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Automated Fabrication of Magnetic Soft Microrobots

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arXiv 2025年

作者： Clancy, Kaitlyn Xie, Siwen Smith, Griffin Onaizah, Onaizah School of Biomedical Engineering McMaster University Hamilton Canada Department of Computing and Software McMaster University Hamilton Canada

The advent of 3D printing has revolutionized many industries and has had similar improvements for soft robots. However, many challenges persist for these functional devices. Magnetic soft robots require the addition of magnetic particles that must be correctly oriented. There is a significant gap in the automated fabrication of 3D geometric structures with 3D magnetization direction. A fully automated 3D printer was designed to improve accuracy, speed, and reproducibility. This design was able to achieve a circular spot size (voxels) of 1.6mm in diameter. An updated optical system can improve the resolution to a square spot size of 50µm by 50µm. The new system achieves higher resolution designs as shown through magneto-mechanical simulations. Various microrobots including’worm’,’gripper’ and’zipper’ designs are evaluated with the new spot size. Copyright © 2025, The Authors. All rights reserved.

关键词： Microfabrication

Automated Fabrication of 3D Printed Magnetic Soft Robots With Programmable 3D Magnetizations

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Advanced Robotics Research

Advanced Robotics Research 2025年第1期1卷

作者： Jackson Sholdice Kaitlyn Clancy Karolina Teresinska Carl Castro Lauren Watson Onaizah Onaizah Department of Computing and Software McMaster University Hamilton Canada School of Biomedical Engineering McMaster University Hamilton Canada

Magnetic soft robots (MSRs) are a viable tool for many biomedical applications, such as targeted drug delivery and minimally invasive surgery, since they can be actuated remotely using external magnetic fields. These robots are developed by programming ferromagnetic domains with specific magnetizations using magnetic particles embedded in a flexible substrate. Existing fabrication methods rely on partially automated or manual processes, which limit production rates and realistic design iterations. To address these challenges, a fully automated workflow that translates robot simulations into an instruction set for a stereolithography 3D printer is presented. In this process, a rotating permanent magnet is used to program 3D magnetizations by reorienting hard magnetic particles within a photosensitive resin. Geometric resolutions of 1.6 mm are achieved with a layer height of 0.1 mm, enabling the creation of structures 14 layers thick. Beam bending tests identify an optimal 6:1 resin-to-magnetic particle mass ratio, yielding a maximum deflection angle of 80°. Demonstrated applications include rolling and climbing locomotion in a maze and independent control of each arm in a multiarmed robot. By enabling fast, repeatable production of MSRs within 30 min, this automated system shortens the feedback loop from design to application, advancing their potential as a biomedical tool.

关键词： 3D printers fabrication soft magnetic robots soft robotics stereolithography stimuli responsive materials

Managing Service Dependency for Cloud Reliability: The Industrial Practice

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arXiv 2022年

作者： Yang, Tianyi Li, Baitong Shen, Jiacheng Su, Yuxin Yang, Yongqiang Lyu, Michael R. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong School of Software Engineering Sun Yat-Sen Univeristy Zhuhai China Computing and Networking Innovation Lab Huawei Cloud Shenzhen China

Interactions between cloud services result in service dependencies. Evaluating and managing the cascading impacts caused by service dependencies is critical to the reliability of cloud systems. This paper summarizes the dependency types in cloud systems and demonstrates the design of the Dependency Management System (DMS), a platform for managing the service dependencies in the production cloud system. DMS features the full-lifecycle support for service reliability (i.e., initial service deployment, service upgrade, proactive architectural optimization, and reactive failure mitigation) and refined characterization of the intensity of dependencies. © 2022, CC BY.

关键词： software reliability

A PREPROCESSING PERSPECTIVE FOR QUANTUM MACHINE LEARNING CLASSIFICATION ADVANTAGE USING NISQ ALGORITHMS

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arXiv 2022年

作者： Mancilla, Javier Pere, Christophe Stafford Computing LLC 16192 Coastal Highway LewesDEDE 19958 United States INTRIQ Department of Computer Science and Software Engineering Université Laval Québec Canada

Quantum Machine Learning (QML) hasn’t yet demonstrated extensively and clearly its advantages compared to the classical machine learning approach. So far, there are only specific cases where some quantum-inspired techniques have achieved small incremental advantages, and a few experimental cases in hybrid quantum computing are promising considering a mid-term future (not taking into account the achievements purely associated with optimization using quantum-classical algorithms). The current quantum computers are noisy and have few qubits to test, making it difficult to demonstrate the current and potential quantum advantage of QML methods. This study shows that we can achieve better classical encoding and performance of quantum classifiers by using Linear Discriminant Analysis (LDA) during the data preprocessing step. As a result, Variational Quantum Algorithm (VQA) shows a gain of performance in balanced accuracy with the LDA technique and outperforms baseline classical classifiers. © 2022, CC BY.

关键词： Machine learning

A Novel Logic-Based Adaptive Monitoring for Composite Edge Services

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A Novel Logic-Based Adaptive Monitoring for Composite Edge S...

IEEE International Conference on Web Services (ICWS)

作者： Deng Zhao Zhangbing Zhou Xiao Xue Jin Diao Sami Yangui Bo Liu Walid Gaaloul School of Information Engineering China University of Geosciences (Beijing) Beijing China Department of Computer Science TELECOM SudParis Evry France College of Intelligence and Computing Tianjin University Tianjin China LAAS-CNRS University of Toulouse Toulouse France School of Software Engineering Beijing University of Technology Beijing China

With the wide-adoption of edge computing, the functionalities of Internet of Things (IoT) devices can be encapsulated as edge services, to facilitate domain applications through edge service compositions. Considering the capacity-fluctuating and resource-varying of IoT devices, edge service monitoring is essential to guarantee the healthy of their compositions at runtime. Current techniques focus mostly on the monitoring of atomic edge services, which, however, are inadequate for that of inter-and composite services. Besides, constraints to be monitored are usually pre-specified, although certain parameters may have to be adapted online according to execution context. To address these challenges, this paper proposes a novel logic-based adaptive monitoring mechanism, to achieve the interpretation of temporal constraints and time-dependent QoS constraints upon intra-, inter-, and composite services. Leveraging our proposed Compositional Signal Temporal Logic (CSTL) with extended compositional modalities and online parameter settings, constraints can be converted to CSTL formulae, and QoS variations and temporal violations are interpreted qualitatively and quantitatively at runtime. Extensive experiments are conducted upon publicly-available datasets, and evaluation results demonstrate that our CSTL performs better than baseline techniques in terms of expressiveness, applicability, and robustness.

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DSD-MatchingNet:Deformable sparse-to-dense feature matching for learning accurate correspondences

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Virtual Reality & Intelligent Hardware 2022年第5期4卷 432-443页

作者： Yicheng ZHAO Han ZHANG Ping LU Ping LI Enhua WU Bin SHENG Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China ZTE Corporation Shenzhen 518057China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen 518057China Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China School of Design The Hong Kong Polytechnic UniversityHong Kong 999077China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

Background Exploring correspondences across multiview images is the basis of various computer vision ***,most existing methods have limited accuracy under challenging *** To learn more robust and accurate correspondences,we propose DSD-MatchingNet for local feature matching in this ***,we develop a deformable feature extraction module to obtain multilevel feature maps,which harvest contextual information from dynamic receptive *** dynamic receptive fields provided by the deformable convolution network ensure that our method obtains dense and robust ***,we utilize sparse-to-dense matching with symmetry of correspondence to implement accurate pixel-level matching,which enables our method to produce more accurate *** Experiments show that our proposed DSD-MatchingNet achieves a better performance on the image matching benchmark,as well as on the visual localization ***,our method achieved 91.3%mean matching accuracy on the HPatches dataset and 99.3%visual localization recalls on the Aachen Day-Night dataset.

关键词： Image matching Deformable convolution network Sparse-to-dense matching

MDAU-Net: A Multi-Scale U-Net with Dual Attention Module for Pavement Crack Segmentation

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MDAU-Net: A Multi-Scale U-Net with Dual Attention Module for...

International Conference on Intelligent System and Knowledge engineering, ISKE

作者： Zaid Al-Huda Bo Peng Mugahed A. Al-antari Riyadh Nazar Ali Algburi Radhwan A. A. Saleh Khaled Moghalles School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Department of Artificial Intelligence College of Software & Convergence Technology Sejong University Seoul Korea Aeronautical Technical engineering College of Technical Engineering Al-Farahidi University Baghdad Iraq Department of Mechatronics Engineering Kocaeli University Kocaeli Turkey School of Information Science and Technology Southwest Jiaotong University Chengdu China

Crack segmentation is a critical and demanding task in pavement engineering. Recent studies using Convolutional Neural Networks (CNNs) or vision Transformer (ViT) for crack segmentation have shown promising results, but there are still challenges due to the inhomogeneous crack intensity, complex pavement environments, limited labeled training datasets, and noise in most crack images. These challenges make it difficult to distinguish cracks from noise. To address these challenges, we propose MDAU-Net, a multi-scale U-Net with dual attention module based on an encoder-decoder architecture for pavement crack segmentation. MDAU-Net exploits multi-scale input to preserve spatial awareness in all encoder layers, and the dual attention module (DAM) combines global average pooling (GAP) attention module and position attention (PC) module to extract more comprehensive and discriminative features for crack boundary recognition and improve crack segmentation effectiveness. For optimizing the segmentation network and capturing detailed crack information, a hybrid weighted segmentation loss function is developed. Comprehensive experimental evaluations on two benchmark datasets (DeepCrack and Crack500) demonstrate that MDAU-Net achieves state-of-the-art segmentation performance. These results show that MDAU-Net is a promising approach for pavement crack segmentation and can be used to improve the safety and durability of pavement infrastructure.

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FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection

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FishEye8K: A Benchmark and Dataset for Fisheye Camera Object...

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Gochoo, Munkhjargal Otgonbold, Munkh-Erdene Ganbold, Erkhembayar Hsieh, Jun-Wei Chang, Ming-Ching Chen, Ping-Yang Dorj, Byambaa Al Jassmi, Hamad Batnasan, Ganzorig Alnajjar, Fady Abduljabbar, Mohammed Lin, Fang-Pang United Arab Emirates University Department of Computer Science and Software Engineering United Arab Emirates United Arab Emirates University Emirates Center for Mobility Research United Arab Emirates National Yang Ming Chiao Tung University College of AI and Green Energy Taiwan University at Albany - State University of New York NY United States National Yang Ming Chiao Tung University Department of Computer Science Taiwan Mongolian University of Science and Technology Mongolia National Center for High-Performance Computing Taiwan

ISBN: (纸本)9798350302493

With the advance of AI, road object detection has been a prominent topic in computer vision, mostly using perspective cameras. Fisheye lens provides omnidirectional wide coverage for using fewer cameras to monitor road intersections, however with view distortions. To our knowledge, there is no existing open dataset prepared for traffic surveillance on fisheye cameras. This paper introduces an open FishEye8K benchmark dataset for road object detection tasks, which comprises 157K bounding boxes across five classes (Pedestrian, Bike, Car, Bus, and Truck). In addition, we present benchmark results of State-of-The-Art (SoTA) models, including variations of YOLOv5, YOLOR, YOLO7, and YOLOv8. The dataset comprises 8,000 images recorded in 22 videos using 18 fisheye cameras for traffic monitoring in Hsinchu, Taiwan, at resolutions of 1080×1080 and 1280×1280. The data annotation and validation process were arduous and time-consuming, due to the ultra-wide panoramic and hemispherical fisheye camera images with large distortion and numerous road participants, particularly people riding scooters. To avoid bias, frames from a particular camera were assigned to either the training or test sets, maintaining a ratio of about 70:30 for both the number of images and bounding boxes in each class. Experimental results show that YOLOv8 and YOLOR outperform on input sizes 640×640 and 1280×1280, respectively. The dataset will be available on the GitHub link with PASCAL VOC, MS COCO, and YOLO annotation formats. The FishEye8K benchmark will provide significant contributions to the fisheye video analytics and smart city applications. © 2023 IEEE.

关键词： Object detection

SceneGATE: Scene-Graph Based Co-Attention Networks for Text Visual Question Answering

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arXiv 2022年

作者： Cao, Feiqi Luo, Siwen Nunez, Felipe Wen, Zean Poon, Josiah Han, Soyeon Caren School of Computer Science Faculty of Engineering University of Sydney CamperdownNSW2006 Australia Department of Computer Science and Software Engineering School of Physics Maths and Computing University of Western Australia CrawleyWA6009 Australia

Visual Question Answering (VQA) models fail catastrophically on questions related to the reading of text-carrying images. However, TextVQA aims to answer questions by understanding the scene texts in an image-question context, such as the brand name of a product or the time on a clock from an image. Most TextVQA approaches focus on objects and scene text detection, which are then integrated with the words in a question by a simple transformer encoder. The focus of these approaches is to use shared weights during the training of a multi-modal dataset, but it fails to capture the semantic relations between an image and a question. In this paper, we proposed a Scene Graph-Based Co-Attention Network (SceneGATE) for TextVQA, which reveals the semantic relations among the objects, the Optical Character Recognition (OCR) tokens and the question words. It is achieved by a TextVQA-based scene graph that discovers the underlying semantics of an image. We create a guided-attention module to capture the intra-modal interplay between the language and the vision as a guidance for inter-modal interactions. To permit explicit teaching of the relations between the two modalities, we propose and integrate two attention modules, namely a scene graph-based semantic relation-aware attention and a positional relation-aware attention. We conduct extensive experiments on two widely used benchmark datasets, Text-VQA and ST-VQA. It is shown that our SceneGATE method outperforms existing ones because of the scene graph and its attention modules. Copyright © 2022, The Authors. All rights reserved.

关键词： Neural networks