Estimation for Nonlinear Dynamical Systems over Packet-Dropping Networks

Two approaches, extended Kalman filter (EKF) and moving horizon estimation (MHE), are discussed for state estimation for nonlinear dynamical systems over packet-dropping networks. For EKF, we provide sufficient conditions that guarantee a bounded EKF error covariance. For MHE, a natural scheme on organizing the finite horizon window is proposed to handle intermittent observations. A nonlinear programming software package, SNOPT, is employed in MHE and the formulation for constraints is discussed in detail. Examples and simulation results are presented

Modeling of negative autoregulated genetic networks in single cells

We discuss recent developments in the modeling of negative autoregulated genetic networks. In particular, we consider the temporal evolution of the population of mRNA and proteins in simple networks using rate equations. In the limit of low copy numbers, fluctuation effects become significant and more adequate modeling is then achieved using the master equation formalism. The analogy between regulatory gene networks and chemical reaction networks on dust grains in the interstellar medium is discussed. The analysis and simulation of complex reaction networks are also considered.Comment: 15 pages, 4 figures. Published in Gen

Performance and Radiation Testing of a Low-Noise Switched Capacitor Array for the CMS Endcap Muon System.

The 16-channel, 96-cell per channel switched capacitor array ( SCA) ASIC developed at UC Davis for the cathode readout of the cathode strip chambers ( CSC) in the CMS endcap muon system is ready for production. For the final full-sized prototype, the Address Decoder was re-designed and LVDS receivers were incorporated into the chip package. Under precision testing, the chip exhibits excellent linearity within the 1V design range and very low cell-to-cell pedestal variation. Monitored samples of the production design were subjected to exposure to a 63.3 MeV proton beam. The performance of chips after exposures up to 100 krad was within tolerances of an unexposed part

Effects of in-medium vector meson masses on low-mass dileptons from SPS heavy-ion collisions

Using a relativistic transport model to describe the expansion of the fire-cylinder formed in the initial stage of heavy-ion collisions at SPS/CERN energies, we study the production of dileptons with mass below about 1 GeV from these collisions. The initial hadron abundance and their momentum distributions in the fire-cylinder are determined by following the general features of the results from microscopic models based on the string dynamics and further requiring that the final proton and pion spectra and rapidity distributions are in agreement with available experimental data. For dilepton production, we include the Dalitz decay of $\pi ^0$, $\eta$, $\eta^\prime$, $\omega$ and $a_1$ mesons, the direct decay of primary $\rho ^0$, $\omega$ and $\phi$ mesons, and the pion-pion annihilation that proceeds through the $\rho^0$ meson, the pion-rho annihilation that proceeds through the $a_1$ meson, and the kaon-antikaon annihilation that proceeds through the $\phi$ meson. We find that the modification of vector meson properties, especially the decrease of their mass due to the partial restoration of chiral symmetry, in hot and dense hadronic matter, provides a quantitative explanation of the recently observed enhancement of low-mass dileptons by the CERES collaboration in central S+Au collisions and by the HELIOS-3 collaboration in central S+W collisions.Comment: 46 pages, LaTeX, figures available from [email protected], to appear in Nucl. Phys.

GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs

This work studies the problem of stochastic dynamic filtering and state propagation with complex beliefs. The main contribution is GP-SUM, a filtering algorithm tailored to dynamic systems and observation models expressed as Gaussian Processes (GP), and to states represented as a weighted sum of Gaussians. The key attribute of GP-SUM is that it does not rely on linearizations of the dynamic or observation models, or on unimodal Gaussian approximations of the belief, hence enables tracking complex state distributions. The algorithm can be seen as a combination of a sampling-based filter with a probabilistic Bayes filter. On the one hand, GP-SUM operates by sampling the state distribution and propagating each sample through the dynamic system and observation models. On the other hand, it achieves effective sampling and accurate probabilistic propagation by relying on the GP form of the system, and the sum-of-Gaussian form of the belief. We show that GP-SUM outperforms several GP-Bayes and Particle Filters on a standard benchmark. We also demonstrate its use in a pushing task, predicting with experimental accuracy the naturally occurring non-Gaussian distributions.Comment: WAFR 2018, 16 pages, 7 figure

Enhancement of low-mass dileptons in heavy-ion collisions

Using a relativistic transport model for the expansion stage of S+Au collisions at 200 GeV/nucleon, we show that the recently observed enhancement of low-mass dileptons by the CERES collaboration can be explained by the decrease of vector meson masses in hot and dense hadronic matter.Comment: 12 pages, RevTeX, 3 figures available from [email protected]

Curation of the End-of-Term Web Archive

Paper for the 2011 IS&T Archiving Conference. This paper discusses the Classification of the End-of-Term Archive research project at the University of North Texas