CNN-Based Vehicle Target Recognition with Residual Compensation for Circular SAR Imaging

Hu, Rongchun; Peng, Zhenming; Ma, Jun; Li, Wei

doi:10.3390/electronics9040555

Cited by 4 publications

(3 citation statements)

References 65 publications

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“…The accumulator consists of adder and P hw registers, and each register stores one output computed in a full accumulation cycle. The accumulated values are binarized in the activation unit with the thresholding phase defined in (3). This simple comparison involves batch normalization and binarization, as mentioned in Section 2.1.…”

Section: Hardware Architecturementioning

confidence: 99%

“…Deep neural networks (DNNs) have exhibited state-of-the-art accuracy on a wide range of AI tasks, such as image classification, radar signal processing, and object detection [1,2]. Recent DNN models have become even deeper to complete tasks with higher accuracy, which requires a massive amount of computing resources and memory [3,4]. However, many resource-restricted applications call for low-cost and low-power DNN designs, and reaching a relatively lower level of accuracy is often sufficient [5].…”

Section: Introductionmentioning

confidence: 99%

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Reconfigurable Binary Neural Network Accelerator with Adaptive Parallelism Scheme

et al. 2021

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Binary neural networks (BNNs) have attracted significant interest for the implementation of deep neural networks (DNNs) on resource-constrained edge devices, and various BNN accelerator architectures have been proposed to achieve higher efficiency. BNN accelerators can be divided into two categories: streaming and layer accelerators. Although streaming accelerators designed for a specific BNN network topology provide high throughput, they are infeasible for various sensor applications in edge AI because of their complexity and inflexibility. In contrast, layer accelerators with reasonable resources can support various network topologies, but they operate with the same parallelism for all the layers of the BNN, which degrades throughput performance at certain layers. To overcome this problem, we propose a BNN accelerator with adaptive parallelism that offers high throughput performance in all layers. The proposed accelerator analyzes target layer parameters and operates with optimal parallelism using reasonable resources. In addition, this architecture is able to fully compute all types of BNN layers thanks to its reconfigurability, and it can achieve a higher area–speed efficiency than existing accelerators. In performance evaluation using state-of-the-art BNN topologies, the designed BNN accelerator achieved an area–speed efficiency 9.69 times higher than previous FPGA implementations and 24% higher than existing VLSI implementations for BNNs.

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Section: Hardware Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reconfigurable Binary Neural Network Accelerator with Adaptive Parallelism Scheme

et al. 2021

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“…Thus, RNNs are well suited for processing sequential data, and since logging data are connected indepth, RNNs and their variants long short-term memory (LSTM) networks and gated recurrent units (GRU) networks have been introduced into the S-wave velocity prediction (Mehrgini et al, 2017;Zhang et al, 2020) and other rock parameters (Yuan et al, 2022). Moreover, convolutional neural networks (CNNs) have tremendous advantages in feature extraction, thus the CNNs were widely developed and applied in many research fields (Yuan et al, 2018;Hu et al, 2020;Hu et al, 2021), and a combination of RNNs and CNNs for S-wave velocity prediction has been proposed recently (Wang et al, 2022;Zhang et al, 2022). However, the neural networkbased S-wave velocity prediction method has poor generalization and limited labels for establishing S-wave velocity prediction networks, which brings many difficulties to real applications.…”

Section: Introductionmentioning

confidence: 99%

Shear wave velocity prediction based on deep neural network and theoretical rock physics modeling

Feng

Zeng²,

Xu³

et al. 2023

Front. Earth Sci.

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Shear wave velocity plays an important role in both reservoir prediction and pre-stack inversion. However, the current deep learning-based shear wave velocity prediction methods have certain limitations, including lack of training dataset, poor model generalization, and poor physical interpretability. In this study, the theoretical rock physics models are introduced into the construction of the labeled dataset for deep learning algorithms, and a forward simulation of the theoretical rock physics models is utilized to supplement the dataset that incorporates geological and geophysical knowledge. This markedly increases the physical interpretability of the deep learning algorithm. Theoretical rock physics models for two different types of reservoirs, i.e., conventional sandstone and tight sandstone reservoirs, are first established. Then, a full-sample labeled dataset is constructed using these two types of theoretical rock physics models to traverse the elasticity parameter space of the two types of reservoirs through random variation and combination of parameters in the theoretical models. Finally, based on the constructed full-sample labeled dataset, four parameters (P-wave velocity, clay content, porosity, and density) that are highly correlated with the shear wave velocity are selected and combined with a deep neural network to build a deep shear wave velocity prediction network with good generalization and robustness, which can be directly applied to field data. The errors between the predicted shear wave velocity using the deep neural network and the measured shear wave velocity data in the laboratory and the logging data in three real field work areas are less than 5%, which are much smaller than the errors predicted by both Han’s and Castagna’s empirical formula. Furthermore, the prediction accuracy and generalization performance are better than those of these two common empirical formulas. The forward simulation based on theoretical models supplements the training dataset and provides high-quality labels for machine learning. This can considerably improve the interpretability and generalization of models in real applications of a machine learning algorithm.

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SAR target detection based on the optimal fractional Gabor spectrum feature

Peng

Wang

Chen

et al. 2023

Journal of Electronic Science and Technology

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CNN-Based Vehicle Target Recognition with Residual Compensation for Circular SAR Imaging

Cited by 4 publications

References 65 publications

Reconfigurable Binary Neural Network Accelerator with Adaptive Parallelism Scheme

Reconfigurable Binary Neural Network Accelerator with Adaptive Parallelism Scheme

Shear wave velocity prediction based on deep neural network and theoretical rock physics modeling

SAR target detection based on the optimal fractional Gabor spectrum feature

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