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Improving the performance of medical CT image reconstruction on multicore processor
Image reconstruction is observed to be one of the most common problem because of it's large data movement and non-trivial data dependencies. In the past, these problems were tackled by many high performance hardware such as FPGA's and GPGPU's. This also reflects the investemts to be made in these supercomputers for real time reconstruction of clinical computed tomography (CT) applications. Medical imaging systems are employing high performance computing (HPC) technology to meet their time constraints. This paper presents different optimizations to the volume reconstruction and implement it on a commodity hardware such as x86 based multicore system. This paper chooses to perform its implementaion on Intel Xeon X5365 multicore processor. We perform different levels of parallelization and analyse each of them and report their results with respect to serial implementation. The objective of this paper is to understand the constraints of volume reconstruction in multicore architecture and optimize them while preserving the quality of the reconstructed image.
Prin colectarea deşeurilor menajere se înţelege efectuarea operaţiilor de strângere, prelucrare şi transport a acestor deşeuri în vederea valorificării şi neutralizării lor. Operaţia de colectare şi depozitare a deşeurilor urbane revine în sarcina primăriilor, care prin regiile proprii sau prin firme private ce au contracte cu primăriile au sarcina să se ocupe de colectarea, transportul şi depozitarea deşeurilor.
CSE8803 Project: Mortality Prediction in ICU patients
Accurate prognosis and prediction of a patient's current disease state is critical in an ICU. The use of vast amounts of digital medical information can help in predicting the best course of action for the diagnosis and treatment of patients. The proposed technique investigates the strength of using a combination of latent variable models (latent dirichlet allocation) and structured data to transform the information streams into potentially actionable knowledge. In this project, I use Apache Spark to predict mortality among ICU patients so that it can be used as an acuity surrogate to help physicians identify the patients in need of immediate care.
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