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Xiaoping Zeng

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2 papers

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JBHI Journal 2014 Journal Article

Using a Dynamic Tracking Filter to Extract Distortion-Product Otoacoustic Emissions Evoked With Swept-Tone Signals

Jun Deng
Shixiong Chen
Xiaoping Zeng
Guanglin Li

Distortion-product otoacoustic emissions (DPOAEs) are sound energy generated by healthy inner ears when stimulated by two tones. Since DPOAEs are physiologically related with the functional status of the inner ear, they have been widely used as a clinical tool in hearing screening and diagnoses. Currently, almost all DPOAEs recording systems use pure tones as the stimuli and can test only one frequency at a time, resulting in low efficiency and insufficient resolution. In this study, conventional pure tones were replaced by swept tones with time-varying frequencies to overcome the limitation of current DPOAEs measurements. A tracking filter with dynamic center frequencies was proposed to extract the swept-tone DPOAEs from recorded signals with stimulus artifacts and background noises. The results of this study showed that the dynamic tracking filter had great performance in effectively extracting the swept-tone DPOAEs under different noise conditions for both the simulation and experimental data. The spectrogram of the extracted swept-tone DPOAEs could provide useful information to examine the functional status of the inner ear and to identify the detailed frequency regions of the hearing loss. These preliminary findings suggested that the swept-tone DPOAEs might be useful for developing a more efficient and accurate tool for hearing loss screening in the clinic.

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EAAI Journal 2010 Journal Article

Two coding based adaptive parallel co-genetic algorithm with double agents structure

Yongming Li
Xiaoping Zeng
Liang Han
Pin Wang

This paper systematically proposed a multi-population agent co-genetic algorithm with double chain-like agent structure (MPATCGA) to solve the problem of the low optimization precision and long optimization time of simple genetic algorithm in terms of two coding strategy. This algorithm adopted multi-population parallel searching mode, close chain-like agent structure, cycle chain-like agent structure, dynamic neighborhood competition, and improved crossover strategy to realize parallel optimization, and has the characteristics of high optimization precision and short optimization time. Besides, the size of each sub-population is adaptive. The characteristic is very competitive when dealing with imbalanced workload. In order to verify the optimization precision of this algorithm with binary coding, some popular benchmark test functions were used for comparing this algorithm and a popular agent genetic algorithm (MAGA). The experimental results show that MPATCGA has higher optimization precision and shorter optimization time than MAGA. Besides, in order to show the optimization performance of MPATCGA with real coding, the authors used it for feature selection problems as optimization algorithm and compared it with some other well-known GAs. The experimental results show that MPATCGA has higher optimization precision (feature selection precision). In order to show the performance of the adaptability of size of sub-populations, MPATCGA with sub-populations with same size and MPATCGA with sub-populations with different size are compared. The experimental results show that when the workload on different sub-populations becomes not same, the adaptability will adaptively change the size of different sub-population to obtain precision as high as possible.

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