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Generalized Tensor-Aided Channel Estimation for Hardware Impaired Device Identification

IEEE Transactions on Vehicular technology 2025年

作者： Wu, Qi Sui, Zeping Ngo, Hien Quoc Wan, Qun Matthaiou, Michail School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China National Key Laboratory on Blind Signal Processing Chengdu610041 China Queen’s University Belfast BelfastBT3 9DT United Kingdom School of Computer Science and Electronics Engineering University of Essex ColchesterCO4 3SQ United Kingdom Department of Electronic Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of

In this paper, we investigate the joint generalized channel estimation and device identification problem in Internet of Things (IoT) networks under multipath propagation. To fully utilize the received signal, we decompose the generalized channel into three components: transmitter hardware characteristics, path gains, and angles of arrival. By modeling the received signals as parallel factor (PARAFAC) tensors, we develop alternating least squares (ALS)-based algorithms to simultaneously estimate the generalized channels and identify the transmitters. Simulation results show that the proposed scheme outperforms both Khatri-Rao Factorization (KRF) and the conventional least squares (LS) method in terms of channel estimation accuracy and achieves performance close to the derived Cramér–Rao lower bound. © 2025 IEEE. All rights reserved,

关键词： Tensors

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MCFTNet: Multimodal Cross-layer Fusion Transformer Network for Hyperspectral and LiDAR Data Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Huang, Wei Wu, Tianren Zhang, Xueyu Li, Liangliang Lv, Ming Jia, Zhenhong Zhao, Xiaobin Ma, Hongbing Vivone, Gemine Xinjiang University School of Computer Science and Technology Urumqi830017 China Xinjiang University Key Laboratory of Signal Detection and Processing Urumqi830017 China Guangxi University School of Computer and Electronic Information Nanning530004 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Tsinghua University Department of Electronic Engineering Beijing100084 China National Research Council Tito85050 Italy NBFC National Biodiversity Future Center Palermo90133 Italy

Remote sensing image classification is a popular yet challenging field. Many researchers have combined convolutional neural networks (CNNs) and Transformers for hyperspectral image (HSI) classification tasks. However, in traditional Transformers, shallow-level information does not propagate well to deeper layers, which can lead to spatial variations and overfitting. Moreover, traditional Transformer models use an external classification (CLS) token that is randomly initialized and often struggles to generalize effectively. In this paper, we combine the strengths of HSI and light detection and ranging (LiDAR) data, using LiDAR as an external CLS token, which significantly enhances classification accuracy and reliability. We propose a new multimodal cross-layer fusion transformer network (MCFTNet), integrating CNNs with the latest Transformer networks. It includes a CNN for extracting spatial features and a hybrid cross-patch attention mechanism (HyCrossAttention) for land cover classification, leveraging LiDAR data to generate CLS and HSI patch tokens. More importantly, to reduce the loss of valuable information during the layer-by-layer propagation, we designed cross-layer skip connections. Through adaptive learning, these cross-layer fusions help address the gradient vanishing problem in deep networks while preserving early-layer features. This enables the model to better integrate information from different layers, enhancing both its stability and performance. We carried out in - depth experiments on commonly - used benchmark datasets, specifically, the University of Houston dataset, the Trento dataset, and the University of Southern Mississippi Gulf Park (MUUFL) dataset. We compared the results of the proposed MCFTNet model with those obtained from state-of-the-art Transformer models, classical CNNs, and traditional classifiers. As a result, the MCFTNet model outshone them all in terms of performance. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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A new adaptive time delay estimation algorithm based on MMSE criterion

A new adaptive time delay estimation algorithm based on MMSE...

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2013 IEEE International Conference on signal processing, Communications and Computing, ICSPCC 2013

作者： Liang, Qiao Hui, Zheng National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (纸本)9781479910274

In this paper, we adress the problem of time delay estimation between signals received at two spatially separated sensors at low signal-to-noise ration levels. By replacing the instantaneous square error in ETDE with time mean of square error as the cost function, we develop a new adaptive time delay estimation algorithm based on MMSE criterion (MMSE-TDE). The proposed algorithm significantly improve the performance at low SNRs, while it has the same convergence speed with the traditional ETDE algorithm. Convergence analysis is given and simulation results are also included to validate the performance of the MMSE-TDE. © 2013 IEEE.

关键词： Cost functions

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A tor anonymity attack experiment platform driven by raspberry pi 11

A tor anonymity attack experiment platform driven by raspber...

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11th International Conference on Prognostics and System Health Management, PHM-Jinan 2020

作者： Wang, Qi Cao, Weiquan National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (纸本)9781728151816

Private Tor network is essential for analyzing security issues both for Tor project as well as for numerous researchers. However, current research work mainly focus on improving scalability and efficiency by simulating traffic discretely with single node and restoration level by deploying a complete private network with high cost. In this paper, we propose a novel methodology constructing real private tor network by editing and controlling Raspberry Pis, called Tor anonymity attack experiment platform. Compared with other platforms of the same type, the platform has the advantages of low cost, easy to scale, high quality of real-time data transmission, high degree of network protocol restoration as well as convenient configuration. In order to demonstrate the platform can be used as a private and complete Tor anonymous network experiment environment, we conduct a source tracing attack as a study case. Experimental results and theoretical analysis both show that the platform can support researchers to carry out numerous Tor network security research with high degree of restoration efficiently and cheaply. © 2020 IEEE.

关键词： Restoration

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Vehicles categorization in complex background

Vehicles categorization in complex background

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2014 International Conference on Applied sciences, Engineering and technology, ICASET 2014

作者： Xiang, Ying Zhuo Yang, Dong Mei Yan, Ji Kun National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (纸本)9783038351849

This paper presents a novel approach to categorize multi-view vehicles in complex background using only two dimension characteristic vectors instead of high dimension vectors. Vehicles have large variability of models and the view-point makes the appearance change dramatically. Significant characteristics should be chosen as the evidence to categorize. In this paper, we categorize the vehicles into two categories - cars and lorries. Line detection method is used and calculating the average line length and the number of parallel lines as the two characteristics. A linear classifier is trained using 30 different view cars and lorries as the training set and an 10 additional different cars and lorries as the testing set. © (2014) Trans Tech Publications, Switzerland.

关键词： Vehicles

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Reconstruct LDPC codes in an error-free environment 4

Reconstruct LDPC codes in an error-free environment

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4th International Conference on Computer science and Network technology, ICCSNT 2015

作者： Bao, Xin Huang, Yuanling Gan, Wenya You, Ling National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (纸本)9781467381727

This paper presents two algorithms to recover LDPC codes in an error-free environment. Both algorithms reconstruct the sparse parity-check matrices of LDPC codes based on elementary row transformation, and are equivalent to recognize LDPC codes. The first one is appropriate to the LDPC codes whose parity-check matrices have the double-diagonal structure;the second one is a general algorithm that ensures the second-best solution for every type of LDPC codes. Simulation results show that, these algorithms work well with most of LDPC standards and protocols, including 802.16e, 802.11n, DVB-S2 GJB7296 and GB20600. © 2015 IEEE.

关键词： Errors

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Hash technology for connection management in high-speed networks

Hash technology for connection management in high-speed netw...

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2014 International Conference on Applied sciences, Engineering and technology, ICASET 2014

作者： Liu, Rong Ji Li, Yun Zhao Cao, Chun Xia National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (纸本)9783038351849

Connection management uses hash table. Hash function and data structure are both the bottleneck technology. For balancing computation cost and uniformity of hash function, three new hash functions were devised--the Random number Mapping XOR(RMX) Hash Function based on random number table mapping, the Segment Sampling XOR(SSX) Hash Function and Byte by Byte Shift XOR(BBSX) Hash Function based on segment generation and bit operations. The results show that these three functions are all better than IP Shift-XOR(IPSX) Hash Function on uniformity. This paper analyzed memory access performance and miss rate of multiple slots hash table, and presented the technical idea for shorting packet process time in connection management. Simulation for multiple slots hash table is described. © (2014) Trans Tech Publications, Switzerland.

关键词： Hash functions

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Performance Evaluation of an Anomaly-Detection Algorithm for keystroke-Typing Based Insider Detection

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Tsinghua science and technology 2018年第5期23卷 513-525页

作者： Liang He Zhixiang Li Chao Shen National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu 610041 China Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an 710004 China

keystroke dynamics is the process to identify or authenticate individuals based on their typing rhythm behaviors. Several classifications have been proposed to verify a user＇s legitimacy, and the performances of these classifications should be confirmed to identify the most promising research direction. However, classification research contains several experiments with different conditions such as datasets and methodologies. This study aims to benchmark the algorithms to the same dataset and features to equally measure all performances. Using a dataset that contains the typing rhythm of 51 subjects, we implement and evaluate 15 classifiers measured by Fl-measure, which is the harmonic mean of a false-negative identification rate and false-positive identification rate. We also develop a methodology to process the typing data. By considering a case in which the model will reject the outsider, we tested the algorithms on an open set. Additionally, we tested different parameters in random forest and k nearest neighbors classifications to achieve better results and explore the cause of their high performance. We also tested the dataset on one-class classification and explained the results of the experiment. The top-performing classifier achieves an Fl-measure rate of 92% while using the normalized typing data of 50 subjects to train and the remaining data to test. The results, along with the normalization methodology, constitute a benchmark for comparing the classifiers and measuring the performance of keystroke dynamics for insider detection.

关键词： keystroke dynamics insider identification Fl-measure normalization one-class classification

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Research on target model and echeos spatial correlation of distributed radar

Research on target model and echeos spatial correlation of d...

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IET International Radar Conference 2015

作者： Song, K.K. Sun, T. Peng, H.F. Li, K. National Key Laboratory of Science and Technology On Blind Signal Processing ChengDu China

ISBN: (纸本)9781785610387

The spatial correlation of target echoes is important to jointly process signals received at each radar substation in distributed radar system. On the background of space surveillance with two independent monostatic radars, a three-dimensional cylinder target scattering model and echo signal model are built combined the orbital motion. The echoes spatial correlation is analysed and correlation coefficient which varies with the included angle between two radars and target is derived. The reasonableness of target scattering model and validity of theoretical analysis are proved via simulation examples.

关键词： Space-based radar

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Heterogeneous-attributes enhancement deep framework for network embedding

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Frontiers of Computer science 2021年第6期15卷 121-131页

作者： Lisheng QIAO Fan ZHANG Xiaohui HUANG Kai LI Enhong CHEN Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230022China National Key Laboratory of Blind Signal Processing Chengdu 610041China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230022China

Network embedding,which targets at learning the vector representation of vertices,has become a crucial issue in network ***,considering the complex structures and heterogeneous attributes in real-world networks,existing methods may fail to handle the inconsistencies between the structure topology and attribute ***,more comprehensive techniques are urgently required to capture the highly non-linear network structure and solve the existing inconsistencies with retaining more *** that end,in this paper,we propose a heterogeneous-attributes enhancement deep framework(HEDF),which could better capture the non-linear structure and associated information in a deep learningway,and effectively combine the structure information of multi-views by the combining *** this line,the inconsistencies will be handled to some extent and more structure information will be preserved through a semi-supervised *** extensive validations on several real-world datasets show that our model could outperform the baselines,especially for the sparse and inconsistent situation with less training data.

关键词： network embedding heterogeneous-attributes deep framework inconsistent

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