检索结果-内蒙古大学图书馆

Space-Air-Ground Integrated Multi-domain Network Resource Orchestration based on Virtual Network Architecture: a DRL Method

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Zhang, Peiying Wang, Chao Kumar, Neeraj Liu, Lei Qingdao266580 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Qingdao266580 China Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Deemed to be University Patiala147004 India Department of Computer Science and Information Engineering Asia University Taichung41354 Taiwan School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China

Traditional ground wireless communication networks cannot provide high-quality services for artificial intelligence (AI) applications such as intelligent transportation systems (ITS) due to deployment, coverage and capacity issues. The space-air-ground integrated network (SAGIN) has become a research focus in the industry. Compared with traditional wireless communication networks, SAGIN is more flexible and reliable, and it has wider coverage and higher quality of seamless connection. However, due to its inherent heterogeneity, time-varying and self-organizing characteristics, the deployment and use of SAGIN still faces huge challenges, among which the orchestration of heterogeneous resources is a key issue. Based on virtual network architecture and deep reinforcement learning (DRL), we model SAGIN’s heterogeneous resource orchestration as a multi-domain virtual network embedding (VNE) problem, and propose a SAGIN cross-domain VNE algorithm. We model the different network segments of SAGIN, and set the network attributes according to the actual situation of SAGIN and user needs. In DRL, the agent is acted by a five-layer policy network. We build a feature matrix based on network attributes extracted from SAGIN and use it as the agent training environment. Through training, the probability of each underlying node being embedded can be derived. In test phase, we complete the embedding process of virtual nodes and links in turn based on this probability. Finally, we verify the effectiveness of the algorithm from both training and testing. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

Telecom networking with a Diamond Quantum Memory

学校读者我要写书评

暂无评论

PRX Quantum 2024年第1期5卷 010303-010303页

作者： Eric Bersin Madison Sutula Yan Qi Huan Aziza Suleymanzade Daniel R. Assumpcao Yan-Cheng Wei Pieter-Jan Stas Can M. Knaut Erik N. Knall Carsten Langrock Neil Sinclair Ryan Murphy Ralf Riedinger Matthew Yeh C.J. Xin Saumil Bandyopadhyay Denis D. Sukachev Bartholomeus Machielse David S. Levonian Mihir K. Bhaskar Scott Hamilton Hongkun Park Marko Lončar Martin M. Fejer P. Benjamin Dixon Dirk R. Englund Mikhail D. Lukin Lincoln Laboratory Massachusetts Institute of Technology Lexington Massachusetts 02421 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics Harvard University Cambridge Massachusetts 02138 USA John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge Massachusetts 02138 USA E. L. Ginzton Laboratory Stanford University Stanford California 94305 USA Institut für Laserphysik und Zentrum für Optische Quantentechnologien Universität Hamburg Hamburg 22761 Germany The Hamburg Centre for Ultrafast Imaging Hamburg 22761 Germany AWS Center for Quantum Networking Boston Massachusetts 02135 USA Department of Chemistry and Chemical Biology Harvard University Cambridge Massachusetts 02138 USA

Practical quantum networks require interfacing quantum memories with existing channels and systems that operate in the telecom band. Here we demonstrate low-noise, bidirectional quantum frequency conversion that enables a solid-state quantum memory to directly interface with telecom-band systems. In particular, we demonstrate conversion of visible-band single photons emitted from a silicon-vacancy (SiV) center in diamond to the telecom O band, maintaining low noise (g2(0)<0.1) and high indistinguishability (V=89±8%). We further demonstrate the utility of this system for quantum networking by converting telecom-band time-bin pulses, sent across a lossy and noisy 50-km deployed fiber link, to the visible band and entangling them with a diamond quantum memory with fidelity F≥87±2.5%. These results demonstrate the viability of SiV quantum memories integrated with telecom-band systems for scalable quantum networking applications.

关键词： Frequency conversion Quantum memories Quantum networks Quantum optics with artificial atoms Quantum state transfer Diamond Fibers

Adoption of Internet of Things for Smart Monitoring of Aquaponics

学校读者我要写书评

暂无评论

Adoption of Internet of Things for Smart Monitoring of Aquap...

International Conference on Computing and networking Technology (ICCNT)

作者： Kapil Joshi Priya Priya Vishal Kaushik Harishchander Anandaram Arati Mohapatro Anil Kumar Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India School of Agriculture Graphic Era Hill University Dehradun Uttarakhand India School of Computer Science UPES Dehradun India Centre for Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu India Dept of Computer Science Indian Academy Degree College(Autonomous) Bengaluru India School of Computing DIT University Dehradun India

Aquaponics is a cutting-edge, clever, and ecological technology for agriculture that combines both aquaculture and hydroponics to produce vegetable products prototype. This method promises to be environmentally friendly and viable due to its high-water consumption efficacy, lack of use of pesticides, and decreased fertilizer use. The main task is to make aquaponics viable and dependable at a business scale because interest in it is growing. By recognizing, being listed, and thoroughly describing each of the characteristics detected in aquaponics, as well as the smart platforms and Internet of Things (IoT) devices in the examined literature, this article hopes to support study towards a feasible business aquaponics option. The suggested study also identifies future efforts needed for mechanized aquaponics and possible voids in the existing literature. This is anticipated the fact that the system for aquaponics backed by smart control units will benefit in terms of profitability, intelligence, accuracy, and effectiveness within the context of the research studies examined in this paper.

关键词：

Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection

学校读者我要写书评

暂无评论

Computational Intelligence and Neuroscience 2022年 2022卷 8501738页

作者： Krishnaraj, N. Sivakumar, B. Kuppusamy, Ramya Teekaraman, Yuvaraja Thelkar, Amruth Ramesh Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Electrical and Electronics Engineering Sri Sairam College of Engineering Karnataka Bangalore562106 India Department of Electronic and Electrical Engineering The University of Sheffield SheffieldS1 3JD United Kingdom Faculty of Electrical and Computer Engineering Jimma Institute of Technology Jimma University Jimma Ethiopia

Due to the exponential growth of high-quality fake photos on social media and the Internet, it is critical to develop robust forgery detection tools. Traditional picture- and video-editing techniques include copying areas of the image, referred to as the copy-move approach. The standard image processing methods physically search for patterns relevant to the duplicated material, restricting the usage in enormous data categorization. On the contrary, while deep learning (DL) models have exhibited improved performance, they have significant generalization concerns because of their high reliance on training datasets and the requirement for good hyperparameter selection. With this in mind, this article provides an automated deep learning-based fusion model for detecting and localizing copy-move forgeries (DLFM-CMDFC). The proposed DLFM-CMDFC technique combines models of generative adversarial networks (GANs) and densely connected networks (DenseNets). The two outputs are combined in the DLFM-CMDFC technique to create a layer for encoding the input vectors with the initial layer of an extreme learning machine (ELM) classifier. Additionally, the ELM model's weight and bias values are optimally adjusted using the artificial fish swarm algorithm (AFSA). The networks' outputs are supplied into the merger unit as input. Finally, a faked image is used to identify the difference between the input and target areas. Two benchmark datasets are used to validate the proposed model's performance. The experimental results established the proposed model's superiority over recently developed approaches. © 2022 N. Krishnaraj et al.

关键词： Generative adversarial networks

Learning Disentangled Representations Using Dormant Variations

学校读者我要写书评

暂无评论

Learning Disentangled Representations Using Dormant Variatio...

International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Kanmani Palaniappan Ushasukhanya S T.Y.J Naga Malleswari Prabha Selvaraj Vijay Kumar Burugari Department of DSBS SRM Institute of Science and Technology Chennai India Department of Networking and Communications SRM Institute of Science and Technology Chennai India School of Computer Science and Engineering VIT-AP University Amaravathi India Department of CSE Koneru Lakshmaiah Education Foundation K L University Guntur India

A disentangled representation is one in which each variable in the latent space is sensitive to one single generative factor and is relatively dormant to other factors. Disentanglement results in an incisive latent representation of the image which can be used for downstream tasks such as reinforcement learning and supervised learning. The discrete generative factors in image datasets are hard to capture in the form of a latent space and in order to perform efficient interpolations it requires smooth and continuous latent spaces in order to address this by disentangling the important factors of the input image in the latent space. Subsequently post training the model should be able to generate different versions of the input image by varying features/attributes. A technique Hybrid Optimized GAN using Dormant Variants (HOGDV) is proposed which can be deployed in multiple places if the number is made variable and works on a wide variety of data distribution.

关键词： Training Interpolation Supervised learning Reinforcement learning Task analysis Machine intelligence

Mixture of Experts for Intelligent Networks: A Large Language Model-enabled Approach

学校读者我要写书评

暂无评论

Mixture of Experts for Intelligent Networks: A Large Languag...

International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Hongyang Du Guangyuan Liu Yijing Lin Dusit Niyato Jiawen Kang Zehui Xiong Dong In Kim School of Computer Science and Engineering Nanyang Technological University Singapore Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of Automation Guangdong University of Technology and Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Optimizing various wireless user tasks poses a significant challenge for networking systems because of the expanding range of user requirements. Despite advancements in Deep Reinforcement Learning (DRL), the need for customized optimization tasks for individual users complicates developing and applying numerous DRL models, leading to substantial computation resource and energy consumption and can lead to inconsistent outcomes. To address this issue, we propose a novel approach utilizing a Mixture of Experts (MoE) framework, augmented with Large Language Models (LLMs), to analyze user objectives and constraints effectively, select specialized DRL experts, and weigh each decision from the participating experts. Specifically, we develop a gate network to oversee the expert models, allowing a collective of experts to tackle a wide array of new tasks. Furthermore, we innovatively substitute the traditional gate network with an LLM, leveraging its advanced reasoning capabilities to manage expert model selection for joint decisions. Our proposed method reduces the need to train new DRL models for each unique optimization problem, decreasing energy consumption and AI model implementation costs. The LLMenabled MoE approach is validated through a general maze navigation task and a specific network service provider utility maximization task, demonstrating its effectiveness and practical applicability in optimizing complex networking systems.

关键词： Wireless communication Energy consumption Navigation Computational modeling Logic gates Probability Throughput

A Hybrid Approach for Binary Classification of Imbalanced Data

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Tsai, Hsin-Han Yang, Ta-Wei Wong, Wai-Man Chou, Cheng-Fu Department of Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan Graduate Institute of Networking and Multimedia National Taiwan University Taipei10617 Taiwan Information Technology Office National Taiwan University Hospital Taipei100225 Taiwan

Binary classification with an imbalanced dataset is challenging. Models tend to consider all samples as belonging to the majority class. Although existing solutions such as sampling methods, cost-sensitive methods, and ensemble learning methods improve the poor accuracy of the minority class, these methods are limited by overfitting problems or cost parameters that are difficult to decide. We propose HADR, a hybrid approach with dimension reduction that consists of data block construction, dimentionality reduction, and ensemble learning with deep neural network classifiers. We evaluate the performance on eight imbalanced public datasets in terms of recall, G-mean, and AUC. The results show that our model outperforms state-of-the-art methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning systems

Multi-Functional Reconfigurable Intelligent Surface: System Modeling and Performance Optimization

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Wang, Wen Ni, Wanli Tian, Hui Eldar, Yonina C. Zhang, Rui The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Math and CS Faculty Weizmann Institute of Science Rehovot7610001 Israel School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

In this paper, we propose and study a multifunctional reconfigurable intelligent surface (MF-RIS) architecture. In contrast to conventional single-functional RIS (SF-RIS) that only reflects signals, the proposed MF-RIS simultaneously supports multiple functions with one surface, including reflection, refraction, amplification, and energy harvesting of wireless signals. As such, the proposed MF-RIS is capable of significantly enhancing RIS signal coverage by amplifying the signal reflected/refracted by the RIS with the energy harvested. We present the signal model of the proposed MF-RIS, and formulate an optimization problem to maximize the sum-rate of multiple users in an MF-RIS-aided non-orthogonal multiple access network. We jointly optimize the transmit beamforming, power allocations as well as the operating modes and parameters for different elements of the MF-RIS and its deployment location, via an efficient iterative algorithm. Simulation results are provided which show significant performance gains of the MF-RIS over SF-RISs with only some of its functions available. Moreover, we demonstrate that there exists a fundamental trade-off between sum-rate maximization and harvested energy maximization. In contrast to SF-RISs which can be deployed near either the transmitter or receiver, the proposed MF-RIS should be deployed closer to the transmitter for maximizing its communication throughput with more energy harvested. Copyright © 2023, The Authors. All rights reserved.

关键词： Transmitters

Integrated Phononic Waveguides in Diamond

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Ding, Sophie Weiyi Pingault, Benjamin Shao, Linbo Sinclair, Neil Machielse, Bartholomeus Chia, Cleaven Maity, Smarak Lončar, Marko John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States QuTech Kavli Institute of Nanoscience Delft University of Technology PO Box 5046 Delft2600 GA Netherlands Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States AWS Center for Quantum Networking BostonMA02135 United States

Efficient generation, guiding, and detection of phonons, or mechanical vibrations, are of interest in various fields including radio frequency communication, sensing, and quantum information. Diamond is an important platform for phononics because of the presence of strain-sensitive spin qubits, and its high Young’s modulus which allows for low-loss gigahertz devices. We demonstrate a diamond phononic waveguide platform for generating, guiding, and detecting gigahertz-frequency surface acoustic wave (SAW) phonons. We generate SAWs using interdigital transducers integrated on AlN/diamond and observe SAW transmission at 4-5 GHz through both ridge and suspended waveguides, with wavelength-scale cross sections (~1 μm2) to maximize spin-phonon interaction. This work is a crucial step for developing acoustic components for quantum phononic circuits with strain-sensitive color centers in diamond. Copyright © 2023, The Authors. All rights reserved.

关键词： Diamonds