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Xi'an University of Technology

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Research library12linked papers
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Selected work

Representative Papers

Local Epistemic Uncertainty Guided Active Sampling for Plug-and-play Diffusive Image Restoration

Aug 07, 2026

Existing diffusion-based image restoration methods struggle with spatially non-uniform degradation due to their reliance on fixed data constraints and uniform sampling steps, often leading to structural distortions, detail loss, and computational redundancy. This work proposes the LEADer framework, which introduces local epistemic uncertainty into diffusion-based image restoration for the first time. In the spatial domain, it dynamically modulates the strength of null-space priors based on pixel-wise uncertainty, enabling adaptive data consistency enforcement. In the temporal domain, it leverages the trace of uncertainty to prune the sampling trajectory, achieving efficient and adaptive inference. Evaluated across multiple state-of-the-art diffusion-based image restoration models, LEADer significantly improves restoration quality while substantially reducing sampling time, incurs negligible memory overhead, and guarantees strict data consistency along with deterministic error bounds.

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ACZ-GSeg: Adaptive Concentric Zone-based Two-stage Ground Segmentation for LiDAR Point Clouds

Jul 13, 2026

This study addresses the under-segmentation of ground points in sparse long-range point clouds, which is exacerbated by terrain undulations and interference from non-ground structures. To tackle this challenge, the authors propose a two-stage ground segmentation method based on an adaptive concentric zone model. In the coarse segmentation stage, dynamic sector partitioning balances local point density, and plane fitting is guided by a minimum-height seed constraint combined with height-decay weighting. The fine segmentation stage incorporates reflectance intensity consistency to select high-confidence ground points and refines ambiguous regions using neighborhood height stability. By innovatively integrating geometric and intensity features, the proposed approach achieves F1 scores of 97.66% on SemanticKITTI and 99.36% on RUBY-PLUS, significantly enhancing both segmentation accuracy and robustness.

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T-RBFT: A Scalable and Efficient Byzantine Consensus Based on Trusted Execution Environment for Consortium Blockchain

Apr 17, 2026

This work addresses the high communication overhead and limited performance of traditional Byzantine Fault Tolerance (BFT) consensus protocols in permissioned blockchains, even under the assumption that a majority of nodes are honest. To overcome these limitations, the paper proposes T-RBFT, a two-layer hybrid consensus mechanism leveraging Trusted Execution Environments (TEEs). T-RBFT dynamically partitions nodes into shards: within each shard, an optimized Raft protocol efficiently processes requests, while inter-shard consensus is achieved through a lightweight BFT protocol assisted by TEEs. This design substantially reduces communication complexity and latency, significantly enhancing throughput and scalability without compromising security. Experimental results demonstrate that T-RBFT outperforms existing two-layer consensus approaches in both efficiency and robustness.

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Recent publications

Latest Papers

Local Epistemic Uncertainty Guided Active Sampling for Plug-and-play Diffusive Image Restoration

Aug 07, 2026

Existing diffusion-based image restoration methods struggle with spatially non-uniform degradation due to their reliance on fixed data constraints and uniform sampling steps, often leading to structural distortions, detail loss, and computational redundancy. This work proposes the LEADer framework, which introduces local epistemic uncertainty into diffusion-based image restoration for the first time. In the spatial domain, it dynamically modulates the strength of null-space priors based on pixel-wise uncertainty, enabling adaptive data consistency enforcement. In the temporal domain, it leverages the trace of uncertainty to prune the sampling trajectory, achieving efficient and adaptive inference. Evaluated across multiple state-of-the-art diffusion-based image restoration models, LEADer significantly improves restoration quality while substantially reducing sampling time, incurs negligible memory overhead, and guarantees strict data consistency along with deterministic error bounds.

0 citationsRead paper

ACZ-GSeg: Adaptive Concentric Zone-based Two-stage Ground Segmentation for LiDAR Point Clouds

Jul 13, 2026

This study addresses the under-segmentation of ground points in sparse long-range point clouds, which is exacerbated by terrain undulations and interference from non-ground structures. To tackle this challenge, the authors propose a two-stage ground segmentation method based on an adaptive concentric zone model. In the coarse segmentation stage, dynamic sector partitioning balances local point density, and plane fitting is guided by a minimum-height seed constraint combined with height-decay weighting. The fine segmentation stage incorporates reflectance intensity consistency to select high-confidence ground points and refines ambiguous regions using neighborhood height stability. By innovatively integrating geometric and intensity features, the proposed approach achieves F1 scores of 97.66% on SemanticKITTI and 99.36% on RUBY-PLUS, significantly enhancing both segmentation accuracy and robustness.

0 citationsRead paper

T-RBFT: A Scalable and Efficient Byzantine Consensus Based on Trusted Execution Environment for Consortium Blockchain

Apr 17, 2026

This work addresses the high communication overhead and limited performance of traditional Byzantine Fault Tolerance (BFT) consensus protocols in permissioned blockchains, even under the assumption that a majority of nodes are honest. To overcome these limitations, the paper proposes T-RBFT, a two-layer hybrid consensus mechanism leveraging Trusted Execution Environments (TEEs). T-RBFT dynamically partitions nodes into shards: within each shard, an optimized Raft protocol efficiently processes requests, while inter-shard consensus is achieved through a lightweight BFT protocol assisted by TEEs. This design substantially reduces communication complexity and latency, significantly enhancing throughput and scalability without compromising security. Experimental results demonstrate that T-RBFT outperforms existing two-layer consensus approaches in both efficiency and robustness.

0 citationsRead paper