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Enhancing Data Collection in Wide-Area Internet of Things: A Comprehensive Low-Overhead Approach 22

Enhancing Data Collection in Wide-Area Internet of Things: A...

引用

22nd International conference on Optical Communications and Networks, ICOCN 2024

作者： Li, Anyi Zhang, Bin Peng, Wei Xu, Yuwei Zhao, Feng Zhao, Baokang Xin, Qin School of Computer National University of Defense Technology Changsha China University of the Faroe Islands Faroe Islands

ISBN: (纸本)9798350367652

The rapid development of the Internet of Things (IoT) has led to the widespread application of widearea IoT, which offers the advantages of low power consumption and wide coverage. Nevertheless, the scarcity of bandwidth resources poses a significant challenge to the data transmission efficiency of wide-area IoT. Furthermore, the data encoding algorithms currently employed for message payloads tend to exhibit high redundancy and low efficiency. In order to enhance the data transmission efficiency of widearea IoT, our research has yielded the following outcomes: (1) A Template-Based Data Encoding Algorithm (TempEncode) is proposed, which employs data templates to define the message structure. The data template is employed by both senders and receivers, enabling the message payload to be compressed and overhead reduced. (2), a Low Overhead and Enhancing Data Collection (LEAD) approach has been devised for wide-area IoT based on the Message Queuing Telemetry Transport (MQTT) protocol and integrating TempEncode with Robust Header Compression. The experimental results demonstrate that LEAD has reduced the packet size by approximately 50.2% on average in comparison to the original MQTT protocol, thereby achieving efficient data transmission performance. © 2024 IEEE.

关键词： Encoding (symbols)

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TCP-do: Enhancing TCP performance with Delay Oscillation Frequency Analysis 15

TCP-do: Enhancing TCP Performance with Delay Oscillation Fre...

引用

15th International conference on Information and Communication Technology Convergence, ICTC 2024

作者： Lee, Sueun Lee, Chaeyeong Paek, Jeongyeup Chung-Ang University Department of Computer Science & Engineering Seoul Korea Republic of

ISBN: (纸本)9798350364637

As demand for ultra-high-speed Internet rises, the flaws in traditional congestion control methods based on packet loss are becoming clear. In wireless networks, packet loss often results from interference and collision, not actual congestion, causing inefficiencies. Delay-based congestion control algorithms which typically use round-trip time (RTT) as a key metric have been proposed to address these challenges. However, RTT alone cannot fully capture queuing delays, and small variations in delay measurements can cause significant fluctuations in the sending rate, leading to packet loss or inefficient link utilization. This study introduces TCP-do, a novel protocol that shifts the focus from static delay measurements to analyzing delay oscillation frequency. TCP-do monitors dynamic delay fluctuations and precisely adjusts the congestion window (cwnd), greatly enhancing delay-based congestion control especially in environments with frequent delay variations. © 2024 IEEE.

关键词： Packet loss

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Mesh Deformation Scheme for high Quality 3D Model Reconstruction

Mesh Deformation Scheme for High Quality 3D Model Reconstruc...

引用

2023 IEEE International conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Park, Jung Suk Seo, Bong-Seok Kim, Dong Ho Seoul National University of Science and Technology It Media Engineering Seoul Korea Republic of Seoul National University of Science and Technology Information Technology and Media Engineering Seoul Korea Republic of Seoul National University of Science and Technology Department of Smart Ict Convergence Engineering Seoul Korea Republic of

ISBN: (纸本)9798350333336

This paper proposes a new mesh deformation scheme in a 3D reconstruction in which the performance deteriorates when an object of an unlearned category is input in a 3D model generation network. The proposed method uses a new loss function to generate appropriate 3D reconstruction outputs even for inputs of unlearned categories of 3D models. We compare the performance with the existing method so as to prove the validity of this method. The simulation results show that the proposed loss function improves the generalization ability of the 3D reconstruction with good performance even for inputs of unlearned categories. © 2023 IEEE.

关键词： 3D modeling

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Optimization method for 3D visualization of mobile medical images based on user interaction prediction

Optimization method for 3D visualization of mobile medical i...

引用

2024 International conference on Image, Signal Processing, and Pattern Recognition, ISPP 2024

作者： Zhai, Tongtong Cui, Yong Lin, Yusong School of Cyber Science and Engineering Zhengzhou University Zhengzhou China School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China Collaborative Innovation Center for Internet Healthcare Zhengzhou University Zhengzhou China Hanwei IoT Institute Zhengzhou University Zhengzhou China

ISBN: (纸本)9781510680425

With the advancement in mobile device performance and the growing demand for 3D visualization of medical images, achieving efficient 3D visualization on mobile devices has become paramount. In this study, we dynamically select rendering methods and quality based on different interaction methods and process dynamics during the user exploring volume data, aiming to swiftly address user interaction requests. Additionally, we employ a trajectory prediction model by leverage of LSTM+MDN to discern inherent patterns in virtual camera parameter sequences from users' historical interaction statistics, predicting virtual camera motion trajectories for pre-rendering and further reducing waiting time for remote rendering. Through comprehensive experimental evaluations encompassing various network conditions, medical image data, and devices, we validate the efficacy of the proposed rendering optimization strategy and prediction model. The experimental outcomes demonstrate a 10% reduction in overall network transmission volume while preserving image quality and response time, thus enhancing user satisfaction with high-quality visualization and interactive exploration of mobile medical images. © 2024 SPIE.

关键词： Rendering (computer graphics)

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Adaptive Pyramid Particle Swarm Algorithm with Competitive and Cooperative Strategies 7

Adaptive Pyramid Particle Swarm Algorithm with Competitive a...

引用

7th IEEE Information Technology, Networking, Electronic and Automation Control conference, ITNEC 2024

作者： Bai, Junting Yan, Jin Ablameyko, Sergey V. Wang, Jian Qingdao China Belarusian State University Faculty of Applied Mathematics and Computer Science Minsk220030 Belarus

ISBN: (纸本)9798350370782

Particle swarm optimization algorithms play an important role in various optimization problems by simulating the collaboration and information sharing among individuals in natural systems and using the local optimal solutions of individuals in the population to guide the global search. In this paper, based on the particle swarm algorithm with pyramid topology, the adaptive pyramid particle swarm algorithm (APPSO) is proposed by innovating the update of particles through the use of parameter adaptive competition and cooperation strate-gies. The algorithm adds adaptive strategies to improve the accuracy performance of the algorithm and introduces the concept of iterative stagnation to maintain the diversity of the population. In this paper, the proposed algorithm (APPSO) is compared with six algorithms on the CEC2022 test set, and extensive experimental results show that APPSO has the ability to accurately converge to the global optimum. © 2024 IEEE.

关键词： Optimization algorithms

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Local-Global Spatio-Temporal Encoding with Adaptive Brain Region Signal Segmentation for Mild Cognitive Impairment Detection

Local-Global Spatio-Temporal Encoding with Adaptive Brain Re...

引用

2024 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Jie Wu, Luohuang Kuang, Hulin Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Hunan Changsha410000 China

ISBN: (纸本)9798350386226

Resting-state Functional Magnetic Resonance Imaging (rs-fMRI) has been widely used in Mild Cognitive Impairment (MCI) detection. However, most existing methods rely on fixed sliding windows to capture the temporal variations in brain connectivity and fail to model temporal features effectively. To address these limitations, we propose a spatio-temporal graph Transformer framework. First, we segment rs-fMRI data into distinct segments using the multivariate Gaussian distribution of brain region signals and remove redundant segments based on cumulative Jensen-Shannon divergence. Next, we extract features for each brain region and time point within segments, using these temporal features to guide spatial feature aggregation. A position encoder is designed to leverage temporal features across segments to capture both local and global spatio-temporal information. Finally, we employ a population graph framework as the classifier, with spatio-temporal features and demographic data of each subject forming the edge weights, to output the detection results. Our method achieves 92.80%, 81.89%, and 82.80% accuracy in MCI detection, early MCI detection, and late MCI detection tasks on the ADNI3 and ADNI2 datasets, respectively, outperforming existing methods. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Attention Mechanism-Based Dense Upsampling of Transfer Learning Mask RCNN for Improved Object Segmentation

Attention Mechanism-Based Dense Upsampling of Transfer Learn...

引用

2024 IEEE International conference on Cybernetics and Innovations, ICCI 2024

作者： Comia, Lysa V. Festijo, Enrique D. Technological Institute Of The Philippines Graduate School Program Manila City Philippines

ISBN: (纸本)9798350370096

Object segmentation is a key task in computer vision, with applications ranging from self-driving to medical imaging. Leveraging deep learning approaches, notably Transfer Learning (TL), presents a potential path for improving segmentation accuracy and flexibility across a wide range of visual scenarios. However, standard TL-based algorithms often fail to capture complex object features and properly traverse complicated visual environments. In this paper, it presents a unique strategy that combines attention mechanisms with dense upsampling approaches within the Mask R-CNN framework, to improve the model's ability to distinguish fine-grained features and contextual cues that are critical for correct segmentation. Drawing on previous research initiatives, it expands on insights gained from studies investigating graph-based segmentation, tailored loss functions, and context-aware upsampling modules. The findings show excellent progress, with mean Average Precision (mAP) of 0.952, Average Recall at Precision=0.5 (RP@50) of 0.9173, Average Recall at Precision=0.75 (RP@75) of 0.8967, and an average AUC-ROC of 0.9368. These results demonstrate the effectiveness of the suggested technique for creating high-resolution segmentation masks while maintaining spatial information and adding contextual signals. Finally, the findings signal a considerable advance in object segmentation capabilities, opening the path for improved performance in a wide range of real-world applications. © 2024 IEEE.

关键词： Signal sampling

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A Noc Centric Low Overhead Multi-chip Interconnection Technology 23

A Noc Centric Low Overhead Multi-chip Interconnection Techno...

引用

23rd IEEE International conference on high performance Computing and Communications, 7th IEEE International conference on Data Science and Systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Hu, Xing Zhang, Yang Mao, Li Shen, Jie Xing, Zuocheng College of Computer National University of Defense Technolgy ChangSha China College of Microelectronics Xi'an University of Electronic Science and Technology Xi'an China

ISBN: (纸本)9781665494571

Improving the performance of processors is one of the main tasks of high-performance processor design. It is an effective way to improve the processor's performance through high-speed inter-chip interconnection, which utilizes multiple chips to build one unified computing system with higher performance. The existing multi-chip interconnection technologies, such as CCIX, CXL and NVLink, are faced with the problems of not open protocol, high hardware overhead and difficulty to reuse with existing high-speed peripherals. Considering the limitation of current multi-chip interconnection, we adopt the network centered idea to design FT_Link open architecture, which realizes the interconnection of multiple chips in a low-cost way and enables requests to access other chip spaces transparently. FT_Link system is built on common logic layer and physical layer of the existing high-speed peripherals, making it flexible and versatile. Tests and synthesis results show that FT-Link technology has the feature of high throughput and low latency with very small hardware overhead. Its performance bottleneck depends on the FIFO depth of the high-speed peripheral logical layer which it adopts. © 2021 IEEE.

关键词： program processors Protocols Smart cities Throughput Physical layer Hardware Hazards

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An emotional EEG signal classification research based on deep learning 3

An emotional EEG signal classification research based on dee...

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3rd International conference on computer Vision, Image and Deep Learning and International conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Cai, Baoqing Quan, Wei Li, Qi Song, Yu Changchun University of Science and Technology School of Computer Science and Technology Changchun China Zhongshan Institute of Changchun University of Science and Technology Zhongshan China

ISBN: (纸本)9781665459112

For the task of classifying emotional EEG signals with rich high-dimensional features in the time and frequency domains, this paper proposes a novel deep learning algorithm, SincNet-E, inspired by the speech recognition model SincNet. The convolutional structure of SincNet is adapted so that it can be considered as consisting of two pooling layers alternately superimposed with three convolutional groups. Subsequently, the LSTM structure is added after the convolutional structure to enhance its extraction of time-dependent features in the signal data. This novel model effectively improves the classification performance of the deep learning algorithm for emotional EEG data, ultimately achieving an accuracy of about 83.89% without the need for manual feature extraction. © 2022 IEEE.

关键词： Convolutional neural networks

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An Efficient and Privacy-concerned Multi-user Access Mechanism for EMRs 24

An Efficient and Privacy-concerned Multi-user Access Mechani...

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24th IEEE International conference on high performance Computing and Communications, 8th IEEE International conference on Data Science and Systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Li, Yihan Zeng, Shengke Feng, Yawen Zhao, Zhiliang School of Computer and Software Engineering Xihua University Chengdu China

ISBN: (纸本)9798350319934

In the medical system, it is essential to protect the privacy of electronic medical records (EMRs) especially in cloud-based system. Adopting cloud service has brought great convenience and high efficiency, such as data storage and processing. However, storing sensitive records to cloud is susceptible to be leaked without encryption. Therefore, many public key encryption with keyword search (PEKS) schemes have been proposed to search on encrypted data so as to protect the security and privacy of the outsourced data. In order to prevent the keyword guessing attack (KGA) existing in PEKS schemes, the authentication mechanism with private key of the sender is considered. Nevertheless, this kind of solution reveals the identity of the uploaders (the sender), say patients. Indeed, the patients in the medical system are reluctant to leak neither their medical records nor their identites. Besides, some of these schemes can not be extended to multi-user scenarios. To address the above issues, we propose an efficient and privacy-concerned multi-user access mechanism (M-PEKS) for Electronic Medical Records, in which the records are not only encrypted semantically secure with KGA resistant but also the identities of the uploaders cannot be leaked. Besides, our scheme is suitable for the multi-receiver scenario. That is, the same record only needs to be encrypted once for each keyword, no matter how many receivers our scheme have. Finally, we show that our scheme has much better performance in storage and computation than other related schemes for sharable EMRs. © 2022 IEEE.

关键词： Public key cryptography

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