Joint Diagonalization Applied to the Detection and Discrimination of Unexploded Ordnance

Efforts to discriminate buried unexploded ordnance from harmless surrounding clutter are often hampered by the uncertainty in the number of buried targets that produce a given detected signal. We present a technique that helps determine that number with no need for data inversion. The procedure is based on the joint diagonalization of a set of multistatic response (MSR) matrices measured at different time gates by a time-domain electromagnetic induction sensor. In particular, we consider the Naval Research Laboratory’s Time-Domain Electromagnetic Multisensor Towed Array Detection System (TEMTADS), which consists of a 5×5 square grid of concentric transmitter/receiver pairs. The diagonalization process itself generalizes one of the standard procedures for extracting the eigenvalues of a single matrix; in terms of execution time, it is comparable to diagonalizing the matrices one by one. We present the method, discuss and illustrate its mathematical basis and physical meaning, and apply it to several actual measurements carried out with TEMTADS at a test stand and in the field at the former Camp Butner in North Carolina. We find that each target in a measurement is associated with a set of nonzero time-dependent MSR eigenvalues (usually three), which enables estimation of the number of targets interrogated. These eigenvalues have a characteristic shape as a function of time that does not change with the location and orientation of the target relative to the sensor. We justify analytically and empirically that symmetric targets have pairs of eigenvalues with constant ratios between them

Finite sets of dd-planes in affine space

Let $A$ be a subvariety of affine space $\mathbb{A}^n$ whose irreducible components are $d$-dimensional linear or affine subspaces of $\mathbb{A}^n$. Denote by $D(A)\subset\mathbb{N}^n$ the set of exponents of standard monomials of $A$. We show that the combinatorial object $D(A)$ reflects the geometry of $A$ in a very direct way. More precisely, we define a $d$-plane in $\mathbb{N}^n$ as being a set $\gamma+\oplus_{j\in J}\mathbb{N}e_{j}$, where #J=d and $\gamma_{j}=0$ for all $j\in J$. We call the $d$-plane thus defined to be parallel to $\oplus_{j\in J}\mathbb{N}e_{j}$. We show that the number of $d$-planes in $D(A)$ equals the number of components of $A$. This generalises a classical result, the finiteness algorithm, which holds in the case $d=0$. In addition to that, we determine the number of all $d$-planes in $D(A)$ parallel to $\oplus_{j\in J}\mathbb{N}e_{j}$, for all $J$. Furthermore, we describe $D(A)$ in terms of the standard sets of the intersections $A\cap\{X_{1}=\lambda\}$, where $\lambda$ runs through $\mathbb{A}^1$.Comment: 31 pages, 8 figure

On the Hidden Order in URu2_{2}Si2_{2} --- Antiferro Hexadecapole Order and its Consequences

An antiferro ordering of an electric hexadecapole moment is discussed as a promising candidate for the long standing mystery of the hidden order phase in URu$_{2}$Si$_{2}$. Based on localized $f$-electron picture, we discuss the rationale of the selected multipole and the consequences of the antiferro hexadecapole order of $xy(x^{2}-y^{2})$ symmetry. The mean-field solutions and the collective excitations from them explain reasonably significant experimental observations: the strong anisotropy in the magnetic susceptibility, characteristic behavior of pressure versus magnetic field or temperature phase diagrams, disappearance of inelastic neutron-scattering intensity out of the hidden order phase, and insensitiveness of the NQR frequency at Ru-sites upon ordering. A consistency with the strong anisotropy in the magnetic responses excludes all the multipoles in two-dimensional representations, such as $(O_{yz},O_{zx})$. The expected azimuthal angle dependences of the resonant X-ray scattering amplitude are given. The $(x^{2}-y^{2})$-type antiferro quadrupole should be induced by an in-plane magnetic field along $[110]$, which is reflected in the thermal expansion and the elastic constant of the transverse $(c_{11}-c_{12})/2$ mode. The $(x^{2}-y^{2})$-type [$(xy)$-type] antiferro quadrupole is also induced by applying the uniaxial stress along $[110]$ direction [$[100]$ direction]. A detection of these induced antiferro quadrupoles under the in-plane magnetic field or the uniaxial stress using the resonant X-ray scattering provides a direct redundant test for the proposed order parameter.Comment: 10 pages, 10 figures, 5 table

Magneto inductive communication system for underwater wireless sensor networks

Underwater wireless sensor networks have found a number of applications in underwater environment monitoring, infrastructure monitoring, military applications and ocean exploration. Among the four possible means of underwater wireless communication, namely acoustic, electromagnetic (EM), magneto-inductive (MI) and optics communication, MI communication enjoys the advantages of being low cost and robust equally in air, water and soil. This dissertation presents design and implementation of a low-power and low-cost MI sensor network node that is suited for long-term deployment of underwater and underground infrastructure monitoring, such as bridge scour and levee scour monitoring. The designed MI sensor node combat the directionality of the single coil MI communication by utilizing 3D coil to both transmit and receive. The presented MI sensor node is tested in air and underwater to show robustness and reliability. The sensor node is designed after thorough analysis and evaluation of various MI challenges such as directionality, short range, decoupling due to mis-alignment of coils, and effect of metal structure. A communication range of 40 m has been achieved by the prototype sensor node. The prototyping cost of a sensor node is less than US$100 and will be drastically reduced at volume production. A novel and an energy efficient medium access control (MAC) protocol based on the carrier sense medium access (CSMA) has also been implemented for the designed sensor node to improve throughput in a dense network --Abstract, page iv