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Abstract:We report a 51 year old male with open double segmental fracture of both bones of left leg after being allegedly hit by a car. Patient was initially treated by toileting, debridement and nailing, followed by bone grafting at a later date. Segmental tibial fractures are uncommon and double segmental fractures/ trifocal fractures are rarely seen, literature search revealed that only three such cases and only one treated previously by intramedullary nail. Intramedullary nail remains effective treatment modality of such fractures and these fractures often require bone grafting.

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Computing the exact maximum likelihood or maximum a posteriori estimate of the environment is computationally expensive in many practical distributed sensing settings. We argue that this computational difficulty can be overcome by increasing the number of sensor measurements. Based on our work on the connection between error correcting codes and sensor networks, we propose a new algorithm which extends the idea of sequential decoding used to decode convolutional codes to estimation in a sensor network. In a simulated distributed sensing application, this algorithm provides accurate estimates at a modest computational cost given a sufficient number of sensor measurements. Above a certain number of sensor measurements this algorithm exhibits a sharp transition in the number of steps it requires in order to converge, leading to the potentially counter-intuitive observation that the computational burden of estimation can be reduced by taking additional sensor measurements.