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Stochastic Event-triggered Variational Bayesian Filtering

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arXiv 2022年

作者： Lv, Xiaoxu Duan, Peihu Duan, Zhisheng Chen, Guanrong Shi, Ling The State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China Department of Electrical Engineering City University of Hong Kong Hong Kong Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

This paper proposes an event-triggered variational Bayesian filter for remote state estimation with unknown and time-varying noise covariances. After presetting multiple nominal process noise covariances and an initial measurement noise covariance, a variational Bayesian method and a fixed-point iteration method are utilized to jointly estimate the posterior state vector and the unknown noise covariances under a stochastic event-triggered mechanism. The proposed algorithm ensures low communication loads and excellent estimation performances for a wide range of unknown noise covariances. Finally, the performance of the proposed algorithm is demonstrated by tracking simulations of a vehicle. Copyright © 2022, The Authors. All rights reserved.

关键词： Kalman filters

Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark

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arXiv 2022年

作者： Afshar, Parnian Mohammadi, Arash Plataniotis, Konstantinos N. Farahani, Keyvan Kirby, Justin Oikonomou, Anastasia Asif, Amir Wee, Leonard Dekker, Andre Wu, Xin Haque, Mohammad Ariful Hossain, Shahruk Hasan, Md. Kamrul Kamal, Uday Hsu, Winston Lin, Jhih-Yuan Rahman, M. Sohel Ibtehaz, Nabil Foisol, Sh.M. Amir Lam, Kin-Man Guang, Zhong Zhang, Runze Channappayya, Sumohana S. Gupta, Shashank Dev, Chander The Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada The Department of Electrical and Computer Engineering University of Toronto Toronto Canada RockvilleMD United States The Frederick National Laboratory for Cancer Research FrederickMD United States The Department of Medical Imaging Sunnybrook Health Sciences Centre University of Toronto Toronto Canada The Electrical and Computer Engineering Department Concordia University Montreal Canada The Department of Radiation Oncology MAASTRO Clinic Maastricht Netherlands Maastricht University Maastricht Netherlands The Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh The Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan The Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh The Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong India

Lung cancer is one of the deadliest cancers, and in part its effective diagnosis and treatment depend on the accurate delineation of the tumor. Human-centered segmentation, which is currently the most common approach, is subject to inter-observer variability, and is also time-consuming, considering the fact that only experts are capable of providing annotations. Automatic and semi-automatic tumor segmentation methods have recently shown promising results. However, as different researchers have validated their algorithms using various datasets and performance metrics, reliably evaluating these methods is still an open challenge. The goal of the Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark created through 2018 IEEE Video and Image Processing (VIP) Cup competition, is to provide a unique dataset and pre-defined metrics, so that different researchers can develop and evaluate their methods in a unified fashion. The 2018 VIP Cup started with a global engagement from 42 countries to access the competition data. At the registration stage, there were 129 members clustered into 28 teams from 10 countries, out of which 9 teams made it to the final stage and 6 teams successfully completed all the required tasks. In a nutshell, all the algorithms proposed during the competition, are based on deep learning models combined with a false positive reduction technique. Methods developed by the three finalists show promising results in tumor segmentation, however, more effort should be put into reducing the false positive rate. This competition manuscript presents an overview of the VIP-Cup challenge, along with the proposed algorithms and results. © 2022, CC BY.

关键词： computerized tomography

Object Detection, Recognition, Deep Learning, and the Universal Law of Generalization

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arXiv 2022年

作者： Rustom, Faris B. Öğmen, Haluk Yazdanbakhsh, Arash Department of Psychological and Brain Sciences Computational Neuroscience and Vision Lab Center for Systems Neuroscience Boston University BostonMA United States Department of Electrical & Computer Engineering Laboratory of Perceptual and Cognitive Dynamics University of Denver DenverCO United States

Object detection and recognition are fundamental functions underlying the success of species. Because the appearance of an object exhibits a large variability, the brain has to group these different stimuli under the same object identity, a process of generalization. Does the process of generalization follow some general principles or is it an ad-hoc "bag-of-tricks"? The Universal Law of Generalization provided evidence that generalization follows similar properties across a variety of species and tasks. Here we test the hypothesis that the internal representations underlying generalization reflect the natural properties of object detection and recognition in our environment rather than the specifics of the system solving these problems. By training a deep-neural-network with images of "clear" and "camouflaged" animals, we found that, with a proper choice of category prototypes, the generalization functions are monotone decreasing, similar to the generalization functions of biological systems. Our findings support the hypothesis of the study. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

SPP-CNN: An Efficient Framework for Network Robustness Prediction

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arXiv 2023年

作者： Wu, Chengpei Lou, Yang Wang, Lin Li, Junli Li, Xiang Chen, Guanrong The College of Computer Science Sichuan Normal University Chengdu610066 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks. This kind of network robustness is typically measured by the time-consuming attack simulation, which returns a sequence of values that record the remaining connectivity and controllability after a sequence of node- or edge-removal attacks. For improvement, this paper develops an efficient framework for network robustness prediction, the spatial pyramid pooling convolutional neural network (SPP-CNN). The new framework installs a spatial pyramid pooling layer between the convolutional and fully-connected layers, overcoming the common mismatch issue in the CNN-based prediction approaches and extending its generalizability. Extensive experiments are carried out by comparing SPP-CNN with three state-of-the-art robustness predictors, namely a CNN-based and two graph neural networks-based frameworks. Synthetic and real-world networks, both directed and undirected, are investigated. Experimental results demonstrate that the proposed SPP-CNN achieves better prediction performances and better generalizability to unknown datasets, with significantly lower time-consumption, than its counterparts. © 2023, CC BY.

关键词： Complex networks

Equalizer zero-determinant strategy in discounted repeated Stackelberg asymmetric game

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arXiv 2023年

作者： Zhaoyang, Cheng Guanpu, Chen Yiguang, Hong Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai210201 China

This paper focuses on the performance of equalizer zero-determinant (ZD) strategies in discounted repeated Stackerberg asymmetric games. In the leader-follower adversarial scenario, the strong Stackelberg equilibrium (SSE) deriving from the opponents’ best response (BR), is technically the optimal strategy for the leader. However, computing an SSE strategy may be difficult since it needs to solve a mixed-integer program and has exponential complexity in the number of states. To this end, we propose to adopt an equalizer ZD strategy, which can unilaterally restrict the opponent’s expected utility. We first study the existence of an equalizer ZD strategy with one-to-one situations, and analyze an upper bound of its performance with the baseline SSE strategy. Then we turn to multi-player models, where there exists one player adopting an equalizer ZD strategy. We give bounds of the sum of opponents’ utilities, and compare it with the SSE strategy. Finally, we give simulations on unmanned aerial vehicles (UAVs) and the moving target defense (MTD) to verify the effectiveness of our approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Equalizers

Topological Hyperbolic Lattices

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Physical Review Letters 2020年第5期125卷 053901-053901页

作者： Sunkyu Yu Xianji Piao Namkyoo Park Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul 08826 Korea

Non-Euclidean geometry, discovered by negating Euclid’s parallel postulate, has been of considerable interest in mathematics and related fields for the description of geographical coordinates, Internet infrastructures, and the general theory of relativity. Notably, an infinite number of regular tessellations in hyperbolic geometry—hyperbolic lattices—are expected to extend Euclidean Bravais lattices and the consequent wave phenomena to non-Euclidean geometry. However, topological states of matter in hyperbolic lattices have yet to be reported. Here we investigate topological phenomena in hyperbolic geometry, exploring how the quantized curvature and edge dominance of the geometry affect topological phases. We report a recipe for the construction of a Euclidean photonic platform that inherits the topological band properties of a hyperbolic lattice under a uniform, pseudospin-dependent magnetic field, realizing a non-Euclidean analog of the quantum spin Hall effect. For hyperbolic lattices with different quantized curvatures, we examine the topological protection of helical edge states and generalize Hofstadter’s butterfly, by employing two empirical parameters that measure the edge confinement and defect immunity. We demonstrate that the proposed platforms exhibit the unique spectral-magnetic sensitivity of topological immunity in highly curved hyperbolic planes. Our approach is applicable to general non-Euclidean geometry and enables the exploitation of infinite lattice degrees of freedom for band theory.

关键词： Quantum spin Hall effect Topological effects in photonic systems Topological phases of matter Geometry

Spanning trees of recursive scale-free graphs

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Physical Review E 2022年第2期105卷 024312-024312页

作者： C. Tyler Diggans Erik M. Bollt Daniel ben-Avraham Clarkson Center for Complex Systems Science Clarkson University Potsdam New York 13699 USA Department of Physics Clarkson University Potsdam New York 13699 USA Air Force Research Laboratory: Information Directorate Rome New York 13441 USA Department of Electrical and Computer Engineering Clarkson University Potsdam New York 13699 USA

We present a link-by-link rule-based method for constructing all members of the ensemble of spanning trees for any recursively generated, finitely articulated graph, such as the Dorogovtsev-Goltsev-Mendes (DGM) net. The recursions allow for many large-scale properties of the ensemble of spanning trees to be analytically solved exactly. We show how a judicious application of the prescribed growth rules selects for certain subsets of the spanning trees with particular desired properties (small world, extended diameter, degree distribution, etc.), and thus approximates and/or provides solutions to several optimization problems on undirected and unweighted networks. The analysis of spanning trees enhances the usefulness of recursive graphs as sophisticated models for everyday life complex networks.

关键词： Coupled oscillators Exhaustive network search Network formation & growth Network optimization

A2G Channel Measurement and Characterization via TNN for UAV Multi-Scenario Communications

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A2G Channel Measurement and Characterization via TNN for UAV...

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Kai Mao Qiuming Zhu Fuqiao Duan Yanheng Qiu Maozhong Song Wei Fan Yang Miao The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China The Radio Systems Faculty of Electrical Engineering Computer Science and Mathematics (EEMCS) University of Twente The Netherlands The Department of Electronic Systems Aalborg University Aalborg Denmark

ISBN: (纸本)9781665435413

Unmanned aerial vehicle (UAV) is considered as an important component for future communication networks. In this paper, an air-to-ground (A2G) channel sounder is designed and implemented for UAV communication channel measurement and characterization. The channel impulse response (CIR) extraction is implemented on a field programmable gate array (FPGA) to improve extraction efficiency. Based on the channel characteristics under measured (or baseline) scenarios, a transfer learning neural network (TNN) framework is also proposed to predict the channel characteristics of other unmeasured (or transferred) scenarios. In the proposed framework, the baseline matrices of neural network parameters are obtained from the measurement data of baseline scenarios. The ray tracing (RT) simulation data is only used to obtain the extrapolation matrices where we utilize imperfect digital map and do not require a highly accurate RT simulation. Then the neural network driven by the baseline and extrapolation matrices is used to predict the channel characteristics of transferred scenarios. To verify the proposed prediction method, the channel characteristics including path loss, K-factor, and root mean square delay spread of a near-urban scenario are firstly measured. Then, the corresponding channel characteristics of a transferred dense-urban scenario are predicted by the proposed TNN method and validated by the measurement data. It is shown that the predicted channel characteristics are well consistent with the measured ones.

关键词： Extrapolation Neural networks Transfer learning Prediction methods Ray tracing Autonomous aerial vehicles Loss measurement

Joint Optimization of Wind Farm Layout Considering Optimal Control

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arXiv 2021年

作者： Chen, Kaixuan Lina, Jin Qiu, Yiwei Liu, Feng Song, Yonghua State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing100087 China Department of Electrical and Computer Engineering University of Macau 999078 China

The wake effect is one of the leading causes of energy losses in offshore wind farms (WFs). Both turbine placement and cooperative control can influence the wake interactions inside the WF and thus the overall WF power production. Traditionally, greedy control strategy is assumed in the layout design phase. To exploit the potential synergy between the WF layout and control so that a system-level optimal layout can be obtained with the greatest energy yields, the layout optimization should be performed with cooperative control considerations. For this purpose, a novel two-stage WF layout optimization model is developed in this paper. Cooperative WF control of both turbine yaw and axis-induction are considered in the WF layout design. However, the integration of WF control makes the layout optimization much more complicated and results in a large-scale nonconvex problem, hindering the application of current layout optimization methods. To increase the computational efficiency, we leverage the hierarchy and decomposability of the joint optimization problem and design a decomposition-based hybrid method (DBHM). Instead of manipulating all the variables simultaneously, the joint optimization problem is decomposed into several subproblems and their coordination. Case studies are carried out on different WFs. It is shown that WF layouts with higher energy yields can be obtained by the proposed joint optimization compared to traditional separate layout optimization. Moreover, the computational advantages of the proposed DBHM on the considered joint layout optimization problem are also demonstrated. © 2021, CC BY.

关键词： Electric utilities