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Flexible, Super-stable, and Multifunctional Capacitive Pressure Sensors with Tilted Micropillar Array by Inclined Laser Ablation and Demolding

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IEEE Sensors Journal 2025年第6期25卷 9367-9374页

作者： Xu, Tianlong Li, Kaixin Zhou, Yihao Zhang, Dawei Sheng, Bin College of Optical Electrical and Computer Engineering Unversity of Shanghai for Science and Technology Shanghai 200093 China. Shanghai Key Laboratory of Modern Optical Systems Engineering Research Center of Optical Instruments and System Shanghai 200093 China

Flexible capacitive pressure sensors have garnered considerable interest across diverse applications, including medical monitoring, electronic skin, and robotic tactile systems, owing to their straightforward fabrication process, low energy consumption, and robust stability. In this paper, we introduce a new inclined laser ablation (ILA) combined with demolding technique for the fabrication of flexible capacitive sensors featuring a microstructured dielectric layer composed of a tilted micropillar array (TMA). The design parameters of TMA were systematically optimized, including the tilt angle of the micropillars, array gap, aspect ratio, and bottom film thickness. The resulting capacitive pressure sensor, equipped with 75° tilted micropillar arrays (aspect ratio of 5:1), demonstrates exceptional sensitivity (1.12 kPa-1) within the low-pressure range (0-2 kPa), rapid response and recovery times, a low pressure detection limit of 0.25 Pa, and transient response stability factor as high as 243.9. The proposed capacitive pressure sensor was validated for the detection and recognition of human motions when attached to various body parts. Additionally, the sensors also demonstrated significant potential for diverse applications, including tactile sensing, and Morse code communication. © 2025 IEEE.

关键词： Aspect ratio

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PPDO:a privacy-preservation-aware delay optimization task-offloading algorithm for collaborative edge computing

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Frontiers of Information technology & Electronic Engineering 2025年第1期26卷 27-41页

作者： Chao JING Jianwu XU College of Computer Science and Engineering Guilin University of TechnologyGuilin 541004China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin 541004China

Although collaborative edge computing(CEC)systems are beneficial in enhancing the performance of mobile edge computing(MEC),the issue of user privacy leakage becomes prominent during task *** address this issue,we design a privacy-preservation-aware delay optimization task-offloading algorithm(PPDO)in a CEC *** considering location and usage pattern privacy protection,we establish a privacy task model to interfere with the edge server and ensure user *** address the extra delay arising from privacy protection,we subsequently leverage a Markov decision processing(MDP)policy-iteration-based algorithm to minimize delays without compromising *** simultaneously accelerate the MDP operation,we develop an extension that improves the PPDO by optimizing the action ***,a comprehensive simulation was conducted using the edge user allocation(EUA)*** results demonstrated that PPDO achieves an optimal trade-off between privacy protection and delay with a minimum delay compared with existing ***,we examined the advantages and disadvantages of improving PPDO.

关键词： Collaborative edge computing Task offloading Privacy protection Markov decision process

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Laser Scribed CNTs/PEEK/TPU Composites Film for High-performance Stretchable Electrodes

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IEEE Sensors Journal 2025年第10期25卷 16666-16674页

作者： Jiang, Caiyun Tang, Lei Hou, Jian Ding, Guqiao Sheng, Bin University of Shanghai for Science and Technology College of Optical Electrical and Computer Engineering Shanghai200093 China Engineering Research Center of Optical Instruments and System Shanghai Key Laboratory of Modern Optical Systems Shanghai200093 China Chinese Academy of Sciences State Key Laboratory of Materials for Integrated Circuits Shanghai Institute of Microsystem and Information Technology Shanghai200050 China

In this paper, an elastic composite material comprising carbon nanotubes (CNTs), polyether-ether-ketone (PEEK), and thermoplastic polyurethane (TPU) is presented, in which direct laser-induced graphitization was used to fabricate high-performance stretchable electrodes. The introduction of CNTs can enhance the conductivity and mechanical strength of the composites, thus improving the overall performance of the sensors. Flexible strain sensors with porous structures were prepared by appropriate encapsulation methods, which exhibited superior gauge factors of 72, 367, and 972 in the 0-30%, 30-44%, and 44-53% strain regions, respectively. Furthermore, it features a rapid response time (100 ms), excellent stability and durability, and is capable of detecting both large-scale human movement and monitoring of small physical signals. In addition, patterned laser-induced graphene interdigital electrodes (LIG-IDEs) were fabricated on composites using laser light. Highly sensitive humidity sensors were obtained using graphene oxide as the humidity-sensing material for respiratory monitoring and non-contact sensing. The thermoplastic properties of TPU allow the straight fiber to be thermoformed into highly stretchable helical electrode, which was of a helix index of 5 and with a high-quality factor (Q=130) at 1250% strain. In conclusion, the preparation of LIG on stretchable composite films by direct laser scribing shows a promising approach for large-scale fabrication of wearable devices. © 2001-2012 IEEE.

关键词： Humidity sensors

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A multi-channel image-based method for flight delay prediction in terminal area 4

A multi-channel image-based method for flight delay predicti...

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2024 4th International Conference on Intelligent Traffic systems and Smart City, ITSSC 2024

作者： Yang, Yihan Chen, Haiyan Yang, Longwu State Key Laboratory of Air Traffic Management System Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9781510686304

With the rapid development of civil aviation, flight delays have become increasingly critical. Effectively predicting flight delays has become a research hotspot in recent years. In this paper, we propose a novel flight delay prediction method based on image recognition by applying deep learning techniques. We first rasterize the target terminal area into a grid image of size 25×25. Then, we convert the arrival and departure traffic flow data collected over some time into colors and fill them into the corresponding grids to obtain the arrival and departure traffic flow images. On this basis, we design the prediction model Attention-Complementary Hybrid Network (ACHNet), which can be used for multi-channel image feature fusion. It takes arrival and departure traffic flow images as inputs and fuses the spatial information of these two types of images through the fusion branch. We use the Convolutional Neural Network (CNN)-based prediction model to predict flight delays on arrival and departure traffic flow image sets respectively, and compare the prediction results with those of ACHNet. The experimental results show that the fusion of multi-channel image features can effectively improve the performance of flight delay prediction. © 2025 SPIE.

关键词： Convolutional neural networks

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RDM: A Light Field Super-Resolution Model Improved with Retinex Decomposition 16

RDM: A Light Field Super-Resolution Model Improved with Reti...

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16th International Conference on Signal Processing systems, ICSPS 2024

作者： Lyu, Wenqi Ke, Wei Sheng, Hao Ma, Xiao Liu, Lixue Faculty of Applied Sciences Macao Polytechnic University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9781510689251

Light field cameras hold significant value in applications such as depth estimation, 3D video acquisition, and image super-resolution. Compared to traditional single-image super-resolution methods, light field images can capture object information from multiple spatial and angular perspectives, which is more conducive to capturing detail information. However, current advanced light field super-resolution models still exhibit issues like blurriness, jaggedness, and dimness in 4x light field super-resolution. To address this, we propose a novel light field super-resolution framework that utilizes the Retinex decomposition concept to solve common problems such as ghosting and overlap, thereby improving image quality. By decomposing the light field image into illumination and reflection components and processing them separately, our model significantly enhances detail preservation and color consistency. Experimental results on multiple datasets demonstrate that our model exhibits outstanding performance, both subjectively and objectively, highlighting the effectiveness of this framework and its broad application potential in the field of computational imaging. © 2025 SPIE.

关键词： Image acquisition

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SkeletonMix: A Mixup-Based Data Augmentation Framework for Skeleton-Based Action Recognition

SkeletonMix: A Mixup-Based Data Augmentation Framework for S...

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

作者： Zongye Zhang Huanyu Zhou Qingjie Liu Yunhong Wang State Key Laboratory of Virtual Reality Technology and System Beihang University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Skeleton-based human action recognition has received widespread attention for its robustness to changes in the background and appearance of actors compared to the RGB modality. Data augmentation is widely used to explicitly regularize the model to prevent overfitting, especially when the number of labeled samples is scarce. However, compared to various augmentation methods available for the RGB modality, there are fewer works on the skeleton modality, especially a model-agnostic augmentation method that can be easily integrated into multiple models. We address the problem by proposing a comprehensive data augmentation framework named SkeletonMix, which contains a pair sample selection module for mixup and random augmentations tailored for skeleton modality. SkeletonMix is a non-learning framework and can be applied to different models in a plug-and-play manner. Extensive experiments on NTU RGB+D, NTU RGB+D 120 and PKU-MMD datasets demonstrate the effectiveness of our proposed framework under limited labeled training data. Our proposed method boosts the performance by a maximum of 7.5% on scarce training data setup (5% of training data).

关键词： Training data Speech recognition Signal processing Data augmentation Skeleton Robustness Data models Human activity recognition Speech processing Overfitting

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Segmentation of CAD models using hybrid representation

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虚拟现实与智能硬件(中英文) 2025年第2期7卷 188-202页

作者： Claude UWIMANA Shengdi ZHOU Limei YANG Zhuqing LI Norbelt MUTAGISHA Edouard NIYONGABO Bin ZHOU State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Peace-Bay Academy of Virtual Reality Shenyang 110004China China Aerospace Systems Engineering Corporation Beijing 100070China Department of Signal and Information Processing Beihang UniversityBeijing 100191China Department of Computer Science University of SherbrookeSherbrooke J1C0A1Canada

In this paper,we introduce an innovative method for computer-aided design(CAD)segmentation by concatenating meshes and CAD *** previous CAD segmentation methods have achieved impressive performance using single representations,such as meshes,CAD,and point ***,existing methods cannot effectively combine different three-dimensional model types for the direct conversion,alignment,and integrity maintenance of geometric and topological ***,we propose an integration approach that combines the geometric accuracy of CAD data with the flexibility of mesh representations,as well as introduce a unique hybrid representation that combines CAD and mesh models to enhance segmentation *** combine these two model types,our hybrid system utilizes advanced-neural-network techniques to convert CAD models into mesh *** complex CAD models,model segmentation is crucial for model retrieval and *** partial retrieval,it aims to segment a complex CAD model into several simple *** first component of our hybrid system involves advanced mesh-labeling algorithms that harness the digitization of CAD properties to mesh *** second component integrates labelled face features for CAD segmentation by leveraging the abundant multisemantic information embedded in CAD *** combination of mesh and CAD not only refines the accuracy of boundary delineation but also provides a comprehensive understanding of the underlying object *** study uses the Fusion 360 Gallery *** results indicate that our hybrid method can segment these models with higher accuracy than other methods that use single representations.

关键词： B-RepNet hybrid segmentation CAD models classification MeshCNN MeshCAD-Net

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Formalization of Android Activity-Fragment Multitasking Mechanism and Static Analysis of Mobile Apps

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Formal Aspects of Computing 2025年第2期37卷 1-86页

作者： He, Jinlong Wu, Zhilin Chen, Taolue Key Laboratory of System Software Chinese Academy of Sciences Institute of Software Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China School of Computing and Mathematical Sciences Birkbeck University of London Northern Ireland London United Kingdom

The multitasking mechanism between activities and fragments plays a fundamental role in the Android operating system, which involves a wide range of features, including launch modes, intent flags, task affinities, and structured activities containing fragments. All of them are being widely used in Android apps, both open source and commercial ones. In this article, we present a formal semantics of the Android multitasking mechanism between activities and fragments, which accommodates all the important features and gives insofar the most comprehensive and accurate formalization. In particular, our semantics is formulated based on multi-stack systems, and fully captures the behavior of task stacks and activity stacks regarding fragments. Based on the semantics, we provide new static analysis algorithms, which are both multi-stack aware and fragment sensitive, thus achieve more precise static analysis for Android apps. We validate our approach by extensive experiments on both open source and commercial Android apps. The results highlight the benefits of the considering the semantics of the multitasking mechanism between activities and fragments in static analysis, and confirm the efficacy of our approach. © 2025 Copyright held by the owner/author(s).

关键词： Android multitasking mechanism activities and fragments formal semantics stack unboundedness static analysis

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Joint Sensing and Computation Incentive Mechanism for Mobile Crowdsensing Networks: A Multiagent Reinforcement Learning Approach

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IEEE Internet of Things Journal 2025年第9期12卷 13033-13046页

作者： Zhao, Nan Sun, Yiling Pei, Yiyang Niyato, Dusit Hubei University of Technology Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Wuhan430061 China Singapore Institute of Technology Information and Communications Technology Cluster Tampines Singapore Nanyang Technological University School of Computer Science and Engineering Jurong West Singapore

Mobile crowdsensing (MCS) is a novel sensing paradigm by utilizing mobile users (MUs) to collect data from environment. Considering the finite sensing and computing resources of MUs, it is crucial to inspire MUs to take part in crowdsensing willingly. In this study, a multiagent-deep-reinforcement-learning (DRL)-based incentive mechanism is investigated to tackle the joint data sensing and computing issues. Specifically, due to the heterogeneity of sensing tasks, multiple MCS platforms (MCPs) motivate MUs to participate in different tasks. The interaction between MCPs and MUs is modeled as a multileader-multifollower Stackelberg game with Stackelberg equilibrium proved by derivation. Moreover, the Stackelberg game is transformed as a Markov decision process (MDP) to deal with a multiagent DRL method without any prior knowledge. Due to the continuous high-dimensional action space of multiple MCPs and MUs, a multiagent double actors deep deterministic policy gradient (MA-DADDPG) algorithm is proposed to obtain the optimal sensing data size, computing resource, and incentive payment policies. Extensive simulation results illustrate the effectiveness of the proposed crowdsensing incentive mechanism. © 2014 IEEE.

关键词： Deep reinforcement learning

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Assessment of Multilevel Intransitive Noninterference by Nonblocking Analysis

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IEEE Transactions on Automatic Control 2025年第5期70卷 2902-2917页

作者： Zhong, Wenjing Zhao, Jinjing Hu, Hesuan Xidian University School of Electro-Mechanical Engineering Xi'an710071 China National Key Laboratory of Science and Technology on Information System Security Beijing100101 China Nanyang Technological University School of Computer Science and Engineering College of Engineering 639798 Singapore Xi'an Jiaotong University State Key Laboratory for Manufacturing Systems Engineering Xi'an710054 China

Security of system behavior is a kind of information flow security, which is achieved by confusing the intruders via the indistinguishability of system behaviors. Noninterference is a typical notion to describe information flow security, for which multilevel intransitive noninterference (MINI) is an advanced variant. Since there is a lack of rigorous approach to assessing MINI, this article achieves so via observability theory. For systems modeled by labeled Petri nets (LPNs), two MINI properties, i.e., positive MINI (PMINI) and bipolar MINI (BMINI), are considered. First, a necessary and sufficient condition for their assessment is established via language equivalence. Language equivalence analyses for PMINI and BMINI are based on the existing trace equivalence and the proposed INI bisimulation, respectively. INI bisimulation is more comprehensive to describe negative noninterference than bisimulation. Second, another necessary and sufficient condition is established after the transformation of MINI assessment problem to nonblocking analysis problem. The core of such a problem transformation is the stepwise construction of nonblocking analyzer. This stepwise construction allows MINI assessment to proceed online before terminating at an appropriate time. In addition, this stepwise construction fully employs the concurrency of LPNs so that MINI can be assessed in a multithreaded way. Both online and multithreaded MINI assessments can improve assessment efficiency. © 1963-2012 IEEE.

关键词： Petri nets

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