AI conference paper library

RadioStream: A Wideband Multi-Antenna SDR Platform

Adel Heidari, Jeeva Keshav Sattianarayanin, Rohith Reddy Vennam, Aaron Tartz, Agrim Gupta, Kishore Rajendran, Raviteja Devara, Lakshman Bhaskaran, Dinesh Bharadia, Radha Krishna Ganti

Software-defined radios are central to wireless systems research, but their host-centric streaming interfaces grow fragile exactly where modern experiments push hardest: wide bandwidth, many antennas, and fine slot-level control. The root difficulty is a timing-domain mismatch: the FPGA and RF front-end run on a deterministic, sample-clock-driven timeline, while the host operates asynchronously under operating-system scheduling, and traditional stacks resolve this by forcing the host to keep pace with the radio, producing late packets, underflows, and overflows at wideband rates. We present RadioStream, a radio-centric, slot-synchronous SDR architecture that makes the slot, rather than the raw sample stream, the unit of coordination across the host-radio boundary. The FPGA owns slot timing and time-critical radio actions and exports slot boundaries to the host as explicit slot-tick Ethernet packets, while the host runs asynchronously behind slot-aligned buffers over a lightweight fronthaul, with a two-slot look-ahead for control. A DPDK-based host path integrates directly with frameworks such as GNU Radio, Python, and MATLAB. On a 100G interface, RadioStream sustains 90 Gbps concurrently on uplink and downlink with no packet drops, streams 2 GHz of aggregated RF bandwidth, bounds slot-tick jitter below 600 ns at the 99th percentile, and applies per-slot control reaching the RF output in under 500 ns. We demonstrate RadioStream across communication, mmWave beam sweeping, and sub-6 GHz multi-antenna sensing workloads, showing that reliable, high-throughput streaming and deterministic fine-grained control hold together in practice.

ActiveSight: Enabling Continuous Wearable Perception through Algorithm-Hardware Co-Designed Multimodal Semantic Gating

Zhihao Ren, Xiaoyu Fang, Sohom Sen, Tao Han, Nirwan Ansari

Continuous wearable AI perception must capture transient, meaningful events throughout the day, yet always-on visual processing rapidly exhausts batteries of compact wearables. We present ActiveSight, an algorithm-hardware co-designed multimodal gating system that exploits the complementarity between audio and vision: audio is energy-efficient but perceptually limited, while vision is accurate but too power-hungry to run continuously. ActiveSight uses a low-power audio front-end as an always-on trigger and activates a unified CLIP-based detector only when visual semantic filtering is needed. The detector performs keyword-guided semantic filtering with reconfigurable text anchors, transmitting only relevant frames without on-device retraining. To enable on-wearable execution, we design a custom FPGA accelerator featuring the PatchDW dataflow, which mitigates the memory bottleneck of depthwise convolutions and achieves 44.3 GOPS/W in PL-side active power. Evaluation on Ego4D shows that ActiveSight achieves 87.7% event hit rate at 453 mW average power, reduces average power by 3× compared with continuous visual streaming, and reaches 88.5% hit rate in live smart-glasses deployment.

SwimHR: Wearable-Free Heart Rate Monitoring for Swimmers via Water-Mediated ECG Sensing

Jiting Liu, Yue Gong, Zeyu Luo, Billy Chun Lung So, Qijia Shao

Heart rate (HR) monitoring during swimming is crucial for athlete performance optimization and physiological assessment. Yet existing solutions remain unsatisfactory. ECG chest straps require tight skin contact and are often uncomfortable during swimming, while photoplethysmography (PPG) sensors suffer severe accuracy degradation due to swimming-induced motion artifacts. We present SwimHR, the first wearable-free HR monitoring system that captures electrocardiogram (ECG) signals transmitted through pool water without any on-body device. To enable practical pool-scale underwater ECG sensing, we (1) derive and validate a physical model of ECG transmission in water and quantify environmental attenuation in swimming pools through simulation, (2) design a lane-scale sensing architecture with a bias-electrode-free acquisition circuit for reliable full-lane coverage, and (3) develop a U-Net-based pipeline for robust R-peak detection under stroke-dependent swimming artifacts. We evaluate SwimHR with 34 swimmers (12 amateur and 22 professional) across four strokes and multiple pools. SwimHR achieves a mean HR error of 1.0 bpm with a standard deviation of 0.9 bpm relative to the ECG ground truth, significantly outperforming PPG-based wearables. SwimHR demonstrates the feasibility of accurate, real-time, wearable-free HR monitoring in standard swimming pool settings while avoiding the discomfort of body-worn sensors.

Cross-Band RF Radiance Field Modeling via 3D Gaussian Splatting and Propagation Mixture-of-Experts

Zechen Li, Hao Pan, Yu Lu, Yiheng Bian, Yongjian Fu, Yezhou Wang, Lanqing Yang, Guangtao Xue

As multi-protocol radio-frequency (RF) environments (e.g., RFID, Wi-Fi, and 5G) become increasingly prevalent, practical RF modeling is expected to overcome single-band limitations and achieve zero-shot generalization across a broad spectrum. This requires a unified representation of frequency-dependent electromagnetic (EM) propagation rather than modeling each band separately. In this paper, we present XBandGS, a unified cross-band RF radiance field modeling framework built on 3D Gaussian Splatting (3DGS). Unlike prior 3DGS-based RF modeling methods restricted to single-frequency fitting, XBandGS introduces an EM propagation Mixture-of-Experts (MoE) that disentangles and learns the frequency-specific behaviors of different physics-guided propagation modes within a given 3D environment. In addition, our approach enables the effective use of non-co-located datasets measured at various locations for different frequency bands. Trained on sparse frequencies, XBandGS can predict RF radiance fields for arbitrary unseen frequencies across a broad spectrum. We evaluate the cross-band RF Power Angular Spectrum (PAS) reconstruction capability of XBandGS across diverse 3D environments, including 7 simulated indoor scenes spanning 1–94 GHz and 6 real-world scenes (4 indoor and 2 outdoor) spanning 0.7–5.8 GHz. Results show that XBandGS achieves high-fidelity PAS reconstruction in unseen frequency bands, with an average SSIM of 0.822 on simulated traces and 0.650 on real-world datasets.

TulipTender: A Mobile 3D Parkinson's Disease Assessment System Using a Single Camera

Meng Xue, Yizhen Zhang, HY Wong, Yanni Yang, GHF Chan, YF Cheung, Nancy Y lp, Qian Zhang

Parkinson's disease (PD) assessment currently relies on subjective, episodic expert observations that often miss critical symptom fluctuations. Existing mobile methods are either impractical for home use or fail to capture the full spectrum of motor deficits. In this paper, we present TulipTender, a system that runs entirely on a consumer laptop using only its built-in camera, without any cloud offloading. TulipTender introduces a multi-scale 3D human modeling framework that jointly recovers full-body motion and fine hand articulations in metric space from a single video. This enables both millimeter-scale finger-tapping and meter-scale gait analysis. Furthermore, it suppresses visual artifacts, such as specular reflections and floor penetration, in uncontrolled home environments via a physics-aware purification framework. The system then extracts interpretable kinematic variables mirroring Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) descriptors. These variables are processed by a lightweight hierarchical cross-modal transformer (HCT) for accurate, explainable score prediction. Evaluated on an RTX 4090 with 70 participants across diverse settings, including 43 inpatients, 20 outpatients, 2 nursing-home residents, and 5 home-based participants, TulipTender achieves F1-scores of 82% and 84% for finger tapping and gait, respectively. The full pipeline also runs locally on a MacBook Pro, supporting the feasibility of privacy-preserving assessment on consumer hardware.

Thor: Fine-grained Per-UE Baseband Routing in vRAN

Xin Zhe Khooi, Satis Kumar Permal, Dong Hyeok Kim, Robert Schmidt, Min Suk Kang, Mun Choon Chan

Today's virtualized and disaggregated RAN (vRAN) architectures bind all User Equipments (UEs) in a cell to a single PHY layer (L1) implementation, leaving operators with no ability to run multiple L1s concurrently and select different L1 processing for different UEs. As such, this precludes the possibility of incremental L1 updates, A/B testing of new L1s, and prevents any form of service differentiation at the L1. In this paper, we present Thor, a practical system that introduces a new primitive for vRANs: fine-grained per-UE baseband routing. Thor enables operators to route each UE to its designated L1 instance by interposing lightweight, standards-compliant middleboxes at the FAPI and O-RAN fronthaul interfaces. These middleboxes safely split and merge control, data, and IQ samples across multiple concurrently running L1 implementations while preserving cell-wide shared context and meeting strict slot-level deadlines. We prototype Thor on a standard-compliant 5G testbed and show that it is robust and incurs negligible overhead. Finally, we demonstrate Thor's per-UE baseband routing primitive using incremental L1 updates as an example case study.

Designing, Deployment and Field Testing of C2Stack for Networked Intelligent Software-Defined UAVs

Maxwell McManus, Josh Zhang, Sidharth Santhinivas, Yuqing Cui, Prem Sagar Pattanshetty Vasanth Kumar, Chenzhi Zhao, Nicholas Mastronarde, George Sklivanitis, Dimitris A. Pados, Elizabeth S. Bentley, Zhangyu Guan

Unmanned Aerial Vehicles (UAVs) are emerging as critical enablers of next-generation wireless networking and autonomous systems. Despite their potential, deploying and testing networked UAV systems in real-world environments remains challenging, largely due to the absence of well-developed, end-to-end, ready-to-use protocol stacks. To fill this gap, we present C2Stack, a configurable protocol stack and experimental framework designed for real-time control, evaluation, and optimization of UAV networks. C2Stack incorporates a modular control plane, referred to as the C2Stack Network Operating System (CNOS), alongside a programmable data plane that exposes APIs for cross-layer algorithm development, digital twin integration, and autonomous swarm control.

DeRa: Deep-Range Low-Power Wide Area IoT via Ultra-sensitive Signal Detection

Jialuo Du, Jingkai Lin, Zhichao Cao

LoRa extends communication range by operating well below the noise floor, yet this capability hinges on reliable packet detection—an overlooked bottleneck. Carrier frequency offset (CFO) introduces deterministic inter-chirp phase shifts that prevent coherent preamble accumulation; because all existing detectors fall back on non-coherent, magnitude-only addition, they forfeit substantial signal power. We present DeRa, a phase-coherent LoRa detection and decoding framework built on three contributions: (1) a phase-coherent detection method that exploits the relationship between CFO and inter-chirp phase evolution to detect weak packets; (2) analytical compensation of compound CFO-induced scaling distortions—timing drift, frequency migration, and energy leakage—so that coherent accumulation remains effective even under 40 kHz CFO, eliminating the need for expensive oscillator hardware; and (3) a CRC-guided, phase-compensated iterative decoder that coherently recombines split symbol energy and closes the detection-to-decoding feedback loop. On COTS LoRa devices across SF 7–12, DeRa lowers the detection threshold by 4.3–4.8 dB and the decoding threshold by 3.3–3.9 dB over state-of-the-art baselines, while maintaining O(N log N) per-symbol complexity and consistent gains in real-world field experiments.

Chronos: Scalable and Cost-Effective Cloud-based RAN Emulation System

Ujjwal Pawar, Andrew E. Ferguson, Yufeng Xia, Xenofon Foukas, Mahesh Marina, Bozidar Radunovic

The trend towards softwarized and AI driven radio access network (RAN) in 5G and beyond has created a need for RAN emulators to test and enhance cellular network performance prior to real-world deployment. However, large-scale RAN emulation remains challenging due to the complexity of the RAN protocol stack and the high cost of hardware-based solutions. Software alternatives are more affordable but current solutions sacrifice scalability or fidelity. This paper presents Chronos, a cloud-based RAN emulation system that overcomes these challenges by abstracting the compute-intensive PHY layer, virtualizing emulation time, introducing RAN slot level synchronization, and scalable middlebox based traffic forwarding. Chronos operates at the FAPI boundary: it models PHY outcomes that affect higher-layer behavior, while deliberately excluding complex physical layer signal processing. Experiments using OpenAirInterface based prototype on cloud infrastructure demonstrate Chronos's ability to emulate a RAN with 10,000 mobile devices and 10,000 base stations on 130 cloud instances (with 6400 CPU cores), two orders of magnitude larger scale than existing solutions, reflecting its potential for enabling scalable RAN emulation in an affordable manner, all while maintaining fidelity.

Wideband Real-Time Adaptive Full-Duplex Radios

Alon S. Levin, Igor Kadota, Bo Zhang, Sasank Garikapati, Manav Kohli, Harish Krishnaswamy, Mingoo Seok, Gil Zussman

Experience: Unveiling GNSS-RTK in the Wild: A 2.5-Year Nationwide Study via Last-Mile Delivery

Anlan Yu, Zhiqing Hong, Jinkun Li, Shuting Pan, Haotian Wang, Yi Ding, Daqing Zhang

GNSS-Real-Time Kinematic (RTK) is designed to deliver precise GNSS positioning, yet its in-the-wild performance on smartphones (i.e., accessibility, precision, and utility) remains unknown. We present the first nationwide, in-the-wild measurement of GNSS-RTK, conducted over 2.5 years across 370 cities in China. Specifically, we leverage the operational processes of JD Logistics, a leading Chinese delivery company serving over 500 million customers, to enable nationwide GNSS-RTK measurement at scale. During daily operations, delivery couriers routinely traverse different areas with smartphones for positioning, enabling low-cost GNSS-RTK data collection at scale. The collected data provides extensive spatio-temporal coverage and repeated sampling, providing a unique opportunity for comprehensive GNSS-RTK performance measurement. Our large-scale measurement reveals that RTK achieves 92.3% overall accessibility, improves precision by 0.95 m over standalone GNSS, and yields an estimated $1.37M in recurring annual benefits from anomaly delivery detection and delivery distance measurement, together with $1.2M in one-time benefits from address coordinate mining.

Hark: Breaking Receiver Power Limits of LoRa

Amalinda Gamage, Pramuka Sooriya Patabandige, Spanddhana Sara, Ambuj Varshney

A long-standing challenge for LoRa devices has been to enable ultra-low-power reception that supports always-on listening while preserving the standard LoRa PHY and compatibility with existing COTS transmitters. Currently, COTS LoRa radios fail to support spontaneous actuation due to the prohibitive receive power (~43.5 mW) required to listen continuously, which effectively locks out applications that fall under LoRaWAN Class C. Recent works achieve substantially lower receive power, but either require modified physical layers and coordinated helper transmissions, or forgo dechirping and the range it provides. To this end, we present Hark, an ultra-low-power LoRa receiver that enables always-on listening at 1.90 mW while fully demodulating and decoding standard frames from unmodified COTS transmitters. We achieve this by pairing a novel digital wake-up pipeline with an analog tunnel-diode oscillator frontend that exhibits autodyning and is disciplined by a shared, unsynchronized, unmodulated carrier emitter. Specifically, Hark samples the channel at a fraction of the Nyquist rate to minimize compute and ADC power, yet successfully demodulates standard LoRa frames even below the noise floor. Hark demonstrates a communication range of nearly 500 m while still reducing receive power by over 22× compared to the COTS LoRa receiver. Hark therefore targets deployments where standard LoRa transmitters must remain unmodified and a receiver-side carrier emitter can be shared across multiple receivers.

Monitoring Electrical Transformer Mechanics via Crowdsourced Imaging

Shihan Zhao, Yunan Lu, Lingang Li, Donghui Dai, Lei Yang

Electrical transformers are critical to power grids, and their mechanical integrity directly impacts system stability. Existing monitoring approaches, such as accelerometer, acoustic, and piezoelectric sensing, require intrusive installation and are often costly or impractical for real-time assessment. To overcome these limitations, we propose Transcope, a computational imaging framework that remotely infers transformer mechanical states by analyzing the illumination variations of a transformer-connected bulb. The key principle is vibration-to-flicker coupling, where abnormal mechanical states induce voltage ripples that modulate bulb brightness and create a visual proxy for internal vibrations. To this end, we develop a flicker-aware physical model to characterize the mapping from bulb images to flicker signals, and further design a diffusion-based framework to reconstruct vibration signals from weak visual observations. Extensive experiments on a real-world testbed with more than 100k samples show that Transcope achieves 0.070 MAE and 19.08 dB SNR in vibration reconstruction, and reaches 98.79% accuracy and 96.19% macro F1-score in anomaly diagnosis. These results demonstrate the promise of reliable, non-contact transformer monitoring using ordinary cameras.

Prism: Efficient Zeroth-Order Fine-Tuning for On-Device LLMs

Yongjian Fu, Wenyu Qi, Yizhe Zhao, Xiaohang Sui, Jun Lu, Yongheng Deng, Hao Pan, Ju Ren

On-device fine-tuning of Large Language Models (LLMs) is becoming increasingly important for personalized AI assistants and adaptive agents. However, standard backpropagation imposes substantial training-state memory overheads that often exceed the stringent resource budgets of mobile platforms. To address this, Zeroth-order (ZO) optimization leverages forward-only probing instead of backpropagation, drastically reducing memory footprints and offering a compelling alternative for on-device adaptation. Nonetheless, scaling ZO fine-tuning to billion-scale trainable parameter spaces remains challenging, as the cost of recovering reliable update directions scales poorly with dimensionality under mobile constraints. In this paper, we present Prism, a mobile-native infrastructure for practical billion-scale on-device ZO fine-tuning. Prism re-architects the ZO loop around a scalable probing substrate, bringing both per-step training latency and memory overhead closer to the inference footprint. We implement Prism end to end on commodity smartphones and evaluate it across diverse tasks and multiple model scales. Prism achieves accuracy comparable to that of state-of-the-art ZO fine-tuning baselines while reducing per-step latency by 53.4%-76.3% and peak memory by 10.6%-13.8% relative to the best baseline in each setting. These results demonstrate the practical feasibility of billion-scale on-device ZO fine-tuning for LLMs under real mobile constraints. We will release our code to support future research.

MetaVenom: Field Programmable Fully Passive Metasurfaces

Baicheng Chen, Justin Chen, Wuqiong Zhao, Kai Zheng, Xinyu Zhang

Emerging high frequency wireless sensing and communication systems often leverage metasurfaces to modulate the signal propagation, thereby transcending Snell's law. Yet, existing metasurfaces are either active, which is constantly sustained by electrical power, or passive, which cannot be reconfigured. In this paper, we explore a quasi-dynamic meta-surface using electrically morphable ferrofluid that enables a new category of metasurface design for wireless sensing and communication. We first characterize how ferrofluid material interact with high-frequency millimeter-wave signals. Next, we present a practical fabrication method of quasi-dynamic metasurface using thermoplastic 3D printing. We then develop a model to effectively manipulate the wireless signal propagation using an array of field-programmable ferrofluid cells. Our extensive evaluation results reveal the capabilities of a novel quasi-dynamic metasurface as an alternative to existing wireless tags for joint radar communication, and beamforming metasurfaces for enhancing millimeter-wave network coverage.

Exploring the Feasibility of Full-Body Muscle Activation Sensing with Insole Pressure Sensors

Hao Zhou, Mahanth Gowda

Muscle activation initiates contractions that drive human movement, and understanding it provides valuable insights for injury prevention and rehabilitation. Yet, sensing muscle activation is barely explored in the rapidly growing mobile health market. Traditional methods for muscle activation sensing rely on specialized electrodes, such as surface electromyography, making them impractical, especially for long-term usage. In this paper, we introduce Press2Muscle, the first system to unobtrusively infer muscle activation using insole pressure sensors. The key idea is to analyze foot pressure changes resulting from full-body muscle activation that drives movements. To handle variations in pressure signals due to differences in users' gait, weight, and movement styles, we propose a data-driven approach to dynamically adjust reliance on different foot regions and incorporate easily accessible biographical data to enhance Press2Muscle's generalization to unseen users. We conducted an extensive study with 30 users. Under a leave-one-user-out setting, Press2Muscle achieves a root mean square error of 0.025, marking a 19% improvement over a video-based counterpart. A robustness study validates Press2Muscle's ability to generalize across user demographics, footwear types, and walking surfaces. Additionally, we showcase muscle imbalance detection and muscle activation estimation under free-living settings with Press2Muscle, confirming the feasibility of muscle activation sensing using insole pressure sensors in real-world settings.

ATM-SLAM: Adaptive Temporal Memory Visual SLAM in Severe Rainy Conditions

Baodong Chen, Wei Wang, Lang Wei, Zijian Wan, Chiyu Zhou, Ting Zhu

Visual Simultaneous Localization and Mapping (vSLAM) plays a crucial role in autonomous vehicles and robotics by enabling real-time localization and mapping in unknown environments. However, as current vSLAM systems are primarily designed for ideal weather conditions they often experience significant accuracy degradation and sudden operational breakdowns in severe rainy conditions (> 10 mm/h), posing serious safety hazards. To fill the gap, we propose ATM-SLAM, a novel learning-based vSLAM framework that overcomes the intense visual interference under severe rainy conditions and enhances system robustness. ATM-SLAM incorporates two key modules: (i) an Adaptive Feature Encoder (AFE), which adaptively prioritizes rain-robust features across consecutive frames, (ii) a Temporal Memory Integration (TMI) module, which integrates memorized correlation information to recover and stabilize localization under distorted frames. We extensively evaluate ATM-SLAM on a consumer vehicle, a robotic platform, and widely used public datasets across diverse scenarios. The evaluation results demonstrate that ATM-SLAM can achieve accurate and reliable localization and mapping in heavy rain. Moreover, to facilitate future research on vSLAM under severe rainy conditions, we collected the Severe Rainy SLAM Dataset (SR-SLAM), a large-scale real-world dataset, and plan to release it as a dedicated benchmark for evaluating vSLAM robustness.

RF-Tactrix: Fingerprint-Inspired Battery-Free, Wireless Force Sensing And Localization on 2D Surfaces

Yuzheng Dong, Yan Liang, Xuan Li, Xiaojiang Chen, Ju Wang

Accurate sensing and localization of surface force are crucial in various applications, including muscle rehabilitation, robotic manipulation of fragile objects, and finger rehabilitation training. However, existing technologies struggle to achieve high-precision and continuous force sensing, due to the crosstalk interference from dense sensor arrays or ambient light interference from optical sensors or reliance on batteries and power-hungry components.

Breaking Coordinate Overfitting: Geometry-Aware WiFi Sensing for Cross-Layout 3D Pose Estimation

Songming Jia, Yan Lu, Bin Liu, Xiang Zhang, Peng Zhao, Xinmeng Tang, Yelin Wei, JinYang Huang, Huan Yan, Zhi Liu

WiFi-based 3D human pose estimation offers a low-cost and privacy-preserving alternative to vision-based systems for smart interaction. However, existing approaches rely on visual 3D poses as supervision and directly regress CSI to a camera-based coordinate system. We find that this practice leads to coordinate overfitting: models memorize deployment-specific WiFi transceiver layouts rather than only learning activity-relevant representations, resulting in severe generalization failures. To address this challenge, we present PerceptAlign, the first geometry-conditioned framework for WiFi-based cross-layout pose estimation. PerceptAlign introduces a lightweight coordinate unification procedure that aligns WiFi and vision measurements in a shared 3D space using only two checkerboards and a few photos. Within this unified space, it encodes calibrated transceiver positions into high-dimensional embeddings and fuses them with CSI features, making the model explicitly aware of device geometry as a conditional variable. This design forces the network to disentangle human motion from deployment layouts, enabling robust and, for the first time, layout-invariant WiFi pose estimation. To support systematic evaluation, we construct the largest cross-domain 3D WiFi pose estimation dataset. Experiments show that PerceptAlign reduces cross-domain error by more than 60% compared to state-of-the-art baselines. These results establish geometry-conditioned learning as a viable path toward practical WiFi sensing.