estimation

(1) We propose a new method to obtain 3D human pose data as baseline data, which combines the advantages of 2D human pose estimation methods and 3D human pose estimation methods.

In the extraction of human pose data, the key step is human pose estimation. The 3D human pose estimation method is mainly based on image and single feature.

Area estimation can be also carried out in a logic simulator if the number of gates and their area in its structural description are accounted.

In this paper, a joint power dissipation and occupied area estimation methodology in VHDL is given.

There are two steps in basic Kalman Filter estimation process [19].

The estimation is started with an initial value for state variables and covariance matrix ([X.sub.0], [P.sub.0]).

They correspond to the diagonal elements of [[OMEGA].sub.z] and the estimation of [A.sub.1], separately.

Until now, we get the paired estimation of [mathematical expression not reproducible] which represent cos [[theta].sub.i], sin [[theta].sub.i]cos [[phi].sub.i], and sin [[theta].sub.i]sin [[phi].sub.i], respectively.

We also derive the approximated lower bound for the mean square error (MSE) of second-hop channel estimation. Finally, we validate the performance of the proposed schemes with the given simulation results.

Obviously, it can be seen from (6) that three terms are included in the interference V for second-hop channel estimation, where the first term represents the interference of unknown cooperative data and others indicate the equivalent noise, consisting of the relay-propagated noise (the noise propagated from R) and the local noise at D.