基于一种新的阈值函数的小波图像去噪

文中在D. L. Donoho和I. M. Johnstone提出的小波阈值去噪基础上,提出了一种改进的阈值函数。该阈值函数采用双阈值模式,通过逐渐增大对小波系数的缩减力度来处理双阈值之间的小波系数,尽可能多地保留有用信息,直至小波系数缩减为零。在这里引入了一个控制变量来调节系数的缩减幅度。与传统的软、硬阈值方法相比,改进的阈值函数最大的优点是函数连续且减小了估计系数的误差。通过仿真实验,从视觉和客观评价标准（峰值信噪比和均方根误差）上验证了新阈值函数去噪的有效性。     

仪表测控系统中信号干扰的抑制

介绍工业现场中仪表测控系统的干扰以及干扰的抑制法。     

面向交互应用的HEVC编码性能分析

结合现行的视频编码标准H.264/AVC,分析HEVC的具体编码技术,并对HEVC编码算法的编码效率和编码复杂度进行了对比研究。以交互应用视频序列为样本,对HEVC的具体性能进行测试,结果表明,在交互领域,和H.264/AVC相比,HEVC编码效率获得有大约50%的提升,编码复杂度也增加到了170%左右。     

CRS共反射面元叠加技术在加蓬地区的应用

CRS共反射面元叠加方法是一种基于非均匀介质,且不依赖于宏观速度模型,由反射面元的位置、倾角、曲率三参数代替叠加速度来刻画反射界面的成像方法。它利用相邻道集数据之间的相似性,尽可能地集中邻域道集的能量参与叠加,从而压制了随机噪声,增强了反射波同相轴的连续性,很好地提高了地震资料的信噪比。加蓬地区的构造复杂、地震采集资料信噪比低,通过CRS共反射面元叠加技术的应用,成像效果较常规地震资料处理方法的成像效果有显著的提高,说明该技术值得在类似复杂区域进行推广和应用。     

用选择性声强技术分析车内噪声产生的原因

本文介绍了选择性声强技术在轿车车内噪声识别中的应用。通过对国产轿车在给定工况下的测试分析，确定出试验车发动机仓内最大噪声的位置、司机耳旁辐射噪声产生的原因，为进一步降低该车噪声提供了参考。     

汽车排气的净化与消声

研究汽车排气的净化与消声及其相互关系,列出相应的计算和经验公式。通过净化、消声,油耗综合试验,研制出实用的JX—系列净化消声器,CO净化率>95％,CH_x净化率>70％,噪声数值此原消声器降低1～3 dB(A),比国家标准降低5～6 dB(A),油耗略低。     

减振降噪阻尼材料及其应用

介绍国内外减振材料的发展概况、着重阐述自行研制的S2复合阻尼钢板的各种性能及在多种结构上的抑振降噪效果，并对S2复合阻尼钢板的抑振降噪机理进行了初步的探讨。实践证明，S2复合阻尼钢板是一种抑振降噪的有效材料。     

Development of a noise prediction software NASEFA and its application in medium-to-high frequency ranges

For the analysis of noise problems in medium-to-high frequency ranges, the energy flow boundary element method (EFBEM) has been studied. EFBEM is numerical analysis method of energy flow analysis (EFA), and solves energy governing equations using a boundary element method in complex structures. Based on EFBEM, a noise prediction software, “noise analysis system by energy flow analysis” (NASEFA), was developed. For effective maintenance, NASEFA is composed of three main modules: the translator, the model converter, and the main solver. The translator changes the FE model to the NASEFA BE model, and the model converter changes the BE model to an EFBE model, including various data, such as structural materials, medium properties, sources, and boundary conditions. NASEFA then solves the acoustic energy density and intensity on boundary and in the field. Moreover, it analyzes interior and exterior noise problems for single and multiple domains in two and three dimensions. Finally, for the validation of the software developed, interior and exterior noise predictions of various structures were performed. The results obtained with NASEFA were compared with those of the commercial SEA program and experiment. From these comparative studies, the usefulness of NASEFA was established.     

一种基于像素梯度信息的背景减除法

讨论了背景模型的更新参数与模型精度的关系。通过精确的梯度背景模型值间接估计当前帧中背景像素理论上的期望梯度值。以高斯模型为基础,将当前帧背景像素的实际梯度值与其理论上的期望值进行比较,计算偏差概率,以此为基础,形成不依赖于局部纹理的梯度特征的相似性度量方法。再用梯度特征的相似度量化地调整差分图像在各像素点处的二值化阈值,实现像素值信息与梯度信息的融合使用。实验表明,本方法对前景分割有一定的改善效果。     

The frequency partitioned block modified filtered-x NLMS with orthogonal correction factors for multichannel Active Noise Control

The Normalized Least Mean Square (NLMS) algorithm with a filtered-x structure (FxNLMS) is a widely used adaptive algorithm for Active Noise Control (ANC) due to its simplicity and ease of implementation. One of the major drawbacks is its slow convergence. A modified filtered-x structure (MFxNLMS) can be used to moderately improve the speed of convergence, but it does not offer a huge improvement. A greater increase in the speed of convergence can be obtained by using the MFxNLMS algorithm with orthogonal correction factors (M-OCF), but the usage of orthogonal correction factors also increases the computational complexity and limits the usage of the M-OCF in massive real-time applications. However, Graphics Processing Units (GPUs) are well known for their potential for highly parallel data processing. Therefore, GPUs seem to be a suitable platform to ameliorate this computational drawback. In this paper, we propose to derive the M-OCF algorithm to a partitioned block-based version in the frequency domain (FPM-OCF) for multichannel ANC systems in order to better exploit the parallel capabilities of the GPUs. The results show improvements in the convergence rate of the FPM-OCF algorithm in comparison to other NLMS-type algorithms and the usefulness of GPU devices for developing versatile, scalable, and low-cost multichannel ANC systems.