arXiv subjects

Zebin Huang

Publications and source records attributed to Zebin Huang.

9 recordsLinked to original sources

arXiv2025

Electrically Pumped Terahertz Frequency Comb Based on Actively Mode-locked Resonant Tunneling Diode

Terahertz (THz) frequency combs (TFCs) are promising for numerous applications in spectroscopy, metrology, sensing, and wireless communications. However, the practical applications of TFCs have been hindered thus far by the need for cryogenic cooling, limited bandwidth, and bulky configuration, largely due to the lack of advanced THz sources. Here, we report an electrically pumped, room-temperature, broadband, compact, and tunable TFC by integrating an actively mode-locked resonant tunneling diode (RTD) in a metallic waveguide. By injecting a strong, continuous-wave radio frequency (RF) signal into a direct-current-driven RTD oscillator, we experimentally demonstrate a broadband, offset-free comb spectrum spanning more than one octave, from 0.14 to 0.325 THz at room temperature. Moreover, we show that the repetition frequency of the TFC can be tuned from 0.9 to 50 GHz by the injected RF signal, and a 120 Gbps (16 Gbps per channel) data rate can be achieved in the TFC-based high-speed single-channel (multi-channel) wireless communications.

physics.app-ph↗

arXiv2025

Framework for Robust Motion Planning of Tethered Multi-Robot Systems in Marine Environments

This paper introduces CoralGuide, a novel framework designed for path planning and trajectory optimization for tethered multi-robot systems. We focus on marine robotics, which commonly have tethered configurations of an Autonomous Surface Vehicle (ASV) and an Autonomous Underwater Vehicle (AUV). CoralGuide provides safe navigation in marine environments by enhancing the A* algorithm with specialized heuristics tailored for tethered ASV-AUV systems. Our method integrates catenary curve modelling for tether management and employs Bezier curve interpolation for smoother trajectory planning, ensuring efficient and synchronized operations without compromising safety. Through simulations and real-world experiments, we have validated CoralGuides effectiveness in improving path planning and trajectory optimization, demonstrating its potential to significantly enhance operational capabilities in marine research and infrastructure inspection.

cs.RO↗

arXiv2025

Broadband Terahertz Frequency Comb Based on Actively Mode Locked Resonant Tunneling Diode

The frequency combs characterized by their phase-coherent equidistant spectral modes and precise frequency scales of broadband spectrum, have made them an indispensable part of contemporary physics. A terahertz (THz) frequency comb is a key asset for THz technology applications in spectroscopy, metrology, communications, and sensing. However, the THz frequency comb technologies are comparatively underdeveloped compared to the optical frequency domain, primarily attributed to the deficiency of advanced THz generation components. In this paper, we innovatively demonstrate a compact THz frequency comb source based on a resonant tunneling diode (RTD) through active mode locking technique. By injecting a strong continuous-wave radio frequency (RF) signal via the bias line into a RTD oscillator integrated within a WR-5 hollow metallic waveguide package, we observe a broadband comb spectrum spanning from 140 to 325 GHz. The mode spacing is directly determined by the frequency of the injected RF signal, providing a wide tuning range of approximately 40 GHz. We also employ the proposed frequency comb source as the local oscillator in a coherent transmitter. In particular, this is the first all-electrical compact THz frequency comb source, and the transmission demonstration paves the way to next-generation communication.

physics.app-ph↗

arXiv2025

Text2Schema: Filling the Gap in Designing Database Table Structures based on Natural Language

People without a database background usually rely on file systems or tools such as Excel for data management, which often lead to redundancy and data inconsistency. Relational databases possess strong data management capabilities, but require a high level of professional expertise from users. Although there are already many works on Text2SQL to automate the translation of natural language into SQL queries for data manipulation, all of them presuppose that the database schema is pre-designed. In practice, schema design itself demands domain expertise, and research on directly generating schemas from textual requirements remains unexplored. In this paper, we systematically define a new problem, called Text2Schema, to convert a natural language text requirement into a relational database schema. With an effective Text2Schema technique, users can effortlessly create database table structures using natural language, and subsequently leverage existing Text2SQL techniques to perform data manipulations, which significantly narrows the gap between non-technical personnel and highly efficient, versatile relational database systems. We propose SchemaAgent, an LLM-based multi-agent framework for Text2Schema. We emulate the workflow of manual schema design by assigning specialized roles to agents and enabling effective collaboration to refine their respective subtasks. We also incorporate dedicated roles for reflection and inspection, along with an innovative error detection and correction mechanism to identify and rectify issues across various phases. Moreover, we build and open source a benchmark containing 381 pairs of requirement description and schema. Experimental results demonstrate the superiority of our approach over comparative work.

cs.DB↗

arXiv2025

MarineGym: A High-Performance Reinforcement Learning Platform for Underwater Robotics

This work presents the MarineGym, a high-performance reinforcement learning (RL) platform specifically designed for underwater robotics. It aims to address the limitations of existing underwater simulation environments in terms of RL compatibility, training efficiency, and standardized benchmarking. MarineGym integrates a proposed GPU-accelerated hydrodynamic plugin based on Isaac Sim, achieving a rollout speed of 250,000 frames per second on a single NVIDIA RTX 3060 GPU. It also provides five models of unmanned underwater vehicles (UUVs), multiple propulsion systems, and a set of predefined tasks covering core underwater control challenges. Additionally, the DR toolkit allows flexible adjustments of simulation and task parameters during training to improve Sim2Real transfer. Further benchmark experiments demonstrate that MarineGym improves training efficiency over existing platforms and supports robust policy adaptation under various perturbations. We expect this platform could drive further advancements in RL research for underwater robotics. For more details about MarineGym and its applications, please visit our project page: https://marine-gym.com/.

cs.RO↗

arXiv2025

High Gain and Broadband Metalens Antenna for Terahertz Communication

Terahertz (THz) metalens antennas with compact planar structures have demonstrated significant potential in enhancing gain and aperture efficiency through beam convergence. However, research on THz wireless communication systems utilizing metalens antennas remains limited, primarily due to insufficient collaborative enhancement in gain and bandwidth in THz transceiver design. In this paper, we propose a high gain metalens antenna transceiver and demonstrate its application for THz communication. The system employs a horn antenna integrated with a 3D-printed bracket to enhance the metalens gain and operating bandwidth, where the metalens adopts a "sandwich" architecture composed of a V-shaped copper resonator, a dielectric substrate, and a grating. The resonant design inside the metalens facilitates high polarization conversion efficiency and full phase modulation across a 0{\deg} to 360{\deg} range at frequency between 0.20 to 0.30 THz band. Experimental results demonstrate a peak gain of 36.1 dBi and aperture efficiency of 54.45% at 0.244 THz, with a 3 dB bandwidth exceeding 33 GHz. A prototype communication system incorporating the metalens transceiver achieves a bit error rate (BER) reduction by three orders of magnitude compared to conventional horn antennas and supports a maximum data rate of 100 Gbps. This proposed metalens offer a high-gain, compact solution for achieving high data rate THz communications, driving advancements in 6G communication network.

physics.app-ph↗

arXiv2024

SpasticMyoElbow: Physical Human-Robot Interaction Simulation Framework for Modelling Elbow Spasticity

Robotic devices hold great potential for efficient and reliable assessment of neuromotor abnormalities in post-stroke patients. However, spasticity caused by stroke is still assessed manually in clinical settings. The limited and variable nature of data collected from patients has long posed a major barrier to quantitatively modelling spasticity with robotic measurements and fully validating robotic assessment techniques. This paper presents a simulation framework developed to support the design and validation of elbow spasticity models and mitigate data problems. The framework consists of a simulation environment of robot-assisted spasticity assessment, two motion controllers for the robot and human models, and a stretch reflex controller. Our framework allows simulation based on synthetic data without experimental data from human subjects. Using this framework, we replicated the constant-velocity stretch experiment typically used in robot-assisted spasticity assessment and evaluated four types of spasticity models. Our results show that a spasticity reflex model incorporating feedback on both muscle fibre velocity and length more accurately captures joint resistance characteristics during passive elbow stretching in spastic patients than a force-dependent model. When integrated with an appropriate spasticity model, this simulation framework has the potential to generate extensive datasets of virtual patients for future research on spasticity assessment.

cs.RO↗

arXiv2024

MarineGym: Accelerated Training for Underwater Vehicles with High-Fidelity RL Simulation

Reinforcement Learning (RL) is a promising solution, allowing Unmanned Underwater Vehicles (UUVs) to learn optimal behaviors through trial and error. However, existing simulators lack efficient integration with RL methods, limiting training scalability and performance. This paper introduces MarineGym, a novel simulation framework designed to enhance RL training efficiency for UUVs by utilizing GPU acceleration. MarineGym offers a 10,000-fold performance improvement over real-time simulation on a single GPU, enabling rapid training of RL algorithms across multiple underwater tasks. Key features include realistic dynamic modeling of UUVs, parallel environment execution, and compatibility with popular RL frameworks like PyTorch and TorchRL. The framework is validated through four distinct tasks: station-keeping, circle tracking, helical tracking, and lemniscate tracking. This framework sets the stage for advancing RL in underwater robotics and facilitating efficient training in complex, dynamic environments.

cs.RO↗

arXiv2024

Replication of Impedance Identification Experiments on a Reinforcement-Learning-Controlled Digital Twin of Human Elbows

This study presents a pioneering effort to replicate human neuromechanical experiments within a virtual environment utilising a digital human model. By employing MyoSuite, a state-of-the-art human motion simulation platform enhanced by Reinforcement Learning (RL), multiple types of impedance identification experiments of human elbow were replicated on a musculoskeletal model. We compared the elbow movement controlled by an RL agent with the motion of an actual human elbow in terms of the impedance identified in torque-perturbation experiments. The findings reveal that the RL agent exhibits higher elbow impedance to stabilise the target elbow motion under perturbation than a human does, likely due to its shorter reaction time and superior sensory capabilities. This study serves as a preliminary exploration into the potential of virtual environment simulations for neuromechanical research, offering an initial yet promising alternative to conventional experimental approaches. An RL-controlled digital twin with complete musculoskeletal models of the human body is expected to be useful in designing experiments and validating rehabilitation theory before experiments on real human subjects.

cs.RO↗