Sentence examples for both data quantization and from inspiring English sources

This paper studies the approach of model predictive control (MPC) for the non-linear systems under networked environment where both data quantization and packet loss may occur.

The likelihood function, in which both data quantization and channel imperfection are considered, is calculated in three different scenarios.

Attention is focused on solving the problem of event-triggered filter design subject to parameter uncertainties, data quantization, and communication delay in a unified frame.

Considering network-induced delays, data quantization and aperiodic sampling intervals, a network-based framework which allows each agent to be remotely controlled over the communication network is established.

We introduce a Resource-Aware Model Analysis (RAMA) method, and remove the bottleneck involving multipliers by bit-level XNOR and shifting operations, and the bottleneck of parameter access by data quantization and optimized on-chip storage.

The purpose of this paper is to present the efficient data quantization and fixed-point representation that are devised for the architectures of MMSE turbo-equalizer [6] and Max-Log-Map turbo-demapper [5].

Sections 3 and 4 describe, respectively, the adopted algorithms for turbo-equalization and turbo-demapping, discuss the required operations to implement the algorithms, present the required fixed-point arithmetic and data quantization, and finally show their influence on error-rate performance.

First, several data quantization cases and their effects on the snow network design problems were explored.

For further reducing computation resources and transmission cost, the sampled data, self-trigger scheme and data quantization are employed together.

The problem of event-triggered L2 L∞ state estimation for a class of discrete-time neural networks with mixed time delays, sensor saturations and data quantization is investigated in this paper.

Fixed-point arithmetic and data quantization affect the performance of algorithmic implementation.