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A Scalable Deep Attention Mechanism of Instance Segmentation for the Investigation of Chromosome

SLAS technology 2025年 100306页

作者： Neelam Umbreen Sara Ali Hasan Sajid Yasar Ayaz Shrooq Alsenan Yunyoung Nam So Yeon Kim Muhammad Baber Sial National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: neelamumbreen.phd@smme.edu.pk. National University of Sciences and Technology (NUST) Islamabad Pakistan Suzhou Industrial Park (SIP) Monash Research Institute of Science and Technology Monash University 8Q9C+R6W Suzhou Jiangsu China. School of Mechanical and Manufacturing Engineering (SMME) National University of Sciences and Technology (NUST) Islamabad NUST-COVENTRY Human-Robot Interaction (NC-HRI) Laboratory Pakistan Intelligent Field Robotics Laboratory (IFRL) National Center for Artificial Intelligence (NCAI) National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: sarababer@smme.nust.edu.pk. National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: hasan.sajid@smme.nust.edu.pk. Intelligent Field Robotics Laboratory (IFRL) National Center for Artificial Intelligence (NCAI) National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: yasar@smme.nust.edu.pk. Information Systems Department College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia. Electronic address: shalsenan@pnu.edu.sa. Emotional and Intelligent Child Care Convergence Research Center Soonchunhyang University Asan 31538 Korea. Electronic address: ynam@sch.ac.kr. Department of Artificial Intelligence Department of Software and Computer Engineering Ajou University. Electronic address: jebi1771@ajou.ac.kr. School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China. Electronic address: babersial@***.

Chromosome segmentation in metaphase images is a critical yet challenging task in cytogenetics and genomics due to the inherent complexity, variability in chromosome shapes, and the scarcity of high-quality annotated datasets. This study proposes a robust instance segmentation framework that integrates an automated annotation pipeline with an enhanced deep learning architecture to address these challenges. A novel dataset is introduced, comprising metaphase images and corresponding karyograms, annotated with precise instance segmentation information across 24 chromosome classes in COCO format. To overcome the labor-intensive manual annotation process, a feature-based image registration technique leveraging SIFT and homography is employed, enabling the accurate mapping of chromosomes from karyograms to metaphase images and significantly improving annotation quality and segmentation performance. The proposed framework includes a custom Mask R-CNN model enhanced with an Attention-based Feature Pyramid Network (AttFPN), spatial attention mechanisms, and a LastLevelMaxPool block for superior multi-scale feature extraction and focused attention on critical regions of the image. Experimental evaluations demonstrate the model's efficacy, achieving a mean average precision (mAP) of 0.579 at IoU = 0.50:0.95, surpassing the baseline Mask R-CNN and Mask R-CNN with AttFPN by 3.94% and 5.97% improvements in mAP and AP50, respectively. Notably, the proposed architecture excels in segmenting small and medium-sized chromosomes, addressing key limitations of existing methods. This research not only introduces a state-of-the-art segmentation framework but also provides a benchmark dataset, setting a new standard for chromosome instance segmentation in biomedical imaging. The integration of automated dataset creation with advanced model design offers a scalable and transferable solution, paving the way for tackling similar challenges in other domains of biomedical and cytogenetic imagi

关键词： Chromosome segmentation Instance segmentation, cytogenetics Mask R-CNN attention mechanism biomedical imaging multi-scale feature fusion

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Integrating Artificial Intelligence with Real-time Intracranial EEG Monitoring to Automate Interictal Identification of Seizure Onset Zones in Focal Epilepsy

arXiv

引用

arXiv 2018年

作者： Varatharajah, Yogatheesan Berry, Brent Cimbalnik, Jan Kremen, Vaclav van Gompel, Jamie Stead, Matt Brinkmann, Benjamin Iyer, Ravishankar Worrell, Gregory Electrical and Computer Engineering University of Illinois UrbanaIL61801 United States Mayo Systems Electrophysiology Laboratory Department of Neurology Mayo Clinic RochesterMN55905 United States Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague 6166 36 Czech Republic Department of Neurosurgery Mayo Clinic RochesterMN55905 United States Department of Physiology & Biomedical Engineering Mayo Clinic RochesterMN55905 United States

An ability to map seizure-generating brain tissue, i.e., the seizure onset zone (SOZ), without recording actual seizures could reduce the duration of invasive EEG monitoring for patients with drug-resistant epilepsy. A widely-adopted practice in the literature is to compare the incidence (events/time) of putative pathological electrophysiological biomarkers associated with epileptic brain tissue with the SOZ determined from spontaneous seizures recorded with intracranial EEG, primarily using a single biomarker. Clinical translation of the previous efforts suffers from their inability to generalize across multiple patients because of (a) the inter-patient variability and (b) the temporal variability in the epileptogenic activity. Here, we report an artificial intelligence-based approach for combining multiple interictal electrophysiological biomarkers and their temporal characteristics as a way of accounting for the above barriers and show that it can reliably identify seizure onset zones in a study cohort of 82 patients who underwent evaluation for drug-resistant epilepsy. Our investigation provides evidence that utilizing the complementary information provided by multiple electrophysiological biomarkers and their temporal characteristics can significantly improve the localization potential compared to previously published single-biomarker incidence-based approaches, resulting in an average area under ROC curve (AUC) value of 0.73 in a cohort of 82 patients. Our results also suggest that recording durations between ninety minutes and two hours are sufficient to localize SOZs with accuracies that may prove clinically relevant. The successful validation of our approach on a large cohort of 82 patients warrants future investigation on the feasibility of utilizing intra-operative EEG monitoring and artificial intelligence to localize epileptogenic brain tissue. Broadly, our study demonstrates the use of artificial intelligence coupled with careful feature engineering in

关键词： Support vector machines

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Bifurcations and Application of the Vallis – Model

Bifurcations and Application of the Vallis – Model

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International Conference on Computational Cybernetics (ICCC)

作者： Bálint Nagy György Molnár Antal Joós Enikő Nagy Department of Mathematics Institute of Computer Engineering University of Dunaújváros Dunaújváros Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary Department of Natural Sciences Kandó Kálmán Faculty of Electrical Engineering Institute of Electrophysics Óbuda University Budapest Hungary Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary János Csere Apáczai Faculty Széchenyi University Győr Hungary John von Neumann Faculty Antal Bejczy Center for Intelligent Robotics institution">Institute of Cyberphysical Systems Óbuda University Budapest Hungary

ISBN: (数字)9798350350159

ISBN: (纸本)9798350350166

Investigating chaotic systems is a fertile field considering that chaos is an exotic chapter in the theory of differential equations, and it has useful applications in the theory of communications. In this work a well-known chaotic system is investigated from the bifurcation point of view and some applications are also introduced. The data studied are also visualized for better clarity. The results observed can be helpful in the encryption methods of the implementation of different communication channels.

关键词： Visualization Chaotic communication Communication systems Computational modeling Data visualization Computational cybernetics Differential equations

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DAC-h3: A proactive robot cognitive architecture to acquire and express knowledge about the world and the self

arXiv

引用

arXiv 2017年

作者： Moulin-Frier, Clément Fischer, Tobias Petit, Maxime Pointeau, Grégoire Puigbo, Jordi-Ysard Pattacini, Ugo Low, Sock Ching Camilleri, Daniel Nguyen, Phuong Hoffmann, Matej Chang, Hyung Jin Zambelli, Martina Mealier, Anne-Laure Damianou, Andreas Metta, Giorgio Prescott, Tony J. Demiris, Yiannis Dominey, Peter Ford Verschure, Paul F.M.J. Laboratory for Synthetic Perceptive Emotive and Cognitive Systems Universitat Pompeu Fabra Barcelona08002 Spain Barcelona Spain Personal Robotics Laboratory Department of Electrical and Electronic Engineering Imperial College London SW7 2AZ United Kingdom Robot Cognition Laboratory INSERM U846 Stem Cell and Brain Research Institute Bron69675 France Italian Institute of Technology iCub Facility Via Morego 30 Genova Italy Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague Department of Computer Science University of Sheffield United Kingdom

This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users. Copyright © 2017, The Authors. All rights reserved.

关键词： Human robot interaction

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Neural-iLQR: A Learning-Aided Shooting Method for Trajectory Optimization

Neural-iLQR: A Learning-Aided Shooting Method for Trajectory...

引用

IEEE International Conference on robotics and Biomimetics

作者： Zilong Cheng Yulin Li Kai Chen Jianghua Duan Jun Ma Tong Heng Lee Department of Electrical and Computer Engineering National University of Singapore Singapore Division of Emerging Interdisciplinary Areas The Hong Kong University of Science and Technology Hong Kong SAR China Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Hong Kong SAR China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen China

Iterative linear quadratic regulator (iLQR) has gained wide popularity in addressing trajectory optimization problems with nonlinear system models. However, as a model-based shooting method, it relies heavily on an accurate system model to update the optimal control actions and the trajectory determined with forward integration, thus becoming vulnerable to inevitable model inaccuracies. Recently, substantial research efforts in learning-based methods for optimal control problems have been progressing significantly in addressing unknown system models, particularly when the system has complex interactions with the environment. Yet an incremental machine learning method or a deep neural network is normally required to fit a substantial scale of sampling motion data. Existing works mainly focus on capturing the whole picture of the motion pattern from trial data (which is essentially noisy and hard to identify with any simple predefined structures) for complex robot systems. Instead, we present Neural-iLQR, a learning-aided shooting method over the unconstrained control space, in which a neural network with a simple structure is used to drive the optimization process towards optimal direction. Through comprehensive evaluations on two illustrative control tasks using three different kinds of lightweight network structures, the proposed method is shown to outperform the conventional iLQR significantly with fast and continuous cost convergence in the presence of inaccuracies in system models, which demonstrates the generalizability and robustness of the proposed learning-aided method.

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Bearing-only formation control under persistence of excitation

arXiv

引用

arXiv 2020年

作者： Tang, Zhiqi Cunha, Rita Hamel, Tarek Silvestre, Carlos Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Portugal I3S-CNRS Université Côte d'Azur Nice-Sophia Antipolis France I3S-CNRS Institut Universitaire de France Nice-Sophia Antipolis France Department of Electrical and Computer Engineering The Faculty of Science and Technology The University of Macau China

This paper addresses the problem of bearing-only formation control in d (d ≥ 2)-dimensional space by exploring persistence of excitation (PE) of the desired bearing reference. By defining a desired formation that is bearing PE, distributed bearing-only control laws are proposed, which guarantee exponential stabilization of the desired formation only up to a translation vector. The key outcome of this approach relies in exploiting the bearing PE to significantly relax the conditions imposed on the graph topology to ensure exponential stabilization, when compared to the bearing rigidity conditions, and to remove the scale ambiguity introduced by bearing vectors. Simulation results are provided to illustrate the performance of the proposed control method. Copyright © 2020, The Authors. All rights reserved.

关键词： Stabilization

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Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age

引用

Nature Ecology & Evolution 2025年 1-12页

作者： Stephen Pringle Jessica C. Fisher Gail E. Austen Jake E. Bicknell Nicolas J. Deere Mohammad S. Farhadinia Charlie J. Gardner Richard A. Griffiths Daniel J. Ingram Matthew J. Struebig Natalie Yoh Zoe G. Davies Martin Dallimer Mark A. Goddard Katherine C. R. Baldock Miranda T. Prendergast-Miller Léni K. Le Goff Emma Hart Simón C. Smith Simon J. Langdale Sara-Adela Abad Marc Ancrenaz Fabio Angeoletto Fernando Auat Cheein Joseph J. Bailey Lindsay F. Banin Cristina Banks-Leite Aliyu S. Barau Reshu Bashyal Adam J. Bates Jon Bielby Serge Wich Christopher D. Williams Petra Bosilj Marc Hanheide Emma R. Bush Simon J. Butler Dan Carpenter Christopher F. Clements Antoine Cully Kendi F. Davies Brett A. Melbourne Michael Dodd Rosie Drinkwater Don A. Driscoll Guillaume Dutilleux Mads Dyrmann David P. Edwards Aisyah Faruk Richard Field Franziska Schrodt Robert J. Fletcher Chris W. Foster Richard Fox Richard M. Francksen Aldina M. A. Franco Alison M. Gainsbury Ioanna Giorgi Giovanni L. Masala Salua Hamaza Matt W. Hayward Marcus Hedblom Thorunn Helgason Sui P. Heon Kevin A. Hughes Edmund R. Hunt George Jackson-Mills Kelly Jowett Timothy H. Keitt Laura N. Kloepper Stephanie Kramer-Schadt Jim Labisko Frédéric Labrosse Jenna Lawson Nicolas Lecomte Ricardo F. de Lima Nick A. Littlewood Harry H. Marshall Lindsay C. Maskell Eleni Matechou Barbara Mazzolai Alistair McConnell Aslan Miriyev Eric Djomo Nana Alessandro Ossola Sarah Papworth Catherine L. Parr Ana Payo-Payo Gad Perry Nathalie Pettorelli Marcus Rowcliffe Rajeev Pillay Simon G. Potts Lan Qie Carl D. Soulsbury Persie Rolley-Parnell Stephen J. Rossiter Heather Rumble Jon P. Sadler Christopher J. Sandom Asiem Sanyal Sarab S. Sethi Adi Shabrani Robert Siddall Robbert P. H. Snep Margaret C. Stanley Philip A. Stephens P. J. Stephenson Matthew Studley Martin Svátek Gilbert Tang Nicholas K. Taylor Kate D. L. Umbers Robert J. Ward Patrick J. C. White Mark J. Whittingham Ibrahim B. Yakubu Syed A. R. Zaidi Anna Zmarz Joeri A. Zwerts Durrell Institute of Conservation and Ecology (DICE) School of Natural Sciences University of Kent Canterbury UK Centre for Environmental Policy Imperial College London London UK Department of Geography and Environmental Sciences Northumbria University Newcastle upon Tyne UK School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK Synthotech Ltd Milner Court Hornbeam Square Harrogate UK Mechanical Engineering Department University College London London UK HUTAN SWD Kota Kinabalu Malaysia Programa de Pós-Graduação em Gestão e Tecnologia Ambiental da Universidade Federal de Rondonópolis Rondonópolis Brasil Department of Engineering Harper Adams University Newport UK Applied Ecology Research Group School of Life Sciences Anglia Ruskin University Cambridge UK Centre for Ecology and Hydrology Penicuik UK Department of Life Sciences Imperial College London Silwood Park Campus Ascot UK Department of Urban and Regional Planning Faculty of Earth and Environmental Sciences Bayero University Kano Nigeria Greenhood Nepal Kathmandu Nepal Animal Rural & Environmental Sciences Nottingham Trent University Nottinghamshire UK School of Biological and Environmental Sciences Liverpool John Moores University Liverpool UK Lincoln Centre for Autonomous Systems University of Lincoln Lincoln UK Royal Botanic Garden Edinburgh Edinburgh UK School of Biological Sciences University of East Anglia Norwich Research Park Norwich UK Digital Ecology Limited Bristol UK School of Biological Sciences University of Bristol Bristol UK Department of Computing Imperial College London London UK Department of Ecology and Evolutionary Biology University of Colorado Boulder CO USA Faculty of Science Technology Engineering and Mathematics (STEM) The Open University Walton Hall Milton Keynes UK Palaeogenomics Group Faculty of Veterinary Medicine Ludwig Maximilian University Munich Munich Germany School of Life and Environmental Sciences

With biodiversity loss escalating globally, a step change is needed in our capacity to accurately monitor species populations across ecosystems. Robotic and autonomous systems (RAS) offer technological solutions that may substantially advance terrestrial biodiversity monitoring, but this potential is yet to be considered systematically. We used a modified Delphi technique to synthesize knowledge from 98 biodiversity experts and 31 RAS experts, who identified the major methodological barriers that currently hinder monitoring, and explored the opportunities and challenges that RAS offer in overcoming these barriers. Biodiversity experts identified four barrier categories: site access, species and individual identification, data handling and storage, and power and network availability. robotics experts highlighted technologies that could overcome these barriers and identified the developments needed to facilitate RAS-based autonomous biodiversity monitoring. Some existing RAS could be optimized relatively easily to survey species but would require development to be suitable for monitoring of more ‘difficult’ taxa and robust enough to work under uncontrolled conditions within ecosystems. Other nascent technologies (for instance, new sensors and biodegradable robots) need accelerated research. Overall, it was felt that RAS could lead to major progress in monitoring of terrestrial biodiversity by supplementing rather than supplanting existing methods. Transdisciplinarity needs to be fostered between biodiversity and RAS experts so that future ideas and technologies can be codeveloped effectively.

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Hydrothermally Synthesis of Binary Niznfe2o4-Cnt Nanocomposite for Energy Storage Application

SSRN

引用

SSRN 2023年

作者： Ghadimi, Laleh Saleh Tabrizi, Amin Goljanian Rezaie, Ehsan Parvini, Elahe Tavakoli, Mahmoud Abouzari-Lotf, Ebrahim Hajalilou, Abdollah Arsalani, Nasser Pahlevani, Majid Department of Electrical and Computer Engineering Queen's University KingstonON Canada Research Laboratory of Polymer Department of Organic and Biochemistry Faculty of Chemistry University of Tabriz Iran Laboratory for Advanced Materials Faculty of Natural Sciences Comenius University Bratislava842 15 Slovakia Soft and Printed Microelectronics Laboratory Institute of Systems and Robotics University of Coimbra Coimbra Portugal Electrochemical Energy Storage Helmholtzstraße 11 UlmD-89081 Germany

Our research aimed to enhance carbon-based electrochemical double-layer capacitors (EDLCs) by incorporating faradic redox processes. We synthesized a Ni0.5Zn0.5Fe2O4-Carbon Nanotube (NZF-CNT) nanocomposite via hydrothermal methods at various pH levels. X-ray diffraction (XRD) analysis at pH 12 confirmed the formation of spinel nickel-zinc ferrite with 15 nm particles. X-ray photoelectron spectroscopy (XPS) verified cation distribution changes, and high-resolution transmission electron microscopy (HRTEM) revealed well-arranged NZF crystals around CNTs. The successful fusion of NZF and CNTs into the nanocomposite framework was demonstrated. NZF-CNT exhibited a remarkable specific capacitance of approximately 368 F/g at 1.25 A/g, surpassing pure ferrite's 110 F/g. Even after 5000 charge/discharge cycles at 1 A/g, it retained 88.5% of its initial capacitance. Our findings showcase the potential of NZF-CNT as a promising material for energy storage devices, offering a unique combination of EDLC and pseudo-capacitive properties. This approach opens new avenues for improving supercapacitor performance and their diverse applications. © 2023, The Authors. All rights reserved.

关键词： Supercapacitor

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Symbolic Models for Interconnected Impulsive systems

Symbolic Models for Interconnected Impulsive Systems

引用

IEEE Conference on Decision and Control

作者： Sadek Belamfedel Alaoui Adnane Saoud Pushpak Jagtap Abdalla Swikir School of Computer Science at Mohammed VI Polytechnical University Benguerir Morocco Centrale-Supelec University Paris-Saclay Gif-sur-Yvette France Robert Bosch Center for Cyber-Physical Systems Indian Institute of Science Bangalore Chair of Robotics and Systems Intelligence and MIRMI Technical University Munich (TUM) Munich Germany Department of Electrical and Electronic Engineering Omar Al-Mukhtar University (OMU) Albaida Libya Centre for Tactile Internet with Human-in-the-Loop (CeTI) Dresden Germany

In this paper, we present a compositional method-ology for constructing symbolic models of nonlinear interconnected impulsive systems. Our approach relies on the concept of “alternating simulation function” to establish a relation-ship between concrete subsystems and their symbolic models. Assuming some small-gain type conditions, we develop an alternating simulation function between the symbolic models of individual subsystems and those of the nonlinear interconnected impulsive systems. To construct symbolic models of nonlinear impulsive subsystems, we propose an approach that depends on incremental input-to-state stability and forward completeness properties. Finally, we demonstrate the advantages of our framework through a case study.

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Simultaneously Calibration of Multi Hand-Eye Robot System Based on Graph

arXiv

引用

arXiv 2023年

作者： Zhou, Zishun Ma, Liping Liu, Xilong Cao, Zhiqiang Yu, Junzhi The Institute of Automation Chinese Academy of Sciences Beijing100190 China The College of Electrical Engineering and Computer Science Australian National University Canberra2601 Australia The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The State Key Laboratory for Turbulence and Complex System Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Peking University Beijing100871 China

Precise calibration is the basis for the vision-guided robot system to achieve high-precision operations. systems with multiple eyes (cameras) and multiple hands (robots) are particularly sensitive to calibration errors, such as micro-assembly systems. Most existing methods focus on the calibration of a single unit of the whole system, such as poses between hand and eye, or between two hands. These methods can be used to determine the relative pose between each unit, but the serialized incremental calibration strategy cannot avoid the problem of error accumulation in a large-scale system. Instead of focusing on a single unit, this paper models the multi-eye and multi-hand system calibration problem as a graph and proposes a method based on the minimum spanning tree and graph optimization. This method can automatically plan the serialized optimal calibration strategy in accordance with the system settings to get coarse calibration results initially. Then, with these initial values, the closed-loop constraints are introduced to carry out global optimization. Simulation experiments demonstrate the performance of the proposed algorithm under different noises and various hand-eye configurations. In addition, experiments on real robot systems are presented to further verify the proposed method. © 2023, CC BY-SA.

关键词： Multipurpose robots

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