Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Article Citation - WoS: 10Citation - Scopus: 13On the Characterization of Cognitive Tasks Using Activity-Specific Short-Lived Synchronization Between Electroencephalography Channels(Elsevier, 2021) Olcay, B. Orkan; Özgören, Murat; Karaçalı, BilgeAccurate characterization of brain activity during a cognitive task is challenging due to the dynamically changing and the complex nature of the brain. The majority of the proposed approaches assume stationarity in brain activity and disregard the systematic timing organization among brain regions during cognitive tasks. In this study, we propose a novel cognitive activity recognition method that captures the activity-specific timing parameters from training data that elicits maximal average short-lived pairwise synchronization between electroencephalography signals. We evaluated the characterization power of the activity-specific timing parameter triplets in a motor imagery activity recognition framework. The activity-specific timing parameter triplets consist of latency of the maximally synchronized signal segments from activity onset Delta t, the time lag between maximally synchronized signal segments t, and the duration of the maximally synchronized signal segments w. We used cosine-based similarity, wavelet bi-coherence, phase-locking value, phase coherence value, linearized mutual information, and cross-correntropy to calculate the channel synchronizations at the specific timing parameters. Recognition performances as well as statistical analyses on both BCI Competition-III dataset IVa and PhysioNet Motor Movement/Imagery dataset, indicate that the interchannel short-lived synchronization calculated using activity-specific timing parameter triplets elicit significantly distinct synchronization profiles for different motor imagery tasks and can thus reliably be used for cognitive task recognition purposes. (C) 2021 Elsevier Ltd. All rights reserved.Article Citation - WoS: 10Citation - Scopus: 10Periodic Disturbance Estimation Based Adaptive Robust Control of Marine Vehicles(Elsevier, 2021) Kurtoğlu, Deniz; Bıdıklı, Barış; Tatlıcıoğlu, Enver; Zergeroğlu, ErkanTracking control of marine vessels in the presence of parametric uncertainty and additive periodic disturbances is considered. For optimal estimation of environmental forces, periodic disturbance estimation method inspired from Fourier series expansion have been applied. Stability of the closed–loop system and the convergence of the tracking error under the closed–loop operation are established via Lyapunov based arguments. Simulation studies are provided to support the theoretical results and the effectiveness of the proposed method. © 2020 Elsevier LtdArticle Citation - WoS: 18Citation - Scopus: 31Applied Mel-Frequency Discrete Wavelet Coefficients and Parallel Model Compensation for Noise-Robust Speech Recognition(Elsevier, 2006) Tüfekçi, Zekeriya; Gowdy, John N.; Gürbüz, Sabri; Patterson, EricInterfering noise severely degrades the performance of a speech recognition system. The Parallel Model Compensation (PMC) technique is one of the most efficient techniques for dealing with such noise. Another approach is to use features local in the frequency domain, such as Mel-Frequency Discrete Wavelet Coefficients (MFDWCs). In this paper, we investigate the use of PMC and MFDWC features to take advantage of both noise compensation and local features (MFDWCs) to decrease the effect of noise on recognition performance. We also introduce a practical weighting technique based on the noise level of each coefficient. We evaluate the performance of several wavelet-schemes using the NOISEX-92 database for various noise types and noise levels. Finally, we compare the performance of these versus Mel-Frequency Cepstral Coefficients (MFCCs), both using PMC. Experimental results show significant performance improvements for MFDWCs versus MFCCs, particularly after compensating the HMMs using the PMC technique. The best feature vector among the six MFDWCs we tried gave 13.72 and 5.29 points performance improvement, on the average, over MFCCs for -6 and 0 dB SNR, respectively. This corresponds to 39.9% and 62.8% error reductions, respectively. Weighting the partial score of each coefficient based on the noise level further improves the performance. The average error rates for the best MFDWCs dropped from 19.57% to 16.71% and from 3.14% to 2.14% for -6 dB and 0 dB noise levels, respectively, using the weighting scheme. These improvements correspond to 14.6% and 31.8% error reductions for -6 dB and 0 dB noise levels, respectively. (c) 2006 Elsevier B.V. All rights reserved.Article Citation - WoS: 67Citation - Scopus: 78Chirp Group Delay Analysis of Speech Signals(Elsevier, 2007) Bozkurt, Barış; Couvreur, Laurent; Dutoit, ThierryThis study proposes new group delay estimation techniques that can be used for analyzing resonance patterns of short-term discrete-time signals and more specifically speech signals. Phase processing or equivalently group delay processing of speech signals are known to be difficult due to large spikes in the phase/group delay functions that mask the formant structure. In this study, we first analyze in detail the z-transform zero patterns of short-term speech signals in the z-plane and discuss the sources of spikes on group delay functions, namely the zeros closely located to the unit circle. We show that windowing largely influences these patterns, therefore short-term phase processing. Through a systematic study, we then show that reliable phase/group delay estimation for speech signals can be achieved by appropriate windowing and group delay functions can reveal formant information as well as some of the characteristics of the glottal flow component in speech signals. However, such phase estimation is highly sensitive to noise and robust extraction of group delay based parameters remains difficult in real acoustic conditions even with appropriate windowing. As an alternative, we propose processing of chirp group delay functions, i.e. group delay functions computed on a circle other than the unit circle in z-plane, which can be guaranteed to be spike-free. We finally present one application in feature extraction for automatic speech recognition (ASR). We show that chirp group delay representations are potentially useful for improving ASR performance. (c) 2007 Elsevier B.V. All rights reserved.Article Citation - WoS: 14Citation - Scopus: 17Evaluation of Synchronization Measures for Capturing the Lagged Synchronization Between Eeg Channels: a Cognitive Task Recognition Approach(Elsevier, 2019) Olcay, Bilal Orkan; Karaçalı, BilgeDuring cognitive, perceptual and sensory tasks, connectivity profile changes across different regions of the brain. Variations of such connectivity patterns between different cognitive tasks can be evaluated using pairwise synchronization measures applied to electrophysiological signals, such as electroencephalography (EEG). However, connectivity-based task recognition approaches achieving viable recognition performance have been lacking from the literature. By using several synchronization measures, we identify time lags between channel pairs during different cognitive tasks. We employed mutual information, cross correntropy, cross correlation, phase locking value, cosine similarity and nonlinear interdependence measures. In the training phase, for each type of cognitive task, we identify the time lags that maximize the average synchronization between channel pairs. These lags are used to calculate pairwise synchronization values with which we construct the train and test feature vectors for recognition of the cognitive task carried out using Fisher's linear discriminant (FLD) analysis. We tested our framework in a motor imagery activity recognition scenario on PhysioNet Motor Movement/Imagery and BCI Competition-III IVa datasets. For PhysioNet dataset, average performance results ranging between % 51 and % 61 across 20 subjects. For BCI Competition-III dataset, we achieve an average recognition performance of % 76 which is above the minimum reliable communication rate (% 70). We achieved an average accuracy over the minimum reliable communication rate on the BCI Competition-III dataset. Performance levels were lower on the PhysioNet dataset. These results indicate that a viable task recognition system is achievable using pairwise synchronization measures evaluated at the proper task specific lags.Article Citation - WoS: 6Citation - Scopus: 10Surface Chemistry Dependent Toxicity of Inorganic Nanostructure Glycoconjugates on Bacterial Cells and Cancer Cell Lines(Elsevier, 2023) Sancak, Sedanur; Yazgan, İdris; Bayarslan, Aslı Uğurlu; Ayna, Adnan; Evecen, Senanur; Taşdelen, Zehra; Gümüş, Abdurrahman; Sönmez, Hamide Ayçin; Demir, Mehmet Ali; Demir, Sosin; Bakar, Fatma; Dilek Tepe, HafizeSurface functionalized nanostructures have outstanding potential in biological applications owing to their target-specific design. In this study, we utilized laboratory synthesized carbohydrate-derivatives (i.e., galactose, mannose, lactose, and cellobiose derivatives) for aqueous one-pot synthesis of gold (Au) and silver (Ag) nanostructure glycoconjugates (NSs), and iron metal-organic framework glycoconjugates (FeMOFs). This work aims to test whether differences in the surface chemistry of the inorganic nanostructures play roles in revealing their toxicities towards bacterial cells and cancerous cell lines. As of the first step, biological activity of AuNSs, AgNSs, and FeMOFs were tested against a variety of gram (−) and gram (+) bacterial strains, where AgNSs possessed moderate to high antibacterial activities against all the tested bacterial strains, while AuNSs and FeMOFs showed their bacterial toxicity mostly depending on the strain. Minimum inhibitory concentration (MIC) and Minimum bactericidal concentration (MBC) determination studies were performed for the nanostructure glycoconjugates, for which μg/mL MBC values were obtained such as (Cellobiose p-aminobenzoic acid_AgNS) CBpAB_AgNS gave 50 μg/mL MBC value for P.aeruginosa and S.kentucy. The activity of selected sugar ligands and corresponding glycoconjugates were further tested on MDA-MB-231 breast cancer and A549 lung cancer cell lines, where selective anticancer activity was observed depending on the surface chemistry as well. Besides, D-penicillamine was introduced to galectin specific sugar ligand coated AuNS glycoconjugates, which showed very strong anticancer activities even at low doses. Overall, the importance of this work is that the surface chemistry of the inorganic nanostructures can be critical to reveal their toxicity towards bacterial cells and cancerous cell lines.