1.3-μm Quantum-well InGaAsP, AlGaInAs, and InGaAsN laser material gain: A theoretical study

Due to the keen interest in improving the high-speed and high-temperature performance of 1.3-μm wavelength lasers, we compare, for the first time, the material gain of three different competing active layer materials, namely InGaAsP-InGaAsP, AlGaInAs-AlGaInAs, and InGaAsN-GaAs. We present a theoretical study of the gain of each quantum-well material system and present the factors that influence the material gain performance of each system. We find that AIGaInAs and InGaAsN active layer materials have substantially better material gain performance than the commonly used InGaAsP, both at room temperature and at high temperature.

Learning based automatic face annotation for arbitrary poses and expressions from frontal images only

Statistical approaches for building non-rigid deformable models, such as the Active Appearance Model (AAM), have enjoyed great popularity in recent years, but typically require tedious manual annotation of training images. In this paper, a learning based approach for the automatic annotation of visually deformable objects from a single annotated frontal image is presented and demonstrated on the example of automatically annotating face images that can be used for building AAMs for fitting and tracking. This approach employs the idea of initially learning the correspondences between landmarks in a frontal image and a set of training images with a face in arbitrary poses. Using this learner, virtual images of unseen faces at any arbitrary pose for which the learner was trained can be reconstructed by predicting the new landmark locations and warping the texture from the frontal image. View-based AAMs are then built from the virtual images and used for automatically annotating unseen images, including images of different facial expressions, at any random pose within the maximum range spanned by the virtually reconstructed images. The approach is experimentally validated by automatically annotating face images from three different databases. © 2009 IEEE.

III-V compound semiconductor nanowires for optoelectronic device applications

GaAs and InP based III-V compound semiconductor nanowires were grown epitaxially on GaAs (or Si) (111)B and InP (111)B substrates, respectively, by metalorganic chemical vapor deposition using Au nanoparticles as catalyst. In this paper, we will give an overview of nanowire research activities in our group. In particular, the effects of growth parameters on the crystal structure and optical properties of various nanowires were studied in detail. We have successfully obtained defect-free GaAs nanowires with nearly intrinsic exciton lifetime and vertical straight nanowires on Si (111)B substrates. The crystal structure of InP nanowires, i.e., WZ or ZB, can also be engineered by carefully controlling the V/III ratio and catalyst size. © 2011 World Scientific Publishing Company.