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IEEE Transactions on Biometrics, Behavior, and Identity science 2024年第3期6卷 289-289页

作者： Liao, Shengcai Liu, Xiaoming Tistarelli, Massimo Chellappa, Rama Kittler, Josef United Arab Emirates University College of Information Technology Al Ain United Arab Emirates Michigan State University Department of Computer Science and Engineering East LansingMI48824 United States University of Sassari Computer Vision Laboratory Sassari07100 Italy Johns Hopkins University Department of Electrical and Computer Engineering BaltimoreMD21218 United States Johns Hopkins University Department of Biomedical Engineering BaltimoreMD21218 United States University of Surrey Centre for Vision Speech and Signal Processing GuildfordGU2 7XH United Kingdom

The 2022 International Joint Conference on Biometrics (IJCB 2022) was held at Abu Dhabi, United Arab Emirates, from October 10 to October 13, 2022. This marked the first biometrics conference held in the Middle East. The IJCB 2022 conference combined two major biometrics conferences: the IEEE Biometrics Theory, Applications, and Systems (BTAS) conference and the International Conference on Biometrics (ICB). The IJCB 2022 conference was technically co-sponsored by both IAPR TC4 (Technical Committee on Biometrics) and the IEEE Biometrics Council. © 2019 IEEE.

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An Octagonal Iterative Fractal Antenna with Notch Band and UWB Characteristics

An Octagonal Iterative Fractal Antenna with Notch Band and U...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Yong Cai Shuping He Xingang Ren Zhixiang Huang Anhui Province Key Laboratory of Target Recognition and Feature Extraction Luan China Key Laboratory of Electromagnetic Environmental Sensing Anhui University Hefei China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Institute of Physical Science and Information Technology Anhui University Hefei China

ISBN: (纸本)9781665409889

In this paper, based on the rectangular iterative technique, we propose an omnidirectional UWB fractal antenna by using microstrip line as feed. The antenna takes regular octagonal shape as patch element. Meanwhile, the iterative fractal and notch technique has been adopted to optimize the UWB antenna performance. After multiple iterations of the configured octagonal patch element, the optimized UWB performance can be obtained. The simulation results reveal that the operating bandwidth of the antenna meets Federal Communications Commission (FCC) broadband standard. Moreover, in order to avoid the electromagnetic interference in the X-band identified by the IEEE 521-2002 standard, we further adopt notch techniques and add the U-shaped gap to the microstrip line to achieve electromagnetic shielding. The impedance matching has been achieved for the antenna by imposing the slot on the ground. After optimizing the slot structure, the reflection of the antenna has reduced in the whole operating frequency band. The overall size of the ultra-wideband fractal antenna was designed to $25 \times 35 mm^{2}$ and was composed of FR-4 material with the relative dielectric constant $\varepsilon_{r} = 4.4$ and loss tangent angle $\tan \theta = 0.02$. After optimization, the antenna has good impedance matching and meets the UWB antenna omnidirectional standard. In 3.1–15.4GHz (excluding X-band), and the reflection coefficient S 11 is lower than -10 dB, voltage standing wave ratio (VSWR) is less than 2, maximum gain reaches 6dB. The results indicated that our proposed fractal antenna is promising for UWB antenna applications.

关键词： Slot antennas Impedance matching Voltage FCC Ultra wideband antennas Fractals Microstrip

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21-cm foreground removal using AI and frequency-difference technique

arXiv

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arXiv 2023年

作者： Shi, Feng Chang, Haoxiang Zhang, Le Shan, Huanyuan Zhang, Jiajun Zhou, Suiping Jiang, Ming Wang, Zitong School of Aerospace Science And Technology Xidian University Xi’an710126 China Peng Cheng Laboratory No.2 Xingke 1st Street Shenzhen518000 China School of Physics and Astronomy Sun Yat-sen University 2 Daxue Road Tangjia Zhuhai519082 China CSST Science Center for the Guangdong-Hong Kong-Macau Greater Bay Area Zhuhai519082 China Nandan Road 80 Shanghai200030 China University of Chinese Academy of Sciences Beijing100049 China National Key Laboratory of Radar Signal Processing Xidian University Xi’an710126 China

The deep learning technique has been employed in removing foreground contaminants from 21-cm intensity mapping, but its effectiveness is limited by the large dynamic range of the foreground amplitude. In this study, we develop a novel foreground removal technique grounded in U-Net networks. The essence of this technique lies in introducing an innovative data preprocessing step specifically, utilizing the temperature difference between neighboring frequency bands as input. Combining with the frequency difference, we refer to our method as the UNet-fd (UNet frequency-difference), where the U-Net structure is the same as that in deep21. Based on our tests, we demonstrate that this frequency-difference preprocessing technique can substantially reduce the dynamic range of foreground amplitudes by approximately two orders of magnitude. This reduction proves to be highly advantageous for the U-Net foreground removal. We observe that the HI signal can be reliably recovered, as indicated by the cross-correlation power spectra showing unity agreement at the scale of k 0.3 hMpc−1 in the absence of instrumental effects. Moreover, accounting for the systematic beam effects, our reconstruction displays consistent auto-correlation and cross-correlation power spectrum ratios at the 1σ level across scales k 0.1 hMpc−1, with only a 10% reduction observed in the cross-correlation power spectrum at k 0.2 hMpc−1. The effects of redshift-space distortion are also reconstructed successfully, as evidenced by the quadrupole power spectra matching with the target truth. In order to test how thermal noise affects the performance of our method, we simulated various white noise levels in the map. This shows the mean cross-correlation ratio R¯cross 0.8 when the level of the thermal noise is smaller than or equal to that of the HI signal. In comparison, our method outperforms the traditional Principal Component Analysis (PCA) method. The PCA-derived cross-correlation ratios are underestimated by

关键词： Power spectrum

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Learning Parallax Attention for Stereo Image Super-Resolution

Learning Parallax Attention for Stereo Image Super-Resolutio...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Longguang Wang Yingqian Wang Zhengfa Liang Zaiping Lin Jungang Yang Wei An Yulan Guo College of Electronic Science and Technology National University of Defense Technology National Key Laboratory of Science and Technology on Blind Signal Processing

ISBN: (纸本)9781728132945

Stereo image pairs can be used to improve the performance of super-resolution (SR) since additional information is provided from a second viewpoint. However, it is challenging to incorporate this information for SR since disparities between stereo images vary significantly. In this paper, we propose a parallax-attention stereo super-resolution network (PASSRnet) to integrate the information from a stereo image pair for SR. Specifically, we introduce a parallax-attention mechanism with a global receptive field along the epipolar line to handle different stereo images with large disparity variations. We also propose a new and the largest dataset for stereo image SR (namely, Flickr1024). Extensive experiments demonstrate that the parallax-attention mechanism can capture correspondence between stereo images to improve SR performance with a small computational and memory cost. Comparative results show that our PASSRnet achieves the state-of-the-art performance on the Middle bury, KITTI 2012 and KITTI 2015 datasets.

关键词： stereo imaging SAVANNAH RIVER OPERATIONS OFFICE Strontium images super-resolution Imagery (Psychotherapy) Image parallax information

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AaN: Anti-adversarial Noise - A Novel Approach for Securing Machine Learning-based Wireless Communication Systems

TechRxiv

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TechRxiv 2023年

作者： Hamza, Anis Amazigh Guesmi, Amira Dayoub, Iyad Amrouche, Abderrahmane Alouani, Ihsen Univ. Polytechnique Hauts-de-France CNRS Univ. Lille UMR 8520 IEMN Institut d’Electronique de Microélectronique et de Nanotechnologie ValenciennesF-59313 France INSA Hauts-de-France ValenciennesF-59313 France Laboratory of Speech Communication and Signal Processing Faculty of Electronics and Computer Science University of Science and Technology Houari Boumediene Bab Ezzouar Algiers16111 Algeria Queen’s University Belfast Belfast United Kingdom XLIM UMR 7252 Laboratory University of Limoges Limoges87000 France University of Limoges France

Machine Learning (ML) is becoming a cornerstone enabling technology for the next generation of wireless systems. This is mainly due to the high performance achieved by these data-driven models in addressing problems in communication that are challenging to solve using the classical methods. However, ML models are known to be vulnerable to adversarial attacks;maliciously crafted low-magnitude signals that are designed to mislead ML models. More specifically, the propagation nature of the electromagnetic signals makes the wireless domain even more critical compared to other applications like computer vision where the attacker is physically constrained by the victim’s immediate neighborhood to be efficient. While several works showed the practicality of these attacks in the wireless domain, the main countermeasure is adversarial training. However, this approach results in a considerable accuracy loss, which makes the very utility of ML questionable. In this paper, we address this problem with a new approach tailored to wireless communication contexts. Specifically, we propose a new defense that leverages the physical properties of the wireless propagation to enhance ML-based wireless communication systems against adversarial attacks. We propose Anti-adversarial Noise (AaN), where the Base Station (BS) broadcasts a carefully crafted defensive signal that is designed to counter the impact of any adversarial noise. We specifically focus on ML-based modulation recognition. However, the proposed method is not specific to this application and can be generalized to other ML-based communication use cases. Our results show that our proposed defense can enhance models’ robustness by up to 44% without loosing utility. © 2023, CC BY.

关键词： Machine learning

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A Modified Information Embedding Scheme for the Integrated System of Radar and Communication

A Modified Information Embedding Scheme for the Integrated S...

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2019 IEEE International Conference on signal, Information and Data processing, ICSIDP 2019

作者： Wu, Wenhua Cao, Yunhe Liu, Yutao National Laboratory of Radar Signal Processing Xidian University Xi'an China Science and Technology on Communication Networks Laboratory Shi jia zhuang China

ISBN: (纸本)9781728123455

This paper has proposed a modified information embedding scheme for the integrated system of radar and communication without influence on the mainlobe. Exploiting the waveform diversity of multiple-input multiple-output (MIMO) radar, the beamforming weight vectors are designed for constellation mapping to increase the data rate. Simulation results are presented to demonstrate the theoretical analysis. © 2019 IEEE.

关键词： Beamforming

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Software in the natural world: A computational approach to hierarchical emergence

arXiv

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arXiv 2024年

作者： Rosas, Fernando E. Geiger, Bernhard C. Luppi, Andrea I. Seth, Anil K. Polani, Daniel Gastpar, Michael Mediano, Pedro A.M. Department of Informatics University of Sussex United Kingdom Sussex Centre for Consciousness Science and Sussex AI University of Sussex United Kingdom Center for Psychedelic Research and Centre for Complexity Science Department of Brain Science Imperial College London United Kingdom Center for Eudaimonia and Human Flourishing University of Oxford United Kingdom Know-Center GmbH Graz Austria Signal Processing and Speech Communication Laboratory Graz University of Technology Graz Austria Montreal Neurological Institute McGill University Canada Department of Computer Science University of Hertfordshire Hatfield United Kingdom School of Computer and Communication Sciences EPFL Lausanne Switzerland Department of Computing Imperial College London United Kingdom Division of Psychology and Language Sciences University College London United Kingdom

Understanding the functional architecture of complex systems is crucial to illuminate their inner workings and enable effective methods for their prediction and control. Recent advances have introduced tools to characterise emergent macroscopic levels;however, while these approaches are successful in identifying when emergence takes place, they are limited in the extent they can determine how it does. Here we address this important limitation by developing a computational approach to emergence, which characterises macroscopic processes in terms of their computational capabilities. Concretely, we articulate a view on emergence based on how software works, which is rooted on a mathematical formalisation of how macroscopic processes can express self-contained informational, interventional, and computational properties. This framework reveals a hierarchy of nested self-contained processes that determines what computations take place at what level, which in turn delineates the functional architecture of a complex system. This approach is illustrated on paradigmatic models from the statistical physics and computational neuroscience literature, which are shown to exhibit macroscopic processes that are akin to software in human-engineered systems. Overall, this framework enables a deeper understanding of the multi-level structure of complex systems, revealing specific ways in which they can be efficiently simulated, predicted, and controlled. Copyright © 2024, The Authors. All rights reserved.

关键词： Statistical Physics

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Disentangled Generation Network for Enlarged License Plate Recognition and A Unified Dataset

arXiv

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arXiv 2022年

作者： Li, Chenglong Yang, Xiaobin Wang, Guohao Zheng, Aihua Tan, Chang Jia, Ruoran Tang, Jin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Lab of Intelligent Computing and Signal Processing Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China iFLYTEK Co. Ltd. Hefei230088 China

License plate recognition plays a critical role in many practical applications, but license plates of large vehicles are difficult to be recognized due to the factors of low resolution, contamination, low illumination, and occlusion, to name a few. To overcome the above factors, the transportation management department generally introduces the enlarged license plate behind the rear of a vehicle. However, enlarged license plates have high diversity as they are non-standard in position, size, and style. Furthermore, the background regions contain a variety of noisy information which greatly disturbs the recognition of license plate characters. Existing works have not studied this challenging problem. In this work, we first address the enlarged license plate recognition problem and contribute a dataset containing 9342 images, which cover most of the challenges of real scenes. However, the created data are still insufficient to train deep methods of enlarged license plate recognition, and building large-scale training data is very time-consuming and high labor cost. To handle this problem, we propose a novel task-level disentanglement generation framework based on the Disentangled Generation Network (DGNet), which disentangles the generation into the text generation and background generation in an end-to-end manner to effectively ensure diversity and integrity, for robust enlarged license plate recognition. Extensive experiments on the created dataset are conducted, and we demonstrate the effectiveness of the proposed approach in three representative text recognition frameworks. © 2022, CC BY.

关键词： Optical character recognition

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On Error Performance and Concatenated Coding of Polar Codes in AWGN Channels

On Error Performance and Concatenated Coding of Polar Codes ...

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第四届遥感与无线通信国际会议

作者： Jing Jin Rui Deng Tiansheng Liu Liping Li Key Laboratory of Intelligent Computing and Signal Processing (Ministry of Education) Anhui University Department of Electronic Engineering and Information Science University of Science and Technology

In additive white Gaussian noise（AWGN） channels, construction of polar codes is needed for every operating signal-to-noise ratio（SNR）. Recently, the proposal of the design-SNR reduces the computation effort in constructing polar codes. In this paper, we prove that although the BER performance of the design-SNR construction is not affected, the packet-error-rate（PER） performance is degraded compared with the point-by-point construction. Therefore, a concatenation scheme is proposed to improve the degraded PER performance. Results show the validity of the proposed concatenation scheme when employing the design-SNR construction.

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Joint multi-task cascade for instance segmentation

Joint multi-task cascade for instance segmentation

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作者： Wen, Yaole Hu, Fuyuan Ren, Jinchang Shang, Xinru Li, Linyan Xi, Xuefeng School of Electronic & Information Engineering Suzhou University of Science and Technology SuzhouJiangsu215009 China Centre for Signal and Image Processing University of Strathclyde Glasgow United Kingdom Suzhou Institute of Trade & Commerce SuzhouJiangsu215009 China Virtual Reality Key Laboratory of Intelligent Interaction and Application Technology of Suzhou Suzhou University of Science and Technology SuzhouJiangsu215009 China Suzhou Key Laboratory for Big Data and Information Service Suzhou University of Science and Technology SuzhouJiangsu215009 China

Instance segmentation requires both pixel-level classification accuracy and high-level semantic features at the target instance level, which is very challenging, and the cascade structure can effectively improve both of these problems. To make full use of the relationship between detection and segmentation, this paper proposes a joint multi-tasking cascade structure, which is not simply to cascade the two tasks of detection and segmentation, but to unitedly put them into multi-stage processing, and especially to integrate the information at different stages of the mask branch. The entire structure can effectively utilize the superior characteristics of each stage in the matter of detection and segmentation, thus improving the quality of mask prediction. The feature fusion process is introduced in the full convolution networks (FCN) branch, and the high-level and low-level features are effectively fused to enhance the contextual information of the picture semantic features. The experiments demonstrate the better results on the COCO dataset. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Semantics

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