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IEEE Conference on Nanotechnology

作者： Hailiang Zhou Bingcai Sui Yaqing Chi Minxuan Zhang Yue Hao National laboratory of parallel and distributed processing National University of Defense Technology Changsha China Department of Microelectronics Xidian University Xi'an China

ISBN: (纸本)9781424448326

In CNFETs, the Drain Induced Barrier Lowing (DIBL) effect is still a primitive problem and open for further study. Based on a numerical model developed with the Non-Equilibrium Green's Function (NEGF) approach in real space, this paper studied the DIBL effect in Carbon Nanotube-FETs (CNFETs). Some interesting phenomena of the impact of DIBL on the energy band have been noticed. For example, the relationship between the degree of barrier lowing and the drain-source voltage is not linear but of a step-like quantization, and the energy band shape (gradient or aclinic) is closely related to Fermi-level. For the first time, a detailed explanation of the underlying physical mechanism of the observed DIBL effect is presented according to the electronic structure, the effective capacitance model and the quantum capacitance model of CNFETs. Then the correlation between DIBL effect and the insulator thickness, insulator dielectric constant, CNT diameter and channel length is studied. At the end, a conclusion is made by summarizing the differences between the DIBL effect in CNFETs and that in short channel MOSFETs, and several approaches is presented to reduce the leakage current resulting from the DIBL effect.

关键词： Carbon nanotubes Quantum capacitance Dielectrics and electrical insulation Numerical models Green's function methods Voltage Quantization Shape Dielectric constant MOSFETs

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Towards Building Efficient Content-Based Publish/Subscribe Systems over Structured P2P Overlays

Towards Building Efficient Content-Based Publish/Subscribe S...

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International Conference on parallel processing (ICPP)

作者： Shengdong Zhang Ji Wang Rui Shen Jie Xu National Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha China School of Computing University of Leeds Leeds UK

In this paper, we introduce a generic model to deal with the event matching problem of content-based publish/subscribe systems over structured P2P overlays. In this model, we claim that there are three methods (event-oriented, subscription-oriented and hybrid) to make all the matched pairs (event, subscription) meet in a system. By theoretically analyzing the inherent problem of both event-oriented and subscription-oriented methods, we propose PEM (Popularity-based Event Matching), a variant of hybrid method. PEM can achieve better trade-off between event processing load and subscription storage load of a system. PEM has been verified through both mathematical and simulation-based evaluation.

关键词： Subscriptions Decision support systems Publishing Load modeling Mathematical model Bandwidth Routing

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An ordering-based approach to the evaluation of trust systems

An ordering-based approach to the evaluation of trust system...

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International Conference on Communication Software and Networks, ICCSN

作者： Wei Liu Yang-Bin Tang Huai-Min Wang Gang Lu School of Computer National University of Defense Technology Changsha Hunan China National Laboratory for Parallel and Distributed Processing Changsha Hunan China

Trust systems provide a promising way to build trust relationships among users in distributed and opening systems. However, it is difficult to make quantitatively comparative analysis on different trust systems because of the different application settings and the lack of effective measures. This paper constructs a framework of trust systems in terms of linear algebra, which helps us model and implement different systems in a uniform way. Besides, we propose an ordering-based approach to evaluating trust systems, then give two relevant ordering-base measures. The experiment results suggests that our method provides an effective way to analyze and evaluate trust systems.

关键词： Computational modeling Blogs

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HPDedup: A Hybrid prioritized data deduplication mechanism for primary storage in the cloud 33

HPDedup: A Hybrid prioritized data deduplication mechanism f...

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33rd International Conference on Massive Storage Systems and technology, MSST 2017

作者： Wu, Huijun Wang, Chen Fu, Yinjin Sakr, Sherif Zhu, Liming Lux, Kai Data CSIRO University of New South Wales Australia PLA University of Science and Technology China Science and Technology on Parallel and Distributed Laboratory State Key Laboratory of High Performance Computing State Key Lab. of High-end Server and Storage Technology Coll. of Computer Natl. Univ. of Def. Technol. China

Eliminating duplicate data in primary storage of clouds increases the cost-efficiency of cloud service providers as well as reduces the cost of users for using cloud services. Most existing primary deduplication techniques either use inline caching to exploit locality in primary workloads or use postprocessing deduplication running in system idle time to avoid the negative impact on I/O performance. However, neither of them works well in the cloud servers running multiple services or applications for the following two reasons: Firstly, the temporal locality of duplicate data writes may not exist in some primary storage workloads thus inline caching often fails to achieve good deduplication ratio. Secondly, the post-processing deduplication allows duplicate data to be written to disks, therefore does not provide the benefit of I/O deduplication and requires high peak storage capacity. This paper presents HPDedup, a Hybrid Prioritized data Deduplication mechanism to deal with the storage system shared by applications running in co-located virtual machines or containers by fusing an inline and a post-processing process for exact deduplication. In the inline deduplication phase, HPDedup gives a fingerprint caching mechanism that estimates the temporal locality of duplicates in data streams from different VMs or applications and prioritizes the cache allocation for these streams based on the estimation. HPDedup also allows different deduplication threshold for streams based on their spatial locality to reduce the disk fragmentation. The post-processing phase removes duplicates whose fingerprints are not able to be cached due to weak temporal locality from disks. The hybrid deduplication mechanism significantly reduces the amount of redundant data written to the storage system while maintaining inline data writing performance. Our experimental results show that HPDedup clearly outperforms the state-of-the-art primary storage deduplication techniques in terms of inline cac

关键词： Efficiency

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An Architecture of parallel Tiled QRD Algorithm for MIMO-OFDM Systems

An Architecture of Parallel Tiled QRD Algorithm for MIMO-OFD...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Cang Liu Chuan Tang Zuocheng Xing Lirui Chen Yang Zhang Guitao Fu National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Beijing Satellite Navigation Center Beijing China

ISBN: (纸本)9781509032068

The QR decomposition (QRD) has been extensively adopted in the transceiver processor of Multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) systems. The antenna configuration of future MIMO-OFDM system is very flexible. Therefore, the QRD architecture should also has the flexibility feature to decompose various dimensional channel response matrices. However, the existing QRD hardware architectures for MIMO-OFDM systems mainly focus on several fixed dimensional matrices. Due to the flexibility and scalability of parallel tiled QRD algorithm, it is very suitable for future MIMO-OFDM systems. A versatile hardware architecture (Ver_Arch) is designed for the bottleneck operations of parallel tiled QRD algorithm in this paper. Based on the designed Ver_Arch, we also design a QRD architecture for 4×4 real matrix. To the best of our knowledge, this is the first paper that presents a completed QRD hardware architecture based on the parallel tiled QRD algorithm for MIMO-OFDM wireless communication systems.

关键词： Matrix decomposition Hardware Algorithm design and analysis Computer architecture Wireless communication MIMO OFDM

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Learning non-local image diffusion for image denoising

arXiv

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arXiv 2017年

作者： Qiao, Peng Dou, Yong Feng, Wensen Chen, Yunjin National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha410073 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing

Image diffusion plays a fundamental role for the task of image denoising. Recently proposed trainable nonlinear reaction diffusion (TNRD) model defines a simple but very effective framework for image denoising. However, as the TNRD model is a local model, the diffusion behavior of which is purely controlled by information of local patches, it is prone to create artifacts in the homogenous regions and over-smooth highly textured regions, especially in the case of strong noise levels. Meanwhile, it is widely known that the non-local selfsimilarity (NSS) prior stands as an effective image prior for image denoising, which has been widely exploited in many nonlocal methods. In this work, we are highly motivated to embed the NSS prior into the TNRD model to tackle its weaknesses. In order to preserve the expected property that end-to-end training is available, we exploit the NSS prior by a set of non-local filters, and derive our proposed trainable non-local reaction diffusion (TNLRD) model for image denoising. Together with the local filters and influence functions, the non-local filters are learned by employing loss-specific training. The experimental results show that the trained TNLRD model produces visually plausible recovered images with more textures and less artifacts, compared to its local versions. Moreover, the trained TNLRD model can achieve strongly competitive performance to recent state-of-theart image denoising methods in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Copyright © 2017, The Authors. All rights reserved.

关键词： Image denoising

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Maximizing Uniform Multicast Throughput in Multi-Channel Dense Wireless Sensor Networks 12

Maximizing Uniform Multicast Throughput in Multi-Channel Den...

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12th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2016

作者： Jiao, Xianlong Chen, Guirong Wang, Xiaodong Chen, Yuli Yang, Li Information and Navigation College Air Force Engineering University Xi'an710077 China College of Information System and Management National University of Defense Technology Changsha410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Chongqing Guanyinqiao Elementary School Chongqing400020 China Chongqing Liangjiangxinqu Renhe Experimental School Chongqing400021 China

ISBN: (纸本)9781509056965

This paper investigates the problem of maximizing uniform multicast throughput (MUMT) for multi-channel dense wireless sensor networks, where all nodes locate within one-hop transmission range and can communicate with each other on multiple orthogonal channels. This kind of networks show wide application in the real world, and maximizing uniform multicast throughput for these networks is worth deep studying. Previous researches have proved MUMT problem is NP-hard. However, previous researches are either hard to implement, or use too many relay nodes to complete the multicast task, and thus incur high overhead or poor performance. To efficiently solve MUMT problem, we adopt the concept of the maximum independent set with the size constraint, and present one novel Single-Broadcast based Multicast algorithm called SBM based on the concept. We prove that SBM algorithm achieves a constant ratio to the theoretical throughput upper bound. Extensive experimental results demonstrate that, SBM performs better than existing work in terms of both the uniform multicast throughput and the total number of transmissions. © 2016 IEEE.

关键词： Throughput

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A Novel Conceptual Fish-like Robot Inspired by Rhinecanthus Aculeatus

A Novel Conceptual Fish-like Robot Inspired by Rhinecanthus ...

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Tianjiang Hu Guangming Wang Lincheng Shen Fei Li Institute of Automation National University of Defense Technology Changsha China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

This paper proposes a novel conceptual underwater bio-robot inspired by Rhinecanthus aculeatus, which belongs to median and/or paired fin (MPF) propulsion fish and impresses researchers with agility by cooperative undulation of the dorsal-and-anal fins. Such a fish-like robot is anticipated to outperform the conventional aquatic robots in maneuverability and stability for oceanic exploitation necessities, e.g. benthonic mineral exploration. To begin with, a specimen of R. aculeatus was filmed in a glass aquarium (150cm times 50cm times 60cm) in which artificial seawater was maintained at 26 degC or so. Afterwards, we analyzed a few characteristics in morphology and locomotion with image processing and other approaches. The morphological and kinematical bionic inspirations were summarized, and in succession, we elaborately delineated the design scheme of our conceptual robotic fish including the schematic architectures, the structural and outside form, and the undulatory multi-fin propulsor

关键词： Robots Marine animals Robotics and automation Propulsion Minerals Automotive engineering Remotely operated vehicles Stability Morphology Underwater vehicles

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An empirical study on the structure evolution of deep learning models: taking SAR image processing a case study

An empirical study on the structure evolution of deep learni...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Huanxi Liu Xiang He Dawei Feng Han Bao National Laboratory for Parallel and Distributed Processing (PDL) National University of Defense Technology China School of Information and Communication National University of Defense Technology China

With the continuous improvement on model performance, deep learning models have been widely deployed and achieved promising outcomes in various fields in recent years. However, due to the escalating volumes of training data and the complexity of application problems, it becomes more and more challenging to design a neural network with better performance by hand. Analysing the evolution of typical neural network structures has important reference significance for designing a network structure. In this paper, we select the open source models in SAR image processing for an empirical analysis on the evolution of neural network structures. We analyse the evolution of 239 open source deep learning models from the aspects of framework, computing unit, model computation amount and the combined use of various computing units. Results reveal that preference and co-occurrence exist in computing units, while the average number of convolution, activation and normalization layer increases significantly over time. Model complexity shows an overall upward trend, and the characteristics of SAR image are more and more taken into consideration during the model structure design.

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DEEP IMAGE CLUSTERING USING CONVOLUTIONAL AUTOENCODER EMBEDDING WITH INCEPTION-LIKE BLOCK

DEEP IMAGE CLUSTERING USING CONVOLUTIONAL AUTOENCODER EMBEDD...

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IEEE International Conference on Image processing

作者： Qiang Wang Jiaqing Xu Rongchun Li Peng Qiao Ke Yang Shijie Li Yong Dou National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China School of Computer National University of Defense Technology Changsha China

Image clustering is one of the challenging tasks in machine learning, and has been extensively used in various applications. Recently, various deep clustering methods has been proposed. These methods take a two-stage approach, feature learning and clustering, sequentially or jointly. We observe that these works usually focus on the combination of reconstruction loss and clustering loss, relatively little work has focused on improving the learning representation of the neural network for clustering. In this paper, we propose a deep convolutional embedded clustering algorithm with inception-like block (DCECI). Specifically, an inception-like block with different type of convolution filters are introduced in the symmetric deep convolutional network to preserve the local structure of convolution layers. We simultaneously minimize the reconstruction loss of the convolutional autoencoders with inception-like block and the clustering loss. Experimental results on multiple image datasets exhibit the promising performance of our proposed algorithm compared with other competitive methods.

关键词： Convolutional codes Convolution Clustering algorithms Image reconstruction Decoding Clustering methods Task analysis

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