Sentence examples for more accurate quantization from inspiring English sources

Many agencies go even further, advocating the use of several targets in order to allow for a more accurate quantization of the variables involved in the digitization process.

We first observe that the optimized solution prescribes a more accurate quantization at higher frequencies, since these convey more information on the time delay, as per the CRB (13), while all frequencies contribute in equal manner to the estimate of the phase offset as per (14).

A two-stage feedback scheme has been presented in[13] where in the first step a coarse estimates of all user channels are feedback to the BS then in the second step only N t users are selected to feed back more accurate channel quantization.

This process reduces the gel-to-gel variation and allows more accurate and sensitive proteomic quantization [ 20, 21].

If the CPD1 estimation error stays within the dashed line, it can be said that the CPD1 estimation using Equation (14) provides more accurate results than the direct quantization of CPD1.

PSB novelty is in a more accurate coefficient selection, taking into account quantization and coefficient relevancy.

In [12], the authors presented an approach that relies on cluster center initialization and color quantization allowing faster and more accurate convergence such that it is suitable to segment very large color images.

Furthermore, they are significantly smaller than the maximum quantization error, which shows that the proposed methods are more beneficial because it is possible to obtain a more accurate CPD1 using smaller bits than when quantizing and transmitting the CPD1 itself.

Compared with Bianchi and Piva's approaches [13, 14], the proposed approach provides more accurate detection results due to (i) better estimation of the quantization error caused by shifted JPEG compression and (ii) more discriminative information investigated by the adaptive DCT mode selection approach in Section 4.1 compared with that only DC mode is investigated in [13].

In contrast, the GMM approach is more accurate, as it allows good likelihood ratio estimates while providing a finer feature space quantization through the soft assignment possibility.

Thereafter, two or more distinctly SILAC-labeled cell populations can be mixed and analyzed in one MS experiment, allowing accurate quantization of proteins from the different cellular states.