Time-reversion of a hybrid state stochastic difference system witha jump-linear smoothing application

The reversion in time of a stochastic difference equation in a hybrid space with a Markovian solution is presented. The reversion is obtained by a martingale approach, which previously led to reverse time forms for stochastic equations with Gauss-Markov or diffusion solutions. The reverse time equations follow from a particular noncanonical martingale decomposition, while the reverse time equations for Gauss-Markov and diffusion solutions followed from the canonical martingale decomposition. The need for this noncanonical decomposition stems from the hybrid state-space situation. Moreover, the nonGaussian discrete-time situation leads to reverse time equations that incorporate a Bayesian estimation step. The latter step is carried out for linear systems with Markovian switching coefficients, and the result is shown to provide the solution to the problem of fixed-interval smoothing. For an application of this smoothing approach to a trajectory with sudden maneuvers, simulation results are given to illustrate the practical use of the reverse time equations obtained     

Dual effect, certainty equivalence, and separation in stochastic control

In this paper the various policies in fixed end-time stochastic control are discussed first. The emphasis is on the difference between the feedback and closed-loop policies. It is shown how the closed-loop policy has the important property that it can be actively adaptive, while the feedback policy can only be passively adaptive. The feature of being actively adaptive is possible when the control has a dual effect, i.e., in addition to its effect on the state it affects the state uncertainty. The intimate connection between the neutrality (lack of dual effect) and certainty equivalence properties for a class of problems is proved. This new result is then used to widen the class of problems for which it was previously known that the certainty equivalence property holds.     

Isolation of Marine Paracoccus sp. Ss63 from the Sponge Sarcotragus sp. and Characterization of its Quorum-Sensing Chemical-Signaling Molecules by LC-MS/MS Analysis

Gram-negative bacteria often use N-acyl homoserine lactones (AHLs) as quorum-sensing (QS) signal molecules. The present study aimed to identify and characterize the production of AHLs by Paracoccus sp. Ss63, which was isolated from the Mediterranean sponge Sarcotragus sp. High performance liquid chromatography – high resolution tandem mass spectrometry (LC-HRMS/MS) provided evidence for the production, by Paracoccus sp. Ss63, of twelve saturated long-chain AHLs (3-OH-C10-HSL, 3-OH-C12-HSL, C10-HSL, 3-OH-C14-HSL, C12-HSL, 3-O-C16-HSL, 3-OH-C16-HSL, C14-HSL, 3-OH-C18-HSL, C16-HSL, 3-O-C18-HSL, and C18-HSL), along with four putative unsaturated long-chain AHLs (C10 : 1-HSL, 3-OH-C18 : 1-HSL, C16 : 1-HSL, and C18 : 1-HSL). The distribution of Paracoccus sp. Ss63 in different sponge species, as well as in seawater and marine sediment samples, suggests a mixed lifestyle for this bacterium, i.e., free-living and host-associated. The high pH of seawater is likely to inactivate the AHL signal molecules, and limit the possibility of AHLs to accumulate to the concentration required for QS-based gene regulation. We hypothesize that Paracoccus sp. Ss63 utilizes quorum sensing to sense and respond to the different environments it experiences, with an active QS only when found inside enclosed niches (host-associated and at lower pH).     

The interacting multiple model algorithm for systems with Markovianswitching coefficients

An important problem in filtering for linear systems with Markovian switching coefficients (dynamic multiple model systems) is the management of hypotheses, which is necessary to limit the computational requirements. A novel approach to hypotheses merging is presented for this problem. The novelty lies in the timing of hypotheses merging. When applied to the problem of filtering for a linear system with Markovian coefficients, the method is an elegant way to derive the interacting-multiple-model (IMM) algorithm. Evaluation of the IMM algorithm shows that it performs well at a relatively low computational load. These results imply a significant change in the state of the art of approximate Bayesian filtering for systems with Markovian coefficients     

Application of stochastic control theory to resource allocation under uncertainty

The subject of this paper is the application of stochastic control theory to resource allocation under uncertainty. In these problems it is assumed that the results of a given allocation of resources are not known with certainty, but that a limited number of experiments can be performed to reduce the uncertainty. The problem is to develop a policy for performing experiments and allocating resources on the basis of the outcome of the experiments such that a performance index is optimized. The problem is first analyzed using the basic stochastic dynamic programming approach. A computationally practical algorithm for obtaining an approximate solution is then developed. This algorithm preserves the "closed-loop" feature of the dynamic programming solution in that the resulting decision policy depends both on the results of past experiments and on the statistics of the outcomes of future experiments. In other words, the present decision takes into account the value of future information. The concepts are discussed in the context of the general problem of allocating resources to repair machines where it is possible to perform a limited number of diagnostic experiments to learn more about potential failures. Illustrative numerical results are given.     

From receiver operating characteristic to system operatingcharacteristic: evaluation of a track formation system

The authors discuss the application of multitarget tracking techniques in combination with a Markov chain model to the evaluation of a track formation system based on a composite logic. Target track detection probability is computed in the presence of false alarms. The target track detection probability versus the false track probability is plotted; the values of target detection probability and the false alarm probability are varied to conform to the system's operating signal-to-noise ratio. This allows choice of the detector's operating point so to satisfy the overall system requirements. A procedure for inferring the overall system capacity in terms of the minimum spacing of targets that ensures tracking with a certain reliability is presented. The entire methodology is illustrated by a case study of an actual system     

Tracking in a cluttered environment with probabilistic data association

This paper presents a new approach to the problem of tracking when the source of the measurement data is uncertain. It is assumed that one object of interest (‘target’) is in track and a number of undesired returns are detected and resolved at a certain time in the neighbourhood of the predicted location of the target's return. A suboptimal estimation procedure that takes into account all the measurements that might have originated from the object in track but does not have growing memory and computational requirements is presented. The probability of each return (lying in a certain neighborhood of the predicted return, called ‘validation region’) being correct is obtained—this is called ‘probabilistic data association’ (PDA). The undesired returns are assumed uniformly and independently distributed. The estimation is done by using the PDA method with an appropriately modified tracking filter, called PDAF. Since the computational requirements of the PDAF are only slightly higher than those of the standard filter, the method can be useful for real-time systems. Simulation results obtained for tracking an object in a cluttered environment show the PDAF to give significantly better results than the standard filter currently in use for this type of problem.