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Programmable directional color dynamics using plasmonics

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Microsystems & Nanoengineering 2024年第1期10卷 325-334页

作者： Gyurin Kim Doeun Kim Soeun Ko Jang-Hwan Han Juhwan Kim Joo Hwan Ko Young Min Song Hyeon-Ho Jeong school of Electrical Engineering and Computer Science Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea Department of Semiconductor Engineering Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea Artificial Intelligence(AI)graduate school Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea

Adaptive multicolor filters have emerged as key components for ensuring color accuracy and resolution in outdoor visual ***,the current state of this technology is still in its infancy and largely reliant on liquid crystal devices that require high voltage and bulky structural ***,we present a multicolor nanofilter consisting of multilayered‘active’plasmonic nanocomposites,wherein metallic nanoparticles are embedded within a conductive polymer *** nanocomposites are fabricated with a total thickness below 100 nm using a‘lithography-free’method at the wafer level,and they inherently exhibit three prominent optical modes,accompanying scattering phenomena that produce distinct dichroic reflection and transmission ***,a pivotal achievement is that all these colors are electrically manipulated with an applied external voltage of less than 1 V with 3.5 s of switching speed,encompassing the entire visible ***,this electrically programmable multicolor function enables the effective and dynamic modulation of the color temperature of white light across the warm-to-cool spectrum(3250 K-6250 K).This transformative capability is exceptionally valuable for enhancing the performance of outdoor optical devices that are independent of factors such as the sun’s elevation and prevailing weather conditions.

关键词： directional prevailing exceptional

Modifying Lyapunov exponent of chaotic map by self-cascading

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Science China(Technological Sciences) 2024年第7期67卷 2203-2214页

作者： YI ChenLong LI ChunBiao LI YongXin XIA Ming HUA ZhongYun school of Electronic and Information Engineering Nanjing University of Information Science&TechnologyNanjing 210044China school of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing 210044China school of Computer Science and technology Harbin Institute of Technology Shenzhen Graduate SchoolShenzhen 518055China

The self-cascade(SC) method is an effective technique for chaos enhancement and complexity increasing in chaos ***, the controllable self-cascade(CSC) method allows for more accurate control of Lyapunov exponents of the discrete map. In this work, the SC and CSC systems of the original map are derived, which enhance the chaotic performance while preserving the fundamental dynamical characteristics of the original map. Higher Lyapunov exponent of chaotic sequences corresponding to higher frequency are obtained in SC and CSC systems. Meanwhile, the Lyapunov exponent could be linearly controlled with greater flexibility in the CSC system. The verification of the numerical simulation and theoretical analysis is carried out based on the platform of CH32.

关键词： self-cascade controllable self-cascade chaos enhancement chaos control

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Fast Cross-Platform Binary Code Similarity Detection Framework Based on CFGs Taking Advantage of NLP and Inductive GNN

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Chinese Journal of Electronics 2024年第1期33卷 128-138页

作者： Jinxue PENG Yong WANG Jingfeng XUE Zhenyan LIU school of Computer Science and technology Beijing Institute of Technology

Cross-platform binary code similarity detection aims at detecting whether two or more pieces of binary code are similar or not. Existing approaches that combine control flow graphs(CFGs)-based function representation and graph convolutional network(GCN)-based similarity analysis are the best-performing ones. Due to a large amount of convolutional computation and the loss of structural information, the use of convolution networks will inevitably bring problems such as high overhead and sometimes inaccuracy. To address these issues, we propose a fast cross-platform binary code similarity detection framework that takes advantage of natural language processing(NLP)and inductive graph neural network(GNN) for basic blocks embedding and function representation respectively by simulating extracting structural features and temporal features. GNN’s node-centric and small batch is a suitable training way for large CFGs, it can greatly reduce computational overhead. Various NLP basic block embedding models and GNNs are evaluated. Experimental results show that the scheme with long short term memory(LSTM)for basic blocks embedding and inductive learning-based Graph SAGE(GAE) for function representation outperforms the state-of-the-art works. In our framework, we can take only 45% overhead. Improve efficiency significantly with a small performance trade-off.

关键词： Training Semantics Process control Binary codes Feature extraction Natural language processing Graph neural networks

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Multi-Robot Cooperative Localization: Time-Varying Unobservable Subspace Analysis and Consistent Algorithm Design

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Journal of Systems Science & Complexity 2024年第5期37卷 1937-1955页

作者： HAO Ning HE Fenghua YAO Yu school of Astronautics Harbin Institute of Technology

This paper investigates the problem of cooperative localization(CL) for a multi-robot system(MRS) under dynamic measurement topology, which involves a group of robots collectively estimating their poses with respect to a common reference frame using ego-motion measurements and robot-to-robot relative measurements. The authors provide a theoretical analysis of the time-varying unobservable subspace and propose a consistent cooperative localization algorithm. First, the authors introduce the relative measurement graph(RMG) to represent the relative pose measurements obtained by the MRS at each instant. Then, the authors derive the local observability matrix over a time *** equivalent relationship is established between the local observability matrix and the spectral matrices of the RMG. Moreover, the authors present a method for constructing the unobservable subspace based on the RMG under different topology conditions. Based on this analysis, the authors design a consistent cooperative localization algorithm that satisfies the constraints of the time-varying unobservable subspace. An analytical optimal solution is derived for the constrained optimization *** Carlo numerical simulations are conducted to demonstrate the consistency and accuracy of the proposed method.

关键词： Consistency cooperative localization multi-robot system null space relative measurement graph time-varying unobservable subspace

Deep Guided Attention Network for Joint Denoising and Demosaicing in Real Image

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Chinese Journal of Electronics 2024年第1期33卷 303-312页

作者： Tao ZHANG Ying FU Jun ZHANG school of Computer Science and technology Beijing Institute of Technology Advanced Reasearch institute of Multidisciplinary Science Beijing Institute of Technology

Denoising(DN) and demosaicing(DM) are the first crucial stages in the image signal processing pipeline. Recently, researches pay more attention to solve DN and DM in a joint manner, which is an extremely undetermined inverse problem. Existing deep learning methods learn the desired prior on synthetic dataset, which limits the generalization of learned network to the real world data. Moreover, existing methods mainly focus on the raw data property of high green information sampling rate for DM, but occasionally exploit the high intensity and signalto-noise(SNR) of green channel. In this work, a deep guided attention network(DGAN) is presented for real image joint DN and DM(JDD), which considers both high SNR and high sampling rate of green information for DN and DM, respectively. To ease the training and fully exploit the data property of green channel, we first train DN and DM sub-networks sequentially and then learn them jointly, which can alleviate the error accumulation. Besides, in order to support the real image JDD, we collect paired raw clean RGB and noisy mosaic images to conduct a realistic dataset. The experimental results on real JDD dataset show the presented approach performs better than the state-of-the-art methods, in terms of both quantitative metrics and qualitative visualization.

关键词： Training Visualization Inverse problems Green products Noise reduction Pipelines Signal processing

Multiple-dimensioned defect engineering for graphite felt electrode of vanadium redox flow battery

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Carbon Energy 2024年第2期6卷 143-153页

作者： Yingqiao Jiang Yinhui Wang Gang Cheng Yuehua Li Lei Dai Jing Zhu Wei Meng Jingyu Xi Ling Wang Zhangxing He school of Chemical Engineering North China University of Science and TechnologyTangshanChina Tsinghua Shenzhen International graduate school Institute of Materials ResearchTsinghua UniversityShenzhenChina

The scarcity of wettability,insufficient active sites,and low surface area of graphite felt(GF)have long been suppressing the performance of vanadium redox flow batteries(VRFBs).Herein,an ultra-homogeneous multipledimensioned defect,including nano-scale etching and atomic-scale N,O codoping,was used to modify GF by the molten salt ***_(4)Cl and KClO_(3) were added simultaneously to the system to obtain porous N/O co-doped electrode(GF/ON),where KClO_(3) was used to ultra-homogeneously etch,and O-functionalize electrode,and NH4Cl was used as N dopant,***/ON presents better electrochemical catalysis for VO_(2)+/VO_(2)+ and V3+/V2+ reactions than only O-functionalized electrodes(GF/O)and *** enhanced electrochemical properties are attributed to an increase in active sites,surface area,and wettability,as well as the synergistic effect of N and O,which is also supported by the density functional theory ***,the cell using GF/ON shows higher discharge capacity,energy efficiency,and stability for cycling performance than the pristine cell at 140 mA cm^(−2) for 200 ***,the energy efficiency of the modified cell is increased by 9.7% from 55.2% for the pristine cell at 260 mA cm^(−2).Such an ultra-homogeneous etching with N and O co-doping through“boiling”molten salt medium provides an effective and practical application potential way to prepare superior electrodes for VRFB.

关键词： graphite felt molten salt N O co-doping ultra-homogeneous etching vanadium redox flow battery

GraphSTGAN:Situation understanding network of slow-fast high maneuvering targets for maritime monitor services of IoT data

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Digital Communications and Networks 2024年第3期10卷 620-630页

作者： Guanlin Wu Haipeng Wang Yu Liu You He Shenzhen International graduate school Tsinghua UniversityShenzhen518071China institute of Space Science and Applied technology Harbin Institute of TechnologyShenzhen518055China Department of Electronic Engineering Tsinghua UniversityBeijing100084China

With the rapid growth of the maritime Internet of Things(IoT)devices for Maritime Monitor Services(MMS),maritime traffic controllers could not handle a massive amount of data in *** unmanned MMS,one of the key technologies is situation ***,the presence of slow-fast high maneuvering targets and track breakages due to radar blind zones make modeling the dynamics of marine multi-agents difficult,and pose significant challenges to maritime situation *** order to comprehend the situation accurately and thus offer unmanned MMS,it is crucial to model the complex dynamics of multi-agents using IoT big ***,previous methods typically rely on complex assumptions,are plagued by unstructured data,and disregard the interactions between multiple agents and the spatial-temporal correlations.A deep learning model,Graph Spatial-Temporal Generative Adversarial Network(GraphSTGAN),is proposed in this paper,which uses graph neural network to model unstructured data and uses STGAN to learn the spatial-temporal dependencies and *** experiments show the effectiveness and robustness of the proposed method.

关键词： Internet of things Multi-agents Graph neural network Maritime monitoring services

Hyperspectral image super resolution using deep internal and self-supervised learning

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CAAI Transactions on Intelligence technology 2024年第1期9卷 128-141页

作者： Zhe Liu Xian-Hua Han graduate school of Science and technology for Innovation Yamaguchi UniversityYamaguchiJapan graduate school of Artificial Intelligence and Science Rikkyo UniversityTokyoJapan

By automatically learning the priors embedded in images with powerful modelling ca-pabilities,deep learning-based algorithms have recently made considerable progress in reconstructing the high-resolution hyperspectral(HR-HS)*** previously collected large-amount of external data,these methods are intuitively realised under the full supervision of the ground-truth ***,the database construction in merging the low-resolution(LR)HS(LR-HS)and HR multispectral(MS)or RGB image research paradigm,commonly named as HSI SR,requires collecting corresponding training triplets:HR-MS(RGB),LR-HS and HR-HS image simultaneously,and often faces dif-ficulties in *** learned models with the training datasets collected simultaneously under controlled conditions may significantly degrade the HSI super-resolved perfor-mance to the real images captured under diverse *** handle the above-mentioned limitations,the authors propose to leverage the deep internal and self-supervised learning to solve the HSI SR *** authors advocate that it is possible to train a specific CNN model at test time,called as deep internal learning(DIL),by on-line preparing the training triplet samples from the observed LR-HS/HR-MS(or RGB)images and the down-sampled LR-HS ***,the number of the training triplets extracted solely from the transformed data of the observation itself is extremely few particularly for the HSI SR tasks with large spatial upscale factors,which would result in limited reconstruction *** solve this problem,the authors further exploit deep self-supervised learning(DSL)by considering the observations as the unlabelled training ***,the degradation modules inside the network were elaborated to realise the spatial and spectral down-sampling procedures for transforming the generated HR-HS estimation to the high-resolution RGB/LR-HS approximation,and then the reconstruction errors of the observations were formulated for mea

关键词： computer vision deep learning deep neural networks hyperspectral image enhancement

An in-situ polymerized interphase engineering for high-voltage allsolid- state lithium-metal batteries

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Nano Research 2024年第4期17卷 2687-2692页

作者： Lu Nie Shaojie Chen Mengtian Zhang Tianyi Gao Yuyao Zhang Ran Wei Yining Zhang Wei Liu school of Physical Science and technology ShanghaiTech UniversityShanghai 201210China Tsinghua-Berkeley Shenzhen institute&Tsinghua Shenzhen International graduate school Tsinghua UniversityShenzhen 518055China

All-solid-state lithium batteries(ASSLBs)have attracted great interest due to their promising energy density and strong ***,the interface issues,including large interfacial resistance between electrode and electrolyte and low electrochemical stability of solid-state electrolytes against high-voltage cathodes,have restricted the development of high-voltage ***,we report an ASSLB with stable cycling by adopting a conformal polymer interlayer in-situ formed at the Li_(6.4)La_(3)Zr_(1.4)Ta_(0.6)O_(12)(LLZTO)–cathode *** polymer can perfectlyﬁll the voids and create a stable interface contact between LLZTO and *** addition,the electric field across the polymer interlayer is reduced compared with pure solid polymer electrolyte(SPE),which facilitates the electrochemical stability with high-voltage *** all-solid-state Li|LLZTO-SPE|LiFe_(0.4)Mn_(0.6)PO_(4)(LMFP)cells achieve a low interface impedance,high specific capacity,and excellent cycling *** work presents an effective and practical strategy to rationally design the electrode–electrolyte interface for the application of high-voltage ASSLBs.

关键词： all-solid-state lithium batteries interface contact electric field electrochemical stability