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Free-space beam shaping and steering based on a silicon optical phased array

Photonics Research 2023年第12期11卷 2093-2099页

作者： WENBO REN QINGQING LIANG JIJUN FENG HAIPENG LIU JIANHUA HU SHUO YUAN XINCHENG XIA WEI JIANG QIWEN ZHAN HEPING ZENG Shanghai Key Laboratory of Modern Optical System Engineering Research Center of Optical Instrument and System(Ministry of Education)School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Chongqing Key Laboratory of Precision Optics Chongqing Institute of East China Normal UniversityChongqing 401120China State Key Laboratory of Precision Spectroscopy East China Normal UniversityShanghai 200241China

In this study,we present a method for free-space beam shaping and steering based on a silicon optical phased array,which addresses the theoretical limitation of traditional bulk *** theoretically analyze the beam propagation properties with changes in the applied *** beam profiles can be shaped by varying the phase combination,while a high-order quasi-Bessel beam can be generated with a cubic change to the phase *** simulated results are validated further experimentally,and they match one another *** steering can be achieved with a field of view as large as 140°,which has potential benefits for practical *** presented method is expected to have broad application prospects for optical communications,free-space optical interconnects,and light detection and ranging.

关键词： optical beam optics

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The Frontier of Geospatial AI Deep Learning Applications in Data Mining and Spatial Analysis

The Frontier of Geospatial AI Deep Learning Applications in ...

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2024 IEEE International Conference on Intelligent systems and Advanced Applications, ICISAA 2024

作者： Mishra, Ajit Kumar Verma, Vikash Manivannan, R. Kumar, N. Krishna Srividya, Mudunuru Sharma, Sanskriti IES College of Technology Department of Electrical & Electronics Engineering M.P Bhopal462044 India GGITS Dept of Artificial Intelligence & Machine Learning M.P Jabalpur India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Computer Science & Engineering TN Chennai India Guru Nanak Institutions Technical Campus Ibrahimpatam Dept.of CSE Telangana Hyderabad India MLR Institute of Technology Dept. of CSE Telangana Hyderabad India GGITS Dept of Computer Science & Business System M.P Jabalpur India

ISBN: (纸本)9798331539948

Modern artificial intelligence uses geographical data mining and regional analysis to analyze large datasets. Deep learning is a new geographic AI method. Example: Geographic Deep Mining Network (GDMN). The Spatial Feature Extractor (SFE), Temporal Geospatial Attention Mechanism (TGAM), and Geospatial Data Fusion Module (GDFM) comprise this technique. We examined six popular approaches and found GDMN performed best. It worked successfully for several reasons. GDMN always outperforms its competitors in memory, F1 score, IoU, training time, and inference time. This is always true. These findings suggest that GDMN might boost geospatial AI's data mining and location analysis abilities. After all the effort, we have developed a solid basis for location intelligence, making it useful in various subject areas. Because it's versatile, mapping specialists may use GDMN for better environmental monitoring or city development. Deep learning helps GDMN make better space choices and uncover insights in large, challenging data sets that no one knew about. GDMN's cutting-edge approaches make space and time research and prediction simpler. This wide perspective offers a more full and all-encompassing picture of today's challenges than previous techniques. This makes it impossible to overstate how significant this work is to location intelligence. Its unmatched research and insights usher in a new era. © 2024 IEEE.

关键词： Adversarial machine learning

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Detail-Enhancing Framework for Reference-Based Image Super-Resolution

arXiv

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arXiv 2024年

作者： Wang, Zihan Xiong, Ziliang Tang, Hongying Yuan, Xiaobing Shanghai Institution of Micro-system and Information Technology University of Chinese Academy of Science China Computer Vision Laboratory Department of Electrical Engineering LiU Sweden School of Information Science and Technology Shanghai Tech University China

Recent years have witnessed the prosperity of reference-based image super-resolution (Ref-SR). By importing the high-resolution (HR) reference images into the single image super-resolution (SISR) approach, the ill-posed nature of this long-standing field has been alleviated with the assistance of texture transferred from reference images. Although the significant improvement in quantitative and qualitative results has verified the superiority of Ref-SR methods, the presence of misalignment before texture transfer indicates room for further performance improvement. Existing methods tend to neglect the significance of details in the context of comparison, therefore not fully leveraging the information contained within low-resolution (LR) images. In this paper, we propose a Detail-Enhancing Framework (DEF) for reference-based super-resolution, which introduces the diffusion model to generate and enhance the underlying detail in LR images. If corresponding parts are present in the reference image, our method can facilitate rigorous alignment. In cases where the reference image lacks corresponding parts, it ensures a fundamental improvement while avoiding the influence of the reference image. Extensive experiments demonstrate that our proposed method achieves superior visual results while maintaining comparable numerical outcomes. Copyright © 2024, The Authors. All rights reserved.

关键词： Textures

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Quantum noise of dark pulse microcombs

Quantum noise of dark pulse microcombs

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CLEO: QELS_Fundamental science, QELS 2022

作者： Lao, Chenghao Jin, Xing Chang, Lin Xie, Weiqiang Shu, Haowen Wang, Xingjun Bowers, John E. Yang, Qi-Fan State Key Laboratory for Artificial Microstructure and Mesoscopic Physics Frontiers Science Center for Nano-Optoelectronics School of Physics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States State Key Laboratory of Advanced Optical Communications System and Networks Department of Electronics School of Electronics Engineering and Computer Science Peking University Beijing100871 China

We investigate the fundamental timing jitter of dark pulse microcombs generated in an AlGaAs microresonator, where the quantum-limited frequency noise of the 91-GHz repetition rate is measured to be 0.5 Hz2/Hz. ©... 详细信息

ISBN: (纸本)9781557528209

关键词： Aluminum gallium arsenide

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DRCTL: A Disorder-Resistant Computation Translation Layer Enhancing the Lifetime and Performance of Memristive CIM Architecture

DRCTL: A Disorder-Resistant Computation Translation Layer En...

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IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Heng Zhou Bing Wu Huan Cheng Jinpeng Liu Taoming Lei Dan Feng Wei Tong Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology School of Computer Science and Technology Huazhong University of Science and Technology Ministry of Education of China

ISBN: (数字)9798350350579

ISBN: (纸本)9798350350586

The memristive Computing-in-Memory (CIM) sys-tem can efficiently accelerate matrix-vector multiplication (MVM) operations through in-situ computing. The data layout has a significant impact on the communication performance of CIM systems. Existing software-level communication opti-mizations aim to reduce communication distance by carefully designing static data layouts, while wear-leveling (WL) and error mitigation methods use dynamic scheduling to enhance system reliability, resulting in randomized data layouts and increased communication overhead. Besides, existing CIM compilers di-rectly map data to physical crossbars and generate instructions, which causes inconvenience for dynamic scheduling. To address these challenges of balancing communication performance and reliability while coordinating existing CIM compilers and dy-namic scheduling, we propose a disorder-resistant computation translation layer (DRCTL), which improves system lifetime and communication performance through co-optimization of data layout and dynamic scheduling. It consists of three parts: (1) We propose an address conversion method for dynamic scheduling, which updates the addresses in the instruction stream after dynamic scheduling, thereby avoiding recompilation. (2) Dynamic scheduling strategy for reliability improvement. We propose a hierarchical wear-leveling (HWL) strategy, which reduces communication by increasing scheduling granularity. (3) Communication optimization for dynamic scheduling. We propose data layout-aware selective remapping (LASR), which helps dynamic scheduling methods improve communication lo-cality and reduce latency by exploiting data dependencies. The experiments demonstrate that HWL extends lifetime by 100.3-205.9 x compared to not using WL. Even with a slight lifetime decrease compared to the state-of-the-art WL (TIWL), it still supports continuous neural network training for 7 years. After applying LASR to HWL, the number of execution cycles, energy consumption

关键词： Training Energy consumption Processor scheduling Layout Dynamic scheduling In-memory computing Common Information Model (computing) system-on-chip Reliability Optimization

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CorcPUM: Efficient Processing Using Cross-Point Memory via Cooperative Row-Column Access Pipelining and Adaptive Timing Optimization in Subarrays 23

CorcPUM: Efficient Processing Using Cross-Point Memory via C...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Chengning Wang Dan Feng Wei Tong Jingning Liu Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China (School of Computer Science and Technology Huazhong University of Science and Technology) Wuhan China

ISBN: (纸本)9798350323481

Emerging cross-point memory can in-situ perform vector-matrix multiplication (VMM) for energy-efficient scientific computation. However, parasitic-capacitance-induced row charging and discharging latency is a major performance bottleneck of subarray VMM. We propose a memory-timing-compliant bulk VMM processing-using-memory design with row access and column access co-optimization from rethinking of read access commands and μ-op timing. We propose row-level-parallelism-adaptive timing termination mechanism to reduce tail latency of tRCD and tRP by exploiting row nonlinear charging and bulk-interleaved row-column-cooperative VMM access mechanism to reduce tRAS and overlap CL without increasing column ADC precision. Evaluations show that our design can achieve 5.03× performance speedup compared with an aggressive baseline.

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No Free Lunch Theorem for Privacy-Preserving LLM Inference

arXiv

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arXiv 2024年

作者： Zhang, Xiaojin Fei, Yulin Kang, Yan Chen, Wei Fan, Lixin Jin, Hai Yang, Qiang Huazhong University of Science and Technology China WeBank China Hong Kong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Individuals and businesses have been significantly benefited by Large Language Models (LLMs) including PaLM, Gemini and ChatGPT in various ways. For example, LLMs enhance productivity, reduce costs, and enable us to focus on more valuable tasks. Furthermore, LLMs possess the capacity to sift through extensive datasets, uncover underlying patterns, and furnish critical insights that propel the frontiers of technology and science. However, LLMs also pose privacy concerns. Users’ interactions with LLMs may expose their sensitive personal or company information. A lack of robust privacy safeguards and legal frameworks could permit the unwarranted intrusion or improper handling of individual data, thereby risking infringements of privacy and the theft of personal identities. To ensure privacy, it is essential to minimize the dependency between shared prompts and private information. Various randomization approaches have been proposed to protect prompts’ privacy, but they may incur utility loss compared to unprotected LLMs prompting. Therefore, it is essential to evaluate the balance between the risk of privacy leakage and loss of utility when conducting effective protection mechanisms. The current study develops a framework for inferring privacy-protected Large Language Models (LLMs) and lays down a solid theoretical basis for examining the interplay between privacy preservation and utility. The core insight is encapsulated within a theorem that is called as the NFL (abbreviation of the word No-Free-Lunch) Theorem. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

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DRSC: Dual-Reweighted Siamese Contrastive Learning Network for Cross-Domain Rotating Machinery Fault Diagnosis With Multi-Source Domain Imbalanced Data

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IEEE Internet of Things Journal 2025年

作者： Bi, Yuanguo Fu, Rao Jiang, Cunyu Zhang, Xiaoling Li, Fengyun Zhao, Liang Han, Guangjie Northeastern University School of Computer Science and Engineering Shenyang110169 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110169 China Shenyang University of Technology Institute of Artificial Intelligence Shenyang110870 China Shenyang Aerospace University School of Computer Science Shenyang110135 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

To enhance the reliability of rotating machinery, cross-domain fault diagnosis becomes vital for detecting faults under unknown operating conditions. However, multi-source domain imbalanced data present significant challenges, as divergent label distributions across domains cause complex domain-class shifts and degrade the performance of cross-domain fault diagnosis. Moreover, diagnostic models often struggle to learn features from minority classes due to label imbalance within each domain, which may degrade the performance in diagnosing these minority classes. To address these challenges, we propose a Dual-Reweighted Siamese Contrastive learning network (DRSC) for cross-domain fault diagnosis with multi-source domain imbalanced data. In DRSC, we design a Siamese feature extractor based on a wide-kernel convolutional neural network to capture short-term characteristics and leverage the convenience in extracting domain-invariant features. Subsequently, to alleviate domain-class shifts, we design a reweighted contrastive domain-class alignment mechanism that strategically pulls domain-class pairs together while pushing other health conditions away. Finally, to enable the diagnostic model to learn from minority health conditions, a reweighted health condition classifier is developed by assigning higher weights to the minority classes. Evaluation results on two public datasets illustrate DRSC outperforms comparison models in cross-domain fault diagnosis. © 2014 IEEE.

关键词： Convolutional neural networks

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CoSLR: Contrastive Chinese Sign Language Recognition with prior knowledge And Multi-Tasks Joint Learning

CoSLR: Contrastive Chinese Sign Language Recognition with pr...

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

作者： Tian Yang Cong Shen Tiantian Yuan School of Computer Science and Engineering Tianjin University of Technology Tianjin China Engineering Research Center of Learning-Based Intelligent System (MOE) Tianjin China Technical College for the Deaf Tianjin University of Technology Tianjin China

Perceiving by computer vision, Sign Language Recognition (SLR) obtains the advantage of transforming the posture video into a sentence, compared with the methods of sensors to collect signals. However, learning representative features from a multimodal perspective is challenging. To this end, this study proposes a multi-task joint learning framework termed Contrastive Learning-based Sign Language Recognition Network (CoSLR) for Chinese sign language, which embeds text representation into the general video-based framework of SLR. In virtue of the profound ability of the pre-trained multimodal encoders, they are employed as processing modules to extract features from the original input video and text. Then, a contrastive learning between the video representation and corresponding token embedding is utilized to the feature extractor training. Finally, the linear combination of contrast and cross-entropy loss functions drives the end-to-end network to converge. Experiments show that the 1.27% WER of CoSLR has outperformed the state-of-the-art works in the comparison.

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Advances on the investigation of landslides by space-borne synthetic aperture radar interferometry

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地球空间信息科学学报（英文版） 2024年第3期27卷 602-623页

作者： Roberto Tomás Qiming Zeng Juan M.Lopez-Sanchez Chaoying Zhao Zhenhong Li Xiaojie Liu María I.Navarro-Hernández Liuru Hu Jiayin Luo Esteban Díaz William T.Szeibert José Luis Pastor Adrián Riquelme Chen Yu Miguel Cano Departamento de Ingeniería Civil University of AlicanteAlicanteSpain Institute of Remote Sensing and Geographic Information System School of Earth and Space SciencePeking UniversityBeijingChina Institute for Computer Research(IUII) University of AlicanteAlicanteSpain College of Geological Engineering and Geomatics Chang'an UniversityXi'anChina Departamento de Ingeniería Civil University of AlicanteAlicanteSpain School of Civil Engineering Lanzhou University of TechnologyLanzhouChina Departamento de Ingeniería Civil University of AlicanteAlicanteSpain Land Satellite Remote Sensing Application Center(LASAC) Ministry of Natural Resources of P.R.ChinaBeijingChina The First Topographic Surveying Brigade of Ministry of Natural Resources of the People's Republic of China Xi'anChina Sixense Departamento SatéliteL'Hospitalet de LlobregatBarcelonaSpain

Landslides are destructive geohazards to people and infrastructure,resulting in hundreds of deaths and billions of dollars of damage every ***,mapping the rate of deforma-tion of such geohazards and understanding their mechanics is of paramount importance to mitigate the resulting impacts and properly manage the associated *** this paper,the main outcomes relevant to the joint European Space Agency(ESA)and the Chinese Ministry of science and Technology(MOST)Dragon-5 initiative cooperation project ID 59,339"Earth observation for seismic hazard assessment and landslide early warning system"are *** primary goals of the project are to further develop advanced SAR/InSAR and optical techniques to investigate seismic hazards and risks,detect potential landslides in wide regions,and demonstrate EO-based landslide early warning system over selected *** work only focuses on the landslide hazard content of the project,and thus,in order to achieve these objectives,the following tasks were developed up to now:a)a procedure for phase unwrap-ping errors and tropospheric delay correction;b)an improvement of a cross-platform SAR offset tracking method for the retrieval of long-term ground displacements;c)the application of polarimetric SAR interferometry(PolInSAR)to increase the number and quality of monitoring points in landslide-prone areas;d)the semiautomatic mapping and preliminary classification of active displacement areas on wide regions;e)the modeling and identification of landslides in order to identify triggering factors or predict future displacements;and f)the application of an InSAR-based landslide early warning system on a selected *** achieved results,which mainly focus on specific sensitive regions,provide essential assets for planning present and future scientific activities devoted to identifying,mapping,characterizing,monitoring and predicting landslides,as well as for the implementation of early warning systems.

关键词： InSAR SAR offset tracking PolInSAR landslide mapping modeling early warning monitoring

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