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IEEE Conference on Global Communications (GLOBECOM)

作者： Pengxu Wang Xingjian Zhang Yuan Ma Hao Zhu Jian Jiao Qinyu Zhang Department of Electronics and Information Engineering Harbin Institute of Technology (Shenzhen) Shenzhen China Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Harbin Institute of Technology (Shenzhen) Shenzhen China Peng Cheng Laboratory Shenzhen China State Key Laboratory of Radio Frequency Heterogeneous Integration Guangdong Key Laboratory of Intelligent Information Processing and the College of Electronic and Information Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Wideband signal recognition is a crucial task in the cognitive wireless communication, involving accurate classification of different signal types, modulation types, center frequencies, etc. However, most conventional approaches treat the recognition of different parameters as multiple independent tasks, and often face performance bottlenecks due to the complexity and diversity of wideband signals. To overcome these challenges, we propose a multi-task learning (MTL) network that integrates multiple tasks of signal recognition into an end-to-end model to accomplish spectrum sensing, modulation recognition, and signal classification simultaneously. By employing a shared feature extraction network and a multi-task classification header, the proposed framework effectively captures the correlations and shared information among different tasks, thereby enhancing overall recognition performance. To validate the effectiveness of the proposed scheme, we compare its performance with other state-of-the-art recognition and classification networks. Experimental results demonstrate the significant performance of the proposed MTL network in spectrum sensing, modulation recognition, and signal classification tasks.

关键词： Wireless communication Correlation Accuracy Frequency modulation Face recognition Pattern classification Multitasking Sensors Global communication Wideband

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Fault Diagnosis for Small Samples Based on VMR

Fault Diagnosis for Small Samples Based on VMR

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Chinese Automation Congress (CAC)

作者： Xuejin Wu Yanghu Hu Yali Wu Department of Information and Control Engineering Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China State Key Laboratory of Metal Extrusion and Forging Equipment Technology China National Heavy Machinery Research Institute Co. Ltd Xi'an China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

The scarcity of fault samples presents a significant challenge for fault diagnosis in industrial processes. Conventional methods often fail to deliver satisfactory results when dealing with small sample sizes. In addressing the issue of small sample fault diagnosis in industrial processes, this paper utilizes the Variational Mode Reconstruction(VMR) algorithm to screen the best components decomposed by the Variational Mode Decomposition(VMD) through the envelope entropy then random weighting them and signal reconstruction to achieve data enhancement, to enrich the sample size and to prevent the model overfitting phenomenon due to the scarcity of samples. Meanwhile, this paper integrates Convolutional Neural Network (CNN) and Long Short-term Memory Neural Network (LSTM) to propose a network model of spatio-temporal feature fusion for feature extraction and classification. The experiments demonstrate that this model not only enhances data diversity but also ensures consistency before and after enhancement, effectively mitigating overfitting. Furthermore, it guarantees high recognition and generalization rates in fault diagnosis.

关键词： Fault diagnosis Training Neural networks Rolling bearings Feature extraction Data models signal reconstruction Convolutional neural networks Long short term memory Overfitting

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Fusing SAR Images and Social Media Data Through Domain Adaptation and Land Cover information: A Case of 2017 Houston Flood Event

Fusing SAR Images and Social Media Data Through Domain Adapt...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Zhenjie Liu Yong Deng Jun Li Javier Plaza Antonio Plaza Hubei Key Laboratory of Intelligent Geo-Information Processing and School of Computer Science China University of Geosciences Wuhan China Changjiang Institute of Survey Planning Design and Research Wuhan China Hyperspectral Computing Laboratory University of Extremadura Caceres Spain

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Global climate change leads to increasing frequency and severity of urban flood disasters, which restricts human sustainable development. So far, many studies have explored the potential of fusing remote sensing images and social media data for monitoring urban flood disasters. However, the SAR flooded characteristics of different land cover in complex urban environment are generally ignored. In this paper, we develop a new method for the fusion of heterogeneous SAR images and social media data by using domain adaptation and land cover information. The 2017 Houston flood event is taken as a case study for evaluation. According to our experiments, compared with traditional methods of optimal transport and geographic optimal transport, the proposed method can align the geo-tagged tweets with location uncertainty to nearby flooded areas of low-, medium-, and high-intensity developed areas with more remarkable performance.

关键词： Uncertainty Social networking (online) Disasters Urban areas Land surface radar polarimetry Floods

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

DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

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

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

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 is available at https://***/CVI-SZU/DEGSTalk.

关键词： Hair Training Three-dimensional displays Dynamics signal processing Rendering (computer graphics) Noise measurement Speech processing Faces Videos

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Multi-user Dual-function radar-communications System Based on Circulating Code Array

Multi-user Dual-function Radar-communications System Based o...

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2021 CIE International Conference on radar, radar 2021

作者： Zhang, Qiuyue Feng, Cunqian Gu, Yabin Zhang, Linrang Zhou, Yu Chen, Zhanye Air and Missile Defense College Air Force Engineering University Xi'an China Shannxi Huanghe Group Company Ltd. Xi'an China Xidian University National Laboratory of Radar Signal Processing Xi'an China Chongqing University Chongqing Key Laboratory of Space Information Network and Intelligent Information Fusion Chongqing China

ISBN: (纸本)9781665498142

To ease the shortage of the spectrum resource, a dual-function radar-communications (DFRC) system based on circulating code array is proposed in this paper. The relationship between the spectrum of the synthesized signal and the spatial angle is firstly analyzed. By utilizing this relationship, different frequency bands can be allocated to multiple different communication users located in different spatial directions. radar and communication receiving processing procedures are analyzed secondly. At the communication receiver, the relationship between the spectrum of the transmitted basic waveform and the spectrum of the synthesized waveform of circulating code array provides the possibility to demodulate the communication symbols from the received signal. The proposed DFRC system requires only one transmitted basic waveform to implement multi-user communication transmission and radar detection simultaneously. It is verified that the designed method can use different modulation schemes to simultaneously transmit information to multiple communication users located in different directions without sacrificing radar detection performance. Simulation results are presented to validate the performance of the designed DFRC system. © 2021 IEEE.

关键词： BER circulating code array DFRC radar detection

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An Improved Differential Evolution Algorithm Integrating the Plant Evolutionary Strategy

SSRN

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SSRN 2024年

作者： Zhang, Xiaoming Cheng, Hongwei Li, Jun Li, Tingjuan Zhang, Panpan Tian, Ye Institutes of Physical Science and Information Technology Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China Qinghai Institute of Science and Technology Information Xining810008 China School of Computer Science and Technology Anhui University Hefei230601 China

Differential evolution algorithm (DE) and their variants often encounter a decrease in population diversity and convergence rate along with the evolution, resulting in local optima and premature convergence. To address this issue, this paper proposes an improved differential evolution integrating the plant evolutionary strategy, named DEPES, leveraging the exploitation capability of DE and the exploration capability of the plant population distribution evolutionary strategy. First, a tri-subpopulation optimization framework with a mutation strategy is introduced to cope with diversity maintenance, convergence enhancement, and local optima a Cauchy mutation operator based on plant population distribution evolutionary model is employed to enhance the exploration capability of the mutation strategy. Second, a parameter control strategy is performed on the superior and inferior solutions to balance the exploitation and exploration of the population. Third, a covariance matrix-based crossover strategy is designed to minimize interference from local population information, consequently generating high-quality solutions. The superior efficiency of DEPES is fully verified by comparing it with advanced DE-based variants and other algorithms on the IEEE CEC2020 test suite and engineering design problems. © 2024, The Authors. All rights reserved.

关键词： Covariance matrix

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Multipath Imaging for Vehicle Targets in Non-Los Urban SAR

Multipath Imaging for Vehicle Targets in Non-Los Urban SAR

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yixin Wang Xiaolan Qiu Xuejiao Wen Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Beijing China Chinese Academy of Sciences Aerospace Information Research Institute Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China National Key Laboratory of Microwave Imaging Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Laboratory of Spatial Information Intelligent Processing System Suzhou Aerospace Information Research Institute Suzhou China University of Science and Technology of China Hefei China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Integrating Synthetic Aperture radar (SAR) imaging with unmanned aerial vehicles (UAVs) plays a crucial role in urban area surveillance and situational awareness, benefiting from the low cost, small size, and high flexibility of SAR carried by drones. However, the dense arrangement of high-rise buildings in urban environments creates Urban Canyons and numerous visual blind spots due to occlusion, which weakens the perception capability of SAR. Additionally, SAR imaging results of moving vehicles on the roads between buildings result in severe defocusing due to their non-cooperative motion. In this paper, we establish a vehicle signal model for SAR imaging with UAVs that considers the vehicle body’s translation and the wheels’ rotation. The range history modulation and imaging characteristics of returns caused by translation and micro-motion are derived. Simulation results validate the correctness of the theoretical analysis, and the proposed theory helps explain SAR imaging results, providing support for high-precision focusing and three-dimensional imaging of non-line-of-sight (NLOS) SAR images.

关键词： Visualization Surveillance Simulation Roads Urban areas Buildings Wheels

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Learning Guided Implicit Depth Function With Scale-Aware Feature Fusion

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IEEE transactions on image processing : a publication of the IEEE signal processing Society 2025年 34卷 3309-3322页

作者： Yifan Zuo Yuqi Hu Yaping Xu Zhi Wang Yuming Fang Jiebin Yan Wenhui Jiang Yuxin Peng Yan Huang Jiangxi University of Finance and Economics School of Information Management Jiangxi Provincial Key Laboratory of Multimedia Intelligent Processing (No. 2024SSY03141) Jiangxi Nanchang China Peking University Wangxuan Institute of Computer Technology Beijing China University of Technology Sydney Australian Artificial Intelligence Institute Sydney NSW Australia

Recently, the single image super-resolution based on implicit image function is a hot topic, which learns a universal model for arbitrary upsampling scales. By contrast, color-guided depth map super-resolution is less explored based on implicit function learning. The related research faces three questions. First, is it also necessary and applicable to fuse the depth feature and the color feature in the encoder with continuous upsampling scales? Second, is the scale information in the encoder as important as that in the decoder? Third, how to efficiently and effectively model the affinity of location distance and content similarity within cross domains in the decoder? This paper proposes a transformer-based network to answer the above questions, which includes a depth super-resolution branch and a guidance extraction branch. Specifically, in the encoder, the effective implicit cross transformer is designed to fuse the guidance from the color feature with continuous coordinate mapping. In addition, the unrelated guidance is filtered out by correlation evaluation in the high-dimension feature space. Unlike the scale only introduced in the decoder, this paper additionally embeds the scale into the position encoding and the feed-forward network in the encoder to learn the scale-aware feature representation. In the decoder, the high-resolution depth feature is reconstructed by using the internal prior and the external guidance. The internal prior is implemented by implicit self-attention in the depth super-resolution branch, and the external guidance is exploited via implicit cross-attention between both branches. Finally, the above decoded features are complementary to generate the high-resolution depth map. The sufficient experiments on the synthetic and real datasets for in-distribution and out-of-distribution upsampling scales validate the improved performance. The code and the models are public via https://***/NaNRan13/GIDF.

关键词： Guided Depth Map Super-resolution Implicit Image Function Local Implicit Transformer Vision Transformer

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Mimicking synaptic plasticity based on TiO2/MgO film resistive switching device

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Materials Science in Semiconductor processing 2025年 197卷

作者： Pan, Jinyan Li, Jing He, Ruotong Huang, Qiao Li, Tiejun Yan, Lingsen Liu, Yan He, Hongyang Gao, Yunlong School of Ocean Information Engineering Jimei University Fujian Xiamen361021 China Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing Jimei University Fujian Xiamen361021 China Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen University Fujian Xiamen361102 China

The TiO2/MgO composite film was prepared via atomic layer deposition, exhibiting superior resistive switching (RS) performance compared to pure MgO film, including a higher resistance ratio (Roff/Ron) and enhanced retention. The Ag/TiO2/MgO/ITO device exhibited gradual RS characteristics, making it suitable for neuromorphic applications. The device also displayed excellent multilevel RS properties analogous to synaptic plasticity. When a series of positive or negative scanning voltages with progressively increasing amplitudes were applied, the resistance of the device switched and remained at correspondingly higher or lower states, effectively mimicking synaptic potentiation or depression in an amplitude-dependent manner. Furthermore, long-term potentiation and depression behaviors were observed under the time-dependent stimulation. As the pulse voltage amplitude increased, fewer pulses were required to set the device to a specific state. Conversely, lower voltage necessitated more pulses to facilitate Ag ions migration and conductive filament formation. This dynamic mimics synaptic weight modulation, where the evaluation of filaments over time corresponds to synaptic adjustment. Therefore, applying higher or lower amplitude voltages for longer or shorter durations aligns with the observation, where varying stimuli to the presynaptic membrane modulate synaptic weight, thereby strengthening (and weakening) the synaptic signal. The pulse amplitude and duration thresholds modulate the RS behavior of the composite TiO2/MgO film effectively mimic the learning and forgetting processes of human brain memory. © 2025

关键词： Composite films

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Enhance Terahertz Image Resolution via Complex-valued convolutional neural networks 3

Enhance Terahertz Image Resolution via Complex-valued convol...

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3rd International Conference on Defence Technology, ICDT 2022

作者： Hu, Nairui Qi, Feng Li, Yufeng Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Aerospace University Liaoning Shenyang110136 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

Terahertz imaging technology is widely used in nondestructive testing (NDT), human security inspection, far-field imaging systems, and so on. Improving image resolution is always a topic of discussion. For visible image and image super-resolution, the convolutional neural network (CNN) has achieved a better improvement effect than the traditional algorithm. Compared to optics, it is quite feasible to gain both the amplitude and phase information in THz imaging, the Complex-valued convolutional neural network (CV-CNN) is better suited for the field of terahertz imaging than the real-valued neural network. In this paper, A lightweight complex-valued neural network is constructed which enhances terahertz imaging in a vector network analyzer (VNA) imaging system. Compared to CNN, CV-CNN has a better peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), and it is significantly less vulnerable to overfitting. Phase information can be used well at the same time, which is impossible for CNN. The network is trained using the MNIST dataset and verified by using simulated and measured data obtained from a 200Ghz imager. © Published under licence by IOP Publishing Ltd.

关键词： Image resolution

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