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Volkan Y. Senyurek
Researcher at University of Alabama
Publications - 41
Citations - 621
Volkan Y. Senyurek is an academic researcher from University of Alabama. The author has contributed to research in topics: Structural health monitoring & Computer science. The author has an hindex of 13, co-authored 34 publications receiving 394 citations. Previous affiliations of Volkan Y. Senyurek include Florida International University & Marmara University.
Papers
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Journal ArticleDOI
Machine Learning-Based CYGNSS Soil Moisture Estimates over ISMN sites in CONUS
TL;DR: The results demonstrated here indicate that the proposed technique can confidently provide SM estimates over lightly-vegetated areas with vegetation water content (VWC) less than 5 kg/m2 and relatively low spatial heterogeneity.
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Detection of cuts and impact damage at the aircraft wing slat by using Lamb wave method
TL;DR: In this article, the actual wing slats of a Boeing 737 aircraft were used for detection of typical damages observed at the normal operating conditions, and the cuts and impact damages of the slats were considered.
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Contact and non-contact approaches in load monitoring applications using surface response to excitation method
Shervin Tashakori,Amin Baghalian,Muhammet Unal,Hadi Fekrmandi,Volkan Y. Senyurek,Dwayne McDaniel,Ibrahim N. Tansel +6 more
TL;DR: In this paper, the performance of the surface response to excitation (SuRE) method was evaluated with the conventional piezoelectric elements and scanning laser vibrometer used as contact and non-contact sensors, respectively, for monitoring the presence of loads on the surface.
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Cigarette Smoking Detection with an Inertial Sensor and a Smart Lighter
TL;DR: The proposed method identified smoking events from the cigarette lighter data and estimated puff counts by detecting hand-to-mouth gestures in the IMU data by a Support Vector Machine (SVM) classifier and suggests that an IMU and instrumented lighter may potentially be used in studies of smoking behavior under natural conditions.
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A CNN-LSTM neural network for recognition of puffing in smoking episodes using wearable sensors
TL;DR: A novel algorithm for automatic detection of puffs in smoking episodes by using a combination of Respiratory Inductance Plethysmography and Inertial Measurement Unit sensors and CNN-LSTM based neural network architecture to sufficiently detect puffing episodes in free-living condition is proposed.