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45th Jubilee International Convention on Information, Communication and Electronic Technology, MIPRO 2022

作者： Horvat, M. Stojanovic, A. Kovacevic, Z. University of Zagreb Faculty of Electrical Engineering and Computing Department of Applied Computing Zagreb Croatia Zagreb University of Applied Sciences Department of Computer Science and Information Technology Zagreb Croatia

ISBN: (纸本)9789532331035

Emotions are an omnipresent and important factor in the interaction and communication between people. Since emotions are an indispensable part of human life, it would accelerate the progress of artificial intelligence and other fields of science that require data about emotions if they could be adequately described by computer systems. Today there are many different theories of affect, but few of them are used in affective computing. Other areas of computing also benefit from structured and expressive data models of the affective domain, such as human-computer interaction and brain-computer interfaces. Typical tasks include automated recognition and analysis of emotional states, mental fatigue, individual motivation, vigilance and stress resilience. In this paper four often used models of emotion and cognitive behavior are listed and their properties explained: discrete, dimensional, appraisal and action tendency models. For each model, algorithms are provided for similarity measures that can be used to determine the relatedness between different stimulation and estimation artefacts in their respective emotion spaces. The goal of this article is to help professionals find the optimal emotion model for their research and quickly become familiar with data modelling of affective states. © 2022 Croatian Society MIPRO.

关键词： Brain computer interface

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Empower Hub: Sustainable Innovative Navigation and Smart Interior Design 3

Empower Hub: Sustainable Innovative Navigation and Smart Int...

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3rd International Conference on Sustainable Mobility Applications, Renewables and Technology, SMART 2024

作者： Abdalla, Manoon Nasri Al Falasi, Mohammed El-Messiry, Haythem School of Architecture and Interior Design Canadian university Dubai United Arab Emirates School of Engineering Applied science and Technology Canadian university Dubai United Arab Emirates The Department of Computer Science Canadian University Dubai United Arab Emirates

ISBN: (纸本)9798331532925

This paper will conclude the integration between smart interior design and Artificial Intelligence including sustainability within design principles and functions. Presenting "Empower hub" a center that is specifically devoted to people with physical disabilities that concludes AI in the infrastructure. Using sustainable materials and energy-efficient lighting are the objectives. This can include solar power smart systems and adaptive technology, which greatly assist people with various types of impairments, and it is not adequately integrated in many sectors. The design focus is to address the special needs of people with visual, auditory, or mobility impairments, utilize state-of-the-art adaptive technology. Therefore, navigation assistance and motion detection systems are being proposed for the center. The smart prototypes will process data and sensors that will detect movements for smooth access. In addition to proximity sensors that can be vocal to ensure audio assistance. Most of those sensors will be powered by solar energy to ensure the sustainable factor of the project in addition to reducing energy consumption. Overall, the research will conclude the integration of AI and interior design while applying sustainable design principles in interior design with the integration of machine learning to benefit people of determination in order of accessibility and function to allow them to give back to society by giving them easy access to ensure their safety and wellbeing. © 2024 IEEE

关键词： Architectural design

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Intelligent Systems for Data Driven Agriculture: Enhancing Farmer Productivity Through Automation and Artificial Intelligence

Intelligent Systems for Data Driven Agriculture: Enhancing F...

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Smart Systems for Electrical, Electronics, Communication and computer engineering (ICSSEECC), International Conference on

作者： S. Lokesh Arvind Madhavan RM Prakash Ramanathan Krteen Anand Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Tamilnadu India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

Numerous obstacles endanger the livelihoods of smallholder farmers and global food security. Crop output losses due to plant diseases alone are estimated to be between 15% to 25% every year. However, farmers' capacity to recognize and treat illnesses in a timely manner is hampered by things like a lack of agronomic expertise, limited access to actionable insights, and information gaps. The work proposes a comprehensive end-to-end web application that uses machine learning models to help farmers with configurable expert advice, smart farm management, and automatic disease detection. Thus, enabling farmers to identify the infection using their smart phones and obtain crop - fertilizer recommendations for the crops to be treated in the farmer's area based on historical data analysis of yield-influencing elements such as geography, weather, and soil. The proposed system will employ Generative Artificial Intelligence, such as ChatGPT's natural language processing and generation capabilities, to engage in conversational interactions with farmers. The work achieves close to 95% accuracy after utilizing 30 epochs in the area of disease detection and 97% accurate models are achieved for fertilizer outputs. These features along with it's high accuracy can assist farmers in making informed decisions, utilising the data obtained to reduce crop output losses.

关键词： Smart agriculture Productivity Plant diseases Accuracy Neural networks Crops Soil

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Dissipative soliton generation and real-time dynamics in microresonator-filtered fiber lasers

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Light(science & Applications) 2022年第11期11卷 2676-2686页

作者： Mingming Nie Bowen Li Kunpeng Jia Yjun Xie Jingjie Yan Shining Zhu Zhenda Xie Shu-Wei Huang Department of Electrial Computer and Energy EngineeringUniversity of Colorado BoulderBoulderColorado 80309USA School of Electronic Science and Engineering National Laboratory of Solid State MicrostructuresCollege of Engineering and Applied SciencesSchool of Physicsand Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210093China

Optical frequency combs in microresonators(microcombs)have a wide range of applications in science and technology,due to its compact size and access to considerably larger comb *** recent successes,the problems of self-starting,high mode efficiency as well as high output power have not been fully addressed for conventional soliton *** demonstration of laser cavity soliton microcombs by nesting a microresonator into a fiber cavity,shows great potential to solve the *** we study the dissipative soliton generation and interaction dynamics in a microresonator-fltered fiber laser in both theory and *** bring theoretical insight into the mode-locking principle,discuss the parameters effect on soliton properties,and provide experimental guidelines for broadband soliton *** predict chirped bright dissipative soliton with flat-top spectral envelope in microresonators with normal dispersion,which is fundamentally forbidden for the externally driven ***,we experimentally achieve soliton microcombs with large bandwidth of~10 nm and high mode efficiency of 90.7%.Finally,by taking advantage of an ultrahigh-speed time magnifier,we study the real-time soliton formation and interaction dynamics and experimentally observe soliton Newton's *** study will benefit the design of the novel,high-efficiency and self-starting microcombs for real-world applications.

关键词： resonator fiber soliton

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Effective Prediction of Energy Consumption in Automated Guided Vehicles with Recurrent and Convolutional Neural Networks

Effective Prediction of Energy Consumption in Automated Guid...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Benecki, Pawel Kostrzewa, Daniel Grzesik, Piotr Shubyn, Bohdan Syu, Jia-Hao Lin, Jerry Chun-Wei Sunderam, Vaidy Mrozek, Dariusz Silesian University of Technology Department of Applied Informatics Gliwice Poland Lviv Polytechnic National University Department of Telecommunications Lviv Ukraine National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan Silesian University of Technology Department of Distributed Systems and Informatics Devices Gliwice Poland Emory University Department of Computer Science AtlantaGA30322 United States

ISBN: (纸本)9798350324457

Detection and prediction of failures in Automated Guided Vehicles (AGV) are essential for the uninterrupted operation of production plants. Anomaly detection is usually achieved by comparing expected measurement values with actual observations. Thus, it is crucial to predict telemetry signals properly. In this paper, we research the prediction of energy consumption using state-of-the-art Artificial Neural Networks architectures (SCINet) compared with other Recurrent Neural Network (RNN) approaches on the data streams acquired from CoBotAGV. We especially focus on the possibility of applying feature weighting. We show that it can improve prediction capabilities. We also investigate resource utilization in terms of time to fit the embedded AGV environment. © 2023 IEEE.

关键词： anomaly detection automated guided vehicles feature weighting Industry 4.0 time series prediction

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Enhancing Space Exploration with Real-Time Streaming Data Analysis

Enhancing Space Exploration with Real-Time Streaming Data An...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Sharma, Tarunika Gatla, Anitha Lakhanpal, Sorabh Dhasmana, Gulshan Lalitha, Yaragudipati Sri Ziara, Sajjad Department of Applied Sciences New Horizon College of Engineering Bangalore India Department of Information Technology Institute of Aeronautical Engineering Dundigal Hyderabad500043 India Lovely Professional University Phagwara India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Information Technology GRIET Bachupally Telangana Hyderabad India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (纸本)9798350355338

Space travel has inspired creativity and science. In this ever-changing and unpredictable environment, real-time streaming data analysis helps make missions safer and more efficient. This research discusses and assesses three strategies to improve space exploration using sophisticated data analysis. RNNs detect weird things in real time in the first technique. This detects system issues and environmental threats early. Second, Convolutional Neural Networks (CNNs) automate guidance and scientific research. This helps the robot decide without human input. Bayesian networks accurately forecast solar flares and geomagnetic storms in the third technique. We simulate experiments to demonstrate how these strategies may enhance human safety, mission success, and science. Real-time streaming data processing may revolutionize how we solve global riddles as space research advances. © 2024 IEEE.

关键词： Convolutional neural networks

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Secure 3D Directional Modulation Using Subarrays Based on Planar Frequency Diverse Array with Nonuniform Frequency Offsets

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IEEE Transactions on Communications 2024年第6期73卷 4017-4032页

作者： You, Byungha Lee, In-Ho Jung, Haejoon Duong, Trung Q. Shin, Hyundong Kyung Hee University Department Of Electronics And Information Convergence Engineering Yongin-si17104 Korea Republic of Hankyong National University School Of Electronic And Electrical Engineering Anseong17579 Korea Republic of Memorial University Faculty Of Engineering And Applied Science St. John'sNLA1C 5S7 Canada Queen's University Belfast School Of Electronics Electrical Engineering And Computer Science BelfastBT7 1NN United Kingdom Kyung Hee University Department Of Electronic Engineering Yongin17104 Korea Republic of

Physical-layer security (PLS) is a new paradigm for secure communication without requiring secret key exchange and management. Moreover, PLS with frequency diverse subarray (FDSA) can better control information leakage in the angle-range domain, which mitigates the security weakness of the phased array caused by its lack of range resolution. In this paper, we propose a three-dimensional (3D) directional modulation (DM) using randomized radiation with FDSA for enhanced PLS, employing a planar array. In addition, nonuniform frequency offsets (FOs) are considered as FO configurations (FOCs) for FDSA to concentrate on the mainlobe and suppress the undesired sidelobes in 3D space, where logarithmically increasing FOC (L-FOC), Hamming window-based FOC (H-FOC), and piecewise trigonometric FOC (P-FOC) are introduced. Characterizing the process of selecting the random subsets for randomized radiation, we provide the exact analysis of the secrecy rate of the proposed scheme. Moreover, FOs applied to FDSA and the number of random subsets are optimized with a genetic algorithm (GA)-based optimization strategy. We evaluate the proposed schemes in terms of secrecy rate and vulnerable volume, where the simulation results verify our analysis and show that nonuniform FOCs are a more favorable choice for FDSA compared to uniform FOC (U-FOC). © 1972-2012 IEEE.

关键词： Array signal processing Three-dimensional displays Security Frequency modulation Vectors Frequency diversity Symbols Antenna radiation patterns Wireless communication Phased arrays

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Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

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computers, Materials & Continua 2023年第11期77卷 2579-2594页

作者： Abdennasser Dahmani Yamina Ammi Nadjem Bailek Alban Kuriqi Nadhir Al-Ansari Salah Hanini Ilhami Colak Laith Abualigah El-Sayed M.El-kenawy Department of Mechanical Engineering Faculty of Science and TechnologyGIDD Industrial Engineering and Sustainable Development LaboratoryUniversity of RelizaneBourmadiaRelizane48000Algeria Laboratory of Biomaterials and Transport Phenomena(LBMPT) University of MedeaUrban PoleMedea26000Algeria Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of TamanghassetTamanrasset10034Algeria Sustainable Development and Computer Science Laboratory Faculty of Sciences and TechnologyAhmed Draia University of AdrarAdrarAlgeria CERIS Instituto Superior TécnicoUniversidade de LisboaLisbon1049-001Portugal Civil Engineering Department University for Business and TechnologyPristina10000Kosovo Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Engineering and Architecture Faculty Nisantasi UniversityIstanbulTurkey Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia Department of Computing and Information Systems School of Engineering and TechnologySunway University MalaysiaPetaling Jaya27500Malaysia Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running *** into renewable energy sources such as solar energy is being pursued to counter *** energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs *** study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global *** hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA *** from two stations in Algeria with different climatic zones were used to develop the *** error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all ***,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

关键词： Renewable energy energy prediction global irradiation artificial intelligence BFGS quasi-Newton training algorithm

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Italian-Chinese Neural Machine Translation: results and lessons learnt 23

Italian-Chinese Neural Machine Translation: results and less...

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3rd ACM Conference on Information Technology for Social Good, GoodIT 2023

作者： Delnevo, Giovanni Im, Marcus Tse, Rita Lam, Chan-Tong Tang, Su-Kit Salomoni, Paola Pau, Giovanni Ghini, Vittorio Mirri, Silvia Department of Computer Science and Engineering University of Bologna Cesena Italy Faculty of Applied Sciences Macao Polytechnic University China Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence Ministry of Education Macao Polytechnic University China

ISBN: (纸本)9798400701160

Today, access to the Internet provides access to various forms of knowledge like free online lecture series offered by prestigious universities, massive open online courses, films and books, and Wikipedia. In addition, it is possible to join online communities on any topic of interest, get to know people with common interests, exchange thoughts and participate in debates. To enable access to these unprecedented knowledge bases, it is crucial to be able to translate texts into any language known by users. For this reason, Machine Translation has been a very active research field for the last thirty years. In this paper, we investigate the task of Chinese-Italian translations by exploiting Neural Machine Translation approaches. We trained several deep neural networks starting from two already available datasets containing Chinese-Italian parallel corpora. Then, we compared their performance against some of the most common machine translation services freely available online. In particular, we take advantage of Microsoft Translator, Google Translate, DeepL, and ModernMT. © 2023 Owner/Author.

关键词： Neural machine translation

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Retraction Note: Enhancing low latency and high performance in clustered-based wireless sensor network routing using genetic algorithm for 5G networks

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Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Banu, Firdouse Udhayamoorthi, M. Ramana Gupta Nallagattla, Venkata Phaneendra Maguluri, Lakshmana Mahesh Kumar, N. B. Jayachitra, S. Department of Applied Information System Applied College King Khalid University Khamis Mushaiyt Saudi Arabia Department of Computer Science and Design Karpagam College of Engineering (Autonomous) Coimbatore India Department of CSE Prasad V. Potluri Siddhartha Institute of Technology Vijayawada India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul India

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