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arXiv 2024年

作者： Kozlowski, Wojciech Kuipers, Fernando A. Smets, Rob Turkovic, Belma QuTech Delft University of Technology Delft2628 CD Netherlands The Networked Systems Group Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands SURF Utrecht3511 EP Netherlands TNO The Hague2595 DA Netherlands

Quantum entanglement is so fundamentally different from a network packet that several quantum network stacks have been proposed;one of which has even been experimentally demonstrated. Several simulators have also been developed to make up for limited hardware availability, and which facilitate the design and evaluation of quantum network protocols. However, the lack of shared tooling and community-agreed node architectures has resulted in protocol implementations that are tightly coupled to their simulators. Besides limiting their reusability between different simulators, it also makes building upon prior results and simulations difficult. To address this problem, we have developed QuIP: a P4-based Quantum Internet Protocol prototyping framework for quantum network protocol design. QuIP is a framework for designing and implementing quantum network protocols in a platform-agnostic fashion. It achieves this by providing the means to flexibly, but rigorously, define device architectures against which quantum network protocols can be implemented in the network programming language P416. QuIP also comes with the necessary tooling to enable their execution in existing quantum network simulators. We demonstrate its use by showcasing V1Quantum, a completely new device architecture, implementing a link- and network-layer protocol, and simulating it in the existing simulator NetSquid. © 2024, CC BY.

关键词： Network architecture

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Drive-Charge Dilemma in Electric Mobility: Price of Anarchy and Data Analytics Standpoint 5

Drive-Charge Dilemma in Electric Mobility: Price of Anarchy ...

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5th International Conference on Advanced Communication Technologies and Networking, CommNet 2022

作者： Mahrez, Zineb Sabir, Fssaid Saad, Walid Badidi, Elarbi Hassan Ii University of Casablanca Nest Research Group Lri Lab Ensem Morocco University of Quebec at Montreal Departrment of Computer Science MontrealQCH2L 2C4 Canada Wireless VT Department of Computer Science and Software Engineering Virginia Tech VA United States College of Information Technology Uae University Department of Computer Science and Software Engineering United Arab Emirates

ISBN: (数字)9781665450546

ISBN: (纸本)9781665450546

The advent of electric mobility has created new costs that are closely linked to and influenced by the actions and behaviors of electric vehicle (EV) drivers. These include time-, energy-, and risk-related costs that each driver seeks to reduce by adjusting his or her strategy. In this paper, an electric vehicle charging dilemma using a non-cooperative routing game with selfish players is formulated. We assume that an electric vehicle plans a trip from one place to another and, in the process, must choose a specific route, stop at a roadside station, decide on the charging station, and charge its battery by a certain amount. The strategy of EVs to drive or stop depends on factors related to battery level, waiting status at charging stations, availability of charging types, and traffic load both on roads and at stations. The research problem aims to solve the EV driver's dilemma and determine the route that the EV driver must take to optimize his travel cost.' To solve the dilemma, we propose to build a simulation model based on input data obtained from historical records of U.S. government sources, as described in the paper. © 2022 IEEE.

关键词： Data Analytics

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SMOG: Accelerating Subgraph Matching on GPUs

SMOG: Accelerating Subgraph Matching on GPUs

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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Zhibin Wang Ziheng Meng Xue Li Xi Lin Long Zheng Chen Tian Sheng Zhong State Key Laboratory for Novel Software Technology Nanjing University Alibaba Group“ National Engineering Research Center for Big Data Technology and System/ Services Computing Technology and System Lab/Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Zhejiang Lab

Subgraph matching is a crucial problem in graph theory with diverse applications in fields, such as bioinformatics, social networks and recommendation systems. Accelerating subgraph matching can be greatly facilitated by GPUs, which offer exceptional parallelism and high memory bandwidth. By leveraging the power of multiple GPU cards, subgraph matching can be scaled to achieve unprecedented levels of performance. In this paper, we propose SMOG, an abbreviation for. Subgraph Matching On Multi-Card GPUs. It is a general, high-performance and scalable sub graph matching system that utilizes multi-card GPUs. To address the issue of duplication resulting from subgraph automorphism, SMOG introduces a two-step approach. Firstly, it analyzes the symmetry within the subgraph. Then, it adaptively adjusts the graph preprocessing and generates subgraph-aware GPU codes tailored to the given subgraph. Furthermore, SMOG leverages multi-level parallelism by designing the specific strategy for each level, enabling it to scale from 1 to 1,024 GPU cards, resulting in an extraordinary $553\times$ speedup. We evaluate SMOG on various subgraph queries and datasets. The experimental results demonstrate that SMOG outperforms the triangle-specific system TRUST with an average speedup of $2.94\times$ . And it performs significantly better than the subgraph matching system RPS by $203.55\times$ and the graph processing system Gunrock by 35, $455.52\times$ on average.

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A 32-Channel Time-to-Digital Converter with 20-ps Resolution for ToF Applications 4

A 32-Channel Time-to-Digital Converter with 20-ps Resolution...

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4th IEEE International Conference on Circuits and Systems, ICCS 2022

作者： Ke, Qin Yu, Hang Li, Yan Ma, Siguang Xi, Daoming Chen, Rui Shenzhen University Shenzhen Laboratory of Ic Design for Iot College of Computer Science and Software Engineering Shenzhen China Physical Electronics Group of Jihua Laboratory Foshan China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Huazhong University of Science and Technology Department of Biomedical Engineering Wuhan China

ISBN: (数字)9781665460361

ISBN: (纸本)9781665460361

Single-photon avalanche diodes (SPAD) enable sensitive detection of single photons under ultra-weak light conditions. Combined with direct time-of-flight (ToF) measurements, SPAD sensors are widely used in 3D imaging systems. Time-to-Digital Converter (TDC) can accurately measure time intervals in picoseconds, which is widely used for direct TOF measurements. This paper proposes a 32-channel TDC of two-level architecture containing a delay locked loop (DLL) and a vernier delay line (VDL) loop to increase timing accuracy. Designed in a 180 nm CMOS process, the TDC occupies an area of 4.1 mm × 3.6 mm and achieves 20 ps resolution at 2 Msps measurement rate. © 2022 IEEE.

关键词： Particle beams

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Initial Assessment of the Influence of Robustness on the Weighted Tardiness for a Scheduling Problem with High Demand Volatility Based on a Simulation Model

Initial Assessment of the Influence of Robustness on the Wei...

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2021 Winter Simulation Conference, WSC 2021

作者： Letonja, Ziga Furian, Nikolaus Pan, Johannes Vossner, Siegfried Reuter-Oppermann, Melanie Institute of Engineering and Business Informatics Graz University of Technology Graz8010 Austria Technical University of Darmstadt Information Systems-Software Digital Business Group Darmstadt64289 Germany

ISBN: (纸本)9781665433112

Nowadays, customer behavior changes rapidly, resulting in high demand volatility throughout immensely globalized supply chains, especially from key-account customers. Hence, requiring not only excellent products produced in the shortest time but also abiding to defined due dates. In this paper, we propose a multi-objective large neighborhood search (MO-LNS) algorithm with a frozen period focusing solely on minimizing the total weighted tardiness of a two-stage production with random rush orders. The total weighted tardiness is set as the primary objective function and the robustness as the secondary one. The algorithm is implemented in a discrete event simulation (DES) model to observe the effects of varying objective function weights at different levels of order dynamism. The results suggest that there is not a single dominant weight pair, but rather a range, which dominates others. Thus, using a pair from the dominant range increases the likelihood of reducing the tardiness over a period. © 2021 IEEE.

关键词： Supply chains

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Transformers in Small Object Detection: A Benchmark and Survey of State-of-the-Art

arXiv

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arXiv 2023年

作者： Rekavandi, Aref Miri Rashidi, Shima Boussaid, Farid Hoefs, Stephen Akbas, Emre Bennamoun, Mohammed Department of Computer Science and Software Engineering The University of Western Australia Australia Department of Electrical Electronics and Computer Engineering The University of Western Australia Australia Defence Science and Technology Group Australia Department of Computer Engineering Middle East Technical University Turkey

Transformers have rapidly gained popularity in computer vision, especially in the field of object recognition and detection. Upon examining the outcomes of state-of-the-art object detection methods, we noticed that transformers consistently outperformed well-established CNN-based detectors in almost every video or image dataset. While transformer-based approaches remain at the forefront of small object detection (SOD) techniques, this paper aims to explore the performance benefits offered by such extensive networks and identify potential reasons for their SOD superiority. Small objects have been identified as one of the most challenging object types in detection frameworks due to their low visibility. We aim to investigate potential strategies that could enhance transformers’ performance in SOD. This survey presents a taxonomy of over 60 research studies on developed transformers for the task of SOD, spanning the years 2020 to 2023. These studies encompass a variety of detection applications, including small object detection in generic images, aerial images, medical images, active millimeter images, underwater images, and videos. We also compile and present a list of 12 large-scale datasets suitable for SOD that were overlooked in previous studies and compare the performance of the reviewed studies using popular metrics such as mean Average Precision (mAP), Frames Per Second (FPS), number of parameters, and more. Researchers can keep track of newer studies on our web page, which is available at: https://***/arekavandi/Transformer-SOD. © 2023, CC BY-NC-SA.

关键词： Object recognition

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Cooperative Spectrum Sensing Method against Spectrum Sensing Data Falsification Attack

Cooperative Spectrum Sensing Method against Spectrum Sensing...

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Asia-Pacific Network Operations and Management Symposium (APNOMS)

作者： Xiaojun Wu Zhenghan Tang Yaya Lu Daolong Wu Haitao Xiao Yong Tang School of Software Engineering Xi’an Jiaotong University Xi’an China Key Laboratory of Technology on Datalink China Electronics Technology Group Corporation (CETC) 20th Institute Xi’an China School of Information and Communication Engineering Xi’an Jiaotong University Xi’an China

Spectrum sensing is a key technology of cognitive radio, by which Sensing Users (SUs) are able to scan and detect a specific spectrum to obtain patterns of spectrum usage and discover spectrum holes. Cooperative Spectrum Sensing (CSS) can effectively improve the efficiency and accuracy of spectrum sensing in Flying Ad-hoc NETwork (FANET). However, the dynamic topology and the lack of authentication management make it difficult for FANET to resist Spectrum Sensing Data Falsification (SSDF) attack, thus one distributed CSS method against SSDF is proposed. In this method, each SU is regarded as an agent, which performs spectrum sensing independently and interacts with neighbor nodes through reinforcement learning, where a clustering method of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is introduced to calculate rewards and guide neighbor selection. Each SU is used as a local fusion center for data fusion where a sliding window reputation mechanism is proposed to assign consensus weights. Experimental results show that in various SSDF attack scenarios, the proposed method can effectively identify and resist the interference of malicious users, as well as improving the efficiency and accuracy of CSS.

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Deep Learning Application for Urban Change Detection from Aerial Images 7

Deep Learning Application for Urban Change Detection from Ae...

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7th International Conference on Geographical Information Systems Theory, Applications and Management, GISTAM 2021

作者： Fyleris, Tautvydas Kriščiūnas, Andrius Gružauskas, Valentas Čalnerytė, Dalia Kaunas University of Technology Faculty of Informatics Department of Software Engineering Lithuania Kaunas University of Technology Faculty of Informatics Department of Applied Informatics Lithuania Kaunas University of Technology School of Economics and Business Sustainable Management Research Group Lithuania

ISBN: (纸本)9789897585036

Urban growth estimation is an essential part of urban planning in order to ensure sustainable regional development. For such purpose, analysis of remote sensing data can be used. The difficulty in analysing a time series of remote sensing data lies in ensuring that the accuracy stays stable in different periods. In this publication, aerial images were analysed for three periods, which lasted for 9 years. The main issues arose due to the different quality of images, which lead to bias between periods. Consequently, this results in difficulties in interpreting whether the urban growth actually happened, or it was identified due to the incorrect segmentation of images. To overcome this issue, datasets were generated to train the convolutional neural network (CNN) and transfer learning technique has been applied. Finally, the results obtained with the created CNN of different periods enable to implement different approaches to detect, analyse and interpret urban changes for the policymakers and investors on different levels as a map, grid, or contour map. © 2021 by SCITEPRESS - Science and technology Publications, Lda.

关键词： Urban growth

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Efficient Classification of Live Sensor Data on Low-Energy IoT Devices with Simple Machine Learning Methods 19thth

Efficient Classification of Live Sensor Data on Low-Energy...

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19th International Conference on Computer Aided Systems Theory, EUROCAST 2024

作者： Hanreich, Martin Krauss, Oliver Zwettler, Gerald Research and Development Research group AIST University of Applied Sciences Upper Austria Hagenberg4232 Austria Faculty for Informatics Communications and Media Department of Media Technology and Design University of Applied Sciences Upper Austria Hagenberg4232 Austria Faculty for Informatics Communications and Media Department of Software Engineering University of Applied Sciences Upper Austria Hagenberg4232 Austria

ISBN: (纸本)9783031829598

This paper addresses the implementation of time series classification on Internet of Things (IoT) devices with minimal resource usage while maintaining required accuracy levels. Although IoT devices have grown more powerful, optimizing resource usage remains essential to reduce costs and extend battery life. This work explores the practical aspects of developing and deploying time series classification models on IoT devices, emphasizing non-neural network models due to their lower resource demands. A detection system utilizing multiple predictions and a voting-based mechanism is constructed to enhance prediction stability. The input data comprises multivariate sensor streams of the same length. All computations are performed on the device, leveraging edge computing principles without external server dependency. This approach demonstrates how to balance accuracy and resource efficiency in IoT-based time series classification, offering a practical framework for similar applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Energy-Efficiency IoT-Devices Machine Learning Time-Series-Classification

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Communication Interference Recognition Based on Improved Deep Residual Shrinkage Network

Communication Interference Recognition Based on Improved Dee...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Xiaojun Wu Yaya Lu Zhenghan Tang Daolong Wu Haitao Xiao Zhongzheng Sun School of Software Engineering Xi'an Jiaotong University Xi'an China Key Laboratory of Technology on Datalink China Electronics Technology Group Corporation(CETC) 20th Institute Xi'an China School of Information and Communication Engineering Xian Jiaotong University Xi'an China

In complex battlefield environments, Flying Ad-hoc NETwork (FANET) faces challenges of manually extracting communication interference signals features, low recognition rate in strong noise environment, and inability to recognize unknown interference types. To solve these problems, one Simple Non-local Correction Shrinkage (SNCS) module is constructed, which modifies the soft threshold function in the traditional denoising method and embeds it into the neural network, so that the threshold can be adjusted adaptively. Local Importance-based Pooling (LIP) is introduced to enhance the useful features of interference signals to reduce noise in the downsampling process, and the joint loss function is constructed by combining cross-entropy loss and center loss to jointly train the model. To distinguish unknown class interference signals, the acceptance factor is proposed, and the One Class Support Vector Machine Simplified Non-local Residual Shrinkage Network (OCSVM-SNRSN) model with the ability of both known class recognition and new class rejection is constructed by combining OCSVM and SNRSN. Experimental results show that the recognition accuracy of the OCSVM-SNRSN model is the highest in the scenario of low Jamming Noise Ratio (JNR). The accuracy is increased by about 4%-9% compared with other methods on the known class interference signal dataset, and the recognition accuracy reaches 99% when the JNR is -6dB. At the same time, compared with other methods, the False Positive Rate (FPR) for recognizing unknown class interference signals drops to 9%.

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