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Energy-Efficient Data Offloading for Earth Observation Satellite Networks

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arXiv 2024年

作者： He, Lijun Jia, Ziye Wang, Juncheng Wang, Feng Lansard, Erick Yuen, Chau School of Software Northwestern Polytechnical University China Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics China Department of Computer Science Hong Kong Baptist University China Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Electrical and Electronics Engineering Nanyang Technological University Singapore

In Earth Observation Satellite Networks (EOSNs) with a large number of battery-carrying satellites, proper power allocation and task scheduling are crucial to improving the data offloading efficiency. As such, we jointly optimize power allocation and task scheduling to achieve energy-efficient data offloading in EOSNs, aiming to balance the objectives of reducing the total energy consumption and increasing the sum weights of tasks. First, we derive the optimal power allocation solution to the joint optimization problem when the task scheduling policy is given. Second, leveraging the conflict graph model, we transform the original joint optimization problem into a maximum weight independent set problem when the power allocation strategy is given. Finally, we utilize the genetic framework to combine the above special solutions as a two-layer solution for the joint optimization problem. Simulation results demonstrate that our proposed solution can properly balance the sum weights of tasks and the total energy consumption, achieving superior system performance over the current best alternatives. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy utilization

Mechanical Vibrations on a Deployable Nanosatellite Antenna: SAR Performance Analysis

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Mechanical Vibrations on a Deployable Nanosatellite Antenna:...

European Conference on Antennas and Propagation, EuCAP

作者： Andrew C. M. Austin Timothy Pelham Simone Mencarelli Annalisa Tresoldi Mohammed Dabboor Guglielmo Aglietti Michael J. Neve Communication Systems and Networks Group University of Bristol Bristol UK Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand Te Pūnaha Ātea—Auckland Space Institute University of Auckland Auckland New Zealand Science and Technology Branch Environment and Climate Change Canada Dorval QC Canada

The mechanical vibrations of a high-strain glass-fibre composite deployable X-band synthetic aperture radar (SAR) antenna structure are investigated using a finite element method. The antenna is approximately $\mathrm{2}\ \mathrm{m}\times \mathrm{0}.\mathrm{3}\ \mathrm{m}$ , and exhibits natural frequencies at 2.8 Hz and 7.6 Hz. The radiation patterns for a reflectarray formed from the structure are calculated using a ray-tracing approach for the nominal and mechanically deformed cases. The mechanically deformed antenna pattern is defocused, and a SAR system model shows this reduces the signal-to-noise ratio of a point scattering target by approximately 6 dB.

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Training Differentially Private Models with Secure Multiparty Computation

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arXiv 2022年

作者： Pentyala, Sikha Railsback, Davis Maia, Ricardo J.M. Melanson, David Dowsley, Rafael Nascimento, Anderson De Cock, Martine School of Engineering and Technology University of Washington Tacoma United States Department of Computer Science University of Brasilia Brazil Department of Software Systems and Cybersecurity Monash University Australia Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium Mila - Quebec Ai Institute Canada

We address the problem of learning a machine learning model from training data that originates at multiple data owners, while providing formal privacy guarantees regarding the protection of each owner's data. Existing solutions based on Differential Privacy (DP) achieve this at the cost of a drop in accuracy. Solutions based on Secure Multiparty Computation (MPC) do not incur such accuracy loss but leak information when the trained model is made publicly available. We propose an MPC solution for training DP models. Our solution relies on an MPC protocol for model training, and an MPC protocol for perturbing the trained model coefficients with Laplace noise in a privacy-preserving manner. The resulting MPC+DP approach achieves higher accuracy than a pure DP approach, while providing the same formal privacy guarantees. Our work obtained first place in the iDASH2021 Track III competition on confidential computing for secure genome analysis. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

Physics-informed Inverse Design of Multi-bit Programmable Metasurfaces

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arXiv 2024年

作者： Xu, Yucheng Yang, Jia-Qi Fan, Kebin Wang, Sheng Wu, Jingbo Zhang, Caihong Zhan, De-Chuan Padilla, Willie J. Jin, Biaobing Chen, Jian Wu, Peiheng Key Laboratory of Optoelectronic Devices and Systems with Extreme Performances of MOE School of Electronic Science and Engineering Nanjing University Nanjing210023 China Purple Mountain Laboratories Nanjing211111 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Electrical and Computer Engineering Duke University Box 90291 DurhamNC27708 United States

Emerging reconfigurable metasurfaces offer various possibilities in programmatically manipulating electromagnetic waves across spatial, spectral, and temporal domains, showcasing great potential for enhancing terahertz applications. However, they are hindered by limited tunability, particularly evident in relatively small phase tuning over 270o, due to the design constraints with time-intensive forward design methodologies. Here, we demonstrate a multi-bit programmable metasurface capable of terahertz beam steering, facilitated by a developed physics-informed inverse design (PIID) approach. Through integrating a modified coupled mode theory (MCMT) into residual neural networks, our PIID algorithm not only significantly increases the design accuracy compared to conventional neural networks but also elucidates the intricate physical relations between the geometry and the modes. Without decreasing the reflection intensity, our method achieves the enhanced phase tuning as large as 300o. Additionally, we experimentally validate the inverse designed programmable beam steering metasurface, which is adaptable across 1-bit, 2-bit, and tri-state coding schemes, yielding a deflection angle up to 68o and broadened steering coverage. Our demonstration provides a promising pathway for rapidly exploring advanced metasurface devices, with potentially great impact on communication and imaging technologies. © 2024, CC0.

关键词： Tuning

A Modified Symbiotic Organism Search Algorithm with Lévy Flight for software Module Clustering Problem 1

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5th International Conference on Electrical, Control and computer engineering, InECCE 2019

作者： Zainal, Nurul Asyikin Zamli, Kamal Z. Din, Fakhrud Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Gambang KuantanPahang26300 Malaysia Department of Computer Science & IT University of Malakand Khyber Pakhtunkhwa Pakistan

ISBN: (数字)9789811523175

ISBN: (纸本)9789811523168

To date, there are much increasing trends on adopting parameter free meta-heuristic algorithms for solving general optimization problems. With parameter free algorithms, there are no parameter controls for tuning. As such, the adoption of parameter free meta-heuristic algorithms is often straightforward. On the negative note, exploration (i.e. roaming the search space thoroughly) and exploitation (i.e. manipulating the current known best neighbor) are pre-set. As the search spaces are problem dependent, any pre-set exploration and exploitation can lead to entrapment in local optima. In this paper, we investigate the use of Lévy flight to enhance the exploration of a parameter free meta-heuristic algorithm, called Modified Symbiotic Organism Search Algorithm (MSOS), via its population initialization. Our experimentations involving the software module clustering problems have been encouraging, as MSOS gives competitive results against existing selected parameter free meta-heuristic algorithms. For all the given module clustering problems, MSOS generates overall best mean results. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

An Efficient Searchable Encryption Framework with Weighted Keywords for the Internet of Medical Things (IoMT)

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TechRxiv

TechRxiv 2022年

作者： Wang, Haiyan Li, Yuan He, Ying Luo, Fucai Yu, F. Richard Ming, Zhong Wang, Shulan The College of Computer Science and Software Engineering Shenzhen University Shenzhen China The Department of New Networks Pengcheng Laboratory Shenzhen China The Department of Systems and Computer Engineer Carleton University OttawaON Canada The College of Big Data and Internet Shenzhen Technology University Shenzhen China

With the increasing popularity of smart terminals, the Internet of Things (IoT) vastly expands the influence of information technology and creates great values to our society. The Internet of Medical Things (IoMT) is a typical application of the IoT specifically for medical and healthcare related purposes. In IoMT, personal health records (PHRs) generated by intelligent equipment are usually stored in a cloud sever managed by a semi-trusted cloud provider. However, there is still a possibility of the exposure of private information to unauthorized parties. In order to protect a patient’s private information, and ensure that doctors can search a patient’s PHRs without decrypting all ciphertexts, an efficient searchable encryption framework with weighted keywords is proposed in this paper. In this framework, we implement a weighted keyword model to raise the accuracy of search results, and reduce the computational and storage overhead. Besides, compared with the existing schemes, the framework is supportable of multi-search mechanism, which is adaptable to more application scenarios. Finally, the efficiency and security of this framework is proved through security and performance analysis in this paper. © 2022, CC BY.

关键词： Cryptography

Assurance monitoring of learning enabled cyber-physical systems using inductive conformal prediction based on distance learning

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arXiv 2021年

作者： Boursinos, Dimitrios Koutsoukos, Xenofon Department of Electrical Engineering and Computer Science Institute for Software Integrated Systems Vanderbilt University NashvilleTN United States

Machine learning components such as deep neural networks are used extensively in Cyber-Physical systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance monitoring of learning-enabled CPS based on the conformal prediction framework. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. By leveraging conformal prediction, the approach provides well-calibrated confidence and ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. We demonstrate the approach using three data sets of mobile robot following a wall, speaker recognition, and traffic sign recognition. The experimental results demonstrate that the error rates are well-calibrated while the number of alarms is very small. Further, the method is computationally efficient and allows real-time assurance monitoring of CPS. © 2021, CC BY.

关键词： Embedded systems

Efficient Resource Allocation and User Association in NOMA-Enabled Vehicular-Aided HetNets with High Altitude Platforms

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arXiv 2024年

作者： Nauman, Ali Maashi, Mashael Alkahtani, Hend K. Al-Wesabi, Fahd N. Aljehane, Nojood O. Assiri, Mohammed Ibrahim, Sara Saadeldeen Khan, Wali Ullah The Department of Information and Communication Engineering Yeungnam University Korea Republic of The Department of Software Engineering College of Computer and Information Sciences King Saud University Po box 103786 Riyadh11543 Saudi Arabia The Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia The Department of Computer Science College of Science & Art at Mahayil King Khalid University Saudi Arabia The Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia The Department of Computer Science College of Sciences and Humanities- Aflaj Prince Sattam bin Abdulaziz University Aflaj 16273 Saudi Arabia The Department of Computer and Self Development Preparatory Year Deanship Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia University of Luxembourg Luxembourg

The increasing demand for massive connectivity and high data rates has made the efficient use of existing spectrum resources an increasingly challenging problem. Non-orthogonal multiple access (NOMA) is a potential solution for future heterogeneous networks (HetNets) due to its high capacity and spectrum efficiency. In this study, we analyze an uplink NOMA-enabled vehicular-aided HetNet, where multiple vehicular user equipment (VUEs) share the access link spectrum, and a high-altitude platform (HAP) communicates with roadside units (RSUs) through a backhaul communication link. We propose an improved algorithm for user association that selects VUEs for HAPs based on channel coefficient ratios and terrestrial VUEs based on a caching-state backhaul communication link. The joint optimization problems aim to maximize a utility function that considers VUE transmission rates and cross-tier interference while meeting the constraints of backhaul transmission rates and QoS requirements of each VUE. The joint resource allocation optimization problem consists of three sub-problems: bandwidth allocation, user association, and transmission power allocation. We derive a closed-form solution for bandwidth allocation and solve the transmission power allocation sub-problem iteratively using Taylor expansion to transform a non-convex term into a convex one. Our proposed three-stage iterative algorithm for resource allocation integrates all three sub-problems and is shown to be effective through simulation results. Specifically, the results demonstrate that our solution achieves performance improvements over existing approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Heterogeneous networks

Hyperparameter optimization in cardio vascular disease prediction

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AIP Conference Proceedings 2025年第1期3159卷

作者： Saraswathi Kuppusamy Renukadevi Thangavel Deepan Kumar Janani Yamunadevi Department of Computer Technology-UG Kongu Engineering College Perundurai Erode Tamilnadu India BSc Software Systems Department of Computer Technology-UG Erode Kongu Engineering College Tamilnadu India

A condition affecting the heart with blood vessels is called Cardio Vascular Disease (CVD). It is the main reason for death. Early detection can help prevent or lessen it, which lowers mortality. Various study articles describe the application of algorithmic machine learning to identify cardiac diseases. When the algorithm is applied to the dataset’s records, a faster and more precise prediction of cardiovascular illnesswill enable the patient to receive therapy. Cardiologists can make judgments quickly with the aid of these projections. The suggested study employs self-defined Decision Tree, random forest, Logistic Regressions, Support Vector Machine (SVM), grid search to identify the presenceof cardiovascular illness. We examine and assess its performance to forecast it.

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