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A variety of genome-wide mapping techniques have made it possible to chart these properties at high resolution.
Mapping techniques have been developed for three different types of NoC topologies – Mesh, Mesh-of-Tree (MoT), and Butterfly-Fat-Tree (BFT).
Recently, several different surface property mapping techniques have become available that work at much higher speeds, leading to significantly increased data throughput [18 20].
Promising novel T1/T2 mapping techniques have recently been introduced that provide quantitative measurements of tissue changes, minimizing subjectivity and errors common in conventional threshold-based post-processing methods [12, 13, 14].
In addition, studies using cortical surface mapping techniques have identified additional frequency-specific fields in individual subjects.
These quantitative trait locus (QTL) mapping techniques have been successful in identifying regions of the genome that control phenotypic variation, but have been less productive when it comes to the identification of causative functional DNA variants or, more importantly, how these variants act at the molecular level to drive phenotypes [1].
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The use of mapping techniques has been described as second-best compared to primary collection of data [ 9], but remain one of the most practical solutions available when no utility measure has been collected.
The DQ, GDQ and mapping technique have been used to develop DQEM and DQFDM solution algorithms for solving scientific and engineering problems having irregular domain configurations.
Tone-mapping techniques have been developed to address the problem.
HDR tone-mapping techniques have been developed to compress the dynamic range of the image for displaying on prevalent monitors, which the dynamic range is often smaller.
In order to overcome this limitation of LGE, T1-mapping techniques have been developed which permit the assessment of diffuse fibrosis.
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