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Magnetic‑free chiral eigenmode spectroscopy for simultaneous sensitive measurement of optical rotary dispersion and circular dichroism

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eLight 2024年第1期4卷 160-171页

作者： Wenpeng Zhou Ya‑Ping Ruan Haodong Wu Han Zhang Jiang‑Shan Tang Zhenda Xie Lei Tang Yu Wang Yue-E Ji Kunpeng Jia Cheng‑Wei Qiu Yan‑Qing Lu Keyu Xia College of Engineering and Applied Sciences Nanjing UniversityNanjing 210023China National Laboratory of Solid State Microstructures Nanjing UniversityNanjing 210023China School of Physics Nanjing UniversityNanjing 210023China School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China Department of Electrical and Computer Engineering Engineering Drive 3National University of SingaporeSingapore 117583Singapore Hefei National Laboratory Hefei 230088China

Chirality,defined by Lord Kelvin,refers to the geometric symmetry property of an object that cannot be superposed onto its mirror image using rotations and *** material’s chirality can be probed with light as the optical activity:optical rotary dispersion(ORD)and circular dichroism(CD).It is still challenging to yield extremely sensitive ORD and CD for very weak chirality and measure both *** ringdown polarimetry has been reported to improve ORD detection sensitivity with the absence of equally important CD signature,at the price of high cavity finesse near 400,frequency-locking sophistication,and large magnetic ***,we report a unique recipe to demonstrate the simultaneous measurement of ORD and the CD by separately observing the chiral eigenmode spectra from a bowtie optical cavity with a finesse about 30,without resorting to frequency locking or magnetic *** obtain a sensitivity of2.7×10^(−3)deg/√Hz for ORD,8.1×10^(−6)/√Hz for CD,and a spectral resolution of 0.04 pm within a millisecond-scale *** present a cost-effective yet ultrasensitive account for chiral chromatography,the conformational dynamics and chiroptical analysis of biological samples which particularly exhibit weak and narrow spectral signals.

关键词： Chiral material Optical rotary dispersion Circular dichroism Cavity-enhanced chiral eigenmode spectroscopy

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Towards Mental stress Detection in University Students Based on RBF and Extreme Learning Based Approach 1

Towards Mental stress Detection in University Students Based...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Anand, Taruna Alam, Mohammad Shabbir Akila, D. Yadav, Manjushri Janardan Mavliya, Anil Kumar Saran Kumar, A. Graphic Era Deemed to be University Department of Humanities and Social Sciences Dehradun India College of Computer Science and Information Technology Jazan university Department of Computer science Jizan Saudi Arabia Saveetha College of Liberal Arts and Sciences SIMATS Department of Computer Applications Tamil Nadu Chennai India International Institute of Business Studies Department of Management Bengluru India COER College of Nursing and Paramedical Sciences Uttarkhand Roorkee India Bannari Amman Institute of Technology Computer Science and Engineering Erode India

ISBN: (纸本)9798350371802

A lot of individuals associate stressful situations with bad experiences. It is concerning that mental health issues are so common, particularly among younger people. The once carefree generation is now under a great deal of stress. Modern society's elevated stress levels are associated with an array of serious health issues, such as an increased risk of suicide, heart disease, depression, and stroke. Within the scope of our research, we are attempting to quantify the degree to which students experience internet-related anxiety in the seven days leading up to an exam. Model training, feature selection, and preprocessing are all tremendously dependent on sequencing. Images with consistent size and intensity can be achieved by removing noise and blinks during this preprocessing step. Classifier performance improvement via feature selection aims to normalize and increase accuracy by utilizing favorable attributes. Feature selection is crucial during the training of a StressNet-C-BiLSTM model. When compared to two state-of-the-art algorithms, BiLSTM and CNN, the suggested technique outperforms them. The results demonstrated a significant improvement, with an accuracy rate of 97.58%. © 2024 IEEE.

关键词： Health risks

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A Secure and Reliable Supply chain management approach integrated with IoT and Blockchain 2

A Secure and Reliable Supply chain management approach integ...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Ashraf, Humaira Hanif, Maria Ihsan, Uswa Al-Quayed, Fatima Humayun, Mamoona Jhanjhi, N.Z. International Islamic University Islamabad Department of Computer Science and Software Engineering Islamabad Pakistan Jouf University College of Computer and Information Sciences Department of Computer Science Sakakah Saudi Arabia Jouf University College of Computer and Information Sciences Department of Information Systems Sakakah Saudi Arabia Malaysia Applied Science Research Center Applied Science Private University Amman11937 Jordan

ISBN: (纸本)9798350335644

Nowadays, secure and reliable management of logistics is highly needed. Logistics is the delivery of goods from producers to legitimate consumers in accurate amounts and good conditions. The use of low-capable sensor nodes in smart logistics makes it vulnerable to many security threats. Smart logistics necessitated the delivery of suitable information to the authorized person at the appropriate time and place, which is only feasible with a stable infrastructure. This paper presents an authentication scheme together with blockchain technology to provide a secure supply chain management system. The presented authentication mechanism is based on standard KERBOROS scheme which is a ticket-based scheme. The scheme is evaluated by BAN logic which shows that it is an effective scheme in terms of improved response time and encryption/decryption time along with key generation time. © 2023 IEEE.

关键词： Supply chain management

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Learning Multiresolution Features for Unsupervised Anomaly Localization on Industrial Textured Surfaces

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 127-139页

作者： Tao, Xian Yan, Shaohua Gong, Xinyi Adak, Chandranath Institute of Automation Chinese Academy of Sciences Beijing100190 China Binzhou Institute of Technology Binzhou256606 China CAS Engineering Laboratory for Intelligent Industrial Vision Beijing100190 China School of Artificial Intelligence University of ChineseAcademy of Sciences Beijing100049 China Department of Computer Science and Engineering Indian Institute of Technology Bihar Patna801106 India

In industrial quality assessment, monitoring whether the textured product contains defects is a critical step. Compared to a large number of defect-free images that are easy to obtain, anomaly samples are limited and vary randomly in size and type. It is challenging to develop an automatic and accurate texture defect localization system that only uses normal images for training. In this article, a multiresolution feature learning network is proposed to detect various texture defects in an unsupervised manner. A robust pretrained model is first employed to extract the perceptual features from the input image, then the perceptual features of various layers are fed to the corresponding multiscale autoencoder framework. This hierarchical alignment strategy aids in receiving multilevel information for locating anomalies of various sizes. Moreover, a residual attention module is embedded in the architecture to further improve the detection performance. Our proposed method has achieved state-of-the-art performance on the texture dataset of MVTecAD. We also extended the experiment to the real industrial texture datasets, and its detection result is better than the major existing advanced techniques. © 2020 IEEE.

关键词： Textures

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Collision Detection in LoRaWAN Using Machine Learning 22

Collision Detection in LoRaWAN Using Machine Learning

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22nd IEEE International Symposium on Intelligent Systems and Informatics, SISY 2024

作者： Abdoun, Nabil Abboud, Samira Altaleb, Haya Rajnai, Zoltan Department of Liberal Education Beirut Lebanon Department of Computer Science Angers France Ó buda University Bánki Donát Faculty of Mechanical and Safety Engineering Budapest Hungary

ISBN: (纸本)9798350385601

In this paper, we investigate the application of machine learning techniques as a productive solution for carrying out LoRaWAN packet collision detection. Our focus is on a common channel that is shared by several LoRa end-devices via the asynchronous ALOHA protocol, with the aim of delivering data packets in IoT scenarios. We evaluate the effectiveness of two algorithms in this setup: Random Forest and Decision Tree. Higher efficiency is exhibited for identification and likelihood of False Positive Rate, even when packets transmissions collide. © 2024 IEEE.

关键词： Random forests

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Assessment of Heart-Attack Prediction using Fuzzy Rule Based System 8

Assessment of Heart-Attack Prediction using Fuzzy Rule Based...

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Saratkar, Saniya Chaudhari, Aarti Thute, Trupti Raut, Rohini Thakre, Gayatri Kumar, Hemant Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha442107 India

ISBN: (纸本)9798350391770

This paper evaluates the prediction of heart attacks with a fuzzy rule-based system (FRBS). Since heart disease is the world's leading cause of death, precise prediction models are crucial for prevention and early intervention. With its reputation for managing imprecise and ambiguous data, fuzzy logic presents a viable method for raising prediction accuracy in medical diagnostics. In order to capture the complexity and unpredictability of cardiovascular risk variables, fuzzy rules are used in this work to investigate the creation and assessment of a fuzzy rule-based system (FRBS) for heart attack prediction. The efficacy and dependability of the FRBS in identifying those at high risk of heart attack are shown via testing and comparison with conventional prediction models. The results highlight fuzzy logic's potential to improve cardiovascular predictive analytics. © 2024 IEEE.

关键词： Prediction models

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Fake Currency Note Recognition using Extreme Learning Machine 2

Fake Currency Note Recognition using Extreme Learning Machin...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Kumar, Rajeev Lamba, Anil Kumar Mohammed, Sayed Asokan, Anju Aswal, Upendra Singh Kolavennu, Subbarao Gopal Narayan Singh University Faculty of Information Technology Bihar Jamuhar India Psna College of Engineering and Technology Ece Tamilnadu Dindigul India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India School of Computer Science and Engineering Geeta University Delhi India Sri Eshwar College of Engineering Kinathukadavu Department of Computer and Communication Engineering Tamilnadu Coimbatore India Sphoorthy Engineering College Cse Cyber Security Department Telangana Hyderabad India

ISBN: (纸本)9781665456302

Color printing technology has made it simpler for forgers to make and copy large numbers of fake currency. The production of legible banknotes was once the sole purview of print shops, but today even a novice printer with a simple laser printer can do the job. Because of this, the prevalence of counterfeit money has increased dramatically. A number of problems, including corruption and the existence of 'black money,' have hounded India for a long time. There is also a serious problem with the circulation of counterfeit currency. The outcome is a system that can detect fake currency with greater speed and precision. It is a serious crime to create counterfeit currency, which is money that has not been authorized by the government. The vast majority of them do this professionally. The circulation of counterfeit currency retards economic development and devalues legitimate currency. In this study, it uses image processing to the problem of identifying fake Indian notes. This technique finishes the initial acquisition before the image is processed further. Pre-processing includes actions such as cropping, smoothing, and modifying the image;converting the image to greyscale;applying image segmentation;extracting features;and resizing the image. The model is then trained using an ELM. Proposed model exceeds both the CNN and SVM models in terms of accuracy, at around 97.8 percent. © 2023 IEEE.

关键词： Crime

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Design of Automated Opinion Mining Model Using Optimized Fuzzy Neural Network

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computers, Materials & Continua 2022年第5期71卷 2543-2557页

作者： Ala’A.Eshmawi Hesham Alhumyani Sayed Abdel Khalek Rashid A.Saeed Mahmoud Ragab Romany F.Mansour Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of SciencesTaif UniversityTaif21944Saudi Arabia Centre for Artificial Intelligence in Precision Medicine King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Sentiment analysis or Opinion Mining (OM) has gained significant interest among research communities and entrepreneurs in the recentyears. Likewise, Machine Learning (ML) approaches is one of the interestingresearch domains that are highly helpful and are increasingly applied in severalbusiness domains. In this background, the current research paper focuses onthe design of automated opinion mining model using Deer Hunting Optimization Algorithm (DHOA) with Fuzzy Neural Network (FNN) abbreviatedas DHOA-FNN model. The proposed DHOA-FNN technique involves fourdifferent stages namely, preprocessing, feature extraction, classification, andparameter tuning. In addition to the above, the proposed DHOA-FNN modelhas two stages of feature extraction namely, Glove and N-gram ***, FNN model is utilized as a classification model whereas GTOA isused for the optimization of parameters. The novelty of current work is thatthe GTOA is designed to tune the parameters of FNN model. An extensiverange of simulations was carried out on the benchmark dataset and the resultswere examined under diverse measures. The experimental results highlightedthe promising performance of DHOA-FNN model over recent state-of-the-arttechniques with a maximum accuracy of 0.9928.

关键词： Opinion mining sentiment analysis fuzzy neural network metaheuristics feature extraction classification

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The Intersection of computer engineering and Intellectual Property Law 15

The Intersection of Computer Engineering and Intellectual Pr...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Bansal, Shweta Sharma, Sachin Sagar, Sipra Ahmad, Shamim Mishra, Rahul Pandey, Arvind Kumar K. R. Mangalam University School of Engineering & Technology Department of Computer Science & Engineering Haryana Gurugram122103 India Dev Bhoomi Uttarakhand University School of Engineering and Computer Uttarakhand 248007 India Invertis University Department of Applied Sciences Uttar Pradesh Bareilly243123 India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India Arka Jain University Department of Computer Science and Information Technology Jharkahnd Jamshedpur India

ISBN: (纸本)9798350370249

laptop engineering and intellectual belongings regulation are beautiful fields, however their intersection has grow to be increasingly more extensive in current years. With technology advancing speedy, computer engineering has end up important in all industries, even as highbrow assets law has turn out to be critical in protecting the rights of creators and innovators. computer engineering involves the layout, improvement, and implementation of pc hardware and software program application systems. The era maintains to conform, computer engineers are constantly growing new merchandise and generation that push the bounds of innovation. however, with the ones improvements come demanding situations related to highbrow assets rights. As such, laptop engineers must have complete understanding of intellectual belongings regulation to shield their creations and navigate criminal problems, patent infringement, and alternate mystery robbery. however, highbrow assets regulation encompasses a set of criminal policies and rules that guard ideas, creations, and improvements from unauthorized use or duplication. © 2024 IEEE.

关键词： Creations Duplication Industries Protecting Unauthorized

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Gender Differences in School Students' Perceptions Towards engineering: A Case Study from Rural South India 14

Gender Differences in School Students' Perceptions Towards E...

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14th IEEE Global engineering Education Conference, EDUCON 2023

作者： Subheesh, N.P. Ayisha, E.A. Akash, Vijay Akshay, R.S. Sarath, S. Yadhukrishna, K. Indian Institute of Technology Madras Department of Humanities and Social Sciences Tamil Nadu Chennai India Federal Institute of Science and Technology Department of Computer Science and Engineering Kerala Ernakulam India St. Joseph's College of Engineering and Technology Palai Department of Computer Science and Engineering Kerala Kottayam India Government Engineering College Barton Hill India Department of Electronics and Communication Engineering Kerala Trivandrum India Accenture Solutions Pvt Ltd Tamil Nadu Coimbatore India College of Engineering Munnar India Department of Computer Science and Engineering Kerala Idukki India

ISBN: (纸本)9798350399431

Gender typecasts have prevailed in higher education since time immemorial. Previous research studies have demonstrated the incongruity in gender representation in almost every higher education sector, including engineering education. Gender-specific variation in perceptions towards engineering education and the engineering profession is a significant area of interest. However, relatively little research has been conducted on gender differences in school students' attitudes and perceptions of engineering. In addition, no such previous study has surveyed students of schools on the rural outskirts. The current study intends to address this research gap by investigating the insights of school students who participated in the 'Rural Students' Technology Enhancement Program (R-STEP).' R-STEP was a technical training program organised by the IEEE Education Society (EdSoc) Kerala Chapter exclusively for less-privileged school students from the rural outskirts of Kerala, a southern state of India. The research study critically analysed the R-STEP participants' responses to a questionnaire survey. The questionnaire was in the regional language, Malayalam, comprising five questions each on engineering education and profession. A total of 220 school students from various districts of Kerala participated in the survey. While 62.5% of female survey respondents indicated that they want to be engineers in the future, it was a more significant 86% in the case of males. Rather interestingly, 16.1% of female participants reported that they do not want to be engineers, compared to only 3.1% of males. This research-worthy fivefold difference between male and female attitudes is critically deliberated in the paper, along with other findings from the study. The paper concludes with a few recommendations to address rural school students' gender-specific attitudes towards engineering. © 2023 IEEE.

关键词： Students

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