Risk-sensitive filters for recursive estimation of motion fromimages

In this paper, an extended risk-sensitive filter (ERSF) is used to estimate the motion parameters of an object recursively from a sequence of monocular images. The effect of varying the risk factor &thetas; on the estimation error is examined. The performance of the filter is compared with the extended Kalman filter (EKF) and the theoretical Cramer-Rao lower bound. When the risk factor &thetas; and the uncertainty in the measurement noise are large, the initial estimation error of the ERSF is less than that of the corresponding EKF The ERSF is also found to converge to the steady state value of the error faster than the EKF. In situations when the uncertainty in the initial estimate is large and the EKF diverges, the ERSF converges with small errors. In confirmation with the theory, as &thetas; tends to zero, the behavior of the ERSF is the same as that of the EKF     

An optimal procedure to extract interaction potentials for protein folding

A formidable challenge in molecular biology is the prediction of the three-dimensional structures of the native state of proteins from their sequence of amino acids. An essential step to solve this problem is the extraction of the coarse-grained interaction potentials between the amino acids. Here we outline preliminary results of a strategy that accomplishes such goal with the search of those potentials which are able to recognize the native state of a protein as a stable local minimum. The method is implemented by exploiting several numerical and analytical tools which have been recently developed by our group. The results are extremely promising: despite the fact that we have used simple forms for Hamiltonians, the extracted potentials are able to stabilize simultaneously at least 10 proteins of different classes to an average distance (per residue) less than 3 Å from the native state.     

A mixed norm performance measure for the design of multiratefilterbanks

A mixed norm performance measure is presented to design the synthesis filters of a multirate filterbank. The mixed norm performance measure is based on the energy as well as the peak value of the error signal. Mathematically, the performance measure minimizes the l₂ -norm of the error signal subject to the l_∞-norm of the error being bounded by some positive value v (this imposes a bound on the peak value of the error signal). The design problem is shown to be that of a mixed ℋ₂/ℋ_∞ optimization problem. The theory of linear matrix inequalities (LMIs) offers a tractable solution to such multiobjective synthesis problems. The synthesis filters designed with the new performance measure are compared with those obtained by similar induced norm minimization techniques in terms of degree of reconstruction, order of the synthesis filters, SNR, and aliasing distortion     

Temporal redundancy methods using risk-sensitive filtering andparameter estimation to detect failures in neutronic sensors duringreactor start-up and steady-state operation

In this paper, simple linear single-input-single output models of the reactor-regulating system have been considered, and risk-sensitive filtering and risk-sensitive parameter-estimation techniques have been used to obtain temporal-redundancy relations. While a risk-sensitive filter is used to provide a temporally redundant measurement of reactivity from an analytically redundant process parameter during a reactor start-up, a risk-sensitive parameter estimation technique is used to obtain temporally redundant measurements during steady state operation