Myoelectric signals are nervous impulses that activate skeletal muscles. Deciphering the messages encoded in myoelectric signals could open the door to the next generation of prosthetics and UI devices, creating seamless human machine interfaces that do not rely on physical controls. Unfortunately, myoelectric signal interpretation remains a challenging problem. The current state of the art is to use machine learning algorithms to classify patterns of myoelectric signals into gestures that can be interpreted by prosthetics or UI systems.
This project proposes to add a Drug Order and Pharmacy module to OpenMRS, a computer software for electronic medical records. With extensive studies of existing software including Practice Fusion, Bahmni, Dominica and in-depth discussions with the medical and developer communities about the specific features needing improvement in existing software, we have developed the UI/UX - more on this in the following chapters.
The aim of this project is to develop Drawing and Document Annotation module for OpenMRS - an open-source electronic medical records management system. The motivation for the project is the legacy drawing module which features basic tools to draw and annotate the drawing and link them to the patient's medical records within OpenMRS. By providing better drawing and annotation tools with workflow as per the current version of OpenMRS, the project is an improvement to the legacy drawing module.
This project was designed to upgrade the existing OpenMRS Visit Notes Analysis module. This version of the module lets the user access the underlying visit notes data with more ease - For a particular patient, all visit notes available in the database are displayed as a chart over time, and the user can view a word cloud of problems/treatments/tests by varying the date range, entity type and number of terms. The user can then select a few terms for which a detailed timeline can be viewed (including related terms).
A brain-computer interface (BCI) takes signals from a human brain as input to a computer system. The goal of BCI systems is to allow a human operator to control a computer application by mental activity alone. Research in this field has advanced considerably in recent years, with the advent of powerful new machine learning technologies. However, many obstacles currently inhibit BCI research, including cost, access to equipment, and domain-specific expertise challenges at the intersection of neuroscience and computer science.
Aggregation operators are functions that are used to aggregate individual suitability degrees and compute an overall suitability degree for evaluating decisions. One of the goals of this project is to analyze and compare various suitability aggregators. The emphasis is on new forms of interpolative aggregators, as well as new aggregators that combine idempotent and non-idempotent properties.
Although vast amounts of work have been done in development of benchmark suites for evaluation of computer systems, reliable performance prediction of arbitrary programs is elusive. This project attempts to ll this gap by applying decomposition and predicting performance of programs in terms of weighted compositions of other programs whose performance is known.