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RSMA-assisted SHAPTINs: secrecy performance under imperfect hardware and channel estimation errors

Neural Computing and Applications 2024年 1-18页

作者： Feng, Zhou Kefeng, Guo Jian, Cheng Sunder Ali, Khowaja Kapal, Dev Reddy, Gadekallu Thippa Hussam Al, Hamadi School of Information Technology Yancheng Institute of Technology Yancheng224051 China School of Space Information Space Engineering University Beijing101407 China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing210016 China Faculty of Computing Digital and Data Technological University Dublin DublinD07EWV4 Ireland ADAPT Centre and Department of Computer Science Munster Technological University Cork BishopstownT12 P928 Ireland Department of institute of intelligent systems University of Johannesburg Auckland Park Johannesburg2006 South Africa Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Division of Research and Development Lovely Professional University Phagwara144001 India College of Engineering and IT College of Engineering and IT University of Dubai PO Box 14143 Dubai United Arab Emirates

Satellite high aerial platform terrestrial integrated networks have become the hot topic these years, which have been regarded as the major part of the intelligence of things for future networks. During this work, we investigate the secrecy performance for rate-splitting multiple access-assisted satellite high aerial platform terrestrial integrated networks. Besides, imperfect hardware and channel estimation errors are considered in the secrecy networks. Moreover, to enhance the energy utilization efficiency, rate-splitting multiple access scheme is utilized into the considered network, which is prior to that of the non-orthogonal multiple access scheme. What's more, to enhance the satellite transmission, multiple high aerial platforms are used to forward the transmission along with multiple eavesdroppers. In addition, the direct transmission link is not considered in the secrecy networks due to the heavy fading and obstacles. Relied on the former considerations, the exact and asymptotic analysis for the secrecy performance is further obtained to confirm the rightness of the analysis. Finally, some representative Monte Carlo simulations are carried out to validate the obtained results. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Asymptotic analysis

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Performance optimization of microbial electrolysis cell (MEC) for palm oil mill effluent (POME) wastewater treatment and sustainable Bio-H2 production using response surface methodology (RSM)

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2022年第34期47卷 15464-15479页

作者： Chandrasekhar, Abudukeremu Kadier ab Junying Wang ab K. Wang, Junying Chandrasekhar, K. Abdeshahian, Peyman Islam, M. Amirul Ghanbari, Farshid Bajpai, Mukul Katoch, Surjit Singh Bhagawati, Prashant Basavaraj Li, Hui Kalil, Mohd Sahaid Hamid, Aidil Abdul Abu Hasan, Hassimi Ma, Peng-Cheng Laboratory of Environmental Science and Technology The Xinjiang Technical Institute of Physics and Chemistry Key Laboratory of Functional Materials and Devices for Special Environments Chinese Academy of Sciences Urumqi 830011 China Center of Material and Opto-electronic Research University of Chinese Academy of Sciences Beijing 100049 China School of Civil and Environmental Engineering Yonsei University Seoul 03722 Republic of Korea Department of Biotechnology Engineering School of Lorena University of São Paulo São Paulo 12602-810 Brazil Laboratory for Quantum Semiconductors and Photon-based BioNanotechnology Department of Electrical and Computer Engineering Faculty of Engineering Université de Sherbrooke Sherbrooke Québec J1K 2R1 Canada Research Center for Environmental Contaminants (RCEC) Abadan University of Medical Sciences Abadan Iran Environmental Engineering Lab Civil Engineering Department National Institute of Technology Hamirpur Hamirpur Himachal Pradesh 177005 India Faculty of Engineering Department of Civil Engineering Annasaheb Dange College of Engineering and Technology Ashta Maharashtra 416301 India Department of Chemical and Process Engineering Faculty of Engineering and Built Environment National University of Malaysia (UKM) UKM Bangi 43600 Selangor Malaysia School of Biosciences and Biotechnology Faculty of Science and Technology National University of Malaysia (UKM) UKM Bangi 43600 Selangor Malaysia Research Centre for Sustainable Process Technology Faculty of Engineering and Built Environment National University of Malaysia (UKM) UKM Bangi 43600 Selangor Malaysia

Microbial electrolysis cells (MECs) are a new bio-electrochemical method for converting organic matter to hydrogen gas (H-2). Palm oil mill effluent (POME) is hazardous wastewater that is mostly formed during the crude oil extraction process in the palm oil industry. In the present study, POME was used in the MEC system for hydrogen generation as a feasible treatment technology. To enhance biohydrogen generation from POME in the MEC, an empirical model was generated using response surface methodology (RSM). A central composite design (CCD) was utilized to perform twenty experimental runs of MEC given three important variables, namely incubation temperature, initial pH, and influent dilution rate. Experimental results from CCD showed that an average value of 1.16 m(3) H-2/m(3) d for maximum hydrogen production rate (HPR) was produced. A second-order polynomial model was adjusted to the experimental results from CCD. The regression model showed that the quadratic term of all variables tested had a highly significant effect (P < 0.01) on maximum HPR as a defined response. The analysis of the empirical model revealed that the optimal conditions for maximum HPR were incubation temperature, initial pH, and influent dilution rate of 30.23 ?, 6.63, and 50.71%, respectively. Generated regression model predicted a maximum HPR of 1.1659 m(3) H-2/m(3) d could be generated under optimum conditions. Confirmation experimentation was conducted in the optimal conditions determined. Experimental results of the validation test showed that a maximum HPR of 1.1747 m(3) H-2/m(3) d was produced. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Microbial electrolysis cell (MEC) Hydrogen production Palm oil mill effluent (POME) Wastewater Optimization Response surface methodology (RSM)

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A Generalized Outage Prediction Model for Various Types of Extreme Climate Events in Texas

A Generalized Outage Prediction Model for Various Types of E...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Lee, Jangjae Lee, Sangkeun Matthew Paal, Stephanie German Chinthavali, Supriya Texas A&m University Zachry Department of Civil and Environmental Engineering College StationTX77843 United States Oak Ridge National Laboratory Computer Science and Mathematics Division Oak RidgeTN37831 United States Oak Ridge National Laboratory Geospatial Science and Human Security Division Oak RidgeTN37831 United States

ISBN: (纸本)9798350362480

This study proposes a generalized model for predicting power outages for various types of extreme weather events. To accomplish the objective of this research, diverse features (e.g., weather data, geographical features, socio-demographic data, and infrastructure information) were leveraged as independent features, while the target variable was the number of customers who had problems with their electricity at the county level. Using the time of occurrence of extreme weather as defined by the National Weather Service, the top ten influential events were selected using the impact index based on cumulative power outages due to each type of extreme weather event. Additionally, a generalized model was created to predict power outages using weather data from one hour before the outage, outage data from one hour prior, as well as socio-demographic, geographic, and infrastructure information and this model was evaluated. The model was developed in two ways: first, as an Ensemble model trained using individual extreme weather events, and second, as a Unified model using all types of extreme weather conditions. As a result of evaluating the model using mean directional accuracy (MDA), one of the evaluation metrics, the ensemble model showed an accuracy of over 0.4 for weather event types such as Winter Storms, Cold/Wind Chill, Frost/Freeze, and Ice Storms. Although this study focused on creating a model specific to Texas, it is possible to expand the data to develop a nationwide model. © 2024 IEEE.

关键词： Extreme weather

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OFDM Based Image Transmission through Plastic Optical Fibers for Low Rate Internet of Things Applications

OFDM Based Image Transmission through Plastic Optical Fibers...

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IEEE Southeastcon

作者： Shalin Itty Jade Carranza Pedro Luis Carro María Ángeles Losada Alicia López Javier Mateo Gascón Eduardo Lleida Alfonso Ortega Dayana Ribas Yi Wang Neo Antoniades Dwight Richards Jessica Jiang Electrical and Computer Engineering Department Manhattan College Riverdale New York USA Engineering & Environmental Sciences Department College of Staten Island The City University of New York New York USA Electronic and Communications Engineering Department Aragón Institute of Engineering Research University of Zaragoza Spain

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

This study proposes and investigates an image transmission system that utilizes Orthogonal Frequency-division Multiplexing (OFDM) and Software-Defined Radio (SDR) to transmit images over Plastic Optical Fiber (POF) in Internet of Things (IoT) scenarios. The initial system focused on transmitting and receiving grayscale images due to the resource limitations of an IoT-like OFDM signals and the SDR system. To broaden the applicability of this system, the deep learning library, DeOldify, was used to recolor the received images. Different hardware and software parameters were tested, focusing on various frequencies and laser biases to observe how they affected and to determine which conditions performed best. Finally, results show the feasibility of the proposed system according to usual metrics as Bit Error Rate (BER) and decoded colored image examples are presented.

关键词： Measurement Plastic optical fiber OFDM Image communication Bit error rate Software Libraries Hardware Internet of Things Software radio

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4D-Printed Shape Memory Polymer Based Solar Tracker 49

4D-Printed Shape Memory Polymer Based Solar Tracker

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49th IEEE Photovoltaics Specialists Conference, PVSC 2022

作者： Tytov, Serhii Al-Modaf, Fhad Su, Shicheng El-Atab, Nazek National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' 37 prospect Peremohy 03056 Ukraine Electrical and Computer Engineering Computer Electrical Mathematical Science and Engineering Division Thuwal23955-6900 Saudi Arabia University of Electronic Science and Technology of China 610056 China

ISBN: (数字)9781728161174

ISBN: (纸本)9781728161174

In this work, soft actuators made of shape memory polymers (SMPs) are used to actuate and control a solar tracker in response to heat generated by sunlight. To achieve this, a thermo-mechanical design of a solar tracker is 4D printed. The results show that the black-colored elliptical-shaped solar tracker can shrink and tilt by up to 30° when exposed to sunlight with a response time of 30 s, enabling the solar cell to remain exposed to the highest light intensity and therefore, allowing for continuously optimizing the solar cell power output. © 2022 IEEE.

关键词： Solar energy

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SAR onboard Broadband C-band Circularly Polarized Antenna for In situ Volcanic Lava Observation 8

SAR onboard Broadband C-band Circularly Polarized Antenna fo...

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8th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2023

作者： Yoshimoto, Yuki Miyazaki, Takahiro Gao, Steven Pramono, Subuh Kashihara, Hisato Ito, Koichi Sumantyo, Josaphat Tetuko Sri Takahashi, Ayaka Santosa, Cahya Edi Naito, Motoyuki Sumitomo Metal Mining Co. Ltd. Computer Aided Engineering and Development Dept. Tokyo Japan Chiba University Dept. of Environmental Remote Sensing Chiba Japan The Chinese University of Hong Kong Dept. of Electronic Engineering Hong Kong Hong Kong Sebelas Maret University Dept. of Electrical Engineering Surakarta Indonesia Chiba University Center for Environmental Remote Sensing Chiba Japan Teikyo University Dept. of Aerospace Engineering Utsunomiya Japan National Research and Innovation Agency Research Center for Aeronautics Technology Bogor Indonesia

ISBN: (纸本)9798350393590

Estimating the volcanic eruption scale is important for mitigating eruption hazards. While the eruption scale can be explained by the eruption mode and its temporal variation, the dominant factor is expected to be the formation reaction of volcanic products, but there are still many unknowns, including the relationship with the thermal history. To solve this problem, we proposed a broadband circularly polarized microwave antenna for a synthetic aperture radar (SAR) sensor that is suitable for in situ observation of volcanic materials such as lava. As a result of fabricating the proposed antenna, the antenna characteristics were almost the same as the simulation one, with a center frequency of 5.394 GHz, IBW=32.6%, ARBW=23.7%, and Gain=12.0dBic. The measurement results indicate that the proposed antenna has loss and radiation characteristics suitable for the purpose. © 2023 IEEE.

关键词： Synthetic aperture radar

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Dynamic Resource Allocation in LTE Radio Access Network Using Machine Learning Techniques

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Journal of computer and Communications 2023年第6期11卷 73-93页

作者： Eric Michel Deussom Djomadji Ivan Basile Kabiena Valery Nkemeni Ayrton Garcia Belinga À Njere Michael Ekonde Sone Department of Electrical and Electronic Engineering College of Technology University of Buea Buea Cameroon Division of ICT National Advanced School of Post Telecommunication and ICT University of Yaounde I Yaounde Cameroon Department of Computer Engineering and Telecommunications National Advanced School of Engineering University of Douala Douala Cameroon Department of Computer Engineering Faculty of Engineering and Technology University of Buea Buea Cameroon

Current LTE networks are experiencing significant growth in the number of users worldwide. The use of data services for online browsing, e-learning, online meetings and initiatives such as smart cities means that subscribers stay connected for long periods, thereby saturating a number of signalling resources. One of such resources is the Radio Resource Connected (RRC) parameter, which is allocated to eNodeBs with the aim of limiting the number of connected simultaneously in the network. The fixed allocation of this parameter means that, depending on the traffic at different times of the day and the geographical position, some eNodeBs are saturated with RRC resources (overused) while others have unused RRC resources. However, as these resources are limited, there is the problem of their underutilization (non-optimal utilization of resources at the eNodeB level) due to static allocation (manual configuration of resources). The objective of this paper is to design an efficient machine learning model that will take as input some key performance indices (KPIs) like traffic data, RRC, simultaneous users, etc., for each eNodeB per hour and per day and accurately predict the number of needed RRC resources that will be dynamically allocated to them in order to avoid traffic and financial losses to the mobile network operator. To reach this target, three machine learning algorithms have been studied namely: linear regression, convolutional neural networks and long short-term memory (LSTM) to train three models and evaluate them. The model trained with the LSTM algorithm gave the best performance with 97% accuracy and was therefore implemented in the proposed solution for RRC resource allocation. An interconnection architecture is also proposed to embed the proposed solution into the Operation and maintenance network of a mobile network operator. In this way, the proposed solution can contribute to developing and expanding the concept of Self Organizing Network (SON) used in 4

关键词： RRC Resources 4G Network Linear Regression Convolutional Neural Networks Long Short-Term Memory Precision

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Acoustic characterization of a cavitation tunnel for ship propeller noise studies

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Journal of Ocean engineering and Science 2025年第3期10卷 330-341页

作者： Bocanegra, J.A. Borelli, D. Gaggero, T. Picó, R. Tani, G. DIME – Department of Mechanics Energetics Management and Transportation Thermal Engineering and Environmental Conditioning Division University of Genoa Via All'Opera Pia 15-A (I) 16145 Genoa Italy DITEN - Department of Electrical Electronic Telecommunication Engineering and Naval Architecture University of Genoa Genoa Italy Instituto de Investigación para la Gestión Integrada de Zonas Costeras (IGIC) Universitat Politècnica de València Paranimf 1 València Gandia 46730 Spain

The noise radiated by ship propellers can be studied by analyzing scaled models in a cavitation tunnel. However, transferring the measurements made in the enclosed space of the tunnel to the expected values in a full-scale propeller (in a free field) presents a challenge for noise measurements. An acoustic characterization of the DITEN cavitation tunnel, including a study of normal modes and acoustic response to harmonic sources, is presented to understand the primary acoustic behaviour of the enclosure and its propagation characteristics. A three-dimensional numerical model was developed to simulate the DITEN Genoa University cavitation tunnel and evaluate the influence of elements such as a dynamometer, fins, impeller, and source position on the acoustic response of the tunnel. The study showed the influence of the mentioned elements on the acoustic response of the tunnel, and the numerical predictions were validated by comparing them with the experimental campaign in the low-mid frequency range. The study provides valuable insights into the behaviour of sound waves in a cavitation tunnel and the influence of various factors, which can help design and optimize cavitation tunnels and related experiments. © 2024

关键词： Cavitation noise Cavitation tunnel Propeller noise Radiated noise Ship propellers Underwater noise

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Fish weight prediction using empirical and data-driven models in aquaculture systems

Fish weight prediction using empirical and data-driven model...

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Control Technology and Applications (CCTA),

作者： Xiao Chen Ibrahima N’Doye Fahad Aljehani Taous-Meriem Laleg-Kirati Computer Electrical and Mathematical Science and Engineering Division (CEMSE) King Abdullah University of Science and Technology (KAUST) Saudi Arabia Environmental Science and Engineering Program Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Saudi Arabia National Institute for Research in Digital Science and Technology (INRIA) Paris-Saclay France

ISBN: (数字)9798350370942

ISBN: (纸本)9798350370959

Predicting fish growth trajectory is crucial in achieving better control for the growing phase of the fish in aquaculture systems. However, fish biomass sensors that directly measure the fish’s weight are currently unavailable. Additionally, the fish growth model that relies on the fish weight is affected by environmental and feeding management factors, raising the difficulty of predicting the weight. This paper proposes comparative empirical and data-driven models for fish weight prediction. The empirical fish growth model relies directly on the growth ratio, an essential component for optimizing fish growth, while the data-driven model is based on long short-term memory (LSTM). Since the exact daily food ratio, proportional to the body weight of a growing fish, cannot be prescribed, we optimize the growth ratio of the empirical model in a sliding window framework based on the previous growth periods and then predict the fish weights a few days ahead through a nonlinear exponential regression approach. The LSTM method uses a normal distribution to generate multiple datasets over the experimental fish weight range and interpolate these data to provide a good prediction. To this end, we propose a weighted sum optimization method that combines the individual cost of the LSTM and empirical methods to achieve better short-term prediction performance. The simulation results demonstrate the effectiveness of the proposed models in predicting the fish weight and show that the combined weighted sum approach reduces the testing errors by 61.3% and 70.9% compared to the empirical and LSTM methods, respectively. Furthermore, an increasing specific growth rate (SGR) for both LSTM and combined models was observed in the short-term predictions.

关键词： Weight measurement Costs Biological system modeling Simulation Predictive models Fish Trajectory

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Rapid Amplification of Single-Stranded DNA by On-Bead Plasmonic PCR in an Automated Microfluidic System

Rapid Amplification of Single-Stranded DNA by On-Bead Plasmo...

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IEEE International Conference on Micro Electro Mechanical Systems

作者： Anni Hu Yang Bu Yuze Liu Yung Ching Lee Levent Yobas Division of Bioengineering The Hong Kong University of Science and Technology Hong Kong Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

This paper presents a novel automated microfluidic system for rapid plasmonic on-beads polymerase chain reaction (PCR). The highly integrated system automates essential PCR workflows, including sample loading, thermal cycling, purification washes, and elution utilizing pneumatic valves. Additionally, gold nanorods are introduced into the PCR system to leverage their efficient photothermal conversion properties to accelerate the PCR reaction. The platform completes 20 PCR cycles within 12 minutes to amplify single-stranded DNA (ssDNA), demonstrating potential for various rapid ssDNA PCR applications, such as aptamer selection and generation.

关键词： Gold Purification Loading DNA System integration Plasmons Valves Thermal loading Microfluidics Nanorods

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