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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...

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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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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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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...

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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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Distributed MPC for Tracking based on Reference Trajectories

Distributed MPC for Tracking based on Reference Trajectories

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第三十三届中国控制会议

作者： Langwen Zhang Jingcheng Wang Zhengfeng Liu Kang Li Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China School of Electronics Electrical Engineering and Computer Science Queen's University Belfast

ISBN: (纸本)9781479947249

In this paper, we consider the control of large-scale processes with both input and state couplings. A distributed model predictive control(MPC) strategy for tracking based on the reference trajectories is presented. The proposed distributed MPC strategy requires decomposing a large-scale system into several smaller ones and solving convex optimization problems independently. Distributed MPC tracking strategies for unconstrained and constrained processes are presented, respectively. An iterative algorithm is presented to coordinate the distributed MPC controllers. The proposed algorithm is applied to a four-tank process to demonstrate the effectiveness.

关键词： distributed MPC tracking iterative algorithm reference trajectories

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On stability analysis and synthesis of reset control systems with output uncertainties

On stability analysis and synthesis of reset control systems...

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第三十三届中国控制会议

作者： Guanglei Zhao Jingcheng Wang Kang Li Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China School of Electronics Electrical Engineering and Computer ScienceQueens University BelfastUK

ISBN: (纸本)9781479947249

This paper considers reset control systems with output *** present sufficient conditions for the quadratic stability and finite L2 gain ***,the results are extended to piecewise quadratic stability which is much less ***,an iterative algorithm is proposed to design the reset *** the obtained results are given as linear matrix inequalities(LMIs) that can be solved *** examples are given to illustrate the results.

关键词： reset control output uncertainties quadratic stability iterative algorithm

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Approximate Maximum Common Sub-graph Isomorphism Based on Discrete-Time Quantum Walk

Approximate Maximum Common Sub-graph Isomorphism Based on Di...

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International Conference on Pattern Recognition

作者： Kai Lu Yi Zhang Kai Xu Yinghui Gao Richard C. Wilson Science and Technology on Parallel and Distributed Processing Laboratory and College of Computer National University of Defense Technology Changsha P.R. China College of Electronic Science and Engineering National University of Defense Technology Changsha P.R. China Department of Computer Science University of York York UK

Maximum common sub-graph isomorphism (MCS) is a famous NP-hard problem in graph processing. The problem has found application in many areas where the similarity of graphs is important, for example in scene matching, video indexing, chemical similarity and shape analysis. In this paper, a novel algorithm Qwalk is proposed for approximate MCS, utilizing the discrete-time quantum walk. Based on the new observation that isomorphic neighborhood group matches can be detected quickly and conveniently by the destructive interference of a quantum walk, the new algorithm locates an approximate solution via merging neighborhood groups. Experiments show that Qwalk has better accuracy, universality and robustness compared with the state-of-the-art approximate MCS methods. Meanwhile, Qwalk is a general algorithm to solve the MCS problem approximately while having modest time complexity.

关键词： Approximation algorithms Interference Algorithm design and analysis Accuracy Time complexity Quantum computing Noise

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