In this paper, we consider sensor data scheduling with communication energy constraint. A sensor has to decide whether to send its data to a remote estimator or not due to the limited available communication energy. We construct effective sensor data scheduling schemes that minimize the estimation error and satisfy the energy constraint. Two scenarios are studied: the sensor has sufficient comp...

This article presents a framework for performing ensemble and hybrid data assimilation in a weak-constraint four-dimensional variational data assimilation system (w4D-Var). A practical approach is considered that relies on an ensemble of w4D-Var systems solved by the incremental algorithm to obtain flow-dependent estimates to the model error statistics. A proof-of-concept is presented in an ide...

This paper considers state estimation of linear systems using analog amplify and forwarding with multiple sensors, for both multiple access and orthogonal access schemes. Optimal state estimation can be achieved at the fusion center using a time varying Kalman filter. We show that in many situations, the estimation error covariance decays at a rate of 1/M when the number of sensors M is large. ...

Errors in clinical research databases are common but relatively little is known about their characteristics and optimal detection and prevention strategies. We have analyzed data from several clinical research databases at a single academic medical center to assess frequency, distribution and features of data entry errors. Error rates detected by the double-entry method ranged from 2.3 to 26.9%...

In this paper, the time optimal feedrate planning problem under confined feedrate, axis velocity, axis acceleration, axis jerk, and axis tracking error for a high order CNC servo system is studied. The problem is useful in that the full ability of the CNC machine is used to enhance the machining productivity while keeping the machining precision under a given level. However, the problem is comp...

We consider the problem of training a binary sequential classifier under an error rate constraint. It is well known that for known densities, accumulating the likelihood ratio statistics is time optimal under a fixed error rate constraint. For the case of unknown densities, we formulate the learning for sequential detection problem as a constrained density ratio estimation problem. Specifically...

A novel feedback control method for robotic manipulators with random communication delays by combining the optimal P-type iterative learning control (ILC) idea with a minimum tracking error entropy control strategy is presented. The control design is formulated as an optimisation problem with a proper performance index and a constraint. In specific, the performance index implies the idea of the...

Abstract. This paper presents rigorous filtering methods for continuous constraint satisfaction problems based on linear relaxations. Filtering or pruning stands for reducing the search space of constraint satisfaction problems. Discussed are old and new approaches for rigorously enclosing the solution set of linear systems of inequalities, as well as different methods for computing linear rela...

Specifications are key to improving software reliability as well as documenting precisely the intended behavior of software. Writing specifications is still perceived as expensive. Of course, writing implementations is at least as expensive, but is hardly questioned because there is currently no real alternative. Our goal is to give specifications a more balanced role compared to implementation...

Abstract : This paper is concerned with relational Support Vector Machines, at the intersection of Support Vector Machines (SVM) and relational learning or Inductive Logic Programming (ILP). The so-called phase transition framework, primarily developed for constraint satisfaction problems (CSP), has been extended to ILP, providing relevant insights into the limitations and difficulties thereof....