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On-chip quantum NOON state sensing for temperature and humidity

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Chinese Physics B 2024年第10期33卷 15-20页

作者： Weihong Luo Chao Wu Yuxing Du Chang Zhao Miaomiao Yu Pingyu Zhu Kaikai Zhang Ping Xu institute for Quantum information&State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China

A maximal photon number entangled state,namely NOON state,can be adopted for sensing with a quantum *** this work,we designed silicon quantum photonic chips containing two types of Mach-Zehnder interferometerswherein the two-photon NOON state,sensing element for temperature or humidity,is *** with classicallight or single photon case,two-photon NOON state sensing shows a solid enhancement in the sensing resolution *** the first demonstration of on-chip quantum photonic sensing,it reveals the advantages of photonic chips forhigh integration density,small-size,stability for multiple-parameter sensing serviceability.A higher sensing precision isexpected to beat the standard quantum limit with a higher photon number NOON state.

关键词： quantum sensing NOON state photonic chip

Efficient and robust CNN-LSTM prediction of flame temperature aided light field online tomography

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Science China(Technological Sciences) 2024年第1期67卷 271-284页

作者： NIU ZhiTian QI Hong SUN AnTai REN YaTao HE MingJian GAO BaoHai School of Energy Science and Engineering Harbin Institute of TechnologyHarbin 150001China Key Laboratory of Aerospace Thermophysics Ministry of Industry and Information TechnologyHarbin 150001China

Light field tomography,an optical combustion diagnostic technology,has recently attracted extensive attention due to its easy implementation and ***,the conventional iterative methods are high data throughput,low efficiency and time-consuming,and the existing machine learning models use the radiation spectrum information of the flame to realize the parameter field measurement at the current *** is still an offline measurement and cannot realize the online prediction of the instantaneous structure of the actual turbulent combustion *** this work,a novel online prediction model of flame temperature instantaneous structure based on deep convolutional neural network and long short-term memory(CNN-LSTM)is *** method uses the characteristics of local perception,shared weight,and pooling of CNN to extract the threedimensional(3D)features of flame temperature and outgoing radiation ***,the LSTM is used to comprehensively utilize the ten historical time series information of high dynamic combustion flame to accurately predict 3D temperature at three future moments.A chaotic time-series dataset based on the flame radiation forward model is built to train and validate the performance of the proposed CNN-LSTM *** is proven that the CNN-LSTM prediction model can successfully learn the evolution pattern of combustion flame and make accurate predictions.

关键词： temperature prediction convolutional neural network long short-term memory light field imaging online tomography

Multispectral point cloud superpoint segmentation

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Science China(Technological Sciences) 2024年第4期67卷 1270-1281页

作者： WANG QingWang WANG MingYe ZHANG ZiFeng SONG Jian ZENG Kai SHEN Tao GU YanFeng Faculty of information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Computer Technologies Application Kunming 650500China School of Electronics and information Engineering Harbin Institute of TechnologyHarbin 150001China

The multitude of airborne point clouds limits the point cloud processing *** are grouped based on similar points,which can effectively alleviate the demand for computing resources and improve processing ***,existing superpoint segmentation methods focus only on local geometric structures,resulting in inconsistent spectral features of points within a *** feature inconsistencies degrade the performance of subsequent ***,this study proposes a novel Superpoint Segmentation method that jointly utilizes spatial Geometric and Spectral information for multispectral point cloud superpoint segmentation(GSI-SS).Specifically,a similarity metric that combines spatial geometry and spectral information is proposed to facilitate the consistency of geometric structures and object attributes within segmented *** the formation of the primary superpoints,an intersuperpoint pointexchange mechanism that maximizes feature consistency within the final superpoints is *** are conducted on two real multispectral point cloud datasets,and the proposed method achieved higher recall,precision,F score,and lower global consistency and feature classification *** experimental results demonstrate the superiority of the proposed GSI-SS over several state-of-the-art methods.

关键词： multispectral point cloud superpoint segmentation over-segmentation spatial-spectral joint metric

Fault-tolerant identity-based encryption from SM9

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Science China(information Sciences) 2024年第2期67卷 104-117页

作者： Xiaohong LIU Xinyi HUANG Zhaohui CHENG Wei WU Fujian Provincial Key Laboratory of network Security and Cryptology College of Computer and Cyber SecurityFujian Normal University Artificial Intelligence Thrust Information HubThe Hong Kong University of Science and Technology (Guangzhou) Olym information Security technology Ltd. Center for Applied Mathematics of Fujian Province School of Mathematics and StatisticsFujian Normal University

This paper initiates the formal study of attribute-based encryption within the framework of SM9,the Chinese National Cryptography Standard for Identity-Based Cryptography, by presenting two new faulttolerant identity-based encryption(FIBE) schemes. Our first scheme uses the same private-key/ciphertext structure as the original SM9 algorithm and operates in a small attribute universe. As a result, it can be effectively and smoothly integrated into the information systems using SM9. In the random oracle model,we prove that our scheme is ciphertext-indistinguishable against fuzzy selective-identity and chosen-plaintext attacks under the(k + 3)-DBDHI assumption. Our second design is a large universe FIBE scheme based on SM9 that is ciphertext-indistinguishable against chosen-plaintext attacks in the random oracle model under the(f, g)-GDDHE assumption. Finally, we compare the communication and computing costs of our schemes to those of other classical ones. The comparison shows that our schemes have comparable performance as others. We believe that our findings will accelerate the applications of SM9 in modern information systems such as cloud computing and blockchain.

关键词： attribute-based encryption identity-based encryption fault-tolerant SM9

Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU network

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Computers, Materials & Continua 2024年第5期79卷 3067-3087页

作者： Arnab Dey Samit Biswas Dac-Nhuong Le Department of Computer Science and technology Indian Institute of Engineering Science and TechnologyShibpurHowrah711103India Faculty of information technology Haiphong UniversityHaiphong180000Vietnam

Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd datas

关键词： Workout action recognition video stream action recognition residual network GRU attention

A review on current development of thermophotovoltaic technology in heat recovery

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International Journal of Extreme Manufacturing 2024年第2期6卷 297-323页

作者： Shuni Chen Yanming Guo Qinghui Pan Yong Shuai Ministry of Industry and information technology Key Laboratory of Aerospace Thermophysics School of Energy Science and EngineeringHarbin Institute of TechnologyHarbin 150001People’s Republic of China

The burning of fossil fuels in industry results in significant carbon emissions,and the heat generated is often not fully *** high-temperature industries,thermophotovoltaics(TPVs)is an effective method for waste heat *** review covers two aspects of high-efficiency TPV systems and industrial waste heat *** the system level,representative results of TPV complete the systems,while selective emitters and photovoltaic cells in the last decade are *** key points of components to improve the energy conversion efficiency are further analyzed,and the related micro/nano-fabrication methods are *** the application level,the feasibility of TPV applications in high-temperature industries is shown from the world waste heat utilization *** potential of TPV in waste heat recovery and carbon neutrality is illustrated with the steel industry as an example.

关键词： thermophotovoltaic waste heat recovery micro/nano-fabrication carbon neutrality

Single-step-etched ultra-compact metamaterial grating coupler enabled by a hierarchical inverse design approach

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Science China(Physics,Mechanics & Astronomy) 2024年第2期67卷 119-126页

作者： Qiao Wang Ruiqi Luo Nan Liu Maojing Hou Bo Xiong Guandong Liu Wei Ma Intelligent network Research institute Zhejiang LabHangzhou 311100China State Key Laboratory of Modern Optical Instrumentation College of Information Science and Electronic EngineeringZhejiang UniversityHangzhou 310027China

With the concept of metamaterials introduced into integrated photonics,subwavelength structures have gained popularity for their ability to create devices with ultra-compact size,high performance,and versatile ***,traditional metamaterial design methods are usually based on empirical templates and physical approximations,lacking the ability to design free-form metamaterial structures and optimize entire devices *** this work,we propose a hierarchical inverse design approach that combines a conventional effective refractive index based metamaterial structures design with a follow-up global topology *** empirical metamaterial grating coupler design based on effective refractive index engineering faces inaccurate index extraction and insufficient approximation of wavevector matching conditions,which deteriorates coupling efficiency,especially for fully-etched devices with the decreased tapering ***,a subsequent overall topology optimization step can well compensate for the negative effect of the shrinking device footprint to increase the efficiency of the metamaterial grating *** demonstrate a 23μm×10μm ultra-compact metamaterial grating coupler with single-stepetched to couple light between a fiber and a 500 nm single-mode silicon waveguide in the *** measurement shows an insertion loss of 3.17 dB and a 3 dB bandwidth of 77 nm,making it the smallest footprint device ever reported.

关键词： waveguide hierarchical inverse

Multi-layer network embedding on scc-based network with motif

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Digital Communications and networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory for Ubiquitous network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of information and Communication Engineering Hainan UniversityHaikou 570228China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

Attention-Enhanced Voice Portrait Model Using Generative Adversarial network

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Computers, Materials & Continua 2024年第4期79卷 837-855页

作者： Jingyi Mao Yuchen Zhou YifanWang Junyu Li Ziqing Liu Fanliang Bu School of information network Security People’s Public Security University of ChinaBeijing100038China

Voice portrait technology has explored and established the relationship between speakers’ voices and their facialfeatures, aiming to generate corresponding facial characteristics by providing the voice of an unknown *** to its powerful advantages in image generation, Generative Adversarial networks (GANs) have now beenwidely applied across various fields. The existing Voice2Face methods for voice portraits are primarily based onGANs trained on voice-face paired datasets. However, voice portrait models solely constructed on GANs facelimitations in image generation quality and struggle to maintain facial similarity. Additionally, the training processis relatively unstable, thereby affecting the overall generative performance of the model. To overcome the abovechallenges,wepropose a novel deepGenerativeAdversarialnetworkmodel for audio-visual synthesis, namedAVPGAN(Attention-enhanced Voice Portrait Model using Generative Adversarial network). This model is based ona convolutional attention mechanism and is capable of generating corresponding facial images from the voice ofan unknown speaker. Firstly, to address the issue of training instability, we integrate convolutional neural networkswith deep GANs. In the network architecture, we apply spectral normalization to constrain the variation of thediscriminator, preventing issues such as mode collapse. Secondly, to enhance the model’s ability to extract relevantfeatures between the two modalities, we propose a voice portrait model based on convolutional attention. Thismodel learns the mapping relationship between voice and facial features in a common space from both channeland spatial dimensions independently. Thirdly, to enhance the quality of generated faces, we have incorporated adegradation removal module and utilized pretrained facial GANs as facial priors to repair and enhance the clarityof the generated facial images. Experimental results demonstrate that our AVP-GAN achieved a cosine similarity of0.511, ou

关键词： Cross-modal generation GANs voice portrait technology face synthesis