矢量中值滤波改进算法研究

在众多彩色图像滤波算法中,矢量中值滤波算法应用最为广泛,但VMF算法在处理每一像素时,计算复杂度较高,这就限制了它在一些领域中(例如实时系统)的应用.在保留VMF算法优点的前提下,通过数据的重用,降低计算的复杂度.实验结果表明,一定程度的数据重用能有效提高算法的运行效率.     

一种基于SAT的FPGA映射优化算法

为了提高FPGA技术映射算法的质量,提出了一种基于布尔可满足性(SAT)的算法,在实现系统功能的前提下,可以将一个子电路映射到数量最少的查看表中。通过将该算法迭代应用于已经完成映射的电路局部,在许多情况下,可以使用更少的查看表来完成。     

谈局域网的性能优化

在局域网运行和维护的过程中,网络管理员不仅要确保网络能够提供正常的服务,还要采取各种措施保证网络能够可靠、安全、高效地运行,从整个网络的角度提高网络的性能。     

关于最大团问题的一种新算法

最大团问题是图论中重要的NP完全问题,目前求解最大团问题的方法只适合某些特殊的图,活则消耗时间长,求解效率低。该文提出了一种新的算法,蚁群算法来解决最大团问题。蚁群优化算法是一种基于自然启发的算法,是一种解决组合优化问题的有效方法。实验结果显示,算法的有效性。     

无线传感器网络中最大化簇寿命的优化模型

分簇是无线传感器网络中节省节点能量的一种有效方法.研究在传感器节点均具有数据融合能力时,网络的分簇方式以及最大化簇的寿命, 提出用簇与簇头节点的能耗向量来具体刻画每个簇能量消耗的方法,建立了最大化簇寿命的整数线性规划模型,并应用该模型分析了两种基本的分簇机制下簇的寿命,提出了对就近点分簇机制下簇结构的一种调整算法.仿真实验表明,这种调整可在缩短网络时延的同时有效地延长簇的寿命.     

A decision support framework for metrics selection in goal-based measurement programs: GQM-DSFMS

Software organizations face challenges in managing and sustaining their measurement programs over time. The complexity of measurement programs increase with exploding number of goals and metrics to collect. At the same time, organizations usually have limited budget and resources for metrics collection. It has been recognized for quite a while that there is the need for prioritizing goals, which then ought to drive the selection of metrics. On the other hand, the dynamic nature of the organizations requires measurement programs to adapt to the changes in the stakeholders, their goals, information needs and priorities. Therefore, it is crucial for organizations to use structured approaches that provide transparency, traceability and guidance in choosing an optimum set of metrics that would address the highest priority information needs considering limited resources. This paper proposes a decision support framework for metrics selection (DSFMS) which is built upon the widely used Goal Question Metric (GQM) approach. The core of the framework includes an iterative goal-based metrics selection process incorporating decision making mechanisms in metrics selection, a pre-defined Attributes/Metrics Repository, and a Traceability Model among GQM elements. We also discuss alternative prioritization and optimization techniques for organizations to tailor the framework according to their needs. The evaluation of the GQM-DSFMS framework was done through a case study in a CMMI Level 3 software company.     

A Gauss–Newton approach to joint image registration and intensity correction

We develop a new efficient numerical methodology for automated simultaneous registration and intensity correction of images. The approach separates the intensity correction term from the images being registered in a regularized expression. Our formulation is consistent with the existing non-parametric image registration techniques, however, an extra additive intensity correction term is carried throughout. An objective functional is formed for which the corresponding Hessian and Jacobian is computed and employed in a multi-level Gauss–Newton minimization approach. In this paper, our experiments are based on elastic regularization on the transformation and total variation on the intensity correction. Validations on dynamic contrast enhanced MR abdominal images for both real and simulated data verified the efficacy of the model.     

A maintenance centric approach to the view selection problem

The View Selection Problem is an optimization problem designed to enhance query performance through the pre-computation and storage of select views given resource constraints. Assuring the materialized views can be updated within a reasonable time frame has become a chief concern for recent models. However, these methods are crafted simply to fit a solution within a feasible range and not to minimize the resource intensive maintenance process. In this paper, we submit two novel advances in terms of model formulation and solution generation to reduce maintenance costs. Our proposed model, the Minimum-Maintenance View Selection Problem, combines previous techniques to minimize and constrain update costs. Furthermore, we define a series of maintenance time reducing principles in solution generation embodied in a constructor heuristic. The model and constructor heuristic are evaluated using an existing clinical data warehouse and state-of-the-art heuristics. Our analysis shows our model produces the lowest-cost solution relative to extant models. Also, they indicate algorithms seeded with our constructor heuristic to be superior solutions to all other methods tested.     

Optimizing XML queries: Bitmapped materialized views vs. indexes

Optimizing queries using materialized views has not been addressed adequately in the context of XML due to the many limitations associated with the definition and usability of materialized views in traditional XML query evaluation models.     

Sensor deployment based on fuzzy graph considering heterogeneity and multiple-objectives to diagnose manufacturing system

In this paper, a methodology is presented to generate an optimized sensor deployment deciding sensor types, numbers, and locations to accurately monitor fault signatures in manufacturing systems. Sensor deployment to robustly monitor operation parameters is the corner stone for diagnosing manufacturing systems. However, current literature lacks investigation in methodologies that handle heterogeneity among sensor properties and consider multiple-objective optimization involved in the sensor deployment. We propose a quantitative fuzzy graph based approach to model the cause–effect relationship between system faults and sensor measurements; analytic hierarchy process (AHP) was used to aggregate the heterogeneous properties of the sensor–fault relationship into single edge values in fuzzy graph, thus quantitatively determining the sensor's detectability to fault. Finally sensor–fault matching algorithms were proposed to minimize fault unobservability and cost for the whole system, under the constraints of detectability and limited resources, thus achieving optimum sensor placement. The performance of the proposed strategy was tested and validated on different manufacturing systems (continuous or discrete); various issues discussed in the methodology were demonstrated in the case studies. In the continuous manufacturing case study, the results illustrated that compared with signed directed graph (SDG), the proposed fuzzy graph based methodology can greatly enhance the detectability to faults (from SDG's 0.699 to fuzzy graph's 0.772). In the discrete manufacturing case study, results from different optimization approaches were compared and discussed; the detectability of sensors to faults also increased from SDG's 0.61 to fuzzy graph's 0.65. The two case study results show that the proposed approach overcame the qualitative approach such as signed directed graph's deficiency on handling sensor heterogeneity and multiple objectives; the proposed approach is systematic and robust; it can be integrated into diagnosis architecture to detect faults in other complex systems.