Crossing Probabilities and ARAIM Temporal Correlation

A natural requirement to impose upon the life distribution of a component is that after inspection at some randomly chosen time to check whether it is still functioning, its life distribution from the time of checking should be bounded below by some specified distribution which may be defined by external considerations. Furthermore, the life distribution should ideally be minimal in the partial ordering obtained from the conditional probabilities. We prove that these specifications provide an apparently new characterization of the DFRA class of life distributions with a corresponding result for IFRA distributions. These results may be transferred, using Slepian's lemma, to obtain bounds for the boundary crossing probabilities of a stationary Gaussian process.

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Exploiting temporal correlation in pitch-adaptive speech enhancement

Speech Communication ◽

10.1016/j.specom.2019.05.001 ◽

2019 ◽

Vol 111 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Johannes Stahl ◽

Pejman Mowlaee

Keyword(s):

Speech Enhancement ◽

Temporal Correlation

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Spatial-Temporal Correlation Learning for Real-Time Video Deinterlacing

2021 IEEE International Conference on Multimedia and Expo (ICME) ◽

10.1109/icme51207.2021.9428121 ◽

2021 ◽

Author(s):

Yuqing Liu ◽

Xinfeng Zhang ◽

Shanshe Wang ◽

Siwei Ma ◽

Wen Gao

Keyword(s):

Real Time ◽

Temporal Correlation ◽

Correlation Learning ◽

Video Deinterlacing

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Learning spatio-temporal correlation filter for visual tracking

Neurocomputing ◽

10.1016/j.neucom.2021.01.057 ◽

2021 ◽

Vol 436 ◽

pp. 273-282

Author(s):

Youmin Yan ◽

Xixian Guo ◽

Jin Tang ◽

Chenglong Li ◽

Xin Wang

Keyword(s):

Visual Tracking ◽

Temporal Correlation ◽

Correlation Filter ◽

Spatio Temporal

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A Spatio-Temporal Local Association Query Algorithm for Multi-Source Remote Sensing Big Data

Remote Sensing ◽

10.3390/rs13122333 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2333

Author(s):

Lilu Zhu ◽

Xiaolu Su ◽

Yanfeng Hu ◽

Xianqing Tai ◽

Kun Fu

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Temporal Correlation ◽

Local Association ◽

Sensing Data ◽

Multi Scale ◽

Correlation Networks ◽

Efficient Integration ◽

Spatio Temporal ◽

Query Algorithm

It is extremely important to extract valuable information and achieve efficient integration of remote sensing data. The multi-source and heterogeneous nature of remote sensing data leads to the increasing complexity of these relationships, and means that the processing mode based on data ontology cannot meet requirements any more. On the other hand, the multi-dimensional features of remote sensing data bring more difficulties in data query and analysis, especially for datasets with a lot of noise. Therefore, data quality has become the bottleneck of data value discovery, and a single batch query is not enough to support the optimal combination of global data resources. In this paper, we propose a spatio-temporal local association query algorithm for remote sensing data (STLAQ). Firstly, we design a spatio-temporal data model and a bottom-up spatio-temporal correlation network. Then, we use the method of partition-based clustering and the method of spectral clustering to measure the correlation between spatio-temporal correlation networks. Finally, we construct a spatio-temporal index to provide joint query capabilities. We carry out local association query efficiency experiments to verify the feasibility of STLAQ on multi-scale datasets. The results show that the STLAQ weakens the barriers between remote sensing data, and improves their application value effectively.

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