86,136 research outputs found
Distributed Nonparametric Sequential Spectrum Sensing under Electromagnetic Interference
A nonparametric distributed sequential algorithm for quick detection of
spectral holes in a Cognitive Radio set up is proposed. Two or more local nodes
make decisions and inform the fusion centre (FC) over a reporting Multiple
Access Channel (MAC), which then makes the final decision. The local nodes use
energy detection and the FC uses mean detection in the presence of fading,
heavy-tailed electromagnetic interference (EMI) and outliers. The statistics of
the primary signal, channel gain or the EMI is not known. Different
nonparametric sequential algorithms are compared to choose appropriate
algorithms to be used at the local nodes and the FC. Modification of a recently
developed random walk test is selected for the local nodes for energy detection
as well as at the fusion centre for mean detection. It is shown via simulations
and analysis that the nonparametric distributed algorithm developed performs
well in the presence of fading, EMI and is robust to outliers. The algorithm is
iterative in nature making the computation and storage requirements minimal.Comment: 8 pages; 6 figures; Version 2 has the proofs for the theorems.
Version 3 contains a new section on approximation analysi
Hypothesis Test for Manifolds and Networks
Statistical inference of high-dimensional data is crucial for science and engineering. Such high-dimensional data are often structured. For example, they can be data from a certain manifold or a large network. Motivated by the problems that arise in recommendation systems, power systems, and social media, etc., this dissertation aims to provide statistical modeling for such problems and perform statistical inferences. This dissertation focus on two problems. (i) statistical modeling for smooth manifold and inferences for the corresponding characteristic rank; (ii) detection of change-points for sequential data in a network. For the first topic. We start with the rank selection problem in the matrix completion problem. We addressed the problem of rank identifiability in minimum rank matrix completion problem and proposed a statistical model for the low-rank matrix approximation problem. We then generalize the problem to a more general smooth manifold. For the second topic. We study the problem of cascading failure motivated by the study of the power system. We proposed a model for failure propagation and a fast algorithm to perform the test procedure of detecting the cascading failure. The other problem we study in change-points detection is to detect the change of event data. We use the multivariate Hawkes process to capture the self and cross excitation between the events and proposed a test procedure base on scan score statistics.Ph.D
A Fusion Framework for Camouflaged Moving Foreground Detection in the Wavelet Domain
Detecting camouflaged moving foreground objects has been known to be
difficult due to the similarity between the foreground objects and the
background. Conventional methods cannot distinguish the foreground from
background due to the small differences between them and thus suffer from
under-detection of the camouflaged foreground objects. In this paper, we
present a fusion framework to address this problem in the wavelet domain. We
first show that the small differences in the image domain can be highlighted in
certain wavelet bands. Then the likelihood of each wavelet coefficient being
foreground is estimated by formulating foreground and background models for
each wavelet band. The proposed framework effectively aggregates the
likelihoods from different wavelet bands based on the characteristics of the
wavelet transform. Experimental results demonstrated that the proposed method
significantly outperformed existing methods in detecting camouflaged foreground
objects. Specifically, the average F-measure for the proposed algorithm was
0.87, compared to 0.71 to 0.8 for the other state-of-the-art methods.Comment: 13 pages, accepted by IEEE TI
Detection and localization of change-points in high-dimensional network traffic data
We propose a novel and efficient method, that we shall call TopRank in the
following paper, for detecting change-points in high-dimensional data. This
issue is of growing concern to the network security community since network
anomalies such as Denial of Service (DoS) attacks lead to changes in Internet
traffic. Our method consists of a data reduction stage based on record
filtering, followed by a nonparametric change-point detection test based on
-statistics. Using this approach, we can address massive data streams and
perform anomaly detection and localization on the fly. We show how it applies
to some real Internet traffic provided by France-T\'el\'ecom (a French Internet
service provider) in the framework of the ANR-RNRT OSCAR project. This approach
is very attractive since it benefits from a low computational load and is able
to detect and localize several types of network anomalies. We also assess the
performance of the TopRank algorithm using synthetic data and compare it with
alternative approaches based on random aggregation.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS232 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
- …