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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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    Correction to: online failure diagnosis in interdependent networks (Operations Research Forum, (2021), 2, 1, (10), 10.1007/s43069-021-00055-2)
    (Springer International Publishing, 2021) Akbari, Vahid; N/A; Shiri, Davood; PhD Student; Graduate School of Sciences and Engineering; N/A
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    Head-worn mixed reality projection display application
    (Association for Computing Machinery, 2014) Kade, Daniel; N/A; Department of Media and Visual Arts; Department of Electrical and Electronics Engineering; Akşit, Kaan; Özcan, Oğuzhan; Ürey, Hakan; PhD Student; Faculty Member; Faculty Member; Department of Media and Visual Arts; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Social Sciences and Humanities; College of Engineering; N/A; 12532; 8579
    The main goal of this research is to develop a mixed reality (MR) application to support motion capture actors. This application allows seeing and exploring a digital environment without occluding the actor's visual field. A prototype is built by combining a retro-reflective screen covering surrounding walls and a headband consisting of a laser scanning projector with a smartphone. Built-in sensors of a smartphone provide navigation capabilities in the digital world. The integrated system has some unique advantages, which are collectively demonstrated for the first time: (i) providing fixed field-ofview (50o in diagonal), fixed retinal images at full-resolution, and distortion-free images that are independent of the screen distance and shape; (ii) presenting different perspectives to the users as they move around or tilt their heads, (iii) allowing a focus-free and calibration-free display even on non-flat surfaces using laser scanning technology, (iv) enabling multiple users to share the same screen without crosstalk due to the use of retro-reflectors, and (v) producing high brightness pictures with a projector of only 15 lm; due to a high-gain retro-reflective screen. We demonstrated a lightweight, comfortable to wear and low cost head-mounted projection display (HMPD) which acts as a stand-a-lone mobile system. Initial informal functionality tests have been successfully performed. The prototype can also be used as a 3D stereo system using the same hardware by additionally mounting polarized glasses and an active polarization rotator, while maintaining all of the advantages listed above.
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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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    Nanosensor networks for smart health care
    (Elsevier, 2020) Abbasi, Naveed A.; Department of Electrical and Electronics Engineering; N/A; N/A; Department of Electrical and Electronics Engineering; Akan, Özgür Barış; Khan, Tooba; Civaş, Meltem; Çetinkaya, Oktay; Faculty Member; PhD Student; PhD Student; Other; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; 6647; N/A; N/A; N/A
    Advent of nanoscale sensors has paved the way for countless applications envisioned in the concept of a Smart City. In this chapter, we are focusing on one of the most fundamental requirements of the smart city, that is, smart health care. Great advancements in personal health care are expected with the emergence of nanosensing devices; however, single nanosensor is limited in its processing power and storage; thus we need to form network of nanosensors for any health-care application. In this chapter, we first elaborate the communication paradigms for nanosensor network. Moreover, we discuss various smart health-care applications such as smart drug delivery, body area network, implantable devices to treat injuries or malfunctions, and Internet of Nano Things. In the end, we highlight the implementation challenges for the nanosensor network for biomedical applications.