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2011 IEEE International Conference on Technologies for Practical Robot Applications, TePRA 2011

作者： Ratti, Jayant Vachtsevanos, George Robotics Program School of Electrical and Computer Engineering United States School of Electrical and Computer Engineering United States

ISBN: (纸本)9781612844824

Micro Aerial Vehicles (MAVs) have gained a significant amount of research lately, with a number of universities and industry sponsors paving the way with micro flying robots to perform Intelligence, Surveillance and Reconnaissance (ISR) Missions. However, much of the work done in flapping wing MAVs till date has not shown performance improvements over their VTOL, rotary-wing counterparts. Research and development over the years has shown that insects and birds have unmatched flying capabilities in the Low Reynolds Number Regime. Their phenomenal flight performance is attributed to among others the highly energy efficient actuation systems & their power to weight ratios, efficient aerodynamics & wing configurations, novel & high-speed flight-control and processing. The paper proposes and illustrates the developments thus far towards the design of a novel, high endurance, flapping wing micro aerial robot for surveillance and reconnaissance. The entire design and development efforts, including aerodynamics, actuation-mechanisms, flight controls, embedded controls & processing and finally flight testing is abridged and presented herein. © 2011 IEEE.

关键词： Aerodynamics

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From Monocular Vision to Autonomous Action: Guiding Tumor Resection via 3D Reconstruction

arXiv

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arXiv 2025年

作者： Acar, Ayberk Smith, Mariana Al-Zogbi, Lidia Watts, Tanner Li, Fangjie Li, Hao Yilmaz, Nural Scheikl, Paul Maria d’Almeida, Jesse F. Sharma, Susheela Branscombe, Lauren Ertop, Tayfun Efe Webster, Robert J. Oguz, Ipek Kuntz, Alan Krieger, Axel Wu, Jie Ying Department of Computer Science Vanderbilt University NashvilleTN37235 United States Department of Mechanical Engineering Johns Hopkins University BaltimoreMD21211 United States Robotics Center Kahlert School of Computing University of Utah Salt Lake CityUT84112 United States Department of Mechanical Engineering Vanderbilt University NashvilleTN37235 United States Virtuoso Surgical NashvilleTN37205 United States Department of Mechanical Aerospace and Biomedical Engineering University of Tennessee KnoxvilleTN37996 United States

Surgical automation requires precise guidance and understanding of the scene. Current methods in the literature rely on bulky depth cameras to create maps of the anatomy, however this does not translate well to space-limited clinical applications. Monocular cameras are small and allow minimally invasive surgeries in tight spaces but additional processing is required to generate 3D scene understanding. We propose a 3D mapping pipeline that uses only RGB images to create segmented point clouds of the target anatomy. To ensure the most precise reconstruction, we compare different structure from motion algorithms’ performance on mapping the central airway obstructions, and test the pipeline on a downstream task of tumor resection. In several metrics, including post-procedure tissue model evaluation, our pipeline performs comparably to RGB-D cameras and, in some cases, even surpasses their performance. These promising results demonstrate that automation guidance can be achieved in minimally invasive procedures with monocular cameras. This study is a step toward the complete autonomy of surgical robots. © 2025, CC BY-NC-ND.

关键词： Image segmentation

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NuSegDG: Integration of heterogeneous space and Gaussian kernel for domain-generalized nuclei segmentation

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Knowledge-Based Systems 2025年 322卷

作者： Lou, Zhenye Xu, Qing Jiang, Zekun He, Xiangjian Li, Chenxin Chen, Zhen Wang, Yi He, Maggie M. Duan, Wenting Sichuan University Pittsburgh Institute Sichuan University Chengdu China School of Computer Science Computer Vision and Intellifent Perception Laboratory and Municipal Digital Port Technology Key Laboratory University of Nottingham Ningbo Zhejiang China West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China Centre for Artificial Intelligence and Robotics (CAIR) Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong School of Software Dalian University of Technology Dalian 116600 China Department of Electronic Engineering The Chinese University of Hong Kong 999077 Hong Kong Department of Cardiology Gold Coast University Hospital QLD Australia School of Computer Science University of Lincoln Lincoln LN6 7TS United Kingdom

Domain-generalized nuclei segmentation refers to the generalizability of models to unseen domains based on knowledge learned from source domains and is challenged by various image conditions, cell types, and stain strategies. Recently, the Segment Anything Model (SAM) has made great success in universal image segmentation by interactive prompt modes (e.g., point and box). Despite its strengths, the original SAM presents limited adaptation to medical images. Moreover, SAM requires providing manual bounding box prompts for each object to produce satisfactory segmentation masks, so it is laborious in nuclei segmentation scenarios. To address these limitations, we propose a domain-generalizable framework for nuclei image segmentation, abbreviated to NuSegDG. Specifically, we first devise a Heterogeneous Space Adapter (HS-Adapter) to learn multi-dimensional feature representations of different nuclei domains by injecting a small number of trainable parameters into the image encoder of SAM. To alleviate the labour-intensive requirement of manual prompts, we introduce a Gaussian-Kernel Prompt Encoder (GKP-Encoder) to generate density maps driven by a single point, which guides segmentation predictions by mixing position prompts and semantic prompts. Furthermore, we present a Two-Stage Mask Decoder (TSM-Decoder) to effectively convert semantic masks to instance maps without the manual demand for morphological shape refinement. Based on our experimental evaluations, the proposed NuSegDG demonstrates state-of-the-art performance in nuclei semantic and instance segmentation, exhibiting superior domain generalization capabilities. The source code is available at https://***/xq141839/NuSegDG. © 2025 Elsevier B.V.

关键词： Domain generalization Foundation model Nuclei segmentation Parameter-efficient fine-tuning

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Compressive and energy absorption properties of titanium hybrid lattice structures for bioimplant applications fabricated via LPBF

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Materials and Design 2025年 254卷

作者： Karmiris-Obratański, Panagiotis Georgakopoulos-Soares, Ilias Cichocki, Kamil Lisiecka-Graca, Paulina Papazoglou, Emmanouil L. Advanced Manufacturing Laboratory Department of Manufacturing Systems Faculty of Mechanical Engineering and Robotics AGH University of Krakow Krakow Poland School of Chemical Engineering Department of Materials Science and Engineering National Technical University of Athens Heroon Polytechniou 9 Athens15780 Greece Institute for Personalized Medicine Department of Biochemistry and Molecular Biology The Pennsylvania State University College of Medicine HersheyPA United States Faculty of Metals Engineering and Computer Science AGH University of Krakow Adama Mickiewicza Ave. 30 Kraków30-059 Poland School of Mechanical Engineering Section of Manufacturing Technology National Technical University of Athens Heroon Polytechniou 9 Athens15780 Greece

The compressive properties of lattice structures are strongly influenced by lattice design, density, material, and manufacturing method. Recent advancements in manufacturing, particularly Additive Manufacturing, have enabled the fabrication of complex lattice structures from advanced materials. Building on this progress, the present study investigates the compressive properties and energy absorption capabilities of four distinct lattice structures at three relative densities (20 %, 30 %, and 40 %), specifically tailored for implant applications. The lattices, fabricated from Ti6Al4V ELI alloy using the Laser Powder Bed Fusion (LPBF) method, include a hybrid Fluorite-BCC lattice, a SplitP TPMS skeletal lattice, a stochastic Voronoi lattice, and a hybrid Voronoi-TPMS Gyroid lattice. Compressive properties, including the Elastic Modulus of Compression (EMC), Compressive Yield Strength, and Ultimate Compressive Strength, were systematically analyzed alongside energy absorption capabilities. The Gibson-Ashby model was employed to quantify the relationship between compressive properties and relative density. Additionally, Digital Image Correlation and high-resolution imaging during compression tests provided detailed insights into deformation mechanisms and collapse behavior. The findings offer valuable insights into the compressive performance of these novel hybrid lattices, highlighting their potential applicability in bioimplant applications, with the Fluorite-BCC lattice exhibits the highest EMC and the Fluorite-BCC and SplitP lattices exhibit higher energy absorption capabilities. © 2025 The Author(s)

关键词： Machinability

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Medical Image Segmentation by Improved Nested Unet 2nd

Medical Image Segmentation by Improved Nested Unet

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2nd International Conference on Intelligent Systems and Data science, ISDS 2024

作者： Tran, Song-Toan Le, Minh-Hai Nguyen, Thai-Son Nghi, Vinh-Khanh Nguyen, Thanh-Nguyen School of Engineering and Technology Tra Vinh University Tra Vinh87000 Viet Nam Resource Development Institute Tra Vinh University Tra Vinh87000 Viet Nam PhD program in Computer Science Tra Vinh University Tra Vinh87000 Viet Nam Facilities Management Department Tien Giang University Tien Giang84000 Viet Nam

ISBN: (纸本)9789819796120

Deep learning is revolutionizing the field of medical image segmentation. The U-shaped (Unet) model, with its encoder-decoder architecture and skip connections, has become the dominant architecture for this task. However, variations like Nested-Unet are necessary to address the challenge of adapting network size for datasets of varying scales. This study proposes a novel model based on a multilayer Unet architecture that leverages all output features within nodes of a Nested Unet network. We evaluate the proposed model on four key medical image datasets: electron microscopy (EM), nuclei, polyps (CVC-ClinicDB), and skin lesions. The model achieves impressive Dice scores: 94.08% for EM, 92.68% for nuclei, 87.20% for skin lesions, and 93.02% for the CVC-ClinicDB dataset. These outstanding results demonstrate the superior performance of our proposed model compared to recent advancements in the field. Remarkably, the model achieves this performance while maintaining a size that is half that of a traditional Unet model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Supporting the Pedagogical Assessment Process in Educational Institutions with Social Background Data Analysis and WTCAi System Integration

Supporting the Pedagogical Assessment Process in Educational...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Éva Karl György Molnár József Cserkó Beáta Orosz Eniko Nagy Várkerti Primary School Várpalota Hungary Széchenyi University Doctoral School of Multidisciplinary Engineering Sciences (MMTDI) Győr Hungary Kandó Kálmán Faculty of Electrical Engineering Budapest Óbuda University Hungary János Faculty of Humanities Education and Social Sciences Széchenyi University Apáczai Csere Győr Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Óbuda University Doctoral School of Innovation Management Budapest Hungary Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty of Informatics Óbuda University Budapest Hungary

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

Primary educational institutions aim to prepare children for further education and successful integration into the workforce. To consistently achieve this, they must meet the demands and expectations imposed by the constantly changing economic and social environments. Research shows that applying a constructive pedagogical approach is the most successful path, best fitting the expectations above. An essential and integral part of the educational process is the pedagogical supervision and evaluation procedure, aimed to assess performance and improve and support the learning process. A sad feature of the current system is that it needs to support individual learning paths sufficiently. Additionally, communication issues arising from the generational gap between teachers and students jeopardize the validity of the assessment tools. Educational institutions' advancement and continuous development can only be realized if the pedagogical supervision and evaluation system progresses and evolves to meet new challenges. Our research aims to develop such a method. We are working on creating a complex evaluation method supported by artificial intelligence (WTCAi - When The Child Ask with AI), which suggests appropriate items for assessment and evaluation to subject teachers. To achieve our main objective, we have planned to collect additional social background data from our students. Through analyzing these data, we hope to uncover numerous correlations that significantly influence the obtained data and results, thereby steering the development of the supervision-evaluation system in the most appropriate direction.

关键词： Economics Data analysis Correlation Fitting Education System integration Artificial intelligence Informatics Machine intelligence

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Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG Dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

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Crack Catcher AI — Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI — Enabling Smart Fracture Mechanics Approa...

引用

Annual International Symposium on Reliability Physics

作者： Gabriel Preston Greer Gonzolez Andrea S. Karnyoto Fitya S. Mozar Matthew M. Henry Mahmud Isnan Kuncahyo S. Nugroho Henry Candra Muhamad Doris Dianing N. N. Putri Tyas K Sari Bens Pardamean Endang Djuana Derrric Speaks Arief S. Budiman Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls OR USA Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls OR USA Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta Indonesia Computer Sci. Dept. BINUS Grad. Program - Master of Computer Sci. Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas TRISAKTI Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas TRISAKTI Jakarta Indonesia Industrial Eng. Dept. BINUS Grad. Program – Master of Industrial Eng. Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher AI uses novel smart fracture mechanics approach with Artificial Intelligence (AI) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability.

关键词： Semiconductor device measurement Three-dimensional displays Accuracy Semiconductor device reliability Silicon Artificial intelligence Bonding Stress Three-dimensional integrated circuits Stress measurement

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Gumbel-Softmax Flow Matching with Straight-Through Guidance for Controllable Biological Sequence Generation

arXiv

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arXiv 2025年

作者： Tang, Sophia Zhang, Yinuo Tong, Alexander Chatterjee, Pranam Department of Biomedical Engineering Duke University United States Management and Technology Program University of Pennsylvania United States Center of Computational Biology Duke-NUS Medical School Singapore Mila Quebec AI Institute Canada Université de Montréal Canada Department of Computer Science Duke University United States Department of Biostatistics and Bioinformatics Duke University United States

Flow matching in the continuous simplex has emerged as a promising strategy for DNA sequence design, but struggles to scale to higher simplex dimensions required for peptide and protein generation. We introduce Gumbel-Softmax Flow and Score Matching, a generative framework on the simplex based on a novel Gumbel-Softmax interpolant with a time-dependent temperature. Using this interpolant, we introduce Gumbel-Softmax Flow Matching by deriving a parameterized velocity field that transports from smooth categorical distributions to distributions concentrated at a single vertex of the simplex. We alternatively present Gumbel-Softmax Score Matching which learns to regress the gradient of the probability density. Our framework enables high-quality, diverse generation and scales efficiently to higher-dimensional simplices. To enable training-free guidance, we propose Straight-Through Guided Flows (STGFlow), a classifier-based guidance method that leverages straight-through estimators to steer the unconditional velocity field toward optimal vertices of the simplex. STGFlow enables efficient inference-time guidance using classifiers pre-trained on clean sequences, and can be used with any discrete flow method. Together, these components form a robust framework for controllable de novo sequence generation. We demonstrate state-of-the-art performance in conditional DNA promoter design, sequence-only protein generation, and target-binding peptide design for rare disease treatment. © 2025, CC BY-NC-ND.

关键词： Velocity distribution

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GAITGen: Disentangled Motion-Pathology Impaired Gait Generative Model – Bringing Motion Generation to the Clinical Domain

arXiv

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arXiv 2025年

作者： Adeli, Vida Mehraban, Soroush Mirmehdi, Majid Whone, Alan Filtjens, Benjamin Dadashzadeh, Amirhossein Fasano, Alfonso Iaboni, Andrea Taati, Babak University of Toronto Computer Science Department Canada Vector Institute Canada KITE Research Institute UHN Canada University of Toronto Institute of Biomedical Engineering Canada University of Bristol School of Computer Science United Kingdom University of Bristol Translational Health Science United Kingdom University of Toronto Data Sciences Institute Canada University of Toronto Department of Medicine Division of Neurology Canada Krembil Research Institute UHN Canada Edmond J. Safra Program in Parkinson’s Disease UHN Canada University of Toronto Department of Psychiatry Canada Centre for Mental Health UHN Canada

Gait analysis is crucial for the diagnosis and monitoring of movement disorders like Parkinson’s Disease. While computer vision models have shown potential for objectively evaluating parkinsonian gait, their effectiveness is limited by scarce clinical datasets and the challenge of collecting large and well-labelled data, impacting model accuracy and risk of bias. To address these gaps, we propose GAITGen, a novel framework that generates realistic gait sequences conditioned on specified pathology severity levels. GAITGen employs a Conditional Residual Vector Quantized Variational Autoencoder to learn disentangled representations of motion dynamics and pathology-specific factors, coupled with Mask and Residual Transformers for conditioned sequence generation. GAITGen generates realistic, diverse gait sequences across severity levels, enriching datasets and enabling large-scale model training in parkinsonian gait analysis. Experiments on our new PD-GaM (real) dataset demonstrate that GAITGen outperforms adapted state-of-the-art models in both reconstruction fidelity and generation quality, accurately capturing critical pathology-specific gait features. A clinical user study confirms the realism and clinical relevance of our generated sequences. Moreover, incorporating GAITGen-generated data into downstream tasks improves parkinsonian gait severity estimation, highlighting its potential for advancing clinical gait analysis. © 2025, CC BY-NC-SA.

关键词： Pathology

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