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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 1357-1368页

作者： Wang, Chenlong Ai, Bo He, Ruisi Yang, Mi Zhou, Shun Yu, Long Zhang, Yuxin Qiu, Zhicheng Zhong, Zhangdui Fan, Jianhua Beijing Jiaotong University Frontiers Science Center for Smart High-Speed Railway System School of Electronics and Information Engineering Beijing100044 China National University of Defense Technology Sixty-Third Research Institute Nanjing210007 China

With the advancement of communication technology, there is a higher demand for high-precision and high-generalization channel path loss models as it is fundamental to communication systems. For traditional stochastic and deterministic models, it is difficult to strike a balance between prediction accuracy and generalizability. This paper proposes a novel deep learning-based path loss prediction model using satellite images. In order to efficiently extract environment features from satellite images, residual structure, attention mechanism, and spatial pyramid pooling layer are developed in the network based on expert knowledge. Using a convolutional network activation visualization method, the interpretability of the proposed model is improved. Finally, the proposed model achieves a prediction accuracy with a root mean square error of 5.05 dB, demonstrating an improvement of 3.07 dB over a reference empirical propagation model. © 2023 CCBY.

关键词： Prediction models

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Seastar: A Cache-Efficient and Load-Balanced Key-Value Store on Disaggregated Memory

Seastar: A Cache-Efficient and Load-Balanced Key-Value Store...

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2024 IEEE International Conference on Cluster Computing, CLUSTER 2024

作者： Du, Jingwen Wang, Fang Feng, Dan Zeng, Dexin Yi, Sheng Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System China School of Computer Science and Technology Huazhong University of Science and Technology China Shenzhen Huazhong University of Science and Technology Research Institute China

ISBN: (纸本)9798350358711

In modern datacenters, memory disaggregation un-packs monolithic servers to build independent network-connected compute and memory pools, greatly improving resource uti-lization. Existing memory-disaggregated key-value stores adopt ownership sharing or ownership partitioning for request scheduling and they all show poor performance and scalability. The former generates multiple cache misses and multiple network round trips due to poor cache locality, while the latter fails to fully use resources on all Compute Nodes (CNs) due to load imbalance. In this paper, we propose Seastar, a fast and scalable key-value store that achieves high cache locality as well as load balancing. To enable high concurrency and ensure linearizability, Seastar exploits a self-verification version chain to provide out-of-place atomic updates and ensure consistent reads. To improve cache locality, Seastar leverages a pair-based ownership assigning scheme to assign the processing permission of requests corre-sponding to each key to a CN pair through two independent hash functions. To ensure load balancing among CNs, Seastar proposes a decentralized power-of-two-choices routing scheme to route the requests to the less-loaded CNs in CN pairs for handling. Experimental results demonstrate that Seastar improves the throughput by up to 6.7x and significantly reduces the latency compared with state-of-the-art memory-disaggregated key-value stores. © 2024 IEEE.

关键词： Scalability

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Self-organizing Competitive Neural Network Based Adaptive Sparse Representation for Magnetotelluric Data Denoising 10

Self-organizing Competitive Neural Network Based Adaptive Sp...

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10th International Conference on Environmental and engineering Geophysics, ICEEG 2023

作者： Liu, Xiaoqiong Li, Guang Li, Jin Zhou, Xiaohui Gu, Xianjie Zhou, Cong Gong, Meng Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System East China University of Technology Nanchang330013 China College of Information Science and Engineering Hunan Normal University Changsha410081 China

The existing sparse decomposition denoising methods for magnetotelluric (MT) data need to set the iterative stop condition manually, which not only has a large workload and high difficulty, but also easily causes subjective bias. To this end, we propose a new adaptive sparse representation method for MT data denoising. First, the data to be processed is divided into high-quality segments and noisy segments by machine learning algorithm. Then, the characteristic parameters of high-quality segments are calculated, and the boundary value of the characteristic parameters is taken as the threshold. The threshold has two functions, one is as a criterion for signal-to-noise identification, and the other is as an iterative stop condition for subsequent sparse decomposition. Finally, the optimized orthogonal matching pursuit algorithm is used to separate the signal and noise of the noisy segments, and the denoised segments and high-quality segments are combined to obtain the complete denoised MT data. The field data processing results show that this method is a fully automatic and intelligent MT data denoising method. It greatly improves the signal-to-noise ratio and the apparent resistivity-phase curves. © Published under licence by IOP Publishing Ltd.

关键词： Signal to noise ratio

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Ethiopic Cryptography: An Efficient Hybrid Encryption Algorithm for Reducing Cybersecurity Attack 11th

Ethiopic Cryptography: An Efficient Hybrid Encryption Algori...

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11th EAI International Conference on Advancement of science and technology, ICAST 2023

作者： Akele, Tadesse Birara Bahiru, Tadesse Kebede Molla, Tibebu Legesse Fentie, Shambel Dessale Belay, Tadesse Destaw Marye, Almaz Yemataw Getaneh, Mersha Derese Department of Information System Kombolcha Institute of Technology Wollo University Kombolcha Ethiopia Department of Information Technology Kombolcha Institute of Technology Wollo University Kombolcha Ethiopia Deparment of Software Engineering Kombolcha Institute of Technology Wollo University Kombolcha Ethiopia Department of Computer Science Kombolcha Institute of Technology Wollo University Kombolcha Ethiopia

ISBN: (纸本)9783031641503

Cryptography is an essential tool in ensuring the security of data during network communication. Network and service providers prioritize data security to ensure secure data exchanges. Ethiopic cryptography is a unique encryption method that utilizes the Amharic language, prevalent in Ethiopia. Unlike Latin alphabets, Amharic consists of 287 characters, making decryption challenging. The encryption process involves converting the text to Amharic and then mapping it to a pair of randomly chosen two-bit English alphabets, creating an intermediate cipher. The Ethiopic cryptography algorithm employs a hybrid approach, using the RSA algorithm for secure key exchange and the Advanced Encryption Standard algorithm for text confidentiality. The algorithm comprises three phases—translation, mapping, and encryption—contributing to its effectiveness in securing data over the network. Decryption requires familiarity with the Amharic language and mapping details. The algorithm demonstrates a stronger avalanche effect (97%) in comparison with Blowfish and DES. The algorithm is proposed and evaluated using the design science research methodology, and its performance is validated through simulation using MATlab and BunnaScript tools, considering factors like the avalanche effect, time complexity, and memory consumption. The evaluation shows that the suggested solution operates faster, requires lesser time for data encryption, and uses less memory than other methods in use. Overall, the proposed Ethiopic cryptography algorithm proves to be an efficient and effective method for securing data during network communication, offering stronger text confidentiality and an improved avalanche effect. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： MATlab

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Large circuit models: opportunities and challenges

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science China(information sciences) 2024年第10期67卷 25-66页

作者： Lei CHEN Yiqi CHEN Zhufei CHU Wenji FANG Tsung-Yi HO Ru HUANG Yu HUANG Sadaf KHAN Min LI Xingquan LI Yu LI Yun LIANG Jinwei LIU Yi LIU Yibo LIN Guojie LUO Hongyang PAN Zhengyuan SHI Guangyu SUN Dimitrios TSARAS Runsheng WANG Ziyi WANG Xinming WEI Zhiyao XIE Qiang XU Chenhao XUE Junchi YAN Jun YANG Bei YU Mingxuan YUAN Evangeline F.Y.YOUNG Xuan ZENG Haoyi ZHANG Zuodong ZHANG Yuxiang ZHAO Hui-Ling ZHEN Ziyang ZHENG Binwu ZHU Keren ZHU Sunan ZOU Huawei Noah’s Ark Lab School of Integrated Circuits Peking University Faculty of Electrical Engineering and Computer Science Ningbo University Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering The Chinese University of Hong Kong School of Integrated Circuits Southeast University Huawei HiSilicon Peng Cheng Laboratory School of Computer Science Peking University School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University School of Artificial Intelligence Shanghai Jiao Tong University

Within the electronic design automation(EDA) domain, artificial intelligence(AI)-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an “AI4EDA” approach falls short of achieving a holistic design synthesis and understanding,overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This study argues for a paradigm shift from AI4EDA towards AI-rooted EDA from the ground up, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, register-transfer level(RTL) designs, circuit netlists,and physical layouts. We champion the creation of large circuit models(LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-rooted philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound “shift-left” in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design-tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area(PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems' capabilities.

关键词： AI-rooted EDA large circuit models (LCMs) multimodal circuit representation learning circuit optimization

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TRNet: Two-Tier Recursion Network for Co-Salient Object Detection

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IEEE Transactions on Circuits and systems for Video technology 2025年第6期35卷 5844-5857页

作者： Cong, Runmin Yang, Ning Liu, Hongyu Zhang, Dingwen Huang, Qingming Kwong, Sam Zhang, Wei Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Northwestern Polytechnical University Brain and Artificial Intelligence Laboratory School of Automation Xi'an710129 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing101408 China Lingnan University Hong Kong

Co-salient object detection (CoSOD) is to find the salient and recurring objects from a series of relevant images, where modeling inter-image relationships plays a crucial role. Different from the commonly used direct learning structure that inputs all the intra-image features into some well-designed modules to represent the inter-image relationship, we resort to adopting a recursive structure for inter-image modeling, and propose a two-tier recursion network (TRNet) to achieve CoSOD in this paper. The two-tier recursive structure of the proposed TRNet is embodied in two stages of inter-image extraction and distribution. On the one hand, considering the task adaptability and inter-image correlation, we design an inter-image exploration with recursive reinforcement module to learn the local and global inter-image correspondences, guaranteeing the validity and discriminativeness of the information in the step-by-step propagation. On the other hand, we design a dynamic recursion distribution module to fully exploit the role of inter-image correspondences in a recursive structure, adaptively assigning common attributes to each individual image through an improved semi-dynamic convolution. Experimental results on five prevailing CoSOD benchmarks demonstrate that our TRNet outperforms other competitors in terms of various evaluation metrics. The code and results of our method are available at https://***/rmcong/TRNetTCSVT2025. © 1991-2012 IEEE.

关键词： HTTP

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Nanodiamond/Ti_(3)C_(2) MXene-coated quartz crystal microbalance humidity sensor with high sensitivity and high quality factor

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Rare Metals 2024年第6期43卷 2719-2729页

作者： Yao Yao Qiao Chen Yan-Qi Li Xian-He Huang Wei-Wei Ling Zhe-MiaoXie Jia-QiWang Chang-Ming Chen College of Communication Engineering Chengdu University of Information TechnologyChengdu 610225China School of Automation Chongqing University of Posts and TelecommunicationsChongqing 400065China School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Advanced Micro-/Nano-Devices Lab Department of Systems Design EngineeringUniversity of WaterlooWaterlooON N2L 3GlCanada

To address the challenge of achieving both high sensitivity and a high quality factor in quartz crystal microbalance(QCM)humidity sensors,a nanodiamond(ND)/Ti_(3)C_(2)MXene composite-coated QCM humidity sensor was *** material characteristics of ND,Ti_(3)C_(2)MXene,and ND/Ti_(3)C_(2)MXene composite were analyzed by transmission electron microscopy(TEM)and Fourier transform infrared(FTIR)*** experimental results demonstrated that the hydrophilic ND nanoparticles coated on Ti_(3)C_(2) MXene nanosheet prevented the self-stacking of Ti_(3)C_(2)MXene and enhanced the sensitivity of Ti_(3)C_(2) MXene-based QCM humidity ***,the high mechanical modulus of Ti_(3)C_(2) MXene material helped ND/Ti_(3)C_(2)MXene composite-coated QCM humidity sensor to achieve a high quality factor(>20,000).ND/Ti_(3)C_(2)MXene compositecoated QCM humidity sensor exhibited a sensitivity of 82.45 Hz/%RH,a humidity hysteresis of 1.1%RH,fast response/recovery times,acceptable repeatability,and good stability from 11.3%RH to 97.3%*** response mechanism of ND/Ti_(3)C_(2) MXene composite-coated QCM humidity sensor was analyzed in combination with a bi-exponential kinetic adsorption ***,the potential application of ND/Ti_(3)C_(2)MXene composite-coated QCM humidity sensor was demonstrated through its frequency response to wooden blocks with different moisture contents.

关键词： Humidity sensor Quartz crystal microbalance Nanodiamond Ti_(3)C_(2)MXene High quality factor

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Research on 3D Positioning technology of UWB Single Base Station 14th

Research on 3D Positioning Technology of UWB Single Base Sta...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Zhang, Jingjing Huang, Lu Su, Jia Yang, Zihan Yi, Qingwu School of Information Science and Engineering Hebei University of Science and Technology Hebei050018 China State Key Laboratory of Satellite Navigation System and Equipment Technology The 54th Research Institute of China Electronics Technology Group Corporation Hebei050081 China

ISBN: (纸本)9789819969272

In recent years, in the context of smart cities and the internet of everything, with the rapid development of the intelligent indoor environment, the demand for indoor location services in many industries has become higher and higher, and the need for real-time location of personnel has become more and more urgent. In this paper, a UWB-based circular antenna array single base station is designed for indoor space single base station 3D positioning problem, and the joint Time of Arrival (TOA)/Angle of Arrival (AOA) positioning estimation algorithm is studied. In terms of direction finding, a five-array element direction finding model is established using a uniform circular array, and the Phase Difference of Arrival (PDOA) algorithm is combined to obtain the signal arrival angle information, and TOA is used to complete the distance measurement between the base station and the label, to achieve the calculation of label location information. Also, for the consideration of improving the accuracy of angle measurement, the ambiguity resolution method of antenna array element phase difference for long baseline is proposed. Finally, the system performance was tested and verified in the experimental environments. The results show that the UWB single base station can be used to achieve indoor 3D positioning, its positioning accuracy is better than 1m. Furthermore, the technology can effectively solve problems such as the high deployment cost of multiple base stations, complicated system construction, and so on in practical applications with more excellent application and promotion value. © 2024, Aerospace information Research Institute.

关键词： Base stations

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Enhanced electrical performance in CaBi_(4)Ti_(4)O_(15)ceramics through synergistic chemical doping and texture engineering

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Journal of Materiomics 2024年第2期10卷 471-479页

作者： Le Zhao Guohao Li Xiao Zhai Weijie Kuai Yuzhi Zhai Gang Tian Zhigang Gai Minglei Zhao Juan Du Hongjun Zhang Limei Zheng School of Information and Automation Engineering Qilu University of Technology(Shandong Academy of Sciences)Jinan250353China School of Physics State Key Laboratory of Crystal MaterialsShandong UniversityJinan250100China Center for Optics Research and Engineering(CORE) Key Laboratory of Laser and Infrared System of Ministry of EducationShandong UniversityQingdaoShandong266237China College of Materials Science and Engineering Liaocheng UniversityLiaocheng252059China Functional Materials and Acoustooptic Instruments Institute School of Instrumentation Science and EngineeringHarbin Institute of TechnologyHarbin150080China

High-temperature piezoelectric materials with excellent piezoelectricity,low dielectric loss and large resistivity are highly desired for many industrial sectors such as aerospace,aircraft and nuclear *** a synergistic design strategy combining microstructural texture and chemical doping is employed to optimize CaBi 4Ti 4O15(CBT)ceramics with bismuth layer *** textured microstructure with an orientation factor of 80%e82%has been successfully achieved by the spark plasma sintering ***,by doping MnO_(2),both advantages of hard doping and sintering aids are used to obtain the excellent electrical performance of d_(33)=27.3 pC/N,tandδ-0.1%,Q_(31)~2,307 and electrical re-sistivityρ~6.5×10^(10)Ω·*** to 600℃,the 0.2%(in mass)Mn doped CBT ceramics still exhibit high performance of d_(33)=26.4 pC/N,r~1.5×10^(6)Ω·cm and tandδ~15.8%,keeping at an applicable level,thus the upper-temperature limit for practical application of the CBT ceramics is greatly *** work paves a new way for developing and fabricating excellent high-temperature piezoelectric materials.

关键词： CaBi_(4)Ti_(4)O_(15) Piezoelectricity Mechanical quality factor Resistivity

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Multi-dimensional information Extraction and Utilization in Smart Fiber-optic Distributed Acoustic Sensor (sDAS)

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Journal of Lightwave technology 2024年第19期42卷 1-13页

作者： Wu, Huijuan Liu, Xinyu Wang, Xinlei Wu, Yongxin Liu, Yiyu Wang, Yufeng Rao, Yunjiang Key Lab of Optical Fiber Sensing &amp Communications (Ministry of Education) School of Information and Communication Engineering University of Electronic Science &amp Technology of China Chengdu Sichuan China

Current fiber-optic distributed acoustic sensor (DAS) target recognition technologies continue to prioritize updating the feature learning tools while neglecting varying contributions of information from different dimensions. Few works focus on designing recognition algorithms from the perspective of multi-dimensional information extraction and utilization. In this paper, we introduce an end-to-end three-dimensional attention-assisted convolutional neural network (3-D ACNN) into DAS for the first time. This novel approach allows for automatic and concurrent extracttion of three-dimensional input in time, frequency, and space, aiming to cooperatively recognize sensing targets with higher accuracy. Our new scheme also includes comparative examination of 2-D and 3-D attention mechanisms to leverage the most efficient information in various dimensions, thereby enhancing recognition accuracy while maintaining computation efficiency. Field tests demonstrate that the 3-D CNN outperforms commonly used 2-D networks with only time-frequency (T-F), time-space (T-S), or space-frequency (S-F) inputs, improving recognition accuracy from 95.47% to 98.67%. Moreover, the proposed 3-D ACNN can further improve accuracy up to 99.33%, outperforming the basic 3-D CNN. It is also found that T-F information in DAS is richer and more recognized than T-S and S-F information. The use of 3-D attention yields better results than 2-D attention. Additionally, the processing time required for each spatial sensing point is only 0.22 ms on a commercial GPU (NVIDIA GeForce (R) GTX 1080 Ti), which is approximately 1/171 of the time required for the equivalent 2-D network with the same T-F input (38.52 ms). The 3-D ACNN enabled smart fiber-optic DAS (sDAS) represents a significant breakthrough, enabling fast and accurate multi-dimensional collaborative recognition. This advancement is expected to have a substantial impact for distributed sensing applications. © 2024 IEEE. Personal use is permitted, but

关键词： 3-D ACNN attention mechanism DAS signal recognition Î¦-OTDR

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