Adaptive Discretization for Computerized Tomography

Two adaptive discretization frameworks are tested for computerized tomography (CT) data reconstruction. Removal of inactive pixels is primary motivation. Efficient and user independent entropy optimized masking is employed for spatial filtering purposes. Density of nodes at high gradient of reconstructed physical property is used as adaptation criterion. An alternative option, independent from noisy projection data and nature of the physical properties, is also discussed. Sensitivity analysis between the uniform and nonuniform (evolved via adaptive route) reconstruction grid reveals the utility of nonuniform grids. Iterative and transform based reconstruction techniques are used. Outcomes are tested successfully on three real world projection data from two different compact CT setups and one commercial high-resolution micro-CT scanner.     

基于多元神经网络融合的分布式资源空间文本分类研究

针对实体产业对科技资源的服务需求,以服务效应作为资源文本分类标准,提出一种基于多元神经网络融合的分布式资源空间文本分类模型。设计了包含词嵌入层、卷积层、双向门控循环单元层、注意力机制层和softmax层的多元神经网络通路;在此基础上采用基于需求—效应—资源分类策略,完成了从定性科技资源需求到定量资源服务效应求解,再到定性科技资源输出的映射变换,重点解决了分布式科技资源局部和全局语义特征形式多样、文本长距离依赖特征显著、重要资源信息难以准确识别的问题,进而从分布式科技资源空间中快速准确地获取效应知识,提升实体产业产品研发效率和创新能力;通过万方专利科技资源数据集验证了所提方法的可行性和有效性,为更加全面地挖掘资源文本特征和按需服务实体产业提供了一种新的思路和手段。     

Beta-Binomial删除信道中的喷泉码文件传输协议

由于命名数据网络(NDN,Named-Data Networking)无环路、逐包、逐跳转发的特点,使得数据包回传成功率降低,而传统的TCP/IP协议中的ARQ和ACK机制对于多播会话不再适用.由于NDN中的传输信道可以等效为二进制删除信道,因此可以通过应用层编码来实现文件的可靠传输.传统的信道编码技术如卷积码、级联码和RS码等复杂度较高,而将NDN与低复杂度的喷泉码的结合可以实现分布式的存储架构,因而可通过喷泉编码在应用层协议中实现可靠的纠删机制,保证整体文件的传输可靠性.以往的研究一般是基于确定的删除概率信道模型,但是由于网络的异构性和信道噪声等因素影响,可能会造成信道丢包概率呈随机性分布.因此,本文在Beta-Binomial分布模型的前提下,根据贝叶斯统计的先验信息和中心极限定理,对随机概率下的纠删信道的文件可靠传输协议进行了数学建模和理论推导.仿真结果显示此模型更具有普适性,此传输协议可在信道状况未知的前提下从理论上求出最小发包数,减少冗余编码包,提高文件整体的投递成功率,在保证传输可靠性的同时有效提升协议传输效率.     

信息技术在医院病案统计中的应用

伴随着我国综合实力的不断提升,信息技术获得了突飞猛进的发展,如今,医疗改革如火如荼地进行,医院的信息管理工作同以往相比较也实现了极大的发展。病案统计工作是指相关工作人员对医院中患者的信息进行全面统计,若是这项工作完成得足够出色,会给医院后续的信息查询工作带来诸多方便和快捷,也能够在某种程度上使医院管理效率和质量实现一定幅度的提升。因此,将信息技术和医院的病案工管理工作进行密切结合,就成为病案管理人员应当重点思考的问题。病案统计工作是医院管理工作中的一个重点环节,病案统计结果能够为医院整体绩效带来十分深远的影响。因此,投入更多时间和精力来提高对这项工作的重视,显得十分关键和重要。文章对信息技术在医院病案统计中的应用展开了分析。     

A deep neural network based classifier for brain tumor diagnosis

Classification process plays a key role in diagnosing brain tumors. Earlier research works are intended for identifying brain tumors using different classification techniques. However, the False Alarm Rates (FARs) of existing classification techniques are high. To improve the early-stage brain tumor diagnosis via classification the Weighted Correlation Feature Selection Based Iterative Bayesian Multivariate Deep Neural Learning (WCFS-IBMDNL) technique is proposed in this work. The WCFS-IBMDNL algorithm considers medical dataset for classifying the brain tumor diagnosis at an early stage. At first, the WCFS-IBMDNL technique performs Weighted Correlation-Based Feature Selection (WC-FS) by selecting subsets of medical features that are relevant for classification of brain tumors. After completing the feature selection process, the WCFS-IBMDNL technique uses Iterative Bayesian Multivariate Deep Neural Network (IBMDNN) classifier for reducing the misclassification error rate of brain tumor identification. The WCFS-IBMDNL technique was evaluated in JAVA language using Disease Diagnosis Rate (DDR), Disease Diagnosis Time (DDT), and FAR parameter through the epileptic seizure recognition dataset.     

Bioecology of macrobenthic communities in the microtidal monsoonal Kodungallur–Azhikode Estuary,southwest coast of India

A comprehensive study of the macrobenthic communities in the Kodungallur–Azhikode Estuary (KAE) was conducted during 2009–2011 period. A total of 18,846 organisms were collected, with 60% being malacostracans, followed by polychaetes (20%), molluscs (9%) and ‘others’ group (11%). A total of 79 species in 71 genera belonging to 49 families were identified, with 33 spp. being polychaetes, 26 spp. being malacostracans, 11 spp. being molluscs and 9 spp. being in the ‘others’ group. A single species of opportunistic amphipod (Americorophium triaeonyx) comprised more than 62.05% of the total numerical abundance of macrobenthos. The other dominant species were Obelia bidentata, Arcuatula senhousia, Cirolana fluviatilis, Prionospio cirrifera and Capitella sp. Canonical correspondence analysis (CCA) results indicated environmental parameters such as water column salinity, turbidity, sediment Eh, substratum type, chlorophyll‐a concentration, depth and organic matter in sediment were the significant factors influencing the distribution of macrobenthic species in the KAE. The present study provides baseline information for future KAE benthic studies.     

Extraction of Distinguished Hyperbolic Trajectories for 2D Time-Dependent Vector Field Topology

This paper does two main contributions to 2D time-dependent vector field topology. First, we present a technique for robust, accurate, and efficient extraction of distinguished hyperbolic trajectories (DHT), the generative structures of 2D time-dependent vector field topology. It is based on refinement of initial candidate curves. In contrast to previous approaches, it is robust because the refinement converges for reasonably close initial candidates, it is accurate due to its adaptive scheme, and it is efficient due to its high convergence speed. Second, we provide a detailed evaluation and discussion of previous approaches for the extraction of DHTs and time-dependent vector field topology in general. We demonstrate the utility of our approach using analytical flows, as well as data from computational fluid dynamics.