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CA-Res2UNet++: a Deep Residual UNet Based Method for Brain Tumor Segmentation in Multimodal MRI 2

CA-Res2UNet++: a Deep Residual UNet Based Method for Brain T...

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2nd International conference on Biomedical and Intelligent Systems, IC-BIS 2023

作者： Hu, Yujiao Chen, Weiting Wu, Wenjing Long, Jianghao MOE Research Center of Software / Hardware Co-Design Engineering Shanghai Key Laboratory of Trustworthy Computing East China Normal University China

ISBN: (纸本)9781510666733

In recent years, segmentation of the multimodal brain tumor image puts forward high requirements for performance. To meet the accuracy requirements, we propose a multimodal brain tumor image segmentation method based on UNet and deep residual learning, which can make full use of multimodal information in order to obtain details. The model achieves the information extraction and integration of multimodal images at different scales by introducing a multiscale feature extraction module, a coordinate attention module, and a dense atrous spatial pyramid pooling module. The results show that the proposed method can fully exploit the multi-scale context information of multimodal images, alleviate the problem of blurred edges in segmentation regions, and achieve good performance on popular evaluation metrics. © 2023 SPIE.

关键词： Magnetic resonance imaging

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Multi-scale Transformation Based Image Representation and Analysis of Amplitude-integrated EEG 2

Multi-scale Transformation Based Image Representation and An...

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2nd International conference on Biomedical and Intelligent Systems, IC-BIS 2023

作者： Wu, Wenjing Chen, Weiting Hu, Yujiao MOE Research Center of Software / Hardware Co-Design Engineering Shanghai Key Laboratory of Trustworthy Computing East China Normal University China

ISBN: (纸本)9781510666733

Amplitude-integrated electroencephalography (aEEG) is widely adopted for recognizing neonatal neurological disorders in clinics. Previous work has mainly analyzed aEEGs from a time series perspective, while clinicians are more concerned about images. This paper studies the efficacy of image representations in aEEG interpretation. To this end, we employ Amplitude-frequency contour map (AFCM) to express information on local amplitude distributions and overall temporal trend. During its generation, Multi-scale Box-cox transformation is introduced to flexibly control the degree of amplitude compression and capture details in different amplitude regions. Moreover, one- and two-dimensional features are extracted and combined to enhance model predictions. The experimental results show the potential of image representation for aEEG recognition. This method can provide new ideas to analyze time series signals like aEEGs from the perspective of combining time series and image. © 2023 SPIE.

关键词： Electroencephalography

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Satellite Federated Fine-Tuning for Foundation Models: Architecture design and System Optimization

Satellite Federated Fine-Tuning for Foundation Models: Archi...

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2024 IEEE Global communications conference, GLOBEcoM 2024

作者： Zhu, Yan Yang, Peng Zhu, Jingyang Wen, Dingzhu Wang, Ting Zhou, Yong Shi, Yuanming Jiang, Chunxiao East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China

ISBN: (纸本)9798350351255

With the surge in the number of low earth orbit (LEO) satellites, continuous research has emerged on using satellite data to train artificial intelligence models. On one hand, traditional centralized training on the ground is not feasible due to privacy concerns and limited bandwidth for downloading raw satellite data. On the other hand, due to the limited energy and computational capability of satellites, training directly on satellites suffers from prolonged latency, especially for large models. To alleviate these issues, we propose a novel satellite-ground collaborative federated fine-tuning architecture, where ground stations (GSs) and satellites collaboratively train a global model without the need for data downloads. In this proposed architecture, satellites serve as edge devices and the ground server serves as a coordinator. However, the short satellite-ground communication windows caused by the high mobility of satellites and the substantial intra-orbit data transmission bring special challenges to the transmission process of federated edge learning. To tackle these challenges, we carefully design the satellite-ground collaborative fine-tuning architecture and utilize an optimized ring all-reduce algorithm and network flow algorithm to enhance the intra-orbit and ground-satellite transmissions, respectively. Experimental results demonstrate that our proposed architecture significantly reduces the training time by 40% compared to training solely on satellite. © 2024 IEEE.

关键词： Satellite ground stations

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Archer: Adaptive Memory compression with Page-Association-Rule Awareness for High-Speed Response of Mobile Devices 23

Archer: Adaptive Memory Compression with Page-Association-Ru...

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23rd USENIX conference on File and Storage Technologies, FAST 2025

作者： Li, Changlong Zhu, Zongwei Wang, Chao Liu, Fangming Xu, Fei Sha, Edwin H.-M. Zhou, Xuehai School of Computer Science and Technology East China Normal University China Jianghuai Advance Technology Center Hefei230026 China MoE Engineering Research Center of Hardware/Software Co-Design Technology and Application China School of Software Engineering University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Huazhong University of Science and Technology China Peng Cheng Laboratory China

ISBN: (纸本)9781939133458

In mobile systems, memory can be compressed page-by-page to save space. This approach is widely adopted because memory data is accessed by page. However, this paper shows that the system response speed is significantly limited by page-grained compression. In this paper, we observe that approximately a quarter of anonymous memory pages are highly correlated, even though the association is implicit. Inspired by this, we propose Archer, an association-rule-aware memory compression framework in mobile systems. Archer demonstrates that memory in mobile devices should be compressed using flexible granularity, rather than relying solely on traditional page compression. To further integrate association-rule mining techniques into system design, we redesign the LRU mechanism and propose an adaptive memory compression region. Experimental results show that the average app launching speed is 1.55x faster when enabling Archer, and the average photographic speed and frame rate increase by 1.42x and 1.31x, respectively, compared to the state-of-the-art. © 2025 FAST. All Rights Reserved.

关键词： Portable equipment

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OpenNet: Incremental Learning for Autonomous Driving Object Detection with Balanced Loss

OpenNet: Incremental Learning for Autonomous Driving Object ...

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2023 IEEE International conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Zezhou Cao, Guitao Xi, Xidong Wang, Jiangtao East China Normal University MoE Engineering Research Center of SW/HW Co-design Technology and Application Shanghai China Software Engineering Institute East China Normal University Shanghai China

ISBN: (纸本)9798350337020

Automated driving object detection has always been a challenging task in computer vision due to environmental uncertainties. These uncertainties include significant differences in object sizes and encountering the class unseen. It may result in poor performance when traditional object detection models are directly applied to automated driving detection. Because they usually presume fixed categories of common traffic participants, such as pedestrians and cars. Worsely, the huge class imbalance between common and novel classes further exacerbates performance degradation. To address the issues stated, we propose OpenNet to moderate the class imbalance with the Balanced Loss, which is based on Cross Entropy Loss. Besides, we adopt an inductive layer based on gradient reshaping to fast learn new classes with limited samples during incremental learning. To against catastrophic forgetting, we employ normalized feature distillation. By the way, we improve multi-scale detection robustness and unknown class recognition through FPN and energy-based detection, respectively. The Experimental results upon the coDA dataset show that the proposed method can obtain better performance than that of the existing methods. © 2023 IEEE.

关键词： Class Imbalance Incremental Learning Meta-Learning Open World Object Detection

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NL2CTL: Automatic Generation of Formal Requirements Specifications via Large Language Models 1

引用

25th International conference on Formal engineering Methods, ICFEM 2024

作者： Zhao, Mengyan Tao, Ran Huang, Yanhong Shi, Jianqi Qin, Shengchao Yang, Yang National Trusted Embedded Software Engineering Technology Research Center East China Normal University Shanghai China Hardware/Software Co-Design Technology and Application Engineering Research Center East China Normal University Shanghai China Guangzhou Institute of Technology Xidian University Xi’an China ICTT and ISN Laboratory Xidian University Xi’an China Software Engineering Institute East China Normal University Shanghai China

ISBN: (数字)9789819606177

ISBN: (纸本)9789819606160

Reducing the gap between natural language requirements and precise formal specifications is a critical task in requirements engineering. In recent years, requirement engineering is becoming increasingly complex alongside the growing intricacy of system engineering. Most requirements are expressed in natural language, which can be incomplete and ambiguous. However, formal languages with strict semantics can accurately represent certain temporal logic properties and allow for automated verification and analysis. This often limits the application of verification techniques, as writing formal specifications is a manual, error-prone, and time-consuming task. To address this, this paper proposes a framework that leverages Large Language Models (LLMs) to achieve automated conversion of natural language requirements to computation Tree Logic (CTL). To address the issue of dataset scarcity, we leveraged the interactive and generative capabilities of LLMs. By constructing a random generation algorithm and utilizing prompt engineering, we generated an NL-CTL dataset using LLMs. The generated dataset was then used to fine-tune the T5-Large model, enhancing its generative capacity. To improve generalization, this paper proposes the use of the GPT-3.5 Atomic Proposition (AP) Recognition method, which eliminates the constraints of using the framework across different domains. A series of experimental evaluations showed that the fine-tuned LLM achieved an accuracy of 46.4%, whereas the LLM with few-shot learning using only prompt engineering achieved only 2% accuracy, demonstrating the feasibility of this approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Semantics

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MACEdge: Real-Time Video Analytics Based on Multi-Access collaborative Edge computing

MACEdge: Real-Time Video Analytics Based on Multi-Access Col...

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IEEE International conference on Systems, Man and Cybernetics

作者： Dian Zhong Minghua Zhu Binbin Huang MOE Engineering Research Center for Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China

Video analysis typically requires a significant amount of computing resources and energy. Traditional cloud-based video analysis relies on concentrating computing resources in the cloud, which puts a tremendous load on network bandwidth and introduces poor user experience due to latency. Edge computing offers a solution by offloading a portion of the video analysis tasks to the edge, significantly reducing the pressure on bandwidth and latency. However, the fixed video configurations (resolution, frame rate, etc.) uploaded to the edge may not be optimal for specific situations. Moreover, using a low video configuration makes it difficult for edge video analysis systems to recognize environmental changes, while using a high video configuration poses challenges to latency and bandwidth. Therefore, in this paper, we propose the MACEdge-an edge video analysis framework by leveraging the multi-access capability of the edge gateway to enhance edge perception by accessing heterogeneous sensors. We offer an algorithm based on Q Learning for dynamic adaptive adjustment of sensor threshold and video configurations. In experiments using real-world data, our collaborative solution improved accuracy and reduced high bandwidth costs compared to other video configuration adjustment benchmarks.

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QueryEdge: Real-Time Muti-Video Query in Edge-Cloud collaborative System

QueryEdge: Real-Time Muti-Video Query in Edge-Cloud Collabor...

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IEEE International conference on Systems, Man and Cybernetics

作者： Jihua Zhong Yannian Niu Minghua Zhu MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China

The real-time query of surveillance video plays a significant role in many fields such as public safety, smart city, and abnormality monitoring. However, with the exponential growth of surveillance video data, traditional cloud-based intelligent video processing faces significant challenges in terms of latency and bandwidth, while the pure edge computing approach is deficient in query accuracy due to its lack of computational power. Existing edge cloud collaboration approaches, such as SurveilEdge, focus on real-time target queries within a single video stream and do not show promising results during target queries across multiple video streams. For this reason, this paper proposes Query Edge, an edge-cloud collaborative real-time query system for multiple video streams. Specifically, we design a real-time query system based on an edge-cloud collaboration framework to achieve highly accurate and low-latency target query services in multiple video streams. In addition, we introduce a prioritization mechanism and a load-balancing strategy in the query task scheduling process to further improve query efficiency. The evaluation proves that QueryEdge has a significant improvement in query latency and bandwidth consumption compared with pure cloud computing, pure edge computing, and SurveilEdge.

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Slim-MY: A Lightweight Network for Power Line Defect Detection with FPGA Acceleration

Slim-MY: A Lightweight Network for Power Line Defect Detecti...

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International conference on Parallel and Distributed Systems (ICPADS)

作者： Xiaoyang Zhang Wenjie Chen Zihao Zhou Zhuowen Duan MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China

The power line is a crucial component of the power system. However, long-term exposure to the natural environment can lead to various defects, such as burrs, corrosion, and corona. These defects seriously threaten the safe operation of the power system. Traditional manual inspections and image processing methods are time-consuming, labor-intensive, and have lower accuracy. In light of these, we propose a lightweight network called Slim-MY. It utilizes lightweight feature extraction and fusion modules, significantly reducing computational and parameter overhead while ensuring defect detection accuracy. Furthermore, to accelerate the post-processing process, we propose the FP-NMS algorithm. It parallelizes the sorting and overlapping box removal parts of the NMS algorithm, making it more suitable for deployment on devices that support parallel computing, such as FPGA. By testing on our dataset of power line defects, the Slim-MY network achieves a detection accuracy and computation of 92.7% and 1.1 GFLOPs respectively, which outperforms most other networks. By accelerating on the KV260 FPGA, the Slim-MY network achieves an inference speed of 94 fps, and the FP-NMS algorithm’s execution speed is 14.8 times that of the GTX 1080Ti. It indicates the feasibility and efficiency of the method we propose in power line defect detection tasks.

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LWSA: A Learning-Based Workflow Scheduling Algorithm for Energy-Efficient UAV Delivery System

LWSA: A Learning-Based Workflow Scheduling Algorithm for Ene...

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International conference on Parallel and Distributed Systems (ICPADS)

作者： Jiali Wang Yutong Ye Ting Wang Mingsong Chen MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China

Due to their fast speed and easy deployment, Unmanned Aerial Vehicles (UAVs) have been widely used across various sectors, such as earthquake rescue, medical assistance, and smart agriculture. However, UAVs in delivery networks face significant challenges due to limited battery life and computational capabilities, particularly for tasks that entail intensive computing workflows. In this context, Multi-access Edge computing (MEC), which provides computing resources in close proximity to mobile terminal devices, has emerged as a promising solution. UAVs can offload computing tasks to MEC resources across diverse Internet of Things (IoT) environments. Although task offloading can enhance their task processing capability, it simultaneously brings additional costs, encompassing data transmission time and energy consumption. To address these issues, this paper proposes a novel workflow scheduling method based on the Proximal Policy Optimization (PPO) algorithm, aimed at optimizing UAV energy consumption within MEC environments. The proposed approach establishes a learning-based workflow scheduling strategy harnessing the adaptability of the PPO algorithm to manage dynamic and intricate scenarios, which facilitates efficient task allocation to optimal computational resources while accounting for flight time constraints. Extensive experiments conducted on various well-known scientific workflow benchmarks in real-world UAV delivery networks validate the effectiveness of our method. compared with state-of-the-art methods, our approach significantly reduces UAV energy consumption and task completion time, simultaneously increasing UAV’s effective payload capacity.

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