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Variable Length Multi-Objective Whale Optimization for Trust Aware Data Gathering in Wireless Sensor Network

IEEE Access 2025年 13卷 32100-32121页

作者： Saad, Mohammed Ayad Jaafar, Rosmina Chellappan, Kalaivani Faculty of Engineering and Built Environment Department of Electrical Electronics and System Engineering Selangor Bangi43600 Malaysia Al-Kitab University Department of Medical Instrumentations Technique Engineering Kirkuk36001 Iraq Universitas Airlangga Biomedical Engineering Study Program Faculty of Science and Technology Surabaya60115 Indonesia

Efficient data collection in wireless sensor networks (WSNs) is crucial. While traditional approaches rely on stationary data sinks, the use of mobile sinks, like unmanned aerial vehicles (UAVs), has shown promise in enhancing data-gathering capabilities. However, existing methods often overlook the importance of trust, leaving the network susceptible to malicious or faulty nodes. To address this issue, we propose a novel trust-aware data-gathering algorithm, Variable Length Multi-Objective Whale Optimization Data Gathering (VLMOWO-DG). Our algorithm simultaneously optimizes energy consumption, delay, and trust while employing mobile sinks to collect data from sensor nodes. By considering trust as a key performance metric, we improve data reliability and security. The VLMOWO-DG algorithm introduces flexibility through variable-length solution representation and application-oriented operators, allowing for efficient exploration of the solution space. Simulation results demonstrate that our proposed algorithm significantly outperforms established benchmarks, NSGA-II and NSGA-III, in terms of domination and hypervolume. This leads to a remarkable improvement of 200% in data gathering performance. © 2015 The Authors.

关键词： Data collection

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Interfacial-Layer-Free Ge0.95Si0.05Nanosheet FeFETs

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IEEE Transactions on Electron Devices 2024年第3期71卷 1758-1763页

作者： Hsieh, Wan-Hsuan Chen, Yu-Rui Liu, Yi-Chun Zhao, Zefu Lee, Jia-Yang Tu, Chien-Te Huang, Bo-Wei Wang, Jer-Fu Lee, M.H. Liu, C.W. Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan Institute and Undergraduate Program of Electro-Optical Engineering Graduate School of Advance Technology National Taiwan University Taipei10617 Taiwan National Taiwan University Graduate School of Advance Technology Taipei10617 Taiwan Graduate Institute of Electronics Engineering Graduate Institute of Photonics and Optoelectronics Graduate School of Advanced Technology National Taiwan University Department of Electrical Engineering Taipei10617 Taiwan

The substantial memory window (MW) of 1.8 V is achieved by utilizing stacked two nanosheet (NS) gate-all-around (GAA) Ge0.95 Si0.05 ferroelectric field-effect transistors (FeFETs), employing a low write voltage of 2 V. The channel phosphorus concentration 1E18 cm-3 is used to erase the absence of interfacial layer (IL) can reduce the operation voltage. An isotropic wet etching process is employed for channel release. The stacked two NSs offer the advantages of reducing cell-to-cell variation and doubling the read current. Additionally, the stability of data storage is demonstrated with the data retention of >1E4 s, linearly extrapolated to a span of 10 years, and exhibiting high endurance >1E11 cycles. The thermal budget is as low as 400 °C. © 1963-2012 IEEE.

关键词： III-V semiconductors

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Measurement of renal blood flow in a rat model using diffuse correlation spectroscopy

Measurement of renal blood flow in a rat model using diffuse...

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Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: Biomedical Optics

作者： Nakabayashi, Mikie Ono, Yumie Electrical Engineering Program Graduate School of Science and Technology Meiji University Kanagawa Japan Department of Electronics and Bioinformatics School of Science and Technology Meiji University Kanagawa Japan

ISBN: (纸本)9781957171340

关键词： Detectors Laser sources Lasers Near infrared Photon counting Spectroscopy

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Optimal Location to Use Solar Energy in an Urban Situation

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Computers, Materials & Continua 2023年第4期75卷 815-829页

作者： Ngakan Ketut Acwin Dwijendra Indrajit Patra N.Bharath Kumar Iskandar Muda Elsayed M.Tag El Din Faculty of Engineering Udayana UniversityBali80361Indonesia An Independent Researcher NIT DurgapurWest Bengal700027India Department of Electrical and Electronics Engineering Vignan’s Foundation for Science Technology and ResearchGuntur522001India Department of Doctoral Program Faculty Economic and BusinessUniversitas Sumatera UtaraMedan20222Indonesia Electrical Engineering Department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt

This study conducted in Lima, Peru, a combination of spatial decisionmaking system and machine learning was utilized to identify potentialsolar power plant construction sites within the city. Sundial measurementsof solar radiation, precipitation, temperature, and altitude were collectedfor the study. Gene Expression programming (GEP), which is based on theevolution of intelligent models, and Artificial Neural Networks (ANN) wereboth utilized in this investigation, and the results obtained from each werecompared. Eighty percent of the data was utilized during the training phase,while the remaining twenty percent was utilized during the testing phase. Onthe basis of the findings, it was determined that the GEP is the most suitablenetwork for predicting the location. The test state’s Nash-Sutcliffe efficiency(NSE) was 0.90, and its root-mean-square error (RMSE) was 0.04. Followingthe generation of the final map based on the results of the GEP model, itwas determined that 9.2% of the province’s study area is suitable for theconstruction of photovoltaic solar power plants, while 53.5% is acceptable and37.3% is unsuitable. The ANN model reveals that only 1.7% of the study areais suitable for the construction of photovoltaic solar power plants, while 66.8%is acceptable and 31.5% is unsuitable.

关键词： Solar energy renewable energy machine learning

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Phase-transition-free rivets for layered oxide potassium cathodes

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Nano Research 2024年第11期17卷 9671-9678页

作者： Jie Chen Apparao M. Rao Caitian Gao Jiang Zhou Limei Cha Xiaoming Yuan Bingan Lu School of Physics and Electronics Hunan University Changsha 410082 China Department of Physics and Astronomy Clemson Nanomaterials Institute Clemson University Clemson SC USA School of Materials Science and Engineering Central South University Changsha 410083 China Materials Science and Engineering program Guangdong Technion-Israel Institute of Technology Shantou 515063 China Materials Science and Engineering program Technion-Israel Institute of Technology Haifa 32000 Israel MATEC Key Lab Guangdong Technion-Israel Institute of Technology Shantou 515063 China Hunan Key Laboratory of Nanophotonics and Devices School of Physics Central South University Changsha 410078 China

As a cathode material for potassium-ion batteries (PIBs), manganese-based layered oxides have attracted widespread attention due to their low cost, ease of synthesis, and high performance. However, the Jahn-Teller effect caused by Mn3+ and the irreversible phase transformation of the structure leads to poor cycle stability, limiting the development of layered oxides in PIBs. Herein, we demonstrate the use of phase-transition-free CaTiO_(3) as rivets in K_(0.5)Mn_(0.9)Ti_(0.1)O_(2) by a simple solid-state method. As verified by the in situ X-ray diffraction, the CaTiO_(3) rivets effectively prevent the slippage of the transition metal layer during charge and discharge, inhibiting structural degradation. As a result, the obtained K_(0.5)Mn_(0.9)Ti_(0.1)O_(2)-0.02CaTiO_(3) shows excellent cycling stability and rate performance, with high capacities of 119.3 and 70.1 mAh·g^(-1) at 20 and 1000 mA·g^(-1), respectively. At 200 mA·g^(-1), the capacity retention remains 94.7% after more than 300 cycles. This work represents a new avenue for designing and optimizing layered cathode materials for PIBs and other batteries.

关键词： potassium cathodes layered oxide phase-transition-free perovskite rivets

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A methodology for coffee price forecasting based on extreme learning machines

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Information Processing in Agriculture 2022年第4期9卷 556-565页

作者： Carolina Deina Matheus Henrique do Amaral Prates Carlos Henrique Rodrigues Alves Marcella Scoczynski Ribeiro Martins Flavio Trojan Sergio Luiz Stevan Jr Hugo Valadares Siqueira Department of Production Engineering Federal University of Rio Grande do Sul90.035-190Brazil Department of Electronics Federal University of TechnologyParana 84017-220Brazil Graduate Program in Production Engineering Federal University of TechnologyParana 84017-220Brazil

This work introduces a methodology to estimate coffee prices based on the use of Extreme Learning *** process is initiated by identifying the presence of nonstationary components,like seasonality and *** components are withdrawn if they are ***,the temporal lags are selected based on the response of the Partial Autocorre-lation Function *** predictors,we address the following models:Exponential Smooth-ing(ES),Autoregressive(AR)and Autoregressive Integrated and Moving Average(ARIMA)models,Multilayer Perceptron(MLP)and Extreme Learning Machines(ELMs)neural *** computational results based on three error metrics and two coffee types(Ara-bica and Robusta)showed that the neural networks,especially the ELM,can reach higher performance levels than the other *** methodology,which presents preprocess-ing stages,lag selection,and use of ELM,is a novelty that contributes to the coffee prices forecasting field.

关键词： Coffee price forecasting Linear models Artificial neural networks Computational intelligence

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Mini-YOLOX: A Lightweight Network for Real-Time Embedded Applications 66

Mini-YOLOX: A Lightweight Network for Real-Time Embedded App...

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2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： El-Zeiny, Ahmed N. Hassan, Adham Mostafa, Hassan Khalil, Ahmed H. Cairo University Electronics and Communications Engineering Department Egypt University of Science and Technology Nanotechnology and Nanoelectronics Program Zewail City of Science and Technology Giza12578 Egypt

ISBN: (纸本)9798350302103

In this paper, we present an efficient real-time object detector designed for embedded system applications. Although increased memory and power consumption pose significant challenges, our network adopts techniques to overcome them. Depthwise and pointwise separable convolutions are used across the backbone, neck, and head of the network to reduce the number of parameters. To compensate for accuracy loss, we employ several techniques including starting with a larger baseline, using depthwise separable convolution mixed with blueprint separable convolution in the backbone, replacing the SiLU activation function with the Mish activation function, and switching from Adam to SGD optimization algorithms. These techniques help to achieve a state-of-the-art object detection network called Mini-YOLOX, which optimizes YOLOX-S from the YOLOX series and reaches only ∽57% of its parameter size and ∽52% of its floating-point operations with better accuracy. Mini-YOLOX achieves impressive results with only 5.08M parameters and 13.95 GFLOPs, resulting in a 40.9 AP score in just 5.71 ms. © 2023 IEEE.

关键词： convolutional neural network (CNN) embedded applications Real-time object detector YOLOX

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A Comparative Study of DeepLabCut and Other Open-Source Pupillometry Data Analysis Algorithms – Which to Choose?

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Machine Graphics and Vision 2024年第2期33卷 77-90页

作者： Badkul, Amitesh Mishra, Sonakshi Kommajosyula, Srinivasa P. Department of Electrical & Electronics Engineering Birla Institute of Technology and Science Hyderabad Campus Jawahar Nagar Hyderabad India Department of Pharmacy Birla Institute of Technology and Science Hyderabad Campus Jawahar Nagar Hyderabad India Ph.D. Program in Computer Science The Graduate Center The City University of New York New YorkNY United States

Pupillometry measures pupil size, and several open-source algorithms are available to analyse pupillometry data. However, only a few studies compared these algorithms’ accuracy and computational resources. This study aims to compare the accuracy of computer vision-based algorithms (Swirski, Starburst, PuRe, ElSe, ExCuSe algorithms) and the machine learning algorithm, DeepLabCut, to the double-blinded human examiners (gold-standard). Training of DeepLabCut with different architectures and a variable number of markers (2-9 markers) was done on an open-source dataset. The duration of training was statistically longer for the ResNet152 model compared to the MobileNet model. The pupil diameters in computer vision-based software such as PuRe, Starburst, and Swirski were statistically different from human measurements. MobileNet 2 and 3 marker models were the closest to the human measurements. In conclusion, this work highlights the efficiency of lower marker models based on MobileNet architecture in DeepLabCut, which consumes fewer computational resources and is more accurate. © 2024 Institute of Information technology, Warsaw University of Life Sciences - SGGW. All rights reserved.

关键词： Adversarial machine learning

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The Effect of Vehicles Speed on the Performance of VANET Protocol 1

The Effect of Vehicles Speed on the Performance of VANET Pro...

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1st International Conference on Advanced engineering and Technologies, ICONNIC 2023

作者： Qasim, Osama A. Mahdi, Dania Mohammed Noori, Mohammed S. Ali, Rabei Raad Sari, Christy Atika Rachmawanto, Eko Hari Northern Technical University Department of Computer Engineering Technology Mosul Iraq Uniersiti Tenaga Nasional Department of Electrical and Electronics Engineering Selangor Malaysia University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia

ISBN: (纸本)9798350306484

At present, ad hoc networks are getting a lot of attention, because they have many features that differ from the rest of the networks and because they are technically advanced. In routing protocols, the Vehicular Ad-hoc network (VANET) differ in performance because each protocol has different variables, such as variable density, speed, and traffic scenarios. In this work, we proposed topology-based routing namely Ad hoc On Demand Distance Vector (AODV) protocol. The AODV protocol has been simulated in case of the movement of vehicles at a fixed velocity at 60km/h, and in case of randomly changing the vehicle's locations in the network. There are three parameters used to investigate the performance of the proposed AODV protocol which are packet delivery ratio, and overheads. The Simulation results show a significant decrease from 0.55 to 0.82 in the packet delivery ratio. On the other hand, the performance of the AODV protocol demonstrates effective communication for the VANETs. © 2023 IEEE.

关键词： Vehicular ad hoc networks

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Comparative Assessment of Off-shore Wind Converters and Wave Energy Converters in the Philippines 15

Comparative Assessment of Off-shore Wind Converters and Wave...

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15th IEEE International Conference on Humanoid, Nanotechnology, Information technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Alquiza, Laurence Keith P. Recto, King Harold A. Loresco, Pocholo James M. Villalobos, Jesus Ateneo de Manila University Department of Electronics Computer and Communications Engineering Quezon City Philippines Feu Institute of Technology Department of Electronics Engineering Manila Philippines Institut Catholique d'Arts et Métiers International Program in General Engineering Toulouse France

ISBN: (纸本)9798350381177

The Republic of the Philippines is confronted with rebuilding its energy landscape, which now depends heavily on imported fossil fuels for a substantial supply. The department of Energy (DOE) has established lofty objectives to enhance the nation's renewable energy (RE) capability;nevertheless, these objectives are still to be achieved. This research supports DOE's goals by studying other possible renewable energy sources. In particular, the primary aim of this research is to examine the viability of Offshore Wind Converters (OWCs) and Wave Energy Converters (WECs) as viable sustainable energy options for the Philippines. Ocean wave converters (OWCs) provide inherent benefits in terms of dependability and have widespread societal acceptance. Conversely, wave energy converters (WECs) harness the vast energy potential contained within ocean waves. A comparative evaluation was undertaken to analyze the differences between these two potential renewable energy sources. The assessment concludes that OWCs possess a minor advantage over WECs regarding their economic viability and higher societal acceptability. It recommended that the government adopts a diversified energy portfolio, which may include the incorporation of WECs to effectively navigate the changing dynamics of the energy sector, enhance sustainability, and ensure the long-term security of the nation's energy supply. © 2023 IEEE.

关键词： Water waves

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