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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

作者： Bilal, Asif Quddious, Abdul Votsi, Haris Kanno, Atsushi Kawanishi, Tetsuya Antoniades, Marco A. Iezekiel, Stavros University of Cyprus EMPHASIS Research Centre Nicosia Cyprus University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus University of Cyprus KIOS Research Center for Excellence Nicosia Cyprus National Institute of Information and Communications Technology Tokyo Japan Waseda University Tokyo Japan Ryerson University Department of Electrical Computer & Biomedical Engineering TorontoON Canada

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

This paper discusses a dual-layer metasurface (MSF) superstrate, consisting of double split-ring resonator cells in the 28 GHz 5G mm-wave frequency band. The MSF superstrate is added above a conventional patch antenna, and an enhancement in the performance of the patch antenna in terms of bandwidth and gain is achieved. This is due to the negative index metamaterial (NIM) properties of the designed MSF superstrate. In this design, the impedance bandwidth is improved from 2.6 GHz to 3.6 GHz, while the gain of the patch antenna is increased from 6.59 dBi to 12.9 dBi. The simplicity of the design to achieve a higher gain and broader bandwidth makes it a suitable candidate for applications in 5G mm-wave communications. © 2022 IEEE.

关键词： Microstrip antennas

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Performance and Radiation Testing of the Coral TPU Co-processor for AI Onboard Satellites

Performance and Radiation Testing of the Coral TPU Co-proces...

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Data Handling & Data Processing Conference (EDHPC), European

作者： George Lentaris Vasileios Leon Chronis Sakos Dimitrios Soudris Antonios Tavoularis Alessandra Costantino Cesar Boatella Polo Department of Informatics & Computer Engineering University of West Attica Greece School of Electrical & Computer Engineering National Technical University of Athens Greece European Space Agency European Space Research & Technology Centre Netherlands

COTS HW and SW components become crucial for advancing AI in space applications. The CAIRS21 ESA project considers Coral TPU as a candidate AI co-processor in avionics and examines its suitability in terms of performance, programmability/productivity, and radiation tolerance. The current paper focuses on our custom methodology for evaluating TPU, including TID and SEE effects. We assume representative AI benchmarks and irradiation profiles for LEO missions. We define a series of actions and measurements to derive thorough conclusions while avoiding increased time/budget procedures. Additionally, the paper summarizes key findings and a preliminary analysis of results from actual tests. Overall, TPU shows significant benefits when executing embedded AI, as well as promising Krad(Si) and SEE cross-section values.

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Uncertainty Aware Implicit Image Function for Arbitrary-Scale Super-Resolution

Uncertainty Aware Implicit Image Function for Arbitrary-Scal...

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IEEE International Conference on Image Processing

作者： Swastik Jena Saptarshi Panda Bunil Kumar Balabantaray Rajashree Nayak Dept. of Computer Science and Engineering National Institute of Technology Meghalaya India Dept. of Civil Engineering National Institute of Technology Meghalaya India Centre for Data Science JIS Institute of Advanced Studies and Research Kolkata India

The most integral step in computer vision and image processing tasks is the representation of images. Recently, continuous image parameterization using Implicit Neural Representations (INR) has shown a great advantage over discrete representations due to their spatial invariance. This property immediately finds application in the context of single-image super-resolution (SISR) at an arbitrary scale. However, most super-resolution models, including the INR-based Local Implicit Image Function (LIIF), produce only a single output, failing to address the ill-posedness of SISR. Moreover, these models tend to optimize a mean-squared-error (MSE) based loss function which causes blurring and structural distortion in regions exhibiting a high degree of variance (details). Our work proposes a novel uncertainty-aware self-supervised methodology (U-LIIF) that extends on LIIF, to reduce the blurriness and deals with the ill-posedness of SISR. Our U-LIIF does not require any re-training and yields diversified high-resolution images by leveraging model uncertainty. The efficacy of the proposed method is validated by substantial experiments on various benchmark datasets.

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Quantitative Applications of Weather Satellite Data for Nowcasting:Progress and Challenges

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Journal of Meteorological research 2024年第3期38卷 399-413页

作者： Jun LI Jing ZHENG Bo LI Min MIN Yanan LIU Chian-Yi LIU Zhenglong LI WPaul MENZEL Timothy J.SCHMIT John L.CINTINEO Scott LINDSTROM Scott BACHMEIER Yunheng XUE Yayu MA Di DI Han LIN Innovation Centre for Fengyun Meteorological Satellite(FYSIC) National Satellite Meteorological CentreChina Meteorological AdministrationBeijing100081China School of Atmospheric Sciences and Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies Sun Yat-sen University and Southern Laboratory of Ocean Science and EngineeringZhuhai519082China Key Laboratory of Geographic Information Science(Ministry of Education) East China Normal UniversityShanghai200241China Research Center for Environmental Changes Academia SinicaTaipei11529China Cooperative Institute for Meteorological Satellite Studies University of Wisconsin-MadisonMadisonWI53706USA Sensors and Calibration Branch STAR/NESDISMadisonWI53706USA Wuxi University Wuxi214105China School of Computer Science Chengdu University of Information TechnologyChengdu610225China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science&TechnologyNanjing210044China Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education National and Local Joint Engineering Research Center of Satellite Geospatial Information TechnologyFuzhou UniversityFuzhou350108China

Monitoring and predicting highly localized weather events over a very short-term period,typically ranging from minutes to a few hours,are very important for decision makers and public *** these events usually relies on radar observations through monitoring and *** advanced high-resolution imaging and sounding observations from weather satellites,nowcasting can be enhanced by combining radar,satellite,and other data,while quantitative applications of those data for nowcasting are advanced through using machine learning *** applications include monitoring the location,impact area,intensity,water vapor,atmospheric instability,precipitation,physical properties,and optical properties of the severe storm at different stages(pre-convection,initiation,development,and decaying),identification of storm types(wind,snow,hail,etc.),and predicting the occurrence and evolution of the *** observations can provide information on the environmental characteristics in the preconvection stage and are very useful for situational awareness and storm *** paper provides an overview of recent progress on quantitative applications of satellite data in nowcasting and its challenges,and future perspectives are also addressed and discussed.

关键词： weather satellite quantitative applications nowcasting pre-convection

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Observation of Su-Schrieffer-Heeger Topological Model Band Structure in Integrated LNOI Coupled Ring Cavities 16

Observation of Su-Schrieffer-Heeger Topological Model Band S...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Dinh, Hiep X. Balcytis, Armandas Ozawa, Tomoki Ota, Yasutomo Baba, Toshihiko Iwamoto, Satoshi Mitchell, Arnan Nguyen, Thach G. Rmit University Integrated Photonics and Applications Centre MelbourneVIC3000 Australia Advanced Institute for Materials Research Tohoku University Sendai980-8577 Japan Keio University Department of Applied Physics and Physico-Informatics Yokohama223-8522 Japan Yokohama National University Department of Electrical and Computer Engineering Yokohama240-8501 Japan The University of Tokyo Research Center for Advanced Science and Technology Tokyo153-8904 Japan

We report synthetic frequency dimension Su-Schrieffer-Heeger model and its band structure observation using coupled ring cavities on an integrated photonic chip. Intra-cell and inter-cell couplings between hybridized ... 详细信息

ISBN: (纸本)9798350372076

关键词： Photonic integrated circuits

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Personalized Multimedia Recommendation Systems Using Higher-Order Tensor Singular-Value-Decomposition

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IEEE Transactions on Broadcasting 2024年第1期70卷 148-160页

作者： Chang, Shih Yu Wu, Hsiao-Chun Yan, Kun Chen, Xinjia Huang, Scott Chih-Hao Wu, Yiyan San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for AI Applications Chungli32003 Taiwan Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Department of Information and Telecommunication Guilin541004 China Northwestern State University Department of Engineering Technology NatchitochesLA71497 United States National Tsing Hua University Department of Electrical Engineering Hsinchu300 Taiwan Communications Research Centre Canada OttawaK2H 8S2 Canada

Because of rapid growth of multimedia data over the Internet, the infobesity has been emerging in recent years. Many recommender systems (RSs) have been proposed using a variety of techniques, including artificial intelligence, machine learning, statistical analysis, and data mining. Nowadays, E-commerce companies such as Amazon, eBay, Netflix, etc. have applied RSs conveniently to provide better customer services than before by enabling customized multimedia-content recommendations. It is well known that classical RSs are almost all based on the user-item attribute matrices. However, this two-fold user-item relationship should be extended to the emerging many-fold relationship among an arbitrary number of attribute groups so that the corresponding new RSs may lead to better performances. Therefore, in this paper, we introduce a novel personalized multimedia recommender system (PMRS) according to our proposed new parallel higher-order tensor singular-value-decomposition (PHOTSVD) algorithm. This PHOTSVD algorithm is capable of accommodating an arbitrary number of entities such as social-media channels, users, queries, webpages, etc. in a robust RS. Furthermore, we also investigate the cold start and data bias problems critical to RSs. Finally, we compare the performances of our proposed new PMRS and three other existing tensor-based RSs over realworld data with respect to the well-adopted metric, namely normalized root-mean-square error (NRMSE). Our proposed new PMRS greatly outperforms the other three existing tensor-based RSs in terms of NRMSE. © 1963-12012 IEEE.

关键词： Singular value decomposition

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Towards Cost-Optimal Policies for DAGs to Utilize IaaS Clouds with Online Learning

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IEEE Transactions on Services Computing 2025年

作者： Wu, Xiaohu Yu, Han Casale, Giuliano Gao, Guanyu Beijing University of Posts and Telecommunications National Engineering Research Center of Mobile Network Technologies Beijing China Nanyang Technological University WeBank-NTU Joint Research Centre on Fintech Singapore Singapore Nanyang Technological University School of Computer Science and Engineering Singapore Singapore Imperial College London Department of Computing London United Kingdom Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

Premier cloud service providers (CSPs) offer on-demand and spot instances (i.e., virtual machines) with time-varying features in availability and price. While interacting with a CSP, what concerns users is the process of cost-effectively utilizing these instances, possibly in addition to self-owned instances. A job in data-intensive applications is represented by a directed acyclic graph (DAG) whose nodes represent tasks and the DAG can further be transformed into a chain of tasks. The key to achieving cost efficiency is determining the allocation of a specific deadline to each task, as well as the allocation of different types of instances to the task. In this paper, we make some mild assumptions on the usage of various instances and give an analytical understanding of the expected behaviors while utilizing instances. Based on this, we propose informed heuristic policies to determine the allocation of deadlines and instances. The policies are parametric to support the usage of online learning to infer the optimal values against the dynamics of cloud markets. Finally, intuitive greedy policies are used as baselines to validate the effectiveness of the proposed analytical solutions. The cost improvement is up to 23.58% when spot and on-demand instances are considered and up to 50.08% when self-owned instances are also considered. © 2008-2012 IEEE.

关键词： Cloud platforms

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SW-LCM: A Scalable and Weakly-supervised Land Cover Mapping Method on a New Sunway Supercomputer

SW-LCM: A Scalable and Weakly-supervised Land Cover Mapping ...

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International Symposium on parallel and Distributed Processing (IPDPS)

作者： Yi Zhao Juepeng Zheng Haohuan Fu Wenzhao Wu Jie Gao Mengxuan Chen Jinxiao Zhang Lixian Zhang Runmin Dong Zhenrong Du Sha Liu Xin Liu Shaoqing Zhang Le Yu Tsinghua University Beijing China Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Tsinghua University Beijing China National Supercomputing Center in Wuxi Wuxi China National Research Center of Parallel Computer Engineering and Technology Beijing China Qingdao Pilot National Laboratory for Marine Science and Technology Qingdao China

High-resolution land cover mapping (LCM) is an important application for studying and understanding the change of the earth surface. While deep learning (DL) methods demonstrate great potential in analyzing satellite images, they largely depend on massive high-quality labels. This paper proposes SW-LCM, a Scalable and Weakly-supervised two-stage Land Cover Mapping method on a new Sunway Supercomputer. Our method consists of a k-means clustering module as a first stage, and an iterative deep learning module as a second stage. With the k-means module providing a good enough starting point (taking inaccurate results as noisy labels), the deep learning module improves the classification results in an iterative way, without any labelling efforts required for processing large scenarios. To achieve efficiency for country-level land cover mapping, we design a customized data partition scheme and an on-the-fly assembly for k-means. Through careful parallelization and optimization, our k-means module scales to 98,304 computing nodes (over 38 million cores), and provides a sustained performance of 437.56 PFLOPS, in a real LCM task of the entire region of China; the iterative updating part scales to 24,576 nodes, with a performance of 11 PFLOPS. We produce a 10-m resolution land cover map of China, with an accuracy of 83.5% (10-class) or 73.2% (25-class), 7% to 8% higher than best existing products, paving ways for finer land surveys to support sustainability-related applications.

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Diagnosis and Identification of Citrus Canker Growth Rate Using Machine Learning

Diagnosis and Identification of Citrus Canker Growth Rate Us...

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Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Naureen Zainab Hammad Afzal Fahim Arif Abdul Ghafoor Naima Iltaf Muhammad Zakria Muhammad Javed Hyder Rafia Ahsan Department of Computer Software Engineering National University of Sciences and Technology Islamabad Pakistan National Agricultural Research Centre (NARC) Park Road Crop Diseases Research Institute (CDRI) Islamabad Pakistan Department of Mechanical Engineering Capital University of Science and Technology (CUST) Islamabad Pakistan

Citrus canker is a bacterial disease caused by Xanthomonas citri pv citri. Mainly the research in the field of Citrus canker has been done on fruits; early detection of disease on leaves can help in adopting preventive measures at early stage. The present study focuses on the detection of Citrus canker in leaves and classification of six different growth stages/levels of disease: water soaking, yellow initiation, chlorosis, and blister formation, canker infection (50%), canker infection (100%). The primary dataset was developed by taking images of citrus leaves infected by X. citri for the specified time. The proposed strategy is based on the detection of the lesion spot on citrus leaves and the classification of citrus disease stages/level was done to evaluate the growth rate of citrus canker through six distinct growth levels and evaluate affected area of leaf with each passing day. The entire processing includes Pre-processing, Segmentation, Features extraction and perform Classification using four different classifiers which include SVM, KNN, Naive Bayes, and Neural Network. The efficiency of different classifiers was 96.77% by SVM, followed by KNN 90%, NB 84% and NN 82% respectively. The lowest execution time was achieved by NB 0.011s, followed by KNN 0.015s, SVM 0.021s, and N.N. 0.216s.

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Modelling and Optimization of Compound Lever-Based Displacement Amplifier in a Mems Accelerometer

SSRN

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SSRN 2024年

作者： Jani, Nikul Bhupendrabhai Tirupathi, Rakesh Menon, P. Krishna Pandey, Ashok Kumar Indian Institute of Technology Hyderabad Telangana Kandi502284 India Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Research Centre Imarat Telangana Hyderabad500069 India

Compliant mechanisms have been realized as a potential tool to enhance the sensitivity of MEMS accelerometers through displacement amplification. In this article, optimization of a compound lever-based compliant mechanism has been carried out for the inclusion in MEMS accelerometer. The compliant displacement amplifier has been studied using the pseudo-rigid body approach and expressions for the displacement and fundamental natural frequency have been derived for the MEMS accelerometer. Further, for a range of values of the selected parameters, a detailed comparison has been made between the outcomes of the considered pseudo-rigid body-based approach and FEA simulations. Based on the derived mathematical model, optimization has been carried out for the best performance of the MEMS accelerometer. Using switched capacitor-based signal conditioning, output voltage has been calculated for the given input signal. Performance of displacement amplifier based MEMS accelerometer has been compared with the conventional one for sensitivity and nonlinearity. In this work, using a comparatively smaller size of proof mass, the higher figure of merits has been achieved in terms of natural frequency and amplified displacement of sense comb fingers. © 2024, The Authors. All rights reserved.

关键词： Compliant mechanisms

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