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Polarization-induced photocurrent switching effect in heterojunction photodiodes

Nano Research 2023年第4期16卷 5503-5510页

作者： Dingbo Chen Yu-Chang Chen Guang Zeng Yu-Chun Li Xiao-Xi Li Dong Li Chao Shen Nan Chi Boon S.Ooi David Wei Zhang Hong-Liang Lu State Key Laboratory of ASIC and System Shanghai Institute of Intelligent Electronics&SystemsSchool of MicroelectronicsFudan UniversityShanghai 200433China Key Laboratory for Information Science of Electromagnetic Waves Department of Communication Science and EngineeringFudan UniversityShanghai 200433China Photonics Laboratory Division of ComputerElectricaland Mathematical Sciences and EngineeringKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia

The unipolar photocurrent in conventional photodiodes(PDs)based on photovoltaic effect limits the output modes and potential versatility of these devices in *** photodiodes with photocurrent switching are emerging as a promising solution for obtaining photoelectric devices with unique and attractive functions,such as optical logic ***,we design an all-solid-state chip-scale ultraviolet(UV)PD based on a hybrid GaN heterojunction with engineered bipolar polarized electric *** introducing the polarization-induced photocurrent switching effect,the photocurrent direction can be switched in response to the wavelength of incident light at 0 V *** particular,the photocurrent direction exhibits negative when the irradiation wavelength is less than 315 nm,but positive when the wavelength is longer than 315 *** device shows a responsivity of up to−6.7 mA/W at 300 nm and 5.3 mA/W at 340 nm,*** particular,three special logic gates in response to different dual UV light inputs are demonstrated via a single bipolar PD,which may be beneficial for future multifunctional UV photonic integrated devices and systems.

关键词： GaN heterostructure ultraviolet(UV)photodiodes bipolar photocurrent optical logic device

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An efficient and resilience linear prefix approach for mining maximal frequent itemset using clustering

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Journal of Safety science and Resilience 2025年第1期6卷 93-104页

作者： M.Sinthuja S.Pravinthraja B K Dhanalakshmi H L Gururaj Vinayakumar Ravi G Jyothish Lal Department of ISE M.S.Ramaiah Institute of Technology BengaluruVisvesvaraya Technological UniversityBelagaviKarnataka590018India Department of CSE Presidency UniversityBengaluruIndia Department of Computer Science and Engineering BMS institute of Technology&ManagementBengaluruVisvesvaraya Technological UniversityBelagaviKarnataka590018India Department of Information Technology Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipalIndia Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia Center for Computational Engineering and Networking(CEN) Amrita School of EngineeringAmrita Vishwa VidyapeethamCoimbatoreIndia

The numerous volumes of data generated every day necessitate the deployment of new technologies capable of dealing with massive amounts of data *** is the case with Association Rules,a tool for unsupervised data mining that extracts information in the form of IF-THEN *** various approaches for extracting frequent itemset(prior step before mining association rules)in extremely large databases have been presented,the high computational cost and shortage of memory remain key issues to be addressed while processing enormous *** objective of this research is to discover frequent itemset by using clustering for preprocessing and adopting the linear prefix tree algorithm for mining the maximal frequent *** performance of the proposed CL-LP-MAX-tree was evaluated by comparing it with the existing FP-max *** was performed with the three different standard datasets to record evidence to prove that the proposed CL-LP-MAX-tree algorithm outperform the existing FP-max algorithm in terms of runtime and memory consumption.

关键词： Clustering Data mining Frequent itemset mining Linear prefix tree Maximal frequent itemset mining

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Evolving Safety Protocols: Deep Learning-Enabled Detection of Personal Protective Equipment 1

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4th International Conference on computer Vision, High-Performance Computing, Smart Devices, and Networks, CHSN 2023

作者： Alahmid, Mustafa Bhimani, Kishankumar Saradva, Khushbu Ghildiyal, Sushil Saeedi, Sayed Aryan Ali, Saraa Yan, Han Filippov, Evgeny Faculty of Computer Science and Technology Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Laboratory on AI for Computational Biology Faculty of Computer Science HSE University Moscow Russia Department of Computer Science and Engineering Indian Institute of Technology Ropar Punjab India Laboratory of Methods for Big Data Analysis Faculty of Computer Science HSE University Moscow Russia Deggendorf Institute of Technology Bavaria Germany

ISBN: (数字)9789819777945

ISBN: (纸本)9789819777938

To give shift in safety protocols, we have employed advanced deep learning algorithms and frameworks (Shrestha and Mahmood in IEEE Access 7:53,040–53,065, 2019 [25]) to construct an innovative AI model. The designed model detects the usage of personal protective equipment (PPE) (Personal protective equipment. Geneva: World Health Organization, 2020 [18]) by workers in high-risk industries such as construction and manufacturing. We have used Google’s TensorFlow object detection API (Sai and Sasikala in Object detection and count of objects in image using tensor flow object detection API, pp 542–546, 2019 [22]) to modify and train a model for dual purposes: PPE detection and face recognition. The state-of-the-art of this research is to substantially enhance safety compliance by addressing the prevalent issue of PPE non-compliance. To emphasis this, we have developed a pioneering software prototype that synergizes PPE detection with a face recognition-based clock-in system. This prototype demonstrates impressive object detection metrics with a mean average precision (mAP) of 0.9 for vests and 0.85 for helmets. Moreover, it exhibited efficient face recognition with a successful threshold range of 17–20%. The implementation of AI in our system promises significant enhancements to worker safety, while concurrently reducing the financial burden associated with big hazards and accidents. Beyond the development and performance of the system, this paper provides a thorough exploration of the encountered challenges, potential real-world applications (particularly in employee monitoring and clock-in systems), and the future implications of this study on research and practical applications in the field of AI-integrated safety compliance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Software prototyping

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LinkMed: Entity Recognition and Relation Extraction from Clinical Notes in Spanish

LinkMed: Entity Recognition and Relation Extraction from Cli...

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2023 Iberian Languages Evaluation Forum, IberLEF 2023

作者： Muñoz-Castro, Carlos Carvallo, Andrés Rojas, Matías Aracena, Claudio Guerra, Rodrigo Pizarro, Benjamín Dunstan, Jocelyn National Center for Artificial Intelligence Chile Faculty of Physical and Mathematical Sciences Universidad de Chile Chile Department of Computer Science Pontificia Universidad Católica de Chile Chile Institute for Mathematical and Computational Engineering Pontificia Universidad Católica de Chile Chile Center for Mathematical Modeling Universidad de Chile Chile Millenium Institute Foundational Research on Data Chile

Relation extraction is an essential component of Natural Language Processing (NLP) and significantly influences information retrieval and structured information extraction. Within clinical notes, the task is needed to establish connections among illnesses, therapies, indications, and other medical concepts. Motivated by the above, in this work, we propose a two-step model approach for entity linking;in the first step, we solve entity recognition, and in the second, a relation classification approach. We evaluated our approach in a Spanish corpus of the TESTLINK challenge in IberLEF2023 (Iberian Languages Evaluation Forum), comprising 81 clinical notes to train and 80 clinical notes to test. Our results show competitive performance with a precision of 0.47, recall of 0.43, and F1-score of 0.45, presenting an effective strategy for relation extraction from clinical notes in Spanish. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Information retrieval

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Conflict-Free Coloring on Claw-Free Graphs and Interval Graphs 47

Conflict-Free Coloring on Claw-Free Graphs and Interval Grap...

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47th International Symposium on mathematical Foundations of computer science, MFCS 2022

作者： Bhyravarapu, Sriram Kalyanasundaram, Subrahmanyam Mathew, Rogers The Institute of Mathematical Sciences HBNI Chennai India Department of Computer Science and Engineering Indian Institute of Technology Hyderabad India

ISBN: (纸本)9783959772563

A Conflict-Free Open Neighborhood coloring, abbreviated CFON*coloring, of a graph G = (V,E) using k colors is an assignment of colors from a set of k colors to a subset of vertices of V (G) such that every vertex sees some color exactly once in its open neighborhood. The minimum k for which G has a CFON*coloring using k colors is called the CFON*chromatic number of G, denoted by X*ON(G). The analogous notion for closed neighborhood is called CFCN*coloring and the analogous parameter is denoted by X*CN(G). The problem of deciding whether a given graph admits a CFON*(or CFCN*) coloring that uses k colors is NP-complete. Below, we describe briefly the main results of this paper. For k ≥ 3, we show that if G is a K1,k-free graph then X*ON(G)= O(k2 log Δ), where Δ denotes the maximum degree of G. Debski and Przybylo in [J. Graph Theory, 2021] had shown that if G is a line graph, then X*CN(G)= O(log Δ). As an open question, they had asked if their result could be extended to claw-free (K1,3-free) graphs, which are a superclass of line graphs. Since it is known that the CFCN*chromatic number of a graph is at most twice its CFON*chromatic number, our result positively answers the open question posed by Debski and Przybylo. We show that if the minimum degree of any vertex in G is Ω(Δ logϵ Δ) for some ϵ ≥ 0, then X*ON(G)= O(log1+ϵ Δ). This is a generalization of the result given by Debski and Przybylo in the same paper where they showed that if the minimum degree of any vertex in G is Ω(Δ), then X*ON(G)= O(log Δ). We give a polynomial time algorithm to compute X*ON(G) for interval graphs G. This answers in positive the open question posed by Reddy [Theoretical Comp. science, 2018] to determine whether the CFON*chromatic number can be computed in polynomial time on interval graphs. We explore biconvex graphs, a subclass of bipartite graphs and give a polynomial time algorithm to compute their CFON*chromatic number. This is interesting as Abel et al. [SIDMA, 2018] had shown tha

关键词： Coloring

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Performance Analysis of Various Machine Learning Techniques for Mental Health Tracking 2nd

Performance Analysis of Various Machine Learning Techniques ...

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2nd International Conference on Deep sciences for Computing and Communications, IconDeepCom 2023

作者： Poongothai Kasthuri Mariammal Yeturu, Jahnavi Department of Computational Intelligence S.R.M Institute of Science and Technology TamilNadu Chennai India Department of Computer Science Arulmigu Subramania Swamy Arts and Science College Thoothukudi628 907 India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Avadi Chennai India Department of Computer Science Dr. V. S. Krishna Govt. Degree and PG College Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9783031689048

Artificial intelligence technologies called "machine learning" automatically build models from data in order to improve decision-making. An examination of ML that can be utilized for mental illness treatment has been prompted by the upsurge in mental health diseases and the directive for efficient medical sustenance. The quality of life for patients is improved and treatment outcomes are increased when mental health issues are identified early. Therefore, it is critical to address basic mental health issues that, if left unattended, could develop into more serious issues. The analysis and diagnosis of medical data can today be accomplished with the help of machine learning algorithms. The conventional (CCTV) system for tracking patient activity is ineffective and expensive, and employing a sensor-based system is particularly challenging because of depleted battery life. The real goal is to provide a critical review, and literature review, about all the machine learning techniques which are supervised-based. These techniques include Random Forest Classification, KNN Classifier, Logistic Regression, Decision Tree Classifier, Bagging, Boosting, & Stacking. And to determine the most effective machine-learning technique for monitoring someone’s mental health. It has been found that Accuracy and ROC Curve plays an important role for comparing the ML models. The outcomes clearly show that stacking generated more precise outcomes by showing the accuracy of 82%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Electronic health record

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Revolutionizing Cloud Computing: Evaluating the Influence of Blockchain and Consensus Algorithms 3

Revolutionizing Cloud Computing: Evaluating the Influence of...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Saravanan, T.R. Rathinam, Anantha Raman Lenin, J. Komathi, A. Bharathi, B. Murugan, S. Srm Institute of Science and Technology Department of Computational Intelligence Chennai Tamil Nadu Kattankulathur India Kl University Department of Computer Science and Engineering Guntur Andhra Pradesh Vijayawada India Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Karnataka Bengaluru India Nadar Saraswathi College of Arts and Science College Department of Computer Science Tamilnadu Theni India R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350319125

Combining Proof of Space (PoSpace) and Proof of Replication (PoRep) with interoperability protocols and distributed file systems in cloud computing may change the game. This combination has the potential to usher in a paradigm change in cloud infrastructures, bringing with it new heights of safety, scalability, and efficacy. Both PoSpace and PoRep provide new ways of storing information on blockchain networks. The energy consumption of PoSpace is much lower than that of conventional Proof of Work (PoW) algorithms because it uses unused storage space as a resource for consensus. In contrast, PoRep strengthens the robustness of the cloud system by evaluating the storage replication between nodes to guarantee data integrity. Protocols for interoperability across blockchains are crucial to their capacity to communicate with one another. They make it easier to trade information and assets, leading to a more cohesive ecosystem that goes beyond the boundaries of different blockchains. Increased connectivity and productivity in the cloud are both benefits of interoperability. By distributing data storage over a network, distributed file systems usher in a new era of administration efficiency. © 2023 IEEE.

关键词： Cloud Computing Data Storage Distributed File Systems Efficiency Interoperability Protocols Proof of Replication (PoRep) Proof of Space (PoSpace) Resilience Scalability Security

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A Classification of Face Recognition System Using Faster Region-Based Convolutional Neural Network 2

A Classification of Face Recognition System Using Faster Reg...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Maheswari, M. Alabdeli, Haider Aruna, T.M. Reddy, R. Archana Revathi, R. School of Computing Srm Institue of Science and Technology Department of Computational Intelligence Kattankulathur India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Artificial Intelligence and Machine Learning Bengaluru India School of Computer Science and Engineering Sr University Warangal India Karpagam Academy of Higher Education Department of Computer Science Echanari Coimbatore India

ISBN: (纸本)9798350373110

In recent times, face recognition is a kind of biometric authentication which is widely used for security purpose in many areas like image processing, pattern recognition. However, recognizing the human face was very difficult as the poses in the input images vary, various conditions of light, brightness, expressions, ages. To overcome from it, a proposed method was introduced in this paper known as Faster Region-based Convolutional Neural Network (RCNN). The main advantage of using the proposed method is it will minimize the training process time as it consisting of two fully connected layers namely classifier and regression layer. Initially, the proposed method was implemented on ORL database consisting of 40 classes, then the images in the database undergoes image resizing and enhancing in the preprocessing stage. Where, Image resizing id done to reduce images size so that every image is same in size that will minimize the processing time and image enhancing was done to adjust the brightness of the image. In further, for extracting the features from preprocessed images, CNN is used, so that extracted features are given to the classification stage to detect the face of human or not. In detecting the faces, proposed Faster RCNN achieved high accuracy of 98.64% when compared with other existing methods like IFC, EFRA and VGG16. © 2024 IEEE.

关键词： Convolutional neural networks

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Integrating Digital Twins with AI for Real-Time Intrusion Detection in Smart Infrastructure Networks

Integrating Digital Twins with AI for Real-Time Intrusion De...

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2024 International Conference on Intelligent Algorithms for computational Intelligence Systems, IACIS 2024

作者： Sasikala, M. Mahaboob John, Y.M. Jothi, B. Nandhini, S. Senthil Kumar, S. Sona College of Technology Department of Information Technology Salem India Mahendra College of Engineering Ece Department Salem India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Karpagam Academy of Higher Education Computer Science and Engineering Coimbatore India R.M.K. College of Engineering and Technology Department of Mechanical Engineering Thiruvallur India

ISBN: (纸本)9798350360660

The integration of Digital Twins with Artificial Intelligence (AI) represents a revolutionary approach to enhancing real-time intrusion detection in smart infrastructure networks. Digital Twins, as virtual replicas of physical assets, enable continuous monitoring and predictive analysis, while AI provides advanced analytical capabilities for identifying and mitigating potential security threats. This paper presents a comprehensive framework that leverages Digital Twins in conjunction with AI algorithms to detect and respond to intrusions in smart infrastructure networks. By simulating network behavior and employing machine learning models to analyze data patterns, the proposed system ensures robust security measures. One of the proposed techniques involves the use of a hybrid anomaly detection algorithm combining Autoencoders and Recurrent Neural Networks (RNN). The Autoencoder is utilized to learn a compact representation of the normal network behavior by reducing dimensionality and reconstructing inputs, while the RNN captures temporal dependencies and sequences within the network data. This hybrid model enhances the detection of both known and unknown intrusion patterns by focusing on deviations from the learned normal behavior. The integration of these AI components with Digital Twins enables a more nuanced and accurate understanding of network anomalies, thereby improving the system's ability to detect and respond to intrusions promptly. Experimental results demonstrate the efficacy of this approach in real-time threat detection, showcasing significant improvements in response times and accuracy. This integration not only enhances the resilience of smart infrastructure but also paves the way for future advancements in secure and intelligent network management. © 2024 IEEE.

关键词： Network intrusion

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Properties of the Reed-Muller-Fourier Spectra of Maiorana-McFarland Bent Functions 53

Properties of the Reed-Muller-Fourier Spectra of Maiorana-Mc...

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53rd IEEE International Symposium on Multiple-Valued Logic, ISMVL 2023

作者： Moraga, Claudio Stanković, Radomir S. Stanković, Milena Tu Dortmund University Faculty of Computer Science Dortmund44221 Germany Faculty of Electronic Engineering Department of Computer Science Niš18000 Serbia Mathematical Institute Serbian Academy of Sciences and Arts Belgrade11000 Serbia

The Reed-Muller-Fourier spectrum of p-valued (p prime, p > 2) Maiorana-McFarland bent functions is studied. It is shown that this spectrum exhibits a unique structure, which allows characterizing these functions an... 详细信息

ISBN: (纸本)9781665464161

关键词： Fourier transforms

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