Unsupervised Electromagnetic Target Classification by Self-organizing Map Type Clustering

2010-07-08
Katilmis, T. T.
Ekmekci, E.
Sayan, Gönül
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.

Suggestions

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...
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...
Image Annotation by Semi-Supervised Clustering Constrained by SIFT Orientation Information
Sayar, Ahmet; Yarman-Vural, Fatos T. (2008-10-29)
Methods developed for image annotation usually make use of region clustering algorithms. Visual codebooks are generated from the region clusters of low level features. These codebooks are then, matched with the words of the text document related to the image, in various ways. In this paper, we supervise the clustering process by using the orientation information assigned to each interest point of Scale-invariant feature transform (SIFT) features to generate a visual codebook. The orientation information pro...
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.