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Design of Capsule Robot for Magnetic Gastric Biopsy and Drug Administration

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Design of Capsule Robot for Magnetic Gastric Biopsy and Drug...

IEEE International Conference on Mechatronics and Automation

作者： Zhihui Xu Zongze Li Shoubin Liu Chengzhi Hu Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China School of Mechanical and Electrical Engineering Harbin Institute of Technology Shenzhen China

Capsule robots have emerged as a promising tool for the early diagnosis of gastrointestinal diseases. Unlike conventional gastroscopy, they offer the potential for minimally invasive or even non-invasive treatment, and can alleviate discomfort and pain in patients. However, current capsule robots have some limitations. For instance, they lack stomach tissue biopsy and drug administration functions, and the capsule robot used for biopsy does not consider the tolerance of gastrointestinal tissue. Similarly, the capsule robot used for drug administration does not account for the outlet speed of drug administration. This paper proposes a new biopsy method using a magnetically manipulated capsule robot and analyzes the principles governing the robot's manipulation during the biopsy and drug administration processes. Furthermore, an ANSYS gastric model was established and used to simulate and experimentally analyze damage to the gastric tissue model from different types of biopsy needles and their moving speeds. The appropriate type of biopsy needle and moving speed were determined to prevent damage to the gastric tissue. The study also used FLUENT to determine the appropriate thrust at the entrance of the drug cavity and the impact of the drug density on the drug outlet velocity to ensure complete adherence of the drug to the lesion.

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Effects of Robotic Expertise and Task Knowledge on Physical Ergonomics and Joint Efficiency in a Human-Robot Collaboration Task

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Effects of Robotic Expertise and Task Knowledge on Physical ...

IEEE-RAS International Conference on Humanoid Robots

作者： Matteo Pantano Arianna Curioni Daniel Regulin Tobias Kamps Dongheui Lee Technology department Siemens Aktiengesellschaft Munich Germany Department of Electrical and Computer Engineering Human-Centered Assistive Robotics Technical University of Munich Munich Germany Autonomous Systems Institut für Computertechnik Technische Uni-versitat Wien Vienna Austria Institute of Robotics and Mechatronics German Aerospace Center Wessling Germany

With the trend of low batch manufacturing, more and more small and medium enterprises are leaning towards adopting collaborative robots to increase productivity and improve operator well-being. However, robots are often programmed by robot experts rather than the operators effectively working with the machine. Therefore, operators may perceive low levels of task autonomy due to the unpredictability of robot motions. Empowering operators to make their own choices regarding robot motions can improve such feelings. However, research in cognitive science shows that allowing operators to decide on robot motions in a collaborative task could be influenced by how people consider their travel path and their partner's action. To better understand these relations, considering preliminary results from a previous study, we designed a user study where we tested operators' decisions in a collaborative task where groups of robot experts and novices were asked to choose their preferred task configuration among four possible options that differed in terms of operators' physical ergonomics and robot travel path. Our results show that robotic experts prioritize joint team work rather than their ergonomics. Contrarily, novices prioritize individual efforts and tend to reduce the robot travel path while keeping their travel path constant, maintaining good physical ergonomics. In conclusion, providing operators with task decision autonomy can be advantageous, but operator background must be considered to ensure optimal physical ergonomics and travel paths.

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells

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Application of Deep Learning Method to Estimate Bottomhole P...

IEEE International Symposium on Industrial Electronics (ISIE)

作者： Haibo Cheng Shichao Li Peng Zeng Valeriy Vyatkin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry.

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Distilling Tiny and Ultra-fast Deep Neural Networks for Autonomous Navigation on Nano-UAVs

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arXiv 2024年

作者： Lamberti, Lorenzo Bellone, Lorenzo Macan, Luka Natalizio, Enrico Conti, Francesco Palossi, Daniele Benini, Luca The Department of Electrical Electronic and Information Engineering University of Bologna Italy The Autonomous Robotics Research Center Technology Innovation Institute United Arab Emirates CNRS LORIA University of Lorraine France The Dalle Molle Institute for Artificial Intelligence USI-SUPSI Switzerland The Integrated Systems Laboratory ETH Zürich Switzerland

Nano-sized unmanned aerial vehicles (UAVs) are ideal candidates for flying Internet-of-Things smart sensors to collect information in narrow spaces. This requires ultra-fast navigation under very tight memory/computation constraints. The PULP-Dronet convolutional neural network (CNN) enables autonomous navigation running aboard a nano-UAV at 19 frame/s, at the cost of a large memory footprint of 320 kB - and with drone control in complex scenarios hindered by the disjoint training of collision avoidance and steering capabilities. In this work, we distill a novel family of CNNs with better capabilities than PULP-Dronet, but memory footprint reduced by up to 168× (down to 2.9 kB), achieving an inference rate of up to 139 frame/s;we collect a new open-source unified collision/steering 66 k images dataset for more robust navigation;and we perform a thorough in-field analysis of both PULP-Dronet and our tiny CNNs running on a commercially available nano-UAV. Our tiniest CNN, called Tiny-PULP-Dronet v3, navigates with a 100% success rate a challenging and never-seen-before path, composed of a narrow obstacle-populated corridor and a 180° turn, at a maximum target speed of 0.5 m/s. In the same scenario, the SoA PULP-Dronet consistently fails despite having 168× more parameters. Copyright © 2024, The Authors. All rights reserved.

关键词： Drones

Edge computing: A neural network implementation on an iot device 5

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Edge computing: A neural network implementation on an iot de...

5th Experiment at International Conference, exp.at 2019

作者： Barreto, Ricardo Lobo, Jorge Menezes, Paulo ISR-Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Portugal

ISBN: (纸本)9781728136370

This demonstration showcases the use of reconfigurable logic to implement edge computing for IoT devices able to provide specific information from raw data produced from some sensor, e.g. a camera or microphone, instead of the raw data itself. In what concerns the embedded processing capabilities, the focus is image processing using convolutional neuronal networks (CNN). This approach is clearly distinct from the current trends in IoT devices of using cloud computing to process the collected data. We intend a twist on the established paradigm and pursue an edge computing approach. Since we are targeting small and simple devices, we need some low power solution for the CNN computation. The demonstration will be made on a Terasic DE1 (SoC) reconfigurable system, with a field programmable gate array (FPGA), and hardwired ARM processor to build the IoT device. The collected data from the CNN computation, is transmitted using an IoT protocol to a broker. © 2019 IEEE.

关键词： Edge computing

3DSGrasp: 3D Shape-Completion for Robotic Grasp

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arXiv 2023年

作者： Mohammadi, Seyed S. Duarte, Nuno F. Dimou, Dimitris Wang, Yiming Taiana, Matteo Morerio, Pietro Dehban, Atabak Moreno, Plinio Bernardino, Alexandre Del Bue, Alessio Santos-Victor, José Vislab Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Department of Marine Electrical Electronic and Telecommunications Engineering University of Genoa Italy Genoa Italy Fondazione Bruno Kessler Trento Italy

Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose a novel grasping strategy, named 3DSGrasp, that predicts the missing geometry from the partial PCD to produce reliable grasp poses. Our proposed PCD completion network is a Transformer-based encoder-decoder network with an Offset-Attention layer. Our network is inherently invariant to the object pose and point's permutation, which generates PCDs that are geometrically consistent and completed properly. Experiments on a wide range of partial PCD show that 3DSGrasp outperforms the best state-of-the-art method on PCD completion tasks and largely improves the grasping success rate in real-world scenarios. The code and dataset will be made available upon acceptance. Copyright © 2023, The Authors. All rights reserved.

关键词： Network layers

Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angiogram Images Utilizing Soft Computing Techniques

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Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angio...

International Conference on Inventive Computation Technologies (ICICT)

作者： Anjaneya L. H K. Maheswari N. Revathi Siva. K. P Manzoore Elahi M. Soudagar Joshuva Arockia Dhanraj Department of Electrical and Electronics Engineering Bapuji Institute of Engineering and Technology Davangere Karnataka India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Hyderabad Telangana India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Techinical Sciences Chennai Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Padur Chennai Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

It is always a challenging task for radiologists to detect Cerebral Aneurysms (CA). Unenhanced magnetic resonance angiography (MRA) has become a popular screening tool because of its non-invasiveness, lack of contrast, and ionizing radiation. Because of its screening role, MRA requires a high recognition rate. This paper presents Machine Learning (ML) methods for detecting CA in MRA data. MRA scans of normal and aneurysms are collected and evaluated. This research study is divided into two sections: segmentation and classification. First, K-Means Clustering (KMC) is used for aneurysm segmentation from MR images. Second, image classification is done. For that, the appropriate features are retrieved using feature extraction and selection. Then Gray Level Run Length Matrix (GLRLM) is used for feature extraction and the Genetic Algorithm (GA) for feature selection. The GLRLM and GA features are provided independently to the ML models for training and testing. The ML models used in the research work are Artificial Neural Networks (ANN) and Modified Fuzzy C-Means (MFCM). The metrics are employed to evaluate the GLRM and GA features with ANN and MFCM. The combination of GA features with ANN produces the best results when compared to other combinations. The GA with ANN obtains an outstanding 98.33% accuracy, 100% specificity and precision, 96.67% sensitivity, and 98.31% F1 score.

关键词： Training Image segmentation Sensitivity Artificial neural networks Aneurysm Feature extraction Reliability

Vertical Federated Learning with Missing Features During Training and Inference

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arXiv 2024年

作者： Valdeira, Pedro Wang, Shiqiang Chi, Yuejie CMU IST United States IBM Research United States Department of Electrical and Computer Engineering Carnegie Mellon University United States Institute for Systems and Robotics Instituto Superior Técnico Portugal IBM T. J. Watson Research Center United States

Vertical federated learning trains models from feature-partitioned datasets across multiple clients, who collaborate without sharing their local data. Standard approaches assume that all feature partitions are available during both training and inference. Yet, in practice, this assumption rarely holds, as for many samples only a subset of the clients observe their partition. However, not utilizing incomplete samples during training harms generalization, and not supporting them during inference limits the utility of the model. Moreover, if any client leaves the federation after training, its partition becomes unavailable, rendering the learned model unusable. Missing feature blocks are therefore a key challenge limiting the applicability of vertical federated learning in real-world scenarios. To address this, we propose LASER-VFL, a vertical federated learning method for efficient training and inference of split neural network-based models that is capable of handling arbitrary sets of partitions. Our approach is simple yet effective, relying on the strategic sharing of model parameters and on task-sampling to train a family of predictors. We show that LASER-VFL achieves a O(1/√T) convergence rate for nonconvex objectives in general, O(1/T) for sufficiently large batch sizes, and linear convergence under the Polyak-Lojasiewicz inequality. Numerical experiments show improved performance of LASER-VFL over the baselines. Remarkably, this is the case even in the absence of missing features. For example, for CIFAR-100, we see an improvement in accuracy of 21.4% when each of four feature blocks is observed with a probability of 0.5 and of 12.2% when all features are observed. © 2024, CC BY.

关键词： Federated learning

A Sim-to-Real Deep Learning-based Framework for Autonomous Nano-drone Racing

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arXiv 2023年

作者： Lamberti, Lorenzo Cereda, Elia Abbate, Gabriele Bellone, Lorenzo Morinigo, Victor Javier Kartsch Barcis, Michal Barcis, Agata Giusti, Alessandro Conti, Francesco Palossi, Daniele Department of Electrical Electronic and Information Engineering University of Bologna Italy Dalle Molle Institute for Artificial Intelligence USI-SUPSI Switzerland Autonomous Robotics Research Center Technology Innovation Institute United Arab Emirates Integrated Systems Laboratory ETH Zürich Switzerland

Autonomous drone racing competitions are a proxy to improve unmanned aerial vehicles’ perception, planning, and control skills. The recent emergence of autonomous nano-sized drone racing imposes new challenges, as their ∼10 cm form factor heavily restricts the resources available onboard, including memory, computation, and sensors. This paper describes the methodology and technical implementation of the system winning the first autonomous nano-drone racing international competition: the "IMAV 2022 Nanocopter AI Challenge." We developed a fully onboard deep learning approach for visual navigation trained only on simulation images to achieve this goal. Our approach includes a convolutional neural network for obstacle avoidance, a sim-to-real dataset collection procedure, and a navigation policy that we selected, characterized, and adapted through simulation and actual in-field experiments. Our system ranked 1st among seven competing teams at the competition. In our best attempt, we scored 115 m of traveled distance in the allotted 5-minute flight, never crashing while dodging static and dynamic obstacles. Sharing our knowledge with the research community, we aim to provide a solid groundwork to foster future development in this field. Copyright © 2023, The Authors. All rights reserved.

关键词： Drones