A method-of-moments (MoM) analysis is applied to the problem of determining late-time resonances of dielectric bodies of revolution buried in a lossy layered medium, with application to ...plastic-land-mine identification. To make such an analysis tractable, we have employed the method of complex images to evaluate the layered-medium Green's function. The application of this method to resonant structures characterized by complex resonant frequencies, introduces numerical issues not manifested at real frequencies (i.e., for driven problems) with such discussed here in detail. Numerical results are presented for several buried targets in which we demonstrate, for example, the spiraling character of the resonant frequencies of particular targets as a function of the target depth.
We propose two new methods for wideband array signal imaging for targets situated in unknown random media. First, a normalized coherent interferometric (N-CINT) imaging algorithm is developed based ...on coherent interferometric (CINT) imaging theory, yielding improved imaging performance with experimental data. Second, a phase-difference analysis (PDA) method is proposed to significantly reduce computation time and to improve imaging quality. The parameters in the two methods are determined adaptively by optimizing an objective function. Experiments are carried out for electromagnetic scattering using a linear antenna array, providing a demonstration of these methods.
Bayesian compressive sensing and projection optimization Ji, Shihao; Carin, Lawrence
ACM International Conference Proceeding Series; Vol. 227: Proceedings of the 24th international conference on Machine learning; 20-24 June 2007,
06/2007
Conference Proceeding
Open access
This paper introduces a new problem for which machine-learning tools may make an impact. The problem considered is termed "compressive sensing", in which a real signal of dimension N is measured ...accurately based on K << N real measurements. This is achieved under the assumption that the underlying signal has a sparse representation in some basis (e.g., wavelets). In this paper we demonstrate how techniques developed in machine learning, specifically sparse Bayesian regression and active learning, may be leveraged to this new problem. We also point out future research directions in compressive sensing of interest to the machine-learning community.
Previous models for video captioning often use the output from a specific layer of a Convolutional Neural Network (CNN) as video features. However, the variable context-dependent semantics in the ...video may make it more appropriate to adaptively select features from the multiple CNN layers. We propose a new approach for generating adaptive spatiotemporal representations of videos for the captioning task. A novel attention mechanism is developed, that adaptively and sequentially focuses on different layers of CNN features (levels of feature "abstraction"), as well as local spatiotemporal regions of the feature maps at each layer. The proposed approach is evaluated on three benchmark datasets: YouTube2Text, M-VAD and MSR-VTT. Along with visualizing the results and how the model works, these experiments quantitatively demonstrate the effectiveness of the proposed adaptive spatiotemporal feature abstraction for translating videos to sentences with rich semantics.
A superresolution signal processing algorithm is used for the identification of wavefronts from the fields scattered from several canonical targets. Particular wave objects that are examined are ...single and multiple edge diffraction, scattering from flat and curved surfaces, cone diffraction, and creeping waves. The scattering data are computed numerically via the method of moments (MoM) and are processed using a modified matrix-pencil algorithm. General properties of superresolution processing of such data-independent of the particular algorithm used-are assessed through an examination of the Cramer-Rao (C-R) bounds for basic scattering scenarios.
We present a probabilistic model for tensor decomposition where one or more tensor modes may have side-information about the mode entities in form of their features and/or their adjacency network. We ...consider a Bayesian approach based on the Canonical PARAFAC (CP) decomposition and enrich this single-layer decomposition approach with a two-layer decomposition. The second layer fits a factor model for each layer-one factor matrix and models the factor matrix via the mode entities' features and/or the network between the mode entities. The second-layer decomposition of each factor matrix also learns a binary latent representation for the entities of that mode, which can be useful in its own right. Our model can handle both continuous as well as binary tensor observations. Another appealing aspect of our model is the simplicity of the model inference, with easy-to-sample Gibbs updates. We demonstrate the results of our model on several benchmarks datasets, consisting of both real and binary tensors.
Experimental and theoretical results are presented for ultra wide-band (UWB) synthetic aperture radar (SAR) signatures of buried anti-tank and anti-personnel mines. Such are characterized by ...resonance-like peaks as well as valleys, across the 50-1200 MHz bandwidth considered. Consequently, frequency subbanding is used to highlight one target over another, of application to discriminating targets (mines) from clutter.
We propose a multi-task learning (MTL) framework for non-linear classification, based on an infinite set of local experts in feature space. The usage of local experts enables sharing at the ...expert-level, encouraging the borrowing of information even if tasks are similar only in subregions of feature space. A kernel stick-breaking process (KSBP) prior is imposed on the underlying distribution of class labels, so that the number of experts is inferred in the posterior and thus model selection issues are avoided. The MTL is implemented by imposing a Dirichlet process (DP) prior on a layer above the task-dependent KSBPs.
Music analysis with a Bayesian dynamic model Lu Ren; Dunson, D.B.; Lindroth, S. ...
2009 IEEE International Conference on Acoustics, Speech and Signal Processing,
2009-April
Conference Proceeding
A Bayesian dynamic model is developed to model complex sequential data, with a focus on audio signals from music. The music is represented in terms of a sequence of discrete observations, and the ...sequence is modeled using a hidden Markov model (HMM) with time-evolving parameters. The model imposes the belief that observations that are temporally proximate are more likely to be drawn from HMMs with similar parameters, while also allowing for ldquoinnovationrdquo associated with abrupt changes in the music texture. Segmentation of a given musical piece is constituted via the model inference and the results are compared with other models and also to a conventional music-theoretic analysis.
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