Unsupervised Electromagnetic Target Classification by Self-organizing Map Type Clustering

Date

2010-07-08

Author

Katilmis, T. T.
Ekmekci, E.
Sayan, Gönül

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

163
views

0
downloads

In this study, design of a completely unsupervised electromagnetic target classifier will be described based on the use of Self-Organizing Map (SOM) type artificial neural network training and Wigner distribution (WD) based target feature extraction technique. The suggested classification method will be demonstrated for a target library of four dielectric spheres which have exactly the same size but slightly different permittivity values.

URI

https://hdl.handle.net/11511/54270

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

HYPERSPECTRAL UNMIXING BASED VEGETATION DETECTION WITH SEGMENTATION Özdemir, Okan Bilge; Soydan, Hilal; Çetin, Yasemin; Duzgun, Sebnem (2016-07-15) This paper presents a vegetation detection application with semi-supervised target detection using hyperspectral unmixing and segmentation algorithms. The method firstly compares the known target spectral signature from a generic source such as a spectral library with each pixel of hyperspectral data cube employing Spectral Angle Mapper (SAM) algorithm. The pixel(s) with the best match are assumed to be the most likely target vegetation locations. The regions around these potential target locations are furt...
Multi-time-scale input approaches for hourly-scale rainfall-runoff modeling based on recurrent neural networks Ishida, Kei; Kiyama, Masato; Ercan, Ali; Amagasaki, Motoki; Tu, Tongbi (2021-11-01) This study proposes two effective approaches to reduce the required computational time of the training process for time-series modeling through a recurrent neural network (RNN) using multi-time-scale time-series data as input. One approach provides coarse and fine temporal resolutions of the input time-series data to RNN in parallel. The other concatenates the coarse and fine temporal resolutions of the input time-series data over time before considering them as the input to RNN. In both approaches, first, ...
Asynchronous design of systolic array architectures in cmos İsmailoğlu, Ayşe Neslin; Aşkar, Murat; Department of Electrical and Electronics Engineering (2008) In this study, delay-insensitive asynchronous circuit design style has been adopted to systolic array architectures to exploit the benefits of both techniques for improved throughput. A delay-insensitivity verification analysis method employing symbolic delays is proposed for bit-level pipelined asynchronous circuits. The proposed verification method allows datadependent early output evaluation to co-exist with robust delay-insensitive circuit behavior in pipelined architectures such as systolic arrays. Reg...
An adaptive, energy-aware and distributed fault-tolerant topology-control algorithm for heterogeneous wireless sensor networks Deniz, Fatih; Bagci, Hakki; KÖRPEOĞLU, İBRAHİM; Yazıcı, Adnan (2016-07-01) This paper introduces an adaptive, energy-aware and distributed fault-tolerant topology control algorithm, namely the Adaptive Disjoint Path Vector (ADPV) algorithm, for heterogeneous wireless sensor networks. In this heterogeneous model, we have resource-rich supernodes as well as ordinary sensor nodes that are supposed to be connected to the supernodes. Unlike the static alternative Disjoint Path Vector (DPV) algorithm, the focus of ADPV is to secure supernode connectivity in the presence of node failures...
Multiobjective evolutionary feature subset selection algorithm for binary classification Deniz Kızılöz, Firdevsi Ayça; Coşar, Ahmet; Dökeroğlu, Tansel; Department of Computer Engineering (2016) This thesis investigates the performance of multiobjective feature subset selection (FSS) algorithms combined with the state-of-the-art machine learning techniques for binary classification problem. Recent studies try to improve the accuracy of classification by including all of the features in the dataset, neglecting to determine the best performing subset of features. However, for some problems, the number of features may reach thousands, which will cause too much computation power to be consumed during t...

Citation Formats

T. T. Katilmis, E. Ekmekci, and G. Sayan, “Unsupervised Electromagnetic Target Classification by Self-organizing Map Type Clustering,” 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54270.