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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Han, Changhao Qin, Jun Yang, Qipeng Zheng, Zhao Shu, Haowen Zhang, Yunhao Wu, Yichen Sun, Yu Lu, Junde Zhou, Yan Ge, Zhangfeng Wang, Lei He, Zhixue Yu, Shaohua Hu, Weiwei Peng, Chao Bowers, John E. Wang, Xingjun State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100192 China Frontiers Science Center for Nano-optoelectronics Peking University Beijing100871 China Peng Cheng Laboratory Shenzhen518055 China Peking University Yangtze Delta Institute of Optoelectronics Nantong226010 China

We experimentally demonstrate PAM-4 optical transmission beyond 224 Gbps based on an ultrahigh-bandwidth slow-light silicon modulator in C-band with the combination of the artificial neural network equalizers. © ... 详细信息

关键词： Light modulators

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Swallowing Assessment using High-Resolution Cervical Auscultations and Transformer-based Neural Networks

Swallowing Assessment using High-Resolution Cervical Auscult...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Ayman Anwar Yassin Khalifa Erin Lucatorto James L. Coyle Ervin Sejdic Department of Electrical and Computer Engineering Faculty of Applied Science & Engineering University of Toronto Toronto Canada Center for Research Computing and Information Technology Analytics University of Pittsburgh Pittsburgh PA USA Systems and Biomedical Engineering Department Faculty of Engineering Cairo University Giza Egypt Department of Communication Science and Disorders School of Health and Rehabilitation Sciences University of Pittsburgh Pittsburgh PA USA Department of Electrical and Computer Engineering Faculty of Applied Science & Engineering University of Toronto and North York General Hospital Toronto Canada

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Swallowing assessment is a crucial task to reveal swallowing abnormalities. There are multiple modalities to analyze swallowing kinematics, such as videofluoroscopic swallow studies (VFSS), which is the gold standard method, and high-resolution cervical auscultation (HRCA), which is a noninvasive technique that uses a triaxial accelerometer attached to the patient’s neck. Deep learning models play an essential role in data driven analysis of swallowing landmarks using VFSS and/or HRCA as input data. Most of these models utilize convolutional and recurrent neural networks. Here, we investigate the ability of transformers to analyze swallowing kinematics; specifically upper esophageal sphincter opening and laryngeal vestibule closure using HRCA signals. We tested the model using an independent test dataset to assess the generalizability of the proposed network. The proposed network achieved an average detection accuracy higher than 90% and 85% for both segmentation tasks, which outperform the hybrid neural networks from the literature, and the model obtained high-performance measures for the independent dataset, showing the transformers’ ability to generalize on unseen data.

关键词： Accuracy Sensitivity Recurrent neural networks Convolution Kinematics Transformers Feature extraction Solids Data models Signal resolution

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Risk Management in Robotic Process Automation

Risk Management in Robotic Process Automation

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Dileep Kumar Yendluri Ramya Thatikonda Reshmi Thatikonda Jainath Ponnala M. Kempanna A. Bhuvanesh Department of Computer Science University of the Cumberlands Williamsburg USA Department of Information Technology University of the Cumberlands Williamsburg USA Department of Computer Information Systems University of Central Missouri Williamsburg USA Department of Electronics and Communication Engineering Jawaharlal Technological University Hyderbad India Department of Artificial Intelligence and Machine Learning Bangalore Institute of Technology Bangalore India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India

The paper investigates the nuances of RPA risk management, detailing the difficulties, tendencies, and approaches organizations must adopt to integrate RPA into their operations successfully. From automation to optimization, RPA’s impact on industry workflow is revolutionary. However, RPA introduces a spectrum of technical, operational, business, and compliance risks alongside its benefits. Amidst the intricate landscape of RPA, this paper sheds light on key risks. It offers practical strategies for tackling them, enabling organizations to confidently unlock automation’s transformative power. Trends like advanced analytics fusion, AI-driven risk analysis, and blockchain utilization will likely influence future RPA risk management. In addition, the paper discusses evolving challenges, including ethical issues, breakneck technology evolution, and digital security dangers. Using an in-depth examination of RPA risk management, this paper prepares organizations for the ever-changing automation environment with informed decisions and proactive measures.

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Wavelength-Division-Multiplexed Transmission and Dispersion Compensation Enabled by Soliton Microcombs and Cascaded Microrings

Wavelength-Division-Multiplexed Transmission and Dispersion ...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Yuanbin Liu Hongyi Zhang Jiacheng Liu Liangjun Lu Jiangbing Du Yu Li Zuyuan He Jianping Chen Linjie Zhou Andrew W. Poon Department of Electronic Engineering State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Shanghai Jiao Tong University Shanghai China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu China Department of Electronic and Computer Engineering Photonic Device Laboratory The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

We demonstrate wavelength-division-multiplexed data transmission and dispersion compensation of 25 Gb/s $\times 9$ on-off-keying signals over a 20-km singlemode fiber using an integrated single-soliton microcomb and... 详细信息

ISBN: (纸本)9798350369311

关键词： Solitons Lasers and electrooptics Wavelength division multiplexing Data communication Quantum cascade lasers Electro-optical waveguides Optical fiber dispersion Fiber lasers

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Characterization of Microservice Response Time in Kubernetes: A Mixture Density Network Approach

Characterization of Microservice Response Time in Kubernetes...

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International Conference on Network and Service Management

作者： Lorenzo Manca Davide Borsatti Filippo Poltronieri Mattia Zaccarini Domenico Scotece Gianluca Davoli Luca Foschini Genady Ya. Grabarnik Larisa Shwartz Cesare Stefanelli Mauro Tortonesi Walter Cerroni University of Bologna Bologna Italy Distributed Systems Research Group University of Ferrara Ferrara Italy Department of Mathematics and Computer Science St. John's University Queens NY USA Operational Innovation IBM TJ Watson Research Center NY USA

The use of microservice-based applications is becoming more prominent also in the telecommunication field. The current 5G core network, for instance, is already built around the concept of a “Service Based Architecture”, and it is foreseeable that 6G will push even further this concept to enable more flexible and pervasive deployments. However, the increasing complexity of future networks calls for sophisticated platforms that could help network providers with their deployments design. In this framework, a central research trend is the development of digital twins of the physical infrastructures. These digital representations should closely mimic the behavior of the managed system, allowing the operators to test new configurations, analyze what-if scenarios, or train their reinforcement learning algorithms in safe environments. Considering that Kubernetes is becoming the de-facto standard platform for container orchestration and microservice-based application lifecycle management, the implementation of a Kubernetes digital twin requires an accurate characterization of the microservice response time, possibly leveraging suitable Machine Learning techniques trained with measurement data collected in the field. In this paper we introduce a new methodology, based on Mixture Density Networks, to accurately estimate the statistical distribution of the response time of microservice-based applications. We show the improvement in performance with respect to simulation-based inference procedures proposed in literature.

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Employee Churn Retention System Using Association Rule Mining Techniques

Employee Churn Retention System Using Association Rule Minin...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), International Conference on

作者： Palanikumar S M.P. Flower Queen Resmi H B A. Salinda Eveline Suniram Department of Computer Science and Business Systems Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai Tamilnadu India Department of Electrical and Electronics Engineering SRM TRP Engineering College Trichy Department of Electronics and Communication LBS Institute of Technology Kerala India Department of Computer Science St. Alphonsa College of Arts and Science College Soosaipuram Tamilnadu India

Employees are one of the main assets of a company therefore prediction of employee churn rate is of high importance. Employee churn or attrition rate is the total number of employees leaving the company at a particular interval of time. Employee attrition can bring financial loss to the company as the company has to immediately fill the vacant position with an efficient and trained employee to prevent further lose in the business. Here we predict the employee churn based on several factors collected from the employee data and also finds the perfect employee who can fill the vacant position. For calculating employee attrition and finding the right employee to fill the vacancy we have used the FP growth calculation on the human resource data and the results are compared with that of Apriori algorithm. This prediction will be very helpful to the companies as it reduces the human resource cost.

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Signal Attenuation Modeling and Simulation for 5G and Beyond P2P Wireless communication Networks in FR2 Bands: Comparison within M-Region in Nigeria 2

Signal Attenuation Modeling and Simulation for 5G and Beyond...

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2nd International Conference on Multidisciplinary Engineering and Applied science, ICMEAS 2023

作者： Alozie, Emmanuel Faruk, Nasir Abdulkarim, Abubakar Usman, Aliyu D. Olayinka Imam-Fulani, Yusuf Garba, Salisu Adewole, Kayode S. Oloyede, Abdulkarim A. Sowande, Olugbenga A. Abdullahi Baba, Bashir Musa, Abdulwaheed Imoize, Agbotiname Lucky Adediran, Yinusa A. Olagunju, Hawau I. Taura, Lawan S. Sule Lamido University Department of Information Technology Kafin Hausa Nigeria Sule Lamido University Directorate of Information and Communication Technology Kafin Hausa Nigeria Ahmadu Bello University Department of Electrical Engineering Kafin Hausa Nigeria Kampala International University Department of Electrical and Telecommunications Engineering Kampala Uganda Ahmadu Bello University Department of Electronics and Telecommunication Engineering Zaria Nigeria University of Ilorin Department of Telecommunication Science Ilorin Nigeria Sule Lamido University Department of Software Engineering Kafin Hausa Nigeria University of Ilorin Department of Computer Science Ilorin Nigeria Sule Lamido University Department of Cyber Security Kafin Hausa Nigeria Kwara State University Department of Electrical and Computer Engineering Malete Nigeria University of Johannesburg Institute for Intelligent Systems Johannesburg South Africa University of Lagos Department of Electrical and Electronics Engineering Lagos Nigeria Ruhr University Institute of Digital Communication Department of Electrical Engineering and Information Technology Bochum Germany University of Ilorin Department of Electrical and Electronics Engineering Ilorin Nigeria Sule Lamido University Department of Physics Kafin Hausa Nigeria

ISBN: (纸本)9798350358834

ITU-R categorized Nigeria into four rain regions (i.e., M, N, P, and Q) depending on the atmospheric conditions. Previous works that have conducted rain and attenuation modeling and simulations have assumed similar signal propagation behavior and parameters within locations categorized under the same region. This paper aims to explore these assumptions by conducting an extensive radio propagation simulations of point-to-point microwave links with a clear line-of-sight in order to estimate the total path loss (attenuation), excess path loss, and flat fade margins for each of the locations within the M-regions, i.e., Kano, Sokoto, and Adamawa. Results obtained showed that in all the locations, the loss monotonically increases with distance, with Sokoto having the highest level of signal losses and fading, while, Adamawa has the lowest. The deviation for both total loss, excess loss, and flat fading margin was found to be between 15 dB across the region. © 2023 IEEE.

关键词： Location

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A Novel Roi-Based Deep Learning Approach for Early Glaucoma Detection Using Retinal Fundus Images

SSRN

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SSRN 2024年

作者： Alutaibi, Ahmed I. Baihan, Abdullah Sharma, Sunil Kumar Raja Kumar, R. Ahiln, A. Department of Machine Learning and Blockchain College of computer and information sciences Majmaah University Majmaah11952 Saudi Arabia Department of Computer Science Community College King Saud University Riyadh11437 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Majmaah University Majmaah11952 Saudi Arabia Department of Mathematics Sathyabama Institute of Science and Technology Tamil Nadu Chennai119 India Department of Electronics and Communication Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627 152 India

Glaucoma is a neuro-degenerative eye condition that affects the retina and was brought on by a rise in intraocular pressure. Globally, it is the second leading cause of blindness after diabetes, and if not diagnosed early, can result in total blindness. Segmentation-based handmade characteristics in automated CADx systems is designed to evaluate this eye disease in retinal images. This research proposes a unique deep neural network based on ROI extraction for the early diagnosis and classification of glaucoma using retinal fundus images. The gathered images are pre-processed using a contrast stretching adaptive bilateral (CAS) filter to increase the image quality without loss of detail. Subsequently, employ the RoI extraction technique to segment the optic nerve since the progressive eye disease caused by optic nerve injury results in loss of visual field. Afterward, the features were recovered using a multilayer approach and the Gated Dilated Convolutional Neural Network (GD-CNN) architecture from raw pixel intensity data. Finally, the most discriminatory deep characteristics are used to divide the images being tested into categories for normal and abnormal (glaucoma-affected) conditions. The experimental results illustrate the GD-CNN model's resilience using the specific metrics of sensitivity, accuracy, specificity, recall, precision, and F1 score. Detection and categorization for glaucoma are accomplished with an average classification accuracy of 99.81%. © 2024, The Authors. All rights reserved.

关键词： Adaptive filters

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Introducing the Editorial Board—Part III

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IEEE Technology and Society Magazine 2024年第3期43卷 19-21页

作者： Ketra Schmitt Simon T. Powers Pamela Tudge Centre for Engineering in Society Gina Cody School of Engineering and Computer Science Concordia University Montreal QC Canada Division of Computing Science and Mathematics Faculty of Natural Sciences University of Stirling Stirling U.K Systems Risk Laboratory Concordia University Montreal QC Canada Department of Sociology and Communication Studies Alexander College Burnaby BC Canada

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Modeling and Simulation of Total Attenuation of P2P NLOS 5G and Beyond Wireless communication systems Operating in FR2 Bands in Nigeria 2

Modeling and Simulation of Total Attenuation of P2P NLOS 5G ...

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2nd International Conference on Multidisciplinary Engineering and Applied science, ICMEAS 2023

作者： Alozie, Emmanuel Faruk, Nasir Abdulkarim, Abubakar Usman, Aliyu D. Imam-Fulani, Yusuf Olayinka Garba, Salisu Adewole, Kayode S. Oloyede, Abdulkarim A. Sowande, Olugbenga A. Baba, Bashir Abdullahi Musa, Abdulwaheed Imoize, Agbotiname Lucky Adediran, Yinusa A. Olagunju, Hawau I. Taura, Lawan S. Sule Lamido University Department of Information Technology Kafin Hausa Nigeria Sule Lamido University Directorate of Information and Communication Technology Kafin Hausa Nigeria Ahmadu Bello University Department of Electrical Engineering Nigeria Kampala International University Department of Electrical and Telecommunications Engineering Kansanga Kampala Uganda Ahmadu Bello University Department of Electronics and Telecommunication Engineering Nigeria University of Ilorin Department of Telecommunication Science Ilorin Nigeria Sule Lamido University Department of Software Engineering Kafin Hausa Nigeria University of Ilorin Department of Computer Science Ilorin Nigeria Sule Lamido University Department of Cyber Security Kafin Hausa Nigeria Kwara State University Department of Electrical and Computer Engineering Malete Nigeria University of Johannesburg Institute for Intelligent Systems Johannesburg South Africa University of Lagos Department of Electrical and Electronics Engineering Lagos Akoka Nigeria Ruhr University Institute of Digital Communication Department of Electrical Engineering and Information Technology Bochum Germany University of Ilorin Department of Electrical and Electronics Engineering Ilorin Nigeria Sule Lamido University Department of Physics Kafin Hausa Nigeria

ISBN: (纸本)9798350358834

Signal propagation in a particular region differs from another due to differences in atmospheric, climatic and environmental properties, distinct terrain and clutter features. Adequate analysis is essential to understand the radio propagation behavior in a particular region. The ITU-R designated four rain regions, M, N, P, and Q, for Nigeria representing the rain rate distribution and also provided further classifications based on ground conductivity, among other salient parameters. Based on these classifications, this paper utilized the EDX Signal Pro software® to model and simulate a typical Point-to-Point (P2P), Non-Line of Sight (NLOS) link scenarios for 5G networks. The objective was to estimate and compare the total loss, excess loss, and flat fade margins for each rain region in Nigeria. Results obtained from the comparison showed that signals propagating in region N experience the highest level of losses and fading, while, region Q has the least losses and fading. © 2023 IEEE.

关键词： 5G mobile communication systems

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