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Onsite spatial perception and UAV acquisition path planning

Scientia Sinica Informationis 2025年第5期55卷 1051-1072页

作者： Zhang, Xingming Xie, Ke Huang, Pengdi Liu, Yilin Huang, Hui Visual Computing Research Center College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China

With the emergence of lightweight consumer-grade unmanned aerial vehicles and advancements in 3D reconstruction techniques, reconstructing large-scale urban scenes using aerial photogrammetry has become feasible. Traditional methods typically follow an “explore-then-exploit” strategy: the initial flight conducts a sparse scan of the scene using a predefined pattern and performs a preliminary rough reconstruction. Subsequently, an optimal second flight path is planned based on the coarse model to facilitate dense image acquisition. However, these methods present several challenges: a lengthy overall process and high computational demands onsite. Moreover, coarse models often fail to accurately capture detailed features within the scene structure, such as corners, structural lines, and planes, resulting in overly smooth geometric information. To address these issues, we introduce a novel rapid onsite spatial perception method based on aerial imagery. This method utilizes RGB image data and surface normal data for the precise detection and extraction of planar structures, quickly recovering the depth information of each planar structure based on structure-from-motion (SFM) data and surface normals. Compared to pixel-level multi-view stereo (MVS) methods, this approach offers significant advantages in terms of speed. Additionally, we propose an innovative path planning algorithm. This algorithm is capable of generating an initial set of candidate viewpoints and, while maintaining reconstruction quality, minimizes the number of viewpoints and plans the shortest flight path to achieve optimal scene coverage and reconstruction accuracy. Our work enables immediate onsite data analysis of aerial images, allowing for prompt analysis results obtained on the spot, significantly reducing onsite operational time and computational costs. © 2025 science Press. All rights reserved.

关键词： onsite spatial perception UAV path planning urban scene reconstruction

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Streamlining Classroom and Laboratory Management in University: Enhancing Problem Reporting Processes

Streamlining Classroom and Laboratory Management in Universi...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Qamar H. Naith Department of Software Engineering College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Managing the classroom and lab activities in the university and reporting process for the problems that occur during lectures is a hard process that needs more consideration. This study addresses challenges faced by technicians and academic staff in university classrooms, emphasizing the need for a streamlined automated issue reporting system. This paper proposes a comprehensive system aimed at improving communication, efficiency, and effectiveness in addressing classroom issues, benefiting technicians, students, and lecturers. Quantitative Analysis and evaluation of the system's user experience from instructors and technicians showed highly positive outcomes, indicating its high effectiveness in managing classroom issues and improving overall classroom management. Moreover, the statistical test revealed no statistically significant differences (α= 0.05) in the willingness to use the classroom issue management system between technicians and academic staff, indicating similar user perceptions and readiness for both groups to utilize the system.

关键词： Surveys Productivity Bridges Statistical analysis User experience Stakeholders Usability Information technology

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Energy Oriented Three-Tier Computation Offloading Scheme in Maritime Edge Computing Network

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8126-8140页

作者： Zhang, Hongxia Xi, Shiyu Shang, Bodong Zhang, Peiying Wu, Sheng Jiang, Chunxiao Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Eastern Institute of Technology Eastern Institute for Advanced Study Ningbo315200 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China

With the development of maritime continuing to grow, large-scale maritime wireless devices (MWDs) are being deployed for various maritime applications. The rapid development of maritime Internet of Things (IoT) and growing demand for these applications have led to an increasing number of computation-intensive and resource-sensitive tasks. Considering the limited computing capacity and energy of MWDs, it is challenging to transmit and process massive maritime computing tasks in an energy efficient manner. In this paper, we propose a three-tier maritime edge computing system in which low earth orbit (LEO) satellite and offshore base station (OBS) support to provide communication and computing services for MWDs. The system optimizes association variable, task partitioning, transmission power, and computing resource allocation to reduce system energy consumption under latency constraints. The optimization problem is formulated as a mixed integer non-linear programming (MINLP) optimization problem. We decompose the original problem into four subproblems and design algorithms to solve them. Firstly, the association variable optimization subproblem is solved by slack variable method. Then, we adopt quadratic transformation and design a difference of convex algorithm to solve transmission power optimization subproblem. Further, task partitioning subproblem is solved by obtaining upper and lower bounds of offloading task size by strict deduction, and then solved by the standard convex method. Finally, Lagrangian dual method is utilized to deal with joint computing resource optimization subproblem of LEO satellite and OBS. In simulation results, the proposed algorithm saves 39.3% of system energy consumption compared to benchmark schemes. © 1967-2012 IEEE.

关键词： Computation offloading

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Mutual Reconstruction-based Linear Discriminant Hashing for Image Retrieval 7

Mutual Reconstruction-based Linear Discriminant Hashing for ...

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7th International Conference on Vision, Image and Signal Processing, ICVISP 2023

作者： Huang, Danlu Lai, Zhihui Kong, Heng College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Breast and Thyroid Surgery BaoAn Central Hospital of Shenzhen Shenzhen China

ISBN: (纸本)9781839539848

Recently, hash learning has attracted much attention due to its ability to convert high-dimensional data into compact binary codes for efficient retrieval and storage. However, most classification-based supervised hashing methods neglect the original goal of increasing the similarity of similar samples while reducing the similarity of different samples in the learned hash code. In this paper, we combine the idea of linear discriminant analysis with hash learning by designing a new scatter matrix and propose a supervised hash learning framework to learn optimal discriminative hash codes for image retrieval. To solve the problem of reducing information loss during the learning process, we design a novel method via mutual reconstruction between binary hash codes and original features. Thus, a method named Mutual Reconstruction-based Linear Discriminant Hashing (RDAH) is obtained, which further improves the discriminative ability of the learned hash code. We test the performance of RDAH on three image datasets to show the superiority on large-scale image retrieval. © The Institution of engineering & Technology 2023.

关键词： Image retrieval

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A software Execution Data Component Identification Algorithm Based on Spectral Clustering 10

A Software Execution Data Component Identification Algorithm...

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10th International Conference on Dependable Systems and Their Applications, DSA 2023

作者： Tang, Yahui Rao, Mengqi Wei, Qingjie Chen, Feng Liu, Xin Chongqing University of Posts and Telecommunications School of Software Engineering ChongQing China Chongqing University of Posts and Telecommunications College of Computer Science and Technology ChongQing China

ISBN: (纸本)9798350304770

In order to understand the architecture of software system and facilitate the future maintenance of the software, we can reconstruct the software system as a component-based software system. To obtain the component information of the software, it is necessary to identify components based on the information contained in the software execution data. This paper presents a software Execution data component identification algorithm based on Spectral Clustering (SESC). The input of the algorithm is the software execution event log, and the output is an evaluation report on the quality of the identified components, yielding the log of software execution events with component information. By using four software execution event logs, we compare the proposed SESC with five component identification algorithms based on the community detection algorithm. SESC takes the number of calls between classes into consideration, so it can identify components more accurately, and has certain advantages in time performance. © 2023 IEEE.

关键词： Clustering algorithms

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Locating What You Need: Towards Adapting Diffusion Models to OOD Concepts In-the-Wild 38

Locating What You Need: Towards Adapting Diffusion Models to...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yang, Jianan Gao, Chenchao Xiao, Zhiqing Zhao, Junbo Wu, Sai Chen, Gang Wang, Haobo College of Computer Science and Technology Zhejiang University China Institute of Blockchain and Data Security China School of Software Technology Zhejiang University China International School of Information Science and Engineering Dalian University of Technology China

The recent large-scale text-to-image generative models have attained unprecedented performance, while people established adaptor modules like LoRA and DreamBooth to extend this performance to even more unseen concept tokens. However, we empirically find that this workflow often fails to accurately depict the out-of-distribution concepts. This failure is highly related to the low quality of training data. To resolve this, we present a framework called Controllable Adaptor Towards Out-of-Distribution Concepts (CATOD). Our framework follows the active learning paradigm which includes high-quality data accumulation and adaptor training, enabling a finer-grained enhancement of generative results. The aesthetics score and concept-matching score are two major factors that impact the quality of synthetic results. One key component of CATOD is the weighted scoring system that automatically balances between these two scores and we also offer comprehensive theoretical analysis for this point. Then, it determines how to select data and schedule the adaptor training based on this scoring system. The extensive results show that CATOD significantly outperforms the prior approaches with an 11.10 boost on the CLIP score and a 33.08% decrease on the CMMD metric. © 2024 Neural information processing systems foundation. All rights reserved.

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Large Language Models Improve Alzheimer's Disease Diagnosis Using Multi-Modality Data 1

Large Language Models Improve Alzheimer's Disease Diagnosis ...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Feng, Yingjie Xu, Xiaoyin Zhuang, Yueting Zhang, Min School of Software Zhejiang University Hangzhou China College of Biomedical Engineering & Instrument Science Zhejiang University Hangzhou China School of Computer Science Zhejiang University Hangzhou China

ISBN: (纸本)9798350358780

In diagnosing challenging conditions such as Alzheimer's disease (AD), while imaging is an important reference, non-image data such as patient information, genetic data, medication information, cognitive and memory tests also play a very important role. However, limited by the ability of artificial intelligence models to mine such information, most of the existing models only use image data, and cannot make comprehensive use of non-image data. We based our approach on an existing pretrained large language model (LLM) to enhance the model's ability to utilize non-image data on diagnosing AD. Our test results on the ADNI dataset show that the approached achieved state-of-the-art (SOTA) performance. © 2023 IEEE.

关键词： Diagnosis

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High-quality Trajectory Generation for Autonomous Driving: A Lightweight Federated Learning-based Diffusion Model

High-quality Trajectory Generation for Autonomous Driving: A...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Gao, Runquan Kang, Jiawen Lai, Bingkun Xu, Minrui Sun, Geng Zhang, Tao Zhang, Weiting Yang, Dong Guangdong University of Technology School of Automation Guangzhou China Nanyang Technological University School of Computer Science and Engineering Singapore Jilin University College of Computer Science and Technology Changchun China Beijing Jiaotong University School of Software Engineering Beijing China Beijing Jiaotong University School of Electronic and Information Engineering Beijing China

ISBN: (纸本)9798350351255

Vehicle trajectory data plays a pivotal role in simulation testing for autonomous driving. Hence, there exist well-established trajectory generation methods employing deep generative models to generate trajectories mapping the distribution of the original dataset, thereby augmenting existing trajectory datasets. However, these methods typically rely on large datasets gathered by governmental or organizational entities for central training, which may pose data privacy, security, and accessibility issues. Therefore, it is challenging to generate high-quality traffic trajectory data while preserving privacy which involves a delicate balance between these two objectives. To deal with this challenge, we introduce Federated Learning into the diffusion model and propose a Federated Learning-based diffusion model (FedDifftraj) to generate traffic trajectory data. Unlike existing central training methods, FedDifftraj aggregates model parameters uploaded by different vehicles and then updates a global model. Additionally, there is a substantial communication overhead incurred during the training of the federated diffusion model. Therefore, we quantize the local diffusion model before uploading it to the parameter server. Through extensive simulations on real-world datasets, FedDifftraj can generate high-quality traffic trajectory data that is consistent with the results of the central training while preserving privacy and reducing communication overhead by 93.74% when utilizing 8-bit quantization. © 2024 IEEE.

关键词： Differential privacy

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ChatGPT, Copilot, Gemini, SciSpace and Wolfram versus higher education assessments: an updated multi-institutional study of the academic integrity impacts of Generative Artificial Intelligence (GenAI) on assessment, teaching and learning in engineering

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Australasian Journal of engineering Education 2024年第2期29卷 126-153页

作者： Nikolic, Sasha Sandison, Carolyn Haque, Rezwanul Daniel, Scott Grundy, Sarah Belkina, Marina Lyden, Sarah Hassan, Ghulam M. Neal, Peter Faculty of Engineering and Information Sciences University of Wollongong Wollongong Australia School of Science Technology and Engineering University of the Sunshine Coast Sippy Downs Australia School of Professional Practice & Leadership University of Technology Sydney Sydney Australia School of Chemical Engineering University of New South Wales Sydney Australia College Western Sydney University Sydney Australia School of Engineering University of Tasmania Hobart Australia School of Computer Science & Software Engineering University of Western Australia Perth Australia

More than a year has passed since reports of ChatGPT-3.5’s capability to pass exams sent shockwaves through education circles. These initial concerns led to a multi-institutional and multi-disciplinary study to assess the performance of Generative Artificial Intelligence (GenAI) against assessment tasks used across 10 engineering subjects, showcasing the capability of GenAI. Assessment types included online quiz, numerical, oral, visual, programming and writing (experimentation, project, reflection and critical thinking, and research). Twelve months later, the study was repeated using new and updated tools ChatGPT-4, Copilot, Gemini, SciSpace and Wolfram. The updated study investigated the performance and capability differences, identifying the best tool for each assessment type. The findings show that increased performance and features can only heighten academic integrity concerns. While cheating concerns are central, opportunities to integrate GenAI to enhance teaching and learning are possible. While each GenAI tool had specific strengths and weaknesses, ChatGPT-4 was well-rounded. A GenAI Assessment Security and Opportunity Matrix is presented to provide the community practical guidance on managing assessment integrity risks and integration opportunities to enhance learning. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Artificial intelligence

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Bug localization based on syntactical and semantic information of source code

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Journal of Systems engineering and Electronics 2023年第1期34卷 236-246页

作者： YAN Xuefeng CHENG Shasha GUO Liqin College of Computer Science Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 211106China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System EngineeringBeijing 100854China

The existing software bug localization models treat the source file as natural language, which leads to the loss of syntactical and structure information of the source file. A bug localization model based on syntactical and semantic information of source code is proposed. Firstly, abstract syntax tree(AST) is divided based on node category to obtain statement sequence. The statement tree is encoded into vectors to capture lexical and syntactical knowledge at the statement ***, the source code is transformed into vector representation by the sequence naturalness of the statement. Therefore,the problem of gradient vanishing and explosion caused by a large AST size is obviated when using AST to the represent source code. Finally, the correlation between bug reports and source files are comprehensively analyzed from three aspects of syntax, semantics and text to locate the buggy code. Experiments show that compared with other standard models, the proposed model improves the performance of bug localization, and it has good advantages in mean reciprocal rank(MRR), mean average precision(MAP) and Top N Rank.

关键词： bug report abstract syntax tree code representation software bug localization

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