联合式电路保护有助于防止损坏DVB网络设备

随着广播、电信和IT业的融合，未来的电话和数字电视都将成为互联网上的应用，这样就需要采用新型的客户端设备（CPE）和网络基础设施，以适应更高数据传输速率的要求，并满足新出现的DVB—T／S／C标准以及符合当前和提议中的通讯要求。     

Decentralized fault detection and diagnosis via sparse PCA based decomposition and Maximum Entropy decision fusion

This paper proposes an approach for decentralized fault detection and diagnosis in process monitoring sensor networks. The sensor network is decomposed into multiple, potentially overlapping, blocks using the Sparse Principal Component Analysis algorithm. Local predictions are generated at each block using Support Vector Machine classifiers. The local predictions are then fused via a Maximum Entropy algorithm. Empirical studies on the benchmark Tennessee Eastman Process data demonstrated that the proposed decentralized approach achieves accuracy comparable to that of the fully centralized approach, while offering benefits in terms of fault tolerance, reusability, and scalability.     

Outage performance of dual‐hop relaying systems over extended generalized‐K fading channels

The outage performance of the amplify‐and‐forward relaying strategies over mutually uncorrelated extended generalized‐K fading channels is addressed in this paper. The attention is dedicated to the analyses of the noise‐limited and also interference‐limited environment. The new analytical expression for outage probability of observed relaying system in the presence of thermal noise is derived using the method for approximating equivalent signal‐to‐noise ratio. In addition, the outage performance is studied for the dual‐hop system when only the single dominant co‐channel interference is inherent at the relay and destination node. The correctness of the proposed mathematical derivations is verified by simulations. Copyright © 2014 John Wiley & Sons, Ltd.     

Supervised clustering of label ranking data using label preference information

This paper studies supervised clustering in the context of label ranking data. The goal is to partition the feature space into K clusters, such that they are compact in both the feature and label ranking space. This type of clustering has many potential applications. For example, in target marketing we might want to come up with K different offers or marketing strategies for our target audience. Thus, we aim at clustering the customers’ feature space into K clusters by leveraging the revealed or stated, potentially incomplete customer preferences over products, such that the preferences of customers within one cluster are more similar to each other than to those of customers in other clusters. We establish several baseline algorithms and propose two principled algorithms for supervised clustering. In the first baseline, the clusters are created in an unsupervised manner, followed by assigning a representative label ranking to each cluster. In the second baseline, the label ranking space is clustered first, followed by partitioning the feature space based on the central rankings. In the third baseline, clustering is applied on a new feature space consisting of both features and label rankings, followed by mapping back to the original feature and ranking space. The RankTree principled approach is based on a Ranking Tree algorithm previously proposed for label ranking prediction. Our modification starts with K random label rankings and iteratively splits the feature space to minimize the ranking loss, followed by re-calculation of the K rankings based on cluster assignments. The MM-PL approach is a multi-prototype supervised clustering algorithm based on the Plackett-Luce (PL) probabilistic ranking model. It represents each cluster with a union of Voronoi cells that are defined by a set of prototypes, and assign each cluster with a set of PL label scores that determine the cluster central ranking. Cluster membership and ranking prediction for a new instance are determined by cluster membership of its nearest prototype. The unknown cluster PL parameters and prototype positions are learned by minimizing the ranking loss, based on two variants of the expectation-maximization algorithm. Evaluation of the proposed algorithms was conducted on synthetic and real-life label ranking data by considering several measures of cluster goodness: (1) cluster compactness in feature space, (2) cluster compactness in label ranking space and (3) label ranking prediction loss. Experimental results demonstrate that the proposed MM-PL and RankTree models are superior to the baseline models. Further, MM-PL is has shown to be much better than other algorithms at handling situations with significant fraction of missing label preferences.     

Effect of radiation-induced oxide-trapped charge on mobility inp-channel MOSFETs

It is shown that radiation-induced oxide-trapped charge contributes to an increase in mobility in p-channel MOSFETs. A new scattering mechanism involving retardation of surface-roughness scattering due to oxide-trapped charge is proposed in order to explain the observed mobility increase