素数域[GF(P)]上椭圆曲线快速标量乘算法的研究

基于求逆转换为乘法的思想,利用仿射坐标提出了直接计算椭圆曲线上[7P]的算法,该算法运算量为I+23M+10S,比现有的算法节省了一次求逆运算,同时也给出了直接计算[7kP]的快速算法,该算法比重复计算[k]次[7P]更有效。结合多基数系统将这些新算法应用到标量乘法中,实验结果表明,在NIST推荐的椭圆曲线上,新算法的效率优于徐凯平等人所提的算法及传统的ternary-binary、3-NAF、Dimitro算法,相交处I/M可降至2.4。     

一种基于多尺度空间的图像水印算法

当前数字水印对常见的图像处理攻击如高斯噪声、中值滤波、有损压缩等具有较强的抵抗能力,但其抵抗剪切、缩放等几何攻击的能力较差。因此,为提高数字水印的鲁棒性,结合尺度不变特征变换算子和视觉感知模型,提出一种基于不变特征的水印嵌入方法。基于广义高斯分布模型对小波系数进行刻画,并根据二值假设检验理论确定水印的检测阈值,由此进一步给出水印虚警和漏警之间的工作特性曲线关系。实验仿真结果表明提出的水印方法在抗压缩、叠加噪声、缩放、剪切、旋转等攻击时具有较好的鲁棒性能。     

基于混合特征的移动机器人图像匹配算法

针对移动机器人定位过程中视觉图像处理速度慢以及特征点提取与匹配实时性、准确性差的问题,提出了基于颜色矩的改进SIFT分级图像匹配算法。首先改进SIFT算法,扩大极值点检测范围;采用Sobel算子计算特征点的梯度方向和幅值;以向量夹角为准则度量SIFT特征相似性,提高SIFT特征提取与匹配的速度和精度。图像匹配时先采用颜色矩对环境图像序列进行相似性排序,改进SIFT特征,再与排序后图像依次进行精确匹配,分级匹配提高了移动机器人的定位速度和精度。实验结果表明：与原SIFT相比,改进SIFT提高了特征向量的显著性,误匹配率降低约9.2%,特征点数量减少约20%;分级匹配提高了图像匹配速度和精度,SIFT特征计算量减小60%,总体耗时缩短40%。达到移动机器人定位实时性和鲁棒性的目的。     

基于地理信息的电力线全景图像信息融合方法

提出一种基于地理信息的电力线全景图像拼接方法。首先利用自动巡检机器人同步采集电力线的可见光图像和红外图像,采用尺度不变特征变换(SIFT)特征匹配算法分别拼接成全景图。其中红外全景图用于电力线的故障检测,可见光全景图对故障作进一步的可视化辅助检测,实现了故障信息、GPS信息和全景图像的融合,能够较精确地确定故障处的实际地理位置,提高了对电力线的维修效率。     

Rotation-Invariant Texture Classification Using Circular Gabor Wavelets Based Local and Global Features

Rotation-invariant texture classification is one of the most challenging problems in computer vision. This paper presents a new and effective method for rotation-invariant texture classification based on the circular Gabor wavelets. The Gabor representation has been shown to be optimal in the sense of minimizing the joint two-dimensional uncertainty in space and frequency. The circular Gabor filter is completely circularly symmetric. So, the circular Gabor wavelets are constructed to decompose an image into multiple scales （subbands） and are rotation invariant. Two group features can be constructed by the mean and variance of the circular Gabor filtered images, and rotation invariant. The experimental results show that the proposed method has comparatively high correct classification rates.     

基于深度学习预测有机光伏电池能量转换效率

采用一种针对有机化合物提出的类语言分子描述符对哈佛清洁能源项目数据库（CEPDB）中29000个有机太阳能电池供体分子进行描述,分子将基于最近邻子图理论被分解成片段（词）,并利用广度优先搜索算法将片段排列成一定的序列（句子）,在每个片段的信息被嵌入一个数值向量后,每个分子可表示为一个信息矩阵。在此基础上,通过一个深层神经网络提取嵌入信息,并与对应材料的光电转换效率（PCE）关联,获得了决定系数（R²）为0.97、均方误差（MSE）为0.16的预测结果。与现有方法的比较表明该方法在精度上具有竞争力。在建模过程中引入注意力机制,识别出了几个对PCE值具有决定性意义的分子片段,可为有机光伏材料的逆向设计提供指导信息。     

An architecture of deep learning in QSPR modeling for the prediction of critical properties using molecular signatures

Deep learning rapidly promotes many fields with successful stories in natural language processing. An architecture of deep neural network (DNN) combining tree-structured long short-term memory (Tree-LSTM) network and back-propagation neural network (BPNN) is developed for predicting physical properties. Inspired by the natural language processing in artificial intelligence, we first developed a strategy for data preparation including encoding molecules with canonical molecular signatures and vectorizing bond-substrings by an embedding algorithm. Then, the dynamic neural network named Tree-LSTM is employed to depict molecular tree data-structures while the BPNN is used to correlate properties. To evaluate the performance of proposed DNN, the critical properties of nearly 1,800 compounds are employed for training and testing the DNN models. As compared with classical group contribution methods, it can be demonstrated that the learned DNN models are able to provide more accurate prediction and cover more diverse molecular structures without considering frequencies of substructures.     

两传感器广义系统信息融合Kalman滤波器

基于矩阵理论和时域上的Kalman滤波理论,针对两传感器广义离散随机线性系统进行研究,运用矩阵约当分解,将一类广义系统化为正常系统,并利用标量加权准则下的最优信息融合Kalman滤波理论,给出一类两传感器广义系统Kalman信息融合滤波器,同单传感器情形相比,可大大提高滤波精度,具有实际应用价值。