Real-time access to overhead Low-Earth Orbit (LEO) satellite imagery from a handheld device can have transformative applications: tracking wild-fire, natural disasters and weather events. Today, real-time access to images from LEO satellites overhead is challenging to obtain. LEO satellite ground receivers are bulky, expensive and sparsely deployed in the world. Despite the exponential increase in LEO small satellites orbiting the planet today-there is a significant time gap between an image capture on such a satellite and users who need it the most in remote and ecologically-sensitive regions. This paper presents SelfieStick, a novel satellite receiver system that explores reducing this barrier of access to real-time satellite imagery data using a single low cost (< $ 30) tiny receiver. SelfieStick's core approach takes advantage of the multiplicity of overhead Low-Earth Orbit satellites due to their exponential rise in recent years. While signals from such satellites may be individually weak, especially at a low-cost receiver, SelfieStick stitches together noisy RF captures containing underlying images of the same part of the Earth across many such satellites to generate clean Earth images. This is made possible by combining weak signals in the RF domain (rather than the traditional image domain) after appropriately transforming and aligning the RF signals accounting for different satellite perspectives, their orbits and wireless channels. A detailed experimental evaluation on the RTL-SDR platform on satellite captures from the NOAA constellation demonstrates a PSNR improvement of 5 dB through combining of images across 10 satellites.

This paper presents a radio-frequency (RF) based assistive technology for voice impairments (i.e., dysphonia), which occurs in an estimated 1% of the global population. We specifically focus on acquired voice disorders where users continue to be able to make facial and lip gestures associated with speech. Despite the rich literature on assistive technologies in this space, there remains a gap for a solution that neither requires external infrastructure in the environment, battery-powered sensors on skin or body-worn manual input devices. We present RFTattoo, which to our knowledge is the first wireless speech recognition system for voice impairments using batteryless and flexible RFID tattoos. We design specialized wafer-thin tattoos attached around the user's face and easily hidden by makeup. We build models that process signal variations from these tattoos to a portable RFID reader to recognize various facial gestures corresponding to distinct classes of sounds. We then develop natural language processing models that infer meaningful words and sentences based on the observed series of gestures. A detailed user study with 10 users reveals 86% accuracy in reconstructing the top-100 words in the English language, even without the users making any sounds.

LoRa is a popular Low-Power Wide-Area Networking (LP-WAN) technology that allows devices powered by a ten year AA battery to connect to radio infrastructure miles away. One of the most promising features of LoRa is the ability to track the location of radios from a distance, enabling applications ranging from inventory tracking, smart infrastructure monitoring and structural health sensing. Yet, state-of-the-art LoRa localization systems experience errors of several tens or even hundreds of meters in location tracking, owing to the narrow bandwidth and limited battery life of LoRa devices.

Should you decide to launch a nano-satellite today in Low-Earth Orbit (LEO), the cost of renting ground station communication infrastructure is likely to significantly exceed your launch costs. While space launch costs have lowered significantly with innovative launch vehicles, private players, and smaller payloads, access to ground infrastructure remains a luxury. This is especially true for smaller LEO satellites that are only visible at any location for a few tens of minutes a day and whose signals are extremely weak, necessitating bulky and expensive ground station infrastructure.

Full duplex communication has been well explored in the past decade, with several physical layer designs proposed for effectively doubling the throughput of mobile devices. However, these systems are far from being well-deployed in the Wi-Fi and cellular applications, where most of the research has been done. In this paper, we investigate some of the fundamental pragmatic challenges in deploying full duplex that explain the reluctance from industry players in deploying this feature in commercial end-user devices and base stations at a large scale. Upon doing so, we identify that the problems lie not quite at the radio layer -- but at layers below and above it. At the hardware layer, we find that the power and complexity of IC-implementations of full duplex far exceeds that of other existing technologies that achieve similar throughput gain, such as multi-user MIMO. We further identified how higher-layer cellular and Wi-Fi traffic patterns and MAC protocols are fundamentally ill-suited to the full duplex paradigm. We report empirical analysis from the power consumption of an IC-implementation of a full duplex cancellation circuit, demonstrating that full duplex cancellation circuitry would consume at least 57 percent more power than a MIMO system achieving identical throughput gains. We also show how current traffic patterns reduce the benefits of full duplex to 1.25X instead of the expected 2X.

mm-Wave has emerged as an attractive high-speed wireless communication paradigm owing to the high available bandwidth at mm-wave frequencies. Full-Duplex has the potential to double the available capacity in the mm-wave bands by enabling simultaneous radio transmission and reception. While full-duplex has been extensively studied in sub-6 GHz bands, this paper exposes the unique challenges in porting this capability to mm-wave frequencies.

Tire wear is a leading cause of automobile accidents globally. Beyond safety, tire wear affects performance and is an important metric that decides tire replacement, one of the biggest maintenance expense of the global trucking industry. We believe that it is important to measure and monitor tire wear in all automobiles. Current approach to measure tire wear is manual and extremely tedious. Embedding sensor electronics in tires to measure tire wear is challenging, given the inhospitable temperature, pressure and dynamics of the tire. Further, off-tire sensors placed in the well such as laser range-finders are vulnerable to road debris that may settle in tire grooves.

In this paper, we demonstrate Osprey, a tire wear sensor presented in [4]. Osprey makes use of commodity automotive, mmWave RADAR, places it in the tire well of automobiles to image the tire and then measures the tire wear. Osprey measures accurate tire wear continuously while being resilient to road debris and without embedding any electronics in tires. Osprey achieves this by building a super resolution algorithm based on Inverse Synthetic Aperture RADAR imaging and by embedding thin metallic strips along coded patterns in the grooves to combat debris. Here, we implement Osprey on a tire rotation rig and demonstrate the ability to measure tire wear (with and without debris) accurately and detect potentially harmful foreign objects.