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Smartphones are excellent mobile sensing platforms, with the microphone in particular being exercised in several audio inference applications. We take smartphone audio inference a step further and demonstrate for the first time that it's possible to accurately estimate the number of people talking in a certain place -- with an average error distance of 1.5(More)
Radio frequency based device-free passive localization has been proposed as an alternative to indoor localization because it does not require subjects to wear a radio device. This technique observes how people disturb the pattern of radio waves in an indoor space and derives their positions accordingly. The well-known multipath effect makes this problem(More)
Radio frequency based device-free passive (DfP) localization techniques have shown great potentials in localizing individual human subjects, without requiring them to carry any radio devices. In this study, we extend the DfP technique to count and localize multiple subjects in indoor environments. To address the impact of multipath on indoor radio signals,(More)
We explore the problem of detecting whether a device has moved within a room. Our approach relies on comparing summaries of received signal strength measurements over time, which we call descriptors. We consider descriptors based on the differences in the mean, standard deviation, and histogram comparison. In close to 1000 mobility events we conducted, our(More)
—Asset tracking is an important application domain for wireless sensor networks. However, continuous tracking of a large number of items at the individual item level over a significant period of time is still not feasible. There are two main obstacles. The first is the need for efficient, low-power communication protocols. Many current protocols employ(More)
Device-free passive localization (DfP) techniques can localize human subjects without wearing a radio tag. Being convenient and private, DfP can find many applications in ubiquitous/pervasive computing. Unfortunately, DfP techniques need frequent manual recalibration of the radio signal values, which can be cumbersome and costly. We present SenCam, a(More)
A typical wireless sensor network consists of many small sensors that collect instrument data around their locations and forward it to a central location for data processing. These networks can be deployed to monitor livestock and agricultural assets, products in a store, patients in a hospital, and so on. In many cases sensors have to be densely deployed,(More)
Today, people have the opportunity to opt-in to usage-based automotive insurances for reduced premiums by allowing companies to monitor their driving behavior. Several companies claim to measure only speed data to preserve privacy. With our elastic pathing algorithm, we show that drivers can be tracked by merely collecting their speed data and knowing their(More)
Device-free passive (DfP) localization is proposed to localize human subjects indoors by observing how the subject disturbs the pattern of the radio signals without having the subject wear a tag. In our previous work, we have proposed a probabilistic classification based DfP technique, which we call PC-DfP in short, and demonstrated that PC-DfP can classify(More)
We trained a convolutional neural network (CNN) to map raw pixels from a single front-facing camera directly to steering commands. This end-to-end approach proved surprisingly powerful. With minimum training data from humans the system learns to drive in traffic on local roads with or without lane markings and on highways. It also operates in areas with(More)