Finite-element model-based fault diagnosis, a case study of a ceramic pressure sensor structure

Finite-element (FE) analysis is an efficient aid in the design optimisation of modern electronic devices. Simulation tools have dramatically reduced the product design cycle. The model validated with actual prototypes can also be used for other purposes such as failure analysis, fatigue prediction, reliability studies, etc. In this paper we present a case study of application of FE model in fault localisation in ceramic pressure sensor structures. The sensing elements are thick-film resistors acting as strain gauges, which translate the strain into an electrical signal. In the design phase, FE analysis was used to analyse the sensitivity of the thick-film resistors to the applied pressure. The same model was used for non-destructive fault diagnosis and troubleshooting of the prototype series. Selected examples illustrate the approach. FE model can also be used in the production test process. Since simulations are rather time consuming, quick fault localisation can be performed by a fault dictionary while in-depth diagnosis is performed by FE analysis.     

Plasma Extracellular Vesicles Play a Role in Immune System Modulation in Minimal Hepatic Encephalopathy

Minimal hepatic encephalopathy (MHE) is associated with changes in the immune system including an increased pro-inflammatory environment and altered differentiation of CD4+ T lymphocytes. The mechanisms remain unknown. Changes in extracellular vesicle (EV) cargo including proteins and miRNAs could play a main role as mediators of immune system changes associated with MHE. The aim was to assess whether plasma EVs from MHE patients played a role in inducing the pro-inflammatory environment and altered differentiation of CD4+ T lymphocyte subtypes in MHE patients. We characterized the miRNA and protein cargo of plasma EVs from 50 cirrhotic patients (27 without and 23 with MHE) and 24 controls. CD4+ T cells from the controls were cultured with plasma EVs from the three groups of study, and the cytokine release and differentiation to CD4+ T-cell subtypes were assessed. Plasma EVs from MHE patients had altered miRNA and protein contents, and were enriched in inflammatory factors compared to the controls and patients without MHE. EVs from MHE patients modulated the expression of pro-inflammatory IL-17, IL-21, and TNF-α and anti-inflammatory TGF-β in cultured CD4+ T lymphocytes, and increased the proportion of Th follicular and Treg cells and the activation of Th17 cells. In conclusion, plasma EVs could play an important role in the induction of immune changes observed in MHE.     

Convolutional neural networks combined with feature selection for radio-frequency fingerprinting

Radio-frequency fingerprinting is a technique for the authentication and identification of wireless devices using their intrinsic physical features and an analysis of the digitized signal collected during transmission. The technique is based on the fact that the unique physical features of the devices generate discriminating features in the transmitted signal, which can then be analyzed using signal-processing and machine-learning algorithms. Deep learning and more specifically convolutional neural networks (CNNs) have been successfully applied to the problem of radio-frequency fingerprinting using a spectral domain representation of the signal. A potential problem is the large size of the data to be processed, because this size impacts on the processing time during the application of the CNN. We propose an approach to addressing this problem, based on dimensionality reduction using feature-selection algorithms before the spectrum domain representation is given as an input to the CNN. The approach is applied to two public data sets of radio-frequency devices using different feature-selection algorithms for different values of the signal-to-noise ratio. The results show that the approach is able to achieve not only a shorter processing time; it also provides a superior classification performance in comparison to the direct application of CNNs.     

Analysis of light scattering in amorphous Si:H solar cells by a one‐dimensional semi‐coherent optical model

A one‐dimensional semi‐coherent optical model for thin‐film solar cells is presented. The optical circumstances at flat interfaces are addressed and the situation at rough interfaces in the model is described for the case of direct (coherent) incident and scattered (incoherent) incident light. After the model has been experimentally verified, analysis of the light scattering process in hydrogenated amorphous silicon (a‐Si:H) p–i–n solar cells is carried out. The influence of the interface root‐mean‐square roughness and the effect of different angular distribution functions of diffused light on quantum efficiency and short‐circuit current are investigated by the optical model. Copyright © 2002 John Wiley & Sons, Ltd.