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i-Glove: An Intelligent Glove System Based on Deep Learning to Support Deaf-blind Individuals in Recognizing Banknotes

i-Glove: An Intelligent Glove System Based on Deep Learning ...

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robotics, automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE international conference on

作者： Fahad Mohammad Hossain Rafit Alavi Yeamin Safat Gobinda Chandra Sarker Nafisa Noor Ekram Hossain Department of Computer Science and Engineering Mymensingh Engineering College Mymensingh Bangladesh Department of Robotics and Mechatronics Engineering University of Dhaka Dhaka Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

The deaf-blind community faces significant challenges in identifying banknotes due to simultaneous hearing and vision impairments, which complicate their ability to conduct daily financial transactions. Recent advances in Artificial Intelligence (AI)-based assistive technologies offer promising potential to address these challenges. This study presents the "i-Glove," a wearable device specifically developed to assist the deaf-blind community in Bangladesh with accurate banknote identification. The i-Glove is designed to recognize nine common Bangladeshi banknotes (e.g., BDT 20, 50, 100) through a camera module integrated on the palmar side of the glove, which captures real-time images of the banknotes. These images are then processed by a convolutional neural network (CNN) model to accurately classify each banknote. Identified notes are translated into unique vibration patterns that are delivered to the user through a shaftless vibration motor embedded in the glove, providing intuitive, non-visual communication. Compared to previous approaches, our model demonstrates superior performance even with a compact dataset, achieving an overall F1-score accuracy of 96.55%, with class-specific precision, recall, and F1-score metrics ranging from 90% to 100%. Additionally, a dedicated application is developed which supports real-time banknote detection and interfaces seamlessly with the glove. The proposed system represents a significant step forward in assistive technology for the deaf-blind community, empowering the users with greater autonomy in financial transactions.

关键词： Vibrations Visualization Accuracy Image recognition Tactile sensors Assistive technologies Motors Real-time systems Convolutional neural networks Wearable devices

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Machine Learning-Driven Demand Prediction for Off-Grid PV-Integrated EV Charging Stations

Machine Learning-Driven Demand Prediction for Off-Grid PV-In...

引用

robotics, automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE international conference on

作者： Sowrov Komar Shib Durjoy Roy Dipto Md Tanvir Rahman Md Mostafizur Rahman Riadul Islam Abu Shufian Dept. of Electrical & Electronic Engg American International University-Bangladesh Dhaka Bangladesh Dept. of Management Information System Lamar University Texas United States Dept. of Electrical & Electronic Engg Lamar University Texas United States

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

The rapid growth of electric vehicles (EVs) has led to an increasing demand for charging infrastructure, particularly in off-grid locations. Developing off-grid EV charging stations powered by photovoltaic (PV) systems is crucial to support this expansion. However, the variability of solar power presents challenges in maintaining efficient and reliable charging services. This study focuses on developing off-grid EV charging stations and optimizing load demand prediction using machine learning models to enhance the operational efficiency of these charging stations. Various algorithms, including Random Forest, Decision Tree, SVM, Logistic Regression, and Naïve Bayes, were employed to model and predict load demand based on historical data and real-time inputs. The results indicate that the Random Forest and Decision Tree models achieved the highest accuracy, at 93% and 92%, respectively. The effectiveness of machine learning in dynamically adjusting power distribution according to predicted demand is highlighted, reducing reliance on traditional energy grids and supporting the sustainable development of EV charging solutions in remote areas. Integrating these predictive models allows for better management of energy resources, ensuring a consistent and cost-effective power supply and facilitating the broader adoption of renewable energy in EV infrastructure.

关键词： Renewable energy sources Machine learning algorithms Accuracy Biological system modeling Predictive models Electric vehicle charging Decision trees Reliability Random forests Load modeling

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A Helical Bistable Soft Gripper Enable by Pneumatic Actuation

A Helical Bistable Soft Gripper Enable by Pneumatic Actuatio...

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IEEE international conference on robotics and automation (ICRA)

作者： Xuanchun Yin Junliang Xie Pengyu Zhou Sheng Wen Jiantao Zhang College of Engineering South China Agricultural University Guangzhou China College of Mathematics and Informatics South China Agricultural University Guangzhou China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

There are many instances of helical mechanisms that are used to efficiently grasp different objects with various shapes and sizes in nature. Inspired by the helical grasping in the nature, we proposed a helical bistable soft gripper with high load capacity and energy saving. An off-the-shelf bistable steel shell (BSS) as the stiff element was inserted into a 3D printing soft helical exoskeleton to achieve coiling around and holding the objects without energy consumption. Two air pouches were designed as the actuator to control the transition between the two stable states. To facilitate gripper design, a simplified model of the gripper was conducted, and the geometric parameters of the gripper are listed in a table for reference. The transition pressures between the two stable states were experimentally characterized. Moreover, we conduct experiments to demonstrate the capability of the gripper in two working modes. The gripper exhibits coiling diameters ranging between 40 mm and 60 mm and is successfully attached to various slender objects of different geometries with a maximum holding force of 92.67 N (up to 135.1 times of its mass) in hanging mode. Finally, the gripper was integrated into a robot arm and successfully grasped different objects, and the maximum grasping weight is 221.6 g in the grasping mode.

关键词： Geometry Shape Force Exoskeletons Grasping Pneumatic systems Three-dimensional printing

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Bionic Design and Kinematic Evaluation of a Stiffness-Adjustable Spinal Exoskeleton for Trunk Support

Bionic Design and Kinematic Evaluation of a Stiffness-Adjust...

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Service robotics (ICoSR), international conference on

作者： Yuhan Yin Aibin Zhu Peng Xu Jing Zhang Jie Liu Systems and Shaanxi Key Laboratory of Intelligent Robots Institute of Robotics & Intelligent Xi'an Jiaotong University Xi'an China Honghui Hospital Xi'an Jiaotong University Xi'an China School of Mechanical Engineering Yanshan University Qinhuangdao China

ISBN: (数字)9798350367119

ISBN: (纸本)9798350367126

In recent years, the field of medical rehabilitation utilizing back-support exoskeleton has demonstrated significant potential for application. However, existing exoskeletons still present issues such as insufficient comfort and safety. This paper proposes the design of a passive exoskeleton with variable stiffness for supporting the human trunk. The primary structure consists of a symmetrically distributed chain-type configuration on both sides of the spine, allowing for adjustable stiffness to assist users during complex body movements. In comparison to traditional rigid exoskeletons, this design not only enhances flexibility but also improves overall exoskeleton performance. Kinematic analysis of the exoskeleton reveals a high degree of freedom. Experimental results demonstrate that the exoskeleton provides a wide range of motion, with average ranges of 63° flexion, 33° extension, 25° lateral flexion, and 18° axial rotation when worn.

关键词： Service robots Exoskeletons Kinematics Back Safety Motion control

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Universal design of an Embedded Hardware for Educational robotics based on Atmega328P

Universal design of an Embedded Hardware for Educational rob...

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Embedded Systems and Artificial Intelligence (ESAI), international conference on

作者： Mohammed El Miri Said El Khaldi Ismael Driouch Loukmane Elkhaldi Experimentation and Modeling in Mechanics and Energy Systems Team National School of Applied Sciences ENSAH Abdelmalek Essaadi University Al hoceima Morocco Sciences and Advanced Technologies National School of Applied Sciences ENSATE Abdelmalek Essaadi University Tetouan Morocco

ISBN: (数字)9798331522056

ISBN: (纸本)9798331522063

This paper presents the design and development of an universal and cost-effective embedded hardware platform for educational robotics applications. The core of the system utilizes the popular Atmega328P microcontroller, known for its robustness and affordability. The design prioritizes minimalism principles while maintaining essential functionalities for basic robot control and sensors integration. The hardware system includes four stepper motor drivers, four H-bridge motor drivers, a 16-channel PWM controller and GPIO connections for sensors and user command. The software design provides a versatile framework for controlling motors, reading sensor data, and enabling communication between devices, making the platform easy to program and customize. Our design makes the system ideal for educational and research settings, allowing users to explore robotics concepts without the constraints of complex and expensive hardware.

关键词： Embedded systems Software design Education Robot sensing systems Motors Hardware Sensor systems Robustness Sensors Robots

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Detecting Plant Leaves Based on Vision Transformer Enhanced YOLOv5 3

Detecting Plant Leaves Based on Vision Transformer Enhanced ...

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3rd international conference on Pattern Recognition and Machine Learning, PRML 2022

作者： Li, Xue Fan, Wenqiang Wang, Yi Zhang, Liankuan Liu, Zhixin Xia, Chunlei Yantai University School of Physics and Electronic Information Yantai China Naval Aeronautical University Coastal Defense College Yantai China South China Agricultural University College of Mathematics and Informatics Guangzhou China Agricultural High-tech Industrial Demonstration Area of the Yellow Technology Innovation Center for Comprehensive Utilization of Saline-alkali Land China Chinese Academy of Sciences Yantai Institute of Coastal Zone Research Yantai China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

With the advancement in the field of computer vision and the development of automated systems for agricultural production, object detection techniques have been widely used in the task of plant leaf detection. However, detecting plant leaves in field condition is challenging due to severe occlusion and variation in leaf position. In this work, we implement plant leaf detection by YOLOv5 and combine transformer techniques to improve detection accuracy. Transformers blocks are integrated into the original backbone and the prediction heads of YOLOv5 by replacing modules in the original YOLOv5 network. Two kinds of transformer blocks, vision transformer and swin transformer, are utilized to modify the YOLOv5 model, separately. The proposed method combines local features with global features and refines the information division to achieve the effect of improving detection accuracy and speed. The performance of the improved model is evaluated using plant leaf images taken under field conditions. The average detection accuracy (mAP) of YOLOv5 with vision transformer is 92.2%, which is an improvement of 1.2% compared with the baseline model (YOLOv5) while mAP of swin transformer based YOLOv5 is 92.6% which is 1.6% improvement compared with the baseline model (YOLOv5). Experimental results show that the transformer modules can effectively improve detection accuracy of plant leaves, especially, in dense leaf scene. Swin transformer based YOLOv5 presents the best detection accuracy due to the introduction of localization, hierarchy, and translation invariance structure of swin transformer. This work could provide solutions for agricultural automation applications, e.g., plant growth monitoring. © 2022 IEEE.

关键词： Object detection

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Improvement of Depth Mapping and Occlusion Removal Method Using Integral Imaging

Improvement of Depth Mapping and Occlusion Removal Method Us...

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international conference on control, automation and Systems ( ICCAS)

作者： Haruka Sakanoue Hyun-Woo Kim Myungjin Cho Min-Chul Lee Department of Computer Science and Networks Kyushu Institute of Technology Iizuka Fukuoka Japan School of ICT Robotics and Mechanical Engineering IITC Hankyong National University Anseong Kyonggi-do South Korea

In this paper, we propose the depth mapping and occlusion removal method using integral imaging (InIm). InIm is a method to obtain 3D information by capturing and reconstructing an object from multiple viewpoints. InIm can remove occlusion using parallax obtained by photographing an object from multiple viewpoints. However, occlusion removal uses a unit matrix to calculate the number of overlaps, which results in an overall darkening of the image. To solve this problem, this paper aims to improve occlusion removal by using the matrix of the mask image to calculate the number of overlaps. InIm can provide depth mapping that shows the distance from an object as pixel values. However, when there is an occlusion in front of the object, it is difficult to provide depth mapping object due to the influence of the occlusion. To solve this problem, we propose a method of depth mapping and occlusion removal method using InIm. Our method is expected to make great progress in improving safety in the field of autonomous driving.

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Real-Time Sentiment Analytics: Integrating NLP for Social Media Insight 3

Real-Time Sentiment Analytics: Integrating NLP for Social Me...

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3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Senthil Pandi, S. Kumar, P. Salman Latheef, T.A. Naresh Kumar, A. Rajalakshmi Engineering College Department of Cse Chennai India Rec Department of Cse Chennai India University of Sussex Department of Informatics Brighton United Kingdom

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542375

The social networking sites of Instagram, WhatsApp, Twitter, and YouTube have led to this tremendous volume of user-generated content in text, images, and videos. Now all these elements-from marketing to direct customer engagement-become contingent upon the analysis of the emotional context of such multimodal data. This research strongly advocates an architecture that unifies NLP for text-based content and deep learning for the visual data into emotion categorization because traditional techniques for sentiment analysis cannot merge the two input modalities into meaningful and actionable insights. While EfficientNet covers the visual inputs such as images and video frames, GPT-2, optimized with LoRA, classifies the emotions within the text comments and captions. Some pipelines are included in the real-time processing modular architecture. The outputs are executed through a chatbot on an interactive frontend constructed with ReactJS. Other salient features include ensemble-based data fusion, improved pretreatment pipelines, and scalable back-end deployment. Results: This depicts a reasonably high accuracy and scale in different platforms. The overall multimodal framework would, therefore, allow organizations to extract maximum potential from the knowledge available on social media and thereby transform the process of emotion analytics. © 2025 IEEE.

关键词： Tweets

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Advanced AI Framework for Enhanced Detection and Assessment of Abdominal Trauma: Integrating 3D Segmentation with 2D CNN and RNN Models

Advanced AI Framework for Enhanced Detection and Assessment ...

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robotics, Artificial Intelligence and Intelligent control (RAIIC), international conference on

作者： Liheng Jiang Xuechun Yang Chang Yu Zhizhong Wu Yuting Wang New York University New York USA TikTok Inc Milpitas USA Northeastern University Boston USA Google Inc Mountain View USA FASTTEK GLOBAL Livonia USA

ISBN: (数字)9798350352627

ISBN: (纸本)9798350352634

Trauma is a significant cause of mortality and diagnostic methods for traumatic injuries, such as X-rays, CT scans, and MRI, are often time-consuming and dependent on medical expertise, which can delay critical interventions. This study explores the application of artificial intelligence (AI) and machine learning (ML) to improve the speed and accuracy of abdominal trauma diagnosis. We developed an advanced AI-based model combining 3D segmentation, 2D Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) to enhance diagnostic performance. Our model processes abdominal CT scans to provide real-time, precise assessments, thereby improving clinical decision-making and patient outcomes. Comprehensive experiments demonstrated that our approach significantly outperforms traditional diagnostic methods, as evidenced by rigorous evaluation metrics. This research sets a new benchmark for automated trauma detection, leveraging the strengths of AI and ML to revolutionize trauma care.

关键词： Measurement Solid modeling Three-dimensional displays Recurrent neural networks Computed tomography Machine learning Predictive models

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Brain Tumor Diagnosis Using a Hybrid Method of BoVW, LBP, and Neural Networks

Brain Tumor Diagnosis Using a Hybrid Method of BoVW, LBP, an...

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robotics, automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE international conference on

作者： Nowshad Hasan Md Saiful Islam Md. Maskat Sharif Arafat Anam Chowdhury Miskatur Rahman Md Tohedur Rahaman Khan Md Rokibul Fahad Khan Anamul Bahar Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh Department of Computer Science Enginnering North South University Dhaka Bangladesh Department of Computer Science Enginnering International Islamic University Chittagong Chittagong Bangladesh Department of Electronics and Telecommunication Engineering International Islamic University Chittagong Chittagong Bangladesh Department of Computer Science Enginnering University of London London United Kingdom

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

Brain tumor is one of the most deadly diseases today. Early identification is the key in treatment and automated method of image classification can help in this process. But, there are significant obstacles in precisely identifying brain tumors and increasing the classification accuracy for different types of tumors. In order to address the problem, the work suggests a hybrid model for effectively detecting and classifying brain tumors. First, the obtained pictures are preprocessed with resizing, application of sharpening filter and final stage of contrast enhancement. Further preprocessing in carried out using the K-mean clustering algorithm Next, Feature extraction is done by using the BoVW together with LBP which deal with the overall and region-specific texture features respectively. Following that, the seven statistical features are extracted, and then they are utilized as input to the artificial neural network classifier in order to obtain the classification of the glioma, meningioma, and pituitary tumor. The training function ‘Levenberg-Marquardt’ is also used in this work. This method provides 99.0 % of accuracy on Dataset I and 99.2% accuracy on Dataset II. Moreover, in relation to other modern methods, the selected seven statistical features employed in this study are relatively limited in demonstrating this performance.

关键词： Training Accuracy Brain tumors Artificial neural networks Feature extraction Brain modeling Reliability Robots Medical diagnostic imaging Image classification

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