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Articles by Zhang Deyun
Total Records ( 3 ) for Zhang Deyun
  Zhang Jun , Gao Lei and Zhang Deyun
  In order to solve the problem of real-time speech quality assessment, a high-performance algorithm, Efficient Psychoacoustics Evaluation of Speech Quality (EPESQ), based on psychoacoustics model, is proposed. The process of EPESQ is: The original and corresponding degraded speech samples are first preprocessed by overall gain compensation and IRS (Intermediate Reference System) filtering. Then both signals are transformed to their loudness presentation by a series of consecutive steps: windowed fast Fourier transform, frequency warping to Mel-scale and loudness mapping. The loudness presentations are compared in different time-frequency cell to get the differences called Disturbance. Disturbances are aggregated over time and frequency and then the result is processed by a cognitive formula to generate the final evaluation score. Experimental results show that EPESQ performs a 37.5% reduction in running time and 51.9% in memory occupation to the P.862 algorithm with only a 7.8% decrease in average correlation to listener opinions. EPESQ is a high-performance algorithm and suitable for real-time applications. It has been implemented in our Internet voice communication system as a self-evaluating component.
  Liu Zunxiong , Xie Xin and Zhang Deyun
  Minimax Probability Machine Regression (MPMR) is proposed for chaotic time series global prediction in this study. In MPMR, regression function maximizes the minimum probability that future predication will be within an |Å to the true regression function. Multi-step predictions up to 20 steps were done on Mackey-Glass chaotic time series with MPMR and Local Weighted Linear Regression (LWLR). The results demonstrate that MPMR have better prediction performance, compared with LWLR. Kernel function shape parameter and regression tube value will influence the MPMR-based system performance. In experiments, the cross validation methods are employed to choose the two parameters.
  Fu Peng and Zhang Deyun
  It is very difficult to find feasible QoS (Quality of service) routes in the mobile ad hoc networks (MANETs), because of the nature constrains of it, such as dynamic network topology, wireless communication link and limited process capability of nodes. In order to reduce average cost in flooding path discovery scheme of the traditional MANETs routing protocols and increase the probability of success in finding QoS feasible paths and we proposed a heuristic and distributed route discovery method named RLGAMAN that supports QoS requirement for MANETs in this study. This method integrates a distributed route discovery scheme with a reinforcement learning (RL) method that only utilizes the local information for the dynamic network environment; and the route expand scheme based on genetic algorithms (GA) method to find more new feasible paths and avoid the problem of local optimize. We investigate the performance of the RLGAMAN by simulation experiment bed in NS2. Compared with traditional method, the experiment results showed the network performance is improved obviously and RLGAMAN is efficient and effective.
 
 
 
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