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2021 Asia communications and Photonics Conference, ACP 2021

作者： Cao, Yuwen Zhu, Lanxin Zhou, Tong Yang, Chunxia Chen, Yanjun Li, Zhengbin State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Beijing100871 China School of Software and Microelectronics Peking University Beijing102600 China Youfu Technology Co. Ltd Hangzhou311200 China

A portable rotational sensor based on a modified dual-polarization fiber optic gyroscope is demonstrated, in which the excess relative intensity noise is effectively compensated achieving an enhanced sensitivity of 70... 详细信息

ISBN: (纸本)9781957171005

关键词： Gyroscopes

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LoRaWAN and Biosensor-Assisted Smart City Framework to Mitigate the Effects of Lung Diseases

LoRaWAN and Biosensor-Assisted Smart City Framework to Mitig...

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Modeling, Simulation & Intelligent Computing (MoSICom), International Conference on

作者： K M Abubeker Michaelraj Kingston Roberts C Prajitha S Rinesh A Shafeena Karim S. Jeevanandham Department of Electronics and Communication Engineering Amal Jyothi College of Engineering (Autonomous) Kerala India Department of Electronics and Communication Engineering Centre for Advances in Signal Processing and Wireless Sensor Networks Sri Eshwar College of Engineering Coimbatore India Department of Electronics and Communication Engineering Centre for Interdisciplinary Research Karpagam Academy of Higher Education Coimbatore India Department of Computer Science and Engineering V.S.B College of Engineering Technical Campus Coimbatore India Department of Information Technology Sri Ramakrishna Engineering College Coimbatore India

ISBN: (数字)9798331533311

ISBN: (纸本)9798331533328

Urbanization and industrialization in cities present substantial obstacles to air quality, resulting in detrimental effects on public health, including respiratory health. This research introduces an Internet of Things (IoT) framework for a smart city environment using LoRaWAN and sensor networks. The framework utilizes the ESP32 LoRaWAN module to provide real-time monitoring of essential air quality metrics, such as carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), temperature, and humidity. Integrating biosensors enhances accuracy by identifying distinct biological indicators linked to respiratory well-being. The proposed work aims to use the framework in an urban setting within a global context, demonstrating its capacity to reduce the effects of lung disorders. The ESP32 LoRaWAN module enables effective communication within the sensor network up to 20 kilometers, guaranteeing a robust and adaptable solution for smart city applications. The developed framework provides a scalable and novel method for developing healthier and more sustainable cities, decreasing respiratory disorders as urban settings develop.

关键词： Temperature sensors Smart cities Pulmonary diseases LoRaWAN Air quality Real-time systems Biosensors Internet of Things Public healthcare Organic compounds

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Numerical Analyzation of Distributed Feedback Lasers with Different Phase Shift Structures

Numerical Analyzation of Distributed Feedback Lasers with Di...

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2021 Asia communications and Photonics Conference, ACP 2021

作者： Wang, Bo Zhou, Peiqi Wang, Xingjun State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics School of Electronics Engi-neering and Computer Science Peking University Beijing100871 China Institute of Microelectronics School of Electronics Engineering and Computer Science Peking University Beijing100871 China Frontier Science Center for Nano-optoelectronics Peking University Beijing100871 China Peking University Yangtze Delta Institute of Optoelectronics Nantong226010 China

We optimize phase shift structures of distributed feedback (DFB) lasers. For the multi-distributed phase shift laser, the output slope can reach up to 51.90% when the optimal cavity length is 2.2 mm. © Optica Pub...

ISBN: (纸本)9781957171005

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Deep Reinforcement Learning Based Multistage Profit Aware Task Scheduling Algorithm for Computing Power Network

Deep Reinforcement Learning Based Multistage Profit Aware Ta...

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IEEE Conference on Global communications (GLOBECOM)

作者： Xiaoyao Huang Remington R. Liu Bo Lei Guanglun Huang Baoxian Zhang Network Technology Institute Research Institute China Telecom China School of Information and Communication Engineering Beijing University Of Posts and Telecommunications China School of Computer Science and Information Security Guilin University of Electronic Technology China Research Center of Ubiquitous Sensor Networks University of Chinese Academy of Sciences China

Computing power network (CPN), which integrates heterogeneous computing resources and communication network, can tackle the challenges brought by the pervasiveness of mobile and Internet of Things applications. In this paper, we study the optimization of task scheduling in a CPN network by considering the unbalancing between task distribution and resource cost. The design objective is to maximize the system profit while satisfying tasks' delay requirements. We formulate this problem as an integer programming problem. To address this NP-hard problem, we propose a Deep Reinforcement Learning (DRL) based multistage profit-aware task scheduling algorithm which first makes coarse grained task allocation using DRL among regions and then determines an optimized intra-region task assignment by using profit-aware balancing algorithm. Extensive simulations are conducted for performance evaluation and the results show the high performance of the proposed algorithm as compared with baseline algorithms.

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A Survey on Indoor Visible Light Positioning Systems: Fundamentals, Applications, and Challenges

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IEEE communications Surveys and Tutorials 2024年第3期27卷 1656-1686页

作者： Zhu, Zhiyu Yang, Yang Chen, Mingzhe Guo, Caili Cheng, Julian Cui, Shuguang Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering and Institute for Data Science and Computing Coral GablesFL33146 United States Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China The University of British Columbia Faculty of Applied Science School of Engineering KelownaBCV1V 1V7 Canada 518172 Hong Kong

The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses. © 1998-2012 IEEE.

关键词： Resource allocation

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E-Buddy for Rescue Child Labour

E-Buddy for Rescue Child Labour

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BIT Conference (BITCON), International

作者： Ajmeera Kiran Prithiviraj Ananth P Veera Prakash N. Kumareshan Ramesh Kumar Ayyasamy P. Chinnasamy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of CSE Sona College of Technology Salem Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Ananthapuramu Andhra Pradesh Department of ECE Professor/Centre for Advances in Signal Processing and Sensor Networks Sri Eshwar College of Engineering Coimbatore Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

Ebuddy is a ground breaking platform dedicated to rescuing and rehabilitating child laboures. By leveraging advanced technology, this initiative effectively connects with at-risk children, providing them with crucial education and support services. With a mission to break the vicious cycle of poverty and exploitation, Ebuddy collaborates with local communities and organizations to raise awareness about child labor and advocate for policies that safeguard children's rights. This platform offers comprehensive support services aimed at addressing the children's physical, emotional, and psychological well-being. By providing counseling, rehabilitation, and necessary medical assistance, Ebuddy empowers children to leave their past behind and embrace a better future.

关键词： Employee welfare Pediatrics Education Psychology Morphology Organizations

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Skin Cancer Classification using Machine Learning Techniques

Skin Cancer Classification using Machine Learning Techniques

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BIT Conference (BITCON), International

作者： I. Sapthami Ch. Srilakshmi Batta Saranya N. Kumareshan Ramesh Kumar Ayyasamy P. Chinnasamy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Information Technology VNR VJIET Bachupally Hyderabad India AIDS Department CBITGandipet Hyderabad India Department of ECE Professor/Centre for Advances in Signal Processing and Sensor Networks Sri Eshwar College of Engineering Coimbatore Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Srivilliputtur TamilNadu

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

The death rate from skin cancer has radically grown due to a lack of understanding about the indications and prevention measures. It is one of the most lethal types of cancers, and the mortality rate due to skin cancer has risen sharply. Cancer must be diagnosed as early as possible to halt its growth and prevent metastasis. Even though there are numerous varieties of skin cancer, melanoma, basal cell carcinoma, and squamous cell carcinoma are considered the three most dangerous types of skin cancer. This research, using the CNN image-processing technology and machine learning, tries to detect and classify into the many kinds of skin cancer. Artificial intelligence (AI) based algorithms for automated skin cancer classification have consistently outperformed human professionals in several clinical studies, laying the groundwork for a future in which AI will be widely used.

关键词： Squamous cell carcinoma Prevention and mitigation Mortality Melanoma Machine learning Data augmentation Skin Classification algorithms Convolutional neural networks Skin cancer

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Stochastic Geometry Based Modelling and Analysis of Uplink Cooperative Satellite-Aerial-Terrestrial networks for Nomadic communications with Weak Satellite Coverage

arXiv

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arXiv 2024年

作者： Dong, Wen-Yu Yang, Shaoshi Zhang, Ping Chen, Sheng The School of Information and Communication Engineering Beijing University of Posts and Telecommunications The Key Laboratory of Universal Wireless Communications Ministry of Education China The Key Laboratory of Mathematics and Information Networks Ministry of Education Beijing100876 China The School of Information and Communication Engineering Beijing University of Posts and Telecommunications China The State Key Laboratory of Networking and Switching Technology Beijing100876 China The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Cooperative satellite-aerial-terrestrial networks (CSATNs), where unmanned aerial vehicles (UAVs) are utilized as nomadic aerial relays (A), are highly valuable for many important applications, such as post-disaster urban reconstruction. In this scenario, direct communication between terrestrial terminals (T) and satellites (S) is often unavailable due to poor propagation conditions for satellite signals, and users tend to congregate in regions of finite size. There is a current dearth in the open literature regarding the uplink performance analysis of CSATN operating under the above constraints, and the few contributions on the uplink model terrestrial terminals by a Poisson point process (PPP) relying on the unrealistic assumption of an infinite area. This paper aims to fill the above research gap. First, we propose a stochastic geometry based innovative model to characterize the impact of the finite-size distribution region of terrestrial terminals in the CSATN by jointly using a binomial point process (BPP) and a type-II Matérn hard-core point process (MHCPP). Then, we analyze the relationship between the spatial distribution of the coverage areas of aerial nodes and the finite-size distribution region of terrestrial terminals, thereby deriving the distance distribution of the T-A links. Furthermore, we consider the stochastic nature of the spatial distributions of terrestrial terminals and UAVs, and conduct a thorough analysis of the coverage probability and average ergodic rate of the T-A links under Nakagami fading and the A-S links under shadowed-Rician fading. Finally, the accuracy of our theoretical derivations are confirmed by Monte Carlo simulations. Our research offers fundamental insights into the system-level performance optimization for the realistic CSATNs involving nomadic aerial relays and terrestrial terminals confined in a finite-size region. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Planning Algorithm for Scientific Workflows: A Cost-Aware and Makespan Approach Based on Locust-Inspired Algorithm

SSRN

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SSRN 2023年

作者： Ala’anzy, Mohammed Alaa Othman, Mohamed Hanapi, Zurina Mohd Mohamed, Nader Ahmad, Zulfiqar Almaty040900 Kazakhstan Department of Communication Technology and Networks Universiti Putra Malaysia UPM Selangor D.E. Serdang43400 Malaysia Universiti Putra Malaysia UPM Selangor D.E. Serdang43400 Malaysia Department of Computer Science and Information Systems Pennsylvania Western University California PA15419 United States Department of Computer Science and Information Technology Hazara University Mansehra21300 Pakistan

Job scheduling of scientific workflows in cloud computing is an NP-hard optimisation problem. Efficiently scheduling the tasks in a workflow remains a challenging and open research problem that needs to be addressed. Scientific workflows are data-intensive;whereas, high powered computational resources are needed for handling them. Therefore, cloud computing is an impressive paradigm to handle such massive amounts of data effectively on a pay-as-you-use basis. In cloud computing, cost and execution time limitations are increasingly becoming significant concerns. The challenges of rising cost and time are particularly tough for scientific workflows since they need complex data processing through the communication network. In this paper, we propose a novel planning algorithm based on a locust-inspired algorithm for scientific workflow in a cloud environment for heterogeneous systems. This algorithm aims to optimise the scheduling and execution of tasks in such a way that it can be applied to a wide range of systems, with the goal of minimising overall costs and maximising efficiency. The proposed algorithm generates a task list based on the locust-inspired positioning of the tasks whereas the task list will consist the priority and balance tasks. This task list is used to submit tasks to the heterogeneous virtual machines, taking into account the queue list and the earliest finish times in order to optimise the scheduling of the tasks. Moreover, the child-parent tasks and the link latency are also considered in the scheduling of the tasks. In light to this, the makespan and financial costs have improved in accordance with this method, which has successfully demonstrated its effectiveness. The proposed algorithm is assessed using real-world dataset of data-exhaustive workflows and compared to state-of-the-art algorithms. The performance evaluation result demonstrates that our proposed algorithm could effectively reduce the cost and the makespan of the most of the invest

关键词： Cloud computing

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Augmented V2V Radio Fingerprinting With Heterogeneous Federated Scattering Network for Autonomous Vehicle Road Cooperation

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Tiantian Xu, Dongyang Ma, Jing Alharbi, Abdullah Yu, Keping Guizani, Mohsen Leung, Victor C.M Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an710049 China Xi'an Jiaotong University School of Information and Communication Engineering Xi'an710049 China Xidian University State Key Laboratory of Integrated Services Networks China King Saud University Computer Science Department Community College Riyadh11437 Saudi Arabia Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan 999041 United Arab Emirates Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen528060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Augmented Internet of vehicle (IoV) network, providing natural infrastructure support for autonomous vehicle road cooperation (VRC), has been deemed as one of key enablers for autonomous VRC while simultaneously facing the challenge of impersonation, tampering or replay attack towards the high layer digital certificate for critical information. In this paper, we propose a heterogeneous scattering fingerprinting network (HSFNet) which amalgamates wavelet scattering and deep learning to achieve lightweight, rapid and reliable physical layer authentication by radio frequency fingerprinting (RFF) which employs the unique inherent hardware impairments within for vehicle-to everything (V2X) communication. Particularly, we first exploit a wavelet scattering network to extract RFF coefficients and eliminate redundancies within heterogeneous datasets. The establishment of a high-information-density datasets facilitates the efficient processing of extensive heterogeneous data, especially for resource-limited vehicles nodes. To improve the interpretability, we combine the multi-scale coefficients of input waveform and shapley values, formulating a complete interpretable framework and completing parameters tuning. Finally, considering heterogeneous applications in practical scenarios, we design a distributed federated learning framework which not only completes distributed training but also protects privacy with the resources-constrained intelligent IoV nodes. Experimental results demonstrate that the proposed scheme maintains excellent performance while utilizing only about 25.7% of original samples, significantly improving identification accuracy. Furthermore, the identification accuracy of federated learning ultimately converges to 98.1%. © 1967-2012 IEEE.

关键词： Deep learning

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