Wireless Source Localization Utilizing An Airborne Interfrometry

This paper investigates the problem of localization of co-channel transmitters or primary users (PUs) using an array mounted on a moving aerial platform. As a practical alternative for a sensor network to pursue the localization task, the proposed Phase Interferometric Source Localization (PISL) technique utilizes a moving sensor that measures phase difference between two antennas mounted on the platform. Due to the sparse nature of PUs' distribution in the region, we model the localization task as a simple basis-pursuit denoising framework and introduce a reconstruction method using a sparse recovery algorithm to discover locations of unknown PUs based on the phase difference measurements. We show that the ratio of distance between two antennas to the carrier-frequency wavelength is the critical parameter making the localization feasible. We also propose a scheme for sensor motion design in order to maximize the number of detectable PUs based on mutual coherence property. Since the motion optimization problem is very hard to address we develop a simple geometric relaxation to address the problem. The simulation results show that PISL can precisely recover the map of PUs with only few measurements and also reveal that sensor motion path can have determinate effect on the localization accuracy. PISL performance is compared with an state-of-the-art technique that utilizes adaptive beamforming and results show the superiority of PISL results in localization accuracy.

Wireless Source Localization Utilizing An Airborne Interfrometry

This paper investigates the problem of localization of co-channel transmitters or primary users (PUs) using an array mounted on a moving aerial platform. As a practical alternative for a sensor network to pursue the localization task, the proposed Phase Interferometric Source Localization (PISL) technique utilizes a moving sensor that measures phase difference between two antennas mounted on the platform. Due to the sparse nature of PUs' distribution in the region, we model the localization task as a simple basis-pursuit denoising framework and introduce a reconstruction method using a sparse recovery algorithm to discover locations of unknown PUs based on the phase difference measurements. We show that the ratio of distance between two antennas to the carrier-frequency wavelength is the critical parameter making the localization feasible. We also propose a scheme for sensor motion design in order to maximize the number of detectable PUs based on mutual coherence property. Since the motion optimization problem is very hard to address we develop a simple geometric relaxation to address the problem. The simulation results show that PISL can precisely recover the map of PUs with only few measurements and also reveal that sensor motion path can have determinate effect on the localization accuracy. PISL performance is compared with an state-of-the-art technique that utilizes adaptive beamforming and results show the superiority of PISL results in localization accuracy.

Efficient Processing of All Nearest Neighbor Queries in Dynamic Road Networks

The increasing trend of GPS-enabled smartphones has led to the tremendous usage of Location-Based Service applications. In the past few years, a significant amount of studies have been conducted to process All nearest neighbor (ANN) queries. An ANN query on a road network extracts and returns all the closest data objects for all query objects. Most of the existing studies on ANN queries are performed either in Euclidean space or static road networks. Moreover, combining the nearest neighbor query and join operation is an expensive procedure because it requires computing the distance between each pair of query objects and data objects. This study considers the problem of processing the ANN queries on a dynamic road network where the weight, i.e., the traveling distance and time varies due to various traffic conditions. To address this problem, a shared execution-based approach called standard clustered loop (SCL) is proposed that allows efficient processing of ANN queries on a dynamic road network. The key concept behind the shared execution technique is to exploit the coherence property of road networks by clustering objects that share common paths and processing the cluster as a single path. In an empirical study, the SCL method achieves significantly better performance than competitive methods and efficiently reduces the computational cost to process ANN queries in various problem settings.

Characterizing the intrinsic properties of individual XFEL pulses via single-particle diffraction

With each single X-ray pulse having its own characteristics, understanding the individual property of each X-ray free-electron laser (XFEL) pulse is essential for its applications in probing and manipulating specimens as well as in diagnosing the lasing performance. Intensive research using XFEL radiation over the last several years has introduced techniques to characterize the femtosecond XFEL pulses, but a simple characterization scheme, while not requiring ad hoc assumptions, to address multiple aspects of XFEL radiation via a single data collection process is scant. Here, it is shown that single-particle diffraction patterns collected using single XFEL pulses can provide information about the incident photon flux and coherence property simultaneously, and the X-ray beam profile is inferred. The proposed scheme is highly adaptable to most experimental configurations, and will become an essential approach to understanding single X-ray pulses.

Frobenius Structures

A Frobenius structure is a monoid together with a comonoid, which satisfies an interaction law. Frobenius structures have a powerful graphical calculus and we prove a normal form theorem that makes them easy to work with. The Frobenius law itself is justified as a coherence property between daggers and closure of a category. We prove classification theorems for dagger Frobenius structures: in Hilb in terms of operator algebras and in Rel in terms of groupoids. Of special interest is the commutative case—as for Hilbert spaces this corresponds to a choice of basis—and provides a powerful tool to model classical information. We discuss phase gates and the state transfer protocol—as well as modules for Frobenius structures—and show how we can use these to model measurement, controlled operations and quantum teleportation.

The Dynamic Measurement of Impinging Sheet Thickness via Partial Coherent Interferometry

In this work, the thicknesses of a series of impinging sheets formed by two ethanol jets under different jet velocities are measured and compared with the theoretical model via a non-intrusive technique, the partial coherent interferometry. An interferometer with the calibrated partial coherence property is used to record the interference pattern by passing one optical path through the impinging sheet. The thickness is measured by analyzing the change of degree of coherence before and after the sheet insertion. The Reynolds numbers and Weber numbers of this experiment range from 269 to 370 and 35 to 67, respectively. The experimental results show that the jet velocity controls the size of the sheet but not affects the thickness distribution. The measured thicknesses are different from the theoretical predictions and indicate that the velocity inside the sheet may not be a constant along the radial direction.

Dimensionality Reduction of Hyperspectral Image Using Spatial-Spectral Regularized Sparse Hypergraph Embedding

Many graph embedding methods are developed for dimensionality reduction (DR) of hyperspectral image (HSI), which only use spectral features to reflect a point-to-point intrinsic relation and ignore complex spatial-spectral structure in HSI. A new DR method termed spatial-spectral regularized sparse hypergraph embedding (SSRHE) is proposed for the HSI classification. SSRHE explores sparse coefficients to adaptively select neighbors for constructing the dual sparse hypergraph. Based on the spatial coherence property of HSI, a local spatial neighborhood scatter is computed to preserve local structure, and a total scatter is computed to represent the global structure of HSI. Then, an optimal discriminant projection is obtained by possessing better intraclass compactness and interclass separability, which is beneficial for classification. Experiments on Indian Pines and PaviaU hyperspectral datasets illustrated that SSRHE effectively develops a better classification performance compared with the traditional spectral DR algorithms.