Frequency-domain approach to model-reduction problem

Model reduction is performed in the frequency domain by matching the first Taylor-series-expansion coefficients of the square magnitude of the original and the approximating transfer functions at suitable frequencies. The method is computationally simple and may lead to a satisfactory approximation while preserving stability and minimum-phase characteristics.     

G.729语音编码器在DSP上的实时实现

为满足音视频的同时需求,提出了基于TMS320C64X系列DSP的G.729语音编码器软、硬件设计方案,并着重阐述了在实时实现过程中进行优化的关键技术。经测试表明,优化后的单路语音编码占用了5%左右的CPU资源,比优化前降低了80%,保留了更多的资源用于视频编码,为在同一块DSP芯片上实现音频、视频编码提供可能。     

嵌入式系统中自适应背光的设计与实现

针对嵌入式产品中设备背光自适应调节的应用需求,本文使用光敏晶体管获取外界光强对应的电信号,采用UCB1x00芯片进行模数转换,同时利用软件实时采样、分析此数值,并通过处理器PXA255芯片的GPIO口模拟PWM实现了一种自适应调节设备背光的方案.测试结果表明,该实现方案是实际可行的并且具有良好的稳定性及通用性.     

遗传禁忌神经网络模型及其在电力负荷预测中的应用

针对神经网络权值学习算法的不足,结合遗传算法和禁忌算法各自的优势,建立了遗传禁忌神经网络预测模型.实际应用表明,与遗传算法相比,该模型具有收敛速度快、预测精度高等优点,具有满意的预测效果.     

一种基于H.264的视频加密算法研究

H.264是一个面向未来IP和无线网络的视频编码标准,应用范围越来越广泛,因而安全加密技术逐步成为研究的热点。论文针对H.264的特点,提出一种利用数据加密标准DES的输出反馈模式OFB对H.264少量视频数据进行加密的算法,所选取的数据来自于DCT变换后的DC及部分AC系数。理论分析和实验结果表明,该算法选择加密部分视频数据,减小了加密的数据量,从而降低了加密系统的计算复杂度,具有较快的加解密速度,提高了实时性和安全性,且适合于移动多媒体、无线多媒体网络的数据流的安全传送。     

基于CC标准的安全操作系统测评系统研究

安全操作系统在计算机系统安全中的重要作用越来越引起人们的重视,如何测评操作系统安全性成为一个重要的课题。结合CC标准,论文给出了一个自主设计的安全操作系统的测评系统(SOSES),并对安全操作系统所能达到的安全级别进行半自动化测评,也就是在操作员的参与下对安全操作系统的等级进行量化测评。     

基于GANO算法的电力系统中长期负荷预测研究

进行负荷预测时,由于中长期负荷历史数据较少而制约因素较多,因此预测难度较大。在分析了灰色预测和神经网络预测优缺点的基础上,提出了多因素灰色神经网络组合预测模型(GANO)。该模型首先采用灰色GM(1,n)模型处理多因素的影响,进而利用BP神经网络训练电力历史负荷数据,最后利用统计方差的倒数建立较为理想的优化组合预测模型。该优化模型结合了各模型优点且综合考虑了电力负荷的多种制约因素。经算例验证,优于单一历史负荷预测模型,有效地提高了中长期负荷预测精度。     

加固显示器抗干扰滤波器设计

着重介绍加固显示器抗干扰滤波器设计的方法与技巧。介绍了共模电流对显示器干扰的影响,提出了消除干扰源的措施,给出了滤波器电路与阻抗的关系以及抗干扰滤波器在工程设计中的注意事项,并结合实践中的实例进行设计与应用。     

An adaptive and efficient dimension reduction model for multivariate wireless sensor networks applications

Wireless sensor networks (WSNs) applications are growing rapidly in various fields such as environmental monitoring, health care management, and industry control. However, WSN's are characterized by constrained resources especially; energy which shortens their lifespan. One of the most important factors that cause a rapid drain of energy is radio communication of multivariate data between nodes and base station. Besides, the dynamic changes of environmental variables pose a need for an adaptive solution that cope with these changes over the time. In this paper, a new adaptive and efficient dimension reduction model (APCADR) is proposed for hierarchical sensor networks based on the candid covariance-free incremental PCA (CCIPCA). The performance of the model is evaluated using three real sensor networks datasets collected at Intel Berkeley Research Lab (IBRL), Great St. Bernard (GSB) area, and Lausanne Urban Canopy Experiments (LUCE). Experimental results show 33.33% and 50% reduction of multivariate data in dynamic and static environments, respectively. Results also show that 97–99% of original data is successfully approximated at cluster heads in both environment types. A comparison with the multivariate linear regression model (MLR) and simple linear regression model (SLR) shows the advantage of the proposed model in terms of efficiency, approximation accuracy, and adaptability with dynamic environmental changes.     

On the Repeatability and Quality of Keypoints for Local Feature-based 3D Object Retrieval from Cluttered Scenes

3D object recognition from local features is robust to occlusions and clutter. However, local features must be extracted from a small set of feature rich keypoints to avoid computational complexity and ambiguous features. We present an algorithm for the detection of such keypoints on 3D models and partial views of objects. The keypoints are highly repeatable between partial views of an object and its complete 3D model. We also propose a quality measure to rank the keypoints and select the best ones for extracting local features. Keypoints are identified at locations where a unique local 3D coordinate basis can be derived from the underlying surface in order to extract invariant features. We also propose an automatic scale selection technique for extracting multi-scale and scale invariant features to match objects at different unknown scales. Features are projected to a PCA subspace and matched to find correspondences between a database and query object. Each pair of matching features gives a transformation that aligns the query and database object. These transformations are clustered and the biggest cluster is used to identify the query object. Experiments on a public database revealed that the proposed quality measure relates correctly to the repeatability of keypoints and the multi-scale features have a recognition rate of over 95% for up to 80% occluded objects.