用于植物病虫害诊断的多光谱成像系统

准确重建被测目标的颜色信息对实现可靠的植物病虫害诊断具有十分重要的意义。文章提出把多光谱成像技术应用于植物病虫害诊断,所采集的多光谱图像可以从光谱维和图像维反映被测目标的特征信息。在此基础上,实验采用16个窄带滤色片、单色面阵CCD、积分球混合光源照明和标准观测环境建立了能进行适时、无损检测的多光谱成像系统。并利用该设备对Macbeth色卡中8个色卡进行光谱和颜色重建,重建的结果与光谱辐射度计的测量结果进行了比较。通过对光谱匹配角度和CIE标准色差分析,证明这种多光谱成像系统能够准确、稳定地重建出目标的光谱信息和颜色信息。     

扫描多光谱显微成像

文本描述了一种新的显微成像方法,它把光学扫描显微成像技术与成像多光谱技术巧妙地结合在一起,形成扫描多光谱显微成像技术.文中叙述了系统的结构及工作原理,介绍了所采用的图像处理系统,给出了实验结果,并进行了分析讨论.     

自适应多光谱光声成像技术研究

光声成像技术是利用激光照射组织产生超声波成像的新型医学影像技术.在传统光声成像中,由于组织体内复杂的成分与环境会对入射光波产生较大的扰动而导致波前畸变、图像分辨率下降,从而降低诊断的准确性.为了克服这一影响,本文提出了一种自适应多光谱光声成像技术.该技术利用自适应光学技术可有效地降低组织对光波扰动的影响,提高系统成像分辨率与图像对比度.此外,该系统还融合了多光谱成像技术,可在多种波长下对目标成像,从而更好地进行组织结构识别、组分分析等.实验结果表明,该系统十分适用于复杂的生物组织光声成像,可极大地增强光声成像性能,在生物医学领域具有广阔的应用前景.     

成像光谱技术在农作物信息诊断中的研究进展

传统的农作物信息诊断方法存在劳动强度大、诊断时间长、操作技术要求高、受人为主观因素影响大等缺陷,限制了农作物信息诊断的实时性和准确性.成像光谱技术能够通过IHl时获得农作物的图像以及光谱信息.实现对农作物生长状况、病虫害等信息的快速、无损检测,已在作物信息诊断中得到越来越广泛的应用,为农业的信息化提供了技术支持.文章概述了成像光谱技术的原理,重点介绍了其在农作物种子成分检测、种子品种分类、种子病虫害检测、田问植株长势监测、田间植株病虫害检测中的国内外最新研究进展,分析了成像光谱技术应用于农作物信息诊断的难点,并对其发展方向进行了展望.     

利用成像光谱技术的光纤光栅多传感器复用

根据光纤光栅多传感器复用时信号的光谱特征，提出了采用成像光谱技术对复用信号进行解调的方案。在分析了成像光谱复用方案原理、推导了其测量分辨率及复用能力等主要性能指标的基础上，给出了验证实验系统及相应实验的结果。     

基于多光谱成像的光谱反射率重建

一些对颜色重现要求较高的应用领域需要获取目标表面上各点的光谱反射率。大多数天然物质表面的光谱反射率曲线比较平滑,可视为几个基向量的线性组合。基于这一原理的多光谱成像技术可以准确快速地重建目标表面的光谱反射率。通过对NCS色卡进行主成分分析,得出了彩色印刷品光谱反射率的基向量。建立了一个多光谱成像系统,用以比较基向量个数不同对反射率重建效果的影响。     

面向多光谱卫星成像的广义光谱超分辨率

针对多种多光谱卫星成像模式,对原始光谱超分辨率概念进行扩充,提出一种联合数据驱动与模型驱动的深度学习算法。模拟构建的多个数据集,讨论了不同多光谱成像模式下的光谱超分辨率之间的差异,验证了所构建算法的稳健性,提高了现有多光谱卫星影像利用率,对光谱超分辨率的更广泛情形进行了概括。     

基于多光谱遥感成像链模型的系统信噪比分析

信噪比(SNR)是评价多光谱遥感成像性能的重要指标,在设计多光谱遥感成像仪的最初阶段应进行分析,从而确定各分系统相关参数。多光谱遥感系统的成像链模型综合考虑辐射源、地物光谱反射、大气辐射传输、光学系统成像、分光元件特性、探测器光谱响应和相机噪声等各个环节,可用于进行成像过程端对端的完整分析。以基于滤光片阵列的多光谱遥感系统为例,采用MODTRAN软件进行大气辐射传输计算,对不同太阳天顶角下,不同目标地物计算像面的照度,根据电荷耦合器件探测器的噪声模型,计算出不同工作条件下多光谱遥感系统的SNR。通过对SNR的分析,可给出该类型多光谱遥感系统获得最佳性能的工作条件,并能够结合使用要求进行光学系统参数的优化选择。     

高速多光谱 TDI CCD 成像电路系统

为实现多光谱TDI CCD的高速高信噪比成像,利用可空间应用的多光谱TDI CCD传感器研制出了高性能成像电路系统。该系统以现场可编程门阵列（ FPGA）为核心逻辑单元,带有RS422外围通信控制接口,并采用CAMERALINK接口输出图像数据。系统具有动态推扫成像的能力,可同时输出全色和彩色两种模式的图像数据。利用灰度条纹的靶标对传感器的3个多光谱（ R、G、B）感光区标定白平衡,利用彩色条纹的靶标对系统进行成像测试,在驱动频率为15 MHz的情况下,系统单片CCD输出的图像数据率达到1.2 Gbps。试验结果表明,获取全色图像的信噪比达到了53.56 dB,各多光谱图像的信噪比较高的也在40 dB以上,满足空间对地高分辨多光谱遥感成像的技术指标要求,对高速空间多光谱遥感相机的研制具有借鉴意义。     

基于多光谱成像技术的宫颈脱落细胞DNA定量分析研究

常用的宫颈癌筛查方法有TBS(the bethesda system)分类法和细胞DNA定量分析法两种,而同时利用多重染色方法在同一张细胞涂片上对细胞质进行巴氏染色和对细胞核进行Feulgen染色进行宫颈癌筛查的研究仍然是空白。多重染色筛查方法的难点在于非DNA物质的吸光度会干扰DNA物质的吸光度。因此建立了一套多光谱成像系统,并基于吸光度的线性叠加特性,利用多元线性回归方法建立了吸光度剥离模型,通过该模型成功地将DNA物质的吸光度剥离出来进行DNA定量分析,实现了两种常用方法的完美结合。通过一系列实验证明了,利用模型剥离出的DNA物质的吸光度与实测的DNA物质的吸光度在检验水平为1%的情况下在统计学上没有显著差异,而且实际应用测试中的统计数据也显示在置信水平为99%的情况下使用该分析方法筛查得到的四倍体细胞的DNA指数的置信区间与癌细胞的DNA指数判别区间没有交集,验证了这种基于多光谱成像技术的多重染色的DNA定量分析方法的准确性和可行性,在宫颈癌乃至其他癌症的早期诊断和筛查中有巨大的市场应用潜力与广阔的应用前景。     

基于光谱图像的森林病虫害自动检测方法

我国天然林区分布范围广,地形复杂,依靠传统的护林员巡检方式进行林木病虫害防治,效率较低,难于及时发现早期的林木病虫害,可能因此错过防治的最佳时机。针对该问题,设计了一种基于多光谱图像检测林木病虫害的深度学习网络,研发了一套检测软件,通过无人机挂飞实验,利用搭建的深度学习网络,完成林区染病区检测,对检测结果进行了分析。     

粉末涂料电脑配色的人工神经网络模型

提出一种基于多层BP人工神经网络的粉末涂料配方预测模型;用BP算法人工神经网络建立粉末涂料反射样品的标准色度参数与配方浓度参数之间的映射关系。把人工神经网络的配方预测模型应用到典型的粉末涂料样品的测配色实验过程中。实验结果表明,基于多隐层BP网的模型可以实现粉末涂料样品的配方浓度空间与标准三刺激值颜色空间的相互映射,对64个节点的平均训练精度达到了1个CIELAB色差单位。     

应用BP神经网络识别内层表面细小缺陷的研究

内层表面细小凹坑的识别是超声无损评估的一个难点。本文利用人工神经网络对于信号的分类功能,建构和训练了一个BP神经网络,并用它对尺寸为1mm的圆锥形和半球形两种凹坑成功地进行了识别。研究表明,应用凹坑回波的DCT谱作为缺陷特片输入,可使BP神经网络的训练和缺陷识别既快捷又有效。     

Particle identification using artificial neural networks at BESⅢ

A multilayered perceptrons' neural network technique has been applied in the particle identification at BESⅢ. The networks are trained in each sub-detector level. The NN output of sub-detectors can be sent to a sequential network or be constructed as PDFs for a likelihood. Good muon-ID, electron-ID and hadron-ID are obtained from the networks by using the simulated Monte Carlo samples.     

涂料颜色配方预测的人工神经网络模型实验研究

人工神经网络技术近年来在颜色空间变换或色域映射的研究中显示了特有的效果。人工神经网络用于粉末涂料配方的预测,其特点是用BP算法神经网络建立粉末涂料反射样品的标准色度参数与配方浓度参数之间的映射关系。通过对典型颜色样本训练和预测,结果表明基于多隐层BP网的模型可以实现粉末涂料样品的配方浓度空间与标准三刺激值颜色空间的相互映射,即用BP网络实现粉末涂料的配方预测。     

变参数混沌时间序列的神经网络预测研究

研究一类复杂变参数混沌系统时间序列的预测问题.首先构造一个变参数Logistic映射,分析变参数混沌系统的特点,指出动力学特征不断变化的这类系统不存在恒定形状的吸引子;结合Takens嵌入定理和神经网络理论,阐述神经网络方法预测具有恒定吸引子形状的混沌系统可行的原因,分析研究其用于预测变参数混沌系统的潜在问题.变参数Ikeda系统的神经网络预测试验验证了理论分析结果,试验还表明,简单增大预测训练样本数可能降低泛化预测精度,训练集的选择对这类系统的泛化预测效果影响极大,指出混沌时间序列预测实用化必须研究解决这类变参数混沌系统的预测. 关键词： 混沌 预测 神经网络 变参数系统     

Detection of material property errors in handbooks and databases using artificial neural networks with hidden correlations

The authors have discovered a systematic, intelligent and potentially automatic method to detect errors in handbooks and stop their transmission using unrecognised relationships between materials properties. The scientific community relies on the veracity of scientific data in handbooks and databases, some of which have a long pedigree covering several decades. Although various outlier-detection procedures are employed to detect and, where appropriate, remove contaminated data, errors, which had not been discovered by established methods, were easily detected by our artificial neural network in tables of properties of the elements. We started using neural networks to discover unrecognised relationships between materials properties and quickly found that they were very good at finding inconsistencies in groups of data. They reveal variations from 10 to 900% in tables of property data for the elements and point out those that are most probably correct. Compared with the statistical method adopted by Ashby and co-workers [Proc. R. Soc. Lond. Ser. A 454 (1998) p. 1301, 1323], this method locates more inconsistencies and could be embedded in database software for automatic self-checking. We anticipate that our suggestion will be a starting point to deal with this basic problem that affects researchers in every field. The authors believe it may eventually moderate the current expectation that data field error rates will persist at between 1 and 5%.     

Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks

Ultrasonic pulse velocity technique is one of the most popular non-destructive techniques used in the assessment of concrete properties. However, it is very difficult to accurately evaluate the concrete compressive strength with this method since the ultrasonic pulse velocity values are affected by a number of factors, which do not necessarily influence the concrete compressive strength in the same way or to the same extent. This paper deals with the analysis of such factors on the velocity-strength relationship. The relationship between ultrasonic pulse velocity, static and dynamic Young’s modulus and shear modulus was also analyzed. The influence of aggregate, initial concrete temperature, type of cement, environmental temperature, and w/c ratio was determined by our own experiments. Based on the experimental results, a numerical model was established within the Matlab programming environment. The multi-layer feed-forward neural network was used for this purpose. The paper demonstrates that artificial neural networks can be successfully used in modelling the velocity-strength relationship. This model enables us to easily and reliably estimate the compressive strength of concrete by using only the ultrasonic pulse velocity value and some mix parameters of concrete.     

Composite MR image reconstruction and unaliasing for general trajectories using neural networks

In rapid parallel magnetic resonance imaging, the problem of image reconstruction is challenging. Here, a novel image reconstruction technique for data acquired along any general trajectory in neural network framework, called “Composite Reconstruction And Unaliasing using Neural Networks” (CRAUNN), is proposed. CRAUNN is based on the observation that the nature of aliasing remains unchanged whether the undersampled acquisition contains only low frequencies or includes high frequencies too. Here, the transformation needed to reconstruct the alias-free image from the aliased coil images is learnt, using acquisitions consisting of densely sampled low frequencies. Neural networks are made use of as machine learning tools to learn the transformation, in order to obtain the desired alias-free image for actual acquisitions containing sparsely sampled low as well as high frequencies. CRAUNN operates in the image domain and does not require explicit coil sensitivity estimation. It is also independent of the sampling trajectory used, and could be applied to arbitrary trajectories as well. As a pilot trial, the technique is first applied to Cartesian trajectory-sampled data. Experiments performed using radial and spiral trajectories on real and synthetic data, illustrate the performance of the method. The reconstruction errors depend on the acceleration factor as well as the sampling trajectory. It is found that higher acceleration factors can be obtained when radial trajectories are used. Comparisons against existing techniques are presented. CRAUNN has been found to perform on par with the state-of-the-art techniques. Acceleration factors of up to 4, 6 and 4 are achieved in Cartesian, radial and spiral cases, respectively.