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The uniqueness of the fixed point is proved below.
We omit the details since a more general theorem is proved below.
In this paper, we improve Theorems E and F and obtain the main result Theorem 1.1 which is proved below in Section 3. Theorem 1.1.
It is proved below that the formula of B and h of the presented algorithm is suitable in the region of the polar axis.
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The memory is proven below to associate loaded pairs correctly and to associate close by values otherwise (see Materials and Methods (MM)).
So, as it would be proven below, at the bottom-up stage of the DP algorithm it would be sufficient to assign to each node a in the tree G four values: left(a), right(a), Z(a), and X(a).
The fractional hazard rate is given by Lemma 2.1 with results that are proved below.
It will be proved below that the stability condition (46) is feasible so that it is always possible to design an asymptotically stable state observer.
Choosing various zero or non-zero values for a, b, g, h gives rise to the different results which are proved below.
For zero-mean complex Gaussian input, additive white noise and root-Nyquist prototype filter, the output for each subchannel is proved to follow the distributions below: ℋ 0 : T i y i ∼ G N, σ s 2 c i 2 σ s 2 c i 2 + σ w 2 ℋ 1 : T i y i ∼ G N, σ s 2 c i 2 σ w 2 (84).
It is proved that T follows the distributions below: T = σ w 2 2 χ 2 N 2 If ℋ 0, σ w 2 + σ s 2 2 χ 2 N 2 If ℋ 1 (8).
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