Background modeling is currently used to detect moving objects in video acquired from static cameras. Numerous statistical methods have been developed over the recent years. The aim of this paper is firstly to provide an extended and updated survey of the recent researches and patents which concern statistical background modeling and secondly to achieve a comparative evaluation. For this, we firstly classified the statistical methods in terms of category. Then, the original methods are reminded and discussed following the challenges met in video sequences. We classified their respective improvements in terms of strategies used. Furthermore, we discussed them in terms of the critical situations they claim to handle. Finally, we conclude with several promising directions for future research. The survey also discussed relevant patents.
Rapid advances in Wireless Sensor Networks (WSNs) indicate that they are becoming increasingly complex. Consequently users and applications are becoming more demanding. Due to unique characteristics of WSNs, like small dimensions and limited resources and capabilities, Quality of Service (QoS) is imposed as one of the key factors of WSNs. In this paper, we surveyed two main approaches for QoS provisioning in WSNs: layered and cross-layer approach. QoS provisioning with layered approach is surveyed in three WSN layers: MAC, network and transport layer. Current developments show that they can be efficiently used for QoS provisioning. However, they consider QoS only as layer specific isolated set of problems and they are highly dependent on the performance of other layers. Cross-layer approach does not have the restrictions as layered approach and hence can dispose with information from all layers of the communication protocol stack. Although it has huge potential to become the most efficient solution for QoS provisioning in WSNs, current development indicate that there are still many issues and challenges that need to be overcome. Since the concept of the QoS is relatively new in WSNs, there are not a large number of patents currently dealing with this issue, however but in coming years a large increase in the number of such patents is expected. Available patents in this domain are described in the paper.
Distance-based k-nearest-neighbors (KNN) retrieval is usually adopted to find nearest neighbors for the target case in case-based reasoning (CBR). However, in practical application, the similarity metrics calculated by this means often estimate the actual situation with bias because different features have different units and magnitudes. To avoid this problem, method of feature normalization is usually employed before the implementation of case retrieval. In this study on performance of CBR in classification, distance-based case retrieval was, respectively, implemented on eight binary classification data sets, including two credit scoring datasets and one bankruptcy prediction dataset, after, respectively, implementing seven feature normalized methods on the datasets. The results show that: 1) The two data normalization methods of T3, namely: scaling feature values into the range of 0 to 1 by revised minimum-maximum normalization, and T7, namely: the use of normalization utility function, helped CBR perform the best on 3 out of 8 data sets; 2) the data normalization method of T4, namely: dividing by the maximum value of the feature or maximum normalization, made CBR perform better on 7 out of 8 data sets than the other methods; 3) The data normalization method of T7 helped CBR improve classification performance on 6 out of 8 data sets. As a whole, T7 and T4, followed by T3/T2(minimum-maximum normalization) and T1(Z-score)/T6(Standard deviation normalization), are the best feature normalization methods for distance- based CBR in classification (including bankruptcy prediction and credit scoring problems), as they obtain the top ranking scores in the experiment. In this patent paper, we contribute the current literature on the understanding of how should we choose the most adequate normalization method that could be applied on the data collected in a particular system.
In a parallel system, nodes communicate with each other by exchanging messages. Different topologies exist for arranging processors in a network based on the architecture of the network; or based on the fact that a network is a multiprocessor or multi computer network. A honeycomb network is considered as a multiprocessor / multi-computer interconnection network where each node represents a processor/computer and each line represents a link between two computers. In this paper a new addressing schema is presented for the honeycomb network; which can be used in many levels. The rest of the paper defines some methods of mapping the honeycomb into bus, tree, grid in addition to a proposed cluster-based architecture. Mapping the honeycomb requires however some compromising; such as ignoring some links or adding others. Recent patent and research advances aim to find methods for reducing the complexity of mapping honeycomb into other topologies.