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40th IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2015

作者： He, Bryan Wein, Alexander Srinivasan, Lakshminarayan Department of Computer Science California Institute of Technology United States Department of Mathematics Massachusetts Institute of Technology United States Neural Signal Processing Laboratory Stanford University United States

ISBN: (纸本)9781467369978

Conventional Nyquist sampling and reconstruction of square waves at a finite rate will always result in aliasing because square waves are not band limited. Based on methods for signals with finite rate of innovation (FRI), generalized Analog Thresholding (gAT-n) is able to sample square waves at a much lower rate under ideal conditions. The target application is efficient, real-time, implantable neurotechnology that extracts spiking neural signals from the brain. This paper studies the effect of integrator noise and quantization error on the accuracy of reconstructed square waves. We explore realistic values for integrator noise and input signal amplitude, using specifications from the Texas Instruments IVC102 integrator chip as a first-pass example because of its readily-available data sheet. ADC resolution is varied from 1 to 16 bits. This analysis indicates that gAT-1 is robust against these hardware non-idealities where gAT-2 degrades less gracefully, which makes gAT-1 a prime target for hardware implementation in a custom integrated circuit. © 2015 IEEE.

关键词： analog thresholding compressed sensing electrode arrays finite rate of innovation massive-scale neural recording neurotechnology optical methods square wave sub-Nyquist

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Phoneme Recognition Based on Deep Belief Network

Phoneme Recognition Based on Deep Belief Network

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International Conference on Information System and Artificial Intelligence (ISAI)

作者： Yue Xie Cai-Rong Zou Rui-Yu Liang Hua-Wei Tao Laboratory of Underwater Acoustic Signal Processing Southeast University Nanjing China College of Electrical Engineering Guangzhou University Guangzhou China College of Communication Engineering Nanjing Institute of Technology Nanjing China School of Information Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9781509015863

To improve the accuracy of phoneme recognition in continuous speech recognition, a new method based on the deep belief network which can extract the posterior probability of phonemeis proposed. Firstly, a deep belief network is pre-trained layer by layer with Restricted Boltzmann Machine, then by adding an output layer called "softmax" to the network, a deep neural network detecting the posterior probability of phoneme can be created. Subsequently, Backward Propagation algorithm is applied to fine-tune the weights discriminatively and make them better at predicting the probability distribution over the states of hidden Markov models. Finally the sequence of the predicted probability distribution is fed into a standard Viterbi decoder. The experiments show that the proposed method has a better performance on the TIMIT dataset than traditional ways.

关键词： Speech recognition Hidden Markov models Training Speech Data models Speech processing Acoustics

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Proportional integral sliding mode observer for uncertain Takagi Sugeno systems with unknown inputs 7

Proportional integral sliding mode observer for uncertain Ta...

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7th International Conference on Modelling, Identification and Control, ICMIC 2015

作者： Elleuch, Ilyes Khedher, Atef Othmen, Kamel Ben Research Laboratory in Automatic Signal Processing and Imaging NationalSchool of Engineers Monastir Tunisia Research Laboratory: LARA Automatic ENIT Higher Institute of Applied Science and Technology Gafsa Tunisia

ISBN: (纸本)9780956715753

This paper deals with the problem of state and faults estimation for nonlinear uncertain systems described by Takagi Sugeno structures (named multiple models). In this work, only actuator are considered, they are considered as unknown inputs. The state and faults estimation is made using a structure of sliding mode observer where an integral term is added. This structure of observer is named proportional integral sliding mode observer. The integral term permits the fault estimation. The observer convergence conditions are formulated in LMI form allowing to compute the observer gains. The proposed proportional integral sliding mode observer is applied in the case of a numerical example showing the efficiency of the fault and state estimation. © 2015 University of Al Qayrawan, Tunisia.

关键词： State estimation

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Two Novel Cellular Neural Networks Based on Mem-elements

Two Novel Cellular Neural Networks Based on Mem-elements

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第三十四届中国控制会议

作者： SHEN Yi JIA Zhen Zhen WANG Xiao Ping LIU Yang School of Automation Huazhong University of Science and Technology Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Research Institute of Huazhong University of Science and Technology in Shenzhen

ISBN: (纸本)9781467374439

Two types of novel cellular neural networks based on mem-elements are proposed, namely, MC-CNN and *** MC-CNN lets a memcapacitor replace the conventional linear capacitor of a cellular neural network cell. This improvement takes advantage of the nanoscale of memcapacitor and its natural nonlinearity, which makes the MC-CNN more compact and the output function simplified. Mathematical analysis of stability and simulation of image processing is presented to verify the feasibility and performance of MC-CNN. The EM-CNN is an economical improvement of the memristor synapse cellular neural network. In the EM-CNN, based on the symmetry of CNN templates, the amount of memristors and voltage-controlled current source is largely reduced. Thus, the EM-CNN is not only economical on the fabricating cost of CNN but also possesses a simpler cell structure which is beneficial to better implementation of CNN.

关键词： Cellular Neural Network,Memcapacitor,Memristor,image processing

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Speech Recognition and Synthesis Algorithm for Digital Hearing Aids under Background Noise

Speech Recognition and Synthesis Algorithm for Digital Heari...

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International Conference on Information System and Artificial Intelligence (ISAI)

作者： Ming-Yang Zhang Cai-Rong Zou Rui-Yu Liang Li Zhao Laboratory of Under water Acoustic Signal Processing Southeast University Nanjing China College of Electrical Engineering Guangzhou University Guangzhou China College of Communication Engineering Nanjing Institute of Technology Nanjing China School of Information Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9781509015863

The difficulty of speech comprehension under background noise greatly influenced the use of hearing aids. To address this problem, this paper proposes a new hearing aids algorithm based on speech recognition and synthesis. This method is based on the pure speech to build a parameters database. Under the real noisy scene, implement speech recognition for the input speech, and then extract the corresponding parameters according to the results of the recognition and synthesize the pure speech. To improve the noise robustness of speech recognition system, we use a newly proposed kernel power flow orientation coefficients (KPOCs) as the characteristic parameters for training and testing. Because the speech synthesis is based on the parameters of pure speech, the output speech almost does not contain noise, and the difficulty of speech comprehension has great improvement.

关键词： Speech Speech recognition Hidden Markov models Feature extraction Speech processing Kernel Hearing aids

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Improving secrecy performance via device-to-device jamming in cellular networks

Improving secrecy performance via device-to-device jamming i...

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International Conference on Wireless Communications and signal processing (WCSP)

作者： Jian Ouyang Min Lin Wei-Ping Zhu Kang An Lei Wang Institute of Signal Processing and Transmission Nanjing University of Posts and Telecommunications Nanjing China Nanjing Institute of Telecommunication Technology Nanjing China Department of Electrical and Computer Engineering Concordia University Montreal QC Canada College of Communications Engineering PLA University of Science and Technology Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China

ISBN: (纸本)9781509028610

In this paper, we study the secrecy performance of a cellular network, where a device-to-device (D2D) communication shares the spectral resources with a multi-antenna base station (BS). By assuming known the channel state information (CSI) of the legitimate user and using the maximal ratio transmission (MRT) at the BS, we first derive a closed-form expression of the ergodic secrecy capacity (ESC) for the wiretap channel as well as the ergodic capacity (EC) for the D2D link. Then, we present a simple yet efficient power allocation scheme to maximize the secrecy performance while satisfying the D2D transmission requirement. Finally, numerical results are given to demonstrate the merit of our study, concluding that the interference from the D2D communication can be exploited as a friendly jammer to enhance the security transmission in cellular networks.

关键词： Device-to-device communication Cellular networks Antennas Resource management Jamming Security Optimized production technology

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Two novel cellular neural networks based on mem-elements 34

Two novel cellular neural networks based on mem-elements

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34th Chinese Control Conference, CCC 2015

作者： Shen, Yi Zhenzhen, Jia Xiaoping, Wang Yang, Liu School of Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Wuhan China Research Institute of Huazhong University of Science and Technology in Shenzhen Shenzhen China

ISBN: (纸本)9789881563897

Two types of novel cellular neural networks based on mem-elements are proposed, namely, MC-CNN and EM-CNN. The MC-CNN lets a memcapacitor replace the conventional linear capacitor of a cellular neural network cell. This improvement takes advantage of the nanoscale of memcapacitor and its natural nonlinearity, which makes the MC-CNN more compact and the output function simplified. Mathematical analysis of stability and simulation of image processing is presented to verify the feasibility and performance of MC-CNN. The EM-CNN is an economical improvement of the memristor synapse cellular neural network. In the EM-CNN, based on the symmetry of CNN templates, the amount of memristors and voltage-controlled current source is largely reduced. Thus, the EM-CNN is not only economical on the fabricating cost of CNN but also possesses a simpler cell structure which is beneficial to better implementation of CNN. © 2015 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Cellular Neural Network image processing Memcapacitor Memristor

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Aperiodic sampled-data sliding-mode control of fuzzy systems with communication delays via the event-triggered method

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IEEE Transactions on Fuzzy Systems 2016年第5期24卷 1048-1057页

作者： Wen, Shiping Huang, Tingwen Yu, Xinghuo Chen, Michael Z. Q. Zeng, Zhigang School of Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan430074 China Texas A and M University at Qatar Doha23874 Qatar Platform Technologies Research Institute RMIT University MelbourneVIC3001 Australia Department of Mechanical Engineering University of Hong Kong Hong Kong

This paper studies the aperiodic sampled-data control for the sliding-mode control (SMC) scheme of fuzzy systems with communication-induced delays via the event-triggered method. In practice, it is impossible to update control in continuous manner;thus, an event-based control technique has become popular with the advantage that the control task is executed only if it is triggered by an event. In this paper, the event-based sliding-mode control (ESMC) is designed for each linear subsystem of the global fuzzy model first. Then, the conditions for "fuzzily" amalgamated ESMC are discussed to stabilize the global fuzzy model. This ensures that the SMCis executed only when necessary. Furthermore, the results are extended to the fuzzy systems with communication-induced delays. Finally, case studies are carried out to demonstrate the effectiveness of the derived results. © 2015 IEEE.

关键词： Fuzzy systems

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Measuring effects of different noises in a model using ISI-distance methods

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International Journal of Biomathematics 2015年第4期8卷 17-36页

作者： Jingyi Qu Rubin Wang Ying Du Tianjin Key Laboratory for Advanced Signal Processing College of Electronic Information EngineeringCivil Aviation University Tianjin 300300 P. R. China Institute for Cognitive Neurodynamics School of Science East China University of Science and Technology Shanghai 200237 P. R. China

This paper examines the effects of current and conductance noises in a minimal Hodgkin- Huxley type model of a cold receptor neuron. Current noise enters the membrane equation directly while conductance noise is propagated through the activation variables. Compared with common used interspike interval method, ISI-distance is a simple complementary approach to measure the different effects of current and conductance noises. ISI-distance extracts information from the interspike intervals by evaluating the ratio of instantaneous firing rates, which is parameter-free, time scale-independent and easy to visualize. Simulation results show that the most significant differences between different noise implementations in a pacemaker-like tonic firing regime at the transition to chaotic burst discharges.

关键词： Neural spike train current and conductance noise ISI-distance cold receptormodel impulse pattern.

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image QUALITY ASSESSMENT BASED ON DCT SUBBAND SIMILARITY

IMAGE QUALITY ASSESSMENT BASED ON DCT SUBBAND SIMILARITY

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IEEE International Conference on image processing

作者： Amnon Balanov Arik Schwartz Yair Moshe Nimrod Peleg Signal and Image Processing Laboratory (SIPL) Department of Electrical Engineering Technion - Israel Institute of Technology

ISBN: (纸本)9781479983407

Measuring image quality becomes increasingly important due to the many applications involving digital imaging and communication. image quality assessment aims to develop a visual quality metric that correlates well with human visual perception. In this paper, we present a full-reference image quality assessment technique based on DCT Subbands Similarity (DSS). The proposed technique exploits important characteristics of human visual perception by measuring change in structural information in subbands in the discrete cosine transform (DCT) domain and weighting the quality estimates for these subbands. The proposed technique was tested with public image datasets and shows higher correlation with subjective results than state-of-the-art techniques. Another advantage of the proposed technique is its low computational cost.

关键词： image quality assessment (IQA) Objective quality measure Discrete Cosine Transform (DCT) image quality Visual Perception discrete cosine transforms DCT gene Subbands Digital images Human Characteristics Sight

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