EMI Spy: Harnessing electromagnetic interference for low-cost, rapid prototyping of proxemic interaction

We present a wearable system that uses ambient electromagnetic interference (EMI) as a signature to identify electronic devices and support proxemic interaction. We designed a low cost tool, called EMI Spy, and a software environment for rapid deployment and evaluation of ambient EMI-based interactive infrastructure. EMI Spy captures electromagnetic interference and delivers the signal to a user's mobile device or PC through either the device's wired audio input or wirelessly using Bluetooth. The wireless version can be worn on the wrist, communicating with the user;s mobile device in their pocket. Users are able to train the system in less than 1 second to uniquely identify displays in a 2-m radius around them, as well as to detect pointing at a distance and touching gestures on the displays in real-time. The combination of a low cost EMI logger and an open source machine learning tool kit allows developers to quickly prototype proxemic, touch-to-connect, and gestural interaction. We demonstrate the feasibility of mobile, EMI-based device and gesture recognition with preliminary user studies in 3 scenarios, achieving 96% classification accuracy at close range for 6 digital signage displays distributed throughout a building, and 90% accuracy in classifying pointing gestures at neighboring desktop LCD displays. We were able to distinguish 1- and 2-finger touching with perfect accuracy and show indications of a way to determine power consumption of a device via touch. Our system is particularly well-suited to temporary use in a public space, where the sensors could be distributed to support a popup interactive environment anywhere with electronic devices. By designing for low cost, mobile, flexible, and infrastructure-free deployment, we aim to enable a host of new proxemic interfaces to existing appliances and displays

Control of amplifier flows using subspace identification techniques

International audienceA realistic, efficient and robust technique for the control of amplifier flows has been investigated. Since this type of fluid system is extremely sensitive to upstream environmental noise, an accurate model capturing the influence of these perturbations is needed. A subspace identification algorithm is not only a convenient and effective way of constructing this model, it is also re a l is tic in the sense that it is based on input and output data measurements only and does not require other information from the detailed dynamics of the fluid system. This data-based control design has been tested on an amplifier model derived from the Ginzburg-Landau equation, and no significant loss of efficiency has been observed when using the identified instead of the exact model. Even though system identification leads to a realistic control design, other issues such as state estimation, have to be addressed to achieve full control efficiency. In particular, placing a sensor too far downstream is detrimental, since it does not provide an estimate of incoming perturbations. This has been made clear and quantitative by considering the relative estimation error and, more appropriately, the concept of a visibility length, a measure of how far upstream a sensor is able to accurately estimate the flow state. It has been demonstrated that a strongly convective system is characterized by a correspondingly small visibility length. In fact, in the latter case the optimal sensor placement has been found upstream of the actuators, and only this configuration was found to yield an efficient control performance. This upstream sensor placement suggests the use of a feed-forward approach for fluid systems with strong convection. Furthermore, treating upstream sensors as inputs in the identification procedure results in a very efficient and robust control. When validated on the Ginzburg-Landau model this technique is effective, and it is comparable to the optimal upper bound, given by full-state control, when the amplifier behaviour becomes convection-dominated. These concepts and findings have been extended and verified for flow over a backward-facing step at a Reynolds number Re = 350. Environmental noise has been introduced by three independent, localized sources. A very satisfactory control of the Kelvin-Helmholtz instability has been obtained with a one-order-of-magnitude reduction in the averaged perturbation norm. The above observations have been further confirmed by examining a low-order model problem that mimics a convection-dominated flow but allows the explicit computation of control-relevant measures such as observability. This study casts doubts on the usefulness of the asymptotic notion of observability for convection-dominated flows, since such flows are governed by transient effects. Finally, it is shown that the feed-forward approach is equivalent to an optimal linear-quadratic-Gaussian control for spy sensors placed sufficiently far upstream or for sufficiently convective flows. The control design procedure presented in this paper, consisting of data-based subspace identification and feed-forward control, was found to be effective and robust. Its implementation in a real physical experiment may confidently be carried out

Data security and trading framework for smart grids in neighborhood area networks

Due to the drastic increase of electricity prosumers, i.e., energy consumers that are also producers, smart grids have become a key solution for electricity infrastructure. In smart grids, one of the most crucial requirements is the privacy of the final users. The vast majority of the literature addresses the privacy issue by providing ways of hiding user’s electricity consumption. However, open issues in the literature related to the privacy of the electricity producers still remain. In this paper, we propose a framework that preserves the secrecy of prosumers’ identities and provides protection against the traffic analysis attack in a competitive market for energy trade in a Neighborhood Area Network (NAN). In addition, the amount of bidders and of successful bids are hidden from malicious attackers by our framework. Due to the need for small data throughput for the bidders, the communication links of our framework are based on a proprietary communication system. Still, in terms of data security, we adopt the Advanced Encryption Standard (AES) 128bit with Exclusive-OR (XOR) keys due to their reduced computational complexity, allowing fast processing. Our framework outperforms the state-of-the-art solutions in terms of privacy protection and trading flexibility in a prosumer-to-prosumer design

PlaceRaider: Virtual Theft in Physical Spaces with Smartphones

As smartphones become more pervasive, they are increasingly targeted by malware. At the same time, each new generation of smartphone features increasingly powerful onboard sensor suites. A new strain of sensor malware has been developing that leverages these sensors to steal information from the physical environment (e.g., researchers have recently demonstrated how malware can listen for spoken credit card numbers through the microphone, or feel keystroke vibrations using the accelerometer). Yet the possibilities of what malware can see through a camera have been understudied. This paper introduces a novel visual malware called PlaceRaider, which allows remote attackers to engage in remote reconnaissance and what we call virtual theft. Through completely opportunistic use of the camera on the phone and other sensors, PlaceRaider constructs rich, three dimensional models of indoor environments. Remote burglars can thus download the physical space, study the environment carefully, and steal virtual objects from the environment (such as financial documents, information on computer monitors, and personally identifiable information). Through two human subject studies we demonstrate the effectiveness of using mobile devices as powerful surveillance and virtual theft platforms, and we suggest several possible defenses against visual malware