The Binary Decision Diagram (BDD) based compilation schemes are quite suitable for representing configuration knowledge bases. It is well-known that the size of BDDs are very sensitive to the variable order. In this paper, we study the variable ordering problem of BDDs when they represent a configuration knowledge. Such a study is crucial to better understand the BDD-based compilation schemes. ...

In this paper, we propose improved versions of normalized least mean square (NLMS) algorithm: single and multiple -variable step size normalized least mean square (VSSNLMS) algorithms for echo cancellation. The presented algorithms exhibit faster convergence rate in comparison to NLMS algorithm. Simulation results employing standard figure of merits show how the algorithms perform better than N...

Proposed is a novel variable step size normalized subband adaptive filter (NVSS-NSAF) algorithm, which assigns an individual step size for each subband by minimizing the mean-square of the noise-free a posterior subband error. Furthermore, a noniterative shrinkage method is used to recover the noise-free a priori subband error from the noisy subband error signal. Simulation results using the co...

Set sharing is an abstract domain in which each concrete object is represented by the set of local variables from which it might be reachable. It is a useful abstraction to detect parallelism opportunities, since it contains definite information about which variables do not share in memory, i.e., about when the memory regions reachable from those variables are disjoint. Set sharing is a more pr...

Venn diagrams, flow charts, tree structures, node networks, to name just a few-or to the great lengths that computer companies go to advertise the graphic capabilities of their products, to see that charts and graphs have enormous appeal to people. All of this is true despite the fact that in virtually every case, the same information can be communicated by nonpictorial means: tables of numbers...

This paper presents an axiomatic framework for influence diagram computation, which allows reasoning with partially ordered values of utility. We show how an algorithm based on sequential variable elimination can be used to compute the set of maximal values of expected utility (up to an equivalence relation). Formalisms subsumed by the framework include decision making under uncertainty based o...

Influence diagrams are probabilistic graphical models used to represent and solve sequential decision problems under uncertainty. Sharp numerical values are required to quantify probabilities and utilities. This might be an issue with real models, whose parameters are typically obtained from expert judgements or partially reliable data. We consider an interval-valued quantification of the param...

The set U(n) of n × n unitary matrices forms a compact topological group with respect to the matrix multiplication and the usual topology, therefore there exists a unique (up to the scalar multiplication) translation invariant measure on U(n), the so-called Haar measure. We will consider a random variable Un which maps from a probability space to U(n), and take its values uniformly from U(n), i...

This dissertation deals with the problem of characterizing the controllers which are potentially optimal in the robustness optimization for (weighted) normalized coprime factor/gap metric uncertainty, which is the basis for the H1 Loop Shaping Design Method. This dissertation considers single-input-single-output systems. Given a plant P and a stabilizing controller C, we ask if C can be obtaine...