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Author

Ping Fu

Bio: Ping Fu is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topic(s): Ant colony optimization algorithms & Biochip. The author has an hindex of 5, co-authored 24 publication(s) receiving 124 citation(s).

Papers
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Journal ArticleDOI
TL;DR: A novel dictionary training method for sparse reconstruction for enhancing the similarity of sparse representations between the low resolution and high resolution MRI block pairs through simultaneous training two dictionaries.
Abstract: Magnetic Resonance Imaging (MRI) data collection is influenced by SNR, hardware, image time, and other factors. The super-resolution analysis is a critical way to improve the imaging quality. This work presents a framework of super-resolution MRI via sparse reconstruction, and this method is promising to solve the data collection limitations. A novel dictionary training method for sparse reconstruction for enhancing the similarity of sparse representations between the low resolution and high resolution MRI block pairs through simultaneous training two dictionaries. Low resolution MRI blocks generate the high resolution MRI blocks with proposed sparse representation (SR) coefficients. Comprehensive evaluations are implemented to test the feasibility and performance of the SR–MRI method on the real database.

59 citations

Proceedings ArticleDOI
18 Jul 2012
TL;DR: A novel method for Super-Resolution Medical image based sparse representation with two coupled dictionaries to solve the problem of MR image resolution owing to the limitations of hardware and acquisitions is proposed.
Abstract: In this paper, we propose a novel method for Super-Resolution Medical image based sparse representation, with the aim to solve the problem of MR image resolution owing to the limitations of hardware and acquisitions. With two coupled dictionaries the sparse representation of a low resolution medical image blocks is used to generate a high resolution. Some evaluations are implemented to compare with previous method, and the proposed algorithm has its advantage on super-resolution.

13 citations

Proceedings ArticleDOI
Wenbin Zheng1, Hongtao Yin1, Wang Anqi1, Ping Fu1, Bing Liu1 
03 Jun 2017
TL;DR: The recognition principle for reading recognition of the pointer's angle by using Hough transform is proposed, and the reading of thepointer is decided by the linear relationship between the panel's scale and the angle of the instrument's dial revolutionary.
Abstract: To effectively resolve the automatic recognition of analog pointer measuring instruments, a reading recognition method based on Hough transform is proposed. In order to simplify the following procedure, image graying and binary, mathematical morphology, including dilation and thinning, edge recognition was applied to the system. We proposed the recognition principle for reading recognition of the pointer's angle by using Hough transform. And the reading of the pointer is decided by the linear relationship between the panel's scale and the angle of the instrument's dial revolutionary. Considering that in most practical cases, the panel cannot be horizontal, so the procedure of tilt correction is employed in this paper. Finally, the software is written in MATLAB. The recognition system is achieved in LabVIEW, where the front panel of the reading system is developed. The experimental results demonstrate that our method is feasible for pointer instrument recognition.

11 citations

Proceedings ArticleDOI
14 May 2018
TL;DR: A method to capture the timestamps based on specialized hardware Field Programmable Gate Array (FPGA) between the physical layer and MAC layer and can eliminate the delay jitter which is caused by the network protocol stack to improve the synchronization accuracy.
Abstract: IEEE 1588 defines a precision time protocol, which is widely used in distributed test and measurement systems. It is very important to capture the timestamps of the location that can affect the synchronization accuracy seriously in the synchronization process. In this paper, we proposed a method to capture the timestamps based on specialized hardware Field Programmable Gate Array (FPGA) between the physical layer and MAC layer. We designed IEEE 1588 message detection module and frequency compensation clock to detect IEEE 1588 message and record the timestamps, respectively. This method can eliminate the delay jitter which is caused by the network protocol stack to improve the synchronization accuracy. The test experiments results show that 97.76% of the synchronization deviation is located within ±40nS.

6 citations

Journal ArticleDOI
01 Sep 2021-Energy
TL;DR: A SOH prediction model that evaluates the prediction uncertainty using data from different batches of batteries under actual working conditions not only quantitatively evaluates the credibility of the prediction model in absence of true values, but also filtering training data to improve the model accuracy and avoid overfitting.
Abstract: The state of health (SOH) is a key parameter for fault diagnoses and safety early warnings in the life cycle of lithium batteries in electric vehicles. The SOH prediction model generally uses the experimental data from the same batch of batteries in the same environment. These data may cause “overfitting” to the model as the attenuation of lithium batteries varies depending on the batch and working condition, especially in actual use. And there is a risk of serious deviation in the prediction result if there is no true value of the model. This paper proposes a SOH prediction model that evaluates the prediction uncertainty using data from different batches of batteries under actual working conditions. It not only quantitatively evaluates the credibility of the prediction model in absence of true values, but also filtering training data to improve the model accuracy and avoid overfitting. The model produces evaluation uncertainty for the prediction result based on the Gaussian process regression (GPR) method. Experiments' results show that the evaluation uncertainty is better than the prediction variance of GPR. The accuracy of the prediction model using the minimum evaluation uncertainty as the training data screening is an order of magnitude higher than that using all data for training.

6 citations


Cited by
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Journal ArticleDOI
Linwei Yue1, Huanfeng Shen1, Jie Li1, Qiangqiang Yuan1, Hongyan Zhang1, Liangpei Zhang1 
TL;DR: This paper aims to provide a review of SR from the perspective of techniques and applications, and especially the main contributions in recent years, and discusses the current obstacles for future research.
Abstract: Super-resolution (SR) technique reconstructs a higher-resolution image or sequence from the observed LR images As SR has been developed for more than three decades, both multi-frame and single-frame SR have significant applications in our daily life This paper aims to provide a review of SR from the perspective of techniques and applications, and especially the main contributions in recent years Regularized SR methods are most commonly employed in the last decade Technical details are discussed in this article, including reconstruction models, parameter selection methods, optimization algorithms and acceleration strategies Moreover, an exhaustive summary of the current applications using SR techniques has been presented Lastly, the article discusses the current obstacles for future research

267 citations

Journal ArticleDOI
17 Jun 2014
TL;DR: The working principles, applications, merits, and demerits of these methods has been discussed in detail along with their other technical issues followed by present status and future trends.
Abstract: Under the alternating electrical excitation, biological tissues produce a complex electrical impedance which depends on tissue composition, structures, health status, and applied signal frequency, and hence the bioelectrical impedance methods can be utilized for noninvasive tissue characterization. As the impedance responses of these tissue parameters vary with frequencies of the applied signal, the impedance analysis conducted over a wide frequency band provides more information about the tissue interiors which help us to better understand the biological tissues anatomy, physiology, and pathology. Over past few decades, a number of impedance based noninvasive tissue characterization techniques such as bioelectrical impedance analysis (BIA), electrical impedance spectroscopy (EIS), electrical impedance plethysmography (IPG), impedance cardiography (ICG), and electrical impedance tomography (EIT) have been proposed and a lot of research works have been conducted on these methods for noninvasive tissue characterization and disease diagnosis. In this paper BIA, EIS, IPG, ICG, and EIT techniques and their applications in different fields have been reviewed and technical perspective of these impedance methods has been presented. The working principles, applications, merits, and demerits of these methods has been discussed in detail along with their other technical issues followed by present status and future trends.

205 citations

Journal ArticleDOI
TL;DR: To develop a super‐resolution technique using convolutional neural networks for generating thin‐slice knee MR images from thicker input slices, and compare this method with alternative through‐plane interpolation methods.
Abstract: PURPOSE To develop a super-resolution technique using convolutional neural networks for generating thin-slice knee MR images from thicker input slices, and compare this method with alternative through-plane interpolation methods. METHODS We implemented a 3D convolutional neural network entitled DeepResolve to learn residual-based transformations between high-resolution thin-slice images and lower-resolution thick-slice images at the same center locations. DeepResolve was trained using 124 double echo in steady-state (DESS) data sets with 0.7-mm slice thickness and tested on 17 patients. Ground-truth images were compared with DeepResolve, clinically used tricubic interpolation, and Fourier interpolation methods, along with state-of-the-art single-image sparse-coding super-resolution. Comparisons were performed using structural similarity, peak SNR, and RMS error image quality metrics for a multitude of thin-slice downsampling factors. Two musculoskeletal radiologists ranked the 3 data sets and reviewed the diagnostic quality of the DeepResolve, tricubic interpolation, and ground-truth images for sharpness, contrast, artifacts, SNR, and overall diagnostic quality. Mann-Whitney U tests evaluated differences among the quantitative image metrics, reader scores, and rankings. Cohen's Kappa (κ) evaluated interreader reliability. RESULTS DeepResolve had significantly better structural similarity, peak SNR, and RMS error than tricubic interpolation, Fourier interpolation, and sparse-coding super-resolution for all downsampling factors (p < .05, except 4 × and 8 × sparse-coding super-resolution downsampling factors). In the reader study, DeepResolve significantly outperformed (p < .01) tricubic interpolation in all image quality categories and overall image ranking. Both readers had substantial scoring agreement (κ = 0.73). CONCLUSION DeepResolve was capable of resolving high-resolution thin-slice knee MRI from lower-resolution thicker slices, achieving superior quantitative and qualitative diagnostic performance to both conventionally used and state-of-the-art methods.

173 citations

Journal ArticleDOI
TL;DR: A graph-based redundant wavelet transform is introduced to sparsely represent magnetic resonance images in iterative image reconstructions and outperforms several state-of-the-art reconstruction methods in removing artifacts and achieves fewer reconstruction errors on the tested datasets.
Abstract: Compressed sensing magnetic resonance imaging has shown great capacity for accelerating magnetic resonance imaging if an image can be sparsely represented. How the image is sparsified seriously affects its reconstruction quality. In the present study, a graph-based redundant wavelet transform is introduced to sparsely represent magnetic resonance images in iterative image reconstructions. With this transform, image patches is viewed as vertices and their differences as edges, and the shortest path on the graph minimizes the total difference of all image patches. Using the l1 norm regularized formulation of the problem solved by an alternating-direction minimization with continuation algorithm, the experimental results demonstrate that the proposed method outperforms several state-of-the-art reconstruction methods in removing artifacts and achieves fewer reconstruction errors on the tested datasets.

113 citations

Journal ArticleDOI
TL;DR: This work proposes a novel residual learning-based SR algorithm for MRI, which combines both multi-scale GRL and shallow network block-based local residual learning (LRL), which works effectively in capturing high-frequency details by learning local residuals.
Abstract: Spatial resolution is one of the key parameters of magnetic resonance imaging (MRI). The image super-resolution (SR) technique offers an alternative approach to improve the spatial resolution of MRI due to its simplicity. Convolutional neural networks (CNN)-based SR algorithms have achieved state-of-the-art performance, in which the global residual learning (GRL) strategy is now commonly used due to its effectiveness for learning image details for SR. However, the partial loss of image details usually happens in a very deep network due to the degradation problem. In this work, we propose a novel residual learning-based SR algorithm for MRI, which combines both multi-scale GRL and shallow network block-based local residual learning (LRL). The proposed LRL module works effectively in capturing high-frequency details by learning local residuals. One simulated MRI dataset and two real MRI datasets have been used to evaluate our algorithm. The experimental results show that the proposed SR algorithm achieves superior performance to all of the other compared CNN-based SR algorithms in this work.

57 citations