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This matching may be assessed in the lung as a whole, or in individual or in sub-groups of gas-exchanging units in the lung. On the other side Ventilation-perfusion mismatch is the term used when the ventilation and the perfusion of a gas exchanging unit are not matched. The actual values in the lung vary depending on the position within the lung.
Vector quantization (VQ) is a classical quantization technique from signal processing that allows the modeling of probability density functions by the distribution of prototype vectors. Developed in the early 1980s by Robert M. Gray , it was originally used for data compression .
It was prominently criticized in economics by Robert LaLonde (1986), [7] who compared estimates of treatment effects from an experiment to comparable estimates produced with matching methods and showed that matching methods are biased. Rajeev Dehejia and Sadek Wahba (1999) reevaluated LaLonde's critique and showed that matching is a good ...
Different matching methods, such as velocity matching and position matching, are designed to improve the speed and accuracy of the alignment. Different factors will affect the accuracy of the transfer alignment, such as reference information delay, mounting error, sensor measurement error, lever-arm effect [2] and flexure of the carrier body.
In the context of the MPEG-4 Audio (MPEG-4 Part 3), TwinVQ is an audio codec optimized for audio coding at ultra low bitrates around 8 kbit/s.. TwinVQ is one of the object types defined in MPEG-4 Audio, published as subpart 4 of ISO/IEC 14496-3 (for the first time in 1999 - a.k.a. MPEG-4 Audio version 1).
In graph theory, a priority matching (also called: maximum priority matching) is a matching that maximizes the number of high-priority vertices that participate in the matching. Formally, we are given a graph G = ( V , E ) , and a partition of the vertex-set V into some k subsets, V 1 , …, V k , called priority classes .
Prediction by partial matching (PPM) is an adaptive statistical data compression technique based on context modeling and prediction. PPM models use a set of previous symbols in the uncompressed symbol stream to predict the next symbol in the stream. PPM algorithms can also be used to cluster data into predicted groupings in cluster analysis.
Matching pursuit should represent the signal by just a few atoms, such as the three at the centers of the clearly visible ellipses. Matching pursuit (MP) is a sparse approximation algorithm which finds the "best matching" projections of multidimensional data onto the span of an over-complete (i.e., redundant) dictionary .