新课程改革下的高中信息技术教学方法

高中信息技术新课程改革中对信息技术学科提出了新的要求,就是要以提升学生的信息素养为宗旨,培养高中生发展成为具有良好信息素养的公民。那么为着这样一个目标,我们该如何展开我们的教学呢？     

浅谈中职单片机课程对数学的要求

单片机技术的发展迅速,被广泛地应用到各个领域,现在一些中职已经陆续开设单片机课程了,我教了两年的单片机课程,该课程是在二年一期开设,刚开始都是给学生补习数学知识,占用了单片机的教学时间.如果我们能在数学教学和数学教材上面适当的做一些改动,就能为单片机的教学提供更好的支撑.我们学校为此做了相关的教学实践,并取得了一定的教学效果.     

接口协议智能编解码方法研究

针对当前复杂信息系统仿真中,关于接口协议编解码方法的缺陷,从接口协议的存储、程序设计的数据结构和编解码流程几个方面,给出了复杂信息系统仿真中接口协议编解码方法的详细设计过程。针对接口协议的复杂性和灵活性,提出了一种新颖的编解码方法,有效的解决了接口协议种类多、编解码方式灵活等问题;而且,与传统的方法进行了比较,从而可以看出其编解码的优点。仿真结果证明,编解码方法思路新颖、编解码速度快、可移植性好。     

The upper and lower bounds of the information-hiding capacity of digital images

The information-hiding capacity of a digital image is the maximum information that can be hidden in that image, while the lower limit of information hiding is the minimum detectable information capacity. This paper proposes a new method of information-hiding capacity bounds analysis that is based on the neural network theories of attractors and attraction basins. With this method, the processes for determining upper and lower limits of information hiding, are unified within a single theoretical framework. Results of research show that the theory of attraction basins of neural networks can be used to determine the upper limit of information hiding and the theory of attractors of neural networks can be used to determine the lower limit of information hiding.     

Four-scanning attack on hierarchical digital watermarking method for image tamper detection and recovery

In a recent paper presented by Lin et al., a block-based hierarchical watermarking algorithm for digital images is proposed. It adopts parity check and the intensity-relation check to conduct the experiment of image tamper detection. Their experimental results indicate that the precision of tamper detection and localization is 99.6% and 100% after level-2 and level-3 inspections, respectively. The proposed attacks demonstrate that this watermarking algorithm is fundamentally flawed in that the attacker can tamper a watermarked image easily without being detected. In this paper, a four-scanning attack aimed to Lin et al.'s watermarking method is presented to create tampered images. Furthermore, in case they use encryption to protect their 3-tuple-watermark, we proposed a blind attack to tamper watermarked images without being detected. Experimental results are given to support and enhance our conclusions, and demonstrate that our attacks are successful in tampering watermarked images.     

“How do you know that I don’t understand?” A look at the future of intelligent tutoring systems

Many software systems would significantly improve performance if they could adapt to the emotional state of the user, for example if Intelligent Tutoring Systems (ITSs), ATM’s, ticketing machines could recognise when users were confused, frustrated or angry they could guide the user back to remedial help systems so improving the service. Many researchers now feel strongly that ITSs would be significantly enhanced if computers could adapt to the emotions of students. This idea has spawned the developing field of affective tutoring systems (ATSs): ATSs are ITSs that are able to adapt to the affective state of students. The term “affective tutoring system” can be traced back as far as Rosalind Picard’s book Affective Computing in 1997.This paper presents research leading to the development of Easy with Eve, an ATS for primary school mathematics. The system utilises a network of computer systems, mainly embedded devices to detect student emotion and other significant bio-signals. It will then adapt to students and displays emotion via a lifelike agent called Eve. Eve’s tutoring adaptations are guided by a case-based method for adapting to student states; this method uses data that was generated by an observational study of human tutors. This paper presents the observational study, the case-based method, the ATS itself and its implementation on a distributed computer systems for real-time performance, and finally the implications of the findings for Human Computer Interaction in general and e-learning in particular. Web-based applications of the technology developed in this research are discussed throughout the paper.     

Evaluation of Localized Semantics: Data, Methodology, and Experiments

We present a new data set of 1014 images with manual segmentations and semantic labels for each segment, together with a methodology for using this kind of data for recognition evaluation. The images and segmentations are from the UCB segmentation benchmark database (Martin et al., in International conference on computer vision, vol. II, pp. 416–421, 2001). The database is extended by manually labeling each segment with its most specific semantic concept in WordNet (Miller et al., in Int. J. Lexicogr. 3(4):235–244, 1990). The evaluation methodology establishes protocols for mapping algorithm specific localization (e.g., segmentations) to our data, handling synonyms, scoring matches at different levels of specificity, dealing with vocabularies with sense ambiguity (the usual case), and handling ground truth regions with multiple labels. Given these protocols, we develop two evaluation approaches. The first measures the range of semantics that an algorithm can recognize, and the second measures the frequency that an algorithm recognizes semantics correctly. The data, the image labeling tool, and programs implementing our evaluation strategy are all available on-line (kobus.ca//research/data/IJCV_2007). We apply this infrastructure to evaluate four algorithms which learn to label image regions from weakly labeled data. The algorithms tested include two variants of multiple instance learning (MIL), and two generative multi-modal mixture models. These experiments are on a significantly larger scale than previously reported, especially in the case of MIL methods. More specifically, we used training data sets up to 37,000 images and training vocabularies of up to 650 words. We found that one of the mixture models performed best on image annotation and the frequency correct measure, and that variants of MIL gave the best semantic range performance. We were able to substantively improve the performance of MIL methods on the other tasks (image annotation and frequency correct region labeling) by providing an appropriate prior.     

RBF neural networks for solving the inverse problem of backscattering spectra

This paper investigates a new method to solve the inverse problem of Rutherford backscattering (RBS) data. The inverse problem is to determine the sample structure information from measured spectra, which can be defined as a function approximation problem. We propose using radial basis function (RBF) neural networks to approximate an inverse function. Each RBS spectrum, which may contain up to 128 data points, is compressed by the principal component analysis, so that the dimensionality of input data and complexity of the network are reduced significantly. Our theoretical consideration is tested by numerical experiments with the example of the SiGe thin film sample and corresponding backscattering spectra. A comparison of the RBF method with multilayer perceptrons reveals that the former has better performance in extracting structural information from spectra. Furthermore, the proposed method can handle redundancies properly, which are caused by the constraint of output variables. This study is the first method based on RBF to deal with the inverse RBS data analysis problem.     

基于时间序列法的风电场风速预测研究

针对时间序列的超短期风速预测进行研究,采用Box.Jenkins时间序列分析方法,通过Maflab软件,在达坂城风电场30 m处.每10 min采集一次得到的风速数据,建立ARMA模型,实现提前一小时的风速预测,为更长时间(半天、一天或两天)的风速预测提供理论基础.     

基于LSSVM的木材干燥建模研究

针对木材干燥过程的强非线性特点,提出以最小二乘支持向量机LSSVM建立木材干燥基准模型.通过实验用小型木材干燥窑实际干燥过程中采集的数据作为训练样本进行仿真实验,结果表明基于LSSVM的木材干燥模型预测输出能够准确反映干燥过程木材含水率的变化,模型结构简单、预测精度高、泛化能力强,验证了LSSVM对木材干燥过程建模是一种可行而有效的方法.