Browsing by Author "İnce, N. F."

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Open Access
A novel objective function minimization for sparse spatial filters
(IEEE, 2014) Onaran, İ.; İnce, N. F.; Çetin, A. Enis
Common spatial pattern (CSP) method is widely used in brain machine interface (BMI) applications to extract features from the multichannel neural activity through a set of spatial projections. The CSP method easily overfits the data when the number of training trials is not sufficiently large and it is sensitive to daily variation of multichannel electrode placement, which limits its applicability for everyday use in BMI systems. To overcome these problems, the amount of channels that is used in projections, should be limited. We introduce a spatially sparse projection (SSP) method that exploits the unconstrained minimization of a new objective function with approximated l\ penalty. The SSP method is employed to classify the two class EEG data set. Our method outperforms the standard CSP method and provides comparable results to £o norm based solution and it is associated with less computational complexity.
Open Access
A signal representation approach for discrimination between full and empty hazelnuts
(IEEE, 2007) Onaran, İbrahim; İnce, N. F.; Tevfik, A. H.; Çetin, A. Enis
We apply a sparse signal representation approach to impact acoustic signals to discriminate between empty and full hazelnuts. The impact acoustic signals are recorded by dropping the hazelnut shells on a metal plate. The impact signal is then approximated within a given error limit by choosing codevectors from a special dictionary. This dictionary was generated from sub-dictionaries that are individually generated for the impact signals corresponding to empty and full hazelnut. The number of codevectors selected from each sub-dictionary and the approximation error within initial codevectors are used as classification features and fed to a Linear Discriminant Analysis (LDA). We also compare this algorithm with a baseline approach. This baseline approach uses features which describe the time and frequency characteristics of the given signal that were previously used for empty and full hazelnut separation. Classification accuracies of 98.3% and 96.8% were achieved by the proposed approach and base algorithm respectively. The results we obtained show that sparse signal representation strategy can be used as an alternative classification method for undeveloped hazelnut separation with higher accuracies.
Open Access
Subset selection with structured dictionaries in classification
(EURASIP, 2007) İnce, N. F.; Göksu, F.; Tewfik, A. H.; Onaran, İbrahim; Çetin, A. Enis
This paper describes a new approach for the selection of discriminant time-frequency features for classification. Unlike previous approaches that use the individual discrimination power of expansion coefficients, the proposed approach selects a subset of features by implementing a classifier directed pruning of an initial redundant set of candidate features. The candidate features are calculated from a structured redundant time-frequency analysis of the signal, such as an undecimated wavelet transform. We show that the proposed approach has a performance that is as good as or better than traditional classification approaches while using a much smaller number of features. In particular, we provide experimental results to demonstrate the superior performance of the algorithm in the area of impact acoustic classification for food kernel inspection. The proposed algorithm achieved 91.8% and 98.5% classification accuracies in separating open shell from closed shell pistachio nuts and discriminating between empty and full hazelnuts respectively. Traditional methods used in this area resulted in 82% and 97% classification accuracies respectively.
Open Access
Wheat and hazelnut inspection with impact acoustics time-frequency patterns
(ASABE, 2007-06) İnce, N. F.; Onaran, İbrahim; Tewfik, A. H.; Kalkan, H.; Pearson, T.; Çetin, A. Enis; Yardimci, Y.
Kernel damage caused by insects and fungi is one of the most common reason for poor flour quality. Cracked hazelnut shells are prone to infection by cancer producing mold. We propose a new adaptive time-frequency classification procedure for detecting cracked hazelnut shells and damaged wheat kernels using impact acoustic emissions recorded by dropping wheat kernels or hazelnut shells on a steel plate. The proposed algorithm is based on a flexible local discriminant bases (F-LDB) procedure. The F-LDB method combines local cosine packet analysis and a frequency axis clustering approach which supports individual time and frequency band adaptation. Discriminant features are extracted from the adaptively segmented acoustic signal, sorted according to a Fisher class separability criterion, post processed by principal component analysis and fed to linear discriminant. We describe experimental results that establish the superior performance of the proposed approach when compared with prior techniques reported in the literature or used in the field. Our approach achieved classification accuracy in paired separation of undamaged wheat kernels from IDK, Pupae and Scab damaged kernels with 96%, 82% and 94%. For hazelnuts the accuracy was 97.1%.