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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Santos, Leonardo Moon, Brady Scherer, Sebastian Nguyen, Hoa Van School of Engineering Federal University of Minas Gerais MG Belo Horizonte Brazil Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States Department of Electrical and Computer Engineering Curtin University BentleyWA6102 Australia

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Path planning for autonomous search and tracking of multiple objects is a critical problem in applications such as reconnaissance, surveillance, and data gathering. Due to the inherent competing objectives of searching for new objects while maintaining tracks for found objects, most current approaches rely on multi-objective planning methods, leaving it up to the user to tune parameters to balance between the two objectives, usually based on heuristics or trial and error. In this paper, we introduce UniSaT (Unified Search and Track), a novel unifiedobjective formulation for the search and track problem based on Random Finite Sets (RFS). Our approach models unknown and known objects using a combined generalized labeled multi-Bernoulli (GLMB) filter. For unseen objects, UniSaT leverages both cardinality and spatial prior distributions, allowing it to operate without prior knowledge of the exact number of objects in the search space. The planner maximizes the mutual information of this unified belief model, creating balanced search and tracking behaviors. We demonstrate our work in a simulated environment, presenting both qualitative results and quantitative improvements over a multi-objective method. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Object tracking

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Fundus Image Classification via an Integrated Deep Learning Model and Random Forest for Glaucoma Diagnostics 2nd

Fundus Image Classification via an Integrated Deep Learnin...

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2nd International Conference on Intelligent Manufacturing and robotics, ICIMR 2024

作者： Zeng, Haotian Cong, Jiheng Hong, Hantong Yu, Zihan Zhang, Fan Li, Guojie School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China School of Robotics Xi’an Jiaotong-Liverpool University Suzhou China Hebei University of Environmental Engineering Qinhuangdao China School of Electrical Engineering Electronics and Computer Science University of Liverpool Liverpool United Kingdom

ISBN: (纸本)9789819639489

Glaucoma is a leading cause of irreversible blindness, often worsened by delayed diagnosis. Traditional diagnostic methods have limitations in early detection and require significant expertise. Recent advancements in machine learning and deep learning have shown promise in enhancing the diagnostic accuracy and efficiency of glaucoma detection using fundus imagery. This study presents a four-stage method for glaucoma classification. Fundus images are processed using VGG16 and ViT for feature extraction, capturing both local and global features. These features are then fused and reduced in dimensionality using Principal Component Analysis. Finally, the reduced features are classified using a Random Forest classifier. The integrated feature fusion model demonstrates significant improvements in diagnostic performance, achieving higher accuracy, specificity, and sensitivity in distinguishing between non-referable and referable glaucoma classes compared to traditional methods. Specifically, our model achieved an accuracy of 94.2%, an F1 score of 94.2%, a sensitivity of 94.44%, and a specificity of 94.0%. The use of both CNNs and ViTs for feature extraction leverages their strengths, resulting in a more effective diagnostic tool. The combination of CNNs, ViTs, and Random Forest classifiers, along with advanced data augmentation techniques, shows substantial potential for early glaucoma detection and ongoing monitoring. This approach addresses the limitations of current machine learning models and enhances diagnostic accuracy and efficiency, making it a promising tool for clinical settings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ophthalmology

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Exploiting Cross-Modal Cost Volume for Multi-sensor Depth Estimation 17th

Exploiting Cross-Modal Cost Volume for Multi-sensor Depth E...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Kim, Janghyun Shin, Ukcheol Heo, Seokyong Park, Jinsun Department of Information Convergence Engineering Pusan National University Busan Korea Republic of Robotics Institute Carnegie Mellon University Pittsburgh United States School of Computer Science and Engineering Pusan National University Busan Korea Republic of

ISBN: (纸本)9789819609680

Single-modal depth estimation has shown steady improvement over the years. However, relying solely on a single imaging sensor such as RGB and near-infrared (NIR) cameras can result in unreliable and erroneous depth estimation, particularly in challenging lighting conditions such as low-light or sudden lighting change scenarios. Thereby, several approaches have leveraged multiple sensors for robust depth estimation. However, the effective fusion method that maximally utilizes multi-modal sensor information still requires further investigation. With this in mind, we propose a multi-modal cost volume fusion strategy with cross-modal attention, incorporating information from both cross-spectral and single-modality pairs. Our method initially constructs low-level cost volumes that consist of modality-specific (ı.e., single modality) and modality-invariant (ı.e., cross-spectral) volumes from multi-modal sensors. These cost volumes are then gradually fused using bidirectional cross-modal fusion and unidirectional LiDAR fusion to generate a multi-sensory cost volume. Furthermore, we introduce a straightforward domain gap reduction approach to learn modality-invariant features and depth refinement techniques through cost volume-guided propagation. Experimental results demonstrate that our method achieves SOTA (State-of-the-Art) performance under diverse environmental changes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Depth perception

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II-NVM: Enhancing Map Accuracy and Consistency With Normal Vector-Assisted Mapping

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IEEE robotics and Automation Letters 2025年第6期10卷 5465-5472页

作者： Zhao, Chengwei Li, Yixuan Jian, Yina Xu, Jie Wang, Linji Ma, Yongxin Jin, Xinglai Xi'an Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an710049 China Hangzhou Qisheng Intelligent Techology Company Ltd Hangzhou311217 China Columbia University in the City of New York Department of Computer Science New YorkNY10027 United States Harbin Institute of Technology State Key Laboratory of Robotics and Systems Harbin150001 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore

SLAM technology plays a crucial role in indoor mapping and localization. A common challenge in indoor environments is the "double-sided mapping issue", where closely positioned walls, doors, and other surfaces are mistakenly identified as a single plane, significantly hindering map accuracy and consistency. To addressing this issue this letter introduces a SLAM approach that ensures accurate mapping using normal vector consistency. We enhance the voxel map structure to store both point cloud data and normal vector information, enabling the system to evaluate consistency during nearest neighbor searches and map updates. This process distinguishes between the front and back sides of surfaces, preventing incorrect point-to-plane constraints. Moreover, we implement an adaptive radius KD-tree search method that dynamically adjusts the search radius based on the local density of the point cloud, thereby enhancing the accuracy of normal vector calculations. To further improve real-time performance and storage efficiency, we incorporate a Least Recently Used (LRU) cache strategy, which facilitates efficient incremental updates of the voxel map. The code is released as open-source and validated in both simulated environments and real indoor scenarios. Experimental results demonstrate that this approach effectively resolves the "double-sided mapping issue" and significantly improves mapping precision. Additionally, we have developed and open-sourced the first simulation and real-world dataset specifically tailored for the "double-sided mapping issue". © 2016 IEEE.

关键词： Mapping

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A General Framework for Clustering and Distribution Matching with Bandit Feedback

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IEEE Transactions on Information Theory 2025年第3期71卷 2116-2139页

作者： Yavas, Recep Can Huang, Yuqi Tan, Vincent Y. F. Scarlett, Jonathan CNRS@CREATE DesCartes Program CREATE Tower 138602 Singapore National University of Singapore Department of Computer Science Queenstown119077 Singapore National University of Singapore School of Computing Department of Information Systems and Analytics Queenstown119077 Singapore National University of Singapore Department of Mathematics Department of Electrical and Computer Engineering Queenstown119077 Singapore Institute of Data Science National University of Singapore Department of Computer Science The Department of Mathematics Queenstown119077 Singapore

We develop a general framework for clustering and distribution matching problems with bandit feedback. We consider a K-armed bandit model where some subset of K arms is partitioned into M groups. Within each group, the random variable associated to each arm follows the same distribution on a finite alphabet. At each time step, the decision maker pulls an arm and observes its outcome from the random variable associated to that arm. Subsequent arm pulls depend on the history of arm pulls and their outcomes. The decision maker has no knowledge of the distributions of the arms or the underlying partitions. The task is to devise an online algorithm to learn the underlying partition of arms with the least number of arm pulls on average and with an error probability not exceeding a pre-determined value δ. Several existing problems fall under our general framework, including finding M pairs of arms, odd arm identification, and N-ary clustering of K arms belong to our general framework. We derive a non-asymptotic lower bound on the average number of arm pulls for any online algorithm with an error probability not exceeding δ. Furthermore, we develop a computationally-efficient online algorithm based on the Track-and-Stop method and Frank-Wolfe algorithm, and show that the average number of arm pulls of our algorithm asymptotically matches that of the lower bound. Our refined analysis also uncovers a novel bound on the speed at which the average number of arm pulls of our algorithm converges to the fundamental limit as δ vanishes. © 1963-2012 IEEE.

关键词： Random variables

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Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari Surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, Supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Blockchain-Enabled Secure Offloading for VEC: A Multi-Agent Reinforcement Learning Approach

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2978-2995页

作者： Lu, Xiaozhen Xiao, Liang Xiao, Yilin Xiong, Zehui Liu, Zhe Zhang, Yanyong Zhuang, Weihua Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China Xiamen University Department of Information and Communication Engineering Xiamen361005 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design 487372 Singapore Zhejiang Lab Hangzhou310027 China University of Science and Technology of China School of Computer Science and Technology Hefei230026 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Vehicular edge computing (VEC) helps improve the task computational performance of vehicles on roads but has difficulty in defending against eavesdropping and selfish attacks simultaneously. In this paper, we design a reputation-based smart contract with blockchain and propose a multi-agent reinforcement learning (RL) based secure offloading scheme for VEC against both eavesdropping and selfish attacks. This scheme has a three-level hierarchical structure for each vehicle and uses the reputations obtained from the blockchain as the basis to optimize the edge node selection, offloading ratio, and power allocation, which aims to reduce the task computational latency, the vehicle energy consumption and eavesdropping rate. By using a punishment function based on the constraints, this scheme avoids exploring dangerous policies that can cause task failure or severe data leakage. A multi-agent deep RL-based secure offloading scheme is proposed for vehicles with sufficient resources, which evaluates the long-term risk rather than the punishment function to further improve the secure offloading performance. The regret bound is analyzedand the cumulative reward upper bound is provided. Simulation results verify the effectiveness of our schemes as compared with the benchmark. © 2004-2012 IEEE.

关键词： Computation offloading

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Breast Tumor Classification with Fine-Tuned Hyperparameter Training using Deep Learning Models

Breast Tumor Classification with Fine-Tuned Hyperparameter T...

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2025 AI-Driven Smart Healthcare for Society 5.0, AdSoc5.0 2025

作者： Maini, Yash Singh, Sanjay Kumar Saxena, Pranshu University School of Automation and Robotics Guru Gobind Singh Indraprastha University East Delhi Campus Surajmal Vihar Delhi110092 India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798331536336

Breast tumor classification plays a crucial role in early detection and treatment planning for breast cancer. In recent years, deep learning models have shown remarkable success in image-based medical diagnosis. This study focuses on improving breast tumor classification using fine-tuned hyperparameter training of deep learning models. By optimizing key parameters such as learning rate, batch size, and network architecture, the classification accuracy of models like ResNet, AlexNet, GoogleNet and VGG is enhanced. The proposed approach leverages advanced techniques such as transfer learning, data augmentation, and hyperparameter optimization algorithms (Bayesian Optimization along with population-based training) to achieve robust performance on breast cancer datasets. The proposed method is able to achieve 98.46% accuracy while classifying breast cancer dataset. Experimental results demonstrate significant improvements in classification accuracy, precision, recall, and F1-score compared to conventional approaches, making the fine-tuned models more suitable for clinical applications. The findings suggest that hyperparameter tuning in deep learning models is critical to improving model performance and generalization in breast tumor classification tasks. © 2025 IEEE.

关键词： Deep learning

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Deep-Learning Approach for An Analysis of Real-estate Prices and Transactions

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IEEE Access 2025年 13卷 89248-89265页

作者： Yang, Cheng-Hong Lee, Borcy Lin, Yu-Da Tainan University of Technology Department of Information Management Tainan Taiwan National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung Taiwan Kaohsiung Medical University Ph.D. Program in Biomedical Engineering Kaohsiung Taiwan Kaohsiung Medical University School of Dentistry Kaohsiung Taiwan Kaohsiung Medical University Drug Development and Value Creation Research Center Kaohsiung Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan

Research on the study of houses, condominiums and buildings in Taiwan’s metropolitan areas continues to be an important area of research. In real estate forecasting and analysis, methods such as statistical analysis and questionnaire data collection are widely used. However, when multidimensional data is considered, these methods are time-consuming and inadequate. This study aimed to build a real estate forecasting model that can adapt to a changing environment. Data were collected from public government databases, the collected data were standardized for accurate clustering, an appropriate data clustering algorithm was applied to the standardized data, and cross-statistical analysis was performed to verify the adopted algorithm. We used a deep learning based on autoencoder algorithm to increase the accuracy of the clustering analysis. A double-bottom map particle swarm optimization (DBM-PSO) clustering algorithm was then used to determine the optimal clustering solution. Cluster analysis and deep learning were conducted on data collected from public websites to understand the factors that led to the sustained increase in housing prices in Taiwan over the past decade. The results of this study indicate that three key factors—the number of real estate transactions, the average unit price of real estate transactions, and the building material and construction index—significantly affected real estate prices in Taiwan. Our results could help researchers and governments to focus on specific aspects of real estate development without being influenced by other related factors. In addition, the relationships between real estate trends and the aforementioned three key factors were determined to obtain valuable information that can enable the Taiwanese government to regulate the property market and prevent excessive growth. The framework proposed in this paper allows researchers and governments to focus on specific aspects of real estate development without being influenced b

关键词： Houses

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