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An improved E-passport system with secured IoT and wireless communication technology

AIP Conference Proceedings 2022年第1期2452卷

作者： T. Vignesh K. K. Thyagharajan R. Beaulah Jeyavathana Prasanna Kumar R. 1Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India 2Department of ECE R.M.D Engineering College Chennai India 3Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam Chennai India 4Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India

A stepped forward E-Passports with IoT devices assumes an essential element in momentum research. Additionally, getting the data, placed away on E-Passport is also an essential difficulty. In this paper, we've got proposed a stepped forward far- off E-Visa framework with simple stage protection. The number one aim of this advanced system is to devise and foster an excessive stage tremendous far-off identity and Savvy card which conveys the identity subtleties and visa limits. Radio Frequency Identification (RFID) is a programmed ID innovation this is using Radio recurrence signals. Utilizing RFID labels as opposed to identity and visa information to conquer the paper works and document lacking difficulty with IoT devices improvements the superior protection highlights of an identity. Also, this advanced system provides high level security in customer information storage.

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Erratum: “Rare earth-implanted lithium niobate: Properties and on-chip integration” [Appl. Phys. Lett. 115, 071104 (2019)]

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Applied Physics Letters 2022年第9期120卷

作者： Xiaodong Jiang Dongmin Pak Arindam Nandi Yi Xuan Mahdi Hosseini 1Birck Nanotechnology Center School of Electrical and Computer Engineering Purdue University West Lafayette Indiana 47907 USA 2Department of Physics and Astronomy Purdue University West Lafayette Indiana 47907 USA

The authors would like to add one affiliation for two of the authors of the original version of the published article.1 Affiliation to the department of Physics

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Modeling of the pernicious chemical sensor in the terahertz regime

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AIP Conference Proceedings 2022年第1期2357卷

作者： Etu Podder Md. Bellal Hossain Md. Ekhlasur Rahaman Abdullah Al-Mamun Bulbul Rayhan Habib Jibon Sajib Kabiraj M. Raihan 1Electronics and Communication Engineering Discipline Khulna University Khulna-9208 Bangladesh 2Department of Electronics and Telecommunication Engineering (ETE) Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj Bangladesh 3Department of Computer Science and Engineering North Western University Khulna Bangladesh

Several chemicals are dangerously injurious to the breathing system of human being and so, the exact diagnosis of the pernicious chemicals is essential. Therefore, we present a PCF sensor to detect these pernicious chemicals such as Sarin, Soman, Tabun and in this work, Zeonex is considered as bulk material. The COMSOL Multiphysics V-5.5 is used to design and analysis of the sensor model and the investigation of the proposed sensor’s performance is accomplished by shifting the terahertz frequency. We have found that relatively enhanced sensitivity (almost 90.93%, 92.39%, and 94.12 % for Sarin, Soman, and Tabun at 2.5 THz) with negligible light confinement loss (around 1.043e×10-14 cm-1, 7.62×10−15 cm−1, and 6.15×10-15 cm-1 for Sarin, Soman, and Tabun at 2.5 THz) is achieved by the sensor which the foremost requirement to provide the best performance by any biosensor or chemical sensor. Besides, the existing modern fabrication technologies can be applied to fabricate the sensor.

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Enabling Research through the SCIP Optimization Suite 8.0

arXiv

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arXiv 2023年

作者： Bestuzheva, Ksenia Besançon, Mathieu Chen, Wei-Kun Chmiela, Antonia Donkiewicz, Tim van Doornmalen, Jasper Eifler, Leon Gaul, Oliver Gamrath, Gerald Gleixner, Ambros Gottwald, Leona Graczyk, Christoph Halbig, Katrin Hoen, Alexander Hojny, Christopher van der Hulst, Rolf Koch, Thorsten Lübbecke, Marco Maher, Stephen J. Matter, Frederic Mühmer, Erik Müller, Benjamin Pfetsch, Marc E. Rehfeldt, Daniel Schlein, Steffan Schlösser, Franziska Serrano, Felipe Shinano, Yuji Sofranac, Boro Turner, Mark Vigerske, Stefan Wegscheider, Fabian Wellner, Philipp Weninger, Dieter Witzig, Jakob Zuse Institute Berlin Department AIS2T Takustr. 7 Berlin14195 Germany ZIB-AIS2T Germany School of Mathematics and Statistics Beijing Institute of Technology Beijing100081 China RWTH Aachen University Lehrstuhl für Operations Research Kackertstr. 7 Aachen52072 Germany Eindhoven University of Technology Department of Mathematics and Computer Science Netherlands ZIB-AIS2T I2DAMO GmbH Englerallee 19 Berlin14195 Germany ZIB-AIS2T HTW Berlin Germany Friedrich-Alexander Universität Erlangen-Nürnberg Department of Data Science Cauerstr. 11 Erlangen91058 Germany University of Twente Department of Discrete Mathematics and Mathematical Programming P.O. Box 217 Enschede7500 AE Netherlands Technische Universität Berlin Software and Algorithms for Discrete Optimization Straße des 17. Juni 135 Berlin10623 Germany ZIB Department A2IM Takustr. 7 Berlin14195 Germany RWTH Aachen-Lehrstuhl für OR Germany University of Exeter College of Engineering Mathematics and Physical Sciences Exeter United Kingdom Technische Universität Darmstadt Fachbereich Mathematik Dolivostr. 15 Darmstadt64293 Germany TU Darmstadt Fachbereich Mathematik Germany ZIB-A2IM Germany TU Berlin Germany ZIB-A2IM Software and Algorithms for Discrete Optimization Institute of Mathematics TU Berlin Germany GAMS Software GmbH c/o ZIB-AIS2T Germany

The SCIP Optimization Suite provides a collection of software packages for mathematical optimization centered around the constraint integer programming framework SCIP. The focus of this paper is on the role of the SCIP Optimization Suite in supporting research. SCIP's main design principles are discussed, followed by a presentation of the latest performance improvements and developments in version 8.0, which serve both as examples of SCIP's application as a research tool and as a platform for further developments. Further, the paper gives an overview of interfaces to other programming and modeling languages, new features that expand the possibilities for user interaction with the framework, and the latest developments in several extensions built upon *** Codes 90C05, 90C10, 90C11, 90C30, 90C90, 65Y05 Copyright © 2023, The Authors. All rights reserved.

关键词： Integer programming

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Fast recurrent neural network approach for automatic detection of lung nodules

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AIP Conference Proceedings 2022年第1期2519卷

作者： Balachandra Pattnaik B. Suseela T. R. Ganesh Babu S. Anitha 1Department of Electrical and Computer Engineering Wollega University Nekemte Ethiopia Africa 2Department of Electronics and Communication Engineering Government Polytechnic College Keelakanavai Perambalur-621 104 India 3Department of Electronics and Communication Engineering Muthayammal Engineering College Rasipuram-637408. India 4Department of Electronics and Communication Engineering Muthayammal Engineering College Rasipuram-637408 India

The cancer death in concern to lung cancer is increased compared to other cancers worldwide. The presence of nodules in the lung indicates the chances of getting lung cancer in the future. In this paper, an accurate computer-Aided Detection (CAD) system for lung nodules detection using computer Tomography (CT) images is proposed. It consists of four important modules such as preprocessing; Two-Successive Segmentation Process (TS2P), Rule-Based Refinement Pass (RBRP), and Detection Module (***). The lung C.T. CT image is de-noised using the Wiener filter in the preprocessing module. In the TS2P module, the right and left lungs are segmented at first, and in the next stage, the nodules and vessels are segmented. Then, the RBRP module is designed to remove the vessels with the help of geometrical features. Finally, the nodules are detected using a deep learning approach in the ***. The proposed method is validated on 888 lung *** images, and a mean average precision of 96.75% and sensitivity of 97% with 2 false positives per image were obtained.

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Cs-137 activity in fly ash from thermal electric power stations

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AIP Conference Proceedings 2022年第1期2466卷

作者： I. K. Ahmed H. N. B. Khalaf Y. A. M. Mostafa 1Department of Computer Networks Engineering College of Information Engineering Al-Nahrain University Iraq 2Department of Physics Faculty of Science Minia University 61519 El-Minia Egypt 3Ural Federal University Physics and Technology Institute 620078 Ekaterinburg Russia

Radioactive 137Cs has a great concern because of its long half-life (30.2 year) and its similarity to potassium which is an essential element for humans. It is well known that Cs is distributed in surface soils or specifically adsorbed on illicit clay minerals and finally enters to organisms. 137Cs is made through nuclear accidents and processes is an example of anthropogenic radionuclides. In the present work, 137Cs specific activity concentration is measured with a high efficiency gamma ray spectroscopy NaI(Tl) detector in fly ash samples from two thermal electric power stations in the south of Iraq (Al-Naserya and Al-Musaeb). The 137Cs specific activity concentration are founded with ranges from 20±4.8 Bq kg-1 to 120±27.6 Bq kg−1 at Al-Naserya and 17±4.4 Bq kg-1 to 86±19.8 Bq kg−1 at Al-Musaeb station. Some of these values are alerted us to take care about the received dose because these concentrations are higher than background and world-wide limit (37 Bq kg-1 From UNSCEAR). Also, the presented 137Cs specific activity concentration results point out an additional shot to the measured effective dose rate, which can be predicted to be the 137Cs and cosmic radiation. By accounting for the contribution of 137Cs in the estimated absorbed dose rate, applying the conversion coefficient 0.136 (nGy h−1 per Bq kg−1). If a human inhale the air every day for 70 years, the radiation dose must be less than the dose limit (1 mSv y−1). In case 137CS is present, the criterion is the activity of 137Cs /60≦1.

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Convolutional neural network-based MRI brain tumor classification system

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AIP Conference Proceedings 2022年第1期2519卷

作者： M. Amanullah J. Visumathi Naresh Sammeta Maram Ashok 1Department of Information Technology Aalim Muhammed Salegh College of Engineering Chennai Tamil Nadu India 2Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India 3Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Tamil Nadu India 4Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Secunderabad Telangana India

A brain tumor, the cause of more death rate among all cancers, is diagnosed using uncontrollable cell growth and abnormal brain cell partitioning. The recent progress in Deep Learning (DL) neural network technology aids the health service department in medical imaging diagnose several death-causing diseases. The visual learning of image recognition manually may result in fault detection and can be overcome by the most prevalent task of machine learning. In our paper, the Convolutional Neural Network (CNN) model is designed with data augmentation and image processing techniques to identify the brain MRI scan images into cancerous or non-cancerous and classify various brain tumor types. The performance comparison of our proposed CNN model uses the Transfer Learning (TL) method. With a very small dataset, the experimental result shows that our model is very effective at low computational power with less complexity and achieves better accuracy compared to an existing model.

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Covid heuristic analysis using machine learning

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AIP Conference Proceedings 2022年第1期2393卷

作者： Pravin R. Kshirsagar Makarand Upadhyaya Pankaj Dadheech T. Yuvaraj C. A. Sathiya Moorthy 1Department of Electronics and Communication Engineering AVNIET Hyderabad India 2Department of management & marketing University of Bahrain Bahrain 3Computer science & Engineering Swami Keshvanand institute of technology Jaipur Rajasthan India 4Department of Electrical and Electronics Engineering Saveetha School of Engineering Chennai India 5Department of Electronics and Communication Engineering CMS College of engineering Namakkal India

The newer avid-19 corona virus created havoc for patients with a variety of complications that prompted health practitioners around the world to develop new technologies and treatment plans. Technologies based on Machine Learning (ML) have been a major factor in addressing complex issues and many businesses have been able to develop and adapt to the COVID-19 challenges. The diagnosis of illness can be used with different AI methods to monitor the present havoc. Since Machine Learning (ML) approaches have been commonly used in other domain fields, a great deal of demand is now being made for ML-supported diagnostic systems to screen, monitor, and forecast void-19 spread and find a cure. The article presents an overview of the role of ML to combat the virus so far, especially from the perspective of screening, prognosis, and vaccine.

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Enhancement of smart grid technology using end-to-end reinforcement learning in deep q-network

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AIP Conference Proceedings 2022年第1期2555卷

作者： Dhruvi Bansal Mitashi Bansal Kusum Tharani Meenu Gupta Bhawna Goyal Ayush Dogra 1Department of Electrical & Electronics Engineering Bharati Vidyapeeth’s college of Engineering New Delhi India 110063 2Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Noida India 201313 3Department of Computer Science and Engineering Chandigarh University Punjab India 140413 4Department of ECE Chandigarh University Mohali India 140413 5Ronin Institute Montclair NJ USA 07043

This paper reviews the complex task problems which are out of reach for a simple machine. So, there is a need for a solution for such a problem, so the solution is Reinforcement Learning with deep Q-Network. Reinforcement learning techniques are now being researched for their applicability in a wide range of situations. Perhaps because of the rising complexity and unpredictability in the generation and distribution sector of power systems, traditional approaches frequently encounter congestion while attempting to handle decision and control issues that are out of reach for a basic machine. Deep Reinforcement Learning (DRL) is one of these data-driven approaches that is considered true Artificial Intelligence (AI). DRL is a hybrid of Deep Learning (DL) and Reinforcement Learning (RL). Our study examines the fundamental concepts, models, methods, and approaches of DRL. It also presents power system applications such as smart grids, energy management, demand response, the electricity market, operational control, and many others. Furthermore, current advancements in DRL, the coupling of RL with other classical techniques, and the prospects and problems of its applications in the power system are explored.

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Internet connected patient healthcare monitoring in smart phones using Raspberry Pi

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AIP Conference Proceedings 2022年第1期2519卷

作者： Porandla Srinivas K. Kalimuthu C. Balarengadurai C. Viswanathan 1Department of Information Technology Malla Reddy Institute of Engineering and Technology Secunderabad Telangana India 2Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Tamil Nadu India 3Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Dundigal Telangana India 4Department of Computer Science and Engineering Chennai Institute of Technology Malayambakkam Tamil Nadu India

Today the Environment of health has established research and knowledge based on Technology-oriented wireless sensing node. Patients are confronted with the problem of unanticipated death because of the particular cause of cardiac difficulties and attacks that occur at the necessary time owing to a lack of adequate care for patients. In contemporary health industry, the use of medical gadgets is significant, which is causing a lot of disruption in the patient’s space as well as the doctor’s analysis. As a result, we highlight our major healthcare electronics initiatives in this paper, with an emphasis on electronic medical records. Uniqueness and ease of use characterize the designed systems, which were planned with a very high level of efficiency (so-called "smarter" gadgets). It was created as a handheld gadget to allow for real-time rescue in the event of an accident without the requirement for continuous patient care of the data. We also show a low-cost computerized healthcare practice that can analyze respiratory activities in real time, non-invasively, and continuously.

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