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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 173-181页

作者： Kant, Ravi Saini, Poonam Kumari, Julee Defence Electronics Application Laboratory Defence Research and Development Organisation Dehradun248001 India Department of Computer Science and Engineering Punjab Engineering College Chandigarh148023 India Centre for Development of Advanced Computing Mohali160071 India

Unmanned aerial vehicles (UAV's) safe flight is one of the most important tasks of ground control stations (GCS). However, sometimes due to communication failure between ground data terminal (GDT) and UAV, GCS is not able to ensure a safe flight, which could be hazardous. In such conditions, the accurate UAV position finding prediction becomes essential to serve as sustenance for UAVs. Hence, this article proposes a UAV's position finding prediction model using a deep neural network application for successfully pointing the GDT toward UAV in real time. The proposed work consists of three components, namely data preprocessing, long short-term memory autoencoder (LSTM-AE)-based deep learning model, and mathematical formulation to calculate the ground radar look angle. First, we use different preprocessing techniques on a historical raw dataset for better visualization and generate patterns. To achieve this, Tensor flow along with Keras packages have been used. Next, an LSTM autoencoder deep learning model is applied to the same dataset in order to predict an accurate 4-D position of the UAV. Finally, the output from the LSTM autoencoder model is used in a mathematical model to calculate the GDT look-angles called Azimuth and Elevation. Both the parameters have been used to point the GDT toward the predicted point on the Airspace. We evaluate our proposed model against famous evaluation matrices, namely mean absolute error, mean square error, and root-mean-square error to validate the experimental results. © 2022 IEEE.

关键词： Real time systems

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High-dimensional nonlinear variable separation solutions and novel wave excitations for the(4+1)-dimensional Boiti-Leon-Manna-Pempinelli equation

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Communications in Theoretical Physics 2024年第11期76卷 1-11页

作者： Zu-feng Liang Xiao-yan Tang Wei Ding Department of Physics Hangzhou Normal UniversityHangzhou 310036China School of Mathematical Sciences Key Laboratory of Mathematics and Engineering Applications(Ministry of Education)&Shanghai Key Laboratory of PMMPEast China Normal UniversityShanghai 200241China School of Computer Science and Technology Shanghai Normal UniversityShanghai 200234China

Considering the importance of higher-dimensional equations that are widely applied to real nonlinear problems,many(4+1)-dimensional integrable systems have been established by uplifting the dimensions of their corresponding lower-dimensional integrable ***,an integrable(4+1)-dimensional extension of the Boiti-Leon-Manna-Pempinelli(4DBLMP)equation has been proposed,which can also be considered as an extension of the famous Korteweg-de Vries equation that is applicable in fluids,plasma physics and so *** is shown that new higher-dimensional variable separation solutions with several arbitrary lowerdimensional functions can also be obtained using the multilinear variable separation approach for the 4DBLMP *** addition,by taking advantage of the explicit expressions of the new solutions,versatile(4+1)-dimensional nonlinear wave excitations can be *** an illustration,periodic breathing lumps,multi-dromion-ring-type instantons,and hybrid waves on a doubly periodic wave background are discovered to reveal abundant nonlinear structures and dynamics in higher dimensions.

关键词： (4+1)-dimensional Boiti-Leon-Manna-Pempinelli equation variable separation solution periodic breathing lumps multi-dromion-ring-type instanton hybrid waves on a doubly periodic wave background

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Analyzing the Impact of Security Measures on Wi-Fi Quality of Service 2

Analyzing the Impact of Security Measures on Wi-Fi Quality o...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Kopcho, Thomas J. Fouda, Mostafa M. Krome, Cameron J. Quach, Anna T. Olaveson, Israel G. Idaho State University Department of Computer Science PocatelloID United States Idaho State University Department of Electrical and Computer Engineering PocatelloID United States Ai Data & Visualization Idaho National Laboratory Idaho FallsID United States

ISBN: (纸本)9798331529529

This study examines the relationship between Quality of Service (QoS) and security in Wi-Fi networks. Using a detailed dataset consisting of QoS metrics of file transfers under various security configurations, we analyze the impact of these settings on network performance. Our findings provide a quantification to accurately show that different encryption protocols, such as TKIP and CCMP, significantly affect QoS, with disabling security generally improving performance. The study provides a framework for optimizing security settings to enhance both security and QoS in future wireless networks. © 2024 IEEE.

关键词： Wireless local area networks (WLAN)

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A Lightweight AI Model for Anomaly Detection in Wireless Networks 2

A Lightweight AI Model for Anomaly Detection in Wireless Net...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Kopcho, Thomas J. Fouda, Mostafa M. Krome, Cameron J. Idaho State University Department of Computer Science PocatelloID United States Idaho State University Department of Electrical and Computer Engineering PocatelloID United States Idaho National Laboratory Ai Data & Visualization Idaho FallsID United States

ISBN: (纸本)9798331529529

Detecting network anomalies is critical for wireless network security and reliability. Traditional AI methods often require substantial computational resources, particularly when deployed on cloud servers, leading to increased network load and latency. In contrast, deploying AI models directly on end devices enables real-time data processing at the source, facilitating faster anomaly detection and mitigation. We propose a lightweight hierarchical AI model where initial inferences are made by a simple model and outputs confidence scores. Using a modified TOPSIS-based method, we determine an optimal confidence score threshold. Inferences below this threshold are forwarded to a more complex model for accurate analysis, reducing overall computational demands while maintaining high detection performance. Our approach is tested on a 5 G network dataset, demonstrating competitive performance compared to other anomaly detection models. © 2024 IEEE.

关键词： 5G mobile communication systems

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Revolutionizing Personalized Health: The Frontier of Wearable Biomolecule Sensors Through 3D Printing Innovation

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Biomedical Materials and Devices 2025年第2期3卷 818-834页

作者： Rajendran, Jerome Esfandyarpour, Rahim Department of Electrical Engineering and Computer Science University of California Irvine 92697 CA United States Laboratory for Integrated Nano BioElectronics Innovation The Henry Samueli School of Engineering University of California Irvine 92697 CA United States

This review article delves into the innovative intersection of 3D-printed technologies and wearable chemical sensors, highlighting a forward-thinking approach to biomarker monitoring. It emphasizes the transformative role of additive manufacturing in the development of wearable devices tailored for the precise detection of chemical biomarkers, crucial for proactive disease management and health assessment. By offering a detailed exploration of how 3D printing of nanomaterials contributes to pioneering sensor designs, this review underscores the practicality of sensor wearability, ensuring comfort and efficacy for users. We address the challenges of material resilience, sensor durability, and efficient data communication, while also charting the significant trends and future directions that promise to redefine the landscape of flexible and wearable chemical sensors. Through a comprehensive analysis, this article aims to showcase the pivotal advancements and ongoing innovations in the field, emphasizing the critical impact of 3D printing on enhancing personalized healthcare and wearable diagnostics. Graphical Abstract: (Figure presented.) © The Author(s) 2024.

关键词： 3D printing Biomarker monitoring Chemical sensors Flexible Personalized healthcare Wearable

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Belief Combination of Classifiers for Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 652-667页

作者： Zuowei Zhang Songtao Ye Yiru Zhang Weiping Ding Hao Wang IEEE the Research Center for Optical Fiber Sensing Zhejiang Laboratory the School of Automation Northwestern Polytechnical University the School of Cyber Engineering Xidian University the Department of Computer Science CY Cergy Paris Université the School of Information Science and Technology Nantong University the School with of Cyber Engineering Xidian University the Department of Computer Science Norwegian University of Science and Technology

Data with missing values,or incomplete information,brings some challenges to the development of classification,as the incompleteness may significantly affect the performance of *** this paper,we handle missing values in both training and test sets with uncertainty and imprecision reasoning by proposing a new belief combination of classifier(BCC)method based on the evidence *** proposed BCC method aims to improve the classification performance of incomplete data by characterizing the uncertainty and imprecision brought by *** BCC,different attributes are regarded as independent sources,and the collection of each attribute is considered as a ***,multiple classifiers are trained with each subset independently and allow each observed attribute to provide a sub-classification result for the query ***,these sub-classification results with different weights(discounting factors)are used to provide supplementary information to jointly determine the final classes of query *** weights consist of two aspects:global and *** global weight calculated by an optimization function is employed to represent the reliability of each classifier,and the local weight obtained by mining attribute distribution characteristics is used to quantify the importance of observed attributes to the pattern *** comparative experiments including seven methods on twelve datasets are executed,demonstrating the out-performance of BCC over all baseline methods in terms of accuracy,precision,recall,F1 measure,with pertinent computational costs.

关键词： Classifier fusion classification evidence theory incomplete data missing values

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Blood Pressure Estimation through Photoplethysmography and Machine Learning Models 9

Blood Pressure Estimation through Photoplethysmography and M...

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9th International Conference on Control, Instrumentation and Automation, ICCIA 2023

作者： Mohammadi, Hanieh Tarvirdizadeh, Bahram Alipour, Khalil Ghamari, Mohammad Laboratory Faculty of New Sciences and Technologies Department of Mechatronics Engineering Tehran Iran Kettering University Department of Electrical and Computer Engineering FlintMI United States

ISBN: (纸本)9798350366013

Blood pressure (BP) is a crucial health element, the fluctuations of which may have profound implications for an individual's well-being. Traditional methods of measuring BP, such as cuff-based and invasive devices, are not only uncomfortable but also unable to provide continuous monitoring. In response to this need, we propose a cuffless, continuous, and noninvasive BP measurement system utilizing photoplethysmograph (PPG) signals and machine learning (ML) models. PPG, an optical volumetric measurement technique, can identify alterations in blood volume within the tissue's vascular network. In this study, PPG signals obtained from diverse individuals underwent preprocessing and feature extraction. Subsequently, a feature selection technique was employed to identify suitable features. These selected features were then employed to train and evaluate ML models. Ultimately, we identified optimal regression models for independent estimation of diastolic blood pressure (DBP) and systolic blood pressure (SBP). Our findings indicate that the random forest (RF) model, in combination with the SelectFromModel feature selection method, outperformed other models. This model yielded significant outcomes, achieving a root mean square error (RMSE) of 6.61 for DBP and 10.06 for SBP, highlighting its superior performance in BP estimation. © 2023 IEEE.

关键词： Blood pressure

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Computational Fluid Dynamics Modeling Analysis of a Martian Rotorcraft With Individual Blade Control1

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ASME Letters in Dynamic Systems and Control 2023年第2期3卷 021005页

作者： Schulman, Paul Berndt, Samuel-Hunter Roman, Christiahn Tan, Xiaobo Department of Electrical and Computer Engineering Michigan State University East LansingMI48824 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States

Computational fluid dynamics (CFD) analysis was conducted on a proposed blade root-actuated individual blade control (IBC) system for future Martian rotorcraft. IBC offers many potential benefits to rotary-winged exploration of Mars, including precision control of rotor blade forces. This study seeks to provide an estimate of rotor blade force and system power as a basis for concept feasibility analysis and experimental prototyping. ANSYS FLUENT was used to compute blade pitching moment, lift, and drag under various feathering waveforms, amplitudes, biases, and frequencies. It is determined that the rapid feathering characteristic of IBC has a non-negligible impact on blade forces. It is also found that actuators with power ratings on the order of 101 W are likely sufficient for blade actuation on Martian rotorcraft. © 2024 American Society of Mechanical Engineers (ASME). All rights reserved.

关键词： Vibration analysis

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RADepthNet:Reflectance-aware monocular depth estimation

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Virtual Reality & Intelligent Hardware 2022年第5期4卷 418-431页

作者： Chuxuan LI Ran YI Saba Ghazanfar ALI Lizhuang MA Enhua WU Jihong WANG Lijuan MAO Bin SHENG Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Department of State Key Laboratory of Computer Science University of MacaoMacao 999078China Shanghai University of Sports Shanghai 200438China

Background Monocular depth estimation aims to predict a dense depth map from a single RGB image,and has important applications in 3D reconstruction,automatic driving,and augmented ***,existing methods directly feed the original RGB image into the model to extract depth features without avoiding the interference of depth-irrelevant information on depth-estimation accuracy,which leads to inferior *** To remove the influence of depth-irrelevant information and improve the depth-prediction accuracy,we propose RADepthNet,a novel reflectance-guided network that fuses boundary ***,our method predicts depth maps using the following three steps:(1)Intrinsic Image *** propose a reflectance extraction module consisting of an encoder-decoder structure to extract the depth-related *** an ablation study,we demonstrate that the module can reduce the influence of illumination on depth estimation.(2)Boundary Detection.A boundary extraction module,consisting of an encoder,refinement block,and upsample block,was proposed to better predict the depth at object boundaries utilizing gradient constraints.(3)Depth Prediction *** use an encoder different from(2)to obtain depth features from the reflectance map and fuse boundary features to predict *** addition,we proposed FIFADataset,a depth-estimation dataset applied in soccer *** Extensive experiments on a public dataset and our proposed FIFADataset show that our method achieves state-of-the-art performance.

关键词： Monocular depth estimation Deep learning Intrinsic image decomposition

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NegCPARBP: Enhancing Privacy Protection for Cross-Project Aging-Related Bug Prediction Based on Negative Database

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IEEE Transactions on Emerging Topics in Computing 2025年第2期13卷 283-298页

作者： Zhao, Dongdong Liu, Zhihui Zhang, Fengji Liu, Lei Keung, Jacky Wai Yu, Xiao Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan China Wuhan University of Technology Chongqing Research Institute Chongqing China City University of Hong Kong Department of Computer Science Hong Kong Xi'an Jiaotong University School of Electronic Science and Engineering Xi'an China Zhejiang University State Key Laboratory of Blockchain and Data Security Hangzhou China

The emergence of Aging-Related Bugs (ARBs) poses a significant challenge to software systems, resulting in performance degradation and increased error rates in resourceintensive systems. Consequently, numerous ARB prediction methods have been developed to mitigate these issues. However, in scenarios where training data is limited, the effectiveness of ARB prediction is often suboptimal. To address this problem, CrossProject Aging-Related Bug Prediction (CPARBP) is proposed, which utilizes data from other projects (i.e., source projects) to train a model aimed at predicting potential ARBs in a target project. However, the use of source-project data raises privacy concerns and discourages companies from sharing their data. Therefore, we propose a method called Cross-Project AgingRelated Bug Prediction based on Negative Database (NegCPARBP) for privacy protection. NegCPARBP first converts the feature vector of a software file into a binary string. Secondly, the corresponding Negative DataBase (NDB) is generated based on this binary string, containing data that is significantly more expressive from the original feature vector. Furthermore, to ensure more accurate prediction of ARB-prone and ARB-free files based on privacy-protected data (i.e., maintain the data utility), we propose a novel negative database generation algorithm that captures more information about important features, using information gain as a measure. Finally, NegCPARBP extracts a new feature vector from the NDB to represent the original feature vector, facilitating data sharing and ARB prediction objectives. Experimental results on Linux, MySQL, and NetBSD datasets demonstrate that NegCPARBP achieves a high defense against attacks (privacy protection performance reaching 0.97) and better data utility compared to existing privacy protection methods. © 2025 IEEE.

关键词： Differential privacy

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