The availability of low-cost microwave components today enables the development of various high-frequency sensors and radars, including Ground-based Synthetic Aperture Radar (GBSAR) systems. Similar to optical images, radar images generated by applying a reconstruction algorithm on raw GBSAR data can also be used in object classification. The reconstruction algorithm provides an interpretable representation of the observed scene, but may also negatively influence the integrity of obtained raw data due to applied approximations. In order to quantify this effect, we compare the results of a conventional computer vision architecture, ResNet18, trained on reconstructed images versus one trained on raw data. In this process, we focus on the task of multi-label classification and describe the crucial architectural modifications that are necessary to process raw data successfully. The experiments are performed on a novel multi-object dataset RealSAR obtained using a newly developed 24 GHz (GBSAR) system where the radar images in the dataset are reconstructed using the Omega-k algorithm applied to raw data. Experimental results show that the model trained on raw data consistently outperforms the image-based model. We provide a thorough analysis of both approaches across hyperparameters related to model pretraining and the size of the training dataset. This, in conclusion, shows how processing raw data provides overall better classification accuracy, it is inherently faster since there is no need for image reconstruction and it is therefore useful tool in industrial GBSAR applications where processing speed is critical.

Due to the COVID-19 pandemic that bursted out this year, online education has become a main type of education and an entire education system was forced to make a switch from classrooms to World Wide Web. The impact of the students receiving education online for the entire semester on their final grade was analysed and the results are shown in the paper. These results are obtained by analysing the academic course Mathematical Analysis 2, held online at the Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia, in the summer semester of 2020. Furthermore, the number of views of the lectures in relation to the time left until the final exam was checked in order to investigate the students' studying habits. Unsurprisingly, the most noticeable effect here is the last minute cramming. Nevertheless, the overall results show the pros and cons of online education, as well as the huge potential that online education has, even when performed at the current state.

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