A novel approach to design low-cost two-stage frequency-response masking filters

The multistage frequency-response masking (FRM) technique is widely used to reduce the complexity of a filter when the transition bandwidth is extremely small. In this brief, a real generalized two-stage FRM filter without any constraint on the subfilters or the interpolation factors was proposed. New principles and equations were deduced to determine the design parameters. The subfilters were then jointly optimized using non-linear optimization. Experiential results show that when the proposed algorithm obtains different solutions with the conventional algorithm, the solution of the proposed approach is better with less number of filter coefficients and sometimes with lower delay as well than the conventional two-stage FRM, which can lead to a reduced hardware cost in applications

A reconfigurable sound wave decomposition filterbank for hearing aids based on nonlinear transformation

Hearing impaired people have their own hearing loss characteristics and listening preferences. Therefore hearing aid system should become more natural, humanized and personalized, which requires the filterbank in hearing aids provides flexible sound wave decomposition schemes, so that patients are likely to use the most suitable scheme for their own hearing compensation strategy. In this paper, a reconfigurable sound wave decomposition filterbank is proposed. The prototype filter is first cosine modulated to generate uniform subbands. Then by non-linear transformation the uniform subbands are mapped to nonuniform subbands. By changing the control parameters, the nonlinear transformation changes which leads to different subbands allocations. It provides four different sound wave decomposition schemes without changing the structure of the filterbank. The performance of the proposed reconfigurable filterbank was compared with that of fixed filerbanks, fully customizable filterbanks and other existing reconfigurable filterbanks. It is shown that the proposed filterbank provides satisfactory matching performance as well as low complexity and delay, which make it suitable for real hearing aid applications

Corporate Governance, CSR disclosure and Firm performance -- based on listed companies in China

This dissertation estimates the association among corporate governance, CSR disclosure and enterprise performance. It emphasizes the role of strong governance mechanisms on CSR disclosure and these two factors’ interaction effect on firm performance. Three characteristics are chosen to represent internal governance mechanisms -- board size, board independence and CEO-chairman duality and one characteristic is chosen to represent external mechanisms -- the level of market competitiveness. The quantitative method is used in this dissertation based on the sample data of 198 listed enterprises from ten different industries during the year 2014-2016 in China. In terms of the association between CSR disclosure and corporate governance, the results show that the effect of board size and board independence on CSR disclosure is significantly positive while CEO duality shows a negative impact. Additionally, there is no significant influence of market competitiveness on CSR disclosure. In terms of the correlation among corporate governance, CSR disclosure and firm performance, the results exhibit that the moderating impact of board size on the relationship between CSR disclosure and firm performance is significantly positive while the moderating impact of market competitiveness is significantly negative. Additionally, board independence and CEO duality do not have moderating impact on the correlation between CSR disclosure and firm performance. Keywords: Corporate Governance; CSR Disclosure, Firm Performanc

A weak fault diagnosis method for rotating machinery based on compressed sensing and stochastic resonance

Vibration signals used for rotating machinery fault diagnosis often constitute large amount of data. It is a big challenge to extract faults feature information from these data. Recently, a new sampling framework called compressed sensing has been proposed, which enables the recovery from a small set of measured data if the signals are sparse or compressible. In reality, the sparseness of the signals is not very well due to noise, so it is difficult and unavailing to recover the whole signal. Thus, a new mechanical fault diagnosis method is proposed in this paper. First, the machine fault vibration signals are pretreated by stochastic resonance. By this way, the fault signal drowned by noise is amplified and the sparseness of the signals is enhanced, which make it possible to apply compressed sensing. Second, fault features are extracted directly from the compressed data without recovering completely, which reduces the dimensionality of the measurement data and the complexity of algorithm. Finally, the effectiveness of the proposed method is proved by the experiments