基于交流比例积分控制器的单相逆变器

提出一种电感电流、输出电压瞬时值反馈控制和负载电流前馈控制相结合的复合控制策略。在此基础上,研制了一台800W的单相逆变器。实验结果表明,提出的控制策略改善了逆变器输出电压的波形,提高了系统的暂态特性。     

Prediction mode modulated data-hiding algorithm for H.264/AVC

In this paper, a data-hiding algorithm with large data payload for H.264/AVC is proposed. The secret information is embedded by modulating the prediction modes of 4?×?4 luminance blocks. If the best mode does not match the information bit, the prediction mode should be modulated by replacing the best mode with the substitute mode. The substitute mode is the one with the least Lagrangian cost among those having different parity with the best mode. Due to high secrecy and easy reproducibility of chaos, the secret information is first encrypted by a chaotic sequence and then a small number of luminance blocks used for data embedding are randomly selected in each macroblock based on another chaotic sequence. The usefulness of multilevel chaotic keys and privacy of the number of 4?×?4 luminance blocks enhance the security. The hidden information can be extracted directly from the encoded stream without resorting to the original video, and merely requires decoding the intra-prediction mode from bit stream rather than decoding the whole video. Experimental results show that the proposed hiding scheme can effectively embed information with little bit rate increase and almost no quality degradation.     

基于网络断层扫描的传感器网络链路丢包研究

文中提出了一种基于网络断层扫描的无线传感器网络链路丢包率测量方法,通过边界节点的丢包率来估计传感器内部链路丢包的情况。根据无线传感器网络数据聚合的特点,提出了网络逻辑拓扑和链路报文丢包模型,在将链路报文丢失率推测问题形式化为MLE问题的基础上,用引入修正因子的μ的EM算法来推测链路报文丢失率。NS2仿真结果证明,μ-EM算法推测的链路报文丢失率与预设值更加接近,特别是在大规模网络中μ-EM算法在精确度和有效性方面均明显优于EM算法。     

基于VC＋＋．NET的数据访问技术与实现

为了完成数据管理系统开发中专项数据的组织调用和管理,设计将数据存人到数据库表的结构化字段中,使用VC＋＋程序开发语言和ADO数据访问技术,结合使用ActiveX控件对数据进行安全存储和组织访问,从而达到了简单、安全、快速操作数据库中的数据的目的。文中介绍了微软ADO数据访问技术体系结构、对象模型,以及存储访问数据的原理与方法,并在此基础上给出了基于VC＋＋．NET和ADO数据访问技术存储访问SQLServer数据库数据的程序规划和详细程序代码,此方法已成功应用在数据管理系统的开发中。     

基于Turbo码的OFDM-IDMA系统干扰消除算法

基于交织多址系统,通过分析串行干扰消除和并行干扰消除两种方式的结构特点和纠错性能,结合两者优势,给出适用于OFDM-IDMA技术的串并结合干扰消除方式。通过构建等效的信道传输模型,在Turbo编码和OFDM条件下,将串并结合干扰消除方式与传统串行干扰消除方式进行性能对比。仿真结果表明,采用串并结合干扰消除技术的OFDM-IDMA系统,能够以较少的纠错性能损失为代价实现低时延多用户通信的目的。     

Extraction of melody from polyphonic music using modified morlet wavelet

In this paper, the extraction of melody for polyphonic music is discussed. The method proposes a simple but effective modification in the Morlet wavelet as a modified Morlet wavelet (MMW). The pitch detection concept is applied to the transformed wavelet music signal for primary melody estimation and extraction. The proposed method is then compared with three other methods reported in the literature (the method focused on Short Time Fourier Transform, multi-resolution Fast Fourier Transform, and Morlet Wavelet). The percentage gross pitch error (GPE) parameter is considered for purposes of comparison. In comparison, it is observed that the proposed approach has the lowest GPE. The simulation results also present a lower percentage error in the predominant pitch frequency estimation for the proposed method.     

Paradigm and performance analysis of distributed frequent itemset mining algorithms based on Mapreduce

FrequentItemsetMining (FIM) is one of the most important data mining tasks and is the foundation of many data mining tasks. In Big Data era, centralized FIM algorithms cannot meet the needs of FIM for big data in terms of time and space, so Distributed Frequent Itemset Mining (DFIM) algorithms have been designed to meet the above challenges. In this paper, LocalGlobal and RedistributionMining which are two main paradigms of DFIM algorithm are discussed; Two algorithms of these paradigms on MapReduce named LG and RM are proposed while MapReduce is a popular distributed computing model, and also the related work is discussed. The experimental results show that the RM algorithm has better performance in terms of computation and scalability of sites, and can be used as the basis for designing the DFIM algorithm based on MapReduce. This paper also discusses the main ideas of improving the DFIM algorithms based on MapReduce.     

RAFNet: RGB-D attention feature fusion network for indoor semantic segmentation

Semantic segmentation based on the complementary information from RGB and depth images has recently gained great popularity, but due to the difference between RGB and depth maps, how to effectively use RGB-D information is still a problem. In this paper, we propose a novel RGB-D semantic segmentation network named RAFNet, which can selectively gather features from the RGB and depth information. Specifically, we construct an architecture with three parallel branches and propose several complementary attention modules. This structure enables a fusion branch and we add the Bi-directional Multi-step Propagation (BMP) strategy to it, which can not only retain the feature streams of the original RGB and depth branches but also fully utilize the feature flow of the fusion branch. There are three kinds of complementary attention modules that we have constructed. The RGB-D fusion module can effectively extract important features from the RGB and depth branch streams. The refinement module can reduce the loss of semantic information and the context aggregation module can help propagate and integrate information better. We train and evaluate our model on NYUDv2 and SUN-RGBD datasets, and prove that our model achieves state-of-the-art performances.