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Improved Covariance Model Parameter Estimation Using Rna Thermodynamic Properties, Jennifer A. Smith, Kay C. Wiese
Improved Covariance Model Parameter Estimation Using Rna Thermodynamic Properties, Jennifer A. Smith, Kay C. Wiese
Electrical and Computer Engineering Faculty Publications and Presentations
Covariance models are a powerful description of non-coding RNA (ncRNA) families that can be used to search nucleotide databases for new members of these ncRNA families. Currently, estimation of the parameters of a covariance model (state transition and emission scores) is based only on the observed frequencies of mutations, insertions, and deletions in known ncRNA sequences. For families with very few known members, this can result in rather uninformative models where the consensus sequence has a good score and most deviations from consensus have a fairly uniform poor score. It is proposed here to combine the traditional observed-frequency information with …
Human Image Preference And Document Degradation Models, Chris Hale, Elisa H. Barney Smith
Human Image Preference And Document Degradation Models, Chris Hale, Elisa H. Barney Smith
Electrical and Computer Engineering Faculty Publications and Presentations
Because most degraded documents are created by people, the preferences individuals have in relation to degraded documents are quite important. Their preferences may determine whether or not the documents they created are appropriate for machines. The goal of this study was to find relationships between preference and several parameters of a scanner degradation model. It was found that the difference in binarization threshold and the difference in edge displacement caused by the degradation both had strong linear relationships to preference. The width of the point spread function did not show such a relationship. These relationships were counterintuitive because degraded characters …
Rna Gene Finding With Biased Mutation Operators, Jennifer A. Smith
Rna Gene Finding With Biased Mutation Operators, Jennifer A. Smith
Electrical and Computer Engineering Faculty Publications and Presentations
The use of genetic algorithms for non-coding RNA gene finding has previously been investigated and found to be a potentially viable method for accelerating covariance-model-based database search relative to full dynamic-programming methods. The mutation operators in previous work chose new alignment insertion and deletion locations uniformly over the length of the model consensus sequence. Since the covariance models are estimated from multiple known members of a non-coding RNA family, information is available as to the likelihood of insertions or deletions at the individual model positions. This information is implicit in the state-transition parameters of the estimated covariance models. In the …
Investigation Of Single Pmosfet Gate Oxide Degradation On Nor Logic Circuit Operability, David Estrada
Investigation Of Single Pmosfet Gate Oxide Degradation On Nor Logic Circuit Operability, David Estrada
McNair Scholars Research Journal
The impact of gate oxide degradation of a single pMOSFET on the performance of the CMOS NOR logic circuit has been examined using a switch matrix technique. A constant voltage stress of -4.0V was used to induce a low level of degradation to the 2.0nm gate oxide of the pMOSFET. Characteristics of the CMOS NOR logic circuit following gate oxide degradation are analyzed in both the DC and V-t domains. The NOR gate rise time increases by approximately 30%, which may lead to timing or logic errors in high frequency digital circuits. Additionally, the voltage switching point of the NOR …
Evolvable Reconfigurable Hardware Framework For Edge Detection, Nader I. Rafla
Evolvable Reconfigurable Hardware Framework For Edge Detection, Nader I. Rafla
Electrical and Computer Engineering Faculty Publications and Presentations
Systems on Reconfigurable Chips contain rich resources of logic, memory, and processor cores on the same fabric. This platform is suitable for implementation of Evolvable Reconfigurable Hardware Architectures (ERHA). It is based on the idea of combining reconfigurable Field Programmable Gate Arrays (FPGA) along with genetic algorithms (GA) to perform the reconfiguration operation. This architecture is a suitable candidate for implementation of early-processing stage operators of image processing such as filtering and edge detection. However, there are still fundamental issues need to be solved regarding the on-chip reprogramming of the logic. This paper presents a framework for implementing an evolvable …