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LithoHoD: A Litho Simulator-Powered Framework for IC Layout Hotspot Detection

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arXiv 2024年

作者： Shao, Hao-Chiang Chen, Guan-Yu Lin, Yu-Hsien Lin, Chia-Wen Fang, Shao-Yun Tsai, Pin-Yian Liu, Yan-Hsiu The Institute of Data Science and Information Computing National Chung Hsing University Taichung402202 Taiwan The Department of Electrical Engineering National Tsing Hua University Hsinchu300044 Taiwan The Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu300044 Taiwan The Department of Electrical Engineering National Taiwan University of Science and Technology Taipei106335 Taiwan The Product Engineering Department United Microelectronics Corporation Hsinchu300094 Taiwan The Development of Smart Manufacturing United Microelectronics Corporation Hsinchu300094 Taiwan

Recent advances in VLSI fabrication technology have led to die shrinkage and increased layout density, creating an urgent demand for advanced hotspot detection techniques. However, by taking an object detection network as the backbone, recent learning-based hotspot detectors learn to recognize only the problematic layout patterns in the training data. This fact makes these hotspot detectors difficult to generalize to real-world scenarios. We propose a novel lithography simulator-powered hotspot detection framework to overcome this difficulty. Our framework integrates a lithography simulator with an object detection backbone, merging the extracted latent features from both the simulator and the object detector via well-designed cross-attention blocks. Consequently, the proposed framework can be used to detect potential hotspot regions based on i) the variation of possible circuit shape deformation estimated by the lithography simulator, and ii) the problematic layout patterns already known. To this end, we utilize RetinaNet with a feature pyramid network as the object detection backbone and leverage LithoNet as the lithography simulator. Extensive experiments demonstrate that our proposed simulator-guided hotspot detection framework outperforms previous state-of-the-art methods on real-world data. © 2024, CC BY-NC-ND.

关键词： Integrated circuit layout

A Survey on Indoor Visible Light Positioning Systems: Fundamentals, Applications, and Challenges

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arXiv 2024年

作者： Zhu, Zhiyu Yang, Yang Chen, Mingzhe Guo, Caili Cheng, Julian Cui, Shuguang The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Faculty of Applied Science School of Engineering The University of British Columbia KelownaBCV1V 1V7 Canada The Chinese University of Hong Kong Shenzhen518172 China

The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

DSCOT: An NFT-Based Blockchain Architecture for the Authentication of IoT-Enabled Smart Devices in Smart Cities

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arXiv 2022年

作者： Khalil, Usman Malik, Owais Ahmed Hong, Ong Wee Uddin, Mueen School of Digital Science Universiti Brunei Darussalam Jalan Tungku Link GadongBE1410 Brunei Institute of Applied Data Analytics Universiti Brunei Darussalam Jalan Tungku Link GadongBE1410 Brunei College of Computing and Information Technology University for Science and Technology Doha24449 Qatar

Smart city architecture brings all the underlying architectures, i.e., Internet of Things (IoT), Cyber-Physical Systems (CPSs), Internet of Cyber-Physical Things (IoCPT), and Internet of Everything (IoE), together to work as a system under its umbrella. The goal of smart city architecture is to come up with a solution that may integrate all the real-time response applications. However, the cyber-physical space poses threats that can jeopardize the working of a smart city where all the data belonging to people, systems, and processes will be at risk. Various architectures based on centralized and distributed mechanisms support smart cities;however, the security concerns regarding traceability, scalability, security services, platform assistance, and resource management persist. In this paper, private blockchain-based architecture Decentralized Smart City of Things (DSCoT) is proposed. It actively utilizes fog computing for all the users and smart devices connected to a fog node in a particular management system in a smart city, i.e., a smart house or hospital, etc. Non-fungible tokens (NFTs) have been utilized for representation to define smart device attributes. NFTs are unique and non-interchangeable units of data stored on a digital ledger and are widely used in blockchain-based solutions to represent unique assets. NFTs in the proposed DSCoT architecture provide devices and user authentication (IoT) functionality. DSCoT has been designed to provide a smart city solution that ensures robust security features such as Confidentiality, Integrity, Availability (CIA), and authorization by defining new attributes and functions for Owner, User, Fog, and IoT devices authentication. The evaluation of the proposed functions and components in terms of Gas consumption and time complexity has shown promising results. Comparatively, the Gas consumption for minting DSCoT NFT showed approximately 27%, and a DSCoT approve() was approximately 11% more efficient than the PUF-based N

关键词： Smart contract

Intelligent Network Optimisation for Beyond 5G Networks Considering Packet Drop Rate

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Intelligent Network Optimisation for Beyond 5G Networks Cons...

IEEE International Conference on Industrial Technology (ICIT)

作者： Haitham Mahmoud Adel Aneiba Ziming He Fei Tong Liucheng Guo Taufiq Asyhari Ziwei Wang Zhen Gao College of Computing Birmingham City University Birmingham United Kingdom System LSI Samsung Electronics Samsung Cambridge Solution Centre Cambridge United Kingdom Tangi0 LTD (TG0) London United Kingdom Data Science Department Monash University Tangerang Indonesia School of Engineering Lancaster University Lancaster United Kingdom School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

To meet the growing expectations for fast and dependable connectivity, Novel approaches such as reinforcement learning-based resource allocation and network slicing are essential to consider. Enhancing network intelligence through the use of deep reinforcement learning and machine learning may increase capacity, lower latency and congestion, improve energy efficiency, and open up new revenue opportunities and business models. However, the current body of research falls short in terms of thoroughly exploring network slicing datasets and taking packet drop likelihood into account when allocating resources. With reference to current benchmarks, this paper presents a network slicing strategy and illustrates its efficacy using seven machine learning algorithms (ANN, SVM, RF, DT, GNG, LDA, and RT). To prioritize packets that run the risk of being dropped, a priority algorithm is also developed. To improve network performance, a resource allocation technique is used that is based on the mathematical study of packet drop rate. Utilizing deep reinforcement learning and genetic algorithms, the distribution of tasks across Cloud and Edge resources is further improved according to network slice characteristics. In spite of high network traffic, this guarantees constant service availability.

关键词： Support vector machines Radio frequency Machine learning algorithms Network slicing Telecommunication traffic Deep reinforcement learning Resource management

Focus on the Stability of Large Systems: Toward Automatic Prediction and Analysis of Vulnerability Threat Intelligence

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Focus on the Stability of Large Systems: Toward Automatic Pr...

IEEE International Conference on data science in Cyberspace (DSC)

作者： Shiwen Song Qiong Wu Xin Zheng Peng Wang Yuchen Dou Zhongwen Li Lidong Zhai Institute of Information Engineering Chinese Academy of Sciences BeiJing China Bag Data Academy Zhongke Beijing China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665418164

With the increase in the number of users and business volume, the business systems of Internet companies are becoming more and more complex, resulting in a surge in the number of alarms. A large number of dirty alarms add a huge workload to security operations, which indirectly pose a large number of threats to business systems. At present, most systems use the method of accessing third-party Threat Intelligence to assist operators to realize automatic handling of alarms. However, this method has lagging and accuracy problems, making this work always difficult to meet the requirements of fast and accurate. This article proposes a new method for gathering vulnerability Threat Intelligence, which can obtain vulnerability information in advance of security announcements issued by security vendors. By analyzing the vulnerability disclosure process, this method obtains vulnerability information from the original source submitted by open source mail group, of developers. We used NLP technology and XGBoost model to automatically analyze the vulnerability information, and finally generate FINTEL. The experimental result shows that this method has an accuracy of 93%, and can obtain vulnerability information 10h to 7 days before security vendors release. The scope of application covers all open source code repositories and some closed source repositories.

关键词： Machine learning algorithms Scalability Prediction methods Stability analysis Security Surges Research and development

Governing Online Rumor: An Uncertain Intervention Timing Model for Online Rumor Propagation from the Perspective of Government

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Journal of Uncertain Systems

Journal of Uncertain Systems 2024年第1期17卷

作者： Jin, Meiling Liu, Fengming Ning, Yufu Zhou, Jian School of Management Shanghai University Shanghai 200444 China Business School Shandong Normal University Jinan 250014 China School of Information Engineering Shandong Youth University of Political Science Jinan 250103 China Smart Healthcare Big Data Engineering and Ubiquitous Computing Characteristic Laboratory in Universities of Shandong Jinan 250103 China Confucius Institute University of Bahrain Bahrain

In view of different types of online rumors, when the governance institutions (government departments) intervene to achieve the least cost and the best effect is a core issue of rumor governance. Based on the perspective of the government, under the premise of limited human, material and financial resources, and considering the different impacts of government intervention opportunities on the spread of different types of rumors, this paper establishes an uncertain multi-objective network rumor transmission intervention timing model, so as to achieve the optimal allocation of government intervention opportunities for different types of online rumors, minimize the total cost and maximize the inhibition effect of rumor spreading. Then, the equivalent form of the model is given, and the model is solved. In addition, numerical experiments are conducted on three types of rumors during the COVID-19 pandemic. The experimental results show that when the types of online rumors and the focus of government departments on cost and governance effect are different, the optimal time for intervention will change. Finally, combined with the experimental results, corresponding countermeasures and suggestions are put forward for the supervision of online rumors by government departments. © 2023 World Academic Union. All rights reserved.

关键词： Online rumors rumor propagation timing of intervention uncertain programming

A review of computer graphics approaches to urban modeling from a machine learning perspective

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Frontiers of information Technology & Electronic Engineering 2021年第7期22卷 915-925页

作者： Tian FENG Feiyi FAN Tomasz BEDNARZ Department of Computer Science and Information Technology La Trobe UniversityVIC 3086Australia Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China Expanded Perception and Interaction Centre University of New South WalesNSW 2021Australia CSIRO's Data61 NSW 2015Australia

Urban modeling facilitates the generation of virtual environments for various scenarios about *** requires expertise and consideration,and therefore consumes massive time and computation ***,related tasks sometimes result in dissatisfaction or even *** challenges have received significant attention from researchers in the area of computer ***,the burgeoning development of artificial intelligence motivates people to exploit machine learning,and hence improves the conventional *** this paper,we present a review of approaches to urban modeling in computer graphics using machine learning in the literature published between 2010 and *** serves as an overview of the current state of research on urban modeling from a machine learning perspective.

关键词： Urban modeling Computer graphics Machine learning Deep learning

Predicting Crops Based on Soil and Environmental Characteristics Using ARIMA and LSTM Model

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Predicting Crops Based on Soil and Environmental Characteris...

Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： U Naresh kumar K Madhana Mohan S. Pragadeeswaran Jobin M Scaria Baseera A D. Seenivasan Department of Artificial intelligence & Data science Annamacharya University Rajampet India Department of Electronics and Instrumentation Engineering Sri Sairam Engineering College Chennai India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Mt. Eriama Campus School of Information Technology IBS University Port Moresby Papua New Guinea School of Computing Science and Engineering VIT Bhopal University Sehore India Department of Computer Science and Business Systems M. Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Food producers are under pressure to meet rising demand as the world's population and natural resources diminish. Overuse of pesticides and fertilizers has caused soil erosion and land degradation. Future food needs will require environmentally sustainable farming strategies. The suggested strategy includes model training, feature extraction, sensor integration, and model preparation. data preparation involves processing data before storing it machine-readable. SFFS and RFE select features. The model was trained using a hybrid ARIMA-LSTM technique, which blends deep learning and time-series forecasting. The ARIMA-LSTM model outperforms solo ARIMA and LSTM models with an average accuracy of 93.54%. Increased accuracy shows the model's capacity to anticipate sustainable agriculture approaches. data-driven and environmentally sustainable ways are needed to overcome global food production concerns, according to this study. When paired with good preprocessing and feature selection, ARIMA-LSTM can boost agricultural productivity while minimizing environmental impact.

关键词： Training Accuracy Food manufacturing Biological system modeling Soil Predictive models Feature extraction Agriculture Long short term memory Meteorology

DeepOPF-V: Solving AC-OPF problems efficiently

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arXiv 2021年

作者： Huang, Wanjun Pan, Xiang Chen, Minghua Low, Steven H. School of Data Science City University of Hong Kong Department of Information Engineering The Chinese University of Hong Kong Department of Computing and Mathematical Sciences California Institute of Technology

AC optimal power flow (AC-OPF) problems need to be solved more frequently in the future to maintain stable and economic power system operation. To tackle this challenge, a deep neural network-based voltage-constrained approach (DeepOPF-V) is proposed to solve AC-OPF problems with high computational efficiency. Its unique design predicts voltages of all buses and then uses them to reconstruct the remaining variables without solving non-linear AC power flow equations. A fast post-processing process is developed to enforce the box constraints. The effectiveness of DeepOPF-V is validated by simulations on IEEE 118/300-bus systems and a 2000-bus test system. Compared with existing studies, DeepOPF-V achieves decent computation speedup up to four orders of magnitude and comparable performance in optimality gap and preserving the feasibility of the solution. Copyright © 2021, The Authors. All rights reserved.

关键词： Acoustic generators