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Distortion minimization for multimedia transmission in NOMA HAP-UAV integrated aerial access networks

Chinese Journal of Aeronautics 2022年第9期35卷 81-94页

作者： Guobin ZHANG Zhu HAN Hang XIN Xin WEI Boya DI School of Electrical Engineering and Intelligentization Dongguan University of TechnologyDongguan 523808China Department of Electrical and Computer Engineering and Department of Computer Science University of HoustonTX77004USA School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China Department of Computing Imperial CollegeLondon SW72AzUK

Aerial access networks have been envisioned as a promising 6G solution to enhance the ground communication systems in both coverage and capacity. To better utilize the spectrum and fully explore different channel characteristics, this paper constructs an integrated network comprising the High Altitude Platform(HAP) and Unmanned Air Vehicles(UAVs) with the NonOrthogonal Multiple Access(NOMA) technology. In order to improve the transmission quality of images and videos, a power management scheme is proposed to minimize the distortion of the transmissions from the HAP and UAVs to the terminals. The power control is formulated as a non-convex problem constrained by the maximal transmit power and the minimal terminal rate requirements. The variable substitution and the first-order Tailor’s expansion is used to transform it into a sequence of convex problems, which are subsequently solved through the gradient projection method. Simulation demonstrates the signal distortion and error rate improvement achieved by the proposed algorithm.

关键词： Aerial Access Networks High Altitude Platform Non-Orthogonal Multiple Access Sixth-Generation Unmanned Air Vehicles

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Performance Analysis of Denoising Filters for Ultrasound Fetal Head Images

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SN computer science 2025年第2期6卷 1-13页

作者： Prasad, Kauleshwar Patnaik, Pawan Kumar Agrawal, Anupam Department of Computer Science and Engineering Bhilai Institute of Technology Chhattisgarh Durg India Department of Electrical and Electronics Engineering Bhilai Institute of Technology Chhattisgarh Durg India

Deep learning has significantly advanced image processing in the medical domain, including the analysis of ultrasound (US) fetal images for prediction of fetal growth retardation. Before analysis of fetal images there is need of correction since they are usually adulterated by noise during acquisition of image. In the recent years, there are various deep learning based denoising methods in which U-Net have attracted the attention of many researchers. This paper describes the comparative study of different filters such as mean filter, gaussian filter, anisotropic diffusion, non-local means (NLM) filter and U-Net based on their performance on denoising fetal images prior to the prediction of fetal growth retardation (FGR). In this article peak signal to noise ratio (PSNR), mean square error (MSE), similarity index measure (SSIM), peak signal to interference ratio (PSIM) and multi scale structural similarity index measure (MSIM) values are analyzed for the performance of these filters. This paper reveals that U-Net performs better in image quality evaluation when compared to other filtering algorithms, based on testing and analytical findings. We have also discussed the significance of MSE, PSNR, SSIM, PSIM, MSIM and U-Net in this article. This article also covers the advantages, disadvantages, and characteristics of different filtering methods, including U-Net. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Denoising techniques Fetal growth retardation U-Net

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A multicore-based algorithm for optimal multi-way number partitioning

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International Journal of Information Technology (Singapore) 2023年第6期15卷 2929-2940页

作者： Abdelsalam, Kamel M. K. Khamis, Soheir M. Bahig, Hatem M. Bahig, Hazem M. Computer Science Division Mathematics Department Faculty of Science Ain Shams University Cairo Egypt Information and Computer Science Department College of Computer Science and Engineering Ha’il University Ha’il Saudi Arabia

The Multi-Way Number Partitioning (MWNP) problem is to divide a multiset of n numbers into k subsets in such a way that the largest subset sum is minimized. MWNP is an NP-hard combinatorial optimization problem which requires a high computational time. It has applications in many areas, such as processor scheduling and public key encryption. In this paper, we design a fast parallel algorithm that finds an exact solution for the MWNP problem based on the recursive principle of optimality. The practical studies on a multicore system and data set generated in the range [1 , 2 ⁴⁸- 1] show that the proposed parallel algorithm is able to reduce the running time of the corresponding sequential algorithm. The results of the parallel algorithm show that, using 16 cores, the algorithm achieves, on average, a speedup of 10 and a percentage 90 % of improvement in running time over its sequential counterpart. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Multicore systems Number partitioning Optimization, NP-hard Parallel algorithms

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Hybrid Technique-based Optimal Energy Management in Smart Home Appliances

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IEIE Transactions on Smart Processing and Computing 2024年第5期13卷 425-434页

作者： Shirley, C.P. Depaa, R.A.B. Priya, A. Sarala, R. Solanki, Rajdeep Singh Malini, K.V. Venkatakrishna Reddy, Ch. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Civil Engineering Dr. M.G.R Educational and Research Institute Maduravoyal Chennai95 India School of Architecture Koneru Lakshmaiah Educational Foundation Andhra Pradesh Guntur India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai Rameshwaram High Road Viraganoor Madurai625009 India Department of Computer Science Medi-Caps University Indore India Department of Electrical and Electronics Engineering Sri Sairam College of Engineering Anekal India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad75 India

This manuscript presents a hybrid method for optimal energy management in smart home appliances. The proposed approach combines the Ebola Optimization Search Algorithm (EOSA) with the performance of spiking neural networks (SNN). The key objective of the proposed strategy is cost reduction through day-ahead load scheduling while also considering the best demand response (DR), and self-consumption of photovoltaic (PV). The EOSA method manages air conditioning (AC) and ensures thermal comfort. In addition, the SNN method was used to predict the optimal system control signal. Comparative analysis with existing systems, such as PSO, RFA, and BCO, showed that the proposed strategy resulted in 13% cost savings. The proposed system considered parameters, such as solar radiation, occupant presence, and electricity prices. The proposed technique is implemented in the MATLAB software, and the performance is compared with existing approaches. The efficiency of the proposed approach is higher than the existing techniques. © 2024 The Institute of Electronics and Information Engineers.

关键词： Demand response

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CPDZ: A Credibility-Aware and Privacy-Preserving Data Collection Scheme with Zero-Trust in Next-Generation Crowdsensing Networks

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2183-2199页

作者： Tang, Jianheng Fan, Kejia Yang, Shihao Liu, Anfeng Xiong, Neal N. Song, Houbing Herbert Leung, Victor C. M. Central South University School of Computer Science and Engineering Changsha410083 China Peking University School of Computer Science Beijing100871 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States Department of Information Systems BaltimoreMD21250 United States Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Next-Generation Crowdsensing Networks (NGCNs) have become increasingly critical for smart cities, where data privacy and quality are pivotal concerns. Traditional trust mechanisms in crowdsensing mainly rely on static trust models, which are insufficient for dynamic security requirements. Zero-Trust security represents a promising opportunity, yet coming with notable challenges in NGCNs, including Unknown Workers Online Recruitment (UWOR), Information Elicitation Without Verification (IEWV), Privacy Preserving Data Evaluation (PPDE), and Dynamic Trust Abrupt Shift (DTAS). To address these challenges, we propose a Credibility-aware and Privacy-preserving Data collection scheme with Zero-trust (CPDZ) for secure and quality data collection in NGCNs. First, our CPDZ scheme encompasses a quality worker recruitment strategy with combinatorial multi-armed bandit models, utilizing Thompson Sampling for the secure and efficient resolution of the UWOR. Second, an active dispatching scheme for unmanned aerial vehicles is crafted to collect data as a gold standard to assist in overcoming the IEWV challenge. Third, as for the PPDE challenge, we propose a lightweight privacy-preserving scheme for dependable truth discovery and secure trust verification. Fourth, the DTAS challenge is managed by a dual verification scheme that integrates short-term and long-term trust assessments, ensuring stability and adaptability of the zero-trust security in our CPDZ scheme. Experiments confirm the superiority of our CPDZ scheme, showing a 12.5% increase in recruitment revenue and a 57.8% reduction in relative error compared to existing approaches. © 1983-2012 IEEE.

关键词： Differential privacy

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Microscale fiber-integrated vector magnetometer with on-tip field biasing using N-V ensembles in diamond microcrystals

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Physical Review Applied 2024年第3期22卷 034029页

作者： Jonas Homrighausen Frederik Hoffmann Jens Pogorzelski Peter Glösekötter Markus Gregor Department of Physics Engineering Department of Electrical Engineering and Computer Science

In quantum sensing of magnetic fields, ensembles of nitrogen-vacancy centers in diamond offer high sensitivity, high bandwidth and outstanding spatial resolution while operating in harsh environments. Moreover, the orientation of defect centers along four crystal axes forms an intrinsic coordinate system, enabling vector magnetometry within a single diamond crystal. While most vector magnetometers rely on a known bias magnetic field for full recovery of three-dimensional (3D) field information, employing external 3D Helmholtz coils or permanent magnets results in bulky, laboratory-bound setups, impeding miniaturization of the device. Here, a novel approach is presented that utilizes a fiber-integrated microscale coil at the fiber tip to generate a localized uniaxial magnetic field. The same fiber-tip coil is used in parallel for spin control by combining dc and microwave signals in a bias tee. To implement vector magnetometry using a uniaxial bias field, the orientation of the diamond crystal is preselected and then fully characterized by rotating a static magnetic field in three planes of rotation. The measurement of vector magnetic fields in the full solid angle is demonstrated with a shot-noise-limited sensitivity of 19.4nT/Hz1/2 and microscale spatial resolution while achieving a fiber sensor head cross section of less than 1mm2.

关键词： Magneto-optics Magnetometry Quantum sensing Devices Nitrogen vacancy centers in diamond Optical fibers Fluorescence Optically detected magnetic resonance

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Self-Adaptive Physics Informed Neural Network for Paper Insulation Degree of Polymerization Prediction 50

Self-Adaptive Physics Informed Neural Network for Paper Insu...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Alquennah, Alamera Nouran AlShaikh Saleh, Mohammad Ghrayeb, Ali Abu-Rub, Haitham Refaat, Shady S. Al-Hajri, Mohammed Khatri, Sunil Department of Electrical and Computer Engineering Texas A&M University College StationTX United States Department of Electrical and Computer Engineering Texas A&M University at Qatar Qatar Foundation Doha Qatar Department of Electrical and Computer Engineering College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar School of Physics Engineering and Computer Science University of Hertfordshire United Kingdom Doha Qatar

ISBN: (纸本)9781665464543

This paper proposes a self adaptive physics informed neural network (SAPINN) model to predict the degree of polymerization (DP) of oil-impregnated paper insulation to quantify the level of degradation and the remaining useful lifetime. The prediction is performed based on historical DP values and the corresponding prediction time step, which are used as input data points to the proposed model. The DP mathematical model is used to constrain the training phase of the AI-model through a weighted sum loss function. The weights of this loss function are adjusted for each epoch through a self-adaptive weighting method to determine the relative importance of the data component and the mathematical model throughout the training by defining these weights as trainable parameters. The trained model is then tested using different datasets which are not part of the training phase. The training and testing datasets are generated synthetically through an algorithm that considers the deviation from the ideal DP degradation curve and incorporates actual measurement noise. The performance of the proposed SAPINN is compared to the baseline PINN and NN (in the absence of physics) to highlight the importance of embedding the mathematical model and the self adaptation algorithm, and theses experiments demonstrate that SAPINN significantly enhances the DP prediction. © 2024 IEEE.

关键词： Forecasting

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Position: AI/ML Influencers Have a Place in the Academic Process 41

Position: AI/ML Influencers Have a Place in the Academic Pro...

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41st International Conference on Machine Learning, ICML 2024

作者： Weissburg, Iain Xie Arora, Mehir Wang, Xinyi Pan, Liangming Wang, William Yang Department of Electrical and Computer Engineering University of California Santa Barbara United States Department of Computer Science University of California Santa Barbara United States

As the number of accepted papers at AI and ML conferences reaches into the thousands, it has become unclear how researchers access and read research publications. In this paper, we investigate the role of social media influencers in enhancing the visibility of machine learning research, particularly the citation counts of papers they share. We have compiled a comprehensive dataset of over 8,000 papers, spanning tweets from December 2018 to October 2023, alongside controls precisely matched by 9 key covariates. Our statistical and causal inference analysis reveals a significant increase in citations for papers endorsed by these influencers, with median citation counts 2-3 times higher than those of the control group. Additionally, the study delves into the geographic, gender, and institutional diversity of highlighted authors. Given these findings, we advocate for a responsible approach to curation, encouraging influencers to uphold the journalistic standard that includes showcasing diverse research topics, authors, and institutions. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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Secured Cyber Security Algorithm for Healthcare System Using Blockchain Technology

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Intelligent Automation & Soft Computing 2023年第2期35卷 1889-1906页

作者： D.Doreen Hephzibah Miriam Deepak Dahiya Nitin C.R.Rene Robin Computational Intelligence Research Foundation(CIRF) Chennai600023TamilnaduIndia College of Computer and Information Sciences Majmaah UniversityMajmaah11952Saudi Arabia Department of Electrical Engineering and Computer Science College of Engineering and Applied ScienceUniversity of CincinnatiCincinnati45221OHUnited States Department of Computer Science and Engineering Sri Sairam Engineering CollegeChennai600044TamilnaduIndia

Blockchain technology is critical in cyber *** most recent cryptographic strategies may be hacked as efforts are made to build massive elec-tronic *** of the ethical and legal implications of a patient’s medical data,cyber security is a critical and challenging problem in *** image secrecy is highly vulnerable to various types of *** a result,designing a cyber security model for healthcare applications necessitates extra caution in terms of data *** resolve this issue,this paper proposes a Lionized Golden Eagle based Homomorphic Elapid Security(LGE-HES)algorithm for the cybersecurity of blockchain in healthcare *** blockchain algorithm preserves the security of the medical image by performing hash *** execution of this research is carried out by MATLAB *** suggested fra-mework was tested utilizing Computed Tumor(CT)pictures and MRI image data-sets,and the simulation results revealed the proposed model’s profound *** the simulation,94.9%of malicious communications were recognized and identiﬁed effectively,according to the total outcomes *** suggested model’s performance is also compared to that of standard approaches in terms of Root Mean Square Error(RMSE),Peak Signal to Noise Ratio(PSNR),Mean Square Error(MSE),time complexity,and other factors.

关键词： Healthcare system cybersecurity security data sharing blockchain

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Logistic Regression based Sentiment Analysis System: Rectify

Logistic Regression based Sentiment Analysis System: Rectify

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2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

作者： Singh, Harsh Pratap Singh, Nagendra Mishra, Anuprita Sen, Santosh Kumar Swarnkar, Mamta Pandey, Deepak Shri Vaishnav Vidyapeeth Vishwavidyalaya Department of Computer Science and Engineering Indore India Trinity College of Engineering & Technology Department of Electrical Engineering Karimnagar India IES College of Technology Department of Electrical and Electronics Engineering Bhopal462044 India IES University Department of Electrical and Electronics Engineering Bhopal426044 India Corporate institute of Science and Technology Department of Computer Science and Engineering Bhopal505001 India MANIT Department of Computer Science and Engineering Bhopal India TIT Department of EEE Bhopal India

ISBN: (纸本)9798350374100

The detection of various reactions using computer vision, machine learning, and artificial intelligence is a rapidly growing field of research. In this paper, we present a sentiment analysis model based on the Python, NLTK (natural language toolkit) libraries, and machine learning algorithms that can detect multiple reactions from the public on a single platform. The proposed Sentiment Analysis System deals with the very usual problem faced by different companies, manufacturers, and sellers about knowing the customer review for their product. The model classifies the public reviews and the normal text as positive, negative, or neutral. This proposed system is categorised into two parts, one being a field-specific sentiment analysis and the other being a generalised system that can judge any particular word or sentence to be either positive, negative, or neutral. The proposed model is also capable of representing a huge dataset (input as a csv file) in the form of a graph, which can be easily understood by the desired person, and the graphical representation is possible with the Matplotlib library of Python. The model's performance is evaluated using several metrics, such as precision, recall, and others. The model's accuracy and efficiency make it a promising tool for sentiment analysis, which can be used by anyone in any field. In conclusion, the system can work for any specific field provided the data set, and the generalized way can help out with random sentences, and the model's potential can be boosted with some further research. © 2024 IEEE.

关键词： Python

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