小球藻、球等鞭金藻和螺旋藻生物量高光谱成像的可视化研究

微藻高效培养是微藻生物能源开发利用的关键和前提,而在营养充足的培养条件下生长迅速但较易受到环境污染和影响,因此微藻生长过程中对其生长状况进行监测意义重大。高光谱成像技术同时拥有丰富物质品质信号的优点和图像包含丰富品质分布空间信息的优点,可为微藻的快速无损检测提供新的方法和手段。分别采集小球藻、球等鞭金藻和螺旋藻三种微藻各45个样本的高光谱图像,并提取样本感兴趣区域(ROI)的平均光谱。利用连续投影算法(SPA)波长优选之后,取30个建模集样本的光谱数据与其相应的生物量建立多元线性回归(MLR)模型,对15个预测集样本的生物量进行预测,小球藻、球等鞭金藻和螺旋藻预测相关系数(r)分别为0.950,0.969和0.961,预测均方根误差(RMSEP)为0.010 2,0.010 7和0.017 1,获得了较好的预测精度。最后,用所建MLR模型对预测集图像上每个像素点的生物量加以预测,采用Matlab图像编程处理将不同的生物量用不同的颜色表示,最终以伪彩图的形式实现藻液生物量的可视化。研究结果表明,高光谱成像技术对小球藻和螺旋藻藻液生物量的可视化效果较好,对球等鞭金藻的预测效果还需要进一步改进。本研究为实现微藻生长信息的快速获取和进一步开展微藻生物质能源利用奠定了一定的研究基础。     

Matrix approaches to approximate solutions of variational inequalities in Hilbert spaces

A matrix approach to approximating solutions of variational inequalities in Hilbert spaces is introduced. This approach uses two matrices: one for iteration process and the other for regularization. Ergodicity and convergence (both weak and strong) are studied. Our methods combine new or well-known iterative methods (such as the original Mann’s method) with regularized processes involved regular matrices in the sense of Toeplitz.     

Statistical and Visual Analysis of Audio,Text, and Image Features for Multi-Modal Music Genre Recognition

We present a multi-modal genre recognition framework that considers the modalities audio, text, and image by features extracted from audio signals, album cover images, and lyrics of music tracks. In contrast to pure learning of features by a neural network as done in the related work, handcrafted features designed for a respective modality are also integrated, allowing for higher interpretability of created models and further theoretical analysis of the impact of individual features on genre prediction. Genre recognition is performed by binary classification of a music track with respect to each genre based on combinations of elementary features. For feature combination a two-level technique is used, which combines aggregation into fixed-length feature vectors with confidence-based fusion of classification results. Extensive experiments have been conducted for three classifier models (Naïve Bayes, Support Vector Machine, and Random Forest) and numerous feature combinations. The results are presented visually, with data reduction for improved perceptibility achieved by multi-objective analysis and restriction to non-dominated data. Feature- and classifier-related hypotheses are formulated based on the data, and their statistical significance is formally analyzed. The statistical analysis shows that the combination of two modalities almost always leads to a significant increase of performance and the combination of three modalities in several cases.     

DISCRETE GRADIENT METHOD FOR IMAGE-BASED BIOMECHANICAL STRESS ANALYSIS 1)

介绍一种可用于计算生物力学的离散梯度方法,此方法可利用离散的点云模型直接进行数值模拟分析而不需要传统的几何模型。将离散梯度法应用于点云模型需要首先确定模型中点之间的相邻关系和每个点所分配的材料体积,然后通过用广义的有限差分的形式定义了梯度插值向量,并以此向量来近似函数在每个离散点上的梯度。从弱形式出发,推导建立了适用于弹性固体大变形问题的求解器,并具有和有限元法中双线性四边形单元一致的准确性和收敛性。着重描述了一种可以从医学图像中快速提取材料点并建立点云模型的方法,以及利用三角划分和重心划分确定材料点之间的相邻关系和每个材料点体积的具体过程。通过腹主动脉瘤膨胀的静力学模拟分析,展示了离散梯度法的实用性和准确性。该算法实现了基于医学图像进行生物力学分析的过程自动化,为病体特异性的研究和治疗提供便利和实用的工具。