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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Tomoda, Hiroko Machinaga, Akihiro Takase, Kan Harada, Jun Kashiwazaki, Takahiro Umeki, Takeshi Miki, Shigehito Terai, Hirotaka Yabuno, Masahiro China, Fumihiro Okuno, Daichi Takeda, Shuntaro Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan NTT Device Technology Labs NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan Advanced ICT Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi Kobe651-2492 Japan Graduate School of Engineering Kobe University 1-1 Rokko-dai Kobe Nada657-0013 Japan

We theoretically and experimentally proved that the generation rate of squeezed single photon states can be increased by generalized photon subtraction method. Our work will encourage universal and fault-tolerant cont... 详细信息

ISBN: (纸本)9781957171395

关键词： Squeezed light

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Automating Weed Detection Through Hyper Spectral Image Analysis

Automating Weed Detection Through Hyper Spectral Image Analy...

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Optimization computing and Wireless Communication (ICOCWC), International Conference on

作者： Shambhu Bharadwaj Prabhu A Vipin Solanki Department of Computing Sciences and Information Technology Teetthanker Mahaveer University Moradabad Uttar Pradesh India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of Applied Science Sanskriti University Mathura Uttar Pradesh India

Weed detection is an essential assignment in agricultural settings. Negative environmental results, crop yield loss, and mechanical weeding hard work prices related to weed management have necessitated the improvement of automation solutions to discover and treat weeds. Hyperspectral imaging (HSI) is a promising method that can produce abundant statistics about the spectral residences of flowers. This generation has been used in weed detection packages to classify weeds from crops, and these days deep mastering has been used to provide high accuracy prices in this discipline. In this abstract, we explore the utility of HSI for weed detection. We define current demanding situations that require further research earlier than automatic weed detection structures using HSI grow to be widely available. In particular, the presently available algorithms lack robustness and scalability, and further improvements in gadget-gaining knowledge of algorithms and techniques are wished to conquer those constraints, in addition to advancing the computational capabilities of these structures. Moreover, we discuss the potential of HSI as a weed detection answer in various contexts, including agroforestry and precision farming. In conclusion, we advise that the software of HSI for automatic weed detection has the massive capability to reduce labor fees related to weed manipulation, improve farming performance, and in the end, boom crop yields. Weed detection is a first-rate challenge inside the agricultural enterprise, as guide weed control is costly, time-consuming, and the correct identity of weeds is rigid. Hyper Spectral photo evaluation (HSIA) offers an alternative to guide weed detection, considering the rapid and effective mapping of weed-infested land without the need for guide labor. HSIA may be used to routinely detect the spectral signature of weed species, allowing for correct identity and brief remedy. This method uses hyperspectral scanners to gather spectral records, whic

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Development of Automated Techniques for Object-Oriented Image Analysis in Hyper Spectral Images

Development of Automated Techniques for Object-Oriented Imag...

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Optimization computing and Wireless Communication (ICOCWC), International Conference on

作者： Monika Abrol Rajendra P. Pandey Rahul Pawar Department of Applied Science Sanskriti University Mathura Uttar Pradesh India Department of Computing Sciences and Information Technology Teetthanker Mahaveer University Moradabad Uttar Pradesh India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India

the development of computerized strategies for item-orientated image evaluation in Hyper Spectral photos (HSI), an emerging field of applying machine-gaining knowledge of synthetic intelligence, has ended up an increasing number of crucial in a selection of domain names. This kind of analysis calls for a particular and correct illustration of the gadgets of interest from the hyperspectral photos. For this reason, characteristic extraction, classifiers, and clustering techniques have been proposed if you want to come across and classify them greenly. The maximum, not unusual feature extraction techniques used to extract statistics from HSI consist of radiometry, spectral band shapes, and spectral correlation. These function extraction strategies produce specific characteristic descriptors that can be utilized in aggregate with item classifiers and clustering solutions to detect and classify the objects gift in the HSI. Characteristic extraction strategies, together with Radiometric Normalized distinction flora Index (NDVI) and significant components analysis (PCA), have been observed to achieve success in numerous scenarios. Classifiers, linear and nonlinear SVM, neural networks, and choice bushes are the most famous strategies for reading HSI. Using a single this kind of strategy has been seen to offer the most straightforward restricted outcomes; however, using a combination of those strategies has been visible to enhance the classification performance.

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Opposition flow control for reducing skin-friction drag of a turbulent boundary layer

arXiv

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arXiv 2023年

作者： Dacome, Giulio Mörsch, Robin Kotsonis, Marios Baars, Woutijn J. Faculty of Aerospace Engineering Delft University of Technology Delft2629 HS Netherlands Institute of Applied Mathematics and Scientific Computing University of the Bundeswehr Munich Neubiberg85577 Germany

This work explores the dynamic response of a turbulent boundary layer to large-scale reactive opposition control, at a friction Reynolds number of Reτ ≈ 2 240. A surface-mounted hot-film is employed as the input sensor, capturing large-scale fluctuations in the wall-shear stress, and actuation is performed with a single on/off wall-normal blowing jet positioned 2.4δ downstream of the input sensor, operating with an exit velocity of vj = 0.4U∞. Our control efforts follow the work by Abbassi et al. [Int. J. Heat Fluid Fl. 67, 30-41, 2017;1], but includes a control-calibration experiment and a performance assessment using PIV- and PTV-based flow field analyses. With the control-off calibration-experiment conducted a-priori, a transfer kernel is identified so that the velocity fluctuations targeted by control are estimated based on the upstream hot-film’s signal. The controller targets large-scale high-speed zones when operating in "opposing" mode and low-speed zones in the "reinforcing" mode. A desynchronised mode was tested for reference and consisted of a statistically similar control mode, but without synchronization to the incoming velocity fluctuations. An energy-attenuation of about 40 % is observed for the opposing control mode in the frequency band corresponding to the passage of large-scale motions. This proves the effectiveness of the control in targeting large-scale motions, since an energy-intensification of roughly 45 % occurs for the reinforcing control mode instead. And relatively no change in energy, within the wall-normal range targeted, appears with the desynchronized control mode. Skin friction coefficients are inferred from PTV data to yield a direct measurement of the wall-shear stress. Results indicate that the opposing control logic is able to lower the wall-shear stress by about 3 % with respect to desynchronised control, and by roughly 10 % with respect to the uncontrolled flow. A FIK-decomposition of the skin-friction coefficient was performed

关键词： Reynolds number

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Pulsed Homodyne Measurement of Multi-Mode Squeezed Vacua for Large Scale Quantum Computation

Pulsed Homodyne Measurement of Multi-Mode Squeezed Vacua for...

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Nomura, Takefumi Endo, Mamoru Kashiwazaki, Takahiro Umeki, Takeshi Nehra, Rajveer Takase, Kan Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan NTT Device Technology Labs. NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan Department of Electrical and Computer Engineering University of Massachusetts-Amherst AmherstMA01003 United States Department of Physics University of Massachusetts-Amherst AmherstMA01003 United States

Optical quantum computers require a large number of squeezed vacua. In this research, 36 squeezed spectral modes were produced with type-0 lithium niobate waveguide, thus demonstrating its scalability as a resource fo... 详细信息

ISBN: (纸本)9781957171395

关键词： Quantum computers

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Skin Melanoma Segmentation using VGG-UNet with Adam/SGD Optimizer: A Study 3

Skin Melanoma Segmentation using VGG-UNet with Adam/SGD Opti...

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3rd International Conference on Intelligent computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Rajinikanth, Venkatesan Kadry, Seifedine Damasevicius, Robertas Sankaran, D. Abed Mohammed, Mazin Chander, Shrinithi Noroff University College Faculty of Applied Computing and Technology Kristiansand94612 Norway Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland St. Joseph's College of Engineering Omr Department of Electronics and Instrumentation Engineering Chennai600119 India University of Anbar College of Computer Science and Information Technology Ramadi31001 Iraq St. Joseph's College of Engineering Omr Department of Computer Science and Engineering Chennai600119 India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Skin is a major sensory organs and the irregularity in the skin causes various issues. Skin cancer is one major medical emergency, and appropriate detection and treatment are essential. Clinical level skin cancer assessment is normally performed using the Dermoscopy Images (DI). This research aims to develop and implement the VGG-UNet scheme to extract and assess the abnormal section in DI with improved accuracy. This plan includes;(i) Image collection and resizing, (ii) Segmentation of skin lesion in DI, (iii) Comparing the segmented section with the Ground-Truth (GT), and (iv) Performance evaluation and validation. The merit of the proposed technique is confirmed by computing the necessary performance metrics. In this work, the performance of the proposed technique is verified with different optimizers (Adam/SGD) and the average pooling. The experimental work is executed on the ISIC2016 challenge dataset, and the merit of this scheme is verified with Jaccard, Dice, and Accuracy. © 2022 IEEE.

关键词： Diseases

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Ensemble Nonlinear Support Vector Machine Approach for Predicting Chronic Kidney Diseases

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Computer Systems Science & Engineering 2022年第9期42卷 1273-1287页

作者： S.Prakash P.Vishnu Raja A.Baseera D.Mansoor Hussain V.R.Balaji K.Venkatachalam Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and TechnologyCoimbatore641062India Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of ECE Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic

Urban living in large modern cities exerts considerable adverse effectson health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urbanizedcountries. The primary objective of this work is to introduce and develop predictive analytics for predicting CKDs. However, prediction of huge samples isbecoming increasingly difficult. Meanwhile, MapReduce provides a feasible framework for programming predictive algorithms with map and reduce *** relatively simple programming interface helps solve problems in the scalability and efficiency of predictive learning algorithms. In the proposed work, theiterative weighted map reduce framework is introduced for the effective management of large dataset samples. A binary classification problem is formulated usingensemble nonlinear support vector machines and random forests. Thus, instead ofusing the normal linear combination of kernel activations, the proposed work creates nonlinear combinations of kernel activations in prototype examples. Furthermore, different descriptors are combined in an ensemble of deep support vectormachines, where the product rule is used to combine probability estimates ofdifferent classifiers. Performance is evaluated in terms of the prediction accuracyand interpretability of the model and the results.

关键词： Chronic disease classification iterative weighted map reduce machine learning methods ensemble nonlinear support vector machines random forests

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Generation of Over-8-dB Squeezed Light by a Broadband Waveguide Optical Parametric Amplifier Toward Fault-Tolerant Quantum Computers

Generation of Over-8-dB Squeezed Light by a Broadband Wavegu...

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Conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Takahiro Kashiwazaki Taichi Yamashima Koji Enbutsu Takushi Kazama Asuka Inoue Kosuke Fukui Mamoru Endo Takeshi Umeki Akira Furusawa NTT Device Technology Labs NTT Corporation Atsugi Kanagawa Japan Department of Applied Physics School of Engineering The University of Tokyo Tokyo Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing Wako Saitama Japan

ISBN: (纸本)9798350345995

Measurement-based optical quantum computers (MBOQCs) using continuous-variables (CVs) of traveling wave of light have a potential for ultra-fast fault-tolerant quantum information processing [1]. In CV MBOQCs, broadband high-level squeezed light is the most essential quantum resource. Broad bandwidth not only enables high-clock frequency computation, but also contributes to the realization of large-scale quantum computers using time-domain-multiplexed quantum entanglement. This is because squeezed light with a broader bandwidth enables us to define many more quantum wave packets in a narrow time range. On the other hand, high-level squeezing contributes to high-fidelity quantum operation and suppression of quantum errors. For example, over-8.3-dB squeezing enables us to achieve a fault-tolerant CV MBOQC with Gottesman-Kitaev-Preskill (GKP) coding [2].

关键词： Information processing Optical amplifiers Optical computing Photonic entanglement Quantum information Scanning electron microscopy

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applied Linguistics with Mixed Leader Optimizer Based English Text Summarization Model

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Intelligent Automation & Soft computing 2023年第6期36卷 3203-3219页

作者： Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Abdullah Mohamed Manar Ahmed Hamza Ishfaq Yaseen Abu Sarwar Zamani Mohammad Mahzari Department of Applied Linguistics College of LanguagesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The term‘executed linguistics’corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related *** exponential generation of text data on the Internet must be leveraged to gain knowledgeable *** extraction of meaningful insights from text data is crucial since it can provide value-added solutions for business organizations and *** Automatic Text Summarization(ATS)process reduces the primary size of the text without losing any basic components of the *** current study introduces an applied Linguistics-based English Text Summarization using a Mixed Leader-Based Optimizer with Deep Learning(ALTS-MLODL)*** presented ALTS-MLODL technique aims to summarize the text documents in the English *** accomplish this objective,the proposed ALTS-MLODL technique pre-processes the input documents and primarily extracts a set of ***,the MLO algorithm is used for the effectual selection of the extracted *** the text summarization process,the Cascaded Recurrent Neural Network(CRNN)model is exploited whereas the Whale Optimization Algorithm(WOA)is used as a hyperparameter *** exploitation of the MLO-based feature selection and the WOA-based hyper-parameter tuning enhanced the summarization *** validate the perfor-mance of the ALTS-MLODL technique,numerous simulation analyses were *** experimental results signify the superiority of the proposed ALTS-MLODL technique over other approaches.

关键词： Text summarization deep learning hyperparameter tuning applied linguistics multi-leader optimizer

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EmoNet: Unveiling Affective States Through Convolutional Neural Networks in Textual Emotion Classification 5

EmoNet: Unveiling Affective States Through Convolutional Neu...

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5th International Conference on Intelligent computing, Communication, Networking and Services, ICCNS 2024

作者： Salloum, Said Tahat, Khalaf Mansoori, Ahmed Alfaisal, Raghad Tahat, Dina School of Science Engineering and Environment University of Salford Manchester United Kingdom United Arab Emirates University and Yarmouk University Media and Creative Industries Jordan United Arab Emirates University Media and Creative Industries Department United Arab Emirates Universiti Pendidikan Faculty of Computing and Meta-Technology Tanjung Malim Malaysia Al Ain University Applied Sociology Department United Arab Emirates

ISBN: (纸本)9798350354690

In the burgeoning field of natural language processing, emotion classification from textual data has emerged as a critical task with applications ranging from sentiment analysis to mental health assessment. This paper explores the utilization of Convolutional Neural Networks (CNNs), traditionally dominant in image processing, for classifying emotions in text. Our proposed CNN model leverages the inherent hierarchical structure of language to identify and learn emotion-specific features, with an emphasis on capturing contextual n-grams through convolutional filters. The approach is substantiated by a comprehensive dataset, subjected to rigorous preprocessing and vectorization via TF-IDF to convert text into a numerical format suitable for deep learning. The model's architecture is meticulously crafted, incorporating convolutional layers followed by global max pooling and dense layers, culminating in a softmax activation function tailored for multi-class classification. Our findings demonstrate the model's robustness, achieving a notable accuracy of 96.08% on the test set. This high level of precision is further corroborated by the Receiver Operating Characteristic (ROC) analysis, revealing exceptional area under the curve (AUC) values across various emotion categories. The results suggest that CNNs hold significant promise for emotion recognition tasks in textual data, providing an effective framework for future explorations in the domain. © 2024 IEEE.

关键词： Convolutional neural networks

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