Browsing by Author "Yanushkevich, Svetlana N"

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    Cough Event Recognition Using Signal-Processing Based Feature Sets and Machine Learning, with Tri-Axial Accelerometer Sensor Worn at Multiple Body Points
    (2021-12-22) Doddabasappla Basavarajappa, Kruthi; Vyas, Rushi J.; Murari, Kartikeya; Yanushkevich, Svetlana N; Medeiros de Souza, Roberto
    Human activity recognition (HAR) from time-series accelerometer and gyroscope sensor data has seen tremendous progress in recent years. Laying, standing, sitting, walking, walking down, and walking upstairs are the daily human activities that are commonly classified using sensor data. Cough is a common human activity and is also a symptom of various diseases including the novel coronavirus disease 2019 (COVID-19). Cough detection and classification are well investigated in recent literature to varying levels of success. But, in most of the studies, sensor data for cough activity is collected during still conditions such as sitting or standing and from a specific location such as chest and neck only. The body position of data recording considerably impacts the data, significantly affecting the classification accuracy. In our study, we place tri-axial accelerometer sensors at different spots on the human body where a smartphone or wearable device such as earphones or headphones are commonly worn. We studied the data with statistical and machine learning (ML) based signal processing methods to find the best accelerometer sensor position to detect coughing events accurately on the human body. Our study finds the most suitable sensor position for cough recognition considering the noise introduced by walking and considering different human heights. The proposed multi-band frequency-domain features such as Spectral Summation, Spectral Maximum, and Spectral Spread of acceleration signal offer higher classification accuracy for cough activity.
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    Decision Diagram Techniques for Micro- and Nanoelectronic Design Handbook
    (Taylor & Francis/CRC Press, 2005-12-22) Yanushkevich, Svetlana N; Miller, D. Michael; Shmerko, Vlad P; Stankovic, Radomir S
    Taking an applied perspective, Decision Diagram Techniques for Micro- and Nanoelectronic Design Handbook provides a comprehensive, up-to-date collection of DD techniques. Experts with more than forty years of combined experience in both industrial and academic settings demonstrate how to apply the techniques to full advantage with more than 400 examples and illustrations. Beginning with the fundamental theory, data structures, and logic underlying DD techniques, they explore a breadth of topics from arithmetic and word-level representations to spectral techniques and event-driven analysis. The book also includes abundant references to more detailed information and additional applications.
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    Gauss–Laguerre wavelet textural feature fusion with geometrical information for facial expression identification
    (Spinger Open, 2012-09-25) Poursaberi, Ahmad; Noubari, Hossein Ahmadi; Gavrilova, Marina; Yanushkevich, Svetlana N