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Preoperative imaging in primary hyperparathyroidism patients using 4DCT subtraction maps, a report of three cases

Radiology Case Reports 2023年第8期18卷 2814-2822页

作者： Krol, Jorian P. Veerbeek, Tessa Deden, Laura N. Joosten, Frank B.M. Slump, Cornelis H. Oyen, Wim J.G. Department of Radiology & Nuclear Medicine Rijnstate Hospital Arnhem Netherlands Department of Robotics and Mechatronics Faculty of Electrical Engineering Mathematics and Computer Sciences University of Twente Enschede Netherlands Department of Biomedical Sciences and Humanitas Clinical and Research Centre Department of Nuclear Medicine Humanitas University Milan Italy Department of Radiology & Nuclear Medicine Radboud University Medical Centre Nijmegen Netherlands

Four-dimensional computed tomography (4DCT) is one of the preoperative imaging modalities that can be used to localize a parathyroid adenoma in primary hyperparathyroidism patients however, sensitivity differs in literature and could be improved especially for multiglandular hyperplasia or double adenomas. The most robust feature on the 4DCT for the differentiation between parathyroid adenoma and thyroid gland tissue is arterial enhancement. To make this better visible, we have developed a subtraction map that shows arterial enhancement as a color scale to increase sensitivity for 4DCT. In this report of 3 cases, we present the usefulness of this subtraction map in a 54-year-old male, a 57-year-old female and a 51-year-old male. Subtraction maps may increase sensitivity for 4DCT, especially for multiglandular hyperplasia or double adenomas. © 2023

关键词： Four dimensional computed tomography Parathyroid adenoma Parathyroid hyperplasia Primary hyperparathyroidism Subtraction map

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A Soft e-Textile Sensor for Enhanced Deep Learning-based Shape Sensing of Soft Continuum Robots

A Soft e-Textile Sensor for Enhanced Deep Learning-based Sha...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Eric Vincent Galeta Ayman A. Nada Sabah M. Ahmed Victor Parque Haitham El-Hussieny Department of Mechatronics and Robotics Egypt-Japan University of Science and Technology Alexandria Egypt Electrical Engineering Department Faculty of Engineering Assiut University Egypt Department of Modern Mechanical Engineering Waseda University Tokyo Japan

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The safety and accuracy of robotic navigation hold paramount importance, especially in the realm of soft continuum robotics, where the limitations of traditional rigid sensors become evident. Encoders, piezoresistive, and potentiometer sensors often fail to integrate well with the flexible nature of these robots, adding unwanted bulk and rigidity. To overcome these hurdles, our study presents a new approach to shape sensing in soft continuum robots through the use of soft e-textile resistive sensors. This sensor, designed to flawlessly integrate with the robot’s structure, utilizes a resistive material that adjusts its resistance in response to the robot’s movements and deformations. This adjustment facilitates the capture of multidimensional force measurements across the soft sensor layers. A deep Convolutional Neural Network (CNN) is employed to decode the sensor signals, enabling precise estimation of the robot’s shape configuration based on the detailed data from the e-textile sensor. Our research investigates the efficacy of this e-textile sensor in determining the curvature parameters of soft continuum robots. The findings are encouraging, showing that the soft e-textile sensor not only matches but potentially exceeds the capabilities of traditional rigid sensors in terms of shape sensing and estimation. This advancement significantly boosts the safety and efficiency of robotic navigation systems.

关键词： Training Shape Navigation Estimation Sensor phenomena and characterization Robot sensing systems Sensors Safety Convolutional neural networks Continuum robots

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Invisible Servoing: A Visual Servoing Approach with Return-Conditioned Latent Diffusion

Invisible Servoing: A Visual Servoing Approach with Return-C...

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2025 International Conference on Unmanned Aircraft Systems, ICUAS 2025

作者： Gerges, Bishoy Bazzana, Barbara Botteghi, Nicolò Aboudorra, Youssef Franchi, Antonio University of Twente Department of Applied Mathematics Enschede 7500AE Netherlands University of Twente Robotics and Mechatronics group Faculty of Electrical Engineering Mathematics and Computer Science Enschede 7500AE Netherlands Sapienza University of Rome Department of Computer Control and Management Engineering Rome 00185 Italy

ISBN: (纸本)9798331513283

In this paper, we present a novel visual servoing (VS) approach based on latent Denoising Diffusion Probabilistic Models (DDPMs), that explores the application of generative models for vision-based navigation of UAVs (Uncrewed Aerial Vehicles). Opposite to classical VS methods, the proposed approach allows reaching the desired target view, even when the target is initially not visible. This is possible thanks to the learning of a latent representation that the DDPM uses for planning and a dataset of trajectories encompassing target-invisible initial views. A compact representation is learned from raw images using a Cross-Modal Variational Autoencoder. Given the current image, the DDPM generates trajectories in the latent space driving the robotic platform to the desired visual target. The approach has been validated in simulation using two generic multi-rotor UAVs (a quadrotor and a hexarotor). The results show that we can successfully reach the visual target, even if not visible in the initial view. A video summary with simulations can be found in: https://***/2Hb3nkkcszE. © 2025 IEEE.

关键词： Target drones

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Direct Biologically-Inspired Sliding-Mode Control of a Rotorcraft System 32

Direct Biologically-Inspired Sliding-Mode Control of a Rotor...

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32nd Mediterranean Conference on Control and Automation, MED 2024

作者： Alvarez-Munoz, J.U. Escareno, J. Garcia Carrillo, L.R. Scola, I. Rubio Labbani-Igbida, O. Institut Polytechnique de Sciences Avancées Ivry-sur-Seine94200 France XLIM Research Institute UMR-CNRS University of Limoges Robotics and Mechatronics Department 87060 France Klipsch School of Electrical and Computer Engineering New Mexico State University Las CrucesNM88003 United States INTI CONICET and UNR Department of Industrial Products Engineering Rosario Argentina

ISBN: (纸本)9798350395440

In the context of nonlinearity and uncertainty in the control systems, this work presents a novel control strategy based on a biologically-inspired learning robust controller for the attitude stabilization of a Vertical-Take-Off and Landing (VTOL) rotorcraft. Initially, the attitude's VTOL model based on quaternion formalism is detailed to subsequently develop the corresponding control law. Such controller consists of a Brain Emotional Learning-Based Intelligent Control (BELBIC) mechanism, combined with a Super-Twisting Sliding-Mode Control (STSMC) law. By using this architecture, the nonlinear sliding-mode equivalent control term is estimated via the BELBIC model, which allows the controller not to be fully dependent on the system model. Furthermore, closed loop stability is verified through Lyapunov's theory. Finally, for validation and assessment purposes of the overall control strategy, an extensive numerical simulation stage is conducted. © 2024 IEEE.

关键词： Sliding mode control

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A Soft e-Textile Sensor for Enhanced Deep Learning-based Shape Sensing of Soft Continuum Robots

arXiv

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arXiv 2024年

作者： Galeta, Eric Vincent Nada, Ayman A. Ahmed, Sabah M. Parque, Victor El-Hussieny, Haitham Department of Mechatronics and Robotics Egypt-Japan University of Science and Technology Alexandria Egypt Electrical Engineering Department Faculty of Engineering Assiut University Egypt Department of Modern Mechanical Engineering Waseda University Tokyo Japan

The safety and accuracy of robotic navigation hold paramount importance, especially in the realm of soft continuum robotics, where the limitations of traditional rigid sensors become evident. Encoders, piezoresistive, and potentiometer sensors often fail to integrate well with the flexible nature of these robots, adding unwanted bulk and rigidity. To overcome these hurdles, our study presents a new approach to shape sensing in soft continuum robots through the use of soft e-textile resistive sensors. This sensor, designed to flawlessly integrate with the robot's structure, utilizes a resistive material that adjusts its resistance in response to the robot's movements and deformations. This adjustment facilitates the capture of multidimensional force measurements across the soft sensor layers. A deep Convolutional Neural Network (CNN) is employed to decode the sensor signals, enabling precise estimation of the robot's shape configuration based on the detailed data from the e-textile sensor. Our research investigates the efficacy of this etextile sensor in determining the curvature parameters of soft continuum robots. The findings are encouraging, showing that the soft e-textile sensor not only matches but potentially exceeds the capabilities of traditional rigid sensors in terms of shape sensing and estimation. This advancement significantly boosts the safety and efficiency of robotic navigation systems. © 2024, CC BY.

关键词： Robots

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Logarithmically Quantized Distributed Optimization over Dynamic Multi-Agent Networks

arXiv

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arXiv 2024年

作者： Doostmohammadian, Mohammadreza Pequito, Sérgio Mechatronics Department Faculty of Mechanical Engineering Semnan University Semnan Iran Department of Electrical and Computer Engineering Institute for Systems and Robotics Instituto Superior Técnico University of Lisbon Portugal

Distributed optimization finds many applications in machine learning, signal processing, and control systems. In these real-world applications, the constraints of communication networks, particularly limited bandwidth, necessitate implementing quantization techniques. In this paper, we propose distributed optimization dynamics over multi-agent networks subject to logarithmically quantized data transmission. Under this condition, data exchange benefits from representing smaller values with more bits and larger values with fewer bits. As compared to uniform quantization, this allows for higher precision in representing near-optimal values and more accuracy of the distributed optimization algorithm. The proposed optimization dynamics comprise a primary state variable converging to the optimizer and an auxiliary variable tracking the objective function's gradient. Our setting accommodates dynamic network topologies, resulting in a hybrid system requiring convergence analysis using matrix perturbation theory and eigenspectrumanalysis. © 2024, CC BY.

关键词： Support vector machines

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A Study of Cantaloupe Fruit Classification Using Deep Learning Algorithms

A Study of Cantaloupe Fruit Classification Using Deep Learni...

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2024 International Conference on Control, robotics and Informatics, ICCRI 2024

作者： Tran, Huy Q. Le, Quoc Ngoc Ha, Cheolkeun Van Nguyen, Tan Tan, Quoc Phan Nguyen Tat Thanh University Robotics and Mechatronics Research Group Faculty of Engineering and Technology Viet Nam Faculty of Information and Technology SaigonTech Ho Chi Minh City Viet Nam University of Ulsan Department of Aerospace Engineering Ulsan Korea Republic of Thu Dau Mot University Faculty of Engineering and Technology Viet Nam Saigon University Faculty of Information Technology Viet Nam

ISBN: (纸本)9798350380309

This study focuses on identifying cantaloupes using deep learning algorithms. Subsequently, the cantaloupes are categorized into two groups (green and yellow) based on their distinct shape and color characteristics. We also recognized the unripe cantaloupe using several recent deep-learning algorithms. Experimental results from the dataset collected in Tay Ninh province, Vietnam, demonstrated classification accuracies of Yolov8, SSD Mobilenet V2, and Faster - RCNN are impressive, especially the Yolov8 model, which achieved an average accuracy of nearly 97%. © 2024 IEEE.

关键词： Adversarial machine learning

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FAST-LIVO2 on Resource-Constrained Platforms: LiDAR-Inertial-Visual Odometry With Efficient Memory and Computation

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IEEE robotics and Automation Letters 2025年第8期10卷 7931-7938页

作者： Bingyang Zhou Chunran Zheng Ziming Wang Fangcheng Zhu Yixi Cai Fu Zhang Mechatronics and Robotic Systems (MaRS) Laboratory Department of Mechanical Engineering University of Hong Kong Hong Kong SAR China Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden

This paper presents a lightweight LiDAR-inertial-visual odometry system optimized for resource-constrained platforms. It integrates a degeneration-aware adaptive visual frame selector into error-state iterated Kalman filter (ESIKF) with sequential updates, improving computation efficiency markedly while maintaining a similar level of robustness. Additionally, a memory-efficient mapping structure combining a locally unified visual-LiDAR map and a long-term visual map achieves a good trade-off between performance and memory usage. Extensive experiments on x86 and ARM platforms demonstrate the system's robustness and efficiency. On the Hilti dataset, our system achieves a 33% reduction in per-frame runtime and 47% lower memory usage compared to FAST-LIVO2, with only a 3 cm increase in RMSE. Despite this slight accuracy trade-off, our system remains competitive, outperforming state-of-the-art (SOTA) LIO methods such as FAST-LIO2 and most existing LIVO systems. These results validate the system's capability for scalable deployment on resource-constrained edge computing platforms.

关键词： Laser radar Visualization Odometry Computational efficiency Robustness State estimation Cameras Accuracy Sensors Program processors

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Open-Source, Low-Cost, and Bimodal Amateur Radio Communication Paradigm for Scientific CanSats

Open-Source, Low-Cost, and Bimodal Amateur Radio Communicati...

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robotics, Control and Automation engineering (RCAE), International Conference on

作者： Carrington Chun Billy Kihei Sumit Chakravarty M. Hassan Tanveer Department of Robotics and Mechatronics Engineering Kennesaw State Univeristy Marietta GA Department of Electrical and Computer Engineering Kennesaw State Univeristy Marietta GA

To facilitate the development of accessible space-bound robotic explorers, this work demonstrates a novel communication architecture intended for scientific CanSats. As low-cost terrestrial simulation platforms for orbital satellites, CanSats provide a valuable proving ground for new technologies or techniques. CanSats are also a considerably more accessible technological frontier than orbital CubeSats, as CanSats are lower in cost to develop and retain fewer operational requirements. Multiple low-cost CanSats and a simple Base Station were developed using Commercial-Off- The-Shelf (COTS) components. The bimodal modulation technique utilizes Amplitude Shift Keying (ASK) as a means for transferring data packets between the devices, while the delay between multiple ASK trains modulates additional Pseudo Morse Code (PMC) information that is audibly decodable with a handheld amateur radio receiver or digitally with a Software Defined Radio (SDR). The presented framework could be a valuable addition to telemetry beacons for orbital satellites that are seeking to engage the open-source or amateur radio communities.

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An Improved Method for Identifying SSVEP Single-Channel Target Frequency for BCI Systems 3

An Improved Method for Identifying SSVEP Single-Channel Targ...

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3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication engineering, ETECTE 2024

作者： Wahab, Adeel Khan, Umar Shahbaz Nawaz, Tahir Akbar, Hassan Shah, Syed Tayyab Hussain Khan, Zulfiqar Hasan Department of Mechatronics Engg National University of Sciences and Technology IslamabadH-12 Pakistan National Centre of Robotics and Automation National University of Sciences and Technology IslamabadH-12 Pakistan Department of Basic Sciences and Humanities National University of Sciences and Technology IslamabadH-12 Pakistan Faculty of Electrical Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi Pakistan

ISBN: (纸本)9798331504861

Steady-state visual evoked potential (SSVEP) is a brain response to flickering visual stimuli in the occipital area. SSVEP detection methods usually utilize multichannel configurations, which can be intricate and uncomfortable. Conversely, single-channel configurations are simple and user-friendly, but attaining the necessary accuracy is difficult, requiring the development of a reliable and accurate single-channel technique. For this purpose, this study introduces a hybrid VMD-LGBM approach to single-channel SSVEP detection. The proposed technique utilizes VMD as a preprocessor that decomposes the SSVEP signal into harmonic, intrinsic mode functions (IMFs), and noisy IMFs. To separate harmonic IMFs from noisy IMFs, the crest factor of the cross-correlation function was introduced. Then the restored SSVEP signal is used to extract features using power spectral density analysis (Welch's approach), followed by classification using a Light Gradient Boosting Machine. Our proposed approach performs better than available techniques in terms of classification accuracy and information transfer rate. The proposed single-channel approach provides better efficiency and user-friendliness for practical applications. ©2024 IEEE.

关键词： Harmonic analysis

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