Two photon couplings of the lightest isoscalars from BELLE data

Amplitude Analysis of two photon production of $\pi\pi$ and ${\overline K}K$, using S-matrix constraints and fitting all available data, including the latest precision results from Belle, yields a single partial wave solution up to 1.4 GeV. The two photon couplings of the $\sigma/f_0(500)$, $f_0(980)$ and $f_2(1270)$ are determined from the residues of the resonance poles.Comment: 11 pages, 3 figures, extended for detail

Estimation Schemes for Networked Control Systems Using UDP-Like Communication

In this work we consider a class of networked control systems (NCS) when the control signal is sent to the plant via a UDP-like communication protocol, the controller sends a communication packet to the plant across a lossy network but the controller does not receive any acknowledgement signal indicating the status of reception/delivery of the control packet. Standard observer based estimators assume the estimator has knowledge of what control signal is applied to the plant, but under the UDP-like communication scheme the estimator does not know what control is applied. Continuing previous work, we present a simple estimation algorithm consisting of a state estimator and mode observer. For single input systems we can add an extra control signal that guarantees recovery of the fate of the control packet. Using a modified state feedback with the added input we can guarantee the estimation error is bounded as is the expected value of the state. This extra input is removed and sufficient conditions on the system properties are given to assure the estimation remain bounded. Comparisons are made between the algorithm presented and the method of unknown input observer. Simulations are provided to demonstrate the algorithm

Kalman Filtering Over A Packet Dropping Network: A Probabilistic Approach

We consider the problem of state estimation of a discrete time process over a packet dropping network. Previous pioneering work on Kalman filtering with intermittent observations is concerned with the asymptotic behavior of E[P_k], i.e., the expected value of the error covariance, for a given packet arrival rate. We consider a different performance metric, Pr[P_k ≤ M], i.e., the probability that P_k is bounded by a given M, and we derive lower and upper bounds on Pr[P_k ≤ M]. We are also able to recover the results in the literature when using Pr[P_k ≤ M] as a metric for scalar systems. Examples are provided to illustrate the theory developed in the paper

Kalman Filtering Over a Packet-Dropping Network: A Probabilistic Perspective

We consider the problem of state estimation of a discrete time process over a packet-dropping network. Previous work on Kalman filtering with intermittent observations is concerned with the asymptotic behavior of E[P_k], i.e., the expected value of the error covariance, for a given packet arrival rate. We consider a different performance metric, Pr[P_k ≤ M], i.e., the probability that P_k is bounded by a given M. We consider two scenarios in the paper. In the first scenario, when the sensor sends its measurement data to the remote estimator via a packet-dropping network, we derive lower and upper bounds on Pr[P_k ≤ M]. In the second scenario, when the sensor preprocesses the measurement data and sends its local state estimate to the estimator, we show that the previously derived lower and upper bounds are equal to each other, hence we are able to provide a closed form expression for Pr[P_k ≤ M]. We also recover the results in the literature when using Pr[P_k ≤ M] as a metric for scalar systems. Examples are provided to illustrate the theory developed in the paper

An Estimation Algorithm for a Class of Networked Control Systems Using UDP-Like Communication Schemes

In this work we consider a class of networked control systems (NCS) when the control signal is sent to the plant via a UDP-like communication protocol. In this case the controller sends a communication packet to the plant across a lossy network, but the controller does not receive any acknowledgement signal indicating the status of the control packet. Standard observer based estimators assume the estimator has knowledge of what control signal is applied to the plant. Under the UDP-like protocol the controller/estimator does not have explicit knowledge whether the control signals have been applied to the plant or not. We present a simple estimation and control algorithm that consists of a state and mode observer as well as a constraint on the control signal sent to the plant. For the class of systems considered, discrete time LTI plants where at least one of the states that is directly affected by the input is also part of the measurement vector, the estimator is able to recover the fate of the control packet from the measurement at the next timestep and exhibit better performance than other naive schemes. For single-input-single-output (SISO) systems we are able to show convergence properties of the estimation error and closed loop stability. Simulations are provided to demonstrate the algorithm and show its effectiveness

"It's Not Too Aggressive": Key Features of Social Branding Anti-Tobacco Interventions for High-Risk Young Adults.

Purpose. Peer crowd-targeted campaigns are a novel approach to engage high-risk young adults in tobacco use prevention and cessation. We elicited the perspectives of young adult key informants to understand how and why two social branding interventions were effective: (1) "COMMUNE," designed for "Hipsters" as a movement of artists and musicians against Big Tobacco, and (2) "HAVOC," designed for "Partiers" as an exclusive, smoke-free clubbing experience. Design. Qualitative study (27 semistructured qualitative phone interviews). Setting. Intervention events held in bars in multiple U.S. cities. Participants: Twenty-seven key informants involved in COMMUNE or HAVOC as organizers (e.g., musicians, event coordinators) or event attendees. Measures. We conducted semistructured, in-depth interviews. Participants described intervention events and features that worked or did not work well. Analysis. We used an inductive-deductive approach to thematically code interview transcripts, integrating concepts from intervention design literature and emergent themes. Results: Participants emphasized the importance of fun, interactive, social environments that encouraged a sense of belonging. Anti-tobacco messaging was subtle and nonjudgmental and resonated with their interests, values, and aesthetics. Young adults who represented the intervention were admired and influential among peers, and intervention promotional materials encouraged brand recognition and social status. Conclusion. Anti-tobacco interventions for high-risk young adults should encourage fun experiences; resonate with their interests, values, and aesthetics; and use subtle, nonjudgmental messaging

A charging model for three-axis stabilized spacecraft

A charging model was developed for geosynchronous, three-axis stabilized spacecraft when under the influence of a geomagnetic substorm. The differential charging potentials between the thermally coated or blanketed outer surfaces and metallic structure of a spacecraft were determined when the spacecraft was immersed in a dense plasma cloud of energetic particles. The spacecraft-to-environment interaction was determined by representing the charged particle environment by equivalent current source forcing functions and by representing the spacecraft by its electrically equivalent circuit with respect to the plasma charging phenomenon. The charging model included a sun/earth/spacecraft orbit model that simulated the sum illumination conditions of the spacecraft outer surfaces throughout the orbital flight on a diurnal as well as a seasonal basis. Transient and steady-state numerical results for a three-axis stabilized spacecraft are presented

Kalman Filtering with Uncertain Process and Measurement Noise Covariances with Application to State Estimation in Sensor Networks

Distributed state estimation under uncertain process and measurement noise covariances is considered. An algorithm based on sensor fusion using Kalman filtering is investigated. It is shown that if the covariances are decomposed into a known nominal covariance plus an uncertainty term, then the uncertainty of the actual estimation error covariance for the Kalman filter grows linearly with the size of the uncertainty term. This result is extended to the sensor fusion scheme to give an upper bound on the actual error covariance for the fused state estimate. Examples are provided to illustrate how the theory can be applied in practice