Elektrik - Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineeringhttp://hdl.handle.net/20.500.12566/172024-03-29T00:16:30Z2024-03-29T00:16:30ZTransfer entropyGençağa, Denizhttp://hdl.handle.net/20.500.12566/19972024-03-25T13:42:08Z2018-01-01T00:00:00ZTransfer entropy
Gençağa, Deniz
2018-01-01T00:00:00ZConfounding factor analysis for vocal fold oscillationsGençağa, Denizhttp://hdl.handle.net/20.500.12566/19962024-03-25T13:17:50Z2023-01-01T00:00:00ZConfounding factor analysis for vocal fold oscillations
Gençağa, Deniz
This paper provides a methodology to better understand the relationships between different aspects of vocal fold motion, which are used as features in machine learning-based approaches for detecting respiratory infections from voice recordings. The relationships are derived through a joint multivariate analysis of the vocal fold oscillations of speakers. Specifically, the multivariate setting explores the displacements and velocities of the left and right vocal folds derived from recordings of five extended vowel sounds for each speaker (/aa/, /iy/, /ey/, /uw/, and /ow/). In this multivariate setting, the differences between the bivariate and conditional interactions are analyzed by information-theoretic quantities based on transfer entropy. Incorporation of the conditional quantities reveals information regarding the confounding factors that can influence the statistical interactions among other pairs of variables. This is demonstrated on a vector autoregressive process where the analytical derivations can be carried out. As a proof of concept, the methodology is applied on a clinically curated dataset of COVID-19. The findings suggest that the interaction between the vocal fold oscillations can change according to individuals and presence of any respiratory infection, such as COVID-19. The results are important in the sense that the proposed approach can be utilized to determine the selection of appropriate features as a supplementary or early detection tool in voice-based diagnostics in future studies.
2023-01-01T00:00:00ZA magnetic sensor for artery pressure monitoringAslan, Menduh FurkanKerbouche, OmarNavruz, Tuğba SelcenBeyaz, Mustafa İlkerhttp://hdl.handle.net/20.500.12566/19802024-03-20T12:52:06Z2023-01-01T00:00:00ZA magnetic sensor for artery pressure monitoring
Aslan, Menduh Furkan; Kerbouche, Omar; Navruz, Tuğba Selcen; Beyaz, Mustafa İlker
—This paper reports a magnetic sensing device for in-vivo artery pressure monitoring. The device consists of multiple magnets and hall sensors arranged orthogonally on a flexible substrate. The novelty of the device lies in the component plurality and their geometrical configuration, which enhances pressure sensitivity. Several designs involving different number of components have been investigated through Finite Element simulations. The design that incorporates two cross-positioned magnets and sensors exhibited an eight-fold improvement in artery expansion response compared to the traditional single magnet-sensor configuration. In-vitro experiments carried on a surgical latex tube demonstrated a five-fold improvement in pressure sensitivity. Further development of this device will enable continuous monitoring of blood pressure after organ transplantations.
2023-01-01T00:00:00ZIs cell-free massive MIMO (CF-MMIMO) equivalent to the already existing and commercialized pCell technology?Iqbal, SadiqHamamreh, Jehad M.http://hdl.handle.net/20.500.12566/19792024-03-20T12:06:45Z2021-01-01T00:00:00ZIs cell-free massive MIMO (CF-MMIMO) equivalent to the already existing and commercialized pCell technology?
Iqbal, Sadiq; Hamamreh, Jehad M.
Distributed antenna system (DAS) is a highly diversified concept that can help lower radiated power, maximize coverage, and improve spectral efficiency. Yet, the most exciting feature of DAS is the distribution of remote radio heads (RRH) or antennas over a wide geographical area, which allows the use of less complex signal processing techniques resulting in the reduction of systems’ size and cost. These features of DAS were utilized in network MIMO, distributed MIMO, multi-cell MIMO, and distributed-input distributed-output (DIDO) systems to move away from the centralized approach as in conventional multi-user and massive MIMO systems. In this tutorial paper, we first talk about MIMO systems with their relevant technologies by categorizing them into two main approaches according to their working principles and systematic architecture; the first approach is based on a centralized (or collocated) architecture, whereas the second is based on a decentralized (or distributed) architecture. We then deep dive into all the different technologies related to these architectures one by one and explain each in detail with much more focus on the similarities and differences between them. After that, we exposition the main target of this study, which is to answer the following question "Is cell-free massive MIMO (CF-MMIMO) equivalent to the already existing and commercialized pCell technology?". To confidently answer this question, we comprehensively and deeply study the two technologies considering the differences in their system models and look into their conceptual formulations as well as their respective channel models. More particularly, we detail CF-MMIMO and pCell architectures while giving special attention to pCell’s SDR wireless platform and stating why it is crucial for the current wireless systems. Then, we present the understandings that are acquired after careful observations and provide recommendations based upon them. In short, this study aims to focus the reader attention on the fact that all the distributed and cellfree MIMO-related technologies work on the same principle, and the way these systems are built makes them fundamentally equivalent, yet, they are displayed quite differently using different terminologies and perspectives, which can cause confusion to readers and end up misleading future researchers.
The authors shed light on MIMO systems with their relevant technologies by categorizing them into two approaches; 1) centralized (co-located), which covers most conventional MIMO schemes, and 2) decentralized (distributed) approach, which covers both pCell & CF-MMIMO.
2021-01-01T00:00:00Z