检索结果-内蒙古大学图书馆

International Conference on Network Protocols

作者： Hanwen Zhang He Huang Yu-E Sun Zhaojie Wang School of Computer Science & Technology Soochow University Suzhou China School of Rail Transportation Soochow University Suzhou China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

Spread estimation is an essential issue in high-speed networks with wide applications, such as network billing, quality of service, anomaly detection, etc. As a promising technique, sketch can efficiently estimate per-flow spread with only a small memory cost. Many studies focus on improving the performance of sketches. However, these works primarily focus on optimizing the counter level or sketch level without considering the scenario of multi-spread estimation, which is crucial for improving memory utilization and detecting anomalies. In this paper, we propose an efficient flow information compression algorithm based on the on-chip/off-chip hybrid framework to estimate multiple flow spreads. In the on-chip memory, we filter out non-duplicates and sample them to the off-chip space for recording. In the off-chip memory, we compress each sampled non-duplicate to a carefully designed bit-cube. When the measurement is finished, we separate corresponding KV-flows from a specific flow based on the user query. Then, we rebuild this flow to a subset group based on the duplicate number of each KV-flow. Finally, according to the Multi-set theory, we derive an accurate multi-spread estimate formula to solve this issue with a high throughput and small on-chip memory usage. Furthermore, we evaluate the performance of our proposed estimator using real Internet traffic traces downloaded from CAIDA. Experiments show that, compared to the state-of-the-art, our proposal achieves a 97.2% lower average relative error in per-destination source flow spread estimation with a tight on-chip memory, e.g., 320KB. And our proposed method achieves 31.86 higher processing throughput.

关键词：

来源：评论

学校读者我要写书评

暂无评论

DSP Assembler Auto-Generation Technique and ToolChain Integration 21

DSP Assembler Auto-Generation Technique and ToolChain Integr...

引用

21st IEEE International Conference on Communication Technology, ICCT 2021

作者： Wang, Wanli Wu, Jun Ren, Haoqi Zhang, Zhifeng Tongji University Electronics and Information Engineering College Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Fudan University School of Computer Science Shanghai200438 China

ISBN: (纸本)9781665432061

To promote the application of DSP, a fast and accurate toolchain generation method must be realized. Unlike the traditional manual method, in this paper, we propose a toolchain generation algorithm based on the architecture description language, and migrate the toolchain to adapt to the specified processor architecture. The algorithm uses the DSP architecture description as input to generate intermediate expressions, then uses lexical analysis and syntax analysis tools to migrate the assembly toolchain. In addition, an integrated development environment is developed based on Eclipse, which creates a friendly human-computer interface. Experiments show that this toolchain generation algorithm runs fast and accurately, which solves problems of the development and application of the DSP toolchain, and creates favorable conditions for the promotion of DSP. © 2021 IEEE.

关键词： Syntactics

来源：评论

学校读者我要写书评

暂无评论

Predictive Resilience Assessment of Road Networks Based on Dynamic Multi-Granularity Graph Neural Network

SSRN

引用

SSRN 2024年

作者： Zang, Di Ding, Yongjie Zhao, Jiayi Tang, Keshuang Zhu, Hong Key Laboratory of Embedded System and Service Computing Ministry of Educaiton Department of Computer Science and Technology Tongji University No. 4800 Cao’an Road Shanghai China Key Laboratory of Road and Traffic Engineering Ministry of Education College of Transportation Engineering Tongji University No. 4800 Cao’an Road Shanghai China

Due to the influence of global climate anomalies, abnormal weather conditions such as heavy rainfall have become more frequent in recent years, posing a significant threat to the operation of transportation systems. An effective assessment of the resilience of the transportation system before and during heavy rain can alert the transportation department to take necessary emergency actions. However, existing methods for assessing the rainfall resilience of transportation networks mostly suffer from the following problems: 1) Simulation methods for modeling rainfall impacts lack realism;2) After-the-fact evaluations of resilience cannot offer advance warning prior to or during a heavy rain event. To address above problems, we present a novel resilience assessment methodology for evaluating the resilience of road networks in real-time during heavy rainfall scenarios. In this methodology, we propose the temporal decomposition-based dynamic multi-granularity graph neural network (TD2MG2NN) for long-term traffic speed forecasting, providing a perspective on the future evolution of traffic states for accurate resilience assessment. In addition, we construct a composite traffic resilience indicator, designed to comprehensively reflect changes in the spatial-temporal resilience of the transportation system during heavy rain. Experimental results on four publicly real datasets indicate that the prediction performance of TD2MG2NN outperforms state-of-the-art models. The assessment results for the transportation road network in California demonstrate that the comprehensive resilience indicator is superior to single functional resilience indicator and the real-time methodology for evaluating resilience can accurately depict and predict the operation of the road network system under heavy rainfall scenarios. © 2024, The Authors. All rights reserved.

关键词： Roads and streets

来源：评论

学校读者我要写书评

暂无评论

A Better Cardinality Estimator with Fewer Bits, Constant Update Time, and Mergeability

A Better Cardinality Estimator with Fewer Bits, Constant Upd...

引用

IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Yang Du He Huang Yu-E Sun Kejian Li Boyu Zhang Guoju Gao School of Computer Science and Technology Soochow University Suzhou China School of Rail Transportation Soochow University Suzhou China Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education Shanghai China

Cardinality estimation is a fundamental problem with diverse practical applications. HyperLogLog (HLL) has become a standard in practice because it offers good memory efficiency, constant update time, and mergeability. Some recent work achieved better memory efficiency, but typically at the cost of impractical update time or losing mergeability, making them incompatible with applications like network-wide traffic measurement. This work presents SpikeSketch, a better cardinality estimator that reduces memory usage of HLL by 37% without sacrificing other crucial metrics. We adopt a bucket-based data structure to promise constant update time, design a smoothed log 4 ranking and a spike coding scheme to compress cardinality observables into buckets, and propose a lightweight mergeable lossy compression to balance memory usage, information loss, and mergeability. Then we derive an unbiased estimator for recovering cardinality from the lossy-compressed sketch. Theoretical and empirical results show that SpikeSketch can work as a drop-in replacement for HLL because it achieves a near-optimal MVP (memory-variance-product) of 4.08 (37% smaller than HLL) with constant update time and mergeability. Its memory efficiency even defeats ACPC and HLLL, the state-of-the-art lossless-compressed sketches using linear-time compression to reduce memory usage.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An intelligent guided-airborne virus detection

An intelligent guided-airborne virus detection

引用

International Conference on Sciences and Techniques of Automatic Control and computer Engineering (STA)

作者： Ahmed Hadded Mossaad Ben Ayed Shaya A. Alshaya Computer and Embedded System Laboratory National Engineering School Of Sfax Sfax University Sfax Tunisia Electronic Industrial ENISoSousse University Tunisia Computer and Embedded System Laboratory ENIS Sfax University Tunisia Sfax Tunisia Department of computer science Faculty of sciences and humanities sciences Majmaah University Dean of digital transformation and e-transactions Prince Sultan University Majmaah Saudi Arabia

ISBN: (纸本)9781665482622

Health has recently faced many challenges, including improving a healthy environment and reducing human life's dangers and economic crises. The last pandemic COVID-19 had badly affected survivor sectors with infection and lockdown exigence. Scientists proposed several solutions to reduce the negative impact of a such pandemic by proposing systems for earlier detection of viruses. The use of metamaterials as an emerging technology in the biosensors field allows a high accuracy. This paper presents a method for detecting and capturing airborne viruses using metasurface technology. The goal is to develop a system that can identify and capture these viruses using FET sensors. The accuracy of the detection is tied to the concentration of aerosols. The model proposes a guided flow of aerosols that positively impacts the detection of viruses through the FET biosensor. The simulation results based on Concentration and airflow velocity delays prove the proposed model's performance.

关键词： COVID-19 computer viruses Pandemics Atmospheric modeling Biological system modeling Field effect transistors Aerosols

来源：评论

学校读者我要写书评

暂无评论

Study and modeling of energy performance of PV/T solar plant for hydrogen production 2nd

Study and modeling of energy performance of PV/T solar plant...

引用

2nd International Conference on Electronic Engineering and Renewable Energy, ICEERE 2020

作者： El Fouas, C. El Manssouri, O. Hajji, B. Tina, G.M. Gagliano, A. Laboratory of Renewable Energy Embedded System and Information Processing National School of Applied Sciences Mohammed First University Oujda60000 Morocco Departments of Electric Electronics and Computer Engineering University of Catania Catania Italy

ISBN: (纸本)9789811562587

Hydrogen production from water electrolysis process is used worldwide as an alternative to conventional fuels. Using photovoltaic thermal system (PV/T) for electrolysis energy supply could be an attractive and innovative option. The PV/T systems are a genuine power production technology, providing simultaneously electricity and heat. In this paper, an attempt is made to investigate the possibility of hydrogen production from PV/T solar plant. Assessment of electrical and thermal performances of this plant is performed based on thermo–electrical dynamic model. Such model was developed taking into account energy balance equations and validated through an experimental data from PV/T power plant installed at the University of Catania, Italy. The results clearly show a good fitting between simulated and experimental results (RMSE% less than 7.00%, and R-squared more than 0.99). At 12 pm, the mean maximum electrical and thermal energy provided by the PV/T solar plant is found about 0.84 kWh/m2 and 0.548 kWh/m2 respectively. In terms of hydrogen production, a considerable amount of hydrogen about 60 ml/min can be provided. © Springer Nature Singapore Pte Ltd. 2021.

关键词： Hydrogen production

来源：评论

学校读者我要写书评

暂无评论

Camouflaged Object Tracking: A Benchmark

arXiv

引用

arXiv 2024年

作者： Guo, Xiaoyu Zhong, Pengzhi Zhang, Hao Huang, Defeng Shao, Huikai Zhao, Qijun Li, Shuiwang The College of Computer Science and Engineering Guilin University of Technology 541006 China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology 541004 China The School of Automation Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The College of Computer Science Sichuan University Sichuan610065 China

Visual tracking has seen remarkable advancements, largely driven by the availability of large-scale training datasets that have enabled the development of highly accurate and robust algorithms. While significant progress has been made in tracking general objects, research on more challenging scenarios, such as tracking camouflaged objects, remains limited. Camouflaged objects, which blend seamlessly with their surroundings or other objects, present unique challenges for detection and tracking in complex environments. This challenge is particularly critical in applications such as military, security, agriculture, and marine monitoring, where precise tracking of camouflaged objects is essential. To address this gap, we introduce the Camouflaged Object Tracking Dataset (COTD), a specialized benchmark designed specifically for evaluating camouflaged object tracking methods. The COTD dataset comprises 200 sequences and approximately 80,000 frames, each annotated with detailed bounding boxes. Our evaluation of 20 existing tracking algorithms reveals significant deficiencies in their performance with camouflaged objects. To address these issues, we propose a novel tracking framework, HiPTrack-MLS, which demonstrates promising results in improving tracking performance for camouflaged objects. COTD and code are avialable at https://***/openat25/ HIPTrack-MLS. © 2024, CC BY-NC-ND.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Formal Verification of Interrupt Isolation for the TrustZone-based TEE

Formal Verification of Interrupt Isolation for the TrustZone...

引用

Asia-Pacific Conference on Software Engineering

作者： Leping Zhang Qianying Zhang Xinyue Wang Ximeng Li Guohui Wang Zhiping Shi Yong Guan College of Information Engineering Capital Normal University Beijing China School of Computer Science and Engineering Beihang University Beijing China Beijing Engineering Research Center of High Reliable Embedded System Beijing China Beijing Key Laboratory of Electronic System Reliability Technology Beijing China

ARM TrustZone is a hardware security technology commonly used to implement the Trusted Execution Environment (TEE), which is a hardware-based isolated execution environment in which security-critical software can execute without interference and is widely applied to embedded systems. Isolation of interrupts in the two security domains of TrustZone is an important security mechanism that plays a vital role in protecting the execution of the TEE. This paper introduces a formal modeling and verification approach for the interrupt isolation mechanism of the TEE based on TrustZone. We propose a model formalizing the TrustZone-based TEE system at the instruction level, which focuses on modeling system behaviors of interrupt handling and captures crucial instructions related to interrupt isolation. We formally define three types of properties for the model: correctness, safety, and information flow security. The verification results in the theorem prover Isabelle/HOL show that the model satisfies the above properties, which indicates that the interrupt isolation mechanism of TrustZone correctly enforces the isolation of interrupts and protects the TEE from being interfered with non-secure interrupts.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Structure-Aware Generative Adversarial Network for Text-to-Image Generation

Structure-Aware Generative Adversarial Network for Text-to-I...

引用

IEEE International Conference on Image Processing

作者： Wenjie Chen Zhangkai Ni Hanli Wang Department of Computer Science and Technology Tongji University Shanghai P. R. China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai P. R. China

Text-to-image generation aims at synthesizing photo-realistic images from textual descriptions. Existing methods typically align images with the corresponding texts in a joint semantic space. However, the presence of the modality gap in the joint semantic space leads to misalignment. Meanwhile, the limited receptive field of the convolutional neural network leads to structural distortions of generated images. In this work, a structure-aware generative adversarial network (SaGAN) is proposed for (1) semantically aligning multimodal features in the joint semantic space in a learnable manner; and (2) improving the structure and contour of generated images by the designed content-invariant negative samples. Experimental results show that SaGAN achieves over 30.1% and 8.2% improvements in terms of FID on the datasets of CUB and COCO when compared with the state-of-the-art approaches.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Predictive Resilience Assessment Featuring Diffusion Reconstruction for Road Networks Under Rainfall Disturbances

SSRN

引用

SSRN 2024年

作者： Zang, Di Wei, Chenguang Zhao, Jiayi Zhang, Junqi Tang, Keshuang Zhu, Hong Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science and Technology Tongji University No. 4800 Cao’an Road Shanghai200092 China Key Laboratory of Road and Traffic Engineering Ministry of Education College of Transportation Engineering Tongji University No. 4800 Cao’an Road Shanghai200092 China

The ability of road networks to withstand external disturbances is a crucial measure of transportation system performance, where resilience distinctly emerges as an effective perspective for its unique insights into the system's resistance and recovery capabilities. In the face of unforeseen resilience disturbance events, predictive and accurate assessment of road network resilience is essential for better traffic regulation and emergency response management. However, existing resilience assessment methods of road networks are insufficient: they lack reliable real-time big-data analysis, do not possess predictive capabilities for guiding decision-making, and have a narrow view with single-dimensional resilience indicators. To address these issues, focusing on rainfall disturbance scenarios, this work introduces a novel resilience assessment method, which is predictive and real-time, consisting of two components: a traffic indicator prediction model and a comprehensive resilience assessment model. Firstly, we propose a two-stage traffic indicator prediction model, namely the Conditional Diffusion-Reconstruction-based Graph Neural Network (CDRGNN), which particularly enhances disturbance-scenario prediction accuracy, thereby providing reliable foresight in aid of the following assessments. Subsequently, we develop a resilience assessment model featuring structural-functional comprehensive resilience indicators established on the shortest-path aggregation, and the overall resilience assessment is performed through comparative analysis using indicators obtained in real time with historical non-disruptive resilience benchmarks. In a case study focusing on heavy rainfall disturbances on a road network in California, USA, abundant experiments and visualizations are conducted to demonstrate the rationality of our proposed comprehensive resilience indicators as well as the precision and reliability of these predictive resilience assessment outcomes. © 2024, The Authors. All

关键词： Diffusion

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：