EUSFLAT 2019 template
Submitted full papers are to be supposed to be of at
least 4 pages length. The authors should submit their
papers electronically, written in English, due to the
given deadline, through a web upload procedure available, see www.eusflat2019.cz).
Management and Processing of Discographic Data with Amazon Elastic MapReduce
The purpose of this report is to explain how – by leveraging on the capabilities of the amazon web services – it is possible to manage and process a set of data that is too large and complex for traditional data processing techniques and technologies.
The report discusses the implementation of a set of services – from the retrieval of external data to its transformation, through the storage on non relational databases and finally the parallel computation on an external cluster – meant for the management of discographic information in order to easily join different data in an agile manner and subsequently perform additional processing based on the joined output.
Sjabloon Kampvoorbereiding volgens richtlijnen Scouts en Gidsen Vlaanderen
Entropy Minimization Based Synchronization Algorithm for Underwater Acoustic Receivers
This paper presents a new entropy minimization criterion and corresponding algorithms that are used for both symbol timing and carrier frequency recovery for underwater acoustic receivers. It relies on the entropy estimation of the eye diagram and the constellation diagram of the received signal. During the parameter search, when perfect synchronization is achieved, the entropy will reach a global minimum, indicating the least intersymbol interference or a restored constellation diagram. Unlike other synchronization methods, this unified criterion can be used to build an all-in-one synchronizer with high accuracy. The feasibility of this method is proven using a theoretical analysis and supported by sea trial measurement data.
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.
TDA Mapper Paper Template
Template for use in the University of Iowa course MATH:3900 Introduction to Math Research.