In this paper, we consider the randommatrix ensemble given by (db, dw)-regular graphs onM black vertices andN white vertices, where db ∈ [N γ , N2/3−γ ] for any γ > 0. We simultaneously prove that the bulk eigenvalue correlation statistics for both normalized adjacency matrices and their corresponding covariance matrices are stable for short times. Combined with an ergodicity analysis of the Dy...

A biomechanical model constrained stochastic finite element framework has been developed to jointly estimate myocardium kinematics and material parameters from medical image sequence. In an extended Kalman filter formulation, we have observed that the augmented state error covariance matrix must be carefully chosen in order to avoid divergence. In this paper, we incorporate confidence measures ...

In this paper we present a novel method for spatial and temporal frequency estimation in the case of uncorrelated sources. By imposing the diagonal structure given in the signal covariance matrix, it is possible to improve the performance of subspace based estimators. The proposed method combines ideas from subspace and covariance matching methods to yield a non-iterative frequency estimation a...

Matching is a relation between object types originally designed to support type-safe subclassing with binary methods. In this paper we generalize it to deal with general covariance by allowing eld and method parameter types to change covariantly in matching types. The result is a signiicant increase in the exibility of subclassing. The generalization is in a diierent direction than the recent p...

Introduction Conclusions References

− In this paper a development of an adaptive Kalman filter through a fuzzy inference system (FIS) is outlined. The adaptation is concerned with the imposition of conditions under which the filter measurement noise covariance matrix R or the process noise covariance matrix Q are estimated. The adaptive adjustment is carried out using a FIS based on the whiteness of the filter innovation sequence...

Covariance matching techniques have recently grown in interest due to their good performances for object retrieval, detection, and tracking. By mixing color and texture information in a compact representation, it can be applied to various kinds of objects (textured or not, rigid or not). Unfortunately, the original version requires heavy computations and is difficult to execute in real time on ...

The focus of this paper is on spatial precoding in correlated multi-antenna channels, where the number of independent data-streams is adapted to trade-off the data-rate with the transmitter complexity. Towards the goal of a low-complexity implementation, a structured precoder is proposed, where the precoder matrix evolves fairly slowly at a rate comparable with the statistical evolution of the ...

The scope of the thesis is to estimate the parameters of continuous-time models used within control and communication from sampled data with high accuracy and in a computationally efficient way. In the thesis, continuous-time models of systems controlled in a networked environment, errors-in-variables systems, stochastic closedloop systems, and wireless channels are considered. The parameters o...

In this paper, we introduce a novel descriptor for employing covariance of motion and appearance features for human action and gesture recognition. In our approach, we compute kinematic features from optical flow and first and second-order derivatives of intensities to represent motion and appearance respectively. These features are then used to construct covariance matrices which capture joint...