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Start date: 2020End date: 2026Type: search.filters.itemtype.Proceedings PaperSubject: search.filters.subject.Computer Science, Theory & MethodsSubject: search.filters.subject.Computer Science, Interdisciplinary ApplicationsSubject: search.filters.subject.Computer Science, Artificial Intelligence

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    Assistive Visual Tool: Enhancing Safe Navigation with Video Remapping in AR Headsets
    (SPRINGER INTERNATIONAL PUBLISHING AG, 2025) Sadeghzadeh, Arezoo; Islam, Md Baharul; Uddin, Md Nur; Aydin, Tarkan; DelBue, A; Canton, C; Pont-Tuset, J; Tommasi, T; Bahcesehir University; State University System of Florida; Florida Gulf Coast University
    Visual Field Loss (VFL) is characterized by blind spots or scotomas that poses detrimental impact on fundamental movement activities of individuals. Addressing the challenges (e.g., low video quality, content loss, high levels of contradiction, and limited mobility assessment) faced by existing Extended Reality (XR) systems as vision aids, we introduce a groundbreaking method that enriches the real-time navigation using Augmented Reality (AR) glasses. Our novel vision aid employs advanced video processing techniques to enhance visual perception in individuals with moderate to severe VFL, bridging the gap to healthy vision. A unique optimal video remapping function, tailored to our selected AR glasses characteristics, dynamically maps live video content to the largest intact region of the Visual Field (VF) map. Our method preserves video quality, minimizing blurriness and distortion. Through a comprehensive empirical user study involving 29 subjects with artificially induced scotomas, statistical analyses of object counting and multi-tasking walking track tests demonstrate the promising performance of our method in enhancing visual awareness and navigation capability in real-time.
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    Advancing WebRTC QoE Assessment with Machine Learning in Real-World Wi-Fi Scenarios
    (IEEE, 2024) Argin, Berke; Demir, Mehmet Ozgun; Salik, Elif Dilek; Onalan, Aysun Gurur; Batum, Oyku Han; Soyak, Ece Gelal; Bahcesehir University
    Video conferencing applications play a key role in enabling use cases like remote working, education, and potentially the metaverse. From the perspective of Internet service providers, predicting the end user's Quality of Experience (QoE) in such applications is critical in allocating the right resources to ensure consistently high QoE. This work addresses the estimation of user QoE from link-layer performance metrics such as transferred packets, queue size, signal strength, and channel occupancy for WebRTC-supported applications. Our study entails collecting a data set capturing various Wi-Fi scenarios in practical environments and training machine learning models on this data to estimate the perceived QoE. Our findings demonstrate improvement in prediction accuracy compared to earlier models and QoE representations, furthermore, we also investigate the explainability of the models with the help of SHAP values.
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    Strategies for the Utilization of Virtual Reality Technologies in the First Year of Architectural Education
    (SPRINGER INTERNATIONAL PUBLISHING AG, 2021) Ceylan, Salih; Lane, HC; Zvacek, S; Uhomoibhi, J; Bahcesehir University
    The first year of architectural education is a crucial period in which students get introduced to the ambiguous nature of architecture and build the foundations of their professional career. It has a dense structure, theoretical and technical courses gathered around the design studio which is the core of architectural education. Its dynamic and flexible nature makes architectural education open for innovations and the implementation of emerging technologies. Accordingly, digital technologies that have a strong relationship with the profession of architecture, also have firm effects on architectural education. Even though it is not common among architectural education institutions around the globe, emerging digital technologies may have a role in the first year of architectural curriculum. One of the digital technologies that can be utilized in the first year of architectural education in virtual reality technologies as they provide an additional medium for experiencing architectural products and alternative methods for designing them. This paper investigates the necessity and potential benefits ofVRtechnologies in the first year of architectural education. Based on a case study conducted among freshman students, the VR technologies prove themselves useful. The paper also presents various methods and strategies, and their potential benefits for the implementation of VR technologies into the different domains of first year architectural curriculum.
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    Picture Fuzzy Cost-Effectiveness Analysis in Health Care
    (SPRINGER INTERNATIONAL PUBLISHING AG, 2024) Haktanir, Elif; Kahraman, C; Onar, SC; Cebi, S; Oztaysi, B; Tolga, AC; Sari, IU; Bahcesehir University
    Cost-effectiveness analysis is a way of making a budget in order to minimize the cost and maximize the service outcome, by making the best and most effective choice among the alternative ways to achieve the planned goals. This method, which is generally used in the field of health care, is handled with picture fuzzy sets in this study, and the uncertainties of the decision makers are reflected more realistically and consistently. The method developed with the proposed new equations can be used in many different areas where decisions need to be made under cost constraints. The developed method is illustrated step by step with the application in the field of SMA disease.
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    Towards Stereoscopic Video Deblurring Using Deep Convolutional Networks
    (SPRINGER INTERNATIONAL PUBLISHING AG, 2021) Imani, Hassan; Islam, Md Baharul; Bebis, G; Athitsos, V; Yan, T; Lau, M; Li, F; Shi, C; Yuan, X; Mousas, C; Bruder, G; Bahcesehir University
    These days stereoscopic cameras are commonly used in daily life, such as the new smartphones and emerging technologies. The quality of the stereo video can be affected by various factors (e.g., blur artifact due to camera/object motion). For solving this issue, several methods are proposed for monocular deblurring, and there are some limited proposed works for stereo content deblurring. This paper presents a novel stereoscopic video deblurring model considering the consecutive left and right video frames. To compensate for the motion in stereoscopic video, we feed consecutive frames from the previous and next frames to the 3D CNN networks, which can help for further deblurring. Also, our proposed model uses the stereoscopic other view information to help for deblurring. Specifically, to deblur the stereo frames, our model takes the left and right stereoscopic frames and some neighboring left and right frames as the inputs. Then, after compensation for the transformation between consecutive frames, a 3D Convolutional Neural Network (CNN) is applied to the left and right batches of frames to extract their features. This model consists of the modified 3D U-Net networks. To aggregate the left and right features, the Parallax Attention Module (PAM) is modified to fuse the left and right features and create the output deblurred frames. The experimental results on the recently proposed Stereo Blur dataset show that the proposed method can effectively deblur the blurry stereoscopic videos.