In this paper, we investigate an optimization of threshold level with energy detection to improve the spectrum sensing performance. Determining threshold level to minimize spectrum sensing error both reduces collision probability with primary user and enhances usage level of vacant spectrum, resulting in improving total spectrum efficiency. However, when determining threshold level, spectrum se...

We propose a general methodology for approximating the Pareto front of multi-criteria optimization problems. Our search-based methodology consists of submitting queries to a constraint solver. Hence, in addition to a set of solutions, we can guarantee bounds on the distance to the actual Pareto front and use this distance to guide the search. Our implementation, which computes and updates the d...

In this paper parallel universes are defined by their relation to multi-criteria optimization combined with an explicit or implicit link for the unambiguous identification of an optimum. As an explicit link function the desirability index is introduced. Desirabilities are also used for restricting the Pareto set to desired parts.

Cognitive Radar is a recently proposed system concept, one of whose most important characteristics is the closed-loop operation. The feedback structure from the receiver to the transmitter enables the optimization of transmission waveforms based on the latest knowledge about targets and environments. In this paper, we propose an improved waveform design method for target recognition in cognitiv...

Recently, there has been substantial interest in using rule-based modelling approaches, such as the Kappa modelling language, to attack the combinatorial intractability of many biochemical systems. These approaches have allowed several novel static analyses to be developed, which motivates broadening their expressivity. In this paper, we build upon prior work giving Kappa an SPO-rewriting seman...

This paper is concerned with the problem of finding a representative sample of Pareto-optimal points inmulti-objective optimization. The Normal Boundary Intersection algorithm is a scalarization scheme for generating a set of evenly spaced Efficient solutions. A drawback of this algorithm is that Pareto-optimality of solutions is not guaranteed. The contributions of this paper are two-fold. Fir...

Random projection has been widely used in data classification. It maps high-dimensional data into a low-dimensional subspace in order to reduce the computational cost in solving the related optimization problem. While previous studies are focused on analyzing the classification performance of using random projection, in this work, we consider the recovery problem, i.e., how to accurately recove...

Many published articles in automatic music classification deal with the development and experimental comparison of algorithms however the final statements are often based on figures and simple statistics in tables and only a few related studies apply proper statistical testing for a reliable discussion of results and measurements of the propositions’ significance. Therefore we provide two simpl...

Dynamic Efficiency and Pareto Optimality in a Stochastic OLG Model with Production and Social Security We analyze the interaction between risk sharing and capital accumulation in a stochastic OLG model with production. We give a complete characterization of interim Pareto optimality. Our characterization also subsumes equilibria with a PAYG social security system. In a competitive equilibrium i...

Recent works in evolutionary multiobjective optimization suggest to shift the focus from solely evaluating optimization success in the objective space to also taking the decision space into account. They indicate that this may be a) necessary to express the users requirements of obtaining distinct solutions (distinct Pareto set parts or subsets) of similar quality (comparable locations on the P...