检索结果-内蒙古大学图书馆

Dual-Band Reflectarray Antennas Using Integrated Resonant and Non-Resonant Natures of Metallic Waveguide Elements at Millimeter Wave Frequencies

引用

Radio Science 2023年第1期58卷 1-13页

作者： Chou, H.-T. Wang, N.-N. Fang, M. Wang, L.-Q. Akkaraekthalin, P. Torrungrueng, D. Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Research Center of Innovation Digital and Electromagnetic Technology Department of Teacher Training in Electrical Engineering Faculty of Technical Education King Mongkut's University of Technology North Bangkok Bangkok Thailand

Pure metallic reflectarray antennas have good power efficiency and low fabrication cost, limited to single-band operation in the past. A dual-band reflectarray antenna is developed for two directional beams by two feeds. The reflecting elements are metal waveguides and have novel properties of both resonant and non-resonant modes at the two frequency bands. The low and high-frequency bands can be easily separated by the cutoff frequency of waveguide modes. The phase-changing mechanisms via ray optics and fundamental waveguide modes, respectively, provide simple formulations for the elemental structure design of directional beams. Radiation characteristics of the linear and circular polarizations are cross-examined with a good performance by full-wave simulations using HFSS, FEKO, and CST at 28 and 60 GHz bands for 5G front-haul network applications. In particular, we have used HFSS to simulate the reflecting elements' basic characteristics of phase variation, FEKO to simulate the linear polarization antenna case, and CST to simulate the circular polarization antenna case, respectively. © 2023. American Geophysical Union. All Rights Reserved.

关键词： Cutoff frequency

来源：评论

学校读者我要写书评

暂无评论

A2D: Anywhere Anytime Drumming

A2D: Anywhere Anytime Drumming

引用

2023 IEEE Region 10 Symposium, TENSYMP 2023

作者： Yadid, Harel Algranti, Almog Levin, Mark Taitler, Ayal Technion - Israel Institute of Technology Faculty of Electrical and Computer Engineering Haifa Israel Technion - Israel Institute of Technology Faculty of Computer Science Haifa Israel University of Toronto Department of Mechanical and and Industrial Engineering TorontoON Canada

ISBN: (纸本)9781665482585

The drum kit, which has only been around for around 100 years, is a popular instrument in many music genres such as pop, rock, and jazz. However, the road to owning a kit is expensive, both financially and space-wise. Also, drums are more difficult to move around compared to other instruments, as they do not fit into a single bag. We propose a no-drums approach that uses only two sticks and a smartphone or a webcam to provide an air-drumming experience. The detection algorithm combines deep learning tools with tracking methods for an enhanced user experience. Based on both quantitative and qualitative testing with humans-in-the-loop, we show that our system has zero misses for beginner level play and negligible misses for advanced level play. Additionally, our limited human trials suggest potential directions for future research. © 2023 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Particle-based Data-driven Nonlinear State Estimation of Model-free Process from Nonlinear Measurements

Particle-based Data-driven Nonlinear State Estimation of Mod...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ghosh, Anubhab Eldar, Yonina C. Chatterjee, Saikat Digital Futures Centre School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Faculty of Mathematics and Computer Science The Weizmann Institute of Science Israel

ISBN: (纸本)9798350368741

We consider the problem of causal filtering of a model-free process from (noisy) nonlinear measurements. The 'model-free process' means that we do not have a state-space model (SSM) of the process dynamics, limiting the use of traditional model-driven filters, such as unscented Kalman filter (UKF) and particle filter (PF). To address the problem we propose a particle-based data-driven nonlinear state estimation (pDANSE) method. In pDANSE, a recurrent neural network (RNN) provides the statistical parameters of a Gaussian prior of the underlying state, and particles are then drawn from the prior to compute the posterior moments. pDANSE is typically trained in a semi-supervised fashion. For our experiments we study the use of half-wave rectification as a nonlinear transformation of measurements. We first show that an unsupervised learning-based method under-performs, and subsequently the semi-supervised learning-based pDANSE performs satisfactorily. Using Lorenz-63 system as benchmark, pDANSE is found to be competitive against a model-driven PF that knows the exact SSM. © 2025 IEEE.

关键词： deep neural network Kalman filter particle filter semi-supervised learning State estimation

来源：评论

学校读者我要写书评

暂无评论

Efficient Optimization of Energy Consumption at Home through Machine Learning 5

Efficient Optimization of Energy Consumption at Home through...

引用

5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Sharada Prasad, N. Lakshmi, Shree M.S. Manjunatha, S. Vekariya, Vipul Benal, Ganga D. Patil, Harshal RNS Institute of Technology Department of Electrical and Electronics Engineering Karnataka Bangalore560098 India Cambridge Institute of Technology Department of Computer Science and Engineering Karnataka Bangalore560036 India Parul University Faculty of Engineering and Technology Parul Institute of Engineering and Technology Department of Computer Science and Engineering Gujarat Waghodia391760 India Symbiosis Institute of Technology Maharashtra Pune412115 India

ISBN: (纸本)9798350395235

In recent years, the imperative need for efficient energy utilization in residential buildings has become increasingly evident due to the unwarranted wastage of electrical energy. This has spurred significant interest in optimizing energy consumption while maintaining user comfort. Accurate energy prediction is a crucial component of this optimization. This study focus on minimizing energy consumption by accurately predicting it through advanced Machine Learning (ML) models and optimization techniques. The study begins with the collection of energy data from a reliable source, Kaggle, comprising 29 features. To streamline the dataset, unnecessary features are discarded, and data normalization is performed to ensure consistency and reliability. Subsequently, ML models, specifically Long Short-Term Memory (LSTM), are designed and optimized through the Genetic Algorithm (GA) and Grey Wolf Optimization (GWO) to fine-tune hyperparameters. The prediction results are evaluated using error values to assess the accuracy and reliability of the models. Notably, our findings indicate that the GWO-LSTM model outperforms the others, exhibiting minimal errors and therefore showcasing its superior predictive capabilities. Accurate energy prediction not only serves as a valuable tool in its own right but also plays a pivotal role in enabling proactive energy management. By accurately forecasting future energy requirements, it becomes possible to optimize energy consumption further. Such precision paves the way for intelligent scheduling of home appliances, which, in turn, leads to significant reductions in energy consumption. © 2024 IEEE.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

Impact of Imperfect SIC in NOMA Enabled Heterogeneous Cellular Network 27

Impact of Imperfect SIC in NOMA Enabled Heterogeneous Cellul...

引用

27th IEEE International Conference on Intelligent engineering Systems, INES 2023

作者： Baghel, Amit Parihar, Abhinav Singh Bhatia, Vimal Bridal, Peter Augustynek, Martin Indian Institute of Technology Department of Electrical Engineering Indore India University of Zilina Faculty of Electrical Engineering and Information Technology Zilina Slovakia VŠB - Technical University of Ostrava Faculty of Electrical and Computer Science Engineering Czech Republic

ISBN: (纸本)9798350328516

To accommodate a large number of users requiring higher data rates and improved outage performance, a heterogeneous cellular network (HCN) uses a multi-tier architecture made up of a macro-base station (MBS) and pico-base station (PBS). Due to its superior spectrum efficiency, non-orthogonal multiple access (NOMA) has been identified as a suitable multiple-access strategy for the upcoming sixth-generation wireless networks. Successive interference cancellation (SIC) is an essential technique in NOMA that enables multiple users to share the same resource while maintaining high performance and fairness. Thus, it is essential to understand the performance guarantees of actual NOMA-enabled HCN systems (HCN-NOMA) using a multi-tier design under both ideal and imperfect SIC using a stochastic geometry. In multi-tier HCN, the outage probability and system throughput of orthogonal multiple access (OMA) and NOMA under perfect and imperfect SIC are evaluated and compared in this paper. It has been found that using NOMA rather than OMA results in performance gains of up to 40% at 25 dB SNR in HCN. Performance increases of up to 30% against OMA at 25 dB SNR can be observed, despite imperfections in the SIC at NOMA. © 2023 IEEE.

关键词： Spectrum efficiency

来源：评论

学校读者我要写书评

暂无评论

Generalized Conditional Displacement

Generalized Conditional Displacement

引用

Optica Quantum 2.0 Conference and Exhibition, Quantum 2.0

作者： Even-Haim, Shiran Diringer, Asaf A. Ruimy, Ron Baranes, Gefen Gorlach, Alexey Hacohen-Gourgy, Shay Kaminer, Ido Faculty of Electrical & Computer Engineering Faculty of Physics and Solid-State Institute Technion - Israel Institute of Technology Israel Massachusetts Institute of Technology CambridgeMA02139 United States

We present a generalization of the conditional displacement operator, conditioned on a qudit ancilla, and discuss possible implementations. This generalization provides a building block for quantum computation with gr... 详细信息

ISBN: (纸本)9781957171272

关键词： Cavity quantum electrodynamics Pulse shaping Quantum communications Quantum computation Quantum electrodynamics Quantum information

来源：评论

学校读者我要写书评

暂无评论

Generalized Conditional Displacement

Generalized Conditional Displacement

引用

2023 Conference on Lasers and Electro-Optics, CLEO 2023

ISBN: (纸本)9781957171258

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

Free-electron interactions with photonic GKP states: error correction and universal quantum computation

Free-electron interactions with photonic GKP states: error c...

引用

Optica Quantum 2.0 Conference and Exhibition, Quantum 2.0

作者： Even-Haim, Shiran Baranes, Gefen Ruimy, Ron Gorlach, Alexey Dahan, Raphael Diringer, Asaf A. Hacohen-Gourgy, Shay Kaminer, Ido Faculty of Electrical & Computer Engineering Faculty of Physics Solid State Institute Technion - Israel Institute of Technology Israel Massachusetts Institute of Technology CambridgeMA02139 United States

We find that interactions of coherently-shaped free electrons with photons enable universal quantum computation and error correction with GKP qubits. The electron can entangle multiple GKP qubits, creating GHZ and clu... 详细信息

ISBN: (纸本)9781957171272

关键词： Integrated photonics Quantum communications Quantum computation Quantum information Two level systems Waveguide modes

来源：评论

学校读者我要写书评

暂无评论

Free-electron interactions with photonic GKP states: error correction and universal quantum computation

Free-electron interactions with photonic GKP states: error c...

引用

2023 Conference on Lasers and Electro-Optics, CLEO 2023

ISBN: (纸本)9781957171258

关键词： Error correction

来源：评论

学校读者我要写书评

暂无评论

Adaptive Prescribed-Time Neural Control of Nonlinear Systems via Dynamic Surface Technique

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第10期5卷 4948-4958页

作者： Wang, Ping Yu, Chengpu Lv, Maolong Zhao, Zilong Beijing Institute of Technology School of Automation Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Air Force Engineering University College of Air Traffic Control and Navigation Xi'an710051 China Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft2628 CD Netherlands

The adaptive practical prescribed-time (PPT) neural control is studied for multiinput multioutput (MIMO) nonlinear systems with unknown nonlinear functions and unknown input gain matrices. Unlike existing PPT design schemes based on backstepping, this study proposes a novel PPT control framework using the dynamic surface control (DSC) approach. First, a novel nonlinear filter (NLF) with an adaptive parameter estimator and a piecewise function is constructed to effectively compensate for filter errors and facilitate prescribed-time convergence. Based on this, a unified DSC-based adaptive PPT control algorithm, augmented with a neural networks (NNs) approximator, is developed, where NNs are used to approximate unknown nonlinear system functions. This algorithm not only addresses the inherent computational complexity explosion associated with traditional backstepping methods but also reduces the constraints on filter design parameters compared to the DSC algorithm that relies on linear filters. The simulation showcases the effectiveness and superiority of the devised scheme by employing a two-degree-of-freedom robot manipulator. © 2024 IEEE.

关键词： Flexible manipulators

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：