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Electronic Information engineering and Computer Technology (EIECT), International Conference on

作者： Chengzhou Liu Jianhui Wang Bin Ba Hao Li Jin Zhang Qiang Jin National Digital Switching System Engineering & Technological Research Center Zhengzhou China Zhengzhou Xinda Institute of Advanced Technology Zhengzhou China

Aiming at the problem that the estimation error of wireless channel parameters in complex electromagnetic environment is too large to meet the full rank channel matrix required for signal separation, this paper proposes a signal separation method based on reconfigurable intelligent surface (RIS) assisted channel regulation. The core idea of the method is to use the ability of RIS to reshape wireless channel parameters, and to achieve signal separation by actively designing the elements in the equivalent channel matrix to make the channel matrix full rank. Theoretical analysis and experimental simulation show that RIS array has the ability to adjust and control channel parameters, and the ability to modify wireless channel parameters can be used to achieve the separation of signals with the same frequency.

关键词： Wireless communication Estimation error Source separation Costs Array signal processing Transmitters Channel estimation

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Virtual Sensor for Door Opening Detection and Anomaly Detection Using Machine Learning

Virtual Sensor for Door Opening Detection and Anomaly Detect...

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International Conference on Intelligent Systems Applications to Power Systems (ISAP)

作者： Almir Neto Luis Gomes Zita Vale Federal Institute of Education Science and Technology of Maranhão (IFMA) São Luís Brazil GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto (P.PORTO) Porto Portugal

ISBN: (数字)9798331531751

ISBN: (纸本)9798331531768

Currently, several studies are being developed using machine Learning with the aim of replacing real sensors with virtual sensors. One of the biggest challenges is obtaining quality data to enable the system modeling. This article proposes the replacement of a light dependent resistor sensor, applied to a refrigerator, which detects whether the door is open or closed and an anomaly test when the door is forgotten open for too long. The use of tiny machine learning is applied to ESP32 to enable the use of regression and classification models to detect the door opening and anomaly. The tested metrics for the regression were the root mean squared error. The comparative results between the virtual and real sensors are satisfactory with an accuracy of 99.59% for classification and a root mean squared error value of 0.01 for regression. Furthermore, a prototype was developed, and the model was embedded in the ESP32 microcontroller.

关键词： Temperature sensors Temperature measurement Microcontrollers Soft sensors Tiny machine learning Neural networks Prototypes Virtualization Intelligent sensors Anomaly detection

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Two-dimensional DOA estimation based on non-circular signals from a tensor perspective

Two-dimensional DOA estimation based on non-circular signals...

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International Conference on Communication Technology (ICCT)

作者： Hao Li Bin Ba Jin Zhang Chengzhou Liu Qiang Jin Liye Zhang National Digital Switching System Engineering & Technological Research Center Zhengzhou China Zhengzhou Xinda Institute of Advanced Technology Zhengzhou China

Coprime planar array (CPA) based on non-circular signals can perform direction-of-arrival (DOA) estimation with high degrees of freedom, but most of the algorithms are based on matrices, which may obscure the structural information of the data and reduce the accuracy of DOA estimation. Aiming at this problem, we propose a DOA estimation algorithm for non-circular signals based on tensor modeling, which realizes high accuracy estimation. The algorithm models the received signal with tensors to obtain the tensor covariance with non-circular signal information. Then the structural covariance tensor is obtained by merging dimensions with the same direction factor by tensor expansion. The tensor decomposition of the structural covariance tensor is then performed to obtain a matrix factor with direction information. Finally the DOAs of the signals are obtained by searching for the spectral peak. Simulation experiments show that under the same conditions, the RMSE performance of the proposed algorithm is better than that of the comparison algorithm, and it has higher angular resolution.

关键词： Tensors Direction-of-arrival estimation Transmission line matrix methods Merging Estimation Planar arrays Communications technology

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Urban Damage Detection Using Temporally Stacked Synthetic Aperture Radar Interferometric Coherence

Urban Damage Detection Using Temporally Stacked Synthetic Ap...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Ryo Natsuaki Ryu Sugimoto Masanobu Shimada Chiaki Tsutsumi Ryosuke Nakamura Department of Electrical Engineering and Information Systems The University of Tokyo Japan Digital Architecture Research Center National Institute of Advanced Industrial Science and Technology Japan Division of Architectural Civil and Environmental Engineering Tokyo Denki University Japan

In this paper, we propose a novel disaster damage detection method using Synthetic Aperture Radar (SAR) interferometric analysis. SAR interferometric coherence analysis is an effective method for disaster monitoring especially in the urban area, where the amplitude of SAR image changes only slightly unless the damage level is high. One drawback of the interferometric coherence-based analysis is the existence of the Cramér-Rao lower bound. That is, a small spatial window for its ensemble averaging leads significant bias while a large window makes its spatial resolution worse. To solve this problem, we propose to extend the ensemble average window towards temporal domain by increasing the number of interferometric pairs. Conventional methods which use multiple interferometric pairs firstly calculate coherence values independently. Instead, the proposed method unifies the interferograms first. We report some preliminary experimental results showing the effectiveness of the proposed method.

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Exploring light-cone distribution amplitudes from quantum computing

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Science China(Physics,Mechanics & Astronomy) 2023年第8期66卷 57-65页

作者： Tianyin Li Xingyu Guo Wai Kin Lai Xiaohui Liu Enke Wang Hongxi Xing Dan-Bo Zhang Shi-Liang Zhu Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum MatterSouth China Normal UniversityGuangzhou 510006China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University.Guangzhou 510006China Department of Physics and Astronomy University of CaliforniaLos Angeles 90095USA Center of Advanced Quantum Studies Department of PhysicsBeijing Normal UniversityBejing 100875China Center for High Energy Physics Peking UniversityBeijing 100871China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Frontier Research Institute for PhysicsSouth China Normal UniversityGuangzhou 510006China Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials School of Physics and Telecommunication EngineeringSouth China Normal UniversityGuangzhou 510006China

Light-cone distribution amplitudes(LCDAs)are essential nonperturbative quantities for theoretical predictions of exclusive highenergy processes in quantum chromodynamics(QCD).We demonstrate the prospect of calculating LCDAs on a quantum computer by applying a recently proposed quantum algorithm,with staggered fermions,to the simulation of the LCDA in the(1+1)-dimensional Nambu-Jona-Lasinio(NJL)model on classical *** agreement between the result from the classical simulation of the quantum algorithm and that from exact diagonalization justifies the proposed quantum *** find that the resulting LCDA in the NJL model exhibits features shared with the LCDAs obtained from the QCD.

关键词： light-cone distribution amplitude quantum algorithm NJL model

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Physics informed neural network (PINN) for noise-robust phase-based magnetic resonance electrical properties tomography 3

Physics informed neural network (PINN) for noise-robust phas...

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3rd URSI Atlantic and Asia Pacific Radio Science Meeting, AT-AP-RASC 2022

作者： Garcia Inda, Adan J. Huang, Shao Y. Immamoglu, Nevrez Yu, Wenwei Chiba University Department of Medical Engineering Chiba Japan National University of Singapore Department of Surgery Singapore Singapore Singapore University of Technology and Design Engineering Product Development Department Singapore Singapore National Institute of Advanced Industrial Science and Technology Digital Architecture Research Center Tokyo Japan Chiba University Center for Frontier Medical Engineering Chiba Japan

ISBN: (纸本)9789463968058

Electrical properties could become a source of contrast for non-calcified tumor tissues. Magnetic Resonance Electrical Property Tomography (MREPT) relies on numerical differentiation to solve the partial differential equations (PDEs) to reconstruct electrical properties. However, the numerical differentiation for derivatives produces artifacts near tissue edges and amplifies noise. In this work, Physics-informed neural networks (PINNs), are employed to estimate derivatives for MREPT with automatic differentiation enabled by neural networks to improve noise robustness and reduce edge artifacts. PINNs require the balance of various loss functions and information from collocation points of ground truth and image boundary to learn to estimate the derivatives. In this work, a warm-up mechanism is used to reduce the complexity of the multiple loss balance. Results showed that even with only three collocation points and boundary conditions, reconstructions can be made from noise-contaminated source images (SNR 50). This proves that PINNs achieved noise-robust estimate of partial derivatives, thus leding to noise-robust conductivity reconstruction. © 2022 URSI.

关键词： Magnetic resonance

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CoMT: A Novel Benchmark for Chain of Multi-modal Thought on Large Vision-Language Models 39

CoMT: A Novel Benchmark for Chain of Multi-modal Thought on ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Cheng, Zihui Chen, Qiguang Zhang, Jin Fei, Hao Feng, Xiaocheng Che, Wanxiang Li, Min Qin, Libo School of Computer Science and Engineering Central South University China Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University China Research Center for SCIR Harbin Institute of Technology Harbin China National University of Singapore Singapore

ISBN: (纸本)157735897X

Large Vision-Language Models (LVLMs) have recently demonstrated amazing success in multi-modal tasks, including advancements in Multi-modal Chain-of-Thought (MCoT) reasoning. Despite these successes, current benchmarks still follow a traditional paradigm with multi-modal input and text-modal output, which leads to significant drawbacks such as missing visual operations and vague expressions. Motivated by this, we introduce a novel Chain of Multi-modal Thought (CoMT) benchmark to address these limitations. Different from the traditional MCoT benchmark, CoMT requires both multi-modal input and multi-modal reasoning output, aiming to mimic human-like reasoning that inherently integrates visual operations. Specifically, CoMT consists of four categories: (1) Visual Creation, (2) Visual Deletion, (3) Visual Update, and (4) Visual Selection to comprehensively explore complex visual operations and concise expression in real scenarios. We evaluate various LVLMs and strategies on CoMT, revealing some key insights into the capabilities and limitations of the current approaches. We hope that CoMT can inspire more breakthroughs on introducing multi-modal generation into the reasoning process. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Modal analysis

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Unmanned aerial vehicle swarm mission reliability modeling and evaluation method oriented to systematic and networked mission

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Chinese Journal of Aeronautics 2021年第2期34卷 466-478页

作者： Lizhi WANG Xuejiao ZHAO Yuan ZHANG Xiaohong WANG Tielin MA Xia GAO Unmanned System Institute Beihang UniversityBejing 100083China School of Reliability and Systems Engineering Beihang UniversityBeijing 100083China Digital Twinning and System Engineering Research Center Yunnan Innovation InstituteBeihang UniversityBejing 650233China Key Laboratory of Advanced Technology of Intelligent Unmanned Flight System of M inistry of Industry and Information Technology Beijing 100083China School of Astronautics Beihang UniversityBejing 100083China

With the development of Unmanned Aerial Vehicle(UAV) system autonomy, network communication technology and group intelligence theory, mission execution in the form of a UAV swarm will be an important realization of future applications. Traditional single-UAV mission reliability modeling methods have been unable to meet the requirements of UAV swarm mission reliability modeling. Therefore, the UAV swarm mission reliability modeling and evaluation method is proposed. First, aimed at the interdependence among the multiple layers, a multi-layer network model of a UAV swarm is established. At the same time, based on the system having the following characteristics—using a mission chain to complete the mission and applying the connectivity of the mission network—the mission network model of a UAV swarm is established. Second, vulnerability and connectivity are selected as two indicators to reflect the reliability of the mission, and aimed at random attack and deliberate attack, vulnerability and connectivity evaluation methods are proposed. Finally, the validity and accuracy of the constructed model are verified through simulations,and the model and selected indicators can meet the reliability requirements of the UAV swarm mission. In this way, this study provides quantitative reference for UAV-swarm-related decisionmaking work and supports the development of UAV-swarm-related work.

关键词： Complex networks Connectivity Mission reliability Unmanned Aerial Vehicles(UAV) Vulnerability

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Tip the Scales: Achieving Balance in Adversarial Examples Across Modalities

Tip the Scales: Achieving Balance in Adversarial Examples Ac...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhenbo Shi Zhidong Yu Yuxuan Zhang Shuchang Wang Xiaoman Liu Wei Yang Liusheng Huang School of Computer Science and Technology University of Science and Technology of China Hefei China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Laboratory for Advanced Computing and Intelligence Engineering Wuxi China School of Software Engineering University of Science and Technology of China Suzhou China Hefei National Laboratory University of Science and Technology of China Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the field of multimodal learning, controlling the training of unimodal encoders from different perspectives is a primary approach to addressing Training Imbalance. However, the inherent capacity limitations of the modality affect the model’s capability. Therefore, generating adversarial examples that can achieve balanced transferability remains a challenging and perplexing problem. In this paper, we propose the InterModality Balanced Attack (MOBA) to address this problem. MOBA leverages Aggregated Modality Perturbation (AMP), which exploits the unbalanced effects of text and image perturbations to maximize the impact on the victim model. AMP capitalizes on the intrinsic feature connections between modalities during the optimization process, adjusting perturbations through Cross-Modality Discrepancy Loss to enhance attack success rates. Additionally, we devise the Transferability-Enhanced Evolution (TEE) to overcome the issue of diminished attack transferability due to model capacity limitations. TEE employs Transfer-Driven Optimization Loss to alleviate overfitting in single models, thereby enhancing the generalization ability.

关键词： Training Adaptation models Perturbation methods Architecture Buildings Signal processing Acoustics Speech processing Optimization Overfitting

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Algorithms for context-awareness route generation 11th

Algorithms for context-awareness route generation

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11th International Symposium on Ambient Intelligence, ISAmI 2020

作者： Pinto, Ricardo Conceição, Luís Marreiros, Goreti GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering Polytechnic of Porto Porto4200-072 Portugal ALGORITMI Centre University of Minho Guimarães4800-058 Portugal

ISBN: (纸本)9783030583552

This work has as its main goal the investigation and experimentation on automatic generation of routes for tourists and visitors of points of interest, considering the knowledge of the routes, the profile of the visitor and the context awareness of the tour. The context of a trip can be taken through various sources of information, such as the location of the tourist, the time of the visit, the weather conditions, as well as relevant aspects and characteristics of the user's activity and profile. The developments of this work are part of TheRoute project, and its main goal is the development of a route generation module that considers the context of the tourist, the trip and the environmental constraints. In order to solve the proposed problem, two algorithmic solutions were developed. One is an adaption of the A* algorithm with cuts, while the other is based on Ant Colony Optimization, a Swarm Intelligence algorithm. The results from the experiments allowed to conclude that the A* with cuts, oriented to the heuristic for the path with the highest score, is the one that obtains the best conjugation of results for the defined satisfaction metrics. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Ant colony optimization

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