Inter-frame Prediction with Fast Weighted Low-rank Matrix Approximation

In the field of video coding, inter-frame prediction plays an important role in improving compression efficiency. The improved efficiency is achieved by finding predictors for video blocks such that the residual data can be close to zero as much as possible. For recent video coding standards, motion vectors are required for a decoder to locate the predictors during video reconstruction. Block matching algorithms are usually utilized in the stage of motion estimation to find such motion vectors. For decoder-side motion derivation, proper templates are defined and template matching algorithms are used to produce a predictor for each block such that the overhead of embedding coded motion vectors in bit-stream can be avoided. However, the conventional criteria of either block matching or template matching algorithms may lead to the generation of worse predictors. To enhance coding efficiency, a fast weighted low-rank matrix approximation approach to deriving decoder-side motion vectors for inter frame video coding is proposed in this paper. The proposed method first finds the dominating block candidates and their corresponding importance factors. Then, finding a predictor for each block is treated as a weighted low-rank matrix approximation problem, which is solved by the proposed column-repetition approach. Together with mode decision, the coder can switch to a better mode between the motion compensation by using either block matching or the proposed template matching scheme

Separable subsets of GFERF negatively curved groups

A word hyperbolic group G is called GFERF if every quasiconvex subgroup coincides with the intersection of finite index subgroups containing it. We show that in any such group, the product of finitely many quasiconvex subgroups is closed in the profinite topology on G

Wavelength dependence of the spectral linewidth of a grating-tuned CW single-frequency external-cavity strained quantum well InGaAs/AlGaAs Grinsch diode laser

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 1994.Includes bibliographical references (p. 91-94).by Long Hsu.Ph.D

Electrical Pulse Triggered Reversible Assembly of Molecular Adlayers

[[abstract]]Reversible adlattice assembly for alkoxy-decorated aromatics is controllable by short electrical pulses.[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

Design of high efficiency Mid IR QCL lasers

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 101-103).The proposed research is a study of designing high-efficiency Mid-IR quantum cascade lasers (QCL). This thesis explores "injector-less" designs for achieving lower voltage defects and improving wall plug efficiencies through highly strain-balanced structures and minimized injector regions. This work contains experimental design work for testing and evaluating Mid-IR QCL performance, simulation work for verifying wavefunction and energy alignment, as well as, Monte Carlo transport simulations for evaluating designs, and finally measuring lasing and spontaneous emission performance for various designs.by Allen Long Hsu.S.M

PGT-Net: Progressive Guided Multi-task Neural Network for Small-area Wet Fingerprint Denoising and Recognition

Fingerprint recognition on mobile devices is an important method for identity verification. However, real fingerprints usually contain sweat and moisture which leads to poor recognition performance. In addition, for rolling out slimmer and thinner phones, technology companies reduce the size of recognition sensors by embedding them with the power button. Therefore, the limited size of fingerprint data also increases the difficulty of recognition. Denoising the small-area wet fingerprint images to clean ones becomes crucial to improve recognition performance. In this paper, we propose an end-to-end trainable progressive guided multi-task neural network (PGT-Net). The PGT-Net includes a shared stage and specific multi-task stages, enabling the network to train binary and non-binary fingerprints sequentially. The binary information is regarded as guidance for output enhancement which is enriched with the ridge and valley details. Moreover, a novel residual scaling mechanism is introduced to stabilize the training process. Experiment results on the FW9395 and FT-lightnoised dataset provided by FocalTech shows that PGT-Net has promising performance on the wet-fingerprint denoising and significantly improves the fingerprint recognition rate (FRR). On the FT-lightnoised dataset, the FRR of fingerprint recognition can be declined from 17.75% to 4.47%. On the FW9395 dataset, the FRR of fingerprint recognition can be declined from 9.45% to 1.09%