Broadband beamforming and direction finding using concentric ring array

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file viewed on (July 18, 2006)Includes bibliographical references.Vita.Thesis (Ph. D.) University of Missouri-Columbia 2005.Dissertations, Academic -- University of Missouri--Columbia -- Electrical and computer engineering.Sensor arrays have been used widely in applications including radar, sonar, seismology, biomedicine, communications and imaging. A very popular type of sensor array is circular array, which has almost invariant array pattern in azimuthal plane. This dissertation considers beamformer design and direction finding for a broadband source using concentric ring array (CRA) that contains many concentric rings of different radii. The multi-ring structure of a CRA has several advantages including the flexibility in array pattern synthesis and adaptive array design. We first proposed three CRA array pattern synthesis techniques, which can be used to control the side lobe and/or main lobe width of the array pattern of a deterministic broadband beamformer. We then proposed a flexible partially adaptive broadband beamformer design based on element-space approach. Finally, we proposed an efficient direction finding technique for broadband source using CRA. The proposed design techniques are corroborated by experiments from simulated as well as measured data

Robust utility maximization with intractable claims

We study a continuous-time expected utility maximization problem in which the investor at maturity receives the value of a contingent claim in addition to the investment payoff from the financial market. The investor knows nothing about the claim other than its probability distribution, hence an ``intractable claim''. In view of the lack of necessary information about the claim, we consider a robust formulation to maximize her utility in the worst scenario. We apply the quantile formulation to solve the problem, expressing the quantile function of the optimal terminal investment income as the solution of certain variational inequalities of ordinary differential equations. In the case of an exponential utility, the problem reduces to a (non-robust) rank--dependent utility maximization with probability distortion whose solution is available in the literature

Compressive Sequential Learning for Action Similarity Labeling

Human action recognition in videos has been extensively studied in recent years due to its wide range of applications. Instead of classifying video sequences into a number of action categories, in this paper, we focus on a particular problem of action similarity labeling (ASLAN), which aims at verifying whether a pair of videos contain the same type of action or not. To address this challenge, a novel approach called compressive sequential learning (CSL) is proposed by leveraging the compressive sensing theory and sequential learning. We first project data points to a low-dimensional space by effectively exploring an important property in compressive sensing: the restricted isometry property. In particular, a very sparse measurement matrix is adopted to reduce the dimensionality efficiently. We then learn an ensemble classifier for measuring similarities between pairwise videos by iteratively minimizing its empirical risk with the AdaBoost strategy on the training set. Unlike conventional AdaBoost, the weak learner for each iteration is not explicitly defined and its parameters are learned through greedy optimization. Furthermore, an alternative of CSL named compressive sequential encoding is developed as an encoding technique and followed by a linear classifier to address the similarity-labeling problem. Our method has been systematically evaluated on four action data sets: ASLAN, KTH, HMDB51, and Hollywood2, and the results show the effectiveness and superiority of our method for ASLAN