Aixia Liu

According to our database1, Aixia Liu
  • authored at least 15 papers between 2004 and 2017.
  • has a "Dijkstra number"2 of five.

Timeline

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Bibliography

2017
On g-extra conditional diagnosability of hypercubes and folded hypercubes.
Theor. Comput. Sci., 2017

2016
g-Good-neighbor conditional diagnosability measures for 3-ary n-cube networks.
Theor. Comput. Sci., 2016

Sufficient conditions for triangle-free graphs to be super k-restricted edge-connected.
Inf. Process. Lett., 2016

2015
The g-Good-Neighbor Conditional Diagnosability of k-Ary n-Cubes under the PMC Modeland MM* Model.
IEEE Trans. Parallel Distrib. Syst., 2015

2013
Panconnectivity of n-dimensional torus networks with faulty vertices and edges.
Discrete Applied Mathematics, 2013

2011
The k-Restricted Edge Connectivity of Balanced Bipartite Graphs.
Graphs and Combinatorics, 2011

2010
Sufficient conditions for lambdak-optimality in triangle-free graphs.
Discrete Mathematics, 2010

Application of geographic image cognition approach in land type classification using Hyperion image: A case study in China.
Int. J. Applied Earth Observation and Geoinformation, 2010

2009
Sufficient conditions for bipartite graphs to be super-k-restricted edge connected.
Discrete Mathematics, 2009

The Risk Neural Network Based Visibility Forecast.
Proceedings of the Fifth International Conference on Natural Computation, 2009

2008
Sufficient Conditions for Super-Arc-Strongly Connected Oriented Graphs.
Graphs and Combinatorics, 2008

k-Restricted edge connectivity for some interconnection networks.
Applied Mathematics and Computation, 2008

2007
A semi-empirical backscattering model for estimation of leaf area index (LAI) of rice in southern China.
Proceedings of the IEEE International Geoscience & Remote Sensing Symposium, 2007

2005
Monitoring desertification in arid and semi-arid areas of China with NOAA-AVHRR and MODIS data.
Proceedings of the IEEE International Geoscience & Remote Sensing Symposium, 2005

2004
Evolving neural network using real coded genetic algorithm (GA) for multispectral image classification.
Future Generation Comp. Syst., 2004


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