软件开发框架模型——现实世界问题的结构化分析

对软件开发框架模型提出了一种新的构化的分析方法,同时介绍了常用的结构化分析的工具及其更实用情况。     

改进的RBF神经网络交通流预测算法

短时交通流预测是实现智能交通控制的前提与基础.提出了一种基于粗神经网络的RBF短时交通流预测算法,该算法在交通流量预测方面明显优于常规RBF神经网络,且具有较高的实时性.     

基于相关算法的电磁流量计抗噪性能研究

针对电磁流量计在低速测量时,信号被噪声淹没不能准确测量的问题,引入了相关检测算法.本文介绍了相关检测算法进行流量测量的基本原理,通过在电磁流量计测量中引入相关检测算法,滤掉了噪声干扰,提高了信号的信噪比,使电磁流量计在低速或者在低信噪比的情况下测量时,性能有较大改善,精度可达0.5%左右.在低流速时能够稳定的显示数据.     

基于OCTEON芯片的高精度流回放系统的设计与实现

介绍网络多核处理器OCTEON和基于OCTEON开发的基本需求,讨论在Linux平台下基于该芯片开发高精度流回放系统的设计与实现过程,包括主机与OCTEON间PCI的通信,高精度流回放的时间戳实现,以及多个流同时回放的设计与实现。对该系统的性能测试表明,基于OCTEON的流回放实现高度并行并且高精度的处理,系统的实现对于在高速Gbps环境下的网络数据流检测、过滤与入侵检测系统具有很高的应用价值。     

一种新型的三参量传感器

单参量传感器（加速度计、速度计或位移计）只能输出一种参量。本文介绍一种三参量转换器,可与任何一种单参量传感器组成一套完整的三参量传感器,可同时输出加速度、速度和位移三种参量。分析了三参量转换器的基本原理及构成三参量传感器的方法,给出了实测数据和相关的技术指标。构成的新型三参量传感器已在工程振动、强地震动等领域的测量中得到应用,取得了满意的效果。     

气/液两相流相关法测速系统的仿真研究

气/液两相流是自然界和工业过程中广泛存在的一种非常典型、复杂的两相流形态。本文以气液两相凝固流为模型将电容层析成象(ECT)技术和相关流速测量技术相结合,针对采样运算所得的电容信号分别采用象素相关、归一化互相关、差动自相关和极性互相关四种相关算法进行了相关测速系统的Matlab仿真。结果表明采用相关测量技术可较好的实现两相流离散相速度的测量。     

On the complexity of bandwidth allocation in radio networks

We define and study an optimization problem that is motivated by bandwidth allocation in radio networks. Because radio transmissions are subject to interference constraints in radio networks, physical space is a common resource that the nodes have to share in such a way, that concurrent transmissions do not interfere. The bandwidth allocation problem we study under these constraints is the following. Given bandwidth (traffic) demands between the nodes of the network, the objective is to schedule the radio transmissions in such a way that the traffic demands are satisfied. The problem is similar to a multicommodity flow problem, where the capacity constraints are replaced by the more complex notion of non-interfering transmissions. We provide a formal specification of the problem that we call round weighting  . By modeling non-interfering radio transmissions as independent sets, we relate the complexity of round weighting to the complexity of various independent set problems (e.g. maximum weight independent set, vertex coloring, fractional coloring). From this relation, we deduce that in general, round weighting is hard to approximate within n^1−ε$n^{1-\epsilon }$ (n$n$ being the size of the radio network). We also provide polynomial (exact or approximation) algorithms e.g. in the following two cases: (a) when the interference constraints are specific (for instance for a network whose vertices belong to the Euclidean space), or (b) when the traffic demands are directed towards a unique node in the network (also called gathering, analogous to single commodity flow).     

Color Subspaces as Photometric Invariants

Complex reflectance phenomena such as specular reflections confound many vision problems since they produce image ‘features’ that do not correspond directly to intrinsic surface properties such as shape and spectral reflectance. A common approach to mitigate these effects is to explore functions of an image that are invariant to these photometric events. In this paper we describe a class of such invariants that result from exploiting color information in images of dichromatic surfaces. These invariants are derived from illuminant-dependent ‘subspaces’ of RGB color space, and they enable the application of Lambertian-based vision techniques to a broad class of specular, non-Lambertian scenes. Using implementations of recent algorithms taken from the literature, we demonstrate the practical utility of these invariants for a wide variety of applications, including stereo, shape from shading, photometric stereo, material-based segmentation, and motion estimation.     

Over-Parameterized Variational Optical Flow

A novel optical flow estimation process based on a spatio-temporal model with varying coefficients multiplying a set of basis functions at each pixel is introduced. Previous optical flow estimation methodologies did not use such an over parameterized representation of the flow field as the problem is ill-posed even without introducing any additional parameters: Neighborhood based methods of the Lucas–Kanade type determine the flow at each pixel by constraining the flow to be described by a few parameters in small neighborhoods. Modern variational methods represent the optic flow directly via the flow field components at each pixel. The benefit of over-parametrization becomes evident in the smoothness term, which instead of directly penalizing for changes in the optic flow, accumulates a cost of deviating from the assumed optic flow model. Our proposed method is very general and the classical variational optical flow techniques are special cases of it, when used in conjunction with constant basis functions. Experimental results with the novel flow estimation process yield significant improvements with respect to the best results published so far.     

Improvements of nonlinear dynamic modeling of hot-film MAF sensor

The operating procedure of engine requires that the mass air flow (MAF) sensor can measure the pulsation air flow accurately. Therefore the MAF sensor must possess fast response speed. It is necessary to study the dynamic characteristic of the MAF sensor. Both the experimental equipments and method used by authors previously are improved to obtain the sensor response data accurately in this paper. The static and dynamic separable modeling method is adopted to build the uniform nonlinear dynamic model of the hot-film MAF sensor in the Hammerstein and Wiener forms, which can describe the large/small flow-rate and positive/negative step responses. The performance indexes are calculated by the actual and model responses.