Dual-Satellite Source Geolocation with Time and Frequency Offsets and Satellite Location Errors

This paper considers locating a static source on Earth using the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements obtained by a dual-satellite geolocation system. The TDOA and FDOA from the source are subject to unknown time and frequency offsets because the two satellites are imperfectly time-synchronized or frequency-locked. The satellite locations are not known accurately as well. To make the source position identifiable and mitigate the effect of satellite location errors, calibration stations at known positions are used. Achieving the maximum likelihood (ML) geolocation performance usually requires jointly estimating the source position and extra variables (i.e., time and frequency offsets as well as satellite locations), which is computationally intensive. In this paper, a novel closed-form geolocation algorithm is proposed. It first fuses the TDOA and FDOA measurements from the source and calibration stations to produce a single pair of TDOA and FDOA for source geolocation. This measurement fusion step eliminates the time and frequency offsets while taking into account the presence of satellite location errors. The source position is then found via standard TDOA-FDOA geolocation. The developed algorithm has low complexity and performance analysis shows that it attains the Cramér-Rao lower bound (CRLB) under Gaussian noises and mild conditions. Simulations using a challenging scenario with a short-baseline dual-satellite system verify the theoretical developments and demonstrate the good performance of the proposed algorithm

Operator Manifold Approach to Geometry and Particle Physics

The question that guides our discussion is how did the geometry and particles come into being. The present theory reveals primordial deeper structures underlying fundamental concepts of contemporary physics. We begin with a drastic revision of a role of local internal symmetries in physical concept of curved geometry. A standard gauge principle of local internal symmetries is generalized. The gravitation gauge group is proposed, which is generated by hidden local internal symmetries. Last two parts address to the question of physical origin of geometry and basic concepts of particle physics such as the fields of quarks with the spins and various quantum numbers, internal symmetries and so forth; also four basic principles of Relativity, Quantum, Gauge and Color Confinement, which are, as it was proven, all derivative and come into being simultaneously. The most promising aspect of our approach so far is the fact that many of the important anticipated properties, basic concepts and principles of particle physics are appeared quite naturally in the framework of suggested theory.Comment: LaTex, 42 pages, email [email protected]

Competition between decay and dissociation of core-excited OCS studied by X-ray scattering

We show the first evidence of dissociation during resonant inelastic soft X-ray scattering. Carbon and oxygen K-shell and sulfur L-shell resonant and non-resonant X-ray emission spectra were measured using monochromatic synchrotron radiation for excitation and ionization. After sulfur, L2,3 -> {\pi}*, {\sigma}* excitation, atomic lines are observed in the emission spectra as a consequence of competition between de-excitation and dissociation. In contrast the carbon and oxygen spectra show weaker line shape variations and no atomic lines. The spectra are compared to results from ab initio calculations and the discussion of the dissociation paths is based on calculated potential energy surfaces and atomic transition energies.Comment: 12 pages, 6 pictures, 2 tables, http://link.aps.org/doi/10.1103/PhysRevA.59.428

Ensemble Kalman filtering for online Gaussian process regression and learning

Gaussian process regression is a machine learning approach which has been shown its power for estimation of unknown functions. However, Gaussian processes suffer from high computational complexity, as in a basic form they scale cubically with the number of observations. Several approaches based on inducing points were proposed to handle this problem in a static context. These methods though face challenges with real-time tasks and when the data is received sequentially over time. In this paper, a novel online algorithm for training sparse Gaussian process models is presented. It treats the mean and hyperparameters of the Gaussian process as the state and parameters of the ensemble Kalman filter, respectively. The online evaluation of the parameters and the state is performed on new upcoming samples of data. This procedure iteratively improves the accuracy of parameter estimates. The ensemble Kalman filter reduces the computational complexity required to obtain predictions with Gaussian processes preserving the accuracy level of these predictions. The performance of the proposed method is demonstrated on the synthetic dataset and real large dataset of UK house prices

Anomalous rotational-alignment in N=Z nuclei and residual neutron-proton interaction

Recent experiments have demonstrated that the rotational-alignment for the $N=Z$ nuclei in the mass-80 region is considerably delayed as compared to the neighboring $N \ne Z$ nuclei. We investigate whether this observation can be understood by a known component of nuclear residual interactions. It is shown that the quadrupole-pairing interaction, which explains many of the delays known in rare-earth nuclei, does not produce the substantial delay observed for these $N=Z$ nuclei. However, the residual neutron-proton interaction which is conjectured to be relevant for $N=Z$ nuclei is shown to be quite important in explaining the new experimental data.Comment: 4 pages, 3 figures, final version accepted by Phys. Rev. C as a Rapid Communicatio

Quasiparticle spectrum of the hybrid s+g-wave superconductors YNi_2B_2C and LuNi_2B_2C

Recent experiments on single crystals of YNi$_2$B$_2$C have revealed the presence of point nodes in the superconducting energy gap Delta(k} at k = (1,0,0), (0,1,0), (-1,0,0), and (0,-1,0). In this paper we investigate the effects of impurity scattering on the quasiparticle spectrum in the vortex state of s+g-wave superconductors, which is found to be strongly modified in the presence of disorder. In particular, a gap in the quasiparticle energy spectrum is found to open even for infinitesimal impurity scattering, giving rise to exponentially activated thermodynamic response functions, such as the specific heat, the spin susceptibility, the superfluid density, and the nuclear spin lattice relaxation. Predictions derived from this study can be verified by measurements of the angular dependent magnetospecific heat and the magnetothermal conductivity.Comment: 8 pages, RevTex, 4 figure

A 3D inﾠvitro model of patient-derived prostate cancer xenograft for controlled interrogation of inﾠvivo tumor-stromal interactions

Patient-derived xenograft (PDX) models better represent human cancer than traditional cell lines. However, the complex in vivo environment makes it challenging to employ PDX models to investigate tumor-stromal interactions, such as those that mediate prostate cancer (PCa) bone metastasis. Thus, we engineered a defined three-dimensional (3D) hydrogel system capable of supporting the co-culture of PCa PDX cells and osteoblastic cells to recapitulate the PCa-osteoblast unit within the bone metastatic microenvironment in vitro. Our 3D model not only maintained cell viability but also preserved the typical osteogenic phenotype of PCa PDX cells. Additionally, co-culture cellularity was maintained over that of either cell type cultured alone, suggesting that the PCa-osteoblast cross-talk supports PCa progression in bone, as is hypothesized to occur in patients with prostatic bone metastasis. Strikingly, osteoblastic cells co-cultured with PCa PDX tumoroids organized around the tumoroids, closely mimicking the architecture of PCa metastases in bone. Finally, tumor-stromal signaling mediated by the fibroblast growth factor axis tightly paralleled that in the in vivo counterpart. Together, these findings indicate that this 3D PCa PDX model recapitulates important pathological properties of PCa bone metastasis, and validate the use of this model for controlled and systematic interrogation of complex in vivo tumor-stromal interactions

A Bezier curve-based generic shape encoder

Existing Bezier curve based shape description techniques primarily focus upon determining a set of pertinent Control Points (CP) to represent a particular shape contour. While many different approaches have been proposed, none adequately consider domain specific information about the shape contour like its gradualness and sharpness, in the CP generation process which can potentially result in large distortions in the object’s shape representation. This paper introduces a novel Bezier Curve-based Generic Shape Encoder (BCGSE) that partitions an object contour into contiguous segments based upon its cornerity, before generating the CP for each segment using relevant shape curvature information. In addition, while CP encoding has generally been ignored, BCGSE embeds an efficient vertex-based encoding strategy exploiting the latent equidistance between consecutive CP. A nonlinear optimisation technique is also presented to enable the encoder is automatically adapt to bit-rate constraints. The performance of the BCGSE framework has been rigorously tested on a variety of diverse arbitrary shapes from both a distortion and requisite bit-rate perspective, with qualitative and quantitative results corroborating its superiority over existing shape descriptors

Enhanced GMM-based Filtering with Measurement Update Ordering and Innovation-based Pruning

The Gaussian mixture model (GMM) has been extensively investigated in nonlinear/non-Gaussian filtering problems. This paper presents two enhancements for GMM-based nonlinear filtering techniques, namely, the adaptive ordering of the measurement update and normalized innovation square (NIS)-based mixture component management. The first technique selects the order of measurement update by maximizing the marginal measurement likelihood to improve performance. The second approach takes the filtering history of a mixture component into account and prunes those components with NIS larger than a threshold to eliminate their impact on the filtering posterior. The advantage of the proposed enhancements is illustrated via simulations that consider source tracking using the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements received at two unmanned aerial vehicles (UAVs). A GMM-cubature quadrature Kalman filter (CQKF) is implemented and its performances with different measurement update and mixture component management strategies are compared. The superior performance obtained via the use of the two proposed techniques is demonstrated

Light propagation in non-trivial QED vacua

Within the framework of effective action QED, we derive the light cone condition for homogeneous non-trivial QED vacua in the geometric optics approximation. Our result generalizes the ``unified formula'' suggested by Latorre, Pascual and Tarrach and allows for the calculation of velocity shifts and refractive indices for soft photons travelling through these vacua. Furthermore, we clarify the connection between the light velocity shift and the scale anomaly. This study motivates the introduction of a so-called effective action charge that characterizes the velocity modifying properties of the vacuum. Several applications are given concerning vacuum modifications caused by, e.g., strong fields, Casimir systems and high temperature.Comment: 13 pages, REVTeX, 3 figures, to appear in Phys. Rev.