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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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MAD-TI: Meta-Path Aggregated-Graph Attention Network for Drug Target Interaction Prediction

MAD-TI: Meta-Path Aggregated-Graph Attention Network for Dru...

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Iranian Conference on electrical engineering (ICEE)

作者： Reza Shami Tanha Maryam Sadighian Arash Zabihian Mohsen Hooshmand Mohsen Afsharchi dept. Computer Science and IT Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan Iran lab. Bioinformatics and Drug Design (LBD) Institute of Biochemistry and Biophysics University of Tehran Tehran Iran dept. Electrical and Computer Engineering University of Zanjan Zanjan Iran

Computational identification of unknown drug-target interactions (DTI) is crucial in locating new drug treat-ments for proteins, viruses, and diseases. This work proposes MAD-TI a meta-path-based, GAT-oriented method to predict DTIs. Our proposed method uses a heterogeneous graph of drugs, targets, diseases, and side effects as the input graph. Then, it applies two graph attention networks to generate the embeddings of drugs and targets. Using the embeddings, it predicts the unknown DTIs. The results show that MAD-TI outperforms the state-of-the-art methods.

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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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Artificial intelligence-assisted generative pretrained transformers for applications of ChatGPT in higher education among graduates

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SN Social Sciences 2024年第2期4卷 1-23页

作者： Prajapati, Jigna B. Kumar, Ashwini Singh, Sudarshan Prajapati, Bhupendra Thakar, Yash Tambe, Prashant R. Ved, Amit Acharya Motibhai Patel Institute of Computer Studies Ganpat University Gujarat Kherva 384012 India Department of Mechanical Engineering School of Engineering and Technology Manav Rachna International Institute of Research and Studies (Deemed to Be University) Haryana Faridabad 121004 India Department of Pharmaceutical Sciences Faculty of Pharmacy Chiang Mai University Chiang Mai 50200 Thailand Office of Research Administration Chiang Mai University Chiang Mai 50200 Thailand Shree S. K. Patel College of Pharmaceutical Education and Research Ganpat University Gujarat Kherva 384012 India Assistant Manager at Incubation Centre Ganpat University Gujarat Kherva 384012 India Technical Campus Akole Maharashtra Nawalewadi 422601 India Department of Electrical Engineering Marwadi University Gujarat Rajkot 360003 India

Education that aims at sustainable development is a growing field, notably over the last few decades. Measuring the level of technology-based educational development has become a necessity for well-rounded educational experience. The availability of numerous artificial intelligence-based software is leading education in dynamic way. Technology based on artificial intelligence (AI) has been advancing constantly and becoming increasingly pervasive in various facets of our lives. AI in education is facilitating bit-sized learning with maintaining a human touch by digital courses and other low-storage study materials. Chatbots and other AI-enabled technologies assisting in enhancing education learning experience;however, their major drawbacks are algorithmic bias. The present study uses a qualitative case study to analyze chat Generative Pre-trained Transformer (ChatGPT) in education among youngsters and explore its potential opportunities in the fields of higher education. Also, the present survey-based analysis highlights the difficulties, risks, limitations of AI in higher education, and recent developments in AI among the graduates. The survey-based analysis indicated that AI-assisted ChatGPT has gained tremendous popularity among researchers of varied fields. The discussion has led to the conclusion that AI holds a significant promise to be the best solution for all future educational developments in a variety of ways. However, safe usage with ethics is required while working with AI from an academic perspective. Furthermore, a broad investigation is required among various users of different streams and age groups to strengthen utilization of AI-enabled technologies for enhance learning process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Artificial intelligence Case study Chatbots ChatGPT GPT Higher education

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Fast Erasure Decoding of Reed-Solomon Codes Based on Decomposition of Vandermonde Matrix

Fast Erasure Decoding of Reed-Solomon Codes Based on Decompo...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Zebing Lin Jingjie Lv Pingping Li Linqi Song Hui Liang Hanxu Hou School of Computer Science and Technology Dongguan University of Technology School of Electrical Engineering & Intelligentization Dongguan University of Technology Shenzhen Institute for Advanced Study University of Electronic Science & Technology of China Department of Computer Science City University of Hong Kong City University of Hong Kong Shenzhen Research Institute

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

Reed-Solomon (RS) codes are widely applied in storage systems. The main bottleneck of RS codes is that the high decoding complexity defined as the number of multiplications and additions. In this paper, we propose a fast decoding algorithm for RS codes that requires fewer number of multiplications. Our fast decoding algorithm is designed by a decomposition of Vandermonde matrices. We show that our decoding algorithm requires fewer number of multiplications than the existing best known algorithm when the number of parities $T$ is large. When the codeword length $N=16$ , our decoding algorithm reduces the number of multiplications by 7% and 29% when $T=4$ and $T=7$ , respectively, compared with the best known decoding algorithm.

关键词： Reed-Solomon codes Wireless communication Codes Upper bound Signal processing algorithms Transforms Signal processing Decoding Matrix decomposition Galois fields

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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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Optimizing Sentiment Analysis on Twitter for Improved Customer Insights: Integrating Bagged CNN and Flamingo Search

Optimizing Sentiment Analysis on Twitter for Improved Custom...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： C. Umarani Jyoti Metan Piyush Kumar Pareek Mahantesh Mathapati Department of Computer Science and Applications Christ Academy Institute for Advanced Studies Bengaluru India Department of ISE Atria Institute of Technology Bengaluru India Department of AIML and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India Department of CSE Amruta Institute of Engineering & Management Sciences Bengaluru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Twitter helps you understand public opinion by revealing what people think, feel, and believe. This study provides a framework for businesses, particularly food delivery companies, to analyse social media data competitively and gain actionable insights. A novel combination of Flamingo Search Optimisation (FSA) and a one-dimensional bagged CNN is applied to Twitter data to improve customer satisfaction research. Our method uses advanced machine learning to analyse and interpret consumer attitudes and trends using Twitter's massive, real-time customer feedback stream. We found that sentiment analysis can now better predict consumer preferences and satisfaction. The study suggests developing more advanced optimisation algorithms, cross-platform sentiment analysis, real-time processing, and natural language processing techniques. Average kappa score: 0.9941, sensitivity: 0.9969, specificity: 0.9972, accuracy: 0.9971, precision: 0.9973. This study prepares companies to improve customer engagement and respond quickly to digital consumer sentiments.

关键词： Sentiment analysis Accuracy Sensitivity Social networking (online) Blogs Customer satisfaction Companies Real-time systems Convolutional neural networks Optimization

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