Synthesis and characterization of nanowire-based anisotropic conductors.

We investigated the potential of commercially available porous templates to be used for the fabrication of functional anisotropic conductors. A galvanostatic deposition technique was used to fabricate arrays consisting of 200 nm diameter nanowires inside the pores of polycarbonate membranes. A tape lift-off procedure allowed the complete removal of any residual metal from both sides of the polymer membrane to form an anisotropic conductive film. The 10 microm thick film has roughly 3 x 10(8) nanowires per cm2, and it showed near zero electrical resistance perpendicular to the surface while appearing completely open to circuits between any points on the surface. The preparation of the film, characterization using SEM, AFM, and resistance measurements are presented. The 1D conductivity of these membranes may have many potential applications for microelectronic interconnects for packaging technologies.

Combining predictive capabilities of transcranial doppler with electrocardiogram to predict hemorrhagic shock.

Hemorrhagic shock (HS) potentially impacts the chance of survival in most traumatic injuries. Thus, it is highly desirable to maximize the survival rate in cases of blood loss by predicting the occurrence of hemorrhagic shock with biomedical signals. Since analyzing one physiological signal may not enough to accurately predict blood loss severity, two types of physiological signals - Electrocardiography (ECG) and Transcranial Doppler (TCD) - are used to discover the degree of severity. In this study, these degrees are classified as mild, moderate and severe, and also severe and non-severe. The data for this study were generated using the human simulated model of hemorrhage, which is called lower body negative pressure (LBNP). The analysis is done by applying discrete wavelet transformation (DWT). The wavelet-based features are defined using the detail and approximate coefficients and machine learning algorithms are used for classification. The objective of this study is to evaluate the improvement when analyzing ECG and TCD physiological signals together to classify the severity of blood loss. The results of this study show a prediction accuracy of 85.9% achieved by support vector machine in identifying severe/non-severe states.