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A number of alternative transcript profiling techniques such as EST frequency analysis, Massive Parallel Signature Sequencing (MPSS) [ 36] and Serial Analysis of Gene Expression (SAGE) [ 37, 38] have potential for much greater depth of coverage compared with microarrays.
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Based on the above analysis, a massive MIMO-based adaptive multi-stream beamforming scheme is proposed.
For the analysis of massive samples containing multiple analytes, the enhancement of detection efficiency is crucial.
The proposed solution can support GIS systems to conduct efficient visibility analysis for massive observers.
Consequently, this goes beyond a statistical analysis of massive data, and also includes models of the generative dynamics underlying it.
In recent years, powerful computational techniques to analyze big data have emerged, making possible the analysis of massive datasets.
For the performance analysis of massive MIMO system, there exists a large set of tools from random matrix theory (see [110 116] and references therein).
Crime and its control analysis involve massive computing challenges due to the large volume of data and complexity of the human behavior.
Hive is designed towards the query and analysis of massive data, which can be used to build a data warehouse for processing big data of smart substation.
Given the large amount of research related to mobile phones, we will focus in this paper on contributions related to the analysis of massive CDR datasets.
The analysis of massive data-files from single MPS analysis can be a major challenge if examining the data for potential polymorphic loci.
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