In this paper, we design and fabricate cost-effective, electronics-free millimeter-wave (mmWave) fiducial markers that support clutter-free detection on radars. Fiducial markers are widely used in camera-based systems to provide spatial information in robotic navigation applications. QR-code-like fiducial markers can be readily printed on paper - low-cost, easy to produce, and electronics-free. Yet, an analogous mmWave solution is yet to appear, which stems from the unique challenge in the mmWave context: its vulnerability to clutter. Existing solutions either trade hardware simplicity for clutter resilience or stay simple but remain vulnerable to environmental multipath. This paper seeks to solve this dilemma.

Extending the range of RF energy harvesting can revolutionize battery-free/low-power sensing and networking. This paper explores the design space for RF infrastructure to charge battery-free devices (e.g. RFID) or devices with coin-cell batteries (e.g. water and security sensors) over much longer range than the state-of-the-art. Rather than rely completely on ambient RF (e.g. TV towers) or dedicated infrastructure (e.g. RFID readers), we explore a middle path - combine RF energy from (nearly) all available major wireless frequency bands and then supplement this with low-cost specially designed RF charging infrastructure to fill in any gaps.

This paper presents NFCapsule, a light-weight, battery-free, and ingestible biomedical sensor that can potentially enable non-invasive detection of active eosinophilic esophagitis (EoE). EoE is an allergen-induced inflammatory condition of the esophagus; its diagnosis generally involves invasive, wired, and time-consuming endoscopy. In contrast, NFCapsule aims to wirelessly detect active EoE by tracking tissue impedance through an ingestible pill that the patient swallows. Specifically, recent biomedical research has shown that active EoE induces observable changes in the electrochemical impedance of the esophagus tissue due to an increase in its intercellular spacing. We design the NFCapsule pill based on RLC resonant circuits and model the target tissue as an impedance component that changes the resonant properties of the pill circuit. Further, the NFCapsule reader identifies the resonant properties of the pill by consistently monitoring the amount of energy transferred to the pill as it goes through the esophagus, and converts this information to estimates of bio-impedance. We implement NFCapsule pill prototypes with flexible polyimide PCBs and gelatin capsules (27 mm in height and 10 mm in diameter) and evaluated NFCapsule with both ionic agarose hydrogel models and ex vivo porcine esophageal tissues (no human patients involved). We show that NFCapsule maintains high classification accuracy under various practical scenarios (e.g., blockage, bending, movement, etc.) and achieves 85% average accuracy between healthy and unhealthy tissue samples.

This paper builds a Near-field Communication (NFC) based localization system that allows ordinary surfaces to locate surrounding objects with high accuracy in the near-field. While there is rich prior work on device-free localization using far-field wireless technologies, the near-field is less explored. Prior work in this space operates at extremely small ranges (a few centimeters), leading to designs that sense close proximity rather than location.

Wireless sensors have enabled a number of key applications. Due to their energy constraints, wireless sensors today communicate occasional short samples or pre-determined summary statistics of the data they collect. This means that computing every additional statistic at high fidelity incurs additional communication and energy overhead. This paper presents Joltik, a framework enabling general, future-proof, and energy-efficient analytics for low power wireless sensors. Joltik is general in that it summarizes sensed data from low-power devices without making assumptions on which specific statistical metric(s) are desired at the cloud and is future-proof, meaning it supports new, unforeseen metrics. Joltik is built upon recent theoretical advances in universal sketching, which can enable a Joltik sensor node to report a compact summary of observed data to enable a large class of statistical summaries. We address key system design and implementation challenges with respect to communication, memory, and computation bottlenecks that arise in practically realizing the potential benefits of universal sketching in the low-power regime. We present a proof-of-concept testbed evaluation of Joltik in LoRaWAN NUCLEO-L476RG boards and sensors. Across a range of realistic datasets, Joltik provides up to a 24.6× reduction in energy cost compared to transmitting raw data and outperforms many natural alternatives (e.g., sub-sampling, custom sketches, compressed sensing, and lossy compression) in terms of energy-accuracy trade-offs.

Coronavirus disease 2019, known as COVID-19, has spread rapidly and infected millions of people around the world. In addition to respiratory droplet spreading, a common mode of contraction of this virus is when individuals touch their face after coming into contact with a contaminated surface. In this poster, we leverage near-field communication (NFC) and propose a system design, NoFaceContact, which can promptly warn users when they attempt to touch their face with the aim of helping to reduce the spread of COVID-19 and improve overall hygiene. A proof-of-concept experiment shows that NoFaceContact can achieve an average communication distance of 8.07 cm and can potentially detect a wide range of face touching poses.

Wireless sensing has demonstrated the potential of using Wi-Fi signals to track people and objects, even behind walls.Yet, prior work in this space aims to merely detect the presence of objects around corners, rather than their type. In this paper, we explore the feasibility of the following re-search question: ?Can commodity Wi-Fi radios detect both the location and type of moving objects around them?". We present IntuWition, a complementary sensing system that can sense the location and type of material of objects in the environment, including those out of line-of-sight. It achieves this by sensing wireless signals reflected off surrounding objects using commodity Wi-Fi radios, whose signals penetrate walls and occlusions. At the core of IntuWition is the idea that different materials reflect and scatter polarized waves in different ways. We build upon ideas from RADAR Polarimetry to detect the material of objects across spatial locations, despite mobility of the sensing device and the hardware non-idealities of commodity Wi-Fi radios. A detailed feasibility study reveals an average accuracy of 95% in line-of-sight and 92% in non-line-of-sight in classifying five types of materials:copper, aluminum, plywood, birch, and human. Finally, we present a proof-of-concept application of our system on an autonomous UAV that uses its onboard Wi-Fi radios to sense whether an occlusion is a person versus another UAV.