检索结果-内蒙古大学图书馆

International Conference on Internet of Things 2024, ICIoT 2024

作者： Yao, Shun Zhou, Jin Chieng, David Kwong, Chiew Foong Lee, Boon Giin Li, Jianqiang Chen, Yusen Next-Generation Internet of Everything Laboratory Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China School of Computer Science University of Nottingham Ningbo China Ningbo China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Zhejiang Communication Investment Group Co. Ltd. Zhejiang China

This paper presents a comprehensive investigation of traffic radar coverage efficiency under different placement strategies in collaboration with Zhejiang Communications Investment Group Company Limited (CICO). The overall goal is to maximise coverage while minimising blind spots and the number of radars, given the constraints of the road. The NSGA-II multi-objective optimisation algorithm is found to be capable of generating a set of optimal solutions for the proposed radar placement configurations. When aiming for 100% coverage, the results indicate that a multi-antenna radar configuration requires the lowest radar density. Overall, this study provides valuable insights to transportation infrastructure owners in determining the best radar placement strategies for road traffic monitoring. © The Institution of engineering & Technology 2024.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Generalized model for the charge transport properties of dressed quantum Hall systems

引用

Physical Review B 2022年第3期105卷 035430-035430页

作者： Kosala Herath Malin Premaratne Advanced Computing and Simulation Laboratory (AχL) Department of Electrical and Computer Systems Engineering Monash University Clayton 3800 VIC Australia

We present a generalized mathematical model for predicting the transport properties of a quantum system exposed to a stationary magnetic field and a high-intensity electromagnetic field. The new formulation, which applies to two-dimensional (2D) dressed quantum Hall systems, is based on Landau quantization theory and the Floquet-Drude conductivity approach. We model our system as a two-dimensional electron gas (2DEG) that interacts with two external fields. To analyze the strong light coupling with the 2DEG, we employ the Floquet theory as a nonperturbative procedure. Moreover, the Floquet-Fermi golden rule is adopted to explore the impurity scattering effects on charge transport in disordered quantum Hall systems. We derive fully analytical expressions to describe longitudinal components in the conductivity tensor in dressed quantum Hall systems. Subsequently, we demonstrate that the conductivity characteristics of quantum Hall systems can be manipulated using strong external light. Our results align with well-established and experimentally verified theoretical descriptions for undressed systems while providing a more generalized analysis of the conductivity characteristics in quantum Hall systems. Thus our model can be applied to accurately interpret the usage of strong external radiation as a tool in nanoscale quantum devices.

关键词： Hall effect Quantum transport 2-dimensional systems Floquet systems Drude model

来源：评论

学校读者我要写书评

暂无评论

Floquet engineering of dressed surface plasmon polariton modes in plasmonic waveguides

引用

Physical Review B 2022年第23期106卷 235422-235422页

作者： Kosala Herath Malin Premaratne Advanced Computing and Simulation Laboratory (AχL) Department of Electrical and Computer Systems Engineering Monash University Clayton 3800 VIC Australia

We present a comprehensive study surface plasmon polariton (SPP) propagation on planar metallic waveguides under a dressing field. We perform a set of analytical calculations of the periodic time-dependent Schrödinger equation to study the interaction of an intense electromagnetic field with a metallic system. We model the strong light coupling to the metallic system using a nonperturbative procedure. In this paper, we show that, by introducing Floquet theory into the dressed metal, the behavior of the Floquet states are similar to the phase-modulated signals in the communication system. This will reveal a new perspective on the underlying science in an intricate, dressed quantum system. Furthermore, we examine the impurity scattering effects on charge transport in disordered plasmonic metals using the generalized Floquet-Fermi golden rule and provide a novel approach to diminish the losses in plasmonic materials using the dressing field. To gauge the effectiveness of our results, we introduce a figure of merit to compare the performance of plasmonic metals, subjective to a dressing field. The calculated dressed SPP characteristics suggest that high-efficiency SPP propagation can be achieved in practice. Our findings open up new directions for state-of-the-art nanoplasmonic devices.

关键词： Surface plasmons Waveguides Nanotechnology

来源：评论

学校读者我要写书评

暂无评论

Distributed Power Allocation for 6-GHz Unlicensed Spectrum Sharing via Multi-agent Deep Reinforcement Learning

Distributed Power Allocation for 6-GHz Unlicensed Spectrum S...

引用

IEEE International Conference on Industrial Technology (ICIT)

作者： Xiang Zhang Arupjyoti Bhuyan Sneha Kumar Kasera Mingyue Ji Department of Electrical and Computer Engineering University of Utah Idaho National Laboratory Kahlert School of Computing University of Utah

We consider the problem of spectrum sharing by multiple cellular operators. We propose a novel deep Reinforcement Learning (DRL)-based distributed power allocation scheme which utilizes the multi-agent Deep Deterministic Policy Gradient (MA-DDPG) algorithm. In particular, we model the base stations (BSs) that belong to the multiple operators sharing the same band, as DRL agents that simultaneously determine the transmit powers to their scheduled user equipment (UE) in a synchronized manner. The power decision of each BS is based on its own observation of the radio environment (RF) environment, which consists of interference measurements reported from the UEs it serves, and a limited amount of information obtained from other BSs. One advantage of the proposed scheme is that it addresses the single-agent non-stationarity problem of RL in the multi-agent scenario by incorporating the actions and observations of other BSs into each BS's own critic which helps it to gain a more accurate perception of the overall RF environment. A centralized-training-distributed-execution framework is used to train the policies where the critics are trained over the joint actions and observations of all BSs while the actor of each BS only takes the local observation as input in order to produce the transmit power. Simulation with the 6 GHz Unlicensed National Information Infrastructure (U-NII)-5 band shows that the proposed power allocation scheme can achieve better throughput performance than several state-of-the-art approaches.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Monitoring the impact of energy conservation measures with Artificial Neural Networks

Monitoring the impact of energy conservation measures with A...

引用

2022 ECEEE Summer Study on Energy Efficiency: Agents of Change

作者： Sarmas, Elissaios Marinakis, Vangelis Doukas, Haris Dimitropoulos, Nikos Zucika, Aija School of Electrical and Computer Engineering Decision Support Systems Laboratory National Technical University of Athens Athens Greece The Latvian Environmental Investment Fund Riga Latvia

ISBN: (纸本)9789198827002

Energy Conservation Measures are mandatory in order to improve buildings' energy performance by using upgraded technologies, systems and installations. However, the lack of accurate techniques for Measurement & Verification (M&V) imposes insurmountable barriers towards their extended financing. The development of precise M&V techniques to estimate energy savings is a critical issue that can be tackled through the adoption of predictive models for the adjusted baseline energy consumption in the reporting period. The most commonly used M&V practices to date are reported in the International Performance Measurement and Verification Protocol (IPMVP), where the most widespread techniques per case for calculating energy savings are defined. More specifically, the IPMVP indicates the adoption of linear regression methods to predict the adjusted baseline energy consumption of a building, exploiting outdoor temperature and heating degree days. In this paper, utilisation of Deep Learning for training energy consumption predictive models is examined, as vast amount of data from Internet of Things devices are available nowadays. Thus, the feedforward Artificial Neural Network (ANN) is proposed for predicting the adjusted baseline energy consumption, using the hour of the day, the day of the week and weather data as training features. The proposed models incorporate both linear and non-linear relationships, in contrast to linear regression methods. To validate the proposed method, an experimental application is implemented, applying the developed models on an educational institution in Latvia. The building has been renovated regarding its heating supply and ventilation system, as well as its enclosing structures insulation. The predictions from the ANN models are compared with the ones from the traditional degree days method, indicating that ANN achieves higher accuracy in energy savings estimation for electricity and diesel fuel consumption. © 2022 European Council for an Energ

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

An Automated Deep Learning Based Muscular Dystrophy Detection and Classification Model

引用

computers, Materials & Continua 2022年第4期71卷 305-320页

作者： T.Gopalakrishnan Periakaruppan Sudhakaran K.C.Ramya K.Sathesh Kumar Fahd N.Al-Wesabi Manal Abdullah Alohali Anwer Mustafa Hilal School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Engineering SRM TRP Engineering CollegeTiruchirappalli621105India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Muscular Dystrophy (MD) is a group of inherited muscular diseases that are commonly diagnosed with the help of techniques such asmuscle biopsy, clinical presentation, and Muscle Magnetic Resonance Imaging(MRI). Among these techniques, Muscle MRI recommends the diagnosis ofmuscular dystrophy through identification of the patterns that exist in musclefatty replacement. But the patterns overlap among various diseases whereasthere is a lack of knowledge prevalent with regards to disease-specific ***, artificial intelligence techniques can be used in the diagnosis ofmuscular dystrophies, which enables us to analyze, learn, and predict forthe future. In this scenario, the current research article presents an automated muscular dystrophy detection and classification model using SynergicDeep Learning (SDL) method with extreme Gradient Boosting (XGBoost),called SDL-XGBoost. SDL-XGBoost model has been proposed to act as anautomated deep learning (DL) model that examines the muscle MRI dataand diagnose muscular dystrophies. SDL-XGBoost model employs Kapur’sentropy based Region of Interest (RoI) for detection purposes. Besides, SDLbased feature extraction process is applied to derive a useful set of featurevectors. Finally, XGBoost model is employed as a classification approach todetermine proper class labels for muscle MRI data. The researcher conductedextensive set of simulations to showcase the superior performance of SDLXGBoost model. The obtained experimental values highlighted the supremacyof SDL-XGBoost model over other methods in terms of high accuracy being96.18% and 94.25% classification performance upon DMD and BMD respectively. Therefore, SDL-XGBoost model can help physicians in the diagnosis of muscular dystrophies by identifying the patterns of muscle fatty replacementin muscle MRI.

关键词： Muscle magnetic resonance imaging XGBoost synergic deep learning roI detection kapur’s entropy muscular dystrophies

来源：评论

学校读者我要写书评

暂无评论

A Training-Free Length Extrapolation Approach for LLMs: Greedy Attention Logit Interpolation (GALI)

arXiv

引用

arXiv 2025年

作者： Li, Yan Zhang, Tianyi Li, Zechuan Han, Soyeon Caren School of Computer Science The University of Sydney Sydney Australia School of Computing and Information Systems The University of Melbourne Melbourne Australia College of Electrical and Information Engineering Hunan University Changsha China

Transformer-based Large Language Models (LLMs) struggle to process inputs exceeding their training context window, with performance degrading due to positional out-of-distribution (O.O.D.) that disrupt attention computations. Existing solutions, fine-tuning and training-free methods, are limited by computational inefficiency, attention logit outliers or loss of local positional information. To address this, we propose Greedy Attention Logit Interpolation (GALI), a training-free length extrapolation method that maximizes the utilization of pretrained positional intervals while avoiding attention logit outliers through attention logit interpolation. The result demonstrates that GALI consistently outperforms state-of-the-art training-free methods. Our findings reveal that LLMs interpret positional intervals unevenly within their training context window, suggesting that extrapolating within a smaller positional interval range yields superior results—even for short-context tasks. GALI represents a significant step toward resolving the positional O.O.D. challenge, enabling more reliable long-text understanding in LLMs. Our implementation of GALI, along with the experiments from our paper, is open-sourced at https://***/AcademyCityL/GALI. © 2025, CC BY.

关键词： Extrapolation

来源：评论

学校读者我要写书评

暂无评论

Adaptive Oracle-Efficient Online Learning

arXiv

引用

arXiv 2022年

作者： Wang, Guanghui Hu, Zihao Muthukumar, Vidya Abernethy, Jacob College of Computing United States School of Electrical and Computer Engineering School of Industrial and Systems Engineering

The classical algorithms for online learning and decision-making have the benefit of achieving the optimal performance guarantees, but suffer from computational complexity limitations when implemented at scale. More recent sophisticated techniques, which we refer to as oracle-efficient methods, address this problem by dispatching to an offline optimization oracle that can search through an exponentially-large (or even infinite) space of decisions and select that which performed the best on any dataset. But despite the benefits of computational feasibility, oracle-efficient algorithms exhibit one major limitation: while performing well in worst-case settings, they do not adapt well to friendly environments. In this paper we consider two such friendly scenarios, (a) "small-loss" problems and (b) IID data. We provide a new framework for designing follow-the-perturbed-leader algorithms that are oracle-efficient and adapt well to the small-loss environment, under a particular condition which we call approximability (which is spiritually related to sufficient conditions provided in (Dudík et al., 2020)). We identify a series of real-world settings, including online auctions and transductive online classification, for which approximability holds. We also extend the algorithm to an IID data setting and establish a "best-of-both-worlds" bound in the oracle-efficient setting. © 2022, CC0.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Accurate, provable, and fast nonlinear tomographic reconstruction: A variational inequality approach

arXiv

引用

arXiv 2025年

作者： Lou, Mengqi Verchand, Kabir Aladin Fridovich-Keil, Sara Pananjady, Ashwin Georgia Institute of Technology School of Industrial and Systems Engineering United States School of Electrical and Computer Engineering United States University of Cambridge Statistical Laboratory United Kingdom Stanford University Department of Electrical Engineering United States

We consider the problem of signal reconstruction for computed tomography (CT) under a nonlinear forward model that accounts for exponential signal attenuation, a polychromatic X-ray source, general measurement noise (e.g. Poisson shot noise), and observations acquired over multiple wavelength windows. We develop a simple iterative algorithm for single-material reconstruction, which we call EXACT (EXtragradient Algorithm for Computed Tomography), based on formulating our estimate as the fixed point of a monotone variational inequality. We prove guarantees on the statistical and computational performance of EXACT under practical assumptions on the measurement process. We also consider a recently introduced variant of this model with Gaussian measurements, and present sample and iteration complexity bounds for EXACT that improve upon those of existing algorithms. We apply our EXACT algorithm to a CT phantom image recovery task and show that it often requires fewer X-ray projection exposures, lower source intensity, and less computation time to achieve similar reconstruction quality to existing methods. © 2025, CC BY.

关键词： computerized tomography

来源：评论

学校读者我要写书评

暂无评论

Antenna Measurements Using Portable Spectrum Analyzers

Antenna Measurements Using Portable Spectrum Analyzers

引用

UNIfied Conference of International Workshop on Defence Applications of Multi-Agent systems, DAMAS 2023, International Conference on Maintenance engineering, IncoME-V 2023, International conference on the Efficiency and Performance engineering Network, TEPEN 2023

作者： Kandregula, Venkat Reddy Zaharis, Zaharias D. Ahmed, Qasim Zeeshan Khan, Faheem A. Hafeez, Maryam Loh, Tian Hong Chochliouros, Ioannis P. Lazaridis, Pavlos I. School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom School of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki541 24 Greece Electromagnetic & Electrochemical Technologies Department National Physical Laboratory Middlesex TeddingtonTW11 0LW United Kingdom Marousi151 24 Greece

ISBN: (纸本)9783031494123

With continuous improvements in wireless products, there is an increasing demand for radio frequency (RF) measurement equipment. Wireless communication relies heavily on antennas, and measuring their performance when deployed in the field is essential to understand the coverage provided. To analyze the performance of the antenna when deployed in the field, the radiation patterns of the antenna were measured. Measuring the radiation patterns of an antenna involves measuring the strength of the electric field in the far-field Fraunhofer region. Spectrum analyzers are widely used to measure the strength of an electric field. Standard spectrum analyzers are heavy and bulky. On the other hand, we propose portable and lightweight spectrum analyzers, such as the Tiny SA Ultra, and the Signal Hound SA44B. The performance of both these small size spectrum analyzers was validated using a signal generator. The performance of SA44B was validated by measuring the radiation patterns of a Printed Log-Periodic Antenna (PLPDA). Measurements were performed at ground level in a sports hall at 800 MHz while maintaining a far-field distance of 30 m. The radiation patterns of the PLPDA were measured with a good accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Log periodic antennas

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：