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Lightweight image super-resolution reconstruction based on mixed attention and global inductive bias network

Multimedia Tools and Applications 2024年 1-17页

作者： Cai, Yuxi Wen, Xiaopeng Wang, Tongguan College of Computer Science Sichuan University Chengdu China Department of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China College of Informatics Huazhong Agricultural University Wuhan China

Convolutional neural network has significantly advanced the field of image super-resolution reconstruction in recent years. The insufficient ability to model global information of hierarchical features, incomplete attention to information, and excessive parameters make some based on convolutional methods face challenges. To address these issues, this paper designs the lightweight image super-resolution reconstruction based on Mixed Attention and Global Inductive bias Network(MAGIN), which directly captures the global information of phased features from the horizontal and vertical directions respectively to produce global inductive bias information. Furthermore, the global spatial attention block is proposed, which can realize the perception of global feature space without redundant operation. To realize more comprehensive attention for features, a mixed attention block arises, which enables the network to attend to features across different dimensions: channel, spatial, and pixel levels. Extensive experimental results demonstrate that the MAGIN delivers more satisfactory results than some other advanced lightweight reconstruction algorithms on several standard test datasets under the condition of considering the network’s performance, parameter and computational complexity. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image reconstruction

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An Efficient Data Collection using Whale Optimization Algorithm (WOA) in Wireless Sensor Network 2

An Efficient Data Collection using Whale Optimization Algori...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Pasupathi, Udhayakumar Gopi Saminathan, A. Manikandan, A. Subhashini, M. Gowrishankar, C. Poornappriya, T.S. Department of Computer Science Tamilnadu India Npr College of Engineering and Technology Dindigul India Srm Institute of Science and Technology Chennai India Department of Computer Science Trichy India Kongunadu College of Engineering and Technology Department of Electrical and Electronics Engineering Tiruchirappalli621215 India Aspire Systems Pvt Ltd Karnataka 560037 India

ISBN: (纸本)9798350388916

Wireless sensor networks (WSNs) are an innovative technology that can be used in critical situations such as battlefields as well as commercial applications including buildings, traffic monitoring, smart homes, habitat monitoring, and more. A wireless sensor network (WSN) comprises small nodes capable of wireless sensing, processing, and communication. One of the most pressing concerns plaguing WSNs today is security. This paper suggests and investigates an energy-efficient cluster head (CH) selection approach using the Whale Optimization Algorithm (WOA) (called WOA-Clustering (WOA-C)) and the Geographic and Energy Aware Routing Algorithm (GEAR). As a result, the suggested method simplifies the decision-making process of energy-aware CHs by utilizing the fitness function that assesses node residual energy and the total energy of neighbouring nodes. Compared to the GWO-C, GABEEC, and PSOMSB protocols, the proposed WOA-GEAR protocol consumes 28%, 22%, and 7.5% less power. The suggested algorithm's network lifetime, power efficiency, buffer occupancy, throughput, and end-to-end latency were evaluated. © 2024 IEEE.

关键词： Decision making

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Pulsed Electromagnetic Field Interaction With a Transmission Line: An Analytical Traveling-Wave Approach Based on Reciprocity

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IEEE Transactions on Antennas and Propagation 2025年第6期73卷 3783-3791页

作者： Stumpf, Martin Antonini, Giulio Lager, Ioan E. Ekman, Jonas The Czech Republic Lerch Laboratory of em Research Department of Radio Electronics Feec Brno University of Technology Technická Brno3082/12 616 00 Czech Republic Luleå University of Technology Department of Computer Science Electrical and Space Engineering Eislab Luleå971 87 Sweden University of l'Aquila UAq Emc Laboratory Department of Industrial and Information Engineering and Economics L'Aquila671 00 Italy Delft University of Technology Terahertz Sensing Group Faculty of Electrical Engineering Mathematics and Computer Science Delft2628 CD Netherlands

Pulsed electromagnetic (EM) field signal transfer from a general EM source distribution to a transmission line (TL) is analyzed with the aid of Lorentz's reciprocity theorem. In this fashion, the transient voltage induced by the impulsive EM source is expressed through the EM fields as radiated by the TL. These transmitted EM fields are expressed in closed form using an analytical procedure that resembles the Cagniard-DeHoop (CdH) technique. The validity of the proposed reciprocity-based methodology is verified with the aid of an alternative analytical solution describing the EM field signal transfer excited by an impulsive vertical electric dipole (VED). Illustrative numerical examples are presented. © 1963-2012 IEEE.

关键词： Time domain analysis

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The Optimum Switching Bilateral of Various Mix Noise Filters to reduce Medical X-Ray Images 12

The Optimum Switching Bilateral of Various Mix Noise Filters...

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12th International electrical engineering Congress, iEECON 2024

作者： Langampol, Kriengkri Prayote, Akara Lee, Wilaiporn Srisomboon, Kanabadee Rajamangala University Of Technology Phra Nakhon Faculty Of Engineering Department Of Computer Engineering Thailand King Mongkut's University Of Technology North Bangkok Faculty Of Applied Science Department Of Computer And Information Science Thailand King Mongkut's University Of Technology North Bangkok Faculty Of Engineering Department Of Electrical And Computer Engineering Thailand

ISBN: (纸本)9798350383591

In the past few years, image processing has been widely adopted for symptom diagnosis of medical application. To achieve accurate analysis, the medical applications require high quality image for applying to the symptom diagnosis applications. However, the medical image quality may drop due to the imperfection of devices and the environment. Noise is one of the most critical factors that reduces the image quality where the noise may form by combining two types of noises. In this paper, we considered four well-known mixed noise filters including TF, BM3D, NCSR and OSBF filters. Since the OSBF filter is the alternative version of SBF filter that requires to determine the optimal weights, therefore we first determine the optimal weights for the medical image under the combination of Gaussian noise and salt-and-pepper noise. Then, we evaluate the filtering performance under three metrics including PSNR, SSIM and filtering time. As a result, OSBF filter achieves the best filtering performance since it can reduce the noise effect, preserve image details and consumes the short filtering time. On the other hand, the filtering performance of the TF filter suffers from the combining of salt-and-pepper noise. © 2024 IEEE.

关键词： Salt and pepper noise

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Mobile Charging Port for Electric Vehicles 4

Mobile Charging Port for Electric Vehicles

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Jeeva, C. Sagar, Lalit Kumar Bindu, K.V. Abinaya, R. Narmatha, M. Shanmuga Priya, M. Sri Sairam Engineering College Department of Electrical and Electronic Engineering Tamil Nadu India Srm Institute of Science and Technology Department of Computer Science Engineering Delhi India R.M.K. College of Engineering and Technology Department of Science and Humanities Tamil Nadu India

ISBN: (纸本)9798331508845

The "Mobile Charging Port for Electric Vehicles"addresses the logistical challenges inherent in the current electric vehicle (EV) charging infrastructure. This innovative solution introduces a solar-powered portable charging unit integrated with electric vehicles, designed to provide emergency power when the vehicle's battery is critically low and the driver is stranded away from established charging stations. The system dynamically calculates the distance between the vehicle's present location and its destination, determining the necessary charge to ensure the vehicle can reach its intended endpoint using a charge controller. By leveraging solar energy for the portable charger, this project enhances the reliability and convenience of EV usage, offering a sustainable and immediate solution to prevent and manage range anxiety in remote locations. © 2024 IEEE.

关键词： Charging stations

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Data Augmentation and Random Multi-Model Deep Learning for Data Classification

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computers, Materials & Continua 2023年第3期74卷 5191-5207页

作者： Fatma Harby Adel Thaljaoui Durre Nayab Suliman Aladhadh Salim EL Khediri Rehan Ullah Khan Computer Science Department Future Academy-Higher Future Institute for Specialized Technological StudiesEgypt Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawar25120Pakistan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Sciences Faculty of Sciences of GafsaUniversity of GafsaGafsaTunisia

In the machine learning(ML)paradigm,data augmentation serves as a regularization approach for creating ML *** increase in the diversification of training samples increases the generalization capabilities,which enhances the prediction performance of classifiers when tested on unseen *** learning(DL)models have a lot of parameters,and they frequently ***,to avoid overfitting,data plays a major role to augment the latest improvements in ***,reliable data collection is a major limiting ***,this problem is undertaken by combining augmentation of data,transfer learning,dropout,and methods of normalization in *** this paper,we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning(RMDL)which uses the association approaches of multi-DL to yield random models for *** present a methodology for using Generative Adversarial Networks(GANs)to generate images for data *** experiments,we discover that samples generated by GANs when fed into RMDL improve both accuracy and model *** across both MNIST and CIAFAR-10 datasets show that,error rate with proposed approach has been decreased with different random models.

关键词： Data augmentation generative adversarial networks classification machine learning random multi-model deep learning

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Identification of nonlinear systems in the frequency domain using two-dimensional Volterra series 10

Identification of nonlinear systems in the frequency domain ...

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10th International Conference on Control, Instrumentation and Automation, ICCIA 2024

作者： Soheili, Masoud Shafieirad, Mohsen Morovati, Tahereh Ronaghi, Erfan Kouhi University of Kashan Department of Electrical and Computer Engineering Kashan Iran Iran University of Science and Technology Department of Electrical Engineering Tehran Iran Shahed University Department of Electrical and Electronic Engineering Tehran Iran

ISBN: (纸本)9798331509972

This paper presents the identification of second-order two-dimensional kernels using the Volterra series model in the frequency domain. The study focuses on nonlinear systems where the input varies over both time and spatial dimensions. The Volterra series, known for its effectiveness in modeling complex nonlinear systems, is extended to two dimensions. The kernels are computed using the least squares method, with the input defined as a product of cosine functions. By simplifying the expressions and applying the Fourier transform, accurate kernel identification is achieved. The results demonstrate the efficacy of this approach in precisely matching the estimated system with the actual system, as evidenced by the minimal identification error. This work contributes to the development of advanced techniques for nonlinear system identification in industrial and scientific applications. © 2024 IEEE.

关键词： Frequency domain analysis

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Frequency Bandwidth Reduction of High-Frequency Intra-Body Propagation Characteristics Used for Attacker's Touch Detection in Fingerprint Authentication 12

Frequency Bandwidth Reduction of High-Frequency Intra-Body P...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Tomura, Shunichi Yamada, Koki Yoshida, Takahiro Tokyo University of Science Graduate School of Engineering Department of Electrical Engineering Tokyo Japan Tokyo University of Agriculture and Technology Department of Electrical Engineering and Computer Science Tokyo Japan Tokyo University of Science Faculty of Engineering Department of Electrical Engineering Tokyo Japan

ISBN: (纸本)9798350340181

In previous studies, we have proposed the attacker's touch detection method in fingerprint authentication using high-frequency intra-body propagation characteristics to detect attacks in that an attacker holds the user's finger/wrist and presses the user's finger to a fingerprint scanner. However, the previous method used a wide range of frequency bands from 300 kHz to 10 MHz, and this caused problems in the circuit scale, cost, and size of the device in development for practical use. Therefore, we searched effective frequency bands and applied K-nearest neighbor with data augmentation in this study. As results, we achieved a 99.1 % touch detection rate with a single frequency and a 99.9 % touch detection rate with a combination of two frequencies. We achieved a significant reduction in the frequency bandwidth of the propagation characteristics. © 2023 IEEE.

关键词： Bandwidth

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Distributed Energy Management for Ship-Integrated Energy System: Secure and Green Sailing

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 8189-8201页

作者： Zhang, Yuxin Xiao, Yang Teng, Fei Li, Tieshan Dalian Maritime University Navigation College Liaoning Dalian116026 China The University of Alabama Department of Computer Science TuscaloosaAL35487 United States Dalian Maritime University Marine Electrical Engineering College Liaoning Dalian116026 China University of Electronic Science and Technology of China School of Automation Engineering Sichuan Chengdu611731 China University of Electronic Science and Technology of China Huzhou313001 China

With increasing the awareness of environmentally friendly, the ship-integrated energy system (S-IES) combined with power and heating networks has become an upcoming trend in the shipbuilding industry. It decreases the consumption of fuel-based resources by integrating renewable energy resources to replace traditional fossil-based generators partly. Meanwhile, with many intelligent devices, the S-IES performs partitioned and autonomous features because of the unique operational structure. Therefore, the normal operation of S-IES is highly vulnerable, which will be bothered by the infeasibility-driven attack for shipboard devices and influenced by the uncertainties of renewable energy resources. To this end, the energy management problem (EMP) under cyber attacks for S-IES is focused on ensuring secure and green sailing in this article. To balance the number of nods and decrease the communication resources among energy devices, a partitioning algorithm is designed to construct the energy router (ER)-based energy hub (EH) for S-IES. Meanwhile, considering the tradeoff relationship between the economy and carbon emission, a multiobjective energy management model with feasible sailing constraints is designed. Then, to ensure the information exchange among EHs using ERs, a distributed resilient energy management strategy with a detection mechanism is proposed to acquire the optimal solutions under cyber attack. Finally, the theoretical analysis verifies the effectiveness of the designed distributed resilient strategy. Simulation results of different scenarios prove the great performance on ensuring secure and green sailing for ships by the constructed energy management model and its distributed resilient strategy. © 2015 IEEE.

关键词： Distributed energy

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MHANDTI: Drug-Target Interaction Prediction Model Based on Heterogeneous Graph Multi-Hop Attention Networks

MHANDTI: Drug-Target Interaction Prediction Model Based on H...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Chen Sun, Jiaqi Xing, Linlin Zhang, Longbo Cai, Hongzhen Guo, Maozu Shandong University of Technology School of Computer Science and Technology Zibo China Shandong University of Technology College of Agricultural Engineering and Food Science Zibo China Beijing University of Civil Engineering and Architecture College of Electrical and Information Engineering Beijing China

ISBN: (纸本)9798350386226

Predicting drug-target interactions (DTI) has become an important step in the drug discovery and drug repositioning process. The biological identification of DTI incurs significant financial and temporal costs, and the deployment of computational methodologies for DTI prediction can substantially curtail both the duration and economic expenditure of drug discovery or repositioning. Drawing from disparate data sources can offer a comprehensive overview and diverse perspectives for predicting drug-target interactions. However, existing methods for processing drug and target information are unable to capture long-range dependency across heterogeneous graphs. To capture more comprehensive drug and target features for DTI prediction, this article proposes a drug-target interaction prediction model based on heterogeneous graph multi-hop attention networks, called MHANDTI. Concretely, the word-level deep convolutional neural network is used to obtain the structural features of drugs and targets as attributes of drug and target entity nodes in heterogeneous graphs. For the processing of heterogeneous graphs, MHANDTI designs a multi-hop attention diffusion layer (MHADL) to establish a connection between the non-direct connection nodes, and summarize the characteristics of the adjacent nodes of the center nodes in several hops. A series of results indicate that the performance of this method is superior to the latest existing frameworks. © 2024 IEEE.

关键词： Convolutional neural networks

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