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IEEE Joint International Information technology and Artificial Intelligence Conference (ITAIC)

作者： Jianyi Zhang Wei Xu Jiaqi Yao Jikun Yan Xin Huang College of Information System Engineering PLA Strategic Support Force Information Engineering University Zhengzhou China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

Speech language classification is an important direction in the field of machine learning. In order to further improve the effect of speech classification, this paper propose a new speech language classification algorithm based on multiple-instance learning with negative instance cleaning, namely NUMI-Biased SVM algorithm. Firstly, extract MFCC features from speech files, regarding each file as an instance bag, and every speech frame as an instance. Secondly, adopt PU learning algorithm to clean negative instances in positive bags to reduce their impact on classification. Finally, multiple-instance learning algorithm is used to classify the speech files. Experiments show that, compared with the SVM algorithm and multiple-instance SVM algorithm, NUMI-Biased-SVM has achieved significant improvement in classification performance.

关键词： Support vector machines Machine learning algorithms Feature extraction Telephone sets Cleaning Classification algorithms Mel frequency cepstral coefficient

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Aiding AUV Localization Using a Dual-Sided Synthetic Aperture Sonar

Aiding AUV Localization Using a Dual-Sided Synthetic Apertur...

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IEEE International Conference on Radar

作者： Shiping Chen Cheng Chi Pengfei Zhang Peng Wang Yu Li Jiyuan Liu Haining Huang Chinese Academy of Sciences Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing Institute of Acoustics Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665468893

Autonomous underwater vehicle (AUV) navigation and localization is crucial and challenging in Global Positioning System-denied environments. AUV carrying synthetic aperture sonars (SASs) can provide precise motion estimation of the platforms. This study focuses on enhancing the performance of AUV localization using dual-sided SASs. The two-dimensional velocities of AUV are estimated using dual-sided SAS echoes. An extended Kalman filter (EKF) is then adopted for dead reckoning using the estimated velocities. The experimental results demonstrate that the proposed method has better localization accuracy than that of the Doppler velocity log (DVL).

关键词： Location awareness Dead reckoning Delay effects Motion estimation Estimation Radar Delay estimation

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A review of dielectric optical metasurfaces for spatial differentiation and edge detection

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Frontiers of Optoelectronics 2021年第2期14卷 187-200页

作者： Lei WAN Danping PAN Tianhua FENG Weiping LIU Alexander A.POTAPOV Department of Electronic Engineering College of Information Science and TechnologyJinan UniversityGuangzhou 510632China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China JNU-IREE RAS Joint Laboratory of Information Techniques and Fractal Signal Processing Jinan UniversityGuangzhou 510632China Key Laboratory of Optoelectronic Information and Sensing Technologies of Guangdong Higher Education Institutes Jinan UniversityGuangzhou 510632China Institute of Radio Engineering and Electronics Russian Academy of SciencesMoscow 125009Russia

Dielectric metasurfaces-based planar optical spatial differentiator and edge detection have recently been proposed to play an important role in the parallel and fast image processing *** the development of dielectric metasurfaces of different geometries and resonance mechanisms,diverse on-chip spatial differentiators have been proposed by tailoring the dispersion characteristics of subwavelength *** review focuses on the basic principles and characteristic parameters of dielectric metasurfaces as first-and second-order spatial differentiators realized via the Green's function *** spatial bandwidth and polarization dependence are emphasized as key properties by comparing the optical transfer flinctions of metasurfaces for different incident wavevectors and *** present the operational capabilities of a two-dimensional spatial differentiator in image information acquisition,edge detection is described to illustrate the practicability of the *** an application example,experimental demonstrations of edge detection for different biological cells and a flower mold are discussed,in which a spatial differentiator and objective lens or camera are integrated in three optical pathway *** realization of spatial differentiators and edge detection with dielectric metasurfaces provides new opportunities for ultrafast information identification in biological imaging and machine vision.

关键词： dielectric metasurfaces spatial differentiator edge detection optical transfer function

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Pre-trained Prompt-driven Community Search

arXiv

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arXiv 2025年

作者： Ni, Li Xu, Hengkai Mu, Lin Zhang, Yiwen Luo, Wenjian Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education School of Computer Science and Technology Anhui University Anhui Hefei230601 China School of Computer Science and Technology Anhui University Anhui Hefei230601 China School of Computer Science and Technology Harbin Institute of Technology Shenzhen518055 China

The "pre-train, prompt" paradigm is widely adopted in various graph-based tasks and has shown promising performance in community detection. Most existing semi-supervised community detection algorithms detect communities based on known ones, and the detected communities typically do not contain the given query node. Therefore, they are not suitable for searching the community of a given node. Motivated by this, we adopt this paradigm into the semi-supervised community search for the first time and propose Pre-trained Prompt-driven Community Search (PPCS), a novel model designed to enhance search accuracy and efficiency. PPCS consists of three main components: node encoding, sample generation, and prompt-driven fine-tuning. Specifically, the node encoding component employs graph neural networks to learn local structural patterns of nodes in a graph, thereby obtaining representations for nodes and communities. Next, the sample generation component identifies an initial community for a given node and selects known communities that are structurally similar to the initial one as training samples. Finally, the prompt-driven fine-tuning component leverages these samples as prompts to guide the final community prediction. Experimental results on five real-world datasets demonstrate that PPCS performs better than baseline algorithms. It also achieves higher community search efficiency than semi-supervised community search baseline methods, with ablation studies verifying the effectiveness of each component of PPCS. Copyright © 2025, The Authors. All rights reserved.

关键词： Semi-supervised learning

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A Recommender System for Metaheuristic Algorithms for Continuous Optimization Based on Deep Recurrent Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2020年第1期1卷 5-18页

作者： Tian, Ye Peng, Shichen Zhang, Xingyi Rodemann, Tobias Tan, Kay Chen Jin, Yaochu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei 230601 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei 230601 China Honda Research Institute Europe Offenbach 63073 Germany Department of Computer Science City University of Hong Kong Kowloon Tong Hong Kong Hong Kong Department of Computer Science University of Surrey Guildford GU2 7XH United Kingdom Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110819 China

As revealed by the no free lunch theorem, no single algorithm can outperform any others on all classes of optimization problems. To tackle this issue, methods for recommending an existing algorithm for solving given problems have been proposed. However, existing recommendation methods for continuous optimization suffer from low practicability and transferability, mainly due to the difficulty in extracting features that can effectively describe the problem structure and lack of data for training a recommendation model. This work proposes a generic recommender system to address the above two challenges. First, a novel method is proposed to represent an analytic objective function of a continuous optimization problem as a tree, which is directly used as the features of the problem. For black-box optimization problems whose objective function is unknown, a symbolic regressor is adopted to estimate the tree structure. Second, a large number of benchmark problems are randomly created based on the proposed tree representation, providing an abundant amount of training data with various levels of difficulty. By employing a deep recurrent neural network, a recommendation model is trained to recommend a most suitable metaheuristic algorithm for white- or black-box optimization, making a significant step forward towards fully automated algorithm recommendation for continuous optimization. Experimental results on 100 000 benchmark problems show that the proposed recommendation model achieves considerably better performance than existing ones, and exhibits high transferability to real-world problems. Impact Statement—Real-world optimization problems, such as aerodynamic design of turbine engines and automated trading, have been successfully solved by metaheuristics. However, practitioners are confronted with the challenge of how to choose an appropriate metaheuristic algorithm to solve a particular instance of these problems. This paper proposes a recommender system that can auto

关键词： Algorithm recommendation continuous optimization problem decision tree deep recurrent neural network metaheuristics symbolic regression

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Corrigendum to “Strong convergence theorem of a new modified Bregman extragradient method to solve fixed point problems and variational inequality problems in general reflexive Banach spaces [Communications in Nonlinear science and Numerical Simulation, 135, (2024): CNSNS 108051]

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Communications in Nonlinear science and Numerical Simulation 2024年 138卷

作者： Huilin Tan Qian Yan Gang Cai Qiaoli Dong School of Mathematical Sciences Chongqing Normal University College of Sciences Chongqing University of Technology Tianjin Key Laboratory for Advanced Signal Processing and College of Science Civil Aviation University of China

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Receiver Distortion Nornalization Method for Specific Emitter Identification

Receiver Distortion Nornalization Method for Specific Emitte...

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IEEE Joint International Information technology and Artificial Intelligence Conference (ITAIC)

作者： Mentkai Shi Yuanling Huang Guiliang Wang University School of Information System Engineering PLA Strategic Support Force Information Engineering University Zhengzhou China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

Specific emitter identification (SEI) is an identification technique that identify the transmitting device with the hardware imperfection. However, the identification features are influenced by the receiver distortions and can be different when the receivers are changed. Previous SEI studies rarely concern the method to normalize the distortions among different receivers. To solve this problem, a SEI receiver normalization method is proposed. In the proposed method, a frequency-domain adaptive equalization system is contained to normalize the distortions in receivers. According to the experiment, this method can effectively eliminate the SEI feature difference caused by receivers. Then the robust of the proposed method is discussed.

关键词： Receivers Adaptive equalizers Distortion Hardware Object recognition Information technology signal to noise ratio

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A Deep Learning-based Super-resolution Model for Bistatic SAR Image

A Deep Learning-based Super-resolution Model for Bistatic SA...

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Electronics, Circuits and Information Engineering (ECIE), International Conference on

作者： Xi Cen Xuan Song Yachao Li Chunfeng Wu School of Life Science and Technology Xidian University Xi’An China Key Laboratory for Radar Signal Processing Xidian University Xi’An China China Sanjiang Space Group Wu Han China

ISBN: (纸本)9781665448109

Target detection and recognition of bistatic SAR image has been widely studied in recently. However, how to accurately detect and recognize targets with low-resolution and small size in the image is still a problem. Image super-resolution reconstruction technology can increase image resolution and expand target size to improve detection and recognition performance. Thus, we in this paper bring deep learning onto the topic of bistatic SAR image super-resolution reconstruction and propose a novel super-resolution reconstruction model (named FSRCNN) for bistatic SAR images. The proposed model is characterized by a feature extractor with different structures, a feedback feature enhancement block, and a feature fusion module. During the experiments of bistatic SAR measured data, our method is proved to perform clearer visual effects than other image super-resolution reconstruction method. Moreover, our model achieves the best value on peak signal to noise ratio and structural similarity, which indicates that our model can effectively super-resolution reconstruct bistatic SAR images.

关键词： Image recognition Target recognition Superresolution Feature extraction Radar polarimetry Integrated circuit modeling Image reconstruction

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HSIGene: A Foundation Model for Hyperspectral Image Generation

arXiv

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arXiv 2024年

作者： Pang, Li Cao, Xiangyong Tang, Datao Xu, Shuang Bai, Xueru Zhou, Feng Meng, Deyu The School of Computer Science and Technology The Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China The School of Mathematics and Statistics Northwestern Polytechnical University Shaanxi Xi’an710021 China The National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China The Key Laboratory of Electronic Information Countermeasure and Simulation of the Education Ministry of China Xidian University Xi’an710071 China The School of Mathematics and Statistics The Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Guangdong Guangzhou510555 China

Hyperspectral image (HSI) plays a vital role in various fields such as agriculture and environmental monitoring. However, due to the expensive acquisition cost, the number of hyperspectral images is limited, degenerating the performance of downstream tasks. Although some recent studies have attempted to employ diffusion models to synthesize HSIs, they still struggle with the scarcity of HSIs, affecting the reliability and diversity of the generated images. Some studies propose to incorporate multi-modal data to enhance spatial diversity, but spectral fidelity cannot be ensured. In addition, existing HSI synthesis models are typically uncontrollable or only support single-condition control, limiting their ability to generate accurate and reliable HSIs. To alleviate these issues, we propose HSIGene, a novel HSI generation foundation model which is based on latent diffusion and supports multi-condition control, allowing for more precise and reliable HSI generation. To enhance the spatial diversity of the training data while preserving spectral fidelity, we propose a new data augmentation method based on spatial super-resolution, in which HSIs are upscaled first, and thus abundant training patches could be obtained by cropping the high-resolution HSIs. In addition, to improve the perceptual quality of the augmented data, we introduce a novel two-stage HSI super-resolution framework, which first applies RGB bands super-resolution and then utilizes our proposed Rectangular Guided Attention Network (RGAN) for guided HSI super-resolution. Experiments demonstrate that the proposed model is capable of generating a vast quantity of realistic HSIs for downstream tasks such as denoising and super-resolution. The code and models are available at https://***/LiPang/HSIGene. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Adaptive Multiple Targets Detection for Fda-Mimo Radar with Gaussian Clutter

SSRN

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SSRN 2022年

作者： Wang, Wen-Qin Bang, Huang Orlando, Danilo Liu, Weijian Lan, Lan School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China Università degli Studi "Niccolò Cusano" Rome00166 Italy Wuhan Electronic Information Institute Wuhan430019 China National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China

This paper presents an adaptive multiple targets detection framework for frequency diverse array multiple-input multiple-output radar embedded in Gaussian clutter with an unknown covariance matrix. To this end, we define the one-range-cell multiple targets model as a summation expression and then design four detectors, i.e., the generalized likelihood ratio test, the two-step GLRT, Rao, and Wald tests for the above newly built problem. Closed-form expressions for the probability of false alarm and the probability of detection are provided to assess the aforementioned detectors. The numerical examples show that the proposed architectures can ensure better detection performance than the considered competitors. Finally, notice that each proposed detector has a specific behavior in terms of robustness to low volumes of data. Thus, the choice of a specific solution is dictated by the operating requirements of the radar system. © 2022, The Authors. All rights reserved.

关键词： Covariance matrix

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