翻转课堂在"Matlab与通信系统仿真"教学中的实践

针对"Matlab与通信系统仿真"课程课堂教学学时有限、学生基础层次不齐、学生被动学习不利于培养其仿真思路的教学现状,本文探讨了如何将翻转课堂应用于该课程的教学过程中,具体阐述了针对该课程的翻转课堂教学模式构建与实施,包括课前学习,课堂以及实验教学,课后反馈以及考核评价.根据学生期末上机成绩分布以及问卷调查,翻转课堂教学模式应用于"Matlab与通信系统仿真"课程能够获得较好的教学效果.     

无线传感网的对分课堂教学实践

阐述无线传感网WSN是物联网工程的核心课程之一，为了提升无线传感网课程教学质量，研究对分课堂教学模式在该课程中的教学策略。从无线传感网课程特点出发，探讨对分课堂教学模式在本课程理论教学中的实施过程。对分课堂教学模式下的教学过程提高了学生的学习积极性，培养了学生的思维能力，有效提升该课程的教学效果。     

翻转课堂在“MATLAB与通信系统仿真”教学中的实践

对于“Matlab与通信系统仿真”课程的教学现状,包括课堂教学学时有限,学生基础层次不齐,且被动式学习不利于培养学生仿真思路,本文探讨了如何将翻转课堂应用于该课程的教学过程中。本文具体阐述了针对该课程的翻转课堂教学模式构建与实施,包括课前学习,课堂以及实验教学,课后反馈以及考核评价。根据学生期末上机成绩分布以及问卷调查,翻转课堂教学模式应用于“Matlab与通信系统仿真”课程能够获得较好的教学效果。     

大学“翻转课堂”式教学系统的分析及设计

为了提升大学课程教学的质量和效率,基于我国翻转课堂教学模式的现状,对重庆交通大学的翻转课堂教学系统进行了分析和设计。文章主要阐述了系统的总体结构、主要功能模块的设计、核心功能模块的设计及其关键技术。通过该系统,可以提升学生的自主学习能力,提升课堂效率,减轻教师的教学压力,实现课堂"翻转"。     

“信号与系统”绪论课教学设计与实施

为适应“互联网+教育”和新工科专业改革的发展需要，教学团队以“信号与系统”绪论课为试点，采用移动智慧教学方案组织课堂教学，贯彻落实PBL教学、OBE理念、课程思政以及同课异构等多种课堂教学和教研模式，依托泛雅智慧教学平台和学习通APP开展智慧课堂教学模式改革。     

基于虚拟仪器的辅助教学系统

本文设计了一套基于虚拟仪器的“模拟电子技术”课程辅助教学系统,系统由便携式“模拟电子技术”实验电路板、数据采集设备、笔记本电脑、上位机软件、投影仪及投影仪幕布组成。本系统应用于“模拟电子技术”课堂教学,较好的将理论课教学与实验课教学融为一体。与仿真软件相比,该系统具有实际的物理意义,增加课程的工程性和实践性,取得了较好...     

面向自动控制原理课程教学的仿真软件开发

“自动控制原理”课程内容抽象，教学难度大。为提高教学质量，开发了面向“自动控制原理”课程教学的仿真软件。基于Matlab GUI开发平台，通过编写算法实现了控制系统的时域、根轨迹和频率响应分析等典型功能，开发了典型工程控制案例“直流调速系统”，通过可视化界面帮助学生理解经典控制理论在实际工程中的应用。该仿真软件使复杂的理论教学变得简单、直观，极大提高了课堂教学效率与教学质量。     

电路课程理实一体化教学模式改革实践

为适应高等教育院校改革发展，推动育人模式创新，对“电路”课程教学模式进行了研究。通过重构课程教学内容体系、开展层次化教学实践设计、制定全程化教学考核方案，构建了以学员为中心、以能力培养为导向的理实一体化教学改革模式，并开展课堂教学实践。该教学模式激发了学员对知识的探索研究兴趣，提升了教员参与教学改革的热情，教学反馈结果表明该改革模式有利于教学质量的提升。     

高职教学中开设“生产工艺”课程的探索

介绍了在高职教学中开设生产工艺课程的具体做法。该课程分课堂教学与实验培训两个环节，使学生通过课堂教学，系统了解工艺管理与技术方面的知识，使学生通过实训环节，真正得到生产工艺的培训。在实训中，彻底改变了传统实验方式，尽量模拟企业的流水线生产方式来组织实训生产，学生在工艺文件的指导下进行操作。尽量创造条件使实训生产与企业的生产方式接轨。经过实践证明，这对提高学生实际工作能力方面已取得一定成效。     

Matlab在"信号与系统"教学中的应用研究

本文针对学生在学习"信号与系统"课程中存在的问题,提出在该课程教学中引入Matlab软件辅助课程教学的思路.通过运用Matlab实现信号时域运算的演示、频域抽样定理等具体实例,说明引入Matlab软件对改善"信号与系统"课程的教学方法和手段,丰富教学内容,提高课堂教学效率具有良好的促进作用.     

A CONDITIONAL RANDOM FIELDS APPROACH TO BIOMEDICAL NAMED ENTITY RECOGNITION

Named entity recognition is a fundamental task in biomedical data mining. In this letter, a named entity recognition system based on CRFs (Conditional Random Fields) for biomedical texts is presented. The system makes extensive use of a diverse set of features, including local features, full text features and external resource features. All features incorporated in this system are described in detail, and the impacts of different feature sets on the performance of the system are evaluated. In order to improve the performance of system, post-processing modules are exploited to deal with the abbrevia- tion phenomena, cascaded named entity and boundary errors identification. Evaluation on this system proved that the feature selection has important impact on the system performance, and the post-processing explored has an important contribution on system performance to achieve better re- sults.     

Intelligent fault diagnosis system of induction motor based on transient current signal

This paper presents a method for induction motor fault diagnosis based on transient signal using component analysis and support vector machine (SVM). The start-up transient current signal is selected as features source for fault diagnosis. Preprocessing of transient current signal is performed using smoothing and discrete wavelet transform to highlight the salient features of faults. In this work, independent component analysis, principal component analysis and their kernel are performed to reduce the dimension of features and to extract the optimal features for classification process. In this work, the influence of the number of component analysis towards diagnosis accuracy is also studied. SVM multi-class classification using one against all strategy is selected for classification tool due to good generalization properties. Performance of the system is validated by applying the system to induction motor faults diagnosis. According to the result, the system has potential to serve an intelligent fault diagnosis system in real application.     

Classifying mammographic mass shapes using the wavelet transform modulus-maxima method

In this article, multiresolution analysis, specifically the discrete wavelet transform modulus-maxima (mod-max) method, is utilized for the extraction of mammographic mass shape features. These shape features are used in a classification system to classify masses as round, nodular, or stellate. The multiresolution shape features are compared with traditional uniresolution shape features for their class discriminating abilities. The study involved 60 digitized mammographic images. The masses were segmented manually by radiologists, prior to introduction to the classification system. The uniresolution and multiresolution shape features were calculated using the radial distance measure of the mass boundaries. The discriminating power of the shape features were analyzed via linear discriminant analysis (LDA). The classification system utilized a simple Euclidean metric to determine class membership. The system was tested using the apparent and leave-one-out test methods. The classification system when using the multiresolution and uniresolution shape features resulted in classification rates of 83% and 80% for the apparent and leave-one-out test methods, respectively. In comparison, when only the uniresolution shape features were used, the classification rates were 72 and 68% for the apparent and leave-one-out test methods, respectively.     

消除指纹纹线中的伪特征

文中介绍了自动指纹识别系统中，灰度指纹图像经过预处理过程得到细化二值图像；其中可能含有大量的伪特征，对匹配操作造成不良影响，导致识别率下降。为了提取出准确的、真实的特征点，选取自动阈值、对短线、断线、小桥等，并利用改进的方法去除，对区域边界的伪特征、三角和岛屿采用新的方法去除。     

基于着色Petri网的电信业务特性集成模型

“业务特性”的快速集成是电信系统发展的趋势,本文提出了基于着色Ｐｅｔｒｉ网（ＣＰ网）的业务特性集成模型。在本方法中,基本的电话系统以及每个业务特性以一个ＣＰ网来模拟,当把一个业务特性集成到系统中时,相应于把代表它的ＣＰ网嵌入系统的网中。本模型既形象化地模拟业务特性的集成,同时可以用数学方法来分析业务特性的行为,可应用于检测和解决业务特性的交互作用。     

基于Ontology的监控视频描述方案

基于对监控视频数据所含信息的层次化分析,提出了一种基于本体论(Ontology)的监控视频层次化描述方案.在此方案中,将监控视频采集器得到的数据分为视频元数据和视频数据分别加以描述.根据所提出的监控视频分析本体,将视频数据进一步分为视觉特征信息、目标对象语义信息、高层语义信息三个层次.最后借鉴领域知识,并使用可扩展标记语言(XML).以实例形式阐述各个层次的描述方法.     

Adaptive Spatiotemporal Feature Extraction and Dynamic Combining Methods for Selective Visual Attention System

This paper introduces a selective attention system that guides users to detect the regions of interest more effectively by adaptively selecting and using spatial and temporal features according to the input images. Although the proposed system is based on a typical bottom-up method, it achieved improvement in the method for extracting features and calculating the saliencies compared to existing studies. In the proposed system, spatial saliencies have dynamic information from which features are adaptively selected according to the input images. Also temporal saliencies in the proposed system have pieces of information for individual moving objects that are associated with each other obtained through multi-resolution feature analysis. In addition, when combining a spatial saliency and a temporal saliency, the activity of the input saliency is measured, and the weights that change dynamically according to the activity are calculated, and the spatial saliency and temporal saliency are combined according to the weights. In order to evaluate the performance of the proposed system, comparative experiments with the existing systems were conducted with diverse experimental images and as a result, it could be seen that the proposed system produces results closer to the results of humans’ visual recognition compared to previous systems.     

脱机手写签名纹理特征分析

文章利用各种方法分析了脱机手写签名的纹理特征．并经比较后提出在签名灰度图像的基础上，利用CDTM(Cross—Diagonal Texture Matrix)方法得到签名的纹理矩阵。在此基础上，计算相关统计特征作为签名的纹理特征。然后建立了相应的鉴别系统，并对签名样本进行了鉴别，实验结果表明利用CDTM提取签名纹理特征的有效性。     

基于多特征和多分类融合的视频概念检测

The rapid growth of multimedia content necessitates powerful technologies to filter, classify, index and retrieve video documents more efficiently. However, the essential bottleneck of image and video analysis is the problem of semantic gap that low level features extracted by computers always fail to coincide with high-level concepts interpreted by humans. In this paper, we present a generic scheme for the detection video semantic concepts based on multiple visual features machine learning. Various global and local low-level visual features are systematically investigated, and kernel-based learning method equips the concept detection system to explore the potential of these features. Then we combine the different features and sub-systems on both classifier-level and kernel-level fusion that contribute to a more robust system. Our proposed system is tested on the TRECVID dataset. The resulted Mean Average Precision (MAP) score is much better than the benchmark performance, which proves that our concepts detection engine develops a generic model and performs well on both object and scene type concepts.     

基于MapReduce的公路视频图像车型分类研究*

分析公路视频图像,从而对经过的车辆进行较高精度的分类,是一个颇且实用价值的课题.如何在保证分类精度的同时提高系统性能,无疑是一个具有挑战性的任务.提出了一个多特征融合的分类框架,结合车辆的全局几何特征、SIFT局部特征,以及Gabor纹理特征对车辆进行分类,提高了分类精度;为了提高系统的性能,设计了基于MapReduce的并行算法,通过对图像分块,实现数据并行.实验结果表明,该方案能够在提高分类精度的基础上仍然保持较高的系统性能.