Browsing by Subject "Time-frequency analysis"

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Open Access
Digital computation of linear canonical transforms
(Institute of Electrical and Electronics Engineers, 2008) Koç A.; Özaktaş, Haldun M.; Candan, C.; Kutay, M. A.
We deal with the problem of efficient and accurate digital computation of the samples of the linear canonical transform (LCT) of a function, from the samples of the original function. Two approaches are presented and compared. The first is based on decomposition of the LCT into chirp multiplication, Fourier transformation, and scaling operations. The second is based on decomposition of the LCT into a fractional Fourier transform followed by scaling and chirp multiplication. Both algorithms take ∼ N log N time, where N is the time-bandwidth product of the signals. The only essential deviation from exactness arises from the approximation of a continuous Fourier transform with the discrete Fourier transform. Thus, the algorithms compute LCTs with a performance similar to that of the fast Fourier transform algorithm in computing the Fourier transform, both in terms of speed and accuracy.
Embargo
Neural dynamics of light temperature under attentional load
(2024-07) Kılıç, Rabia Nur
This thesis investigates the neural dynamics of light temperature under varying levels of attentional load using EEG. The study involved twenty healthy adult participants (13 males, 7 females, mean age = 22.35) who performed rapid vi-sual detection tasks under different correlated color temperatures (CCT) of light: 2000K, 6000K, and 10000K. The experimental design comprised 288 trials per participant, with tasks administered under low and high attentional loads. The results indicated significant variations in EEG time-frequency responses in the alpha (8-13 Hz) frequency band based on attentional load, CCT conditions, and hemisphere. The analysis revealed significant main effects of Load, CCT, and Hemisphere on mean ERSP values. Mean ERSP values were higher under low load conditions compared to high load conditions, indicating that increased at-tentional load enhances alpha suppression and heightens attention. Mean ERSP values were lower in the CCT conditions compared to the control condition, re-flecting greater alpha suppression in the presence of distractor. Additionally, the right hemisphere exhibited significantly lower mean ERSP values compared to the left hemisphere and the midline, indicating lateralization of alpha band activity with greater alpha suppression in the right hemisphere. Behavioral data analysis showed significant differences in response times and accuracy across conditions. Participants demonstrated faster response times under cooler CCTs (6000K and 10000K) compared to the warm colors (2000K). These findings reveal the differ-ential impacts of light temperature and attentional load on behavioral and neural activity, in which our cognitive systems respond to external stimuli.
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%.