在CodeWarrior编译环境下运行μC／OS—Ⅲ

μC／OS-Ⅱ是面向8／16位及低端32位单片机应用的RTOS,而新近推出的μC／OS-Ⅲ则面向高性能32位单片机,如ARMCortex等。以Cortex—M4为内核的Kinetis系列单片机,不仅用于全国大学生飞思卡尔杯智能车竞赛,也用于诸多大学的嵌入式系统教学,官方提供的开发环境是CodeWarrior。本文介绍如何利用CodeWarrior开发环境,将μC／OS-Ⅲ在Kinetis单片机上运行起来,以便将μC／OS-Ⅲ引入教学、科研与应用。     

基于ARM的嵌入式语音存储系统设计

为了有效地节省语音数据的传输带宽和存储系统的磁盘空间,需要在保证语音质量的前提下尽可能降低其编码比特率。本设计采用经过优化的G．729语音压缩编译码算法,以ARM处理器为载体,开发的嵌入式语音存储系统可实现语音信号的海量存储,而且处理速度快、可靠性好、扩展方便。通过严格的测试和评估,该系统能够实现对大量语音数据的压缩和记录,各项指标基本达到了预期的水平。     

Robust ℋ︁∞ PID control for multivariable networked control systems with disturbance/noise attenuation

In this paper, we study the design problem of PID controllers for networked control systems (NCSs) with polyhedral uncertainties. The load disturbance and measurement noise are both taken into account in the modeling to better reflect the practical scenario. By using a novel technique, the design problem of PID controllers is converted into a design problem of output feedback controllers. Our goal of this paper is two‐fold: (1) To design the robust PID tracking controllers for practical models; (2) To develop the robust ??_∞ PID control such that load and reference disturbances can be attenuated with a prescribed level. Sufficient conditions are derived by employing advanced techniques for achieving delay dependence. The proposed controller can be readily designed based on iterative suboptimal algorithms. Finally, four examples are presented to show the effectiveness of the proposed methods. Copyright © 2011 John Wiley & Sons, Ltd.     

Developing feasible and optimal schedules of jobs on one machine

The paper considers the properties of feasible and optimal scheduling of jobs on one machine under constraints on the terms of the beginning and completion of jobs and on partial sequences of job performance. The established properties and the lower-bound estimates of the length of the optimal schedule are used to develop methods for the exact and approximate solutions of the formulated problem by sequential optimization algorithms. The proposed algorithms are illustrated by numerical examples and can be successfully applied to solve these problems in the absence of constraints.     

Comparative evaluation of linear edge detection methods

A comparative evaluation of the most commonly used linear methods for edge detection in grayscale images are presented. Detectors based on the first and second derivatives of image brightness are considered. The method for automatic edge tracking in grayscale images is proposed. The model for assessing errors and artifacts caused by sampling during digitization of real input images is proposed. Investigation of edge detectors isotropy and errors caused by input images sampling is conducted. The advantage of the Isotropic operator for edge tracking is shown. The noise immunity of linear edge detection methods is assessed and the superiority of 3 × 3 gradient operators for noisy images is shown. Isotropic and Sobel operators are identified to be optimal on a basis of sampling errors, output noise level, and computational complexity.     

Control of downrange and crossrange motion of a descent module in a certain range of reentry angles with load factor constraints

Results concerning the terminal guidance algorithm at the reentry of the descent module into the Earth atmosphere are presented. This algorithm simultaneously eliminates predicted deviations in the downrange and crossrange directions. The restriction on the tolerable load factor n ?? 3 is taken into account. Three reentry angles are analyzed: the medium angle ?1.5°, the steep angle ?1.9°, and the flat angle ?1.2°. The motion of the descent module is considered in the disturbed Earth atmosphere represented by the TsNIIMash model. This model includes variations of the atmosphere density and the wind field. It is demonstrated that, depending on the reentry angle, the proposed algorithm guarantees the downrange maneuver zone in the range 1240?C1890 km and the crossrange maneuver zone of 210?C220 km on one side. The maximum guidance error at the altitude of 1 km does not exceed 0.8 km, and the average error is 0.1 km. The algorithm uses up to three bank angle reversals and uses the numerical prediction of the remaining part of the trajectory when choosing the guidance parameters. The concept of the automatic adjustment of the bank angle reference function depending on the entry angle and the prescribed distance to the target point in the maneuver zone is formulated.     

Interactive image segmentation based on level sets of probabilities

In this paper, we present a robust and accurate algorithm for interactive image segmentation. The level set method is clearly advantageous for image objects with a complex topology and fragmented appearance. Our method integrates discriminative classification models and distance transforms with the level set method to avoid local minima and better snap to true object boundaries. The level set function approximates a transformed version of pixelwise posterior probabilities of being part of a target object. The evolution of its zero level set is driven by three force terms, region force, edge field force, and curvature force. These forces are based on a probabilistic classifier and an unsigned distance transform of salient edges. We further propose a technique that improves the performance of both the probabilistic classifier and the level set method over multiple passes. It makes the final object segmentation less sensitive to user interactions. Experiments and comparisons demonstrate the effectiveness of our method.     

Mining blackhole and volcano patterns in directed graphs: a general approach

Given a directed graph, the problem of blackhole mining is to identify groups of nodes, called blackhole patterns, in a way such that the average in-weight of this group is significantly larger than the average out-weight of the same group. The problem of finding volcano patterns is a dual problem of mining blackhole patterns. Therefore, we focus on discovering the blackhole patterns. Indeed, in this article, we develop a generalized blackhole mining framework. Specifically, we first design two pruning schemes for reducing the computational cost by reducing both the number of candidate patterns and the average computation cost for each candidate pattern. The first pruning scheme is to exploit the concept of combination dominance to reduce the exponential growth search space. Based on this pruning approach, we develop the gBlackhole algorithm. Instead, the second pruning scheme is an approximate approach, named approxBlackhole, which can strike a balance between the efficiency and the completeness of blackhole mining. Finally, experimental results on real-world data show that the performance of approxBlackhole can be several orders of magnitude faster than gBlackhole, and both of them have huge computational advantages over the brute-force approach. Also, we show that the blackhole mining algorithm can be used to capture some suspicious financial fraud patterns.