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Analysis and Design of Mobile Model for DTN

Chinese Journal of Electronics 2023年第4期18卷 635-638页

作者： Zhiting Lin Yugui Qu Yusheng Ji Baohua Zhao Department of Computer Science University of Science and Technology of China Hefei China Province Key Laboratory of Software in Computing and Communication Hefei China Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan

Delay tolerant networks may become unexpectedly partitioned due to node mobility or variation in signal strength. However, most widely used models in some relative works are generally very simplistic. In order to exploit intelligent forwarding algorithms, a novel nodal mobile model of delay tolerant networks is presented to map reality with more accuracy. And several approaches are introduced to analyze the network structure, such as n-cliques, n-clans, degree, closeness and betweenness. Our research results showed that the centralizations became smaller when the wireless connections were concerned. It meant that quite a number of nodes became potential relays because of the new structure of networks.

关键词： Wireless communication Analytical models Wireless sensor networks Protocols Social networking (online) Sensitivity analysis Social network theory

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Towards Expanding Precise Timing for Collaborative Its with Deterministic communications in 6G

Towards Expanding Precise Timing for Collaborative Its with ...

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IEEE Conference on Wireless communications and Networking

作者： Hongxing Li Haotian Li Qianhan Gao Guochu Shou Yaqiong Liu Zhigang Guo Yihong Hu State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The demand for precise timing continues to grow with the rapid development of smart cities, where intelligent transportation emerges as a critical application heavily dependent on accurate time distribution. Deterministic communication is anticipated to be a defining feature of nextgeneration mobile networks (6G), enabling end-to-end timecritical applications, and unlocking new possibilities for achieving end-to-end precise time synchronization as well. To cope with the challenge of delivering precise time synchronization across broader areas at a lower cost, this paper proposes a method to achieve end-to-end precise time synchronization by leveraging the deterministic characteristics of communication networks. Specially, the proposed approach adopts the Software-Defined Networking (SDN) paradigm to implement deterministic networking for the seamless integration of Time-Sensitive Networking (TSN) and cellular networks. Furthermore, to mitigate the impact of wireless channel uncertainties on synchronization accuracy, we propose a network delay measurement mechanism based on the principle of time redundancy, designed to reduce end-to-end network delay variation. Experimental results validate the effectiveness of the proposed method, demonstrating its capability to constrain the end-to-end relative time error of wired and wireless converged networks to the microsecond scale, without necessitating modifications to the existing network infrastructure.

关键词： 6G mobile communication Wireless communication Cellular networks Uncertainty Accuracy Measurement uncertainty Transportation Delays Synchronization Software defined networking

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Reconstruction of the Mosul Old City by Procedural Modelling

SSRN

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SSRN 2024年

作者： Hussein, Khalid Ahmed Ismaeel, Emad Hani Ihsan, Rasheed Rebar Sadeeq, Haval Tariq Architecture Engineering Department College of Engineering Nawroz University KRD Duhok Iraq Dept. of Architecture Coll. of Engineering University of Mosul Iraq Computer and Communication Department College of Engineering Nawroz University KRD Duhok Iraq Information Technology Department Technical College of Duhok Duhok Polytechnic University KRD Region Iraq

The old city of Mosul possesses a rich built heritage, featuring distinct architectural styles evolved, incorporating indigenous knowledge and expertise to address functional, climatic, economic, and construction requirements. Tragically, during the military operations in 2016-2017, Mosul Old City experienced extensive devastation, resulting in the loss of numerous landmarks and the irreplaceable built heritage of this city. However, digitization has emerged as a potent heritage preservation and restoration tool. It serves as a vital initial step in reviving deteriorated heritage by providing invaluable information to specialists. In light of this, the present study aims to regenerate the urban facades of Mosul's old city automatically, ensuring they retain their original characteristics. This objective will be accomplished by utilizing procedural modelling techniques and algorithms incorporating specific characteristics as constraints within the regeneration process. The research makes a notable contribution to digital architectural design by employing Shape Grammar analysis to derive rules. Subsequently, these rules are implemented through Python to facilitate the automatic generation of 750 heritage facade designs. Ultimately, the study demonstrates the feasibility of using procedural modelling to recreate the decimated urban fabric, ensuring the restored structures exhibit the same formal and structural characteristics as their predecessors. © 2024, The Authors. All rights reserved.

关键词： Python

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3D Registration in 30 Years: A Survey

arXiv

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arXiv 2024年

作者： Yang, Jiaqi Zhang, Chu'ai Wang, Zhengbao Cao, Xinyue Ouyang, Xuan Zhang, Xiyu Zeng, Zhenxuan Zeng, Zhao Lu, Borui Xia, Zhiyi Zhang, Qian Guo, Yulan Zhang, Yanning School of Computer Science Northwestern Polytechnical University China School of Electronics and Control Engineering Chang'an University China School of Architecture & Urban Planning Shenzhen University China School of Electronics and Communication Engineering Sun Yat-Sen University China

3D point cloud registration is a fundamental problem in computer vision, computer graphics, robotics, remote sensing, and etc. Over the last thirty years, we have witnessed the amazing advancement in this area with numerous kinds of solutions. Although a handful of relevant surveys have been conducted, their coverage is still limited. In this work, we present a comprehensive survey on 3D point cloud registration, covering a set of sub-areas such as pairwise coarse registration, pairwise fine registration, multi-view registration, cross-scale registration, and multi-instance registration. The datasets, evaluation metrics, method taxonomy, discussions of the merits and demerits, insightful thoughts of future directions are comprehensively presented in this survey. The regularly updated project page of the survey is available at https://***/Amyyyy11/3D-Registration-in-30-Years-A-Survey. © 2024, CC BY.

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Fabric Tag#: Envisioning a UXD Solution for Textile Culture Preservation and Fashion Sustainability

Fabric Tag#: Envisioning a UXD Solution for Textile Culture ...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Chiu, Yi-Yun Hsieh, Wei-En Lin, Wei-Chen Tsai, Ning-Wei Wu, Cheng-Han Department of Architecture National Cheng Kung University Tainan Taiwan Department of Psychology National Cheng Kung University Tainan Taiwan Department of Civil Engineering National Cheng Kung University Tainan Taiwan Department of Transportation and Communication Management Science National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9798400713958

This paper explores Fabric Tag#, an innovative solution designed to address the environmental challenges posed by fast fashion while celebrating the cultural and industrial heritage of fabric. Focused on Tainan, once known as the”City of Fabric,” the study investigates the local fabric market through field studies and persona development, revealing key insights for designing a sustainable, user-centered experience. Fabric Tag# integrates physical fabric tags with a digital app, providing users with a deeper connection to fabric through detailed documentation of its material, craftsmanship, and story. The app also supports local fabric retailers by offering market trend insights, inventory management, and a platform for sharing cultural narratives. By blending technology and sustainability, Fabric Tag# aims to foster a community around fabric appreciation, promote sustainable practices, and inspire users to rethink their relationship with textiles in an era dominated by fast fashion. © 2025 Copyright held by the owner/author(s).

关键词： Digital Interaction Fashion Sustainability Physical Textile Cultural Preservation Urban Culture Exploration

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Multi-Target Classification Using Deep Learning Models for Automotive Applications

Multi-Target Classification Using Deep Learning Models for A...

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International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Soumya. A Linga Reddy Cenkeramaddi Chalavadi Vishnu Yash Vinod Lanjewar Krishna Mohan C Department of Computer Science and Engineering Indian Institute of Technology Hyderabad India Department of Information and Communication Technology University of Agder Grimstad Norway Department of Architecture and Regional Planning Indian Institute of Technology Kharagpur India

This paper presents a multi-perspective convolutional neural network (CNN) that extracts the class of objects supporting intelligent transportation systems. The proposed model is the visual geometry group (VGG) backbone network with custom feature extraction blocks, that use multilayer prediction heads. The model addresses both multi-class and multi-object classification tasks utilizing the automotive object detection dataset. The model is designed with multiple prediction heads to classify different objects in an image and enable object count prediction. A publicly available automotive object detection dataset with 19800 images and labels has been utilized. The dataset consists of five primary types of objects: Persons, Trucks, Motorbikes, Cars, and Cyclists. On the dataset, pre-trained models such as VGG, Resenet, EfficientNet, and DenseNet were tested and their classification performance was evaluated. The experimental results illustrate the superiority of the proposed VGG backbone deep learning CNN model in comparison to other pre-trained models.

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Enhanced satellite imagery analysis for post-disaster building damage assessment using integrated ResNet-U-Net model

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Multimedia Tools and Applications 2025年第5期84卷 2689-2714页

作者： Bhardwaj, Diwakar Nagabhooshanam, N. Singh, Ajeet Selvalakshmi, B. Angadi, Sanjeevkumar Shargunam, S. Guha, Tapas Singh, Gurkirpal Rajaram, A. Computer Engineering and Applications GLA University Chaumuhan India Department of Research Analytics Saveetha Dental College and Hospitals Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Aditya University Surampalem Andhra Pradesh India VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of Computing Science and Engineering Tagore Engineering College Rathinamangalam Tamil Nadu Chennai600127 India Department of Computing Science and Engineering Nutan College of Engineering and Research Talegaon Dabhade Maharashtra 410507 India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu Srivilliputhur626126 India Department of CSE Techno International New Town West Bengal Kolkata700156 India Department of Architecture PTU Punjab Mohali140307 India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Tamil Nadu Nagapattinam611002 India

In the aftermath of natural disasters, timely and accurate building damage assessment is crucial for effective disaster response. This research introduces an innovative integrated ResNet-U-Net ensemble model that significantly enhances the accuracy and efficiency of damage detection from satellite imagery. The model combines the deep feature extraction capabilities of ResNet with the precise localization advantages of U-Net, supplemented by attention mechanisms to focus on critical damage indicators. Methodologically, the research employs a dataset of high-resolution satellite images from post-Hurricane Iota, processed through normalization and augmentation techniques. The model demonstrates exceptional accuracy, achieving 96.91% on an unbalanced dataset representative of real-world conditions, and 89.35% on a balanced dataset. This performance indicates a substantial improvement over existing approaches, offering a rapid and reliable tool for aiding disaster relief efforts. The proposed model's computational efficiency and generalizability underscore its potential to transform disaster management practices. Future work will explore the model's application to various disaster types and integration with real-time data processing systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Satellite imagery

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Scalable Deep Learning for Categorization of Satellite Images 7

Scalable Deep Learning for Categorization of Satellite Image...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Swapna, B. Venkatessan, Radhika Taskeen, Fathima Indrapriya, K. Manjula, D. Muthukumar, D Surendiran Dr. M.G.R. Educational and Research Institute Department of Electronics and Communication Engineering Tamil Nadu Chennai India Dr. M.G.R. Educational and Research Institute Faculty of Architecture The Department Tamil Nadu Chennai India Dr. M.G.R. Educational and Research Institute Faculty of Architecture Tamil Nadu Chennai India Karpagam Faculty of Medical Sciences and Research Department of Community Medicine Tamil Nadu Coimbatore India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350341485

Satellite imagery serves a crucial role in diverse applications like disaster response, law enforcement, and environmental monitoring. These tasks often require manual identification of objects and structures within the images. However, due to the extensive geographic regions involved and a limited number of analysts available, there's a growing need for automation. Traditional methods of object detection and classification are proving insufficient and unreliable for addressing this challenge. This is where deep learning comes into play - a collection of machine learning techniques that holds the potential to automate these tasks effectively. In particular, convolutional neural networks (CNNs), a subset of deep learning, have shown remarkable promise in understanding images. This study focuses on applying CNNs to the complex task of recognizing various objects and facilities in high-resolution, multi-spectral satellite images. The research introduces a deep learning system that takes advantage of both satellite metadata and image features to accurately classify the dataset into different categories. The implementation of this system is carried out using Python, leveraging the Keras and TensorFlow libraries. Comparative analysis against existing systems reveals a significant enhancement in overall accuracy offered by this approach. © 2023 IEEE.

关键词： Python

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Multi-agent Reinforcement Learning Based Resource Allocation in End-Edge-Cloud Enabled Industrial Internet of Things

Multi-agent Reinforcement Learning Based Resource Allocation...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and communications (GreenCom)

作者： Yanmei Chen Xiaohuan Li Jin Ye Xun Wang Qian Chen School of Information and Communication Guilin University of Electronic Technology Guilin China School of Computer Electronics and Information Guangxi University Nanning China School of Architecture and Transportation Engineering Guilin University of Electronic Technology Guilin China

The dynamic changes of task requirement and the time-varying distribution of resources in Industrial Internet of Things (IIoT) make a challenge for traditional static resource allocation methods to flexibly adapt to these changes. These can lead to increase latency and energy consumption, which result in low efficiency of resource allocation. In this paper, a resource allocation method based on multi-agent reinforcement learning (MARL) in end-edge-cloud enabled IIoT is proposed. The method builds an end-edge-cloud collaboration resource management model in IIoT scenarios, and constructs the optimization problem of minimizing task latency and energy consumption. Then, the optimization problem is further transformed into a multi-agent Markov decision process (MDP). Furthermore, the multi-agent deep deterministic policy gradient (MADDPG) is adopted to solve the formulated MDP problem. Finally, simulation results demonstrate that the proposed algorithm can significantly reduce the task latency cost by 27% and energy consumption cost by 15% compared with that of the baseline methods.

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Smart Prediction Models for Energy Efficiency in Smart Residential Buildings: A Deep Learning Approach

Smart Prediction Models for Energy Efficiency in Smart Resid...

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2023 International Conference for Technological Engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Vasantharaj, A. Ahamed, M. Irshad Prema, K. Mudigonda, Aditya Sapaev, J.B. Thandapani, Kavitha Kalaignar Karunanidhi Institute of Technology Information Electronics and Communication Engineering India E.G.S. Pillay Engineering College Department of Electronics and Communication Engineerin Tamilnadu Nagapattinam India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India JNIAS School of Planning and Architecture Telangana Hyderabad500034 India National Research University Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Tashkent Uzbekistan Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Vgy Technology Avadi Department of Electronics and Communication Engineering Tamil Nadu Chennai600062 India

ISBN: (纸本)9798350336474

Using power in houses has given rise to several ecological issues, including air pollution. Predicting building energy use is essential for better regulating or reducing energy usage. Machine Learning (ML) techniques have been used to estimate the power use of operating buildings. However, there has been little research on using ML algorithms to anticipate the prospective energy usage of buildings during the initial design phase. When lowering energy-inefficient buildings, it is crucial to tackle the underlying issue: accurately anticipating energy consumption before construction. This is necessary to prevent the future development of environmentally detrimental buildings. This research introduces a new and innovative Hybrid Deep Learning Model for Predicting Energy Efficiency in Smart Residential Buildings (HDL-PEE-SRB). The suggested framework has two steps: cleaning the information and constructing the model. The information cleansing step utilizes pre-processing methods to manipulate the original information and incorporates lag values as supplementary characteristics. During model-building, the hybrid model undergoes training using the processed data. The HDL model combines fully linked layers, Unidirectional Long Short-Term Memory (Uni-LSTM), and bi-directional LSTMs (Bi-LSTM), which are stacked together. The suggested model is assessed using standard energy usage databases, demonstrating higher accuracy than commonly used HDL models. The proposed model surpasses other HDL models, demonstrating the most favorable performance with a MAPE of 1.94% and an accuracy of 98.06%. © 2023 IEEE.

关键词： Forecasting

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