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8th International Conference on Electronic information technology and computer engineering, EITCE 2024

作者： Zheng, Zhiguo Wang, Yujie Chen, Jie Lin, Shuteng Lin, Meirong School of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

With the continuous improvement of people’s living standards in recent years, home intelligence has become a trend. However, traditional locks are easier to pry open or forcibly open due to their structure, resulting in lower security. Additionally, issues such as losing keys or being unable to unlock the door without them often occur. For the sake of security and convenience, this paper designs a smart door lock based on STM32 that integrates various unlocking methods, including RFID unlocking, fingerprint recognition, keypad unlocking, and remote unlocking. Moreover, if someone forcefully strikes the door, an alarm function will be triggered, making it more secure and reliable than traditional mechanical locks and general smart locks. © 2024 Copyright held by the owner/author(s).

关键词： Palmprint recognition

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Boosting the Prediction of Brain Tumor Using Two Stage BiGait Architecture 25

Boosting the Prediction of Brain Tumor Using Two Stage BiGai...

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Khan, Saif-Ur-Rehman Khan, Zia Hossain, Md Zakir Mayumu, Nicanor Yasmin, Farhana Aziz, Younas School of Computer Science and Engineering Central South University Changsha China Curtin University Perth Australia Nanjing University of information Science and Technology Department of Computer and Software Nanjing China

ISBN: (纸本)9798350379037

Deep learning (DL) techniques hold immense promise for revolutionizing medical diagnostics, including brain tumor detection. Detecting malignancies in the brain is fraught with challenges that carry critical implications for accurate and timely diagnosis. Early detection is paramount, and our paper presents a novel approach to address this issue. We introduce the BiGait ensemble transfer learning (TL) architecture tailored for brain tumor classification tasks. Our proposed methodology is a two-stage process that harnesses the power of TL. In the initial phase, we employ two established models, ResNet50 and EfficientNetB1, and fine-tune them using the Kaggle multiclass dataset. This includes the incorporation of supplementary feature layers. We also leverage a stack ensemble solution to combine these feature layers, enhancing the model's overall predictive capabilities. Our approach is designed to shift the input distribution positively, effectively mitigating overfitting and reducing hardware overhead. To validate the effectiveness of our method, we conducted extensive experiments and evaluation. The dataset utilized in this work, publicly available on Kaggle contains 3,264 brain MRIs categorized into four groups: normal (500 images), meningioma (937 images), glioma (926 images), and pituitary tumor (901 images). The experimental results demonstrate that our model surpasses traditional single-model approaches, achieving an impressive accuracy rate of 96% and exhibiting superior generalization capabilities. This advancement in brain tumor classification holds great promise for early diagnosis and improved patient outcomes. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Cloud Computing Network in Remote Sensing-Based Climate Detection Using Machine Learning Algorithms

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 400-408页

作者： Srinivas, Jhade Raju, Ch VV Narasimha Sasikala, C. Bhaskar, Parumanchala Panyala, Amarendra Reddy Priya, Divya Department of Computer Science and Engineering - Data Science K G Reddy College of Engineering and Technology Moinabad Telangana Hyderabad India Information Technology Marri Laxman Reddy Institute of Technology and Management Dundigal Telangana Hyderabad 500043 India Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Andhra Pradesh Ananthapuramu India Department of Computer Science and Engineering Santhiram Engineering College Nandyal India Department of Computer Science and Engineering (Data Science) Malla Reddy University Telangana Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Effective monitoring of the environment over a large area will require mobilization of a considerable amount of information. Otherwise, the use of traditional methods will prove to be costly and would take up so much time. Also, assessing the land cover changes between two times is one of the other methods which can be adopted to measure the environmental sustainability. Using ML techniques for predictive modeling in remote sensing has emerged as a useful approach in solving some of the environmental and climatic challenges. This research proposes a novel method in remote sensing image-based climate detection utilizing cloud computing and machine learning algorithms. Here, the cloud computing module is used in the collection of remote sensing images for the detection of climate changes. Then, the collected image is processed for noise removal as well as normalization. The processed image is segmented, and features are extracted utilizing a fuzzy K-clustering-based contour Gaussian model with graph cut Boltzmann CNN. Experimental analysis is carried out for various remote sensing-based climate change datasets in terms of training accuracy, random precision, AUC, recall, and F-measure. The proposed technique obtained 97% of training accuracy, 95% of random precision, 96% of recall, 93% of AUC, and 94% of F-measure. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate detection Cloud computing Contour Gaussian model Machine learning algorithms Remote sensing images

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Multiplayer Reach-Avoid Differential Games in 3D Space Inspired by Harris'Hawks'Cooperative Hunting Tactics

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Research 2024年第2期2023卷 41-54页

作者： Wanying Ruan Haibin Duan Yongbin Sun Wanmai Yuan Jie Xia State Key Laboratory of Virtual Reality Technology and Systems School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina Peng Cheng Laboratory ShenzhenChina Information Science Academy of CETC BeijingChina

This paper investigates a multiplayer reach-avoid differential game in 3-dimensional(3D)space,which involves multiple pursuers,multiple evaders,and a designated target *** evaders aim to reach the target region,while the pursuers attempt to guard the target region by capturing the *** class of research holds significant practical ***,the complexity of the problem escalates substantially with the growing number of players,rendering its solution extremely *** this paper,the multiplayer game is divided into many subgames considering the cooperation among pursuers,reducing the computational burden,and obtaining numerically tractable strategies for ***,the Apollonius sphere,a fundamental geometric tool for analyzing the 3D differential game,is formulated,and its properties are *** on this,the optimal interception point for the pursuer to capture the evader is derived and the winning conditions for the pursuer and evader are ***,based on the Apollonius sphere,the optimal state feedback strategies of players are designed,and simultaneously,the optimal one-to-one pairings are ***,the Value function of the multiplayer reach-avoid differential game is explicitly given and is proved to satisfy Hamilton-Jacobi-Isaacs(HJI)***,the matching algorithm for the case with pursuers outnumbered evaders is provided through constructing a weighted bipartite graph,and the cooperative tactics for multiple pursuers are proposed,inspired by the Harris'Hawks intelligent cooperative hunting ***,numerical simulations are conducted to illustrate the effectiveness of the theoretical results for both cases where the number of adversary players is equal and unequal between the 2 groups.

关键词： holds satisfy rendering

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Optimized Power Allocation in Downlink NOMA with Imperfect Successive Interference Cancellation

Optimized Power Allocation in Downlink NOMA with Imperfect S...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Biswas, Soumik Bera, Debasish Indian Institute of Information Technology Department of Electronics and Communication Engineering Kalyani India Indian Institute of Information Technology Department of Computer Science and Engineering Kalyani India

ISBN: (纸本)9798350350821

It is expected that physical-layer security will finally thrive to provide an extra layer of defence for a variety of devices in next-generation 6G wireless communication. In this work, physical-layer security using non-orthogonal multiple access (NOMA) with single-input-single-output (SISO) assumptions are considered where the users and eavesdroppers are spatially uniformly deployed. The channels between the transmitter (base station (BS)) and the NOMA users and eavesdroppers are considered to be independent Nakagami-m. We analyse the secrecy sum rate (SSR) of the NOMA system in the case of imperfect successive interfere cancellation (SIC) and propose an optimised power allocation scheme with minimised transmit power requirement to satisfy the Quality of Service (QoS) for all legitimate users. Closed-form solutions for the SSR are also derived. The performance of the proposed schemes has been evaluated both by analysis and by simulations. Results are evaluated for a wide range of the signal-to-noise ratio (SNR). © 2024 IEEE.

关键词： Channel state information

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Graph Dual Adversarial Network for Hyperspectral Image Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 922-932页

作者： Cheng, Yuhu Chen, Yang Kong, Yi Philip Chen, C.L. Wang, Xuesong China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of EducationSchool of Information and Control Engineering Xuzhou221116 China South China University of Technology School of Computer Science and Engineering Guangzhou510006 China

An end-to-end unsupervised domain adaptation method for hyperspectral image (HSI) classification based on a graph dual adversarial network is proposed in this article. First, in order to extract the domain-invariant features of the source and target domains, the rich spectral information and spatial position of HSI are used to construct a spectral-spatial nearest neighbor graph, which is input into the graph convolutional network. Then, a prototype adversarial strategy is proposed, which uses the labeled data of the source domain to reliably calculate the feature prototypes of different classes. Through the prototype adversarial strategy, the distances between different prototypes are appropriately extended, so that the clusters of different classes are far away from their respective decision boundaries, and the discriminability of features are also enhanced. The dual adversarial strategy is composed of the prototype adversarial strategy and the domain adversarial strategy. It is worth noting that the dual adversarial strategy does not require feature extractor and discriminator to work in turn, which can be implemented through a gradient reversal layer. Finally, on the basis of adapting the overall features of both the domains via the domain adversarial strategy, the source- and target-domain features are further adapted by minimizing the correlation alignment loss of each class of samples. Experimental results on two real HSI datasets of Botswana and Kennedy Space Center show the effectiveness of our proposed method. © 2020 IEEE.

关键词： Spectroscopy

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Research on simulation method of atmospheric turbulence effects based on generative adversarial network 24

Research on simulation method of atmospheric turbulence effe...

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3rd International Conference on Signal Processing, computer Networks and Communications, SPCNC 2024

作者： Liu, Dongxu Liu, Zhi School of Electronics and Information Engineering Changchun University of Science and Technology Jilin Changchun China

ISBN: (纸本)9798400710834

In view of the problem that the atmospheric turbulence phase screen generated by the traditional numerical simulation method has insufficient low frequency, this paper proposes a phase screen generation method based on Generative Adversarial Network (GAN). The advantages of this method include its strong robustness to atmospheric turbulence, low model depth, few parameters, and low complexity. Compared with the traditional numerical simulation method, it is found that the GAN proposed in this paper not only compensates for the low-frequency deficiency of the traditional numerical simulation method but also speeds up the generation speed of the phase screen. Simultaneously, the trained phase screen model can be directly used for phase screen generation. © 2024 Copyright held by the owner/author(s).

关键词： Generative adversarial networks

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A Smart of Classification and Regression Tree Algorithms for Detection of Land Cover from Hyper Spectral Data

A Smart of Classification and Regression Tree Algorithms for...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Chaudhary, Chetan Shankar, Shiv Shankar Menaka, C. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Mathematics Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

This paper offers a new approach for land cover detection using classification and regression tree (CART) algorithms for hyperspectral information. The underlying principle for the CART technique is to assemble a decision tree to categorize the far-flung sensing records primarily based on hard and fast model parameters. The proposed technique uses a genetic algorithm for great tuning the parameters for each department of the tree., which enables the CART algorithm to version the spatial variability of the land cowl effectively. Experimental effects on multiple take-a-look-at pictures verify the merits of the proposed CART algorithm in detecting land cowl as it should be from hyperspectral facts. The paper additionally demonstrates the robustness of the proposed algorithm in handling noise and the length of the characteristic area. The results display a full-size development in land cowl detection accuracy over the present methods. © 2024 IEEE.

关键词： algorithms classification demonstrates parameters underlying

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Towards characterizing bug fixes through dependency-level changes in Apache Java open source projects

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science China(information sciences) 2022年第7期65卷 102-120页

作者： Di CUI Lingling FAN Sen CHEN Yuanfang CAI Qinghua ZHENG Yang LIU Ting LIU School of Computer Science and Technology Xi'an Jiaotong University College of Cyber Science Nankai University College of Intelligence and Computing Tianjin University Department of Computer Science Drexel University School of Computer Science and Engineering Nanyang Technological University School of Cyber Science and Engineering Xi'an Jiaotong University

The complexity and diversity of bug fixes require developers to understand bug fixes from multiple perspectives in addition to fine-grained code changes. The dependencies among files in a software system are an important dimension to inform software quality. Recent studies have revealed that most bug-prone files are always architecturally connected with dependencies, and as one of the best practices in the industry,changes in dependencies should be avoided or carefully made during bug fixing. Hence, in this paper, we take the first attempt to understand bug fixes from the dependencies perspective, which can complement existing code change perspectives. Based on this new perspective, we conducted a systematic and comprehensive study on bug fixes collected from 157 Apache open source projects, involving 140456 bug reports and182621 bug fixes in total. Our study results show that a relatively high proportion of bug fixes(30%) introduce dependency-level changes when fixing the corresponding 33% bugs. The bugs, whose fixes introduce dependency-level changes, have a strong correlation with high priority, large fixing churn, long fixing time,frequent bug reopening, and bug inducing. More importantly, patched files with dependency-level changes in their fixes, consume much more maintenance costs compared with those without these changes. We further summarized three representative patch patterns to explain the reasons for the increasing costs. Our study unveils useful findings based on qualitative and quantitative analysis and also provides new insights that might benefit existing bug prediction techniques. We release a large set of benchmarks and also implement a prototype tool to automatically detect dependency-level changes from bug fixes, which can warn developers and remind them to design a better fix.

关键词： empirical software engineering software maintenance software evolution software architecture software design

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Heterogeneous UAV deployment strategies for coverage maximization 24

Heterogeneous UAV deployment strategies for coverage maximiz...

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2nd International Conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Wang, Xi Li, Zhuo Zhang, Xuan School of Information and Electrical Engineering Hunan University of Science and Technology Hunan Xiangtan China

ISBN: (纸本)9798400710681

With the development of UAV technology, UAV sensor networks have been widely used to provide temporary network coverage of an area. At present, homogeneous UAV swarms are commonly used to perform regional coverage tasks. However, Homogeneous UAV swarms are often unable to combine high coverage with reduced wasted coverage area when covering regional boundaries or irregular areas. In contrast, heterogeneous swarms show advantages in terms of flexibility and efficiency and are better suited than homogeneous swarms for coverage in most situations. For heterogeneous UAV networks, different drones have different technical characteristics and coverage ranges. Therefore, this paper proposes an improved molecular force potential field algorithm for regional coverage deployment of heterogeneous UAV swarms to achieve maximum coverage of the target area. We study a problem of UAV network coverage of the same disaster area using different UAV swarms, and the results of the experiment show that the coverage of heterogeneous UAV swarms is significantly better than having homogeneous UAV swarms with similar coverage capabilities. © 2024 Copyright held by the owner/author(s).

关键词： Micro air vehicle (MAV)

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