Generic Coverage Verification Without Location Information Using Dimension Reduction
نویسندگان
چکیده
منابع مشابه
Determining active sensor nodes for complete coverage without location information
Selecting a partial set of sensor nodes for execution and still maintaining the system sensing coverage is an important issue in wireless sensor networks. The previous research was concentrated on the location-aware sensor networks but little attention has been devoted to the location-free environments. This paper describes an approach to determining active nodes for full coverage without locat...
متن کاملFace Verification Advances Using Spatial Dimension Reduction Methods: 2DPCA & SVM
Spatial dimension reduction called Two Dimensional PCA method has recently been presented. The application of this variation of traditional PCA considers images as 2D matrices instead of 1D vectors as other dimension reduction methods have been using. The application of these advances to verification techniques, using SVM as classification algorithm, is here shown. The simulation has been perfo...
متن کاملProjection Penalties: Dimension Reduction without Loss
Dimension reduction is popular for learning predictive models in high-dimensional spaces. It can highlight the relevant part of the feature space and avoid the curse of dimensionality. However, it can also be harmful because any reduction loses information. In this paper, we propose the projection penalty framework to make use of dimension reduction without losing valuable information. Reducing...
متن کاملCost reduction in location management using semi-realtime movement information
This paper introduces a dynamic paging scheme based on the semi-realtime movement information of an individual user, which allows a more accurate predication of the user location at the time of paging. In general, a realtime location tracking scheme may require complex control schemes and incur unacceptably high computation and messaging cost. Our proposed approach, namely velocity paging schem...
متن کاملDimension Reduction by Mutual Information Feature Extraction
During the past decades, to study high-dimensional data in a large variety of problems, researchers have proposed many Feature Extraction algorithms. One of the most effective approaches for optimal feature extraction is based on mutual information (MI). However it is not always easy to get an accurate estimation for high dimensional MI. In terms of MI, the optimal feature extraction is creatin...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE/ACM Transactions on Networking
سال: 2012
ISSN: 1063-6692,1558-2566
DOI: 10.1109/tnet.2012.2190620