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IEEE Transactions on Intelligent Transportation Systems 2024年第7期25卷 6251-6275页

作者： Simona Sacone Department of Computer Science Bioengineering Robotics and Systems Engineering University of Genova Genova Italy

The authors provide a meta-review across nearly 100 extant surveys on unmanned aerial vehicles (UAVs) applications and overview papers, extract their key messages, and investigate the extent of being complementary. Based on the findings of their survey analysis, they developed the so-called AERIAL framework, which aggregates the major challenges on the way to a successful application of UAVs for logistics, mobility, and monitoring. The framework covers the following aspects: 1) acceptance and heterogeneities; 2) economy and demand; 3) reliability and risks; 4) infrastructure and optimization; 5) automation and control; and 6) liability and standardization. They believe that their AERIAL framework and meta-review contribute towards a clearer understanding of the scientific UAV landscape challenges and the identification of potential directions for future research studies.

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Integrating CNN-LSTM Networks with Statistical Filtering Techniques for Intelligent IoT Intrusion Detection 8

Integrating CNN-LSTM Networks with Statistical Filtering Tec...

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8th International Conference on Smart Cities, Internet of Things and Applications, SCIoT 2024

作者： Imani, Fatemeh Kargar, Masoud Assadzadeh, Alireza Bayani, Ali Islamic Azad University of Tabriz Department of Computer Engineering Tabriz Iran Islamic Azad University of Tabriz Robotics and Soft Technologies Research Center Tabriz Iran

ISBN: (纸本)9798350374674

In the rapidly evolving landscape of the Internet of Things (IoT), securing networked devices against malicious intrusions is of paramount importance. This paper presents a novel approach for Intelligent IoT Intrusion Detection, leveraging integrated Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks, along with Statistical Filtering techniques for denoising. By synergistically combining deep learning architectures with statistical filtering, our methodology aims to enhance the accuracy and robustness of intrusion detection systems in IoT environments. Empirical evaluations on diverse datasets, including the Edge-IIoTset Dataset and CoAP-DoS Dataset, demonstrate the effectiveness of our approach, achieving competitive accuracy rates of 99.93% and 94.99% respectively. Furthermore, our model showcases promising computational efficiency, making it suitable for deployment on resource-constrained IoT devices. These results underscore the significance of our research in advancing the state-of-the-art in IoT privacy, paving the way for more resilient and adaptive intrusion detection systems. © 2024 IEEE.

关键词： Internet of things

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Online 3D Deformable Object Classification for Mobile Cobot Manipulation 56

Online 3D Deformable Object Classification for Mobile Cobot ...

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56th International Symposium on robotics, ISR Europe 2023

作者： Nguyen, Khang Dang, Tuan Huber, Manfred Learning and Adaptive Robotics Laboratory Department of Computer Science and Engineering University of Texas at Arlington ArlingtonTX76013 United States

ISBN: (纸本)9783800761418

Vision-based object manipulation in assistive mobile cobots essentially relies on classifying the target objects based on their 3D shapes and features, whether they are deformed or not. In this work, we present an auto-generated dataset of deformed objects specific for assistive mobile cobot manipulation using an intuitive Laplacian-based mesh deformation procedure. We first determine the graspable region of the robot hand on the given object’s mesh. Then, we uniformly sample handle points within the graspable region and perform deformation with multiple handle points based on the robot gripper configuration. In each deformation, we identify the orientation of handle points and prevent self-intersection to guarantee the object’s physical meaning when multiple handle points are simultaneously applied to the mesh at different deformation intensities. We also introduce a lightweight neural network for 3D deformable object classification. Finally, we test our generated dataset on the Baxter robot with two 7-DOF arms, an integrated RGB-D camera, and a 3D deformable object classifier. The result shows that the robot is able to classify real-world deformed objects from point clouds captured at multiple views by the RGB-D camera. The source code is available at https://***/mkhangg/deformable_cobot. © VDE VERLAG GMBH Berlin Offenbach.

关键词： 3D vision deformable object manipulation

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Movement-Based Interaction: An Approach to Visual and Auditory Stimulus and Memory 6

Movement-Based Interaction: An Approach to Visual and Audito...

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6th Experiment at International Conference, exp.at 2023

作者： Nogueira, Maria Rita Menezes, Paulo Institute of Systems and Robotics University of Coimbra Coimbra Portugal University of Coimbra Dept. of Electrical and Computer Engineering Coimbra Portugal

ISBN: (纸本)9798350314403

Movement-based interaction design is an innovative field that leverages the body's natural movements to create intuitive and engaging interfaces. This approach takes a sensorial approach to visual and auditory memory, seeking to enhance user engagement and create more meaningful experiences. The use of movement-based interfaces has significant implications for senior citizens, who may have mobility limitations or cognitive impairments. Using movement-based interfaces, seniors can engage in physical activity and maintain cognitive function, essential aspects for healthy aging. Movement-based interfaces can also help children develop physical coordination, cognitive skills, and social interaction. Furthermore, movement-based interaction design can benefit everyone's physical well-being by promoting physical activity and reducing sedentary behavior. Studies have demonstrated that movement-based interfaces can enhance posture, physical activity levels, and general health. By combining movement with sensory experiences, movement-based interaction design can create a more engaging and enjoyable experience for users, which can motivate them to maintain physical and cognitive health. In this article, we present two case studies that research the effectiveness of movement-based interaction design in enhancing visual and auditory memory. Overall, the results of these case studies suggest that movement-based interaction design is an effective approach to enhancing visual and auditory memory. The introduction of movement-based interfaces can give participants a more enjoyable and memorable experience, improving user engagement and information retention. © 2023 IEEE.

关键词： Machine learning

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Enhanced Local Pattern Transformation Based Feature Extraction for Identification of Parkinson’s Disease Using Gait Signals

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SN computer Science 2023年第2期4卷 1-12页

作者： Klinton Amaladass, P. Subathra, M.S.P. Jeba Priya, S. Sivakumar, Mahima Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India Department of Robotics Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India

One of the most common ailments, especially among the elderly, is Parkinson's disease (PD). Although previous research has demonstrated that heuristics can diagnose Parkinson's disease using decisive signs like tremor, muscular rigidity, movement disorders, and voice disorders, it has also been reported that current approaches, which rely on simple motor tasks, are limited and lack stability and accessibility. The purpose of this study is to identify a novel cost-effective and time-efficient early detection technique for the prediction of this disease using a signal processing feature extraction approach namely, Shifted Extended Local Binary Pattern (S-ELBP) using gait signals. The features extracted using the proposed methods are given as the input to an artificial neural network (ANN) to classify them as Healthy or Parkinson’s. The proposed method has quite promising results when evaluated using different performance metrics. The method has yielded accuracy: 97.6%, specificity: 95.71%, sensitivity: 99, positive predictive value (PPV): 97.2%, negative predictive value (NPV): 98.8%, Matthews correlation coefficient (MCC): 95.4%, F1-score: 97.9%, and geometric mean: 97.19%. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction Parkinson’s disease Parkinson’s gait Shifted extended local binary pattern

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Reactive Multi-agent Coordination using Auction-based Task Allocation and Behavior Trees

Reactive Multi-agent Coordination using Auction-based Task A...

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2023 IEEE Conference on Control Technology and Applications, CCTA 2023

作者： Dahlquist, Niklas Lindqvist, Bjorn Saradagi, Akshit Nikolakopoulos, George Luleå University of Technology Robotics and Ai Group Department of Computer Science Electrical and Space Engineering LuleåSE-97187 Sweden

ISBN: (纸本)9798350335446

This article presents an architecture for multi-agent task allocation and task execution, through the unification of a market-inspired task-auctioning system with Behavior Trees for managing and executing lower level behaviors. We consider the scenario with multi-stage tasks, such as 'pick and place', whose arrival times are not known a priori. In such a scenario, a coordinating architecture is expected to be reactive to newly arrived tasks and the resulting rerouting of agents should be dependent on the stage of completion of their current multi-stage tasks. In the novel architecture proposed in this article, a central auctioning system gathers bids (cost-estimates for completing currently available tasks) from all agents, and solves a combinatorial problem to optimally assign tasks to agents. For every agent, it's participation in the auctioning system and execution of an assigned multi-stage task is managed using behavior trees, which switch among several well-defined behaviors in response to changing scenarios. The auctioning system is run at a fixed rate, allowing for newly added tasks to be incorporated into the auctioning system, which makes the solution reactive and allows for rerouting of some agents (subject to the states of the behavior trees). We demonstrate that the proposed architecture is especially well-suited for multistage tasks, where high costs are incurred when rerouting agents who have completed one or more stages of their current tasks. The proposed framework is experimentally validated in multiple scenarios in a lab environment. A video of a demonstration can be found at: https://***/ZdEkoOOlB2g. © 2023 IEEE.

关键词： Cost estimating

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Performance Evaluation of Machine Learning Classifiers for Sudden Cardiac Arrest Detection

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Journal of The Institution of Engineers (India): Series B 2022年 1-7页

作者： Banerjee, Prakash Bhattacherjee, Saptak Dasgupta, Kousik Sen, Shibaprasad Department of Electrical and Electronics Engineering University of Engineering and Management Kolkata India School of Mechatronics and Robotics Indian Institute of Engineering Science and Technology Shibpur Howrah India Department of Computer Science and Engineering Kalyani Government Engineering College Kalyani India Department of Computer Science and Engineering University of Engineering and Management Kolkata India

Sudden cardiac arrest (SCA) is a type of cardiovascular disease which attacks a person so promptly that it gives minimum time for hospitalization and may also lead to the patient death. In order to save the patient from death, rapid detection of SCA just after the onset of symptoms with a high accuracy is very critical. Although numerous approaches of SCA detection based on electrocardiogram have been carried out, there is still need of standard method for SCA detection with high precession and fast detection. Hence, the present work proposes the use of four machine learning classifiers for detection of SCA based on temporal features extracted from ECG. Performance analysis of the classifiers used in this study on 19 SCA and 20 normal, i.e., healthy subjects, yields classification accuracies of 97.4%, 94.8%, 97.4% and 97.4% for SVM, decision tree, Naïve Bayes and KNN, respectively. Experimental results thus obtained demonstrate the potential of the proposed method as a promising alternative for SCA detection. © 2022, The Institution of Engineers (India).

关键词： Electrocardiograms

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Automatic Cataract and Cholesterol Detection System Based on Recurrent Neural Network (RNN): A Comparison with Convolutional Neural Network (CNN) and Siamese Neural Network (SNN) 1

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19th International Conference of Intelligent Unmanned Systems, ICIUS 2023

作者： Lie, Zener S. Widodo, Jatmiko A. F. Hanesi, Isabel K. P. Moningka, Matthew R. N. Mariana, Y. Astuti, Winda Akmeliawati, Rini Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Product Design program Industrial Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Robotics and Automation Research Group School of Electrical and Mechanical Engineering The University of Adelaide North Terrace Campus Adelaide Australia

ISBN: (数字)9789819765911

ISBN: (纸本)9789819765904

In Indonesia, the majority of blindness cases, approximately 81%, are attributed to cataract. Additionally, 35% of the population has higher cholesterol levels than the standard normal range. The lack of specialized medical equipment, such as the slit-lamp (Ruparelia in Dalhousie Med J 49(1), [1]) bio-microscope, and trained professionals, especially in rural areas, make it difficult to detect these conditions through eye examinations. To address this issue, a supervised machine learning method, the Recurrent Neural Network (RNN), has been utilized to create an iris scanner system that can identify cataract and high cholesterol in a patient. This system will classify eyes into three categories, i.e., cataract, high cholesterol, and normal iris. The computer simulations indicate that this technique is more accurate and requires less training time compared to the existing Convolutional Neural Network (ANN) and Siamese Neural Network (SNN)-based method, while achieving 100% accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Test-Time Adaptation for Nighttime Color-Thermal Semantic Segmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 4893-4904页

作者： Liu, Yexin Zhang, Weiming Zhao, Guoyang Zhu, Jinjing Vasilakos, Athanasios V. Wang, Lin Robotics and Autonomous Systems Thrust Guangzhou511458 China Grimstad4879 Norway Artificial Intelligence Thrust Guangzhou511458 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong999077 Hong Kong

The ability to scene understanding in adverse visual conditions, e.g., nighttime, has sparked active research for color-thermal semantic segmentation. However, it is essentially hampered by two critical problems: 1) the day-night gap of color images is larger than that of thermal images;and 2) the classwise performance of color images at night is not consistently higher or lower than that of thermal images. We propose the first test-time adaptation (TTA) framework, dubbed Night-TTA, to address the problems for nighttime color-thermal semantic segmentation without access to the source (daytime) data during adaptation. Our method enjoys three key technical parts. First, as one modality (e.g., color) suffers from a larger domain gap than that of the other (e.g., thermal), imaging heterogeneity refinement (IHR) employs an interaction branch on the basis of color and thermal branches to prevent cross-modal discrepancy and performance degradation. Then, class aware refinement (CAR) is introduced to obtain reliable ensemble logits based on pixel-level distribution aggregation of the three branches. In addition, we also design a specific learning scheme for our TTA framework, which enables the ensemble logits and three student logits to collaboratively learn to improve the quality of predictions during the testing phase of our Night-TTA. Extensive experiments show that our method achieves state-of-the-art (SoTA) performance with a 13.07% boost in mean intersection over union (mIoU). © 2020 IEEE.

关键词： Color

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Preference detection of the humanoid robot face based on EEG and eye movement

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Neural Computing and Applications 2024年第19期36卷 11603-11621页

作者： Wang, Pengchao Mu, Wei Zhan, Gege Wang, Aiping Song, Zuoting Fang, Tao Zhang, Xueze Wang, Junkongshuai Niu, Lan Bin, Jianxiong Zhang, Lihua Jia, Jie Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI and Robotics Ministry of Education Shanghai Engineering Research Center of AI and Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai200040 China Yiwu Research Institute of Fudan University Zhejiang Yiwu322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

The face of a humanoid robot can affect the user experience, and the detection of face preference is particularly important. Preference detection belongs to a branch of emotion recognition that has received much attention from researchers. Most of the previous preference detection studies have been conducted based on a single modality. In this paper, we detect face preferences of humanoid robots based on electroencephalogram (EEG) signals and eye movement signals for single modality, canonical correlation analysis fusion modality, and bimodal deep autoencoder (BDAE) fusion modality, respectively. We validated the theory of frontal asymmetry by analyzing the preference patterns of EEG and found that participants had higher alpha wave energy for preference faces. In addition, hidden preferences extracted by EEG signals were better classified than preferences from participants' subjective feedback, and also, the classification performance of eye movement data was improved. Finally, experimental results showed that BDAE multimodal fusion using frontal alpha and beta power spectral densities and eye movement information as features performed best, with the highest average accuracy of 83.13% for the SVM and 71.09% for the KNN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electroencephalography

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