Hefei University of Technology

About: Hefei University of Technology is a education organization based out in Hefei, China. It is known for research contribution in the topics: Computer science & Microstructure. The organization has 28093 authors who have published 24935 publications receiving 324989 citations.

A medical image fusion method based on convolutional neural networks

Abstract: Medical image fusion technique plays an an increasingly critical role in many clinical applications by deriving the complementary information from medical images with different modalities. In this paper, a medical image fusion method based on convolutional neural networks (CNNs) is proposed. In our method, a siamese convolutional network is adopted to generate a weight map which integrates the pixel activity information from two source images. The fusion process is conducted in a multi-scale manner via image pyramids to be more consistent with human visual perception. In addition, a local similarity based strategy is applied to adaptively adjust the fusion mode for the decomposed coefficients. Experimental results demonstrate that the proposed method can achieve promising results in terms of both visual quality and objective assessment.

Fortune teller: predicting your career path

Abstract: People go to fortune tellers in hopes of learning things about their future. A future career path is one of the topics most frequently discussed. But rather than rely on "black arts" to make predictions, in this work we scientifically and systematically study the feasibility of career path prediction from social network data. In particular, we seamlessly fuse information from multiple social networks to comprehensively describe a user and characterize progressive properties of his or her career path. This is accomplished via a multi-source learning framework with fused lasso penalty, which jointly regularizes the source and career-stage relatedness. Extensive experiments on real-world data confirm the accuracy of our model.

Novel BiFeO3–BaTiO3–Ba(Mg1/3Nb2/3)O3 Lead-Free Relaxor Ferroelectric Ceramics for Energy-Storage Capacitors

Abstract: A novel lead-free relaxor ferroelectric ceramic of (0.67−x)BiFeO3–0.33BaTiO3–xBa(Mg1/3Nb2/3)O3 [(0.67−x)BF–0.33BT–xBMN, x = 0–0.1] was prepared by a solid-state reaction method. A relatively high maximum polarization Pmax of 38 μC/cm2 and a low remanent polarization Pr of 5.7 μC/cm2 were attained under 12.5 kV/mm in the x = 0.06 sample, leading to an excellent energy-storage density of W ~1.56 J/cm3 and a moderate energy-storage efficiency of η ~75%. Moreover, a good temperature stability of the energy storage was obtained in the x = 0.06 sample from 25°C to 190°C. The achievement of these characteristics was basically attributed to an electric field induced reversible ergodic to ferroelectric phase transition owing to similar free energies near a critical freezing temperature. The results indicate that the (0.67−x)BF–0.33BT–xBMN lead-free realxor ferroelectric ceramic could be a promising dielectric material for energy-storage capacitors.

Early Diagnosis of Breast Cancer.

Abstract: Early-stage cancer detection could reduce breast cancer death rates significantly in the long-term. The most critical point for best prognosis is to identify early-stage cancer cells. Investigators have studied many breast diagnostic approaches, including mammography, magnetic resonance imaging, ultrasound, computerized tomography, positron emission tomography and biopsy. However, these techniques have some limitations such as being expensive, time consuming and not suitable for young women. Developing a high-sensitive and rapid early-stage breast cancer diagnostic method is urgent. In recent years, investigators have paid their attention in the development of biosensors to detect breast cancer using different biomarkers. Apart from biosensors and biomarkers, microwave imaging techniques have also been intensely studied as a promising diagnostic tool for rapid and cost-effective early-stage breast cancer detection. This paper aims to provide an overview on recent important achievements in breast screening methods (particularly on microwave imaging) and breast biomarkers along with biosensors for rapidly diagnosing breast cancer.