Electrical & Electronics Engineering

Permanent URI for this collectionhttps://hdl.handle.net/10679/44

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    ArticlePublicationOpen Access
    Comparative characterization of indoor VLC and MMW communications via ray tracing simulations
    (IEEE, 2023) Hosseinabadi, Fahimeh Aghaei; Eldeeb, H. B.; Bariah, L.; Muhaidat, S.; Uysal, Murat; Electrical & Electronics Engineering; UYSAL, Murat; Hosseinabadi, Fahimeh Aghaei
    The demand for ultra-high-speed indoor wireless connectivity is ever-increasing, which poses unique challenges for the next generation wireless communication system design. This has prompted the exploration of higher frequency bands including millimeter wave (MMW) and visible light bands in addition to the conventional sub-6 GHz band. This paper provides a comprehensive comparison of the propagation channels of these frequency bands under the same indoor environment and scenarios. We adopt ray tracing techniques for site-specific channel modeling, which enables the consideration of the three-dimensional models of the indoor environment and objects inside. It allows us to take into account different frequencies, i.e., 2.4 GHz, 6 GHz, 28 GHz, 60 GHz, 100 GHz, and visible light band as well as different transmitter types, i.e., omnidirectional/directional antennas for radio frequency systems and indoor luminaries for visible light communications (VLC). For different frequencies under consideration, we obtain channel impulse responses (CIRs) and present the channel path losses for various user trajectories in indoor environments. Furthermore, we propose closed-form expressions for the cumulative distribution functions (CDFs) of received power levels for all frequency bands under consideration. Our results demonstrate that VLC channels exhibit lower path loss than that in MMW bands but higher than that of 2.4 GHz band. In addition, it is observed that VLC systems exhibit more sensitivity to shadowing and blockage effects. Our findings further indicate that the characteristics of the propagation channel are greatly influenced by the antenna type. For instance, using omnidirectional and rectangular patch antennas results in lower path loss compared to horn antennas, and this difference becomes more significant as the transmission distance decreases.
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    ArticlePublicationOpen Access
    Depression screening from voice samples of patients affected by parkinson’s disease
    (S. Karger AG, 2019-05-01) Özkanca, Yasin Serdar; Öztürk, M. G.; Ekmekci, Merve Nur; Atkins, D. C.; Demiroğlu, Cenk; Ghomi, R. H.; Electrical & Electronics Engineering; DEMİROĞLU, Cenk; Özkanca, Yasin Serdar; Ekmekci, Merve Nur
    Depression is a common mental health problem leading to significant disability worldwide. It is not only common but also commonly co-occurs with other mental and neurological illnesses. Parkinson's disease (PD) gives rise to symptoms directly impairing a person's ability to function. Early diagnosis and detection of depression can aid in treatment, but diagnosis typically requires an interview with a health provider or a structured diagnostic questionnaire. Thus, unobtrusive measures to monitor depression symptoms in daily life could have great utility in screening depression for clinical treatment. Vocal biomarkers of depression are a potentially effective method of assessing depression symptoms in daily life, which is the focus of the current research. We have a database of 921 unique PD patients and their self-assessment of whether they felt depressed or not. Voice recordings from these patients were used to extract paralinguistic features, which served as inputs to machine learning and deep learning techniques to predict depression. The results are presented here, and the limitations are discussed given the nature of the recordings which lack language content. Our models achieved accuracies as high as 0.77 in classifying depressed and nondepressed subjects accurately using their voice features and PD severity. We found depression and severity of PD had a correlation coefficient of 0.3936, providing a valuable feature when predicting depression from voice. Our results indicate a clear correlation between feeling depressed and PD severity. Voice may be an effective digital biomarker to screen for depression among PD patients.