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GNN-Based Beamforming for Sum-Rate Maximization in MU-MISO Networks

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arXiv 2023年

作者： Li, Yuhang Lu, Yang Ai, Bo Dobre, Octavia A. Ding, Zhiguo Niyato, Dusit School of Computer and Information Technology Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China State Key Laboratory of Rail Traffic Control and Safety the School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China Faculty of Engineering and Applied Science Memorial University St. John’sNLA1C 5S7 Canada Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Department of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

The advantages of graph neural networks (GNNs) in leveraging the graph topology of wireless networks have drawn increasing attentions. This paper studies the GNN-based learning approach for the sum-rate maximization in multiple-user multiple-input single-output (MU-MISO) networks subject to the users’ individual data rate requirements and the power budget of the base station. By modeling the MU-MISO network as a graph, a GNN-based architecture named CRGAT is proposed to directly map the channel state information to the beamforming vectors. The attention-enabled aggregation and the residual-assisted combination are adopted to enhance the learning capability and avoid the oversmoothing issue. Furthermore, a novel activation function is proposed for the constraint due to the limited power budget at the base station. The CRGAT is trained in an unsupervised learning manner with two proposed loss functions. An evaluation method is proposed for the learning-based approach, based on which the effectiveness of the proposed CRGAT is validated in comparison with several convex optimization and learning based approaches. Numerical results are provided to reveal the advantages of the CRGAT including the millisecond-level response with limited optimality performance loss, the scalability to different number of users and power budgets, and the adaptability to different system settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

Robustness of Dynamic Quantum Control: Differential Sensitivity Bounds

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arXiv 2023年

作者： O'Neil, S.P. Weidner, C.A. Jonckheere, E.A. Langbein, F.C. Schirmer, S.G. Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States Quantum Engineering Technology Laboratories H. H. Wills Physics Laboratory Department of Electrical and Electronic Engineering University of Bristol BristolBS8 1FD United Kingdom Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90007 United States School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom Faculty of Science and Engineering Physics Swansea University SwanseaSA2 8PP United Kingdom

Dynamic control via optimized, piecewise-constant pulses is a common paradigm for open-loop control to implement quantum gates. While numerous methods exist for the synthesis of such controls, there are many open questions regarding the robustness of the resulting control schemes in the presence of model uncertainty;unlike in classical control, there are generally no analytical guarantees on the control performance with respect to inexact modeling of the system. In this paper a new robustness measure based on the differential sensitivity of the gate fidelity error to parametric (structured) uncertainties is introduced, and bounds on the differential sensitivity to parametric uncertainties are used to establish performance guarantees for optimal controllers for a variety of quantum gate types, system sizes, and control implementations. Specifically, it is shown how a maximum allowable perturbation over a set of Hamiltonian uncertainties that guarantees a given fidelity error, can be reliably computed. This measure of robustness is inversely proportional to the upper bound on the differential sensitivity of the fidelity error evaluated under nominal operating conditions. Finally, the results show that the nominal fidelity error and differential sensitivity upper bound are positively correlated across a wide range of problems and control implementations, suggesting that in the high-fidelity control regime, rather than there being a trade-off between fidelity and robustness, higher nominal gate fidelities are positively correlated with increased robustness of the controls in the presence of parametric uncertainties. Copyright © 2023, The Authors. All rights reserved.

关键词： Robustness (control systems)

Wireless Voice-controlled Movable Electrical Heating Radiator

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Wireless Voice-controlled Movable Electrical Heating Radiato...

International Conference on Advanced Communication technology (ICACT)

作者： YuXin Li JinPeng Chen Bing-Yuh Lu Xin Li CanJie Yao LinShu Zheng Xin Bai WanQin Jiang Faculty of Electronic Information Engineering Guangdong University of Petrochemical Technology MaoMing City Guangdong China Faculty of Automation Guangdong University of Petrochemical Technology MaoMing City Guangdong China Faculty of Computer Science Guangdong University of Petrochemical Technology MaoMing City Guangdong China

ISBN: (数字)9791188428069

ISBN: (纸本)9781665447027

This study presents a voice-controlled movable electrical heating radiator. The functions are (1) button or voice controlled the radiator by smartphone APP; (2) heat radiation power control, moving control and timing control; and (3) feedback artificial synthesis voice for instruction, temperature feedback, and ultrasonic sensing for anti-collision. The results show the successful rates are over 90% for most of the cases. A better performance and lower cost voice-controlled movable electrical heating radiator has been implemented. The convenience of the radiator elevates the quality of human living life.

关键词： Wireless communication Temperature sensors Wireless sensor networks Timing Sensors Servers Resistance heating

Comparison of Biocode Based Machine Learning and Segmentation Model for Automated Prawn Size Prediction for Real Prawn Farm

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Comparison of Biocode Based Machine Learning and Segmentatio...

Image Processing, electronics and computers (IPEC), Asia-Pacific Conference on

作者： Muhammad Wafiq Abd Zariful Najeebah Az-Zahra Tashim Tiong Hoo Lim Hanif Fakhrurroja Suriayati Chuprat Seno Adi Putra Pengcheng Liu Aida Maryam Basri Electrical and Electronic Engineering Universiti Technologi Brunei Bandar Seri Begawan Brunei Research Center for Smart Mechatronics National Research and Innovation Agency Bandung Indonesia Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Kuala Lumpur Malaysia School of Industrial and System Engineering Telkom Universiti Bandung Indonesia Department of Computer Science University of York York United Kingdom School of Applied Science and Matematics Universiti Technologi Brunei Bandar Seri Begawan Brunei

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

The aquaculture industry has witnessed a significant surge in technological integration over the past decade, leveraging advancements in the Internet of Things (IoT) and Artificial Intelligence (AI) to enhance production efficiency. Monitoring the growth of aquatic cultures, such as fishes and prawns, plays a pivotal role in optimizing production. However, traditional methods, notably in prawn aquaculture in Asia, still heavily rely on stress-inducing techniques like netting and manual measurements. This paper proposes a novel approach using YOLO-based detection algorithm with biocode reference point based on the ArUco design to predict prawn size based on the length and are dimension to analysed the improvement in predicting the size. The propose solution is compared extensively using statistical tests against segmentation model with and without fine-tuning. Initial results show that both YOLOv8 segmentation and YOLOv8 with Segment Anything Model (SAM) give similar results with the actual mean length of 15.7 cm and predicted mean lengths are 20.5 cm and 20.8 cm respectively. However, after fine-tuning the parameter, the accuracy of predicted results improved significantly with 15.8 cm for YOLOv8 segmentation and 16 cm for YOLOv8 with SAM. The mean absolute error of 0.93 cm for YOLOv8 segmentation and 1.10 cm for YOLOv8 with SAM, and the width of 0.48 cm and 0.78 cm respectively.

关键词： Weight measurement Legged locomotion Deep learning Image segmentation Biological system modeling Production Manuals Length measurement Predictive models Internet of Things

Ferrocene-Decorated Graphene Nanosheets Built by Edge-to-Face Π-Π Interaction for Room Temperature Ppb-Level No Sensing

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SSRN

SSRN 2024年

作者： Zhang, Lulu Gao, Yixun Mai, Zhijian Wang, Jianqiang Chang, Yanwei Wang, Fengnan Li, Hao Paoprasert, Peerasak Lee, Yi-Kuen French, Patrick J. Umar Siddiqui, Ahmad M. Zhou, Guofu Wang, Yao National Center for International Research on Green Optoelectronics Guangdong Provincial Key Laboratory of Optical Information Materials and Technology Institute of Electronic Paper Displays South China Academy of Advanced Optoelectronics South China Normal University Guangzhou510006 China Department of Thoracic Oncology State Key Laboratory of Respiratory Diseases The First Affiliated Hospital of Guangzhou Medical University Guangzhou510006 China Department of Chemistry Faculty of Science and Technology Thammasat University Pathumthani 12121 Thailand Department of Mechanical & Aerospace Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Electronic & Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong BE Laboratory EWI Delft University of Technology Delft2628CD Netherlands Najran University Najran11001 Saudi Arabia

The development of materials toward ppb-level nitric oxide (NO) sensing at room temperature remains in high demand for the monitoring of respiratory inflammatory diseases. Here, a supramolecular assembly Fc/rGO was designed and prepared by employing ferrocene (Fc) and reduced graphene oxide (rGO) for NO sensing. The formation of Fc/rGO was confirmed using FT-IR, TEM, and XPS measurements. The Fc/rGO-based sensors exhibited superior NO sensing properties at room temperature including high response (Ra/Rg =1.73, 1 ppm), high selectivity against other exhaled gases, reliable repeatability and stability (less than 4% decrease after 40 days). A practical limit of detection (LOD) of 200 ppb was achieved. The theoretical simulation demonsrtated that ferrocene was assembled via π-π interaction with rGO in edge-to-face configuration which provides relatively lower energy and more stable states. Moreover, the sensing mechanism was investigated based on experimental methods and XPS measurement. This work provides a fresh approach of anchoring iron on graphene for gas sensing via supramolecular methods. © 2024, The Authors. All rights reserved.

关键词： Graphene

Quantum Nanophotonics with Energetic Particles: X-rays and Free Electrons

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arXiv 2024年

作者： Shi, Xihang Lee, Wen Wei Karnieli, Aviv Lohse, Leon Merten Gorlach, Alexey Wong, Lee Wei Wesley Salditt, Tim Fan, Shanhui Kaminer, Ido Wong, Liang Jie Solid State Institute Faculty of Electrical and Computer Engineering Technion – Israel Institute of Technology Haifa3200003 Israel School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore E. L. Ginzton Laboratories Stanford University 348 Via Pueblo StanfordCA United States Institute for X-ray Physics University of Göttingen Friedrich-Hund-Platz 1 Göttingen37077 Germany

Rapid progress in precision nanofabrication and atomic design over the past 50 years has ushered in a succession of transformative eras for moulding the generation and flow of light. The use of nanoscale and atomic features to design light sources and optical elements – encapsulated by the term nanophotonics – has led to new fundamental science and innovative technologies across the entire electromagnetic spectrum, with substantial emphasis on the microwave to visible regimes. In this review, we pay special attention to the impact and potential of nanophotonics in a relatively exotic yet technologically disruptive regime: the high-energy particles such as X-ray photons and free electrons – where nanostructures and atomic design open the doors to unprecedented technologies in quantum science and versatile X-ray sources and optics. As the practical generation of X-rays is intrinsically linked to the existence of energetic free or quasi-free-electrons, our review will also capture related phenomena and technologies that combine free electrons with nanophotonics, including free-electron-driven nanophotonics at other photon energies. In particular, we delve into the demonstration and study of quantum recoil in the X-ray regime, the study of nanomaterial design and free-electron waveshaping as means to enhance and control X-ray radiation, examine the free-electron generation enabled by nanophotonics, and analyze the high-harmonic generation by quasi-free-electrons. We also discuss applications of quantum nanophotonics for X-rays and free electrons, including nanostructure waveguides for X-rays, photon pair enhanced X-ray imaging, mirrors, lenses for X-rays, etc. Our review highlights the uniqueness of the X-ray regime and the enormous potential of quantum nanophotonics to revolutionize it through tailored interactions between photons, free electrons, and the bound electrons of atoms and nanostructures. © 2024, CC BY.

关键词： Photons

Big Data Analytics for Processing Real-time Unstructured Data from CCTV in Traffic Management

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Big Data Analytics for Processing Real-time Unstructured Dat...

2020 International Conference on Data Science and Its Applications, ICoDSA 2020

作者： Hamami, Faqih Dahlan, Iqbal Ahmad Prakosa, Setya Widyawan Somantri, Khamal Fauzan Telkom University School of Industrial and System Engineering Bandung Indonesia Indonesia Defense University Military Engineering Faculty Bogor Indonesia National Taiwan University of Science Technology Electronic and Computer Engineering Taipei Taiwan Universitas Pendidikan Indonesia Faculty of Mathematics and Science Bandung Indonesia

ISBN: (纸本)9781728182353

Todays many devices generate data everywhere and anytime. Data grow massively and becomes complex thing that needs to be handled. Unstructured data is one type of big data that is difficult to process and consists of unstable attributes. In traffic management, CCTVs are installed to monitor the specific location in the highway. CCTV generates unstructured data in image and video format. These data are difficult to process due to the complexity of the data. This research proposes to implement big data analytics to process real-time unstructured data from CCTV into knowledge displayed in web dashboard. We implement the YOLO framework with YoloV4 Architecture and COCO dataset for traffic flow counting and detecting illegal parking which is categorized as abnormal situation. Unstructured data from CCTV then transformed into semi-structured format in JSON. Data also can be visualized in real time to facilitate local authority to understand the highway situation. Historical data are stored in the NoSQL database to deep more knowledge such as vehicle traffic pattern. The proposed system requires the ROI drawing line as trigger to count the passing vehicles. These experiments are conducted from open CCTV for traffic online in Bali Tower Public Streaming. The prototype result is able to detect the object with 10 fps. © 2020 IEEE.

关键词： Big data

Ranking Based Routing Protocol for Residual Energy Estimation in Wireless Sensor Network Based IoT Network

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Ranking Based Routing Protocol for Residual Energy Estimatio...

International Conference on engineering technology and their Applications (IICETA)

作者： Sami Abduljabbar Rashid Ahmed Jamal Ahmed Mustafa Hamid Hassan Ali Hashim Abbas Mohammed I. Habelalmateen Mohammed Ahmed Jubair L. Audah Waleed Al-Azzawi Department of Computer Engineering Techniques Al-Maarif University College Al-Anbar Iraq Department of Computer Technical engineering College of Information Technology Imam Ja’afar Al-Sadiq University Al-Muthanna Iraq Department of Computer Technical Engineering College of Technical Engineering Islamic University Najaf Iraq Wireless and Radio Science Centre (WARAS) Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia Medical technical college Al-Farahidi University Baghdad Iraq

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

Internet of Things (IoT) is widely involved in environmental monitoring. IoT network has a limited energy supply which impacts the network’s lifetime. At present, limited energy is a challenging factor due to limited energy availability. IoT performance is improved with the RPL routing protocol for effective route establishment. With an effective energy harvesting scheme IoT network, residual energy can be improved. Typically, energy harvesting with conventional RPL routing protocol is ineffective. To withstand the limitation associated with energy harvesting in IoT networks with the incorporation of RPL routing is essential. To achieve this, this paper presented an Energy Harvesting Ranked Whale optimization (EHRWO) is developed. Initially, the proposed model utilizes the optimal route establishment with EHRWO with improved security. The operation of EHRWO is performed for static and dynamic operation of the network. The developed work estimates the node energy, harvest energy, routing performance, and distance. Based on the optimization of the whale optimal path is estimated for the transmission of data. With a ranking based approach optimal path for data transmission will be computed. The simulation results expressed the proposed work performance of the parameters such as energy consumption and packet delivery ratio.

关键词： Energy consumption Wireless sensor networks Estimation Whales Routing Routing protocols Whale optimization algorithms

PATE-AAE: Incorporating adversarial autoencoder into private aggregation of teacher ensembles for spoken command classification

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arXiv 2021年

作者： Yang, Chao-Han Huck Siniscalchi, Sabato Marco Lee, Chin-Hui Georgia Institute of Technology AtlantaGA United States Faculty of Computer and Telecommunication Engineering University of Enna Italy Department of Electronic Systems NTNU Trondheim Norway

We propose using an adversarial autoencoder (AAE) to replace generative adversarial network (GAN) in private aggregation of teacher ensembles (PATE), a solution for ensuring differential privacy in speech applications. The AAE architecture allows us to obtain good synthetic speech leveraging upon a discriminative training of latent vectors. Such synthetic speech is used to build a privacy-preserving classifier when non-sensitive data is not sufficiently available in the public domain. This classifier follows the PATE scheme that uses an ensemble of noisy outputs to label the synthetic samples and guarantee Ε-differential privacy (DP) on its derived classifiers. Our proposed framework thus consists of an AAE-based generator and a PATE-based classifier (PATE-AAE). Evaluated on the Google Speech Commands Dataset Version II, the proposed PATE-AAE improves the average classification accuracy by +2.11% and +6.60%, respectively, when compared with alternative privacy-preserving solutions, namely PATE-GAN and DP-GAN, while maintaining a strong level of privacy target at Ε=0.01 with a fixed δ=10-5 © 2021, CC BY-NC-SA.

关键词： Generative adversarial networks