By Simon Haykin
Represents the main accomplished therapy on hand of neural networks from an engineering point of view. Examines all of the very important features of this rising know-how. DLC: Adaptive filters.
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Additional info for Adaptive Filter Theory (3rd Edition)
14) Illuminating the photodiode with a flux of photons with energy greater than the band gap will create an excess minority carrier flux of GLLe. 14 shows that the photodiode current will be linearly proportional to this flux. Since no approximations were necessary to derive this result, we can expect the linear relationship to hold over many orders of magnitude of photon flux. This result is key to the performance of photodiode detectors. The linear photodetection response can be compared to the dependence of the photodiode current on an applied voltage.
This is just mathematics. The most important part of the solution, however is the physics of the problem. This is summarized in the boundary conditions that allow us to solve for A, B, and C. a. When no light is present, ⌬np at (xp = ϱ) = 0. b. When light is present, ⌬np at (xp = ϱ) = GLe 0. To see that this must be so, set the second derivative = 0 in Eq. 6. c. At xp = 0, ⌬np (x = 0) = np0(eqVA/kT – 1). 7) First, note that ͙k ෆ must have units of 1/L, where L is length. 8) where Le = ͙D ෆෆ eeෆ = diffusion length for electrons Then apply the boundary condition at xp = ϱ, ⌬np(xp = ϱ) = GLe: Ae+ϱ + Be–ϱ + C = GLe If this equation is true, A must be zero.
Adaptive Filter Theory (3rd Edition) by Simon Haykin