检索结果-内蒙古大学图书馆

Collaborative Service Provisioning for UAV-Assisted Mobile Edge Computing

Chinese Journal of Electronics 2024年第6期33卷 1504-1514页

作者： Yuben QU Zhenhua WEI Zhen QIN Tao WU Jinghao MA Haipeng DAI Chao DONG The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Colllege of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Xi'an Research Institute of High Technology Department of Information and Communication Noncommissioned Officer Academy of PAP National University of Defense Technology Department of Computer Science and Technology Nanjing University

Unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC), as a way of coping with delaysensitive and computing-intensive tasks, is considered to be a key technology to solving the challenges of terrestrial MEC networks. In this work, we study the problem of collaborative service provisioning(CSP) for UAV-assisted MEC. Specifically, taking into account the task latency and other resource constraints, this paper investigates how to minimize the total energy consumption of all terrestrial user equipments, by jointly optimizing computing resource allocation, task offloading, UAV trajectory, and service placement. The CSP problem is a non-convex mixed integer nonlinear programming problem, owing to the complex coupling of mixed integral variables and non-convexity of CSP. To address the CSP problem, this paper proposes an alternating optimization-based solution with the convergence guarantee as follows. We iteratively deal with the joint service placement and task offloading subproblem, and UAV movement trajectory subproblem, by branch and bound and successive convex approximation, respectively,while the closed form of the optimal computation resource allocation can be efficiently obtained. Extensive simulations validate the effectiveness of the proposed algorithm compared to three baselines.

关键词： Energy consumption Multi-access edge computing Collaboration Programming Autonomous aerial vehicles Minimization Trajectory Resource management Optimization Convergence

来源：评论

学校读者我要写书评

暂无评论

Acute Lymphoblastic Leukemia Detection Using DenseNet Model from Microscopic Blood Smear Images

Acute Lymphoblastic Leukemia Detection Using DenseNet Model ...

引用

International Conference on Data Science and Network engineering, ICDSNE 2023

作者： Prasath, Navamani Jasthi, Monika Saikia, Rabul Loganathan, Muralidaran Devi, Salam Shuleenda Department of Electronics and Communication Engineering NIT Meghalaya Meghalaya Shillong793003 India Department of Computer Science and Information System Engineering St. Joseph University Dae Es Salaam Tanzania United Republic of

ISBN: (纸本)9789819967544

Acute lymphoblastic leukemia (ALL) is a cancerous condition that affects the bone marrow and blood. It is a fast developing illness that, if not identified and treated as soon as possible, could be fatal. ALL is often identified by looking at blood and bone marrow smears under a microscope. Leukemia can be detected and classified using detailed cytochemical tests. However, these procedures are expensive, time-consuming, and dependent on the knowledge and skills of the specialists involved. Using image processing techniques that examine microscopic blood smear images to search for the leukemic cells, leukemia can be detected. These methods are simple, quick, cost-effective, and unaffected by the judgments of experts. The suggested study describes a computer-aided diagnosis method that uses deep convolutional neural networks (CNNs) that have already been trained to compare leukemia images to normal images. The public dataset ALL-IDB 2 was used for the proposed research. The study uses the pre-trained model DenseNet-201 for performing the classification. With the DenseNet201 pre-trained networks employed in the study for the ALL_IDB2 dataset, a classification accuracy of 94.6% is achieved. In all of the classifications carried out, optimization strategies such as cross-validation, fine-tuning, and real-time augmentation are also compared. Also use Pre-trained series models like ResNet-50, VGG-19, Inceptionv3, MobileNet-v2, Xceptionv3, and VGG-16 for performing the comparison. The experimental result gives an improvement in accuracy (17.76, 10.6, and 13.2%) in comparison to the other approaches namely, residual neural network, customized combined CNN, and conVNet neural network, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computer aided diagnosis

来源：评论

学校读者我要写书评

暂无评论

Fine classification of rice paddy using multitemporal compact polarimetric SAR C band data based on machine learning methods

引用

Frontiers of Earth Science 2024年第1期18卷 30-43页

作者： Xianyu GUO Junjun YIN Kun LI Jian YANG Huimin ZOU Fukun YANG School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China College of Global Change and Earth System Science Beijing Normal UniversityBeijing 100875China

Rice is an important food crop for human *** distinguishing different varieties and sowing methods of rice on a large scale can provide more accurate information for rice growth monitoring,yield estimation,and phenological monitoring,which has significance for the development of modern *** polarimetric(CP)synthetic aperture radar(SAR)provides multichannel information and shows great potential for rice monitoring and ***,the use of machine learning methods to build classification models is a controversial *** this paper,the advantages of CP SAR data,the powerful learning ability of machine learning,and the important factors of the rice growth cycle were taken into account to achieve high-precision and fine classification of rice ***,CP SAR data were simulated by using the seven temporal RADARSAT-2 C-band data ***,20-two CP SAR parameters were extracted from each of the seven temporal CP SAR data *** addition,we fully considered the change degree of CP SAR parameters on a time scale(ΔCP_(DoY)).Six machine learning methods were employed to carry out the fine classification of rice *** results show that the classification methods of machine learning based on multitemporal CP SAR data can obtain better results in the fine classification of rice paddies by considering the parameters ofΔCP_(DoY).The overall accuracy is greater than 95.05%,and the Kappa coefficient is greater than *** them,the random forest(RF)and support vector machine(SVM)achieve the best results,with an overall accuracy reaching 97.32%and 97.37%,respectively,and Kappa coefficient values reaching 0.965 and 0.966,*** the two types of rice paddies,the average accuracy of the transplant hybrid(T-H)rice paddy is greater than 90.64%,and the highest accuracy is 95.95%.The average accuracy of direct-sown japonica(D-J)rice paddy is greater than 92.57%,and the highest accuracy is 96.13%.

关键词： compact polarimetric(CP)SAR rice paddy machine learning fine classification multitemporal

来源：评论

学校读者我要写书评

暂无评论

ANFIS-based Multilayered Algorithm for Botnet Detection

ANFIS-based Multilayered Algorithm for Botnet Detection

引用

2023 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous communication, and Computational Intelligence, RAEEUCCI 2023

作者： Thamaraimanalan, T. Venkatesan, C. Ramkumar, M. Sivaramakrishnan, A. Marimuthu, M. Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Hkbk College of Engineering Department of Electronics and Communication Engineering Bangalore India Knowledge Institute of Technology Department of Computer Science and Business System Salem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra pradesh India Sona College of Technology Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798350337426

In this study, a multilayered botnet detection system for network-based botnet detection based on ANFIS is suggested. Two layers make up the suggested system, one of which is utilized for feature extraction and the other for botnet detection. Using mean and variance, the feature extraction layer pulls features from network traffic data. The botnet detection layer employs ANFIS to identify botnet activity based on the collected features. Using the CICIDS2017 dataset, the suggested system's performance is assessed. The experimental findings demonstrate that the suggested algorithm works better than other current botnet detection techniques regarding accuracy, precision, and recall. The proposed system obtained 99.83% accuracy, 99.75% precision, and 99.91 % recall. The proposed multilayered ANFIS-based botnet detection system offers an excellent way to identify botnet activity in a network. It can be utilized in various network security applications, such as threat intelligence, malware detection, and intrusion detection. © 2023 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

A new symbol timing and carrier frequency offset estimation algorithm for OTFS for B5G 2

A new symbol timing and carrier frequency offset estimation ...

引用

2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Thangavel, Gunasekaran Rajan, Sheeba Subbulekshmi, S. Anthonisamy, Arun Ganapathi, Rohini Velankanni, Gowri University of Technology and Applied Sciences Department of Electronics and Communication Engineering Muscat Oman SRM Institute of Science and Technology School of Computing Tiruchirapalli India Prathyusha Engineering College Department of Electronics and Communication Engineering Chennai India Panimalar Engineering College Department of Computer Science and Business System Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Chennai India K. Ramakrishnan College of Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798331534950

In B5G networks, accurate symbol timing estimate and estimation of the carrier frequency offset are both absolutely necessary. Particularly in highly mobile and interference-ridden settings, these factors are of paramount importance for sustaining high-throughput, dependable wireless communication. Estimating symbol timing and carrier frequency offset in OTFS can be difficult due to factors including the time-varying nature of Doppler shifts and interference from surrounding cells as well as the non-stationary nature of wireless channels. In such changing settings, traditional algorithms often fail to produce reliable estimations. To account for frequency changes caused by things like Doppler effects and drift, this research uses Machine Learning-Based Carrier Frequency Prediction Analysis (ML-CFPA) to implement a dynamic carrier frequency offset estimation and correction system. To preserve signal coherence and lower interference in B5G settings, carrier frequency offset adjustment is essential. communication solutions with high performance are required for these applications to ensure they can function normally despite the complexity and changeability of their contexts. The outcomes demonstrate the higher performance of the algorithm in contrast to conventional approaches, highlighting its applicability to B5G networks. Its potential as a crucial enabling technology for the B5G future has been solidified by these simulations, which verify its capacity to maintain reliable communication under a variety of real-world conditions. © 2024 IEEE.

关键词： Graphical user interfaces

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation of Bit-String Normalisation of Fingerprint Database

Performance Evaluation of Bit-String Normalisation of Finger...

引用

2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Bhargava, Neeraj Rathore, Pramod Singh Goswami, Ankur Chauhan, Kapil Panda, Sankarsan School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India Department of Computer Applications Govt. Engineering College Bikaner India Aryabhattt College of Engieering and research Centre India MDS University Ajmer India

ISBN: (纸本)9798350374933

This study presents a comprehensive methodology for fingerprint analysis, employing a sequence of advanced image processing techniques including preprocessing, skeletonization, and minutiae extraction, leading to the generation of a digital bit-string representation. By enhancing image quality through histogram equalization and adaptive thresholding, and by refining fingerprint ridge patterns to single pixel widths via skeletonization, the proposed techniques facilitate precise minutiae extraction. These steps significantly improve the accuracy and reliability of biometric identification systems, as demonstrated by the dense and accurate distribution of minutiae points. The resultant digital encoding supports efficient data storage and rapid matching processes, essential for robust biometric verification. This research underscores the potential of these methodologies to enhance the performance of fingerprint-based security systems and lays the groundwork for future integrations with machine learning for automated and scalable biometric solutions. © 2024 IEEE.

关键词： Musculoskeletal system

来源：评论

学校读者我要写书评

暂无评论

Fingerprint Image Enhancement: Multi-Filter Approaches and Stage Timing Evaluation 4

Fingerprint Image Enhancement: Multi-Filter Approaches and S...

引用

4th International Conference on Sustainable Expert systems, ICSES 2024

作者： Bhargava, Neeraj Rathore, Pramod Singh Goswami, Ankur Chauhan, Kapil Choudhary, Suresh School of Engineering & System Sciences Mds University Ajmer India Manipal University Department of Computer and Communication Engineering Jaipur India Govt. Engineering College Department of Computer Applications Bikaner India Aryabhattt College of Engieering and research Centre India Mds University Ajmer India

ISBN: (纸本)9798331540364

The performance of fingerprint recognition and verification systems heavily depends on the quality of the input fingerprint images. This paper introduces a multi-filter image enhancement strategy tailored for enhancing low-quality fingerprint images. The objective is to enhance the visibility and definition of ridge patterns, tackling problems stemming from irregular skin contacts and contaminated sensor surfaces. Author incorporates a new normalization technique that adjusts for finger flexibility, improving the correction of anomalies and boosting overall image quality, especially in detecting fine details. Experimental evidence shows that technique surpasses previous methods in effectiveness. © 2024 IEEE.

关键词： Gabor filters

来源：评论

学校读者我要写书评

暂无评论

Adaptive Clustering Algorithm for Big Health Data Classification 3

Adaptive Clustering Algorithm for Big Health Data Classifica...

引用

3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Bhargava, Neeraj Rathore, Pramod Singh Jha, Meenakshi Goswami, Ankur School of Engineering & System Sciences Mds University Ajmer India Manipal University Department of Computer and Communication Engineering Jaipur India Mds University Ajmer India Govt. Engineering College Department of Computer Applications Bikaner India

ISBN: (纸本)9798350302288

This paper presents an analysis of tobacco and alcohol consumption among different age groups, using data collected by the World Health Organization (WHO). The analysis quantifies alcohol and tobacco consumption in grams per day, aggregating the total amount consumed by each age group. Consumption of alcohol is considered not only through wine, but also through other mediums such as medicine, inhalers, etc. while tobacco is consumed through smoking, dry tobacco, etc. Our analysis also takes into account whether the cases of consumption were controlled or uncontrolled. The majority of cases analyzed in our study were found to be controlled. Finally, we present graphical representations of the analysis results, highlighting the dependencies between different variables. © 2023 IEEE.

关键词： Tobacco

来源：评论

学校读者我要写书评

暂无评论

Unveiling Feature Significance: Enhancing Classification Accuracy using Chi-Squared Weighted Feature Selection 2

Unveiling Feature Significance: Enhancing Classification Acc...

引用

2nd International Conference on Automation, Computing and Renewable systems, ICACRS 2023

作者： Bhargava, Neeraj Rathore, Pramod Singh Rajput, Deepsh Goswami, Ankur School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India MDS University Ajmer India Department of Computer Applications Govt. Engineering College Bikaner India

ISBN: (纸本)9798350340235

This study delves into the realm of feature selection and its profound impact on enhancing classification accuracy. Leveraging the renowned Fisher's Iris dataset as a well-established benchmark, our investigation revolves around the application of Chi-squared weights to identify attributes crucial for species classification. The Chi-squared weights are a powerful tool, quantifying the associations between features and species labels. Through this approach, we discern the most pivotal attribute, suggesting its pivotal role in distinguishing iris flower species. By selecting features with elevated Chi-squared weights, we curate a focused feature subset that significantly bolsters classification accuracy. The visualization of Chi-squared weights through intuitive bar graphs emerges as an influential tool for gauging feature importance. This graphical representation aids in identifying attributes that possess strong discriminatory capabilities, thereby empowering informed decision-making during feature selection. The study emphasizes the imperative role of feature selection in the domain of machine learning and classification tasks. By incorporating statistical methodologies like the Chi-squared test, we unravel the essence of attribute significance and streamline the process of crafting potent feature subsets. As datasets evolve in complexity, the identification of pivotal attributes becomes indispensable. The proposed methodology provides a valuable approach to address this challenge. In essence, the study advances feature selection techniques by showcasing the potency of Chi-squared weights and their visual representation in enhancing classification outcomes. Our exploration within the Fisher's Iris dataset manifests the broader potential of informed feature selection strategies in elevating the performance of machine learning models. © 2023 IEEE.

关键词： Chi-Square feature ranking Fisher’s Iris IHFS

来源：评论

学校读者我要写书评

暂无评论

Improving Facial Recognition Accuracy Using Local Binary Patterns 3

Improving Facial Recognition Accuracy Using Local Binary Pat...

引用

3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Bhargava, Prof Neeraj Rathore, Pramod Singh Saxena, Deepshikha Goswami, Ankur School of Engineering and System Sciences Mds University Ajmer India Manipal University Department of Computer and Communication Engineering Jaipur India Mds University Ajmer India Govt. Engineering College Department of Computer Applications Bikaner India

ISBN: (纸本)9798350302288

The proposed method involves pre-processing of the dataset, which includes normalization and resizing of the images to a standard size. Local binary pattern is then used to extract features from the pre-processed images. These features are used to train a machine learning model, specifically a support vector machine, to recognize faces in the images. The effectiveness of the proposed approach was evaluated using a publicly available dataset, and the results indicate a high level of accuracy in face detection, with a recognition rate of over 90%. The approach was also tested on images with varying contrast and rotation angles, and the results demonstrate its ability to accurately identify faces under such conditions. The proposed method has practical applications in various areas such as surveillance, access control, and forensics. It has the potential to be integrated into security systems for real-time face recognition and identification, improving the accuracy and efficiency of such systems. Furthermore, the method can be extended to include more complex features for more accurate and robust face detection. Overall, this research contributes to the growing body of knowledge on face detection techniques and their practical applications. © 2023 IEEE.

关键词： Face recognition

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：