师范院校计算机专业单片机课程教学改革探讨

针对师范院校计算机专业课程设置的特点,结合单片机课程特点及教学实践,从目前该课程教学和实践中存在的问题入手,分别从课程教学的内容、方式和实验等方面探讨了单片机教学改革和实验的一些思路。     

双目立体视频最小可辨失真模型及其在质量评价中的应用

该文针对目前最小可辨失真(JND)模型只能应用于评价单视点视频的问题,探索一种以双眼亮度关系为基本出发点的双目立体视频的JND模型。首先根据立体视频特性引入双眼亮度关系模型,然后考虑了背景亮度掩盖、纹理掩盖、帧间掩盖、空间时间对比灵敏度、眼睛运动等因素,提出了双目JND模型,最后将该双目JND模型应用到立体视频质量评价方法中。实验结果表明该文提出的质量评价方法符合人类的视觉感知特性,更接近于主观测试结果。     

基于改进DS证据理论和BN的信息安全风险评估

针对信息安全风险评估过程中专家评价意见的多样性以及不确定信息难以量化处理的问题,提出了一种基于改进的DS证据理论与贝叶斯网络(BN)结合的风险评估方法.首先,在充分研究信息安全风险评估流程和要素的基础上,建立了风险评估模型,确定风险影响因素;其次,根据评估模型并结合专家知识构建相应的贝叶斯网络模型,确定贝叶斯网络模型中的条件概率表;再次,利用基于权值分配和矩阵分析的改进DS证据理论融合多位专家对风险影响因素的评价意见;最后,根据贝叶斯网络模型的推理算法,计算被测信息系统处于不同风险等级的概率值,并对结果进行有效性分析.分析表明,将改进后的DS证据理论与贝叶斯网络应用到风险评估过程中,在一定程度上能够提高评估结果的可信度和直观性.     

Robust image retrieval by cascading a deep quality assessment network

The performance of computer vision algorithms can severely degrade in the presence of a variety of distortions. While image enhancement algorithms have evolved to optimize image quality as measured according to human visual perception, their relevance in maximizing the success of computer vision algorithms operating on the enhanced image has been much less investigated. We consider the problem of image enhancement to combat Gaussian noise and low resolution with respect to the specific application of image retrieval from a dataset. We define the notion of image quality as determined by the success of image retrieval and design a deep convolutional neural network (CNN) to predict this quality. This network is then cascaded with a deep CNN designed for image denoising or super resolution, allowing for optimization of the enhancement CNN to maximize retrieval performance. This framework allows us to couple enhancement to the retrieval problem. We also consider the problem of adapting image features for robust retrieval performance in the presence of distortions. We show through experiments on distorted images of the Oxford and Paris buildings datasets that our algorithms yield improved mean average precision when compared to using enhancement methods that are oblivious to the task of image retrieval. ¹     

Mining discriminative spatial cues for aerial image quality assessment towards big data

Evaluating massive-scale aerial/satellite images quality is useful in computer vision and intelligent applications. Traditional local features-based algorithms have achieved impressive performance. However, spatial cues, i.e., geometric property and topological structure, have not been exploited effectively and explicitly. Thus, in this paper, we propose a novel method for image quality assessment towards aerial/satellite images, where discriminative spatial cues are well encoded. More specifically, in order to mine inherent spatial structure of aerial images, each image is segmented into several basic components such as buildings, airport and playground. Afterwards, a weighted region adjacency graph (RAG) is built based on the basic components to represent the spatial feature of each aerial image. We integrate the spatial feature with other transform domain features, and train a support vector regression model to achieve image quality assessment. Experiments demonstrate that our method shows competitive or even better performance compared with several state-of-the-art algorithms.     

Perceptual visual quality assessment using deeply-learned gaze shifting kernel

Image quality assessment (IQA) is a useful technique in computer vision and machine intelligence. It is widely applied in image retrieval, image clustering and image recognition. IQA algorithms generally rely on human visual system (HVS), which can reflect how human perceive salient regions in the image. In this paper, we leverage both low-level features and high-level semantic features to select salient regions, which will be concatenated to form GSPs by the designed saliency-constraint algorithm to mimic human visual system. We design an enhanced IQA index based on the GSPs to calculate the simialrity between reference image and test image to achieve image quality assessment. Experiments demonstrate that our IQA method can achieve satisfactory performance.     

本科毕业设计模式改革的探索

对于理工科院校而言,毕业设计是本科教学中至关重要的最后一个环节,它既是大学四年学生学习成果的检验,又是知识、理论、技能的一次综合全面的应用。但是近年来,由于主客观条件的制约,本科毕业设计在进行的过程中出现了学生重视不够、时间难以保证、质量有所下降等一系列问题。本文针对这些问题,着重探讨了如何在当前就业形势日益严峻的背景下,改革毕业设计的模式,将校企合作定向培养人才与毕业设计结合在一起的新模式。     

FAST深空数据传输速率分析

500 m口径反射面射电望远镜(FAST)是目前世界口径最大、最灵敏的射电天文望远镜.为了分析FAST对深空探测任务的数据传输支持能力,利用数据传输链路预算分析方法,在规定特定工程余量的条件下,详细计算了FAST应用在内行星、外行星及柯伊伯带(Kuiper belt)天体探测任务中的数据传输速率.计算结果表明,FAST等效成口径300 m的地面接收天线,能使目前的深空通信数据下行传输速率提高70~400倍,未来其将会强有力地支持我国的火星、木星等深空探测任务.     

基于加速性能退化试验的板级可靠性评估

传统的可靠性评估方法一般基于失效寿命数据,而目前对于高可靠长寿命的电子产品,很难通过加速试验获得其失效寿命时间。为解决这一矛盾,将性能退化理论引入到传统可靠性评估中,提出了基于失效数据及加速性能退化的可靠性评估的新方法。应用某型雷达24V/2A稳压电源板加速性能退化试验进行验证,结果表明该方法用于高可靠长寿命电子装备的可靠性评估是正确有效的。