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20th International Conference on Frontiers of information technology, FIT 2023

作者： Ettesvoll, Edward Rognerud Schule, Leon Camill Mudassar Yamin, Muhammad Imran, Ali Shariq Norwegian University of Science and Technology Department of Information Security and Communication Technology Gjovik Norway Norwegian University of Science and Technology Department of Computer Science Gjovik Norway

ISBN: (纸本)9798350395785

Confidential Computing (CC) is a fairly new concept encompassing technologies designed to safeguard data during processing, playing a crucial role in data protection throughout its lifecycle. In this paper, we address the problem of ensuring data security from a technical perspective, delving into the intricate workings of technologies enabling CC. While the initial sections focus on establishing fundamental concepts related to CC, including Trusted Execution Environments (TEEs), later parts of the paper present practical prototypes demonstrating the setup of a CC environment on Cloud Computing Platforms (CCPs), such as Microsoft Azure. We specifically showcase two leading technologies in this domain: Intel Software Guard Extensions (SGX) and AMD Secure Encrypted Virtualization (SEV), both harnessing dedicated Central Processing Units (CPUs) from their respective manufacturers. This paper extensively covers the diverse approaches these technologies employ to secure data in use. Additionally, we offer a brief comparative analysis of related technologies as alternatives. Our research not only addresses the problem statement and motivation but also provides insights into significant results achieved in the realm of CC. © 2023 IEEE.

关键词： Program processors

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Model-based deep learning for fiber bundle infrared image restoration

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Defence technology（防务技术） 2023年第9期27卷 38-45页

作者： Bo-wen Wang Le Li Hai-bo Yang Jia-xin Chen Yu-hai Li Qian Chen Chao Zuo Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense Nanjing University of Science and TechnologyNanjingJiangsu Province210094China Smart Computational Imaging(SCI)Laboratory Nanjing University of Science and TechnologyNanjingJiangsu Province210094China Science and Technology on Electro-Optical Information Security Control Lab Tianjin300308China

As the representative of flexibility in optical imaging media,in recent years,fiber bundles have emerged as a promising architecture in the development of compact visual *** to tackling the problems of universal honeycomb artifacts and low signal-to-noise ratio(SNR)imaging in fiber bundles,the iterative super-resolution reconstruction network based on a physical model is *** the constraint of solving the two subproblems of data fidelity and prior regularization term alternately,the network can efficiently“regenerate”the lost spatial resolution with deep *** building and calibrating a dual-path imaging system,the real-world dataset where paired low-resolution(LR)-high-resolution(HR)images on the same scene can be generated *** results on both the United States Air Force(USAF)resolution target and complex target objects demonstrate that the algorithm can restore high-contrast images without pixilated *** the basis of super-resolution reconstruction,compound eye image composition based on fiber bundle is also embedded in this paper for the actual imaging *** proposed work is the first to apply a physical model-based deep learning network to fiber bundle imaging in the infrared band,effectively promoting the engineering application of thermal radiation detection.

关键词： Fiber bundle Deep learning Infrared imaging Image restoration

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Adaptive Time Slot Resource Allocation in SWIPT IoT Networks

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Computer Modeling in Engineering & sciences 2023年第9期136卷 2787-2813页

作者： Yunong Yang Yuexia Zhang Zhihai Zhuo Key Laboratory of Information and Communication Systems Ministry of Information IndustryBeijing Information Science and Technology UniversityBeijing100101China Key Laboratory of ModernMeasurement&Control Technology Ministry of EducationBeijing Information Science&Technology UniversityBeijing100101China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing100101China

The rapid advancement of Internet of Things(IoT)technology has brought convenience to people’s lives;however further development of IoT faces serious challenges,such as limited energy and shortage of network spectrum *** address the above challenges,this study proposes a simultaneous wireless information and power transfer IoT adaptive time slot resource allocation(SIATS)***,an adaptive time slot consisting of periods for sensing,information transmission,and energy harvesting is designed to ensure that the minimum energy harvesting requirement ismet while the maximumuplink and downlink throughputs are ***,the optimal transmit power and channel assignment of the system are obtained using the Lagrangian dual and gradient descent methods,and the optimal time slot assignment is determined for each IoT device such that the sum of the throughput of all devices is *** results show that the SIATS algorithm performs satisfactorily and provides an increase in the throughput by up to 14.4%compared with that of the fixed time slot allocation(FTS)*** the case of a large noise variance,the SIATS algorithm has good noise immunity,and the total throughput of the IoT devices obtained using the SIATS algorithm can be improved by up to 34.7%compared with that obtained using the FTS algorithm.

关键词： Internet of Things simultaneous wireless information and power transfer adaptive time slot resource allocation

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Influence of Physical Parameters on Echo Spectrum of Underwater Target

Influence of Physical Parameters on Echo Spectrum of Underwa...

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2024 OES China Ocean Acoustics, COA 2024

作者： Chen, Yunfei Yu, Dapeng Jia, Bing Wang, Zhenshan Yong, Jintao Wang, Yanjie School of Information and Communication Engineering Dalian University of Technology Dalian China Science and Technology on Underwater Test and Control Laboratory Dalian China

ISBN: (纸本)9798350340297

The elastic scattering features of the underwater target scattering wave depend on the physical parameters of the target, such as the material and structure. Intuitively evaluating the influence of the elastic scattering wave on the characteristics of the whole scattering wave signal is difficult due to the coupling and superposition of the elastic scattering wave and the geometric scattering wave of the target. Based on the different modulation characteristics of broadband echo spectrum produced by target geometry scattering and elastic scattering wave, this paper characterizes and studies the modulation features of echo spectrum and tests the influence of physical parameters on the echo spectrum of underwater target using several simple cylindrical targets with different physical parameters. Moreover, the geometric scattering wave and the elastic scattering wave of the simple target echo are extracted using the fractional Fourier transform (FrFT) domain filtering method, and the influence of physical parameters on the echo spectrum is quantitatively analyzed. © 2024 IEEE.

关键词： Elastic scattering

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Flexible and wearable battery-free backscatter wireless communication system for colour imaging

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npj Flexible Electronics 2024年第1期8卷 761-769页

作者： Jun-Lin Zhan Wei-Bing Lu Cong Ding Zhen Sun Bu-Yun Yu Lu Ju Xin-Hua Liang Zhao-Min Chen Hao Chen Yong-Hao Jia Zhen-Guo Liu Tie-Jun Cui State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China Center for Flexible RF Technology Frontiers Science Center for Mobile Information Communication and SecuritySoutheast UniversityNanjing 210096China College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China

Wireless imaging,equippedwith ultralow power wireless communications and energy harvesting(EH)capabilities,have emerged as battery-free and sustainable ***,the challenge of implementing wireless colour imaging in wearable applications remains,primarily due to high power demands and the need to balance energy harvesting efficiency with device *** address these issues,we propose a flexible and wearable battery-free backscatter wireless communication system specially designed for colour *** system features a hybrid RF-solar EH array that efficiently harvests energy from both ambient RF and visible light energy,ensuring continuous operation in diverse ***,flexible materials allow the working system to conform to the human body,ensuring comfort,user-friendliness,and ***,a compact design utilizing a shared-aperture antenna array for simultaneous wireless information and power transfer(SWIPT),coupled with an optically transparent stacked *** design not only optimizes space but also maintains the performance of both communication and EH *** proposed flexible and wearable systems for colour imaging would have potentially applications in environmental monitoring,object detection,and law enforcement *** approach demonstrates a sustainable and practical solution for the next generation of wearable,power-demanding devices.

关键词： communication antenna battery

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Standard and RL-controled Tabu Search Algorithms Comparison in Capacitated Vehicle Routing Problem Solving 20th

Standard and RL-Controled Tabu Search Algorithms Comparison...

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20th International Conference on Dependability of Computer Systems, DepCoS-RELCOMEX 2025

作者： Bronicki, Jan Nowak, Piotr Faculty of Mechanical Engineering Wroclaw University of Science and Technology Wroclaw Poland Department of Control Systems and Mechatronics Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9783031927331

The Capacitated Vehicle Routing Problem (CVRP) is a key logistics and computer science problem where a vehicle with limited capacity must deliver orders efficiently while minimizing costs. Traditional exact methods struggle with large-scale instances, making metaheuristic approaches like Tabu Search (TS) practical solutions to real-world problems. However, TS performance depends on usually manually tuned neighborhood search strategies. We propose a Tabu Search controlled by reinforcement learning (RL-TS) that dynamically selects the search policy for neighborhood move. Experimental results show that RL-TS improves the quality of the solution over standard TS in multiple CVRP instances and multiple problem sizes. This highlights the potential of machine learning-driven metaheuristics for complex optimization problems. Future work will refine the RL model and explore real-world applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Reinforcement learning

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Research on Radar Anti-deceptive-jamming Method based on Characterization 16

Research on Radar Anti-deceptive-jamming Method based on Cha...

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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Lei, Lei Jiang, Meiqiu Li, JiaChen Guo, Shisheng Cui, Guolong Xia, Bijun Zhu, Lei School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China National Key Laboratory of Electromagnetic Space Security Chengdu China

ISBN: (纸本)9781510689251

Deceptive jamming plays an increasingly important role in radar electronic countermeasures, and there is an urgent need to study new radar anti-deceptive jamming methods. In this paper, four characteristic factors, namely, amplitude variance, amplitude ratio, power ratio and number of correlated signals, are extracted based on the intra pulse characteristic differences between the target and deceptive jamming signals. Based on these four factors, a multi-domain cross-coupled neural network is proposed to distinguish between real targets and deceptive jamming. Finally, the simulation results show that this method has a significant effect on anti-deceptive jamming. © 2025 SPIE.

关键词： Radar countermeasures

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Dynamic Charging and Path Planning for UAV-Powered Rechargeable WSNs Using Multi-Agent Deep Reinforcement Learning

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IEEE Transactions on Automation science and Engineering 2025年 22卷 15610-15626页

作者： Betalo, Mesfin Leranso Leng, Supeng Seid, Abegaz Mohammed Abishu, Hayla Nahom Erbad, Aiman Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Unmanned Aerial Vehicle (UAV)-powered 5G/6G networks integrated with rechargeable wireless sensor networks (RWSNs) offer promising solutions for extending system lifetime, collecting data, and providing computing services and power to sensor nodes (SNs). UAVs offer significant advantages, including exceptional mobility, cost-effective deployment, and the ability to be easily reprogrammed for a wide range of missions. However, the limited onboard power capacity of UAVs, coupled with the lack of dynamic and intelligent charging station (CS) management and inefficient path planning, can lead to SN failure in dynamic mobile environments. To address these challenges, we propose an energy-efficient laser-charged UAV (LCU)-enabled RWSN environment, wherein UAVs, powered by laser beams from ground-based stations, provide services, collect data, and transfer energy to SNs. We formulate a joint optimization problem involving power allocation, dynamic charging strategy (DCS), and path planning to minimize task completion time and sensor node death time. Given the NP-hard nature of the problem, we employ a stochastic game model based on a Markov decision process (MDP) for its solution. To solve this problem, we propose a deep reinforcement learning (DRL) based algorithm that enables real-time charging scheduling decisions while optimizing network performance. We introduce a multi-agent double deep Q-network (MA-DDQN) model to determine the optimal trajectories for all UAVs in large and complex environments. Simulation results demonstrate that the MA-DDQN approach outperforms state-of-the-art techniques, showing significant improvements in terms of average delay, energy consumption, and task completion time. © 2004-2012 IEEE.

关键词： Stochastic models

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DFT-SPREAD COMBINED WITH PTS METHOD TO REDUCE THE PAPR IN RADCOM SYSTEM 10

DFT-SPREAD COMBINED WITH PTS METHOD TO REDUCE THE PAPR IN RA...

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10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Zhi, Ruxin Quart, Cong Xu, Zhan Yang, Xiaolong Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781837242672

In radar communication integration (RADCOM) systems based on orthogonal frequency division multiplexing (OFDM) technology, high peak to average power ratio (PAPR) is one of the main factors affecting system performance. This article analyses the PAPR probability distribution of radar communication integrated signals based on OFDM. In order to reduce the PAPR of integrated signals, the discrete Fourier transform spread spectrum (DFT Spread) technology suitable for traditional OFDM systems has been improved and combined with partial transmission sequences (PTS) to further reduce the PAPR of radar communication integrated signals. The simulation results show that compared with using DFT spread spectrum technology alone, PTS technology alone, and the original signal, the proposed DFT PTS joint algorithm based on linear interpolation reduces PAPR by 1.5 dB, 2.4 dB, and at least 4.1 dB, respectively. Obviously, the DFT-PTS joint algorithm achieved the best PAPR suppression effect. © The Institution of Engineering & technology 2024.

关键词： Orthogonal frequency division multiplexing

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Meta-RFF: Few-Shot Open-Set Incremental Learning for RF Fingerprint Recognition via Multi-phase Meta Task Adaptation 18th

Meta-RFF: Few-Shot Open-Set Incremental Learning for RF Fin...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Li, Taotao Wen, Zhenyu Jiang, Yuting Wang, Jian Su, Jie Hong, Zhen Yu, Li He, Shibo The Institute of Cyberspace Security and the College of Information Engineering Zhejiang University of Technology Hangzhou China University of Science and Technology of China Hefei China College of Control Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9783031714634

RF fingerprinting (RFF) is a non-encrypted authentication technique that provides an additional layer of security for wireless devices, which has a promising application. However, existing RFF recognition techniques that rely on deep learning (DL) are usually available with limited equipment. In actual application scenarios, new wireless devices are constantly appearing, such as unknown drones that appear suddenly in the sky. In such cases, the RF monitoring system should equip the ability to discover the unknown device (i.e., open-set recognition (OSR)) and use captured few samples of new devices incrementally updating knowledge of the system. This requirement brings two challenges: 1) incremental updates from few-shot samples are prone to lead to catastrophic forgetting and over-fitting problems;2) constructing a reliable OSR mechanism for new devices with few-shot samples is difficult. To tackle this challenge, for the first time, we propose a novel few-shot open-set incremental learning (FSOSIL) framework via meta-learning for RFF recognition (Meta-RFF ). The core idea of Meta-RFF is to simulate few-shot RF signal incremental learning by constructing many pseudo-FSOSIL tasks. In particular, to strengthen the OSR capability, we further design RF feature augmentation and open space learning modules. The algorithm is validated on the large-scale aircraft recognition dataset (namely ADS-B), which shows that the close-set accuracy and open-set AUROC of the new class improve the performance by about 10–20% compared to other algorithms with 1-shot. And in 10 increments, our algorithm possesses a lower performance decay rate (about 3%). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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