By Hsiang-Chuan Liu, Wen-Pei Sung, Wenli Yao
Long ago 20 years, laptop technological know-how and data expertise became concerned with many assorted purposes in the course of the international, with a number of items and swift industry prone. they've got not just supplied industries with new tools, new instruments and new items from layout, fabric processing to operation and administration method, yet also are altering the manners, considering types and dealing environments of individuals in all fields.
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The above optimization problem can be solved with OMP algorithm (Tropp & Gilbert 2007) and other sparse recovery algorithms (Tropp & Wright 2010). , e–jπ(dM – 1)sinθi]T (1 ≤ i ≤ L) are the steering vectors, and nv(t) also denotes a complex additive white Gaussian noise vector. Suppose a M × dM matrix G, whose entries are zeros and ones, satisfy A( ) 0 * 1≤ k < j ≤ N | 〈 ϕ k , ψ j 〉 |, (9) where φk is the normal k-th row of Φ, and ψj is the normal j-th column of Ψ. Observe that μ(Φ, Ψ) ∈ [1, N ] (Candes & Wakin 2008).
1] With the development of smart home and mobile device, demand for integration between smart home terminals and mobile devices was growing rapidly. In this paper, based on the existing research results, the main contribution is to combine the XMPP protocol push mechanism and IAIDL language, and put forward a universal data push mechanism suitable for smart home. This mechanism can integrate different terminals in smart home and process internal and external data push services simultaneously. So connections inside and outside of smart home can be better implemented, and expansibility, efficiency and user experience of smart home can also be improved.
The remainder of this paper is described as follows: Sec. 2 introduces the Elman neural network. 2 ELMAN NEURAL NETWORK Elman neural network is a partial recurrent network model first proposed by Elman (Elman 1990). It is a special kind of feed-forward neural network, which has extra local memory neurons and feedback loop. The Elman neural network is capable of approximating a nonlinear system without an explicit physical model. An Elman neural network has four kinds of layers (Fig. 1): input layer, hidden layer, context layer and output layer (Chen 2008).