检索结果-内蒙古大学图书馆

Optimizing land mine detection across diverse mining environments: A hyperspectral data approach with regression models

引用

International Journal of Intelligent Networks 2024年第1期5卷 351-363页

作者： Anand, R. Andrew, J. Makki, Ihab Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka 576104 India Department of Nouvelle Aquitaine France

The detection of landmines, namely anti-tank mines, explosive devices, and unexploded ordnance, is a formidable obstacle for the global community. The visible consequences of unobserved explosives in communities affected by war are characterized by significant devastation and human suffering. In order to effectively tackle this matter, it is imperative to use proactive strategies that focus on the identification and mitigation of these perilous substances prior to their potential infliction of harm. Nevertheless, the majority of current solutions exhibit significant deficiencies, such as exorbitant expenses, inefficiencies, and apprehensions over accuracy. These drawbacks are further compounded by the inherent trade-offs that exist between these elements, where improvements in one area often come at the expense of another. Contrarily, recent breakthroughs in the areas of deep learning, unmanned aerial vehicles, and sensor technologies are being recognized as potentially transformative elements in the domain of landmine identification and removal. This paper presents a thorough examination of recent scholarly investigations that integrate computerized technology in the field of landmine detection. To the extent of our current understanding, there has been no prior investigation that has thoroughly examined this particular domain. The main aim of this study is to investigate the incorporation of machine learning based regression methods in the field of landmine detection. The study specifically emphasizes the identification and resolution of existing issues that hinder the development of efficient automated solutions, hence enhancing performance optimization. The Sum of Sine Curve Fit Regression Model is proposed and proved a powerful and adaptable tool for extracting relevant information from this hyperspectral images. © 2024 The Authors

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Impedance Sensing in CMOS-Embedded Microfluidics Using BEOL Electrodes

引用

Journal of Microelectromechanical Systems 2024年第1期33卷 110-117页

作者： Weng, Wei-Yang Chien, Jun-Chau Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan University of California at Berkeley Department of Electrical Engineering and Computer Sciences BerkeleyCA94720 United States

This paper describes a novel CMOS-embedded microfluidics platform featuring on-chip impedance-sensing electrodes. The platform employs a single-step wet etching process, removing the CMOS back-end-of-line (BEOL) routing metals, to create hollow fluidic channels that can be closely integrated with active circuits. We optimize the process parameters and improve the etch rate by 10Í through screening different metal etchants and applying hydraulic pressure to enhance the etchant byproduct diffusion rates. To integrate on-chip electrodes for impedance sensing, we explore various strategies and present 'via' electrodes that maintain their integrity in the etching process while preserving detection sensitivity. We also investigate the long-term reliability of the platform. Finally, we demonstrate the efficacy of impedance sensing using ionic solutions of varying strengths. [2023-0119] © 1992-2012 IEEE.

关键词： Microfluidics

来源：评论

学校读者我要写书评

暂无评论

Automated Classification of Snow-Covered Solar Panel Surfaces Based on Deep Learning Approaches

引用

computer Modeling in engineering & Sciences 2023年第9期136卷 2291-2319页

作者： Abdullah Ahmed Al-Dulaimi Muhammet Tahir Guneser Alaa Ali Hameed Mohammad Shukri Salman Department of Electrical and Electronics Engineering Karabuk UniversityKarabuk78100Turkey Department of Computer Engineering Istinye UniversityIstanbul34303Turkey College of Engineering and Technology American University of the Middle EastEgaila54200Kuwait

Recently,the demand for renewable energy has increased due to its environmental and economic *** panels are the mainstay for dealing with solar energy and converting it into another form of usable *** panels work under suitable climatic conditions that allow the light photons to access the solar cells,as any blocking of sunlight on these cells causes a halt in the panels work and restricts the carry of these ***,the panels are unable to work under these conditions.A layer of snow forms on the solar panels due to snowfall in areas with low ***,it causes an insulating layer on solar panels and the inability to produce electrical *** detection of snow-covered solar panels is crucial,as it allows us the opportunity to remove snow using some heating techniques more efficiently and restore the photovoltaics system to proper *** paper presents five deep learning models,■-16,■-19,ESNET-18,ESNET-50,and ESNET-101,which are used for the recognition and classification of solar panel *** this paper,two different cases were applied;the first case is performed on the original dataset without trying any kind of preprocessing,and the second case is extreme climate conditions and simulated by generating motion ***,the dataset was replicated using the upsampling technique in order to handle the unbalancing *** conducted dataset is divided into three different categories,namely;all_snow,no_snow,and partial *** fivemodels are trained,validated,and tested on this dataset under the same conditions 60%training,20%validation,and testing 20%for both *** accuracy of the models has been compared and verified to distinguish and classify the processed *** accuracy results in the first case showthat the comparedmodels■-16,■-19,ESNET-18,and ESNET-50 give 0.9592,while ESNET-101 gives *** the second case,the models outperformed their counterparts in the first case by evaluating performan

关键词： Deep learning CNN models image classification solar panels solar panel defect detection

来源：评论

学校读者我要写书评

暂无评论

An authenticated spectrum acquirement and transmission of encrypted images over fusion centre

引用

Multimedia Tools and Applications 2024年 1-24页

作者： V, Thanikaiselvan Y, PiyaDessi S, Renuga devi Shanthini, S. Suba Mahalingam, Hemalatha S, Aashiq Banu R, Dhivya V.M, Padmapriya Amirtharajan, Rengarajan School of Electronics Engineering Vellore Institute of Technology Vellore Vellore India School of Information Technology Vellore Institute of Technology Vellore India Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah22254 Saudi Arabia System Engineering Department Military Technological College Muscat Oman Department of Electronics and Communication Engineering Mepco Schlenk Engineering College Tamilnadu Sivakasi626005 India Department of Computer Sciences Marquette University MilwaukeeWI53233 United States School of Electrical & amp Electronics Engineering SASTRA Deemed University 613 401 Thanjavur India

These days, spectrum insufficiency is the most common issue. If the number of smartphone users who access the radio spectrum grows, the radio spectrum becomes increasingly scarce. Certain types of technology equipment are designed to work only within a specific spectrum range, and there is rivalry in specifications for various other applications. As a result, this paper effectively exploits a licensed user’s unused spectrum to reduce the crowded spectrum and eradicate the Radio spectrum shortage. Licensed primary users (PUs) and secondary users (SUs) would be accepted into a Fusion Centre (FC) based on a spectrum framework with authentication. The approved PUs begin by updating their information to FC1;thenceforth, encryption is performed on the information uploaded to FC1. This information from FC1 is stored in Cloud Storage after encryption using the Blowfish algorithm & De-oxy DNA, a fast encryption algorithm. Following that, FC2, connected to the same cloud environment, downloads the PU data uploaded. The spectrum is then allocated to the authenticated SU linked to FC2 based on signal conditions and priority. This proposed method also avoids any delay in the network and encryption by acquiring a fast encryption algorithm, making the system faster and avoiding the Primary User Emulation (PUE) attack. Later, the time taken for the Tiny Encryption Algorithm (TEA) and the Blowfish/DNA algorithm are compared to validate the run time for different input images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

High-Performance Flow Classification of Big Data Using Hybrid CPU-GPU Clusters of Cloud Environments

引用

Tsinghua Science and Technology 2024年第4期29卷 1118-1137页

作者： Azam Fazel-Najafabadi Mahdi Abbasi Hani H.Attar Ayman Amer Amir Taherkordi Azad Shokrollahi Mohammad R.Khosravi Ahmed A.Solyman Department of Computer Engineering Faculty of EngineeringBu-Ali Sina UniversityHamedan ***Iran Department of Energy Engineering Zarqa UniversityZarqa 13132Jordan Department of Informatics University of OsloOslo 0316Norway Department of Computer Science MalmöUniversityMalmö20506Sweden Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 261100China Department of Electrical and Electronics Engineering NişantaşıUniversityIstanbul 34481742Türkiye

The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific flows by matching them against a set of dynamic rules. This basic process accelerates the processing of data, so that instead of processing singular packets repeatedly, corresponding actions are performed on corresponding flows of packets. In this paper, first, we address limitations on a typical packet classification algorithm like Tuple Space Search (TSS). Then, we present a set of different scenarios to parallelize it on different parallel processing platforms, including Graphics Processing Units (GPUs), clusters of Central Processing Units (CPUs), and hybrid clusters. Experimental results show that the hybrid cluster provides the best platform for parallelizing packet classification algorithms, which promises the average throughput rate of 4.2 Million packets per second (Mpps). That is, the hybrid cluster produced by the integration of Compute Unified Device Architecture (CUDA), Message Passing Interface (MPI), and OpenMP programming model could classify 0.24 million packets per second more than the GPU cluster scheme. Such a packet classifier satisfies the required processing speed in the programmable network systems that would be used to communicate big medical data.

关键词： OpenMP Compute Unified Device Architecture(CUDA) Message Passing Interface(MPI) packet classification medical data tuple space algorithm Graphics Processing Unit(GPU)cluster

来源：评论

学校读者我要写书评

暂无评论

Secure speaker identification in open and closed environments modeled with symmetric comb filters

引用

Multimedia Tools and Applications 2025年第18期84卷 19147-19189页

作者： Shafik, Amira Monir, Mohamad El-Shafai, Walid Khalaf, Ashraf A. M. Nassar, M.M. El-Fishawy, Adel S. El-Din, M. A. Zein Dessouky, Moawad I. El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Minia University Minia61519 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Speech is a fundamental means of human interaction. Speaker Identification (SI) plays a crucial role in various applications, such as authentication systems, forensic investigation, and personal voice assistance. However, achieving robust and secure SI in both open and closed environments remains challenging. To address this issue, researchers have explored new techniques that enable computers to better understand and interact with humans. Smart systems leverage Artificial Neural Networks (ANNs) to mimic the human brain in identifying speakers. However, speech signals often suffer from interference, leading to signal degradation. The performance of a Speaker Identification System (SIS) is influenced by various environmental factors, such as noise and reverberation in open and closed environments, respectively. This research paper is concerned with the investigation of SI using Mel-Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients, with an ANN serving as the classifier. To tackle the challenges posed by environmental interference, we propose a novel approach that depends on symmetric comb filters for modeling. In closed environments, we study the effect of reverberation on speech signals, as it occurs due to multiple reflections. To address this issue, we model the reverberation effect with comb filters. We explore different domains, including time, Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST) domains for feature extraction to determine the best combination for SI in case of reverberation environments. Simulation results reveal that DWT outperforms other transforms, leading to a recognition rate of 93.75% at a Signal-to-Noise Ratio (SNR) of 15 dB. Additionally, we investigate the concept of cancelable SI to ensure user privacy, while maintaining high recognition rates. Our simulation results show a recognition rate of 97.5% at 0 dB using features extracted from speech signals and their DCTs. Fo

关键词： Speech enhancement

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Resilient Smart Power Grid Synchronization Estimation Method for System Resilience with Partial Missing Measurements

引用

CSEE Journal of Power and Energy Systems 2024年第3期10卷 1307-1319页

作者： Yi Wang Yanxin Liu Mingdong Wang Venkata Dinavahi Jun Liang Yonghui Sun School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Henan Engineering Research Center of Power Electronics and Energy Systems Zhengzhou 450001China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6G 2V4Canada School of Engineering Cardiff UniversityCardiff CF243AAUK College of Energy and Electrical Engineering Hohai UniversityNanjing 210098China IEEE

With the increasing demand for power system stability and resilience,effective real-time tracking plays a crucial role in smart grid ***,most studies have focused on measurement noise,while they seldom think about the problem of measurement data loss in smart power grid *** solve this problem,a resilient fault-tolerant extended Kalman filter(RFTEKF)is proposed to track voltage amplitude,voltage phase angle and frequency ***,a threephase unbalanced network’s positive sequence fast estimation model is ***,the loss phenomenon of measurements occurs randomly,and the randomness of data loss’s randomness is defined by discrete interval distribution[0,1].Subsequently,a resilient fault-tolerant extended Kalman filter based on the real-time estimation framework is designed using the timestamp technique to acquire partial data loss ***,extensive simulation results manifest the proposed RFTEKF can synchronize the smart grid more effectively than the traditional extended Kalman filter(EKF).

关键词： Dynamic state estimation Kalman filter partial missing measurements power systems smart grid synchronized measurements

来源：评论

学校读者我要写书评

暂无评论

Enhancing visual clarity in hazy media: a comprehensive approach through preprocessing and feature fusion attention-based dehazing

引用

Multimedia Tools and Applications 2025年第17期84卷 18071-18093页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

Particles in the atmosphere, such as dust and smoke, can cause visual clarity problems in both images and videos. Haze is the result of the interaction between airborne particles and light, which is scattered and attenuated. Hazy media present difficulties in a variety of applications due to the reduced contrast and loss of essential information. In response, dehazing techniques have been introduced to bring hazy videos and images back to clarity. Here, we provide a novel technique for eliminating haze. It comprises preprocessing steps before dehazing. Preprocessing is applied to hazy images through homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE). We present a dehazing technique referred to as the pre-trained Feature Fusion Attention Network (FFA-Net) that directly lets dehazed images be restored from hazy or preprocessed hazy inputs without requiring the determination of atmospheric factors, such as air light and transmission maps. The FFA-Net architecture incorporates a Feature Attention (FA) method to do this task. We assess the proposed technique in a variety of circumstances, including visible frames, Near-Infrared (NIR) frames, and real-world hazy images. Evaluation criteria like entropy, correlation, and Peak Signal-to-Noise Ratio (PSNR) are used to compare the quality of dehazed frames or images to their hazy counterparts. Furthermore, histogram analysis and spectral entropy are adopted to determine the effectiveness of the proposed technique in comparison to existing dehazing techniques. Comparative results are presented for both real-world and simulated environments. The benefits of the proposed technique are demonstrated by a comparison of the results obtained from the standalone pre-trained FFA-Net and the proposed comprehensive methodology. Moreover, a thorough assessment is carried out for comparing the effectiveness of the proposed FFA-Net technique to those of some current dehazing techniques on real hazy images. T

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Physical-layer security in MU-MISO downlink networks against potential eavesdroppers

引用

Digital Communications and Networks 2025年第2期11卷 424-431页

作者： Woong Son Minkyu Oh Heejung Yu Bang Chul Jung C4I R&D Lab. LIG Nex1 Co.Ltd.Yongin16911South Korea Department of Electronics Engineering Chungnam National UniversityDaejeon34134South Korea Department of Electronics and Information Engineering Korea UniversitySejong30019South Korea Department of Electrical and Computer Engineering Ajou UniversitySuwon16499South Korea

Recently,wireless security has been highlighted as one of the most important techniques for 6G mobile communication *** researchers have tried to improve the Physical-Layer Security(PLS)performance such as Secrecy Outage Probability(SOP)and Secrecy Energy-Efficiency(SEE).The SOP indicates the outage probability that the data transmission between legitimate devices does not guarantee a certain reliability level,and the SEE is defined as the ratio between the achievable secrecy-rate and the consumed transmit *** this paper,we consider a Multi-User Multi-Input Single-Output(MU-MISO)downlink cellular network where a legitimate Base Station(BS)equipped with multiple transmit antennas sends secure information to multiple legitimate Mobile Stations(MSs),and multiple potential eavesdroppers(EVEs)equipped with a single receive antenna try to eavesdrop on this *** potential EVE tries to intercept the secure information,i.e.,the private message,from the legitimate BS to legitimate MSs with a certain eavesdropping *** securely receive the private information,each legitimate MS feeds back its effective channel gain to the legitimate BS only when the effective channel gain is higher than a certain threshold,i.e.,the legitimate MSs adopt an Opportunistic Feedback(OF)*** such eavesdropping channels,both SOP and SEE are analyzed as performance measures of PLS and their closed-form expressions are derived *** on the analytical results,it is shown that the SOP of the OF strategy approaches that of a Full Feedback(FF)strategy as the number of legitimate MSs or the number of antennas at the BS ***,the trade-off between SOP and SEE as a function of the channel feedback threshold in the OF strategy is *** analytical results and related observations are verified by numerical simulations.

关键词： Physical-layer security Opportunistic feedback Potential eavesdroppers Secrecy outage probability Secrecy energy-efficiency

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：