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28th International computer Conference, computer Society of Iran, CSICC 2023

作者： Mahdi Ajoodani, Amir Ghafari, Mohammadreza Dennis-Knieriem, Emma School of Electronics Computers Science and Research Branch Azad University Tehran Iran School of Electrical Engineering Amirkabir University of Technology Tehran Iran Brown University Department of Computer Science RI United States

ISBN: (纸本)9798350338195

Today, with the expansion of computer network users, it is difficult to meet the best Quality of Service (QoS) required for end users. Considering the increasing passion for cloud-based services e.g., games on the cloud, improving the QoS provided is also of great importance. According to this, the use of up-to-date technologies such as Software Defined Networks (SDN) and artificial intelligence could help improving the acceptable required QoS by the gamers. The objective of this article, is to deal with this problem by examining some popular games with different resolution qualities. It has been shown that by sending UDP traffic and applying prioritization based on defined rules an eye-catching improvement in jitter would achieve. The experimental results shows that our proposed method improved 34% reduction on jitter for higher priority games and a 3% improvement for all games. © 2023 IEEE.

关键词： Quality of service

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FEM-based modeling of microsphereenhanced interferometry

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Light(Advanced Manufacturing) 2022年第4期3卷 73-85页

作者： Tobias Pahl Lucie Hüser Sebastian Hagemeier Peter Lehmann Measurement Technology Group Faculty of Electrical Engineering and Computer ScienceUniversity of KasselWilhelmshöher Allee 7134121 KasselGermany

To improve the lateral resolution in microscopic imaging,microspheres are placed close to the object’s surface in order to support the imaging process by optical near-field *** microsphere-assisted measurements are part of various recent studies,no generally accepted explanation for the effect of microspheres *** nanojets,enhancement of the numerical aperture,whispering-gallery modes and evanescent waves are usually named reasons in context with microspheres,though none of these effects is proven to be decisive for the resolution *** present a simulation model of the complete microscopic imaging process of microsphere-enhanced interference microscopy including a rigorous treatment of the light scattering process at the surface of the *** model consideres objective lenses of high numerical aperture providing 3D conical illumination and *** enhanced resolution and magnification by the microsphere is analyzed with respect to the numerical aperture of the objective ***,we give a criterion for the achievable resolution and demonstrate that a local enhancement of the numerical aperture is the most likely reason for the resolution enhancement.

关键词： Electromagnetic modeling Microsphere-assisted microscopy Interference microscopy Coherence scanning interferometry Simulation Finite element method

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Physics-aware cross-domain fusion aids learning-driven computer-generated holography

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Photonics Research 2024年第12期12卷 2747-2756页

作者： GANZHANGQIN YUAN MI ZHOU FEI LIU MU KU CHEN KUI JIANG YIFAN PENG ZIHAN GENG Institute of Data and Information Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518071China School of Optoelectronic Engineering Xidian UniversityXi’an 710071China Department of Electrical Engineering City University of Hong KongHong Kong SARChina School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Department of Electrical and Electronic Engineering The University of Hong KongHong Kong SARChina Department of Computer Science The University of Hong KongHong Kong SARChina

The rapid advancement of computer-generated holography has bridged deep learning with traditional optical principles in recent ***,a critical challenge in this evolution is the efficient and accurate conversion from the amplitude to phase domain for high-quality phase-only hologram(POH)*** computational models often struggle to address the inherent complexities of optical phenomena,compromising the conversion *** this study,we present the cross-domain fusion network(CDFN),an architecture designed to tackle the complexities involved in POH *** CDFN employs a multi-stage(MS)mechanism to progressively learn the translation from amplitude to phase domain,complemented by the deep supervision(DS)strategy of middle features to enhance task-relevant feature learning from the initial ***,we propose an infinite phase mapper(IPM),a phase-mapping function that circumvents the limitations of conventional activation functions and encapsulates the physical essence of *** simulations,our proposed method successfully reconstructs high-quality 2K color images from the DIV2K dataset,achieving an average PSNR of 31.68 dB and SSIM of ***,we realize high-quality color image reconstruction in optical *** experimental results highlight the computational intelligence and optical fidelity achieved by our proposed physics-aware cross-domain fusion.

关键词： computer cross holography

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Detection of pneumonia progression in lungs of individuals affected with covid 19 severely using deep learning techniques 3

Detection of pneumonia progression in lungs of individuals a...

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3rd International Conference for Emerging technology, INCET 2022

作者： Gadipudi, Praneeth Teja, Palachuri Sai Yelamancheli, Chandana Sowmya. Thaniserikaran, Adith Mohiuddin, Rayyan Joy, Ajin Srm University Department of Computer Science and Engineering Andhra Pradesh Amaravati India Srm Department of Computer Science and Engineering Institute of Science and Technology Tamil Nadu Kattankulathur India Icfai University Department of Computer Science and Engineering Hyderabad India National Institute of Technology Department of Computer Science and Engineering Telangana Warangal India National Institute of Technology Department of Electrical Engineering Telangana Warangal India JSPMs' Rajarshi Shahu College of Engineering Department of Electronics and Telecommunication Engineering Maharashtra Pune India

ISBN: (纸本)9781665494991

The coronavirus sickness (COVID-19) is a worldwide pandemic that was detected in December 2019 by a Chinese physician in Wuhan, Hubei Province, mainland China. There is presently no licensed human vaccine to combat it. When individuals are close together, COVID-19 spreads more fast. As a consequence, travel constraints are in place to minimize the disease from spreading, and regular Washing the hands is encouraged so that the infections that occur due to the virus. Other indications and symptoms include chest discomfort, sputum production, and a sore throat. COVID19 could lead to a very dangerous disease which is pneumonia which occurs due to the virus. When employing CT scans or Xrays to identify the symptoms that are occuring due to the cause of covid-19 in the last region of the lungs then the accuracy is better than when utilizing RT-PCR. But as there are very less radiologists as compared to the new residents or the people that have come and aslo there has been seen many re examinations occurring of these patients. To solve this kind of the issues or problems that is limiting the CT scans and the x-rays the speed of this procedure must be boosted. This may be done by adding artificial intelligence (AI) approaches into contemporary diagnostic systems. The main motive of the paper is to provide the best accuracy to detect the disease using CNN along with a comparison with the transfer learning approach. © 2022 IEEE.

关键词： computerized tomography

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Generalization Bounds via Conditional f-Information

arXiv

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arXiv 2024年

作者： Wang, Ziqiao Mao, Yongyi School of Computer Science and Technology Tongji University Shanghai China School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

In this work, we introduce novel information-theoretic generalization bounds using the conditional f-information framework, an extension of the traditional conditional mutual information (MI) framework. We provide a generic approach to derive generalization bounds via f-information in the supersample setting, applicable to both bounded and unbounded loss functions. Unlike previous MI-based bounds, our proof strategy does not rely on upper bounding the cumulant-generating function (CGF) in the variational formula of MI. Instead, we set the CGF or its upper bound to zero by carefully selecting the measurable function invoked in the variational formula. Although some of our techniques are partially inspired by recent advances in the coin-betting framework (e.g., Jang et al. (2023)), our results are independent of any previous findings from regret guarantees of online gambling algorithms. Additionally, our newly derived MI-based bound recovers many previous results and improves our understanding of their potential limitations. Finally, we empirically compare various f-information measures for generalization, demonstrating the improvement of our new bounds over the previous bounds. Copyright © 2024, The Authors. All rights reserved.

关键词： Variational techniques

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Internet of Things based Smart Water Leakage Monitoring and Alert System 9

Internet of Things based Smart Water Leakage Monitoring and ...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Keerthana, S. Elango, S. Judgi, T. Vivek, S.K. Ponmurugan, P. Kumar, Neelam Sanjeev J K K Nataraja College of Engineering and Technology Department of Electrical and Electronics Engineering Komarapalayam638183 India Nandha Engineering College Department of Electrical and Electronics Engineering Tamilnadu Erode India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering India Shree Venkateshwara Hi-Tech Engineering College Department of Electrical and Electronics Engineering Gobichettipalayam638455 India Meenakshi Academy of Higher Education and Research Department of Research Chennai600078 India Saveetha School of Engineering SIMATS Department of Biomedical Engineering Chennai India

ISBN: (纸本)9798350397376

It is critical to manage water supply in a safe and efficient manner. Significant amounts of water are lost each year as a result of leaks in water distribution systems (WDN). As a result, locating leaks in a reliable manner is critical. Water leaks can be detected using monitoring software that analyses the flow of water. Apps that track the user's location can be extremely useful. If this technology is widely adopted, it will aid in the resolution of water management issues such as human error and waste of limited water resources. Water leaks can be located predictably using the Internet of Things (Internet of Things). Many countries struggle to get enough water, but the situation is not hopeless. It is critical to accurately locate water leaks in order to reduce system resistance. © 2023 IEEE.

关键词： Water distribution systems

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Development of Programmed Autonomous Electric Heavy Vehicle: An Application of IoT 2

Development of Programmed Autonomous Electric Heavy Vehicle:...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Reddy, P. Nagasekhar Umaeswari, P. Natrayan, L. Choudhary, Ankur Mahatma Gandhi Institute of Technology Department of Electrical and Electronics Engineering Hyderabad500075 India R.M.K Engineering College Department of Computer Science and Business Systems Kavaraipettai601206 India Saveetha School of Engineering Department of Mechanical Engineering Simats India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun248002 India

ISBN: (纸本)9798350346640

The influence of automation in the agriculture and construction industry plays a vital role in the development of the economic backbone of any country. The factors such as power, torque and speed are efficiently controlled by automation devices using Internet of things (IoT). The heavy vehicles are the multi-faced application carrier which is been used in construction, agriculture, mining and other heavy-duty fields. This research focuses on developing a IoT integrated sensor-based obstacle detection system for programmed autonomous heavy vehicles which will improve safety in both on-road and off-road construction. The automation process consists of two main categories: Observing the environment and recognizing the activities. The environment in our case is both highways and rural roads. An intelligent- camera is utilised to capture the activities from the captured video and process the data. A LiDAR sensor performs the obstacle detection process using laser reflection technology and an ultrasonic sensor is used to process the vibration and sound produced by the obstacle or upcoming vehicles. These three sensors are integrated with a controller-based device to be powered by the power distribution system in the programmed autonomous heavy vehicle. © 2023 IEEE.

关键词： Autonomous vehicles

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Slow Convergence of Interacting Kalman Filters in Word-of-Mouth Social Learning

Slow Convergence of Interacting Kalman Filters in Word-of-Mo...

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Annual Allerton Conference on Communication, Control, and Computing

作者： Vikram Krishnamurthy Cristian R. Rojas School of Electrical and Computer Engineering Cornell University Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9798331541033

ISBN: (纸本)9798331541040

We consider word-of-mouth social learning involving $m$ Kalman filter agents that operate sequentially. The first Kalman filter receives the raw observations, while each subsequent Kalman filter receives a noisy measurement of the conditional mean of the previous Kalman filter. The prior is updated by the m-th Kalman filter. When $m=2$ , and the observations are noisy measurements of a Gaussian random variable, the covariance goes to zero as $k^{-1/3}$ for $k$ observations, instead of $O(k^{-1})$ in the standard Kalman filter. In this paper we prove that for $m$ agents, the covariance decreases to zero as $k^{-(2^{m}-1)}$ , i.e, the learning slows down exponentially with the number of agents. We also show that by artificially weighing the prior at each time, the learning rate can be made optimal as $k^{-1}$ . The implication is that in word-of-mouth social learning, artificially re-weighing the prior can yield the optimal learning rate.

关键词： Random variables Kalman filters Noise measurement Standards Convergence

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EEG based stress classification by using difference plots of variational modes and machine learning

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16347-16360页

作者： Salankar, Nilima Qaisar, Saeed Mian School of Computer Science University of Petroleum and Energy Studies Dehradun India Persistent System Limited IT Park Maharashtra Nagpur India Department of Electrical and Computer Engineering EFfat University Jeddah22332 Saudi Arabia Communication and Signal Processing Lab Energy and Technology Research Center Effat University Jeddah22332 Saudi Arabia

The recent trend in healthcare is to use the automated biomedical signals processing for an augmented and precise diagnosis. In this context, an original approach is presented for categorization of stress and non-stress classes by processing the multichannel Electroencephalogram (EEG) signals. The EEG signals are decomposed by using the "Empirical Mode Decomposition" (EMD) and "Variational Mode Decomposition" (VMD), respectively render the "Intrinsic Mode Functions" (IMFs) and Modes. The Second Order Difference Plots (SODPs) are traced for each IMF and Mode. Shapes of SODPs are used to discriminate the two intended categories. The feature space includes areas, Central Tendency Measures (CTMs) and means of SODPs. It is derived from first 7 IMFs and Modes. The identification of stress and non-stress categories is performed by using the robust machine learning algorithms namely, the Support Vector Machine (SVM), Boosting with Random Forest (RF) and Multilayer Perceptron Neural Network (MLPNN). The applicability is tested by using a publicly available EEG signals dataset, recorded during mental arithmetic test. A detailed evaluation is carried out at the subset, individual channel and lobes wise. Noteworthy highest accuracies of subset wise 99.89%, channel wise 98.89% and lobe wise 99.99% are obtained respectively. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Empirical mode decomposition

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A Deep Learning Approach for In-Network Synchrophasor Missing Data Recovery Using Programmable Network Switches

A Deep Learning Approach for In-Network Synchrophasor Missin...

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IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Xiaoyu Guo Yanfeng Qu Gong Chen Dong Jin Electrical Engineering and Computer Science University of Arkansas Fayetteville AR USA Computer Science Illinois Institute of Technology Chicago IL USA

ISBN: (数字)9798350318555

ISBN: (纸本)9798350318562

Phasor measurement unit (PMU) networks deliver accurate and timely measurements, which is essential for managing today’s electric power systems. To ensure data quality and enhance the cyber-resilience of PMU networks against malicious attacks and data errors, this study presents an online PMU missing data recovery scheme by leveraging P4 programmable switches. The data plane incorporates a customized PMU protocol parser that abstracts the necessary payload data for recovery. Recovery processes are executed in the control plane using a pre-trained machine learning model. Both traditional and advanced ML models, such as transformer and TimeGPT, are explicitly employed for data prediction. This approach ensures rapid and precise data recovery. Performance evaluations focus on recovery speed and accuracy, using a real dataset from a campus microgrid. With 20% missing PMU data, the mean absolute percentage error for voltage magnitude is 0.0384%, and the phase angle error discrepancy is approximately 0.4064%.

关键词： Performance evaluation Accuracy Protocols Power measurement Process control Predictive models Transformers Phasor measurement units Smart grids Payloads

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