David Primeaux

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Artificial neural networks (ANNs) are powerful predictors. ANNs, however, essentially function like 'black boxes' because they lack explanatory power regarding input contribution to the model. Various contributive analysis algorithms (CAAs) have been developed to apply to ANNs to illuminate the influences and interactions between the inputs and thus, to(More)
Many natural and biological systems are formed by the process of molecular self-assembly. Molecular self-assembly is defined as the spontaneous organization of molecules under thermodynamic equilibrium conditions into structurally well defined and rather stable arrangements. In this paper, we developed a novel computational methodology to investigate the(More)
This paper proposes an extensible RTOS (real-time operating system) architecture for embedded heterogeneous muti-core processors, which consist of processors with different processing power and functionalities. The architecture splits the RTOS kernel into the two components, the proxy kernel (PK) and user-level kernel (UK). The PK runs on a less powerful(More)
Computations involving very small (or very large numbers) can produce underflow (or overflow) errors, even when these numbers are represented as logarithms. When logarithmic representation is used, algorithms to effect addition and subtraction remain limited in range by underflow or overflow. The algorithms presented here extend the range of these(More)
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