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共 2685 条记录，以下是411-420 订阅

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Rotated Vehicle Object Detection and Tracking on Drone-captured Scenarios 3

Rotated Vehicle Object Detection and Tracking on Drone-captu...

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3rd international conference on Computer Vision and Pattern Analysis, ICCPA 2023

作者： Zhou, Minglong Li, Ning Miao, Lize Zhou, Huiyu College of Eletronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China School of Informatics University of Leicester Leicester United Kingdom

ISBN: (纸本)9781510667563

The vehicle object detection and tracking on drone-captured scenarios will play a significant role in the intelligent transportation in future. In order to provide accurate orientation and scale information, the angle vector module is introduced to improve the model to realize the rotated detection. For the small and densely distributed objects, the C3_TR structure is proposed to improve the detection accuracy. The image weight and the category weight are used in training to improve accuracy on the self-built vehicle dataset with the category imbalance. Finally, the mAP of the improved YOLOv5 detector is 82.4% higher than the baseline model by 8.1 percentage points. The angle parameter is taken into account and the rotated IOU matching is used instead of the horizontal IOU matching to realize the tracking with rotated bounding boxes by DeepSORT. In tracking evaluation results on the self-built dataset, the HOTA, MOTA, MOTP and ID_SW are 76.86%, 92.8%, 81.3% and 2, respectively. © 2023 SPIE.

关键词： Drones

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Predicting Mushroom Edibility with Effective Classification and Efficient Feature Selection Techniques

Predicting Mushroom Edibility with Effective Classification ...

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international conference on robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Md. Samin Morshed Faisal Bin Ashraf Muhammad Usama Islam Md. Shafiur Raihan Shafi Department of Computer Science and Engineering Islamic University of Technology Department of CSE BRAC University School of Computing and Informatics UL Lafayette Department of CSE Southeast University

Machine learning in agriculture has added a new dimension to research field through providing actionable insights for better crop yield. In our work, we have explored various types of mushrooms and utilized efficient feature selection techniques coupled with effective machine learning based classification methods to classify the edibility of mushrooms automatically. Through experimentation with nine distinct machine learning methods and twenty selected features, the result shows that our best model (k-NN) performed significantly better with an accuracy of 99% and F-1 score of 99% in comparison to other machine learning methods. Our research work provided us with valuable actionable insights to devise further scopes in research in the field of mushroom edibility predicting through machine learning.

关键词： Support vector machines Machine learning Signal processing Feature extraction Agriculture Reproducibility of results Robots

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Unifying Classification and Bounding Box Regression Head for Object Detection 3

Unifying Classification and Bounding Box Regression Head for...

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2021 3rd international conference on robotics, Intelligent control and Artificial Intelligence, ICRICA 2021

作者： Gao, Cunzhang Gu, Haitao Yu, Siquan Li, Xingzhen State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Liaoning China University of Chinese Academy of Sciences Shenyang Liaoning China Shenyang University of Technology Shenyang Liaoning China

Object detection usually includes two parts: objection classification and location. At present, the popular object detectors usually use two detection heads: one head is used to predict classification score, and the other one is used to predict the bounding box (bbox), respectively. In this paper, we first stack classification head after feature extract convolutional neural networks of bbox regression head. Then, we establish the classification networks by using a bounding box feature. The bounding box feature is very useful when the classification head uses soft Intersection over Union (IoU) labels. In experiment parts, only using PASCAL VOC 2007 datasets, soft Centerness labels, and soft IoU labels get 50.06 mAP and 52.08 mAP on VOC 2007 test. Compared with FCOS, they have 1.08% and 1.12% improvements. Using PASCAL VOC 2007 and 2012 datasets, our Union A*B head gets 78.71mAP after 12 epochs training with ResNet-101 as backbone and FPN as the neck. Extensive experimental results show that the proposed algorithm is superior to other detection methods. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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Bio-Signal Activated FPGA-Based System for Robotic-Assisted Rehabilitation

Bio-Signal Activated FPGA-Based System for Robotic-Assisted ...

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robotics, automation and Artificial Intelligence (RAAI), IEEE international conference on

作者： Hrishikesh H Pillai Lal Priya P S Kavinga Upul Ekanayaka Jackrit Suthakorn Branesh M. Pillai College of Engineering Trivandrum APJ Abdul Kalam Technological University Thiruvananthapuram Kerala India ACCELR Colombo Western Province Sri Lanka Center for Biomedical and Robotics Technology (BART LAB) Faculty of Engineering Mahidol University Thailand

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

This paper presents a pivotal contribution to multi-modal bio-signal processing for robotic control applications, leveraging cutting-edge Field Programmable Gate Array (FPGA) technology to unlock new frontiers in accuracy, efficiency, and real-time control. The comprehensive hardware implementation strategy is characterized by the strategic utilization of a Fast Fourier Transform/ Inverse Fast Fourier Transform (FFT/IFFT) - based filtering algorithm optimized for Electroencephalogra-phy (EEG) signal processing. Concurrently, an inventive movement classification methodology powered by Electrooculography (EOG) signals is introduced. The core proposition of the paper revolves around the implementation of an EEG and EOG-driven Multi Input Single Output (MISO) control system, intended for the control of rehabilitation robots. The effectiveness of the proposed method was verified by simulation in Lab VIEW. The algorithm has been seamlessly integrated into the My Rio 1900 hardware platform, leveraging the immense processing power of a Xilinx FPGA.

关键词： Electrooculography Signal processing algorithms Filtering algorithms MISO communication control systems Electroencephalography Hardware

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Deep Learning Precision Farming: Identification of Bangladeshi-Grown Fruits Using Transfer Learning-Based Detection 3rd

Deep Learning Precision Farming: Identification of Banglades...

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3rd international conference on Trends in Electronics and Health informatics, TEHI 2023

作者： Siddiki, Marjuk Ahmed Rony, Mohammad Abu Tareq Naim Hossain, Md. Saha, Pritom Islam, Mohammad Shariful Ahmed, Ishtiak Satu, Shoykth Shaharior Ahammad, Mejbah Nazmul Alam, Shah Md. Computer Science and Engineering Daffodil International University Dhaka Bangladesh Noakhali3814 Bangladesh Noakhali3814 Bangladesh Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Computer Science and Engineering Shaikh Burhanuddin Post Graduate College Dhaka Bangladesh Computer Science and Engineering American International University Bangladesh Dhaka Bangladesh Masters of Computer Science University Arizona State University TempeAZ United States

ISBN: (纸本)9789819739363

The dynamic potential of Convolutional Neural Networks (CNN) enhanced by Transfer Learning offers a promising avenue for object recognition within the realm of agriculture. To explore this potential in the context of Bangladeshi agriculture, this study leverages CNN enhanced by Transfer Learning to identify Bangladeshi-grown fruits from a dataset featuring 24 distinct types, including Mango, Jackfruits, and Lychee, among others. Utilizing an extensive dataset that encapsulated high-quality images of these fruits, we embarked on a comparative analysis of six advanced CNN architectures: VGG19, Xception, ResNet152V2, MobileNet, DenseNet201, and NASNetLarge. The results were illuminating. While VGG19 lagged behind with an accuracy of 48%, most models showcased impressive performance, with DenseNet201 emerging as the frontrunner at a remarkable 95% accuracy. The findings not only underscore the transformative role of Transfer Learning in optimizing CNN performance, especially when faced with limited labeled datasets, but also spotlight the applicability of these models in revolutionizing quality control and management within the Bangladeshi agricultural sector. As the demand for automated and accurate agricultural systems gains traction globally, the insights from this research offer a foundation for further exploration and application in similar contexts, demonstrating the scalability and adaptability of Transfer Learning-enhanced CNNs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Design a High-Frequency Dual Active Bridge Converter for Discrete Time Frequency

Design a High-Frequency Dual Active Bridge Converter for Dis...

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Advancements in Electrical, Electronics, Communication, Computing and automation (ICAECA), international conference on

作者： M. Karthik G. Hemalatha M. Mathankumar L. Sreevidya R. Deepalakshmi V. Kandasamy Department of Electrical and Electronics Engineering SRM Madurai College for Engineering and Technology Pottapalayam Tamil Nadu India Department of Robotics and Automation Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Trichy Tamil Nadu India Department of Electrical and Electronics Engineering V.S.B College of Engineering Technical Campus Coimbatore Tamil Nadu India Department of Computer Science Engineering SRM Madurai College for Engineering and Technology Pottapalayam Tamil Nadu India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The dual active bridge (DAB) has become a crucial interface element in today's sophisticated systems. Renewable energy sources produce DC power, which is then stored in batteries, ultra-capacitors, and other energy storage devices. DAB is a crucial part of power exchange between two different buses because of its galvanic separation and bidirectional power transfer capabilities. The operation of the DAB is compatible with a broad range of input voltages and output loads. The creation of DAB is covered in most works. A well-known semiconductor integrated circuit with programmable control parameters is used in this straightforward, inexpensive method.

关键词： Time-frequency analysis Renewable energy sources Costs Bridge circuits Switched reluctance motors Stability analysis Batteries Telecommunication computing Time-domain analysis Stress

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Latent 3D Scene Graph with Aligned Visual-Language Perception for Object-Goal Navigation 3

Latent 3D Scene Graph with Aligned Visual-Language Perceptio...

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3rd international conference on Mechatronics, control and robotics, ICMCR 2025

作者： Zhang, Jianwei Zhou, Kang Yang, Junjie Li, Xiaojian College of Engineering Southern University of Science and Technology Shen Zhen China School of Computer Science City University of Hong Kong Hong Kong Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shen Zhen China

ISBN: (纸本)9798331544546

Object-goal navigation requires agents to accurately locate and navigate to specified target objects in complex indoor environments. Existing methods primarily rely on single visual observations or simple semantic matching, making it difficult to effectively understand 3D scene relationships between target objects, which leads to inefficient path planning during navigation. Furthermore, the feature discrepancy between visual and language modalities poses challenges for accurate target identification. To address these issues, we propose Latent 3D Scene Graph with Aligned Visual-Language Perception (LSGAP), a novel approach that explicitly models 3D scene relationships between objects through depth perception and latent 3D scene graph construction. Meanwhile, we leverage multimodal pretraining strategies to achieve effective visual-language feature alignment, enhancing the model's target recognition capabilities. This end-to-end architectural design improves spatial cognition ability and enables more efficient navigation decision-making. We conduct extensive experimental evaluations in the AI2THOR interactive environment to validate the effectiveness of our navigator. © 2025 IEEE.

关键词： Depth perception

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Deformable Feature Fusion Network for Multi-Modal 3D Object Detection

Deformable Feature Fusion Network for Multi-Modal 3D Object ...

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robotics, Artificial Intelligence and Intelligent control (RAIIC), international conference on

作者： Kun Guo Tong Gan Zhao Ding Qiang Ling Department of Automation University of Science and Technology of China Hefei China Research & Development Department Anhui ShineAuto Autonomous Driving Technology Co. Ltd Hefei China Institute of Intelligent & Networked Automobile Anhui JiangHuai Automobile Group Co. Ltd Hefei China

ISBN: (数字)9798350352627

ISBN: (纸本)9798350352634

LiDAR and cameras are two widely used sensors in 3D object detection. LiDAR point clouds show geometry knowledge of objects, while RGB images provide semantic information, such as color and texture. How to effectively fuse their features is the key to improving detection performance. This paper proposes a Deformable Feature Fusion Network, which performs LiDAR-camera fusion in a flexible way. We present multi-modal features in the bird’s-eye view(BEV), and build a Deformable-Attention Fusion(DAF) module to conduct feature fusion. Besides fusion methods, feature alignment is also important in multi-modal detection. Data augmentation of point clouds may change the projection relationship between RGB images and LiDAR point clouds and causes feature misalignment. We introduce a Feature Alignment Transform(FAT) module and alleviate the problem without introducing any trainable parameters. We conduct experiments on the KITTI dataset to evaluate the effectiveness of proposed modules and the experiment results show that our method outperforms most existing methods.

关键词： Point cloud compression Solid modeling Three-dimensional displays Laser radar Robot kinematics Semantics Object detection

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The effect of oxaliplatin on the immunogenic cell death and cell apoptosis of human merkel cell cancerous tumor

The effect of oxaliplatin on the immunogenic cell death and ...

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3rd international conference on Biomedicine and Bioinformatics Engineering, ICBBE 2023

作者： Zhang, Boyang Department of Cell Molecular Biology University of Toronto TorontoM5R0A3 Canada

ISBN: (纸本)9781643684321

Oxaliplatin, as previously studied in the paper, is a derivative of Cisplatin that is effective in treating the Lewis Lung Carcinoma (LLC)4. As it can actively induce immunogenic cell death of the cancer cells, and result in apoptosis, which increases the therapeutic efficacy in the LLC cancer treatment.4 Merkel cell caner is a type of skin cancer that is rare but highly aggressive, with high metastasizing and reoccurring rate. In this study, we aim the determine the potential of Oxaliplatin to induce apoptosis and ICD in cancerous Merkel cell line MCC1, in associate with the PD-1 inhibitor Nivolumab. The cancer cells will be treated with Oxaliplatin at concentrations 1 mM, 10 mM, or 100 mM. Avelumab and PBS will be used as the positive and negative control, respectively. The treated cells will be measured by checking for tumor size change in confocal microscopy and MTT assay, measuring the ICD using flow cytometry analysis of CRT expression, and conducting Western Blot for Cytokeratin 20 expression. The results of the study will provide insights on the potential of Oxaliplatin as a treatment of Merkel Cell Cancer in the future. © 2023 IOS Press. All rights reserved.

关键词： Cancer cells

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Robotic Drug Delivery in the Healthcare Sector

Robotic Drug Delivery in the Healthcare Sector

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Disruptive Technologies (ICDT), international conference on

作者： Mishthi Rastogi Jagjeevan Kanoujiya Bhakti Agarwal Manipal Institute of Technology Manipal India Symbiosis Institute of Business Management Nagpur Symbiosis International University Pune India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

This paper examines the role of robotics in healthcare, with a focus on robotic drug delivery systems. robotics has significantly advanced drug administration by improving precision, reducing side effects, and increasing patient compliance. Technologies such as RoboCap and PillCam have revolutionized drug delivery by being able to deliver treatment where other methods cannot reach. Pill like robotics can enter the GI tract by swallowing where they perform drug delivery and diagnostics better than conventional practices. It also looks at the applicability of microrobotics in minimally invasive surgeries, which therefore can include targeted drug delivery. However, challenges remain, especially in controlling robotic devices within the body. Ethical concerns, costs, and technical issues need addressing as these technologies evolve. Future research should focus on refining control systems and AI to further enhance robotic drug delivery and improve healthcare outcomes, particularly in underserved regions like India.

关键词： Drugs Performance evaluation Technological innovation Targeted drug delivery Refining Medical services Drug delivery Artificial intelligence Robots Remote control

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