一类单参数假设检验拒绝域的直观构造法

本文提出直观法的理论 ,举出用直观法构造单参数假设检验拒绝域的应用实例 ,并得出构造形如 ( * )式的检验拒绝域的一种方法     

Luminance and Chromaticity Conditions of a Bichromatic Cross Pattern for Chromatically-Uniform Transparency

We can perceive a surface through another surface. This perception is called transparency. It is known that transparency can be perceived even if the stimulus conditions are not consistent with physical conditions for a real transparent surface. In this study, we measured the ranges of luminance and chromaticity of the overlapping area of two crossed layers at which a surface was perceived as chromatically-uniform transparent. As the results, the luminance range of the overlapping area existed around or near the luminance of the inducing area. The upper and lower limits of the luminance range were higher for the dark background than for the light background. Moreover, the chromatic range existed around the additive colormixture line between two chromaticities of the inducing areas for both dark and light backgrounds. This indicates that the perceptual transparency mechanism would divide the color of an additive color mixture into the original colors that exist in the inducing areas. We noticed that the perceptual appearance of the stimulus changed greatly depending on the luminances of the overlapping area and the background. These differences in perceptual appearance would be a factor explaining individual difference and deciding the luminance conditions for transparency.     

An Interpretation Architecture for Deep Learning Models with the Application of COVID-19 Diagnosis

The Coronavirus disease 2019 (COVID-19) has become one of the threats to the world. Computed tomography (CT) is an informative tool for the diagnosis of COVID-19 patients. Many deep learning approaches on CT images have been proposed and brought promising performance. However, due to the high complexity and non-transparency of deep models, the explanation of the diagnosis process is challenging, making it hard to evaluate whether such approaches are reliable. In this paper, we propose a visual interpretation architecture for the explanation of the deep learning models and apply the architecture in COVID-19 diagnosis. Our architecture designs a comprehensive interpretation about the deep model from different perspectives, including the training trends, diagnostic performance, learned features, feature extractors, the hidden layers, the support regions for diagnostic decision, and etc. With the interpretation architecture, researchers can make a comparison and explanation about the classification performance, gain insight into what the deep model learned from images, and obtain the supports for diagnostic decisions. Our deep model achieves the diagnostic result of 94.75%, 93.22%, 96.69%, 97.27%, and 91.88% in the criteria of accuracy, sensitivity, specificity, positive predictive value, and negative predictive value, which are 8.30%, 4.32%, 13.33%, 10.25%, and 6.19% higher than that of the compared traditional methods. The visualized features in 2-D and 3-D spaces provide the reasons for the superiority of our deep model. Our interpretation architecture would allow researchers to understand more about how and why deep models work, and can be used as interpretation solutions for any deep learning models based on convolutional neural network. It can also help deep learning methods to take a step forward in the clinical COVID-19 diagnosis field.     

微纳电极阵列检测丘脑底核电刺激引起的纹状体神经元活动变化

丘脑底核(STN)深部脑刺激(DBS)已成为帕金森病的重要外科治疗手段,然而其确切的作用机理尚不明确.本研究采用微机电系统(MEMS)技术制备了一种16通道植入式微电极阵列(MEA),在MEA表面修饰了铂黑-还原氧化石墨烯-Nafion膜(Pt/RGO/Nafion)纳米材料,用于同步检测麻醉大鼠脑内纹状体神经元在STN电刺激前后多巴胺(DA)含量和动作电位(Spike)发放变化.STN-DBS结果表明,电刺激20 s后,DA含量开始升高,最高达1.72 μmol/L,较高浓度状态保持约50 s后回落至正常水平.与此同时, 检测到在DA上升阶段中间神经元Spike发放活动增强,在保持高于DA正常浓度水平阶段,中等多棘神经元(MSNs)放电频率增加.本研究制备的微电极阵列传感器能够实现脑内多巴胺和电生理的原位实时检测,有望成为神经信息检测的有力工具.     

A diagonal measure and a local distance matrix to display relations between objects and variables

Proper permutation of data matrix rows and columns may result in plots showing striking information on the objects and variables under investigation. To control the permutation first, a diagonal matrix measure D was defined expressing the size relations of the matrix elements. D is essentially the absolute norm of a matrix where the matrix elements are weighted by their distance to the matrix diagonal. Changing the order of rows and columns increases or decreases D. Monte Carlo technique was used to achieve maximum D in the case of the object distance matrix or even minimal D in the case of the variable correlation matrix to get similar objects or variables close together. Secondly, a local distance matrix was defined, where an element reflects the distances of neighboring objects in a limited subspace of the variables. Due to the maximization of D in the local distance matrix by row and column changes of the original data matrix, the similar objects were arranged close to each other and simultaneously the variables responsible for their similarity were collected close to the diagonal part defined by these objects. This combination of the diagonal measure and the local distance matrix seems to be an efficient tool in the exploration of hidden similarities of a data matrix. Copyright © 2009 John Wiley & Sons, Ltd.     

基于双阈值视觉注意模型的图像关注焦点检测

 图像关注焦点（FOA）检测是基于人眼视觉关注模型的图像感兴趣区提取的关键技术。为了更加精确、合理地搜索图像关注焦点,提出一种基于双阈值视觉关注模型的FOA检测算法。算法首先提取图像的亮度、颜色、方向和离散矩变换（DMT）特征,生成各个特征对应的特征图;然后将各特征图合并为一张包含多种特征的显著图;最后根据显著图的灰度直方图建立静态阈值与动态阈值,确定图像关注焦点的位置与数量。实验结果表明：新算法在图像关注焦点检测的准确性上较Itti模型有更为优秀的表现,更符合人眼视觉的关注习惯。     

可见区分光光度法测量水体色度

针对目前水质色度测量方法的局限性,采用分光光度法测量相关色度学参数,通过色度学参数与国家标准方法的色度值建立数值关系,从而测定水体的色度值.该方法不仅保证测量结果与现行国家标准测量结果一致,而且客观性强,稳定可靠,灵敏度更高,也更准确.克服了目视比色法需要水体与标准溶液色调一致的局限性,可以推广到其他色调的废水色度测量...     

基于新型MRTD模型研究扫描型红外热像仪的视距

 基于人眼视觉系统可感知的阈值信噪比,提出预测红外热像仪最小可分辨温差（MRTD）方法。该方法根据频谱理论和红外热成像系统的二维调制传递函数,并结合匹配滤波器的概念,描述4条带标准靶的空间频谱函数。利用此空间频谱函数研究了4条带标准靶经过红外热成像系统的调制传递之后人眼视觉系统的感知信噪比。基于人眼视觉系统可感知的阈值信噪比,建立一个新的MRTD模型。依据此MRTD模型和视距模型,实现红外热像仪视距的预测。仿真结果表明,该方法预测的视距与实验数据有很好的一致性。     

基于后验概率度量的粒子滤波跟踪算法研究

 针对遮挡、光照变化、尺度变化等复杂环境中的视觉跟踪问题,提出一种基于后验概率度量的粒子滤波跟踪算法。由于后验概率指标与Bhattacharyya系数指标相比具有更强的峰值特性,采用后验概率指标作为相似性度量函数,通过粒子的更新、推广、观测、估计等步骤实现跟踪算法。通过对实际视频图像序列进行目标跟踪实验,实验结果表明:传统算法只有约50%的图像能够实现尺度自适应,而本文算法采用传统算法25%的粒子就能够收敛逼近目标的真实轨迹,达到更强的抗遮挡能力,90%以上的图像序列都能够实现良好的尺度自适应效果。