GIS在基础教育中的应用

从信息技术与学科整合的角度,借鉴了GIS在其它领域和国内外教育领域中的应用,设计了由地理、数学、信息技术(IT)等学科数据库和教育综合信息数据库组成的基础教育系统,详细分析了GIS在基础教育系统中的功能,构造了GIS在基础教育系统中的结构,探讨了GIS在基础教育系统中的地理教学桌面系统、空间数据管理系统、因特网服务系统3个关键部分的实现,并展望了GIS在基础教育中的应用.     

小波分析在图像处理中的应用

简要介绍了小波分析的基本原理和分类,及其在图像处理中的应用。重点论述了小波分析在图像增强、降噪、压缩和融合中的应用。通过实例充分说明了小波在图像处理中的实用价值与工程作用。     

生物信息智能化处理的进展

回顾了人工智能在生物信息处理中的进展,重点介绍了神经网络、符号机器学习和遗传算法在处理生物信息中的应用情况.每种技术都列举了相应的例子,包括了蛋白质折叠预测、病毒蛋白酶分裂预测、分类、序列联配、微集阵列基因表达分析等.同时也介绍了其它一些人工智能技术在生物信息学中的应用动向.     

色彩在网站页面设计中的应用探讨

本文首先从色彩的表现力和象征性两方面论述了其在网站页面设计中的重要作用,然后介绍了网站网页色彩设计中的简洁性、主从性、均衡性等原则,最后对色彩在网站页面中的具体运用做了详细地探讨。     

物联网在现代物流业中的应用研究

物流是物联网较早落地的行业之一,物联网的应用从根本上提高了对物品生产、配送、仓储、销售等环节的监控水平。文中通过对物联网体系构架的分析,研究了物联网在物流业中的主要应用领域及其在物流业中的推广障碍,探讨了物联网在物流业中的应用发展趋势。     

嵌入式LINUX在物联网中的应用

本文首先对嵌入式系统的含义进行了简要介绍,其中包括嵌入式LINUX所具有的开源、内核小、效率高等多方面优势;然后介绍了物联网的概念及其四个关键技术;最后对嵌入式LINUX在物联网中的应用进行了分析,其中主要包括嵌入式智能传感器在物联网中的应用、嵌入式RFID技术在物联网的应用以及嵌入式智能技术在物联网中的应用,其中嵌入式智能技术结合了在家居安防系统中的应用进行分析。希望本文的研究,能够为我国嵌入式LINUX在物联网中的实际应用提供一定的理论参考。     

中间件技术在无线网络中的应用研究

该文介绍了中间件技术的产生、定义、分类、优点及中间件技术在无线网络中的应用等五个方面阐述了中间件技术在无线网络系统中的应用。     

分布式网络技术的实时性分析

分析了总线转发、DMA、共享内存以及传统的局域网技术在分布式多处理应用中的优缺点，介绍了RTnet在高速实时网络应用中的优势。     

演绎对象数据库SD-DOOD中集合的应用和实现

基于复杂对象的逻辑推理一直是演绎对象数据库中的研究热点。论文叙述了集合在演绎对象数据库中的应用和实现技术,重点介绍了部分集在规则中的两个作用:列举集合中的所有元素和聚集集合中所有元素。同时讨论了完整集的交、并、划分和差等运算的实现思想。     

JX-300XP系统在300m3/h焦炭制一氧化碳中的应用

介绍了JX-300XP系统在300m3/h.焦炭制一氧化碳中的应用.主要介绍了DCS系统在该装置中的基本结构、硬件组成、系统组态、网络通讯等以及工艺的主要工艺流程的测点、调节回路、连锁控制等.该自控系统在本装置中的应用,提高了生产的自动化水平,保证了生产的连续性和稳定性.     

基于多层结构模型的生物信息分析平台研究*

针对生物信息分析平台的构建,给出一种复合C/S、B/S的多层体系结构模型--BIOCMSM,并以构建新城疫病毒(NDV)生物信息分析平台为例,研究了该多层结构模型的实现过程.实验证明,BIOCMSM较好地解决了生物数据更新、数据集成、应用集成等问题.     

生物信息流中序列分析软件的设计与开发

介绍了基于Java语言的跨平台生物信息分析平台的设计与实现过程，并对软件系统结构、功能模块、关键技术进行了阐述。该系统能完成生物信息流中序列分析工作。它的开发为从事生物信息学研究的人员提供了有效的数据处理和分析工具。     

Applications of Fuzzy Logic in Bioinformatics (Dong Xu et al.; 2008) [Book review]

This book is entirely focused on explaining how fuzzy concepts and approaches can be useful in bioinformatics. It is organized into seven chapters and two very useful appendices. Some of the topics covered include: the fundamental concepts of fuzzy set theory and fuzzy logic; specific examples of using fuzzy set theory and fuzzy logic to address bioinformatics analysis and modeling of data; analysis of microarray data; and future applications and directions for fuzzy computational approaches in molecular biology. Appendix I explains the fundamental biological concepts that are relevant to the biological subjects covered in the book and Appendix II lists and describes a wide variety of free online resources related to molecular biology, bioinformatics, and fuzzy set theory.Researchers in bioinformatics and fuzzy set theory and fuzzy logic will find this book to be an important resource for developing interdisciplinary research between the two fields. Educators can also use this book for a graduate course to both introduce the two fields and stimulate research ideas based on the currently active research topics presented.     

A case study of tuning MapReduce for efficient Bioinformatics in the cloud

The combination of the Hadoop MapReduce programming model and cloud computing allows biological scientists to analyze next-generation sequencing (NGS) data in a timely and cost-effective manner. Cloud computing platforms remove the burden of IT facility procurement and management from end users and provide ease of access to Hadoop clusters. However, biological scientists are still expected to choose appropriate Hadoop parameters for running their jobs. More importantly, the available Hadoop tuning guidelines are either obsolete or too general to capture the particular characteristics of bioinformatics applications. In this study, we aim to minimize the cloud computing cost spent on bioinformatics data analysis by optimizing the extracted significant Hadoop parameters. When using MapReduce-based bioinformatics tools in the cloud, the default settings often lead to resource underutilization and wasteful expenses. We choose k-mer counting, a representative application used in a large number of NGS data analysis tools, as our study case. Experimental results show that, with the fine-tuned parameters, we achieve a total of 4× speedup compared with the original performance (using the default settings). This paper presents an exemplary case for tuning MapReduce-based bioinformatics applications in the cloud, and documents the key parameters that could lead to significant performance benefits.     

A cost saving and load balancing task scheduling model for computational biology in heterogeneous cloud datacenters

The Journal of Supercomputing - Cloud-based scientific workflow systems can play an important role in the development of cost-effective bioinformatics analysis applications. There are differences...     

Pattern recognition techniques for the emerging field of bioinformatics: A review

The emerging field of bioinformatics has recently created much interest in the computer science and engineering communities. With the wealth of sequence data in many public online databases and the huge amount of data generated from the Human Genome Project, computer analysis has become indispensable. This calls for novel algorithms and opens up new areas of applications for many pattern recognition techniques. In this article, we review two major avenues of research in bioinformatics, namely DNA sequence analysis and DNA microarray data analysis. In DNA sequence analysis, we focus on the topics of sequence comparison and gene recognition. For DNA microarray data analysis, we discuss key issues such as image analysis for gene expression data extraction, data pre-processing, clustering analysis for pattern discovery and gene expression time series data analysis. We describe current methods and show how computational techniques could be useful in these areas. It is our hope that this review article could demonstrate how the pattern recognition community could have an impact on the fascinating and challenging area of genomic research.     

生物信息学推荐系统的设计与实现*

在生物信息学系统设计中引进推荐系统,提出具有个性化服务的生物信息学网站模型,完成生物信息学推荐系统的设计和实现,体现出推荐系统在生物信息学中使用的必要性和优越性.     

Grammatical inference with bioinformatics criteria

In this paper we describe both the theoretical and practical results of a novel approach that combines hybrid techniques of association analysis and classical sequentiation algorithms of genomics to generate the grammatical structures of a specific language. We used an application of a compiler generator system that allows a practical application to be developed within the area of grammarware, where the concepts of language analysis are applied to other disciplines, such as bioinformatics. The tool allows the complexity of the obtained grammar to be measured automatically from textual data. A technique involving the incremental discovery of sequential patterns is presented to obtain simplified production rules, and compacted with bioinformatics criteria to make up a grammar.     

基于Spark的基因短序列比对模型

针对生物信息分析中基因短序列比对任务计算耗时长的问题,采用Spark平台、RDD数据集以及分布式文件系统HDFS设计了一种分布式计算模型。采用分而治之的策略将庞大的计算任务分割为多个互不重叠的小任务在分布式集群上并行执行。通过基于位置偏移量等分的数据分区算法实现数据的分发;通过将基因短序列封装入RDD数据集的方法实现了短序列的逐条处理;通过将基因比对算法传入RDD的Map函数的方法实现了基因序列的比对。计算模型的实现使得串行比对算法在分布式集群上可扩展,并显著降低了计算耗时,计算结果可与后续的生物信息分析工作相兼容。实验结果证明计算模型具有较好的稳定性和可扩展性,在Spark集群上取得了优秀的加速比。     

数据挖掘在生物信息学中的应用

生物信息学是一门新兴的交叉学科。人类基因组计划的启动和实施使得核酸?蛋白质数据迅速增长，如何从海量数据中获取有效信息成为生物信息学迫切要解决的问题。数据挖掘与生物信息学有很好的结合点，在生物信息学领域的应用潜力日益受到人们的重视。文中介绍了数据挖掘的概念、生物数据的挖掘步骤，初步探讨了数据挖掘在生物信息领域的应用潜力及生物信息学挖掘工具的开发和应用。研究证明数据挖掘技术是生物信息处理的强有力工具。数据挖掘在生物信息学中的应用将取得更大的进展。