These data suggest that BUS evaluation of upper extremity injuries not involving joints maybe comparable to radiography for identifying fractures, the need for reduction, and the adequacy of reduction in children. If further investigations which include a larger number of lower extremity, growth plate, and joint injuries support our findings, BUS may gain a more prominent role in managing children with all long bone injuries.

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In this paper, we propose an in-node microprocessor-based vehicle classification approach to analyze and determine the types of vehicles passing over a 3-axis magnetometer sensor. Our approach for vehicle classification utilizes J48 classification algorithm implemented in Weka (a machine learning software suite). J48 is a Quinlan's C4.5 algorithm, an extension of decision tree machine learning based on ID3 algorithm. The decision tree model is generated from a set of features extracted from vehicles passing over the 3-axis sensor. The features are attributes provided with correct classifications to the J48 training algorithm to generate a decision tree model with varying degrees of classification rates based on crossvalidation. Ideally, using fewer attributes to generate the model allows for the highest computational efficiency due to fewer features needed to be calculated while minimalizing the tree with fewer branches. The generated tree model can then be easily implemented using nested if-loops in any language on a multitude of microprocessors. In addition, setting an adaptive baseline to negate the effects of the background magnetic field allows reuse of the same tree model in multiple environments. The result of our experiment shows that the vehicle classification system is effective and efficient with the accuracy at nearly 100%.