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Multispectral point cloud superpoint segmentation

Science China(Technological Sciences) 2024年第4期67卷 1270-1281页

作者： WANG QingWang WANG MingYe ZHANG ZiFeng SONG Jian ZENG Kai SHEN Tao GU YanFeng Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Computer Technologies Application Kunming 650500China School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China

The multitude of airborne point clouds limits the point cloud processing *** are grouped based on similar points,which can effectively alleviate the demand for computing resources and improve processing ***,existing superpoint segmentation methods focus only on local geometric structures,resulting in inconsistent spectral features of points within a *** feature inconsistencies degrade the performance of subsequent ***,this study proposes a novel Superpoint Segmentation method that jointly utilizes spatial Geometric and Spectral information for multispectral point cloud superpoint segmentation(GSI-SS).Specifically,a similarity metric that combines spatial geometry and spectral information is proposed to facilitate the consistency of geometric structures and object attributes within segmented *** the formation of the primary superpoints,an intersuperpoint pointexchange mechanism that maximizes feature consistency within the final superpoints is *** are conducted on two real multispectral point cloud datasets,and the proposed method achieved higher recall,precision,F score,and lower global consistency and feature classification *** experimental results demonstrate the superiority of the proposed GSI-SS over several state-of-the-art methods.

关键词： multispectral point cloud superpoint segmentation over-segmentation spatial-spectral joint metric

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Amplitude Method for Reconstructing the Effective Frequency of Electron Collisions on Low-Inclination Ionospheric Paths 9

Amplitude Method for Reconstructing the Effective Frequency ...

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9th IEEE All-Russian Microwave Conference, RMC 2024

作者： Kryukovsky, Andrew S. Mikhaleva, Elizabeth V. Rastyagaev, Dmitry.V. Institute of Information Systems and Engineering and Computer Technologies Russian New University Moscow Russia

ISBN: (纸本)9798331540944

A method for reconstructing the effective electron collision frequency has been developed, based on data on the amplitudes of radio signals obtained during low-angle ionospheric sounding. Ray trajectories and wave vectors have been calculated based on the Hamilton-Lukin bicharacteristic method. The extended Lukin bicharacteristic system has been used to calculate the divergence of radio signals. A satisfactory match between the initial values of the effective collision frequency and the calculated values has been obtained in a numerical experiment. The possibilities of extrapolating the obtained results in reconstructing the altitude profile of the effective electron collision frequency have been analyzed. © 2024 IEEE.

关键词： Ionosphere

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Numerical Modeling of Ray Characteristics Along the Earth's Surface in the Case of an Obliquely Oriented TIDs 9

Numerical Modeling of Ray Characteristics Along the Earth's ...

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9th IEEE All-Russian Microwave Conference, RMC 2024

作者： Kryukovsky, Andrew S. Mikhaleva, ElizabethV. Rastyagaev, Dmitry. V. Institute of Information Systems and Engineering and Computer Technologies Russian New University Moscow Russia

ISBN: (纸本)9798331540944

Based on the system of bicharacteristics, the propagation of decameter-range radio waves on low-inclination paths from the radiation source to the receiver in the presence of traveling inclined ionospheric disturbances (TID) is simulated. The following are investigated: the Doppler frequency shift, the group delay time of the radio signal, and the dependences of the ray exit angles on the TID center position. It is shown that when the TID moves in the upper ionospheric layer, a caustic structure of the 'caustic cusp' type (catastrophe A3) arises, which is manifested in the Doppler curves in the form of S-shaped lines, explained by the formation of three-ray regions and loops during reflection from the TID. © 2024 IEEE.

关键词： Group delay

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Deep learning-based software engineering: progress,challenges, and opportunities

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Science China(information Sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software Engineering Sun Yat-sen University School of Software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of Computer and Information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

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TSCompiler: efficient compilation framework for dynamic-shape models

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Science China(information Sciences) 2024年第10期67卷 67-84页

作者： Xiang LUO Chen ZHANG Chenbo GENG Yanzhi YI Jiahui HU Renwei ZHANG Zhen ZHANG Gianpietro CONSOLARO Fan YANG Tun LU Ning GU Li SHANG School of Computer Science Fudan University School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University School of Computer Science and Technology Harbin Institute of Technology Huawei Technologies Co. Ltd. Huawei Paris Research Center School of Microelectronics Fudan University

Today's deep learning models face an increasing demand to handle dynamic shape tensors and computation whose shape information remains unknown at compile time and varies in a nearly infinite range at runtime. This shape dynamism brings tremendous challenges for existing compilation pipelines designed for static models which optimize tensor programs relying on exact shape values. This paper presents TSCompiler, an end-to-end compilation framework for dynamic shape models. TSCompiler first proposes a symbolic shape propagation algorithm to recover symbolic shape information at compile time to enable subsequent optimizations. TSCompiler then partitions the shape-annotated computation graph into multiple subgraphs and fine-tunes the backbone operators from the subgraph within a hardware-aligned search space to find a collection of high-performance schedules. TSCompiler can propagate the explored backbone schedule to other fusion groups within the same subgraph to generate a set of parameterized tensor programs for fused cases based on dependence analysis. At runtime, TSCompiler utilizes an occupancy-targeted cost model to select from pre-compiled tensor programs for varied tensor shapes. Extensive evaluations show that TSCompiler can achieve state-of-the-art speedups for dynamic shape models. For example, we can improve kernel efficiency by up to 3.97× on NVIDIA RTX3090, and 10.30× on NVIDIA A100 and achieve up to five orders of magnitude speedups on end-to-end latency.

关键词： machine learning tensor compilers dynamic shape operator fusion code generation auto-tuning

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Beyond Integration: Advancing Electricity Metering with Secure and Transparent Blockchain-IoT Solutions 47

Beyond Integration: Advancing Electricity Metering with Secu...

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47th International Conference on Telecommunications and Signal Processing, TSP 2024

作者： Koštál, Kristian Bahar, Muhammad Nasim Numyalai, Sultan Ries, Michal Institute of Computer Engineering and Applied Informatics Slovak University of Technology in Bratislava Faculty of Informatics and Information Technologies Bratislava Slovakia

ISBN: (纸本)9798350365597

As the demand for reliable and accurate electricity metering escalates in the digital era, traditional systems increasingly need to improve due to their centralized nature, prone to inaccuracies, privacy breaches, and cybersecurity threats. This paper proposes a novel solution that harnesses the transformative power of Blockchain and Internet of Things (IoT) technologies to address these critical challenges. By integrating the immutable ledger capabilities of blockchain with the real-time data acquisition prowess of IoT, we introduce a decentralized system that ensures the secure and transparent measurement and billing of electricity for domestic consumers. Adopting Hyperledger Fabric within our framework offers a robust, scalable architecture that supports secure transactions and enhances trust through digital identities for all participants. This system mitigates the vulnerabilities associated with centralized authorities and efficiently monitors and manages energy consumption with unprecedented precision. Through this approach, we aim to revolutionize the electricity metering infrastructure, providing a blueprint for future advancements in the energy sector. © 2024 IEEE.

关键词： Smart contract

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mmAlphabet: Air Writing Alphabet Recognition System Based on mmWave FMCW Radar and Convolutional Neural Network 27th

mmAlphabet: Air Writing Alphabet Recognition System Based on...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Huang, Chao-Wang Wang, Chien-Yao Wang, Jia-Ching Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan Multimedia Technologies Laboratory Institute of Information Science Academia Sinica Taipei Taiwan

ISBN: (纸本)9783031781032

An air writing alphabet recognition system based on the images of temporal spectrogram such as average range-time map, average Doppler-time map and average angle-time map derived from an mmWave FMCW radar is proposed. All 26 English lowercase letters written in midair right above the radar sensor can be recognized. Two valuable radar data sets are introduced in diverse environments such as meeting room, office cubicle and living room etc. The use case is the usage scenarios of laptop computers and mobile phones. In one data set, volunteers write freely in their own handwriting styles including different hand speeds and different stroke orders. In the other data set, volunteers are asked to write according to a prescribed sequence of strokes. Then, the gestures sensed by radar are processed into images of temporal spectrogram to represent the written letter. A convolutional neural network which achieves 98.6% test accuracy is exploited as the classifier to recognize the air written alphabet. In the Leave-One-Subject-Out (LOSO) cross validation, it achieves an average test accuracy of 87.74%. The effectiveness of the proposed alphabet recognition system is extensively verified on the two created data sets for different variants of temporal spectrograms. It can be used to implement natural, intelligent noncontact human machine interface. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Combined CNN-LSTM Deep Learning Algorithms for Recognizing Human Physical Activities in Large and Distributed Manners:A Recommendation System

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computers, Materials & Continua 2024年第4期79卷 351-372页

作者： Ameni Ellouze Nesrine Kadri Alaa Alaerjan Mohamed Ksantini Control and Energy Management Laboratory National Engineering SchoolPreparatory Institute for Engineering StudiesUniversity of SfaxSfax3029Tunisia Higher Institute of Computer Science and Communication Technologies University of SousseSousseBN19RHTunisia College of Computer and Information Sciences Jouf UniversityPO Box 2014SakakahSaudi Arabia

Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic *** and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or ***,smartphones and their associated sensing devices operate in distributed and unstable ***,collecting their data and extracting useful information is a significant *** this context,the aimof this paper is twofold:The first is to analyze human behavior based on the recognition of physical *** the results of physical activity detection and classification,the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical *** system is based on the calculation of calories burned by each user during physical *** this way,conclusions can be drawn about a person’s physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical *** identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory(LSTM),stacked LSTM,and bidirectional *** human activity data contains both spatial and temporal information,we proposed,in this paper,to use of an architecture allowing the extraction of the two types of information *** Convolutional Neural Networks(CNN)has an architecture designed for spatial information,our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal *** results obtained achieved an accuracy of 96%.On the other side,the data learned by these algorithms is prone to error and *** overcome this constraint and improve performance(96%),we proposed to use the

关键词： Human physical activities smartphone sensors deep learning distributed monitoring recommendation system uncertainty healthy calories

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Autoregressive Models for Solving the Problem of Forecasting Active Energy Complexes 5

Autoregressive Models for Solving the Problem of Forecasting...

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5th International Youth Conference on Radio Electronics, Electrical and Power engineering, REEPE 2023

作者： Durova, M.A. Zein, A.N. Borisova, S.V. Mishin, A.A. Kan, D. Osipov, S.K. National Research University Moscow Power Engineering Institute Institute of Information Technologies and Computer Science Moscow Russia

ISBN: (纸本)9798350399523

This paper solves the problem of forecasting active energy complexes using autoregressive models. Forecasting in the energy business is one of the common tasks these days. Nowadays, forecasting helps with long-term strategic planning. However, different application fields and the duration of the forecast sometimes require a radically different approach, and there is no universal model and method that can equally effectively predict any result set. In this research paper, the implementation and analysis of 4 models are introduced: Simple Autoregressive Models (AR), Moving Average Models (MAq), Autoregressive Integrated Moving Average Models (ARIMA), and Seasonal AutoRegressive Integrated and Moving Average Models (SARIMA). © 2023 IEEE.

关键词： Forecasting

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Parallelization of BitColor Algorithm via Multithreading and GPU for Graph Coloring 9

Parallelization of BitColor Algorithm via Multithreading and...

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9th International Conference on computer Science and engineering, UBMK 2024

作者： Kocausta, Burak Terci, Gizem Sungu Bayrakci, Alp Arslan Gebze Technical University Computer Engineering Department Kocaeli Turkey Institute of Information Technologies Gebze Technical University Kocaeli Turkey

ISBN: (纸本)9798350365887

The graph coloring problem, a fundamental NP-hard challenge, has numerous applications in scheduling, register allocation, and network optimization. Traditional sequential algorithms for graph coloring are computationally expensive, particularly for large-scale graphs. In this paper, we propose the Parallel BitColor Algorithm (PBitCo), an extension of the BitColor framework, designed to exploit the parallel processing capabilities of modern CPU and GPU architectures. The PBitCo algorithm utilizes bitwise operations to reduce computation time and employs parallel execution on widely accessible platforms to further enhance performance. We implemented and tested the algorithm on various graph instances, comparing its performance against conventional graph coloring methods. Our results demonstrate that PBitCo achieves significant speedups, with the GPU implementation delivering up to a 10x improvement over baseline methods. © 2024 IEEE.

关键词： Graph algorithms

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