arXiv subjects

Longhao Zou

Publications and source records attributed to Longhao Zou.

7 recordsLinked to original sources

arXiv2026

TAMS: Task-Aware Multi-View Adaptive Streaming for Wireless Telerobotic Manipulation

Wireless telerobotic manipulation relies on timely multi-view video feedback, but the available uplink bandwidth is often limited and dynamic. This paper presents Task-Aware Multi-View Adaptive Streaming (TAMS), a system that allocates video bitrate according to the current manipulation phase. TAMS infers task phase from lightweight robot-side signals and prioritizes the camera view most relevant to the operator while preserving baseline visibility for secondary views. Experiments on a six-degree-of-freedom (6-DoF) teleoperation testbed under three constrained network conditions show that TAMS improves primary view Structural Similarity Index (SSIM), reduces task completion time, and increases trial success rate compared with equal and static allocation baselines. Under the most constrained bandwidth condition, TAMS reduces mean completion time from 68.9 s to 43.9 s relative to equal allocation and increases trial success rate from 48% to 71%. Code is available at: https://github.com/Dzxx623/TAMS.

cs.RO↗

arXiv2026

Point Ladder Tuning: Parameter-Efficient Hierarchical Adaptation for 3D Point Cloud Understanding

Fine-tuning pre-trained point-cloud backbones typically updates all parameters, resulting in substantial computation and memory overhead. More importantly, modern point backbones rely on aggressive tokenization and downsampling, which yields compact global tokens but irreversibly discards fine-grained local geometry, an inherent bottleneck for parameter-efficient adaptation. Consequently, existing PEFT methods that operate only on these coarsened tokens can modulate global semantics but struggle to recover the missing multi-scale locality. We present Point Ladder Tuning (PLT), a locality-aware PEFT framework that performs hierarchical, instance-conditioned adaptation while keeping the backbone frozen. PLT forms a lightweight closed loop: (i) a Hierarchical Ladder Network (HLN) constructs a multi-resolution local feature pyramid directly from raw points; (ii) a Local-Global Fusion (LGF) aligns and fuses local pyramids with intermediate backbone semantics; and (iii) a Dynamic Prompt Generator produces instance-aware multi-scale prompts to modulate the frozen backbone effectively. For dense prediction, we further introduce a lightweight segmentation head that progressively upsamples fused features and leverages backbone priors to refine fine structures. Extensive experiments on classification and dense prediction show that PLT consistently surpasses prior PEFT baselines with minimal tunable parameters. PLT achieves state-of-the-art performance using only 2.71% trainable parameters for classification and 7.69% for dense prediction, and scales favorably to larger backbones, requiring merely 0.36% parameters on PointGPT-L. The code is released at https://github.com/JunLinChang/ECCV2026-PLT.

cs.CV↗

arXiv2026

VISTA: A Benchmark for Real-Time Video Streaming under Network Impairments in Surgical Teleoperation

Real-time video streaming is crucial in surgical teleoperation, yet reproducible evaluation under realistic network impairments remains limited. This paper presents VISTA, a benchmark designed to study how impairments along the forward video path affect received video quality, temporal continuity, and human task performance. VISTA employs Linux Traffic Control with NetEm and a Gilbert-Elliott loss model to emulate five network conditions: Hospital LAN, 5G Urban, 4G Rural, LEO Satellite, and GEO Satellite. The benchmark integrates a standardised peg transfer task with synchronized measurements of network quality of service (QoS), objective video quality (PSNR, SSIM, and VMAF), and temporal continuity through freeze rate, while maintaining a stable reverse control channel. Across 375 experimental trials, network degradation substantially reduced teleoperation performance: success rate decreased from 97% in Hospital LAN to 79% in 5G Urban, 35% in 4G Rural, 71% in LEO Satellite, and 12% in GEO Satellite, while mean task completion time for successful trials increased from 80 s in Hospital LAN to 117 s in 5G Urban, 211 s in 4G Rural, 152 s in LEO Satellite, and 255 s in GEO Satellite. These findings show that network impairments have a direct impact on task completion and success in surgical teleoperation, and provide a reproducible basis for evaluating teleoperation video under realistic network constraints. Source code available at https://github.com/Dzxx623/VISTA.

eess.IV↗

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

FourierCompress: Layer-Aware Spectral Activation Compression for Efficient and Accurate Collaborative LLM Inference

Collaborative large language model (LLM) inference enables real-time, privacy-preserving AI services on resource-constrained edge devices by partitioning computational workloads between client devices and edge servers. However, this paradigm is severely hindered by communication bottlenecks caused by the transmission of high-dimensional intermediate activations, exacerbated by the autoregressive decoding structure of LLMs, where bandwidth consumption scales linearly with output length. Existing activation compression methods struggle to simultaneously achieve high compression ratios, low reconstruction error, and computational efficiency. This paper proposes FourierCompress, a novel, layer-aware activation compression framework that exploits the frequency-domain sparsity of LLM activations. We rigorously demonstrate that activations from the first Transformer layer exhibit strong smoothness and energy concentration in the low-frequency domain, making them highly amenable to near-lossless compression via the Fast Fourier Transform (FFT). FourierCompress transforms activations into the frequency domain, retains only a compact block of low-frequency coefficients, and reconstructs the signal at the server using conjugate symmetry, enabling seamless hardware acceleration on DSPs and FPGAs. Extensive experiments on Llama 3 and Qwen2.5 models across 10 commonsense reasoning datasets demonstrate that FourierCompress preserves performance remarkably close to the uncompressed baseline, outperforming Top-k, QR, and SVD. FourierCompress bridges the gap between communication efficiency (an average 7.6x reduction in activation size), near-lossless inference (less than 0.3% average accuracy loss), and significantly faster compression (achieving over 32x reduction in compression time compared to Top-k via hardware acceleration) for edge-device LLM inference.

cs.DC↗

arXiv2025

TeleSim: A Network-Aware Testbed and Benchmark Dataset for Telerobotic Applications

Telerobotic technologies are becoming increasingly essential in fields such as remote surgery, nuclear decommissioning, and space exploration. Reliable datasets and testbeds are essential for evaluating telerobotic system performance prior to real-world deployment. However, there is a notable lack of datasets that capture the impact of network delays, as well as testbeds that realistically model the communication link between the operator and the robot. This paper introduces TeleSim, a network-aware teleoperation dataset and testbed designed to assess the performance of telerobotic applications under diverse network conditions. TeleSim systematically collects performance data from fine manipulation tasks executed under three predefined network quality tiers: High, Medium, and Low. Each tier is characterized through controlled settings of bandwidth, latency, jitter, and packet loss. Using OMNeT++ for precise network simulation, we record a wide range of metrics, including completion time, success rates, video quality indicators (Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM)), and quality of service (QoS) parameters. TeleSim comprises 300 experimental trials, providing a robust benchmark for evaluating teleoperation systems across heterogeneous network scenarios. In the worst network condition, completion time increases by 221.8% and success rate drops by 64%. Our findings reveal that network degradation leads to compounding negative impacts, notably reduced video quality and prolonged task execution, highlighting the need for adaptive, resilient teleoperation protocols. The full dataset and testbed software are publicly available on our GitHub repository: https://github.com/ConnectedRoboticsLab and YouTube channel: https://youtu.be/Fz_1iOYe104.

cs.NI↗

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↗