Many control applications require the use of both symbolic and numeric computation. An illustrative example is the analysis and controller design of a mechanical system. To begin with, we need to compute the equations of motion. To study local stability we need to compute its linearization. To apply control concepts like feedback linearization we may need to compute the inverse system. All thes...

“Numbers are the highest degree of knowledge. It is knowledge itself.”- Plato“The world built on power numbers.” Pythagoras“Everything around you Shakuntala Devi.The date, we were born on, was not an accident. Each number in our date birth connected with certain energies and vibrations. Similarly, each every letter name (first as well full name) also has particular frequency, vibrations.The com...

When developing programs or websites, it is very convenient to use relational databases, which contain powerful and tools that allow work with data flexibly get the necessary information in a matter of milliseconds. A database consists tables records these tables, each table must have primary key, particular, can be number BIGINT type, unique index record table, allows fetch operation maximum s...

We consider parallel, three-dimensional transonic Euler flow using the PETScFUN3D application, which employs pseudo-transient Newton-Krylov methods. Solving a large, sparse linear system at each nonlinear iteration dominates the overall simulation time for this fully implicit strategy. This paper presents a polyalgorithmic technique for adaptively selecting the linear solver method to match the...

Our work at the National Center for Atmospheric Research includes processing data from research meteorological radars. We evaluate and tune signalprocessing algorithms that process as much as 1500 megabytes of data per hour in real-time. In the past year, we have designed and implemented the Python Environment for Radar Processing (PERP) using Numeric Python to aid in our research. Because Pyth...

Memory for numbers improves with age and experience. We tested the hypothesis that one source of this improvement is a logarithmic-to-linear shift in children’s representations of numeric magnitude. In Experiment 1, we found that linearity of representations improved with age and that the more linear children’s magnitude representations were, the more closely their memory of the numbers approxi...