Recently, microelectromechanical system (MEMS) cantilevers have received significant interest in the domain of Volatile Organic Compounds (VOCs). An analysis of MEMS cantilevers in VOCs is presented in this Review. It examines the different forms of sensors used to detect VOCs. It goes into the conditions that influence MEMS and the strategies used for VOC sensing. It examines research on MEMS cantilevers and other VOC sensing and detection techniques. It shows how MEMS can be used to detect VOCs. Moreover, it presents a comparative study based on the objectives, types of sensors employed, merits, and shortcomings of existing works. This Review intends to explore MEMS cantilevers in VOCs for supporting further research and applications.
Greater reliance on smart and portable electronic devices demands engineers to provide solutions with better performance and minimized demerits. Face Recognition involves the method of associating and confirming the faces. It is fit for distinguishing, following, recognizing, or checking human appearances from a picture or video caught utilizing an advanced camera. Feature extraction is the most significant stage for the achievement of the face recognition framework. The different ways of implementing this project depends on the programming language or algorithms used such as MATLAB, OpenCV, visual basics C#, Viola-Jones algorithm and many more while the core functioning remains the same. In this work, we have implemented face recognition in 3 phases, Phase1 consists of detecting faces and collecting images IDs, Phase 2 involves training the Recognizer and Separating interesting elements and the final phase includes grouping them and putting away in XML records.
Photovoltaic-electric spring (PV-ES) is a promising topology to utilize widespread residential roof-top photovoltaic systems in demand-side management. Power control for an integrated configuration of photovoltaic-electric spring system to achieve dynamic supply-demand balance in power distribution networks is presented. Extraction of maximum power from PV panel using Perturb and Observe algorithm along with boost converter are designed. This power is given as input to the DC link of the Electric Spring. The modeling and design of the integrated system are detailed. Extensive simulations are carried out in MATLAB/Simulink to observe the performance of the PV-ES system. The effectiveness of the proposed topology was verified for changes in line voltage, PV irradiation, and reference power. It was confirmed that the proposed PV-ES precisely controls the active power consumption of the critical load, rigidly regulates the voltage at the point of common coupling (PCC), and follows the variations in reference power available for the smart load. Finally, the expansive performance of ES fed with a PV source was confirmed to be superior over an ES system fed with a DC source.
Deep neural network (DNN) based models are highly acclaimed in medical image classification. The existing DNN architectures are claimed to be at the forefront of image classification. These models require very large datasets to classify the images with a high level of accuracy. However, fail to perform when trained on datasets of small size. Low accuracy and overfitting are the problems observed when medical datasets of small sizes are used to train a classifier using deep learning models such as Convolutional Neural Networks (CNN). These existing methods and models either always overfit when training on these small datasets or will result in classification accuracy which tends towards randomness. This issue stands even when using Transfer Learning (TL), the current standard for such a scenario. In this paper, we have tested several models including ResNet and VGGs along with more modern models like MobileNets on different medical datasets with transfer learning and without transfer learning. We have proposed solid theories as to why there exists a need for a more novel approach to this issue, and how the current methodologies fail when applied to the aforementioned datasets. Larger, more complex models are not able to converge for smaller datasets. Smaller models with less complexity perform better on the same dataset than their larger model counterparts.
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