Small autonomous underwater vehicles (AUVs) that can perceive and explore the environment at low cost are increasingly being deployed for seafloor mapping and biome sensing. A critical sensing ability that many of the larger submarines possess is to perform directional SONAR using arrays of hydrophones to map the environment. Unfortunately, this ability to identify direction-of-arrival (DoA) remains too inaccurate or even infeasible for the small AUVs due to size, weight, and power (SWaP) constraints. This paper presents a new opportunity in augmenting this critical ability using a single hydrophone.

Integrating spatial context into large language models (LLMs) has the potential to revolutionize human-computer interaction, particularly in wearable devices. In this work, we present a novel system architecture that incorporates spatial speech understanding into LLMs, enabling contextually aware and adaptive applications for wearable technologies. Our approach leverages microstructure-based spatial sensing to extract precise Direction of Arrival (DoA) information using a monaural microphone. To address the lack of existing dataset for microstructure-assisted speech recordings, we synthetically create a dataset by using the LibriSpeech dataset. This spatial information is fused with linguistic embeddings from OpenAI’s Whisper model, allowing each modality to learn complementary contextual representations. The fused embeddings are aligned with the input space of LLaMA-3.2 3B model and fine-tuned with lightweight adaptation technique LoRA to optimize for on-device processing. SING supports spatially-aware automatic speech recognition (ASR), achieving a mean error of 25.72°—a substantial improvement compared to the 88.52° median error in existing work—with a word error rate (WER) of 5.3. SING also supports soundscaping, for example, inference how many people were talking and their directions, with up to 5 people and a median DoA error of 16°. Our system demonstrates superior performance in spatial speech understanding while addressing the challenges of power efficiency, privacy, and hardware constraints, paving the way for advanced applications in augmented reality, accessibility, and immersive experiences.

This demonstration presents LiTEfoot, an ultra-low power localization system leveraging ambient cellular signals. To address the limitations of traditional GPS-based tracking systems in terms of power consumption and latency, LiTEfoot employs a non-linear transformation of the cellular spectrum to achieve efficient self-localization. Our design uses a simple envelope detector to realize spectrum folding, enabling the identification of multiple active base stations. The LiTEfoot prototype shows a median localization error of 22 meters in urban areas and 50 meters in rural areas, consuming only 40 μJoules of energy per localization update.

In this paper, we introduce a low-power wide-area cellular localization system, called LiTEfoot. The core architecture of the radio carefully applies non-linear transform of the entire cellular spectrum to obtain a systematic superimposition of the synchronization signals at the baseband. The system develops methods to simultaneously identify all the base stations that are active at any cellular band from the transformed signal. The radio front end uses a simple envelop detector to realize the non-linear transformation. We build on this low-power radio to implement a self-localization system leveraging ambient 4G-LTE signals. We show that the core system can also be extended to other cellular technologies like 5G-NR and NB-IoT. The prototype achieves a median localization error of 22 meters in urban areas and 50 meters in rural areas. It can sense a 3GHz wideband LTE spectrum in 10ms using non-linear intermodulation while consuming 0.9 mJ of energy for a PCB-based implementation and 40 μJ for CMOS simulation. In other words, LiTEfoot tags can last for 11 years on a coin cell while continuously estimating location every 5 seconds. We believe that LiTEfoot will have widespread implications in city-scale asset tracking and other location-based services. The radio architecture can be useful beyond low-power self-localization and can find application in synchronization and communication on battery-less platforms.

Low-power sensor networks are transforming large-scale sensing in precision farming, livestock tracking, climate-monitoring and surveying. Accurate and robust localization in such low-power sensor nodes has never been as crucial as it is today. This paper presents, Sirius, a self-localization system using a single receiver for low-power IoT nodes. Traditionally, systems have relied on antenna arrays and tight synchronization to estimate angle-of-arrival (AoA) and time-of-flight with known access points. While these techniques work well for regular mobile systems, low-power IoT nodes lack the resources to support these complex systems. Sirius explores the use of gain-pattern reconfigurable antennas with passive envelope detector-based radios to perform AoA estimation without requiring any kind of synchronization. It shows a technique to embed direction specific codes to the received signals which are transparent to regular communication channel but carry AoA information with them. Sirius embeds these direction-specific codes by using reconfigurable antennas and fluctuating the gain pattern of the antenna. Our prototype demonstrates a median error of 7 degrees in AoA estimation and 2.5 meters in localization, which is similar to state-of-the-art antenna array-based systems. Sirius opens up new possibilities for low-power IoT nodes.

In this poster, we present a new approach to low-power self-localization for IoT nodes called Sirius. With the rise of low-power sensor networks in precision farming, climate monitoring, and surveying, it has become increasingly critical to accurately and robustly localize low-power sensor nodes. However, traditional systems that rely on antenna arrays and time synchronization are too complex for low-power IoT nodes. To overcome this limitation, Sirius utilizes gain-pattern reconfigurable antennas with passive envelope detector-based radios to estimate angle-of-arrival. This is achieved by embedding direction-specific codes in the received signals, which carry angle-of-arrival information. Our prototype has demonstrated a median error of 7 degrees in AoA estimation and 2.5 meters in localization, comparable to state-of-the-art antenna array-based systems. This new approach opens up exciting possibilities for low-power IoT nodes in various fields.

This paper presents the design and implementation of SPiDR, an ultra-low-power spatial sensing system for miniature mobile robots. This acoustic sensor produces a cross-sectional map of the field-of-view using only one speaker/microphone pair. While it is challenging to have enough spatial diversity of signal with a single omnidirectional source, we leverage sound's interaction with small structures to create a 3D-printed passive filter, called a stencil, that can project spatially coded signals on a region at a fine granularity. The system receives a linear combination of the reflections from nearby objects and applies a novel power-aware depth-map reconstruction algorithm. The algorithm first estimates the approximate locations of the objects in the scene and then iteratively applies fractional multi-resolution inversion. SPiDR consumes only 10mW of power to generate a depth-map in real-world scenario with over 80% structural similarity score with the scene.

This poster presents the design and implementation of SPiDR, an ultra-low-power acoustic imaging system. This imaging system produces a cross-sectional map of the field-of-view using only one speaker/microphone pair. It leverages the fact that sound's interaction with small structures can project spatially coded signals on a region at a fine granularity. We create a 3D-printed passive filter, called a stencil, that can image the scene with a single omnidirectional source and sensor. With spatially coded signal, the system receives a linear combination of the reflections from nearby objects and applies a novel power-aware depth-map reconstruction algorithm. SPiDR consumes only 10mW of power to generate a depth-map in real-world scenario with over 80% structural similarity score with the scene.

This paper presents a low-power and miniaturized design for acoustic direction-of-arrival (DoA) estimation and source localization, called Owlet. The required aperture, power consumption, and hardware complexity of the traditional array-based spatial sensing techniques make them unsuitable for small and power-constrained IoT devices. Aiming to overcome these fundamental limitations, Owlet explores acoustic microstructures for extracting spatial information. It uses a carefully designed 3D-printed metamaterial structure that covers the microphone. The structure embeds a direction-specific signature in the recorded sounds. Owlet system learns the directional signatures through a one-time in-lab calibration. The system uses an additional microphone as a reference channel and develops techniques that eliminate environmental variation, making the design robust to noises and multipaths in arbitrary locations of operations. Owlet prototype shows 3.6° median error in DoA estimation and 10cm median error in source localization while using a 1.5cm × 1.3cm acoustic structure for sensing. The prototype consumes less than 100th of the energy required by a traditional microphone array to achieve similar DoA estimation accuracy. Owlet opens up possibilities of low-power sensing through 3D-printed passive structures.

This demonstration presents a working prototype of Owlet, an alternative design for spatial sensing of acoustic signals. To overcome the fundamental limitations in form-factor, power consumption, and hardware requirements with array-based techniques, Owlet explores wave's interaction with acoustic structures for sensing. By combining passive acoustic microstructures with microphones, we envision achieving the same functionalities as microphone and speaker arrays with less power consumption and in a smaller form factor. Our design uses a 3D-printed metamaterial structure over a microphone to introduce a carefully designed spatial signature to the recorded signal. Owlet prototype shows 3.6° median error in Direction-of-Arrival (DoA) estimation and 10 cm median error in source localization while using a 1.5cm × 1.3cm acoustic structure for sensing.

Driver assistance systems, that rely on vehicular sensors such as cameras, LIDAR and other on-board diagnostic sensors, have progressed rapidly in recent years to increase road safety. Road conditions in developing countries like India are chaotic where roads are not well maintained and thus vehicular sensors alone do not suffice in detecting impending collisions. In this paper, we investigate a collaborative driver assistance system "DRIZY: DRIve eaSY" for such scenarios where inference is drawn from on-board camera feed to alert drivers of obstacles ahead and the cloud uses GPS sensor data uploaded by all vehicles to alert drivers of vehicles in potential collision trajectory. Thus, we combine computer vision and vehicle-to-cloud communication to create comprehensive situational awareness. We prototype our system to consider two types of collisions: vehicle-to-vehicle collisions based on uploading GPS sensor data of vehicles to cloud and vehicle-to-pedestrian collisions based on detecting pedestrians from vehicle's dashboard camera feed. Sensor data processing in each vehicle occurs on smartphone for GPS values which are then uploaded to cloud and on raspberry pi3 for video feeds to make a cost-effective solution. Experiments over both 4G and wireless networks in India show that collaborative driver assistance is feasible in low traffic density within acceptable driver reaction time of <5 sec, but can be limited by the time to process compute-intensive video feeds in real-time. We investigate novel ways to optimize the processing to find an acceptable trade-off.