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First, the correctness of the mutation series extraction is assessed.
Beyond condensation of imaging findings from a single image series, extraction of information from a stack of different series or modalities should be a coherent development to fully exploit the potential of complementary functional imaging in oncology, e.g. PET/CT, MRI and MRS (chemical shift imaging) [ 6].
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Here, we propose detectability of scale-freeness as a benchmark to evaluate time-series extraction settings.
Nuisance covariates, including the global mean and white matter and cerebrospinal fluid signals, were removed from the time series of each condition prior to time-series extraction of ROI [ 28, 33- 35].
In this way, a series of extraction experiments are designed to investigate the effect of different operating conditions such as extraction pressure (85 120 bar), extraction temperature (38 50 °C), CO2 flow rate (0.059 0.354 g/min), mean particle size (0.177 2 mm) and dynamic extraction time (20 120 min) on efficiency of the extracted essential oils.
We also study the use of time series feature extraction methods, in which features are extracted by making assumptions on the underlying mechanism that generate the time series.
In 1884 Sir Charles Algernon Parsons, a British engineer, recognized the advantage of employing a large number of stages in series, allowing extraction of the thermal energy in the steam in small steps.
Seasonal adjustment is an important example of a time series signal extraction procedure.
In our case series, dental extraction was more frequent in the maxilla than the mandible.
A series of extraction and purification steps were designed for the sample pretreatment.
A series of extraction parameters were optimized using the response surface method based on central composite design.
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