Biography

I am pursuing my M.S. degree in the College of Polytechnic Institute, Zhejiang University, Hangzhou, China. My major research interests are Simultaneous Localization And Mapping (SLAM) and autonomous driving perception.

Research and Interests

• Simultaneous Localization And Mapping(SLAM)
• Autonomous Driving Perception

Publications

• Muxuan Gao, Juntao Jiang, Shuangming Lei, Huifeng Wu, Jun Chen, and Yong Liu. OnSort: An O(n) Comparison-Free Sorter for Large-Scale Dataset with Parallel Prefetching and Sparse-Aware Mechanism. IEEE Transactions on Circuits and Systems II: Express Briefs, 72:933-937, 2025.
  [BibTeX] [Abstract] [DOI] [PDF]

  This brief proposes OnSort, a parallel comparison free sorting architecture with O(n) time complexity, utilizing the SRAM structure to support large-scale datasets efficiently. The performance of existing comparison-free sorters is limited by uneven value distribution and variable element numbers. To address these issues, we introduce a parallel prefetching strategy to accelerate the indexing process and a sparse-aware mechanism to narrow the indexing search range. Furthermore, OnSort implements streaming execution through a pipelined design, thereby optimizing the previously overlooked latency of the counting phase. Experimental results show that, under the configuration of sorting 65,536 16-bit data elements, OnSort achieves a 1.97× speedup and a 22.6× throughput-to-area ratio compared to the existing design. The source code is available athttps://github.com/gmx-hub/OnSort.

  @article{gao2025onsort,
  title = {OnSort: An O(n) Comparison-Free Sorter for Large-Scale Dataset with Parallel Prefetching and Sparse-Aware Mechanism},
  author = {Muxuan Gao and Juntao Jiang and Shuangming Lei and Huifeng Wu and Jun Chen and Yong Liu},
  year = 2025,
  journal = {IEEE Transactions on Circuits and Systems II: Express Briefs},
  volume = 72,
  pages = {933-937},
  doi = {10.1109/TCSII.2025.3570797},
  abstract = {This brief proposes OnSort, a parallel comparison free sorting architecture with O(n) time complexity, utilizing the SRAM structure to support large-scale datasets efficiently. The performance of existing comparison-free sorters is limited by uneven value distribution and variable element numbers. To address these issues, we introduce a parallel prefetching strategy to accelerate the indexing process and a sparse-aware mechanism to narrow the indexing search range. Furthermore, OnSort implements streaming execution through a pipelined design, thereby optimizing the previously overlooked latency of the counting phase. Experimental results show that, under the configuration of sorting 65,536 16-bit data elements, OnSort achieves a 1.97× speedup and a 22.6× throughput-to-area ratio compared to the existing design. The source code is available athttps://github.com/gmx-hub/OnSort.}
  }

• Yuehao Huang, Liang Liu, Shuangming Lei, Yukai Ma, Hao Su, Jianbiao Mei, Pengxiang Zhao, Yaqing Gu, Yong Liu, and Jiajun Lv. CogDDN: A Cognitive Demand-Driven Navigation with Decision Optimization and Dual-Process Thinking. In Proceedings of the 33rd ACM International Conference on Multimedia (MM), page 5237–5246, 2025.
  [BibTeX] [Abstract] [DOI] [PDF]

  Mobile robots are increasingly required to navigate and interact within unknown and unstructured environments to meet human demands. Demand-driven navigation (DDN) enables robots to identify and locate objects based on implicit human intent, even when object locations are unknown. However, traditional data-driven DDN methods rely on pre-collected data for model training and decision-making, limiting their generalization capability in unseen scenarios. In this paper, we propose CogDDN, a VLM-based framework that emulates the human cognitive and learning mechanisms by integrating fast and slow thinking systems and selectively identifying key objects essential to fulfilling user demands. CogDDN identifies appropriate target objects by semantically aligning detected objects with the given instructions. Furthermore, it incorporates a dual-process decision-making module, comprising a Heuristic Process for rapid, efficient decisions and an Analytic Process that analyzes past errors, accumulates them in a knowledge base, and continuously improves performance. Chain of Thought (CoT) reasoning strengthens the decision-making process. Extensive closed-loop evaluations on the AI2Thor simulator with the ProcThor dataset show that CogDDN outperforms single-view camera-only methods by 15%, demonstrating significant improvements in navigation accuracy and adaptability. The project page is available at https://yuehaohuang.github.io/CogDDN/.

  @inproceedings{huang2025cog,
  title = {CogDDN: A Cognitive Demand-Driven Navigation with Decision Optimization and Dual-Process Thinking},
  author = {Yuehao Huang and Liang Liu and Shuangming Lei and Yukai Ma and Hao Su and Jianbiao Mei and Pengxiang Zhao and Yaqing Gu and Yong Liu and Jiajun Lv},
  year = 2025,
  booktitle = {Proceedings of the 33rd ACM International Conference on Multimedia (MM)},
  pages = {5237--5246},
  doi = {10.1145/3746027.3755832},
  abstract = {Mobile robots are increasingly required to navigate and interact within unknown and unstructured environments to meet human demands. Demand-driven navigation (DDN) enables robots to identify and locate objects based on implicit human intent, even when object locations are unknown. However, traditional data-driven DDN methods rely on pre-collected data for model training and decision-making, limiting their generalization capability in unseen scenarios. In this paper, we propose CogDDN, a VLM-based framework that emulates the human cognitive and learning mechanisms by integrating fast and slow thinking systems and selectively identifying key objects essential to fulfilling user demands. CogDDN identifies appropriate target objects by semantically aligning detected objects with the given instructions. Furthermore, it incorporates a dual-process decision-making module, comprising a Heuristic Process for rapid, efficient decisions and an Analytic Process that analyzes past errors, accumulates them in a knowledge base, and continuously improves performance. Chain of Thought (CoT) reasoning strengthens the decision-making process. Extensive closed-loop evaluations on the AI2Thor simulator with the ProcThor dataset show that CogDDN outperforms single-view camera-only methods by 15%, demonstrating significant improvements in navigation accuracy and adaptability. The project page is available at https://yuehaohuang.github.io/CogDDN/.}
  }