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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Qiu, Zhihan Zhao, Qinglin Feng, Li Zhou, MengChu Shen, Meng Liu, Yi Sun, Yi School of Computer Science and Engineering Macau University of Science and Technology Taipa China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China School of Computer Science Guangdong University of Technology Guangzhou510006 China Institute of Computing Technology Chinese Academy of Sciences Beijing100045 China

ISBN: (纸本)9798350365221

Blockchain technology, the foundation of cryptocurrencies like Bitcoin, has utility beyond finance due to its decentralized and secure transactional nature. However, today's blockchain networks face the challenge of low transaction throughput due to block propagation delays. In this study, we propose a novel approach that divides large blocks into segments and leverages multi-origin pipelining for segment dissemination, thereby expediting block propagation. The scheme also integrates the use of a Bloom filter for lightweight segment integrity verification to prevent the reception of tampered segments. Simulation results reveal that our scheme outperforms Bitcoin's legacy approach, demonstrating significant improvement in propagation speed, particularly for large blocks, while maintaining a low fork rate. © 2024 IEEE.

关键词： Blockchain

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Improved quantum linear system solver via quantum phase discrimination

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The European Physical Journal Special Topics 2025年 1-11页

作者： Liu, Zihao Li, Guanzhong Li, Lvzhou Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area (Guangdong) Shenzhen China

The problem of solving linear systems is of great significance in both theory and practice, for which quantum solvers have been shown to provide an exponential speedup over the best-known classical solvers. Recently, a quantum linear system solver has been developed with the state-of-the-art complexity [1], which combines two techniques, namely, discrete adiabatic evolution and eigenstate filtering. However, the parameters of the quantum circuit for eigenstate filtering need to be precomputed classically via the discrete Fourier transform, which introduces additional computational overhead. In this paper, we improve the solver by presenting a new eigenstate filtering process termed quantum phase discrimination in which the circuit parameters are given directly in a concise and analytical form with negligible classical overhead, while maintaining the same quantum complexity.

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Nonlinearity and Parametric Amplification of Superconducting Nanowire Resonators in Magnetic Field

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Physical Review Applied 2023年第3期19卷 034024-034024页

作者： M. Khalifa J. Salfi Stewart Blusson Quantum Matter Institute Department of Electrical and Computer Engineering University of British Columbia Vancouver BC Canada

Nonlinear superconducting devices, typically based on Josephson-junction (JJ) nonlinearities, are the basis for superconducting quantum electronics, enabling, e.g., the formation of isolated two-level superconducting qubits and amplifiers. While emerging spin, hybrid spin-superconducting (including Majorana), and nanomagneto-optical quantum systems could benefit tremendously from superconducting nonlinearities, the presence of strong magnetic fields in these systems is incompatible with conventional JJ devices, which are highly sensitive to applied magnetic fields. One potential solution is the use of kinetic inductance (KI) nonlinearity. To date, only linear KI devices have been shown to operate in high magnetic fields, while nonlinear KI device operation in high magnetic fields has received virtually no attention. Here, we study the nonlinearity of superconducting nanowire (NW) KI resonators and their performance as parametric amplifiers for in-plane magnetic fields. We study the Kerr coefficients of NW KI resonators made from 10-nm-thick Nb−Ti−N films with characteristic impedance up to 3 kΩ, and demonstrate both nondegenerate and degenerate parametric amplification, at magnetic fields up to 2 T. We find that narrow KI resonators of width 0.1μm are robust, in terms of gain, dynamic range and noise, to magnetic fields up to approximately 2T, for up to 12 dB (16 dB) of phase-insensitive (phase-sensitive) gain. In comparison, wider KI resonators of width 1μm suffer significant suppression in the gain at fields well below 2 T. Around 8-dB deamplification is also observed for coherent states for a 0.1-μm NW KI resonator, implying the capability of noise squeezing. These results open a pathway to developing nonlinear quantum devices that operate in or generate high magnetic fields such as spin, hybrid spin-superconducting, and magneto-optomechanical devices.

关键词： High magnetic fields Kerr effect Squeezing of quantum noise Superconducting quantum optics Josephson junctions Optical parametric oscillators & amplifiers Superconducting devices Microwave techniques

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Next-Gen Telehealth Ecosystems Using IoT for Virtual Consultations and Support 10

Next-Gen Telehealth Ecosystems Using IoT for Virtual Consult...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Kathija Nasreen, A. Priya, S. Gupta, Nukala Sujata Shoba, L.K. Mohankumar, N. Arunkumar, S. Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Deemed To Be University Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Chaitanya Bharathi Institute of Technology Department of Computer Engineering and Technology Telangana Hyderabad India Srm Institute of Science and Technology Department of Computing Technologies Chenagalpattu Chennai Tamil Nadu Kattankulathur India Symbiosis Institute of Technology Maharashtra Nagpur India Adhiparasakthi Engineering College Department of Computer Applications Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

The research discussed focuses on improving virtual medical consultations and support using Internet of Things (IoT) technologies in telehealth services. It is predicted that IoT devices can assist with creating a new telehealth environment where patients and doctors can communicate easily for remote monitoring, diagnosis, and treatment. The main objective of this unique approach is to expand access to healthcare, especially in poor and rural areas. Connected medical equipment, smart sensors, and wearable technology all play an important role in this system. These IoT components enable gathering real-time and complete patient data, helping doctors make better, more individualized choices and care plans. Data safety, privacy, and system compatibility are some areas where this integration hits a snag. Sensitive patient data and the integrity of the IoT-enabled systems need robust safety measures. These IoT improvements in telemedicine have had a revolutionary effect. Proactive interventions, continuous health monitoring, and individualized care are ways patients might benefit from improved medical care. Virtual consultations are increasingly used in the medical field, and a more connected and data-driven framework for the patient experience is emerging. © 2024 IEEE.

关键词： Internet of things

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Distributionally robust model-based offline reinforcement learning with near-optimal sample complexity

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 9611-9701页

作者： Laixi Shi Yuejie Chi Computing Mathematical Sciences California Institute of Technology Pasadena CA Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA

This paper concerns the central issues of model robustness and sample efficiency in offline reinforcement learning (RL), which aims to learn to perform decision making from history data without active exploration. Due to uncertainties and variabilities of the environment, it is critical to learn a robust policy--with as few samples as possible--that performs well even when the deployed environment deviates from the nominal one used to collect the history dataset. We consider a distributionally robust formulation of offline RL, focusing on tabular robust Markov decision processes with an uncertainty set specified by the Kullback-Leibler divergence in both finite-horizon and infinite-horizon settings. To combat with sample scarcity, a model-based algorithm that combines distributionally robust value iteration with the principle of pessimism in the face of uncertainty is proposed, by penalizing the robust value estimates with a carefully designed data-driven penalty term. Under a mild and tailored assumption of the history dataset that measures distribution shift without requiring full coverage of the state-action space, we establish the finite-sample complexity of the proposed algorithms. We further develop an informationtheoretic lower bound, which suggests that learning RMDPs is at least as hard as the standard MDPs when the uncertainty level is sufficient small, and corroborates the tightness of our upper bound up to polynomial factors of the (effective) horizon length for a range of uncertainty levels. To the best our knowledge, this provides the first provably near-optimal robust offline RL algorithm that learns under model uncertainty and partial coverage.

关键词： offline/batch reinforcement learning distributional robustness pessimism model-based reinforcement learning finite-sample analysis

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Deep Learning-Based Image Analysis to Track Vehicles 2

Deep Learning-Based Image Analysis to Track Vehicles

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Prabakaran, P. Jayasudha, R. Sandhu, Mukta Gupta, Sandeep School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Department of Computer Applications Chennai India Dr.N.G.P.Institute of Technology Department of Mathematics Coimbatore India Shri Vishwakarma Skill University Skill Department of Computer Science and Engineering Haryana Palwal India Department of Electrical Engineering Dehradun India

ISBN: (纸本)9798350371055

There are many ways to track the traffic conditions on roads. With the rise of AI-based image processing technology, there has been a surge in interest in developing traffic monitoring systems that rely on camera vision data. An improved road monitoring system may be derived from the data collected in this research by placing a camera at a junction. The technique detects and classifies vehicles using a deep-learning-based methodology (YOLOv4). By comparing the positions of the vehicle with the map HD, lane-by-lane trajectories of the vehicle may be approximated. The predicted vehicle trajectories are used to calculate each lane's wait lengths and traffic volumes, broken down by travel direction. This technique was evaluated using five distinct metrics: vehicle detection rates, trajectory prediction, type of vehicle categorization, traffic volume estimate, and queue length assessment. Thousands of samples were used to assess its performance. Based on the findings, it is acceptable to assume that the system can identify vehicles with a 98% success rate and an error rate of less than 25% regarding lane-by-lane trip volume estimation and vehicle type classification. Therefore, this study's approach reveals the viability of collecting comprehensive traffic information from a camera set up at a junction. Current traffic monitoring schemes have a reasonable likelihood of being developed by this study's significant contribution, which is a strategy that combines AI with HD map approaches. © 2024 IEEE.

关键词： Artificial Intelligence HD Map Traffic Monitoring System Vehicle Type Classification YOLOv4

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Proactive Hybrid Autoscaling for Container-Based Edge Applications in Kubernetes 19th

Proactive Hybrid Autoscaling for Container-Based Edge Appli...

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19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Zhu, Kaile Shen, Shihao Lan, Shizhan Wang, Xiaofei Zhang, Cheng Qiu, Chao Leung, Victor College of Intelligence and Computing Tianjin University Tianjin China China Mobile Guangxi Institute Co. Ltd. Shanghai China Institute of Technology Tianjin University of Finance and Economics Tianjin China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (纸本)9783031651229

As the rising of the Internet of Things (IoT), edge computing is widely adopted in numerous applications. However, current autoscaling tools are not designed for edge applications and can not utilize the heterogeneous resources of edge nodes efficiently. In this paper, we propose a proactive hybrid autoscaler specifically optimized for edge computing scenario. With the Bidirectional Long Short Term Memory (Bi-LSTM) based load prediction model, the proposed autoscaler is able to predict the future workload and perform scaling operation before it arrives. In addition, a overload compensation algorithm is implemented to mitigate the Quality of Service (QoS) decreasing due to under-prediction. Then, a hybrid scaling method is applied to simultaneously modify the number of pods and their resource quotas without restarting. Experimental results with a real-world workload dataset shows the proposed load prediction model has better accuracy compared with the Long Short Term Memory model and the state-of-the-art statistical analysis model, Autoregressive Integrated Moving Average (ARIMA), which is also more than 350 times slower than our model in prediction speed. Finally, evaluation in a real Kubernetes cluster shows that the proposed proactive hybrid autoscaler outperforms the default Horizontal Pod Autoscaler (HPA) of Kubernetes in terms of both QoS and resource utilization efficiency. © ICST institute for computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Quality of service

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Human-Led Robotic Transportation of Elastic Objects with Adaptive Control 9

Human-Led Robotic Transportation of Elastic Objects with Ada...

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9th International Conference on Robotics and Automation engineering, ICRAE 2024

作者： Cui, Zhenxi Lai, Jiewen Lu, Bo Guo, Yi Chu, Henry K. University of West London School of Computing and Engineering London United Kingdom Chinese University of Hong Kong Department of Electronic Engineering Hong Kong Soochow University School of Mechanical and Electrical Engineering Suzhou China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States The Hong Kong Polytechnic University Department of Mechanical Engineering Hong Kong

ISBN: (纸本)9798331518301

The perception and transportation of soft objects are critical tasks in daily life, and recent sensor advancements enable robots to safely perform these tasks in human-shared environments. This study advances elastic linear object transportation by introducing a collaborative framework where a human leads, providing task-specific instructions to guide the robot. Unlike conventional methods focused on fixed object manipulation, our approach dynamically controls and converges feature points of the elastic object into desired configurations during transportation. We developed an online model estimation technique using a least-squares optimization algorithm, with an exponential forgetting mechanism to adaptively update the model under disturbances from sensors and human interaction. Validated in experiments with a 6-DOF robot and depth camera, our method demonstrated robustness across varied scenarios, achieving faster convergence and reduced positional fluctuation compared to conventional gradient descent approaches. © 2024 IEEE.

关键词： Adaptive control systems

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Brain Tumor Identification Using Data Augmentation and Transfer Learning Approach

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computer Systems Science & engineering 2023年第8期46卷 1845-1861页

作者： K.Kavin Kumar P.M.Dinesh P.Rayavel L.Vijayaraja R.Dhanasekar Rupa Kesavan Kannadasan Raju Arfat Ahmad Khan Chitapong Wechtaisong Mohd Anul Haq Zamil S.Alzamil Ahmed Alhussen Department of Electronics and Communication Engineering Kongu Engineering College638060Tamil NaduIndia Department of Electronics and Communication Engineering Sona College of Technology636005Tamil NaduIndia Department of Computer Science and Engineering Sri Sairam Institute of TechnologyChennai600044Tamil NaduIndia Department of Electrical and Electronics Engineering Sri Sairam Institute of TechnologyChennai600044Tamil NaduIndia Department of Computer Science and Engineering Sri Venkateswara College of EngineeringSriperumbudur602117Tamil NaduIndia Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudur602117Tamil NaduIndia Department of Computer Science College of ComputingKhon Kaen UniversityKhonKaen40002Thailand School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

A brain tumor is a lethal neurological disease that affects the average performance of the brain and can be *** India,around 15 million cases are diagnosed *** mitigate the seriousness of the tumor it is essential to diagnose at the ***,the manual evaluation process utilizing Magnetic Resonance Imaging(MRI)causes a few worries,remarkably inefficient and inaccurate brain tumor ***,the examination process of brain tumors is intricate as they display high unbalance in nature like shape,size,appearance,and ***,a precise and expeditious prognosis of brain tumors is essential for implementing the of an implicit *** computer models adapted to diagnose the tumor,but the accuracy of the model needs to be *** all the above mentioned things,this work aims to identify the best classification system by considering the prediction accuracy out of Alex-Net,ResNet 50,and Inception *** augmentation is performed on the database and fed into the three convolutions neural network(CNN)models.A comparison line is drawn between the three models based on accuracy and *** accuracy of 96.2%is obtained for AlexNet with augmentation and performed better than ResNet 50 and Inception V3 for the 120th *** the suggested model with higher accuracy,it is highly reliable if brain tumors are diagnosed with available datasets.

关键词： AlexNet brain tumor data augmentation inception V3 ResNet 50

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Broadband Measurement of Feibelman d-parameters

Broadband Measurement of Feibelman d-parameters

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Chen, Zeling Yang, Shu Xie, Zetao Hu, Jinbing Xia, Yipu Shen, Yonggen Su, Huirong Xie, Maohai Christensen, Thomas Yang, Yi Department of Physics HK Institute of Quantum Science and Technology The University of Hong Kong Pokfulam Hong Kong Department of Electrical and Photonics Engineering Technical University of Denmark Denmark College of Optical-Electrical Information and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Genuine Optronics Limited Shanghai China

We introduce a general broadband ellipsometric method to measure the quantum optical surface response functions known as Feibelman d-parameters, and demonstrate it using a gold-air interface in the visible-ultraviolet... 详细信息

ISBN: (纸本)9781957171395

关键词： Optical depth

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