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TechRxiv 2022年

作者： Shi, Wanlu Liu, Xiaoguang Li, Yingsong College of Information and Communication Engineering Harbin Engineering University Harbin150001 China School of Microelectronics Southern University of Science and Technology Shenzhen China Key Laboratory of Intelligent Computing Signal Processing Ministry of Education Anhui University Anhui Hefei China

Uniform linear array (ULA) fitting (UF) principle is a newly proposed sparse array (SA) design scheme that aims to design SAs using cascaded ULAs, based on which a number of SAs with desired capacity are devised. In this letter, we aim to introduce the UF idea to devise sparse MIMO arrays with reduced mutual coupling and increased uniform degrees of freedom. However, existing sparse MIMO array design approach needs the transmit and receive arrays in MIMO radar have hole-free difference coarrays (DCAs), which is no longer the best choice for UF principle due to the fact that SAs designed via UF cannot guarantee hole-free DCAs. In this regard, considering the difference coarray of the sum coarray (DCSC) concept, a novel sparse MIMO array design strategy which is suitable for UF principle is proposed. Based on the novel strategy and UF principle, a new sparse MIMO array is designed. Numerical simulations verify the good performance of the proposed sparse MIMO array in high coupling scenarios. © 2022, CC BY.

关键词： MIMO radar

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MSCAP: DNA Methylation Age Predictor based on Multiscale Convolutional Neural Network

MSCAP: DNA Methylation Age Predictor based on Multiscale Con...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Han Wang Ruirui Cai Xizeng Zong Zhiquan He Li Zhang School of Information Science and Technology Institute of Computational Biology Northeast Normal University Changchun China Clinical Research Centre Guangzhou First People’s Hospital School of Medicine South China University of Technology Guangzhou China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China School of Computer Science and Engineering Changchun University of Technology Changchun China

DNA methylation can reflect age-related issues in individuals, and age prediction tools developed based on DNA methylation are called Epigenetic clocks. Epigenetic clocks also serve as potent tools for enhancing researchers’ understanding of the aging process and aim to contribute to the advancement of aging research. The current prevailing approach involves creating clocks using Elastic Net regression modeling, wherein high-age-correlation CpG sites were selected as features in advance. However, pre-selecting CpG sites in advance may inadvertently overlook interactions between distant CpG sites. Deep learning has significant advantages in dealing with vast feature sets due to the tremendous opportunities that the development of deep learning presents for research. Deep learning architectures have demonstrated greater efficiency in acquiring and representing large numbers of intricate features compared to traditional machine learning models. In this study, we proposed a deep learning model based on a multiscale convolutional neural network (MSCNN) for pan-tissue age prediction, called MSCAP. First, for the model to efficiently predict datasets from different sequencing platforms, we chose a common intersection of sites from the Illumina 27K and Illumina 450K platforms (25,789 CpG sites), then trained and tested the model on 93 datasets. Different tissues of the human body are used in the datasets for training and testing. And independently tested on 7 independent datasets against an Epigenetic clock developed by Horvath in 2013 with high prediction accuracy. The results showed that MSCAP could effectively extract key features from a vast number of potential features without advanced feature selection. In addition, MSCAP outperformed the Horvath clock on all seven independent test datasets. This research helped boost the development of epigenetic clock methods and reinforced the value of deep learning in computational biology.

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A Physics-Informed Deep Ray Tracing Network for Regional Channel Impulse Response Estimation

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IEEE Transactions on Wireless Communications 2025年

作者： Wang, Shuchen Gao, Suixiang Yang, Wenguo Zhang, Qiwei Loh, Tian Hong Yang, Yang Qin, Fei University of Chinese Academy of Sciences School of Electronic and Electrical Communication Engineering Beijing100049 China University of Chinese Academy of Sciences School of Mathematical Sciences Beijing100049 China Zhongguancun Laboratory Beijing China Space Star Technology Co. Ltd State Key Laboratory of Space-Ground Integrated Information Technology Beijing100095 China National Physical Laboratory Electromagnetic and Electrochemical Technologies Department TeddingtonTW11 0LW United Kingdom Terminus Group IoT Thrust and the Research Center for Digital World with Intelligent Things at HKUST Beijing100027 China Peng Cheng Laboratory Shenzhen518055 China

In modern wireless communication systems, a profound grasp of the channel impulse response (CIR) is pivotal for optimizing the design and functionality of algorithms and systems, especially for multiple-input-multiple-output (MIMO) technology. Conventional methods for gauging and modeling the channels rely on evaluating spatial points sampled discretely, resulting in limitations in acquiring pertinent channel information across a broad region. To overcome these limitations, this study embeds the physical principles of electromagnetic wave propagation into data-driven deep learning models, achieving second-level regional CIR computing efficiency that is hundreds of times faster. The proposed physics-informed deep ray tracing network (PIDRTN) integrates multiple U-shaped network (U-Net) encoder-decoder blocks, capturing radio wave propagation characteristics within a specific region surrounded by buildings, including two equivalent signal propagation directions in a two-dimensional space and a signal intensity correction term. Then, the network employs a parameter-free nonlinear signal transmission module to emulate the physical principles of signal propagation and obtain accurate CIRs from limited anchor locations, which will iteratively generate CIRs for various times within a specified region subjected to enhancement and denoising operations. Furthermore, the PIDRTN-A model, which utilizes anchor data to improve model accuracy, is proposed. A dataset encompassing diverse fading scenarios is constructed using the ray tracing (RT) method. Extensive experiments demonstrate that the proposed models effectively capture directional and reflective properties of signals;using the RT model as a benchmark, normalized root mean squared errors (NRMSEs) of 0.1226 and 0.0969 are obtained for the PIDRTN and PIDRTN-A models, respectively. © 2002-2012 IEEE.

关键词： Mean square error

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Optimal Routing and Charging Strategy for Shared Electric Vehicle Service

SSRN

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SSRN 2022年

作者： Zeng, Weiliang Han, Yu Yao, Ruoyu Liu, Panlong Liao, Li Xie, Shengli School of automation Guangdong University of Technology Guangdong Guangzhou510006 China Joint International Research Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong Provincial Key Laboratory of Intelligent Transportation System Guangdong510006 China Guangdong Engineering Center of Auto-detection Technology for Intelligent Manufacturing Guangdong University of Technology Guangzhou510006 China

How to jointly optimize the routing and charging strategy is a critical issue for the operation of shared electric vehicle (SEV). Existing studies usually addressed the shared routing problem and electricity charging problem independently based on some strict premises which cannot optimize the operation cost globally. To fill this gap, this study models the routing and charging problem of the SEV system as a general formulation, considering ride-matching, charging amount and charging station selection as a whole. And then the global optimization problem is decomposed into four sub-problems. Particularly, this model enables to optimize the global cost of the SEV fleet while adapting to the time-of-use electric price and satisfying the constraints of passengers’ preferences and vehicle states. The performance of the proposed optimization model is validated using 39,700 rides extracted from the shenzhen taxi dataset. We quantify the success rate of the requests, the average travel time delay, the number of active vehicles and the cost of energy consumption with expanded travel demand levels in the real-world road network by numerical simulation. The simulation results show that the proposed model can reduce 49% of the active vehicles in the road network at the rush hour. Our model also avoids the collective charging behaviors during the power consumption peak time, enabling to relieve the pressure on power supply, and saves more than 20% of the cost of energy. © 2022, The Authors. All rights reserved.

关键词： Energy utilization

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SpaFusion: A multi-level fusion model for clustering spatial multi-omics data

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information Fusion 2025年 124卷

作者： Fuqun Chen Guangchang Cai Ying Li Le Ou-Yang State Key Laboratory of Radio Frequency Heterogeneous Integration Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security College of Electronics and Information Engineering Shenzhen University Shenzhen 518060 China SMBU-MSU-BIT Joint Laboratory on Bioinformatics and Engineering Biology Shenzhen MSU-BIT University Shenzhen 518172 China

Cell type identification is crucial for understanding cellular organization and elucidating the mechanisms underlying disease. Recent advances in spatial multi-omics sequencing technologies have enabled the simultaneous profiling of transcriptomics and proteomics data at shared spatial coordinates, providing new opportunities for cell type identification through spatial omics clustering. However, most existing methods primarily focus on clustering spatial transcriptomics data, and effectively integrating multi-omics data for precise cell clustering remains a critical challenge. In this study, we introduce SpaFusion, a novel multi-level fusion model for clustering spatial multi-omics data. We first construct a high-order cell graph to capture more comprehensive relationships between cells. To extract latent features from both local and global perspectives, we propose an architecture that integrates graph autoencoders and transformer modules. Through a multi-level information fusion strategy, SpaFusion captures both omic-specific features and a unified consensus representation across omics. Finally, a consensus clustering strategy is introduced to facilitate information exchange across hierarchical latent representations, thereby enhancing clustering accuracy. Extensive experiments on three real-world spatial transcriptome–proteome datasets demonstrate that SpaFusion consistently outperforms state-of-the-art methods and provides valuable insights into the spatial organization of cell types and their potential roles in disease mechanisms.

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Robust quantum teleportation via a non-Markovian channel

arXiv

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arXiv 2023年

作者： Wang, Yanfang Xue, Shibei Song, Hongbin Jiang, Min Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China General Education Division Shenzhen Key Lab of Semiconductor Lasers The Chinese University of Hong Kong Guang Dong Shenzhen518116 China School of Electronics and Information Engineering Soochow University Suzhou215006 China

In this paper, we propose a non-Markovian quantum channel approach to mitigating the degradation of the average fidelity in continuous-variable quantum teleportation. The non-Markovian quantum channel is modeled by an augmented system, where ancillary systems are introduced to represent the internal modes of non-Markovian environments. With a proper non-Markovianity, enhanced effects of the channels on continuous-variable quantum teleportation are observed. Also, the logarithmic negativity of entangled states can be effectively maintained so that the decay of the average fidelity of quantum teleportation is mitigated. In addition, the analysis on different teleported quantum states shows that coherent states exhibit stronger robustness than those of squeezed states and cat states in quantum teleportation with a non-Markovian channel. Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum entanglement

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Efficient VVC Intra Prediction Based on Deep Feature Fusion and Probability Estimation

arXiv

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arXiv 2022年

作者： Zhao, Tiesong Huang, Yuhang Feng, Weize Xu, Yiwen Kwong, Sam The Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fuzhou350108 China The Peng Cheng Laboratory Shenzhen518055 China The Department of Computer Science City University of Hong Kong Hong Kong

The ever-growing multimedia traffic has underscored the importance of effective multimedia codecs. Among them, the up-to-date lossy video coding standard, Versatile Video Coding (VVC), has been attracting attentions of video coding community. However, the gain of VVC is achieved at the cost of significant encoding complexity, which brings the need to realize fast encoder with comparable Rate Distortion (RD) performance. In this paper, we propose to optimize the VVC complexity at intra-frame prediction, with a two-stage framework of deep feature fusion and probability estimation. At the first stage, we employ the deep convolutional network to extract the spatial-temporal neighboring coding features. Then we fuse all reference features obtained by different convolutional kernels to determine an optimal intra coding depth. At the second stage, we employ a probability-based model and the spatial-temporal coherence to select the candidate partition modes within the optimal coding depth. Finally, these selected depths and partitions are executed whilst unnecessary computations are excluded. Experimental results on standard database demonstrate the superiority of proposed method, especially for High Definition (HD) and Ultra-HD (UHD) video sequences. Copyright © 2022, The Authors. All rights reserved.

关键词： Video signal processing

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A ROS Based Open Source Simulation Environment for Robotics Beginners

A ROS Based Open Source Simulation Environment for Robotics ...

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International Conference on Robotics and Automation engineering (ICRAE)

作者： Linyu Su Guoping Qiu Wengming Tang Min Chen College of Electronic and Information Engineering Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen University China Super-accurate Vision Science and Technology Ltd Shenzhen China

ISBN: (纸本)9781665406987

This paper presents an open source robot simulation environment based on the robot operating system (ROS). To help novice to learn robotics, we have designed several important experiments that most robotics beginners will need to practice. Our simulation environment provides an efficient, safe, and transferable testing and operating environment for the rapid verification of robot algorithms. Through practicing the experiments in this paper, robotics beginners can learn robotics knowledge faster, better and at a lower cost, which should great facilitate them to operate robots in real-world applications. Source code, simulation video and detailed wiki for the simulation environment are available online at https://***/Suyixiu/robot_sim.

关键词： Electronic publishing Costs Codes Automation Operating systems information services Internet

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Unleashing the Power of CNN and Transformer for Balanced RGB-Event Video Recognition

arXiv

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arXiv 2023年

作者： Wang, Xiao Rong, Yao Wang, Shiao Chen, Yuan Wu, Zhe Jiang, Bo Tian, Yonghong Tang, Jin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei230601 China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University Hefei230601 China School of Computer Science and Technology Anhui University Hefei230601 China The School of Internet Anhui University Hefei230601 China The Peng Cheng Laboratory Shenzhen China The Peng Cheng Laboratory China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China

Pattern recognition based on RGB-Event data is a newly arising research topic and previous works usually learn their features using CNN or Transformer. As we know, CNN captures the local features well and the cascaded self-attention mechanisms are good at extracting the long-range global relations. It is intuitive to combine them for high-performance RGB-Event based video recognition, however, existing works fail to achieve a good balance between the accuracy and model parameters, as shown in Fig. 1. In this work, we propose a novel RGB-Event based recognition framework termed TSCFormer, which is a relatively lightweight CNN-Transformer model. Specifically, we mainly adopt the CNN as the backbone network to first encode both RGB and Event data. Meanwhile, we initialize global tokens as the input and fuse them with RGB and Event features using the BridgeFormer module. It captures the global long-range relations well between both modalities and maintains the simplicity of the whole model architecture at the same time. The enhanced features will be projected and fused into the RGB and Event CNN blocks, respectively, in an interactive manner using F2E and F2V modules. Similar operations are conducted for other CNN blocks to achieve adaptive fusion and local-global feature enhancement under different resolutions. Finally, we concatenate these three features and feed them into the classification head for pattern recognition. Extensive experiments on two large-scale RGB-Event benchmark datasets (PokerEvent and HARDVS) fully validated the effectiveness of our proposed TSCFormer. The source code and pre-trained models will be released at https://***/Event-AHU/TSCFormer Copyright © 2023, The Authors. All rights reserved.

关键词： Large dataset

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Trapping Centers for Carriers Leads to Persistent Photoconductive Effect: Polycrystalline Β-Ga2o3 Used on Neuromorphic information Storage

SSRN

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SSRN 2024年

作者： Xi, Zhaoying Liu, Maosheng Li, Shan Zhang, Jia-Han Jiang, Mingming Zhang, Shaohui Tang, Weihua Liu, Zeng College of Integrated Circuit Science and Engineering Nanjing University of Posts and Telecommunications Nanjing210023 China School of Electronic Information Engineering Inner Mongolia Key Laboratory of Intelligent Communication and Sensing and Signal Processing Inner Mongolia University Hohhot010021 China College of Physics MIIT Key Laboratory of Aerospace Information Materials and Physics Nanjing University of Aeronautics and Astronautics Nanjing211106 China Institute of Biological and Medical Engineering Guangdong Academy of Sciences Guangzhou510316 China Nanjing University China Shenzhen University China Nanjing University of Posts and Telecommunications China

Neuromorphic visual sensors (NVS) address the bottleneck of traditional devices with synaptic behaviors that help solve the challenges of large power consumption and slow processing speeds. The carrier trapping layer plays an important role in the synaptic behavior of NVSs. In this work, we propose a polycrystalline β-Ga2O3 as both photosensitive and carrier trapping material to simplify the multilayer structure of NVSs. The persistent photoconductivity effect of polycrystalline β-Ga2O3 photosensor has been enhanced by utilizing grain boundaries. The trapping effect is thoroughly investigated by characterization and electrical tests. Further, it is demonstrated that the β-Ga2O3 device can mimic biological neuromorphic behavior with a variety of basic synaptic behaviors. Finally, the device is proved to be able to store optical images for more than 520 s and ASCII messages for more than 300 s. It broadens the road for the development of Ga2O3 in neuromorphic memory devices. © 2024, The Authors. All rights reserved.

关键词： Grain boundaries

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