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arXiv 2024年

作者： Wang, Pengyu Tang, Jiawei Lin, Hin Wang Zhang, Fan Wang, Chaoqun Wang, Jiankun Shi, Ling Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong The Department of Electrical and Computer Engineering The University of Alberta Canada The School of Control Science and Engineering Shandong University Shandong China

—Trajectory planning for quadrotors in cluttered environments has been challenging in recent years. While many trajectory planning frameworks have been successful, there still exists potential for improvements, particularly in enhancing the speed of generating efficient trajectories. In this paper, we present a novel hierarchical trajectory planning framework to reduce computational time and memory usage called MINER-RRT*, which consists of two main components. First, we propose a sampling-based path planning method boosted by neural networks, where the predicted heuristic region accelerates the convergence of rapidly-exploring random trees. Second, we utilize the optimal conditions derived from the quadrotor’s differential flatness properties to construct polynomial trajectories that minimize control effort in multiple stages. Extensive simulation and real-world experimental results demonstrate that, compared to several state-of-the-art (SOTA) approaches, our method can generate high-quality trajectories with better performance in 3D cluttered environments. (Video1) Note to Practitioners—The motivation is the problem of planning trajectories for quadrotor autonomous flight in 3D cluttered and complex scenarios such as wild forest exploration and subterranean environment search-and-rescue. Sampling-based path planning methods are suitable for dealing with the complexity of the physical environment but are not convenient for computing dynamics and their differentials. Optimization-based trajectory generation methods are appropriate for handling various high-order constraints but rely on high-quality initial path solutions. Therefore, this paper combines the advantages of the two methods to propose a novel trajectory planning framework that can generate high-quality trajectories for quadrotors faster than many previous algorithms. We conduct numerous simulations and real-world experiments to verify that our method can be effectively deployed in real scenarios and empo

关键词： Deep neural networks

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Enhancement of Two-Photon Fluorescence Microscopy Through Auxiliary Microspheres for Microscopic Observation

Enhancement of Two-Photon Fluorescence Microscopy Through Au...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Feng Zhang Zijian Jin Chaodi Jiang Shuai Yuan Yongliang Yang Xiaoduo Wang School of Electrical & Control Engineering Shenyang Jianzhu University Shenyang Liaoning Province China School of Computer Science and Engineering Shenyang Jianzhu University Shenyang Liaoning Province China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

At present, it is still a great challenge to improve the observation accuracy of two-photon fluorescence microscope. A new method that using auxiliary microspheres as movable components on the sample is proposed to solve this challenge in this study, it can improve high-resolution imaging. In experiments using two-photon fluorescence microscopy, the half-width height (FWHM) of red fluorescent nanoparticles with a diameter of 20 nm was observed to be 296 nm using a primitive microscope system. In this paper by combining barium titanate microspheres with two-photon fluorescence microscopy, the accuracy of the point spread function of the nanoparticles can be improved by a factor of about 1.65, which results in a significant reduction of their half-width height measurements to 179 nm. The research is of great significance to improve the resolution of two-photon fluorescence microscope. This progress is very valuable for studying biological tissues and cell structures, it provides a new impetus for the sustainable development in the microscopic imaging field.

关键词： Nanoparticles Accuracy Barium Microscopy Refractive index High-resolution imaging Fluorescence Titanium compounds Sustainable development Intelligent systems

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Robust Wheel Speed Control of Electric Vehicles with Respect to Backlash and Dead-time in Powertrains

Robust Wheel Speed Control of Electric Vehicles with Respect...

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IEEE Conference on Vehicle Power and Propulsion (VPPC)

作者： Yussuf Shakhin Ahmad Bala Alhassan Minh Cao Ta Binh-Minh Nguyen Ton Duc Do Dept. of Robotics and Mechatronics School of Engineering and Digital Science (SEDS) Nazarbayev University Astana Kazakhstan Dept. of Electrical Engineering and Computer Engineering University of Sherbrooke Sherbrooke Canada Dept. of Advanced Energy The University of Tokyo Tokyo Japan

ISBN: (数字)9798331541606

ISBN: (纸本)9798331541613

The primary advantage of electric vehicles (EVs) lies in their rapid and precise torque generation. Consequently, the research and development of traction control systems that ensure safety, stability, and reliability in EVs are becoming increasingly popular. In this paper, an analysis of the wheel-speed control of an EV under various operating conditions is presented. A double-loop cascade disturbance observer (DOB) was designed to estimate the wheel speed from the estimation of the total disturbance acting on the vehicle. The influence of gear backlash, torque delay, air drag, and road inclination were analyzed to investigate the effectiveness of the control system. The preliminary Matlab simulation analysis demonstrated that the DOB-based control design was effective and robust to the effects of external disturbances.

关键词： Torque Uncertainty Simulation Velocity control Wheels Electric vehicles Stability analysis Disturbance observers Delays Standards

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Efficient deep learning model for classifying lung cancer images using normalized stain agnostic feature method and FastAI-2

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PeerJ computer science 2025年 11卷 1-22页

作者： Saxena, Pranshu Singh, Sanjay Kumar Rashid, Mamoon Alshamrani, Sultan S. Alnfiai, Mrim M. School of Computer Science Engineering and Technology Bennett University Greater Noida India University School of Automation and Robotics Guru Gobind Singh Indraprastha University Delhi India School of Information Communication and Technology Bahrain Polytechnic Isa Town Bahrain Department of Information Technology College of Computers and Information Technology Taif University Taif Saudi Arabia

Background: Lung cancer has the highest global fatality rate, with diagnosis primarily relying on histological tissue sample analysis. Accurate classification is critical for treatment planning and patient outcomes. Methods: This study develops a computer-assisted diagnosis system for non-small cell lung cancer histology classification, utilizing the FastAI-2 framework with a modified ResNet-34 architecture. The methodology includes stain normalization using LAB colour space for colour consistency, followed by deep learning-based classification. The proposed model is trained on the LC25000 dataset and compared with VGG11 and SqueezeNet1_1, demonstrating modified ResNet-34’s optimal balance between depth and performance. FastAI-2 enhances computational efficiency, enabling rapid convergence with minimal training time. Results: The proposed system achieved 99.78% accuracy, confirming the effectiveness of automated lung cancer histopathology classification. This study highlights the potential of artificial intelligence (AI)-driven diagnostic tools to assist pathologists by improving accuracy, reducing workload, and enhancing decision-making in clinical settings. Copyright 2025 Saxena et al.

关键词： Deep learning

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Swish-ResNet Method for Faulty Weld Detection

Swish-ResNet Method for Faulty Weld Detection

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International Workshop on Robot Sensing (ROSE)

作者： Muteb Aljasem Mohammad Mayyas Troy Kevin Duke Mikhail Shilov Zahabul Islam Mohammad Abouheaf Wail Gueaieb Robotics Engineering Bowling Green State University Bowling Green OH USA Mechanical and Manufacturing Engineering Bowling Green State University Bowling Green OH USA School of Electrical Engineering and Computer Science University of Ottawa Ottawa Ontario Canada

ISBN: (数字)9798350362367

ISBN: (纸本)9798350362374

Welding is a fundamental process commonly used in construction and manufacturing industries to effectively join different objects or materials together. There is a growing demand for accurate and dependable weld classification methods due to the increasing complexity and variety of welding applications. Various computer vision and machine learning (ML) based methods have been applied to identify issues with faulty welds. Deep learning (DL) methods typically outperform conventional ML approaches but require more data to train effectively and can overfit when data is limited. In this work, we propose an end-to-end DL method called “swish-ResNet” for reliable classification of good and faulty welds using limited data. We used data augmentation techniques to increase sample diversity and quantity. Furthermore, we employed swish activation in the ResNet-50 model to address issues related to saturation and overfitting. Specifically, we enhanced the ResNet-50 model by incorporating swish activation to improve its ability to detect complex patterns. Additionally, we added four additional dense layers at the end to refine keypoint selection in our method. Experiments conducted on two diverse datasets demonstrate the effectiveness of our swish-ResNet model for the reliable detection of faulty welds.

关键词： Deep learning Manufacturing industries Accuracy Welding Computational modeling Data augmentation Robot sensing systems

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FDCT: Fast Depth Completion for Transparent Objects

arXiv

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arXiv 2023年

作者： Li, Tianan Chen, Zhehan Liu, Huan Wang, Chen The School of Mechanical Engineering University of Science and Technology Beijing Beijing100083 China The Department of Electrical and Computer Engineering McMaster University Canada The Spatial AI & Robotics Lab The Department of Computer Science and Engineering State University of New York BuffaloNY14260 United States

Depth completion is crucial for many robotic tasks such as autonomous driving, 3-D reconstruction, and manipulation. Despite the significant progress, existing methods remain computationally intensive and often fail to meet the real-time requirements of low-power robotic platforms. Additionally, most methods are designed for opaque objects and struggle with transparent objects due to the special properties of reflection and refraction. To address these challenges, we propose a Fast Depth Completion framework for Transparent objects (FDCT), which also benefits downstream tasks like object pose estimation. To leverage local information and avoid overfitting issues when integrating it with global information, we design a new fusion branch and shortcuts to exploit low-level features and a loss function to suppress overfitting. This results in an accurate and user-friendly depth rectification framework which can recover dense depth estimation from RGB-D images alone. Extensive experiments demonstrate that FDCT can run about 70 FPS with a higher accuracy than the state-of-the-art methods. We also demonstrate that FDCT can improve pose estimation in object grasping tasks. The source code is available at https://***/Nonmy/FDCT. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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A3lign-DFER: Pioneering Comprehensive Dynamic Affective Alignment for Dynamic Facial Expression Recognition with CLIP

arXiv

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arXiv 2024年

作者： Tao, Zeng Wang, Yan Lin, Junxiong Wang, Haoran Mai, Xinji Yu, Jiawen Tong, Xuan Zhou, Ziheng Yan, Shaoqi Zhao, Qing Han, Liyuan Zhang, Wenqiang Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China School of Information Science and Technology Fudan University Shanghai China Institute of Automation Chinese Academy of Sciences Beijing China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

The performance of CLIP in dynamic facial expression recognition (DFER) task doesn’t yield exceptional results as observed in other CLIP-based classification tasks. While CLIP’s primary objective is to achieve alignment between images and text in the feature space, DFER poses challenges due to the abstract nature of text and the dynamic nature of video, making label representation limited and perfect alignment difficult. To address this issue, we have designed A3lign-DFER, which introduces a new DFER labeling paradigm to comprehensively achieve alignment, thus enhancing CLIP’s suitability for the DFER task. Specifically, our A3lign-DFER method is designed with multiple modules that work together to obtain the most suitable expanded-dimensional embeddings for classification and to achieve alignment in three key aspects: affective, dynamic, and bidirectional. We replace the input label text with a learnable Multi-Dimensional Alignment Token (MAT), enabling alignment of text to facial expression video samples in both affective and dynamic dimensions. After CLIP feature extraction, we introduce the Joint Dynamic Alignment Synchronizer (JAS), further facilitating synchronization and alignment in the temporal dimension. Additionally, we implement a Bidirectional Alignment Training Paradigm (BAP) to ensure gradual and steady training of parameters for both modalities. Our insightful and concise A3lign-DFER method achieves state-of-the-art results on multiple DFER datasets, including DFEW, FERV39k, and MAFW. Extensive ablation experiments and visualization studies demonstrate the effectiveness of A3lign-DFER. The code will be available in the future. © 2024, CC BY.

关键词： Alignment

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A 4-channel acoustic sensor system for sound source detection of the service robot

A 4-channel acoustic sensor system for sound source detectio...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Jiaxin Hu Bo Lu Jinlin Liu Tiancheng Zhou Xinjian Fan Xiaoang Liu Dianbiao Dong Long Chen School of Mechanical Engineering Northwestern Polytechnical University Xi’an China Robotics and Microsystems Center School of Mechanical and Electric Engineering Soochow University Suzhou China School of Computer Science & Technology Huazhong University of Science and Technology Wuhan China Tianjin Key Laboratory of Power Transmission and Safety Technology for New Energy Vehicles School of Mechanical Engineering Hebei University of Technology Tianjin China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

Constrained by the physical geometry of microphone arrays on the service robot, there exist limitations on both the minimum and maximum frequency bounds along with the coverage area. While the mobility of a microphone array carried by a robot offers the potential for a broader operational frequency range and a more comprehensive perspective, achieving this necessitates a practically more extensive and densely packed array configuration. This is often accomplished by employing non-synchronous measurements in beamforming. Here, we propose a novel approach for locating broadband sound sources using an asynchronous measurement technique that employs Laplacian-enhanced low-rank tensor completion. Our technique analyzes the tensor data structure within broadband signals and incorporates an alternating-direction approach based on multiplier optimization. Through the simulation study using only four physical channels, our Laplacian-enhanced algorithm has demonstrated the ability to generate a highly accurate sound map capturing distinct speech signal sources, thus providing a superior global view compared to the alternative convex surrogate. This advancement holds substantial promise for applications in which precise localization of broadband sound sources is paramount.

关键词： Location awareness Tensors Service robots Simulation Prototypes Microphone arrays Real-time systems

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Toward AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年第11期12卷 16990-16997页

作者： Ahmed, Imran Ahmad, Misbah Siddiqi, Muftooh Ur Rehman Chehri, Abdellah Jeon, Gwangill Anglia Ruskin University School of Computing and Information Science CambridgeCB1 1PT United Kingdom University of the West of England Centre for Machine Vision Bristol Robotics Laboratory BristolBS16 1QY United Kingdom Hartpury University Department of Animal and Agriculture GloucesterGL19 3BE United Kingdom Aston University College of Engineering and Physical Sciences BirminghamB4 7ET United Kingdom Royal Military College of Canada Department of Mathematics and Computer Science KingstonONK7K 7B4 Canada Incheon National University Department of Embedded Systems Engineering Incheon22012 Korea Republic of

In the rapidly advancing field of intelligent transportation systems, integrating artificial intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning-based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios-based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model’s effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model’s capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step toward recognizing the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology. © 2014 IEEE.

关键词： Artificial intelligence (AI) autonomous vehicles deep leaning edge computing Internet of Vehicles (IoV)

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Digital Technology in Health Education Trend Mapping through Bibliometrics Analysis 4

Digital Technology in Health Education Trend Mapping through...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Maulana, Fairuz Iqbal Lestari, Dian Widartha, Vandha Pradwiyasma Haidar, Nirwana Dirgantara, Wahyu School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Airlangga University Faculty of Medicine Surabaya60132 Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia University of Madura Informatics Engineering Study Program Pamekasan Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9781665473279

This study examines the mapping of research data on digital technology in the field of health education using bibliometric analysis method. Data was collected by identifying keywords in the Scopus database and sorting them out to sort the studies from the last 10 years (2012-2021). Through this step, a total of 1482 documents were obtained (articles, journals, proceedings, books, and others). The data is then processed using the VOSViewer instrument to obtain a visualization of the mapping analysis. This study also analyzes the network types of authors and co-authors through the VOSViewer instrument. The result of this study indicates that the most document types published within ten years are Articles (66.5%), Review (22.9%), Conference Paper (4.2%), and others. The most studied subjects are Medicine (55.7%), Nursing (9.0%), Health Professions (8.2%), Social sciences (7.8%), engineering (3.9%), computer science (2.6%), Environmental science (2.6%), Biochemistry-Genetics and Molecular Biology (2.2%), Psychology (1.6%), and Dentistry (1.3%). This study offers a written communication process and the nature and direction of developing descriptive means of counting and analyzing the various phases of communication as well as recognizing the authorship and direction of its symptoms in documents on the subject of digital technology in the health sector. © 2022 IEEE.

关键词： Mapping

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