In this paper, we present a low-complexity ‘reversible variable length code’ (RVLC) decoding scheme for MPEG-4 video that recovers more blocks and sometimes more macroblocks (MBs) from error propagation region of corrupted video packets, as compared to the MPEG-4 scheme. The remaining blocks and MBs are concealed. Simulation studies have been carried out to show that the proposed scheme achieve...

In this paper, we present a low-complexity RVLC decoding scheme (including the effect of DC/AC prediction) that recovers more blocks and sometimes more MBs from error propagation region of corrupted video packets, as compared to the MPEG-4 scheme. The remaining blocks and MBs are concealed, by using maximally smooth error concealment scheme. It is shown that the proposed scheme achieves better ...

The dynamics of populations is frequently subject to intrinsic noise. At the same time unknown interaction networks or rate constants can present quenched uncertainty. Existing approaches often involve repeated sampling of the quenched disorder and then running the stochastic birth-death dynamics on these samples. In this paper we take a different view, and formulate an effective jump process, ...

The performance of high-speed VLSI circuits is increasingly limited by interconnect coupling noise. In this paper we present a closed-form crosstalk noise model with accuracy comparable to that of SPICE for an arbitrary ramp input. We also develop a simplified delay model for estimating delays on coupled RC lines considering input slew times for both aggressor and victim lines. We then apply ou...

Our goal is to facilitate better human-robot collaboration by enabling robots to learn our preferences. To learn preferences, robots need to interact with users. We propose using comparison based learning, which learns preferences by asking a user to compare several alternatives. To minimize user burden, we use active learning. A challenge of comparison based learning is that it can be difficul...

This paper presents a new approach for estimating the state of a linear dynamic system when two different types of uncertainties are present simultaneously. The first type of uncertainty is a stochastic process with given distribution. The second type of uncertainty is only known to be bounded, the exact underlying distribution is unknown. This includes inequality constraints between state vari...

In many regression tasks, in addition to an accurate estimate of the conditional mean of the target distribution, an indication of the predictive uncertainty is also required. There are two principal sources of this uncertainty: the noise process contaminating the data and the uncertainty in estimating the model parameters based on a limited sample of training data. Both of them can be summaris...

Optimization problems due to noisy data solved using stochastic programming or robust optimization approaches require the explicit characterization of an uncertainty set U that models the nature of the noise. Such approaches depend on the modeling of the uncertainty set and suffer from an erroneous estimation of the noise. In this paper, we introduce a framework that considers the uncertain dat...

Geometric fault detection and isolation filters are known for having excellent fault isolation, fault reconstruction and sensitivity properties under small modeling uncertainty and noise. However they are assumed to be sensitive to model uncertainty and noise. This paper proposes a method to incorporate model uncertainty into the design. First, a geometric filter is designed on the nominal plan...