**DSP in Monitoring, Sensing and Measurements**

26 Will-be-set-by-IN-TECH

76 Applications of Digital Signal Processing

Dong, X., Frossard, P., Vandergheynst, P. and Nefedov, N. (2011). Clustering with Multi-Layer

Flake, G., Lawrence, S., Giles, C. and Coetzee, F. (2002). Self-organization and identification

Newman, M.E.J. and Girvan, M. (2004). Finding and evaluating community structure in

Kiukkonen, N., Blom, J., Dousse, O., Gatica-Perez, D. and Laurila, J. (2010). Towards Rich

Kolda, T. and Bader, B. (2009). Tensor decompositions and applications, *SIAM Review*, vol.51,

Lambiotte, R., Delvenne, J.-C. and Barahona, M. (2009). Laplacian Dynamics and Multiscale

Liben-Nowel, D. and Kleinberg, J. (2003). The Link Prediction Problem for Social Networks.

Manning, C., Raghava, P. and Schütze, H. (2008). *Introduction to Information Retrieval*.

Newman, M. E. J. (2004). Fast algorithm for detecting community structure in networks.

Olfati-Saber, R. et al. (2007). Consensus and Cooperation in Networked Multi-Agent Systems.

Strehl A. & Ghosh, J. (2002). Cluster Ensembles - A Knowledge Reuse Framework for Combining Multiple Partitions. *Journal of Machine Learning Research*, 3, pp. 583–617. Tang, L., Wang, W. and Wang X. (2009). Uncovering Groups via Heterogeneous Interaction

Wasserman, S. & Faust, K. (1994). *Social Network Analysis*, Cambridge University Press,

Zachary, W. (1977). An information flow model for conflict and fission in small groups. *Journal*

Mobile Phone Datasets: Lausanne Data Collection Campaign. *Proc. ACM Int. Conf.*

Fortunato, S. (2011). Community detection in graphs. *Physics Reports*, 486, pp. 75–174. Girvan, M. & Newman, M. E. J. (2002). Community structure in social and biological networks.

Graphs: Spectral Perspective. *ArXiv, 1106.2233*.

*Proc. Natl. Acad. Sci. USA*, 99, pp. 7821–7826.

networks. *Physical Review*, E 69, 026113.

Kuramoto, Y. (1975). *Lectuer Notes in Physics*, 30, Springer NY.

Modular Structure in Networks. *ArXiv:0812.1770v3*.

*ACM Int. Conf. on Information and Knowledge Management*.

Analysis. *SDM workshop on Analysis of Dynamic Networks*.

*Pervasive Services*, Berlin.

Cambridge University Press.

*Physical Review*, E 69, 066133.

*IEEE Proceedings*, 95(1), pp. 215–233.

*of Anthropological Research*, 33, pp. 452–473.

pp. 455–500.

Cambridge.

of Web communities. *IEEE Computer* 35, pp. 66–71.

**4** 

*Spain* 

**Comparative Analysis of Three Digital Signal** 

**Processing Techniques for 2D Combination of** 

**Echographic Traces Obtained from Ultrasonic Transducers Located at Perpendicular Planes** 

Miguel A. Rodríguez-Hernández1, Antonio Ramos2 and J. L. San Emeterio2

In certain practical cases of quality control in the manufacturing industry, by means of ultrasonic non-destructive evaluation (NDE), it is very difficult to detect certain types of internal flaw using conventional instrumentation based in ultrasonic transducers located on a unique external surface of the piece under inspection. In these cases, the detection problems are due to the especial flaws orientation or their spatial location, and some

In addition, it is convenient, in a more general scope, to improve the flaw-location in two dimensions, by using several ultrasonic transducers emitting beams from distinct places. In fact, the utilization of more than one detection transducer provides complementary information in the NDE of many pieces. These transducers can be located at the same or at different planes depending on the piece shape and the detection necessities. In any case, the result of such arrangement is a set of ultrasonic traces, which have to be carefully fussed using digital signal processing techniques in order to extract more accurate and more

The usual trend for reducing the mentioned limitations in flaw detection is to increase the number of ultrasonic channels involved in the testing. On the other hand, it is important to reduce this ultrasonic channels number in order to minimize technological costs. In addition, it should be noted that the detection capability also depends on other important factors, because, from a more general point of view, still some physical limitations of the ultrasonic beams remain for a) certain angles of the scanning (Chang and Hsieh 2002), b) for certain complex geometries of the industrial components to be tested (Roy et al 1999) or c) for biological elements in medical diagnosis (Defontaine et al 2004, Reguieg et al 2006). Schemes have been preliminarily proposed in order to improve flaw detection in difficult conditions, trying to resolve these type of aspects well with two transducers and additional digital signal processing of echoes (Chang and Hsieh 2002), or well with several arrays of few elements (Engl and Meier 2002). Other posterior alternative proposals, based on perpendicular scanning from two planes with a reduced transducers number and ultrasonic

technological solutions for it are still pendent to be proposed.

**1. Introduction** 

complete detection results.

*1ITACA. Universitat Politècnica de Valencia* 

*2Lab. Ultrasonic Signal, Systems and Technologies, CSIC. Madrid* 
