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2022 5th International Conference on Advanced Algorithms and Control engineering, ICAACE 2022

作者： Shi, Yangming Wu, Xiaopo Zhu, Ming Automation Department University of Science and Technology of China Hefei China Department of Electronic Engineering University of Science and Technology of China Hefei China

To obtain personalized outcomes for the low-light image enhancement, a novel interactive algorithm based on the well-designed Gamma Curve is proposed to enrich the enhancement techniques. Different from the previous works trying to enhance the image in solely brightness or naturalness by a specific designed deep network, the proposed method is capable of controlling the output results according to the user's preferences by the same framework with different parameters. There would be three main advantages brought by the proposed network: 1) Interactivity, which allows to generate enhancements results according to users' preferences in human-interactive manners;2) Convenience, wherein the model only needs to train for once without using any reference images, and then can obtain results with different brightness during testing by adjusting the hyper-parameter. 3) Fastness, which results from the lightweight network and the excellent properties of the Gamma Curve to make the network operate in extraordinary high speed. Experiments demonstrate the superiority of our algorithm relative to the previous work. In addition, a multi-platform low-illumination enhancement software is explored to facilitate its application for the public. © Published under licence by IOP Publishing Ltd.

关键词： Image enhancement

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LiDAR Point Cloud Map Coding Framework Based on Quadric Surface Fitting and Scene Construction 5

LiDAR Point Cloud Map Coding Framework Based on Quadric Surf...

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5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Sun, Xuebin Wang, Jinxi Yang, Xiaofei School of Electrical Automation and Information Engineering Tianjin University Tianjin China School of Information and Control Engineering China University of Mining and Technology Xuzhou China Shanghai Institute of Optics and Fine Mechanics Department of Aerospace Laser Technology and System Shanghai China

ISBN: (纸本)9798350355925

Due to rapid 3D acquisition advancements, LiDAR point clouds are prevalent in emerging applications like heritage preservation, mobile robotics, and remote sensing. Yet, the sheer data volume poses challenges for transmission and storage, especially in autonomous driving. This paper introduces an efficient coding framework for compressing large dense LiDAR point cloud maps. By recording timestamps and quaternion matrices during map construction, we reduce map compression to sequence compression. The scans are segregated into intra-frames and inter-frames. For intra-frames, a quadric surface fitting intra-prediction technique eliminates spatial redundancy. For inter-frames, by constructing the 3D scene using encoded intra-frames, the inter-frame can be simulated according to its posture and LiDAR intrinsic parameters. Coding focuses on the difference between actual and predicted point cloud data. Decoding reverses this process, reconstructing the point cloud map from the decoded sequence and quaternion matrices. KITTI dataset experiments show the point cloud map can be averagely compressed to 3.43% of its original size while maintaining centimeter-level precision. This outperforms both octree and Google Draco methods. © 2024 IEEE.

关键词： Mobile robots

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Trojan Horse Detection for RISC-V Cores Using Cross-Auditing

Trojan Horse Detection for RISC-V Cores Using Cross-Auditing

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Asian Test Symposium (ATS)

作者： Wei-Po Huang Shi-Yu Huang Chi-Kang Chen Siang-Cheng Huang Electrical Engineering Department National Tsing Hua University Taiwan Electronic System Design Automation

ISBN: (数字)9798331529161

ISBN: (纸本)9798331529178

In security-critical applications, malicious Trojan Horses embedded in a CPU core could impose great threats on the security of an SoC. In this work, we propose a "Trojan-Horse detection framework" using a cross-auditing scheme. Our framework takes a target RISC-V core, and then pairs it up with another reference RISC-V core to conduct the functional simulation using a set of benchmark programs. The "care outputs" of both cores are compared to reveal the potential Trojan Horses in the target core. A set of well-known Trojan Horses are implanted into an open-source RISC-V core to evaluate the effectiveness of this framework. We found that we can successfully detect almost every implanted Trojan Horse as long as it has been activated and manifested by the benchmark programs.

关键词： Threat modeling Power demand Source coding Hardware security Benchmark testing Complexity theory Trojan horses Cryptography

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Multi-state system reliability:An emerging paradigm for sophisticated engineered systems

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Frontiers of engineering Management 2024年第3期11卷 568-575页

作者： Yu LIU Tangfan XIAHOU Qin ZHANG Liudong XING Hong-Zhong HUANG School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Center for System Reliability and Safety University of Electronic Science and Technology of ChinaChengdu 611731China Department of Electrical&Computer Engineering University of Massachusetts DartmouthMA 02747USA

1 Introduction Reliability is defined as the probability that a system or component will perform its required function for a specified period of time under specified operating conditions(Ebeling,2019).Traditional reliability theory assumes that both a system and its components have only two possible states in the operation stage,either perfectly functioning or completely ***,as engineered systems become more sophisticated,involving larger sizes,increased functionality,and greater intelligence,and with advancements in sensing technologies,it has been extensively observed that systems and components exhibit multiple distinct states as they deteriorate over *** failure behavior,damage severity,performance capacity,and operational efficiency of the system and components vary among these states(Lisnianski et al.,2018).It,therefore,necessitates the development of advanced reliability theories and methodologies that are capable of characterizing these systems and components with multi-state nature.

关键词： system state specified

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Internet-mediated automation of a cascade control system-an engineering laboratory experiment

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International Journal of Electrical engineering and Education 2022年第2期59卷 112-129页

作者： Sangeetha, A Lakshmi Bharathi, N. Ganesh, A Balaji Kalaichitra, G. Department of Electronics and Instrumentation Velammal Engineering College Chennai India Department of Electronics and Instrumentation KCG College of Technology Chennai India Electronic System Design Laboratory TIFAC-CORE Velammal Engineering College Chennai India

The paper aims to enhance the controllability of a standalone cascade control system with Internet architecture and promote the system to be accessible by remote students. The user interactive web page is developed by using platform independent *** language and enabled with both monitoring and controlling functionalities. The Internet transmission time delays that are associated with both feed-forward and feedback channels are compensated by using model predictive controller. The performance of cascade control system with and without compensation is validated by measuring indices, such as rise time, peak time and settling time, and also integral square error and integral absolute error. Access privileges among web page users have been added to ensure simultaneous operations as well as security. The cascade control system is an engineering laboratory experiment interfaced with a dedicated computer by using MODBUS protocol and it is made available to the remote students. The user perception analysis is carried out by using a set of questionnaire that observes 85% of positive feedback. © The Author(s) 2019.

关键词： Time delay

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MSMA-Net: A Multi-scale Multidirectional Adaptation Network for Polyp Segmentation

MSMA-Net: A Multi-scale Multidirectional Adaptation Network ...

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2022 RIVF International Conference on Computing and Communication Technologies, RIVF 2022

作者： Le, Trung-Kien Le, Hoang-Minh-Quang Tran, Thi-Thao Pham, Van-Truong School of Electrical and Electronic Engineering Hanoi University of Science and Technology Department of Automation Engineering Hanoi Viet Nam

ISBN: (纸本)9781665461665

Polyps are mostly noncancerous tumors that occur in several locations in the digestive tract but are most com-monly found in the colon. But over time, some colon polyps can develop into cancer, especially Adenomas that need to be detected to remove as soon as possible. In recent years, with the variety of modern techniques for polyps detection, image segmentation using deep learning has always been an appreciated method. However, polyp segmentation in this way also has some trouble such as long dependencies, complexity computation, poor local and global context, and lack of multi-scale context. There have been many researches and techniques proposed to overcome these problems, such as attention mechanisms, atrous spatial pyramid pooling (ASPP), Receptive Field Block(RFB), etc. Inspired by those advances in deep learning, in this work, we inherited and proposed an Efficient Attention Receptive Field Block (EA-RFB Block) and Local Global Fusion (LGF) that ensures the network's multi-scale representation, capturing enough information of both local and global context. Our proposed network, namely MSMA-Net has demonstrated improved performance through two metrics of Intersection over Union (IoU) and Dice Coefficient when compared with other state-of-the-art models in Polyp datasets. © 2022 IEEE.

关键词： Image segmentation

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RESEARCH ON DAMAGE LOCALIZATION TECHNIQUES FOR REUSABLE CARRIER TANK STRUCTURES

RESEARCH ON DAMAGE LOCALIZATION TECHNIQUES FOR REUSABLE CARR...

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2024 CSAA/IET International Conference on Aircraft Utility systems, AUS 2024

作者： Ben, Xu Mei, Yuan Shaopeng, Dong Jiutian, Zhang Fuel System Department Nanjing Engineering Institute of Aircraft Systems AVIC Nanjing China School of Automation Science and Electrical Engineering Beihang University Beijing China Aviation Key Laboratory of Science and Technology on Aero Electromechanical System Integration Nanjing China Ningho Institute of Technology Beihang University Ningbo China

Lamb waves, characterized by slow attenuation, short wavelength, and sensitivity to damage, have been widely utilized in the field of composite material damage detection. In addressing the issue of damage localization in composite curved tank structures, this study proposes a composite tank localization algorithm based on convolutional neural networks (CNNs) using Lamb wave data. The approach transforms the damage localization regression problem into a classification problem through partitioning, achieving damage zone localization in composite tank structures with fewer sensors. The accuracy of the algorithm is more than 98% in the dataset constructed from physical experiments, which verifies its accuracy and applicability. © The Institution of engineering & technology 2024.

关键词： Tanks (containers)

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DR-Unet++: An Approach for Left Ventricle Segmentation from Magnetic Resonance Images 6

DR-Unet++: An Approach for Left Ventricle Segmentation from ...

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6th International Conference on Green technology and Sustainable Development, GTSD 2022

作者： Le, Dinh-Hung Le, Nhat-Minh Le, Khac-Hung Pham, Van-Truong Tran, Thi-Thao School of Electrical and Electronic Engineering Hanoi University of Science and Technology Department of Automation Engineering Hanoi Viet Nam

ISBN: (数字)9781665466288

ISBN: (纸本)9781665466288

Precise and automatic segmentation of the left ventricle (LV) in cardiac magnetic resonance imaging (MRI) can help cardiologists make accurate diagnoses and decisions. In the revolution of Deep Learning, many LV image segmentation models have been developed and shown promising performance. Nevertheless, segmentation of the LV is still a nontrivial task due to challenges in the cardiac MR images such as the presence of intensity inhomogeneity, clutter, and object size variations. In this study, we propose DR- Unet++, an enhanced DR-Unet architecture, for cardiac segmentation tasks. The DR-Unet++ model, which utilizes advanced Deep Learning techniques, provides better feature extraction for the segmentation of desired segmented objects. Our tests on the ACDC dataset and Sunnybrook Cardiac dataset show that DR-Unet++ outperforms other modern models in terms of Dice coefficient and IoU metric and demonstrates its potential in biomedical image segmentation tasks. © 2022 IEEE.

关键词： Magnetic resonance imaging

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Permutation Transient Attention Encoder for None-Line-of-Sight Imaging

Permutation Transient Attention Encoder for None-Line-of-Sig...

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Computational Optical Sensing and Imaging, COSI 2024 - Part of Optica Imaging Congress

作者： Yue, Wenjie Chen, Xiaxu Liu, Gangping Wang, Chutian Lam, Edmund Y. Ke, Jun School of Optics and Photonics Beijing Institute of Technology Beijing100081 China Key Laboratory of Photo-electronic Imaging Technology and System Ministry of Education of China Beijing100081 China Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong

This paper introduces Permutation Transient Attention Encoders as a novel and efficient approach for NLOS reconstruction. Our method, utilizing Transient Attention Blocks and Permute-MLP, enhances transient feature ex... 详细信息

关键词： Electromagnetic transients

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DQN-Based Proactive Trajectory Planning of UAVs in Multi-Access Edge Computing

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Computers, Materials & Continua 2023年第3期74卷 4685-4702页

作者： Adil Khan Jinling Zhang Shabeer Ahmad Saifullah Memon Babar Hayat Ahsan Rafiq School of Electronic Engineering Beijing University of Posts and TelecommunicationsBeijing100876China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China Department of Automation Chongqing University of Posts and TelecommunicationsChongqing400065China

The main aim of future mobile networks is to provide secure,reliable,intelligent,and seamless *** also enables mobile network operators to ensure their customer’s a better quality of service(QoS).Nowadays,Unmanned Aerial Vehicles(UAVs)are a significant part of the mobile network due to their continuously growing use in various *** better coverage,cost-effective,and seamless service connectivity and provisioning,UAVs have emerged as the best choice for telco *** can be used as flying base stations,edge servers,and relay nodes in mobile *** the other side,Multi-access EdgeComputing(MEC)technology also emerged in the 5G network to provide a better quality of experience(QoE)to users with different QoS ***,UAVs in a mobile network for coverage enhancement and better QoS face several challenges such as trajectory designing,path planning,optimization,QoS assurance,mobilitymanagement,*** efficient and proactive path planning and optimization in a highly dynamic environment containing buildings and obstacles are ***,an automated Artificial Intelligence(AI)enabled QoSaware solution is needed for trajectory planning and ***,this work introduces a well-designed AI and MEC-enabled architecture for a UAVs-assisted future *** has an efficient Deep Reinforcement Learning(DRL)algorithm for real-time and proactive trajectory planning and *** also fulfills QoS-aware service provisioning.A greedypolicy approach is used to maximize the long-term reward for serving more users *** results reveal the superiority of the proposed DRL mechanism for energy-efficient and QoS-aware trajectory planning over the existing models.

关键词： Multi-access edge computing UAVs trajectory planning QoS assurance reinforcement learning deep Q network

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