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IEEE Conference on Global Communications (GLOBECOM)

作者： Thang K. Nguyen Thanh V. Pham Hoang D. Le Chuyen T. Nguyen Anh T. Pham Computer Communications Lab. The University of Aizu Japan Department of Mathematical and Systems Engineering Shizuoka University Japan School of Electrical and Electronic Engineering Hanoi University of Science & Technology Vietnam

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

This paper proposes a joint design of probabilistic constellation shaping (PCS) and precoding to enhance the sum-rate performance of multi-user visible light communications (VLC) broadcast channels subject to signal amplitude constraint. In the proposed design, the transmission probabilities of bipolar M-pulse amplitude modulation (M-PAM) symbols for each user and the transmit precoding matrix are jointly optimized to improve the sum-rate performance. The joint design problem is shown to be a complex non-convex problem due to the non-convexity of the objective function. To tackle the problem, the firefly algorithm (FA), a nature-inspired heuristic optimization approach, is employed to solve a local optima to the original non-convex optimization problem. The FA-based approach, however, suffers from high computational complexity. Therefore, we propose a low-complexity design based on zero-forcing (ZF) precoding, which is solved using an alternating optimization (AO) approach. Simulation results reveal that the proposed joint design with PCS significantly improves the sum-rate performance compared to the conventional design with uniform signaling. Some insights into the optimal symbol distributions of the two joint design approaches are also provided.

关键词： Precoding Simulation Heuristic algorithms Symbols Probabilistic logic Numerical simulation Linear programming Global communication Optimization Visible light communication

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Advancements in Lung Cancer Diagnosis: A Comprehensive Study on the Role of PCA, LDA, and t-SNE in Deep Learning Frameworks

Advancements in Lung Cancer Diagnosis: A Comprehensive Study...

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Communication and Networks (ASIANComNet), Asian Conference on

作者： B Vikas Satya Sukumar Makkapati Srinivasa Rao Bogireddy K.S. Balamurugan M. Deepa Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Hyderabad Telangana India Department of Computer Science and Engineering Acharya Nagarjuna University Guntur District A.P India Horizon Systems Inc Phoenix Arizona USA Department of Electronics and Communication Engineering Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India Department of IT Sri Shakthi Institute of Engineering and Technology Coimbatore

ISBN: (数字)9798350367003

ISBN: (纸本)9798350367225

In the ever-evolving domain of medical imaging, the integration of deep learning techniques holds the promise of transformative advancements. This research delved into the potential of employing data transfer within deep learning architectures for the automated detection of three distinct lung cancer types. Leveraging sophisticated methodologies like linear discriminant analysis (LDA), t-SNE, and PCA, the study aimed to enhance accuracy and efficiency in detecting malignancies from lung CT scan images. On rigorous evaluation, the models demonstrated compelling accuracy rates: salivary gland-type lung tumors at $\mathbf{9 0. 5 \%}$, pleomorphic (spindle/giant cell) carcinoma at $88.2 \%$, and primary pulmonary sarcomas at $91.3 \%$. Additionally, ROC curve analysis further highlighted the robust discriminative capability of the models across varied decision thresholds. The promising results accentuate the potential of integrating data transfer techniques with deep learning in a clinical setting. This research not only exhibits a significant stride in lung cancer detection but also paves the path for further innovations in automated medical image analysis.

关键词： Deep learning Analytical models Accuracy Lungs Microprocessors Lung cancer computer architecture Data transfer Tumors Principal component analysis

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Interference reduction in directional wireless networks 17th

Interference reduction in directional wireless networks

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17th International Conference on Distributed Computing and Internet Technology, ICDCIT 2021

作者： Basappa, Manjanna Mishra, Sudeepta Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Hyderabad Campus Shameerpet500078 India Department of Computer Science and Engineering Indian Institute of Technology Ropar Nangal Road Rupnagar140001 India

ISBN: (纸本)9783030656201

In a wireless network using directional transmitters, a typical problem is to schedule a set of directional links to cover all the receivers in a region, such that an adequate data rate and coverage are maintained while minimizing interference. We can model the coverage area of a directional transmitter as an unit triangle and the receiver as a point in the plane. Motivated by this, we first consider a minimum ply covering (MPC) problem. We propose a 2-approximation algorithm for the MPC problem in O((opt+ n) m14opt+1(log opt)) time, where m is the number of transmitters and n is the number of receivers given in the plane, and opt is the maximum number of triangles, in the optimal solution, covering a point in the plane. We also show that the MPC problem is NP-hard, and is not (2 - ϵ) -approximable for any ϵ> 0 unless P = NP. We also study channel allocation in directional wireless networks by posing it as a colorable covering problem, namely, 3-colorable unit triangle cover (3CUTC). We propose a simple 4-approximation algorithm in O(m30n2) time, for this problem. © Springer Nature Switzerland AG 2021.

关键词： Approximation algorithms

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FireXnet: an explainable AI-based tailored deep learning model for wildfire detection on resource-constrained devices

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Fire Ecology 2023年第1期19卷 1-19页

作者： Ahmad, Khubab Khan, Muhammad Shahbaz Ahmed, Fawad Driss, Maha Boulila, Wadii Alazeb, Abdulwahab Alsulami, Mohammad Alshehri, Mohammed S. Ghadi, Yazeed Yasin Ahmad, Jawad Faculty of Engineering and Technology Multimedia University Melaka Malaysia School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK Department of Cyber Security Pakistan Navy Engineering College NUST Karachi Pakistan Robotics and Internet of Things Laboratory Prince Sultan University Riyadh Saudi Arabia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia Department of Computer Science College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Software Engineering Computer Science Al Ain University Abu Dhabi United Arab Emirates

Forests cover nearly one-third of the Earth’s land and are some of our most biodiverse ecosystems. Due to climate change, these essential habitats are endangered by increasing wildfires. Wildfires are not just a risk to the environment, but they also pose public health risks. Given these issues, there is an indispensable need for efficient and early detection methods. Conventional detection approaches fall short due to spatial limitations and manual feature engineering, which calls for the exploration and development of data-driven deep learning solutions. This paper, in this regard, proposes 'FireXnet', a tailored deep learning model designed for improved efficiency and accuracy in wildfire detection. FireXnet is tailored to have a lightweight architecture that exhibits high accuracy with significantly less training and testing time. It contains considerably reduced trainable and non-trainable parameters, which makes it suitable for resource-constrained devices. To make the FireXnet model visually explainable and trustable, a powerful explainable artificial intelligence (AI) tool, SHAP (SHapley Additive exPlanations) has been incorporated. It interprets FireXnet’s decisions by computing the contribution of each feature to the prediction. Furthermore, the performance of FireXnet is compared against five pre-trained models — VGG16, InceptionResNetV2, InceptionV3, DenseNet201, and MobileNetV2 — to benchmark its efficiency. For a fair comparison, transfer learning and fine-tuning have been applied to the aforementioned models to retrain the models on our dataset. The test accuracy of the proposed FireXnet model is 98.42%, which is greater than all other models used for comparison. Furthermore, results of reliability parameters confirm the model’s reliability, i.e., a confidence interval of [0.97, 1.00] validates the certainty of the proposed model’s estimates and a Cohen’s kappa coefficient of 0.98 proves that decisions of FireXnet are in considerable accordance with t

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RELEAD: Resilient Localization with Enhanced LiDAR Odometry in Adverse Environments

arXiv

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arXiv 2024年

作者： Chen, Zhiqiang Chen, Hongbo Qi, Yuhua Zhong, Shipeng Feng, Dapeng Wu, Jin Wen, Weisong Liu, Ming School of Systems Science and Engineering Sun Yatsen University Guangzhou510006 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Department of Aeronautical and Aviation Engineering Hong Kong Polytechnic University Hong Kong

LiDAR-based localization is valuable for applications like mining surveys and underground facility maintenance. However, existing methods can struggle when dealing with uninformative geometric structures in challenging scenarios. This paper presents RELEAD, a LiDAR-centric solution designed to address scan-matching degradation. Our method enables degeneracy-free point cloud registration by solving constrained ESIKF updates in the front end and incorporates multisensor constraints, even when dealing with outlier measurements, through graph optimization based on Graduated Non-Convexity (GNC). Additionally, we propose a robust Incremental Fixed Lag Smoother (rIFL) for efficient GNC-based optimization. RELEAD has undergone extensive evaluation in degenerate scenarios and has outperformed existing state-of-the-art LiDAR-Inertial odometry and LiDAR-Visual-Inertial odometry methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Constrained optimization

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Minimization of fanout in switching networks 15

Minimization of fanout in switching networks

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15th Annual Symposium on Switching and Automata Theory, SWAT 1974

作者： Hayes, John P. Department of Electrical Engineering and Computer Science Program University of Southern California Los AngelesCA90007 United States

The problem of finding the minimum amount of fanout needed to realize a switching function f is investigated. Fanout-free functions are defined, and necessary and sufficient conditions for a function to be fanout-free are derived. A measure '(f) called the input fanout index, is introduced which represents the minimum number of input variables that fan out in any realization of f. I is shown that t(f), can be determined from the prime implicants and implicates of f. Another measure of fanout u(f), which is the minimum numbe r of signal lines that must fan out in any network realizing f is defined, and some of the properties of u(f) are examined. © 2021 IEEE. All rights reserved.

关键词： Switching networks

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From Deep Learning to Interpretable and Explainable Deep Learning in Medical Image Computing: Balancing Innovation with Ethics and Responsibilities

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Procedia computer science 2024年 246卷 302-311页

作者： Abdellah Chehri Imran Ahmed Gwanggil Jeon Animal and Agriculture Department Hartpury University Gloucester UK Department of Mathematics and Computer Science at the Royal Military College of Canada (RMC) Canada School of Computing and Information Science Anglia Ruskin University Cambridge UK Department of Embedded Systems Engineering Incheon National University Incheon Korea

The utilization of Artificial intelligence (AI) and other cutting-edge techniques in the field of medical image analysis has exhibited significant potential. Nevertheless, a significant obstacle that impedes the extensive implementation of these models in the healthcare sector is their restricted interpretability. The concept of explainability is a subject of extensive discussion and debate within the context of utilizing Artificial intelligence in the healthcare domain. Notwithstanding the empirical evidence demonstrating the superior performance of AI-driven systems compared to humans in certain analytical tasks, particularly in the field of medical image computing, these systems still encounter challenges due to their limited explainability. The present study provides a comprehensive assessment of the significance of explainability in the field of medical Artificial intelligence and performs an ethical analysis of the influence of explainability on the incorporation of AI-driven tools in data engineering in medicine and health care. The paper examines various subjects including data security, confidentiality, privacy, fairness, and discrimination, among others.

关键词： Deep Learning Medical Imaging Explainable AI Data Analytics Ethics Responsibilities

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A Multiscale Gradient Fusion Method for Edge Detection in Color Images Utilizing the CBM3D Filter

arXiv

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arXiv 2024年

作者： Wang, Zhuoyue Tao, Yiyi Ma, Danqing Chen, Jiajing Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley United States Department of Computer Science Johns Hopkins University Baltimore United States School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff United States

In this paper, a color edge detection strategy based on collaborative filtering combined with multiscale gradient fusion is proposed. The block-matching and 3D (BM3D) filter are used to enhance the sparse representation in the transform domain and achieve the effect of denoising, whereas the multiscale gradient fusion makes up for the defect of loss of details in single-scale edge detection and improves the edge detection resolution and quality. First, the RGB images in the dataset are converted to XYZ color space images through mathematical operations. Second, the colored block-matching and 3D (CBM3D) filter are used on the sparse images and to remove noise interference. Then, the vector gradients of the color image and the anisotropic Gaussian directional derivative of the two scale parameters are calculated and averaged pixel-by-pixel to obtain a new edge strength map. Finally, the edge features are enhanced by image normalization and non-maximum suppression technology, and on that basis, the edge contour is obtained by double threshold selection and a new morphological refinement method. Through an experimental analysis of the edge detection dataset, the method proposed has good noise robustness and high edge quality, which is better than the Color Sobel, Color Canny, SE and Color AGDD as shown by the PR curve, AUC, PSNR, MSE, and FOM indicators. Copyright © 2024, The Authors. All rights reserved.

关键词： Collaborative filtering

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A Machine Learning Technique for Detection of Diabetes Mellitus

A Machine Learning Technique for Detection of Diabetes Melli...

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Information Technology for Education and Development (ITED)

作者： O.M. Awoniran M.O. Oyelami R.N. Ikono R.F. Famutimi T.I. Famutimi Computer Science Program Bowen University Iwo Nigeria Department of Computer Science and Engineering Obafemi Awolowo University Ile-Ife Nigeria

The need for early detection of diabetes mellitus has led to the development of various intelligent systems using machine learning and artificial intelligence for the recognition of the presence of the disease. However, most of the techniques have yielded a comparatively lower accuracy. This research applied data science techniques to a dataset of diabetes mellitus to improve the accuracy of the prediction of the disease. This was achieved by pre-processing the data with dummy categories and applying principal components analysis for reduced dimensionality. Support vector machine, random forest classifier, and deep neural networks were then used to train the system. Support vector machine, random forest classifier, and deep neural networks yielded accuracies of 0.76, 0.77, and 0.89 respectively. Correspondingly, deep neural networks yielded the highest accuracy. The study concluded that better pre-processing will improve the accuracy of machine learning algorithms in the prediction of diabetes mellitus.

关键词： Deep learning Support vector machines Machine learning algorithms Neural networks Prediction algorithms Diabetes Classification algorithms

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Joint Source Decomposition of Concurrent EEG-FMRI Data in Epilepsy and Control Groups

Joint Source Decomposition of Concurrent EEG-FMRI Data in Ep...

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IEEE International Symposium on Biomedical Imaging

作者： J. Mason Harding Haatef Pourmotabbed Yamin Li Kimberly Rogge-Obando Kate Wang Sarah E. Goodale Shiyu Wang Camden Bibro Bergen Allee Caroline Martin Victoria L. Morgan Dario J. Englot Catie Chang Department of Electrical and Computer Engineering Vanderbilt University Department of Biomedical Engineering Vanderbilt University Department of Computer Science Vanderbilt University Neuroscience Graduate Program Vanderbilt University Vanderbilt Memory and Alzheimer's Center Vanderbilt University Medical Center Department of Neurological Surgery Vanderbilt University Medical Center Department of Radiology Vanderbilt University Medical Center

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

EEG and fMRI are complementary, noninvasive technologies for investigating human brain function. These modalities have been used to uncover large-scale functional networks and their disruptions in clinical populations. Given the high temporal resolution of EEG and high spatial resolution of fMRI, integrating these modalities can provide a more holistic understanding of brain activity. This work explores a multimodal source decomposition technique for extracting shared modes of temporal variation between fMRI BOLD signals and EEG spectral power fluctuations in the resting state. The resulting components are then compared between patients with focal epilepsy and controls, revealing multimodal network differences between groups.

关键词： Neuroimaging Brain Fluctuations Epilepsy Functional magnetic resonance imaging Electroencephalography Pattern recognition Hemodynamics Spatial resolution Signal resolution

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