Alexander Mariakakis

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This paper presents <i>SAIL</i>, a Single Access Point Based Indoor Localization system. Although there have been advances in WiFi-based positioning techniques, we find that existing solutions either require a dense deployment of access points (APs), manual fingerprinting, energy hungry WiFi scanning, or sophisticated AP hardware. We design SAIL using a(More)
The different electronic devices we use on a daily basis produce distinct electromagnetic radiation due to differences in their underlying electrical components. We present MagnifiSense, a low-power wearable system that uses three passive magneto-inductive sensors and a minimal ADC setup to identify the device a person is operating. MagnifiSense achieves(More)
Smartphones and tablets are often used in dynamic environments that force users to break focus and attend to their surroundings, creating a form of "situational impairment." Current mobile devices have no ability to sense when users divert or restore their attention, let alone provide support for resuming tasks. We therefore introduce SwitchBack, a system(More)
Emerging uses of imaging technology for consumers cover a wide range of application areas from health to interaction techniques; however, typical cameras primarily transduce light from the visible spectrum into only three overlapping components of the spectrum: red, blue, and green. In contrast, hyperspectral imaging breaks down the electromagnetic spectrum(More)
The measurement of intraocular pressure (IOP) is an important vital sign for the eye, particularly for the diagnosis of glaucoma. Procedures for measuring IOP have been used by eye care professionals for over 100 years, but those without access to such professionals often go undiagnosed. We present a smartphone-based system that can be operated by minimally(More)
This video presents a demo of indoor localization in multiple settings. In the demo, a user walks with a smartphone and the user's location is shown on the phone's screen in real time. Our system, called Unsupervised Indoor Localization (UnLoc) utilizes the sensor data from smartphones to learn "invisible landmarks" in the environment. Example landmarks(More)
We propose UnLoc [1], an unsupervised indoor localization scheme that bypasses the need for war-driving. Our key observation is that certain locations in an indoor environment present an identifiable signature on one or more sensing dimensions. An elevator, for instance, imposes a distinct pattern on a smartphone's accelerometer; a specific spot may(More)
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