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International Conference on Computer and Applications (ICCA)

作者： Amr Khaled Refaie Mostafa Abdelaziz Mechatronics & Robotics Mechanical Engineering Cairo Egypt The British University in Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

Diabetes mellitus patients must regularly monitor their blood glucose levels to manage glycaemia, typically requiring capillary tests at least three times daily and laboratory tests one to two times per month. These conventional methods involve finger pricking, causing significant discomfort and stress. This study introduces an innovative non-invasive glucose monitoring approach by integrating photoplethysmography (PPG) technology with an artificial intelligence (AI) algorithm, complemented by a mobile application using Flutter, IoT systems, and Firebase cloud for real-time data access. Among the AI models tested, polynomial regression demonstrated superior accuracy in glucose prediction, achieving a Mean Squared Error (MSE) of 14.76 and an R 2 score of 0.729. Despite challenges such as motion artifacts and ambient light interference affecting PPG signals, the system provides an advanced, user-friendly solution for patients and healthcare teams to monitor glucose levels effectively and conveniently.

关键词： Cloud computing Medical services Photoplethysmography Real-time systems Polynomials Glucose Diabetes Artificial intelligence Biomedical monitoring Monitoring

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Pre-Programmable Directional Stiffness in Continuum Robotic Arms: Effects of Truss Configurations

Pre-Programmable Directional Stiffness in Continuum Robotic ...

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International Conference on Computer and Applications (ICCA)

作者： Zahy Y. Elgendy Mahmoud Magdy Mostafa Abdelaziz Mechatronics & Robotics Mechanical Engineering The British University in Egypt Cairo Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

This paper investigates the implementation of trussed designs in modular tendon-driven continuum arms (CAs) to address stiffness and stability limitations while maintaining adaptability. Various truss configurations-Single-Level, Combined-Level, and Alternated trusses-were analyzed using Finite Element Analysis (FEA) to evaluate their impact on flexural and axial stiffness under realistic loading conditions. Results demonstrate that truss placement significantly influences performance, with bottom-level trusses improving flexural stiffness by up to 33 %, middle-level trusses enhancing axial stiffness by 43%. Further, Combined-Level configurations provide superior overall stiffness, with Middle-Bottom trusses achieving a 66.5% improvement in flexural stiffness and a 70.9% increase in axial stiffness, while fully trussed configurations maximize rigidity with an 87.8% increase in flexural stiffness and 98.5% in axial stiffness. Alternated configurations achieve anisotropic stiffness for directional control. Additionally, the proposed trussed wing approach enhances stiffness programmability with reduced complexity, eliminating the need for full segment disassembly or tendon re-routing. The proposed trussed CAs enable tailored stiffness and directional control in robotics, medical devices, and aerospace systems.

关键词： Medical devices Loading Anisotropic Soft robotics Manipulators Three-dimensional printing Stability analysis Finite element analysis Rigidity Tendons

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An exploratory user study towards developing a unified, comprehensive assessment apparatus for deaf signers, specifically tailored for signing avatars evaluation: challenges, findings, and recommendations

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Multimedia Tools and Applications 2024年 1-38页

作者： Imashev, Alfarabi Oralbayeva, Nurziya Sandygulova, Anara Department of Robotics and Mechatronics School of Engineering and Digital Sciences Nazarbayev University 53 Kabanbay Batyr ave. Astana010000 Kazakhstan National Laboratory Astana Nazarbayev University 53 Kabanbay Batyr ave. Astana010000 Kazakhstan

Sign Languages (SL) are natural languages used by those who are deaf or have hearing impairments, as well as by hearing persons, including interpreters, family members, and friends of deaf individuals. Spoken languages rely primarily on verbal and written communication, whereas sign language focuses mainly on visual-gestural communication. In their daily lives, unimodal users (deaf people who only use sign language) cannot always rely solely on textual communication. Research on SL recognition and generation has shown enormous promise in addressing concerns related to inclusivity and accessibility. The research on using signing avatars in human-computer interaction is relatively new. The currently available information suggests that the research strategies used to develop sign language-interpreting avatars have drawbacks that hinder the establishment of a strong, dependable, and persuasive signing system. Therefore, to ensure the meaningful involvement of the deaf population in user studies, it is essential to design a standardized procedure for surveying them and acquiring significant insights to assist future improvements. In order to accomplish this objective, we undertook a thorough examination of several scales and metrics, which we endeavored to precisely customize and modify to suit the subjective assessment of SL agents by deaf signers. We conducted this analysis as an exploratory case study involving deaf signers and individuals who use sign language oftentimes. We aimed to address the question: "What should a methodological framework for evaluating sign language agents by deaf signers entail?" The data obtained from both rounds of this preliminary exploratory user study, as outlined in the current paper, became essential for our future work. During the COVID-19 lockdown period in 2020-2021, we conducted the first iteration solely online. We held the second iteration after a year, still adhering to the limitations imposed by the pandemic. © The Author(s), u

关键词： Audition

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Its All About Context

Its All About Context

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IEEE Southeastcon

作者： Preston Brantley Yusun Chang Razvan Cristian Voicu Department of Robotics & Mechatronics Engineering Kennesaw State University Atlanta Georgia

This paper presents an innovative approach to enhancing context understanding in Generative Pretrained Models (GPTs), a critical step towards achieving Artificial General Intelligence (AGI). While GPTs have significantly advanced natural language processing, their understanding of context remains predominantly limited to linguistic structure. To address this limitation, we introduce a novel layer in the transformer model architecture that computes context weights, integrating both immediate and temporally decaying influences of past tokens. This layer is strategically positioned after the self-attention and before the feed-forward layers, enabling a more nuanced interpretation of sequential language data. Our approach involves the formulation of a decaying temporal factor, which allows the model to consider not only the immediate relevance of tokens but also their historical context. This factor is dynamically adjustable, offering a sophisticated method of context weighting that considers both current and extended contexts. The integration of this context weight into the self-attention mechanism enhances the model's capacity for a deeper, more accurate understanding of language, pushing the boundaries of current AI capabilities towards a system that mirrors human-like intelligence. Our experimental results demonstrate the efficacy of this approach, showing its potential to significantly contribute to the development of AGI.

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Shape Completion with Prediction of Uncertain Regions

arXiv

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arXiv 2023年

作者： Humt, Matthias Winkelbauer, Dominik Hillenbrand, Ulrich DLR Institute of Robotics and Mechatronics Germany Germany

Shape completion, i.e., predicting the complete geometry of an object from a partial observation, is highly relevant for several downstream tasks, most notably robotic manipulation. When basing planning or prediction of real grasps on object shape reconstruction, an indication of severe geometric uncertainty is indispensable. In particular, there can be an irreducible uncertainty in extended regions about the presence of entire object parts when given ambiguous object views. To treat this important case, we propose two novel methods for predicting such uncertain regions as straightforward extensions of any method for predicting local spatial occupancy, one through postprocessing occupancy scores, the other through direct prediction of an uncertainty indicator. We compare these methods together with two known approaches to probabilistic shape completion. Moreover, we generate a dataset, derived from ShapeNet [1], of realistically rendered depth images of object views with ground-truth annotations for the uncertain regions. We train on this dataset and test each method in shape completion and prediction of uncertain regions for known and novel object instances and on synthetic and real data. While direct uncertainty prediction is by far the most accurate in the segmentation of uncertain regions, both novel methods outperform the two baselines in shape completion and uncertain region prediction, and avoiding the predicted uncertain regions increases the quality of grasps for all tested methods. Web: https://***/DLR-RM/shape-completion Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Communication Interchange for Artificial Intelligence Systems

Communication Interchange for Artificial Intelligence System...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Razvan Cristian Voicu Aarush Kaunteya Pande Muhammad Hassan Tanveer Yusun Chang Department of Robotics & Mechatronics Engineering Kennesaw State University Atlanta Georgia

ISBN: (数字)9798350370997

ISBN: (纸本)9798350371000

The rise and proliferation of Artificial Intelligence (AI) technologies are bringing transformative changes to various sectors, signaling a new era of innovation in fields as diverse as medicine, manufacturing, and even day-to-day social interactions. Notable advancements are not just confined to textual understanding, as seen in models like GPT, but also extend to visual cognition through image recognition and more. Beyond surface interactions and predictions, AI finds profound applications in life-saving domains such as medical diagnostics and becomes an integral part of daily life through chatbot- based customer interactions. However, as the horizon of AI expands, a crucial yet often overlooked aspect emerges- the underlying mission-critical infrastructure required to support and deploy these models effectively. The intricacies of efficient communication systems, foundational for real-time AI model operations, take center stage in ensuring the seamless functioning of AI -driven applications. This paper explores the quintessential changes needed in communication paradigms to keep pace with the evolving AI landscape. Specifically, we highlight the pivotal role of multipath communication in enhancing the responsiveness and efficiency of AI applications [1]. As a case in point, we investigate its impact on mission-critical operations in robotics. Through experimentation and analysis, the results elucidate the substantial benefits of this approach, revealing a significant improvement in delay metrics. This work underscores the imperative of aligning communication systems with the ever-growing demands of AI, ensuring that infrastructural capabilities do not lag in the race for innovation.

关键词： Adaptation models Technological innovation Visualization Biological system modeling Mission critical systems Real-time systems Reliability

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Design and Development of IoT Based Weather and Air Quality Monitoring Station

Design and Development of IoT Based Weather and Air Quality ...

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2023 International Conference on robotics and Automation in Industry, ICRAI 2023

作者： Haris, Muhammad Ubaid, Bilal Bibi, Uzma Said, Ameer Us Saqib, Syed Najm Khan, Shahbaz Tufail, Muhammad Advanced Robotics and Automation Lab National Center of Robotics and Automation Peshawar Pakistan University of Engineering and Technology Department of Mechatronics Engineering Peshawar Pakistan Institute of Manufacturing Engineering Management University of Engineering and Applied Sciences Swat Swat Pakistan

ISBN: (纸本)9781665464727

This paper presents the Design and Development of IoT Based Weather and Air Quality Monitoring stations that provide weather-related information for agriculture, and aviation climate forecasting purposes. The prototype is made up of an outdoor unit that consistently monitors numerous climatic variables such as temperature, humidity, barometric pressure, wind speed, wind direction, rainfall, and air quality using sensors with high accuracy. The air quality index(AQI) is also calculated using the sensors values which tell us whether the atmosphere is safe or polluted. ESP32(Microcontroller) which has a WiFi module is used to gather and send the sensor data through the Internet of things (IoT) to a ThingSpeak channel (IoT cloud), an internet platform where the data is saved, analyzed and read the data remotely. The weather station makes use of photovoltaic power and a rechargeable lead-acid battery to sustain the system. This paper also discusses the obvious strengths and shortcomings of the system and suggests plausible upgrades to assist the system in obtaining higher and long-term performance. © 2023 IEEE.

关键词： Internet of things

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The Development and Implementation of a Cost-Effective Educational Robotic Arm Using ROS-MoveIT

The Development and Implementation of a Cost-Effective Educa...

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IEEE Integrated STEM Education Conference (ISEC)

作者： Matthew Eaton M. Hassan Tanveer Department of Robotics & Mechatronics Engineering Kennesaw State University Marietta GA

ISBN: (数字)9798350352801

ISBN: (纸本)9798350352818

This paper introduces a cost-effective robotic arm designed to match the performance of expensive counterparts, addressing the financial barriers hindering the widespread adoption of modern robotic arms in educational settings. The affordability of this robotic arm not only allows for greater student involvement, but its open-source nature fosters a deeper understanding of the robotics development process. This paper details the features and characteristics of the robotic arm design, as well as outlines the opportunities for mechatronics and robotics labs this design provides to students and educators through discussions ranging from the pros and cons of various stepper motor gearbox configurations, and structural material choices, to choices regarding various popular computational platforms. The overall initiative of the paper aims to create a more inclusive learning environment by reducing financial constraints, enabling broader student participation, and contributing to the advancement of robotics education.

关键词： Solid modeling Accuracy Three-dimensional displays Service robots Education Manipulators Control systems

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Optimizing Data Capture Through Object Recognition for Efficient Sensor and Camera Management with a Quadruped Robot

Optimizing Data Capture Through Object Recognition for Effic...

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IEEE Southeastcon

作者： Roxie Reese Aiden Kovarovics Arielle Charles Charles Koduru M. Hassan Tanveer Razvan C. Voicu Department of Robotics & Mechatronics Engineering Kennesaw State University Marietta Georgia

This paper explores the integration of object recognition to advance autonomous mobile robotics, with a specific focus on employing sensor fusion in quadrupedal swarm units, exemplified by the Unitree GO1 robotic dog. The proposed system utilizes an RGB camera and the YOLOv8 machine learning module to enable the robotic dog to identify objects, subsequently triggering predefined actions or activating additional hyper-spectral sensors and cameras. This approach enhances the robot's environmental interaction, allowing it to optimize power efficiency by activating sensors selectively, resulting in a more focused dataset. The technology's applications span various domains, including threat detection in home defense, surveillance of civic structures, and monitoring industrial components. The paper delves into the customization potential through the creation of bespoke datasets and model training, incorporating techniques such as transfer learning and domain adaptation to tailor the system to user-specific requirements. Beyond its immediate applications, the implications of object recognition and AI-assisted robotics extend to diverse scientific communities, offering a versatile tool for a wide array of applications.

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Inverted Stabilization Control of Electric Wheelchairs with Tristar Wheel Mechanism

Inverted Stabilization Control of Electric Wheelchairs with ...

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International Workshop on Advanced Motion Control (AMC)

作者： Naoya Tanabe Takumi Sakai Naoto Sato Yuki Mochida Masami Iwase Dept. of Robotics and Mechatronics Tokyo Denki University Tokyo JAPAN

The objective of this research is to inverted stabilization of an electric wheelchair with a tristar mechanism developed in our research group, which is necessary in the process of stair climbing and descending. Tristar wheel mechanism consists of a Y-shaped carrier and three wheels which are synchronized. Both the Y-shaped carrier and the wheels are controlled by two motors via a planetary gear mechanism. In the stair climbing process accomplished by rotating the Y-shaped carrier, it must pass through the two-wheel inverted state in which the wheelchair must stand by one wheel on each side. Therefore, in this study, a mathematical model of the wheelchair system is derived and an output zeroing control is designed based on the model to realize a two-wheeled inverted state. The effectiveness of the proposed method is demonstrated through numerical simulations.

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