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Combined Microwave Inverse Source and Scattering Solution: High Contrast Targets in Near Field Zone

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Combined Microwave Inverse Source and Scattering Solution: H...

URSI International Symposium on Electromagnetic Theory (EMTS)

作者： Sandra Costanzo Giuseppe Lopez Giuseppe Di Massa Department of Computer Engineering Modelling Electronics and System Science (DIMES) University of Calabria Rende Italy CNR - Institute for Electromagnetic Sensing of the Environment (IREA) Naples Italy ICEmB Inter-University National Research Center on Interactions between Electromagnetic Fields and Biosystems Genova Italy

ISBN: (数字)9798350399288

ISBN: (纸本)9798350399295

In this work, a phaseless solutions of inverse source and scattering problem is addressed. The combination of an inverse source strategy and of an inverse scattering method has been validated by considering a numerical near-field 3D scattering model, with the presence of high permittivity targets. The solution reveals the potentialities of microwaves inverse source/scattering without the use of any phase content.

关键词： Solid modeling Three-dimensional displays Inverse problems Lighting Microwave theory and techniques Numerical models Iterative methods Permittivity Image reconstruction Microwave imaging

SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data

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arXiv 2023年

作者： Fang, Meiling Huber, Marco Fierrez, Julian Ramachandra, Raghavendra Damer, Naser Alkhaddour, Alhasan Kasantcev, Maksim Pryadchenko, Vasiliy Yang, Ziyuan Huangfu, Huijie Chen, Yingyu Zhang, Yi Pan, Yuchen Jiang, Junjun Liu, Xianming Sun, Xianyun Wang, Caiyong Liu, Xingyu Chang, Zhaohua Zhao, Guangzhe Tapia, Juan Gonzalez-Soler, Lazaro Aravena, Carlos Schulz, Daniel Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Norway ID R&D Inc. New York United States School of Cyber Science and Engineering Sichuan University Chengdu China School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China Biometrics and Security Research Group Hochschule Darmstadt Darmstadt Germany I+D Vision Center Santiago Chile

This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks. © 2023, CC BY-NC-SA.

关键词： Competition

Ecological Security Evaluation Algorithm for Resource-Exhausted Cities Based on the PSR Model

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computers, Materials & Continua 2021年第10期69卷 985-1001页

作者： Xiaozhou Li Zhenggang Xu Yuanyuan Fu Qi Jin Yunlin Zhao Neal NXiong Key Laboratory of Forestry Remote Sensing Based Big Data&Ecological Security for Hunan Province Central South University of Forestry and TechnologyChangsha410004China Hunan Urban and Rural Ecological Planning and Restoration Engineering Research Center Hunan City UniversityYiyang413000China College of Information and Electronic Engineering Hunan City UniversityYiyang413000China Department of Mathematics and Computer Science Northeastern State UniversityTahlequah74464USA

Today,resource depletion threatens a number of resource-based cities in *** ecological security problem caused by the long-term exploitation of natural resources is a key issue to be solved in the development of resource-exhausted *** 23 indicators,this study evaluated the ecological security status and development trends of 21 resource-exhausted cities in China from 2011 to *** results showed that from 2011 to 2015,the overall ecological security of this type of city was low,with over 60%of the cities at an unsafe ***,ecological security improved rapidly after 2016,and by 2017,all of the cities had reached the critical safety *** top 10 indicators of ecological security included industrial sulfur dioxide emissions,water supply,agricultural fertilizer application,and forest *** 10 indicators’cumulative contribution to ecological security was 48.3%;among them,reducing industrial sulfur dioxide emissions contributed the most at 5.7%.These findings can help governments better understand the ecological security status of resource-exhausted cities,and it can provide a reference for the allocation of funds and other resources to improve the ecological safety of these cities.

关键词： Ecological security PSR model evaluation resource-exhausted cities

Video smoke removal based on low-rank tensor completion via spatial-temporal continuity constraint

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Video smoke removal based on low-rank tensor completion via ...

作者： Zhu, Hu Xu, Guoxia Liu, Lu Deng, Lizhen Jiangsu Province Key Lab on Image Processing and Image Communication Nanjing University of Posts and Telecommunication Nanjing China Department of Computer Science Norwegian University of Science and Technology Gjovik Norway National Engineering Research Center of Communication and Network Technology Nanjing University of Posts and Telecommunication Nanjing China

Smoke has a very bad effect on the outdoor vision system. Not only are the videos with poor visual effects obtained, but also the quality and structure of the videos are reduced. In this paper, we propose a video smoke removal method based on low-rank tensor completion via spatial-temporal continuity constraint. The proposed method is based on the smoke mixing model and consider the sparseness of smoke and the global and local consistency of clean video. Then, the optimal solution of the smoke removal algorithm model is quickly realized by the Alternating Direction Method of Multiplier. Finally, we evaluate the experiment results of real-world data and simulated data from the visual effects and objective indicators. And the experiment results show that our proposed algorithm can achieve better smoke removal results. © 2021 John Wiley & Sons, Ltd.

关键词： Smoke

OverlapMamba: Novel Shift State Space Model for LiDAR-based Place Recognition

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arXiv 2024年

作者： Xiang, Qiuchi Cheng, Jintao Luo, Jiehao Wu, Jin Fan, Rui Chen, Xieyuanli Tang, Xiaoyu School of Data Science and Engineering Xingzhi College South China Normal University Shanwei516600 China School of Electronic and Information Engineering South China Normal University Guangdong Foshan528225 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong College of Intelligence Science and Technology National University of Defense Technology Changsha China College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China

Place recognition is the foundation for enabling autonomous systems to achieve independent decision-making and safe operations. It is also crucial in tasks such as loop closure detection and global localization within SLAM. Previous methods utilize mundane point cloud representations as input and deep learning-based LiDAR-based Place Recognition (LPR) approaches employing different point cloud image inputs with convolutional neural networks (CNNs) or transformer architectures. However, the recently proposed Mamba deep learning model, combined with state space models (SSMs), holds great potential for long sequence modeling. Therefore, we developed OverlapMamba, a novel network for place recognition, which represents input range views (RVs) as sequences. In a novel way, we employ a stochastic reconstruction approach to build shift state space models, compressing the visual representation. Evaluated on three different public datasets, our method effectively detects loop closures, showing robustness even when traversing previously visited locations from different directions. Relying on raw range view inputs, it outperforms typical LiDAR and multi-view combination methods in time complexity and speed, indicating strong place recognition capabilities and realtime efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making

Investigating value of curriculum reinforcement learning in autonomous driving under diverse road and weather conditions

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arXiv 2021年

作者： Ozturk, Anil Gunel, Mustafa Burak Dagdanov, Resul Vural, Mirac Ekim Yurdakul, Ferhat Dal, Melih Ure, Nazim Kemal ITU Artificial Intelligence and Data Science Research Center Department of Computer Engineering Istanbul Technical University Turkey Faculty of Computer Engineering Bogazici University Turkey

Applications of reinforcement learning (RL) are popular in autonomous driving tasks. That being said, tuning the performance of an RL agent and guaranteeing the generalization performance across variety of different driving scenarios is still largely an open problem. In particular, getting good performance on complex road and weather conditions require exhaustive tuning and computation time. Curriculum RL, which focuses on solving simpler automation tasks in order to transfer knowledge to complex tasks, is attracting attention in RL community. The main contribution of this paper is a systematic study for investigating the value of curriculum reinforcement learning in autonomous driving applications. For this purpose, we setup several different driving scenarios in a realistic driving simulator, with varying road complexity and weather conditions. Next, we train and evaluate performance of RL agents on different sequences of task combinations and curricula. Results show that curriculum RL can yield significant gains in complex driving tasks, both in terms of driving performance and sample complexity. Results also demonstrate that different curricula might enable different benefits, which hints future research directions for automated curriculum training. Copyright © 2021, The Authors. All rights reserved.

关键词： Curricula

A Unified Framework for Foreground and Anonymization Area Segmentation in CT and MRI Data

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arXiv 2025年

作者： Nohel, Michal Ulrich, Constantin Suprijadi, Jonathan Wald, Tassilo Maier-Hein, Klaus Division of Medical Image Computing Heidelberg Germany Department of Biomedical Engineering Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic University hospital Ostrava Department of Deputy director for science research and education Czech Republic Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany NCT Heidelberg A partnership between DKFZ University Medical Center Heidelberg Germany Medical Faculty Heidelberg University of Heidelberg Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany

This study presents an open-source toolkit to address critical challenges in preprocessing data for self-supervised learning (SSL) for 3D medical imaging, focusing on data privacy and computational efficiency. The toolkit comprises two main components: a segmentation network that delineates foreground regions to optimize data sampling and thus reduce training time, and a segmentation network that identifies anonymized regions, preventing erroneous supervision in reconstructionbased SSL methods. Experimental results demonstrate high robustness, with mean Dice scores exceeding 98.5 across all anonymization methods and surpassing 99.5 for foreground segmentation tasks, highlighting the toolkit's efficacy in supporting SSL applications in 3D medical imaging for both CT and MRI images. The weights and code is available at https://***/MIC-DKFZ/Foreground-and- Anonymization-Area-Segmentation. © 2025, CC BY.

关键词： Self-supervised learning

Macroscopic Air-based Molecular Communication using Fluorescein for Sustainable Industrial IoT

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Macroscopic Air-based Molecular Communication using Fluoresc...

International Conference on Advanced Infocomm Technology (ICAIT)

作者： Zhiqi Xia Pengfei Du Dongxian Geng Hanlei Wang Lin Lin Arokiaswami Alphones Yanbing Yang Chen Chen School of Electrical and Electronic Engineering Nanyang Technological University Republic of Singapore Technology and Research (A*STAR) Singapore Institute of Manufacturing Technology (SIMTech) Agency for Science Singapore Republic of Singapore Analysis and Test Center of Sichuan Province Chengdu P.R. China School of Microelectronics and Communication Engineering Chongqing University P.R. China College of Computer Science Sichuan University P.R. China School of Electronics and Information Engineering Tongji University P.R. China

Sustainable Industrial Internet-of-Things (IIoT) or Green IIoT becomes an important research area in IIoT given the large increase in energy prices in the post-COVID era and the continual introduction of carbon emission regulations to industry sectors by the government. The greening of IIoT focuses on developing energy-efficient IIoT to create a sustainable environment. Despite the fact that the dominant solution to sustainable IIoT has yet to emerge, molecular communication is one of the main players to realize extremely low energy consumption for low-speed communication channels. In this paper, a two-stage molecular communication system that reused a surveillance-camera-based receiver for an IIoT scenario is envisioned, in order to achieve a stable communication channel with low Inter-Symbol Interference (ISI) while maintaining low energy consumption. A hardware-in-loop simulation is performed for feasibility check based on bit-error-rate (BER) analysis. Utilizing the correction coefficients obtained from the experiment of concentration model calibration, simulation is further conducted. Simulation results show that the proposed system is able to achieve BER of 0.04 and below in a distance of 20 m with proper configurations.

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Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation

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Incentive Mechanism Design for Unbiased Federated Learning w...

International Conference on Distributed Computing Systems

作者： Bing Luo Yutong Feng Shiqiang Wang Jianwei Huang Leandros Tassiulas Electrical and Computer Engineering Division of Natural and Applied Sciences Duke Kunshan University Kunshan China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China IBM T. J. Watson Research Center NY USA Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Department of Electrical Engineering Institute for Network Science Yale University USA

Incentive mechanism is crucial for federated learning (FL) when rational clients do not have the same interests in the global model as the server. However, due to system heterogeneity and limited budget, it is generally impractical for the server to incentivize all clients to participate in all training rounds (known as full participation). The existing FL incentive mechanisms are typically designed by stimulating a fixed subset of clients based on their data quantity or system resources. Hence, FL is performed only using this subset of clients throughout the entire training process, leading to a biased model because of data heterogeneity. This paper proposes a game-theoretic incentive mechanism for FL with randomized client participation, where the server adopts a customized pricing strategy that motivates different clients to join with different participation levels (probabilities) for obtaining an unbiased and high-performance model. Each client responds to the server's monetary incentive by choosing its best participation level, to maximize its profit based on not only the incurred local cost but also its intrinsic value for the global model. To effectively evaluate clients' contribution to the model performance, we derive a new convergence bound which analytically predicts how clients' arbitrary participation levels and their heterogeneous data affect the model performance. By solving a non-convex optimization problem, our analysis reveals that the intrinsic value leads to the interesting possibility of bi-directional payment between the server and clients. Experimental results using real datasets on a hardware prototype demonstrate the superiority of our mechanism in achieving higher model performance for the server as well as higher profits for the clients.

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