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Article Citation - Scopus: 9Multipath Exploitation in Emitter Localization for Irregular Terrains(Czech Technical University, 2019) Dalveren,Y.; Kara,A.Electronic Support Measures (ESM) systems have many operational challenges while locating radar emitter's position around irregular terrains such as islands due to multipath scattering. To overcome these challenges, this paper addresses exploiting multipath scattering in passive localization of radar emitters around irregular terrains. The idea is based on the use of multipath scattered signals as virtual sensor through Geographical Information System (GIS). In this way, it is presented that single receiver (ESM receiver) passive localization can be achieved for radar emitters. The study is initiated with estimating candidate multipath scattering centers over irregular terrain. To do this, ESM receivers' Angle of Arrival (AOA) and Time of Arrival (TOA) information are required for directly received radar pulses along with multipath scattered pulses. The problem then turns out to be multiple-sensor localization problem for which Time Difference of Arrival (TDOA)-based techniques can easily be applied. However, there is high degree of uncertainty in location of candidate multipath scattering centers as the multipath scattering involves diffuse components over irregular terrain. Apparently, this causes large localization errors in TDOA. To reduce this error, a reliability based weighting method is proposed. Simulation results regarding with a simplified 3D model are also presented. © 2019 RADIOENGINEERING.Article Citation - WoS: 3Citation - Scopus: 5Convolutional Neural Network-Based Vehicle Classification in Low-Quality Imaging Conditions for Internet of Things Devices(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Maiga,B.; Dalveren,Y.; Kara,A.; Derawi,M.Vehicle classification has an important role in the efficient implementation of Internet of Things (IoT)-based intelligent transportation system (ITS) applications. Nowadays, because of their higher performance, convolutional neural networks (CNNs) are mostly used for vehicle classification. However, the computational complexity of CNNs and high-resolution data provided by high-quality monitoring cameras can pose significant challenges due to limited IoT device resources. In order to address this issue, this study aims to propose a simple CNN-based model for vehicle classification in low-quality images collected by a standard security camera positioned far from a traffic scene under low lighting and different weather conditions. For this purpose, firstly, a new dataset that contains 4800 low-quality vehicle images with 100 × 100 pixels and a 96 dpi resolution was created. Then, the proposed model and several well-known CNN-based models were tested on the created dataset. The results demonstrate that the proposed model achieved 95.8% accuracy, outperforming Inception v3, Inception-ResNet v2, Xception, and VGG19. While DenseNet121 and ResNet50 achieved better accuracy, their complexity in terms of higher trainable parameters, layers, and training times might be a significant concern in practice. In this context, the results suggest that the proposed model could be a feasible option for IoT devices used in ITS applications due to its simple architecture. © 2023 by the authors.
