用于锁模光纤激光陀螺的相敏检测

通过采用锁相放大器,演示了一种性能明显提高的锁模光纤激光陀螺.结果表明锁相放大器的输出与转动速率之间具有理想的线性关系,并对两脉冲(在时间间隔测量过程中引起相位误差)之间的光强差不敏感.与以往报道的时间间隔测量相比,演示了长期稳定性提高了二个数量级.     

A Heuristic for Multiple Lot Sizing For an Order Under Variable Yield

Consider a job shop which must completely fill a large make-to-order demand of a product where production yield is highly variable. After a production lot is completed, if the total output of satisfactory units is inadequate to satisfy the demand, then a new run (with associated setup cost) is made. When the output of good units exceeds the demand, then the excess units are scrapped (with possible salvage value). The optimal lot size minimizes the total of production, setup, holding, shortage, and scrap costs. A heuristic is developed based on the incremental cost of increasing the lot size by one unit. The computational ease and excellent cost performance of the heuristic favor its use in place of the mathematically optimal solution obtained by dynamic programming. Real world manufacturing applications and additional properties of the model are also discussed.     

试论中国信息化之路

简要地论述了信息技术的发展历程;探讨了信息化的概念由来与含义,以及发展中国家推进信息化过程中要处理好的几个关系,包括要构造一个大的信息系统,信息产业服务业与制造业之间的关系,信息化和现代化的关系,信息化与工业化关系等;分析了中国信息化的现状,并对未来发展作出了展望。     

An approach to the hardware implementation of wave digital filters

An approach to implement wave digital filter is presented. The implementation method is applicable to wave digital filters containing only 3-ports adaptors, connected with reflection-free ports. All arithmetic operations are performed in a special arithmetic unit. Main features are the fact that the arithmetic units are effectively utilized, and that only a few memories of standard type are needed.     

Harnessing the Power of GPUs to Speed Up Feature Selection for Outlier Detection

Acquiring a set of features that emphasize the differences between normal data points and outliers can drastically facilitate the task of identifying outliers. In our work, we present a novel non-parametric evaluation criterion for filter-based feature selection which has an eye towards the final goal of outlier detection. The proposed method seeks the subset of features that represent the inherent characteristics of the normal dataset while forcing outliers to stand out, making them more easily distinguished by outlier detection algorithms. Experimental results on real datasets show the advantage of our feature selection algorithm compared with popular and state-of-the-art methods. We also show that the proposed algorithm is able to overcome the small sample space problem and perform well on highly imbalanced datasets. Furthermore, due to the highly parallelizable nature of the feature selection, we implement the algorithm on a graphics processing unit （GPU） to gain significant speedup over the serial version. The benefits of the GPU implementation are two-fold, as its performance scales very well in terms of the number of features, as well as the number of data points.     

Continuous Outlier Monitoring on Uncertain Data Streams

Outlier detection on data streams is an important task in data mining. The challenges become even larger when considering uncertain data. This paper studies the problem of outlier detection on uncertain data streams. We propose Continuous Uncertain Outlier Detection （CUOD）, which can quickly determine the nature of the uncertain elements by pruning to improve the efficiency. Furthermore, we propose a pruning approach -- Probability Pruning for Continuous Uncertain Outlier Detection （PCUOD） to reduce the detection cost. It is an estimated outlier probability method which can effectively reduce the amount of calculations. The cost of PCUOD incremental algorithm can satisfy the demand of uncertain data streams. Finally, a new method for parameter variable queries to CUOD is proposed, enabling the concurrent execution of different queries. To the best of our knowledge, this paper is the first work to perform outlier detection on uncertain data streams which can handle parameter variable queries simultaneously. Our methods are verified using both real data and synthetic data. The results show that they are able to reduce the required storage and running time.     

A Survey on Data Dissemination in Wireless Sensor Networks

Wireless sensor networks （WSNs） have been applied in a variety of application areas. Most WSN systems, once deployed, are intended to operate unattended for a long period. During the lifetime, it is necessary to fix bugs, reconfigure system parameters, and upgrade the software in order to achieve reliable system performance. However, manually collecting all nodes back and reconfiguring through serial connections with computer is infeasible since it is labor-intensive and inconvenient due to the harsh deploying environments. Hence, data dissemination over multi-hop is desired to facilitate such tasks. This survey discusses the requirements and challenges of data dissemination in WSNs, reviews existing work, introduces some relevant techniques, presents the metrics of the performance and comparisons of the state-of-the-art work, and finally suggests the possible future directions in data dissemination studies. This survey elaborates and compares existing approaches of two categories： structure-less schemes and structure-based schemes, classified by whether or not the network structure information is used during the disseminating process. In existing literatures, different categories have definite boundary and limited analysis on the trade-off between different categories. Besides, there is no survey that discusses the emerging techniques such as Constructive Interference （CI） while these techniques have the chance to change the framework of data dissemination. In a word, even though many efforts have been made, data dissemination in WSNs still needs some more work to embrace the new techniques and improve the efficiency and practicability further.     

Channel Aware Opportunistic Routing in Multi-Radio Multi-Channel Wireless Mesh Networks

Opportunistic routing （OR） involves multiple candidate forwarders to relay packets by taking advantage of the broadcast nature and multi-user diversity of the wireless medium. Compared with traditional routing （TR）, OR is more suitable for the unreliable wireless link, and can evidently improve the end to end throughput. At present, there are many achievements concerning OR in the single radio wireless network. However, the study of OR in multi-radio wireless network stays the beginning stage. To demonstrate the benefit of OR in multi-radio multi-channel network, we propose a new route metric -- multi-channel expected anypath transmission time （MEATT）, which exploits the channel diversity and resource of multiple candidate forwarders for OR. Based on the new metric, a distributed Mgorithm named Channel Aware Opportunistic Routing （CAOR） is proposed. The simulation results demonstrate that MEATT improves 1.14 and 1.53 times of the average throughput than existing expected anypath transmission time （EATT）and metric of interference and channel switching cost （MIC） respectively. The average delay of MEATT is 17% and 40% lower than those of EATT, MIC, respectively.