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Flexible cooking surfaces have changed the domestic induction heating product paradigm enabling the use of a wider range of cookware materials, shapes, and positions. In order to implement such systems, multiple-output resonant inverters featuring high-performance and high-efficiency operation while achieving a cost-effective implementation are required. This paper proposes a multiple-output zero-voltageswitching resonant inverter for flexible induction heating appliances. The proposed converter features a matrix structure, enabling a cost-effective implementation with a reduced number of power devices while achieving high performance and low switching losses. It has been tested by means of an experimental prototype featuring 48 induction heating coils, proving the feasibility of the proposed approach. INDEX TERMS Induction heating, home appliances, resonant power conversion, inverters.

In this paper we discuss an efficient methodology for the characterization of Microelectrode Recordings (MER) obtained during deep brain stimulation surgery for Parkinson's disease using Support Vector Machines and present the results of a preliminary study. The methodology is based in two algorithms: (1) an algorithm extracts multiple computational features from the microelectrode neurophysiology, and (2) integrates them in the support vector machines algorithm for classification. It has been applied to the problem of the recognition of subcortical structures: thalamus nucleus, zona incerta, subthalamic nucleus and substantia nigra. The SVM (support vector machines) algorithm performed quite well achieving 99.4% correct classification. In conclusion, the use of a computer-based system, like the one described in this paper, is intended to avoid human subjectivity in the localization of the subcortical structures and mainly the subthalamic nucleus (STN) for neurostimulation.