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Start date: 2020End date: 2026Type: search.filters.itemtype.Proceedings PaperAuthor: search.filters.author.Soyak, Ece GelalAuthor: search.filters.author.Karamzadeh, Saeid

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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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    Improving the Robustness of CNN-Based Human Detection in Multiple Raw UWB Radar Datasets
    (IEEE, 2024) Yousefi, Mohammad; Dogan, Emine Berjin; Soyak, Ece Gelal; Karamzadeh, Saeid; Bahcesehir University; Bahcesehir University; Bahcesehir University; Bahcesehir University
    Ultra-wideband (UWB) radar technology has gained significant attention for human detection, vital sign monitoring, and activity recognition applications. In this study, a robust deep-learning model capable of detecting human presence in different environments is developed. The model uses only the raw data obtained directly from the radar without preprocessing. The model is trained and tested on different datasets collected via the UWB radar. Transfer learning has been applied to fine-tune the model on datasets to improve the performance and generalization of the model. This study demonstrates the effectiveness of transfer learning in adapting UWB radar-based human detection models to different environments.
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    Low-Powered Agriculture IoT Systems with LoRa
    (IEEE, 2020) Kokten, Esma; Caliskan, Bahadir Can; Karamzadeh, Saeid; Soyak, Ece Gelal; Aboltins, A; Litvinenko, A; Bahcesehir University; Bahcesehir University
    Monitoring is key to increase the efficiency of food storage in the open field in terms of cost, logistics and quality of the crops. For the long range data transmission in such environments, mobile technologies are not suitable, as the end devices are generally battery-limited. In this work, a prototype has been developed for monitoring goods in storage. The battery life time of this prototype is analysed in terms of calculations as well as measurements, on LoRa technology. Our results show that (i) while sleeping current has the smallest percentage, it has the greatest impact in increasing battery life, (ii) monitoring node shall have low self discharge battery for long battery life, and (iii) sensors are the main power sink that deplete battery.