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Versatile Tasks on Integrated Aerial Platforms Using Only Onboard Sensors: Control, Estimation, and Validation

IEEE Transactions on Robotics 2025年 41卷 3518-3538页

作者： Wang, Kaidi Lai, Ganghua Yu, Yushu Du, Jianrui Sun, Jiali Xu, Bin Franchi, Antonio Sun, Fuchun Beijing Institute of Technology School of Mechatronical Engineering Beijing100081 China Beijing Institute of Technology School of Mechanical Engineering Beijing100081 China University of Twente Robotics and Mechatronics Lab Faculty of Electrical Engineering Mathematics and Computer Science Enschede7500 AE Netherlands Tsinghua University Department of Computer Science and Technology China

Connecting multiple aerial vehicles to a rigid central platform through passive spherical joints holds the potential to construct a fully-actuated aerial platform. The integration of multiple vehicles enhances efficiency in tasks like mapping and object reconnaissance. This paper proposes a control and state estimation framework for the Integrated Aerial Platform (IAP), enabling it to perform versatile tasks like object reconnaissance and physical interactive tasks with only onboard sensors. In the framework, the 6D motion control serves as the low-level controller, while the high-level controller comprises a 6D admittance filter and a perception-aware attitude correction module. The 6D admittance filter, serving as the interaction controller, is adaptable for aerial interaction tasks. The perception-aware attitude correction algorithm is carefully designed by adopting a geometric Model Predictive Controller (MPC). This algorithm, incorporating both offline and online calculations, proves to be well-suited for the intricate dynamics of an IAP. A 6D direct wrench controller is also developed for the IAP. Notably, both the interaction controller and the direct wrench controller operate without reliance on force/torque sensors. Instead, a wrench observer algorithm is devised, considering external disturbances. Additionally, based on the kinematics constraints of the multiple aerials in the platform, a fusion algorithm for multiple Visual-Inertial Odometry (VIO) and kinematics constraints is developed, providing more accurate localization. A prototype of the IAP is constructed, and its capabilities are demonstrated through experiments including perception-aware object reconnaissance, aerial mapping, aerial peg-in-hole task, and 6D contact wrench generation. All experiments are conducted exclusively with onboard sensors. These tasks exemplify the merits of the proposed IAP and validate the effectiveness of the proposed control framework and fusion algorithm. © 2004-2012

关键词： State estimation

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An upgraded variant of backtracking search algorithm and its application to COVID-19 CT images segmentation

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Multimedia Tools and Applications 2025年 1-57页

作者： Debnath, Sanjib Nama, Sukanta Chakraborty, Sanjoy Saha, Apu Kumar Mirjalili, Seyedali Department of Computer Science & Engineering National Institute of Technology Agartala Tripura Agartala799046 India Department of Science & Humanities Gomati District Polytechnic Fulkumari Tripura Udaipur799013 India Department of Information Technology Bir Bikram Memorial College Tripura Agartala India Department of Mathematics National Institute of Technology Agartala Tripura Agartala799046 India Center for Artificial Intelligence Research and Optimization Torrens University Australia Brisbane Australia Faculty of Electrical Engineering and Computer Science VŠB-TU Ostrava Ostrava Czech Republic University Research and Innovation Center Obuda University Budapest Hungary Department of Computer Science & Engineering The ICFAI University Tripura Tripura 799210 India

To improve the performance of the original backtracking search algorithm (BSA), this work combines BSA with a centralized population initialization and an elitism-based local escape operator and proposes a new, improved variant of BSA called CiBSA. BSA's performance is improved by centralized population initialization regarding search capability, convergence speed, and capacity to jump out of the local optimum. The suggested algorithm's elitism-based local escape operator method prevents BSA searching slowness and enhances convergence rates and local search efficiency. The performance of the proposed algorithm has been tested on a set of 54 test suites. An extensive comparison with other recent meta-heuristics algorithms (MA) demonstrates that both methodologies substantially assist BSA in boosting the quality of its solutions and speeding up the convergence rate. The proposed CiBSA is then applied to a real-world problem: image segmentation using Kapur's entropy function. The proposed CiBSA is successfully applied to the segmentation of COVID-19 CT images using Kapur's entropy for grey-scale images. Considering the performance matrices Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), and Intersection over Union (IoU), the suggested CiBSA outperformed conventional MA algorithms. In COVID-19 CT image segmentation trials, CiBSA has superior segmentation capability and adaptability at varied threshold levels than other approaches. As a result, CiBSA has a lot of potential for enhancing COVID-19 diagnosis accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Signal to noise ratio

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Music Therapy-Driven Mood-Based Music Recommendation System Integrating User Emotion, Song Lyrics, and Health Reflections

Music Therapy-Driven Mood-Based Music Recommendation System ...

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

作者： Narayanan, Vyshnav S. Tarafdar, Anurina Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India

ISBN: (纸本)9798331536336

The rapid development of digital media has enabled users to access a colossal choice of music through the internet and electronic devices. Music recommendations are given mostly based on the user’s past listening history and artist preferences. While this approach is effective to some extent, it overlooks the importance of the user’s current mood which is governed by circumstantial as well as health conditions. Music therapy recently is a topic of major interest. This work aims to bridge the gap between music and emotions which is a reflection of health condition and mental state, in music recommendation systems and enhance how people connect with music. The proposed mood-based music recommendation system uses sentiment analysis to assess song lyrics, an interactive chatbot to determine user emotions, and a Random Forest model to categorize song moods based on audio characteristics. Recommendations for music are specifically designed to offer therapeutic advantages, such as stress relief, improved relaxation, or catharsis. The proposed system makes dynamic song recommendations based on the changing emotions of the user, thereby aiding in better absorbance of music. Experimental results demonstrate the system’s high accuracy and potential for improving mental well-being, making it a valuable addition to music streaming platforms and mental health applications. © 2025 IEEE.

关键词： Aseptic conditions

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Movie Recommendation Using Web Crawling 2nd

Movie Recommendation Using Web Crawling

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2nd International Conference on Applied Algorithms, ICAA 2025

作者： Raj, Pronit Kumar, Chandrashekhar Shekhar, Harshit Kumar, Amit Paul, Kritibas Jana, Debasish Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India

ISBN: (纸本)9783031845420

In today’s digital world, streaming platforms offer a vast array of movies, making it hard for users to find content matching their preferences. This paper explores integrating real-time data from popular movie websites using advanced HTML scraping techniques and APIs. It also incorporates a recommendation system trained on a static Kaggle dataset, enhancing the relevance and freshness of suggestions. By combining content-based filtering, collaborative filtering, and a hybrid model, we create a system that utilizes both historical and real-time data for more personalized suggestions. Our methodology shows that incorporating dynamic data not only boosts user satisfaction but also aligns recommendations with current viewing trends. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Data reduction

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Review Rating Prediction Using BiLSTM 14th

Review Rating Prediction Using BiLSTM

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14th International Conference on Frontier Computing, FC 2024

作者： Kim, Yongjun Lee, Sang-Joon Byun, Yung-Cheol Department of Computer Engineering Jeju National University Jeju63243 Korea Republic of Department of Computer Engineering Major of Electronic Engineering Institute of Information Science and Technology Jeju National University Jeju63243 Korea Republic of

ISBN: (纸本)9789819623945

User review ratings are crucial for assessing the quality of restaurants, movies, products, and services online. Traditionally, predicting these ratings involved analyzing the content of user reviews using natural language processing techniques. While this approach allows for the consideration of diverse data by aggregating reviews from all users, it's hindered by the fact that users have unique tendencies and expressions, resulting in varying ratings for the same review. To address this challenge, a novel method is proposed in this paper: a BiLSTM model that incorporates the user's average rating alongside their review content. By doing so, this method aims to enhance prediction accuracy by accounting for individual user characteristics. This approach considers the textual content of reviews and integrates the user's typical rating behavior, resulting in more personalized and precise rating predictions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Comparative Analysis of CNN and SVM Machine Learning Techniques for Plant Disease Detection 4th

Comparative Analysis of CNN and SVM Machine Learning Techniq...

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4th International Conference on Data, engineering, and Applications, IDEA 2022

作者： Adeniyi, Abidemi Emmanuel Madamidola, Olugbenga Ayomide Awotunde, Joseph Bamidele Misra, Sanjay Agrawal, Akshat Department of Computer Science College of Computing and Communication Studies Bowen University Iwo Nigeria Department of Information Technology Federal University of Technology Akure Nigeria Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Department of Applied Data Science Institute for Energy Technology Halden Norway Department of Computer Science & Engineering Amity University Haryana Gurugram122412 India

ISBN: (纸本)9789819700363

The growing advancement of different jobs, skills and businesses has stimulated the spread of diseases. Most of the time, these diseases can cross countries’ borders and spread at a very rapid rate. Avoiding and detecting these infections have proved to be a major problem for our public health and one that must be treated with great importance. Crop disease has haunted, farmers have been victimized for several decades, and a remedy must be offered to halt this damage. As a result, a system should be developed to detect plant disease. Untreated plant diseases ultimately result in a loss to the farmer. Traditional methods of detecting plant disease have become less efficient and inaccurate;thus there is need to improve on the techniques of plant disease detection, as a reliable detection of plant diseases will greatly improve the quality of food crops produced and decrease the threat plant diseases pose to food security. Therefore, this study is aimed at analyzing comparatively, two machine learning language techniques convolutional neural network (CNN) and support vector machine (SVM) in terms of their respective accuracy in detecting plant diseases through their leaves in which both algorithms are used for the automatic diagnosis of plant disease. From the experimental test conducted on various plants using CNN and SVM algorithms, CNN had an accuracy of 98%, while SVM had an accuracy of 62% on various plant leaves used. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Poster: MMEdge: Real-time Multimodal Inference via Cross-Modal Speculative Sensing and Encoding 26

Poster: MMEdge: Real-time Multimodal Inference via Cross-Mod...

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26th International Workshop on Mobile Computing Systems and Applications, HOTMOBILE 2025

作者： Huang, Runxi Ouyang, Xiaomin Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9798400714030

Achieving real-time multimodal inference on devices is crucial for time-critical and privacy-sensitive applications. Existing methods primarily focus on optimizing the latency of model inference while neglecting the data collection process. We introduce MMEdge, an on-device, end-to-end multimodal inference framework. MMEdge employs a cross-modality speculative sensing and encoding approach to enhance inference efficiency and robustness at runtime. © 2025 Copyright held by the owner/author(s).

关键词： Edge computing

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Detection of Caries and Hypodontia using Deep Learning and Explainable AI 18

Detection of Caries and Hypodontia using Deep Learning and E...

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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Kamran, Muhammad Waseem, Muhammad Shafi, Numan Zahid, Abdullah University of Engineering and Technology Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798331508692

In today's world of technology breakthroughs, incorporating cutting-edge procedures has become essential, especially in healthcare, where an early and precise diagnosis is critical for improving patient outcomes and saving lives. Dental caries is an infectious bacterial disease primarily caused by the interaction of oral bacteria with dietary sugars, leading to plaque formation on teeth. Millions of people worldwide are facing this disease. Hypodontia is another dental term that healthcare providers use to describe congenitally missing teeth. We employed an AI-driven healthcare solution by utilizing a transformer-based image classification model, Vision Transformer (VIT), for the detection of dental caries and hypodontia. A dataset containing both periapical and panoramic dental x-rays utilized to gain maximum insights into teeth. The dataset was divided into 60% for training, 20% for testing, and 20% for validation. Data augmentation techniques are applied to enhance the diversity of training data and mitigate overfitting. The proposed model demonstrated remarkable performance and achieved an accuracy of 96.63% with evaluation matrices Precision, Recall, and F1-score of 96.60, 96.63, and 96.57 % respectively. Unlike Convolutional Neural Networks (CNN) which are black boxes, we utilized the Explainable Artificial Intelligence (XAI) models such as LIME, SHAP, and GRAD-CAM to gain insights into the inner workings of the VIT model. The XAI models contributed to fostering trust among medical dentists and medical professionals. © 2024 IEEE.

关键词： Diagnosis

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A Machine Learning and Nature Inspired Algorithm Based Hybrid Approach for Detection of IoT Attacks 4

A Machine Learning and Nature Inspired Algorithm Based Hybri...

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4th International Conference on computer, Communication, Control and information technology, C3IT 2024

作者： Sikdar, Subinoy Kule, Malay Indian Institute of Engineering Science and Technology Department of Computer Science and Technology Howrah Shibpur India

ISBN: (纸本)9798350389128

Internet of Things (IoT) is revolutionizing our daily life, shifting manual systems to computerized ones. With the proliferation of IoT devices connected via the internet, ensuring security against malicious attacks is critical. So, the security of information is the greatest concern in modern days as the IoT devices are attacked by various malicious services. In this paper, we have proposed a hybrid attack detection method using machine learning and nature inspired algorithm. The main objective of this paper is to find out the best feature(s) as well as the best algorithms, which detect the attack accurately. We have used CICIoT2023 dataset to classify the malicious attacks and benign traffic. Decision Tree and One Rule algorithm has been applied on the dataset and both the algorithms reached the accuracy up to 98.22% and 98.99% respectively. An ensemble learning method was also developed using Random Forest algorithm. Furthermore, a hybrid attack detection approach has been implemented using Decision Tree and Cuckoo Search Algorithm, which can perform better than Random Forest model. The hybrid classifier has obtained the accuracy up to 99.40%, whereas the Random Forest model reached the accuracy up to 99.28%. © 2024 IEEE.

关键词： Adversarial machine learning

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Performance Analysis of Constrained Evolutionary Multi-objective Optimization Algorithms on Artificial and Real-World Problems 1

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13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025

作者： Nan, Yang Ishibuchi, Hisao Pang, Lie Meng Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (数字)9789819635382

ISBN: (纸本)9789819635375

Most real-world optimization problems have multiple objectives and constraints. To address constrained multi-objective optimization problems (CMOPs), researchers have proposed many constrained evolutionary multi-objective optimization (EMO) algorithms. They usually evaluate the proposed constrained EMO algorithms on artificial CMOPs in the literature. As a result, some newly specialized constrained EMO algorithms are designed for those artificial problems. Our previous work demonstrated that an old EMO algorithm NSGA-II with a simple constraint handling mechanism can outperform some newly proposed algorithms on real-world CMOPs. In this paper, we further examine the performance of constrained EMO algorithms on artificial and real-world CMOPs. We also analyze the relationship between algorithms’ performance and problems’ properties. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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