arXiv subjects

Tong Hao

Publications and source records attributed to Tong Hao.

5 recordsLinked to original sources

arXiv2026

Transparentize A Shallow Cryosphere: High-Resolution Subsurface Imaging using UAV-Borne GPR A review and prospective

Accurate characterization of shallow cryosphere subsurface structures is critical for understanding snow and ice dynamics, evaluating environmental hazards, and informing climate-related decision making. Recent advances in unmanned aerial vehicles (UAVs) and compact radar instrumentation have enabled UAV-borne ground penetrating radar (GPR) as a non-contact alternative for subsurface sensing in these environments. By combining the safety-constrained mobility of unmanned aerial platforms with the penetration capability of radar, UAV-borne GPR facilitates rapid, wide-coverage and flexible surveys over snow and ice surfaces in cryosphere that are often inaccessible to traditional ground-based methods. This contribution reviews recent progress in UAV-borne GPR for cryosphere investigations, encompassing system architectures, operational strategies, and representative deployment practices. Furthermore, key data-processing methodologies are summarized for their roles in enhancing subsurface imaging and reconstruction fidelity. A case study from Mochou Lake in East Antarctica, illustrates these capabilities, yielding an ice-thickness estimation error of only 0.03 m when benchmarked against drilling measurements. Finally, the remaining challenges, particularly those associated with scanning coverage, real-time data processing, and detection accuracy, are discussed for future cryosphere applications.

eess.SP↗

arXiv2023

3D Shape Knowledge Graph for Cross-domain 3D Shape Retrieval

The surge in 3D modeling has led to a pronounced research emphasis on the field of 3D shape retrieval. Numerous contemporary approaches have been put forth to tackle this intricate challenge. Nevertheless, effectively addressing the intricacies of cross-modal 3D shape retrieval remains a formidable undertaking, owing to inherent modality-based disparities. This study presents an innovative notion, termed "geometric words", which functions as elemental constituents for representing entities through combinations. To establish the knowledge graph, we employ geometric words as nodes, connecting them via shape categories and geometry attributes. Subsequently, we devise a unique graph embedding method for knowledge acquisition. Finally, an effective similarity measure is introduced for retrieval purposes. Importantly, each 3D or 2D entity can anchor its geometric terms within the knowledge graph, thereby serving as a link between cross-domain data. As a result, our approach facilitates multiple cross-domain 3D shape retrieval tasks. We evaluate the proposed method's performance on the ModelNet40 and ShapeNetCore55 datasets, encompassing scenarios related to 3D shape retrieval and cross-domain retrieval. Furthermore, we employ the established cross-modal dataset (MI3DOR) to assess cross-modal 3D shape retrieval. The resulting experimental outcomes, in conjunction with comparisons against state-of-the-art techniques, clearly highlight the superiority of our approach.

cs.CV↗

arXiv2023

Energy Efficiency Optimization for Subterranean LoRaWAN Using A Reinforcement Learning Approach: A Direct-to-Satellite Scenario

The integration of subterranean LoRaWAN and non-terrestrial networks (NTN) delivers substantial economic and societal benefits in remote agriculture and disaster rescue operations. The LoRa modulation leverages quasi-orthogonal spreading factors (SFs) to optimize data rates, airtime, coverage and energy consumption. However, it is still challenging to effectively assign SFs to end devices for minimizing co-SF interference in massive subterranean LoRaWAN NTN. To address this, we investigate a reinforcement learning (RL)-based SFs allocation scheme to optimize the system's energy efficiency (EE). To efficiently capture the device-to-environment interactions in dense networks, we proposed an SFs allocation technique using the multi-agent dueling double deep Q-network (MAD3QN) and the multi-agent advantage actor-critic (MAA2C) algorithms based on an analytical reward mechanism. Our proposed RL-based SFs allocation approach evinces better performance compared to four benchmarks in the extreme underground direct-to-satellite scenario. Remarkably, MAD3QN shows promising potentials in surpassing MAA2C in terms of convergence rate and EE.

cs.IT↗

arXiv2023

On CSI-Free Multi-Antenna Schemes for Massive Wireless-Powered Underground Sensor Networks

Radio-frequency wireless energy transfer (WET) is a promising technology to realize wireless-powered underground sensor networks (WPUSNs) and enable sustainable underground monitoring. However, due to the severe attenuation in harsh underground soil and the tight energy budget of the underground sensors, traditional WPUSNs relying on the channel state information (CSI) are highly inefficient, especially in massive WET scenarios. To address this challenge, we comparatively assess the feasibility of several state-of-the-art CSI-free multi-antenna WET schemes for WPUSNs, under a given power budget. Moreover, to overcome the extremely low WET efficiency in underground channels, we propose a distributed CSI-free system, where multiple power beacons (PBs) simultaneously charge a large set of underground sensors without any CSI. We consider the position-aware K-Means and the position-agnostic equally-far-from-center (EFFC) approaches for the optimal deployment of the PBs. Our results evince that the performance of the proposed distributed CSI-free system can approach or even surpass that of a traditional full-CSI WET strategy, especially when adopting an appropriate CSI-free scheme, applying the advisable PBs deployment approach, and equipping the PBs with an appropriate number of antennas. Finally, we discuss the impact of underground parameters, i.e., the burial depth of devices and the volumetric water content of soil, on the system's performance, and identify potential challenges and research opportunities for practical distributed CSI-free WPUSNs deployment.

cs.IT↗

arXiv2008

Investigation of Vertical Spiral Resonators for Low Frequency Metamaterial Design

This paper thoroughly explores the characteristics of vertical spiral resonators (VSR). They exhibit rela-tively high Q factors and sizes around a few percent of the free space wavelength, which make them ideal candi-dates for assembling metamaterial devices. A quasistatic model of VSR is obtained from simple analytical ex-pressions, and the effects of certain geometrical parameters on the resonant frequency are investigated.

physics.optics↗