Abstract
The estimation of 1 dB compression and third-order intercept points can be obtained after the cross-correlation between dynamic current and output voltage of radio frequency power amplifiers. This estimation is performed using actual power measurements and not power inferred from voltage values. The underlining theory and a correlator that allows implementing this measurement on-chip are presented. The trade-off between measuring voltage and the actual power is also discussed and it is shown that different information concerning the output load is obtained when observing the PA's output voltage and power. Simulation results, obtained with the model of a prototype demonstration chip, show that good accuracy can be obtained with relatively simple measurement conditions. These results include the analysis of optimum stimuli amplitudes and the effect of noise in estimation accuracy.

Abstract

Abstract
This chapter presents a proposal to solve the problem of the adaptation of multimedia services in mobile contexts. The chapter combines context-awareness techniques with user interface modeling to dynamically adapt telecommunications services to user resources, in terms of terminal and network conditions. The solution is mainly characterized by the approach used for resolving the existing dependencies among user interface variables, which is based on the constraints theory. The experiments and tests carried out with these techniques demonstrate a general improvement of the adaptation of multimedia services in mobile environments, in comparison to systems that do not dynamically integrate the user context information in the adaptation process.

Abstract
Many musical works produced in the past are still currently available only as original manuscripts or as photocopies. The preservation of these works requires their digitalization and transformation into a machine-readable format. However, and despite the many research activities on optical music recognition (OMR), the results for handwritten musical scores are far from ideal. Each of the proposed methods lays the emphasis on different properties and therefore makes it difficult to evaluate the efficiency of a proposed method. We present in this article a comparative study of several recognition algorithms of music symbols. After a review of the most common procedures used in this context, their respective performances are compared using both real and synthetic scores. The database of scores was augmented with replicas of the existing patterns, transformed according to an elastic deformation technique. Such transformations aim to introduce invariances in the prediction with respect to the known variability in the symbols, particularly relevant on handwritten works. The following study and the adopted databases can constitute a reference scheme for any researcher who wants to confront a new OMR algorithm face to well-known ones.

Abstract
Ordinal classification is a form of multi-class classification where there is an inherent ordering between the classes, but not a meaningful numeric difference between them. Although conventional methods, designed for nominal classes or regression problems, can be used to solve the ordinal data problem, there are benefits in developing models specific to this kind of data. This paper introduces a new rationale to include the information about the order in the design of a classification model. The method encompasses the inclusion of consistency constraints between adjacent decision regions. A new decision tree and a new nearest neighbour algorithms are then designed under that rationale. An experimental study with artificial and real data sets verifies the usefulness of the proposed approach. © 2010 IEEE.

Abstract
Support vector machines (SVMs) were initially proposed to solve problems with two classes. Despite the myriad of schemes for multiclassification with SVMs proposed since then, little work has been done for the case where the classes are ordered. Usually one constructs a nominal classifier and a posteriori defines the order. The definition of an ordinal classifier leads to a better generalisation. Moreover, most of the techniques presented so far in the literature can generate ambiguous regions. All-at-Once methods have been proposed to solve this issue. In this work we devise a new SVM methodology based on the unimodal paradigm with the All-at-Once scheme for the ordinal classification. © 2010 IEEE.