## 1 Introduction  
Intelligent integrated multi-sensor systems are finding a more and more widespread range of applications in areas including the automotive, aerospace and defense industries, industrial, medical, building automation and security uses, and intelligent house and wear. This remarkable increase of applications is due to the ongoing advances in sensor and integration technology [1-4], computing nodes [5,6], wireless communication [7-9], and signal processing algorithms [10-13]. Such intelligent multi-sensor systems also require, however, a larger variety of sensor electronics and sensor signal processing techniques to be efficiently employed in all these different applications. Currently, a significant part of intelligent sensor systems are still manually created by more or less experienced designers and need to be adapted or reengineered for each new application or different task. The design process goes through the main steps of sensor selection and scene optimization, analog and digital hardware selection or conception, choice of signal and feature processing, dimensionality reduction, and classification. Although the sensor selection, sensor parameter selection and the processing steps of dimensionality reduction and classification are more and more the subject of automation efforts, employing learning and optimization techniques [14-16], the decisive tasks of selection, combination, and parameter setting of heuristic signal processing and feature computation method are currently left to the human designers as a tedious, time and labor consuming task with potentially suboptimal outcome. In particular, the great diversity of available methods and tools from conventional signal processing to computational intelligence techniques imposes severe challenges on the experience and qualifications of the designer. Similar problems have already inspired research and implementation in the field of industrial vision and the design of corresponding image processing systems in the last fifteen years [61,62]. However, image processing and general sensor signal processing show substantial differences due to underlying physical principles. Thus, only inspirations from this work can be abstracted to our field of work and numerous extensions and modifications reflected by our research goals are required. In numerous cases, the utilization of a single sensor is insufficient for deriving important information about particular measured objects. Other research activities have shown the need for multi-sensors or sensor arrays, e.g., in chemical sensing applications, for improving of the decision-making or the overall system performance [2,17]. However, these multi-sensor systems will increase the problem complexity and load on the designer for choosing and combining the proper signal processing and feature computation operators and the setting of the best parameters. In a general approach for intelligent decision making task, e.g., airbag triggering in cars under the constraint of seat occupancy, the sensor signals are computed by operators of feature computation to extract the important features employed in a classifier unit for the recognition task. The features extracted from multi-sensor signals by feature computation operators enrich the dimensionality of data,which as a result increases the computational effort and could decrease the performance of a classifier due to the curse of dimensionality [18]. Therefore, some form of dimensionality reduction is usually applied to reduce the feature space of the data. One of the common dimensionality reduction methods, _i.e._, feature selection, has been applied in the optimization of intelligent sensor system design in order to reduce the cost measurement of overall system with eliminating the unimportant sensors and feature computation operators [19]. The feature computation and classifier selection, as well as the corresponding parameterization are crucial steps in the design process of intelligent multi-sensor systems.