Haze detection using persistent homology

[1]  R. Ghrist Barcodes: The persistent topology of data , 2007 .

[2]  Hamid Krim,et al.  Persistent Homology of Delay Embeddings and its Application to Wheeze Detection , 2014, IEEE Signal Processing Letters.

[3]  Khushboo Mittal,et al.  Topological characterization and early detection of bifurcations and chaos in complex systems using persistent homology. , 2017, Chaos.

[4]  Rodrigo Fernandes de Mello,et al.  Persistent homology for time series and spatial data clustering , 2015, Expert Syst. Appl..

[5]  Marian Gidea,et al.  Topological Data Analysis of Financial Time Series: Landscapes of Crashes , 2017, 1703.04385.

[6]  Mohd Nasir Hassan,et al.  Review of air pollution and health impacts in Malaysia. , 2003, Environmental research.

[7]  N. Ramli,et al.  Monsoonal differences and probability distribution of PM10 concentration , 2010, Environmental monitoring and assessment.

[8]  Therese D. Pigott,et al.  A Review of Methods for Missing Data , 2001 .

[9]  Yuhei Umeda,et al.  Time Series Classification via Topological Data Analysis , 2017, Inf. Media Technol..

[10]  Mohd Armi Abu Samah,et al.  An Overview of the Air Pollution Trend in Klang Valley, Malaysia , 2012 .

[11]  F. Takens Detecting strange attractors in turbulence , 1981 .

[12]  Gunnar E. Carlsson,et al.  Topology and data , 2009 .

[13]  Abdul Aziz Jemain,et al.  Trend and status of air quality at three different monitoring stations in the Klang Valley, Malaysia , 2009, Air quality, atmosphere, & health.