Speckle suppression and contrast enhancement in reconstruction of freehand 3D ultrasound images using an adaptive distance-weighted method

In three-dimensional (3D) freehand ultrasound (US), reconstructing a set of B-scans into a regular voxel array is the key procedure for consequent visualization and analysis. This paper presents a new adaptive interpolation algorithm for computing the voxel array to suppress speckle noises and enhance contrast. The local statistics of homogeneous regions including mean and variance were measured and the ratio of variance to mean was used as homogeneity criteria. For the computation of each voxel, the interpolation method was adaptively determined with respect to its local statistics. If the neighbouring pixels of a voxel satisfied the homogeneity criterion, its value was computed with an arithmetic mean filter. Otherwise, the voxel was probably locating in an inhomogeneous region and an adaptive distance-weighted (ADW) interpolation method was employed to compute its value. A resolution phantom and a subject’s forearm were reconstructed using the proposed algorithm and two other well-known methods - conventional distance-weighted (DW) and voxel nearest neighbourhood (VNN) interpolations. The comparison results demonstrated that the adaptive interpolation algorithm was able to suppress speckles, preserve edges and enhance contrast effectively for the volume reconstruction.     

基于能量色散X射线荧光光谱的鱼粉产地溯源方法研究

鱼粉是一种在养殖业中占有重要地位的高蛋白饲料原料.我国对鱼粉的市场需求很大,但不同产地的鱼粉存在品质差异的问题.为保证鱼粉品质安全,建立鱼粉产地溯源系统具有非常重要的意义.能量色散X射线荧光光谱根据元素辐射X射线荧光光子能量不同,能够检测样品矿物质元素种类和含量.鱼粉所含矿物质元素种类和含量会因鱼粉产地不同而存在差异,...     

基于历史梯度平均方差缩减的协同参数更新方法

随机梯度下降算法(SGD)随机使用一个样本估计梯度,造成较大的方差,使机器学习模型收敛减慢且训练不稳定。该文提出一种基于方差缩减的分布式SGD,命名为DisSAGD。该方法采用历史梯度平均方差缩减来更新机器学习模型中的参数,不需要完全梯度计算或额外存储,而是通过使用异步通信协议来共享跨节点的参数。为了解决全局参数分发存在的“更新滞后”问题,该文采用具有加速因子的学习速率和自适应采样策略：一方面当参数偏离最优值时,增大加速因子,加快收敛速度;另一方面,当一个工作节点比其他工作节点快时,为下一次迭代采样更多样本,使工作节点有更多时间来计算局部梯度。实验表明：DisSAGD显著减少了循环迭代的等待时间,加速了算法的收敛,其收敛速度比对照方法更快,在分布式集群中可以获得近似线性的加速。     

可调实时时间差电子错位散斑技术及应用

本文首次提出一种可调实时时间差电子错位散斑干涉技术,并成功地用于热变形测量,该方法还可用于长时间连续变形和瞬态大变形的位移测量。叙述了该技术的原理,给出了一些应用的实验结果。     

基于有限反馈的数据流数自适应SDMA

针对目前绝大部分有限反馈自适应SDMA由于使用固定满波束发送策略而导致在稀疏网络中严重的用户间干扰的问题,该文提出了一种基于有限反馈的数据流数自适应SDMA,该方案能通过自适应地选择并行发送波束的数目来达到有效控制用户间干扰的目的。具体实现如下：通过用户的有限反馈信息,以及信道的二阶统计信息,基站端使用约束最大似然估计的方法估计出使用不同数目发送波束的发送模式所支持的最大和吞吐量,从而自适应地选择最优的发送模式。仿真结果表明：该文提出的方案能在增加很少反馈量的前提下显著改善现有SDMA方案的性能。     

Joint face and head tracking inside multi-camera smart rooms

The paper introduces a novel detection and tracking system that provides both frame-view and world-coordinate human location information, based on video from multiple synchronized and calibrated cameras with overlapping fields of view. The system is developed and evaluated for the specific scenario of a seminar lecturer presenting in front of an audience inside a “smart room”, its aim being to track the lecturer’s head centroid in the three-dimensional (3D) space and also yield two-dimensional (2D) face information in the available camera views. The proposed approach is primarily based on a statistical appearance model of human faces by means of well-known AdaBoost-like face detectors, extended to address the head pose variation observed in the smart room scenario of interest. The appearance module is complemented by two novel components and assisted by a simple tracking drift detection mechanism. The first component of interest is the initialization module, which employs a spatio-temporal dynamic programming approach with appropriate penalty functions to obtain optimal 3D location hypotheses. The second is an adaptive subspace learning based 2D tracking scheme with a novel forgetting mechanism, introduced to reduce tracking drift and increase robustness. System performance is benchmarked on an extensive database of realistic human interaction in the lecture smart room scenario, collected as part of the European integrated project “CHIL”. The system consistently achieves excellent tracking precision, with a 3D mean tracking error of less than 16 cm, and is demonstrated to outperform four alternative tracking schemes. Furthermore, the proposed system performs relatively well in detecting frontal and near-frontal faces in the available frame views. This work was performed while Zhenqiu Zhang was on a summer internship with the Human Language Technology Department at the IBM T.J. Watson Research Center.     

基于提升框架的一种自适应滤波方法研究

Haar小波和CDF(2,2)小波都可用提升框架的形式表示,该文把它们与无更新算子的Haar小波有机结合到一起,形成了自适应小波变换,随着信号的变化可以自动地选择合适的小波基。仿真结果表明,自适应小波变换对于含噪声的突变信号和光滑信号都有良好的去噪性能,兼具了Haar小波和CDF(2,2)小波各自的优点,适用范围更宽。     

Roughness measurement of metals using a modified binary speckle image and adaptive optics

This paper proposes an integrated roughness measurement system that is based on adaptive optics (AO) and binary analysis of speckle pattern images. The aim of this study was to demonstrate the necessity for AO compensation in regions containing both heat and fluid flow turbulences. A speckle image was obtained by projecting a laser beam onto the specimen surface, and the laser pattern image reflected from the surface was binarized to experimentally correlate the intensity with the surface roughness. In the absence of the AO correction scheme, induced turbulences can severely increase the residual rms error from 0.14 to 1.4 μm. After a real-time closed-loop AO correction, we can reduce the wavefront root mean square (rms) error to 0.12 μm, which not only compensates for the aberration error from induced disturbances but also improves the overall performance of the optical system. In addition, an AO system having different gains was investigated, and a threshold gain value was found to be able to steadily compensate for the wavefront errors in less than 2 s. Measurement results of five steel samples having roughness ranging from 0.2 to 3.125 μm (0.3λ and 5λ, where λ is the diode laser wavelength) demonstrate an excellent correlation between the intensity distribution of binary images and average roughness with a correlation coefficient of 0.9982. Furthermore, the proposed AO-assisted system is in good agreement with the stylus method and less than 9.73% error values can be consistently obtained.     

一种自适应层进式Savitzky‐Golay光谱滤波算法及其应用

可调谐半导体激光吸收光谱技术（TDLAS）利用半导体激光器的可调谐和窄线宽特性,通过选择特定气体的单条吸收线,排除其余气体的干扰,可以实现高精度、高选择性的气体浓度测量,在气体浓度检测系统中具有广泛的应用前景。在不同的应用条件和环境下,需要解决相应的硬件和数据处理方面的技术问题。主要研究TDLAS技术机动车尾气CO组分浓度遥测系统中的光谱数据处理问题,该系统利用路面漫反射回波信号遥测行驶中的机动车尾气CO组分浓度。由于激光扫描光谱回波信号受到漫反射面情况变化、空气环境变化、尾气湍流影响等因素影响,探测器收集到的信号不仅较弱同时也夹杂着多种噪声, 即测量光路信噪比较差, 故提出一种自适应层进式Savitzky-Golay(S-G)平滑滤波算法,实现了对光谱进行滤波处理从而更加准确地反演CO浓度。S-G滤波算法因其原理简单、功能强大、只需设置两个参数（窗口大小、拟合阶数）等优点,已广泛应用于光谱处理。如何正确设置S-G算法参数使滤波效果在去噪不足和过度滤波之间找到平衡点,是该滤波算法应用的一大难题。设计的检测系统中,测量光路光谱信号为非平稳信号,噪声和有效信号幅度时变,最佳窗口大小和多项式阶数随信号动态而变化,且变化区间较大,使用固定参数的S-G滤波器难以达到最佳效果。提出的自适应层进式S-G平滑滤波算法,通过逐层将测量光路光谱信号经过S-G滤波后,与参考光路的光谱信号设置的参考段比对信号相关系数和信号一阶导相关系数的和,以自适应得到逐层最优参数。通过对信噪比从9.81～29.77的10组不同带噪光谱分析验证了该算法的有效性,自适应层进式S-G算法能较好地去除噪声并还原带噪信号所携带的待测气体浓度信息,与带噪光谱对比,吸收光谱峰值最大误差由25.152%降至5.917%,积分吸光度最大误差由18.1%降至3.9%。在实现的系统中,使用自适应层进式S-G算法对测量光路进行滤波处理,并对不同车型、不同排量、燃烧不同油品的机动车在怠速和缓速通过(5 km·h-1)系统时其排放的CO浓度进行实时在线监测。