Browsing by Subject "Pattern analysis"

Now showing 1 - 3 of 3

Open Access
Accurate simulation of reflector antennas by the complex source-dual series approach
(Institute of Electrical and Electronics Engineers, 1995-08) Oğuzer, T.; Altıntaş, A.; Nosich, A. I.
The radiation from circular cylindrical reflector antennas is treated in an accurate manner for both polarizations. The problem is first formulated in terms of the dual series equations and then is regularized by the Riemann-Hilbert problem technique. The resulting matrix equation is solved numeridy with a guaranteed accuracy, and remarkably Little CPU time is needed. The feed directivity is included in the analysis by the complex source point method. Various characteristic patterns are obtained for the front and offset-fed reflector antenna geometries with this analysis, and some comparisons are made with the high frequency techniques. The directivity and radiated power properties are also studied.
Open Access
Global vs local classification models for multi-sensor data fusion
(ACM, 2018) Pippa, E.; Zacharaki, E. I.; Özdemir, A. T.; Barshan, Billur; Megalooikonomou, V.
The aim of this paper is to investigate feature extraction and fusion of information across a number of sensors in different spatial locations to classify temporal events. Although the common feature-level fusion allows capturing spatial dependencies across sensors, the significant increase of feature vector dimensionality does not allow learning the classification models using a small number of samples usually available in practice. In decision-level fusion on the other hand, sensor-specific classification models are trained and subsequently integrated to reach a combined decision. Recent work has shown that decision-level fusion with a global (common for all sensors) classification model, is more appropriate for generalized events that show a (weak or strong) manifestation across all sensors. Although we can hypothesize that the choice of scheme depends on the event type (generalized vs focal/local), the prior work does not provide enough evidence to guide on the choice of fusion scheme. Thus in this work we aim to compare the three data fusion schemes for classification of generalized and non-generalized events using two case scenarios: (i) classification of paroxysmal events based on EEG patterns and (ii) classification of falls and activities of daily living (ADLs) from multiple sensors. The results support our hypothesis that feature level fusion is more beneficial for the characterization of heterogeneous data (based on an adequate number of samples), while sensor-independent classifiers should be selected in the case of generalized manifestation patterns.
Open Access
Morphological surface profile extraction with multiple range sensors
(Elsevier, 2001) Barshan, B.; Başkent, D.
A novel method is described for surface pro"le extraction based on morphological processing of multiple range sensor data. The approach taken is extremely #exible and robust, in addition to being simple and straightforward. It can deal with arbitrary numbers and con"gurations of sensors as well as synthetic arrays. The method has the intrinsic ability to suppress spurious readings, crosstalk, and higher-order re#ections, and process multiple re#ections informatively. The performance of the method is investigated by analyzing its dependence on surface structure and distance, sensor beamwidth, and noise on the time-of-#ight measurements. 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.