基于WinCE的电梯监控系统多串口通信实现

提出了一种基于WinCE的多串口电梯远程监控系统的实现方法.该方法以S3C2440微处理器为核心构建硬件平台,实现了与其他设备的串口通信;同时以WinCE5.0为软件平台,完成多串口分层流驱动的设计.通过文件映射,解决了访问多串口设备时产生的竞态问题.测试结果表明,该系统通信正常、数据稳定,能充分满足电梯监控对串行通信的要求.     

A commentary and correction on the article “Pansharpening by exploiting sharpness of the spatial structure”

This study clarifies the implicit potential deficiency caused by the sparse cardinality parameter k in Rong et al. (2014). In addition, k = β × W × M × N (0.9 ≤ β < 1) is suggested to avert this potential deficiency, where β is a ratio controlling the amount of sparse cardinality, W is the number of multispectral bands and M × N is the size of panchromatic image. With the choice of k suggested in this study, the low rank matrix L and sparse matrix S obtained by Go Decomposition (Zhou and Tao 2011) can be iteratively optimized and solved. Thus, instead of choosing k as W × M × N in Rong et al. (2014), the potential deficiency that L is directly obtained as an analytic solution can be averted.     

Robust optimization of supply chain network design in fuzzy decision system

This paper presents a new robust optimization method for supply chain network design problem by employing variable possibility distributions. Due to the variability of market conditions and demands, there exist some impreciseness and ambiguousness in developing procurement and distribution plans. The proposed optimization method incorporates the uncertainties encountered in the manufacturing industry. The main motivation for building this optimization model is to make tools available for producers to develop robust supply chain network design. The modeling approach selected is a fuzzy value-at-risk (VaR) optimization model, in which the uncertain demands and transportation costs are characterized by variable possibility distributions. The variable possibility distributions are obtained by using the method of possibility critical value reduction to the secondary possibility distributions of uncertain demands and costs. We also discuss the equivalent parametric representation of credibility constraints and VaR objective function. Furthermore, we take the advantage of structural characteristics of the equivalent optimization model to design a parameter-based domain decomposition method. Using the proposed method, the original optimization problem is decomposed to two equivalent mixed-integer parametric programming sub-models so that we can solve the original optimization problem indirectly by solving its sub-models. Finally, we present an application example about a food processing company with four suppliers, five plants, five distribution centers and five customer zones. We formulate our application example as parametric optimization models and conduct our numerical experiments in the cases when the input data (demands and costs) are deterministic, have fixed possibility distributions and have variable possibility distributions. Experimental results show that our parametric optimization method can provide an effective and flexible way for decision makers to design a supply chain network.     

An effective algorithm to detect both smoke and flame using color and wavelet analysis

Fire detection is an important task in many applications. Smoke and flame are two essential symbols of fire in images. In this paper, we propose an algorithm to detect smoke and flame simultaneously for color dynamic video sequences obtained from a stationary camera in open space. Motion is a common feature of smoke and flame and usually has been used at the beginning for extraction from a current frame of candidate areas. The adaptive background subtraction has been utilized at a stage of moving detection. In addition, the optical flow-based movement estimation has been applied to identify a chaotic motion. With the spatial and temporal wavelet analysis, Weber contrast analysis and color segmentation, we achieved moving blobs classification. Real video surveillance sequences from publicly available datasets have been used for smoke detection with the utilization of our algorithm. We also have conducted a set of experiments. Experiments results have shown that our algorithm can achieve higher detection rate of 87% for smoke and 92% for flame.     

PolSAR image compression based on online sparse K-SVD dictionary learning

we present a novel polarimetric synthetic aperture radar (PolSAR) image compression scheme. PolSAR data contains lots of similar redundancies in single-channel and massively correlation between polarimetric channels. So these features make it difficult to represent PolSAR data efficiently. In this paper, discrete cosine transform (DCT) is adopted to remove redundancies between polarimetric channels, simple but quite efficient in improving compressibility. Sparse K-singular value decomposition (K-SVD) dictionary learning algorithm is utilized to remove redundancies within each channel image. Double sparsity scheme will be able to achieve fast convergence and low representation error by using a small number of sparsity dictionary elements, which is beneficial for the task of PolSAR image compression. Experimental results demonstrate that both numerical evaluation indicators and visual effect of reconstructed images outperform other methods, such as SPIHT, JPEG2000, and offline method.     

The subspace global skyline query processing over dynamic databases

The global skyline, as an important variant of the skyline, has been widely applied in multi-criteria decision making, business planning and data mining. In this paper, we extend our early work and propose the maintenance methods to process the subspace global skyline (SGS) queries in dynamic databases. In the previous work, we proposed the index structure RB-tree, which can effectively manage the data to accelerate the subspace global skyline calculation. Also, the basic single SGS algorithm based on RB-tree (SSRB) and the optimized single SGS algorithm (OSSRB) were proposed to process a single SGS query. In addition, the multiple SGS algorithm (MSRB) was proposed to calculate multiple SGS queries by sharing the scan spaces of different queries. In this paper, we design some data structures and propose the maintenance approaches of SSRB, OSSRB and MSRB to cope with updates that happen to data sets. Thus our extended algorithms can be adopted for dynamic data sets. Finally, the experimental results show that the proposed algorithms OSSRB and MSRB have good performance to process SGS queries and they can be easily maintained with dynamic datasets.     

A fuzzy control system with application to production planning problems

A considerable part of the literature on fuzzy sets is devoted to the field of fuzzy control system. In this paper, an alternative control system is introduced to describe a dynamic system with fuzzy white noise. In order to find optimal ways to control such a system, fuzzy optimal control theory is further developed. Specifically, a linear quadratic model is formulated and solved as a fuzzy optimal control problem. The formulation and solution of this model provide an economic interpretation of a production planning model both in the finite horizon and in the infinite horizon.     

A quantitative model for software engineering trends

Many decision-makers in industry, government and academia routinely make decisions whose outcome depends on the evolution of software technology trends. Even though the stakes of these decisions are usually very high, decision makers routinely depend on expert opinions and qualitative assessments to model the evolution of software technology; both of these sources of decision-making information are subjective, are based on opinions rather than facts, and are prone to error. In this paper, we report on our ongoing work to build quantitative models of the evolution of software technology trends. In particular, we discuss how we took specific evolutionary models and merged them into a single (general-purpose) model. The original specific models are derived empirically using statistical methods on trend data we had collected over several years, and have been validated individually; in this paper we further validate the generic (general-purpose) model.     

3D floorplanning of low-power and area-efficient Network-on-Chip architecture

Network-on-Chip (NoC) architectures have been adopted by chip multi-processors (CMPs) as a flexible solution to the increasing delay in the deep sub-micron regime. However, the shrinking feature size limits the performance of NoCs due to power and area constraints. In this paper, we propose three 3D floorplanning methods for a Triplet-based Hierarchical Interconnection Network (THIN) which is a new high performance NoC. The proposed floorplanning methods use both Manhattan and Y-architecture routing architectures so as to improve the performance, reduce the power consumption and area requirement of THIN. A cycle accurate simulator was developed based on Noxim NoC simulator and ORION 2.0 energy model. The proposed floorplanning methods show up to 24.69% energy and 8.84% area reduction at best compared with 3D Mesh. Our analysis concludes that THIN is not only a feasible but also a low-power and area-efficient NoC at physical level.     

基于嵌入式ARM9痕量毒气检测系统的研究

基于卟啉阵列传感器和毒气接触发生化学反应导致其色谱变化的“指纹”特性原理,提出了一种以嵌入式ARM9 S3C2440A为核心、以Linux为操作系统、QT/Embedded为开发工具并利用卟啉化学传感器的痕量毒气检测装置.阐述了系统总体设计,实现了卟啉传感阵列光谱信号采集、信号处理、反应环境监控等主要功能模块及控制软件...