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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Jieming Zhou Tong Zhang Pengfei Fang Lars Petersson Mehrtash Harandi College of Engineering and Computer Science Australian National University Canberra Australia Image and Visual Representation Laboratory EPFL Lausanne Switzerland Imaging and Computer Vision Group CSIRO Canberra Australia Department of Electrical and Computer Systems Engineering Monash University Melbourne Australia

Prior to the introduction of Graph Neural Networks (GNNs), modelling and analyzing irregular data, particularly graphs, was thought to be the Achilles’ heel of deep learning. The core concept of GNNs is to find a representation by recursively aggregating the representations of a central node and those of its neighbor, and many GNNs’ designs have demonstrated its success. However, most only focus on using the first-order information between a node and its neighbors. In this paper, we introduce a central node permutation variant function through a frustratingly simple and innocent-looking modification to the core operation of a GNN, namely the Feature cOrrelation aGgregation (FOG) module, which learns the second-order information from feature correlation between a node and its neighbors in the pipeline. By adding FOG into existing variants of GNNs, we empirically verify this second-order information complements the features generated by original GNNs across a broad set of benchmarks. A tangible boost in the model’s performance is observed where the model surpasses previous state-of-the-art results by a significant margin while employing fewer parameters. e.g., 26.202% improvement on a real-world molecular dataset using graph convolutional networks.

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cVTS: A Constrained Voronoi Tree Search Method for High Dimensional Analog Circuit Synthesis

cVTS: A Constrained Voronoi Tree Search Method for High Dime...

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Design Automation Conference

作者： Aidong Zhao Xianan Wang Zixiao Lin Zhaori Bi Xudong Li Changhao Yan Fan Yang Li Shang Dian Zhou Xuan Zeng Microelectronics Department State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China Department of Electrical Engineering University of Texas Dallas USA Department of Electrical and Computer Engineering University of California San Diego USA School of Data Science Fudan University Shanghai China School of Computer Science Fudan University Shanghai China

A constrained Voronoi tree-based domain decomposition method for high-dimensional Bayesian optimization is proposed to solve large scale analog circuit synthesis problems, which can be formulated as high-dimensional heterogeneous black-box optimization. Hierarchical Voronoi tree progressively breaks down the design space into partitions with implicit performance boundaries such that promising regions are efficiently explored. Fast exploitation is ensured in Voronoi nest via local Bayesian optimization with a few observations. A slice-enhanced Gibbs sampling method is proposed to sample acquisition function cMES in irregular polyhedrons with design constraints. Compared with state-of-the-art methods, cVTS achieves significant speed up without loss of accuracy.

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Reversible Data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

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On the Effects and Optimal Design of Redundant Sensors in Collaborative State Estimation

arXiv

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arXiv 2023年

作者： Ren, Yunxiao Duan, Zhisheng Duan, Peihu Shi, Ling The State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

The existence of redundant sensors in collaborative state estimation is a common occurrence, yet their true significance remains elusive. This paper comprehensively investigates the effects and optimal design of redundant sensors in sensor networks that use Kalman filtering to estimate the state of a random process collaboratively. The paper presents two main results: a theoretical analysis of the effects of redundant sensors and an engineering-oriented optimal design of redundant sensors. In the theoretical analysis, the paper leverages Riccati equations and Symplectic matrix theory to unveil the explicit role of redundant sensors in cooperative state estimation. The results unequivocally demonstrate that the addition of redundant sensors enhances the estimation performance of the sensor network, aligning with the principle of "more is better". Moreover, the paper establishes a precise sufficient and necessary condition to assess whether the inclusion of redundant sensors improves the overall estimation performance. Moving towards engineering-oriented design optimization, the paper proposes a novel algorithm to tackle the optimal design problem of redundant sensors, and the convergence of the proposed algorithm is guaranteed. Numerical simulations are provided to demonstrate the results. Copyright © 2023, The Authors. All rights reserved.

关键词： Kalman filters

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CONTINUOUS-TIME IDENTIFICATION OF DYNAMIC STATE-SPACE MODELS BY DEEP SUBSPACE ENCODING

arXiv

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arXiv 2022年

作者： Beintema, Gerben I. Schoukens, Maarten Tóth, Roland Department of Electrical Engineering Eindhoven University of Technology Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Continuous-time (CT) modeling has proven to provide improved sample efficiency and interpretability in learning the dynamical behavior of physical systems compared to discrete-time (DT) models. However, even with numerous recent developments, the CT nonlinear state-space (NL-SS) model identification problem remains to be solved in full, considering common experimental aspects such as the presence of external inputs, measurement noise, latent states, and general robustness. This paper presents a novel estimation method that addresses all these aspects and that can obtain state-of-the-art results on multiple benchmarks with compact fully connected neural networks capturing the CT dynamics. The proposed estimation method called the subspace encoder approach (SUBNET) ascertains these results by efficiently approximating the complete simulation loss by evaluating short simulations on subsections of the data, by using an encoder function to estimate the initial state for each subsection and a novel state-derivative normalization to ensure stability and good numerical conditioning of the training process. We prove that the use of subsections increases cost function smoothness together with the necessary requirements for the existence of the encoder function and we show that the proposed state-derivative normalization is essential for reliable estimation of CT NL-SS models. © 2022, CC BY.

关键词： Cost functions

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Outage and DMT Analysis of Partition-based Schemes for RIS-aided MIMO Fading Channels

arXiv

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arXiv 2023年

作者： Nicolaides, Andreas Psomas, Constantinos Kraidy, Ghassan M. Yang, Sheng Krikidis, Ioannis The IRIDA Research Centre for Communication Technologies Department of Electrical and Computer Engineering University of Cyprus Cyprus The Department of Electronic Systems Norwegian University of Science and Technology Norway The Laboratory of Signals and Systems CentraleSupélec Paris-Saclay University France

In this paper, we investigate the performance of multiple-input multiple-output (MIMO) fading channels assisted by a reconfigurable intelligent surface (RIS), through the employment of partition-based RIS schemes. The proposed schemes are implemented without requiring any channel state information knowledge at the transmitter side;this characteristic makes them attractive for practical applications. In particular, the RIS elements are partitioned into sub-surfaces, which are periodically modified in an efficient way to assist the communication. Under this framework, we propose two low-complexity partition-based schemes, where each sub-surface is adjusted by following an amplitude-based or a phase-based approach. Specifically, the activate-reflect (AR) scheme activates each sub-surface consecutively, by changing the reflection amplitude of the corresponding elements. On the other hand, the flip-reflect (FR) scheme adjusts periodically the phase shift of the elements at each sub-surface. Through the sequential reconfiguration of each sub-surface, an equivalent parallel channel in the time domain is produced. We analyze the performance of each scheme in terms of outage probability and provide expressions for the achieved diversity-multiplexing tradeoff. Our results show that the asymptotic performance of the considered network under the partition-based schemes can be significantly enhanced in terms of diversity gain compared to the conventional case, where a single partition is considered. Moreover, the FR scheme always achieves the maximum multiplexing gain, while for the AR scheme this maximum gain can be achieved only under certain conditions with respect to the number of elements in each sub-surface. © 2023, CC BY.

关键词： Fading channels

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Exploring Spatial Hybrid User Interface for Visual Sensemaking

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IEEE Transactions on Visualization and computer Graphics 2025年 PP卷 PP页

作者： Tong, Wai Li, Haobo Xia, Meng Kam-Kwai, Wong Pong, Ting-Chuen Qu, Huamin Yang, Yalong Nanchang University School of Mathematics and Computer Sciences Nanchang 330031 China Shenzhen University School of Architecture & Urban Planning Shenzhen 518060 China University of Calgary Department of Geomatics Engineering Calgary T2N 1N4 Canada Wuhan University School of Geodesy and Geomatics Wuhan 430079 China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan 430079 China Finnish Geospatial Research Institute Department of Remote Sensing and Photogrammetry Masala 02430 Finland Wuhan University of Technology School of Safety Science and Emergency Management Wuhan 430079 China Nanjing University of Information Science & Technology School of Remote Sensing & Geomatics Engineering Nanjing 210000 China Nanjing Forestry University College of Civil Engineering Nanjing 210037 China Guangzhou Urban Planning & Design Survey Research Institute Guangzhou 51100 China

We built a spatial hybrid system that combines a personal computer (PC) and virtual reality (VR) for visual sensemaking, addressing limitations in both environments. Although VR offers immense potential for interactive data visualization (e.g., large display space and spatial navigation), it can also present challenges such as imprecise interactions and user fatigue. At the same time, a PC offers precise and familiar interactions but has limited display space and interaction modality. Therefore, we iteratively designed a spatial hybrid system (PC+VR) to complement these two environments by enabling seamless switching between PC and VR environments. To evaluate the system's effectiveness and user experience, we compared it to using a single computing environment (i.e., PC-only and VR-only). Our study results (N=18) showed that spatial PC+VR could combine the benefits of both devices to outperform user preference for VR-only without a negative impact on performance from device switching overhead. Finally, we discussed future design implications. © 1995-2012 IEEE.

关键词： Data visualization

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Iterative Random Training Sampling Convolutional Neural Network for Hyperspectral Image Classification

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IEEE Transactions on Geoscience and remote sensing 2023年 61卷

作者： Chang, Chein-I Liang, Chia-Chen Hu, Peter Fuming University of Maryland Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering BaltimoreMD21250 United States National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan Providence University Department of Computer Science and Information Management Taichung02912 Taiwan University of Maryland School of Medicine R. A. Cowley Shock Trauma Center Shock Trauma Anesthesia Organized Research Center Department of Anesthesia BaltimoreMD21201 United States

Convolutional neural network (CNN) has received considerable interest in hyperspectral image classification (HSIC) lately due to its excellent spectral-spatial feature extraction capability. To improve CNN, many approaches have been directed at exploring the infrastructure of its network by introducing different paradigms. This article takes a rather different approach by developing an iterative CNN that extends a CNN by including a feedback system to repeatedly process the same CNN in an iterative manner. Its idea is to take advantage of a recently developed iterative training sampling spectral-spatial classification (IRTS-SSC) that allows CNN to update its spatial information of classification maps through a feedback spatial fil- tering system via IRTS. The resulting CNN is called iterative random training sampling CNN (IRTS-CNN) with several unique features. First, IRTS-CNN combines CNN and IRTS-SSC into one paradigm, an architecture that has never been investigated in the past. Second, it implements a series of spatial filters to capture spatial information of classified data samples and further feeds this information back via an iterative process to expand the current input data cube for the next iteration. Third, it utilizes the expanded data cube to randomly reselect training samples and then to reimplement CNN iteratively. Last but not least, IRTS-CNN provides a general framework that can implement any arbitrary CNN as an initial classifier to improve its performance through an iterative process. Extensive experiments are conducted to demonstrate that IRTS-CNN indeed significantly improves CNN, specifically when only a small size of limited training samples is used. © 1980-2012 IEEE.

关键词： Iterative methods

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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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Thick Film Interdigitated Electrode Graphite and Carbon Nanotube on Flexible Substrate for Hydrogen Gas Detection

Thick Film Interdigitated Electrode Graphite and Carbon Nano...

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2023 IEEE International Conference on Sensors and Nanotechnology, SENNANO 2023

作者： Abu Bakar, Azlinda Nizar Hamidon, Mohd Yaacob, Mohd Hanif Paiman, Suriati Jaafar, Haslina Wan Jusoh, Wan Nursheila Universiti Putra Malaysia Funtional Device Laboratory Institute of Nanoscience and Nanotechnology Selangor Serdang Malaysia Universiti Putra Malaysia Faculty of Engineering Department of Computer and Communication Systems Selangor Serdang Malaysia Universiti Putra Malaysia Faculty of Science Department of Physics Selangor Serdang Malaysia Universiti Putra Malaysia Faculty of Engineering Department of Electrical and Electronic Selangor Serdang Malaysia Universiti Kuala Lumpur Section of Aerospace Malaysians Institute of Aviation Technology Selangor Dengkil Malaysia

ISBN: (纸本)9798350333312

The comparison on the performance of interdigitated electrode (IDE) graphite and carbon nanotube (CNT) using titanium dioxide-multiwall carbon nanotube (TiO2-MWCNT) composite as sensing materials to detect hydrogen gas is presented in this research work. Screen printing technique has been selected to deposited IDE graphite and CNT conductive pastes as the electrode thick film on a flexible substrate, which is continued by firing at 120°C each. TiO2-MWCNT paste was printed as a sensing layer on top of IDE, followed by firing at 350°C. FESEM was used to characterize the morphological analysis of the IDE film. The operating temperature was varied at 27°C, 50°C and 100°C and concentration varied from 100-1000 ppm for gas hydrogen. The gas sensor demonstrated as n-type gas characteristics where the response of the gas sensor produced increased current from the results. The results purpose when the sensor was exposed to hydrogen gas. Therefore, the ability of IDE graphite and CNT as an electrode to harvest the electron from sensing materials were successfully proven. The IDE CNT showed better response compare to IDE graphite, but both electrodes were able to sense 100 - 1000 ppm at the operating temperature of 27°C, 50°C and 100°C. The optimal of operating temperature for IDE graphite is 50°C and IDE CNT is at room temperature for the hydrogen gas detections in this research work. © 2023 IEEE.

关键词： Thick films

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