abstract time domain reflectometry (tdr) is a widely used method for measuring the dielectric constant (ka) and bulk electrical conductivity (σa) in soils. the tdr-measured σa and ka can be used to calculate the soil solution electrical conductivity, (σp). a theoretical model describing a linear relationship between bulk electrical conductivity, σa, and dielectric constant, ka, in moist soil wa...

Nomenclature F = state transition matrix H = matrix giving the ideal/noiseless connection between measurements and states I = identity matrix k = time index L = noise sensitivity matrix in nonlinear systems N = lag time constant integer P = estimation-error covariance matrix s = arbitrary time step used in inductive proof xk = state at time index k x̂ = state estimate x̂k = a priori state estimat...

water is one of the most vital constituents in plants. in this research, for an estimation of leaf moisture content, the variation of capacitance was employed. the variations were measured via designed and manufactured capacitive sensors. the objective of the research was to estimate leaf moisture content by measuring its capacitance for five agronomic crops. experiments for measuring leaf capa...

We show that strictly positive inductive types, constructed from polynomial functors, constant exponentiation and arbitrarily nested inductive types exist in any Martin-Löf category (extensive locally cartesian closed category with W-types) by exploiting our work on container types. This generalises a result by Dybjer (1997) who showed that non-nested strictly positive inductive types can be re...

The probability distribution P from which the history of our universe is sampled represents a theory of everything or TOE. We assume P is formally describable. Since most (uncountably many) distributions are not, this imposes a strong inductive bias. We show that P (x) is small for any universe x lacking a short description, and study the spectrum of TOEs spanned by two P s, one reflecting the ...

We introduce a new class of time-continuous recurrent neural network models. Instead declaring learning system's dynamics by implicit nonlinearities, we construct networks linear first-order dynamical systems modulated via nonlinear interlinked gates. The resulting models represent with varying (i.e., liquid) time-constants coupled to their hidden state, outputs being computed numerical differe...

This paper introduces a new global-tier interconnect scaling scheme which ensures that inductance effects do not start dominating the overall interconnect performance. It is shown that for unscaled global lines, inductance effects increase as technology scales while for the scaling scheme proposed by ITRS [1], interconnects become extremely resistive and, while inductance effects diminish with ...