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Kuncup Iswandy
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2010 – 2019
- 2011
- [c10]Kuncup Iswandy, Andreas König:
Automated Design of Dependable Intelligent Sensory Systems with Self-x Properties. KES (4) 2011: 155-166 - [c9]Kuncup Iswandy, Andreas König:
Hybrid Virtual Sensor Based on RBFN or SVR Compared for an Embedded Application. KES (2) 2011: 335-344 - 2010
- [c8]Kuncup Iswandy, Stefano Carrella, Andreas König:
Intelligent Magnetic Sensing System for Low Power WSN Localization Immersed in Liquid-Filled Industrial Containers. KES (2) 2010: 361-370
2000 – 2009
- 2009
- [j2]Kuncup Iswandy, Andreas König:
Methodology, Algorithms, and Emerging Tool for Automated Design of Intelligent Integrated Multi-Sensor Systems. Algorithms 2(4): 1368-1409 (2009) - [j1]Kuncup Iswandy, Andreas König:
Fully Evolved Kernel Method Employing SVM Assessment for Feature Computation from Multisensor Signals. Int. J. Comput. Intell. Appl. 8(1): 1-15 (2009) - [c7]Lakshmesha Rao, Kuncup Iswandy, Andreas König:
Cost Effective 3D Localization for Low-Power WSN Based on Modified Sammon's Stress Function with Incomplete Distance Information. WSC 2009: 149-156 - 2008
- [c6]Kuncup Iswandy, Andreas König:
PSO for Fault-Tolerant Nearest Neighbor Classification Employing Reconfigurable, Analog Hardware Implementation in Low Power Intelligent Sensor Systems. HIS 2008: 380-385 - [c5]Kuncup Iswandy, Andreas König:
A Novel Adaptive Resource-Aware PNN Algorithm Based on Michigan-Nested Pittsburgh PSO. ICONIP (2) 2008: 477-484 - 2007
- [c4]Kuncup Iswandy, Andreas König:
A Novel Fully Evolved Kernel Method for Feature Computation from Multisensor Signal Using Evolutionary Algorithms. HIS 2007: 240-245 - 2006
- [c3]Kuncup Iswandy, Andreas König:
Towards Effective Unbiased Automated Feature Selection. HIS 2006: 29 - [c2]Kuncup Iswandy, Andreas König:
Feature-Level Fusion by Multi-Objective Binary Particle Swarm Based Unbiased Feature Selection for Optimized Sensor System Design. MFI 2006: 365-370 - 2004
- [c1]Kuncup Iswandy, Andreas König:
Improvement of Non-Linear Mapping Computation for Dimensionality Reduction in Data Visualization and Classification. HIS 2004: 260-265
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