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21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Tu, Xiaohan Xu, Cheng Liu, Siping Xie, Guoqi Li, Renfa Key Laboratory for Embedded and Network Computing of Hunan Province China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

ISBN: (纸本)9781728120584

As a strategy to obtain dense depth maps from a single image, sparse depths, or the fusion of both (RGBd), depth estimation receives much attention. Usually relying on high-performance workstations, existing depth prediction methods, are not particularly suitable for embedded devices. Moreover, the accuracy of depth estimation needs to be improved. Towards real-time and reliable depth prediction on portable embedded platforms, we propose an encoder-decoder architecture (EDA). Specifically, these are the three contributions of this work. (1) We present a light-weight encoder, which takes three modalities of data as inputs, namely, RGB images, sparse depths, or RGBd. The encoder is efficient to extract features on portable devices. (2) A novel decoder is designed to obtain accurate and high-resolution depth maps. (3) We use the existing TVM to deploy, compile, and optimize EDA on embedded platforms, for further improvements in the computational speed and power of depth estimation. In real scenarios, the optimized EDA achieves remarkable results with low latency. Through extensive experimental evaluation on two public datasets, we show that the optimized EDA outperforms the state of the arts in accuracy and computational resources. To the best of our knowledge, this is the first paper that provides an efficient encoder, learnable decoder, and end-to-end optimized encoder-decoder architecture for precise depth prediction. © 2019 IEEE.

关键词： Forecasting

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Security-aware Signal Packing Algorithm for CAN-based Automotive Cyber-physical Systems

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IEEE/CAA Journal of Automatica Sinica 2015年第4期2卷 422-430页

作者： Yong Xie Liangjiao Liu Renfa Li Jianqiang Hu Yong Han Xin Peng Key Laboratory for Application Technology of Internet of Things of Fujian Educational Bureau Xiamen University of Technology Key Laboratory of Embedded and Networking Computing of Hunan Province Hunan University Key Laboratory on Complex Systems Optimization & Controlling of Hunan High Education Institutions Hunan Institute of Science and Technology

network and software integration pose severe challenges in cyber-security for controller area network (CAN)-based automotive cyber-physical system (ACPS), therefore we employ message authentication code (MAC) to defend CAN against masquerade attack, but the consequent bandwidth overhead makes it a necessity to find the tradeoff among security, real-time and bandwidth utilization for signal packing problem (SPP) of CAN. A mixed-security signal model is firstly proposed to formally describe the properties and requirements on security and real-time for signals, and then a mixed-integer linear programming (MILP) formulation of SPP security-aware signal packing (SASP) is implemented to solve the tradeoff problem, where the bandwidth utilization is improved and the requirements in both security and real-time are met. Experiments based on both society of automotive engineers (SAE) standard signal set and simulated signal set showed the effectiveness of SASP by comparing with the state-of-the-art algorithm. © 2014 Chinese Association of Automation.

关键词： Algorithms Bandwidth Communication channels (information theory) Control system synthesis embedded systems Integer programming

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TMI: Two-dimensional maintainability index for automotive software maintainability measurement

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Journal of Systems Architecture 2025年 166卷

作者： Jiao Xie Jialing Yang Sirong Zhao Jianmei Lei Kaiwei Fan Guoqi Xie Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha 410082 Hunan China Center of Convergence of Automobile and Cyberspace Research Institute of Hunan University in Chongqing Chongqing 401120 China China Automotive Engineering Research Institute Co Ltd Electronic Communication and Software Evaluation Research Center Chongqing 401122 China

The mass adoption of autonomous driving and Advanced Driver-Assistance System (ADAS) technologies increases the complexity and maintenance cost of automotive-embedded software during model-based development. Through model-based development tools (e.g., MATLAB/Simulink), the code is automatically generated by architectural transformation; however, unreasonable software architecture design could make the final generated code be difficult to maintain. Currently, both Microsoft Maintainability Index (MMI) and Bosch Maintainability Index (BMI) are used to measure the maintainability of code, but neither software architecture. We establish a four-layer maintainability index model by analyzing the characteristics of automotive software architecture, software quality model, and industry-related standards. We then propose a Two-dimensional Maintainability Index (TMI), meaning the code and architecture dimensions are simultaneously considered. This is the first work of maintainability measurement to combine code and architecture dimensions while ensuring low redundancy. TMI utilizes nine index metrics to guide architecture refactoring and code optimization, significantly improving software maintainability.

关键词： MATLAB

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Instance-Aware Style-Swap for Disentangled Attribute-Level Image Editing 17th

Instance-Aware Style-Swap for Disentangled Attribute-Level ...

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17th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2022

作者： Zhou, Xinjiao Jiang, Bin Yang, Chao Hu, Haotian Sun, Minyu College of Computer Science and Electronic Engineering Hunan University Hunan Changsha410082 China Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Hunan Changsha410082 China

ISBN: (纸本)9789819923847

Recent studies have shown that attribute-level image editing could be achieved by modifying the latent code of style-based Generative Adversarial network (StyleGAN). However, many existing methods suffer from semantic entanglement, which leads to undesirable changes when editing a specific attribute. To solve this problem, we focus on the S space of StyleGAN2 and propose a simple and effective Optimization-based Instance-Aware Style-Swap method, which could perform a natural transformation effect on arbitrary source images and reference images by searching instance-aware swap coefficients with the guidance of prior information. By further analyzing the learned swapping coefficients, we can identify the style channels connected with a specific target attribute. We verify the effectiveness of our proposed methods in the field of face attribute editing. Extensive experiments have demonstrated that our method can achieve controllable and fine-grained image editing along various attributes. Qualitative and quantitative results show the advantages of our method compared with other semantic image editing methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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Collaborative Multi-head Contextualized Sparse Representations for Real-Time Open-Domain Question Answering 17th

Collaborative Multi-head Contextualized Sparse Representatio...

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17th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2022

作者： Sun, Minyu Jiang, Bin Zhou, Xinjiao Zhang, Bolin Yang, Chao College of Computer Science and Electronic Engineering Hunan University Hunan Changsha410082 China Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Hunan Changsha410082 China

ISBN: (纸本)9789819923847

An efficient method of representing and retrieving information is an essential component of open domain QA. There are question and answer models that allow for real-time responses with speed benefit and scalability. Nonetheless, due to the limitations of existing phrase models, their accuracy is low. In this paper, we improve the contextualized sparse representation to strengthen the connection between contextual information. We achieve better answer retrieval by enhancing the embedding quality of the model for phrase representation. Specifically, based on original contextualized sparse representations, we transform the single self-attention into collaborative multi-head attention so that attention heads can connect and pay attention to crucial information in different context locations. Compared with learning sparse vectors in n-gram vocabulary space by rectified self-attention, collaborative multi-head attention performs better on the SQuAD dataset. Due to the increased efficiency of critical information representation, the model improves to varying degrees on both of the two evaluation metrics. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Vector spaces

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A unified architecture for supporting operations of AES and ECC

A unified architecture for supporting operations of AES and ...

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2011 4th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2011

作者： Wang, Yi Li, Renfa Embedded Systems and Networking Laboratory Hunan Province Key Laboratory of Network and Information Security Hunan University Changsha 410082 China

ISBN: (纸本)9780769545752

In the recent years, embedded systems began to be used in sensitive applications such as personal digital assistants and smart cards. Due to very strict cost and power constrains, the support for cryptography provided by these devices is often limited to either public or private key primitives. This limitation is much more evident in devices where the cryptographic algorithms are implemented using hardware resources. In this paper, we propose an extension of a public-key cryptosystem to support also private-key, and we evaluate our architecture on FPGA platforms. In particular, we propose a new arithmetic unit in which the polynomial modular multiplication of ECC is extended to compute also the polynomial arithmetic operations over binary extended field of AES. We compare our design with an architecture obtained by instantiating state of the art implementation of AES and ECC and we evaluate the trade-offs. The experimental results show that our proposed architecture takes up less hardware resources. Nevertheless, the achieved performances are better compared to the ECC reference core, while the ones compared to AES only implementation are comparable with the state of the art.1 © 2011 IEEE.

关键词： Smart cards

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A Rolling Bearing Fault Diagnosis Method Using Multi-Sensor Data and Periodic Sampling

A Rolling Bearing Fault Diagnosis Method Using Multi-Sensor ...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Zheng, Jianbo Yang, Chao Zheng, Fangrong Jiang, Bin College of Computer Science and Electronic Engineering Hunan University China Key Laboratory for Embedded and Network Computing of Hunan Province China School of Computational Science and Electronics Hunan Institute of Engineering China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

In recent years, bearing fault diagnosis based on deep learning has gradually become the mainstream. However, the existing studies still have some defects, such as unreasonable sampling and incomplete utilization of bearing data, limiting the further improvement of the performance of the fault diagnosis model. This paper proposes a fault diagnosis method using multi-sensor data and periodic sampling to solve the problems above. First, the vibration data of different bearing positions are fused into multi-channel fusion data to improve the defect of insufficient data utilization. Second, based on the sampling length and sampling stride, periodic sampling is carried out for the fusion data to solve the problem of unreasonable sampling. Third, the traditional convolutional neural network is adjusted to extract more detailed fault features and obtain the best recognition effect. Finally, the experimental results verify the effectiveness of the proposed method. © 2022 IEEE.

关键词： Failure analysis

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A decomposition based evolutionary algorithm with angle penalty selection strategy for many-objective optimization 9th

A decomposition based evolutionary algorithm with angle pena...

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9th International Conference on Swarm Intelligence, ICSI 2018

作者： Li, Zhiyong Lin, Ke Nouioua, Mourad Jiang, Shilong Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha China PKU-HKUST Shenzhen-HongKong Institution Shenzhen China

ISBN: (纸本)9783319938141

Evolutionary algorithms (EAs) based on decomposition have shown to be promising in solving many-objective optimization problems (MaOPs). First, the population (or objective space) is divided into K subpopulations (or subregions) by a group of uniform distribution reference vectors. Later, subpopulations are optimized simultaneously. In this paper, we propose a new decomposition based evolutionary algorithm with angle penalty selection strategy for MaOPs (MOEA-APS). In the environmental selection process, in order to prevent the solutions located around the boundary of the subregion from being simultaneously selected into the next generation which will affect negatively on the performance of the algorithm, a new angle similarity measure (AS) is calculated and used to punish the dense solutions. More precisely, after selecting a good solution x for a sub population, the solutions whose angle similarity with x exceeding η or pareto dominated by x will be directly punished. Moreover, The threshold η is not fixed, but decided by the distribution of the solutions around x. This mechanism allows to improve diversity of population. The experimental results on DTLZ benchmark test problems show that the results of the proposed algorithm are very competitive comparing with four other state-of-the-art EAs for MaOPs. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Evolutionary algorithms

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A perspective on Petri Net learning

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Frontiers of Computer Science 2023年第6期17卷 1-3页

作者： Hongda QI Changjun JIANG Key Laboratory of Embedded System and Service Computing(Ministry of Education) Tongji UniversityShanghai 201804China National(Province-Ministry Joint)Collaborative Innovation Center for Financial Network Security Tongji UniversityShanghai 201804China

Petri Nets(PNs)are used for modeling and analyzing discreteevent systems,such as communication protocols,trafficsystems,human-computer interaction,and fault ***’state space explosion problem means that the state spaceof PNs grows exponentially with PNs’*** thefundamental reachability problem is still an NP-Hard problemin *** has been proved that the equivalence problem forthe reachability sets of arbitrary PNs is undecidable except forsome subclass of PNs[1].That is,the reachability problem ofarbitrary PNs cannot be solved ***,there is noefficient and accurate algorithm to solve the problem.10172In recent years,with the emergence of big data and thedevelopment of computing hardware,a series ofbreakthroughs have been achieved in machine learning,suchas AlphaGo,AlphaFold,and ChatGPT[2−4].As a data-drivenapproach,machine learning can learn potential mappingrelationships between inputs and outputs from large-scaledata.

关键词： breakthrough hardware learning

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An Improved algorithm of parameters reconfiguration in cognitive radio networks using ant colony optimization

An Improved algorithm of parameters reconfiguration in cogni...

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2015 3rd International Conference on Manufacturing Engineering and Technology for Manufacturing Growth(METMG 2015)

作者： Shaoyuan Liu Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University

Recent years, cognitive radio techniques have becoming a promising solution to tackle the bottleneck of spectrum scarcity. As an essential characteristics for CRN, the cognitive parameter reconfiguration can automatically adjust the parameters to meet the user demands and adapt to the timevarying environment. However parameter reconfiguration and complex environment cognition are still challenging due to their need for high speed parallel computation and multi-objects optimization. In this aspect, ant colony optimization(ACO) technique, as an intelligent technology to solve the complex issues, is introduced to the reconfiguration process to achieve the assignment. The goal of this paper is to present a parameters reconfiguration scenario using ACO with a comprehensive fitness function for reconfiguration decision making with regard to the multiple and complex objectives. Finally, series of results have demonstrated the stability and validity of the ACO based reconfiguration algorithm in CRN.

关键词： ACO simulation An Improved algorithm of parameters reconfiguration in cognitive radio networks using ant colony optimization

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