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Interacting with Visualization on Mobile Devices
Published in Bongshin Lee, Raimund Dachselt, Petra Isenberg, Eun Kyoung Choe, Mobile Data Visualization, 2021
Matthew Brehmer, Bongshin Lee, John Stasko, Christian Tominski
Despite the different usage contexts for mobile devices and PCs, we note that the distinction between laptop PCs and tablets is beginning to blur. For instance, some Microsoft's Surface devices [77] and others like it are equipped with touchscreens and can be converted between laptop and tablet modes. Meanwhile, tablets such as Apple's iPad Pro [5] boast screens as large as laptops, powerful hardware capabilities as good as many PCs, and peripheral keyboard attachments. These hybrid devices provide affordances to combine bimanual touch- and gesture-based direct manipulation with conventional keyboard, mouse, and trackpad interaction in the context of both WIMP- (Windows, Icons, Menus, Pointer) and post-WIMP interfaces.
A comparison of three surface roughness characterization techniques: photogrammetry, pin profiler, and smartphone-based LiDAR
Published in International Journal of Digital Earth, 2022
Zohreh Alijani, Julien Meloche, Alexander McLaren, John Lindsay, Alexandre Roy, Aaron Berg
LiDAR technology was added to iPhone 12 Pro and iPad Pro (4th generation) for the first time in 2020. The iPhone 12 Pro has three RGB 12 MP rear cameras and a LiDAR sensor. The LiDAR sensor on the iPhone 12 Pro measures the distance to surrounding objects up to 5 m away. These sensors create accurate high-resolution models of small objects with a side length > 10 cm with an absolute accuracy of ± 1 cm (Luetzenburg, Kroon, and Bjørk 2021). Although the type of Apple’s LiDAR seems to be a trade secret, some researchers reported that these sensors may be based on a single-photon avalanche diode (SPAD) coupled with a laser light source (Tontini, Gasparini, and Perenzoni 2020; Murtiyoso et al. 2021). However, it is more likely that the sensor is a solid-state LiDAR (SSL) (Wang et al. 2022) which, in contrast to traditional LiDAR systems with a mechanical rotator that can often be bulky in size, avoids the use of large mechanical parts to ensure higher scalability and reliability (García-Gómez et al. 2020). Therefore, the SSL sensors have recently drawn attention in both academic and industry circles. More information about the SSL sensors can be found at Li et al. (2022). In the current study, all profiles were scanned using the iPhone 12 Pro LiDAR scanner at a distance of 0.3 m from the surface, measured using a rod, placed on the top of the flume, to facilitate a consistent scan and to reduce human error (Figure 1(b)). Generated point clouds were exported in LAS (LASer) format after scanning all profiles.
Advantages of Print Reading over Screen Reading: A Comparison of Visual Patterns, Reading Performance, and Reading Attitudes across Paper, Computers, and Tablets
Published in International Journal of Human–Computer Interaction, 2021
The eye-tracking device used for the experiment was Tobii Pro Glasses 2, which captures real-time user gaze data. Tobii Studio 1.3 and Tobii Pro Lab 1.73 software were employed to extract and analyze the eye-tracking data. As shown in Figure 1, the reading text was presented in three media formats: paper, computer, and tablet. The computer was a 21.5” Apple iMac with a Retina display monitor at a resolution of 1920 × 1080 pixels. The tablets were a Microsoft Surface Pro with PixelSenseTM used in experiment 1. A 12.9” Apple iPad Pro with a Retina display monitor and a resolution of 2732 × 2048 pixels was used in experiment 2. Although the media formats differed, the reading material was designed to have the same page layouts regardless of the device: A4-size paper or 8.3” x 11.7” surface area, 10-point Nanum Myeongjo font, and 8-point line spacing.
Technical characterisation of digital stethoscopes: towards scalable artificial intelligence-based auscultation
Published in Journal of Medical Engineering & Technology, 2023
Youness Arjoune, Trong N. Nguyen, Robin W. Doroshow, Raj Shekhar
Audacity software was used to generate white noise, which was played through one of the two speakers. We recorded the generated sound using one of the digital stethoscopes, but to obtain the raw “WAV” files, we needed device-specific software products: Littmann StethAssist, WAVEko, and Thinklabs Wave. StethAssist was installed on a Macbook Pro, Eko was installed on an iPhone and enabled recording and saving audio files in the cloud. Lastly, the Thinklabs Wave was installed on an iPad Pro. This application enabled recordings from Thinklabs One to be imported as “WAV” files.