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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Kojima, Akira Hiroshima City University Department of Computer and Network Engineering Hiroshima Japan

ISBN: (纸本)9798350324136

We are developing a miniature self-driving car for the design contest at ICCE2024, which will be driven automatically by image processing of camera images. Our implementation uses a AMD/Xilinx SoC FPGA as the central controller. The hardware of the programmable logic part of the SoC FPGA is used for object detection in the camera image and PWM control of the motors. Other processes are software-controlled by the processor of the SoC FPGA. © 2024 IEEE.

关键词： Autonomous driving DPU Object detection Real-Time image processing SoC FPGA

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Deep Learning Based Signal Detection for Quadrature Spatial Modulation System

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China Communications 2024年第10期21卷 78-85页

作者： Shu Dingyun Peng Yuyang Yue Ming Fawaz AL-Hazemi Mohammad Meraj Mirza School of Computer Science and Engineering Macao University of Science and TechnologyMacaoChina Department of Computer and Network Engineering University of JeddahSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia

With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. In QSM, the traditional signal detection methods sometimes are unable to meet the actual requirement of low complexity of the system. Therefore, this paper proposes a signal detection scheme for QSM systems using deep learning to solve the complexity problem. Results from the simulations show that the bit error rate performance of the proposed deep learning-based detector is better than that of the zero-forcing(ZF) and minimum mean square error(MMSE) detectors, and similar to the maximum likelihood(ML) detector. Moreover, the proposed method requires less processing time than ZF, MMSE,and ML.

关键词： bit error rate complexity deep learning quadrature spatial modulation

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Fast Vehicular TSN network Reconfiguration with Application Aware network Synchronization 29

Fast Vehicular TSN Network Reconfiguration with Application ...

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Stöhrmann, Dominik Ernst, Rolf Institute of Computer and Network Engineering Technische Universität Braunschweig Germany

ISBN: (纸本)9798350361230

In-Vehicle networks (IVN) are under intense pressure to be both resource efficient at low cost and flexible enough for the future. The deployment of a large number of new sensors for autonomous driving and self-awareness significantly increases the demand for network resources. In addition, statically designed networks lack flexibility or have large over-provisioning of resources due to the dynamics of network traffic. Application-aware resource management addresses both of these issues. It allows the network to be reconfigured while controlling application requirements, increasing resource utilization, and providing flexibility for future demands. Our timestamp-based reconfiguration protocol ensures fast, lossless reconfiguration of high bandwidth data connections. Based on global time synchronization, it enables precise reconfiguration without increasing the risk of data loss and reduces the risk of inconsistent network configurations by exploiting the object slack created by large object transfers consisting of hundreds of individual packets. Using software-based Linux switches as the network, we demonstrate the use of the reconfiguration protocol under real-world conditions. © 2024 IEEE.

关键词： Resource allocation

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Enhancing the Reliability of NR-V2X Sidelink Broadcast through Relay 29

Enhancing the Reliability of NR-V2X Sidelink Broadcast throu...

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29th IEEE Asia Pacific Conference on Communications, APCC 2024

作者： Aoki, Shiro Tang, Suhua The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan

ISBN: (纸本)9798331517816

NR-V2X sidelink (SL) broadcast is used for the real-time exchange of position and other information between adjacent vehicles. However, its reliability degrades much in non-line-of-sight environments, where Blind ReTransmission (BReTX) does not work well. Relay can effectively address this issue, and a relaybased SL broadcast method was proposed in previous work. However, in this method, each vehicle must detect communication Link Quality (LQ) and exchange LQ Indicators (LQIs) with its neighbors to select a proper relay, which causes much overhead. This paper aims to improve the relay-based SL broadcast from two aspects. (i) To reduce the overhead, we suggest aggregating LQIs per direction, making the overhead of sharing LQIs irrelevant to the number of vehicles. (ii) To further improve reliability, we use an explicit ACK to deal with the potential failure of the transmission from the source vehicle to the relay. Using explicit ACKs allows the source vehicle to retransmit the packet until the relay vehicle correctly receives it, after which the relay vehicle handles the remaining retransmissions. Simulation results confirm that the proposed method, RReTX-ACK, improves the packet dissemination rate by approximately 8.30% within a 200 m distance from the source vehicle in an intersection scenario, compared to BReTX without using relay vehicles. © 2024 IEEE.

关键词： Magnetic levitation vehicles

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Enhancing Security and Flexibility in Smart Locker Systems: A Multi-Authentication Approach with IoT Integration 21

Enhancing Security and Flexibility in Smart Locker Systems: ...

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21st International Learning and Technology Conference, L and T 2024

作者： Balfaqih, Mohammed University of Jeddah Department of Computer and Network Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

The past decade has witnessed the widespread integration of the Internet of Things (IoT) in the evolution of smart locker systems. However, many existing systems rely on a single authentication method, limiting their adaptability. These systems fail to account for potential unavailability of the chosen authentication method due to issues such as lost access cards, damaged cameras, or malfunctioning devices. Furthermore, the vulnerability of relying on a single authentication method, without considering dual-authentication approaches, poses a security risk, as unauthorized access could occur through eavesdropping or code interception. This paper addresses these shortcomings by introducing a smart locker system that incorporates multiple authentication methods, including dual authentication (utilizing both phone number and One Time Password (OTP)), fingerprint recognition, face recognition, and emergency code. Leveraging IoT technology, the system offers a range of authorization methods to grant access permission. The dual authentication method serves as the foundational security layer, significantly enhancing the locker's overall security. The system is developed, implemented, and rigorously evaluated under various scenarios. The outcomes demonstrate superior performance compared to existing systems, particularly in terms of accuracy and flexibility. © 2024 IEEE.

关键词： Internet of things

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Deploying AI-Based Applications with Serverless Computing in 6G networks: An Experimental Study 100

Deploying AI-Based Applications with Serverless Computing in...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Michalke, Marc Muonagor, Chukwuemeka Jukan, Admela Technische Universität Institute of Computer and Network Engineering Braunschweig Germany

ISBN: (纸本)9798331517786

Future 6G networks are expected to heavily utilize machine learning capabilities in a wide variety of applications with features and benefits for both, the end user and the provider. While the options for utilizing these technologies are almost endless, from the perspective of network architecture and standardized service, the deployment decisions on where to execute the AI-tasks are critical, especially when considering the dynamic and heterogeneous nature of processing and connectivity capability of 6G networks. On the other hand, conceptual and standardization work is still in its infancy, as to how to categorize ML applications in 6G landscapes;some of them are part of network management functions, some target the inference itself, while many others emphasize model training. It is likely that future mobile services may all be in the AI domain, or combined with AI. This work makes a case for the serverless computing paradigm to be used to this end. We first provide an overview of different machine learning applications that are expected to be relevant in 6G networks. We then create a set of general requirements for software engineering solutions executing these workloads from them and propose and implement a high-level edge-focused architecture to execute such tasks. We then map the ML-serverless paradigm to the case study of 6G architecture and test the resulting performance experimentally for a machine learning application against a setup created in a more traditional, cloud-based manner. Our results show that, while there is a tradeoff in predictability of the response times and the accuracy, the achieved median accuracy in a 6G setup remains the same, while the median response time decreases by around 25% compared to the cloud setup. © 2024 IEEE.

关键词： Queueing networks

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A Multi-Source Retrieval Question Answering Framework Based on RAG 5

A Multi-Source Retrieval Question Answering Framework Based ...

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5th International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2024

作者： Wu, Ridong Chen, Shuhong Su, Xiangbiao Zhu, Yuankai Liao, Yifei Wu, Jianming Guangzhou University School of Computer Science and Network Engineering Guangzhou China

ISBN: (纸本)9798350352719

With the rapid development of large-scale language models, Retrieval-Augmented Generation (RAG) has been widely adopted. However, existing RAG paradigms are inevitably influenced by erroneous retrieval information, thereby reducing the reliability and correctness of generated results. Therefore, to improve the relevance of retrieval information, this study proposes a method that replaces traditional retrievers with GPT-3.5, leveraging its vast corpus knowledge to generate retrieval information. We also propose a web retrieval based method to implement fine-grained knowledge retrieval, Utilizing the powerful reasoning capability of GPT-3.5 to realize semantic partitioning of problem. In order to mitigate the illusion of GPT retrieval and reduce noise in Web retrieval, we proposes a multi-source retrieval framework, named MSRAG, which combines GPT retrieval with web retrieval. Experiments on multiple knowledge-intensive QA datasets demonstrate that the proposed framework in this study performs better than existing RAG framework in enhancing the overall efficiency and accuracy of QA systems. © 2024 IEEE.

关键词： Question answering

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Distribution-Based Time-Varying Ensemble Scientific Data Reduction for Uncertainty Visualization and Analysis

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Journal of Information Science and engineering 2024年第2期40卷 397-419页

作者： Lin, Zhi-Rong Hsiao, An-Lung Li, Guan Wang, Ko-Chih Department of Computer Science and Information Engineering National Taiwan Normal University Taipei106 Taiwan Computer Network Information Center Chinese Academy of Sciences Beijing100083 China

Scientists often study physical phenomena using computer simulation models. The same simulation can generate different datasets because of different input parameter configurations or internal random variables. Therefore, each grid point is represented by multiple data values from simulation runs, and we call this type of data ensemble dataset. To gain insight into the physical phenomenon, scientists often have to visualize and analyze the ensemble datasets with uncertainty. Distribution-based data representation is a popular approach to handling the ensemble dataset and supporting uncertainty visualization. However, storing a time-varying ensemble dataset needs hundreds or even thousands of times storage size. Given the size of the time-varying ensemble dataset, it is natural to develop storage-reduced data representation to facilitate the time-varying ensemble data exploration. We propose a novel data representation to compactly represent the time-varying scientific data for uncertainty visualization and analysis. Our approach decouples data on the temporal domain into two types of distributions and stores. One distribution summarizes the data values on the temporal domain, and the other distribution describes the occurrence probability of a data value on the temporal domain. Our approach can provide time-varying ensemble scientific data analysis with uncertainty quantification and detailed temporal feature evolution with less storage requirement. © 2024 Institute of Information Science. All rights reserved.

关键词： Probability distributions

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Predicting from a Different Perspective: A Re-ranking Model for Inductive Knowledge Graph Completion 21st

Predicting from a Different Perspective: A Re-ranking Mode...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Iwamoto, Yuki Kaneiwa, Ken Department of Computer Network Engineering Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan

ISBN: (纸本)9789819601240

Rule-induction models have demonstrated great power in the inductive setting of knowledge graph completion. In this setting, the models are tested on a knowledge graph entirely composed of unseen entities. These models learn relation patterns as rules by utilizing subgraphs. Providing the same inputs with different rules leads to differences in the model’s predictions. In this paper, we focus on the behavior of such models. We propose a re-ranking-based model called ReDistLP (Re-ranking with a Distinct Model for Link Prediction). This model enhances the effectiveness of re-ranking by leveraging the difference in the predictions between the initial retriever and the re-ranker. ReDistLP outperforms the state-of-the-art methods in 2 out of 3 benchmarks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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Residual diverse ensemble for long-tailed multi-label text classification

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Science China(Information Sciences) 2024年第11期67卷 92-105页

作者： Jiangxin SHI Tong WEI Yufeng LI National Key Laboratory for Novel Software Technology Nanjing University School of Artificial Intelligence Nanjing University School of Computer Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of Education

Long-tailed multi-label text classification aims to identify a subset of relevant labels from a large candidate label set, where the training datasets usually follow long-tailed label distributions. Many of the previous studies have treated head and tail labels equally, resulting in unsatisfactory performance for identifying tail labels. To address this issue, this paper proposes a novel learning method that combines arbitrary models with two steps. The first step is the “diverse ensemble” that encourages diverse predictions among multiple shallow classifiers, particularly on tail labels, and can improve the generalization of tail *** second is the “error correction” that takes advantage of accurate predictions on head labels by the base model and approximates its residual errors for tail labels. Thus, it enables the “diverse ensemble” to focus on optimizing the tail label performance. This overall procedure is called residual diverse ensemble(RDE). RDE is implemented via a single-hidden-layer perceptron and can be used for scaling up to hundreds of thousands of labels. We empirically show that RDE consistently improves many existing models with considerable performance gains on benchmark datasets, especially with respect to the propensity-scored evaluation ***, RDE converges in less than 30 training epochs without increasing the computational overhead.

关键词： multi-label learning extreme multi-label learning long-tailed distribution multi-label text classification ensemble learning

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