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Fuzzy-Based Automatic Epileptic Seizure Detection Framework

Computers, Materials & Continua 2022年第3期70卷 5601-5630页

作者： Aayesha Muhammad Bilal Qureshi Muhammad Afzaal Muhammad Shuaib Qureshi Jeonghwan Gwak Shaheed Zulfikar Ali Bhutto Institute of Science and Technology IslamabadPakistan Department of Computer Science&IT University of Lakki MarwatKPKPakistan Stockholm University StockholmSweden Department of Computer Science School of Arts and SciencesUniversity of Central AsiaKyrgyz Republic Department of Software Korea National University of TransportationChungju27469Korea Department of Biomedical Engineering Korea National University of TransportationChungju27469Korea Department of AI Robotics Engineering Korea National University of TransportationChungju27469Korea Department of IT&Energy Convergence(BK21 FOUR) Korea National University of TransportationChungju27469Korea

Detection of epileptic seizures on the basis of Electroencephalogram(EEG)recordings is a challenging task due to the complex,non-stationary and non-linear nature of these biomedical *** the existing literature,a number of automatic epileptic seizure detection methods have been proposed that extract useful features from EEG segments and classify them using machine learning *** characterizing features of epileptic and non-epileptic EEG signals overlap;therefore,it requires that analysis of signals must be performed from diverse *** studies analyzed these signals in diverse domains to identify distinguishing characteristics of epileptic EEG *** pose the challenge mentioned above,in this paper,a fuzzy-based epileptic seizure detection model is proposed that incorporates a novel feature extraction and selection method along with fuzzy *** proposed work extracts pattern features along with time-domain,frequencydomain,and non-linear analysis of *** applies a feature selection strategy on extracted features to get more discriminating features that build fuzzy machine learning classifiers for the detection of epileptic *** empirical evaluation of the proposed model was conducted on the benchmark Bonn EEG *** shows significant accuracy of 98%to 100%for normal *** classification cases while for three class classification of normal ***-ictal *** accuracy reaches to above 97.5%.The obtained results for ten classification cases(including normal,seizure or ictal,and seizure-free or inter-ictal classes)prove the superior performance of proposed work as compared to other state-of-the-art counterparts.

关键词： Medical image processing electroencephalography machine learning fuzzy system models seizure detection epileptic seizure virtualization

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SSC- l0 : Sparse Subspace Clustering with the l0 Inequality Constraint 7th

SSC- l0 : Sparse Subspace Clustering with the l0 Inequality...

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Wang, Yangbo Zhou, Jie Lin, Qingshui Lu, Jianglin Gao, Can National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Basic Teaching Department Liaoning Technical University Huludao125105 China College of Engineering Northeastern University BostonMA02115 United States

ISBN: (纸本)9783031476648

Self-expression learning methods often obtain a coefficient matrix to measure the similarity between pairs of samples. However, directly using all points to represent a fixed sample in a class under the self-expression framework may not be ideal, as points from other classes participate in the representing process. To alleviate this issue, this study attempts to achieve representation learning between points only coming from the same class. In practice, it is easier for data points from the same class to represent each other than that from different classes. So, when reconstructing a point, if the number of non-zero elements in the coefficient vector is limited, a model is more likely to select data points from the class where the reconstructed point lies to complete the reconstruction work. Based on this idea, we propose Sparse Subspace Clustering with the l0 inequality constraint (SSC- l0 ). In SSC- l0, the l0 inequality constraint determines the maximum number of non-zero elements in the coefficient vector, which helps SSC- l0 to conduct representation learning among the points in the same class. After introducing the simplex constraint to ensure the translation invariance of the model, an optimization method concerning l0 inequality constraint is formed to solve the proposed SSC- l0, and its convergence is theoretically analyzed. Extensive experiments on well-known datasets demonstrate the superiority of SSC- l0 compared to several state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Learning systems

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Multi-Modal Bayesian Learning, Re-Identification and Tracking of a Target Person for Mobile Robots

SSRN

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SSRN 2024年

作者： Kim, Jinhyung Suh, Il Hong Kwon, Taesoo Division of Intelligent Robot Engineering Hanyang University Seoul04763 Korea Republic of COGA-ROBOTICS Seoul04763 Korea Republic of Department of Computer and Software Hanyang University Seoul04763 Korea Republic of

In this paper, we propose a real-time color-based target person learning and Bayesian person re-identification algorithm for target person tracking of a mobile robot in environments of varying illumination. We also propose an effective framework for simultaneous tracking of a target person and multiple non-target persons (STnT), by use of both color and lidar where the target person can be tracked without being affected by dynamic objects. To show the validity of the proposed tracking method, we collect datasets from challenging environments that feature varying illumination and various occlusions. In addition, we develop an evaluation system to measure the error between the ground truth pose and the pose predicted by our system. Our proposed tracking algorithm was successfully deployed on a service robot, Servinggo, at the international exhibition ROBOTWORLD Korea, and showed good tracking performances in very dynamic exhibition environments. © 2024, The Authors. All rights reserved.

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A Review of Autonomous Glaucomatous Grading via OCT Imagery 2

A Review of Autonomous Glaucomatous Grading via OCT Imagery

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Raja, Hina Hassan, Taimur Hassan, Bilal Seneviratne, Lakmal Dias, Jorge Werghi, Naoufel Nust Biomisa Lab Department of Computer and Software Engineering College of Eme Rawalpindi Pakistan Khalifa University C2PS and Kucars Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Khalifa University Kucars Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Khalifa University Kucars Department of Mechanical Engineering & Robotics Abu Dhabi United Arab Emirates

ISBN: (纸本)9781665498197

Glaucoma is a progressive and degenerative optic neuropathy that damages the optic nerve due to increased intraocular pressure and yields various visual field abnormalities. Any damage to the optic nerve (due to glaucoma) is irreversible because of the incapacity of the retinal nerve fibers to regenerate. However, the glaucomatous progression can be properly cured if it is diagnosed in the early stages. Furthermore, health authorities worldwide have prioritized the early detection and prevention of glaucomatous progression, and in this regard, presenting a comprehensive insight on the recent literature serves as a critical benchmark to both researchers and clinicians. Towards this end, this paper presents a detailed overview of the state-of-the-art clinical and technical methods related to glaucomatous detection, grading, and prognosis. The clinical work focuses on glaucoma diagnosis and progression tracking in different retinal regions such as the retinal layers, optic never head, and lamina cribrosa, while the technical works focus on producing lesion-aware screening and grading of glaucomatous pathologies via optical coherence tomography. © 2022 IEEE.

关键词： Grading

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Multi-Robot System for Mapping and Localization

Multi-Robot System for Mapping and Localization

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International Conference on robotics and Automation engineering (ICRAE)

作者： Glace Varghese Thambbireddygare G. C. Reddy Anjana K Menon Ajay Paul Sreeja Kochuvila Varma Divya R Ravichandra Bhat Navin Kumar Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Bangalore India Robotics - SX/PJ1-EM Bosch Global Software Technologies Bengaluru India

In this work, we developed algorithm for coordinated multi-robot system. It is developed for simultaneous localization and mapping (SLAM). Many novel applications of multi-robot system especially, in the industrial indoor environment and complex/dangerous environment are being envisioned. The proposed algorithm focuses on optimizing navigation and execution of tasks within an environment. We used TurtleBot3 Waffle model and robot operating system (ROS) platform and simulated in Gazebo for localization, navigation and comprehensive coverage. The algorithm achieves effective mapping, localization, navigation, and coverage. Algorithm also ensures seamless coordination among multiple robots within a unified transform-tree (tf-tree) facilitating synchronized movement and a holistic understanding of the environment. Furthermore, an innovative coverage path planning and submap division is achieved using this technique. It is expected that such development would lead to practical applications in the real environment.

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Autonomous Environmental Exploration and Target Tracking Control for Unmanned Surface Vehicles in Uncertain Environment

Autonomous Environmental Exploration and Target Tracking Con...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Kaiwei Zhu Shihan Kong Fang Wu Jinan Sun Junzhi Yu Department of Advanced Manufacturing and Robotics State Key Laboratory for Turbulence and Complex Systems College of Engineering Peking University Beijing China SPIC Nuclear Energy Co. Ltd. China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

In this paper, a set of methods of autonomous obstacle exploration, avoiding, and target-tracking control in the uncertain environment is proposed. First, a secure and reliable obstacle exploration framework via multi-sensors information fusion is developed for USV to autonomously perceive obstacles in unknown environments. Second, a modified dynamic artificial potential field-based path planning method is proposed to dynamically generate a safe and feasible path even without complete prior obstacle position information. Third, a robust sliding mode control law with extended state observer is designed to track expectational target in the dynamical and uncertain environment. Finally, the target-tracking control law has been validated through the simulation experiments. Besides, the proposed methods of obstacle exploration, path planning, and target-tracking control are comprehensively evaluated through the simulation experiments in WEBOTS software, which is closer to the real world.

关键词： Target tracking Observers Path planning software Real-time systems Reliability Vehicle dynamics

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Exploration and Coverage Path Planning of Unknown Environment using Multi-Robot System

Exploration and Coverage Path Planning of Unknown Environmen...

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IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Glace Varghese T Sreeja Kochuvila Navin Kumar Ajay Paul Varma Divya R Samar Shailendra Dept. of ECE Amrita Vishwa Vidyapeetham Bengaluru India Robotics – SX/PJ1-EM Bosch Global Software Technologies Bengaluru India Department of IT and Engineering Melbourne Institute of Technology Melbourne Australia

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

This research paper delves into the intricacies of efficient environment exploration and coverage path planning using multi-robot systems. With a focus on unknown environments, the study addresses the mapping of the environment in 2D, distributing coverage tasks among robots, and coordinating their movements for comprehensive coverage. Various strategies are explored, including Simultaneous localization and mapping (SLAM) using G-mapping, dynamic task distribution, waypoint generation, and coordination mechanisms. Leveraging the collective intelligence of multiple robots, this work aims to optimize coverage while minimizing redundancy and resource consumption. Through simulations and analysis, the effectiveness of the proposed methodology is demonstrated with a coverage efficiency of 98.9%, highlighting the potential of multi-robot system in revolutionizing exploration and coverage path planning in diverse real-world applications.

关键词： Technological innovation Simultaneous localization and mapping Robot kinematics Redundancy Collaboration Collective intelligence Dynamic scheduling

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Autonomous Number Plate Detection System for Vehicle Identification and Tracking

Autonomous Number Plate Detection System for Vehicle Identif...

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Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC), International Conference on

作者： Neha Sri Nareddy Abhinaya Polneni Rekha Baithi Pratyusha Kunchamwar Sunil Kumar Suvarna Durgabhavani Battu Naveena Budda Vikas Agarwal Premchander Munimanda AI IoT and Robotics Lab (AIR Lab) AGVs Group Autonomous Robotics Systems Limited Hyderabad India Department of Information Technology Malla Reddy Engineering College for Women Hyderabad India Ideabytes Software India Private Limited Hyderabad India School of Computer Science Georgia Tech Atlanta USA School of Computer Science and Engineering Malla Reddy Engineering College for Women Hyderabad India

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

The autonomous number plate detection system plays a crucial role in modern transportation, law enforcement, and security infrastructure. Technical approaches using artificial intelligence, machine learning, and computer vision are actively researched to achieve ease of maintenance through real-time vehicle detection. The recent development has drastically improved the accuracy of object detection and recognition systems in deep neural networks. Using these techniques, vehicle number plates can easily be detected autonomously for vehicle identification and tracking. Here, we have used a custom-trained TensorFlow-based number plate detection method to autonomously detect vehicles using images captured by a smartphone, or surveillance camera. It showed an impressive mAP @ 0.5 of 95%, and demonstrated limitations in long-distance detection, leading us to designate it as the optimal vehicle detection model for accuracy. We designed and developed an autonomous number plate detection system for vehicle identification and tracking, which includes web-based, android-based, and embedded-based applications. In addition, we evaluated the proposed autonomous number plate detection system with real-time images and videos. The proposed system achieved real-time vehicle detection and tracking, boasting a commendable 90% detection accuracy.

关键词： Visualization Accuracy Law enforcement System performance Transportation Artificial neural networks Real-time systems Security License plate recognition Videos

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Feature Concatenation-Based Deep Learning Method for Multiclass Wound Classification

Feature Concatenation-Based Deep Learning Method for Multicl...

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Electrical and Computer engineering Researches (ICECER), International Conference on

作者： Muteb Aljasem Mustafa Alhababi Ali Javed Zaid Aldoulah Mohammed Abouheaf Mohammad Mayyas Wasseem Al-Rousan Robotics Engineering Bowling Green State University Bowling Green OH MI USA Electrical and Computer Engineering Wayne State University Detroit MI USA Department of Software Engineering University of Engineering and Technology Taxila Pakistan Electrical and Electronics Engineering Alasala colleges Dammam Saudi Arabia Department of Engineering Technology Hamilton Miami University OH MI USA Department of Electrical Engineering Philadelphia University Amman Jordan

ISBN: (数字)9798331539733

ISBN: (纸本)9798331539740

Chronic wounds represent a significant global healthcare challenge, affecting millions of individuals and leading to substantial financial burdens. Accurate classification of chronic wounds is crucial for guiding effective treatment strategies, improving patient outcomes, and reducing healthcare costs. However, existing methods in chronic wound classification predominantly focus on binary classification, with limited success in multi-class scenarios. Consequently, there is a need for diagnostic solutions that can reliably classify multiple wound types. To overcome these challenges, we present a novel feature concatenation-based deep learning method for reliable multiclass classification of chronic wounds. Specifically, we employed the DenseNet121 and XceptionNet models for generating effective and compact feature maps that are then passed to a Multilayer perceptron Classifier to accurately identify different types of wounds. The ability of DenseNet121 to truncate redundant features and XceptionNet to handle complex features with fewer computational resources allows us to generate compact and efficient feature maps. We evaluated the performance of our method using the publicly available AZH and Medetec datasets, as well as their augmented versions. Multiple augmentation techniques were employed to increase sample size and diversity, addressing overfitting and class imbalance issues. A rigorous experimental study performed on the AZH, Medetec, and augmented collections demonstrates the effectiveness of our method for multiclass wound classification.

关键词： Deep learning Costs Accuracy Computational modeling Medical services Wounds Multilayer perceptrons Computational efficiency Reliability Overfitting

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Design of IoT based Platform Development for Smart Home Appliances Control

Design of IoT based Platform Development for Smart Home Appl...

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International Virtual Conference on Intelligent robotics, Mechatronics and Automation Systems 2021, IRMAS 2021

作者： Kolanur, C.B. Banakar, R.M. Rajneesh, G. Department of Automation and Robotics KLE Technological University Hubballi India Department of Electronics and Communication Engineering KLE Technological University Hubballi India MD Trinity Technologies Software Solutions Hubballi India

The advent of industrial automation has brought about a substantial change in the human lifestyle and the way human interaction with machines. One of the major technological interventions in day-to-day life is the role of automation, which has uplifted the living standards in developed countries and fostered humans' dependency for accomplishing recurring tasks. The latest and most adopted technology for remotely monitoring and controlling any machine or device is the Internet of Things-which has become one of the industry standards for automation. Multiple edge computation capable embedded devices and machines are connected to the internet as independent nodes, which can be monitored and controlled remotely with the use of IoT. Adapting this cutting-edge technology for household appliances to monitor and control them is often seen nowadays. This is fast and reliable but arrives with its own snags such as availability of internet at place where user and also the device/appliance is located, use of single control strategy which hampers and restricts the user always to carry the remote or to stay in the vicinity of the device to be controlled in case of wifi or Bluetooth controlled automation. The proposed research focuses on the adoption of multiple strategies to control each device/appliance to empower the end-user without being restricted to depend on a single strategy remote controlling system. The core part of the proposed research revolves around the Message Queuing Telemetry Transport (MQTT) protocol but can be activated with multiple routes such as touch-based sensing panels, cellphone-based controlling, web-enabled controlling or even gesture-based controlling of the appliances resulting in an ameliorative user experience and interactions with machines. © Published under licence by IOP Publishing Ltd.

关键词： Automation

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