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Full-Text Articles in Engineering
Multiple-Input Translinear Element Networks, Bradley Minch, Paul Hasler, Chris Diorio
Multiple-Input Translinear Element Networks, Bradley Minch, Paul Hasler, Chris Diorio
Bradley Minch
We describe a new class of translinear circuits that accurately embody product-of-power-law relationships in the current signal domain. We call such circuits multiple-input translinear element (MITE) networks. A MITE is a circuit element, which we defined recently that produces an output current that is exponential in a weighted sum of its input voltages. We describe intuitively the basic operation of MITE networks and provide a systematic matrix technique for analyzing the nonlinear relationships implemented by any given circuit. We also show experimental data from three MITE networks that were fabricated in a 1.2-μm double-poly CMOS process.
Mos Translinear Principle For All Inversion Levels, Bradley Minch
Mos Translinear Principle For All Inversion Levels, Bradley Minch
Bradley Minch
In this brief, we derive a translinear principle for alternating loops of saturated MOS transistors that is valid at all levels of inversion starting from a simplified version of the Enz-Krummanacher-Vittoz model of the MOS transistor. This generalized translinear principle reduces to the conventional one when all transistors in a translinear loop are biased in weak inversion and it reduces to the voltage-translinear principle when all transistors in the loop are biased in strong inversion. We show experimental measurements from an alternating loop of four nMOS transistors that was fabricated in a 0.5-mum CMOS process through MOSIS to corroborate the …
Implementing The Lorenz Oscillator With Translinear Elements, Kofi Odame, Bradley Minch
Implementing The Lorenz Oscillator With Translinear Elements, Kofi Odame, Bradley Minch
Bradley Minch
Nonlinear processing is often more suitable than the traditional linear approach is for analyzing biological signals. Unfortunately, digital nonlinear operations are computationaly expensive. In contrast, a large variety of nonlinear operations can efficiently be implemented in analog electronics, operating at real-time speeds. The low level of accuracy generally associated with analog processing is not a concern in this scenario, as biological signals themselves typically have low signal-to-noise ratios. One challenge of analog processing is in its apparently- ad hoc design, and the fact that there is very little wide-spread knowledge of systematically implementing analog electronics to perform arbitrary nonlinear computations. …