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6th International Conference on Renewable Energies Offshore, RENEW 2024

作者： Ahmad, I. M’zoughi, F. Aboutalebi, P. Garrido, I. Garrido, A.J. Automatic Control Group—ACG Institute of Research and Development of Processes—IIDP Department of Automatic Control and Systems Engineering Engineering School of Bilbao—EIB/BIE Faculty of Engineering of Bilbao—EIB/BIE University of the Basque Country—UPV/EHU Bilbao Spain

ISBN: (纸本)9781032905570

In the pursuit of sustainable electricity generation from offshore wind and wave energy, the combination of Floating Offshore Wind Turbines (FOWTs) and Oscillating Water Columns (OWCs) has emerged as a promising solution to enhance the working lifespan of this kind of systems. This study introduces a control-focused modeling approach, using empirical data collected from the widely accepted FAST environment, to implement advanced control laws for these integrated platforms. Leveraging Long Short-Term Memory (LSTM) and Artificial Neural Networks (ANN), the research yields predictive models that effectively capture the platform pitch dynamics so as to optimize control strategies. By integrating advanced machine learning techniques, these approaches address the challenges of modeling floating offshore structures in dynamic environments. In this sense, the neuro-estimators developed in this study closely match the original plant results, offering researchers a straightforward way to implement complex control schemes that could hardly be implemented over the original non-linear system. © 2025 The Author(s).

关键词： Offshore wind turbines

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Design of an Air Pollution Monitoring System Based on a Low-Cost Sensor Node

Design of an Air Pollution Monitoring System Based on a Low-...

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2024 IEEE International Workshop on Metrology for Living Environment, MetroLivEnv 2024

作者： Guerron, Rocio-Alejandra Carni, Domenico-Luca Lanuzza, Marco D'Amore, Francesco Bencardino, Mariantonia Lamonaca, Francesco Taco, Ramiro University of Calabria Department of Computer Engineering Modeling Electronics and Systems Rende Italy Institute for High Performance Computing and Networking Italian National Research Council Rende Italy Institute of Atmospheric Pollution Research Italian National Research Council Rende Italy

ISBN: (纸本)9798350385014

Air quality influences the life of the living beings that inhabit planet Earth. High levels of air pollutants in ambient air are in fact able to affect human health and also ecosystem integrity. Their monitoring allows for regulating the most appropriate air quality indices and verifying whether they are below the established harmful levels. However, this is a task that requires many physical and economic resources. For this reason, the research activity herein presented proposes a low-cost system, which is able to qualitatively evaluate the meteorological conditions and the main pollutants that affect a certain area. Through this proposal, the behavior of Alphasense's electrochemical gas low-cost sensors is studied. Their response to the concentrations of the main air pollutants regulated by the relevant European Directive (2008/50/CE), such as Carbon Monoxide, Nitrogen Dioxide, Ozone, and Sulfur Dioxide, is characterized and analyzed under various environmental conditions of Temperature and Relative Humidity and as a function of the electrical current signal obtained by the chemistry of the sensor. © 2024 IEEE.

关键词： Air quality

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Velocity-aided Channel Estimation for Spatially Selective mmWave Massive MIMO Communications 31

Velocity-aided Channel Estimation for Spatially Selective mm...

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Vlachos, Evangelos Mavrokefalidis, Christos Berberidis, Kostas Industrial Systems Institute ATHENA Research Center Patras Greece Dept. of Computer Engineering and Informatics University of Patras Patras Greece

ISBN: (纸本)9789464593600

This paper focuses on addressing the challenge of estimating multiple-input multiple-output (MIMO) channels for wireless communication between a ground base-station and a moving vehicle. One recently recognised model for time-varying channels incorporates spatial selectivity, which is referred to as beam squint, and is particularly relevant in the millimeter-wave (mmWave) range. In such scenarios, it is essential to account for the beam squint when attempting to recover channel parameters using a training sequence. However, the use of a training sequence alone may be insufficient for this purpose. To overcome this issue, in this work, we propose a channel estimation approach that exploits information provided by the control module of the vehicle, namely its velocity. The estimation problem that is designed, regards the channel both in a parametric and a non-parametric form and the alternating direction method of multipliers is utilised to efficiently solve it. It is demonstrated via simulations that considerable gains can be achieved if information from the control unit of the vehicle can be appropriately introduced and exploited. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Cooperative Five Degrees of Freedom Motion Estimation for A Swarm of Autonomous Vehicles 48

Cooperative Five Degrees of Freedom Motion Estimation for A ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Piperigkos, Nikos Lalos, Aris S. Berberidis, Kostas Anagnostopoulos, Christos University of Patras Computer Engineering and Informatics Dept Rion Patras Greece Industrial Systems Institute Athena Research Center Patras Greece

ISBN: (纸本)9781728163277

In this paper, we propose a novel cooperative-based system that facilitates each autonomous vehicle of the swarm to be fully aware of its 5 degrees of freedom (DOF) motion, i.e., 3D translation and 2D rotation, a very important task for autonomous navigation, known also as simultaneous localization and mapping (SLAM). The novelty is that the interconnected vehicles of the swarm share a common collective task: simultaneously estimating self and neighboring vehicles' 5 DOF by perceiving, transmitting, associating and fusing heterogeneous data, e.g., visual, mechanical, satellite based, etc., relying on different sensor modalities and vehicular communication. The proposed sensor fusion framework is based on the Extended Kalman Filter algorithm, which is reformulated in order to capture cooperative 3D translation and 2D rotation estimation in an alternating fashion. Numerical results using the driving parameters of many cars from CARLA simulator, indicate very promising accuracy in terms of absolute trajectory error. © 2023 IEEE.

关键词： Kalman filters

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An Automatic Threshold Selection Using ALO for Healthcare Duplicate Record Detection with Reciprocal Neuro-Fuzzy Inference System

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computers, Materials & Continua 2023年第3期74卷 5821-5836页

作者： Ala Saleh Alluhaidan Pushparaj Anitha Subbappa Ved Prakash Mishra P.V.Chandrika Anurika Vaish Sarthak Sengupta Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhKSA.P.O.Box:8442811671 Department of Electronics and Communication Engineering National Institute of Technical Teacher Training and ResearchChandigarh160019India Department of Periodontology JSS Dental College&HospitalJSS Academy of Higher Education and ResearchMysuru570015India Department of Computer Science and Engineering Amity UniversityDubai345019United Arab Emirates Department of Management/Economics Prin LN Welingkar Institute of Management Development and ResearchMumbai400019India Department of Management Studies Indian Institute of Information TechnologyAllahabad211012India

Esystems based on EHRs(Electronic health records)have been in use for many years and their amplified realizations have been felt *** still have been pioneering collections of massive volumes of health *** detections involve discovering records referring to the same practical components,indicating tasks,which are generally dependent on several input parameters that experts *** linkage specifies the issue of finding identical records across various data *** similarity existing between two records is characterized based on domain-based similarity functions over different ***-duplication of one dataset or the linkage of multiple data sets has become a highly significant operation in the data processing stages of different data mining *** objective is to match all the records associated with the same *** measures have been in use for representing the quality and complexity about data linkage algorithms,and many other novel metrics have been *** outline of the problem existing in themeasurement of data linkage and de-duplication quality and complexity is *** article focuses on the reprocessing of health data that is horizontally divided among data custodians,with the purpose of custodians giving similar features to sets of *** first step in this technique is about an automatic selection of training examples with superior quality from the compared record pairs and the second step involves training the reciprocal neuro-fuzzy inference system(RANFIS)*** the Optimal Threshold classifier,it is presumed that there is information about the original match status for all compared record pairs(i.e.,Ant Lion Optimization),and therefore an optimal threshold can be computed based on the respective ***,Clinical Decision(CD),and Cork Open research Archive(CORA)data repository help analyze the proposed method with evaluated benchmarks with current techniques.

关键词： Duplicate detection healthcare record linkage dataset pre-processing reciprocal neuro-fuzzy inference system and ant lion optimization fuzzy system

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Multi-Focus Image Fusion: A Systematic Literature Review

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SN computer Science 2025年第2期6卷 1-30页

作者： Basu, Shatabdi Singhal, Sunita Singh, Dilbag Department of Computer Science and Engineering Manipal University Jaipur Ajmer Express Highway Rajasthan Jaipur 303007 India Centre of Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 India Research and Development Cell Lovely Professional University Phagwara 144411 India

Multi-Focus Image Fusion (MFIF) was designed to solve the issue of the limited depth of field inherent in the optical lens of imaging systems. In addition, MFIF extracts all the relevant information from the source image. In this study, we present a systematic review of the current literature to identify and discuss the main requirements and features of MFIF. A systematic literature review was conducted using The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology and 550 articles were extracted from the Springer, Science Direct, and IEEE databases. Based on the extracted data, 74 studies were selected according to the inclusion and exclusion criteria. The findings are presented in four subsections that discuss the most frequently employed fusion methods, activity level measurements, publicly available image datasets, and evaluation indicators used to judge the quality aspect of a fused image in MFIF. According to the results, deep learning methods are preferred as fusion methods, while spatial frequency is the most popular technique for measuring activity levels. Most studies include image feature-based evaluation indicators in addition to the Lytro dataset as input to test the efficacy of the fusion method. There is growing interest in MFIF, but limited literature is available on boundary region fusion methods and novel activity level measurements. Finally, several potential research areas and prospects are identified in the review. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Activity level measurements computer aided manufacturing Evaluation indicators Fusion methods Multi-Focus Image Fusion Systematic literature review

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Decision Tree Machine Learning to Determine Direction of Steering Force for Hospital Bed Push Handle System

Decision Tree Machine Learning to Determine Direction of Ste...

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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Chimehi, Bahareh Wallace, Bruce Carleton University SAM3 Innovation Hub Department of Systems and Computer Engineering Ottawa Canada Carleton University Bruyère Research Institute SAM3 Innovation Hub Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9781665493840

This work demonstrates the use of Decision Tree and Random Forest machine learning to determine the direction of desired movement from the forces applied to two push-handles on a novel patient transfer system the Able Innovations ALTA™ patient transfer system provides a replacement transfer method for residents in care that cannot transfer on their own that have limited mobility. This new system is significantly heavier that a hospital gurney because of the weight of the mechatronics and healthcare professionals will need power assist to transport and position the system. In this work, two loadcell sensor-based prototypes have been used to measure the input forces and direction applied by a user to two handles. These two prototypes have been placed on a hospital bed to simulate the two handles on the right and left provided to hospital staff to push the system. Machine learning is used to analyze sensor measurements from each handle to predict the movement intent. The results are presented for test pushes in 6 directions that include forward and reverse in each of straight, left, and right turns. Two models of Machine Learning (decision tree classifier and random forest classifier) have been used for 8 and 14 features and are shown to be able to predict the direction of push with high accuracy. The accuracy for 8 features using decision tree and random forest has been measured 93.5% and 97.0% respectively. © 2023 IEEE.

关键词： Decision trees

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Approaches in Medical research and Clinical Trials Using Different Software Application 1

Approaches in Medical Research and Clinical Trials Using Dif...

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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Kashinath, Amruta Garagate Mujawar, Apsara Panda, Bijoy Kumar Vatsa, Madhuranjan Agarwal, Purvi Jaywant College of Engineering and Polytechnic Department of Computer Science Engineering Karad India Jaywant Institute of Management Karad India Krishna Vishwa Vidyapeeth "Deemed to Be University" Department of Pharmacy Practic Karad India Akhildev Ipr and Research Services Research and Development Department Delhi India Atharva College of Engineering Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350360707

Clinical trials (CTs) continue to be the cornerstone of secure and efficient medication development. Considering the increasingly data-driven and personalized medical paradigm in healthcare, businesses and regulators must make use of customized artificial intelligence (AI) solutions that facilitate rapid and efficient clinical research. Numerous clinical trials have been carried out by researchers as a result of the growing need for novel treatments and other therapeutic interventions. Treatment trials provide a high quality of evidence, which makes them the primary method for assessing novel treatment therapies. The growing volume of data that needs to be considered while organizing and carrying out clinical trials has resulted in longer trial durations, greater expenses, and worse productivity. Clinical trial designers and administrators may be able to reach a larger audience by utilizing e-technologies, which could also lower costs and increase trial efficiency. E-technology challenges may be met with thoughtful preparation, helpful collaborations, and foresight. Web- and smartphone-based apps are becoming indispensable tools due to their growing function and the public's and scientists' growing usage of these platforms. © 2024 IEEE.

关键词： Personalized medicine

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Sleep-Wake and Body Position Classification with Deep Learning using Pressure Sensor Mat Measurements

Sleep-Wake and Body Position Classification with Deep Learni...

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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Green, Carter Bouchard, Martin Goubran, Rafik Robillard, Rebecca Higginson, Caitlin Lee, Elliott Knoefel, Frank University of Ottawa School of Electrical Engineering and Computer Science OttawaON Canada Bruyere Research Institute University of Ottawa School of Electrical Engineering and Computer Science OttawaON Canada Carleton University Bruyere Research Institute Systems and Computer Engineering OttawaON Canada University of Ottawa Sleep Research Unit U. of Ottawa Institute for Mental Health Research at the Royal School of Psychology OttawaON Canada University of Ottawa Institute for Mental Health Research at the Royal Sleep Research Unit OttawaON Canada Royal Ottawa Mental Health Center Sleep Research Unit U. of Ottawa Institute for Mental Health Research at the Royal Sleep Disorders Clinic OttawaON Canada Carleton University Bruyere Research Institute Faculty of Medicine University of Ottawa Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9781665493840

Polysomnography, the gold standard of sleep measurement, is a time-intensive, costly, and rather invasive procedure. Using under-bed pressure sensor arrays data simultaneously recorded with standard polysomnography, this study demonstrates that deep learning can be used to classify body position and differentiate sleep from wake. All measurements were performed in people with suspected sleep disorders referred for clinical assessments at a sleep laboratory. To perform the classification tasks, we used supervised learning and temporal convolution networks. Performance was assessed with leave-one-out cross validation on 84 participants for body position classification and 70 participants for sleep-wake classification. Results demonstrate that a pressure sensor array placed under the mattress with less than 100 sensors can outperform previous sleep-wake detection methods and is competitive with previous methods for body position classification. Our pressure sensor arrays differ from the mats used in previous work as they use significantly less sensors and are located under the mattress, making them less obtrusive. This tool has great potential as a cost-efficient mean of assessing sleep while reducing patient burden and the workload of specialized staff. © 2023 IEEE.

关键词： Wakes

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SHIS: secure healthcare intelligent scheme in internet of multimedia vehicular environment

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Multimedia Tools and Applications 2024年第40期83卷 87781-87800页

作者： Mangla, Cherry Rani, Shalli Dhiman, Gaurav Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Bikram Government College Punjab Patiala India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Division of Research and Development Lovely Professional University Phagwara144411 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India MEU research Unit Middle East University Amman Jordan

For providing a safe and secure healthcare environment in vehicular multimedia networks has been emerged as a promising area because of data exchange in multimedia format. Because of properties of Long Term Evolution (LTE) with low delay, high data rate, high mobility, an efficient safe environment can be provided to the in healthcare environment for multimedia content (images, audio, text, video). Despite these benefits, due to the high mobility and data transmission using an open channel, i.e., Internet, multimedia vehicular networks are facing many security breaches in recent years. The Internet of Multimedia Vehicular Environment calls for the establishment of a safe and intelligent healthcare system, and the proposed work problem lays the groundwork for those requirements. One of the security breaches is the Denial of Service (DoS) attack in healthcare. To mitigate this issue, in this paper, we propose, SHIS, a model for detection of Imposter location using two parameters (i) packet counter and (ii) average ratio of packet delivery. It helps to detect attacks faster in comparison to the existing approach (MEC-DoS) for healthcare data. We proposed SHIS to detect denial of service attack to find the location of malicious nodes in the multimedia network. Simulation results showed that SHIS outperformed the existing techniques as it has 20% improvement in detection of attacks as compared to the existing techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Denial-of-service attack

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