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Energy Storage: Novel Polygonal Vanadium Oxide Nanoscrolls as Stable Cathode for Lithium Storage (Adv. Funct. Mater. 12/2015)

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Advanced Functional Materials 2015年第12期25卷

作者： Qiulong Wei Shuangshuang Tan Xiaoyi Liu Mengyu Yan Fengchao Wang Qidong Li Qinyou An Ruimin Sun Kangning Zhao Hengan Wu Liqiang Mai State Key Laboratory of Advanced Technology for Materials Synthesis and Processing WUT‐Harvard Joint Nano Key Laboratory Wuhan University of Technology Hubei Wuhan 430070 China CAS Key Laboratory of Mechanical Behavior and Design of Materials Department of Modern Mechanics University of Science and Technology of China Hefei Anhui 230027 China Cullen College of Engineering Department of Electrical and Computer Engineering University of Houston Houston TX USA

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Copyright protection in named data networking

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International Conference on Wireless Communications and Signal processing (WCSP)

作者： Xiaobin Tan Zifei Zhou Cliff Zou Yukun Niu Xin Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China China Department of Electrical Engineering and Computer Science University of Central Florida USA

As a new Internet architecture, Named Data Networking (NDN) decouples location from the data itself to achieve security, scalability, and mobility. Although router-side data caching used in NDN reduces data acquisition delay, it introduces a new copyright protection challenge: how to prevent unauthorized users to retrieve data cached in routers that are out of the control of its publisher? Current approaches that rely on a common encryption key among authorized users cannot protect copyright well since if one authorized user secretly leaks the key, we cannot tell who has leaked the key out. In this paper, we present a split-based scheme to solve this copyright protection problem for large-sized data. The data is split into a large part that could be cached in routers for all users to retrieve, and a small part that is unique for each authorized user. This scheme exploits the fact that in the bit-wise OR operation, both bit 0 and bit 1 can OR with 1 to generate the same result of bit 1. The analysis of our scheme shows that it has a good performance in terms of copyright protection, data retrieval efficiency, and overhead.

关键词： Copyright protection Encryption Watermarking Internet computer architecture Receivers

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Evolutionary membrane computing: A comprehensive survey and new results

Evolutionary membrane computing: A comprehensive survey and ...

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作者： Zhang, Gexiang Gheorghe, Marian Pan, Linqiang Pérez-Jiménez, Mario J. School of Electrical Engineering Southwest Jiaotong University Chengdu 610031 China Department of Computer Science University of Sheffield Regent Court Portobello Street Sheffield S1 4DP United Kingdom Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China Department of Computer Science and Artificial Intelligence University of Sevilla Avda. Reina Mercedes s/n Sevilla 41012 Spain

Evolutionary membrane computing is an important research direction of membrane computing that aims to explore the complex interactions between membrane computing and evolutionary computation. These disciplines are receiving increasing attention. In this paper, an overview of the evolutionary membrane computing state-of-the-art and new results on two established topics in well defined scopes (membrane-inspired evolutionary algorithms and automated design of membrane computing models) are presented. We survey their theoretical developments and applications, sketch the differences between them, and compare the advantages and limitations. © 2014 Elsevier Inc. All rights reserved.

关键词： Surveys

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On the performance of hybrid satellite-terrestrial cooperative networks with interferences 48

On the performance of hybrid satellite-terrestrial cooperati...

引用

48th Asilomar Conference on Signals, Systems and computers, ACSSC 2015

作者： Lin, Min Ouyang, Jian Zhu, Wei-Ping PLA University of Science and Technology Nanjing210007 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Institute of Signal Processing and Transmission Nanjing University of Posts and Telecommunications Nanjing210003 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G1M8 Canada

ISBN: (纸本)9781479982974

The paper investigates the performance of a hybrid satellite-terrestrial cooperative network (HSTCN), where some terrestrial amplify-and-forward (AF) relays are employed to assist the signal transmission from a satellite to a destination, which is corrupted by multiple co-channel interferences (CCIs). In this system, the satellite links undergo Shadowing-Rician fading while the terrestrial links experience Rayleigh fading. Specifically, by assuming that maximum ratio combining (MRC) is used at the destination, we first derive the moment generating function (MGF) of the output signal-to-interference-plus-noise ratio (SINR). With the help of MGF, we then present the analytical average symbol error rate (ASER) expression for the considered cooperative system. Moreover, to gain further insights, the asymptotic ASER analysis in terms of the diversity order and array gain is also developed. Finally, numerical results are given to demonstrate the validity of the performance analysis and the impacts of shadowing parameters, relay number and CCIs on the considered HSTCN. © 2014 IEEE.

关键词： Rayleigh fading

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SEQUENCE TRAINING OF MULTIPLE DEEP NEURAL NETWORKS FOR BETTER PERFORMANCE AND FASTER TRAINING SPEED

SEQUENCE TRAINING OF MULTIPLE DEEP NEURAL NETWORKS FOR BETTE...

引用

IEEE International Conference on Acoustics, Speech and Signal processing

作者： Pan Zhou Lirong Dai Hui Jiang National Engineering Laboratory of Speech and Language Information Processing University of Science and Technology of China Hefei P. R. China Department of Electrical Engineering and Computer Science York University Toronto Canada

ISBN: (纸本)9781479928941

Recently, sequence level discriminative training methods have been proposed to fine-tune deep neural networks (DNN) after the frame-level cross entropy (CE) training to further improve recognition performance of DNNs. In our previous work, we have proposed a new cluster-based multiple DNNs structure and its parallel training algorithm based on the frame-level cross entropy criterion, which can significantly expedite CE training with multiple GPUs. In this paper, we extend to full sequence training for the multiple DNNs structure for better performance and meanwhile we also consider a partial parallel implementation of sequence training using multiple GPUs for faster training speed. In this work, it is shown that sequence training can be easily extended to multiple DNNs by slightly modifying error signals in output layer. Many implementation steps in sequence training of multiple DNNs can still be parallelized across multiple GPUs for better efficiency. Experiments on the Switchboard task have shown that both frame-level CE training and sequence training of multiple DNNs can lead to massive training speedup with little degradation in recognition performance. Comparing with the state-of-the-art DNN, 4-cluster multiple DNNs model with similar size can achieve more than 7 times faster in CE training and about 1.5 times faster in sequence training when using 4 GPUs.

关键词： Functional training Training Fasting speed cerium Neural network Cross Entropy

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DIRECT ADAPTATION OF HYBRID DNN/HMM MODEL FOR FAST SPEAKER ADAPTATION IN LVCSR BASED ON SPEAKER CODE

DIRECT ADAPTATION OF HYBRID DNN/HMM MODEL FOR FAST SPEAKER A...

引用

IEEE International Conference on Acoustics, Speech and Signal processing

作者： Shaofei Xue Ossama Abdel-Hamid Hui Jiang Lirong Dai National Engineering Laboratory of Speech and Language Information Processing University of Science and Technology of China Hefei P. R. China Department of Electrical Engineering and Computer Science York University Toronto Canada

ISBN: (纸本)9781479928941

Recently an effective fast speaker adaptation method using discriminative speaker code (SC) has been proposed for the hybrid DNN-HMM models in speech recognition [1]. This adaptation method depends on a joint learning of a large generic adaptation neural network for all speakers as well as multiple small speaker codes using the standard back-propagation algorithm. In this paper, we propose an alternative direct adaptation in model space, where speaker codes are directly connected to the original DNN models through a set of new connection weights, which can be estimated very efficiently from all or part of training data. As a result, the proposed method is more suitable for large scale speech recognition tasks since it eliminates the time-consuming training process to estimate another adaptation neural networks. In this work, we have evaluated the proposed direct SC-based adaptation method in the large scale 320-hr Switchboard task. Experimental results have shown that the proposed SC-based rapid adaptation method is very effective not only for small recognition tasks but also for very large scale tasks. For example, it has shown that the proposed method leads to up to 8% relative reduction in word error rate in Switchboard by using only a very small number of adaptation utterances per speaker (from 10 to a few dozens). Moreover, the extra training time required for adaptation is also significantly reduced from the method in [1].

关键词： Loudspeakers Speech recognition Error analysis HYBRID

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IMPROVING DEEP NEURAL NETWORKS FOR LVCSR USING DROPOUT AND SHRINKING STRUCTURE

IMPROVING DEEP NEURAL NETWORKS FOR LVCSR USING DROPOUT AND S...

引用

IEEE International Conference on Acoustics, Speech and Signal processing

作者： Shiliang Zhang Yebo Bao Pan Zhou Hui Jiang Lirong Dai National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei Anhui P. R. China Department of Electrical Engineering and Computer Science York University Toronto Canada

ISBN: (纸本)9781479928941

Recently, the hybrid deep neural networks and hidden Markov models (DNN/HMMs) have achieved dramatic gains over the conventional GMM/HMMs method on various large vocabulary continuous speech recognition (LVCSR) tasks. In this paper, we propose two new methods to further improve the hybrid DNN/HMMs model: i) use dropout as pre-conditioner (DAP) to initialize DNN prior to back-propagation (BP) for better recognition accuracy;ii) employ a shrinking DNN structure (sDNN) with hidden layers decreasing in size from bottom to top for the purpose of reducing model size and expediting computation time. The proposed DAP method is evaluated in a 70-hour Mandarin transcription (PSC) task and the 309-hour Switchboard (SWB) task. Compared with the traditional greedy layer-wise pre-trained DNN, it can achieve about 10% and 6.8% relative recognition error reduction for PSC and SWB tasks respectively. In addition, we also evaluate sDNN as well as its combination with DAP on the SWB task. Experimental results show that these methods can reduce model size to 45% of original size and accelerate training and test time by 55%, without losing recognition accuracy.

关键词： Hidden Markov models Training Neural networks Speech recognition Speech Computational modeling Switches

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Speaker adaptive bottleneck features extraction for LVCSR based on discriminative learning of speaker codes

Speaker adaptive bottleneck features extraction for LVCSR ba...

引用

International Symposium on Chinese Spoken Language processing

作者： Changqing Kong Shaofei Xue Jianqing Gao Wu Guo Lirong Dai Hui Jiang National Engineering Laboratory of Speech and Language Information Processing University of Science and Technology of China Iflytek Research Anhui USTC iFlytek Co. Ltd. Department of Electrical Engineering and Computer Science York University

Recently, several fast speaker adaptation methods based on the so-called speaker codes (SC) have been proposed for the hybrid DNN-HMM speech recognition model [1, 2, 3]. In these methods the target speaker features are modified to match the given speaker-independent models or the speaker-independent models are transformed towards one particular speaker based on the discriminative learning of speaker codes. Previous researches have shown that these proposed SC-based adaptation methods are very effective to adapt large DNN models using only a small amount of adaptation data. In this work, we have explored the combination of direct speaker adaptation technique in model space based on speaker codes (mSA-SC) and bottleneck features where mSA-SC is used as an extraction instrument of speaker adaptive bottleneck features. We have evaluated the proposed speaker adaptive bottleneck features extraction method in two speech recognition tasks, namely PSC Mandarin task and large scale 320-hr Switchboard task. Experimental results have verified that it is quite suitable for very large scale tasks. For example, the Switchboard results have shown that it can achieve relative 9% reduction in word error rate on an unsupervised speaker adaptation scheme.

关键词： Hidden Markov models Neural networks Adaptation models Feature extraction Speech recognition Switches Training

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A Cloaking-Based Approach to Protect Location Privacy in Location-Based Services

A Cloaking-Based Approach to Protect Location Privacy in Loc...

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第三十三届中国控制会议

作者： Zheng Jiangyu Tan Xiaobin Cliff Zou Niu Yukun Zhu Jin CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Department of Automation University of Science and Technology of China Department of Electrical Engineering and Computer Science University of Central FloridaOrlandoFL 32816-2362

ISBN: (纸本)9781479947249

With the widespread use of mobile devices, the location-based service(LBS) applications become increasingly popular, which introduces the new security challenge to protect user's location privacy. On one hand, a user expects to report his own location as far as possible away from his real location to protect his location privacy. On the other hand, in order to obtain high quality of service(QoS), users are required to report their locations as accurate as possible. To achieve the dedicated tradeoff between privacy requirement and QoS requirement, we propose a novel approach based on cloaking technique. We also discuss the disadvantage of the traditional general system model and propose an improved model. The basic idea of our approach is to select a sub-area from the generated cloaking area as user's reported location. The sub-area may not contain a user's real location,which prevents an adversary from performing attack with side information. Specifically, by defining an objective function with a novel location privacy metric and a QoS metric, we are able to convert the privacy issue to an optimization problem. Then, location privacy metric and QoS metric are given. To reduce the complexity of the optimization, a heuristic algorithm is *** privacy-preserving analysis and comparison with related work [8], we demonstrate the effectiveness and efficiency of our approach.

关键词： Location-based Services,Location Privacy,k-anonymity,Cloaking Area

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Expectation propagation based iterative group wise detection for large-scale multiuser MIMO-OFDM systems

Expectation propagation based iterative group wise detection...

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IEEE Conference on Wireless Communications and Networking

作者： Sheng Wu Linling Kuang Zuyao Ni Jianhua Lu Defeng David Huang Qinghua Guo Shenzhen Key Laboratory of Wireless Broadband Communication and Signal Processing Shenzhen China Department of Electronic Engineering Tsinghua University Beijing China Tsinghua Space Center Tsinghua University Beijing China Research Institute of Information Technology Tsinghua University Beijing China School of Electrical Electronic and Computer Engineering University of Western Australia Australia School of Electrical Computer and Telecommunications Engineering University of Wollongong Australia

For the spatially correlated multiuser MIMO-OFDM channels, the conventional iterative MMSE-SIC detection suffers from a considerable performance loss. In this paper, we use the factor graph framework to design robust detection algorithms by clustering a group of symbols to combat the spatial correlation and using the principle of expectation propagation to improve message passing. Furthermore, as the complexity of detection becomes one of the issues in the design of large-scale multiuser MIMO-OFDM systems, we propose a low-complexity approximate message-passing algorithm by opening the channel transition node, which eliminates the expensive matrix inversions involved in the MMSE-SIC based algorithms. Finally, numerical results are presented to verify the proposed algorithms.

关键词： Complexity theory MIMO Educational institutions Algorithm design and analysis Vectors OFDM Correlation

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