From the results, we can see that the proposed algorithm achieves a better mode number factor and encoding time reduction with a minimal loss of image quality and a minimal bit increment.
Therefore, it is important to decide the minimal number of bits in floating-point hardware to reduce hardware costs, yet maintain an application's overall accuracy.
Our central contribution here is to examine how neural network-based face detector can employ the minimal number of bits in an FPU to reduce hardware resources, yet maintain a face detector's overall accuracy.
Thus, the minimal number of bits required to store the Bloom filter and the set cFP is approximately (3) n · (1. 44 log 2 (16 k 2. 08 ) + 2. 08 ).
The minimal number of feedback bits required to achieve the full multiplexing gain is summarized in Theorem 1 as following.
Theory is applied here to the selective fading case, where the minimal number of feedback bits required to achieve the full multiplexing is derived.
Based on the observation that time-frequency selective channel slots are correlated, we compute the minimal number of feedback bits required to achieve the full multiplexing gain.
When the wordlength is taken as b-bit, the minimal number of SPT terms for z ̄, which is defined as P z ( z ̄, b ) in (4.3), is computed as a recursive function: Recursive Function ( P z ( z ̄, b ) ). 1.
Thus, when the wordlength is taken as b-bit, the minimal number of SPT terms for some integer z ̄ can be defined by P z ( z ̄, b ) = P w ( 2 - b z ̄, b ).
Then, when the wordlength is taken as b-bit, the minimal number of SPT terms for w ̄ is defined as: P w ( w ̄, b ) = min s ̄ ∑ k = 1 b s ̄ k s. t. w ̄ = ∑ k = 1 b s ̄ k 2 - k. (4.2).
