Browsing by Subject "Sensor units"
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Item Open Access Artificial olfaction inside nanostructured infrared fiber arrays(IEEE, 2011) Yaman, Mecit; Yıldırım, Adem; Bayındır, MehmetNanostructured hollow core fibers are used to demonstrate a new infrared absorption based artificial nose. The sensor unit of the array is a hollow core Bragg fiber that selectively guides incident blackbody radiation and enhances absorption for enhanced sensitivity. © 2011 IEEE.Item Open Access Human activity classification with miniature inertial and magnetic sensor signals(IEEE, 2011) Yüksek, Murat Cihan; Barshan, BillurThis study provides a comparative performance assessment of various pattern recognition techniques on classifying human activities that are performed while wearing miniature inertial and magnetic sensors. Activities are classified using five sensor units worn on the chest, the arms, and the legs. Each sensor unit comprises a tri-axial accelerometer, a tri-axial gyroscope, and a tri-axial magnetometer. The classification techniques compared in this study are: naïve Bayesian (NB), artificial neural networks (ANN), dissimilaritybased classifier (DBC), various decision-tree algorithms, Gaussian mixture model (GMM), and support vector machines (SVM). The methods that result in the highest correct differentiation rates are found to be GMM (99.1%), ANN (99.0%), and SVM (98.9%). © 2011 EURASIP.Item Open Access Human activity recognition using inertial/magnetic sensor units(Springer, Berlin, Heidelberg, 2010) Altun, Kerem; Barshan, BillurThis paper provides a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). Daily and sports activities are classified using five sensor units worn by eight subjects on the chest, the arms, and the legs. Each sensor unit comprises a triaxial gyroscope, a triaxial accelerometer, and a triaxial magnetometer. Principal component analysis (PCA) and sequential forward feature selection (SFFS) methods are employed for feature reduction. For a small number of features, SFFS demonstrates better performance and should be preferable especially in real-time applications. The classifiers are validated using different cross-validation techniques. Among the different classifiers we have considered, BDM results in the highest correct classification rate with relatively small computational cost. © 2010 Springer-Verlag Berlin Heidelberg.Item Open Access Minyatür eylemsizlik duyucuları ve manyetometre sinyallerinin işlenmesiyle insan aktivitelerinin sınıflandırılması(IEEE, 2011-04) Yüksek, Murat Cihan; Barshan, BillurBu çalışmada insan vücuduna yerleştirilen minyatür eylemsizlik duyucuları ve manyetometreler kullanılarak çeşitli aktiviteler örüntü tanıma yöntemleriyle ayırdedilmiş ve karşılaştırmalı bir çalışmanın sonuçları sunulmuştur. Ayırdetme işlemi için basit Bayeşçi (BB) yöntem, yapay sinir ağları (YSA), benzeşmezlik tabanlı sınıflandırıcı (BTS), ceşitli karar ağacı (KA) yöntemleri, Gauss karışım modeli (GKM) ve destek vektör makinaları (DVM) kullanılmıştır. Aktiviteler gövdeye, kollara ve bacaklara takılan beş duyucu ünitesinden gelen verilerin işlenmesiyle ayırdedilmiştir. Her ünite, her biri üç-eksenli olmak üzere birer ivmeölçer, dönüölçer ve manyetometre içermektedir. Çalışmanın sonuçlarına göre, en iyi ilk üç başarı oranı sırasıyla GKM (%99.12), YSA (%99.09) ve DVM (%98.90) yöntemleri ile elde edilmiştir.