基于AdaBoost算法的人脸检测在嵌入式系统中的实现

为解决人脸检测实时性问题,针对AdaBoost算法纯软件实现的瓶颈,提出基于FPGA平台的硬件加速策略,采用流水线处理技术实现积分图像的快速计算。实验使用PowerPC405处理器VirtexTM-Ⅱ Pro平台FPGA,在输入图像大小为352×288像素的条件下,检测速度达到每秒50帧,检测率为98%,误检率约1%,实现了实时人脸检测的要求。     

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。     

一种混沌粒子群算法

针对传统的粒子群算法易陷入局部最小,且算法后期的粒子速度下降过快而失去搜索能力等缺陷,本文提出了一种基于混沌思想的新型粒子群算法。该算法通过生成混沌序列的方式产生惯性权重取代传统惯性权重线性递减的方案,使粒子速度呈现多样性的特点,从而提高算法的全局搜索能力;根据算法中粒子群体的平均粒子速度调节惯性权重,防止粒子速度过早降低而造成的搜索能力下降的问题;最后通过引入粒子群算法系统模型稳定时惯性权重和加速系数之间的约束关系,增强了粒子群算法的局部搜索能力。对比仿真实验表明,本文所提改进的混沌粒子群算法较传统粒子群算法具有更好的搜索性能。     

A two-stage hardware scheduler combining greedy and optimal scheduling

Greedy scheduling heuristics provide a low complexity and scalable albeit particularly sub-optimal strategy for hardware-based crossbar schedulers. In contrast, the maximum matching algorithm for Bipartite graphs can be used to provide optimal scheduling for crossbar-based interconnection networks with a significant complexity and scalability cost. In this paper, we show how maximum matching can be reformulated in terms of Boolean operations rather than the more traditional formulations. By leveraging the inherent parallelism available in custom hardware design, we reformulate maximum matching in terms of Boolean operations rather than matrix computations and introduce three maximum matching implementations in hardware. Specifically, we examine a Pure Logic Scheduler with three dimensions of parallelism, a Matrix Scheduler with two dimensions of parallelism and a Vector Scheduler with one dimension of parallelism. These designs reduce the algorithmic complexity for an N×N$N\times N$ network from O(N³)$O\left(N^3\right)$ to O(1)$O\left(1\right)$, O(K)$O\left(K\right)$, and O(KN)$O\left(KN\right)$, respectively, where K$K$ is the number of optimization steps. While an optimal scheduling algorithm requires K=2N−1$K=2N-1$ steps, by starting with our hardware-based greedy strategy to generate an initial schedule, our simulation results show that the maximum matching scheduler can achieve 99% of the optimal schedule when K=9$K=9$. We examine hardware and time complexity of these architectures for crossbar sizes of up to N=1024$N=1024$. Using FPGA synthesis results, we show that a greedy schedule for crossbars, ranging from 8×8 to 256×256, can be optimized in less than 20 ns per optimization step. For crossbars reaching 1024×1024 the scheduling can be completed in approximately 10 μs with current technology and could reach under 90 ns with future technologies.     

Pulsatile flow of blood using a modified second-grade fluid model

We study the unsteady pulsatile flow of blood in an artery, where the effects of body acceleration are included. The blood is modeled as a modified second-grade fluid where the viscosity and the normal stress coefficients depend on the shear rate. It is assumed that the blood near the wall behaves as a Newtonian fluid, and in the core as a non-Newtonian fluid. This phenomenon is also known as the Fahraeus–Lindqvist effect. The equations are made dimensionless and solved numerically.     

基于MC9S08GB60的汽车电控悬架控制系统设计

对空气悬架的控制是提高车辆行驶平顺性的有效方法。以MC9S08GB60为控制芯片,以YBL6891H型客车的1/4车辆模型为研究对象,设计了一种客车用空气悬架的电子控制单元。该控制单元通过调整空气弹簧的刚度,降低车身垂直加速度,并采用模糊PID控制算法,提高控制精度。试验表明:该电子控制单元能有效降低车身垂直加速度,在改善车辆行驶平顺性的同时,提高了操纵稳定性。     

一种基于偏微分方程的车辆加速度信号自适应降噪方法

提出一种针对MEMS加速度计信号的基于偏微分方程的自适应降噪方法,该方法不仅能有效克服由于传感器本身原因及车载环境振动噪声带来的影响,获得准确的加速度信号,而且实现容易、实时性好.通过对车辆加速度信号进行建模并叠加真实加速度噪声作为仿真信号,将该方法与选用db6小波基、heursure自适应阈值、4层分解的最佳小波进行降噪性能对比,证明在车辆正常行驶的加速度幅值下,该方法不仅能够取得和小波近似的降噪性能,而且很大程度上减少了运算时间.最后通过对实际车载加速度信号的降噪处理和倾角测量中的应用,再次证明该方法在滤除噪声的同时能够较好体现细节信息,很适合应用在对实时性和准确性要求高的实际工程中.     

Observer-invariant histopathology using genetics-based machine learning

Prostate cancer accounts for one-third of noncutaneous cancers diagnosed in US men and is a leading cause of cancer-related death. Advances in Fourier transform infrared spectroscopic imaging now provide very large data sets describing both the structural and local chemical properties of cells within prostate tissue. Uniting spectroscopic imaging data and computer-aided diagnoses (CADx), our long term goal is to provide a new approach to pathology by automating the recognition of cancer in complex tissue. The first step toward the creation of such CADx tools requires mechanisms for automatically learning to classify tissue types—a key step on the diagnosis process. Here we demonstrate that genetics-based machine learning (GBML) can be used to approach such a problem. However, to efficiently analyze this problem there is a need to develop efficient and scalable GBML implementations that are able to process very large data sets. In this paper, we propose and validate an efficient GBML technique——based on an incremental genetics-based rule learner. exploits massive parallelisms via the message passing interface (MPI) and efficient rule-matching using hardware-implemented operations. Results demonstrate that is capable of performing prostate tissue classification efficiently, making a compelling case for using GBML implementations as efficient and powerful tools for biomedical image processing.     

紧凑等离子体环整体加速过程的数值计算

紧凑等离子体环（CT）应用广泛,可以作为聚变驱动源和脉冲X射线源等,对CT加速过程的研究是其应用方面研究的基础．描述了CT加速的基本原理,基于整体加速模型导出数值模拟方程组,采用四阶龙格-库塔法数值求解此方程组．根据Phillips实验室MARADUER的参数和SHIVA—STAR电容器组参数,对CT在同轴加速器内的加速过程进行了数值模拟,定量地分析了影响加速效率的各种因素．计算结果表明：CT的质量越大、加速器越长,加速效率越高,但加速效率存在饱和趋势;电感和电阻越小,加速效率越高;对于一定的电容器初始储能,电容值较小时加速效率越高．     

边坡稳定分析的相对失稳加速度方法

边坡稳定分析是土力学的经典课题,通过稳定分析能够得到边坡的安全系数。但最小安全系数对应的临界滑动面不一定是最先发生滑动的破坏面,失稳加速度临界滑动面则可能最先滑动。失稳加速度方法可以用统一的原理计算边坡和隧洞等所有具有临空面的结构的稳定性;可以利用常规极限平衡法的假定条件进行计算,也可以利用有限元等数值方法的应力计算成果;可以计算失稳加速度,也可以以失稳加速度的模最小来计算安全系数,此时也相当于一种特殊的模式识别方法。本文对失稳加速度方法的提出过程及主要原理进行系统总结,并将其推广到相对失稳加速度和相对安全系数,放松了约束条件,使得它可以具有更加广阔的应用前景。