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arXiv 2024年

作者： Zheng, Tong-Xing Chen, Xin Lan, Lan Ju, Ying Hu, Xiaoyan Liu, Rongke Ng, Derrick Wing Kwan Cui, Tiejun School of Information and Communications Engineering Xi'an Jiaotong University Xi’an710049 China Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi'an Jiaotong University Xi’an710049 China Purple Mountain Laboratories Nanjing211111 China National Key Laboratory of Radar Signal Processing Xidian University Xi’an710071 China School of Telecommunications Engineering Xidian University Xi’an710071 China School of Electronic and Information Engineering Beihang University Beijing100191 China School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia School of Information Science and Engineering Southeast University Nanjing210096 China

Despite the enhanced spectral efficiency brought by the integrated radar and communication technique, it poses significant risks to communication security when confronted with malicious radar targets. To address this issue, a reconfigurable intelligent surface (RIS)-aided transmission scheme is proposed to improve secure communication in two systems, i.e., the radar and communication co-existing (RCCE) system, where a single transmitter is utilized for both radar sensing and communication, and the dual-functional radar and communication (DFRC) system. At the design stage, optimization problems are formulated to maximize the secrecy rate while satisfying the radar detection constraint via joint active beamforming at the base station and passive beamforming of RIS in both systems. Particularly, a zero-forcing-based block coordinate descent (BCD) algorithm is developed for the RCCE system. Besides, the Dinkelbach method combined with semidefinite relaxation is employed for the DFRC system, and to further reduce the computational complexity, a Riemannian conjugate gradient-based alternating optimization algorithm is proposed. Moreover, the RIS-aided robust secure communication in the DFRC system is investigated by considering the eavesdropper's imperfect channel state information (CSI), where a bounded uncertainty model is adopted to capture the angle error and fading channel error of the eavesdropper, and a tractable bound for their joint uncertainty is derived. Simulation results confirm the effectiveness of the developed RIS-aided transmission scheme to improve the secrecy rate even with the eavesdropper's imperfect CSI, and comparisons between both systems reveal that the RCCE system can provide a higher secrecy rate than the DFRC system. Copyright © 2024, The Authors. All rights reserved.

关键词： Conjugate gradient method

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EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domain

arXiv

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arXiv 2024年

作者： Zhang, Wei Cai, Miaoxin Zhang, Tong Zhuang, Yin Mao, Xuerui The Advanced Research Institute of Multidisciplinary Sciences Beijing Institute of Technology Beijing100081 China The School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing314003 China The National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing100081 China

Multi-modal large language models (MLLMs) have demonstrated remarkable success in vision and visual-language tasks within the natural image domain. Owing to the significant diversities between the natural and remote sensing (RS) images, the development of MLLMs in the RS domain is still in the infant stage. To fill the gap, a pioneer MLLM named EarthGPT integrating various multi-sensor RS interpretation tasks uniformly is proposed in this paper for universal RS image comprehension. Firstly, a visual-enhanced perception mechanism is constructed to refine and incorporate coarse-scale semantic perception information and fine-scale detailed perception information. Secondly, a cross-modal mutual comprehension approach is proposed, aiming at enhancing the interplay between visual perception and language comprehension and deepening the comprehension of both visual and language content. Finally, a unified instruction tuning method for multi-sensor multi-task in the RS domain is proposed to unify a wide range of tasks including scene classification, image captioning, region-level captioning, visual question answering (VQA), visual grounding, object detection, etc. More importantly, a dataset named MMRS-1M featuring large-scale multi-sensor multi-modal RS instruction-following is constructed, comprising over 1M image-text pairs based on 34 existing diverse RS datasets and including multi-sensor images such as optical, synthetic aperture radar (SAR), and infrared. The MMRS-1M dataset addresses the drawback of MLLMs on RS expert knowledge and stimulates the development of MLLMs in the RS domain. Extensive experiments are conducted, demonstrating the EarthGPT’s superior performance in various RS visual interpretation tasks compared with the other specialist models and MLLMs, proving the effectiveness of the proposed EarthGPT and offering a versatile paradigm for open-set reasoning tasks. Our code and dataset are available at https://***/wivizhang/EarthGPT. Copyright © 202

关键词： Semantics

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radar range Extended target detection using Prototypical Network

Radar range Extended target detection using Prototypical Net...

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signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Wenhao Ma Mingchen Yuan Xinyang Wang Liang Zhang Yanhua Wang Radar Technology Research Institute Beijing Institute of Technology Beijing China Zhengzhou Research Institute Beijing Institute of Technology Zhengzhou China Xi'an Modern Control Technology Research Institute Xi'an China Electromagnetic Sensing Research Center of CEMEE State Key Laboratory Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded•Real-Time Information: Processing Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Advanced Technology Research Institute Beijing Institute of Technology Jinan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In radar ground stationary or slow-moving target detection, the target detection algorithm based on High-Resolution Range Profile (HRRP) is an important technical approach. Traditional target detection algorithms based on HRRP are difficult to apply in target detection tasks under multiple terrains. To this end, this paper proposes a feature detection algorithm based on deep learning for target polarized HRRP. This paper studies an end-to-end target detection algorithm based on prototypical networks. This algorithm utilizes prototypical networks to extract deep features from input HRRP and constructs the feature distribution of clutter. Then, it calculates the distance between the features of the input test sample and the clutter feature center, and compares it with the threshold calculated based on the false alarm probability to complete the detection. Under the backgrounds of bare soil and highway, the algorithm improves by 22% compared to the traditional energy detection algorithm for two types of vehicle targets at a signal-to-clutter ratio (SCR) of 0dB.

关键词： Road transportation radar detection Object detection radar Soil Probability Feature extraction Classification algorithms Clutter signal to noise ratio

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Thymine-Hg2+-Thymine Strategy in Mof-Based Electrochemical Aptamer Sensor for Paes Detection

SSRN

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SSRN 2024年

作者： Liu, Xiaofang Diao, Zhan Liu, Huan Wang, Qun Lei, Jincan Huo, Danqun Hou, Jingzhou Hou, Changjun Key Laboratory for Biorheological Science and Technology Ministry of Education Bioengineering College Chongqing University Chongqing400044 China Chongqing Engineering and Technology Research Center of Intelligent Rehabilitation and Eldercare Chongqing City Management College Chongqing401331 China Key Laboratory of Condiment Supervision Technology for State Market Regulation Chongqing Institute for Food and Drug Control Chongqing401121 China Chongqing Key Laboratory of Bio-perception & Intelligent Information Processing School of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China

Phthalate acid esters (PAEs), known food contaminants, can lead to reproductive problems, respiratory diseases, childhood obesity, and neuropsychological disorders. This work aims to develop an electrochemical aptamer sensor for the detection of DAP and its analogs. To achieve specific recognition ability for PAEs, We have introduced the thymidine-Hg2+-thymidine strategy for the first time in an electrochemical aptamer sensor, incorporating metal-organic frameworks (MOFs) as the carrier for the aptamer. Additionally, nanoamplification technology was applied by immobilizing MXene on the electrode to intensify the sensor's current signal. In the presence of PAEs, these molecules specifically bound to the aptamers, resulting in competitive release of Hg2+ and aptamers connected to Hg2+, subsequently reducing current signaling. Under optimal experimental conditions, the designed sensor achieved a linear range from 1.65×10-5 mg/mL to 3×10-2 mg/ml and an extremely low detection limit of 8.94×10-6 mg/mL. Furthermore, it exhibited good reliability and practicality in detecting actual samples. © 2024, The Authors. All rights reserved.

关键词： Esters

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Remaining Useful Life Prediction Method of Lithium-Ion Batteries is Based on Variational Modal Decomposition and Deep Learning Integrated Approach

SSRN

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SSRN 2023年

作者： Wang, Shuai Ma, Hongyan Zhang, Yingda Li, Shengyan He, Wei School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing10044 China Institute of Distributed Energy Storage Safety Big Data Beijing10044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing10044 China

A common method based on variational modal decomposition (VMD) and an integrated depth model is proposed to address the problem that it is difficult to precisely anticipate the remaining useful life (RUL) of lithium-ion batteries (LIBs). Initially, VMD is employed to decompose the LIBs capacity data in multiple scales to obtain the signal's global degradation tendency and local random fluctuation components. Then, the global degradation trend and each fluctuation component are modeled using an echo state network (ESN) and a Bayesian optimized long short-term memory (LSTM) network, respectively. The final LIBs RUL prediction results are obtained by integrating the prediction outcomes. On three public LIBs datasets with distinct degradation characteristics, the performance of the proposed model is tested, and alternative prediction algorithms are compared. The results of the experiments show that the proposed model's maximum average absolute percentage error does not exceed 0.43%. The average relative error not more than 0.5%, indicating great accuracy and stability in prediction. © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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Performance Analysis of Direction of Arrival Estimation Based on Deep Learning 2020

Performance Analysis of Direction of Arrival Estimation Base...

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4th International Conference on Digital signal processing, ICDSP 2020

作者： Chen, Min Mao, Xingpeng Gong, Yi School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen Engineering Laboratory of Intelligent Information Processing for IoT Southern University of Science and Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Key Laboratory of Marine Environment Monitoring and Information Processing Ministry of Industry and Information Technology Harbin China Shenzhen Engineering Laboratory of Intelligent Information Processing for IoT Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9781450376877

In this paper, a new efficient direction of arrival (DOA) estimation approach based on the deep neural networks (DNN) is proposed, in which a nonlinear mapping that relates the outputs of the receiving antennas with its associated DOA is learning by using the DNN-based network. The novel network architecture is divided into two stages, the detection phase and the DOA estimation phase. Additional detection network attached in our structure dramatically reduces the size of the training set. It has been shown that the proposed method not only can achieve reasonably high DOA estimation accuracy, but also can reduce the computational complexity required by traditional superresolution DOA estimation algorithms such as multiple signal classification (MUSIC). The computer simulation results are performed to investigate the generalization and effectiveness of the proposed approach in different scenarios. © 2020 ACM.

关键词： Deep neural networks

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Broadband Terahertz Frequency-Domain Emission Spectroscopy of Dast with a Widely Tunable Quasi-Monochromatic Source and Sensitive Up-Conversion Detection

SSRN

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SSRN 2024年

作者： Li, Wei Liu, Pengxiang Fu, Qiaoqiao Qi, Feng Guo, Xu Li, Weifan Wang, Yelong Liu, Zhaoyang Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China Key Laboratory of Liaoning Province in Terahertz Imaging and Sensing Shenyang110169 China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110169 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

A continuous 4-32 THz emission spectrum of organic crystal DAST is obtained with a widely tunable quasi-monochromatic terahertz source and sensitive up-conversion detection. The spectrum is acquired with a frequency resolution of ~0.05 THz, and a highest signal to noise ratio of ~65 dB. signals are detectable at room temperature even in the high absorption regions of DAST, thus most of the absorption peaks of DAST are clearly located. The results verify the previous works well, including density functional theory calculations and other similar experiments. This paper provide a highly sensitive instrument for broadband terahertz spectroscopic characterization. © 2024, The Authors. All rights reserved.

关键词： Emission spectroscopy

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Adversarial Learning Network for Recovering Rppg signals from Low-Resolution Images for Remote Heart Rate Measurement

SSRN

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SSRN 2024年

作者： Zhai, Dezhao Chen, Wei Li, Qinwei Yu, Ming Wu, Hang Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Systems Engineering Institute Academy of Military Sciences People's Liberation Army Tianjin300161 China Tianjin Key Laboratory for Advanced Signal Processing Civil Aviation University of China Tianjin300300 China School of Artificial Intelligence Nankai University Tianjin300381 China

Remote Photoplethysmography (rPPG) is a non-contact method for measuring heart rate (HR) through facial video, which has significant research value and application potential. However, in long-distance vital sign measurement tasks, the low resolution of facial images and insufficient pixels in the facial region severely degrade the quality of the rPPG signals, leading to a sharp decline in HR accuracy. To address this issue, this paper proposes an adversarial learning network for recovering rPPG signals from low-resolution images (ALRSL-HR). First, arbitrary resolution face images are fed into multi-channel network to extract rPPG signal features capturing rich rPPG information, generating rPPG feature blocks, and temporally aggregating for richer feature representation. These features are then inputted into the backbone network to generate adversarial examples of rPPG signals of varying qualities. Finally, a two-stage adversarial loss function is employed to enable the low-quality rPPG signals to learn the periodic characteristics of high-quality rPPG signals, recover the low-quality signals, and narrow the quality gap between rPPG signals from low-resolution images. Extensive experiments were conducted in this paper to validate the effectiveness of our model on the public COHFACE, UBFC-Phys and UBFC-rPPG datasets. The results of the experiments demonstrate that the proposed ALRSL-HR model can effectively recover degraded rPPG signals from low-resolution images. © 2024, The Authors. All rights reserved.

关键词： Photoplethysmography

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Joint Optimization of Carrier Frequency and PRF for Frequency Agile radar Based on Compressed Sensing

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Remote Sensing 2025年第10期17卷 1796-1796页

作者： Zhaoxiang Yang Hao Zheng Yongliang Zhang Junkun Yan Yang Jiang College of Transportation Inner Mongolia University Hohhot 010030 China College of Electronic Information Engineering Inner Mongolia University Hohhot 010021 China Inner Mongolia Key Laboratory of Intelligent Communication and Sensing and Signal Processing Inner Mongolia University Hohhot 010021 China National Key Laboratory of Radar Signal Processing Xidian University Xi’an 710071 China

Frequency agile radar (FAR) exhibits robust anti-jamming capabilities and a superior low probability of intercept performance due to its randomized carrier frequency (CF) and pulse repetition frequency (PRF) hopping sequences. The advent of compressed sensing (CS) theory has effectively addressed the coherent processing challenges of frequency agile signals. Nonetheless, the reconstructed results often suffer from elevated sidelobe levels, which lead to significant sparse recovery errors. The performance of sparse reconstruction is greatly influenced by the correlation between the dictionary matrix columns. Specifically, weaker correlation usually means better target detection performance and lower false alarm probability. Consequently, this paper adopts the maximum coherence coefficient (MCC) between the dictionary matrix columns as the cost function. In addition, in order to reduce the correlation of the dictionary matrix and improve the target detection performance, a genetic algorithm (GA) is employed to jointly optimize the CF hopping coefficients and PRFs of the FAR. The echo of optimized signals is subsequently reconstructed using the alternating direction method of multipliers (ADMM) algorithm. Simulation results demonstrate the effectiveness of the proposal.

关键词： CS dictionary matrix joint agility in CF and PRF GA target detection performance

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SAR target recognition using feature extraction of multi-scale monogenic components 5

SAR target recognition using feature extraction of multi-sca...

引用

IET International radar Conference 2020, IET IRC 2020

作者： Li, Feng Yao, Weijun Li, Yang Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing100081 China

ISBN: (纸本)9781839535635

In this paper, we combine the monogenic signal with the histogram of oriented gradient (HOG) feature, and propose a new low-level feature: Monogenic-HOG (MG-HOG);then use the bag of words (BoW) model to construct the middle-level feature. Most of the existed features are directly extracted from grayscale synthetic aperture radar (SAR) images. As an extended representation of analytic signals in high-dimensional space, the monogenic signal has excellent characteristics such as rotation and scale invariance in the potential extraction process, and it is also valuable in the image field. However, in the past related work, it is mostly used directly as the feature representation of the image. After experimental validation on the moving and stationary target acquisition and recognition (MSTAR) data set, the proposed feature extraction method has better performance than a number of existed methods. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： Feature extraction

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