arXiv subjects

Huanqi Yang

Publications and source records attributed to Huanqi Yang.

10 recordsLinked to original sources

arXiv2026

VoxAnchor: Grounding Speech Authenticity in Throat Vibration via mmWave Radar

Rapid advances in speech synthesis and audio editing have made realistic forgeries increasingly accessible, yet existing detection methods remain vulnerable to tampering or depend on visual/wearable sensors. In this paper, we present VoxAnchor, a system that physically grounds audio authentication in vocal dynamics by leveraging the inherent coherence between speech acoustics and radar-sensed throat vibrations. VoxAnchor uses contactless millimeter-wave radar to capture fine-grained throat vibrations that are tightly coupled with human speech production, establishing a hard-to-forge anchor rooted in human physiology. The design comprises three main components: (1) a cross-modal frame-work that uses modality-specific encoders and contrastive learning to detect subtle mismatches at word granularity; (2) a phase-aware pipeline that extracts physically consistent, temporally faithful throat vibrations; and (3) a dual-stage strategy that combines signal-level onset detection and semantic-level coherence to align asynchronous radar and audio streams. Unlike liveness detection, which only confirms whether speech occurred, VoxAnchor verifies what was spoken through word-level content consistency, exposing localized edits that preserve identity and global authenticity cues. Extensive evaluations show that VoxAnchor achieves robust, fine-grained detection across diverse forgeries (editing, splicing, replay, deepfake) and conditions, with an overall EER of 0.017, low latency, and modest computational cost.

cs.HC↗

arXiv2026

RAGent: Physics-Aware Agentic Reasoning for Training-Free mmWave Human Activity Recognition

Millimeter-wave (mmWave) radar enables privacy-preserving human activity recognition (HAR), yet real-world deployment remains hindered by costly annotation and poor transferability under domain shift. Although prior efforts partially alleviate these challenges, most still require retraining or adaptation for each new deployment setting. This keeps mmWave HAR in a repeated collect-tune-redeploy cycle, making scalable real-world deployment difficult. In this paper, we present RAGent, a deployment-time training-free framework for mmWave HAR that reformulates recognition as evidence-grounded inference over reusable radar knowledge rather than deployment-specific model optimization. Offline, RAGent constructs a reusable radar knowledge base through constrained cross-modal supervision, where a Vision-Language Model (VLM) transfers activity semantics from synchronized videos to paired radar segments without manual radar annotation. At deployment time, RAGent recognizes activities from radar alone by retrieving physically comparable precedents in an explicit kinematic space and resolving the final label through structured multi-role reasoning. The reasoning protocol is further refined offline through zero-gradient self-evolution. Extensive experiments on a self-collected dataset show that RAGent achieves 93.39% accuracy without per-domain retraining or target-domain adaptation, while generalizing robustly across domains.

cs.HC↗

arXiv2026

PowerLens: Taming LLM Agents for Safe and Personalized Mobile Power Management

Battery life remains a critical challenge for mobile devices, yet existing power management mechanisms rely on static rules or coarse-grained heuristics that ignore user activities and personal preferences. We present PowerLens, a system that tames the reasoning power of Large Language Models (LLMs) for safe and personalized mobile power management on Android devices. The key idea is that LLMs' commonsense reasoning can bridge the semantic gap between user activities and system parameters, enabling zero-shot, context-aware policy generation that adapts to individual preferences through implicit feedback. PowerLens employs a multi-agent architecture that recognizes user context from UI semantics and generates holistic power policies across 18 device parameters. A PDL-based constraint framework verifies every action before execution, while a two-tier memory system learns individualized preferences from implicit user overrides through confidence-based distillation, requiring no explicit configuration and converging within 3--5 days. Extensive experiments on a rooted Android device show that PowerLens achieves 81.7% action accuracy and 38.8% energy saving over stock Android, outperforming rule-based and LLM-based baselines, with high user satisfaction, fast preference convergence, and strong safety guarantees, with the system itself consuming only 0.5% of daily battery capacity.

cs.AI↗

arXiv2025

AegisAgent: An Autonomous Defense Agent Against Prompt Injection Attacks in LLM-HARs

The integration of Large Language Models (LLMs) into wearable sensing is creating a new class of mobile applications capable of nuanced human activity understanding. However, the reliability of these systems is critically undermined by their vulnerability to prompt injection attacks, where attackers deliberately input deceptive instructions into LLMs. Traditional defenses, based on static filters and rigid rules, are insufficient to address the semantic complexity of these new attacks. We argue that a paradigm shift is needed -- from passive filtering to active protection and autonomous reasoning. We introduce AegisAgent, an autonomous agent system designed to ensure the security of LLM-driven HAR systems. Instead of merely blocking threats, AegisAgent functions as a cognitive guardian. It autonomously perceives potential semantic inconsistencies, reasons about the user's true intent by consulting a dynamic memory of past interactions, and acts by generating and executing a multi-step verification and repair plan. We implement AegisAgent as a lightweight, full-stack prototype and conduct a systematic evaluation on 15 common attacks with five state-of-the-art LLM-based HAR systems on three public datasets. Results show it reduces attack success rate by 30\% on average while incurring only 78.6 ms of latency overhead on a GPU workstation. Our work makes the first step towards building secure and trustworthy LLM-driven HAR systems.

cs.CR↗

arXiv2025

EmbedGenius: Towards Automated Software Development for Generic Embedded IoT Systems

Embedded IoT system development is crucial for enabling seamless connectivity and functionality across a wide range of applications. However, such a complex process requires cross-domain knowledge of hardware and software and hence often necessitates direct developer involvement, making it labor-intensive, time-consuming, and error-prone. To address this challenge, this paper introduces EmbedGenius, the first fully automated software development platform for general-purpose embedded IoT systems. The key idea is to leverage the reasoning ability of Large Language Models (LLMs) and embedded system expertise to automate the hardware-in-the-loop development process. The main methods include a component-aware library resolution method for addressing hardware dependencies, a library knowledge generation method that injects utility domain knowledge into LLMs, and an auto-programming method that ensures successful deployment. We evaluate EmbedGenius's performance across 71 modules and four mainstream embedded development platforms with over 350 IoT tasks. Experimental results show that EmbedGenius can generate codes with an accuracy of 95.7% and complete tasks with a success rate of 86.5%, surpassing human-in-the-loop baselines by 15.6%--37.7% and 25.5%--53.4%, respectively. We also show EmbedGenius's potential through case studies in environmental monitoring and remote control systems development.

cs.SE↗

arXiv2025

RF Sensing Security and Malicious Exploitation: A Comprehensive Survey

Radio Frequency (RF) sensing technologies have experienced significant growth due to the widespread adoption of RF devices and the Internet of Things (IoT). These technologies enable numerous applications across healthcare, smart homes, industrial automation, and human-computer interaction. However, the non-intrusive and ubiquitous nature of RF sensing - combined with its environmental sensitivity and data dependency - makes these systems inherently vulnerable not only as attack targets, but also as powerful attack vectors. This survey presents a comprehensive analysis of RF sensing security, covering both system-level vulnerabilities - such as signal spoofing, adversarial perturbations, and model poisoning - and the misuse of sensing capabilities for attacks like cross-boundary surveillance, side-channel inference, and semantic privacy breaches. We propose unified threat models to structure these attack vectors and further conduct task-specific vulnerability assessments across key RF sensing applications, identifying their unique attack surfaces and risk profiles. In addition, we systematically review defense strategies across system layers and threat-specific scenarios, incorporating both active and passive paradigms to provide a structured and practical view of protection mechanisms. Compared to prior surveys, our work distinguishes itself by offering a multi-dimensional classification framework based on task type, threat vector, and sensing modality, and by providing fine-grained, scenario-driven analysis that bridges theoretical models and real-world implications. This survey aims to serve as a comprehensive reference for researchers and practitioners seeking to understand, evaluate, and secure the evolving landscape of RF sensing technologies.

cs.HC↗

arXiv2025

LLMKey: LLM-Powered Wireless Key Generation Scheme for Next-Gen IoV Systems

Wireless key generation holds significant promise for establishing cryptographic keys in Next-Gen Internet of Vehicles (IoV) systems. However, existing approaches often face inefficiencies and performance limitations caused by frequent channel probing and ineffective quantization. To address these challenges, this paper introduces LLMKey, a novel key generation system designed to enhance efficiency and security. We identify excessive channel probing and suboptimal quantization as critical bottlenecks in current methods. To mitigate these issues, we propose an innovative large language model (LLM)-based channel probing technique that leverages the capabilities of LLMs to reduce probing rounds while preserving crucial channel information. Instead of conventional quantization, LLMKey adopts a perturbed compressed sensing-based key delivery mechanism, improving both robustness and security. Extensive evaluations are conducted in four real-world scenarios, encompassing V2I (Vehicle-to-Infrastructure) and V2V (Vehicle-to-Vehicle) settings in both urban and rural environments. The results show that LLMKey achieves an average key agreement rate of 98.78\%, highlighting its effectiveness and reliability across diverse conditions.

cs.NI↗

arXiv2024

iRadar: Synthesizing Millimeter-Waves from Wearable Inertial Inputs for Human Gesture Sensing

Millimeter-wave (mmWave) radar-based gesture recognition is gaining attention as a key technology to enable intuitive human-machine interaction. Nevertheless, the significant challenge lies in obtaining large-scale, high-quality mmWave gesture datasets. To tackle this problem, we present iRadar, a novel cross-modal gesture recognition framework that employs Inertial Measurement Unit (IMU) data to synthesize the radar signals generated by the corresponding gestures. The key idea is to exploit the IMU signals, which are commonly available in contemporary wearable devices, to synthesize the radar signals that would be produced if the same gesture was performed in front of a mmWave radar. However, several technical obstacles must be overcome due to the differences between mmWave and IMU signals, the noisy gesture sensing of mmWave radar, and the dynamics of human gestures. Firstly, we develop a method for processing IMU and mmWave data to extract critical gesture features. Secondly, we propose a diffusion-based IMU-to-radar translation model that accurately transforms IMU data into mmWave data. Lastly, we devise a novel transformer model to enhance gesture recognition performance. We thoroughly evaluate iRadar, involving 18 gestures and 30 subjects in three scenarios, using five wearable devices. Experimental results demonstrate that iRadar consistently achieves 99.82% Top-3 accuracy across diverse scenarios.

cs.HC↗

arXiv2024

TransCompressor: LLM-Powered Multimodal Data Compression for Smart Transportation

The incorporation of Large Language Models (LLMs) into smart transportation systems has paved the way for improving data management and operational efficiency. This study introduces TransCompressor, a novel framework that leverages LLMs for efficient compression and decompression of multimodal transportation sensor data. TransCompressor has undergone thorough evaluation with diverse sensor data types, including barometer, speed, and altitude measurements, across various transportation modes like buses, taxis, and MTRs. Comprehensive evaluation illustrates the effectiveness of TransCompressor in reconstructing transportation sensor data at different compression ratios. The results highlight that, with well-crafted prompts, LLMs can utilize their vast knowledge base to contribute to data compression processes, enhancing data storage, analysis, and retrieval in smart transportation settings.

cs.CL↗

arXiv2024

Are You Being Tracked? Discover the Power of Zero-Shot Trajectory Tracing with LLMs!

There is a burgeoning discussion around the capabilities of Large Language Models (LLMs) in acting as fundamental components that can be seamlessly incorporated into Artificial Intelligence of Things (AIoT) to interpret complex trajectories. This study introduces LLMTrack, a model that illustrates how LLMs can be leveraged for Zero-Shot Trajectory Recognition by employing a novel single-prompt technique that combines role-play and think step-by-step methodologies with unprocessed Inertial Measurement Unit (IMU) data. We evaluate the model using real-world datasets designed to challenge it with distinct trajectories characterized by indoor and outdoor scenarios. In both test scenarios, LLMTrack not only meets but exceeds the performance benchmarks set by traditional machine learning approaches and even contemporary state-of-the-art deep learning models, all without the requirement of training on specialized datasets. The results of our research suggest that, with strategically designed prompts, LLMs can tap into their extensive knowledge base and are well-equipped to analyze raw sensor data with remarkable effectiveness.

cs.CL↗