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Super-resolution reconstruction algorithm for terahertz imaging below diffraction limit

Chinese Physics B 2023年第3期32卷 608-612页

作者：王莹祁峰张子旭汪晋宽 School of Communication Science and Engineering Northeastern UniversityShenyang 110819China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of SciencesShenyang 110016China Shenyang Institute of Automation Chinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Beijing 100049China University of Technology Sydney NSW 2007Australia

Terahertz(THz)imaging has drawn significant attention because THz wave has a unique capability to transient,ultrawide spectrum and low photon ***,the low resolution has always been a problem due to its long wavelength,limiting their application of fields practical *** this paper,we proposed a complex one-shot super-resolution(COSSR)framework based on a complex convolution neural network to restore superior THz images at 0.35 times wavelength by extracting features directly from a reference measured sample and groundtruth without the measured *** with real convolution neural network-based approaches and complex zero-shot super-resolution(CZSSR),COSSR delivers at least 6.67,0.003,and 6.96%superior higher imaging efficacy in terms of peak signal to noise ratio(PSNR),mean square error(MSE),and structural similarity index measure(SSIM),respectively,for the analyzed ***,the proposed method is experimentally demonstrated to have a good generalization and to perform well on measured *** COSSR provides a new pathway for THz imaging super-resolution(SR)reconstruction below the diffraction limit.

关键词： terahertz image processing complex convolution neural network

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A ROCKFALL MOTION PARAMETER ESTIMATION METHOD BASED ON TDM-MIMO radar WITH COMBINED DOUBLE/MULTI-PULSE CHANNELS

A ROCKFALL MOTION PARAMETER ESTIMATION METHOD BASED ON TDM-M...

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IET International radar Conference 2023, IRC 2023

作者： Ren, Xiaoyu Qi, Jingshuang Deng, Yunkai Lin, Ruiqi Chen, Youwang Tian, Weiming Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China Chongqing Innovation Center Beijing Institute of Technology Chongqing401120 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China

ISBN: (纸本)9781839539954

MIMO(Multiple-Input Multiple-Output) radar can be effectively applied to the real-time detection of rockfalls, but it is challenging to balance the measurement range of unfuzzy velocity and the real-time detection performance when using TDM(Time Division Multiplexing) waveform. Besides, the estimation accuracy of DOA(Direction-Of-Arrival) of rockfalls is easily disturbed by the motion phase. Aiming at solving the above problems, a rockfall motion parameter estimation method based on TDM-MIMO radar with combined double/multi-pulse channels is proposed in this paper. Firstly, by designing the time sequence of TDM signals, the problem of mutual limitation between the detection range of velocity and real-time detection performance is solved. Secondly, the problem of inaccurate angle estimation caused by Doppler frequency is solved. When there is only one target in the range unit, the motion phase should be compensated first. When multiple targets are located in the same range unit, the velocity of these targets is used to judge the corresponding azimuth, and then velocity-azimuth decoupling is completed. Finally, the effectiveness of the proposed method is verified by simulation experiments. © The Institution of Engineering & Technology 2023.

关键词： Rock bursts

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Positioning Error Compensation by Channel Knowledge Map in UAV Communication Missions

arXiv

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arXiv 2024年

作者： Zhang, Chiya Wang, Ting He, Chunlong School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen518055 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

When Unmanned Aerial Vehicles (UAVs) perform high-precision communication tasks, such as searching for users and providing emergency coverage, positioning errors between base stations and users make it challenging to deploy trajectory planning algorithms. To address these challenges caused by position errors, a framework was proposed to compensate it by Channel Knowledge Map (CKM), which stores channel state information (CSI). By taking the positions with errors as input, the generated CKM could give a prediction of signal attenuation which is close to true positions. Based on that, the predictions are utilized to calculate the received power and a PPO-based algorithm is applied to optimize the compensation. After training, the framework is able to find a strategy that minimize the flight time under communication constraints and positioning error. Besides, the confidence interval is calculated to assist the allocation of power and the update of CKM is studied to adapt to the dynamic environment. Simulation results show the robustness of CKM to positioning error and environmental changes, and the superiority of CKM-assisted UAV communication design. © 2024, CC BY.

关键词： Unmanned aerial vehicles (UAV)

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Improving Bert Fine-Tuning via Stabilizing Cross-Layer Mutual information 48

Improving Bert Fine-Tuning via Stabilizing Cross-Layer Mutua...

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48th IEEE International Conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Li, Jicun Li, Xingjian Wang, Tianyang Wang, Shi Cao, Yanan Xu, Chengzhong Dou, Dejing Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Big Data Lab Baidu Research China University of Alabama Birmingham United States Institute of Information Engineering Chinese Academy of Sciences China State Key Lab of Iotsc University of Macau China

ISBN: (纸本)9781728163277

Fine-tuning pre-trained language models, such as BERT, has shown enormous success among various NLP tasks. Though simple and effective, the process of fine-tuning has been found unstable, which often leads to unexpected poor performance. To increase stability and generalizability, most existing works resort to maintaining the parameters or representations of pre-trained models during fine-tuning. Nevertheless, very little work explores mining the reliable part of pre-learned information that can help to stabilize fine-tuning. To address this challenge, we introduce a novel solution in which we fine-tune BERT with stabilized cross-layer mutual information. Our method aims to preserve the reliable behaviors of cross-layer information propagation, instead of preserving the information itself, of the pre-trained model. Therefore, our method circumvents the domain conflicts between pre-trained and target tasks. We conduct extensive experiments with popular pre-trained BERT variants on NLP datasets, demonstrating the universal effectiveness and robustness of our method. © 2023 IEEE.

关键词： Fine-tuning Stability Mutual information Pre-trained Language Model

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Glancing Future for Simultaneous Machine Translation

Glancing Future for Simultaneous Machine Translation

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Shoutao Guo Shaolei Zhang Yang Feng Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences (ICT/CAS) University of Chinese Academy of Sciences Beijing China

Simultaneous machine translation (SiMT) outputs translation while reading the source sentence. Unlike conventional sequence-to-sequence (seq2seq) training, existing SiMT methods adopt the prefix-to-prefix (prefix2prefix) training, where the model predicts target tokens based on partial source tokens. However, the prefix2prefix training diminishes the ability of the model to capture global information and introduces forced predictions due to the absence of essential source information. Consequently, it is crucial to bridge the gap between the prefix2prefix training and seq2seq training to enhance the translation capability of the SiMT model. In this paper, we propose a novel method that glances future in curriculum learning to achieve the transition from the seq2seq training to prefix2prefix training. Specifically, we gradually reduce the available source information from the whole sentence to the prefix corresponding to that latency. Our method is applicable to a wide range of SiMT methods and experiments demonstrate that our method outperforms strong baselines 1 .

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A radar HRRP Rejection Method Based on Deep Ordinal Rejector with Perturbation Generation

A Radar HRRP Rejection Method Based on Deep Ordinal Rejector...

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Sensors and information Technology (ICSI), International Conference on

作者： Yue Dong Yuhao Wang Bo Tian Penghui Wang Hongwei Liu National Key Laboratory of Radar Signal Processing Xidian University Xi'an China Unit 63961 Chinese People's Liberation Army Beijing China Beijing Institute of Radio Measurement Beijing China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

In real-world applications, radar automatic target recognition systems face a major challenge in the presence of out-of-library (OOL) targets, which are not collected in the databases. Existing methods have investigated on pseudo-OOL. sample generation to train a well-performed OOL target rejector. However, it is impossible to generate all OOL samples due to the diversity and unpredictability of OOL targets. Therefore, we propose a novel unsupervised method based on sample generation, named Deep Ordinal Rejector with Perturbation Generation (DORPG), which eases the requirement of synthesized samples and ranks the samples based on their similarity to training samples. It consists of a support region-aware perturbator, a convolutional encoder and an ordinal rejector, which are optimized in an end-to-end manner. Specifically, the designed perturbator generates samples that demonstrate slight sample-level difference and adjacent feature positions to form negative sample pairs with original training samples. Then the encoder is responsible for feature extraction of these negative sample pairs. Considering the multi-modality of HRRP, the developed ordinal rejector uses the minimum Mahalanobis distance as a rejection criterion and aims to enlarge the distance between negative sample pairs. Thus, DORPG achieves the more compact rejection boundary by suppressing the distribution of IL samples. Experimental results demonstrate the effectiveness of the DORPG in addressing the HRRP OOL target rejection problem.

关键词： Training Target recognition Perturbation methods Face recognition Focusing Feature extraction Robustness Sensors radar applications information technology

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Optimized Statistical Model for Complex Chemical Processes Monitoring

Optimized Statistical Model for Complex Chemical Processes M...

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Data Driven Control and Learning Systems (DDCLS)

作者： Jian Zhang Xiaohe Qu Zhiwei Lei Haifeng Zhang Kai Zhong Datang East China Electric Power Test and Research Institute Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei Anhui China

The traditional kernel principal components analysis (KPCA) and linear discriminant analysis (LDA) have been verified to be two effective approaches for fault detection and diagnosis in recent years. Nevertheless, the conventional method and corresponding improved ones still exposed their deficiencies in some ways. Facing this dilemma, this paper presents a combination of optimized KPCA and modified LDA (OKPCA-MLDA), in which the OKPCA avoids the loss of original features after centralizing data in the eigenspace by adjusting covariance matrix's eigenvalue and transforming the distribution of variables thus providing representative and abundant principal components for the classifier. In addition, the MLDA maximizes a brand new objective function in feature space which achieves better classification performance than the conventional LDA, then utilizing diagnostic thresholds and similarity coefficients to identify the fault types. Based on the combined model, not only the fault detection and diagnosis can be realized simultaneously but also the accuracy of detection and diagnosis can be guaranteed. Furthermore, the simulation experiments on Tennessee Eastman (TE) benchmark process clearly illustrated the superiority of our proposed strategy.

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Lightweight NPU Method Based on Hyper-Threading Technology

Lightweight NPU Method Based on Hyper-Threading Technology

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

作者： Ao Zhang Yongrui Li Wenlong Li Yizhuang Xie Zhihan Zhang Zhu Yang Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Beijing Racobit Electronic Information Technology Co. Ltd Beijing China Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Racobit Electronic Information Technology Co.Ltd

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

With the rapid development of deep learning, the processing power of Neural processing Unit(NPU) continues to improve. However, this leads to an increasing scale of logical resources for NPU, making it challenging to deploy NPU on Field-Programmable Gate Array(FPGA). To address this issue and enable the deployment of high-computational-power NPU on FPGA, this paper proposes a lightweight NPU method based on hyper-threading technology, considering the characteristics of NPU hardware architecture. This method effectively reduces the logical resources usage of the NPU while ensuring only a minimal decrease in computational power, thus enabling successful FPGA deployment. The overall experiment is based on the Virtex UltraScale+ HBM VCU128 FPGA platform. After testing, it was found that the NPU, previously undeployable, could be successfully deployed after lightweight processing. The resource usage ratio of LUTs decreased by about 10%, and the computational power only decreased by 4%. In other words, by using this method, the scale of logical resource usage of the NPU was effectively reduced while ensuring a minimal decrease in computational power, improving the deployment situation of the NPU on FPGA. This approach has certain reference value and significance for the lightweight design of NPU.

关键词： Deep learning Computer architecture Logic gates Data processing Hardware Table lookup Field programmable gate arrays signal resolution Logic arrays Testing

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Modeling and Verification of processing Engines in SAR Imaging SoC

Modeling and Verification of Processing Engines in SAR Imagi...

引用

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

作者： Zhihan Zhang Chao Yang Yizhuang Xie Yongrui Li Ao Zhang Radar Research Lab School of Information and Electoronics Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Shanghai Institute of Satellite Engineering Shanghai China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

With the development of real-time requirements for Synthetic Aperture radar (SAR) imaging technology, SAR imaging processing SoC (System on Chip) has become an important research field at present. The subsystem for SAR imaging processing in the SAR imaging SoC needs to be fully verified during the development process. This article designs a UVM-based random verification environment, focusing on the verification requirements of key computing engines and interaction engines in the SAR imaging processing SoC. At the same time, a modeling method for computing engines and interaction engines is proposed, which can quickly and accurately describe the functions and behaviors of processing engines. Collaborating with the verification environment and the reference C model, the design under test achieves satisfactory coverage results. Consequently, the functionality of the processing engines within the subsystem can be comprehensively and effectively verified. The content studied in this article has certain value for the engineering verification implementation of SAR imaging algorithms in the SoC.

关键词： Computational modeling Focusing Streaming media radar polarimetry Data models Real-time systems Timing System-on-chip Engines Synthetic aperture radar

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radar range Extended target detection using Prototypical Network 2

Radar range Extended target detection using Prototypical Net...

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2nd IEEE International Conference on signal, information and Data processing, ICSIDP 2024

作者： Ma, Wenhao Yuan, Mingchen Wang, Xinyang Zhang, Liang Wang, Yanhua Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Zhengzhou Research Institute Beijing Institute of Technology Zhengzhou450000 China Electromagnetic Sensing Research Center of Cemee State Key Laboratory Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information: Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China

ISBN: (纸本)9798331515669

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. © 2024 IEEE.

关键词： Clutter (information theory)

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