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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Yang, Zhongliao Huang, Huichang Liu, Zeyi Dong, Chen Xu, Li College of Computer and Data Science Fuzhou University Fuzhou350116 China College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9789819998951

Continuous-Flow Microfluidic Biochip (CFMB), with their integrated features, bring traditional biochemical experiments on a single chip to accomplish complex operations and reactions through precise control, efficient reactions and emerging ways of saving reagents. In the field of intelligent digital healthcare, CFMB have attracted a lot of attention. However, traditional manual design schemes can no longer meet the needs of increasingly complex chip architecture design. Therefore, this paper proposes an automated design method for resource binding and module placement of CFMB based on a list scheduling algorithm and an improved Simulated Annealing algorithm. Through the resource binding and scheduling design based on the list scheduling algorithm, an effective scheduling strategy is generated, which effectively improves the biochip execution efficiency. In addition, the improved Simulated Annealing algorithm solves the module placement problem in the biochip in a limited physical space. Compared with some benchmark algorithms, the experimental results demonstrate the effectiveness of the method in the biochip design process and provide a practical framework for further development of CFMB in the field of intelligent digital healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Biochips

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Optically pumped wavelength-tunable lasing from a GaN beam cavity with an integrated Joule heater pivoted on Si

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Chinese Physics B 2023年第5期32卷 478-484页

作者：秦飞飞孙阳杨颖李欣王旭卢俊峰王永进朱刚毅 GaN Optoelectronic Integration International Cooperation Joint Laboratory of Jiangsu Province College of Telecommunications and Information EngineeringNanjing University of Posts and TelecommunicationsNanjing 210003China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology(Nanjing University of Posts and Telecommunications Ministry of Education)Nanjing 210003China Department of Microelectronic Science and Engineering School of Physical Science and TechnologyNingbo UniversityNingbo 315211China College of Physics Nanjing University of Aeronautics and AstronauticsNanjing 211106China

Dynamically tunable laser sources are highly promising for realizing visionary concepts of integrated photonic circuits and other applications. In this paper, a Ga N-based laser with an integrated PN junction heater on Si is *** photoluminescence properties of the Ga N beam cavity are controlled by temperature, and the Joule heater provides electrically driven regulation of temperature. These two features of the cavity make it possible to realize convenient tuning of the lasing properties. The multi-functional Ga N beam cavity achieves optically pumped lasing with a single mode near 362.4 nm with a high Q-factor of 1394. The temperature of this device increases by 0–5℃ under the Joule heating effect. Then, electrical control of the lasing mode is demonstrated. The lasing resonant peak shows a continuous redshift of about 0.5 nm and the device also exhibits dynamic switching of its lasing mode. The lasing modulation can be ascribed to temperature-induced reduction of the bandgap. Our work may be of benefit for external optical modulation in future chip-based optoelectronic devices.

关键词： GaN beam cavity optically pumped lasing dynamically tunable laser source Joule heater

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Gaia: Ground Station-Centric Mobility Management for LEO Satellite networks

Gaia: Ground Station-Centric Mobility Management for LEO Sat...

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IEEE International Conference on Communications (ICC)

作者： Xiaxin Zhou Tian Pan Zhaokun Yang Sirui Su Jun Hu Guohao Ruan Junkai Huang Tao Huang Yunjie Liu Beijing University of Posts and Telecommunications Beijing China Purple Mountain Laboratories Nanjing China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

For mega-scale low Earth orbit (LEO) satellite constellations, the relative position changes between satellites and ground terminals pose challenges for end-to-end TCP session maintenance, which serves as the substrate for many Internet services. Mobile IP resolves the session maintenance issue by in-troducing a binding mechanism between the care-of address and home address; however, this also leads to inefficient triangular routing. Our recently proposed LISP-LEO, through partition-satellite mapping, routes traffic to the service satellite above the destination terminal's partition, addressing the triangular routing problem. However, due to the corner case of partition-satellite mapping, LISP-LEO introduces the issue of the last-hop route selection, as well as the associated per-terminal registration states on the satellite, making the solution non-scalable. In this work, we propose Gaia, a ground station-centric mobility management scheme for LEO satellite networks. Gaia maintains a precise mapping of each ground terminal's IP and its geographical location. When receiving traffic from a source terminal, the access satellite can, based on the geographical location of the destination terminal carried by the traffic, directly locate the satellite above the destination terminal and tunnel the traffic to it. In addition, to reduce the satellite's burden, we add a DNS-like querying mechanism by offloading the mapping of terminal IPs and geographical locations to the ground station. The evaluation shows that Gaia outperforms Mobile IP and LISP-LEO in both end-to-end latency and on-board resource consumption.

关键词： Satellite constellations Satellites Web and internet services Low earth orbit satellites Production Routing Maintenance

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D-Router: Decoupled Content Routers with Remote Content Store

D-Router: Decoupled Content Routers with Remote Content Stor...

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IEEE International Conference on Communications (ICC)

作者： Shuyi Chen Tian Pan Chunyang Wu Guohao Ruan Jiao Zhang Tao Huang Yunjie Liu Beijing University of Posts and Telecommunications Beijing China Purple Mountain Laboratories Nanjing China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Named Data networking (NDN) enables efficient content distribution through in-network caching. However, the additional states of network intermediary nodes make NDN forwarding more burdensome, and the unpredictability of cache hits during forwarding leads to uncertain content retrieval latency. To overcome performance bottlenecks at the router's data plane and enhance network determinism, we propose the decoupled content router with remote content store (D-Router). This novel architecture decouples the local content store (CS) from routers and introduces the remote CS device for pooling important content. When Interest packets arrive at a router whose CS is overloaded, we ensure determinism by forwarding them to the remote CS for processing if the requested content is cached there, preventing blocking before the local CS of routers and potential random cache hits along the forwarding path. The dual-path bypass forwarding is supported through the design of routers and a dual-path routing protocol. D-Router is compatible with traditional NDN. Experiments show notable enhancements in data plane performance, including a 30% reduction in round-trip time (RTT), a 25% increase in throughput, improved determinism, and reduced network jitter. Additionally, the decoupling of CS makes it easier for network administrators to deploy network upgrades.

关键词： Performance evaluation Jitter Throughput Routing protocols

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A Non-Intrusive and Real-Time Data Provenance Method for DDS Systems

A Non-Intrusive and Real-Time Data Provenance Method for DDS...

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International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Siyi Wei Jinbin Tu Yun Wang School of Computer Science and Engineering Southeast University Key Lab of Computer Network and Information Integration MOE Nanjing China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

In the era of big data, because of the escalating data security threats, the importance of data provenance is widely recognized. It facilitates the tracking of data origins and evolution, improving the ability to diagnose errors and detect potential malicious attacks within large-scale software systems. However, conventional log-based provenance methods usually collect data intrusively, leading to strong coupling with the system. Meanwhile, although there are some log-based provenance systems that support runtime analysis, they exhibit inadequate real-time analysis performance. This paper proposes a non-intrusive and real-time approach to data provenance in DDS systems. The data capture is achieved with the help of the Data-Centric Publisher-Subscriber model of DDS, thus avoiding source code modification to the existing system. The collected data is then described in PROV-DDS model to generate RDF data. RDF datasets inherently form a directed and labeled graph. The query over RDF datasets is decomposed into start subqueries to speed up processing with star subgraph signature. The experimental results show that our method provenances effectively with less than 1% time overhead and a lower query response time than RDF-3X and Redland.

关键词： Runtime Source coding Data security Stars Software systems Resource description framework Real-time systems

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EAT: Towards Long-Tailed Out-of-Distribution Detection

arXiv

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arXiv 2023年

作者： Wei, Tong Wang, Bo-Lin Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

Despite recent advancements in out-of-distribution (OOD) detection, most current studies assume a class-balanced in-distribution training dataset, which is rarely the case in real-world scenarios. This paper addresses the challenging task of long-tailed OOD detection, where the in-distribution data follows a long-tailed class distribution. The main difficulty lies in distinguishing OOD data from samples belonging to the tail classes, as the ability of a classifier to detect OOD instances is not strongly correlated with its accuracy on the in-distribution classes. To overcome this issue, we propose two simple ideas: (1) Expanding the in-distribution class space by introducing multiple abstention classes. This approach allows us to build a detector with clear decision boundaries by training on OOD data using virtual labels. (2) Augmenting the context-limited tail classes by overlaying images onto the context-rich OOD data. This technique encourages the model to pay more attention to the discriminative features of the tail classes. We provide a clue for separating in-distribution and OOD data by analyzing gradient noise. Through extensive experiments, we demonstrate that our method outperforms the current state-of-the-art on various benchmark datasets. Moreover, our method can be used as an add-on for existing long-tail learning approaches, significantly enhancing their OOD detection performance. Code is available at: https://***/Stomachache/Long-Tailed-OOD-Detection. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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MCFTNet: Multimodal Cross-layer Fusion Transformer network for Hyperspectral and LiDAR Data Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12803-12818页

作者： Huang, Wei Wu, Tianren Zhang, Xueyu Li, Liangliang Lv, Ming Jia, Zhenhong Zhao, Xiaobin Ma, Hongbing Vivone, Gemine Xinjiang University School of Computer Science and Technology Urumqi830017 China Xinjiang University Key Laboratory of Signal Detection and Processing Urumqi830017 China Guangxi University School of Computer and Electronic Information Nanning530004 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Tsinghua University Department of Electronic Engineering Beijing100084 China National Research Council Tito85050 Italy NBFC National Biodiversity Future Center Palermo90133 Italy

Remote sensing image classification is a popular yet challenging field. Many researchers have combined convolutional neural networks (CNNs) and Transformers for hyperspectral image (HSI) classification tasks. However, in traditional Transformers, shallow-level information does not propagate well to deeper layers, which can lead to spatial variations and overfitting. Moreover, traditional Transformer models use an external classification (CLS) token that is randomly initialized and often struggles to generalize effectively. In this paper, we combine the strengths of HSI and light detection and ranging (LiDAR) data, using LiDAR as an external CLS token, which significantly enhances classification accuracy and reliability. We propose a new multimodal cross-layer fusion transformer network (MCFTNet), integrating CNNs with the latest Transformer networks. It includes a CNN for extracting spatial features and a hybrid cross-patch attention mechanism (HyCrossAttention) for land cover classification, leveraging LiDAR data to generate CLS and HSI patch tokens. More importantly, to reduce the loss of valuable information during the layer-by-layer propagation, we designed cross-layer skip connections. Through adaptive learning, these cross-layer fusions help address the gradient vanishing problem in deep networks while preserving early-layer features. This enables the model to better integrate information from different layers, enhancing both its stability and performance. We carried out in - depth experiments on commonly - used benchmark datasets, specifically, the University of Houston dataset, the Trento dataset, and the University of Southern Mississippi Gulf Park (MUUFL) dataset. We compared the results of the proposed MCFTNet model with those obtained from state-of-the-art Transformer models, classical CNNs, and traditional classifiers. As a result, the MCFTNet model outshone them all in terms of performance. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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A VLAN-based network Testbed for Lightweight Satellite Constellation Emulation

A VLAN-based Network Testbed for Lightweight Satellite Const...

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IEEE International Conference on Communications (ICC)

作者： Yan Zheng Tian Pan Yan Zhang Jiang Liu Tao Huang Yunjie Liu Purple Mountain Laboratories Nanjing China Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Considering the high costs of satellite manufacturing and launch, as well as the complexity of in-orbit debugging, pre-launch emulation on the ground will significantly reduce the development costs of low Earth orbit (LEO) satellite networks. LEO satellite network emulation faces challenges in emulating mega-scale constellations in a lightweight and scalable manner, as well as efficiently handling the frequent link on/off switching for both inter-satellite networks and terrestrial access networks. Existing simulation/emulation tools, such as NS-3, Mininet, QualNet, fall short in addressing these issues effectively. In this work, we propose a lightweight satellite emulation testbed based on Docker containers and the VLAN protocol. In the data plane, our testbed uses Docker containers to emulate satellites/terminals, and uses VETH-pairs and bridges to emulate inter-satellite networks and terrestrial access networks. Furthermore, these virtual network elements can horizontally scale across multiple servers for mega-scale constellation emulation. In the control plane, the real-time constellation topology changes are efficiently emulated through the configuration of VLAN segmentation according to satellite movement patterns. Evaluation shows the testbed's low resource occupancy and high efficiency, with 100 nodes consuming only 1000MB memory and 100 links switching in less than 2s.

关键词： Satellite constellations Satellites Costs Emulation Low earth orbit satellites Switches Containers

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Intelligent Optimization Method for Mobile NOMA network using IMBO

Intelligent Optimization Method for Mobile NOMA Network usin...

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IEEE International Conference on Electronic information and Communication Technology (ICEICT)

作者： Xingyue Fu Lingwei Xu Zhihe Gao Zhe Chen Yufang Li Shubo Cao Qingdao University of Science & Technology Qingdao China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

Non-orthogonal multiple access (NOMA) technology can significantly increase access volume and spectrum efficiency. The introduction of NOMA technology into the mobile communication system can significantly enhance communication performance. The power allocation technology can greatly improve the performance of NOMA systems and has become a research hotspot. However, many research schemes currently have many problems, such as poor system performance and inability to balance between user fairness and service quality. Therefore, this article studies the intelligent optimization for mobile NOMA network using improved Monarch Butterfly Optimization Algorithm(IMBO). First, an $\boldsymbol{N}-\mathbf{Nakagami}$ channel-based mobile cooperative NOMA multi-user network is established. Then, considering the non-ideal communication condition, namely imperfect channel state information (ICSI), the exact expressions of outage probability (OP) for the mobile cooperative NOMA multi-user communication system are derived, and the relation between user power allocation coefficient and the performance of OP is investigated. Finally, an optimization algorithm of power allocation based on the IMBO is put forward, which can get the optimal power allocation coefficient. Simulation results indicate that, compared to the monarch butterfly optimization algorithm(MBO), the artificial fish swarm algorithm(AFSA), and the firefly algorithm(FA), the IMBO is at least 30% more efficient, thereby enhancing the performance of the communication system.

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Multi-Classification Segmentation Method of Gastric Cancer Pathological Images Based on Deep Learning

Multi-Classification Segmentation Method of Gastric Cancer P...

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2024 lEEE International Conference on Advanced information, Mechanical Engineering, Robotics and Automation, AIMERA 2024

作者： Zhou, Hehu Pan, Jingshan Na, Li Ding, Qingyan Zhou, Chengjun Du, Wantong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China The Second Hospital of Shandong University Department of Pathology Jinan China

ISBN: (纸本)9798350343335

Gastric cancer is a serious health threat, and pathological imaging is important in detecting it. These images can assist doctors in accurately determining the location of the cancer, thereby providing an important reference for clinical decision-making. In the field of image processing, deep learning technology is leading to more and more excellent segmentation models. The Trans-Unet model has achieved success in the image segmentation. However, when applying this model to gastric cancer pathological section data, the segmentation boundary appears jagged. We propose three possible solutions. First, we designed an attention connection module to replace the skip connections in the model to enhance the prediction accuracy of the model. Second, we designed a prediction processing unit that takes the model's prediction results as input and uses a Conditional Random Field (CRF) for further prediction calculations. To enable the model to meet the requirements of multi-classification of images, we designed a dynamic weight selection module to enhance the model's accuracy for multi-classification tasks. After our optimization, the optimized model improved by 8% on the DSC evaluation index and 39% on the HD evaluation index. In addition, the jagged boundary problem in the prediction results has also been effectively improved. Through comparative experiments and erosion experiments, we found that the improved method enhances the accuracy of model prediction and reduces the jagged results at the boundary. © 2024 IEEE.

关键词： Health risks

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