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Innovation and Intelligence for informatics, Computing, and Technologies (3ICT), International Conference o

作者： Rania Bouzid Jyotindra Narayan Hassène Gritli Higher Institute of Information and Communication Technologies University of Carthage RISC Laboratory (LR16ES07) Tunis Tunisia Mechatronics and Robotics Laboratory Department of Mechanical Engineering Indian Institute of Technology Guwahati Guwahati India Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Tunis Tunisia

Determining an end-effector position and orientation, known as forward kinematics (FK), is a crucial aspect of robotic manipulation. This research addresses the FK problem using a Feedforward Backpropagation Artificial Neural Network (FFBP-ANN) and examines the network’s performance with different hyperparameters. The input of the FFBP-ANN is primarily formed using three different datasets: fixed-step values, random values, and sinusoidal-signal based values, for a 2-DOF robotic manipulator. The output of the FFBP-ANN is evaluated using FK formulation of the robot manipulator. Thereafter, the FFBP-ANN is trained utilizing the input-output training dataset using The Neural Network Toolbox for MATLAB. The network’s potential is investigated by varying the critical hyperparameters, including the number of hidden neurons and the number of hidden layers. Moreover, the effectiveness of the proposed FFBP-ANN is investigated with three different training optimizers including the utilization of Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient optimization algorithms. During training, by adjusting the network’s weights and biases iteratively, the backpropagation technique allows the network to effectively learn and comprehend the relationship between joint positions and Cartesian coordinates. The comparative results offer valuable insights into the strengths and limitations of different ANN architectures when applied to solving FK problems. In this way, this study focuses on exploring the application of the FFBP-ANN in resolving FK challenges and assisting in the identification of appropriate network types for path-planning tasks.

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Online 4D Ultrasound-Guided Robotic Tracking Enables 3D Ultrasound Localisation Microscopy with Large Tissue Displacements

arXiv

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arXiv 2024年

作者： Yan, Jipeng Tan, Qingyuan Kawara, Shusei Zhu, Jingwen Wang, Bingxue Toulemonde, Matthieu Liu, Honghai Tan, Ying Tang, Meng-Xing Ultrasound Lab for Imaging and Sensing Department of Bioengineering Imperial College London LondonSW7 2AZ United Kingdom the State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150001 China the Department of Bioengineering Imperial College London LondonSW7 2AZ United Kingdom the School of Computing University of Portsmouth PortsmouthPO1 2UP United Kingdom the Department of Mechanical Engineering University of Melbourne ParkvilleVIC3010 Australia

Super-Resolution Ultrasound (SRUS) imaging through localising and tracking microbubbles, also known as Ultrasound Localisation Microscopy (ULM), has demonstrated significant potential for reconstructing microvasculature and flows with sub-diffraction resolution in clinical diagnostics. However, imaging organs with large tissue movements, such as those caused by respiration, presents substantial challenges. Existing methods often require breath holding to maintain accumulation accuracy, which limits data acquisition time and ULM image saturation. To improve image quality in the presence of large tissue movements, this study introduces an approach integrating high-frame-rate ultrasound with online precise robotic probe control. Tested on a microvasculature phantom with translation motions up to 20 mm, twice the aperture size of the matrix array used, our method achieved real-time tracking of the moving phantom and imaging volume rate at 85 Hz, keeping majority of the target volume in the imaging field of view. ULM images of the moving cross channels in the phantom were successfully reconstructed in post-processing, demonstrating the feasibility of super-resolution imaging under large tissue motions. This represents a significant step towards ULM imaging of organs with large motion. © 2024, CC BY-NC-ND.

关键词： Image enhancement

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LMI-Based Robust Control of a Knee Rehabilitation Exoskeleton Robot Using a Nonlinear Composite Controller

LMI-Based Robust Control of a Knee Rehabilitation Exoskeleto...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Sahar Jenhani Hassène Gritli Jyotindra Narayan Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis (ENIT) University of Tunis El Manar Tunis Tunisia RISC Lab (LR16ES07) ENIT University of Tunis El Manar Tunis Tunisia ISTIC University of Carthage Tunis Tunisia Department of Mechanical Engineering Indian Institute of Technology Patna Patna India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The present study proposes a control approach designed to achieve robust stabilization of a 1-DoF knee joint exoskeleton system intended for rehabilitation purposes. The proposed method combines a linear state-feedback control law with a nonlinear control law to address the nonlinear dynamics of the knee-joint exoskeleton, addressing parameter uncertainties and external disturbances. The model simplifies the nonlinear function by assuming it to be bounded by linear constraints. By considering two different expressions of the term existing in the nonlinear controller, two design methods for LMI conditions are developed. These methods use a quadratic Lyapunov function to ensure stability and robustness. The controller’s performance is assessed through simulations under external disturbances, and a comparative analysis confirms its efficacy in maintaining stability despite parametric uncertainties.

关键词： Robust control Uncertain systems Technological innovation Uncertainty Exoskeletons Position control Stability analysis Robustness Nonlinear dynamical systems Robots

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Human activity recognition models in ontology networks

arXiv

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arXiv 2021年

作者： Buoncompagni, Luca Kareem, Syed Yusha Mastrogiovanni, Fulvio The Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Via Opera Pia 13 Genoa16145 Italy Teseo s.r.l. Piazza Montano 2a Genoa16151 Italy

We present Arianna+, a framework to design networks of ontologies for representing knowledge enabling smart homes to perform human activity recognition online. In the network, nodes are ontologies allowing for various data contextualisation, while edges are general-purpose computational procedures elaborating data. Arianna+ provides a flexible interface between the inputs and outputs of procedures and statements, which are atomic representations of ontological knowledge. Arianna+ schedules procedures on the basis of events by employing logic-based reasoning, i.e., by checking the classification of certain statements in the ontologies. Each procedure involves input and output statements that are differently contextualised in the ontologies based on specific prior knowledge. Arianna+ allows to design networks that encode data within multiple contexts and, as a reference scenario, we present a modular network based on a spatial context shared among all activities and a temporal context specialised for each activity to be recognised. In the paper, we argue that a network of small ontologies is more intelligible and has a reduced computational load than a single ontology encoding the same knowledge. Arianna+ integrates in the same architecture heterogeneous data processing techniques, which may be better suited to different contexts. Thus, we do not propose a new algorithmic approach to activity recognition, instead, we focus on the architectural aspects for accommodating logic-based and data-driven activity models in a context-oriented way. Also, we discuss how to leverage data contextualisation and reasoning for activity recognition, and to support an iterative development process driven by domain *** Codes 68T27, 68T35 © 2021, CC BY-SA.

关键词： Pattern recognition

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Time adaptive Gaussian model

arXiv

引用

arXiv 2021年

作者： Ciech, Federico Tozzo, Veronica Department of Bioengineering Robotics Informatics and System Engineering Università degli Studi di Genova Italy Center for System Biology Department of Pathology Massachusetts General Hospital United States Department of Systems Biology Harvard Medical School United States

Multivariate time series analysis is becoming an integral part of data analysis pipelines. Understanding the individual time point connections between covariates as well as how these connections change in time is non-trivial. To this aim, we propose a novel method that leverages on Hidden Markov Models and Gaussian Graphical Models - Time Adaptive Gaussian Model (TAGM). Our model is a generalization of state-of-the-art methods for the inference of temporal graphical models, its formulation leverages on both aspects of these models providing better results than current methods. In particular,it performs pattern recognition by clustering data points in time;and, it finds probabilistic (and possibly causal) relationships among the observed variables. Compared to current methods for temporal network inference, it reduces the basic assumptions while still showing good inference performances. © 2021, CC BY.

关键词： Gaussian distribution

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OWLOOP: A Modular API to Describe OWL Axioms in OOP Objects Hierarchies

arXiv

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arXiv 2021年

作者： Buoncompagni, Luca Kareem, Syed Yusha Mastrogiovanni, Fulvio Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Via Opera Pia 13 Genoa16145 Italy Teseo s.r.l. Piazza Montano 2a Genoa16151 Italy

OWLOOP is an Application Programming Interface (API) for using the Ontology Web Language (OWL) by the means of Object-Oriented Programming (OOP). It is common to design software architectures using the OOP paradigm for increasing their modularity. If the components of an architecture also exploit OWL ontologies for knowledge representation and reasoning, they would require to be interfaced with OWL axioms. Since OWL does not adhere to the OOP paradigm, such an interface often leads to boilerplate code affecting modularity, and OWLOOP is designed to address this issue as well as the associated computational aspects. We present an extension of the OWL-API to provide a general-purpose interface between OWL axioms subject to reasoning and modular OOP objects *** Codes 68T01 © 2021, CC BY-SA.

关键词： Software architecture

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ECPC-IDS: A benchmark endometrial cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions

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Computers in Biology and Medicine 2024年 171卷 108217-108217页

作者： Tang, Dechao Li, Chen Du, Tianmin Jiang, Huiyan Ma, Deguo Ma, Zhiyu Grzegorzek, Marcin Jiang, Tao Sun, Hongzan Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University China Software College Northeastern University Shenyang China Institute of Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Poland Chengdu University of Traditional Chinese Medicine Chengdu China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information Technology China Department of Radiology Shengjing Hospital China Medical University Shenyang China

Background: Endometrial cancer is one of the most common tumors in the female reproductive system and is the third most common gynecological malignancy that causes death after ovarian and cervical cancer. Early diagnosis can significantly improve the 5-year survival rate of patients. With the development of artificial intelligence, computer-assisted diagnosis plays an increasingly important role in improving the accuracy and objectivity of diagnosis and reducing the workload of doctors. However, the absence of publicly available image datasets restricts the application of computer-assisted diagnostic techniques. Methods: In this paper, a publicly available Endometrial Cancer PET/CT Image Dataset for Evaluation of Semantic Segmentation and Detection of Hypermetabolic Regions (ECPC-IDS) are published. Specifically, the segmentation section includes PET and CT images, with 7159 images in multiple formats totally. In order to prove the effectiveness of segmentation on ECPC-IDS, six deep learning semantic segmentation methods are selected to test the image segmentation task. The object detection section also includes PET and CT images, with 3579 images and XML files with annotation information totally. Eight deep learning methods are selected for experiments on the detection task. Results: This study is conduct using deep learning-based semantic segmentation and object detection methods to demonstrate the distinguishability on ECPC-IDS. From a separate perspective, the minimum and maximum values of Dice on PET images are 0.546 and 0.743, respectively. The minimum and maximum values of Dice on CT images are 0.012 and 0.510, respectively. The target detection section's maximum mAP values on PET and CT images are 0.993 and 0.986, respectively. Conclusion: As far as we know, this is the first publicly available dataset of endometrial cancer with a large number of multi-modality images. ECPC-IDS can assist researchers in exploring new algorithms to enhance computer-assisted d

关键词： Object detection

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Autonomous Underwater Intervention

引用

Current robotics Reports 2020年第3期1卷 117-122页

作者： Simetti, Enrico Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Interuniversity Research Center on Integrated System for the Marine Environment Genoa Italy

There is a growing interest and literature on autonomous underwater intervention systems. The purpose of this paper is to provide a review of the recent literature on control systems for autonomous or semi-autonomous underwater manipulation activities, classifying the results based on the type of task executed and the testing environment (simulation, pool, or sea). Amongst underwater intervention tasks, the grasping of objects lying on the seafloor is one of the most studied topics. Several results are given both in pool and sea environments. The perception of such objects might still need further improvements before the system can be considered robust enough. Manipulation of valves while docked has been demonstrated in sea environments. Results on floating valve manipulation or floating inspection through force regulation are still limited to pool environments. Finally, cooperative transportation by multiple agents is still limited to numerical simulation results only. A review of the state of the art of underwater manipulation is presented. First an introduction is given, recalling the fundamental milestones reached in the past on this topic. Then, recent findings on control systems for (semi-)autonomous intervention are presented, subdivided in grasping, valve manipulation and force regulation tasks and cooperative manipulation. Some unconventional systems are also presented.

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Lessons learned about language learning and extended reality frameworks 5

Lessons learned about language learning and extended reality...

引用

5th International Conference of the Virtual and Augmented Reality in Education, VARE 2019

作者： Iacono, Saverio Vercelli, Gianni Department of Informatics Bioengineering Robotics and System Engineering University of Genova Italy

ISBN: (纸本)9788885741423

The knowledge of a second language is an obligation in educational systems of the European Community for many years and the quality of teaching and learning, especially in Italy, could be largely improved for a better outcome. Methods like Game-based Learning and Gamification could improve and change this outcome, combined with new tools used as educational technology in an immersive context. This paper presents some lessons learned from the adoption of VR / AR within two applications related to language learning. In this context, being immersed in an engaging and fun simulated situation is a good way to practice language conversations. In the last few years the great clamor of new VR / AR devices (HMD, controller, tracker) promotes the creation of new frameworks of extended reality for language learning. © 5th International Conference of the Virtual and Augmented Reality in Education, VARE 2019. All rights reserved.

关键词： Educational technology

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Image-guided breast biopsy of MRI-visible lesions with a hand-mounted motorised needle steering tool

arXiv

引用

arXiv 2021年

作者： Lagomarsino, Marta Groenhuis, Vincent Casadio, Maura Welleweerd, Marcel K. Siepel, Françoise J. Stramigioli, Stefano Laboratory Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Drienerlolaan 5 Enschede7522NB Netherlands Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Via Opera Pia 13 Genoa16145 Italy Department of Advanced Robotics Istituto Italiano di Tecnologia Genoa Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy

A biopsy is the only diagnostic procedure for accurate histological confirmation of breast cancer. When sonographic placement is not feasible, a Magnetic Resonance Imaging(MRI)-guided biopsy is often preferred. The lack of real-time imaging information and the deformations of the breast make it challenging to bring the needle precisely towards the tumour detected in pre-interventional Magnetic Resonance (MR) images. The current manual MRI-guided biopsy workflow is inaccurate and would benefit from a technique that allows real-time tracking and localisation of the tumour lesion during needle insertion. This paper proposes a robotic setup and software architecture to assist the radiologist in targeting MR-detected suspicious tumours. The approach benefits from image fusion of preoperative images with intraoperative optical tracking of markers attached to the patient's skin. A hand-mounted biopsy device has been constructed with an actuated needle base to drive the tip toward the desired direction. The steering commands may be provided both by user input and by computer guidance. The workflow is validated through phantom experiments. On average, the suspicious breast lesion is targeted with a radius down to 2.3 mm. The results suggest that robotic systems taking into account breast deformations have the potentials to tackle this clinical challenge. Copyright © 2021, The Authors. All rights reserved.

关键词： Biopsy

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