一种自适应逆控制管道有源消声系统及其实现

在前馈有源噪声控制中，由于次级通道传递函数往往是非最小相位系统，直接使用传统的FXLMS算法时，导致系统的性能下降。提出一种基于NMP逆控制的自适应前馈有源噪声控制FXLMS算法，在电机、风机、通风管道系统上进行实时ANC实验，结果表明较传统的前馈FXLMS算法ANC系统消声性能有明显的改进。     

一种不需要次级通道建模的有源噪声控制算法

对有源噪声控制的研究,绝大部分都是建立在次级通道已知的基础上,或者通过在线或离线辨识来建模次级通道,通过分析滤波-XLMS（FXLMS）算法收敛的几何特性实现有源噪声的无模型控制。子带技术是将信号通过不同频带的滤波器,把信号分解到不同频带的子带中,对各子带信号分别进行相应的处理,并减少处理时间。将两者相结合,运用子带技术,采用过采样无延迟子带结构消除单频噪声、窄带噪声以及宽带噪声,降噪效果明显。相比传统的XLMS滤波算法,该算法降低了运算量,且实现结构简单。     

Factors affecting hazardous waste solidification/stabilization: a review

Solidification/stabilization is accepted as a well-established disposal technique for hazardous waste. As a result many different types of hazardous wastes are treated with different binders. The S/S products have different property from waste and binders individually. The effectiveness of S/S process is studied by physical, chemical and microstructural methods. This paper summarizes the effect of different waste stream such as heavy metals bearing sludge, filter cake, fly ash, and slag on the properties of cement and other binders. The factors affecting strength development is studied using mix designs, including metal bearing waste alters the hydration and setting time of binders. Pore structure depends on relative quantity of the constituents, cement hydration products and their reaction products with admixtures. Carbonation and additives can lead to strength improvement in waste-binder matrix.     

有源消声中声源辐射阻抗规律的理论研究

从声功率准则推导了两种典型消声状态（声抑制和声吸收）条件下,声源辐射阻抗的一般表达式,阐明了消声的物理机制;分析了点声源和偶极源在两种消声状态下辐射阻抗的变化规律。     

A narrowband active noise control system with reference synthesis

Most active noise control (ANC) algorithms require a model of the secondary path (SP). If there are large changes in the SP, online SP modeling algorithms may not be able to follow the changes quickly enough to prevent divergence. In this work, the mirror‐MFxLMS (MMFxLMS) is adapted to narrowband ANC, and the resulting algorithm is analyzed. The MMFxLMS is an algorithm similar to the MFxLMS and FxLMS but has online SP modeling; it is stable for any SP estimate and does not require auxiliary noise. In addition, a technique to alternate between MFxLMS and MMFxLMS is proposed, resulting in the M³FxLMS algorithm, which does not sacrifice performance and assures stability. However, the M³FxLMS requires a reference signal that is not available in feedback ANC. Feedback ANC is important in many practical cases, for instance, in ANC headphones. To solve this problem, a technique is proposed to synthesize a reference formed by a sum of sinusoids using measurements from the error sensor signal. This technique uses the fast Fourier transform to obtain estimates of the harmonic frequencies of the periodic noise components, and then refines and tracks these using a phase‐locked loop (PLL). The resulting algorithm enables a stable ANC system, even under large and sudden changes in the SP. The proposed PLL is also new and has a lock‐in range equal to the full band of the system.     

Multiple Endmember Hyperspectral Sparse Unmixing Based on Improved OMP Algorithm

In conventional linear spectral mixture analysis model, a class is represented by a single endmember. However, the intra-class spectral variability is usually very large, which makes it difficult to represent a class, and in this case, it leads to incorrect unmixing results. Some proposed algorithms play a positive role in overcoming the endmember variability, but there are shortcomings on computation intensive, unsatisfactory unmixing results and so on. Recently, sparse regression has been applied to unmixing, assuming each mixed pixel can be expressed as a linear combination of only a few spectra in a spectral library. It is essentially the same as multiple endmember spectral unmixing. OMP (orthogonal matching pursuit), a sparse reconstruction algorithm, has advantages of simple structure and high efficiency. However, it does not take into account the constraints of abundance non-negativity and abundance sum-to-one (ANC and ASC), leading to undesirable unmixing results. In order to solve these issues, this paper presents an improved OMP algorithm (fully constraint OMP, FOMP) for multiple endmember hyperspectral sparse unmixing. The proposed algorithm overcomes the shortcomings of OMP, and on the other hand, it solves the problem of endmember variability. The ANC and ASC constraints are firstly added into the OMP algorithm, and then the endmember set is refined by the relative increase in root-mean-square-error (RMSE) to avoid over-fitting, finally pixels are unmixed by their optimal endmember set. The simulated and real hyperspectral data experiments show that FOPM unmixing results are ideally comparable and abundance RMSE reduces much lower than OMP and simple spectral mixture analysis (sSMA), and has a strong anti-noise performance. It proves that multiple endmember spectral mixture analysis is more reasonable.