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Start date: 2020End date: 2026Type: search.filters.itemtype.Proceedings PaperSubject: search.filters.subject.Computer Science, Theory & MethodsAuthor: search.filters.author.Karamzadeh, Saeid

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    Characterizing Attenuation of Bumper Modifications: A Comparative Study in the E-Band
    (IEEE, 2024) Neubauer, Michael; Hirschmugl, Michael; Petanjek, David; Kiebach, Helge; Karamzadeh, Saeid; Kolosovs, D; Anstalt fur Verbrennungskraftmaschinen List; Bahcesehir University
    Having knowledge about the attenuation of the materials surrounding a radar sensor is of great importance in the automotive sector. Only then can the manufacturer guarantee the desired functionality of the sensor. Due to the fact that a lot of automotive manufacturers also started integrating radar sensors behind rear bumpers for improved visibility to the back of a car, it is vital to know how those bumpers and possible repair scenarios done to them can affect the radar sensor. This paper proposes a way to measure the attenuation in the E-band of not only square slab samples representing various types of paint and repair scenarios on automotive bumpers, but also actual rear bumpers with different types of repair scenarios. The results are then presented and compared relative to each other.
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    Neural Network-Based Human Detection Using Raw UWB Radar Data
    (IEEE, 2024) Dogan, Emine Berjin; Yousefi, Mohammad; Soyak, Ece Gelal; Karamzadeh, Saeid; Kolosovs, D; Bahcesehir University; Bahcesehir University; Bahcesehir University; Bahcesehir University
    Ultra-Wideband (UWB) radar technology is a widely used technology for human detection and tracking through walls, because of its effectiveness in low-visibility situations. This study demonstrates a neural network-based identification of human presence using raw data obtained directly from the UWB radar. First, measurements have been collected with different human subjects at different positions relative to the UWB radar. A convolutional neural network (CNN) model has been trained on this dataset, to detect the presence of a human. Next, the algorithm effectiveness is deeply investigated using the Gradient-weighted Class Activation Mapping (Grad-CAM) method, and the observations on detected presence are discussed.
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    Evaluating Substrate Influence on Rectangular and Circular Microstrip Patch Antennas for 5G
    (IEEE, 2024) Goksel, Fatih; Karamzadeh, Saeid; Kolosovs, D; Bahcesehir University; Bahcesehir University
    Microstrip Patch Antennas (MSPAs) are favored for their low production cost, compact size, lightweight nature, and adaptability to planar and non-planar surfaces. They are particularly suitable for wideband and multi-frequency operations, making them ideal for sub-6 GHz applications in 5G devices. This study aims to design and compare rectangular and circular MSPAs using six different substrates: FR4 Glass Epoxy (dielectric constant 4.3), ROGERS DT5880 (2.2), ROGERS 4003C (3.3), Teflon (2.1), Arlon AD300A (3), and Alumina (9.9). Using CST Microwave Studio, we first design rectangular MSPAs and then circular MSPAs, maintaining a 1.6 mm substrate height and using Copper (Anneal) for both the patch and ground materials. The target frequency is 5.8 GHz, relevant for 5G applications. By comparing the antenna parameters across different substrates, we aim to identify the optimal substrate and antenna design for future technology needs.