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International Conference on Solid-State and Integrated Circuit Technology

作者： Kwok-Ho Wong Mansun Chan Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Kowloon Hong Kong

ISBN: (数字)9798350361834

ISBN: (纸本)9798350361841

The issue of thermal instability in Bismuth (Bi) contacts hinders the reliability of high-performance molybdenum disulfide (MoS 2 ) transistors in practical applications. Here we present a new method using a semimetal alloy to improve the thermal stability and contact resistance of MoS 2 transistors. Through this approach, the devices with TiBi alloy contacts withstand the backend-of-the-line (BEOL) processes, maintaining a high on-state current after 1 hour of 400 °C annealing. Bi-alloy contacts hold promise for improving MoS 2 -based electronic devices, contributing to future developments in semiconductor technology.

关键词： Performance evaluation Annealing Thermal resistance Field effect transistors Metals Bismuth Circuit stability Sulfur Molybdenum Thermal stability

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An Efficient Attack Detection Framework in Software-Defined Networking using Intelligent Techniques

An Efficient Attack Detection Framework in Software-Defined ...

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electronic engineering (ICEEM), International Conference on

作者： Heba A. Hassan Ezz El-Din Hemdan Mona Shokair Fathi E. Abd El-Samie Walid El-Shafai Department of Electronics and Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Computer Science Security Engineering Laboratory Prince Sultan University Riyadh Saudi Arabia

Recently, Software-Defined Networking (SDN) architecture has offered great benefits due to the separation between the control and network elements such as routers and switches. Unfortunately, the enormous growth of attacks hinders the wide adoption of SDN. Intrusion Detection Systems (IDSs) are used as significant tools to detect and mitigate network attacks of anomalous nature. Several Deep Learning (DL) models, such as Convolutional Neural Networks (CNNs), have been utilized for building IDSs in cyber security in recent years, because they achieve the desired detection results. Nonetheless, little work has been introduced on IDSs in SDN systems. Hence, this paper presents an efficient framework based on intelligent techniques for predicting attacks in SDN systems. This work focuses on the early detection of abnormal attacks existing in an SDN environment. When malicious traffic is identified in an SDN topology, the Artificial Intelligence (AI) module employs Machine Learning (ML) or DL models to identify and stop the attack source. The architecture presented in this research allows for comparing several ML and DL classification techniques that can be used to identify different sorts of network attacks. The proposed framework is tested on the InSDN dataset using several learning models like Random Forest (RF), AdaBoost (AB), K-Nearest Neighbor (KNN), Naïve Bayes (NB), Decision Tree (DT), and Logistic Regression (LR) classifiers. In addition, DL models such as Deep CNN and Long Short Term Memory (LSTM) are considered. The results demonstrate that the proposed Deep CNN model for multi-class attack data classification achieves the highest accuracy of 99.85% compared to RF, AB, KNN, NB, DT, LR, and LSTM classifiers with accuracy levels of 95%, 88 %, 97%, 93 %, 90 %, 98%, and 88.31 %, respectively.

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A 105-dB SFDR 16-bit SAR ADC with a Window Capacitor Calibration Scheme

A 105-dB SFDR 16-bit SAR ADC with a Window Capacitor Calibra...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Qi-Fen Zeng Chia-Hui Tien Yung-Hui Chung Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei City Taiwan

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

A 16-bit 1-MS/s SAR ADC was fabricated in 180nm digital CMOS. To maintain excellent linearity, a window capacitor calibration (WCC) scheme is proposed to estimate capacitor mismatch and obtain corrected bit weights. Different from prior calibration schemes, the WCC scheme can alleviate the offset and flicker noise effects on the calibration accuracy. The total input capacitance is only 4.8 pF. This ADC consumes a total power of 0.91 mW from a 1.8-V supply and occupies an active area of 0.276 mm 2 . After using the proposed WCC scheme, measured INL is improved from -103/+106 LSB to - 1.3/+1.7 LSB. Measured SFDR and SNDR are 105 dB and 83.8 dB, respectively. This prototype ADC achieves a Schreier FOM of 172 dB.

关键词： Accuracy Circuits and systems Capacitors Prototypes Linearity Capacitance Calibration

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A Low-Cost Smart Office Design Framework Using Arduino

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Advances in Internet of Things 2023年第3期13卷 83-108页

作者： Ssemwogerere Rajab Nabagesera Veronica Kawalya Melese Alemsra Tsehayu Logose Masitula Wamwoyo Faruk Rahman Shiddiqur Ekwaro Dominic Mbalyowere Pasis Maritah Nambobi Mutwalibi Sayo Nakeleh Turay Mugerwa Derrick Kasagga Usama Seguya Asad Department of Computer Science & Information Technology Islamic University in Uganda Kampala Uganda College of Computing & Informatics Technology Makerere University Kampala Uganda School of Information and Software Engineering University of Electronic Science & Technology of China Chengdu China School of Computer Science Neijiang Normal University Neijiang China School of Computer Science and Engineering University of Electronic Science & Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science & Technology of China Chengdu China

The main motive of our research work is security enhancement and light energy conservation. This paper describes a study investigating the potential of a controlled office solution by integrating the Internet of Things (IoT) with wireless sensor networks (WSNs). A prototype of a smart office is developed using a global system of mobile Bluetooth and Radio Frequency Identification (RFID) technology. The user can turn on and off the fan remotely at any time. This prototype focuses on security and provides human-friendly assistance when in or out of the Office by integrating a mobile application platform. The innovative automated smart Office is designed with intelligent Security doors, lights, alarms, temperature humidifiers, and bright Liquid Crystal Display (LCD) screens for viewing. Our study has opened up virtual possibilities for producing cheap innovative frameworks in this Generation of IoT and the fifth Generation (5G) technology. Therefore, when implemented, this innovation will ease and improve human quality of life. So, this paper aims to provide a low-cost, effective Internet of the things-based automated smart Office.

关键词： Intelligent Office Automation System Wireless Sensor Networks Internet of Things Ultrasonic Radio Frequency Identification Arduino

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A Two-Stage Classification Model on Detecting Cache-Based Side-Channel Attacks 20

A Two-Stage Classification Model on Detecting Cache-Based Si...

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20th International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Xinchen, Jiang Ailing, Deng Yuxi, Peng Yanping, Xiang Jianwei, Xiang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Hunan University of Technology Department of Computer and Communication Zhuzhou China

ISBN: (纸本)9798350318982

Cache-based side-channel attacks in cloud environments have attracted widespread attention. Many studies have successfully proved that sensitive data can be stolen from co-resident virtual machines by launching these attacks. Relatively various detection and defense methods are also proposed. To address such problems, this paper proposes a novel two-stage classification model that combines random forest and XGBoost, which can classify the samples collected from one process into attack samples, noise samples and normal samples. In the experiments, the accuracy of attack samples and normal samples produced results of 98.89% and 99.83%, respectively, demonstrating that our model is not inferior compared to the current methods in terms of classification performance and outperforms them in terms of implementation difficulty and classification speed. © 2023 IEEE.

关键词： Side channel attack

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Fine-grained Analysis of Stability and Generalization for Stochastic Bilevel Optimization 33

Fine-grained Analysis of Stability and Generalization for St...

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33rd International Joint Conference on Artificial Intelligence, IJCAI 2024

作者： Zhang, Xuelin Chen, Hong Gu, Bin Gong, Tieliang Zheng, Feng College of Informatics Huazhong Agricultural University Wuhan430070 China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan430070 China School of Artificial Intelligence Jilin University Jilin130012 China School of Computer Science and Technology Xi'an Jiaotong University Xi'an710049 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9781956792041

Stochastic bilevel optimization (SBO) has been integrated into many machine learning paradigms recently including hyperparameter optimization, meta learning, reinforcement learning, etc. Along with the wide range of applications, there have been abundant studies on concerning the computing behaviors of SBO. However, the generalization guarantees of SBO methods are far less understood from the lens of statistical learning theory. In this paper, we provide a systematical generalization analysis of the first-order gradient-based bilevel optimization methods. Firstly, we establish the quantitative connections between the on-average argument stability and the generalization gap of SBO methods. Then, we derive the upper bounds of on-average argument stability for single timescale stochastic gradient descent (SGD) and two timescale SGD, where three settings (nonconvex-nonconvex (NC-NC), convex-convex (C-C) and strongly-convex-strongly-convex (SC-SC)) are considered respectively. Experimental analysis validates our theoretical findings. Compared with the previous algorithmic stability analysis, our results do not require the re-initialization of the inner-level parameters before each iteration and are suited for more general objective functions. © 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.

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Enhancing Generalized Few-Shot Semantic Segmentation via Effective Knowledge Transfer

arXiv

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arXiv 2024年

作者： Chen, Xinyue Shi, Miaojing Zhou, Zijian He, Lianghua Tsoka, Sophia Department of Informatics King’s College London United Kingdom College of Electronic and Information Engineering Tongji University China

Generalized few-shot semantic segmentation (GFSS) aims to segment objects of both base and novel classes, using sufficient samples of base classes and few samples of novel classes. Representative GFSS approaches typically employ a two-phase training scheme, involving base class pre-training followed by novel class fine-tuning, to learn the classifiers for base and novel classes respectively. Nevertheless, distribution gap exists between base and novel classes in this process. To narrow this gap, we exploit effective knowledge transfer from base to novel classes. First, a novel prototype modulation module is designed to modulate novel class prototypes by exploiting the correlations between base and novel classes. Second, a novel classifier calibration module is proposed to calibrate the weight distribution of the novel classifier according to that of the base classifier. Furthermore, existing GFSS approaches suffer from a lack of contextual information for novel classes due to their limited samples, we thereby introduce a context consistency learning scheme to transfer the contextual knowledge from base to novel classes. Extensive experiments on PASCAL-5i and COCO-20i demonstrate that our approach significantly enhances the state of the art in the GFSS setting. The code is available at: https://***/HHHHedy/GFSS-EKT. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Comparative Analysis of Network Congestion on IP and Named Data Network 16

Comparative Analysis of Network Congestion on IP and Named D...

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16th International Conference on Telecommunication Systems Services and Applications, TSSA 2022

作者： Arifin, Hasan Nur Syambas, Nana Rachmana Jupriyadi Sucipto, Adi Zaki Hamidi, Eki Ahmad School of Electrical and Informatics Engineering Institut Teknologi Bandung Bandung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Uin Sunan Gunung Djati Bandung Electrical Engineering Department Bandung Indonesia

ISBN: (纸本)9781665455947

Today's data communication needs have shifted from client-server data communication to peer-to-peer. The user's focus is currently on the immediately requested data or the so-called Named Data Network (NDN). Changes to the nature of the app, user needs, and usage patterns have changed significantly, and apps on networks like YouTube, Facebook, and other social networks have revolutionized the idea of user- generated content. One of the problems in the network is network congestion or network failure. Network congestion occurs due to the need for network resources exceeding the capacity of network resources. When network congestion occurs, the NDN forwarding mechanism facilitated by FIB on the IP router cannot detect the congestion condition and will still use the same path. At the same time, the NDN router can detect congestion conditions and react by using alternative paths. The simulation results show that on the NDN router, the forwarding mechanism can maximize the use of link bandwidth faster than the forwarding mechanism on the router-based IP network. In the topology designed, NDN can achieve maximum bandwidth in the 3rd second compared to IP in the 5th second. © 2022 IEEE.

关键词： Bandwidth

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Resampling Effects on Imbalanced Data in Network Intrusion Classification

Resampling Effects on Imbalanced Data in Network Intrusion C...

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International electronics Symposium (IES)

作者： Fayruz Rahma Mahesa Cadi Rajasa Reza Fuad Rachmadi Baskoro Adi Pratomo Mauridhi Hery Purnomo Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Informatics Universitas Islam Indonesia Yogyakarta Indonesia Department of Electrical Engineering Department of Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350391992

ISBN: (纸本)9798350392005

The rapid expansion of network connections has significantly increased network traffic activity, introducing new cybersecurity challenges and heightened vulnerability to cyber attacks. To address these challenges, researchers have leveraged intelligent techniques such as machine learning (ML) to enhance attack detection accuracy in network traffic. However, ML models often face a data imbalance issue in their training sets. This imbalance, typically due to the uneven distribution of attack classes, hampers the classification performance of ML models in network intrusion detection. To mitigate class imbalance, various resampling techniques can be employed. This study evaluates several resampling techniques, including Random Oversampling, SMOTE, ADASYN, Random Undersampling, Tomek Links, and SMOTE-Tomek. Using the UNSW-NB15 dataset, we trained and tested ML models, including Decision Tree, Random Forest, Gradient Boosting, XGBoost, and 1D-CNN algorithms. Our analysis demonstrates that resampling techniques significantly impact the performance of machine learning models. The Tomek Links technique applied to the 1D-CNN model achieved the highest performance, with an accuracy of 75.27%, a precision of 87.58%, and an F1-score of 76.22%. Notably, the best recall score of 67.57% was obtained from the 1D-CNN model without resampling. These findings provide valuable insights for researchers and engineers, aiding in selecting appropriate resampling techniques for developing robust detection models for network traffic attacks.

关键词： Training Accuracy Machine learning algorithms Network intrusion detection Telecommunication traffic Transformers Generative adversarial networks

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Reconfigurable intelligent surfaces for wireless communications:Overview of hardware designs,channel models,and estimation techniques

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Intelligent and Converged Networks 2022年第1期3卷 1-32页

作者： Mengnan Jian George C.Alexandropoulos Ertugrul Basar Chongwen Huang Ruiqi Liu Yuanwei Liu Chau Yuen the Wireless Research Institute ZTE CorporationBeijing 100029China the State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen 518055China Department of Informatics and Telecommunications National and Kapodistrian University of AthensAthens 15784Greece Communications Research and Innovation Laboratory(CoreLab) Department of Electrical and Electronics EngineeringKoçUniversityIstanbul 34450Turkey College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310027China School of Electronic Engineering and Computer Science Queen Mary University of LondonLondonE14NSUK. the Engineering Product Development(EPD)Pillar Singapore University of Technology and DesignSingapore 487372Singapore

The demanding objectives for the future sixth generation(6G)of wireless communication networks have spurred recent research efforts on novel materials and radio-frequency front-end architectures for wireless connectivity,as well as revolutionary communication and computing *** the pioneering candidate technologies for 6G belong the reconfigurable intelligent surfaces(RISs),which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate the incoming electromagnetic field in a wide variety of *** RISs in wireless networks have been recently advocated as a revolutionary means to transform any wireless signal propagation environment to a dynamically programmable one,intended for various networking objectives,such as coverage extension and capacity boosting,spatiotemporal focusing with benefits in energy efficiency and secrecy,and low electromagnetic field *** by the recent increasing interests in the field of RISs and the consequent pioneering concept of the RIS-enabled smart wireless environments,in this paper,we overview and taxonomize the latest advances in RIS hardware architectures as well as the most recent developments in the modeling of RIS unit elements and RIS-empowered wireless signal *** also present a thorough overview of the channel estimation approaches for RIS-empowered communications systems,which constitute a prerequisite step for the optimized incorporation of RISs in future wireless ***,we discuss the relevance of the RIS technology in the latest wireless communication standards,and highlight the current and future standardization activities for the RIS technology and the consequent RIS-empowered wireless networking approaches.

关键词： reconfigurable intelligent surfaces beyond 5G 6G channel modeling hardware architecture channel estimation smart wireless environment standardization

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