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Search Results: 1 - 10 of 11632 matches for " Xiaobo Cai "
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One-Stage Repair and Reconstruction of Craniomaxillofacial Bone Defects  [PDF]
Jianhua Wang, Chao Hu, Gang Zhang, Songbo Qiu, Jun Cai, Xiaobo Wu, Zhao Xiang, Yinghui Tan
Modern Plastic Surgery (MPS) , 2013, DOI: 10.4236/mps.2013.31002

Objective: Severe craniomaxillofacial injuries and craniomaxillofacial tumors can lead to craniomaxillofacial bone defects and deformities. Seriously affect the patients’ appearance and quality of life. So one-stage repair and reconstruction of craniomaxillofacial bone defects is of great significance. The current study summarizes the clinical experience of one-stage repair and reconstruction of craniomaxillofacial bone defects. Material and Methods: Data in one-stage repair and reconstruction of craniomaxillofacial bone defects performed on 13 patients were retrospectively analyzed out of 34 patients with craniomaxillofacial injuries or tumors who received treatment at the outpatient department between January 2002 and March 2011. Surgical indications and approaches were explored after two typical cases were detected. Results: One-stage repair and reconstruction of bone defects was suitable for patients with craniomaxillofacial injuries and excised craniomaxillofacial benign tumors. Adjacent autogenous bones and artificial materials (such as titanium plates, titanium mesh, and so on) work well for the repair of the craniomaxillofacial bone frame and restoration of facial features. Conclusions: Surgical indications should be strictly selected in one-stage repair and reconstruction of craniomaxillofacial bone defects and deformities. Furthermore, the adoption of autogenous bones and artificial materials is a good choice in restoring the craniofacial features.

Reconstruction of Self-Sparse 2D NMR Spectra from Undersampled Data in the Indirect Dimension
Xiaobo Qu,Di Guo,Xue Cao,Shuhui Cai,Zhong Chen
Sensors , 2011, DOI: 10.3390/s110908888
Abstract: Reducing the acquisition time for two-dimensional nuclear magnetic resonance (2D NMR) spectra is important. One way to achieve this goal is reducing the acquired data. In this paper, within the framework of compressed sensing, we proposed to undersample the data in the indirect dimension for a type of self-sparse 2D NMR spectra, that is, only a few meaningful spectral peaks occupy partial locations, while the rest of locations have very small or even no peaks. The spectrum is reconstructed by enforcing its sparsity in an identity matrix domain with ?p (p = 0.5) norm optimization algorithm. Both theoretical analysis and simulation results show that the proposed method can reduce the reconstruction errors compared with the wavelet-based ?1 norm optimization.
A Task-Type-Based Algorithm for the Energy-Aware Profit Maximizing Scheduling Problem in Heterogeneous Computing Systems
Weidong Li,Xi Liu,Xuejie Zhang,Xiaobo Cai
Computer Science , 2015,
Abstract: In this paper, we design an efficient algorithm for the energy-aware profit maximizing scheduling problem, where the high performance computing system administrator is to maximize the profit per unit time. The running time of the proposed algorithm is depending on the number of task types, while the running time of the previous algorithm is depending on the number of tasks. Moreover, we prove that the worst-case performance ratio is close to 2, which maybe the best result. Simulation experiments show that the proposed algorithm is more accurate than the previous method.
Using the Support Vector Machine Algorithm to Predict β-Turn Types in Proteins  [PDF]
Xiaobo Shi, Xiuzhen Hu
Engineering (ENG) , 2013, DOI: 10.4236/eng.2013.510B078

The structure and function of proteins are closely related, and protein structure decides its function, therefore protein structure prediction is quite important.β-turns are important components of protein secondary structure. So development of an accurate prediction method ofβ-turn types is very necessary. In this paper, we used the composite vector with position conservation scoring function, increment of diversity and predictive secondary structure information as the input parameter of support vector machine algorithm for predicting theβ-turn types in the database of 426 protein chains, obtained the overall prediction accuracy of 95.6%, 97.8%, 97.0%, 98.9%, 99.2%, 91.8%, 99.4% and 83.9% with the Matthews Correlation Coefficient values of 0.74, 0.68, 0.20, 0.49, 0.23, 0.47, 0.49 and 0.53 for types I, II, VIII, I’, II’, IV, VI and nonturn respectively, which is better than other prediction.


Cai Xiaobo,Xu Daxin,Dai Quanfa,

大地测量与地球动力学 , 2007,
Abstract: In order to meet the requirement of passivity of the underwater navigation,the gravity anomaly aided inertial navigation system has been used to implement the autonomous underwater navigation.The algorithm has been simulated by use of a simulation tool Simulink.The results indicate that the gravity anomaly aided navigation can be used to get fairly high precision in the areas with relatively large gravity anomaly variation,and therefore reducing the error of the inertial navigation system.
Projected Iterative Soft-thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging
Yunsong Liu,Zhifang Zhan,Jian-Feng Cai,Di Guo,Zhong Chen,Xiaobo Qu
Physics , 2015,
Abstract: Compressed sensing has shown great potentials in accelerating magnetic resonance imaging. Fast image reconstruction and high image quality are two main issues faced by this new technology. It has been shown that, redundant image representations, e.g. tight frames, can significantly improve the image quality. But how to efficiently solve the reconstruction problem with these redundant representation systems is still challenging. This paper attempts to address the problem of applying iterative soft-thresholding algorithm (ISTA) to tight frames based magnetic resonance image reconstruction. By introducing the canonical dual frame to construct the orthogonal projection operator on the range of the analysis sparsity operator, we propose a projected iterative soft-thresholding algorithm (pISTA) and further accelerate it by incorporating the strategy proposed by Beck and Teboulle in 2009. We theoretically prove that pISTA converges to the minimum of a function with a balanced tight frame sparsity. Experimental results demonstrate that the proposed algorithm achieves better reconstruction than the widely used synthesis sparse model and the accelerated pISTA converges faster or comparable to the state-of-art smoothing FISTA. One major advantage of pISTA is that only one extra parameter, the step size, is introduced and the numerical solution is stable to it in terms of image reconstruction errors, thus allowing easily setting in many fast magnetic resonance imaging applications.
Robust recovery of complex exponential signals from random Gaussian projections via low rank Hankel matrix reconstruction
Jian-Feng Cai,Xiaobo Qu,Weiyu Xu,Gui-Bo Ye
Mathematics , 2015,
Abstract: This paper explores robust recovery of a superposition of $R$ distinct complex exponential functions from a few random Gaussian projections. We assume that the signal of interest is of $2N-1$ dimensional and $R<<2N-1$. This framework covers a large class of signals arising from real applications in biology, automation, imaging science, etc. To reconstruct such a signal, our algorithm is to seek a low-rank Hankel matrix of the signal by minimizing its nuclear norm subject to the consistency on the sampled data. Our theoretical results show that a robust recovery is possible as long as the number of projections exceeds $O(R\ln^2N)$. No incoherence or separation condition is required in our proof. Our method can be applied to spectral compressed sensing where the signal of interest is a superposition of $R$ complex sinusoids. Compared to existing results, our result here does not need any separation condition on the frequencies, while achieving better or comparable bounds on the number of measurements. Furthermore, our method provides theoretical guidance on how many samples are required in the state-of-the-art non-uniform sampling in NMR spectroscopy. The performance of our algorithm is further demonstrated by numerical experiments.
Fast Multi-class Dictionaries Learning with Geometrical Directions in MRI Reconstruction
Zhifang Zhan,Jian-Feng Cai,Di Guo,Yunsong Liu,Zhong Chen,Xiaobo Qu
Computer Science , 2015,
Abstract: Objective: Improve the reconstructed image with fast and multi-class dictionaries learning when magnetic resonance imaging is accelerated by undersampling the k-space data. Methods: A fast orthogonal dictionary learning method is introduced into magnetic resonance image reconstruction to providing adaptive sparse representation of images. To enhance the sparsity, image is divided into classified patches according to the same geometrical direction and dictionary is trained within each class. A new sparse reconstruction model with the multi-class dictionaries is proposed and solved using a fast alternating direction method of multipliers. Results: Experiments on phantom and brain imaging data with acceleration factor up to 10 and various undersampling patterns are conducted. The proposed method is compared with state-of-the-art magnetic resonance image reconstruction methods. Conclusion: Artifacts are better suppressed and image edges are better preserved than the compared methods. Besides, the computation of the proposed approach is much faster than the typical K-SVD dictionary learning method in magnetic resonance image reconstruction. Significance: The proposed method can be exploited in undersapmled magnetic resonance imaging to reduce data acquisition time and reconstruct images with better image quality.
A 12 bit 100 MS/s pipelined analog to digital converter without calibration

Cai Xiaobo,Li Fule,Zhang Chun,Wang Zhihua,

半导体学报 , 2010,
Abstract: 本文给出了一个基于0.18um CMOS工艺的12bit 100MS/s的流水线ADC。其中第一级采用了3.5比特结构以降低对电容匹配的要求,采样保持放大器、第一级和第二级均采用了自举开关以改善ADC线性度,后级采用级缩减技术节省了功耗和面积。当输入信号频率为15.5MHz、采样率为100MHz时,该ADC达到了79.8dB的SFDR和10.5bit的有效位数。芯片采用1.8V电压供电,包含输出驱动的总功耗为112mW, 芯片面积为3.51mm2 。

Dai Quanfa,Xu Daxin,Cai Xiaobo,Wang Yong,
,许大欣,蔡小波,王 勇

大地测量与地球动力学 , 2007,
Abstract: At present,gravity matching is one of the best options in underwater inertial aided navigation.Gravity anomalies' matching is the most important navigation estimation method in it.Because of the correlation between the true position and the normal gravity,simplex matching of gravity anomaly can not be realized in practice.The gravity anomalies' matching model is designed with the condition of normal gravity.The results from comparing simulation tests show that modified matching model can improve the effect of gravity anomalies' matching in the area with flat anomalies change.
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