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检索条件"任意字段=Proceedings of the 2020 2nd International Conference on Robotics Systems and Vehicle Technology"

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Hybrid Frequency Domain based Feature Extraction methods for Human Face Recognition 2

Hybrid Frequency Domain based Feature Extraction methods for...

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2nd international conference on Smart Technologies, Communication and robotics, STCR 2022

作者： Abdullah, M. Prakash, S.P. Beshir, Kedir Ftsum Berhe, Abrha Petros Abo, Yibeltal Wachemo University Department of Electrical and Computer Engineering Hosana Ethiopia Bannari Amman Instuite of Technology Sathyamangalam Department of Electronics and Communication Tamilnadu Erode India

ISBN: (纸本)9781665460477

With the rising demands of public safety systems, face recognition has gained extra notice for the researchers in recent times. Real-world face recognition systems require cautious balancing of two important concerns: execution Time, recognition rate. The most important methods for feature extraction and classification are Dimension Reduction-Discrete Cosine Transform (DR-DCT), Dimension Reduction-Discrete Fourier Transform (DR-DFT) and Dimension Reduction-Difference of Gaussian (DoG) along with the Extreme Learning Machine (ELM) classifiers. The feature vector of the proposed algorithm is reduced by means of subspace method Principal Component Analysis (PCA). The execution time of the proposed DR-DFT, DR-DoG and DR-DCT algorithms along with ELM classifier is less when compared to the traditional methods such as Hough transform, Radon Transform, Discrete Fourier Transform (DFT) and Discrete Cosine Transform (DCT) for ORL face database. Similarly, Hybrid methods are proposed by combining DR-DCT & DR-DFT (Hybrid Method 1), DR-DCT & DR-DoG (Hybrid Method 2) and DR-DFT & DR-DoG (Hybrid Method3) along with ELM classifier. Hybrid Method 1 attains 98.50% recognition rate with the feaure size of 60×1 for ORL dataset. It achieves optimal execution time of 0.020 sec. © 2022 IEEE.

关键词： Principal component analysis

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COVR Toolkit - Supporting safety of interactive robotics applications

COVR Toolkit - Supporting safety of interactive robotics app...

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2nd IEEE international conference on Human-Machine systems (ICHMS)

作者： Saenz, Jose Fassi, Irene Prange-Lasonder, Gerdienke B. Valori, Marcello Bidard, Catherine Lassen, Aske B. Bessler-Etten, Jule Business Unit Robot Syst Fraunhofer IFF Magdeburg Germany Natl Res Council Italy Inst Intelligent Ind Technol & Syst Adv Mfg Milan Italy Roessingh Res & Dev Enschede Netherlands Univ Paris Saclay CEA List Palaiseau France Danish Technol Inst Dept Robot Technol Odense Denmark Univ Twente Roessingh Res & Dev Enschede Netherlands

ISBN: (纸本)9781665401708

Collaborative robots (cobots) are increasingly finding use beyond the traditional domain of manufacturing, in areas such as healthcare, rehabilitation, agriculture and logistics. This development greatly increases the size and variations in the level of expertise of cobot stakeholders. This becomes particularly critical considering the role of human safety for collaborative robotics applications. In order to support the wide range of cobot stakeholders, the EU-funded project COVR "Being safe around collaborative and versatile robots in shared spaces" has developed a freely available, web-based Toolkit that offers support to understand how to consider the safety of cobot applications. This paper describes the state of the art for ensuring safety across various life cycle phases in the development and implementation of collaborative robotics applications and highlights how the Toolkit provides practical support during these tasks. The Toolkit aims to be the most comprehensive resource for supporting cobot stakeholders in ensuring the safety of their applications.

关键词： physical human-robot interaction safety collaborative robotics standardization

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Research on 5G Network Slicing technology for Different Scenarios 2

Research on 5G Network Slicing Technology for Different Scen...

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2nd international conference on Electronic Information technology, EIT 2023

作者： Xiao, Juan Yao, Tao Zhang, Mengzhen School of Electrical and Electronic Engineering Wuhan Polytechnic University Hubei Wuhan China China Classification Society Wuhan Branch Hubei Wuhan China

ISBN: (数字)9781510666627

ISBN: (纸本)9781510666610

With the explosive growth of the number, type and quantity of terminal access, 5G networks need to support a large number of diversified business scenarios from the vertical industry to meet the different needs of differentiated services. Network slicing technology is the key to realize the vision of all things interconnection with wide connection, high reliability and ultra-low delay services in 5G. How to unify different network slicing according to the business needs of different scenarios has become an important issue in the implementation of network slicing. This paper first studies the current development status and technical hotspots of 5G network slicing, and then designs the network slicing schemes for different scenarios. Finally designs a network slicing scheme in detail for multi-service in eMBB scenario, and the feasibility of the scheme in practical application is verified Via XLABS simulation platform. © 2023 SPIE.

关键词： 5G mobile communication systems

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Comparative Analysis of High-Performance Computing systems and Machine Learning in Enhancing Cyber Infrastructure: A Multiple Regression Analysis Approach 2

Comparative Analysis of High-Performance Computing Systems a...

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2nd international conference on Innovative Practices in technology and Management, ICIPTM 2022

作者： Torres-Cruz, Fred Sahu, Ajay Kumar Huayhua, Ruben Ticona Julio, Martin Bellido, Merma Limachi, Isaac Ortega Huanca, Julio Cesar Laura Universidad Nacional Del Altiplano De Puno Academic Department Of Statistics And Computer Engineering Puno Peru Ims Engineering College Ghaziabad/Dr Aktu Information Technology Lucknow India Universidad Nacional Del Altiplano De Puno Academic Department Mathematical And Physical Sciences P.O. Box 291 Puno Peru Universidad Nacional De Juliaca Puno-Perú Departamento Académico De Ciencias Básicas Puno Peru

ISBN: (纸本)9781665466431

The invention of advanced digital technologies like machine learning, big data and other tools has disrupting the computing landscape in the current decades. The data corporations like Amazon, Google, Microsoft and others are focusing in tapping new opportunities through better information technology infrastructure, support in delivering higher performance for many companies, individuals and government. The traditional method of sing computing technologies to enhance coyer infrastructure is inadequate with the current development of Industry 5.0, hence the companies are implementing radical approach to understand the current and future requirement of the business and create better cyber infrastructure using high performance computing systems and machine learning. The continuous quest in understanding the overall pattern which shift through high quality data set has disrupted technological platform, the combination of machine learning approaches, robotics, high performance computing systems has boosted the overall performance in information technologies and companies are adopting these novel measures to use high computing methods, deep learning techniques and others for enhancing the cyber infrastructure. Since Machine learning (ML) and High-performance Computing (HPC) are continuously evolving, companies can now able to process the data with higher volume, understand the criticality of business operation and reduce the time to take quick action for sustainable development. Hence this study's is involved in understanding the criticality of implementing ML and HPC measures for supporting the cyber infrastructure requirements of organization in an effective manner. © 2022 IEEE.

关键词： Regression analysis

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Machine Learning Based Non-Invasive Glucose Observation for Diabetes 2

Machine Learning Based Non-Invasive Glucose Observation for ...

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2nd international conference on Smart Technologies, Communication and robotics, STCR 2022

作者： Rajeshwaran, K. Thirunavukkarasu, T. Pooja, S. Puvisha, C. Rajeshkumar, S. Sri Ramakrishna Engineering College Electronics and Communication Engineering Coimbatore India Narasu's Sarathy Institute of Technology Electronics and Communication Engineering Salem India

ISBN: (纸本)9781665460477

In this paper, we discuss overcoming the traditional invasive technique to capture glucose levels and overcome this by using a non-invasive method to monitor glucose levels and other related parameters to give doctors a clear insight on diabetes. The clearer picture which we mention includes the implementation of Machine Learning algorithms for the early prediction and diagnosis of diabetes. The parameters include glucose levels, temperature, and heart rate and it may be very essential to reveal diverse clinical parameters. In modern healthcare systems, the use of IoT plays a vital role in the accessibility and monitoring of diverse patient data. The Internet of things serves as a catalyst for healthcare and performs an outstanding position in a huge variety of healthcare applications. In this venture, the microcontroller is used as a gateway to speak to the diverse sensors which include a temperature sensor and heartbeat sensor, glucose sensor. The microcontroller processes the sensor records and sends them to the cloud and subsequently presents the real-time data tracking of the parameters such as heart rate, temperature, and glucose levels for doctors. The records may be accessed each time with the aid. The records which are stored in the cloud are later used in Machine Learning algorithms to monitor the glucose levels and get a valuable prediction out of it for further monitoring purposes. © 2022 IEEE.

关键词： Glucose

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Analysis of Football Performance Patterns via Load Zone-Based Cluster Analysis Technique 2nd

Analysis of Football Performance Patterns via Load Zone-Base...

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2nd international conference on Intelligent Manufacturing and robotics, ICIMR 2024

作者： Rasid, Aina Munirah Ab Musa, Rabiu Muazu Majeed, Anwar P. P. Abdul Mohamad, Zulkifli Razman, Mohd Azraai Mohd Abdullah, Muhammad Amirul Centre for Fundamental and Continuing Education Universiti Malaysia Terengganu Terengganu Kuala Nerus21030 Malaysia Sunway University No 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia Sports Engineering and Artificial Intelligence Centre College of Engineering MARA University of Technology Selangor Shah Alam40450 Malaysia Innovative Manufacturing Mechatronics and Sports Laboratory Universiti Malaysia Pahang Pahang Darul Makmur Pekan26600 Malaysia

ISBN: (纸本)9789819639489

In recent years, football has undergone substantial changes, requiring players to adapt both mentally and physically. The outcomes of matches depend heavily on the dynamic interplay among teammates and opponents, with players constantly adjusting their positions in response to evolving game situations. Simultaneously, the monitoring of athlete loads has gained considerable attention, driven by technological advancements. It is now standard practice for football clubs worldwide to employ cutting-edge global positioning systems to monitor and assess player performance during matches. This study aims to develop a machine-learning model for clustering football performance based on match load zones derived from these tracking systems. The research focuses on an elite team from the Malaysia Super League (MSL) during the 2022 season. Using Louvain clustering, the study identified three performance levels: low (LP), moderate (MP), and high (HP). An Artificial Neural Network (ANN) was then trained to classify these clusters, achieving an accuracy rate of 86.4%. These findings are of significant value to coaches and sports managers, offering a method to evaluate players based on match demands and load zones. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Global positioning system

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High-performance UV detector based on freestanding AlGaN film by electrochemical etching technology 2

High-performance UV detector based on freestanding AlGaN fil...

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2nd international conference on Optical Communication and Optical Information Processing, OCOIP 2024

作者： Yang, Yuqing Wang, Xingfu Zhang, Lu Wu, Shuanghong School of Optoelectronic Science and Engineering University of Electronic Science and Technology of China No.4 Section 2 North Jianshe Road Chengdu610054 China Faculty of Engineering South China Normal University Taoyuan East Road Nanhai District Foshan528225 China

ISBN: (数字)9781510688995

ISBN: (纸本)9781510688988

Ultraviolet (UV) photodetector (PD) is a device that converts optical signal into electrical signal, serving as a critical component in optoelectronic systems. Due to the leakage effect caused by defects of substrate and buffer layer, the traditional III-nitride semiconductors material UV PDs usually have a large dark current, which seriously restricts the performance of the UV PDs. Herein, freestanding Al0.1Ga0.9N UV PD was prepared by electrochemical (EC) etching technology. Based on the freestanding Al0.1Ga0.9N UV detector, the photoelectric performance was charactered. The freestanding Al0.1Ga0.9N UV PD shows a low dark current below of 1 nA owing to the removing of redundant buffer layer, and exhibits a high responsivity of 0.61 A/W, a high detectivity of 4.1 × 1012 Jones. The time response demonstrates that the rise time and decay time of the Al0.1Ga0.9N UV PD are merely 0.291 and 0.007 s. The Al0.1Ga0.9N PD demonstrates a stable and rapid response time at 325 nm under a low bias of 2 V. This research offers a method for the fabrication and transfer of Al0.1Ga0.9N UV PD, facilitating the development of novel functional devices, including AlGaN heterogeneously integrated devices and flexible devices. © 2025 SPIE.

关键词： Electrochemical etching

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Next-Gen AI in Healthcare: Shaping the Future of Precision and Preventive Medicine 2

Next-Gen AI in Healthcare: Shaping the Future of Precision a...

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2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Tale, Yash Verma, Prateek Pake, Tanushri Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

Healthcare professionals benefit from revolutionary changes because of current developments in artificial intelligence technology, which optimise precision medicine and preventive medical practices. Artificial Intelligence technology transitioned from basic rule-based systems into complex deep learning models that accurately analyse intricate medical data over decades. This paper investigates AI's significant influence on healthcare, including diagnostic and treatment operations, current robotics, and automated patient care systems. AI systems assist medical staff by improving precise diagnoses and simplifying treatments while boosting patient supervision through automated intelligence. The advantages of AI medicine simultaneously trigger crucial legal and moral dilemmas about healthcare data security, automated system bias, and AI-developed judgmental processes. The paper examines these issues regarding AI health care deployment while presenting guidelines to establish responsible and equitable use of AI technology in medical applications. The discussion explores how AI will influence genomic research and robotic surgery while demonstrating its potential to transform healthcare practice globally. This paper examines the complete transformation of contemporary medical care through an analysis of AI effects with identified difficulties and forthcoming potential. © 2025 IEEE.

关键词： Surgery

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Modernizing CPS with Blockchain: Applications, Challenges & Future Directions 2

Modernizing CPS with Blockchain: Applications, Challenges & ...

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2nd international conference on Interdisciplinary Cyber Physical systems, ICPS 2022

作者： Ansar, Syed Anas Singh, Archita Aggrawal, Shruti Yadav, Amitabha Pathak, Prabhash Chandra Khan, Raees Ahmad Babu Banarsi das University Department of Computer Application Lucknow India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India Babasaheb Bhimrao Ambedkar University Department of Information Technology Lucknow India National Post Graduate College Department of DDUKK Lucknow India

ISBN: (数字)9781665470223

ISBN: (纸本)9781665470223

The necessity for ameliorating management and secure process is imperiously required in this tech-driven world with the digitized working environment. The increased usage of the internet and the burgeoning connectivity through IoT have provided a significant opportunity for Cyber-Physical systems (CPS) to preponderate. These are advanced computerized devices that function together to perform specific tasks, respond to human stimuli, and control the physical segments. While this technology is already used in automatic pilot avionics, robotics systems, medical monitoring, industrial control systems, etc. yet the advancement of these systems must comprehend incontrovertible focus on making them efficient and secure. To improve the resilience, dependability, and security of these systems, researchers can incorporate Blockchain technology which has an inbuilt blend of consensual algorithms, secure protocols, and distributed data storage, with the CPS. Moreover, with the advancement of IoT in the upgraded version of CPS, such as autonomous driving, there's a major need to manage big data with low latency and high accuracy. Therefore, deep learning can be applied for systematic analysis of big data, which supports vivid analytic ability. While these emerging technologies set some security standards and enable users to authenticate their digital information, these technologies have the potential to significantly influence the current trend in the field of CyberPhysical systems. This article focuses on a comprehensive analysis of Blockchain applications synthesized with CPS. Furthermore, this article briefly presented the BCPS (Blockchain-enabled CPS) framework for addressing issues in the Industry 4.0 manufacturing system while adopting Blockchain (BC) in CPS. © 2022 IEEE.

关键词： Blockchain

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Research on Repairing Defective Skull Based on Rapid Prototyping Manufacturing technology 2

Research on Repairing Defective Skull Based on Rapid Prototy...

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2nd international conference on Biomedical and Intelligent systems, IC-BIS 2023

作者： Liu, Xudong Li, Yuying He, Cien Li, Xinyi Mo, Zhong School of Biomedical Engineering Guangzhou Xinhua University China

ISBN: (纸本)9781510666733

Skull defects are generally caused by strong blows to the head, and surgical treatment is the preferred therapy for cranial injuries. With the development of computerized three-dimensional reconstruction technology and the improvement of related software development, the use of rapid prototyping technology combined with biologically active artificial bone materials can produce skull models that highly match the patient's skull defect site and promote good bone fusion. Herein, based on CT images of patients with skull defects, combined with information about the patient's brain tissue to obtain the complete original outline of the skull and restore the information of the skull defect site. After 3D reconstruction, a customized rapid prototype manufacturing of the skull defect repair implant is performed using a 3D printer. The resulting skull defect repair implant is very suitable for the site of the skull defect, indicating that the precise manufacture of skull defect repair implants using rapid prototyping technology is feasible, and has important significance for preoperative planning and reducing intraoperative harm during skull defect repair surgery. © 2023 SPIE.

关键词： Rapid prototyping

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