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Electrical Impedance Tomography Image Reconstruction With Attention-Based Deep Convolutional Neural Network
Zichen Wang;Xinyu Zhang;Rong Fu;Di Wang;Xiaoyan Chen;Huaxiang Wang
Electrical impedance tomography (EIT) is a promising functional and structural imaging method in process tomography. However, due to the “soft-field” nature and the high dependence on the prior information, it often suffers serious artifacts in quantitative analysis. Most recently, EIT image quality has significantly improved because of the state-of-the-art (SOTA) deep learning-based models in the...Show More
Multi-Modal and Multi-Scale Fusion 3D Object Detection of 4D Radar and LiDAR for Autonomous Driving
Li Wang;Xinyu Zhang;Jun Li;Baowei Xv;Rong Fu;Haifeng Chen;Lei Yang;Dafeng Jin;Lijun Zhao
Multi-modal fusion overcomes the inherent limitations of single-sensor perception in 3D object detection of autonomous driving. The fusion of 4D Radar and LiDAR can boost the detection range and more robust. Nevertheless, different data characteristics and noise distributions between two sensors hinder performance improvement when directly integrating them. Therefore, we are the first to propose a...Show More
InterFusion: Interaction-based 4D Radar and LiDAR Fusion for 3D Object Detection
Li Wang;Xinyu Zhang;Baowei Xv;Jinzhao Zhang;Rong Fu;Xiaoyu Wang;Lei Zhu;Haibing Ren;Pingping Lu;Jun Li;Huaping Liu
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2022 | Conference Paper |
Cited by: Papers (17)
Many recent works detect 3D objects by several sensor modalities for autonomous driving, where high-resolution cameras and high-line LiDARs are mostly used but relatively expensive. To achieve a balance between overall cost and detection accuracy, many multi-modal fusion techniques have been suggested. In recent years, the fusion of LiDAR and Radar has gained ever-increasing attention, especially ...Show More
HIHU-Net: A Hyper-Information Hybrid U-Net for Image Reconstruction with Electrical Impedance Tomography
2022 IEEE International Conference on Imaging Systems and Techniques (IST)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
Electrical impedance tomography is widely used for visualization of multiphase medium distributions. Due to the ‘soft field’ characteristic and the nonlinear nature, the reconstructions often suffer from the serious artifacts. It is a core to design a framework carefully, which could recover the sharp boundary and accurate conductivity distributions. In this research, we proposed a novel hyper-inf...Show More
Shape Reconstruction for Electrical Impedance Tomography with V2D-Net Deep Convolutional Neural Network
Zichen Wang;Xinyu Zhang;Di Wang;Rong Fu;Xiaoyan Chen;Huaxiang Wang
2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
Year: 2022 | Conference Paper |
Cited by: Papers (7)
Electrical impedance tomography (EIT) has been widely used in industrial and biomedical fields due to its visual and non-invasive natures. EIT reconstructions based on numerical algorithms are sensitive to the measurement noise and suffer from the low spatial resolution. In this paper, A novel image reconstruction method, as referred to V2D-Net is proposed inspired by deep learning method. The met...Show More
V-Shaped Dense Denoising Convolutional Neural Network for Electrical Impedance Tomography
Xinyu Zhang;Zichen Wang;Rong Fu;Di Wang;Xiaoyan Chen;Xiaoyong Guo;Huaxiang Wang
Electrical impedance tomography (EIT) is a state-of-the-art noninvasive imaging method, which is sensitive to the dynamic changes of the lung impedance. However, the nonlinear ill-posed mathematical problem limits its spatial resolution. In order to generate reliable reconstruction image of EIT, a supervised deep convolutional neural network (CNN)—V-shaped dense denoising net (VDD-Net) is proposed...Show More
A Regularization-Guided Deep Imaging Method for Electrical Impedance Tomography
Rong Fu;Zichen Wang;Xinyu Zhang;Di Wang;Xiaoyan Chen;Huaxiang Wang
Electrical Impedance Tomography (EIT) is a promising technique for the process monitoring. However, due to the inherent nonlinearity and ill- posedness of the image reconstruction in EIT, it remains a great challenge for EIT application. A regularization-guided deep imaging (RGDI) method which combines the mathematical structure of the regularization reconstruction method with deep learning is pro...Show More
Deep Autoencoder Imaging Method for Electrical Impedance Tomography
Xiaoyan Chen;Zichen Wang;Xinyu Zhang;Rong Fu;Di Wang;Miao Zhang;Huaxiang Wang
Electrical impedance tomography (EIT) is an effective technique for real-time monitoring, visualization, and analysis of industrial process in a noninvasive manner. However, due to the nonlinear and “soft-field” nature of its inverse problem, image reconstruction of EIT is always limited in image resolution and, in particular, the accuracy of identifying object boundaries. In order to solve the ab...Show More
Structured LISTA for Multidimensional Harmonic Retrieval
Learned iterative shrinkage thresholding algorithm (LISTA), which adopts deep learning techniques to optimize algorithm parameters from labeled training data, can be successfully applied to small-scale multidimensional harmonic retrieval (MHR) problems. However, LISTA becomes computationally demanding for large-scale MHR because the matrix size of the learned mutual inhibition matrix exhibits quad...Show More
Deep Unfolding Network for Block-Sparse Signal Recovery
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2021 | Conference Paper |
Cited by: Papers (7)
Block-sparse signal recovery has drawn increasing attention in many areas of signal processing, where the goal is to recover a high-dimensional signal whose non-zero coefficients only arise in a few blocks from compressive measurements. However, most off-the-shelf data-driven reconstruction networks do not exploit the block-sparse structure. Thus, they suffer from deteriorating performance in bloc...Show More
DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection
Digital receivers are required to recover the transmitted symbols from their observed channel output. In multiuser multiple-input multiple-output (MIMO) setups, where multiple symbols are simultaneously transmitted, accurate symbol detection is challenging. A family of algorithms capable of reliably recovering multiple symbols is based on interference cancellation. However, these methods assume th...Show More
Deep Soft Interference Cancellation for MIMO Detection
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2020 | Conference Paper |
Cited by: Papers (12)
Accurate symbol detection in multiuser multiple-input multiple-output (MIMO) setups, where multiple symbols are simultaneously transmitted, is a challenging task. A family of algorithms capable of reliably recovering multiple symbols is based on interference cancellation. However, these methods assume that the channel is linear, a model which does not reflect many relevant channels, as well as req...Show More
Deep-Sparse Array Cognitive Radar
Ahmet M. Elbir;Satish Mulleti;Regev Cohen;Rong Fu;Yonina C. Eldar
2019 13th International conference on Sampling Theory and Applications (SampTA)
Year: 2019 | Conference Paper |
Cited by: Papers (12)
In antenna array based radar applications, it is often desirable to choose an optimum subarray from a full array to achieve a balance between hardware cost and resolution. Moreover, in a cognitive radar system, the sparse subarrays are chosen based on the target scenario at that instant. Recently, a deep-learning based antenna selection technique was proposed for a single target scenario. In this ...Show More
Iterative Shrinkage Thresholding Algorithm (ISTA) has been widely applied to solve linear inverse problems. To increase the rate of convergence, Learned-ISTA (LISTA) adopts deep learning techniques to learn the optimal algorithm parameters like the mutual inhibition matrices and filter matrices, which significantly reduces the number of iterations. However, the size of the learned mutual inhibitio...Show More
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