An Efficient Polyphase Filter Based Resampling Method for Unifying the PRFs in SAR Data

Variable and higher pulse repetition frequencies (PRFs) are increasingly being used to meet the stricter requirements and complexities of current airborne and spaceborne synthetic aperture radar (SAR) systems associated with higher resolution and wider area products. POLYPHASE, the proposed resampling scheme, downsamples and unifies variable PRFs within a single look complex (SLC) SAR acquisition and across a repeat pass sequence of acquisitions down to an effective lower PRF. A sparsity condition of the received SAR data ensures that the uniformly resampled data approximates the spectral properties of a decimated densely sampled version of the received SAR data. While experiments conducted with both synthetically generated and real airborne SAR data show that POLYPHASE retains comparable performance to the state-of-the-art BLUI scheme in image quality, a polyphase filter-based implementation of POLYPHASE offers significant computational savings for arbitrary (not necessarily periodic) input PRF variations, thus allowing fully on-board, in-place, and real-time implementation

Consensus in the Presence of Multiple Opinion Leaders: Effect of Bounded Confidence

The problem of analyzing the performance of networked agents exchanging evidence in a dynamic network has recently grown in importance. This problem has relevance in signal and data fusion network applications and in studying opinion and consensus dynamics in social networks. Due to its capability of handling a wider variety of uncertainties and ambiguities associated with evidence, we use the framework of Dempster-Shafer (DS) theory to capture the opinion of an agent. We then examine the consensus among agents in dynamic networks in which an agent can utilize either a cautious or receptive updating strategy. In particular, we examine the case of bounded confidence updating where an agent exchanges its opinion only with neighboring nodes possessing 'similar' evidence. In a fusion network, this captures the case in which nodes only update their state based on evidence consistent with the node's own evidence. In opinion dynamics, this captures the notions of Social Judgment Theory (SJT) in which agents update their opinions only with other agents possessing opinions closer to their own. Focusing on the two special DS theoretic cases where an agent state is modeled as a Dirichlet body of evidence and a probability mass function (p.m.f.), we utilize results from matrix theory, graph theory, and networks to prove the existence of consensus agent states in several time-varying network cases of interest. For example, we show the existence of a consensus in which a subset of network nodes achieves a consensus that is adopted by follower network nodes. Of particular interest is the case of multiple opinion leaders, where we show that the agents do not reach a consensus in general, but rather converge to 'opinion clusters'. Simulation results are provided to illustrate the main results.Comment: IEEE Transactions on Signal and Information Processing Over Networks, to appea

Have beliefs in conspiracy theories increased over time?

The public is convinced that beliefs in conspiracy theories are increasing, and many scholars, journalists, and policymakers agree. Given the associations between conspiracy theories and many non-normative tendencies, lawmakers have called for policies to address these increases. However, little evidence has been provided to demonstrate that beliefs in conspiracy theories have, in fact, increased over time. We address this evidentiary gap. Study 1 investigates change in the proportion of Americans believing 46 conspiracy theories; our observations in some instances span half a century. Study 2 examines change in the proportion of individuals across six European countries believing six conspiracy theories. Study 3 traces beliefs about which groups are conspiring against "us,"while Study 4 tracks generalized conspiracy thinking in the U.S. from 2012 to 2021. In no instance do we observe systematic evidence for an increase in conspiracism, however operationalized. We discuss the theoretical and policy implications of our findings

Eliahu I. Jury Historical Perspectives

To me, Eliahu I. Jury is more than an influential researcher who has made a long-lasting impact on the field of discrete-time systems and whose contributions appear in almost every text in control theory. For me, writing about Prof. Eliahu I. Jury takes a more personal tone. He has been my Ph.D. advisor, a coauthor, a colleague, a family friend, a trusted mentor to whom I constantly turn for valuable advice, and a person whom I hold in the highest regard and for whom I have great respect and admiration. After deliberating on how I should begin this tribute, I thought it apt to borrow the citation of the Egleston Medal, the highest award given by the Columbia University Engineering School Alumni Association, which was presented to Prof. Jury in 1999: 'Academician who initiated the field of discrete-time systems, pioneered the z-transforms and created the 'Jury stability test'.' With the burdens and pressures of professional and personal life, it is not often that we get an opportunity to stop and reflect on the pioneering researchers who have paved the way for us. The Egleston Medal citation is sufficient testimony to the long-lasting impact that Prof. Jury has had on discrete-time systems. I was fortunate to receive direction, guidance, and supervision from Prof. Jury while I was a graduate student at the University of Miami and to have him as a mentor and friend to this very day

Model reduction of two-dimensional discrete time and delay systems

The Badreddin-Mansour and balanced procedures of model reduction of discrete time systems are compared. For this purpose, time response errors of both the techniques are utilized. The relationship between two different reduced models arising from the balanced realization, along with their lattice realizations, is obtained.The 2D Badreddin-Mansour reduction procedure is extended to the 2D MIMO discrete time systems utilizing two new canonical forms. It is shown that in case of MIMO separable systems, the reduced model preserves stability. A comparative study of the two canonical forms is also given.Several important properties of 2D gramians and the balanced realization are derived. Through a counterexample, the conjecture that the reduced model will remain stable is proven invalid. Stability properties of the reduced model are looked into. In case of 2D separable systems, several interesting results regarding the gramians, norms, stability, and minimality are derived. An error bound for the frequency response is also presented. The computation of 2D gramians is investigated. For separable systems, this is possible through the solution of two pairs of Lyapunov equations. For non-separable systems, an efficient technique to compute the 2D gramians is developed.The controllability and observability gramians of a certain type of retarded delay differential systems are defined. The notion of canonical realization, balanced realization, and a method of effective model reduction are then developed

Learning Bayesian network parameters from imperfect data: enhancements to the EM algorithm

The recent years have seen many developments in uncertainty reasoning taking place around Bayesian Networks (BNs). BNs allow fast and efficient probabilistic reasoning. One of the key issues that researchers have faced in using a BN is determining its parameters and structure for a given problem. Many techniques have been developed for learning BN parameters from a given dataset pertaining to a particular problem. Most of the methods developed for learning BN parameters from partially observed data have evolved around the Expectation-Maximization (EM) algorithm. In its original form, EM algorithm is a deterministic iterative two-step procedure that converges towards the maximum-likelihood (ML) estimates. The EM algorithm mainly focuses on learning BN parameters from imperfect data where some of the values are missing. However in many practical applications, partial observability results in a wider range of imperfections, e.g., uncertainties arising from incomplete, ambiguous, probabilistic, and belief theoretic data. Moreover, while convergence is to their ML estimates, the EM algorithm does not guarantee convergence to the underlying true parameters. In this paper, we propose an approach that enables one to learn BN parameters from a dataset containing a wider variety of imperfections. In addition, by introducing an early stopping criterion together with a new initialization method to the EM-algorithm, we show how the BN parameters could be learnt so that they are closer to the underlying true parameters than the converged ML estimated parameters

Robust stability of time-variant difference equations with restricted parameter perturbations: Regions in coefficient-space

Suppose rate of change of coefficients of a linear time-variant system modeled via a difference equation is restricted. The work presented herein is an attempt at developing an algorithm that determines regions in coefficient-space where such a system is guaranteed to be globally asymptotically stable. Such information can be extremely useful in many applications. Some previously published related results are consolidated as well