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1st International Conference of Smart Systems and Emerging Technologies, SMART-TECH 2020

作者： Jan, Habab Yar, Hikmat Iqbal, Javed Farman, Haleem Khan, Zahid Koubaa, Anis Sarhad University of Science and It Department of Electrical Engineering Peshawar Pakistan Islamia College Department of Computer Science Peshawar Pakistan Robotics and Internet-of-Things Lab Prince Sultan University Riyadh Saudi Arabia

ISBN: (纸本)9781728174075

Nowadays, the care of elderly peoples is a significant problem and needs more time and attention from the caregivers. In the elderly population, accidental fall is a common cause of injury due to the weakness or any other disease such as high temperature and blood pressure. Moreover, in developing countries, the use of natural gas for older adults is widespread for room heating. The use of a gas heater in a room can be hazardous, especially at night and it has reportedly said to turn OFF the gas heater while going to sleep. However, it is ignored quite often that results in many deaths not only to the elderly but to all age groups. To cope with these issues and to provide a safer environment, especially to the older people where the caretaker can be informed well on-time, a raspberry Pi assisted cost-effective safety system is proposed. The 3-axis accelerometer of gas and flame sensors are used to detect an event when accidental fall occurs precisely, or any gas leakage or flame is detected. The caretaker will be informed using a mobile app or text alert to take action immediately. Results demonstrate that the modules for fall and gas/open flame detection accurately detected true events by 92% and 95% respectively. The primary purpose of the proposed system is to provide a reliable, cost-effective, and efficient method for the safety of the elderly population. © 2020 IEEE.

关键词： Blood pressure

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Design and Development of the FlexBE WebUI with Introductory Tutorials

Journal of Computing Sciences in Colleges

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Journal of Computing sciences in Colleges 2024年第3期40卷

作者： Samuel Raymond Grace Walters Josh Luzier David C. Conner Capable Humanitarian Robotics and Intelligent Systems Lab Department of Physics Computer Science and Engineering Christopher Newport University Newport News VA

Hierarchical Finite State Machines (HFSMs) continue to be a popular strategy for high-level behavioral control in robotics. Within the Robot Operating System (ROS) ecosystem, the Flexible Behavior Engine (FlexBE) provides an easy to use Python implementation with an intuitive user interface for runtime monitoring and "collaborative autonomy." In this paper, we describe the latest evolution of the FlexBE UI, that leverages FastAPI to provide a Python-based native ROS node interface, to a web-based user interface. Additionally, the paper describes a "quick-start" introductory tutorial to state machines using FlexBE. This system provides a natural introduction to HFSM-based techniques.

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PollenNet: A novel architecture for high precision pollen grain classification through deep learning and explainable AI

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Heliyon 2024年第19期10卷 e38596页

作者： Shamrat, F M Javed Mehedi Idna Idris, Mohd Yamani Zhou, Xujuan Khalid, Majdi Sharmin, Sharmin Sharmin, Zeseya Ahmed, Kawsar Moni, Mohammad Ali Department of Computer System and Technology Universiti Malaya Kuala Lumpur 50603 Malaysia School of Business University of Southern Queensland Toowoomba Australia Department of Computer Science and Artificial Intelligence College of Computing Umm Al-Qura University Makkah 21955 Saudi Arabia School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Australia Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive Saskatoon S7N 5A9 SK Canada Group of Bio-photomatiχ Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City (DSC) Birulia Savar Dhaka 1216 Bangladesh AI & Digital Health Technology Artificial Intelligence & Cyber Future Institute Charles Stuart University Bathurst 2795 NSW Australia AI & Digital Health Technology Rural Health Research Institute Charles Stuart University Orange 2800 NSW Australia

Pollen grains play a critical role in environmental, agricultural, and allergy research despite their tiny dimensions. The accurate classification of pollen grains remains a significant challenge, mainly attributable to their intricate structures and the extensive diversity of species. Traditional methods often lack accuracy and effectiveness, prompting the need for advanced solutions. This study introduces a novel deep learning framework, PollenNet, designed to tackle the intricate challenge of pollen grain image classification. The efficiency of PollenNet is thoroughly evaluated through stratified 5-fold cross-validation, comparing it with cutting-edge methods to demonstrate its superior performance. A comprehensive data preparation phase is conducted, including removing duplicates and low-quality images, applying Non-local Means Denoising for noise reduction, and Gamma correction to adjust image brightness. Furthermore, Explainable AI (XAI) is utilized to enhance the interpretability of the model, while Receiver Operating Characteristic (ROC) curve analysis serves as a quantitative method for evaluating the model's capabilities. PollenNet demonstrates superior performance when compared to existing models, with an accuracy of 98.45 %, precision of 98.20 %, specificity of 98.40 %, recall of 98.30 %, and f1-score of 98.25 %. The model also maintains low Mean Squared Error (0.03) and Mean Absolute Error (0.02) rates. The ROC curve analysis, the low False Positive Rate (0.016), and the False Negative Rate (0.017) highlight the reliability and dependability of the model. This study significantly improves the efficacy of classifying pollen grains, indicating an important advancement in the application of deep learning for ecological research. © 2024 The Authors

关键词： Deep learning Explainable AI Image processing Pollen grain classification PollenNet model

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Extraction of Rice Phenological Metrics Using Temporally Correlated Multispectral Drone Imagery

Extraction of Rice Phenological Metrics Using Temporally Cor...

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International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： D. Wasif M.Q. Khan R. Murtaza M.Z. Ahmad Z. Zafar M. Shahzad K. Berns M.M. Fraz School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Aerospace and Geodesy Data Science in Earth Observation Technical University of Munich (TUM) Munich Germany Robotics Research Lab Technical University of Kaiserslautern Germany

The rice crop holds great potential to contribute to the economy of most South Asian agricultural countries. However, due to global warming, changes in weather and climate have made it difficult for farmers to accurately predict the timing of various growth stages in the life cycle of rice. Deciding when to apply fertilizers to maximize yield and pesticides to avoid diseases becomes a challenge with conventional farming techniques. Hence, extracting key phenological metrics and analyzing the growth dynamics of different rice varieties are essential for this purpose. The foremost issue in this process is the lack of available multispectral drone imagery datasets, especially in South-East Asia. Therefore, a novel multispectral dataset of rice crops is presented that consists of three different varieties: Kainat, Hybrid, and Super Kernel in East Punjab, Pakistan. The study’s objective is to use this dataset to model a time series of Normalized Difference Vegetation Index (NDVI) and use the change detection method to map key phenological variables using the rate of change of NDVI. The analysis of available data using phenological metrics represents that the Kainat Basmati variant is growing faster than Hybrid as it reaches its peak about ten days earlier than Hybrid. Overall, our study demonstrates that remotely captured unmanned aerial vehicle (UAV) imagery of rice can streamline the process of predicting phenological metrics of rice and provide farmers with an automated statistical model of the growth stages of various crops.

关键词： Measurement Time series analysis Crops Vegetation mapping Predictive models Autonomous aerial vehicles Kernel

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EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control Framework for Exciting Efficient Oscillations in Mechanical Systems

EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control...

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IEEE Conference on Decision and Control

作者： Andre Coelho Alin Albu-Schaeffer Arne Sachtler Hrishik Mishra Davide Bicego Christian Ott Antonio Franchi Institute of Robotics and Mechatronics of the German Aerospace Center (DLR) Oberpfaffenhofen Germany Dextrous Robotics Inc Memphis TN USA Robotics and Mechatronics Lab Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Netherlands Department of Informatics Technical University of Munich Germany Automation and Control Institute (ACIN) Technical University of Vienna Austria LAAS-CNRS University of Toulouse France

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper proposes a Nonlinear Model-Predictive Control (NMPC) method capable of finding and converging to energy-efficient regular oscillations, which require no control action to be sustained. The approach builds up on the recently developed Eigenmanifold theory, which defines the sets of line-shaped oscillations of a robot as an invariant two-dimensional submanifold of its state space. By defining the control problem as a nonlinear program (NLP), the controller is able to deal with constraints in the state and control variables and be energy-efficient not only in its final trajectory but also during the convergence phase. An initial implementation of this approach is proposed, analyzed, and tested in simulation.

关键词： Analytical models Aerospace electronics Energy efficiency Trajectory Mechanical systems Oscillators Robots

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Advancing Speaker Embedding Learning: Wespeaker Toolkit for Research and Production

SSRN

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SSRN 2024年

作者： Wang, Shuai Chen, Zhengyang Han, Bing Wang, Hongji Liang, Chengdong Zhang, Binbin Xiang, Xu Ding, Wen Rohdin, Johan Silnova, Anna Qian, Yanmin Li, Haizhou Shenzhen Institute of Big Data Guangdong Shenzhen China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Tencent Ethereal Audio Lab Tencent Corporation Shenzhen China Horizon Robotics Beijing China NVIDIA Shanghai China Brno University of Technology Faculty of Information Technology IT4I Centre of Excellence Czech Republic School of Data Science The Chinese University of Hong Kong Guangdong Shenzhen China

Speaker modeling plays a crucial role in various tasks, and fixed-dimensional vector representations, known as speaker embeddings, are the predominant modeling approach. These embeddings are typically evaluated within the framework of speaker verification, yet their utility extends to a broad scope of related tasks including speaker diarization, speech synthesis, voice conversion, and target speaker extraction. This paper presents Wespeaker, a user-friendly toolkit designed for both research and production purposes, dedicated to the learning of speaker embeddings. Wespeaker offers scalable data management, state-of-the-art speaker embedding models, and self-supervised learning training schemes with the potential to leverage large-scale unlabeled real-world data. The toolkit incorporates structured recipes that have been successfully adopted in winning systems across various speaker verification challenges, ensuring highly competitive results. For production-oriented development, Wespeaker integrates CPU- and GPUcompatible deployment and runtime codes, supporting mainstream platforms such as Windows, Linux, Mac and on-device chips such as horizon X3’PI. Wespeaker also provides off-the-shelf high-quality speaker embeddings by providing various pretrained models, which can be effortlessly applied to different tasks that require speaker modeling. The toolkit is publicly available at https://***/wenet-e2e/wespeaker. © 2024, The Authors. All rights reserved.

关键词： Speech recognition

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UniAutoML: A Human-Centered Framework for Unified Discriminative and Generative AutoML with Large Language Models

arXiv

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arXiv 2024年

作者： Guo, Jiayi Chen, Zan Ji, Yingrui Zhang, Liyun Luo, Daqin Li, Zhigang Shen, Yiqin Glasgow College University of Electronic Science and Technology of China Chengdu China School of Engineering University of Glasgow Glasgow United Kingdom Toursun Synbio Shanghai China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Research Institute Ubtech Robotics Corp Shenzhen China Qiyuan Lab. Beijing China Department of Computer Science Johns Hopkins University MD United States

Automated Machine Learning (AutoML) can streamline the complexities of machine learning pipelines. However, existing AutoML frameworks focus on discriminative tasks leaving generative tasks unexplored. Moreover, existing AutoML frameworks often lack interpretability and user engagement during the model development and training process, due to the absence of human-centered designs. To address these challenges, we propose UniAutoML, a human-centered AutoML framework that leverages Large Language Models (LLMs) to unify the automation of both discriminative (e.g., Transformers and CNNs for classification or regression) and generative tasks (e.g., fine-tuning diffusion models or LLMs). UniAutoML enables natural language interactions between users and the framework to provide real-time feedback and progress updates, thus enhancing the interpretability, transparency, and user control, where users can seamlessly modify or refine the model training in response to evolving needs. To ensure the safety and reliability of LLM-generated content, UniAutoML incorporates a safety guard-line that filter inputs and censor outputs. We evaluated UniAutoML’s performance and usability through experiments conducted on eight diverse datasets and user studies involving 25 participants. Our results demonstrate that UniAutoML not only achieves higher performance but also improves user control and trust in the AutoML process, which makes ML more accessible and intuitive for a broader audience. The code for UniAutoML is available at: https://***/richarf22/UniAutoML. © 2024, CC BY.

关键词： Digital elevation model

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Study of Helicopter Optimal Autorotation Landing Procedure in Tail Rotor Drive Failure

Study of Helicopter Optimal Autorotation Landing Procedure i...

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IEEE International Conference on robotics and Biomimetics

作者： Xufei Yan Renliang Chen Shiqiang Zhu Anhuan Xie Jason Gu Research Center for Intelligent Robotics Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China National Key Laboratory of Science and Technology on Rotorcraft Aeromechanics College of Aeronautics Nanjing University of Aeronautics and Astronautics Nanjing Jiangsu China Intelligent Robot Research Center Zhejiang Lab Hangzhou Zhejiang China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

This paper investigates the optimal landing trajectory and control procedure when a helicopter undergoes autorotation due to tail rotor drive failure (TRDF), in which an optimal control methodology is proposed. First, a helicopter flight dynamics model with TRDF was developed. Then, the autorotation in TRDF was converted to be a nonlinear optimal control problem, solved by direct node collocation method and sequential quadratic programming algorithm. Finally, a model helicopter (Z11) with single main rotor and tail rotor was used to demonstrate the proposed approach. An optimal autorotation landing procedure in TRDF was determined accordingly. Results indicate that the airframe will immediately respond to the excess torque generated by the main rotor via yawing, sideslip and rolling when TRDF occurs. The pilot is recommended to shut down the engine and perform a series of critical operations to stabilize the violent yaw and roll movements. In addition, flight test data were used to validate the numerical simulations of autorotation landing. The proposed optimal control approach provides a useful tool to investigate helicopter TRDF autorotation landing procedure.

关键词： Torque Biological system modeling Helicopters Rotors Optimal control Tail Numerical simulation

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Enhancing Motor Imagery EEG Signal Classification with Simplified GoogLeNet

Enhancing Motor Imagery EEG Signal Classification with Simpl...

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International Winter Workshop on Brain-computer Interface (BCI)

作者： Lu Wang Junkongshuai Wang Bo Wen Wei Mu Lusheng Liu Jiaguan Han Lihua Zhang Jie Jia Xiaoyang Kang Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical NeurobiologyAcademy for Engineering & Technology Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Institute of AI & Robotics Institute of Meta-MedicalFudan University Shanghai China Ji Hua Laboratory Guangdong Province Foshan China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China

The brain-computer interface is a novel tool for interaction between the brain and external devices, and is an important tool at the forefront of international brain science research. Motor imagery does not require external stimulation and is one of the most widely used BCI paradigms, and is now widely used to address the treatment of neurological impairments such as paralysis, stroke, and acromegaly. Electrophysiological source imaging is a technique based on the observation of electromagnetic signals from the cerebral cortex, reflecting the electrophysiological activity of brain nerve cells on the scalp surface of the brain. The wavelet transform, on the other hand, is commonly used for the processing of non-smooth signals and aims to extract excellent signal features. This paper combines these two algorithms as feature extraction methods. In recent years, deep learning has been increasingly combined with MI-EEG-based BCI. GoogLeNet is one of the most classical convolutional neural networks and has made a splash in ImageNet image recognition. However, using GoogLeNet to train the BCI public dataset BCI IV IIa and dataset HGD has shown problems such as long training time, overfitting, and gradient disappearance. In response, this paper proposes an improved and streamlined version of GoogLeNet for training based on this dataset to achieve an overall improvement in accuracy, training time, number of parameters, and other indicators. It is useful for the realistic application of MI-based BCI systems.

关键词： Training Wavelet transforms Surface waves Scalp Pattern classification Electrophysiology Streaming media

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No One Left Behind: Real-World Federated Class-Incremental Learning

arXiv

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arXiv 2023年

作者： Dong, Jiahua Li, Hongliu Cong, Yang Sun, Gan Zhang, Yulun Van Gool, Luc The State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China The Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China The University of Chinese Academy of Sciences Beijing100049 China The Department of Civil and Environmental Engineering Hong Kong Polytechnic University Hong Kong The College of Automation Science and Engineering South China University of Technology Guangzhou510640 China The Computer Vision Lab ETH Zürich Zürich8092 Switzerland

Federated learning (FL) is a hot collaborative training framework via aggregating model parameters of decentralized local clients. However, most FL methods unreasonably assume data categories of FL framework are known and fixed in advance. Moreover, some new local clients that collect novel categories unseen by other clients may be introduced to FL training irregularly. These issues render global model to undergo catastrophic forgetting on old categories, when local clients receive new categories consecutively under limited memory of storing old categories. To tackle the above issues, we propose a novel Local-Global Anti-forgetting (LGA) model. It ensures no local clients are left behind as they learn new classes continually, by addressing local and global catastrophic forgetting. Specifically, considering tackling class imbalance of local client to surmount local forgetting, we develop a category-balanced gradient-adaptive compensation loss and a category gradient-induced semantic distillation loss. They can balance heterogeneous forgetting speeds of hard-to-forget and easy-to-forget old categories, while ensure consistent class-relations within different tasks. Moreover, a proxy server is designed to tackle global forgetting caused by Non-IID class imbalance between different clients. It augments perturbed prototype images of new categories collected from local clients via self-supervised prototype augmentation, thus improving robustness to choose the best old global model for local-side semantic distillation loss. Experiments on representative datasets verify superior performance of our model against comparison methods. The code is available at https://***/JiahuaDong/LGA. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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