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Data, Computation and Communication (ICDCC), International Conference on

作者： Suresh Palarimath Pyingkodi Maran Prajoona Valsalan Wilfred Blessing N. R S. J. Kavitha Steffi S College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Applications Kongu Engineering College Erode India Electrical and Computer Engineering Department College of Engineering Dhofar Univesity Salalah Sultanate of Oman College of Computing and Information Sciences Univesity of Technology and Applied Sciences Ibri Sultanate of Oman Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

Due to the ongoing advancements in science and technology, it seems that artificial intelligence and computers have encountered a limitation in their progress. Contemporary scientific research requires more sophisticated interconnection technology to ensure the continued progress of science and technology. Amidst this setting, the technology of Internet of Things (IoT) has progressively been visible to individuals. This technology will significantly impact human lifestyle and quality of life, and the Internet of Things has seen substantial progress. Information technology is intricately intertwined with the lives of citizens. The integration of information technology with the IoT is aimed at maximizing the technological advancement of the Internet of Things, with Internet technology serving as the central component. The use of artificial intelligence and computer intelligence in Internet technology is versatile, and issues pertaining to artificial intelligence and computing intelligence are progressively emerging. Artificial intelligence and computational intelligence play a significant role in the Internet of Things. However, their application process is challenging and lacks creativity, necessitating ongoing improvements at the application level. This study examines the use of artificial intelligence and computational intelligence in the Internet of Things.

关键词： computers Planets Internet of Things Artificial intelligence Information technology Computational intelligence Creativity

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Integrated Sensing and Communication With mmWave Massive MIMO: A Compressed Sampling Perspective

Integrated Sensing and Communication With mmWave Massive MIM...

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作者： Gao, Zhen Wan, Ziwei Zheng, Dezhi Tan, Shufeng Masouros, Christos Ng, Derrick Wing Kwan Chen, Sheng Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology School of Information and Electronics Beijing100081 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250307 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China University College London Department of Electronic and Electrical Engineering LondonWC1E7JE United Kingdom University of New South Wales School of Electrical Engineering and Telecommunications SydneyNSW2052 Australia University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for realizing future wireless systems. In this paper, we propose an ISAC processing framework relying on millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Specifically, we provide a compressed sampling (CS) perspective to facilitate ISAC processing, which can not only recover the high-dimensional channel state information or/and radar imaging information, but also significantly reduce pilot overhead. First, an energy-efficient widely spaced array (WSA) architecture is tailored for the radar receiver, which enhances the angular resolution of radar sensing at the cost of angular ambiguity. Then, we propose an ISAC frame structure for time-varying ISAC systems considering different timescales. The pilot waveforms are judiciously designed by taking into account both CS theories and hardware constraints induced by hybrid beamforming (HBF) architecture. Next, we design the dedicated dictionary for WSA that serves as a building block for formulating the ISAC processing as sparse signal recovery problems. The orthogonal matching pursuit with support refinement (OMP-SR) algorithm is proposed to effectively solve the problems in the existence of the angular ambiguity. We also provide a framework for estimating the Doppler frequencies during payload data transmission to guarantee communication performances. Simulation results demonstrate the good performances of both communications and radar sensing under the proposed ISAC framework. © 2002-2012 IEEE.

关键词： Compressed sensing

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An Addendum to NeBula: Toward Extending Team CoSTAR’s Solution to Larger Scale Environments

IEEE Transactions on Field Robotics

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IEEE Transactions on Field Robotics 2024年 1卷 476-526页

作者： Benjamin Morrell Kyohei Otsu Ali Agha David D. Fan Sung-Kyun Kim Muhammad Fadhil Ginting Xianmei Lei Jeffrey Edlund Seyed Fakoorian Amanda Bouman Fernando Chavez Taeyeon Kim Gustavo J. Correa Maira Saboia Angel Santamaria-Navarro Brett Lopez Boseong Kim Chanyoung Jung Mamoru Sobue Oriana Claudia Peltzer Joshua Ott Robert Trybula Thomas Touma Marcel Kaufmann Tiago Stegun Vaquero Torkom Pailevanian Matteo Palieri Yun Chang Andrzej Reinke Matthew Anderson Frederik E. T. Schöller Patrick Spieler Lillian M. Clark Avak Archanian Kenny Chen Hovhannes Melikyan Anushri Dixit Harrison Delecki Daniel Pastor Barry Ridge Nicolas Marchal Jose Uribe Sharmita Dey Kamak Ebadi Kyle Coble Alexander Nikitas Dimopoulos Vivek Thangavelu Vivek S. Varadharajan Nicholas Palomo Antoni Rosinol Arghya Chatterjee Christoforos Kanellakis Bjorn Lindqvist Micah Corah Kyle Strickland Ryan Stonebraker Michael Milano Christopher E. Denniston Sami Sahnoune Thomas Claudet Seungwook Lee Gautam Salhotra Edward Terry Rithvik Musuku Robin Schmid Tony Tran Ara Kourchians Justin Schachter Hector Azpurua Levi Resende Arash Kalantari Jeremy Nash Josh Lee Christopher Patterson Jennifer G. Blank Kartik Patath Yuki Kubo Ryan Alimo Yasin Almalioglu Aaron Curtis Jacqueline Sly Tesla Wells Nhut T. Ho Mykel Kochenderfer Giovanni Beltrame George Nikolakopoulos David Shim Luca Carlone And Joel Burdick NASA Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA Department of Mechanical and Civil Engineering California Institute of Technology Pasadena CA USA Institut de Robòtica i Informàtica Industrial CSIC-UPC Barcelona Spain Department of Aerospace Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Aeronautics and Astronautics Stanford University Stanford CA USA Department of Electrical And Information Engineering Polytechnic University of Bari Bari Italy Department of Aeronautics and Astronautics Massachusetts Institute of Technology Cambridge MA USA Department of Electrical and Computer Engineering Cornell University Ithaca NY USA Department of Computer Engineering and Software Engineering École Polytechnique de Montréal Montreal QC Canada Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Mechanical Engineering California State University at Northridge Northridge CA USA Department of Earth and Planetary Sciences McGill University Montreal QC Canada Blue Marble Space Institute of Science Seattle WA USA

This article presents an appendix to the original NeBula autonomy solution developed by the Team Collaborative SubTerranean Autonomous Robots (CoSTAR), participating in the DARPA Subterranean Challenge. Specifically, this article presents extensions to NeBula’s hardware, software, and algorithmic components that focus on increasing the range and scale of the exploration environment. From the algorithmic perspective, we discuss the following extensions to the original NeBula framework: 1) large-scale geometric and semantic environment mapping; 2) an adaptive positioning system; 3) probabilistic traversability analysis and local planning; 4) large-scale partially observable Markov decision process (POMDP)-based global motion planning and exploration behavior; 5) large-scale networking and decentralized reasoning; 6) communicationaware mission planning; and 7) multimodal ground–aerial exploration *** demonstrate the application and deployment of the presented systems and solutions in various large-scale underground environments, including limestone mine exploration scenarios as well as deployment in the DARPA Subterranean challenge.

关键词： Planning State estimation Semantics NASA Hardware Three-dimensional displays Systems architecture

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Not All Requirements Prioritization Criteria are Equal at All Times: A Quantitative Analysis

SSRN

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SSRN 2022年

作者： Svensson, Richard Berntsson Torkar, Richard Department of Computer Science and Engineering Chalmers and University of Gothenburg Gothenburg Sweden Stellenbosch Institute for Advanced Study Stellenbosch South Africa

Requirement prioritization is recognized as an important decision-making activity in requirements engineering and software development. Requirement prioritization is applied to determine which requirements should be implemented and released. In order to prioritize requirements, there are several approaches/techniques/tools that use different requirements prioritization criteria, which are often identified by gut feeling instead of an in-depth analysis of which criteria are most important to use. Therefore, in this study we investigate which requirements prioritization criteria are most important to use in industry when determining which requirements are implemented and released, and if the importance of the criteria change depending on how far a requirement has reached in the development process. We conducted a quantitative study of one completed project from one software developing company by extracting 32,139 requirements prioritization decisions based on eight requirements prioritization criteria for 11,110 requirements. The results show that not all requirements prioritization criteria are equally important, and this change depending on how far a requirement has reached in the development process. © 2022, The Authors. All rights reserved.

关键词： Decision making

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Large-Scale Tri-modal Electromagnetic Structure for Ambient RF Energy Harvesting

Large-Scale Tri-modal Electromagnetic Structure for Ambient ...

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International Workshop on Antenna Technology: Small Antennas and Novel Metamaterials, iWAT

作者： Chi-Yuk Chiu Charles Ng Ross Murch Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Kowloon Hong Kong Institute for Advanced Study The Hong Kong University of Science and Technology Kowloon Hong Kong

In this paper, a large-scale electromagnetic (EM) structure is designed and demonstrated for collecting ambient cellular power between 930 to 960 MHz. The novelty of the EM structure is that it is scalable in terms of size or gain, and can produce three high-gain radiation beams pointing to three different directions simultaneously. The construction of the proposed large-scale EM structure consists of a tri-modal patch antenna acting as the basic radiating element. Then, four basic elements are concatenated by three custom-made feed networks forming a 3-port sub-array. When the sub-arrays are linked together into an array and the corresponding three ports are subject to different phase weightings, the EM structure is capable of harnessing low power ambient RF energy concurrently with three different incident angles.

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Physical Knowledge Enhanced Deep Neural Network for Sea Surface Temperature Prediction

arXiv

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arXiv 2023年

作者： Meng, Yuxin Gao, Feng Rigall, Eric Dong, Ran Dong, Junyu Du, Qian Institute of Advanced Oceanography Ocean University of China Qingdao266100 China The Department of Electrical and Computer Engineering Mississippi State University StarkvilleMS39762 United States

Traditionally, numerical models have been deployed in oceanography studies to simulate ocean dynamics by representing physical equations. However, many factors pertaining to ocean dynamics seem to be ill-defined. We argue that transferring physical knowledge from observed data could further improve the accuracy of numerical models when predicting Sea Surface Temperature (SST). Recently, the advances in earth observation technologies have yielded a monumental growth of data. Consequently, it is imperative to explore ways in which to improve and supplement numerical models utilizing the ever-increasing amounts of historical observational data. To this end, we introduce a method for SST prediction that transfers physical knowledge from historical observations to numerical models. Specifically, we use a combination of an encoder and a generative adversarial network (GAN) to capture physical knowledge from the observed data. The numerical model data is then fed into the pre-trained model to generate physics-enhanced data, which can then be used for SST prediction. Experimental results demonstrate that the proposed method considerably enhances SST prediction performance when compared to several state-of-the-art baselines. © 2023, CC BY.

关键词： Surface temperature

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Enhancing Large Vision Model in Street Scene Semantic Understanding through Leveraging Posterior Optimization Trajectory

arXiv

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arXiv 2025年

作者： Kou, Wei-Bin Lin, Qingfeng Tang, Ming Wang, Shuai Ye, Rongguang Zhu, Guangxu Wu, Yik-Chung Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong Shenzhen Research Institute of Big Data Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

To improve the generalization of the autonomous driving (AD) perception model, vehicles need to update the model over time based on the continuously collected data. As time progresses, the amount of data fitted by the AD model expands, which helps to improve the AD model generalization substantially. However, such ever-expanding data is a double-edged sword for the AD model. Specifically, as the fitted data volume grows to exceed the the AD model’s fitting capacities, the AD model is prone to under-fitting. To address this issue, we propose to use a pretrained Large Vision Models (LVMs) as backbone coupled with downstream perception head to understand AD semantic information. This design can not only surmount the aforementioned under-fitting problem due to LVMs’ powerful fitting capabilities, but also enhance the perception generalization thanks to LVMs’ vast and diverse training data. On the other hand, to mitigate vehicles’ computational burden of training the perception head while running LVM backbone, we introduce a Posterior Optimization Trajectory (POT)-Guided optimization scheme (POTGui) to accelerate the convergence. Concretely, we propose a POT Generator (POTGen) to generate posterior (future) optimization direction in advance to guide the current optimization iteration, through which the model can generally converge within 10 epochs. Extensive experiments demonstrate that the proposed method improves the performance by over 66.48% and converges faster over 6 times, compared to the existing state-of-the-art approach. © 2025, CC BY-NC-ND.

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Spectroscopic characterization of a squeezed quantum microcomb on a chip

Spectroscopic characterization of a squeezed quantum microco...

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Frontiers in Optics, FiO 2022

作者： Parnell, Emily A. Yang, Zijiao Jahanbozorgi, Mandana Jeong, Dongin Sun, Shuman Pfister, Olivier Lee, Hansuek Yi, Xu Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA22904 United States Department of Physics University of Virginia CharlottesvilleVA22904 United States Daejeon34141 Korea Republic of Department of Physics Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of

We report spectroscopic characterization of 40 quantum modes within 1 THz optical span for a vacuum squeezed microcomb. An analytical model is developed to illustrate the role of cavity dispersion in determining quant...

ISBN: (纸本)9781557528209

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Strategic Realization of the Highest Possible Pixel Density for Quantum Dot Color Converter with Optimized Performance

Strategic Realization of the Highest Possible Pixel Density ...

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SID International Symposium Digest of Technical Papers, 2023

作者： Chen, Chih-Jung Chen, Kuan-An Kuo, Wei-Hung Wu, Chun-I Shen, Jia-Wun Kuo, Hao-Chung Chiang, Ray-Kuang Taiwan Nanocrystals Corp. Ltd Tainan Taiwan SynthEdge Advanced Materials Corp. Ltd Taoyuan Taiwan Industrial Technology Research Institute Hsinchu Taiwan Hon Hai Precision Industry Ltd Taipei Taiwan Department of Photonics and Graduate Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Chiao Tung University Hsinchu30010 Taiwan Semiconductor Research Center Taipei Taiwan

The light extraction efficiency of QDCC (quantum dot color converter) is low when its aspect ratio (width to thickness ratio) of a subpixel is less than two, which causes the problem in their high-resolution applications. Using QDPR (quantum dot photoresist) of high OD (optical density) values and PenTile subpixel arrangement, an apparent high-resolution application is feasible, in which the QDCC film thickness is less than 2 μm. Further incorporation of yellow color filter film or color purified layer (CPL) on the QDCC film can optimize its optical performance. Theoretically, QDCC with pixel density over 3000 ppi is achievable when it is directly applied to the CMOS substrate. In a trial test QDCC is applied on a glass substrate, which results in pixel density of 2116 ppi and color gamut of NTSC 109.64%. © 2023, John Wiley and Sons Inc. All rights reserved.

关键词： Color

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Over-the-Air Aggregation-Based Federated Learning in Cache-Enabled Wireless Edge Networks

Over-the-Air Aggregation-Based Federated Learning in Cache-E...

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IEEE International Conference on Communications (ICC)

作者： Jun-Pyo Hong Sangjun Park Wan Choi Pukyong National University Busan South Korea School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Electrical and Computer Engineering Seoul National University Seoul South Korea

In conventional federated learning (FL), dataset at the parameter server (PS) is not usually considered but may enhance the performance of FL if available. The benefit of leveraging the dataset at PS can be multiplied in over-the-air aggregation-based FL where combating local gradient update distortion against channel fading is inordinately consequential. In this paper, an over-the-air aggregation framework for communication efficient FL is investigated in cache-enabled wireless edge networks where not only edge devices but also a base station (BS) has its own local dataset. The proposed framework leverages the BS dataset to reduce the number of channel uses necessary for the model convergence and to avoid the overhead incurred by power scale coordination and global channel state information (CSI) acquisition at BS. We present a sufficient condition for convergence to a stationary point without convexity assumption on the objective function. Based on the sufficient condition, a power control method is optimized to facilitate the model convergence without assumptions on power scale coordination and global CSI at BS. Our simulation results validate that BS dataset is beneficial to reduce the number of channel uses for the model convergence and the developed power control method outperforms the conventional method in terms of both convergence rate and converged test accuracy.

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