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    A performance comparison of single-radio multichannel medium access control protocols
    (Institute of Electrical and Electronics Engineers Inc., 2020) Department of Electrical and Electronics Engineering; N/A; N/A; Ergen, Sinem Çöleri; Uçar, Seyhan; Kaytaz, Umuralp; Faculty Member; PhD Student; PhD Student; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; 7211; N/A; N/A
    Single-radio multi-channel Medium Access Control (MAC) protocols aim to transmit in parallel on distinct channels while reducing the hardware cost. Although a variety of MAC protocols have been proposed in this context, no detailed classification and performance comparison is available. In this paper, we first classify previous efforts by their rendezvous characteristics as single- and multi-rendezvous protocols. Multirendezvous protocols have the capability of supporting simultaneous handshaking on different channels whereas with single-rendezvous protocols only asynchronous channel negotiations are allowed. Then, we further classify these protocols according to their spectrum decision mechanisms. We demonstrate the functionality of single- and multi-rendezvous protocols under different scenarios via extensive simulations. Our findings show that multi-rendezvous protocol performs better when the transmission range is low and less number of channels is available. Single-rendezvous protocols, on the other hand, are more suitable for networks with larger traffic loads due to their slot-based decision-making schemes.
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    Intelligent edge computing: state-of-the-art techniques and applications
    (Institute of Electrical and Electronics Engineers Inc., 2020) Department of Computer Engineering; Department of Computer Engineering; N/A; Gürsoy, Attila; Özkasap, Öznur; Gill, Waris; Faculty Member; Faculty Member; PhD Student; Department of Computer Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; 8745; 113507; N/A
    To enable intelligent decisions at the network edge, supervised and unsupervised machine learning techniques and their variations are highly utilized in recent research studies. These include techniques and the corresponding applications such as detecting manufacturing faults in a smart factory setting, monitoring patient activities and health problems in smart health systems, detecting security attacks on the Internet of Things devices, and finding the rare events in the audio signals. In this paper, we present an extensive review of state-of-the-art techniques and applications of intelligent edge computing and provide classification and discussion of various approaches in this field.
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    Use of affective visual Information for summarization of human-centric videos
    (2022) Kopro, Berkay; Department of Computer Engineering; Erzin, Engin; Faculty Member; Department of Computer Engineering; College of Engineering; 34503
    The increasing volume of user-generated human-centric video content and its applications, such as video retrieval and browsing, require compact representations addressed by the video summarization literature. Current supervised studies formulate video summarization as a sequence-to-sequence learning problem, and the existing solutions often neglect the surge of the human-centric view, which inherently contains affective content. In this study, we investigate the affective-information enriched supervised video summarization task for human-centric videos. First, we train a visual input-driven state-of-the-art continuous emotion recognition model (CER-NET) on the RECOLA dataset to estimate activation and valence attributes. Then, we integrate the estimated emotional attributes and their high-level embeddings from the CER-NET with the visual information to define the proposed affective video summarization (AVSUM) architectures. In addition, we investigate the use of attention to improve the AVSUM architectures and propose two new architectures based on temporal attention (TA-AVSUM) and spatial attention (SA-AVSUM). We conduct video summarization experiments on the TvSum and COGNIMUSE datasets. The proposed temporal attention-based TA-AVSUM architecture attains competitive video summarization performances with strong improvements for the human-centric videos compared to the state-of-the-art in terms of F-score, self-defined face recall, and rank correlation metrics.