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Articles 31 - 34 of 34
Full-Text Articles in Biomedical
Classification Algorithms For Genomic Microarray, Rakesh Choudary Malepati
Classification Algorithms For Genomic Microarray, Rakesh Choudary Malepati
UNLV Theses, Dissertations, Professional Papers, and Capstones
The advent of new technologies like DNA micro-arrays provides scientists the ability to gather important information such as the expression levels of almost all the genes within a cell. As the collected data is huge, it is always necessary to use analytical methods to extract important information which can be useful in biological and medical applications. One of such applications is presented in (Van‟t Veer LJ 2002), where the authors used the gene expression values obtained from micro-arrays of breast cancer cells to predict the outcome of the disease. The prediction is based on a supervised classification. While the idea …
System Architecture For An Intelligent Implantable Bio-Telemetry Device, Karthik Kumar Nattamai Kumaresan
System Architecture For An Intelligent Implantable Bio-Telemetry Device, Karthik Kumar Nattamai Kumaresan
UNLV Theses, Dissertations, Professional Papers, and Capstones
Biotelemetry has long been used for environmental and life science research to study animal populations and behavior. The use of implantable bio-telemetric techniques makes it possible to record and study physiological variables during long-term experiments with a minimum disturbance to the animal. Fully implantable telemetric techniques greatly reduce the risk of infection associated with leads and catheters protruding from the skin. In this research the design and implementation of a completely programmable bio-implantable digital system which can measure two physiological signals extended over a period of time is considered. The proposed system consists of a standalone implantable transmitter unit and …
Least Squares Support Vector Machine Based Classification Of Abnormalities In Brain Mr Images, S. Thamarai Selvi, D. Selvathi, R. Ramkumar, Henry Selvaraj
Least Squares Support Vector Machine Based Classification Of Abnormalities In Brain Mr Images, S. Thamarai Selvi, D. Selvathi, R. Ramkumar, Henry Selvaraj
Electrical & Computer Engineering Faculty Research
The manual interpretation of MRI slices based on visual examination by radiologist/physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification system is proposed. This research paper proposes an intelligent classification technique to the problem of classifying four types of brain abnormalities viz. Metastases, Meningiomas, Gliomas, and Astrocytomas. The abnormalities are classified based on Two/Three/ Four class classification using statistical and textural features. In this work, classification techniques based on Least Squares Support Vector Machine (LS-SVM) using textural features computed from the MR images of patient are …
A Study Of Transconductance Degradation In Hemt Using A Self-Consistent Boltzmann-Poisson-Schrodinger Solver, Rahim Khoie
A Study Of Transconductance Degradation In Hemt Using A Self-Consistent Boltzmann-Poisson-Schrodinger Solver, Rahim Khoie
Electrical & Computer Engineering Faculty Research
A self-consistent Boltzmann-Poisson-Schrödinger Solver is used to study the transconductance degradation in high electron mobility transistor (HEMT), which has extensively been reported by both experimental [1]-[8] and computational [9]-[ 13] researchers. As the gate voltage of a HEMT device is increased, its transconductance increases until it reaches a peak value, beyond which, the transconductance is degraded rather sharply with further increase in applied gate bias. We previously reported a two-subband self-consistent Boltzmann-Poisson- Schrödinger Solver for HEMT. [14] We further incorporated an additional self-consistency by calculating field-dependent, energy-dependent intersubband and intrasubband scattering rates due to ionized impurities and polar optical phonons.[15] …