To this end, we introduce a simple transformed sequence to construct a contraction to prove the convergence of solutions, and apply this way solving a class of general equation.
With the help of the transformed sequence { x i * }, we can deduce the following conclusion.
Note that it suffices to show that the transformed sequence { x i * } converges to 1.
Shih, C. & Weinberg, R. A. Isolation of a transforming sequence from a human bladder carcinoma cell line.
We estimate the HMM for these transformed sequences.
A high-resolution T1-weighted anatomical data set encompassing the whole head was acquired in each session by means of a "modified driven equilibrium Fourier transform" sequence (TR = 2250 ms, TE = 26 ms, FA = 9°, matrix size = 256 × 256, FOV = 256 mm2, 192 slices, slice thickness = 1 mm, no gap, total run time= 8 min, 26 s).
In this study, this problem is treated from another viewpoint: Since such a transformed time sequence is a standard Poisson process for an ideal model, statistics related to the Poisson process can be used to construct the confidence band.
If we replace (lambda (t)) by (hat{lambda }(t)), which is a good approximation of the true model, in the above equation, we can also obtain a transformed time sequence that is approximately a Poisson process of rate 1 (the standard Poisson process).
Anatomical T1-weighted images were acquired using a 3-D MDEFT (modified driven equilibrium Fourier transform) sequence.
In particular, networking practices helped to transform sequences themselves into objects that had value as a community resource.
