320 research outputs found
A BRIEF ANALYSIS OF THE REGULATORY EFFECT OF ENGLISH EDUCATION ON THE MENTAL HEALTH OF COLLEGE STUDENTS
AN ANALYSIS OF THE REGULATING EFFECT OF ENGLISH EDUCATION ON COLLEGE STUDENTS\u27 MENTAL HEALTH
Energy Minimization of Portable Video Communication Devices Based on Power-Rate-Distortion Optimization
Digital Object Identifier 10.1109/TCSVT.2008.918802Portable video communication devices operate on
batteries with limited energy supply. However, video compression is computationally intensive and energy-demanding. Therefore, one of the central challenging issues in portable video communication system design is to minimize the energy consumption of video encoding so as to prolong the operational lifetime of portable video devices. In this work, based on power-rate-distortion (P-R-D) optimization, we develop a new approach for energy minimization by exploring the energy tradeoff between video
encoding and wireless communication and exploiting the nonstationary characteristics of input video data. Both analytically and experimentally, we demonstrate that incorporating the third dimension of power consumption into conventional R-D analysis
gives us one extra dimension of flexibility in resource allocation and allows us to achieve significant energy saving. Within the P-R-D analysis framework, power is tightly coupled with rate, enabling us to trade bits for joules and perform energy minimization
through optimum bit allocation. Our experimental studies show that, for typical videos with nonstationary scene statistics, using the proposed P-R-D optimization
technology, the energy consumption of video encoding can be significantly reduced (by up to 50%), especially in delay-tolerant
portable video communication applications
Adaptive Critic Design for Energy Minimization of Portable Video Communication Devices
DOI: 10.1109/ICCCN.2008.ECP.70Portable video communication devices operate on
batteries with limited energy supply. However, video compression is computationally intensive and energy-demanding. Therefore, one of the central challenging issues in portable video
communication system design is to minimize the energy consumption of video encoding so as to prolong the operational lifetime of portable video devices. In this work, we consider a
video encoder as a nonlinear system with a number of encoder parameters to its power consumption. We explore the approach of adaptive critic design to control and optimize the power consumption behavior of a portable video encoding system. Our experimental results demonstrate that this approach is very
efficiently, being able to achieve the optimum performance accurately and robustly.This work has been supported in part by NSF under grant DBI-0529082
Pinning modes and interlayer correlation in high magnetic field bilayer Wigner solids
We report studies of pinning mode resonances in the low total Landau filling
(\nu) Wigner solid of a series of bilayer hole samples with negligible
interlayer tunneling, and with varying interlayer separation d. Comparison of
states with equal layer densities (p,p) to single layer states (p,0) produced
{in situ} by biasing, indicates that there is interlayer quantum correlation in
the solid at small d. Also, the resonance frequency at small d is decreased
just near \nu=1/2 and 2/3, indicating the importance in the solid of
correlations related to those in the fractional quantum Hall effects
Let Segment Anything Help Image Dehaze
The large language model and high-level vision model have achieved impressive
performance improvements with large datasets and model sizes. However,
low-level computer vision tasks, such as image dehaze and blur removal, still
rely on a small number of datasets and small-sized models, which generally
leads to overfitting and local optima. Therefore, we propose a framework to
integrate large-model prior into low-level computer vision tasks. Just as with
the task of image segmentation, the degradation of haze is also
texture-related. So we propose to detect gray-scale coding, network channel
expansion, and pre-dehaze structures to integrate large-model prior knowledge
into any low-level dehazing network. We demonstrate the effectiveness and
applicability of large models in guiding low-level visual tasks through
different datasets and algorithms comparison experiments. Finally, we
demonstrate the effect of grayscale coding, network channel expansion, and
recurrent network structures through ablation experiments. Under the conditions
where additional data and training resources are not required, we successfully
prove that the integration of large-model prior knowledge will improve the
dehaze performance and save training time for low-level visual tasks
Toward Real Flare Removal: A Comprehensive Pipeline and A New Benchmark
Photographing in the under-illuminated scenes, the presence of complex light
sources often leave strong flare artifacts in images, where the intensity, the
spectrum, the reflection, and the aberration altogether contribute the
deterioration. Besides the image quality, it also influence the performance of
down-stream visual applications. Thus, removing the lens flare and ghosts is a
challenge issue especially in low-light environment. However, existing methods
for flare removal mainly restricted to the problems of inadequate simulation
and real-world capture, where the categories of scattered flares are singular
and the reflected ghosts are unavailable. Therefore, a comprehensive
deterioration procedure is crucial for constructing the dataset of flare
removal. Based on the theoretical analysis and real-world evaluation, we
propose a well-developed methodology for generating the data-pairs with flare
deterioration. The procedure is comprehensive, where the similarity of
scattered flares and the symmetric effect of reflected ghosts are realized.
Moreover, we also construct a real-shot pipeline that respectively processes
the effects of scattering and reflective flares, aiming to directly generate
the data for end-to-end methods. Experimental results show that the proposed
methodology add diversity to the existing flare datasets and construct a
comprehensive mapping procedure for flare data pairs. And our method facilities
the data-driven model to realize better restoration in flare images and
proposes a better evaluation system based on real shots, resulting promote
progress in the area of real flare removal
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