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On-chip integrated GeSe_(2)/Si vdW heterojunction for ultravioletenhanced broadband photodetection,imaging,and secure optical communication

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Nano Research 2024年第7期17卷 6544-6549页

作者： Zhiman Zhou Kunxuan Liu Di Wu Yunrui Jiang Ranran Zhuo Pei Lin Zhifeng Shi Yongtao Tian Wei Han Longhui Zeng Xinjian Li School of Physics and Microelectronics Key Laboratory of Material Physics Ministry of EducationZhengzhou UniversityZhengzhou 450052China Department of Electrical and Computer Engineering University of California San DiegoLa JollaCalifornia 92093USA Institute of Microelectronics and Integrated Circuits School of MicroelectronicsHubei UniversityWuhan 430062China

Broadband photodetection,spanning from ultraviolet(UV)to infrared(IR),is pivotal in diverse technological domains including astronomy,remote sensing,environmental monitoring,and medical ***,current commercially available broadband photodetectors,predominately based on conventional narrow-bandgap semiconductors,exhibit limited sensitivity in the UV *** limitation,stemming from the significant energy disparity between the semiconductor bandgap and UV photon,narrows their application ***,we report an innovative approach involving the in-situ van der Waals(vdW)integration of two-dimensional(2D)GeSe_(2)layers onto a Si *** process yields a high-quality GeSe_(2)/Si vdW heterojunction device,which features a broad response range covering from UV to near-IR(NIR)with a greatly-enhanced sensitivity in the UV *** device possesses high responsivities of 325 and 533.4 mA/W,large detectivities of 1.24×10^(13)and 2.57×10^(13)Jones,and fast response speeds of 20.6/82.1 and 17.7/81.0μs under 360 and 980 nm,***,the broadband image sensing and secure invisible optical communication capabilities of the GeSe_(2)/Si heterojunction device are *** work provides a viable approach for UV-enhanced broadband photodetection technology,opening up new possibilities and applications across various scientific and technological domains.

关键词： germanium diselenide van der Waals(vdW)integration ultraviolet enhanced broadband photodetection imaging

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Hydropower Development Towards a Full-Renewable Energy Grid in Indonesia

Hydropower Development Towards a Full-Renewable Energy Grid ...

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2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

作者： Hasanah, Rini Nur Suyono, Hadi Kim, Jinho Muharram, Yunita Muljadi, Eduard Universitas Brawijaya Electrical Engineering Department Malang Indonesia Pacific Northwest National Laboratory RichlandWA United States Auburn University Electrical and Computer Engineering Department AL United States

ISBN: (纸本)9798350320169

This paper describes hydropower development in Indonesia, taking account of the country's proportion of the world energy consumption and its related contribution to environmental protection. The concern is based on the country's geopolitical position and socio-economical role in the future, considering the current energy structure and potential, the portion of renewable energy in the energy mix, and the hydropower role in the renewable energy development. The assessment is focused on the country's commitment to reduce the greenhouse gas emissions, its participation in achieving sustainable development goals, and especially in achieving the country's vision of becoming Golden Indonesia 2045. It is concluded that when all efforts are done to achieve the Indonesia full-renewable energy grid in the future, the hydropower development can be maximally developed using the pumpstorage hydropower system. © 2023 IEEE.

关键词： Sustainable development

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An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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A Threshold Implementation-Based Neural Network Accelerator with Power and Electromagnetic Side-Channel Countermeasures

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IEEE Journal of Solid-State Circuits 2023年第1期58卷 141-154页

作者： Maji, Saurav Banerjee, Utsav Fuller, Samuel H. Chandrakasan, Anantha P. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electronic Systems Engineering Indian Institute of Science Karnataka Bengaluru560012 India Analog Devices Inc. WilmingtonMA01887 United States

With the recent advancements in machine learning (ML) theory, a lot of energy-efficient neural network (NN) accelerators have been developed. However, their associated side-channel security vulnerabilities pose a major concern. There have been several proof-of-concept attacks demonstrating the extraction of their model parameters and input data. This work introduces a threshold implementation (TI) masking-based NN accelerator that secures model parameters and inputs against power and electromagnetic (EM) side-channel attacks. The 0.159 mm2 demonstration in 28 nm runs at 125 MHz at 0.95 V and limits the area and energy overhead to 64% and 5.5× , respectively, while demonstrating security even greater than 2M traces. The accelerator also secures model parameters through encryption and the inputs against horizontal power analysis (HPA) attacks. © 2022 IEEE.

关键词： Side channel attack

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Statistical analysis and method to quantify the impact of measurement uncertainty on dynamic mode decomposition 63

Statistical analysis and method to quantify the impact of me...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Algikar, Pooja Sharma, Pranav Netto, Marcos Mili, Lamine Virginia Polytechnic Institute and State University Bradley Department of Electrical and Computer Engineering VA22043 United States National Renewable Energy Laboratory GoldenCO80401 United States New Jersey Institute of Technology Helen and John C. Hartmann Department of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9798350316339

We apply random matrix theory to study the impact of measurement uncertainty on dynamic mode decomposition. Specifically, when the measurements follow a normal probability density function, we show how the moments of that density propagate through the dynamic mode decomposition. While we focus on the first and second moments, the analytical expressions we derive are general and can be extended to higher-order moments. Furthermore, the proposed numerical method for propagating uncertainty is agnostic of specific dynamic mode decomposition formulations. Of particular relevance, the estimated second moments provide confidence bounds that may be used as a metric of trustworthiness, that is, how much one can rely on a finite-dimensional linear operator to represent an underlying dynamical system. We perform numerical experiments on two canonical systems and verify the estimated confidence levels by comparing the moments with those obtained from Monte Carlo simulations. © 2024 IEEE.

关键词： Probability density function

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TELEX: Two-Level Learned Index for Rich Queries on Enclave-based Blockchain systems

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IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 4299-4313页

作者： Wu, Haotian Tang, Yuzhe Shen, Zhaoyan Tao, Jun Lin, Chenhao Peng, Zhe The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Syracuse University Department of Electrical Engineering and Computer Science United States Shandong University School of Computer Science and Technology China Southeast University School of Cyber Science and Engineering China Xi'an Jiaotong University Faculty of Electronic and Information Engineering China The Hong Kong Polytechnic University Shenzhen Research Institute Hong Kong

Blockchain has become a popular paradigm for secure and immutable data storage. Despite its numerous applications across various fields, concerns regarding the user privacy and result integrity during data queries persist. Additionally, the need for rich query functionalities to harness the full potential of blockchain data remains an area ripe for exploration. In order to address these challenges, our paper first utilizes a framework based on the Trusted Execution Environment (TEE) and oblivious RAM technique to achieve both privacy and data integrity. To enhance the query efficiency over the entire blockchain, we then devise a two-level learned indexing methodology named TELEX within the TEE for both integer and string keys. We also propose different query processing algorithms for versatile query types, including exact queries, aggregate queries, Boolean queries, and range queries. By implementing the prototype and conducting extensive evaluation, we demonstrate the feasibility and remarkable improvement in efficiency compared to existing solutions. © 1989-2012 IEEE.

关键词： Query languages

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Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement 63

Distributed Optimization with Finite Bit Adaptive Quantizati...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Rikos, Apostolos I. Jiang, Wei Charalambous, Themistoklis Johansson, Karl H. Artificial Intelligence Thrust of the Information Hub Guangzhou China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Clear Water Bay Hong Kong Aalto University School of Electrical Engineering Department of Electrical Engineering and Automation Espoo Finland University of Cyprus School of Engineering Department of Electrical and Computer Engineering Nicosia1678 Cyprus Kth Royal Institute of Technology Division of Decision and Control Systems StockholmSE-100 44 Sweden

ISBN: (纸本)9798350316339

In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorithms is essential. In our paper we addresses the challenge of unconstrained distributed optimization. In our scenario each node's local function exhibits strong convexity with Lipschitz continuous gradients. The exchange of information between nodes occurs through 3-bit bandwidth-limited channels (i.e., nodes exchange messages represented by a only 3 -bits). Our proposed algorithm respects the network's bandwidth constraints by leveraging zoom-in and zoom-out operations to adjust quantizer parameters dynamically. We show that during our algorithm's operation nodes are able to converge to the exact optimal solution. Furthermore, we show that our algorithm achieves a linear convergence rate to the optimal solution. We conclude the paper with simulations that highlight our algorithm's unique characteristics. © 2024 IEEE.

关键词： Bandwidth

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Novel Aerial User Equipment Task Offloading Optimization in Integrated 6G Terrestrial and Non-Terrestrial Networks: A Deep Reinforcement Learning Approach

Novel Aerial User Equipment Task Offloading Optimization in ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ei, Nway Nway Hassan, Sheikh Salman Tun, Yan Kyaw Han, Zhu Hong, Choong Seon Kyung Hee University Department of Computer Science and Engineering Yongin17104 Korea Republic of Aalborg University Department of Electronic Systems A. C. Meyers Vænge 15 København2450 Denmark University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States

ISBN: (纸本)9798350351255

This paper investigates a novel network architecture - the 6G integrated terrestrial-non-terrestrial network (ITNTN) with multi-access edge computing (ITNT-MEC). This system aims to bridge the connectivity gap between terrestrial infrastructure and non-terrestrial networks while offering real-time data processing through edge computing. We consider a scenario where aerial user equipments (AUEs) share resources of terrestrial base stations (TBSs) with terrestrial UEs (TUEs). We formulate an optimization problem to minimize the total energy consumption of both AUEs and TUEs. This problem involves joint optimization of AUE association (i.e., TBS or low Earth orbit (LEO) satellite), AUE trajectories, and TBS bandwidth allocation. Due to the dynamic network environment and non-convex optimization characteristics, solving this problem presents a significant challenge. To address this, we propose a novel algorithm that combines block coordinate descent (BCD) with deep deterministic policy gradient (DDPG) and a convex optimization method. Simulation results demonstrate the significant reductions in total energy consumption compared to baseline approaches, achieving improvements of 23%, 36.6%, and 46.5% against DQN-TO, RA, and FT, respectively. © 2024 IEEE.

关键词： Convex optimization

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Deep Learning Enabled Intelligent Healthcare Management System in Smart Cities Environment

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computers, Materials & Continua 2023年第2期74卷 4483-4500页

作者： Hanan Abdullah Mengash Lubna A.Alharbi Saud S.Alotaibi Sarab AlMuhaideb Nadhem Nemri Mrim M.Alnfiai Radwa Marzouk Ahmed S.Salama Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversityTabuk71491Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Science and Arts at MuhayelKing Khalid UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

In recent times,cities are getting smart and can be managed effectively through diverse architectures and *** cities have the ability to support smart medical systems that can infiltrate distinct events(i.e.,smart hospitals,smart homes,and community health centres)and scenarios(e.g.,rehabilitation,abnormal behavior monitoring,clinical decision-making,disease prevention and diagnosis postmarking surveillance and prescription recommendation).The integration of Artificial Intelligence(AI)with recent technologies,for instance medical screening gadgets,are significant enough to deliver maximum performance and improved management services to handle chronic *** latest developments in digital data collection,AI techniques can be employed for clinical decision making *** the other hand,Cardiovascular Disease(CVD)is one of the major illnesses that increase the mortality rate across the ***,wearables can be employed in healthcare systems that instigate the development of CVD detection and *** this motivation,the current study develops an Artificial Intelligence Enabled Decision Support System for CVD Disease Detection and Classification in e-healthcare environment,abbreviated as AIDSS-CDDC *** proposed AIDSS-CDDC model enables the Internet of Things(IoT)devices for healthcare data ***,the collected data is saved in cloud server for *** by,training 4484 CMC,2023,vol.74,no.2 and testing processes are executed to determine the patient’s health *** accomplish this,the presented AIDSS-CDDC model employs data preprocessing and Improved Sine Cosine Optimization based Feature Selection(ISCO-FS)*** addition,Adam optimizer with Autoencoder Gated RecurrentUnit(AE-GRU)model is employed for detection and classification of *** experimental results highlight that the proposed AIDSS-CDDC model is a promising performer compared to other existing models.

关键词： Smart cities e-healthcare decision support system cardiovascular disease deep learning feature selection

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Effective short text classification via the fusion of hybrid features for IoT social data

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Digital Communications and Networks 2022年第6期8卷 942-954页

作者： Xiong Luo Zhijian Yu Zhigang Zhao Wenbing Zhao Jenq-Haur Wang School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Shunde Innovation School University of Science and Technology BeijingFoshan528399China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Department of Electrical Engineering and Computer Science Cleveland State UniversityClevelandOH44115USA Department of Computer Science and Information Engineering National Taipei University of TechnologyTaipei10608China

Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Previous studies mainly tackle these problems by enhancing the semantic information or the statistical information individually. However, the improvement achieved by a single type of information is limited, while fusing various information may help to improve the classification accuracy more effectively. To fuse various information for short text classification, this article proposes a feature fusion method that integrates the statistical feature and the comprehensive semantic feature together by using the weighting mechanism and deep learning models. In the proposed method, we apply Bidirectional Encoder Representations from Transformers (BERT) to generate word vectors on the sentence level automatically, and then obtain the statistical feature, the local semantic feature and the overall semantic feature using Term Frequency-Inverse Document Frequency (TF-IDF) weighting approach, Convolutional Neural Network (CNN) and Bidirectional Gate Recurrent Unit (BiGRU). Then, the fusion feature is accordingly obtained for classification. Experiments are conducted on five popular short text classification datasets and a 5G-enabled IoT social dataset and the results show that our proposed method effectively improves the classification performance.

关键词： Information fusion Short text classi fication BERT Bidirectional encoder representations fr 0om transformers Deep learning Social data

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