Digital Architectures for Adaptive Processing of Measurement Data

In this paper we describe the design of digital architectures suitable for the implementation of measurement data classification based on Support Vector Machines (SVMs). The performance of such architectures are then analyzed. The proposed approach can be applied for solving identification and inverse modelling problems, and for processing complex measurement data. Two very different case studies where real-time processing is of paramount importance are discussed: a nonlinear channel equalization and a high energy physics classification task.