Sampling Mechanisms(采样机制)研究综述
Sampling Mechanisms 采样机制 - However, these advantages have to be “conquered” overcoming the technical challenges of target proximity operations and sampling mechanisms. [1] By introducing time-varying gains into observers, two types of novel continuous-discrete-time observers are presented to estimate state information of agents under two sampling mechanisms, respectively, namely: 1) a synchronous nonuniform sampling (SNS) mechanism and 2) an asynchronous nonuniform sampling (ANS) mechanism. [2] In various settings, limitations of sensing technologies or other sampling mechanisms result in missing labels, where the likelihood of a missing label is an unknown function of the data. [3] Two main disadvantages of minimizers sampling mechanisms are: first, they also do not have good guarantees on the expected size of their samples for every combination of w and k; and, second, indexes that are constructed over their samples do not have good worst-case guarantees for on-line pattern searches. [4] It generalizes and improves over previously-studied random-sampling mechanisms. [5] This in vitro experimental setup provided an excellent model for the in vivo environment in terms of generating peristaltic movements, hence this force analysis will help in developing efficient prototypes for locomotion, anchoring, localization, biopsy, drug delivery, and sampling mechanisms for robotic capsules. [6] This paper is concerned with the non-fragile state estimation (NFSE) problem for a class of delayed genetic regulatory networks (GRNs) under stochastic sampling mechanisms. [7] Existing CPU sampling mechanisms, like SimPoint, reduce per-thread workload, and are ill-suited to GPU programs where reducing the number of threads is critical. [8] for simple sampling mechanisms in small-sized autonomous underwater vehicles (µAUVs). [9] This paper investigates the event-triggered consensus of linear multiagent systems with periodic data sampling mechanisms, where random packet losses are taken into account. [10] In this paper, we propose a novel solution, namely retinomorphic sensing, which integrates fovea-like and peripheral-like sampling mechanisms to generate asynchronous visual streams using a unified representation as the retina does. [11]然而，必须“克服”这些优势，克服目标接近操作和采样机制的技术挑战。 [1] 通过将时变增益引入观察者，提出了两种新颖的连续离散时间观察者来估计代理在两种采样机制下的状态信息，分别是：1）同步非均匀采样（SNS）机制和2）异步非均匀采样（ANS）机制。 [2] 在各种情况下，传感技术或其他采样机制的限制会导致标签丢失，其中丢失标签的可能性是数据的未知函数。 [3] 最小化器采样机制的两个主要缺点是：首先，对于 w 和 k 的每个组合，它们也不能很好地保证其样本的预期大小；其次，基于样本构建的索引对于在线模式搜索没有很好的最坏情况保证。 [4] 它对先前研究的随机抽样机制进行了概括和改进。 [5] 这种体外实验装置在产生蠕动运动方面为体内环境提供了一个极好的模型，因此这种力分析将有助于开发用于机器人胶囊的运动、锚定、定位、活检、药物输送和采样机制的有效原型。 [6] 本文关注随机抽样机制下一类延迟遗传调控网络（GRNs）的非脆弱状态估计（NFSE）问题。 [7] 现有的 CPU 采样机制，如 SimPoint，减少了每个线程的工作量，并且不适合 GPU 程序，因为减少线程的数量是至关重要的。 [8] 用于小型自主水下航行器 (µAUV) 中的简单采样机制。 [9] 本文研究了具有周期性数据采样机制的线性多智能体系统的事件触发共识，其中考虑了随机数据包丢失。 [10] 在本文中，我们提出了一种新颖的解决方案，即视网膜形态传感，它集成了类似中央凹和类似外围的采样机制，以使用视网膜的统一表示来生成异步视觉流。 [11]