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An Improved Lmmse Channel Estimation Algorithm for Mimo-Ofdm system

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SSRN

SSRN 2022年

作者： Tang, Wanwei Wang, Lixia Wang, Zedai Wei, Mingzhe Zhao, Jinlong Yue, Xinwei The Key Laboratory of Intelligent Data Information Processing and Control of Hebei Province Tangshan University Tangshan063000 China The School of Engineering Hong Kong University of Science and Technology 999077 Hong Kong The School of Electronic and Information Engineering Beihang University Beijing100191 China The School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China

This paper studies channel estimation techniques to recover the received signal more accurately and quickly. The classical Least Squares (LS) and Linear Minimum Mean-squared Error (LMMSE) estimation have the problems of poor estimation performance and high complexity. An Fast-LMMSE channel estimation algorithm is proposed to balance the performance and complexity. In order to apply it to the actual system, the corresponding pilot is also designed. Simulation results are provided and the comparison shows the performance gains achieved by the proposed technique over LS and MMSE estimation in SISO and MIMO communication systems. © 2022, The Authors. All rights reserved.

关键词： Channel estimation

Dynamic Passenger Demands Oriented Optimization of Virtually Coupled Train Formation Scheduling

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Dynamic Passenger Demands Oriented Optimization of Virtually...

Chinese Automation Congress (CAC)

作者： Haobei Zhang Tao Tang Hongjie Liu Yinghui Lang School of Electronics and Information Engineering Beijing Jiaotong University Beijing China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China

The research on virtual coupling technology in metros is a hot topic recently. Intelligent automatic train units can flexibly form virtually coupled train formations (VCTFs) when needed, so that the train capacity would be able to adapt to passenger demands, which are changing dynamically with time and space. However, matching the capacity of trains with passenger demands is a difficult decision-making optimization problem. In this work, we propose a collaborative control problem to decide when and how to create a VCTF to meet the dynamic passenger demands in a metro system. A mathematical model is formulated which can simultaneously optimize the number of related train units in a VCTF, the train dwelling time at a station and the running time in a section. Performance indicators such as train load factor and number of stranded passengers are adopted to measure the satisfaction of the operators and passengers. Then, a multi-objective genetic algorithm is adopted to obtain the Pareto optimum solutions. Finally, simulation experiments are conducted, and the relationship between line efficiency and passenger demand in different discrete time periods is analyzed to verify the correctness and feasibility of the model. Compared with the traditional timetabling method, adopting the virtual coupling technology can better adapt the train capacity to meet the time changing passenger demands, which is welcomed by both metro operators and passengers in reality.

关键词： Couplings Road transportation Rails Adaptation models Analytical models Dynamic scheduling Vehicle dynamics

Plasmonic and photonic enhancement of chiral near fields

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arXiv 2022年

作者： Hu, Li Sun, Zhiguang Nie, Yingdong Huang, Yingzhou Fang, Yurui Chongqing Engineering Laboratory for Detection Control and Integrated System School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing400067 China School of Physics Dalian University of Technology Dalian116024 China State Key Laboratory of Coal Mine Disaster Dynamics and Control Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials College of Physics Chongqing University Chongqing400044 China

A chiral near field with a highly contorted electromagnetic field builds a bridge to match the chiral molecules and light wavelengths with large size differences. It significantly enhances the circular dichroism of chiral molecules and has great prospects in chirality sensing, detection, trapping, and other chirality-related applications. Surface plasmons feature outstanding light-trapping and electromagnetic-field-concentrating abilities. Plasmonic chiral nanostructures facilitate light manipulation to generate superchiral near fields. Meanwhile, the nanophotonic structures have attracted significant interest to obtain strong chiral fields due to their unique electromagnetic resonant properties. During the interaction of light and chiral materials, the chiral near field not only bridges the light and chiral molecules but is also responsible for the optical activities. This paper reviews state-of-the-art studies on chiral near field enhancement using plasmonic and photonic nanostructures. We review the principle of chiral electromagnetic fields and the development of plasmonic and photonic nanostructures for near field enhancement. The properties and applications of enhanced chiral near fields for chiral molecule detection, spin-orbit angular interaction, and the generation of the chiral optical force are examined. Finally, we discuss current challenges and provide a brief outlook of this field. Copyright © 2022, The Authors. All rights reserved.

关键词： Dichroism

DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis

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arXiv 2024年

作者： Deng, Kaijun Zheng, Dezhi Xie, Jindong Wang, Jinbao Xie, Weicheng Shen, Linlin Song, Siyang Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China Department of Computer Science University of Exeter United Kingdom

Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code will be publicly available at https://***/CVI-SZU/DEGSTalk. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

A Space-Time Interval Based Protection Method for Virtual Coupling

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A Space-Time Interval Based Protection Method for Virtual Co...

Chinese Automation Congress (CAC)

作者： Jiayu Wang Hongjie Liu Tao Tang Xiaolin Luo Ming Chai School of Electronics and Information Engineering Beijing Jiaotong University Beijing China National Engineering Research Centerof Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China

As a recent hot topic in railways, Virtual Coupling (VC) is aimed at providing more frequent rail services by operating trains closer. Given that current protection methods cannot fulfill the requirements of VC in terms of safety and efficiency, the implementation of VC urgently calls for an advanced protection method that allows successive trains to drive closer on the premise of safety. Therefore, to improve efficiency, after the force analysis of the train, a train emergency braking model that can describe the real process of emergency braking is proposed. The train emergency braking distance calculated from this model is smaller compared with the traditional train emergency braking model. Then, to ensure train safety, the deficiencies of the existing relative braking distance(RBD) based protection methods are analyzed. After adding constraints of time and space to the train emergency braking process and using the train emergency braking model proposed in this paper, a space-time interval based protection method is proposed. Compared with the RBD protection method, this method can ensure that the trains can track closer under the premise of safety, which can fulfill the requirements of VC. Finally, experiments are conducted to verify the safety and efficiency of the proposed methods. When the braking performance of the preceding train is worse than that of the following train, the space-time interval based protection method avoids the train colliding during the braking process, while RBD based protection method cannot avoid train collision. Experimental results numerically show that the minimum safe distance can be reduced by 29%, compared with the RBD based protection method.

关键词： Couplings Resistance Rails Force Rail transportation Safety Trajectory

A Method for Compressed Storage and Retrieval of Triplet Data Based on Cuckoo Hashing

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A Method for Compressed Storage and Retrieval of Triplet Dat...

International Conference on Wavelet Active Media Technology and information processing (ICWAMTIP)

作者： Liu Pengcheng Qinzhi Zhang Shibin Chang Yan Yan Lili School of Cybersecurity (Xin Gu Industrial College) Chengdu University of Information Technology Chengdu China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu China SUGON Industrial Control and Security Center Chengdu China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

This paper proposes a method for compressing and retrieving triple data based on optimized cuckoo hash. Firstly, the optimized cuckoo hash algorithm is utilized to quickly establish node indexes. Then, the relationship information of triples is stored in the form of a sparse matrix according to the relationship type. Finally, the double compression algorithm k 2 -Rel is employed to compress the relationship attribute matrix of stored triple information, aiming to reduce storage space and lower storage costs. Experimental results demonstrate that the optimized cuckoo hash algorithm significantly reduces the time for building node indexes. Furthermore, utilizing the k2-Rel compression algorithm for compressing the relationship information between nodes substantially decreases storage space, ultimately achieving efficient storage and retrieval of triple data.

关键词： Costs Triples (Data structure) Buildings information processing Media Motion pictures Sparse matrices Indexes Compression algorithms

Short-term power load forecasting method based on CNN-SAEDN-Res

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arXiv 2023年

作者： Cui, Yang Zhu, Han Wang, Yijian Zhang, Lu Li, Yang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China College of Information and Electrical Engineering China Agricultural University Beijing100083 China

In deep learning, the load data with non-temporal factors are difficult to process by sequence models. This problem results in insufficient precision of the prediction. Therefore, a short-term load forecasting method based on convolutional neural network (CNN), self-attention encoder-decoder network (SAEDN) and resi- dual-refinement (Res) is proposed. In this method, feature extraction module is composed of a two-dimensional convolutional neural network, which is used to mine the local correlation between data and obtain high-dimensional data features. The initial load fore-casting module consists of a self-attention encoder-decoder network and a feedforward neural network (FFN). The module utilizes self-attention mechanisms to encode high-dimensional features. This operation can obtain the global correlation between data. Therefore, the model is able to retain important information based on the coupling relationship between the data in data mixed with non-time series factors. Then, self-attention decoding is per-formed and the feedforward neural network is used to regression initial load. This paper introduces the residual mechanism to build the load optimization module. The module generates residual load values to optimize the initial load. The simulation results show that the proposed load forecasting method has advantages in terms of prediction accuracy and prediction stability. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

CA-Edit: Causality-Aware Condition Adapter for High-Fidelity Local Facial Attribute Editing

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arXiv 2024年

作者： Xian, Xiaole He, Xilin Niu, Zenghao Zhang, Junliang Xie, Weicheng Song, Siyang Yu, Zitong Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Key Laboratory of Intelligent Information Processing China University of Exeter United Kingdom Great Bay University China

For efficient and high-fidelity local facial attribute editing, most existing editing methods either require additional finetuning for different editing effects or tend to affect beyond the editing regions. Alternatively, inpainting methods can edit the target image region while preserving external areas. However, current inpainting methods still suffer from the generation misalignment with facial attributes description and the loss of facial skin details. To address these challenges, (i) a novel data utilization strategy is introduced to construct datasets consisting of attribute-text-image triples from a data-driven perspective, (ii) a Causality-Aware Condition Adapter is proposed to enhance the contextual causality modeling of specific details, which encodes the skin details from the original image while preventing conflicts between these cues and textual conditions. In addition, a Skin Transition Frequency Guidance technique is introduced for the local modeling of contextual causality via sampling guidance driven by low-frequency alignment. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our method in boosting both fidelity and editability for localized attribute editing. The code is available at https://***/connorxian/CA-Edit. © 2024, CC BY.

关键词：

AQPDCITY dataset: Picture-based PM monitoring in the urban area of big cities

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arXiv 2020年

作者： Zhang, Yonghui Gu, Ke Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System China Beijing Artificial Intelligence Institute China Faculty of Information Technology Beijing University of Technology China

Since Particulate Matters (PMs) are closely related to people’s living and health, it has become one of the most important indicator of air quality monitoring around the world. But the existing sensor-based methods for PM monitoring have remarkable disadvantages, such as low-density monitoring stations and high-requirement monitoring conditions. It is highly desired to devise a method that can obtain the PM concentration at any location for the following air quality control in time. The prior works indicate that the PM concentration can be monitored by using ubiquitous photos. To further investigate such issue, we gathered 1,500 photos in big cities to establish a new AQPDCITY dataset. Experiments conducted to check nine state-of-the-art methods on this dataset show that the performance of those above methods perform poorly in the AQPDCITY dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Air quality