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Author Correction: Ultra-high-granularity detector simulation with intra-event aware generative adversarial network and self-supervised relational reasoning

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Nature communications 2024年第1期15卷 5825页

作者： Baran Hashemi Nikolai Hartmann Sahand Sharifzadeh James Kahn Thomas Kuhr ORIGINS Data Science Lab Technical University Munich Munich Germany. baran.hashemi@origins-cluster.de. Faculty of Physics Ludwig Maximilians University in Munich Munich Germany. Faculty of Computer Science Ludwig Maximilians University in Munich Munich Germany. Helmholtz AI Karlsruhe Germany. Steinbuch Centre for Computing (SCC) Karlsruhe Institute of Technology (KIT) Karlsruhe Germany.

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Advanced Analysis of Arabidopsis thaliana Leaf Traits Using RGB Imaging and Machine Learning

Advanced Analysis of Arabidopsis thaliana Leaf Traits Using ...

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International Symposium on Industrial Electronics (INDEL)

作者： Rijad Sarić Edhem Čustović Martin Trtílek Amila Akagić Mathew G. Lewsey James Whelan La Trobe Institute for Sustainable Agriculture & Food (LISAF) La Trobe University Melbourne VIC Australia Australian Research Council Research Hub for Medicinal Agriculture La Trobe University Melbourne VIC Australia School of Computing Engineering and Mathematical Sciences La Trobe University (LTU) Melbourne VIC Australia Scientific Instruments Australia (SIA) Melbourne VIC Australia Photon Systems Instruments (PSI) Plant Phenotyping Research Centre Drasov Brno Czech Republic Faculty of Electrical Engineering University of Sarajevo (UNSA) Sarajevo Bosnia and Herzegovina State Key Laboratory of Plant Environmental Resilience College of Life Science Zhejiang University (ZJU) P.R. China Provincial International Science and Technology Cooperation Base on Engineering Biology Zhejiang University P.R China

ISBN: (数字)9798350352320

ISBN: (纸本)9798350352337

Image-based high-throughput plant phenotyping utilises various imaging techniques to automatically and non-invasively understand the growth of different plant species. These innovative imaging infrastructures are implemented to monitor plant development over time in indoor or outdoor environments. However, understanding the relationship between genotype and phenotype interactions under different environments remains challenging. This research study demonstrates superior extraction of leaf morphological features of different Arabidopsis thaliana ecotypes by analysing leaf geometry using a sequence of RGB images. Upon successful extraction of anatomical features, leaf length and area are converted into physical coordinates. Furthermore, considering these leaf features as 1D signals, the Fourier Spectrum is analysed, and most descriptive features are selected using PCA. Finally, leaf shape classification is established by training and testing five distinct ML models. A thorough evaluation of selected models demonstrates superiority in classifying two common leaf shapes of Arabidopsis plants.

关键词： Training Shape Biological system modeling Imaging Machine learning Feature extraction Real-time systems Reliability Testing Principal component analysis

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High-technology agriculture system to enhance food security: A concept of smart irrigation system using Internet of Things and cloud computing

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Journal of the Saudi Society of Agricultural sciences 2024年

作者： Morchid, Abdennabi Muhammad Alblushi, Ishaq G. Khalid, Haris M. El Alami, Rachid Sitaramanan, Surendar Rama Muyeen, S.M. Department of Physics Laboratory of Informatics Signals Automation and Cognitivism (LISAC) Faculty of Sciences Dhar El Mahraz Sidi Mohamed Ben Abdellah University BP 1796 Fes-Atlas Fez 30003 Morocco Department of Electrical and Electronics Engineering Higher Colleges of Technology Sharjah 7947 United Arab Emirates College of Engineering and Information Technology University of Dubai Academic City Dubai 14143 United Arab Emirates Department of Electrical and Electronic Engineering Science University of Johannesburg Aukland Park 2006 South Africa Samsung Austin Research and Development Centre (SARC) Advanced Computing Lab (ACL) San Jose CA United States Department of Electrical Engineering Qatar University Doha 2713 Qatar

Context: Food security is highly reliant on agricultural activity to drive the world economy. However, this activity is in great danger due to climatic changes and improper use of irrigation techniques. Consequently, the lives of numerous individuals worldwide are in jeopardy. In light, this paper investigates the promise of smart irrigation systems based on new technology. Objective: To meet the growing demand for water in agriculture, this study presents an intelligent irrigation system that uses cutting-edge technologies of (1) cloud computing, (2) embedded systems, and (3) Internet-of-Things (IoT). The main objective is to demonstrate how this innovative strategy can effectively manage water resources, supporting food security through cutting-edge agricultural technology. Methods: This paper proposes a smart irrigation system based on cutting-edge technologies like the embedded system, Internet of Things (IoT), and cloud computing as a groundbreaking strategy to improve food security through the implementation of advanced agricultural technology. This system supervises real-time monitoring of crucial environmental factors such as (1) moisture, (2) humidity, (3) temperature, and (4) water levels, in smart agriculture practices. In addition, this system employs the latest sensors, including the module (DHT22), water level sensor, and moisture sensors, which are connected to the widely used embedded system (ESP32). The system uses the ThingSpeak cloud and ThingView app to enable wireless communication between the device and the farm owner, enhancing their interaction. The automated control of the two water pumps is based on the readings of various environmental factors. Moreover, this will also present a mathematical-driven function known as linear interpolation to calibrate the water level sensor in percentage. This system was created using the V-model software development approach. Results and conclusion: Farmers can access comprehensive farm data from anywhere i

关键词： Cloud computing Embedded system Food security Intelligent sensors Internet-of-things (IoT) Irrigation system Smart agriculture ThingSpeak platform V model

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Neutral point clamped inverter for enhanced grid connected PV system performance based on hexagonal space vector modulation

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Scientific Reports 2025年第1期15卷 1-21页

作者： Palanisamy, R. Vijayakumar, K. Gulbarga, Mohammad Imtiyaz Al Awadh, Mohammed Hui, Liew Tze Department of Electrical and Electronics Engineering College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chennai 603203 India Faculty of Engineering and Informatics Department of Computer Science & Engineering Ala-Too International University Bishkek Kyrgyzstan Department of Industrial Engineering College of Engineering King Khalid University P.O. Box 394 Abha 61421 Saudi Arabia Center for Engineering and Technology Innovations King Khalid University Abha 61421 Saudi Arabia Centre for Intelligent Cloud Computing (CICC) COE of Advanced Cloud Faculty of Information Science & Technology Multimedia University Jalan Ayer Keroh Lama Bukit Beruang Melaka 75450 Malaysia

The increasing global demand for renewable energy has accelerated the adoption of grid-connected photovoltaic (PV) systems. However, conventional PV inverters often rely on transformers, which add to the system’s cost, size, and power losses. Transformerless inverter topologies have emerged as a promising alternative, offering improved efficiency, compact design, and lower costs. Despite these advantages, challenges such as high total harmonic distortion (THD), common mode voltage (CMV) issues, and neutral current imbalance must be addressed to ensure reliable grid integration. This research investigates a transformerless five-level neutral point clamped (NPC) inverter for grid-connected PV applications, aiming to overcome these challenges. The study focuses on analysing THD, mitigating CMV to enhance system reliability, and optimizing output voltage levels to meet grid standards. The proposed transformerless five-level NPC inverter, incorporating a coupled inductor, is controlled using Hexagonal Space Vector Modulation (HSVM) to improve performance. Additionally, the research evaluates neutral current behavior to ensure stability and compliance with grid codes. Simulation and experimental results validate the proposed system’s effectiveness under varying operating conditions, highlighting its potential as a high-efficiency, cost-effective solution for sustainable energy integration into the grid. © The Author(s) 2025.

关键词： Capacitor voltage unbalance (CVU) Common mode voltage (CMV) Photovoltaic (PV) systems Total harmonic distortion (THD) Transformerless five-level neutral point clamped (NPCI) inverter

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Kurdish Handwritten Character Recognition using Deep Learning Techniques

arXiv

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arXiv 2022年

作者： Ahmed, Rebin M. Rashid, Tarik A. Fattah, Polla Alsadoon, Abeer Bacanin, Nebojsa Mirjalili, Seyedali Vimal, S. Chhabra, Amit IT Department Faculty of Applied Science Tishk International University Erbil Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Software and Informatics Engineering Salahaddin University-Erbil- Erbil Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Sydney Australia Singidunum University Belgrade Serbia Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of Department of Artificial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India

Handwriting recognition is one of the active and challenging areas of research in the field of image processing and pattern recognition. It has many applications that include: a reading aid for visual impairment, automated reading and processing for bank checks, making any handwritten document searchable, and converting them into structural text form, etc. Moreover, high accuracy rates have been recorded by handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. Yet there is no such system available for offline Kurdish handwriting recognition. In this paper, an attempt is made to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. Kurdish (Sorani) contains 34 characters and mainly employs an Arabic\Persian based script with modified alphabets. In this work, a Deep Convolutional Neural Network model is employed that has shown exemplary performance in handwriting recognition systems. Then, a comprehensive dataset was created for handwritten Kurdish characters, which contains more than 40 thousand images. The created dataset has been used for training the Deep Convolutional Neural Network model for classification and recognition tasks. In the proposed system, the experimental results show an acceptable recognition level. The testing results reported a 96% accuracy rate, and training accuracy reported a 97% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and is comparable to the similar model of other languages' handwriting recognition systems. © 2022, CC BY.

关键词： Convolutional neural networks

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Assessing responsible innovation training

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Journal of Responsible technology 2023年 16卷

作者： Stahl, Bernd Carsten Aicardi, Christine Brooks, Laurence Craigon, Peter J. Cunden, Mayen Burton, Saheli Datta Heaver, Martin De Saille, Stevienna De Dolby, Serena Dowthwaite, Liz Eke, Damian Hughes, Stephen Keene, Paul Kuh, Vivienne Portillo, Virginia Shanley, Danielle Smallman, Melanie Smith, Michael Stilgoe, Jack Ulnicane, Inga Wagner, Christian Webb, Helena School of Computer Science University of Nottingham United Kingdom Dept. of Informatics and School of Biomedical Engineering & Imaging Sciences King's College London United Kingdom Information School University of Sheffield Sheffield United Kingdom Horizon Digital Economy Research School of Computer Science University of Nottingham United Kingdom Independent Researcher Dept of Science and Technology Studies University College London United Kingdom ORBIT RRI limited Leicester United Kingdom Dept of Sociological Studies University of Sheffield Sheffield United Kingdom Centre for Computing and Social Responsibility De Montfort University Leicester United Kingdom School of Chemistry University of Bristol United Kingdom Department of Philosophy Faculty of Arts and Social Sciences Maastricht University Netherlands Biodiversity and Conservation Science Department of Biodiversity Conservation and Attractions Wildlife Research Centre Western Australia De Montfort University United Kingdom Lab for Uncertainty in Data and Decision Making (LUCID) School of Computer Science University of Nottingham United Kingdom

There is broad agreement that one important aspect of responsible innovation (RI) is to provide training on its principles and practices to current and future researchers and innovators, notably including doctoral students. Much less agreement can be observed concerning the question of what this training should consist of, how it should be delivered and how it could be assessed. The increasing institutional embedding of RI leads to calls for the alignment of RI training with training in other subjects. One can therefore observe a push towards the official assessment of RI training, for example in the recent call for proposals for centres for doctoral training by UK Research and Innovation. This editorial article takes its point of departure from the recognition that the RI community will need to react to the call for assessment of RI training. It provides an overview of the background and open questions around RI training and assessment as a background of examples of RI training assessment at doctoral level. There is unlikely to be one right way of assessing RI training across institutions and disciplines, but we expect that the examples provided in this article can help RI scholars and practitioners orient their training and its assessment in ways that are academically viable as well as supportive of the overall aims of RI. © 2023

关键词： Assessment Doctoral training Education Responsible innovation

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Inconsistencies among spectral robustness metrics 1

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14th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, Qshine 2018

作者： Wang, Xiangrong Feng, Ling Kooij, Robert E. Marzo, Jose L. Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Computing Science Department Institute of High Performance Computing A*STAR Singapore Singapore iTrust Centre for Research in Cyber Security Singapore University of Technology and Design Singapore Singapore Institute of Informatics and Applications University of Girona Girona Spain

ISBN: (数字)9783030144135

ISBN: (纸本)9783030144128

Network robustness plays a critical role in the proper functioning of modern society. It is common practice to use spectral metrics, to quantify the robustness of networks. In this paper we compare eight different spectral metrics that quantify network robustness. Four of the metrics are derived from the adjacency matrix, the others follow from the Laplacian spectrum. We found that the metrics can give inconsistent indications, when comparing the robustness of different synthetic networks. Then, we calculate and compare the spectral metrics for a number of real-world networks, where inconsistencies still occur, but to a lesser extent. Finally, we indicate how the concept of the R∗-value, a weighted sum of robustness metrics, can be used to resolve the found inconsistencies. © ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering 2019.

关键词： Circuit theory

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Optimizing a parameterized controlled gate with Free Quaternion Selection

arXiv

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arXiv 2024年

作者： Kurogi, Hiroyoshi Endo, Katsuhiro Sato, Yuki Sugawara, Michihiko Wada, Kaito Sugisaki, Kenji Kanno, Shu Watanabe, Hiroshi C. Nakano, Haruyuki Department of Chemistry Graduate School of Science Kyushu University 744 Motooka Nishi-ku Fukuoka819-0395 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-8522 Japan 1-1-1 Umezono Ibaraki Tsukuba305-8568 Japan Toyota Central R & D Labs. Inc. Koraku Mori Building 10F 1-4-14 Koraku Bunkyo-ku Tokyo112-0004 Japan Department of Applied Physics and Physico-Informatics Keio University Hiyoshi 3-14-1 Kohoku-ku Yokohama223-8522 Japan Graduate School of Science and Technology Keio University 7-1 Shinkawasaki Saiwai-ku Kawasaki212-0032 Japan Keio University 2-15-45 Mita Minato-ku Tokyo108-8345 Japan Centre for Quantum Engineering Research and Education TCG Centres for Research and Education in Science and Technology Salt Lake Sector V Kolkata700091 India Mitsubishi Chemical Corporation Science & Innovation Center Yokohama227-8502 Japan Department of Chemistry Faculty of Science Kyushu University 744 Motooka Nishi-ku Fukuoka819-0395 Japan

In variational algorithms, quantum circuits are conventionally parametrized with respect to single-qubit gates. In this study, we parameterize a generalized controlled gate and propose an algorithm to estimate the optimal parameters for locally minimizing the cost value, where we extend the free quaternion selection method, an optimization method for a single-qubit gate. To benchmark the performance, we apply the proposed method to various optimization problems, including the Variational Quantum Eigensolver (VQE) for Ising and molecular Hamiltonians, Variational Quantum Algorithms (VQA) for fidelity maximization, and unitary compilation of time evolution operators. In these applications, the proposed method shows efficient optimization and greater expressibility with shallower circuits than other methods. Furthermore, this method is also capable of generalizing and fully optimizing particle-number-conserving gates, which are in demand in chemical systems applications. Taking advantage of this property, we have actually approximated time evolution operators of molecular Hamiltonian and simulated the dynamics with shallower circuits in comparison to the standard implementation by Trotter decomposition. © 2024, CC BY-NC-SA.

关键词： Hamiltonians

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Pap smear images classification based on surrounding tissues: A comparative study

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AIP Conference Proceedings 2024年第1期2934卷

作者： Mohammed Alsalatie Wan Azani Mustafa Lugain Nammneh Feras Fares Almashakbah Hiam Alquran Yasmin Mohd Yacob The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman 11855 Jordan Faculty of Electrical Engineering Technology Campus Pauh Putra Universiti Malaysia Perlis 02000 Arau Perlis Malaysia Advanced Computing Centre of Excellence (CoE) Universiti Malaysia Perlis (UniMAP) 02000 Arau Perlis Malaysia Faculty of Medicine Jordan University of Science and Technology Ramtha Irbid 22110 Jordan Information Technology Faculty Internet Technology Department Zarqa University Zarqa 13110 Jordan Department of Biomedical Engineering Jordan University of Science and Technology Irbid 22110 Jordan Department of Biomedical Systems and Informatics Engineering Yarmouk University 556 Irbid 21163 Jordan Faculty of Electronic Engineering Technology Campus Pauh Putra Universiti Malaysia Perlis 02000 Arau Perlis Malaysia

Cervical cancer is one of the most known health problems faced by women around the world. Early detection of cervical cancer may reduce the mortality rate. Pap smear images are new techniques used for screening cervical cancer. This paper excludes the nucleus of pap smear images, and the resultant images are classified into seven classes based on the surrounding region nucleus. Automated features are extracted using three pre-trained convolutional neural networks (CNN). The resultant features are twenty-one. The principal component analysis reduces the dimensionality and selects the most significant features into ten features. These features are fed to two types of machine learning algorithms: support vector machine (SVM) classifier and random forest classifier. The support vector machine classifier achieved the highest accuracy for seven classes, reaching 93.1%. This method will help the physicians in the diagnosis of cervical cancer depending on the tissues, not the nucleus. Furthermore, the result can be enhanced using a huge amount of data.

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CyberSentry: Enhancing SCADA Security through Advanced Deep Learning and Optimization Strategies

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International Journal of Critical Infrastructure Protection 2025年

作者： Alaa O. Khadidos Adil O. Khadidos Shitharth Selvarajan Taher Al-Shehari Nasser A Alsadhan Subhav Singh Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia School of Built Environment Engineering and Computing Leeds Beckett University LS1 3HE Leeds U.K Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Computer Skills Department of Self-Development Skill Common First Year Deanship King Saud University 11362 Riyadh Saudi Arabia Computer Science Department College of Computer and Information Sciences King Saud University Riyadh 12372 Saudi Arabia Chitkara Centre for Research and Development Chitkara University Himachal Pradesh-174103 India Division of research and development Lovely Professional University Phagwara Punjab India

SCADA systems form the core of infrastructural facilities, including power grids, water treatment facilities, and industrial processes. Changing cyber threats present increasingly sophisticated attacks against which traditional security models inadequately protect SCADA systems. These traditional models usually have drawbacks in the way of inadequate feature selection, inefficiency in detecting most attacks, and suboptimal parameter tuning, which cause vulnerabilities and reduce resilience in systems. This paper presents CyberSentry, a new security framework designed to overcome limitations so as to provide robust protection for SCADA systems. These three modules makeup CyberSentry: the RMIG feature selection model, tri-fusion net for attack detection, and Parrot-Levy Blend Optimization (PLBO) for parameter tuning. The Recursive Multi-Correlation-based Information Gain (RMIG) feature selection model enhances accuracy in detection by optimizing the set of fatal features through recursive multi-correlation analysis by Information Gain prioritization. The Tri-Fusion Net combines anomaly detection, signature-based detection, and machine learning classifiers to enhance the detection versatility and robustness. The PLBO module ensures efficient and dynamic tuning for the parameters through undocumented Parrot and Levy optimization techniques. The proposed CyberSentry framework integrates, within a unified architecture, anomaly detection, signature-based detection, and machine learning classifiers to enhance the security of SCADA systems against diverse cyber threats. Features extracted in this manner are analyzed using machine learning classifiers that exploit their predictive capabilities for robust threat classification. The proposed approaches are fused within the Tri-Fusion Net to complement each other in areas where the separate methods lack certain strengths. This, therefore, ensures broad threat detection, as is validated by extensive testing with various datasets

关键词： SCADA Systems Security Cyber-Attack Intrusion Detection Deep Learning Feature Selection and Optimization

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