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5th International Conference on Applications of Optics and Photonics, AOP 2022

作者： Araújo, João C. C. Dias, Bernardo Dos Santos, Paulo S. S. De Almeida, J.M.M.M. Coelho, Luís C. C. INESC TEC - Institute for Systems and Computer Engineering Technology and Science FCUP Univ. of Porto R. Do Campo Alegre Porto4169-007 Portugal FCUP University of Porto Rua do Campo Alegre Porto4169-007 Portugal Dep. of Physics Univ. of Trás-os-Montes e Alto Douro Vila Real5001-801 Portugal

The interrogation of optic fiber sensors usually relies in complex and costly equipment with low portability due to their size such as Optical Spectrum Analyzers (OSA) or high-resolution spectrometers. Because of this, micro spectrometer devices, such as Micro-Electromechanical Systems (MEMS) with Fabry-Pérot tunable filters, are emerging as simpler and compact alternatives capable of being used to acquire spectral information in a wide wavelength band. In this work it is described the development of an interrogation system capable of infrared spectroscopy using a MEMS Fabry-Pérot Interferometer (MEMS-FPI) with a spectral response in the 1350nm to 1650nm range. Its performance is tested with the interrogation of long period fiber gratings both as a refractive index sensor and as a temperature sensor. Deconvolution techniques such as Wiener filtering are used to reduce the impact of the tunable filter's impulse response in the measured signal. Results are comparable to those obtained using a typical OSA which shows the system's potential as a cheaper and more transportable alternative. © Published under licence by IOP Publishing Ltd.

关键词： Fiber optic sensors

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Image Captioning Using Inception V3 Transfer Learning Model 6

Image Captioning Using Inception V3 Transfer Learning Model

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6th IEEE International Conference on Communication and Electronics Systems, ICCES 2021

作者： Degadwala, Sheshang Vyas, Dhairya Biswas, Haimanti Chakraborty, Utsho Saha, Sowrav Sigma Institute of Engineering Head of Comp. Eng. Dep. Gujarat Vadodara India Shree Drashti Infotech LLP Nizampura Gujarat Vadodara India Gujarat Technological University Information Technology India Gujarat Technological University Computer Engineering India Gujarat Technological University and Civil Infrastructure Engineering India

ISBN: (纸本)9781665435871

As artificial intelligence has grown rapidly in recent years, picture captioning has attracted the interest of numerous experts, which has become a fascinating and challenging challenge. A critical component of scene analysis which combines machine vision and the natural languages of language processing capabilities is visual subtitles which automatically generate natural language interpretations based on image details. This paper utilizes different NLP strategies for perceiving and clarifying an image meaning in a natural language such as English. The proposed Inception V3 image caption generator model uses CNN (Coevolutionary Neural Networks) and LSTM (Long Short-Term Memory) units. The InceptionV3 model has been educated in 1000 different classes on an ImageNet dataset. The model was imported directly from the Keras module of applications. Remove from the InceptionV3model the last classification layer for the dimension (1343, ) vector. The embedded matrix is used for vocabulary connections. A building matrix is the linear transformation of the original space with important relations into a real-life space. Image captions are commonly used and, for example, important in implementing interaction between humans and the computer. © 2021 IEEE.

关键词： Long short-term memory

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Gait-Based User Identification for Smart Home: When Machine Learning Meets FBG Accelerometers

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IEEE Transactions on Consumer Electronics 2024年

作者： Alhussein, Ghada Chi, Hao Ran Alberto, Nelia Antunes, Paulo Hadjileontiadis, Leontios Radwan, Ayman Domingues, M. Fatima Instituto de Telecomunicações-University of Aveiro Aveiro Portugal Instituto de Telecomunicações I3N and Dep. of Physics-University of Aveiro Aveiro Portugal Khalifa University of Science and Technology Department of Biomedical Engineering and Biotechnology Abu Dhabi United Arab Emirates Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki Greece Instituto de Telecomunicações Instituto Superior Tècnico -University of Lisbon Lisbon Portugal

Advancements in fiber optic sensor technology and telecommunications have paved the way for innovative health monitoring systems. However, previous works in this area have often been limited by the lack of comprehensive datasets, hindering the development of accurate and robust solutions. This paper addresses this gap by presenting a novel dataset and approach to gait-based user identification using a set of four optical Fiber Bragg Grating (FBG) sensor-based accelerometers, integrated into smart home environments. By computing features such as entropy, mean, standard deviation, kurtosis, and skewness from raw signals, and employing three unsupervised machine learning models-K-means, DB-scan, and Gaussian Mixture Model (GMM)-we achieve high accuracy in distinguishing individuals. Our dataset results for each sensor are as follows: for Sensor 1, K-means with a Silhouette Score of 0.3038 and Davies-Bouldin Index of 1.1238;for Sensor 2, K-means with a Silhouette Score of 0.3543 and Davies-Bouldin Index of 0.9659;for Sensor 3, DB-scan with a Silhouette Score of 0.2650 and Davies-Bouldin Index of 0.6855;and for Sensor 4, K-means with a Silhouette Score of 0.3949 and Davies-Bouldin Index of 1.0438. This approach not only enhances user identification but also facilitates personalized healthcare applications and unobtrusive monitoring. © 1975-2011 IEEE.

关键词： Smart homes

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TIoT: A Trust by Design Architecture for Secure and Private Internet of Things (IoT)

TIoT: A Trust by Design Architecture for Secure and Private ...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： H. Alsheakh Tareq Nasser Mahdi Mustafa S. Kadhm Abeer Al Shammari Fatimah I. Abdulsahib Hazeem B Taher Waleed Algriree Marwa Q. Alobaidi Hayder Dhafer Mohammed Department of Computer Science College of Science Mustansiriyah University Baghdad Iraq Clinical Laboratory Science College of Pharmacy Mustansiriyah University Baghdad Iraq IT Directorate Ministry of Science and Technology Baghdad Iraq Department of Computer Science Szechenyi Istvan Egyetem Gyor Hungary Department of Computer Science University of Thi-Qar Thi-Qar Iraq Dep. Of Wireless Communication and Network Engineering University Putra Malaysia Selangor Malaysia National Engineering School of Sfax Sfax University Sfax Tunisia College of Pharmacy Mustansiriyah University Baghdad Iraq

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

The Internet of Things (loT) facilitates the interconnection of a vast array of devices, services, and individuals for data exchange in multi-domain loT ecosystems, the social, physical, and cyber domains. That emerges various security, privacy, and trust issues, such as vulnerabilities, threats, and attacks. Addressing these challenges requires innovative solutions. New controls (solutions) are needed. Trust by design can be one of these controls, supporting security and privacy (among other system components). This paper proposes a Trusted loT (TloT), a trust-by-design model, and highlights the author's principal contributions. First, it advocates for implementing Tlo T through trust services. Second, it outlines an architecture for using trust by design in loT, including the design of its top-level structure and activities. Minor contributions include overviews of loT and trust, as well as looking at security and privacy in terms of security services and privacy services, respectively.

关键词： Privacy Ecosystems Security Internet of Things Arrays

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The Realization Factors of Taiwan's Low Altitude Airspace Economy and UAM: Analysis and Evaluation

The Realization Factors of Taiwan's Low Altitude Airspace Ec...

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IEEE International Conference on e-Business engineering (ICEBE)

作者： Tung-Ling Wu Chih-Ming Kung Guan-Ting Lin Kuen-Yu Tsai Guang-Yun Meng Yu-Che Huang Wan-Shu Cheng Institute of Transportation at Ministry of Transportation and Communications Taiwan R.O.C. Dep. Of Information Technology&Communication Shin Chin Univeristy Kaohsiung Campus Taiwan R.O.C. Dep. of Computer Science and Information Management Providence University Taichung Taiwan R.O.C. Department of Electrical Engineering National Taiwan Univeristy Taipei Taiwan R.O.C. Executive Officer Center for Research on High-Performance Remote Sensing and Urban Informatics NTU Taiwan R.O.C.

ISBN: (数字)9798350365856

ISBN: (纸本)9798350365863

Low altitude airspace economy applications will be of highly effective in a wide range of civil activities and will be a critical element in the smart cities of the future. In view of the main features of unmanned aerial vehicle (UAV), such as automatic piloting system, access to uninhabited areas, low cost compared to road and manned aircraft, shorter delivery time, and the ability to carry heavy payloads, it is important to recognize that UAVs can be used for a wide range of civil activities. With the development of these applications in full swing around the world, in addition to advanced communications technology and other related infrastructure, business demand and a favorable investment climate, clarity, definiteness and feasible regulations in the Taiwanese environment are also essential. Based on the premise of local economic strength, the technical level of the telecommunication industry, and commercial demands, Taiwan should establish a rational standard for the UAV regulatory system as soon as possible, so as to facilitate the development of UAM and to ensure the international competitiveness of the related technologies for market development,

关键词： Industries Technological innovation Transportation Autonomous aerial vehicles Regulation Communications technology Aircraft Standards Drones Investment

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Enhancing value creation in Energy Communities through flexibility management and network ancillary services provision

Enhancing value creation in Energy Communities through flexi...

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2022 ECEEE Summer Study on Energy Efficiency: Agents of Change

作者： Patrão, Carlos Matos, Luisa Landeck, Jorge Klein, Lurian Mendes, Gonçalo da Silva, Nuno Pinho Fulgêncio, Nuno O'Callaghan, Tim Stevens, Peter Institute of Systems and Robotics and Cleanwatts Dep. of Electrical and Computer Engineering University of Coimbra Portugal Cleanwatts Coimbra Portugal University of Coimbra and Cleanwatts LIBPhys Department of Physics University of Coimbra Portugal Lappeenranta-Lahti University of Technology LUT Finland R&D Nester Centro de Investigação em Energia REN Sacavém Portugal Electric Corby Corby Enterprise Centre England United Kingdom

ISBN: (纸本)9789198827002

The Clean Energy for all Europeans policy package has opened the way for a major transition of the European energy landscape towards customer empowerment and local energy markets development. The European Green Deal, the Fit for 55 % package and the European Directives revision are pushing forward Renewable Energy Communities, which will disrupt the energy sector and overcome limitations of existing rigid energy markets. The rise of distributed energy technologies is fostering the development of customer-centered services such as peer-to-peer energy trading. However, adequate tools and business models to interface and engage with energy markets are still lacking. The H2020 FleXunity project is addressing the emerging market needs by developing a technological platform, that combines advanced artificial intelligence algorithms, blockchain technology and demand response services, to exploit new business models that could value the aggregation of small-scale energy assets. Although the concept of these platforms is relatively well known, their real-world implementation and validation is still at an early stage. This paper presents the FleXunity project's main objectives, details of the Virtual Power Plant managing Platform under development and the characteristics of two distinct real world pilot Energy Communities already dep.oyed in the UK and Iberia. It also presents an analysis of the role that Demand Response activities and indep.ndent aggregation could play in Energy Communities, and specifically explores the role of Demand Response in the balancing markets of Finland, UK, Spain, and Portugal. Focusing on the pilot markets, the project also identifies the most promising balancing services to be provided by Energy Communities, by surveying and framing the existing regulatory qualification requirements. © 2022 European Council for an Energy Efficient Economy. All rights reserved.

关键词： Smart power grids

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Next Generation Image Watermarking via Combined DWT-SVD Technique

Next Generation Image Watermarking via Combined DWT-SVD Tech...

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Cyber Resilience (ICCR), International Conference on

作者： Qais Al-Na'amneh Ammar Almomani Ahmad Nasayreh Khalid M.O. Nahar Hasan Gharaibeh Rabia Emhamed Al Mamlook Mohammad Alauthman Department of Cyber Security Applied Science Private University Amman Jordan School of Computing Skyline University College Sharjah United Arab Emirates I. T. department AlHuson University College AlBalqa Applied University Irbid Jordan Department of Computer Sciences Yarmouk University Irbid Jordan Faculty of Information Technology Computer Sciences Yarmouk University Irbid Jordan Dep. of Business Administration Trine University Indiana USA Dep.of Mechanical and Industrial Engineering University of Zawia Tripoli Libya Department of Information Security Faculty of Information Technology University of Petra Amman Jordan

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Personal computers and the Internet are used in different areas and are easier to use. Most data is easy to transmit and duplicate in digital format, and being tampered with and stolen easily leads to issues for content owners and service providers. The Paper describes a secure (tampering-resistant) method for watermarking images for protection and authentication and digital media like audio, video, and multimedia data, and a combination of singular value decomposition (SVD) and Discrete wavelet transform (DWT) for insertion watermark by MATLAB to increase accuracy, simplicity, and flexibility Readability, secure robustness, and imperceptibility the visibility of the image. The proposed algorithm consists of three main steps: Firstly, we decomposed the original image into four sub-bands and the original watermark. Secondly, embed the watermark in the LL sub-band by replacing the singular value for the watermark. Finally, embed a watermark in the other three sub-bands by adding LL values for a watermark in each sub-band in the original image.

关键词： Resistance PSNR Watermarking Transforms Streaming media Robustness Real-time systems

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Convolutional Neural Network Based Deep Neural Network Model for Electrocardiogram Records Classification 23

Convolutional Neural Network Based Deep Neural Network Model...

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Proceedings of the 7th International Conference on Future Networks and Distributed Systems

作者： Ahmed Mahmood Khudhur Mohammed Hasan Alwan Qayssar Al Omairi Omar Abdulkareem Mahmood Yousif I Hammadi Mohammed Saleh Ali Muthanna Ahmed Aziz Ammar Muthanna Information Technology Department Kirkuk University Iraq Department of Communications Engineering University of Diyala Iraq Department of Computer Engineering University of Diyala Iraq Dep. of Medical Instruments Engineering Techniques Bilad Alrafidain University College Iraq Institute of Computer Technologies and Information Security Southern Federal University Russia Department of computer science Benha university Egypt and International Business Management Department Tashkent State University of Economics Uzbekistan Department of Telecommunication Networks and Data Transmission The Bonch Bruevich Saint Petersburg State University of Telecommunications Russia and Department of Applied Probability and Informatics Peoples Friendship University of Russia Russia

ISBN: (纸本)9798400709036

Сardiovascular diseases, also known as CVDs, currently rank as the primary incidence of mortality. The present approach for identifying illnesses involves the analysis of the Electrocardiogram (ECG), an electronic diagnostic device utilized to capture the rhythm of the heart. Regrettably, the process of seeking out specialists to conduct analysis on a substantial volume of electrocardiogram (ECG) data results in a significant dep.etion of medical capabilities. Hence, the utilization of deep learning algorithms for the identification of ECG characteristics has progressively gained prominence. Nevertheless, there exist certain limitations associated with these conventional approaches, which necessitate the need for manual characteristic identification, intricate models, and extensive training duration. This study presents a novel and effective approach for categorizing the five different types of heartbeats in the MIT-BIH Arrhythmia database. The proposed method involves the utilization of a 16-layer deep one-dimensional convolutional neural network, which exhibits both robustness and efficiency in its classification performance. Thus, the designed model is consisting of five-groups, first to fourth groups are the main feature extraction and mapping blocks, while the fifth group is fully connected and classification layers. The findings indicate that the model presented in this study has superior performance in terms of accuracy, precision, and F1-score. The proper classification of medical cases has a significant impact on the conservation of medical resources, hence positively influencing clinical practice. Additionally, the model training phase is characterized by its efficiency, since it does not significantly dep.ete resources.

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Cybersecurity Threats in the Era of AI: Detection of Phishing Domains Through Classification Rules

Cybersecurity Threats in the Era of AI: Detection of Phishin...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Mohammad Aljaidi Ayoub Alsarhan Dimah Al-Fraihat Ahmed Al-Arjan Bashar Igried Subhieh M. El-Salhi Muhammad Khalid Qais Al-Na'amneh Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Information Technology The Hashemite University Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman Jordan Department of Information Technology Faculty of Prince AL-Hussein bin Abdullah II of Information Technology The Hashemite University Jordan Department of Computer Science and Applications The Hashemite University Zarqa Jordan The Hashemite University Zarqa Jordan Department of Computer Sciences and Technology University of Hull Kingston upon Hull UK Dep. of Cyber Security Applied Science Private University Amman Jordan

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Phishing is a cyber threat where attackers create deceptive websites or emails to exploit individuals for fraudulent purposes such as providing sensitive information like usernames, passwords, or financial details. Hence, detecting phishing emails is of paramount importance to protect sensitive information, preventing financial losses, protecting against identity theft, and maintaining trust. This study employs machine learning techniques to classify and categorize websites that are used for phishing attacks. To achieve this, we proposed a classification model for the detection of phishing domains based on classification rules. These rules were formulated based on analyzing and distributing them into three classifiers: domain name features, search engine opinion (SEO) information of the domain, and security scans by security vendors. A total of eleven classification rules were formulated which were distributed as follows: six rules for the first classifier, four rules for the second classifier, and one rule for the third classifier. Each classifier feeds its results to the main classifier, which then evaluates the received results based on its rules. Domain names from 222 confirmed phishing URLs were used to test the suggested model. The model demonstrated exceptional performance, achieving an impressive accuracy rate of 99.1% during testing.

关键词： Adaptation models Accuracy Phishing Identity theft Refining Machine learning Passwords

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Big Data from Space for Precision Agriculture Applications 11

Big Data from Space for Precision Agriculture Applications

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11th International Symposium on Digital Earth, ISDE 2019

作者： Bovolo, F. Bruzzone, L. Fernández-Prieto, D. Paris, C. Solano-Correa, Y.T. Volden, E. Zanetti, M. Fondazione Bruno Kessler Center for Information and Communication Technology Trento Italy Dep. of Information Engineering and Computer Science University of Trento Trento Italy ESRIN Frascati Italy

This paper presents an approach for precision agriculture large scale applications based on the analysis of big data consisting in Satellite Image Time Series (SITS) acquired by ESA Sentinel-2 (S2) satellite constellation. The approach has been developed in the framework of the ESA SEOM - Scientific Exploitation of Operational Missions - S2-4Sci Land and Water project [1]. To focus only on agricultural areas, images are first filtered based on a land cover (LC) map that is generated by updating available old maps by means of recent images. Then S2 SITS are used to analyse agricultural areas. Two macro challenges are therefore considered: (i) automatic update of LC maps and generation of agricultural areas mask;and (ii) unsupervised multi-temporal (MT) fine characterization of land plots. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Precision agriculture

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