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1. Towards Smartphone Operating System Identification

   https://doi.org/10.1109/TDSC.2019.2892482  

   Zhu, Ye; Ruffing, Nicholas; Gurary, Jonathan; Guan, Yong; Bettati, Riccardo (January 2019, IEEE Transactions on Dependable and Secure Computing)
2. POD: A Smartphone That Flies

   https://doi.org/10.1145/3469259.3470490  

   Chen, Guojun; Weiner, Noah; Zhong, Lin (June 2021, Dronet'21: Proceedings of the 7th Workshop on Micro Aerial Vehicle Networks, Systems, and Applications)

   We present POD, a smartphone that flies, as a new way to achieve hands-free, eyes-up mobile computing. Unlike existing drone-carried user interfaces, POD features a smartphone-sized display and the computing and sensing power of a modern smartphone. We share our experience in prototyping POD, discuss the technical challenges facing it, and describe early results toward addressing them. 

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3. A Modular Smartphone for Lending

   https://doi.org/10.1145/3126594.3126633  

   Seyed, Teddy; Yang, Xing-Dong; Vogel, Daniel (October 2017, Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology)

   We motivate, design, and prototype a modular smartphone designed to make temporary device lending trustworthy and convenient. The concept is that the phone can be separated into pieces, so a child, friend, or even stranger can begin an access-controlled interaction with one piece, while the own-er retains another piece to continue their tasks and monitor activity. This is grounded in a survey capturing attitudes towards device lending, and an exploratory study probing how people might lend pieces of different kinds of modular smartphones. Design considerations are generated for a hardware form factor and software interface to support different lending scenarios. A functional prototype combining three smartphones into a single modular device is described and used to demonstrate a lending interaction design. A usability test validates the concept using the prototype. 

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4. Smartphone Screens as Astrometric Calibrators

   https://doi.org/10.1142/S2251171723500095  

   Walk, Aidan; Claveau, Charles-Antoine; Bottom, Michael; Chun, Mark; Jacobson, Shane; Service, Maxwell; Lu, Jessica R. (January 2023, Journal of Astronomical Instrumentation)

   Geometric optical distortion is a significant contributor to the astrometric error budget in large telescopes using adaptive optics. To increase astrometric precision, optical distortion calibration is necessary. We investigate using smartphone Organic Light-Emitting Diode (OLED) screens as astrometric calibrators. Smartphones are low-cost, have stable illumination, and can be quickly reconfigured to probe different spatial frequencies of an optical system’s geometric distortion. In this work, we characterize the astrometric accuracy of a Samsung S20 smartphone, with a view towards providing large format, flexible astrometric calibrators for the next generation of astronomical instruments. We find the placement error of the pixels to be 189[Formula: see text]nm ± 15[Formula: see text]nm Root Mean Square (RMS). At this level of error, milliarcsecond astrometric accuracy can be obtained on modern astronomical instruments. 

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5. Novel Keratoconus Detection Method Using Smartphone

   https://doi.org/10.1109/HI-POCT45284.2019.8962648  

   Askarian, Behnam; Tabei, Fatemehsadat; Tipton, Grace Anne; Chong, Jo Woon (November 2019, Novel Keratoconus Detection Method Using Smartphone)

   Keratoconus is a progressive corneal disease which may cause blindness if it is not detected in the early stage. In this paper, we propose a portable, low-cost, and robust keratoconus detection method which is based on smartphone camera images. A gadget has been designed and manufactured using 3-D printing to supplement keratoconus detection. A smartphone camera with the gadget provides more accurate and robust keratoconus detection performance. We adopted the Prewitt operator for edge detection and the support vector machine (SVM) to classify keratoconus eyes from healthy eyes. Experimental results show that the proposed method can detect mild, moderate, advanced, and severe stages of keratoconus with 89% accuracy on average. 

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