Suchergebnisse - Arbis Subjects::Engineering and Technology::Biomedical Engineering::Biosignal Processing*

Exploring the effects of coded excitations and pulse compression on the performance of magneto-acousto-electrical tomography with magnetic field measurements in mr magnet ; Kodlu uyarımların ve darbe sıkıştırmanın mr mıknatısı içinde manyetik alan ölçümleri ile manyeto-akusto-elektriksel tomografi performansına etkilerinin incelenmesi

Autoren: Gözü, Mehmet Soner Gençer, Nevzat Güneri Department of Electrical and Electronics Engineering

Weitere Verfasser: Gözü, Mehmet Soner Gençer, Nevzat Güneri Department of Electrical and Electronics Engineering

Schlagwörter: Magneto-acousto-electrical tomography, Conductivity imaging, Pulse compression, Coded excitation, Arbis Subjects::Engineering and Technology::Biomedical Engineering::Biosignal Processing, Arbis Subjects::Engineering and Technology::Biomedical Engineering, Arbis Subjects::Engineering and Technology::Electrical and Electronics Engineering

Dateibeschreibung: application/pdf

Relation: https://hdl.handle.net/11511/112684

Verfügbarkeit: https://hdl.handle.net/11511/112684

MULTI-CLASS CLASSIFICATION OF VOICE DISORDERS USING DEEP LEARNING ; DER˙IN ÖGRENME KULLANARAK ÇOK SINIFLI SES ˘ BOZUKLUKLARININ SINIFLANDIRILMASI

Autoren: Rahman, Mehtab Ur Direkoglu, Cem Electrical and Electronics Engineering

Weitere Verfasser: Rahman, Mehtab Ur Direkoglu, Cem Electrical and Electronics Engineering

Schlagwörter: Voice Disorder, Multi-class Classification, OpenL3, VGGish, Deep Learning, Arbis Subjects::Engineering and Technology::Biomedical Engineering::Biosignal Processing, Arbis Subjects::Engineering and Technology::Electrical and Electronics Engineering, Arbis Subjects::Engineering and Technology::Computer Sciences::Artificial Intelligence, Computer Learning and Pattern Recognition::Human Computer Interaction

Dateibeschreibung: application/pdf

Relation: https://hdl.handle.net/11511/108878

Verfügbarkeit: https://hdl.handle.net/11511/108878

Collective anomaly detection in time series using pitch frequency and dissimilarity features ; Pitch frekansı ve benzeşmezlik öznitelikleri kullanılarak zaman serilerinde kollektif anomali tespiti

Autoren: Erkuş, Ekin Can Purutçuoğlu Gazi, Vilda Eken, Aykut et al.

Weitere Verfasser: Erkuş, Ekin Can Purutçuoğlu Gazi, Vilda Eken, Aykut et al.

Schlagwörter: anomaly detection, time series, pitch frequency, dissimilarity, sliding windows, Arbis Subjects::Engineering and Technology, Arbis Subjects::Engineering and Technology::Biomedical Engineering, Arbis Subjects::Engineering and Technology::Biomedical Engineering::Biomedical Image Processing, Arbis Subjects::Engineering and Technology::Biomedical Engineering::Biomedical Image Processing::Other, Arbis Subjects::Engineering and Technology::Biomedical Engineering::Biosignal Processing, Arbis Subjects::Engineering and Technology::Biomedical Engineering::Biosignal Processing::Biosignal Processing, Arbis Subjects::Engineering and Technology::Biomedical Engineering::Other, Arbis Subjects::Engineering and Technology::Biomedical Engineering::Clinical Engineering::Clinical Engineering, Arbis Subjects::Engineering and Technology::Computer Sciences::Artificial Intelligence, Computer Learning and Pattern Recognition, Arbis Subjects::Engineering and Technology::Computer Sciences::Algorithms::Computational Model, Computer Learning and Pattern Recognition::Computer Learning, Computer Learning and Pattern Recognition::Knowledge Engineering, Computer Learning and Pattern Recognition::Pattern Recognition and Image Processing, Arbis Subjects::Engineering and Technology::Computer Sciences::Bioinformatics::Biocomputing, Arbis Subjects::Natural Sciences::Statistics::Statistical Analysis and Applications

Dateibeschreibung: application/pdf

Relation: https://hdl.handle.net/11511/104738

Verfügbarkeit: https://hdl.handle.net/11511/104738

Improvement of Electrocardiographic Imaging Reconstructions: A Physics-guided AI Approach and an Efficient Method for Training Data Reduction ; ELEKTROKARDİYOGRAFİK GÖRÜNTÜLEME GERİÇATIMLARININ İYİLEŞTİRİLMESİ: FİZİK TABANLI YAPAY ZEKA YAKLAŞIMI VE EĞİTİM VERİSİNİN İNDİRGENMESİ İÇİN ETKİN BİR YÖNTEM

Autoren: Uğurlu, Kutay Serinağaoğlu Doğrusöz, Yeşim Department of Electrical and Electronics Engineering

Weitere Verfasser: Uğurlu, Kutay Serinağaoğlu Doğrusöz, Yeşim Department of Electrical and Electronics Engineering

Schlagwörter: Electrocardiographic imaging, Deep learning, Neural networks, Training data, Bayesian MAP, Arbis Subjects::Engineering and Technology, Arbis Subjects::Engineering and Technology::Biomedical Engineering::Biosignal Processing, Arbis Subjects::Engineering and Technology::Biomedical Engineering::Biomedical Image Processing, Arbis Subjects::Engineering and Technology::Computer Sciences::Artificial Intelligence, Computer Learning and Pattern Recognition

Dateibeschreibung: application/pdf

Relation: https://hdl.handle.net/11511/105244

Verfügbarkeit: https://hdl.handle.net/11511/105244

AN INFORMATION THEORETIC REPRESENTATION OF HUMAN BRAIN FOR DECODING MENTAL STATES OF COMPLEX PROBLEM SOLVING ; Karmaşık Problem Çözmenin Zihinsel Durumlarının Bilgi Teorisiyle Temsili

Autoren: Gunal Degirmendereli, Gonul Yarman Vural, Fatoş Tunay Department of Cognitive Sciences

Weitere Verfasser: Gunal Degirmendereli, Gonul Yarman Vural, Fatoş Tunay Department of Cognitive Sciences

Schlagwörter: Brain Decoding, Shannon Entropy, Kullback-Leibler Divergence, Brain Networks, Complex Problem Solving (CPS), Arbis Subjects::Engineering and Technology::Biomedical Engineering::Biosignal Processing, Arbis Subjects::Engineering and Technology::Computer Sciences::Artificial Intelligence, Computer Learning and Pattern Recognition

Dateibeschreibung: application/pdf

Relation: https://hdl.handle.net/11511/96326

Verfügbarkeit: https://hdl.handle.net/11511/96326

Fast EEG based biometrics via mean curve length ; Ortalama eğri uzunluğu kullanılarak gerçekleştirilen hızlı EEG tabanlı biyometri

Autoren: Yahyaei, Reza Özkurt, Tolga Esat Department of Medical Informatics

Weitere Verfasser: Yahyaei, Reza Özkurt, Tolga Esat Department of Medical Informatics

Schlagwörter: Mean Curve Length, Fractal Dimension, EEG, Biometrics, Mahalanobis Classifier, Arbis Subjects::Engineering and Technology::Biomedical Engineering::Biosignal Processing, Arbis Subjects::Engineering and Technology::Computer Sciences::Artificial Intelligence, Computer Learning and Pattern Recognition::Other, Arbis Subjects::Health Sciences

Dateibeschreibung: application/pdf

Relation: https://hdl.handle.net/11511/96714

Verfügbarkeit: https://hdl.handle.net/11511/96714