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Efficient participating media rendering with differentiable regularization

Computational Visual Media 2024年第5期10卷 937-948页

作者： Wenshi Wu Beibei Wang Milos Hasan Lei Zhang Zhong Jin Ling-Qi Yan School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Adobe Research San JoseCA 95110-2704USA Department of Computing Hong Kong PolytechnicUniversityHong Kong 999077China Department of ComjouterScience University of California Santa BarbaraCA 93106USA.

Highly scattering media,such as milk,skin,and clouds,are common in the real *** participating media is challenging,especially for highorder scattering dominant media,because the light may undergo a large number of scattering events before leaving the *** Carlo-based methods typically require a long time to produce noise-free *** on the observation that low-albedo media contain less noise than high-albedo media,we propose reducing the variance of the rendered results using differentiable *** first render an image with low-albedo participating media together with the gradient with respect to the albedo,and then predict the final rendered image with a low-albedo image and gradient image via a novel prediction *** achieve high quality,we also consider the gradients of neighboring frames to provide a noise-free gradient ***,our method can produce results with much less overall eror than equal-time path tracing methods.

关键词： participating media differentiable regularization differentiable rendering volumetric path tracing temporal denoising

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Research on Ultra-wideband Ranging Method in Tunnel Environment 11

Research on Ultra-wideband Ranging Method in Tunnel Environm...

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11th International Conference on Networks, Communication and Computing, ICNCC 2022

作者： Wang, Lei Zhang, Wenbo Qi, Junyan He, Junyi Henan Polytechnic University School of Computer Science and Technology China Henan Key Laboratory of Intelligent Mining Technology and Equipment of Coal Mine China

ISBN: (纸本)9781450398039

Aiming at the serious non-visual range problem in the complex and variable tunneling environment, which leads to the unsatisfactory results of most existing ultra-wideband (UWB) clock drift optimization schemes. We propose a straightforward and powerful high accuracy ranging method for tunnel environment. First, to address the impact of severe occlusion on UWB ranging in tunnels, we propose a simple non-line-of-sight (NLOS) error identification and correction scheme to minimize the clock drift error caused by NLOS. Moreover, for the clock drift problem of two-way ranging (TWR), we employ automatic correction by sending messages twice in a short period of time, taking into account the high mobility of nodes and NLOS range occlusion errors in the correction, which improves the ranging accuracy without increasing the equipment cost. Finally, the distance values are classified and filtered using the DBSCAN clustering algorithm and Gaussian filtering to achieve accurate distance measurements. Experimental results show that the algorithm can achieve a range accuracy of 0.1 m in a non-direct aiming and high maneuverability environment, which has a higher range accuracy compared with other algorithms. © 2022 ACM.

关键词： Clustering algorithms

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Multi-level Teaching Text Classification based on the Fusion of Deep Learning Models

Multi-level Teaching Text Classification based on the Fusion...

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2023 International Conference on Information Education and Artificial Intelligence, ICIEAI 2023

作者： Jin, Guanghao Zhao, Junhua Wang, Yuqing Wang, Jieying Du, Hui Song, Qingzeng School of Artificial Intelligence Beijing Polytechnic China School of Computer Science and Technology Tiangong University China

ISBN: (纸本)9798400716157

Deep learning is widely used in text classification, which can be help the collection of teaching text samples from multiple datasets. On the other side, the variety of texts causes the difficulty of the classification. To solve this problem, we design a multi-level text classification system that fuses multiple models to increase the accuracy of the classification. In more details, we train the deep learning models on some public datasets. Then, we organize these to construct multi-level sets of models. On a text sample, we firstly detect if the dataset classification is needed. Then, we send this sample to the proper level of model set. Finally, we continuously optimize the system based on the collected samples. As the experimental results show, our method can achieve higher accuracy than the existing ones. © 2023 ACM.

关键词： Classification (of information)

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Audio-Text Multimodal Speech Recognition via Dual-Tower Architecture for Mandarin Air Traffic Control Communications

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computers, Materials & Continua 2024年第3期78卷 3215-3245页

作者： Shuting Ge Jin Ren Yihua Shi Yujun Zhang Shunzhi Yang Jinfeng Yang School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic UniversityShenzhen518055China Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen518055China Industrial Training Centre Shenzhen Polytechnic UniversityShenzhen518055China

In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues, we propose a speech-text multimodal dual-tower architecture for speech recognition. It employs cross-modal interactions to achieve close semantic alignment during the encoding stage and strengthen its capabilities in modeling auditory long-distance context dependencies. In addition, a two-stage training strategy is elaborately devised to derive semantics-aware acoustic representations effectively. The first stage focuses on pre-training the speech-text multimodal encoding module to enhance inter-modal semantic alignment and aural long-distance context dependencies. The second stage fine-tunes the entire network to bridge the input modality variation gap between the training and inference phases and boost generalization performance. Extensive experiments demonstrate the effectiveness of the proposed speech-text multimodal speech recognition method on the ATCC and AISHELL-1 datasets. It reduces the character error rate to 6.54% and 8.73%, respectively, and exhibits substantial performance gains of 28.76% and 23.82% compared with the best baseline model. The case studies indicate that the obtained semantics-aware acoustic representations aid in accurately recognizing terms with similar pronunciations but distinctive semantics. The research provides a novel modeling pa

关键词： Speech-text multimodal automatic speech recognition semantic alignment air traffic control communications dual-tower architecture

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Research on Multi-Domain Sample Classification Method Based on Baidu API 23

Research on Multi-Domain Sample Classification Method Based ...

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2023 4th International Conference on Control, Robotics and Intelligent System, CCRIS 2023

作者： Cui, Yuwei Song, Qingzeng Xue, Yongjiang Yu, Jing School of Computer Science and Technology Tiangong University China School of Telecommunication Engineering Beijing Polytechnic China

ISBN: (纸本)9798400708190

This paper proposes a multi-domain sample classification method based on Baidu API's general object recognition function. we used three datasets in the experiment,including CIFAR-10, CIFAR-100, and Mini-ImageNet. For an unknown sample belonging to these three datasets, we first predict which domain it may belong to by using the output results of Baidu API, and then obtain the label of the sample by training a model on that domain. Compared with existing methods, our method reduces the number of high-performance models that need to be trained and reduces the computational difficulty. Experimental results show that our method is more convenient and accurate. © 2023 ACM.

关键词： Image classification

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OblivTime: Oblivious and Efficient Interval Skyline Query Processing Over Encrypted Time-Series Data

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IEEE Transactions on Services Computing 2025年第03期18卷 1602-1617页

作者： Ouyang, Huajie Zheng, Yifeng Wang, Songlei Hua, Zhongyun Harbin Institute of Technology School of Computer Science and Technology Guangdong Shenzhen518055 China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518055 China

Time-series data is prevalent in many applications like smart homes, smart grids, and healthcare. And it is now increasingly common to store and query time-series data in the cloud. Despite the benefits, data privacy concerns in such outsourced services are pressing, making it imperative to embed privacy assurance mechanisms from the outset. Most existing related works have been focused on querying for different types of aggregate statistics. In this paper, we instead focus on the secure support for advanced interval skyline queries, which allow to identify time series that are not dominated by any other time series within a query time interval. This is valuable for time-series data analytics in applications like remote health monitoring (e.g., identifying patients with high heart rates in a certain week). We present OblivTime, a new system framework for oblivious and efficient interval skyline query processing over encrypted time-series data. OblivTime is built from a synergy of time-series data analytics, lightweight cryptography, and GPU parallel computing, achieving stronger security guarantees and lower online query latency over the state-of-the-art prior work. Extensive experiments demonstrate that OblivTime can achieve up to 666\times speedup in online query latency over the state-of-the-art prior work. © 2008-2012 IEEE.

关键词： Structured Query Language

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Research on Intelligent Data Processing Support Model of Government Information System Based on Deep Learning

Research on Intelligent Data Processing Support Model of Gov...

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2025 Asia-Europe Conference on Cybersecurity, Internet of Things and Soft Computing, CITSC 2025

作者： Lu, Dapeng Liu, Haojie Zhuhai College of Science and Technology School of Tourism and Public Administration Guangdong Zhuhai519000 China Henan University of Technology Computer Application Technology Zhengzhou450001 China

ISBN: (纸本)9798331504205

This study constructs a data processing model that supports government information system. By designing a deep learning algorithm suitable for government scenarios, intelligent processing functions such as text analysis and image recognition are integrated into the system, aiming to achieve accurate processing of multi-source government data. The model design includes data preprocessing, feature extraction, algorithm optimization and adaptive learning modules to improve the recognition and analysis capabilities of complex government data. Through system simulation, this paper verifies the effectiveness of the proposed model. The simulation data shows that the model is significantly superior to traditional methods in data processing speed, accuracy and resource utilization. Precise experimental results show that the accuracy of the model in text data classification, image data recognition and other aspects reaches more than 96%. At the same time, it can effectively shorten the processing time in large-scale data processing tasks, providing technical support for the practical application of intelligent government information system. This study provides an efficient and scalable solution for intelligent data processing in government information system. © 2025 IEEE.

关键词： Data assimilation

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Metaverse for Healthcare: Technologies, Challenges, and Vision

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International Journal of Crowd science 2023年第4期7卷 190-199页

作者： Yang, Yihong Zhou, Zhangbing Li, Xiaocui Xue, Xiao Hung, Patrick C. K. Yangui, Sami Beijing100083 China Beijing100083 China Computer Science Department TELECOM SudParis Evry91011 France School of Computer Software College of Intelligence and Computing Tianjin University Tianjin300072 China School of Computer Science and Technology Henan Polytechnic University Jiaozuo454000 China Faculty of Business and Information Technology Ontario Tech University OshawaL1G0C5 Canada LAAS-CNRS University of Toulouse Toulouse31013 France

The continuous enhancement of living conditions imposes higher requirements for medical and healthcare services. Although improved to a certain extent, there still exist critical challenges in current medical pattern, such as the shortage of medical resources, inefficient medical treatment, and limited medical technology level. The metaverse can offer a novel mechanism to address these problems in traditional healthcare domain, and thus, to enhance the quality of medical services. Generally, the metaverse is a dynamic feedback system that facilitates the collaboration and coexistence between the virtual and physical worlds. By fostering collaboration and evolution between intelligent agents in the virtual world, knowledge of this interdependence can be reconstructed in the digital realm. This allows problems existed in the real world to be abstracted and represented in the digital space, where models can be established and computational experiments can be conducted. The outcomes obtained can dynamically guide or control the execution of strategies in the real world, with real-world execution results serving as dynamic data inputs to continually update the virtual world’s model. In addition, this paper summarizes the current research status of different healthcare application scenarios for metaverse, highlights the challenges and vision, and aims to inspire further research in this field. © The author(s) 2023.

关键词： Intelligent agents

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3D Model Reconstruction of CAD Virtual Reality Digital Building Based on BIM Model

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computer-Aided Design and Applications 2023年第S3期20卷 29-42页

作者： Wang, Ziqing Sharma, Ashutosh Hubei Polytechnic University Hubei Huangshi City435000 China School of Computer Science University of Petroleum and Energy Studies Dehradun India Institute of Computer Technology and Information Security Southern Federal University Russia

Virtual reality 3D building model modeling technology depending on BIM model is well suited to today's area of urban planning and construction, and it is extensively utilized in topography mapping and construction project inspection. A technique of constructing virtual reality digital building 3D model based on BIM model is provided to tackle the problem that the accuracy of virtual reality digital building 3D model reconstruction is not high and difficult to operate in practical application. To begin, the BIM model is imported into the digital building 3D model and changed as needed. The 3D model of a CAD virtual reality digital building based on a BIM model is then compared to and optimized against the standard model. Finally, the X model is exported on the BIM model platform and imported into the skyline platform for 3D scene integration when the virtual reality 3D building model modeling is done. The CAD virtual reality digital building three-dimensional model based on BIM model has been proven to be more suited for the present building mode. The results reveal that the performance of the BIM-based model is better than that of the conventional model that the operation is easier, and that performance optimization is enhanced by 20%, making it worthy of widespread adoption and use. © 2023 CAD Solutions, LLC.

关键词： Virtual reality

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Optimal Coding Scheme Selection for LoRa-Based Satellite IoT Transmission 29

Optimal Coding Scheme Selection for LoRa-Based Satellite IoT...

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29th IEEE Asia Pacific Conference on Communications, APCC 2024

作者： Yu, Zhongyang Yan, Yuqian Guo, Lingxi Wang, Wei Xu, Mengmeng Xu, Hengzhou Zhu, Hai Henan University of Engineering School of Computer Science Zhengzhou China Henan University of Technology College of Information Science and Engineering Zhengzhou China North China University of Water Resources and Electric Power School of Mechanical Engineering Zhengzhou China

ISBN: (纸本)9798331517816

Considering the extremely low signal-to-noise ratio (SNR) environment in future satellite internet of Things (IoT) transmission in which the traditional Hamming coded LoRa (Long Range) system in the ground IoT communication is of difficulty to guarantee high reliability, optimal coding scheme selection for the LoRa-based satellite IoT is studied for the first time. In specific, the corresponding system model is described in detail, followed by derivations of two noncoherent detection methods of coded LoRa signals. Subsequently, the channel capacity of the coded LoRa system is provided and used to develop the corresponding relationship between the signal-to-noise ratio (SNR) and the code rate. Based on the obtained relationship and the Monte-Carlo method, it is found that the coded LoRa system with different spreading factors (SFs) can achieve the lowest SNR when the code rate is close to 1/2. Finally, the performance of several coded LoRa systems (including Hamming code, BCH code, RS code, Turbo code, LDPC code and LDGM code with code rates of about 1/2) is compared by means of the Monte-Carlo method. Simulation results show that Turbo or LDPC coded LoRa systems exhibit more than 4.5 dB performance advantages over the Hamming coded LoRa system. © 2024 IEEE.

关键词： Signal to noise ratio

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