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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Ali, Ahmed Gabellieri, Chiara Franchi, Antonio Faculty Robotics and Mechatronics Department Enschede7500 AE Netherlands Sapienza University of Rome Department of Computer Control and Management Engineering Rome00185 Italy

ISBN: (纸本)9798350316339

We propose a new multirotor aerial vehicle class of designs composed of a multi-body structure in which a main body is connected by passive joints to links equipped with propellers. We have investigated some instances of such class, some of which are shown to achieve omnidirectionality while having a minimum number of inputs equal to the main body Degrees of Freedom DoF's, only uni-directional positive thrust propellers, and no internal forces generated at steady state. After dynamics are derived following the Euler-Lagrange approach, an I/O dynamic feedback linearization strategy is then used to show the controllability of any desired pose with stable zero dynamics. We finally verify the developed controller with closed-loop simulations. © 2024 IEEE.

关键词： Feedback linearization

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DeepRice: A deep learning and deep feature based classification of Rice leaf disease subtypes

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Artificial Intelligence in Agriculture 2024年第1期11卷 34-49页

作者： P.Isaac Ritharson Kumudha Raimond X.Anitha Mary Jennifer Eunice Robert Andrew J Department of CSE Karunya Institute of Technology and Sciences CoimbatoreTamilnaduIndia Department of Robotics Engineering Karunya Institute of Technology and SciencesCoimbatoreIndia Department of Mechatronics Engineering Manipal Institute of TechnologyManipal Academy of Higher EducationManipalKarnataka 576104India Department of Computer Science and Engineering Manipal Institute of TechnologyManipal Academy of Higher EducationManipalKarnataka 576104India

Rice stands as a crucial staple food globally,with its enduring sustainability hinging on the prompt detection of rice leaf ***,efficiently detecting diseases when they have already occurred holds paramount importance for solving the cost of manual visual identification and chemical *** the recent past,the identification of leaf pathologies in crops predominantly relies on manual methods using specialized equipment,which proves to be time-consuming and *** study offers a remedy by harnessing Deep Learning(DL)and transfer learning techniques to accurately identify and classify rice leaf diseases.A comprehensive dataset comprising 5932 self-generated images of rice leaves was assembled along with the benchmark datasets,categorized into 9 classes irrespective of the extent of disease spread across the *** classes encompass diverse states including healthy leaves,mild and severe blight,mild and severe tungro,mild and severe blast,as well as mild and severe brown *** meticulous manual labelling and dataset segmentation,which was validated by horticulture experts,data augmentation strategies were implemented to amplify the number of *** datasets were subjected to evaluation using the proposed tailored Convolutional Neural Networks *** performance are scrutinized in conjunction with alternative transfer learning approaches like VGG16,Xception,ResNet50,DenseNet121,Inception ResnetV2,and Inception *** effectiveness of the proposed custom VGG16 model was gauged by its capacity to generalize to unseen images,yielding an exceptional accuracy of 99.94%,surpassing the benchmarks set by existing state-of-the-art ***,the layer wise feature extraction is also visualized as the interpretable AI.

关键词： Rice leaf disease Deep learning CNN Crop yield Agriculture Food security

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Fabrication of Microchannels on Aluminium-Coated Polymethyl Methacrylate (PMMA) Using a Low Power CO2 Laser

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Lasers in Manufacturing and Materials Processing 2024年第4期11卷 868-886页

作者： Okello, Job Lazarus El-Bab, Ahmed M. R. Fath Yoshino, Masahiko El-Hofy, Hassan A. Industrial and Manufacturing Engineering Department Egypt-Japan University of Science and Technology New Borg El-Arab City Egypt Mechatronics and Robotics Engineering Department Egypt-Japan University of Science and Technology New Borg El-Arab City Egypt Department of Mechanical Engineering Tokyo Institute of Technology Tokyo Japan Department of Production Engineering Faculty of Engineering Alexandria University Alexandria Egypt

The emergence of microfluidics as one of the most viable technologies for making various microfluidic devices has greatly enhanced miniaturization in several areas. The utilization of polymethyl methacrylate (PMMA) for making such devices using CO2 lasers has significantly increased due to the flexibility, time, and cost-effectiveness involved. However, attaining microchannels with accurate dimensions is a challenge because of the resulting defects like bulges, high heat-affected zone (HAZ), and surface roughness (Ra). This paper presents a novel attempt to engrave rectangular microchannels on PMMA substrates using a CO2 laser by coating the substrates with a thin layer of 1 μm thickness of 99.95% pure aluminium. The study investigated the effects of power, and speed on the quality of the microchannels. The fabrication was done on both coated and uncoated substrates under the same conditions and the results were compared. Power and speed ranged from 1.5 to 7.5 W and 5 to 17.5 mm/s, respectively. At 1.5 W and 17.5 mm/s, the aspect ratio increased by 37.16%. However, the microchannel depth, width, HAZ, and Ra all reduced by 44.66%, 59.68%, 29.44%, and 64.74% respectively. At 7.5 W and 5 mm/s, the aspect ratio increased by 81.46%. However, the microchannel depth, width, HAZ, and Ra all reduced by 21.84%, 54.64%, 32.36%, and 72.70% respectively. The coated substrates produced better microchannels than the uncoated ones. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Carbon dioxide lasers

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Keypoints Estimation on Partial Point Clouds 6

Keypoints Estimation on Partial Point Clouds

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6th International Conference on robotics and Automation in Industry, ICRAI 2024

作者： Saleem, Maryam Zohaib, Mohammad Naqvi, Syed Abbas Zilqurnain UET Human Centered Robotics Lab Department of Mechatronics and Control Engineering Lahore Pakistan SIGMA Clermont University of Clermont Auvergne Aubiere France

ISBN: (纸本)9798350355703

Estimating 3D keypoints of an object has achieved considerable interest, especially in computer graphics and ma-chine vision. The complexity of the problem increases when the object is noisy, decimated, or partially occluded. To this end, we present an approach to estimate 3D keypoints from the partial PCDs such that they also highlight the object's missing parts. It is based on two modules: shape completion and keypoints estimation. The first module computes the object's missing parts and generates a dense shape which is used by the second module to estimate semantically and geometrically consistent keypoints. The approach also estimates confidence scores that allow the selection of valid keypoint considering the object's structure. We evaluate the proposed method for four different backbones and observe that we get better results using PointNet. Furthermore, the presented results show that the Percentage of Correct Keypoints (PCK) and Dual Alignment Score (DAS) for the keypoints estimated from the dense shapes (36.61 and 76.29) are 23.51 % and 12.14% higher, respectively, compared to those estimated from the partial objects (13.10 and 64.15). © 2024 IEEE.

关键词： 3D modeling

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A deep learning-based model for automated STN localization using local field potentials in Parkinson’s disease

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Soft Computing 2025年第4期29卷 2343-2362页

作者： Hosny, Mohamed Naeem, Mohamed A. Zhu, Minwei Gao, Wenpeng Elshenhab, Ahmed M. Fu, Yili Department of Electrical Engineering Benha Faculty of Engineering Benha University Benha Egypt State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150080 China Department of Mechatronics and Robotics Engineering Faculty of Engineering South Valley University Qena Egypt First Affiliated Hospital Harbin Medical University Harbin150001 China School of Life Science and Technology Harbin Institute of Technology Harbin150080 China Department of Mathematics Faculty of Science Mansoura University Mansoura35516 Egypt

Accurate identification of the subthalamic nucleus (STN) borders is time-consuming, relying heavily on the neurosurgeon expertise in manually interpreting the electrophysiological signals. Local field potentials (LFPs) have garnered imperative attention due to their strong correlation with the STN. However, existing detection models often face challenges with high computational complexity, hyperparameter optimization and lack of explainability, making them unreliable for clinicians. Therefore, this study introduces an explanatory framework using convolutional neural networks (CNN) for detecting the STN region from LFPs. Continuous wavelet transform is employed to convert LFPs signals into scalogram images, which are then processed by sixteen CNN models. We evaluated our framework by examining the impact of various limiting factors on the classification performance, including model size, learning rate (LR), optimizers and data scaling. Deep features are extracted from the top-performing CNN architectures to capture rich representations of the scalograms. These features are then fused and classified using k-nearest neighbour algorithm. Gradient-weighted class activation mapping is used to explain the decisions made by the proposed model. Our approach achieved an accuracy of 99.61%, outperforming individual CNN models for STN localization. The experimental results revealed that CNN models, embedded with additional hyperparameters and layers, generally outperformed smaller models. Besides, low LR significantly enhanced the performance compared to high LR. Moreover, features extracted from untuned networks produced lower performance than tuned networks. The proposed system could revolutionize deep brain stimulation surgery by increasing efficiency and reducing reliance on clinician expertise for STN detection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Wavelet transforms

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A Top-View Hand Gesture Recognition System for IoT Applications 5

A Top-View Hand Gesture Recognition System for IoT Applicati...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Wisener, William J. Rodriguez, Jorge Diaz Ovando, Ammy Woolford, Christopher Patel, Kishan Kennesaw State University Dept. of Robotics and Mechatronics Engineering Department of Physics AtlantaGA United States

ISBN: (纸本)9781665474672

Gesture recognition integrated into Internet of Things (IoT) systems is a growing field with opportunities for improvement. Identifying hand gestures from vertical angles with a cooperative network of cameras can enable a broader collection of control methods for controlling IoT devices within residential and commercial environments. Integrating this smart home framework with other IoT control methodologies also creates a more user-friendly experience. To generate a proof of concept of the effectiveness with this control framework, a convolutional neural network was created using the YOLOv7 deep learning architecture and Roboflow software. This image detection network was connected to a simulated IoT device via Home Assistant home automation software and communicates using the MQTT protocol. The gesture detection system was tested on different camera setups to evaluate the importance of camera position and data sharing within a smart-home device network. © 2023 IEEE.

关键词： Internet of things

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Inpatient Monitoring System: Temperature, Oxygen Saturation, Blood Pressure, Heart Rate, and Infusion Automation Based on ESP 32, IoT, and Mobile Application 7

Inpatient Monitoring System: Temperature, Oxygen Saturation,...

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7th International Conference of Computer and Informatics engineering, IC2IE 2024

作者： Joseph, Irvanda Anthony, Patrick Astuti, Winda Lie, Zener Sukra Iwan Solihin, Mahmud Bina Nusantara University Binus Aso School of Engineering Automotive and Robotics Program Computer Engineering Department Jakarta11480 Indonesia Ucsi University Department of Mechanical and Mechatronics Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798331505530

The implementation of a patient monitoring system and infusion automation which aims to ensure regular and periodic monitoring of inpatient conditions are applied in this system. This embedded system is applied in hospitals and addresses the uneven distribution of healthcare workers in Indonesia, particularly in remote areas. Currently, manual checks by nurses can lead to errors and reduced patient comfort. The improvement supervision is developed in this work. The system monitored body temperature, oxygen saturation, blood pressure, heart rate, and automated infusions. All the embedded systems are connected to Internet of Things (IoT) and a real-time database accessible through an application and web. This system allows doctors and hospital authorities to make decisions based on patient condition history. The testing phase of the system shows four data acquisition devices (temperature, oxygen saturation, blood pressure, and heart rate), an ESP 32, and Firebase as a database, have ability to capture and send real-time data every five seconds and can be accessed by doctors, patients, and hospital authorities. © 2024 IEEE.

关键词： Remote patient monitoring

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Gait dynamic stability evaluation on inclined surfaces for inverted or everted feet walking

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Fuss und Sprunggelenk 2024年第4期22卷 254-268页

作者： Raza, Anam Mahmood, Imran Sultana, Tayyaba Sultana, Salma Dehghani-Sanij, Abbas A. Department of Zoology Government College University Faisalabad Pakistan Mechatronics and Control Engineering Department Faisalabad Campus UET Lahore Pakistan Institute of Design Robotics and Optimisation School of Mechanical Engineering University of Leeds United Kingdom

Background: This study investigates gait dynamic stability on inclined surfaces while walking with foot rotational impairments such as everted and inverted feet. Walking stability is assessed during gait transitional periods i.e. weight loading and unloading phases. Methods: Foot rotational impairments (eversion and inversion) are imitated using wedged foot insoles. A total of ten healthy subjects performed five trials for each of the inverted, everted, and normal feet conditions. Experimental data was collected using a twelve-camera motion capture system and a force plate installed on an inclined platform. Lower limb joint kinematics were computed in Visual-3D motion analysis software to evaluate the degree of foot rotational impairments. Gait dynamic stability was assessed by analysing the centre of mass and centre of pressure signals as neuromechanical input and output responses respectively. Both signals were modelled in time and frequency domains and stability margins were quantified by applying Nyquist and Bode (N&B) methods from control engineering theory. Results: Stability margins quantified from N&B methods illustrated the loading phase as stable and the unloading phase as unstable gait transitions. During the loading phase, both rotational impairments illustrated a decrease in stability (p < 0,05) for the ramp ascent walk and an increase in stability (p < 0,05) for the ramp descent walk. However, there was no difference observed for the respective unloading phases. Results from neuromotor outputs (CoP-velocity) illustrated a strong interlimb correlation (p < 0,001) during gait transitional periods, implying a compensatory stability interaction exists between opposite limbs. Further, stabilities quantified from outputs were found greater in magnitudes than the instability quantified from inputs illustrating the robustness of neuromotor balance control. Conclusions: Results support the hypothesis that gait stability gets affected while walking with rotation

关键词： Balance control Foot Gait stability Inclined walk Rotational impairments

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Investigations on the Design and Simulation of Smart Irrigation Robot for Nurseries 3

Investigations on the Design and Simulation of Smart Irrigat...

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3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems, ACCESS 2023

作者： Abhiram, B. Jha, Swati Sai Prasad, Y. Yogendra Dhanraj, Joshuva Arockia Ramkumar, V. Hindustan Institute of Technology and Science Centre for Automation & Robotics Department of Mechatronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350346169

The irrigation systems in nurseries can be the cause of various water supply errors- ranging from lack of sufficient water supply to over-watering and imbalance in the water pH levels. Based on the statistics recorded by the department of agriculture and farmers welfare, we have conducted we concluded that the water supply management and plant life maintenance are the major problems. Considering all these real-time errors, we have designed a smart irrigation robot functioning on the inputs from a group of soil moisture sensors which helps eliminate the monotonous task of monitoring and keeping records of the change in soil moisture. © 2023 IEEE.

关键词： Irrigation

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3D Semantic Mapping based on RGB-D Camera and LiDAR Sensor in Beach Environment 1

3D Semantic Mapping based on RGB-D Camera and LiDAR Sensor i...

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1st International Conference on robotics, engineering, Science, and Technology, RESTCON 2024

作者： Tan, Chi Jie Ogawa, Shintaro Hayashi, Takamasa Janthori, Titan Tominaga, Ayumu Hayashi, Eiji Kyushu Institute of Technology Department of Creative Informatics Fukuoka Japan National Institute of Technology Kitakyushu College Department of Creative Engineering Robotics and Mechatronics Kitakyushu Japan

ISBN: (纸本)9798350348842

The increasing amount of sea garbage proving to be a huge problem not only for marine life but also causing a tremendous number of troubles to humans living along the coastlines. Especially Japan, that is surrounded by the ocean which has a long coastline that spans thousands of kilometers, and this provides ample opportunities for marine debris to accumulate. This paper proposes a 3D mapping system utilizing the semantic information from the RGBD sensor extracted from a specifically trained state-of-Art deep learning neural network to merge with the spatial information from the LiDAR sensor using Max Fusion Technique. This paper also aims to contribute to the implementation of deep learning and autonomous driving in beach cleaning-related robots and spark interest and raise awareness of the public in beach clean-up activities. It is also shown that the proposed system can achieve a mean accuracy of 98% approximately with the uniquely trained deep learning model. © 2024 IEEE.

关键词： Autonomous vehicles

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