150 research outputs found
Streaming Codes for Channels with Burst and Isolated Erasures
We study low-delay error correction codes for streaming recovery over a class
of packet-erasure channels that introduce both burst-erasures and isolated
erasures. We propose a simple, yet effective class of codes whose parameters
can be tuned to obtain a tradeoff between the capability to correct burst and
isolated erasures. Our construction generalizes previously proposed low-delay
codes which are effective only against burst erasures. We establish an
information theoretic upper bound on the capability of any code to
simultaneously correct burst and isolated erasures and show that our proposed
constructions meet the upper bound in some special cases. We discuss the
operational significance of column-distance and column-span metrics and
establish that the rate 1/2 codes discovered by Martinian and Sundberg [IT
Trans.\, 2004] through a computer search indeed attain the optimal
column-distance and column-span tradeoff. Numerical simulations over a
Gilbert-Elliott channel model and a Fritchman model show significant
performance gains over previously proposed low-delay codes and random linear
codes for certain range of channel parameters
Source-Channel Diversity for Parallel Channels
We consider transmitting a source across a pair of independent, non-ergodic
channels with random states (e.g., slow fading channels) so as to minimize the
average distortion. The general problem is unsolved. Hence, we focus on
comparing two commonly used source and channel encoding systems which
correspond to exploiting diversity either at the physical layer through
parallel channel coding or at the application layer through multiple
description source coding.
For on-off channel models, source coding diversity offers better performance.
For channels with a continuous range of reception quality, we show the reverse
is true. Specifically, we introduce a new figure of merit called the distortion
exponent which measures how fast the average distortion decays with SNR. For
continuous-state models such as additive white Gaussian noise channels with
multiplicative Rayleigh fading, optimal channel coding diversity at the
physical layer is more efficient than source coding diversity at the
application layer in that the former achieves a better distortion exponent.
Finally, we consider a third decoding architecture: multiple description
encoding with a joint source-channel decoding. We show that this architecture
achieves the same distortion exponent as systems with optimal channel coding
diversity for continuous-state channels, and maintains the the advantages of
multiple description systems for on-off channels. Thus, the multiple
description system with joint decoding achieves the best performance, from
among the three architectures considered, on both continuous-state and on-off
channels.Comment: 48 pages, 14 figure
GROUND ROBOT FOR CHARGING MULTIPLE ELECTRIC VEHICLES
A low profile robotic system moves a Wireless Power Transfer (WPT) primary coil under a row of vehicles in a parking lot. The system positions itself underneath an Electric Vehicle (EV), adjusts the position of its primary coil to align with the receiver coil under the vehicle, and transfers a sufficient quantity of energy to charge the vehicle. Once charging is complete, the robot moves down the row of parked vehicles to charge additional vehicles
GANTRY ROBOT FOR CHARGING MULTIPLE ELECTRIC VEHICLES
An overhead gantry robotic system moves a Wireless Power Transfer (WPT) coil across a row of parking spaces in a parking structure. The system detects a vehicle to be charged, adjusts the position of the primary coil over the vehicle, and lowers the primary coil into proximity of a receiver coil in the top of the vehicle
Real-Time Monitoring of Video Quality in IP Networks
This paper investigates the problem of assessing the quality of video transmitted over IP networks. Our goal is to develop a methodology that is both reasonably accurate and simple enough to support the large-scale deployments that the increasing use of video over IP are likely to demand. For that purpose, we focus on developing an approach that is capable of mapping network statistics, e.g., packet losses, available from simple measurements, to the quality of video sequences reconstructed by receivers. A first step in that direction is a loss-distortion model that accounts for the impact of network losses on video quality, as a function of application-specific parameters such as the video codec and loss recovery technique, coded bit rate, packetization, video characteristics, etc. The model, although accurate, is poorly suited to large-scale, on-line monitoring, because of its dependency on many parameters that are difficult to estimate in real-time. As a result, we introduce a relative quality metric that bypasses this problem by measuring video quality against a quality benchmark that the network is expected to provide. The approach offers a lightweight video quality monitoring solution that is suitable for large-scale deployments. We assess its feasibility and accuracy through extensive simulations and experiments
Real-Time Monitoring of Video Quality in IP Networks
This paper investigates the problem of assessing the quality of video transmitted over IP networks. Our goal is to develop a methodology that is both reasonably accurate and simple enough to support the large-scale deployments that the increasing use of video over IP are likely to demand. For that purpose, we focus on developing an approach that is capable of mapping network statistics, e.g., packet losses, available from simple measurements, to the quality of video sequences reconstructed by receivers. A first step in that direction is a loss-distortion model that accounts for the impact of network losses on video quality, as a function of application-specific parameters such as video codec, loss recovery technique, coded bit rate, packetization, video characteristics, etc. The model, although accurate, is poorly suited to large-scale, on-line monitoring, because of its dependency on parameters that are difficult to estimate in real-time. As a result, we introduce a relative quality metric (rPSNR) that bypasses this problem by measuring video quality against a quality benchmark that the network is expected to provide. The approach offers a lightweight video quality monitoring solution that is suitable for large-scale deployments. We assess its feasibility and accuracy through extensive simulations and experiments
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