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SSRN 2024年

作者： Jiang, Xiangming Gao, Tianqi Gong, Maoguo Jiang, Fenlong Zhang, Mingyang Liu, Jieyi School of Electronic Engineering The Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University No. 2 South TaiBai Road Xi’an710071 China School of Computer Science The Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University No. 2 South TaiBai Road Xi’an710071 China

Hyperspectral unmixing can provide the composition of ground objects, while change detection can identify the changes of the same region over time. Therefore, unmixing based hyperspectral change detection can investigate changes in a subpixel-level. It can provide not only whether the change happens or not but also how to change. This paper will gradually establish an unconstrained sparse unmixing based change detection model and design a multiobjective optimization sparse unmixing approach (termed MoSU-CD) to solve it for more subpixel-level change details. Compared with the existing sparse unmixing based change detection approaches, MoSU-CD directly unmixes the difference map of multi-temporal images rather than multi-temporal images themselves respectively. Then it explores the change details of one substance in each pixel based on the unmixing results, i.e., endmembers and abundances. In addition, in order to improve the change detection accuracy, an ensemble decision strategy is designed for the Pareto optimal solutions obtained by multiobjective optimization method. Based on this strategy, the subpixel-level changes can be aggregated to generate an aggressive pixel-level change map. The experimental results on synthetic and real data sets demonstrate that the proposed MoSU-CD not only outperforms the benchmark and state-of-art approaches in terms of pixel-level change detection but also provides additional subpixel-level change information. © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Agricultural Water Conservation through AI-Enhanced Solar IoT Networks

Agricultural Water Conservation through AI-Enhanced Solar Io...

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Electronics and Renewable systems (ICEARS), International Conference on

作者： S. P. Maniraj Monisha J R. Amutha R. Kavitha P. J. Shriidhar C. Srinivasan Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamilnadu India Department of Computer Science and Engineering (Cyber Security) R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering ULTRA College of Engineering and Technology Madurai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Vinayaka Missions Kirupananda Variyar Engineering College Vinayaka Missions Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha Universityi Chennai Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

With the increasing water shortage and climatic uncertainty, creative strategies for effective water management in agriculture are necessary. This research investigates the integration of Artificial Intelligence (AI), particularly Artificial Neural Networks (ANN), with solar-powered Internet of Things (IoT) networks to enhance water utilization in agricultural activities. The proposed system facilitates accurate irrigation scheduling using real-time data from soil moisture sensors, meteorological stations, and crop health monitoring devices. The ANN model analyses this data to predict ideal irrigation schedules and volumes, minimizing water waste while improving crop yields. The solar-powered IoT framework provides sustainability and reduces dependence on traditional energy sources. Field trials indicate that the AI-enhanced system may promote water saving by as much as 30% relative to traditional irrigation techniques. Moreover, using AI enables adaptive learning, enabling the system to enhance its irrigation tactics using historical and real-time data. This novel method conserves water resources and fosters sustainable agriculture practices, coinciding with global initiatives to address water shortages and assure food security. The findings underscore the potential of technology-based solutions to revolutionize agricultural water management for a sustainable future.

关键词： Irrigation Accuracy Food security Artificial neural networks Water conservation Real-time systems Internet of Things Sustainable development Water resources Intelligent sensors

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Fine-Tuned Yolo V-10X: Real-Time Object Detection for Autonomous Vehicles Under Multiple Weather Conditions

Fine-Tuned Yolo V-10X: Real-Time Object Detection for Autono...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Safa Teboulbi Seifeddine Messaoud Mohamed Ali Hajjaji Mohamed Atri Abdellatif Mtibaa Higher Institute of Computer Sciences and Mathematics of Monastir Monastir University Monastir Tunisia Faculty of Sciences of Monastir Monastir University Monastir Tunisia Higher Institute of Applied Sciences and Technology of Sousse Sousse University Sousse Tunisia Computer Engineering Department College of Computer Science King Khalid University Abha Saudi Arabia Systems Integration & Emerging Energies Laboratory National Engineering School of Sfax Sfax University Sfax Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Detecting objects such as vehicles, buildings, pedestrians, and road signs is indispensable to advancing the concept of autonomous and self-driving cars. Furthermore, an autonomous vehicle (AV) must accurately detect its surrounding environment to operate reliably. Most object detection (OD) techniques perform adequately under typical weather conditions, including cloudy or sunny days. However, their efficiency decreases significantly when exposed to Adverse Weather Conditions (AWCs), including days with sandstorm, rain, fog or snow. Complex and computationally costly models are required to achieve high accuracy rates. In this study, we present an improved OD system in AWCs for autonomous vehicles (AVs) using the single-stage deep learning (DL) algorithm YOLO (You Only Look Once) version 10. To evaluate our system, Vehicle Detection in Adverse Weather Nature (DAWN) dataset is used. It comprises real-world images captured under various types of AWCs. The experimental findings confirm that the suggested method is effective and surpasses state-of-the-art OD approaches under AWCs.

关键词： YOLO Adaptation models Accuracy Rain Smart cities Snow Roads Real-time systems Reliability Meteorology

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Multiscale Feature-guided Adversarial Examples Quality Assessment via Hierarchical Perception of Human Visual System

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IEEE Transactions on Big Data 2024年

作者： Wen, Wenying Huang, Minghui Dong, Li Zhang, Yushu Fang, Yuming Jiangxi University of Finance and Economics School of Computing and Artificial Intelligence Jiangxi Nanchang China Jiangxi Provincial Key Laboratory of Multimedia Intelligent Processing Jiangxi Nanchang China Ningbo University Faculty of Electrical Engineering and Computer Science Zhejiang Ningbo China

Deep neural networks (DNNs) reveal significant robustness deficiencies due to their susceptibility to being misled by small and imperceptible adversarial examples, thus it is crucial to improve the robustness of DNNs against such harmful perturbations. The current Lp specification ignores differences in human visual perception when measuring similarity, and most existing image quality assessment (IQA) methods and adversarial example datasets lack subjective scores for evaluation. In this paper, we construct a new database of adversarial examples, called the AED, which contains 35 original images, 1050 adversarial examples, and the corresponding subjective scores of adversarial examples. Then, a novel full-reference IQA model for the quality evaluation of the adversarial examples is proposed by taking into full consideration the hierarchical perception of human visual system (HVS) and the outstanding capabilities of the multi-scale feature extraction network in feature extraction. Specifically, a feature encoding network that uses continuous convolution layers to pre-extract features and expand the receptive field of the image is employed. To simulate the HVS hierarchical perception, the features of different scales are further obtained by designing a multi-scale feature extraction network. The structural similarity scores of the feature maps at different scales are calculated for jointly arriving at the final IQA score of the adversarial examples. Experimental results have demonstrated that our proposed model is closer to the perception of HVS in small imperceptible distortions evaluation of adversarial examples compared with other classical and state-of- the-art models. © 2015 IEEE.

关键词： Adversarial examples HVS hierarchical perception Lp norms multiscale feature

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User-Centric Interactive AI for Distributed Diffusion Model-based AI-Generated Content

arXiv

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Shen, Xuemin Kim, Dong In The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Chinese University of Hong Kong Shenzhen China The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Distributed Artificial Intelligence-Generated Content (AIGC) has attracted increasing attention. However, it faces two significant challenges: how to maximize the subjective Quality of Experience (QoE) and how to enhance the energy efficiency, which are particularly pronounced in widely adopted Generative Diffusion Model (GDM)-based AIGC services for image generation. In this paper, we propose a novel user-centric Interactive AI (IAI) approach for service management, with a distributed GDM-based AIGC framework, prioritizing efficient and collaborative GDM deployment. Specifically, we restructure the GDM's inference process, i.e., the denoising chain, to enable users' semantically similar prompts to share a portion of diffusion steps. Furthermore, to maximize the users' subjective QoE, we propose a IAI approach, i.e., Reinforcement Learning With Large Language Models Interaction (RLLI), which utilizes Large Language Model (LLM)-empowered generative agents to replicate users interaction, providing real-time and subjective QoE feedback that reflects a spectrum of user personalities. Lastly, we present the GDM-based Deep Deterministic Policy Gradient (G-DDPG) algorithm, adapted to the proposed RLLI framework, for effective communication and computing resource allocation while considering user subjective personalities and dynamic wireless environments in decision-making. Simulation results show that G-DDPG can increase the sum QoE by 15%, compared with the conventional DDPG algorithm. © 2023, CC BY.

关键词： Reinforcement learning

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IRS-Enhanced Anti-Jamming Precoding Against DISCO Physical Layer Jamming Attacks

arXiv

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arXiv 2024年

作者： Huang, Huan Zhang, Hongliang Cai, Yi Zhang, Yunjing Lee Swindlehurst, A. Han, Zhu Electronic Information School Soochow University Suzhou China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of Houston Houston United States Center for Pervasive Communications and Computing University of California Irvine United States

Illegitimate intelligent reflective surfaces (IRSs) can pose significant physical layer security risks on multi-user multiple-input single-output (MU-MISO) systems. Recently, a DISCO approach has been proposed an illegitimate IRS with random and time-varying reflection coefficients, referred to as a "disco" IRS (DIRS). Such DIRS can attack MU-MISO systems without relying on either jamming power or channel state information (CSI), and classical anti-jamming techniques are ineffective for the DIRS-based fully-passive jammers (DIRS-based FPJs). In this paper, we propose an IRS-enhanced anti-jamming precoder against DIRS-based FPJs that requires only statistical rather than instantaneous CSI of the DIRS-jammed channels. Specifically, a legitimate IRS is introduced to reduce the strength of the DIRS-based jamming relative to the transmit signals at a legitimate user (LU). In addition, the active beamforming at the legitimate access point (AP) is designed to maximize the signal-to-jamming-plus-noise ratios (SJNRs). Numerical results are presented to evaluate the effectiveness of the proposed IRS-enhanced anti-jamming precoder against DIRS-based FPJs. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel state information

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Controlling Ultrafast Photoemission via Simultaneous Laser Mixing and Shaping

Controlling Ultrafast Photoemission via Simultaneous Laser M...

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2023 Ultrafast Optics, UFO 2023

作者： Lemons, Randy Hirschman, Jack Neveu, Nicole Duris, Joseph Marinelli, Agostino Durfee, Charles Carbajo, Sergio SLAC National Accelerator Laboratory Stanford University 2575 Sand Hill Rd Menlo ParkCA94025 United States Colorado School of Mines 1500 Illinois St GoldenCO80401 United States Department of Electrical and Computer Engineering Department of Physics and Astronomy University of California Los AngelesCA90095 United States

We present a novel, versatile framework to generate W-level temporally shaped, near transform-limited, UV picosecond pulses via non-colinear sum frequency generation and demonstrate it producing temporally flattop, high-power UV pulses capable of enhancing femtosecond- and attosecond-level electron and X-ray free electron lasers brightness. © 2023 The Author(s).

关键词： Electromagnetic pulse

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Learning Unknown Lagrange Dynamical systems with Guaranteed Persistency of Excitation

Learning Unknown Lagrange Dynamical Systems with Guaranteed ...

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International Conference on systems and Control (ICSC)

作者： A. Samanis P. S. Trakas X. Papageorgiou K. J. Kyriakopoulos C. P. Bechlioulis Control Systems Lab School of Mechanical Engineering National Technical University of Athens Greece Department of Electrical and Computer Engineering University of Patras Greece

ISBN: (数字)9781665465076

ISBN: (纸本)9781665465083

In this paper, we present a methodology that ensures a priori that all possible unknown dynamics of the system within a compact set of operation will be excited. A controller is used to make sure that the system with unknown dynamics will follow the reference trajectory and Radial Basis Function (RBF) neural networks are employed to estimate the unknown nonlinearities. The persistency of excitation condition is guaranteed as a prerequisite to achieve accurate estimation of the unknown nonlinear terms and efficient learning. A simulation example clarifies the proposed approach and verifies the aforementioned assertions.

关键词： Neural networks Estimation Control systems Trajectory Nonlinear dynamical systems

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Effects of Salt Spray on c-Si Photovoltaic Modules in the Brazilian Region

Effects of Salt Spray on c-Si Photovoltaic Modules in the Br...

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IEEE Conference Photovoltaic Specialists (PVSC)

作者： Mendelsson R.M. Neves Allan Silveira Hugo S. Alvarez Rodrigo M. Garcia Francisco C. Marques Marcelo G. Villalva Dept. of Systems and Energy - DSE School of Electrical and Computer Engineering Campinas Brazil Communications Department - DECOM School of Electrical and Computer Engineering Campinas Brazil Dept. Research and Development - R&D Build Your Dreams Company - BYD Campinas Brazil Dept. of Applied Physics - DFA Institute of Physics Gleb Wataghin Campinas Brazil

Brazil has great potential for this type of energy generation due to its geographic location, allowing the development of viable photovoltaic (PV) projects in several regions. its use in places close to the sea has increased, with its use on boats and even resorts and hotels. This proximity to the sea requires attention to the local salinity, more precisely to the saline mist. This article will describe the methodology used to carry out the salinity resistance test of PV modules, choosing a specific classification of corrosive atmosphere according to the brazilian environment on the coast where the module will be placed in real conditions.

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Silicon photonic microresonator-based high-resolution line-by-line pulse shaping

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Nature Communications 2024年第1期15卷 7878页

作者： Cohen, Lucas M. Wu, Kaiyi Myilswamy, Karthik V. Fatema, Saleha Lingaraju, Navin B. Weiner, Andrew M. School of Electrical and Computer Engineering Purdue University West Lafayette 47907 IN United States The Johns Hopkins University Applied Physics Laboratory Laurel 20723 MD United States

Optical pulse shaping stands as a formidable technique in ultrafast optics, radio-frequency photonics, and quantum communications. While existing systems rely on bulk optics or integrated platforms with planar waveguide sections for spatial dispersion, they face limitations in achieving finer (few- or sub-GHz) spectrum control. These methods either demand considerable space or suffer from pronounced phase errors and optical losses when assembled to achieve fine resolution. Addressing these challenges, we present a foundry-fabricated six-channel silicon photonic shaper using microresonator filter banks with inline phase control and high spectral resolution. Leveraging existing comb-based spectroscopic techniques, we devise a system to mitigate thermal crosstalk and enable the versatile use of our on-chip shaper. Our results demonstrate the shaper’s ability to phase-compensate six comb lines at tunable channel spacings of 3, 4, and 5 GHz. Specifically, at a 3 GHz channel spacing, we showcase the generation of arbitrary waveforms in the time domain. This scalable design and control scheme holds promise in meeting future demands for high-precision spectral shaping capabilities. © The Author(s) 2024.

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