By Cesare Alippi
ISBN-10: 3319052772
ISBN-13: 9783319052779
ISBN-10: 3319052780
ISBN-13: 9783319052786
Addressing present problems with which any engineer or computing device scientist might be acutely aware, this monograph is a reaction to the necessity to undertake a brand new computational paradigm because the methodological foundation for designing pervasive embedded platforms with sensor features. the necessities of this paradigm are to manage complexity, to restrict rate and effort intake and to supply version and cognition talents permitting the embedded approach to engage proactively with the genuine international. the search for such intelligence calls for the formalization of a brand new new release of clever structures in a position to make the most advances in electronic architectures and in sensing applied sciences. The e-book sheds mild at the conception in the back of intelligence for embedded platforms with particular concentration on:
· robustness (the robustness of a computational movement and its evaluation)
· intelligence (how to imitate the variation and cognition talents of the human mind)
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Additional info for Intelligence for Embedded Systems: A Methodological Approach
Sample text
1. The number of independent values can be written as the ratio between the domain interval of the data and the value Im = 2λση of the probabilistic indistinguishability interval, ση being the uncertainty standard deviation associated with the measurement process. Finally, the number of independent points Ip is Ip = b−a +1 2λση if we require the interval extremes to be represented. As before, a straight assignment would be x1 = a, x2 = a + Im , . . , xIp = b. The number of significant bits is now p = ≺log2 Ip ∪ where ≺·∪ is the ceiling operator.
The extended analysis dealing with the perturbation in the large framework will be presented in Chap. 7. 1 Linear Functions Let us consider linear function y = f (x) = λ T x, where λ ∈ Θ ≤ Rd and x are d-dimensional column vectors representing the parameters and the inputs of the linear function, respectively. In the sequel, the parameter vector λ is assumed to be constant and given, otherwise differently specified. 1 The Additive Perturbation Model A perturbation ηx affecting the inputs, say according to an additive signal plus noise perturbation model x p = x + ηx, generates the perturbed value y p = λ T x p .
D. (x, y) random variable vector. 9) assesses the accuracy of a given model according to the loss function L (y, f (λ, x)). The optimal parameter λ o yielding the optimal model f (λ o , x) constrained by the particular choice of the model family f (λ, x), is 40 3 Uncertainty, Information, and Learning Mechanisms λ o = arg minλ∈Θ V¯ (λ ). However, we do not have access to px,y and only the data set Z N is available. 10) i=1 where Dη (x − xi , y − yi ) is the Dirac function. 9) leads to the Empirical Risk N 1 ⎨ VN (λ ) = L(yi , f (λ, xi )).