**4. Industrial quality control**

In the field of quality control there are two main elements which play an important role: the presence of sensors used to capture data, such as signals or images, and the adopted computational intelligence techniques (Piuri & Scotti, 2005). The quality monitoring includes the use of signal measurements or machine visual systems in order to allow a standardized and non-invasive control of industrial production processes. The computational intelligence techniques comprise the formalisation of the mechanism which allows the extraction of useful information from the images and its interpretation for the purposes the systems it is designed for; therefore it may also include components such as neural networks, fuzzy systems and evolutionary computer algorithms. A generic quality control system needs to manage techniques belonging to several scientific areas, such as depicted in Fig.2.

Fig. 2. Generic scheme of quality control system

In the following, a brief explanation of all the blocks included in Fig. 2 is provided.

## **4.1 Data acquisition**

The data acquisition is a typical problem concerning measurements systems. A lot of studies demonstrate how the computational intelligence techniques can improve the performance of sensors from both the static and the dynamic point of view (Ferrari & Piuri, 2003). The sensor modules can be able to self-calibrate and also reduce the unexpected non-linearities. Also eventual errors can be detected and, if necessary, corrected (Wandell et al., 2002). Images are usually acquired by cameras in digital format.
