976 nm高功率半导体激光芯片是光纤激光器的核心部件，具有极为重要的产业价值。报道了课题组在高效率高功率半导体激光芯片的设计、制作与测试方面的研究成果。为了最大限度地提高器件的功率转换效率，同时满足苛刻的寿命要求，在设计上采用双非对称大光腔波导结构，同时对量子阱结构、波导结构、掺杂以及器件结构进行了优化；在外延生长方面，系统地优化了生长工艺参数，确保了外延材料具有极高的内量子效率及低内损耗。大量测试表明：所制作的器件（腔长为5 mm、发光条宽为200 μm的芯片）在室温、连续波（CW）测试条件下，阈值电流约为1 A，斜率效率为1.14 W/A；当电流为9 A时，最高功率转换效率高达72.4%；当电流为30 A时，输出功率达到29.4 W，功率转换效率为61.3%；对应于95%光场能量的水平远场发散角低至8.7°。上述参数性能已经达到了国际同类产品的先进水平。

报道了应用于掺铒光纤放大器（EDFA）的高功率单模980 nm半导体激光芯片和泵浦模块。所研制的单基横模980 nm激光芯片的kink-free输出功率可达1650 mW，最高热反转功率可达2.4 W。利用此芯片研制了14 pin蝶形封装模块，采用光纤光栅进行波长锁定，实现了单模输出功率超过1300 mW以及从阈值到1300 mW的大动态范围的波长锁定，边模抑制比（SMSR）大于30 dB，峰值波长为974.5 nm±0.5 nm，光谱半峰全宽（FWHM）小于0.5 nm，带内功率占比（PIB）大于95%。

采用化学水浴沉积法在不同氨水用量下制备了Cu(In,Ga)Se2太阳能电池的缓冲层CdS薄膜，根据化学平衡动力学计算出混合溶液中反应粒子的初始浓度、pH值和离子积，利用台阶仪、扫描电子显微镜(SEM)、X射线衍射仪(XRD)、量子效率测试仪(EQE)和IV测试仪对制备样品的薄膜厚度、表面形貌、晶体结构、量子效率和光电转换效率进行了表征和分析。结果表明：提高氨水用量可以抑制同质反应，促进异质反应，使CdS薄膜晶体结构从立方相向六方相转变，晶粒形状从柳絮状向颗粒状转变，晶粒尺寸逐渐增大，粒径分布更加均匀，薄膜表面更加平整，制备电池的EQE、Voc、Jsc、FF、Rs等电学参数得到优化，光电转换效率从7.64%提高到13.60%。

This paper attempts to estimate diagnostically relevant measure, i.e., Arteriovenous Ratio with an improved retinal vessel classification using feature ranking strategies and multiple classifiers decision-combination scheme. The features exploited for retinal vessel characterization are based on statistical measures of histogram, different filter responses of images and local gradient information. The feature selection process is based on two feature ranking approaches (Pearson Correlation Coe±cient technique and Relief-F method) to rank the features followed by use of maximum classification accuracy of three supervised classifiers (k-Nearest Neighbor, Support Vector Machine and Naive Bayes) as a threshold for feature subset selection. Retinal vessels are labeled using the selected feature subset and proposed hybrid classification scheme, i.e., decision fusion of multiple classifiers. The comparative analysis shows an increase in vessel classification accuracy as well as Arteriovenous Ratio calculation performance. The system is tested on three databases, a local dataset of 44 images and two publically available databases, INSPIRE-AVR containing 40 images and VICAVR containing 58 images. The local database also contains images with pathologically diseased structures. The performance of the proposed system is assessed by comparing the experimental results with the gold standard estimations as well as with the results of previous methodologies. Overall, an accuracy of 90.45%, 93.90% and 87.82% is achieved in retinal blood vessel separation with 0.0565, 0.0650 and 0.0849 mean error in Arteriovenous Ratio calculation for Local, INSPIRE-AVR and VICAVR dataset, respectively.

Magnetic resonance imaging (MRI) has been a prevalence technique for breast cancer diagnosis. Computer-aided detection and segmentation of lesions from MRIs plays a vital role for the MRIbased disease analysis. There are two main issues of the existing breast lesion segmentation techniques: requiring manual delineation of Regions of Interests (ROIs) as a step of initialization; and requiring a large amount of labeled images for model construction or parameter learning, while in real clinical or experimental settings, it is highly challenging to get su±cient labeled MRIs. To resolve these issues, this work proposes a semi-supervised method for breast tumor segmentation based on super voxel strategies. After image segmentation with advanced cluster techniques, we take a supervised learning step to classify the tumor and nontumor patches in order to automatically locate the tumor regions in an MRI. To obtain the optimal performance of tumor extraction, we take extensive experiments to learn parameters for tumor segmentation and classification, and design 225 classifiers corresponding to different parameter settings. We call the proposed method as Semi-supervised Tumor Segmentation (SSTS), and apply it to both mass and nonmass lesions. Experimental results show better performance of SSTS compared with five state-of-the-art methods.