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2017 9th IEEE International Conference on Communication Software and Networks (ICCSN 2017)

作者： Zeng Chen Keren Wang Zhengguo Xu Jiaqi Yao National Key Laboratory of Science and Technology on Blind Signal Processing

ISBN: (纸本)9781509038237;9781509038220

With the popularity of the mobile phones and location-based social networks, rich location data has become widely available nowadays, enabling study on friendship detection based on human mobility. However, in some circumstances, limited by data collection techniques, only discrete location（such as location IDs） can be fetched which leads to methods of detecting friendship based on distance metric is unrealistic. This paper aims to detect friendship among users based on their mobility data where locations are represented by location IDs. By considering each user’s preference for location and each location’s popularity, the difference between friends and strangers gets larger than using merely frequency of meeting events. However, we discover that measure values obtained may vary greatly among different users and propose to maintain a threshold for each user. In addition, an online updating algorithm is presented to update thresholds when a new user’s mobility data is available. Experiments conducted on MIT Reality Mining project dataset show that the proposed updating method is practical and the proposed multi-threshold model outperforms the state-of-theart methods proposed by Wang et al.

关键词： friendship detection multi-threshold mobility spatiotemporal online update

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Flow Visualization for Complex Fluid Flows via A Structure-enhanced Motion Estimator

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Chen, Jun Wang, He Hao, Zhifeng Cai, Zemin Mei, Ling Foshan University School of Industrial Design and Ceramic Art Foshan528000 China Foshan University school of Mechatronic Engineering and Automation Foshan528231 China Shantou University College of Science Department of Mathematics Guangdong 515063 China Shantou University Department of Electronic Engineering Guangdong Shantou515063 China Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Techniques Guangdong Shantou515063 China Wuhan University of Science and Technology School of Information Science and Engineering 430081 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation 430074 China Western Michigan University Department of Mechanical and Aerospace Engineering KalamazooMI49008 United States

Flow visualization through motion estimation using time-sequenced images plays a significant role in analyzing and understanding complex flow phenomena, and it is widely used in meteorology, oceanography, medicine, astronomy, experimental fluid mechanics, etc. However, it is difficult for current motion estimators to adapt to illumination changes, remove instable perturbation, and capture diverse motion patterns. In this paper, a novel flow visualization tool is developed to address these issues by employing a structure-enhanced motion estimator composed of a data term and a regularization term. Specifically, a statistical correlation descriptor is designed for the data term to improve the accuracy of motion estimation by enhancing both illumination robustness and matching discrimination. Inspired by the strong distinguishability of a structure-texture distribution in a local window, a structure-enhanced regularizer that considers the physical mechanism of fluid diffusion is introduced to capture different motion patterns, enhance prominent flow structures, and remove unnecessary ripples or textures caused by instable perturbation or noise. The experimental results demonstrate that our approach significantly outperforms current motion estimators in handling illumination changes and predicting complex fluid flows, and it also achieves state-of-the-art evaluation results on the public fluid flow datasets. Furthermore, the designed flow visualization tool successfully captures diverse motion patterns in Jupiter’s White Ovals, which is crucial for understanding the physical mechanisms behind their formation and sustenance. © 1991-2012 IEEE.

关键词： Motion estimation

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Short-Time Variational Mode Decomposition

arXiv

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arXiv 2025年

作者： Jia, Hao Cao, Pengfei Liang, Tong Caiafa, Cesar F. Sun, Zhe Kushihashi, Yasuhiro Grau, Antoni Bolea, Yolanda Duan, Feng Solé-Casals, Jordi School of Medicine Nankai University Tianjin China Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Tianjin China Universitat Politècnica de Catalunya Barcelona Spain Mechanical Systems Engineering Nippon Institute of Technology Saitama Japan Villa Elisa Argentina Computational Bioengineering Laboratory Faculty of Health Data Science Juntendo University Tokyo Japan Data and Signal Processing Research Group University of Vic-Central University of Catalonia Vic Spain

Variational mode decomposition (VMD) and its extensions like Multivariate VMD (MVMD) decompose signals into ensembles of band-limited modes with narrow central frequencies. These methods utilize Fourier transformations to shift signals between time and frequency domains. However, since Fourier transformations span the entire time-domain signal, they are suboptimal for non-stationary time series. We introduce Short-Time Variational Mode Decomposition (STVMD), an innovative extension of the VMD algorithm that incorporates the Short-Time Fourier transform (STFT) to minimize the impact of local disturbances. STVMD segments signals into short time windows, converting these segments into the frequency domain. It then formulates a variational optimization problem to extract band-limited modes representing the windowed data. The optimization aims to minimize the sum of the bandwidths of these modes across the windowed data, extending the cost functions used in VMD and MVMD. Solutions are derived using the alternating direction method of multipliers, ensuring the extraction of modes with narrow bandwidths. STVMD is divided into dynamic and non-dynamic types, depending on whether the central frequencies vary with time. Our experiments show that non-dynamic STVMD is comparable to VMD with properly sized time windows, while dynamic STVMD better accommodates non-stationary signals, evidenced by reduced mode function errors and tracking of dynamic central frequencies. This effectiveness is validated by steady-state visual-evoked potentials in electroencephalogram signals. Copyright © 2025, The Authors. All rights reserved.

关键词： Cost functions

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Azimuth resolution improvement of spaceborne SAR images with nearly nonoverlapped doppler bandwidth

Azimuth resolution improvement of spaceborne SAR images with...

引用

IET International radar Conference 2013

作者： Liu, Yan-Yang Li, Zhen-Fang Suo, Zhi-Yong Bao, Zheng National Key Laboratory of Science and Technology on Radar Signal Processing Xidian Univ. Xi'an Shanxi 710071 China

ISBN: (纸本)9781849196031

This paper deals with the azimuth resolution improvement problems via the coherent combination of the SAR images with nearly zero-overlapped Doppler bandwidth, which corresponding to large along-track baseline. Prior to the combination of the images, the phase bias contributions from the atmospheric propagation delays and baseline measurement errors should be calibrated. The interferogram of the azimuth pre-filtered SAR images are adopted in the classical method, whose performance falls down when the overlapped spectra comes to zero. In this paper, the maximum sharpness optimization is adopted to deal this situation. The proposed method has been tested by processing the real data.

关键词： Synthetic aperture radar

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Feature-Consistent Cyclic Domain Adversarial Network for Unsupervised Transfer Diagnosis of the Autoimmune Disease Igg4-Rd by Raman Spectroscopy

SSRN

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SSRN 2025年

作者： Zhang, Ziyang Chen, Cheng Qin, Wentao Wu, Xue Liu, Yang Wu, Lijun Lv, Xiaoyi Chang, Chenjie Chen, Xinya Chen, Zhiyuan Xu, Qianxi Li, Zhuoyu Ma, Mengsi Chen, Chen Xinjiang University College of Software Urumqi830046 China Xinjiang Cloud Computing Application Laboratory Xinjiang University Karamay834099 China School of Computer Science and Technology Xinjiang University Urumqi830046 China Key Laboratory of signal detection and processing Xinjiang University Urumqi830046 China Department of Rheumatology＆Immunology People's Hospital of Xinjiang Uygur Autonomous Region Xinjiang Urumqi China Xinjiang Clinical Research Center for Rheumatoid arthritis Xinjiang Urumqi China

Abstracts IgG4-related disease (IgG4-RD) is a multi-organ immune disorder characterized by systemic involvement, diverse pathogenesis, and rarity, which complicates its diagnosis. Traditional medical diagnostic models, predominantly based on supervised learning in machine learning or deep learning frameworks, struggle to address the scarcity of labeled data due to the diagnostic challenges of IgG4-RD. To overcome this limitation, this study proposes a novel framework based on Feature-Consistent Cyclic Domain Adversarial Networks (FCCDAN). The framework first trains a classification model on the source domain for diagnosing source domain diseases under supervised conditions. Subsequently, leveraging the source domain model, the encoder is guided to extract semantic features, while feature consistency loss is integrated into both the generator and discriminator to align the semantic features of the target domain with those of the source domain. Finally, the aligned features are processed through a pre-trained linear mapping to achieve unsupervised classification and diagnosis in the target domain. The proposed model utilizes common autoimmune diseases as a knowledge source, enabling unsupervised transfer diagnosis for IgG4-RD, and demonstrates generalizability through experiments on other disease datasets, achieving an average diagnostic accuracy of 90.62%. This represents improvements of 4.14%, 9.82%, and 10.51% over baseline models, respectively. FCCDAN expands the applicability of Raman spectroscopy in disease diagnosis, maintaining high performance even in label-scarce environments. The overall research workflow also establishes a robust paradigm for the application and advancement of domain adaptation techniques in this context. © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Tolerance Granulation Based Community Detection Algorithm

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Tsinghua science and technology 2015年第6期20卷 620-626页

作者： Shu Zhao Wang Ke Jie Chen Feng Liu Menghan Huang Yanping Zhang Jie Tang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Center of Information Support & Assurance TechnologySchool of Computer Science and TechnologyAnhui University School of Computer Science and Technology Tsinghua University

Community structure is one of the most important features in real networks and reveals the internal organization of the vertices. Uncovering accurate community structure is effective for understanding and exploiting networks. Tolerance Granulation based Community Detection Algorithm(TGCDA) is proposed in this paper, which uses tolerance relation(namely tolerance granulation) to granulate a network hierarchically. Firstly, TGCDA relies on the tolerance relation among vertices to form an initial granule set. Then granules in this set which satisfied granulation coefficient are hierarchically merged by tolerance granulation operation. The process is finished till the granule set includes one granule. Finally, select a granule set with maximum granulation criterion to handle overlapping vertices among some granules. The overlapping vertices are merged into corresponding granules based on their degrees of affiliation to realize the community partition of complex networks. The final granules are regarded as communities so that the granulation for a network is actually the community partition of the *** on several datasets show our algorithm is effective and it can identify the community structure more accurately. On real world networks, TGCDA achieves Normalized Mutual Information(NMI) accuracy 17.55% higher than NFA averagely and on synthetic random networks, the NMI accuracy is also improved. For some networks which have a clear community structure, TGCDA is more effective and can detect more accurate community structure than other algorithms.

关键词： tolerance relation community tolerance granulation

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Reliable Multi-Modal Object Re-Identification via Modality-Aware Graph Reasoning

arXiv

引用

arXiv 2025年

作者： Wan, Xixi Zheng, Aihua Wang, Zi Jiang, Bo Tang, Jin Ma, Jixin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University Hefei230601 China School of Biomedical Engineering Anhui Medical University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China School of Computing and Mathematical Sciences University of Greenwich LondonSE10 9LS United Kingdom

Multi-modal data provides abundant and diverse object information, crucial for effective modal interactions in Re-Identification (ReID) tasks. However, existing approaches often overlook the quality variations in local features and fail to fully leverage the complementary information across modalities, particularly in the case of low-quality features. In this paper, we propose to address this issue by leveraging a novel graph reasoning model, termed the Modality-aware Graph Reasoning Network (MGRNet). Specifically, we first construct modality-aware graphs to enhance the extraction of fine-grained local details by effectively capturing and modeling the relationships between patches. Subsequently, the selective graph nodes swap operation is employed to alleviate the adverse effects of low-quality local features by considering both local and global information, enhancing the representation of discriminative information. Finally, the swapped modality-aware graphs are fed into the local-aware graph reasoning module, which propagates multimodal information to yield a reliable feature representation. Another advantage of the proposed graph reasoning approach is its ability to reconstruct missing modal information by exploiting inherent structural relationships, thereby minimizing disparities between different modalities. Experimental results on four benchmarks (RGBNT201, Market1501-MM, RGBNT100, MSVR310) indicate that the proposed method achieves state-of-the-art performance in multi-modal object ReID. The code for our method will be available upon acceptance. Copyright © 2025, The Authors. All rights reserved.

关键词： Modal analysis

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Complex-wave retrieval based on blind signal separation

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Chinese Optics Letters 2006年第1期4卷 11-14页

作者：柴晓冬周成鹏冯兆艳王银花左言胜 Department of Electronic Science and Technology University of Science and Technology of ChinaHefei 230026 Key Laboratory of Intelligent Computing and Signal ProcessingMinistry of EducationAnhui UniversityHefei 230039 Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationAnhui UniversityHefei 230039

In tile process of the reconstruction of digital holography. the traditional methods of diffraction and filtration are commonly adopted to recover the original complex-wave signal. Influenced by twin-image and zero-order terms, the above-mentioned methods, however, either limit tile field of vision or result in the loss of the amplitude and phase. A new method for complex-wave retrieval is presented, which is based on blind signal separation. Three frames of holograms are captured by a charge coupled device （CCD） camera to form an observation signal. The term containing only amplitude and phase of complex-wave is separated, by means of independent component analysis, from the observation signal, which effectively eliminates the zero-order term. Finally. the complex-wave retrieval of pure phase wavefront is achieved. Experimental results show that this method can better recover the amplitude and phase of the original complex-wave even when there is a frequency spectrum mixture in the hologram.

关键词： Charge coupled devices Image reconstruction Independent component analysis signal interference signal processing Spectrum analysis

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A Long Synthetic Aperture Passive Localization Method Using Two Planes

A Long Synthetic Aperture Passive Localization Method Using ...

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2021 CIE International Conference on radar, radar 2021

作者： Chen, Bowei Wang, Yuqi Sun, Guang-Cai Xing, Mengdao Zhang, Zijing Yang, Xiaoniu Xidian University National Laboratory of Radar Signal Processing Xi'an China Science and Technology on Communication Information Security Control Laboratory Jiaxing China

ISBN: (纸本)9781665498142

A long synthetic aperture passive localization algorithm is proposed in this paper. First of all, the Doppler change rate of signal can be measured by the image contrast and then, grid points are divided within the maximum possible latitude and longitude of the emitter location, the Doppler change rate at each grid point can be calculated by using known position and velocity of the receiver. The grid points at which the calculated Doppler change rate is equal to the value measured by the received signal form an isoline. Two receivers can draw two isolines on the ground and the intersection of the isolines gives estimate of the emitter location. © 2021 IEEE.

关键词： Azimuth focusing Doppler change rate Passive localization Synthetic aperture localization

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Data Association with Passive Angle Measurements 10

Data Association with Passive Angle Measurements

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10th International Conference on Control, Automation and Information sciences, ICCAIS 2021

作者： Wang, Linhai Li, Zhuyu Zhou, Shenghua Zhang, Xinyu Xing, Xiaoyu Gou, Mingjiang Xidian University National Laboratory of Radar Signal Processing Xi'an710071 China Science and Technology on Electromagnetic Scattering Laboratory Beijing100854 China

ISBN: (纸本)9781665440295

Some passive sensors can measure only relative angles of signals and in a distributed sensor network, it is interesting to associate measurements of angles from distributed sensors and then to estimate the positions of signal sources. This paper studies the data association problem and the intersection localization problem for a distributed passive sensor network. Under the assumption that the locations of the sensors are known a priori, different signal directions of distributed passive sensors constitute different lines and those with sufficiently small minimal distances are deemed to be from the same signal sources. Meanwhile, for associated signals, the signal location estimate is the average position of the points over the lines with minimal distances to each other. The impacts of angle estimation accuracy and platform self-localization accuracy are analyzed in numerical results, indicating that this method can achieve a high data association and positioning performance with a low computation cost. © 2021 IEEE.

关键词： Sensor networks

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