An adaptive discrete-time ILC strategy using fuzzy systems for iteration-varying reference trajectory tracking

In this article, a novel fuzzy systems based on adaptive Iterative Learning Control (ILC) strategy is presented to deal with a class of non-parametric nonlinear discrete-time systems which perform iteration-varying reference trajectory tracking. Using the technique of fuzzy systems to compensate for the non-parametric uncertainty of the discrete-time system dynamics, the proposed adaptive ILC scheme can well track the iteration-varying reference trajectory beyond the initial time points. The convergence of the fuzzy systems based adaptive ILC algorithm is guaranteed by theoretical analysis, and a numerical example is given to illustrate the effectiveness of the adaptive ILC scheme.

     

基于Cortex-A8智能家居系统的设计与实现

本文设计了一套低成本无线智能家居系统的解决方案,具有安全,可靠,稳定的显著特点,以及智能家居系统市场的广大前景.本文还阐明了系统的体系结构,详细介绍了系统软件和硬件部分的设计与实现。并对关键技术(GPRS、24L01)的流程做了详细的阐述。     

Speech authentication by semi-fragile speech watermarking utilizing analysis by synthesis and spectral distortion optimization

This paper proposes an improved semi-fragile speech watermarking scheme by quantization of linear prediction (LP) parameters, i.e., the inverse sine (IS) parameters. The spectral distortion due to watermark embedding is controlled to meet the ‘transparency’ criterion in speech coding. A modified bit allocation algorithm combined with watermarking is developed to determine the quantization step so that the ‘transparency’ requirement is satisfied. Due to the statistical nature, the LP coefficients estimated from the watermarked speech signal are different from the watermarked LP coefficients even in the absence of attacks. This effect is the cause of increase in decoding error and minimum authentication length. To tackle this problem, an Analysis by Synthesis (AbS) scheme is developed to reduce the difference between the estimated LP coefficients and the watermarked ones. The watermark detection threshold and minimum authentication length are then derived according to the probability of error and the signal to noise ratio (SNR) requirements. Experimental results show that the proposed AbS based method can effectively reduce the difference between the watermarked IS parameter and the extracted IS parameter when there is no attacks. In addition, the modified bit allocation algorithm can automatically find the appropriate quantization step used in the odd-even modulation so that the transparency requirement is satisfied.     

An update-based step-wise optimal cache replacement for wireless data access

Many network applications requires access to most up-to-date information. An update event makes the corresponding cached data item obsolete, and cache hits due to obsolete data items become simply useless to those applications. Frequently accessed but infrequently updated data items should get higher preference while caching, and infrequently accessed but frequently updated items should have lower preference. Such items may not be cached at all or should be evicted from the cache to accommodate items with higher preference. In wireless networks, remote data access is typically more expensive than in wired networks. Hence, an efficient caching scheme considers both data access and update patterns can better reduce data transmissions in wireless networks. In this paper, we propose a step-wise optimal update-based replacement policy, called the Update-based Step-wise Optimal (USO) policy, for wireless data networks to optimize transmission cost by increasing effective hit ratio. Our cache replacement policy is based on the idea of giving preference to frequently accessed but infrequently updated data, and is supported by an analytical model with quantitative analysis. We also present results from our extensive simulations. We demonstrate that (1) the analytical model is validated by the simulation results and (2) the proposed scheme outperforms the Least Frequently Used (LFU) scheme in terms of effective hit ratio and communication cost.     

Fast and effective color-based object tracking by boosted color distribution

In this paper, we propose a novel tracking algorithm, boosted color distribution (BCD), for tracking color objects. There exist three contributions in this paper. First, we propose a novel online gentle boost (OGB) algorithm for online learning. The essential idea of OGB is composed of two aspects: online updating candidate weak classifiers, and then choosing and combining them in a boosting way. Second, we design a novel weak classifier, log color feature distribution ratio, which focuses on the difference of color distributions rather than individual samples and provides a simple yet effective manner of mining color features for object tracking. Third, by combining our OGB algorithm and our log color features, we develop a fast yet effective color-based object tracking algorithm. Numerous experiments demonstrate that our tracking algorithm is better than or not worse than some state-of-the-art tracking algorithms on some public sequences.Overall, this paper presents a novel BCD algorithm for color object tracking that achieves good results at a fast speed.     

径流式低水头大流量小水电站的坝型选择

本文以福生水电站设计的坝型选择为例,综合阐速了利用大中型水电站发电尾水,选择适合的坝型建低水头大流量小水电站具有可观的前景.     

浅谈师范院校计算机探究式教学

为了弥补师范院校计算机课堂教学工作中组织教学难度大、个别指导难度大、教学效果难保证、不能照顾所有需要帮助的学生的缺陷,我们在计算机课堂教学中采用探究式的教学.     

浅谈政府公文交换系统的发展

在社会信息化的不断推进过程中,各级政府的办公自动化需求也在不断升级,公文交换系统多样化且技术更新加速,随着改革的深入和我国加入WTO,公文交换系统的建设已经成为当前我国信息化建设的首要任务.     

Supervised classification of share price trends

Share price trends can be recognized by using data clustering methods. However, the accuracy of these methods may be rather low. This paper presents a novel supervised classification scheme for the recognition and prediction of share price trends. We first produce a smooth time series using zero-phase filtering and singular spectrum analysis from the original share price data. We train pattern classifiers using the classification results of both original and filtered time series and then use these classifiers to predict the future share price trends. Experiment results obtained from both synthetic data and real share prices show that the proposed method is effective and outperforms the well-known K-means clustering algorithm.     

Polarization discrimination between repeater false-target and radar target

High fidelity repeater false-target badly affects a radar system’s detecting, tracking, and data processing. It is an available approach of confronting false-target for radar that discriminates firstly and then eliminates. Whereas for the technique progress about the repeater false-target jam, it is more and more difficult to discriminate this jam in the time-domain, frequency-domain, or space-domain. The technique using polarization information to discriminate the target and false-target is discussed in th...