Deep Sea Robotic Imaging Simulator

Nowadays underwater vision systems are being widely applied in ocean research. However, the largest portion of the ocean - the deep sea - still remains mostly unexplored. Only relatively few image sets have been taken from the deep sea due to the physical limitations caused by technical challenges and enormous costs. Deep sea images are very different from the images taken in shallow waters and this area did not get much attention from the community. The shortage of deep sea images and the corresponding ground truth data for evaluation and training is becoming a bottleneck for the development of underwater computer vision methods. Thus, this paper presents a physical model-based image simulation solution, which uses an in-air texture and depth information as inputs, to generate underwater image sequences taken by robots in deep ocean scenarios. Different from shallow water conditions, artificial illumination plays a vital role in deep sea image formation as it strongly affects the scene appearance. Our radiometric image formation model considers both attenuation and scattering effects with co-moving spotlights in the dark. By detailed analysis and evaluation of the underwater image formation model, we propose a 3D lookup table structure in combination with a novel rendering strategy to improve simulation performance. This enables us to integrate an interactive deep sea robotic vision simulation in the Unmanned Underwater Vehicles simulator. To inspire further deep sea vision research by the community, we release the source code of our deep sea image converter to the public (https://www.geomar.de/en/omv-research/robotic-imaging-simulator)

Research Tendencies in the Discipline of Distance Education (2015-2022): Examination of Doctoral Theses in Higher Education in Turkey

The discipline of distance education is evolving and becoming mainstream, and this view requires examining research tendencies in the field. Motivated by this justification, it can be argued that understanding distance education to the fullest extent is possible partially through examining changes in theory and practice, as research conducted in the field reflects changes, dynamics, and perspectives. In this regard, the purpose of this paper is to present the research tendencies in doctoral theses in the Turkish higher education context with a specific focus on distance education. In line with this aim, a total of 265 doctoral theses published between 2015 and 2022 were examined through data mining and analytics approaches. The analysis of the titles through t-SNE analysis revealed four broad themes. These are: (1) more emphasis on learning processes; (2) the comparison of online technologies and online learning spaces; (3) a strong focus on educational technologies; and (4) the limitations emerging from comparative studies. The examination of the abstracts through text-mining identified the following themes: (1) the methodological vicious circle, the pursuit of methodological perfection, and lack of critical perspectives; (2) the tendency to use online [educational] technologies; (3) the comparison of distance and face-to-face education; and (4) the design of social interaction and communication in distance education processes. Finally, the analysis of the keywords through word clouds surfaced the following research tendencies: (1) Technology-supported distance education processes; (2) the wide use of educational technologies; (3) focusing on issues related to the learners in distance education. The paper concludes with implications and recommendations for future research directions

Detection and Microwave Imaging of Conducting Objects Buried Very Closely to the Air-Soil Boundary

Down-looking Ground Penetrating Radar (GPR) is an ultra-wideband electromagnetic sensor which has important applications such as IED and landmine detection, locating people in earthquake rescue operations, detection of archeological sites, mapping ice thickness or quantification of sedimentary structures in geophysical applications. The very first and important step in target detection by GPR is the removal of ground reflections caused by the air-soil boundary as these undesired signals are usually much stronger than the signals reflected and scattered from the buried targets. Ground reflections are well-known for their deteriorating effects on detection rate and false alarm rate in GPR applications. When a target is buried in a reasonable depth such as five centimeters or more, the ground reflections and the first returns from the buried object can be well separated in time, thus the removal of ground reflections turns out to be a standard procedure. However, if the burial depth is very small, the early returns from the target may be mistakenly removed together with the ground reflections. In such a case, a shallowly buried conducting target may go completely unnoticed. In this study, we will investigate the problem of detection and imaging of various conducting targets which are buried only one centimeter below the air-soil interface. The test targets are chosen to be a water-filled rectangular prism made of plastic; a thin rectangular prism coated by aluminum foil; two metal rods of the same length one with circular cross-section and the other one with a square shaped cross-section. After GPR-based measurements are recorded for these targets, a preprocessing method based on energy features and background removal will be used to eliminate air-ground reflections from the raw GPR A-Scan signals. C-Scan data sets, which are the collections of measured A-Scan signals recorded in cross-track and down-track directions, will be used for subsurface microwave imaging to sense the presence of the buried targets, and to figure out their shapes, if possible