175 research outputs found
A QoS-Aware Scheduling Algorithm for High-Speed Railway Communication System
With the rapid development of high-speed railway (HSR), how to provide the
passengers with multimedia services has attracted increasing attention. A key
issue is to develop an effective scheduling algorithm for multiple services
with different quality of service (QoS) requirements. In this paper, we
investigate the downlink service scheduling problem in HSR network taking
account of end-to-end deadline constraints and successfully packet delivery
ratio requirements. Firstly, by exploiting the deterministic high-speed train
trajectory, we present a time-distance mapping in order to obtain the highly
dynamic link capacity effectively. Next, a novel service model is developed for
deadline constrained services with delivery ratio requirements, which enables
us to turn the delivery ratio requirement into a single queue stability
problem. Based on the Lyapunov drift, the optimal scheduling problem is
formulated and the corresponding scheduling service algorithm is proposed by
stochastic network optimization approach. Simulation results show that the
proposed algorithm outperforms the conventional schemes in terms of QoS
requirements.Comment: 6 pages, 3 figures, accepted by IEEE ICC 2014 conferenc
Adaptive Computation of an Elliptic Eigenvalue Optimization Problem with a Phase-Field Approach
In this paper, we discuss adaptive approximations of an elliptic eigenvalue
optimization problem in a phase-field setting by a conforming finite element
method. An adaptive algorithm is proposed and implemented in several two
dimensional numerical examples for illustration of efficiency and accuracy.
Theoretical findings consist in the vanishing limit of a subsequence of
estimators and the convergence of the relevant subsequence of
adaptively-generated solutions to a solution to the continuous optimality
system.Comment: 36 pages, 24 figures, 2 table
SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection
Vision-based vehicle detection approaches achieve incredible success in
recent years with the development of deep convolutional neural network (CNN).
However, existing CNN based algorithms suffer from the problem that the
convolutional features are scale-sensitive in object detection task but it is
common that traffic images and videos contain vehicles with a large variance of
scales. In this paper, we delve into the source of scale sensitivity, and
reveal two key issues: 1) existing RoI pooling destroys the structure of small
scale objects, 2) the large intra-class distance for a large variance of scales
exceeds the representation capability of a single network. Based on these
findings, we present a scale-insensitive convolutional neural network (SINet)
for fast detecting vehicles with a large variance of scales. First, we present
a context-aware RoI pooling to maintain the contextual information and original
structure of small scale objects. Second, we present a multi-branch decision
network to minimize the intra-class distance of features. These lightweight
techniques bring zero extra time complexity but prominent detection accuracy
improvement. The proposed techniques can be equipped with any deep network
architectures and keep them trained end-to-end. Our SINet achieves
state-of-the-art performance in terms of accuracy and speed (up to 37 FPS) on
the KITTI benchmark and a new highway dataset, which contains a large variance
of scales and extremely small objects.Comment: Accepted by IEEE Transactions on Intelligent Transportation Systems
(T-ITS
An Adaptive Phase-Field Method for Structural Topology Optimization
In this work, we develop an adaptive algorithm for the efficient numerical
solution of the minimum compliance problem in topology optimization. The
algorithm employs the phase field approximation and continuous density field.
The adaptive procedure is driven by two residual type a posteriori error
estimators, one for the state variable and the other for the objective
functional. The adaptive algorithm is provably convergent in the sense that the
sequence of numerical approximations generated by the adaptive algorithm
contains a subsequence convergent to a solution of the continuous first-order
optimality system. We provide several numerical simulations to show the
distinct features of the algorithm.Comment: 30 pages, 10 figure
Context-aware and Scale-insensitive Temporal Repetition Counting
Temporal repetition counting aims to estimate the number of cycles of a given
repetitive action. Existing deep learning methods assume repetitive actions are
performed in a fixed time-scale, which is invalid for the complex repetitive
actions in real life. In this paper, we tailor a context-aware and
scale-insensitive framework, to tackle the challenges in repetition counting
caused by the unknown and diverse cycle-lengths. Our approach combines two key
insights: (1) Cycle lengths from different actions are unpredictable that
require large-scale searching, but, once a coarse cycle length is determined,
the variety between repetitions can be overcome by regression. (2) Determining
the cycle length cannot only rely on a short fragment of video but a contextual
understanding. The first point is implemented by a coarse-to-fine cycle
refinement method. It avoids the heavy computation of exhaustively searching
all the cycle lengths in the video, and, instead, it propagates the coarse
prediction for further refinement in a hierarchical manner. We secondly propose
a bidirectional cycle length estimation method for a context-aware prediction.
It is a regression network that takes two consecutive coarse cycles as input,
and predicts the locations of the previous and next repetitive cycles. To
benefit the training and evaluation of temporal repetition counting area, we
construct a new and largest benchmark, which contains 526 videos with diverse
repetitive actions. Extensive experiments show that the proposed network
trained on a single dataset outperforms state-of-the-art methods on several
benchmarks, indicating that the proposed framework is general enough to capture
repetition patterns across domains.Comment: Accepted by CVPR202
- …