We propose to significantly extend our work in EEG-based emotion detection for automated expressive performances of algorithmically composed music for affective communication and induction. This new system involves music composed and expressively performed in real-time to induce specific affective states, based on the detection of affective state in a human listener. Machine learning algorithms...

Increased volatility in electricity prices and new emerging demand side management opportunities call for efficient tools for the optimal operation of powerintensive processes. In this work, a general discrete-time model is proposed for the scheduling of power-intensive process networks with various power contracts. The proposed model consists of a network of processes represented by Convex Reg...

In this article we implement a stochastic modeling technique for simulating the communication between processors and arbitration among buses for an embedded SoC. The stochastic models implemented with queues have been used to estimate, through simulation of different arbitration policies, the power consumption and delays, as well as estimate average or worst case scenarios that could occur with...

In this article we implement a stochastic modeling technique for simulating the communication between processors and arbitration among buses. The stochastic models implemented with queues have been used to estimate through simulation of different arbitration policies the power consumption and delays, as well as estimate average or worst case scenarios that could occur with different architectur...

A Monte-Carlo approach to solving a stochastic jump transition model for active-region energy (Wheatland and Glukhov, Astrophys. J. 494, 1998; Wheatland, Astrophys. J. 679, 2008) is described. The new method numerically solves the stochastic differential equation describing the model, rather than the equivalent master equation. This has the advantages of allowing more efficient numerical soluti...

In this paper, we present new theoretical and experimental results for bidirectional A∗ search. Unlike most previous research on this topic, our results do not require assumptions of either consistent or balanced heuristic functions for the search. Our theoretical work examines new results on the worst-case number of node expansions for inconsistent heuristic functions with bounded estimation e...

This paper describes an industrial application of case-based reasoning (CBR) in the aluminium die-casting industry: the setting of parameters of a pressure diecasting machine for a specific die. As well as describing the application, the paper shows how the system has evolved. The use of cases has given the system a flexibility which has enabled it to be used in ways that were not foreseen at t...

A maximum likelihood estimate for frequency carrier, phase leakage and amplitude of digital PSK modulated signals is presented. The three optimum parameters obtained are analyzed and compared with suboptimum realizations in the synchronization process. In order to achieve an efficient system a final structure is closed where each method is selected in order to get not only appropriated performa...

In the recent years, the use of workflows has significantly expanded from its original domain of business processes towards new areas. The increasing demand for individual and more flexible workflows asks for new methods that support domain experts to create, monitor, and adapt workflows. The emergent field of process-oriented case-based reasoning addresses this problem by proposing methods for...