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17th International Conference on Complex Medical engineering, CME 2023

作者： Fu, Hao Guo, Di School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9798350316117

Nuclear Magnetic Resonance (NMR) spectroscopy is one of the most promising analytical chemistry techniques, although it takes a long time to acquire data. Three-dimensional NMR spectra offer higher resolution. The Low Rank Hankel Matrix (LRHM) method reconstructs three-dimensional undersampled NMR data by transforming each 2D indirect plain slice into a block Hankel matrix. However, this reconstruction process involves a considerable time overhead. In this work, we introduce a block Hankel matrix based on a sliding window method specifically designed for 3D NMR, to accelerate spectrum reconstruction from NUS signals. This approach can effectively reduce the size of the Hankel matrix for faster reconstruction. Experiments on both synthetic and real data demonstrate that the proposed method achieves the fastest reconstruction speed compared to other methods, without compromising the quality of the spectra. © 2023 IEEE.

关键词： Nuclear magnetic resonance spectroscopy

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Multimodal Learning data Analysis and Algorithmic Teaching Effectiveness Evaluation Model Construction

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International Journal of High Speed Electronics and Systems 2024年第0期

作者： Tu, Lihui Hong, Huanjie School of Computer and Data Engineering NingboTech University Ningbo315100 China

Introduction:A combination of internal characteristics, such as motivation, personality, beliefs, and dispositions that combine with external circumstances to affect student results is referred to as teaching efficacy. The English-speaking environment becomes a major problem as students learn by interaction. To communicate effectively, they do not acquire the speaking and listening abilities necessary in English. Objective:This study examines the impact of computer programming courses and learning analytics on student's computer programming skills. Methods:In this study, we proposed a novel Dung Beetle Optimized Flexible Random Forest (DBO-FRF) to evaluate the teaching effectiveness. In this study, 175 students' data were collected for teaching effectiveness. Using student data, prediction model was constructed based on the attributes of the students, their past academic records, their interactions with online resources, and their advancement in laboratory work related to programming. The proposed method is compared to other traditional algorithms. Results and conclusion:The proposed method is implemented using Python software. The expected performance in the course and, in the instance that any submitted programs failed to meet the requirements, a programming recommendation from one of the class's top students. The result shows the proposed method achieved better performance in terms of accuracy, precision, recall, and F1-score. This decreased the performance gap between students who performed lower and those who performed higher, enabling students who adjusted their programs to learn more. © 2025 World Scientific Publishing Company.

关键词： Students

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NMR Metabolite Spectrum Denoising with Improved U-Net++ 17

NMR Metabolite Spectrum Denoising with Improved U-Net++

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17th International Conference on Complex Medical engineering, CME 2023

作者： Fang, Wenjie Fu, Hao Guo, Di School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9798350316117

Nuclear Magnetic Resonance (NMR) spectroscopy serves as a significant tool for capturing essential information in chemical reactions. However, in practical applications, NMR signals are frequently afflicted by Gaussian noise, leading to challenges in sequence analysis and quantification. In this paper, we have devised a deep neural network named NMRDnet, with the Unet++ architecture at its core, incorporating modules such as CBAM and soft-thresholding. This network was trained using a simulated dataset based on the exponential characteristics of NMR spectroscopic signals. The experimental results show that the NMRDnet network can effectively remove the noise and restore the original signal. It is superior to the low-rank Hankel matrix denoising method in maintaining peak signal strength and reducing signal distortion. © 2023 IEEE.

关键词： Nuclear magnetic resonance spectroscopy

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A High-Quality Adaptive Video Reconstruction Optimization Method Based on Compressed Sensing

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computer Modeling in engineering & Sciences 2023年第10期137卷 363-383页

作者： Yanjun Zhang Yongqiang He Jingbo Zhang Yaru Zhao Zhihua Cui Wensheng Zhang School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan030024China Department of Big Data and Intelligent Engineering Shanxi Institute of TechnologyYangquan045000China School of Computer Science Beijing University of TechnologyBeijing100124China The Institute of Automation Chinese Academy of Sciences(CAS)Beijing100049China

The video compression sensing method based onmulti hypothesis has attracted extensive attention in the research of video codec with limited ***,the formation of high-quality prediction blocks in the multi hypothesis prediction stage is a challenging *** resolve this problem,this paper constructs a novel compressed sensing-based high-quality adaptive video reconstruction *** includes the optimization of prediction blocks(OPBS),the selection of searchwindows and the use of neighborhood ***,the OPBS consists of two parts:the selection of blocks and the optimization of prediction *** combine the high-quality optimization reconstruction of foreground block with the residual reconstruction of the background block to improve the overall reconstruction effect of the video *** addition,most of the existing methods based on predictive residual reconstruction ignore the impact of search windows and reference frames on ***,Block-level search window(BSW)is constructed to cover the position of the optimal hypothesis block as much as *** maximize the availability of reference frames,Nearby reference frame information(NRFI)is designed to reconstruct the current *** proposed method effectively suppresses the influence of the fluctuation of the prediction block on reconstruction and improves the reconstruction *** results showthat the proposed compressed sensing-based high-quality adaptive video reconstruction optimization method significantly improves the reconstruction performance in both objective and supervisor quality.

关键词： Compressed sensing OPBS block-level search window nearby reference frame information evolutionary algorithm

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DSAFF-Net:A Backbone Network Based on Mask R-CNN for Small Object Detection

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computers, Materials & Continua 2023年第2期74卷 3405-3419页

作者： Jian Peng Yifang Zhao Dengyong Zhang Feng Li Arun Kumar Sangaiah Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computing Science and Engineering Vellore Institute of Technology(VIT)Vellore632014India

Recently,object detection based on convolutional neural networks(CNNs)has developed *** backbone networks for basic feature extraction are an important component of the whole detection ***,we present a new feature extraction strategy in this paper,which name is *** this strategy,we design:1)a sandwich attention feature fusion module(SAFF module).Its purpose is to enhance the semantic information of shallow features and resolution of deep features,which is beneficial to small object detection after feature fusion.2)to add a new stage called D-block to alleviate the disadvantages of decreasing spatial resolution when the pooling layer increases the receptive *** method proposed in the new stage replaces the original method of obtaining the P6 feature map and uses the result as the input of the regional proposal network(RPN).In the experimental phase,we use the new strategy to extract *** experiment takes the public dataset of Microsoft Common Objects in Context(MS COCO)object detection and the dataset of Corona Virus Disease 2019(COVID-19)image classification as the experimental object *** results show that the average recognition accuracy of COVID-19 in the classification dataset is improved to 98.163%,and small object detection in object detection tasks is improved by 4.0%.

关键词： Small object detection classification RPN MS COCO COVID-19

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Improved Deep Matrix Factorization for Signal Recovery in Random Frequency Diversity Array Radar 2

Improved Deep Matrix Factorization for Signal Recovery in Ra...

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2nd IEEE International Conference on Signal, Information and data Processing, ICSIDP 2024

作者： Guo, Dayan Lin, Zijian Huang, Tianyao Qi, Yue He, Jie Zhang, Xiao-Ping Wang, Xueqian Li, Gang University of Science and Technology Beijing School of Computer & Communication Engineering Beijing China Tsinghua Shenzhen International Graduate School Institute of Data and Information Shenzhen China Tsinghua University Department of Electronic Engineering Beijing China

ISBN: (纸本)9798331515669

Motivated by the success of recent deep learning researches on radar, we consider a deep learning based matrix factorization method for suppressing the high range-angle side-lobes in random frequency diversity array (FDA) radars. While the existing deep matrix factorization approach is designed for real-valued situations and signals of FDA radars are complex-valued cases, we propose two methods to extend the existing approach into complex-valued framework: 1) adjusting the neural network structures and activation functions for complex-valued operations, and 2) constructing a real-valued representation of the original complex-valued input. Simulation results demonstrate that the proposed methods outperform traditional ones in multi-target scenarios. © 2024 IEEE.

关键词： Matrix factorization

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Distributed Channel Estimation and Optimization for 6D Movable Antenna: Unveiling Directional Sparsity

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IEEE Journal on Selected Topics in Signal Processing 2025年第2期19卷 349-365页

作者： Shao, Xiaodan Zhang, Rui Jiang, Qijun Park, Jihong Quek, Tony Q. S. Schober, Robert Friedrich-Alexander-University Erlangen-Nurnberg Institute for Digital Communications Erlangen91054 Germany Shenzhen Research Institute of Big Data The Chinese University of HongKong School of Science and Engineering Shenzhen518172 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore The Chinese University of HongKong School of Science and Engineering Shenzhen518172 China Singapore University of Technology and Design 487372 Singapore

Six-dimensional movable antenna (6DMA) is an innovative and transformative technology to improve wireless network capacity by adjusting the 3D positions and rotations of antennas/antenna surfaces based on the channel spatial distribution. To achieve optimal antenna positions and rotations, acquiring statistical channel state information (CSI) is essential for 6DMA systems. However, existing works assume that a central processing unit (CPU) jointly processes the signals of all 6DMA surfaces. This incurs prohibitively high processing cost and latency for channel estimation due to the vast numbers of 6DMA candidate positions/rotations and antenna elements. Therefore, we propose a distributed 6DMA processing architecture to reduce the processing complexity of the CPU by equipping each 6DMA surface with a local processing unit (LPU). Furthermore, we unveil for the first time the directional sparsity property of the 6DMA channels with respect to distributed users, where each user has significant channel gains only for a (small) subset of 6DMA position-rotation pairs. Based on this property, we propose a practical three-stage protocol for the 6DMA system and corresponding algorithms to conduct statistical CSI acquisition for all 6DMA candidate positions/rotations, 6DMA position/rotation optimization based on statistical CSI, and instantaneous CSI estimation for user data transmission with optimized 6DMA positions/rotations. Simulation results show that the proposed channel estimation algorithms achieve higher accuracy than benchmark schemes, while requiring lower pilot overhead. Moreover, the proposed 6DMA system with statistical CSI-based position/rotation optimization achieves a higher ergodic sum rate than fixed-position and fluid antenna systems, even if the latter have perfect instantaneous CSI. © 2025 IEEE.

关键词： Channel estimation

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A Systematic Survey of Automatic Detection of Lung Diseases from Chest X-Ray Images: COVID-19, Pneumonia, and Tuberculosis

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SN computer Science 2024年第2期5卷 229页

作者： Koyyada, Shiva Prasad Singh, Thipendra P. School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun 248007 India Data Sciences uGDXIT (Formerly INSOFE) Telangana Hyderabad 500082 India School of Computer Science Engineering and Technology Bennett University NCR-Delhi Greater Noida 201310 India

Chest X-ray images (CXR) can convey a great deal about a patient’s condition;hence, the standard chest radiograph should be reconsidered. Interpretation of radiographs is challenging and requires skilled people to determine lung disease without false positives and negatives. A detailed investigation addressing lung diseases COVID-19, Pneumonia, and Tuberculosis is presented here with the goal of assisting investigators in constructing models that automatically identify lung diseases. This paper is presented in three folds. The first is an exploration of how research has progressed from classic feature engineering approaches to deep learning methods;the second is how these are used to identify the listed diseases using radiology images such as Chest X-rays (CXRs);and the third is the future path way of research to detect these diseases. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Chest X-ray image Convolutional neural networks COVID-19 Pneumonia Transfer learning Tuberculosis

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Calligraphy Alphabet Perception Using Artificial Intelligence 9

Calligraphy Alphabet Perception Using Artificial Intelligenc...

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9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Venkatesh, S. Jeevitha, D. Gnanaselvi, J. Anitha Srm Institute of Science And Technology Department of Data Science And Business Systems School of Computing Chennai Kattankulathur India Jeppiaar Engineering College Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350365092

The human brain has a simple time analyzing and processing images. The brain is able to rapidly deconstruct and distinguish an image's various components when the eye perceives it. With the Convolutional Neural Network (CNN) as its foundation, this research suggests deep learning conceptual models. When the algorithms are compared, it becomes clear that CNN-based classification of handwritten alphabets performs better than other algorithms in terms of accuracy. The Manual Net, Alex Net, and LeNet Architectures are among the CNN algorithms employed in this research. The convulational layer, max pooling, flattening, feature assortment, rectifier lined unit, and completely linked softmaxx layers are each components of the aforementioned designs. The proposed network is tested using an image dataset comprising 530 training photos and 2756 testing images. The top precision and cost-efficient model will be used in the Django context to build a handler line for supplying the appeal to be recognized and obtaining the productivity outcome of recognized appeal. © 2024 IEEE.

关键词： Convolutional neural networks

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IoT-Enhanced Agricultural Water Management System Utilizing Conventional Machine Learning Methods

IoT-Enhanced Agricultural Water Management System Utilizing ...

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2023 IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023

作者： Singh, Jagendra Garg, Neha Kukreja, Sonal Saini, Mayank Singh, Ishaan Mall, Shachi Bennett University School of Computer Science Engineering and Technology Greater Noida India Publicis Sapient Manager Data Science Greater Noida India Galgotias University School of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350393415

This study delves into IoT-enhanced Agricultural Water Management, aspiring to transform farming for enhanced crop output and sustainability. It focuses on an intelligent system incorporating a sensor network and varied machine learning methods for climate monitoring and yield prediction in tomato cultivation. Notably, the Convolutional Neural Network (CNN) stood out with a 95.2% accuracy rate in predicting yields, highlighting its potential as a pivotal tool in effective irrigation management. This adaptive, data-driven system promises advanced water management, addressing pressing global concerns of food security and resource conservation by aligning agricultural practices with environmental parameters and enabling heightened crop productivity. © 2023 IEEE.

关键词： Water management

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