Wireless sensing: Material identification and localization

Wireless signals are everywhere around us, and they have truly revolutionized the world by all standards. When one thinks of this revolution, one envisions the advances in wireless communication—TV broadcasts, FM radios, WiFi, Bluetooth, cellular mobile phones, and even wireless chips inside the human body. What gets less appreciated, however, is that wireless signals can also be a powerful sensor. The fact that wireless signals touch and penetrate all objects in our environment, and bounce back, make them a powerful lens to view our world through. This thesis focuses on using wireless signals as sensors. We will explore how modifications to wireless signal propagation can reveal the physical properties of the materials that these signals have passed through. This enables identification of materials without touching them or performing any chemical analysis on them. We will show the ability to distinguish between closely related liquids, such as Pepsi and Coca-Cola, or distilled water and mineral water, by simply passing wireless signals through the liquids, and analyzing the signals that emerge on the other side. The propagation delay of wireless signals when passing through air can reveal the distance between a transmitter and a receiver. We show how this primitive can be extended for localization with applications to sports, battlefields, and emergency response. Through modifications to the distance measurement mechanisms, we show how localization is possible even when wireless devices are constantly under motion. We end by discussing future directions in which both of these sensing techniques can be extended. Under the right conditions, it might be possible to localize an object to 5mm precision with applications in robotic machines, augmented reality, and virtual reality. We then discuss the possibility of using reflections of wireless signals, for example, to determine soil moisture content in agricultural fields

Robust Indoor Localization with Ranging-IMU Fusion

Indoor wireless ranging localization is a promising approach for low-power and high-accuracy localization of wearable devices. A primary challenge in this domain stems from non-line of sight propagation of radio waves. This study tackles a fundamental issue in wireless ranging: the unpredictability of real-time multipath determination, especially in challenging conditions such as when there is no direct line of sight. We achieve this by fusing range measurements with inertial measurements obtained from a low cost Inertial Measurement Unit (IMU). For this purpose, we introduce a novel asymmetric noise model crafted specifically for non-Gaussian multipath disturbances. Additionally, we present a novel Levenberg-Marquardt (LM)-family trust-region adaptation of the iSAM2 fusion algorithm, which is optimized for robust performance for our ranging-IMU fusion problem. We evaluate our solution in a densely occupied real office environment. Our proposed solution can achieve temporally consistent localization with an average absolute accuracy of $\sim$0.3m in real-world settings. Furthermore, our results indicate that we can achieve comparable accuracy even with infrequent (1Hz) range measurements

Extension of Self: what it means to be human in a digital world

Presented in-person in the Scholars Event Theater at the Georgia Tech Library and online via YouTube on September 15, 2022 at 4:00 p.m.Eve Brown is a multi-disciplinary artist based in Atlanta, Georgia. They are inspired by psychology, the yogic path, Buddhist thought, negative space, non-binary nature, anti-ableism, resisting capitalist structures, and impressionist painters. Brown’s experience with chronic illness has impacted and inspired their work, especially provoking thought around what it means to be an artist outside of capitalist modes of production and the ableist expectations placed on artists to produce. Brown’s current project explores the paradoxical necessity and tension of the extension of self via social media as a chronically ill human.Dr. Ashutosh Dhekne is an assistant professor in the School of Computer Science at Georgia Tech. His research interests span wireless localization and sensing, the Internet of Things, and mobile computing. Dr. Dhekne received the NSF CAREER award for wireless localization and sensing in 2022. Dr. Dhekne conceived the idea of TechMyMoves when daydreaming of an expressive indoor space.Award-winning interdisciplinary artist, former medical doctor and scientist, Dr. Bojana Ginn works at the intersection of art, science, and technology. Ginn is a recipient of the Ellsworth Kelly Award, granted by the Foundation for Contemporary Art in New York. Finalist for the World Technology Award in Art, Ginn is a fellow at The World Technology Network, New York. Her work has been exhibited at the Venice Architectural Biennale, Museum of Art and Design in New York, Espronceda Institute of Art and Culture in Barcelona, Museum of Contemporary Art of Georgia in Atlanta, David J. Sencer CDC Museum in Atlanta, Cyber Center in Augusta, GA, and many others.Noura Howell's work explores emotion recognition technologies, which claim to infer insights about emotional experience based on biosensory data, data about people’s bodies, thoughts, and behaviors. What can (and can’t) this data say about how we feel? How might this data shape the way we feel, and shape how we relate to ourselves and others? She explores these questions by building biosensing artifacts and inviting people to experience these artifacts with friends or strangers. Howell is an assistant professor in Digital Media at Georgia Tech.Birney Robert is the sole principal investigator on the Microsoft and Center for 21st Century Universities grant, awarded in the Fall of 2021. For this grant, she will curate two exhibits on Georgia Tech’s campus. The current exhibit, “Extension of Self: What is means to be human in a digital world,” explores core questions about identity and how we interact with art and technology in today’s digital world. Robert is also an events coordinator at the College of Computing at Georgia Tech, where she manages 10 events per year across a range of programs. Prior to coming to Georgia Tech, Robert worked at Sandler Hudson Gallery, coordinating exhibits, events, and communications. She still serves as a freelance art consultant and curator.Cedric Stallworth is the Inaugural Associate Dean for Inclusive Excellence in the College of Computing at Georgia Tech. Stallworth has administered educational programs at Georgia Tech for more than two decades, most recently as the Assistant Dean for Outreach, Enrollment and Community. In his new role, Stallworth will have overall responsibility for developing and advancing diversity and equity initiatives within the College.Runtime: 58:08 minutesFour artists from the Extension of Self exhibit sit on a panel with Birney Robert to discuss their art practice and how they navigate the digital world and identity. They question the role that digital technology plays while integrating it into their work to create interactive art for the viewer to explore. Our identities will continue to be complex and full of multiplicities. We invite you to take some time to interact with these pieces and to ask yourself what it means to be human in a digital world

Weasels killing Frogs

This paper presents the design, implementation and evaluation of milliMap, a single-chip millimetre wave (mmWave) radar based indoor mapping system targetted towards low-visibility environments to assist in emergency response. A unique feature of milliMap is that it only leverages a low-cost, off-the-shelf mmWave radar, but can reconstruct a dense grid map with accuracy comparable to lidar, as well as providing semantic annotations of objects on the map. milliMap makes two key technical contributions. First, it autonomously overcomes the sparsity and multi-path noise of mmWave signals by combining cross-modal supervision from a co-located lidar during training and the strong geometric priors of indoor spaces. Second, it takes the spectral response of mmWave reflections as features to robustly identify different types of objects e.g. doors, walls etc. Extensive experiments in different indoor environments show that milliMap can achieve a map reconstruction error less than 0.2m and classify key semantics with an accuracy of ~ 90%, whilst operating through dense smoke