Yun-Heng Wang

Bio: Yun-Heng Wang is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Sparse approximation & Kernel embedding of distributions. The author has an hindex of 3, co-authored 3 publications receiving 87 citations.

Medical Image Super-resolution Analysis with Sparse Representation

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.

Enhanced Performance of Ru-Based Infrared Imaging Sensor Array With Electrospun Thermal Isolation Structure

Abstract: Predicting and optimizing thermal conductivity of materials used in infrared thermal imaging sensors is an effective method to improve thermal isolation performance, which can substantially reduce the thermal crosstalk among pixels. This article reports an electrospun poly(vinylidene fluoride-trifluorethylene) (P(VDF-TrFE)) nanofiber film used as an adiabatic support structure in thermal sensor array and investigates its effect on imaging performance of thermal sensor. Based on solid heat transfer simulation, the thermal conductivities of electrospun P(VDF-TrFE) films with different packing ratios were theoretically calculated, showing a significant decrease compared with the spin-coated counterpart. The temperature increment of thermal sensor using nanofiber film with 43.71% packing ratio is calculated as 1.68 times higher than that using spin-coated film. An average temperature rise of 1.73 times was experimentally obtained, and the thermal response time was also reduced to half, compared with the sensor using spin-coated film. The method of improving thermal isolation performance using electrospun fibrous film could be of great advantage to high-sensitivity infrared imaging sensor.

Phenanthroline-carbolong interface suppress chemical interactions with active layer enabling long-time stable organic solar cells

Abstract: To restrain the chemical reaction at cathode interface of organic solar cells, two cathode interfacial materials are synthesized by connecting phenanthroline with carbolong unit. Consequently, the D18:L8-BO based organic solar cell with double-phenanthroline-carbolong achieve the highest efficiency of 18.2%. Double-phenanthroline-carbolong with larger steric hindrance and stronger electron-withdrawing property confirms to suppress the interfacial reaction with norfullerene acceptor, resulting the most stable device. Double-phenanthroline-carbolong based device can sustain 80% of its initial efficiency for 2170 h in dark N2 atmosphere, 96 h under 85 oC and keep 68% initial efficiency after been illuminated for 2200 h, which are significantly better than bathocuproin based devices. Moreover, superb interfacial stability of double-phenanthroline-carbolong cathode interface enables thermal posttreatment of organic sub-cell in perovskite/organic tandem solar cells and obtained a remarkable efficiency of 21.7% with excellent thermal stability, which indicates the potentially wide application of phenanthroline-carbolong materials for stable and efficient solar device fabrications.

Bioelectrical Impedance Methods for Noninvasive Health Monitoring: A Review.

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.

Super-resolution musculoskeletal MRI using deep learning.

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.