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TIDE: a Timing-Deterministic and Efficient Executor for micro-ROS

TIDE: a Timing-Deterministic and Efficient Executor for micr...

IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social computing (CPSCom) Green computing and Communications (GreenCom)

作者： Zilong Wang Songran Liu Xu Jiang Dong Ji Yi Wang Key Laboratory of Intelligent Computing in Medical Image Northeastern University China

The micro-ROS is widely used to bridge the gap between resource-constrained microcontrollers and larger processors in ROS-based robotic applications. We found that there are some performance issues in micro-ROS because of the unreasonable design of the micro-ROS default RCLC Executor. To improve the timing predictability and run-time efficiency in micro-ROS, we have designed and implemented a new executor called the TIDE Executor, and conducted comprehensive experiments to evaluate the TIDE Executor. The experimental results demonstrate that our TIDE Executor exhibits superior performance compared to the default RCLC Executor.

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A Pulmonary Artery-Vein Separation algorithm based on the relationship between sub-trees 2020

A Pulmonary Artery-Vein Separation algorithm based on the re...

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The Fourth International Symposium on image computing and Digital Medicine

作者： Wenjun Tan Key Laboratory of Intelligent Computing in Medical Image Ministry of Education China

ISBN: (纸本)9781450389686

This article presents a combined algorithm of pulmonary artery-vein (A/V) separation considering both global and local information, including: the transformation of geometric graph, sub-tree separation, and A/V classification. During the process, geometric graphs are firstly built based on the centerlines; A separation of the adhesion points is performed by applying the information stored in the data structure of the nodes and links in the geometric graphs; Sub-trees are then combined according to the local and global information; The combined sub-trees are growing into vascular trees using 2D area growth method; And finally, classification of the arterial and venous vascular trees based on the knowledge of anatomy. An average accuracy of 85% has been achieved of the overall process with a processing time range between 40 s to 50 s, which indicates good performance and stability of this algorithm.

关键词： algorithm Artery-Vein Separation

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A Cross-Domain Ontology Semantic Representation Based on NCBI-BlueBERT Embedding

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Chinese Journal of Electronics 2022年第5期31卷 860-869页

作者： ZHAO Lingling WANG Junjie WANG Chunyu GUO Maozu Faculty of Computing Harbin Institute of Technology Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical University Beijing Key Laboratory of Intelligent Processing for Building Big Data School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

A common but critical task in biological ontologies data analysis is to compare the difference between ontologies. There have been numerous ontologybased semantic-similarity measures proposed in specific ontology domain, but it still remains a challenge for crossdomain ontologies comparison. An ontology contains the scientific natural language description for the corresponding biological aspect. Therefore, we develop a new method based on natural language processing(NLP) representation model bidirectional encoder representations from transformers(BERT) for cross-domain semantic representation of biological ontologies. This article uses the BERT model to represent the word-level of the ontologies as a set of vectors, facilitating the semantic analysis or comparing the biomedical entities named in an ontology or associated with ontology terms. We evaluated the ability of our method in two experiments: calculating similarities of pair-wise disease ontology and human phenotype ontology terms and predicting the pair-wise of proteins interaction. The experimental results demonstrated the comparative performance. This gives promise to the development of NLP methods in biological data analysis.

关键词： human phenotype ontology cross-domain semantic representation natural language description pair-wise disease ontology data analysis bidirectional encoder representations NLP protein interaction diseases proteins natural language processing ontologies (artificial intelligence) natural language processing representation neural nets bioinformatics semantic-similarity measures cross-domain ontology semantic representation semantic analysis NCBI-BlueBERT embedding biological ontology data analysis

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A segmentation method of airway from chest CT image based on VGG-Unet neural network

A segmentation method of airway from chest CT image based on...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Shao, Wei Wang, Zhe Zhang, Ziming Zhou, Qinghua Wang, Ruoyu Tan, Wenjun Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110189 China Affiliated Zhongshan Hospital of Dalian University Oncology Department Dalian116001 China

ISBN: (纸本)9781665468190

Segmenting the pulmonary airway in CT images to obtain its anatomical structure information can help doctors better understand the patient's condition and determine the treatment plan. However, due to the blurred boundary of the pulmonary airway in CT images, it has a high degree of overlap with the surrounding pulmonary parenchyma and other tissues, making segmentation difficult. In this paper, a method based on the separation of pulmonary airway in CT images is investigated. Based on the original U-net network, using the idea of migration learning, the U-net and VGG16 network with partially similar network structure are fused to form a VGG-Unet joint neural network, which not only accelerates the model training and image segmentation efficiency, but also ensures the accuracy of image segmentation. The experimental data source used CT images acquired from the partner hospital. The algorithm in this paper was used to segment and extract the CT images of airway of lungs with an accuracy of 86.52%, and the segmentation efficiency was high and stable. © 2022 IEEE.

关键词： image segmentation

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Adaptive delay-energy balanced partial offloading strategy in Mobile Edge computing networks

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Digital Communications and Networks 2023年第6期9卷 1310-1318页

作者： Shumei Liu Yao Yu Lei Guo Phee Lep Yeoh Branka Vucetic Yonghui Li School of Computer Science and Engineering Northeastern UniversityShenyang110819China Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyang110819China School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China School of Electrical and Information Engineering University of SydneySydneyNSW2006Australia

Mobile Edge computing(MEC)-based computation offloading is a promising application paradigm for serving large numbers of users with various delay and energy *** this paper,we propose a flexible MECbased requirement-adaptive partial offloading model to accommodate each user's specific preference regarding delay and energy *** address the dimensional differences between time and energy,we introduce two normalized parameters and then derive the computational overhead of processing *** from existing works,this paper considers practical variations in the user request patterns,and exploits a flexible partial offloading mode to minimize computation overheads subject to tolerable delay,task workload and power *** the resulting problem is non-convex,we decouple it into two convex subproblems and present an iterative algorithm to obtain a feasible offloading *** experiments show that our proposed scheme achieves a significant improvement in computation overheads compared with existing schemes.

关键词： Mobile edge computing(MEC) Delay Energy consumption Dynamic balance Partial computation offloading

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A transformer-based framework for temporal health event prediction with graph-enhanced representations

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Journal of Biomedical Informatics 2025年 166卷 104826页

作者： Liu, Tianci Liang, Lizhong Che, Chao Liu, Yunjiong Jin, Bo Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Liaoning Dalian116622 China School of Software Engineering Dalian University Liaoning Dalian116622 China School of Computer Science and Engineering Sun Yat-Sen University Guangdong Guangzhou510006 China Affiliated Hospital of Guangdong Medical University Guangdong Zhanjiang524013 China The Marine Biomedical Research Institute School of Ocean and Tropical Medicine Guangdong Medical University Guangdong Zhanjiang524023 China School of Innovation and Entrepreneurship Dalian University of Technology Liaoning Dalian116024 China

Objective: Deep learning approaches have demonstrated significant potential in predicting temporal health events in recent years. However, existing methods have not fully leveraged the complex interactions among comorbidities and have overlooked imbalances and temporal irregularities in admission records. Methods: This study proposes GLT-Net, a deep learning approach that combines Graph Learning with Transformer framework to tackle these challenges. GLT-Net begins by constructing a patient association graph to generate unique representations for each individual. At the same time, the hierarchical structure of diagnosis codes is utilized to pre-train the diagnosis code embeddings. Subsequently, a comorbidity association matrix is created to illustrate the relationships between comorbidities, and graph neural networks are employed to enhance the feature representations of diagnosis codes. Finally, a Transformer-Encoder framework captures the dependencies in historical admission records by incorporating time information. Results: We demonstrate our approach on two tasks in temporal health event predcition. Experimental results on real-world datasets show that GLT-Net outperforms baseline models in forecasting temporal health events. Additionally, a case study demonstrates the effectiveness of GLT-Net in predicting health events. Conclusion: Understanding progression patterns over time, comorbidity associations, and patient characterization is essential for predicting temporal health events. Our study provides new insights and methods for a deeper understanding of patient health status and disease trends. Moreover, our model can be extended to other data sources, enhancing its versatility. © 2025 Elsevier Inc.

关键词： Electronic health record

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Dual-Domain deep prior guided sparse-view CT reconstruction with multi-scale fusion attention

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Scientific Reports 2025年第1期15卷 1-20页

作者： Wu, Jia Lin, Jinzhao Jiang, Xiaoming Zheng, Wei Zhong, Lisha Pang, Yu Meng, Hongying Li, Zhangyong School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing 400065 China School of Medical Information and Engineering Southwest Medical University Luzhou 646000 China Chongqing Engineering Research Center of Medical Electronics and Information Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China Key Laboratory of Intelligent Computing for Advanced Manufacturing Chongqing University of Posts and Telecommunications Chongqing 400065 China School of Optoelectronic Engineering Chongqing University of Posts and Telecommunications Chongqing China Department of Electronic and Electrical Engineering College of Engineering Design and Physical Sciences Brunel University London Uxbridge UB8 3PH United Kingdom

Sparse-view CT reconstruction is a challenging ill-posed inverse problem, where insufficient projection data leads to degraded image quality with increased noise and artifacts. Recent deep learning approaches have shown promising results in CT reconstruction. However, existing methods often neglect projection data constraints and rely heavily on convolutional neural networks, resulting in limited feature extraction capabilities and inadequate adaptability. To address these limitations, we propose a Dual-domain deep Prior-guided Multi-scale fusion Attention (DPMA) model for sparse-view CT reconstruction, aiming to enhance reconstruction accuracy while ensuring data consistency and stability. First, we establish a residual regularization strategy that applies constraints on the difference between the prior image and target image, effectively integrating deep learning-based priors with model-based optimization. Second, we develop a multi-scale fusion attention mechanism that employs parallel pathways to simultaneously model global context, regional dependencies, and local details in a unified framework. Third, we incorporate a physics-informed consistency module based on range-null space decomposition to ensure adherence to projection data constraints. Experimental results demonstrate that DPMA achieves improved reconstruction quality compared to existing approaches, particularly in noise suppression, artifact reduction, and fine detail preservation. © The Author(s) 2025.

关键词： Deep prior Model-based optimization Multi-scale fusion attention Physics-informed consistency Sparse-view CT reconstruction

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A Hybrid Feature Selection Method Based on Binary Differential Evolution and Feature Subset Correlation for Microarray Data∗

A Hybrid Feature Selection Method Based on Binary Differenti...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Xie, Weidong Li, Wei Fang, Yushan Chi, Yuhuan Yu, Kun Northeastern University Computer Science and Engineering Shenyang China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Shenyang China Northeastern University Medicine and Bioinformation Engineering Shenyang China

ISBN: (纸本)9781665468190

Obtaining essential genes from microarray data that can diagnose diseases can be very useful for researchers to understand diseases and develop drugs. However, the high computational cost due to the 'curse of dimensionality' and the high redundancy among features limit the application of evolutionary algorithms to the feature selection problem for high-dimensional data. This paper proposes a two-stage hybrid feature selection method to address this problem. In the first stage, a simple and efficient filtering me thod is used to initially filter redundant features, reduce the feature dimensionality, and reduce the search space of the evolutionary algorithm in the second stage. In the second stage, we propose an improved differential evolution algorithm. We redesign the binary quantization of the differential evolution algorithm for the characteristics of microarray data and improve the algorithm's variation process to balance the algorithm's search efficiency and accuracy. In addition, we define the redundancy of feature subsets and add it to the fitness function to reduce the redundancy of the final feature subsets. The proposed method is compared with classical feature selection methods and advanced hybrid feature selection methods on eight publicly available microarray data, and the effectiveness and advancement of the proposed method are demonstrated. © 2022 IEEE.

关键词： Feature Selection

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MambaMIM: Pre-training Mamba with state space token interpolation and its application to medical image segmentation

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medical image Analysis 2025年 103卷 103606页

作者： Tang, Fenghe Nian, Bingkun Li, Yingtai Jiang, Zihang Yang, Jie Liu, Wei Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou215123 China Suzhou Institute for Advanced Research USTC Jiangsu Suzhou215123 China Department of Automation Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Jiangsu Provincial Key Laboratory of Multimodal Digital Twin Technology Jiangsu Suzhou215123 China State Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China Anhui Hefei230026 China

Recently, the state space model Mamba has demonstrated efficient long-sequence modeling capabilities, particularly for addressing long-sequence visual tasks in 3D medical imaging. However, existing generative self-supervised learning methods have not yet fully unleashed Mamba's potential for handling long-range dependencies because they overlook the inherent causal properties of state space sequences in masked modeling. To address this challenge, we propose a general-purpose pre-training framework called MambaMIM, a masked image modeling method based on a novel TOKen-Interpolation strategy (TOKI) for the selective structure state space sequence, which learns causal relationships of state space within the masked sequence. Further, MambaMIM introduces a bottom-up 3D hybrid masking strategy to maintain a masking consistency across different architectures and can be used on any single or hybrid Mamba architecture to enhance its multi-scale and long-range representation capability. We pre-train MambaMIM on a large-scale dataset of 6.8K CT scans and evaluate its performance across eight public medical segmentation benchmarks. Extensive downstream experiments reveal the feasibility and advancement of using Mamba for medical image pre-training. In particular, when we apply the MambaMIM to a customized architecture that hybridizes MedNeXt and Vision Mamba, we consistently obtain the state-of-the-art segmentation performance. The code is available at: https://***/FengheTan9/MambaMIM. © 2025

关键词： image segmentation

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An empirical study of translation rule extraction with multiple parsers

An empirical study of translation rule extraction with multi...

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23rd International Conference on Computational Linguistics, Coling 2010

作者： Xiao, Tong Zhu, Jingbo Zhang, Hao Zhu, Muhua Natural Language Processing Lab. Northeastern University China Key Laboratory of Medical Image Computing Ministry of Education China

Translation rule extraction is an important issue in syntax-based Statistical Machine Translation (SMT). Recent studies show that rule coverage is one of the key factors affecting the success of syntaxbased systems. In this paper, we first present a simple and effective method to improve rule coverage by using multiple parsers in translation rule extraction, and then empirically investigate the effectiveness of our method on Chinese- English translation tasks. Experimental results show that extracting translation rules using multiple parsers improves a string-to-tree system by over 0.9 BLEU points on both NIST 2004 and 2005 test corpora.

关键词： Extraction

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