This paper addresses the problem of locating error sources in an erroneous combinational or sequential circuit. We use a fault simulation-based technique to approximate each internal signal’s correcting power. The correcting power of a particular signal is measured in terms of the signal’s correctable set, namely, the maximum set of erroneous input vectors or sequences that can be corrected by ...

Clustering large data is a fundamental problem with a vast number of applications. Due to the increasing size of data, practitioners interested in clustering have turned to distributed computation methods. In this work, we consider the widely used kcenter clustering problem and its variant used to handle noisy data, k-center with outliers. In the noise-free setting we demonstrate how a previous...

Superstrings have many applications in data compression and genetics. However, the decision version of the shortest superstring problem is N P-complete. In this paper we examine the complexity of approximating shortest superstrings. There are two basic measures of the approximations: the length factor and the compression factor. The well known and practical approximation algorithm is the sequen...

Sequential Compressive Sensing, which may be widely used in sensing devices, is a popular topic of recent research. This paper proposes an online recovery algorithm for sparse approximation of sequential compressive sensing. Several techniques including warm start, fast iteration, and variable step size are adopted in the proposed algorithm to improve its online performance. Finally, numerical ...

In healthcare systems, one of the important actions is related to perishable products such as red blood cells (RBCs) units that its consumption management in different periods can contribute greatly to the optimality of the system. In this paper, main goal is to enhance the ability of medical community to organize the RBCs units’ consumption in way to deliver the unit order timely with a focus ...

Applications in machine learning, optimization, andcontrol require the sequential selection of a few system elements,such as sensors, data, or actuators, to optimize the systemperformance across multiple time steps. However, in failure-proneand adversarial environments, sensors get attacked, data getdeleted, and actuators fail. Thence, traditional sequential designparadi...

An approximation tothe sequential updating of the distribution flocation parameters of a linear time series model is developed for non-normal observations. The behaviour of the resulting non-linear recursive filtering algorithm isexamined and shown to have certain desirable properties for a variety of non-normal error distributions. Illustrative examples are given and relationships with previou...

We present an algorithm for computing both functional dependency and unateness of combinational and sequential Boolean functions represented as logic networks. The algorithm uses SAT-based techniques from Combinational Equivalence Checking (CEC) and Automatic Test Pattern Generation (ATPG) to compute the dependency matrix of multi-output Boolean functions. Additionally, the classical dependency...