Fadel Adib

See through walls with WiFi

Wi-Fi signals are typically information carriers between a transmitter and a receiver. In this paper, we show that Wi-Fi can also extend our senses, enabling us to see moving objects through walls and behind closed doors. In particular, we can use such signals to identify the number of people in a closed room and their relative locations. We can also identify simple gestures made behind a wall, and combine a sequence of gestures to communicate messages to a wireless receiver without carrying any transmitting device. The paper introduces two main innovations. First, it shows how one can use MIMO interference nulling to eliminate reflections off static objects and focus the receiver on a moving target. Second, it shows how one can track a human by treating the motion of a human body as an antenna array and tracking the resulting RF beam. We demonstrate the validity of our design by building it into USRP software radios and testing it in office buildings.

Clearing the RF smog: making 802.11n robust to cross-technology interference

Recent studies show that high-power cross-technology interference is becoming a major problem in todays 802.11 networks. Devices like baby monitors and cordless phones can cause a wireless LAN to lose connectivity. The existing approach for dealing with such high-power interferers makes the 802.11 network switch to a different channel; yet the ISM band is becoming increasingly crowded with diverse technologies, and hence many 802.11 access points may not find an interference-free channel. This paper presents TIMO, a MIMO design that enables 802.11n to communicate in the presence of high-power cross-technology interference. Unlike existing MIMO designs, however, which require all concurrent transmissions to belong to the same technology, TIMO can exploit MIMO capabilities to decode in the presence of a signal from a different technology, hence enabling diverse technologies to share the same frequency band. We implement a prototype of TIMO in GNURadio-USRP2 and show that it enables 802.11n to communicate in the presence of interference from baby monitors, cordless phones, and microwave ovens, transforming scenarios with a complete loss of connectivity to operational networks.

RF-compass: robot object manipulation using RFIDs

Modern robots have to interact with their environment, search for objects, and move them around. Yet, for a robot to pick up an object, it needs to identify the objects orientation and locate it to within centimeter-scale accuracy. Existing systems that provide such information are either very expensive (e.g., the VICON motion capture system valued at hundreds of thousands of dollars) and/or suffer from occlusion and narrow field of view (e.g., computer vision approaches). This paper presents RF-Compass, an RFID-based system for robot navigation and object manipulation. RFIDs are low-cost and work in non-line-of-sight scenarios, allowing them to address the limitations of existing solutions. Given an RFID-tagged object, RF-Compass accurately navigates a robot equipped with RFIDs toward the object. Further, it locates the center of the object to within a few centimeters and identifies its orientation so that the robot may pick it up. RF-Compasss key innovation is an iterative algorithm formulated as a convex optimization problem. The algorithm uses the RFID signals to partition the space and keeps refining the partitions based on the robots consecutive moves.We have implemented RF-Compass using USRP software radios and evaluated it with commercial RFIDs and a KUKA youBot robot. For the task of furniture assembly, RF-Compass can locate furniture parts to a median of 1.28 cm, and identify their orientation to a median of 3.3 degrees.

Faster GPS via the sparse fourier transform

GPS is one of the most widely used wireless systems. A GPS receiver has to lock on the satellite signals to calculate its position. The process of locking on the satellites is quite costly and requires hundreds of millions of hardware multiplications, leading to high power consumption. The fastest known algorithm for this problem is based on the Fourier transform and has a complexity of O(n log n), where n is the number of signal samples. This paper presents the fastest GPS locking algorithm to date. The algorithm reduces the locking complexity to O(n√(log n)). Further, if the SNR is above a threshold, the algorithm becomes linear, i.e., O(n). Our algorithm builds on recent developments in the growing area of sparse recovery. It exploits the sparse nature of the synchronization problem, where only the correct alignment between the received GPS signal and the satellite code causes their cross-correlation to spike. We further show that the theoretical gain translates into empirical gains for GPS receivers. Specifically, we built a prototype of the design using software radios and tested it on two GPS data sets collected in the US and Europe. The results show that the new algorithm reduces the median number of multiplications by 2.2x in comparison to the state of the art design, for real GPS signals.

Capturing the human figure through a wall

We present RF-Capture, a system that captures the human figure -- i.e., a coarse skeleton -- through a wall. RF-Capture tracks the 3D positions of a persons limbs and body parts even when the person is fully occluded from its sensor, and does so without placing any markers on the subjects body. In designing RF-Capture, we built on recent advances in wireless research, which have shown that certain radio frequency (RF) signals can traverse walls and reflect off the human body, allowing for the detection of human motion through walls. In contrast to these past systems which abstract the entire human body as a single point and find the overall location of that point through walls, we show how we can reconstruct various human body parts and stitch them together to capture the human figure. We built a prototype of RF-Capture and tested it on 15 subjects. Our results show that the system can capture a representative human figure through walls and use it to distinguish between various users.

Emotion recognition using wireless signals

This paper demonstrates a new technology that can infer a persons emotions from RF signals reflected off his body. EQ-Radio transmits an RF signal and analyzes its reflections off a persons body to recognize his emotional state (happy, sad, etc.). The key enabler underlying EQ-Radio is a new algorithm for extracting the individual heartbeats from the wireless signal at an accuracy comparable to on-body ECG monitors. The resulting beats are then used to compute emotion-dependent features which feed a machine-learning emotion classifier. We describe the design and implementation of EQ-Radio, and demonstrate through a user study that its emotion recognition accuracy is on par with state-of-the-art emotion recognition systems that require a person to be hooked to an ECG monitor.

Interference alignment by motion

Recent years have witnessed increasing interest in interference alignment which has been demonstrated to deliver gains for wireless networks both analytically and empirically. Typically, interference alignment is achieved by having a MIMO sender precode its transmission to align it at the receiver. In this paper, we show, for the first time, that interference alignment can be achieved via motion, and works even for single-antenna transmitters. Specifically, this alignment can be achieved purely by sliding the receivers antenna. Interestingly, the amount of antenna displacement is of the order of one inch which makes it practical to incorporate into recent sliding antennas available on the market. We implemented our design on USRPs and demonstrated that it can deliver 1.98× throughput gains over 802.11n in networks with both single-antenna and multi- antenna nodes.

Minding the Billions: Ultra-wideband Localization for Deployed RFID Tags

State-of-the-art RFID localization systems fall under two categories. The first category operates with off-the-shelf narrowband RFID tags but makes restrictive assumptions on the environment or the tags movement patterns. The second category does not make such restrictive assumptions; however, it requires designing new ultra-wideband hardware for RFIDs and uses the large bandwidth to directly compute a tags 3D location. Hence, while the first category is restrictive, the second one requires replacing the billions of RFIDs already produced and deployed annually. This paper presents RFind, a new technology that brings the benefits of ultra-wideband localization to the billions of RFIDs in todays world. RFind does not require changing todays passive narrowband RFID tags. Instead, it leverages their underlying physical properties to emulate a very large bandwidth and uses it for localization. Our empirical results demonstrate that RFind can emulate over 220MHz of bandwidth on tags designed with a communication bandwidth of only tens to hundreds of kHz, while remaining compliant with FCC regulations. This, combined with a new super-resolution algorithm over this bandwidth, enables RFind to perform 3D localization with sub-centimeter accuracy in each of the x/y/z dimensions, without making any restrictive assumptions on the tags motion or the environment.

Demo: real-time breath monitoring using wireless signals

This demo presents Vital-Radio, a wireless sensing technology that monitors breathing remotely, without requiring any body contact. Vital-Radio operates by transmitting a low-power wireless signal and monitoring its reflections off the human body. It uses these reflections to track motion associated with breathing, i.e., the chest movements caused by inhaling and exhaling. The demo will enable any person to sit in front of the device and check that it tracks their inhale and exhale process. The person may hold his/her breath and check that the device detects the breath holding event in real-time.

Enabling deep-tissue networking for miniature medical devices

We present IVN (In-Vivo Networking), a system that enables powering up and communicating with miniature sensors implanted or injected in deep tissues. IVN overcomes fundamental challenges which have prevented past systems from powering up miniature sensors beyond superficial depths. These challenges include the significant signal attenuation caused by bodily tissues and the miniature antennas of the implantable sensors. IVNs key contribution is a novel beamforming algorithm that can focus its energy toward an implantable device, despite its inability to estimate its channel or its location. We implement a multi-antenna prototype of IVN, and perform extensive evaluations via in-vitro, ex-vivo, and in-vivo tests in a pig. Our results demonstrate that it can power up and communicate with millimeter-sized sensors at over 10 cm depths in fluids, as well as battery-free tags placed in a central organ of a swine. The implications of our new beamforming technology extend beyond miniature implantables. In particular, our results demonstrate that IVN can power up off-the-shelf passive RFIDs at distances of 38 m, i.e., 7.6X larger than the operation range of the same RFIDs.