Abstract-Dynamic behavior of the Internet's transmission resources makes it difficult to provide perceptually good quality of streaming video. MPEG-4 Fine-Grained Scalable coding is proposed to deal with this problem by distributing the data in enhancement layers over a wide range of bit rates. However, encoded video also exhibits significant data rate variability to provide a consistent quality video. We are, therefore, faced with the problem of trying to accommodate the mismatch between the available bandwidth variability and the encoded video variability. In this paper, we investigate quality adaptation of the layered VBR video generated by MPEG-4 FGS. Our goal is to develop a quality adaptation scheme that maximizes perceptual video quality through minimizing quality variation while at the same time increasing the usage of available bandwidth. We develop an optimal adaptation scheme and an online heuristic based on whether the network conditions are known a priori. Experimental results show that the online heuristic as well as the optimal adaptation algorithm provide consistent video quality when used over both TFRC and TCP.
Consistent demands on semiconductor manufacturers to produce circuits with increased density and complexity have made stringent process control an issue of growing importance in the industry. Recent work has shown that neural networks offer great promise in modeling complex fabrication processes such as reactive ion etching (RIE). Motivated by these results, this paper explores the use of neural networks for real-time, model-based feedback control of RIE. This objective is accomplished in part by constructing a predictive model for the system, which can be inverted (or approximately inverted) to achieve the desired control. The efficacy of this approach will be demonstrated using experimental data from an actual RIE process to examine real-time control of critical process responses such as etch rate, uniformity, selectivity, and anisotropy.
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