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Integrated Optics: Devices, Materials, and Technologies XXVIII 2024

作者： El Shamy, Raghi S. Li, Xun Swillam, Mohamed A. Faculty of Engineering Department of Electrical and Computer Engineering McMaster University HamiltonONL8S 4L8 Canada School of Science and Engineering Department of Physics The American University in Cairo New Cairo11835 Egypt

ISBN: (纸本)9781510670389

Refractive index (RI) sensors based on silicon nitride on insulator (SiNOI) waveguide platform are designed and fabricated. SiNOI offers many advantages among which CMOS compatibility, low propagation losses, tolerance to temperature and fabrication variations as well as wide transparency range. The designed RI sensors include micro-ring resonators (MRRs), Mach-Zehnder Interferometers (MZIs) and loop-terminated MZIs (LT-MZIs) operating at both visible and near-infrared wavelengths. The sensors include strip and slot based sensing arms for chemical and biological sensing. These different components and the whole spectrum were designed and optimized using finite difference eigenmode (FDE) and finite difference time domain (FDTD) solvers. The sensors were fabricated using electron beam lithography in a SiN multi-project wafer (MPW). © 2024 SPIE.

关键词： Silicon nitride

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Graph Convolutional Networks based Non-Small Cell Lung Cancer Identification using RNA-seq Data from Blood Samples

Graph Convolutional Networks based Non-Small Cell Lung Cance...

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2023 IEEE EMBS Special Topic Conference on Data science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Ben Ali, Ferid Adeleke, Sola Mporas, Iosif The School of Physics Engineering and Computer Science University of Hertfordshire United Kingdom Curenetics Ltd The Royal Marsden Nhs Foundation Trust London United Kingdom

ISBN: (纸本)9798350383386

A methodology for the identification of non-small cell lung cancer from blood samples, combining feature selection methods followed by Graph Convolutional Networks (GCN) with Genetic Algorithm (GA) optimization is presented. The methodology was tested on RNA-seq data from the GSE207586 dataset. The evaluation results showed that mixing the top 100 features from different feature selections and modeling with GCN offered the highest identification performance among all evaluated *** Relevance: Ability to diagnose and correctly classify lung cancer subtypes non-invasively have been elusive up until recently. However, current solutions often rely on combination of imaging (chest X-ray, low dose CT scan) with blood sample and clinical data. While this could push the boundary in the non-invasive screening, diagnosis, and classification of lung cancers, it would be challenging to deploy in the primary care or in remote secondary care settings. This current research has demonstrated the combination of gene feature selection techniques and graph convolution network approaches could lead to the detection and subclassification of lung cancers with a very high diagnostic accuracy just from a single blood draw. © 2023 IEEE.

关键词： Feature Selection

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Towards Net Zero - A Hybrid Energy Harvesting System for Sustainable Wireless Sensor Networks in Industrial Applications (Part 1 Review)

Towards Net Zero - A Hybrid Energy Harvesting System for Sus...

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2023 Photonics and Electromagnetics Research Symposium, PIERS 2023

作者： Ukala, Azunka N. Thomas, Martin Ogbodo, Eugene A. Cao, Tiffany Kesieme, Uchenna School of Physics Engineering and Computer Science University of Hertfordshire College Lane Hertfordshire HatfieldAL10 9AB United Kingdom

ISBN: (纸本)9798350312843

This paper investigates the effectiveness of utilizing a 2.4 GHz patch antenna for energy harvesting in a hybrid system that incorporates photovoltaic (PV) technology as an additional energy source. The proposed system is designed to extract energy from both the RF and light sources, which can provide a sustainable energy supply for equipment within a manufacturing environment. The 2.4 GHz patch antenna is utilized to capture RF energy from the surrounding environment, while the PV system generates energy from a light source (including sunlight). The performance of the proposed hybrid system is assessed in terms of energy efficiency and power output. Previous studies have highlighted the potential of using hybrid energy systems for power generation in wireless sensor networks [1], [2]. Energy harvesting from multiple sources can provide a reliable energy supply that is less dependent on a single source of energy. The use of PV systems in combination with RF energy harvesting has been previously explored [3], [4]. However, the potential of using a 2.4 GHz patch antenna in a hybrid system that incorporates PV technology has not been thoroughly investigated. Simulation results show that the hybrid system proposed in this study can achieve a marginally higher energy harvesting efficiency compared to using only the patch antenna or the PV system alone. This indicates that a combination of RF and light energy sources can be an effective approach for energy harvesting in wireless sensor networks. However, it is important to note that more work is required to generate energy significant enough to power other devices within a manufacturing environment. The proposed hybrid system has the potential to provide a sustainable energy supply for wireless sensor networks in manufacturing environments. The results of this study suggest that a combination of PV and RF energy harvesting can enhance the energy harvesting efficiency of wireless sensor networks. This finding is consistent

关键词： Wireless sensor networks

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Robust AI-Enabled Electronic Components Authentication and Anti -Counterfeiting 9

Robust AI-Enabled Electronic Components Authentication and A...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Banu, E. Afreen Priyanka, R. Thiruramanathan, P. Senthilnathan, T. Vithya, V.T. Vinoth, K. Saveetha School of Engineering Simats Department of Computer Science Engineering Tamil Nadu Chennai602105 India S.A Engineering College Department of Computer Science Engineering Tamil Nadu Chennai600077 India Ramco Institute of Technology Department of Physics Tamil Nadu Rajapalayam626117 India Sri Venkateswara College of Engineering Department of Applied Physics Tamil Nadu Sriperumbudur602117 India Rmk College of Engineering And Technology Department of Computer Science Engineering Tamil Nadu Puduvoyal601206 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science And Technology Department of Electrical And Electronics Engineering Tamil Nadu Chennai600062 India

ISBN: (纸本)9798350365092

In consumer electronics, aircraft, health care, and defense, fake electronic parts compromise system security and reliability. This study presents an AI-powered electrical component authentication and anti-counterfeiting system for efficiency and robustness. To prevent counterfeit electronic components, a multi-layered defense uses cutting-edge authentication and AI algorithms. Deep learning models for picture identification, spectroscopic analysis, and other cutting-edge component physical attribute evaluation. A decentralized blockchain platform can record the whole supply chain from producers to consumers. Smart contracts automate and enforce quality standards, eliminating many counterfeiting layers. An Integrated sensors and triggers detect physical tampering, alarm, and authenticate. Tracking and reporting component movements in real time permits quick response to suspicious activity. Stakeholders can easily validate components with AI algorithms via web or mobile apps. NFC, RFID, and QR code authentication is simple. To prevent counterfeiting, the framework specifies a stringent electrical component validation method. Advanced AI and physical and digital security measures provide comprehensive electrical component security throughout their lifespan. © 2024 IEEE.

关键词： Authentication

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Unsupervised Learning for Insider Threat Prediction: A Behavioral Analysis Approach

Unsupervised Learning for Insider Threat Prediction: A Behav...

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Security of Information and Networks (SIN), International Conference on

作者： Rahat Mehmood Priyanka Singh Zoe Jeffery School of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia

ISBN: (数字)9798331509736

ISBN: (纸本)9798331509743

Most of the devastating cyber-attacks are caused by insiders with access privileges inside an organization. The main reason of insider attacks being more effective is that they don't have many security barriers before they get into the critical resources of the system. Different machine learning techniques have been previously utilized to identify insider threats within cy-bersecurity domain whereas research done in predicting insider attacks is not significant. Moreover, machine learning models used for prediction and detection face a critical limitation as they require training on labeled datasets, rendering them less effective for real-time data streams which lack threat presence indicators. This work presents an unsupervised machine learning approach that predicts insider threat using behavior analysis for real-time threat data. Patterns are identified in user behavior, to make predictions about benign and malicious insiders. Features are selected by analyzing activities performed. Selected features are utilized to feed machine learning model which extracts anomalous behavior among users, using anomalies in their activity patterns followed by learning methods for threat detection. A dataset that contains selected features from CERT r4.2 is used to make predictions. The performance of Isolation Forest (iForest) is compared with other algorithms of the same category including One-class SVM, Local Outlier Factor (LOF) and DBSCAN to evaluate the new approach. The iForest shows the best performance accuracy 80 percent and recall 84.2 percent.

关键词： Training Support vector machines Machine learning Feature extraction Rendering (computer graphics) Real-time systems Threat assessment Security Unsupervised learning Streams

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Mode-Symmetry-Assisted Optical Pulling by Bound States in the Continuum

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Physical Review Letters 2024年第25期132卷 253802-253802页

作者： Hang Li Yongyin Cao Rui Feng Bojian Shi Yuzhi Shi Yang Chen Dongliang Gao Tongtong Zhu Donghua Tang Fangkui Sun Cheng-Wei Qiu Weiqiang Ding Institute of Advanced Photonics School of Physics Institute of Precision Optical Engineering School of Physics Science and Engineering Chinese Academy of Sciences Key Laboratory of Mechanical Behavior and Design of Materials Department of Precision Machinery and Precision Instrumentation School of Physical Science and Technology Collaborative Innovation Center of Suzhou Nano Science and Technology School of Optoelectronic Engineering and Instrumentation Science Department of Physics School of Science Department of Electrical and Computer Engineering Collaborative Innovation Center of Extreme Optics

Aside from optical pushing and trapping that have been implemented successfully, the transportation of objects backward to the source by the optical pulling forces (OPFs) has attracted tremendous attention, which was usually achieved by increasing the forward momentum of light. However, the limited momentum transfer between light and object greatly constrains the amplitudes of OPFs. Here, we present a mechanism to generate strong interactions between object and background through the bound states in the continuums, which can generate large OPFs without increasing the forward momentum of light. The underlying physics is the extraction of momentum from the designed background lattice units assisted by mode symmetry. This work paves the way for extraordinary optical manipulations and shows great potential for exploring the momenta of light in media.

关键词： Mechanical effects of light on material media Nanophotonics Photonic crystals

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Efficient Optimal Control of Open Quantum Systems 19

Efficient Optimal Control of Open Quantum Systems

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19th Conference on the Theory of Quantum Computation, Communication and Cryptography, TQC 2024

作者： He, Wenhao Li, Tongyang Li, Xiantao Li, Zecheng Wang, Chunhao Wang, Ke Center for Computational Science and Engineering MIT CambridgeMA United States School of Physics Peking University Beijing China Center on Frontiers of Computing Studies School of Computer Science Peking University Beijing China Department of Mathematics Pennsylvania State University University ParkPA United States Department of Computer Science and Engineering Pennsylvania State University University ParkPA United States

ISBN: (纸本)9783959773287

The optimal control problem for open quantum systems can be formulated as a time-dependent Lindbladian that is parameterized by a number of time-dependent control variables. Given an observable and an initial state, the goal is to tune the control variables so that the expected value of some observable with respect to the final state is maximized. In this paper, we present algorithms for solving this optimal control problem efficiently, i.e., having a poly-logarithmic dependency on the system dimension, which is exponentially faster than best-known classical algorithms. Our algorithms are hybrid, consisting of both quantum and classical components. The quantum procedure simulates time-dependent Lindblad evolution that drives the initial state to the final state, and it also provides access to the gradients of the objective function via quantum gradient estimation. The classical procedure uses the gradient information to update the control variables. At the technical level, we provide the first (to the best of our knowledge) simulation algorithm for time-dependent Lindbladians with an 1-norm dependence. As an alternative, we also present a simulation algorithm in the interaction picture to improve the algorithm for the cases where the time-independent component of a Lindbladian dominates the time-dependent part. On the classical side, we heavily adapt the state-of-the-art classical optimization analysis to interface with the quantum part of our algorithms. Both the quantum simulation techniques and the classical optimization analyses might be of independent interest. © Wenhao He, Tongyang Li, Xiantao Li, Zecheng Li, Chunhao Wang, and Ke Wang.

关键词： Optimal control systems

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Black-hole-inspired thermal trapping with graded heat-conduction metadevices

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National science Review 2023年第2期10卷 131-138页

作者： Liujun Xu Jinrong Liu Peng Jin Guoqiang Xu Jiaxin Li Xiaoping Ouyang Ying Li Cheng-Wei Qiu Jiping Huang Department of Physics State Key Laboratory of Surface Physics and Key Laboratory of Micro and Nano Photonic Structures (MOE) Fudan University Department of Electrical and Computer Engineering National University of Singapore Graduate School of China Academy of Engineering Physics School of Materials Science and Engineering Xiangtan University Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical InstrumentationZJU-Hangzhou Global Scientific and Technological Innovation CenterZhejiang University International Joint Innovation Center Key Laboratory of Advanced Micro/Nano Electronic Devices and Smart Systems of Zhejiang The Electromagnetics Academy of Zhejiang University Zhejiang University

The curved space-time produced by black holes leads to the intriguing trapping *** far,metadevices have enabled analogous black holes to trap light or sound in laboratory ***,trapping heat in a conductive environment is still challenging because diffusive behaviors are *** by black holes,we construct graded heat-conduction metadevices to achieve thermal trapping,resorting to the imitated advection produced by graded thermal conductivities rather than the trivial solution of using insulation materials to confine thermal *** experimentally demonstrate thermal trapping for guiding hot spots to diffuse towards the *** heat-conduction metadevices have advantages in energy-efficient thermal regulation because the imitated advection has a similar temperature field effect to the realistic advection that is usually driven by external energy *** results also provide an insight into correlating transformation thermotics with other disciplines,such as cosmology,for emerging heat control schemes.

关键词： thermal trapping graded metadevices imitated advection

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Injections Attacks Efficient and Secure Techniques Based on Bidirectional Long Short Time Memory Model

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computers, Materials & Continua 2023年第9期76卷 3605-3622页

作者： Abdulgbar A.R.Farea Gehad Abdullah Amran Ebraheem Farea Amerah Alabrah Ahmed A.Abdulraheem Muhammad Mursil Mohammed A.A.Al-qaness School of Big Data&Software Engineering Chongqing UniversityChongqing401331China Department of Management Science Engineering Dalian University of TechnologyDalian116024China Software College Northeastern UniversityShenyang110169China Department of Information Systems College of Computer and Information ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Management Science and Engineering South China University of TechnologyGuangzhou510641China Department of Computer Engineering and Mathematics University of Rovira i VirgiliTarragonaSpain College of Physics and Electronic Information Engineering Zhejiang Normal UniversityJinhua321004China

E-commerce,online ticketing,online banking,and other web-based applications that handle sensitive data,such as passwords,payment information,and financial information,are widely *** web developers may have varying levels of understanding when it comes to securing an online *** Query language SQL injection and cross-site scripting are the two vulnerabilities defined by the OpenWeb Application Security Project(OWASP)for its 2017 Top Ten List Cross Site Scripting(XSS).An attacker can exploit these two flaws and launch malicious web-based actions as a result of these *** published articles focused on these attacks’binary *** article described a novel deep-learning approach for detecting SQL injection and XSS *** datasets for SQL injection and XSS payloads are combined into a single *** dataset is labeledmanually into three labels,each representing a kind of *** work implements some pre-processing algorithms,including Porter stemming,one-hot encoding,and the word-embedding method to convert a word’s text into a *** model used bidirectional long short-term memory(BiLSTM)to extract features automatically,train,and test the payload *** payloads were classified into three types by BiLSTM:XSS,SQL injection attacks,and *** outcomes demonstrated excellent performance in classifying payloads into XSS attacks,injection attacks,and non-malicious ***’s high performance was demonstrated by its accuracy of 99.26%.

关键词： Web security SQL injection XSS deep learning RNN LSTM BiLSTM

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GRB 220408B:A Three-episode Burst from a Precessing Jet

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Research in Astronomy and Astrophysics 2023年第11期23卷 214-224页

作者： Zijian Zhang Yi-Han Iris Yin Chenyu Wang Xiangyu Ivy Wang Jun Yang Yan-Zhi Meng Zi-Ke Liu Guo-Yin Chen Xiaoping Fu Huaizhong Gao Sihao Li Yihui Liu Xiangyun Long Yong-Chang Ma Xiaofan Pan Yuanze Sun Wei Wu Zirui Yang Zhizhen Ye Xiaoyu Yu Shuheng Zhao Xutao Zheng Tao Zhou Qing-Wen Tang Qiurong Yan Rong Zhou Zhonghai Wang Hua Feng Ming Zeng Bin-Bin Zhang School of Astronomy and Space Science Nanjing UniversityNanjing 210093China School of Physics Nanjing UniversityNanjing 210093China Department of Astronomy Tsinghua UniversityBeijing 100084China Key Laboratory of Modern Astronomy and Astrophysics Nanjing UniversityMinistry of EducationNanjing 210093China School of Information Engineering Nanchang UniversityNanchang 330031China Department of Engineering Physics Tsinghua UniversityBeijing 100084China Department of Health Physics China Institute for Radiation ProtectionTaiyuan 300000China Key Laboratory of Radiation Physics and Technology of Ministry of Education College of Physics of Sichuan UniversityChengdu 610065China School of Artificial Intelligence Nanjing UniversityNanjing 210093China Key Laboratory of Particle and Radiation Imaging Tsinghua UniversityMinistry of EducationBeijing 100084China Department of Computer Science and Technology Nanjing UniversityNanjing 210093China School of Chemistry and Chemical Engineering Nanjing UniversityNanjing 210093China College of Engineering and Applied Sciences Nanjing UniversityNanjing 210093China Department of Physics School of Physics and Materials ScienceNanchang UniversityNanchang 330031China Purple Mountain Observatory Chinese Academy of SciencesNanjing 210023China

Jet precession has previously been proposed to explain the apparently repeating features in the light curves of a few gamma-ray bursts(GRBs).In this paper,we further apply the precession model to a bright GRB 220408B by examining both its temporal and spectral consistency with the predictions of the *** one of the recently confirmed GRBs observed by our GRID CubeSat mission,GRB 220408B is noteworthy as it exhibits three apparently similar emission ***,the similarities are reinforced by their strong temporal correlations and similar features in terms of spectral evolution and spectral *** analysis demonstrates that these features can be well explained by the modulated emission of a Fast-Rise-Exponential-Decay(FRED)shape light curve intrinsically produced by a precessing jet with a precession period of 18.4_(-0.2)~(+0.2)s,a nutation period of11.1_(-0.2)~(+0.2)s and viewed *** study provides a straightforward explanation for the complex yet similar multiepisode GRB light curves.

关键词： gamma-rays:general (stars:)gamma-ray burst:individual(GRB 220408B) stars:jets

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