, Shreyas, Krishna Teja Chitty-Venkata, Kanishk Arya, and Arun K. Somani. "ConVision Benchmark: A Contemporary Framework to Benchmark CNN and ViT Models." AI 5, no. 3 (2024): 1132-1171. doi: https://doi.org/10.3390/ai5030056. Copyright: © 2024 by the authors.This article is an open access article distributed under the terms and conditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)