Direct determination of the ambipolar diffusion length in strained InxGa1−xAs/InP quantum wells by cathodoluminescence

The ambipolar diffusion length is measured in strained InxGa1−xAs/InP quantum wells for several mole fractions in the interval 0.3<x<0.8 by cathodoluminescence. The ambipolar diffusion length is found to have a significantly higher value in the lower indium mole fraction samples corresponding to tensile-strained wells. This longer diffusion length for the tensile samples is consistent with results of carrier lifetime experiments by M. C. Wang, K. Kash, C. E. Zah, R. Bhat, and S. L. Chuang [Appl. Phys. Lett. 62, 166 (1993)]

Domain-Specific Computing Architectures and Paradigms

We live in an exciting era where artificial intelligence (AI) is fundamentally shifting the dynamics of industries and businesses around the world. AI algorithms such as deep learning (DL) have drastically advanced the state-of-the-art cognition and learning capabilities. However, the power of modern AI algorithms can only be enabled if the underlying domain-specific computing hardware can deliver orders of magnitude more performance and energy efficiency. This work focuses on this goal and explores three parts of the domain-specific computing acceleration problem; encapsulating specialized hardware and software architectures and paradigms that support the ever-growing processing demand of modern AI applications from the edge to the cloud. This first part of this work investigates the optimizations of a sparse spatio-temporal (ST) cognitive system-on-a-chip (SoC). This design extracts ST features from videos and leverages sparse inference and kernel compression to efficiently perform action classification and motion tracking. The second part of this work explores the significance of dataflows and reduction mechanisms for sparse deep neural network (DNN) acceleration. This design features a dynamic, look-ahead index matching unit in hardware to efficiently discover fine-grained parallelism, achieving high energy efficiency and low control complexity for a wide variety of DNN layers. Lastly, this work expands the scope to real-time machine learning (RTML) acceleration. A new high-level architecture modeling framework is proposed. Specifically, this framework consists of a set of high-performance RTML-specific architecture design templates, and a Python-based high-level modeling and compiler tool chain for efficient cross-stack architecture design and exploration.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162870/1/lchingen_1.pd

Effects of Confined Structures on Pool Boiling Heat Transfer

Pool boiling heat transfer is one of the most effective heat transfer processes used in a host of applications. The enhancement of pool boiling has been studied for decades by considering a variety of surface modifications and configurations. For that reason, this study investigates the effects of the confined structures on the pool boiling heat transfer. The confined structures consist of flanges, a plate with a central orifice and a mesh with a central orifice. The diameters of the plate orifice are 2 and 4 mm; the diameter of the mesh orifice size are 2.5, 3.5, 4.5 mm. By comparing the boiling curves of different confined structures, the effects of confinement on heat transfer performance can be evaluated. An infrared camera and a high speed camera were used to capture bubble images and for measuring surface temperature. Furthermore, a pump assisted system was used to determine the effect of confinement on vapor quality and system pressure. The test results show that pool boiling heat transfer can be generally enhanced by using confined structures. The level of enhancement depends on the orifice size of the plate and the mesh. Smaller orifice size leads to higher heat transfer enhancement. The results of the pump assisted test indicate that the boiling heat transfer enhancement can be attributed to the bubble coalescence process and the induced shear flow caused by the coalesced bubble departure. Results also indicate than an increase of vapor generation (quality) and the induced shear flow rate (shear stress) can be found when increasing the level of confinement on the pool boiling process. Furthermore, using a mesh in confined structure can provide a higher heat transfer coefficient when compared to the no-mesh cases when a pump is used to facilitate bubble departure. In summary, the results show that using meshes leads to better heat transfer performance, which cannot be replicated using a solid surface as confinement structure

Effects of Confined Structures on Pool Boiling Heat Transfer

Pool boiling heat transfer is one of the most effective heat transfer processes used in a host of applications. The enhancement of pool boiling has been studied for decades by considering a variety of surface modifications and configurations. For that reason, this study investigates the effects of the confined structures on the pool boiling heat transfer. The confined structures consist of flanges, a plate with a central orifice and a mesh with a central orifice. The diameters of the plate orifice are 2 and 4 mm; the diameter of the mesh orifice size are 2.5, 3.5, 4.5 mm. By comparing the boiling curves of different confined structures, the effects of confinement on heat transfer performance can be evaluated. An infrared camera and a high speed camera were used to capture bubble images and for measuring surface temperature. Furthermore, a pump assisted system was used to determine the effect of confinement on vapor quality and system pressure. The test results show that pool boiling heat transfer can be generally enhanced by using confined structures. The level of enhancement depends on the orifice size of the plate and the mesh. Smaller orifice size leads to higher heat transfer enhancement. The results of the pump assisted test indicate that the boiling heat transfer enhancement can be attributed to the bubble coalescence process and the induced shear flow caused by the coalesced bubble departure. Results also indicate than an increase of vapor generation (quality) and the induced shear flow rate (shear stress) can be found when increasing the level of confinement on the pool boiling process. Furthermore, using a mesh in confined structure can provide a higher heat transfer coefficient when compared to the no-mesh cases when a pump is used to facilitate bubble departure. In summary, the results show that using meshes leads to better heat transfer performance, which cannot be replicated using a solid surface as confinement structure

Charge Transport in Organic Molecular Semiconductors from First Principles: The Band-Like Hole Mobility in Naphthalene Crystal

Predicting charge transport in organic molecular crystals is notoriously challenging. Carrier mobility calculations in organic semiconductors are dominated by quantum chemistry methods based on charge hopping, which are laborious and only moderately accurate. We compute from first principles the electron-phonon scattering and the phonon-limited hole mobility of naphthalene crystal in the framework of ab initio band theory. Our calculations combine GW electronic bandstructures, ab initio electron-phonon scattering, and the Boltzmann transport equation. The calculated hole mobility is in very good agreement with experiment between 100$-$300 K, and we can predict its temperature dependence with high accuracy. We show that scattering between inter-molecular phonons and holes regulates the mobility, though intra-molecular phonons possess the strongest coupling with holes. We revisit the common belief that only rigid molecular motions affect carrier dynamics in organic molecular crystals. Our work provides a quantitative and rigorous framework to compute charge transport in organic crystals, and is a first step toward reconciling band theory and carrier hopping computational methods.Comment: 7 pages, 4 figures, Accepted by Phys. Rev.

THE IMPACT OF KNOWLEDGE MANAGEMENT PRACTICES IN IMPROVING STUDENT LEARNING OUTCOMES

The thesis is about knowledge management in education: how to create quality knowledge through the e-learning environment which is positively related to students’ perceptions of their learning outcomes; and secondly, how to develop communities of practice to ensure effective transfer of tacit knowledge to improve student learning. An effective knowledge management system must address both the creation and transfer of explicit as well as tacit knowledge. This research set forth that tacit knowledge must be converted into high quality explicit knowledge through the e-learning environment. The success in converting educator’s tacit knowledge into explicit knowledge to be internalised by the learner as tacit knowledge is very much depended on information quality as the medium for the conversion process. Thus, in this thesis, information quality is an essential concept to examine in the conversion process. This is to ensure that learners are able to derive quality tacit knowledge from this information. Information quality is always relative and depends on the individual or group of students who are evaluating it. Thus, any standardising of information quality has to match to a considerable large group of students’ cognitive structures. This research provides an empirical investigation of the relationship between information quality and student learning outcomes. Data for this study were collected by means of questionnaires through the survey manager in the Blackboard Learning System and were evaluated through a combination of multiple regression analysis. Data analysis revealed evidence that the relationship between the quality of information and student learning outcomes is systematically measurable, in that measurements of information quality can be used to predict student learning outcomes, and that this relationship is, for the most part, positive. Furthermore, this research set forth the conceptual review of developing communities of practice (CoPs) to transfer sustained tacit knowledge effectively to improve student learning