SOM-Based Approach for the Analysis and Classification of Synchronous Impulsive Noise of an In-Ship PLC System

The interest in wideband data transmission over power line communications has increased rapidly. This technology offers a convenient and inexpensive medium to transmit data, reducing the number of cables. This advantage is particularly appealing in many fields, like the railway, naval, and aeronautical ones. Nevertheless, several problems have to be faced to obtain a high data rate. In particular, the presence of noise makes the transmission difficult, degrading the quality of received signals and prohibiting the full application of these communication frameworks. In this paper the behaviour of an in-ship powerline communication system is analyzed in the presence of synchronous periodic impulsive noise. Such noise is modelled at source and its effects on the transmission of wideband signals are evaluated by means of a simulation circuit model. The obtained results allow to identify the characteristics of the channel and the critical conditions due to noise. Subsequently, an unsupervised technique based on principal component analysis and fuzzy c-mean classifier detects the presence and classifies the specific noises. Numerical results show that the proposed approach enables to achieve this target accurately under different operating conditions, proving to be an effective tool to enhance the performances of the considered technology

Signal Processing for NDE

Nowadays, testing and evaluating of industrial equipment using nondestructive tests, is a fundamental step in the manufacturing process. The complexity and high costs of manufacturing industrial components, require examinations in some way about the quality and reliability of the specimens. However, it should be noted, that in order to accurately perform the nondestructive test, in addition to theoretical knowledge, it is also essential to have the experience and carefulness, which requires special courses and experience with theoretical education. Therefore, in the traditional methods, which are based on manual testing techniques and the test results depend on the operator, there is the possibility of an invalid inference from the test data. In other words, the accuracy of conclusion from the obtained data is dependent on the skill and experience of the operator. Thus, using the signal processing techniques for nondestructive evaluation (NDE), it is possible to optimize the methods of nondestructive inspection, and in other words, to improve the overall system performance, in terms of reliability and system implementation costs. In recent years, intelligent signal processing techniques have had a significant impact on the progress of nondestructive assessment. In other words, by automating the processing of nondestructive data and signals, and using the artificial intelligence methods, it is possible to optimize nondestructive inspection methods. Hence, improve overall system performance in terms of reliability and Implementation costs of the system. This chapter reviews the issues of intelligent processing of nondestructive testing (NDT) signals

Multimodal piezoelectric devices optimization for energy harvesting

The use of the piezoelectric effect to convert ambient vibration into useful electrical energy constitutes one of the most studied areas in Energy Harvesting (EH) research. This paper presents a typical cantilevered Energy Harvester device, which relates the electrical outputs to the vibration mode shape easily. The dynamic strain induced in the piezoceramic layer results in an alternating voltage output. The first six modes of frequencies and the deformation pattern of the beam are carried out basing on an eigenfrequency analysis conducted by the MEMS modules of the COMSOL Multiphysic® v3.5a to perform the Finite Element Analysis of the model. Subsequently, the piezoelectric material is cut around the inflection points to minimize the voltage cancellation effect occurring when the sign changes in the material. This study shows that the voltage produced by the device, increases in as the dimensions of the cuts vary in the piezoelectric layer. Such voltage reaches the optimum amount of piezoelectric material and cuts positioning. This proves that the optimized piezoelectric layer is 16% more efficient than the whole piezoelectric layer