177 research outputs found
On Two Simple and Effective Procedures for High Dimensional Classification of General Populations
In this paper, we generalize two criteria, the determinant-based and
trace-based criteria proposed by Saranadasa (1993), to general populations for
high dimensional classification. These two criteria compare some distances
between a new observation and several different known groups. The
determinant-based criterion performs well for correlated variables by
integrating the covariance structure and is competitive to many other existing
rules. The criterion however requires the measurement dimension be smaller than
the sample size. The trace-based criterion in contrast, is an independence rule
and effective in the "large dimension-small sample size" scenario. An appealing
property of these two criteria is that their implementation is straightforward
and there is no need for preliminary variable selection or use of turning
parameters. Their asymptotic misclassification probabilities are derived using
the theory of large dimensional random matrices. Their competitive performances
are illustrated by intensive Monte Carlo experiments and a real data analysis.Comment: 5 figures; 22 pages. To appear in "Statistical Papers
An improved MOEA/D algorithm for multi-objective multicast routing with network coding
Network coding enables higher network throughput, more balanced traffic, and securer data transmission. However, complicated mathematical operations incur when packets are combined at intermediate nodes, which, if not operated properly, lead to very high network resource consumption and unacceptable delay. Therefore, it is of vital importance to minimize various network resources and end-to-end delays while exploiting promising benefits of network coding.
Multicast has been used in increasingly more applications, such as video conferencing and remote education. In this paper the multicast routing problem with network coding is formulated as a multi-objective optimization problem (MOP), where the total coding cost, the total link cost and the end-to-end delay are minimized simultaneously. We adapt the multi-objective evolutionary algorithm based on decomposition (MOEA/D) for this MOP by hybridizing it with a population-based incremental learning technique which makes use of the global and historical information collected to provide additional guidance to the evolutionary search. Three new schemes are devised to facilitate the performance improvement, including a probability-based initialization scheme, a problem-specific population updating rule, and a hybridized reproduction operator. Experimental results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art MOEAs regarding the solution quality and computational time
Optimized Live 4K Video Multicast
4K videos are becoming increasingly popular. However, despite advances in
wireless technology, streaming 4K videos over mmWave to multiple users is
facing significant challenges arising from directional communication,
unpredictable channel fluctuation and high bandwidth requirements. This paper
develops a novel 4K layered video multicast system. We (i) develop a video
quality model for layered video coding, (ii) optimize resource allocation,
scheduling, and beamforming based on the channel conditions of different users,
and (iii) put forward a streaming strategy that uses fountain code to avoid
redundancy across multicast groups and a Leaky-Bucket-based congestion control.
We realize an end-to-end system on commodity-off-the-shelf (COTS) WiGig
devices. We demonstrate the effectiveness of our system with extensive testbed
experiments and emulation
Neural Video Recovery for Cloud Gaming
Cloud gaming is a multi-billion dollar industry. A client in cloud gaming
sends its movement to the game server on the Internet, which renders and
transmits the resulting video back. In order to provide a good gaming
experience, a latency below 80 ms is required. This means that video rendering,
encoding, transmission, decoding, and display have to finish within that time
frame, which is especially challenging to achieve due to server overload,
network congestion, and losses. In this paper, we propose a new method for
recovering lost or corrupted video frames in cloud gaming. Unlike traditional
video frame recovery, our approach uses game states to significantly enhance
recovery accuracy and utilizes partially decoded frames to recover lost
portions. We develop a holistic system that consists of (i) efficiently
extracting game states, (ii) modifying H.264 video decoder to generate a mask
to indicate which portions of video frames need recovery, and (iii) designing a
novel neural network to recover either complete or partial video frames. Our
approach is extensively evaluated using iPhone 12 and laptop implementations,
and we demonstrate the utility of game states in the game video recovery and
the effectiveness of our overall design
- …