Multisensor Data Fusion for High Quality Data Analysis and Processing in Measurement and Instrumentation

Multisensor data fusion （MDF） is an emerging technology to fuse data from multiple sensors in order to make a more accurate estimation of the environment through measurement and detection. Applications of MDF cross a wide spectrum in military and civilian areas. With the rapid evolution of computers and the proliferation of micro-mechanical/electrical systems sensors, the utilization of MDF is being popularized in research and applications. This paper focuses on application of MDF for high quality data analysis and processing in measurement and instrumentation. A practical, general data fusion scheme was established on the basis of feature extraction and merge of data from multiple sensors. This scheme integrates artificial neural networks for high performance pattern recognition. A number of successful applications in areas of NDI （Non-Destructive Inspection） corrosion detection, food quality and safety characterization, and precision agriculture are described and discussed in order to motivate new applications in these or other areas. This paper gives an overall picture of using the MDF method to increase the accuracy of data analysis and processing in measurement and instrumentation in different areas of applications.     

A novel feature extraction approach for microarray data based on multi-algorithm fusion

Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with reduced complexity and improved generalization capabilities. Depending on the purpose of gene selection, two types of feature extraction algorithms including ranking-based feature extraction and set-based feature extraction are employed in microarray gene expression data analysis. In ranking-based feature extraction, features are evaluated on an individual basis, without considering inter-relationship between features in general, while set-based feature extraction evaluates features based on their role in a feature set by taking into account dependency between features. Just as learning methods, feature extraction has a problem in its generalization ability, which is robustness. However, the issue of robustness is often overlooked in feature extraction. In order to improve the accuracy and robustness of feature extraction for microarray data, a novel approach based on multi-algorithm fusion is proposed. By fusing different types of feature extraction algorithms to select the feature from the samples set, the proposed approach is able to improve feature extraction performance. The new approach is tested against gene expression dataset including Colon cancer data, CNS data, DLBCL data, and Leukemia data. The testing results show that the performance of this algorithm is better than existing solutions.     

Deep learning a boon for biophotonics?

This review covers original articles using deep learning in the biophotonic field published in the last years. In these years deep learning, which is a subset of machine learning mostly based on artificial neural network geometries, was applied to a number of biophotonic tasks and has achieved state‐of‐the‐art performances. Therefore, deep learning in the biophotonic field is rapidly growing and it will be utilized in the next years to obtain real‐time biophotonic decision‐making systems and to analyze biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement. Additionally, we discuss the potential use of deep learning for spectroscopic data including spectral data preprocessing and spectral classification. We conclude this review by addressing the potential applications and challenges of using deep learning for biophotonic data.     

Use of artificial neural networks for analysis of the factors affecting particle size in mebudipine nanoemulsion

In this study, a nanoemulsion containing mebudipine [composed of ethyl oleate (oil phase), Tween 80 (T80), Span 80 (S80) (surfactants), polyethylene glycol 400, ethanol (cosurfactants), and deionized water] was prepared with the aim of improving its bioavailability for an effective antihypertensive therapy. Particle size of the formulation was measured by dynamic light scattering. Then, artificial neural networks were used in identifying factors that influence the particle size of the nanoemulsion. Three variables, namely, amount of surfactant system (T80?+?S80), amount of polyethylene glycol, and amount of ethanol as cosurfactants, were considered as input values and the particle size was used as output. The developed model showed that all the three inputs had some degrees of effect on particles size: increasing the value of each input decreased the size. Furthermore, amount of surfactant was found to be the dominant factor in controlling the final particle size of nanoemulsion.

Communicated by Ramaswamy H. Sarma     

神经网络自编码器算法在癌症信息学研究中的应用

癌症已经被广泛认为是高度异质性的疾病,癌症的早期诊断、分型和预后已成为癌症研究的关注重点.在大数据时代,对海量癌症生物医学数据进行高效的数据挖掘是生物信息学面临的重要挑战.自编码器(Autoencoder)作为神经网络的一种典型模型,能够通过无监督的方式高效地学习输入数据的特征,进而对生物数据进行整合与挖掘.文中首先介...     

Image analysis of low magnification images of fine needle aspirates of the breast produces useful discrimination between benign and malignant cases

Image analysis of low magnification images of fine needle aspirates of the breast produces useful discrimination between benign and malignant cases
Fine needle aspirates of the breast (FNAB) ( n =362; 204 malignant, 158 benign), prepared by cytocentrifuge methods and stained by the Papanicolaou technique, were analysed using a semi‐automated image analysis system at a low magnification which precluded resolution of nuclear detail. The measured parameters were integrated optical density, fractal textural dimension, number of cellular objects (single cells and contiguous groups of cells), distance between cellular objects (mean, s.d., skewness and kurtosis), area of cellular objects (mean, s.d., skewness, kurtosis) and the nearest neighbour statistic. The cases were divided into a 200‐case training set and a 162‐case test set. Analysis was performed by logistic regression and the multi‐layer Perceptron type of artificial neural network. Logistic regression and the neural network produced similar performances with a sensitivity of 82–83%, specificity 85% and a positive predictive value for a malignant result of 85%. A non‐parametric analysis of all the predictor variables showed that all except the mean area of cellular objects and the s.d. of this measurement were significant discriminants ( P <0.05), but most were highly interrelated and this was reflected in the selection of only three predictor variables by forward and backward conditional logistic regression. This study shows that much diagnostic information is present in low power views of FNAB, and that image analysis could form the basis of a semi‐automated decision‐support aid.     

Artificial neural network based experimental design procedure for enhancing fermentation development

Conventional experimental design techniques are available to assist in the optimization of fermentation processes, but due to the nonlinearities in the bioprocess, they are limited in their effectiveness. This problem is further complicated with recombinant systems as a result of the additional complexities of the process. This article describes a general strategy using artificial neural networks as an alternative approach to fermentation process development laboratory are presented for the neural network based procedures. (c) 1994 John Wiley & Sons, Inc.     

基于神经网络的生化过程预估优化控制

综观历史与现状,生化过程数学模型的建立是很困难的。不多的一些数学模型也往往由于精度低,应用范围窄而无法在实际中应用。这是由于生化过程的机理非常复杂,具有高度非线性和时变特性。并且不同于一般物理过程的是生化过程是个物理上不可逆的过程。近几年来,人工神经网络(ANN)得到了迅速发展,并被广泛应用到各个领域。同样,ANN也为生化过程控制提供了一种新方法。本文以工业生产中发酵过程的补料控制为例,叙述了ANN如何用干生化过程预估和优化控制。相应地,对结果进行了分析讨论。     

PLS-ANN判别分析自体荧光光谱识别胃癌

本文对58例胃癌病人离体标本的癌浆膜和正常浆膜进行以308nm为激发光的自体荧光光谱检测,采用多因素分析法进行光谱信息提取,以识别胃癌。研究表明偏最小二乘法结合神经网络法（简称PLS—ANN）进行判别分析,诊断胃癌的灵敏度为86％,特异度为100％,准确率为93％,有望成为手术中快速识别胃癌在胃壁的浸润范围的有效方法。     

基于多源遥感数据的生态保护修复项目区监测方法评述

回顾了山水林田湖草生态保护修复项目的实施背景,针对生态保护修复项目监测监管范围广、技术难等问题,强调了基于多源遥感数据开展项目遥感监测的重要性与必要性。从监测指标拟定、遥感地物信息提取、多源遥感数据融合、动态变化检测等方面评述了基于多源遥感数据的生态保护修复项目区监测方法,包括基于中高空间分辨率遥感数据的地物信息提取、融合机器学习的非线性混合像元分析、基于混合像元分析的时空融合等。在总结技术和工作推进方面的优势、局限基础上,提出要结合实际工作,持续优化国土空间生态保护修复监测指标;充分挖掘遥感数据解析的相关算法潜力,提升地物信息提取和混合像元分析的精度;加强时空融合算法与变化检测方法的研究探索,加强相关方法的实践应用;以“山水林田湖草生态保护修复工程试点”项目为平台,建立稳定的国土空间生态保护修复遥感监测运行机制,加强科技创新,形成技术标准,指导工作开展。