检索结果-内蒙古大学图书馆

Environment-Tolerant Trust Opportunity Routing based on Reinforcement Learning for Internet of Underwater Things

IEEE Transactions on Mobile Computing 2025年

作者： He, Yu Han, Guangjie Hou, Yun Lin, Chuan Hohai University College of Information Science and Engineering Changzhou213200 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education Changzhou213200 China Hohai University College of Computer Science and Software Engineering Nanjing211100 China Northeastern University Software College Shenyang110819 China

The Internet of Underwater Things (IoUT) has garnered significant interest due to its potential applications in monitoring underwater environments. However, the unique characteristics of acoustic communication, such as long propagation delays and high attenuation, present considerable obstacles for achieving efficient and dependable data transmission. Opportunistic routing is a crucial technique for enhancing packet delivery ratios by selecting a set of forwarding nodes and utilizing their cooperative forwarding to boost network throughput. Nevertheless, choosing an excessive number of forwarding nodes can lead to wasteful energy usage and extended communication delays. Moreover, the overlooked trustworthiness of forwarded nodes in most research works can undermine the effectiveness of opportunistic routing. Therefore, this study presents a novel trust opportunistic routing scheme that employs reinforcement learning to achieve resilience in constantly changing underwater settings. The combination of reinforcement learning and trust management enables the proposed opportunistic routing scheme to adapt to the unstable underwater environment and unknown malicious attacks. Initially, a method is introduced for measuring environmental fitness by considering multiple trust factors, including communication success rate, data reliability, and location dynamics. The proposed scheme then uses reinforcement learning to develop a reliable opportunistic routing method based on quantified state information. This component employs the obtained state to formulate action strategies and obtains reward values from environmental inputs. The reward update equation integrates these qualities to optimize the deployment of superior action strategies, finally achieving trust opportunistic routing for underwater data collection. Fundamental experimental results demonstrate that the proposed protocol performs exceptionally well in demanding underwater conditions, outperforming existing method

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

HiLoTs: High-Low Temporal Sensitive Representation Learning for Semi-Supervised LiDAR Segmentation in Autonomous Driving

arXiv

引用

arXiv 2025年

作者： Lin, R.D. Weng, Pengcheng Wang, Yinqiao Ding, Han Han, Jinsong Wang, Fei School of Software Engineering Xi’an Jiaotong University China School of Computer Science and Technology Xi’an Jiaotong University China College of Computer Science and Technology Zhejiang University China

LiDAR point cloud semantic segmentation plays a crucial role in autonomous driving. In recent years, semi-supervised methods have gained popularity due to their significant reduction in annotation labor and time costs. Current semi-supervised methods typically focus on point cloud spatial distribution or consider short-term temporal representations, e.g., only two adjacent frames, often overlooking the rich long-term temporal properties inherent in autonomous driving scenarios. In driving experience, we observe that nearby objects, such as roads and vehicles, remain stable while driving, whereas distant objects exhibit greater variability in category and shape. This natural phenomenon is also captured by LiDAR, which reflects lower temporal sensitivity for nearby objects and higher sensitivity for distant ones. To leverage these characteristics, we propose HiLoTs, which learns high-temporal sensitivity and low-temporal sensitivity representations from continuous LiDAR frames. These representations are further enhanced and fused using a cross-attention mechanism. Additionally, we employ a teacher-student framework to align the representations learned by the labeled and unlabeled branches, effectively utilizing the large amounts of unlabeled data. Experimental results on the SemanticKITTI and nuScenes datasets demonstrate that our proposed HiLoTs outperforms state-of-the-art semi-supervised methods, and achieves performance close to LiDAR+Camera multimodal approaches. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

An Encoder-Decoder Model Based On Spiking Neural Networks For Address Event Representation Object Recognition

引用

IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Du, Sichun Zhu, Haodi Zhang, Yang Hong, Qinghui Hunan University College of Computer Science and Electronic Engineering Changsha418002 China Shenzhen University Computer Vision Institute School of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

Address event representation (AER) object recognition task has attracted extensive attention in neuromorphic vision processing. The spike-based and event-driven computation inherent in the spiking neural network (SNN) provides an energy-saving solution for AER object recognition. However, SNN with spike timing dependent plasticity (STDP) learning rule has not achieved satisfying AER object recognition performance. This work proposes an SNN-based encoder-decoder model to improve the recognition performance of AER objects. An STDP-based locally connected spiking neural network (LC-SNN) is proposed as an encoder to extract rich spatiotemporal features from AER event flows more flexibly. After the encoder extracts and learns primary features, we propose a fully connected spiking neural network (FC-SNN) based on the reward-modulated spike-timing-dependent plasticity (R-STDP) learning rule as a decoder to learn higher-level features for classification. In addition, we improved the winner-take-all (WTA) mechanisms and R-STDP learning rule in the decoder based on the reward and punish decision, enabling the network to perform better. The experiments are performed on the N-MNIST, MNIST-DVS, and the DVS Gesture datasets, improving the accuracy of the best existing plasticity-based SNN by 0.19%, 0.27% and 1.35%, respectively. © 2016 IEEE.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

A High-Gain DC-DC Boost Converter for Solar Microgrid Systems Using a Switching Inductor and a Capacitor 1

A High-Gain DC-DC Boost Converter for Solar Microgrid System...

引用

1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Prasanth, S. Priyadarsini, S. Lakshmi, R. Sneha, G. Ajitha, S.S. Saravanakumar, R. Narendra, Konnaiah Gari Unnamalai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Kovilpatti India PSR Engineering College Department of Computer Science and Engineering Tamil Nadu Sivakasi India Siddharth Institute of Engineering and Technology Department of Elecrical and Electroncis Engineering Puttur India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Iconix Software Solution Tamil Nadu Tirunelveli India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electonics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331518578

This paper outlines the design and implementation of a solar microgrid-specific high-gain DC-DC booster converter that makes use of a variable inductor and capacitors. To improve the suitability of photovoltaic (PV) panels for incorporation into microgrids, the proposed converter efficiently increases their low-voltage outputs. Conventional boost converters can't offer that kind of voltage increase because they lose too much performance and put too much strain on their internal components. The converter increases the voltage gain dramatically while maintaining a high degree of efficiency thanks to a switching inductor-capacitor network. The main breakthrough is an inventive multi-stage energy transfer and storage device that cleverly employs a switching inductor and capacitor. This configuration increases the output voltage while decreasing the voltage and current ripples, which in turn reduces the amount of electromagnetic interference (EMI) and increases the life of the components. Thorough assessments of the converter's working via theoretical modeling and simulation studies confirm its performance across several load and input situations common to solar microgrids. The prototype's testing findings show a significant voltage increase and a peak efficiency of over 95% when compared to traditional boost converters. Moreover, the converter's reliable performance over a wide range of voltage sources and workload scenarios proves its suitability for practical solar applications. As a result, the proposed high-gain boost converter will greatly improve the efficiency and reliability of solar microgrids, leading to a rise in the use of renewable energy sources in DGS. © 2025 IEEE.

关键词： Microgrids

来源：评论

学校读者我要写书评

暂无评论

Heterogeneous Graph Transfer Learning for Category-aware Cross-Domain Sequential Recommendation 25

Heterogeneous Graph Transfer Learning for Category-aware Cro...

引用

34th ACM Web Conference, WWW 2025

作者： Xu, Zitao Chen, Xiaoqing Pan, Weike Ming, Zhong College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University Shenzhen Technology University Shenzhen China

ISBN: (纸本)9798400712746

Cross-domain sequential recommendation (CDSR) is proposed to alleviate the data sparsity issue while capturing users’ sequential preferences. However, most existing methods do not explore the item transition patterns across different domains and can also not be applied to a multi-domain scenario. Moreover, previous methods rely on overlapping users as bridges to transfer knowledge, which struggles to capture the complex associations across domains without sufficient overlapping users. In this paper, we introduce item attributes into CDSR, and propose a heterogeneous graph transfer learning method to address these issues. Specifically, we construct a cross-domain heterogeneous graph to allow the association of user, item, and category nodes from different domains, and enhance the flexibility of the model by enabling message propagation between more nodes through edge expansion based on the semantic similarity and co-occurrence probability. In addition, we devise meta-paths from different perspectives for nodes at item, user and category levels to guide information aggregation, which can transfer knowledge across domains and reduce the reliance on the number of overlapping users. We further design attention modules to capture users’ dynamic preferences from the item sequences they have interacted with in each domain, and explore the transition patterns within category sequences which reflect users’ coarse-grained preferences. Finally, we perform knowledge transfer across different domains, and predict the most likely items that users will interact with in each domain. Extensive empirical studies on three real-world datasets indicate that our HGTL significantly outperforms the state-of-the-art baselines in all cases. © 2025 Copyright held by the owner/author(s).

关键词： Transfer learning

来源：评论

学校读者我要写书评

暂无评论

A Virtual Network Mapping Algorithm Based on Time

引用

Chinese Journal of Electronics 2025年第1期23卷 31-36页

作者： Ming Jiang Zhiyang Zhao Min Zhang Jingfan Tang Chunming Wu Xiao Min Institute of Software and Intelligent Technology Hangzhou Dianzi University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Mathematics and Information Engineering College of Jiaxing University Jiaxing China

As one of the challenges for network virtualization, virtual network embedding which maps Virtual network (VN) to the substrate network and allocates resources according to the requirements of VN in an efficient way has gained great attention. Existing algorithms generally make their decision according to the present available substrate network resource, especially bandwidth. This paper proposes a time-based VN embedding algorithm. A probability model is formulated to obtain the maximum probability that the available resources of substrate network can be used by succeeding VN requests. The probability is set as the weight of the node and the greedy algorithm is employed to embed the virtual node. The reciprocal of the probability is set as the weight of the link and the shortest path algorithm is employed to embed the virtual link. Simulation experiments show that the proposed algorithm increases the acceptance rate and the revenue compared to the existing algorithms.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Vectorized Merge Sort on ARM NEON 24th

A Hybrid Vectorized Merge Sort on ARM NEON

引用

24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Zhou, Jincheng Zhang, Jin Zhang, Xiang Xiao, Tiaojie Ma, Di Gong, Chunye School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China College of Computer National University of Defense Technology Hunan Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China National Supercomputer Center in Tianjin Tianjin300457 China

ISBN: (纸本)9789819615506

Sorting algorithms are the most extensively researched topics in computer science and serve for numerous practical applications. Although various sorts have been proposed for efficiency, different architectures offer distinct flavors to the implementation of parallel sorting. In this paper, we propose a hybrid vectorized merge sort on ARM NEON, named NEON Merge Sort for short (NEON-MS). In detail, according to the granted register functions, we first identify the optimal register number to avoid the register-to-memory access, due to the write-back of intermediate outcomes. More importantly, following the generic merge sort framework that primarily uses sorting network for column sort and merging networks for three types of vectorized merge, we further improve their structures for high efficiency in an unified asymmetry way: 1) it makes the optimal sorting networks with few comparators become possible;2) hybrid implementation of both serial and vectorized merges incurs the pipeline with merge instructions highly interleaved. Experiments on a single FT2000+ core show that NEON-MS is 3.8 and 2.1 times faster than std::sort and boost::block_sort, respectively, on average. Additionally, as compared to the parallel version of the latter, NEON-MS gains an average speedup of 1.25. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Memory architecture

来源：评论

学校读者我要写书评

暂无评论

Dancer: Dynamic Compression and Quantization Architecture for Deep Graph Convolutional Network

Dancer: Dynamic Compression and Quantization Architecture fo...

引用

Design, Automation and Test in Europe Conference and Exhibition

作者： Yunhao Dong Zhaoyu Zhong Yi Wang Chenlin Ma Tianyu Wang College of Computer Science and Software Engineering Shenzhen University Shenzhen China State Key Laboratory of Radio Frequency Heterogeneous Integration

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Graph Convolutional Networks (GCNs) have been widely applied in fields such as social network analysis and recommendation systems. Recently, deep GCNs have emerged, enabling the exploration of deeper hidden information. Compared to traditional shallow GCNs, deep GCNs feature significantly more layers, leading to considerable computational and data movement challenges. Processing-In-Memory (PIM) offers a promising solution for efficiently handling GCNs by enabling near-data computation, thus reducing data transfer between processing units and memory. However, previous work mainly focused on shallow GCNs and has shown limited performance with deep GCNs. In this paper, we present Dancer, an innovative PIM-based GCN accelerator. Dancer optimizes data movement during the inference process, significantly improving efficiency and reducing energy consumption. Specifically, we introduce a novel compressed graph storage architecture and a dynamic quantization technique to minimize data transfers at each layer of the GCN. Additionally, through a detailed analysis of weight dynamics changes, we propose a sparsity propagation strategy to further alleviate the computational and data transfer burden between layers. Experimental results demonstrate that, compared to current state-of-the-art methods, Dancer achieves 3.7× speedup, 7.6× energy efficiency, and reduces of 9.6× DRAM access on average.

关键词： Training Energy consumption Quantization (signal) Graph convolutional networks Social networking (online) Memory management Random access memory Europe Data transfer Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Analysis of converting -circuit into -circuit

引用

EPJ Quantum Technology 2025年第1期12卷 1-14页

作者： Luo, Qing-bin Ding, Lang Yang, Guo-wu Li, Xiao-yu College of Intelligent Systems Science and Engineering Hubei Minzu University Enshi China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Big data research Center & School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

A $\mathfrak{C^{*}}$ -circuit, which was proposed in Asiacypt 2022 by Huang and Sun (Advances in cryptology – ASIACRYPT 2022, pp. 614–644, 2022), can directly perform calculations with the existing quantum states, thereby reducing the use of quantum resources in quantum logic synthesis. We theoretically prove how to convert a $\mathfrak{C^{0}}$ -circuit into the corresponding $\mathfrak{C^{*}}$ -circuit through two lemmas and one theorem. The first lemma proves the interchangeability of CNOT gates and NOT gates by using the equivalence of quantum circuits. The second lemma proves that adding CNOT gates to the front of a quantum circuit whose initial states are all $|0\rangle $ s will not change the output states of the circuit. The theorem is used to describe what kind of $\mathfrak{C^{0}}$ -circuit can be transformed into $\mathfrak{C^{*}}$ -circuit, and the correctness of this transformation is proved. Our work will provide a theoretical basis for converting $\mathfrak{C^{0}}$ -circuit to $\mathfrak{C^{*}}$ -circuit. Then applying the theoretical analysis results to the multiplication over $\text{GF}(2^{8})$ , the constructed quantum circuit needs 27 Toffoli gates and 118 CNOT gates, which is 15 fewer Toffoli gates and 43 CNOT gates than the current best result. This shows that the method of constructing quantum circuits by using the conversion of $\mathfrak{C^{0}}$ -circuit to $\mathfrak{C^{*}}$ -circuit is very efficient.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Traffic Prediction Using Lightweight Large Model in UAV-Assisted Mobile Computing for Time-Critical Consumer Electronics

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Jiang, Yunzhe Liu, Yinqiu Cui, Huajun Liu, Jinghuan Mao, Yingchi He, Xiaoming University of Electronic Science and Technology of China College of Communication and Information Engineering Chengdu China Nanyang Technological University School of Computer Science and Engineering Singapore PowerChina Beijing Engineering Corporation Limited Digital Intelligence Research Institute Beijing China Hohai University College of Computer Science and Software Engineering Nanjing China Nanjing University of Posts and Telecommunications College of Internet of Things Nanjing China

Unmanned Aerial Vehicles (UAVs) have emerged as an important category of consumer electronics, offering versatile applications and enhanced operational efficiency. In the context of Mobile Edge Computing (MEC), UAVs can improve task execution and service quality by providing dynamic allocation of computational and data processing resources at the network edge. However, one critical issue in UAV-assisted MEC is task traffic prediction, which directly impacts the efficiency of task offloading strategies. Inspired by the recent success of large models, we propose a novel Lightweight Large Model with Temporal-Spatial Attention (L-LMTSA), designed to maximize task traffic prediction accuracy and minimize the overall computation latency. Specifically, L-LMTSA integrates a Temporal Attention Model (TAM) and a Spatial Attention Model (SAM) to capture multi-dimensional information, such as task flow location and temporal characteristics. In addition, we develop a pre-trained lightweight GPT-2 model by employing a one-dimensional convolutional input encoding layer and fine-tuning its parameters for predictive accuracy. Additionally, we optimize the UAV's trajectory and task offloading strategy using a three-dimensional artificial potential field informed by the predicted task traffic. Extensive experiments demonstrate that L-LMTSA not only improves training speed and prediction accuracy but also significantly reduces computational latency, resulting in more efficient UAV-assisted MEC experiences for consumers. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：