Filtering of Mixed Data Streams with Orthogonal Polarization up to 50 Gbps in Micro-Ring/Bus Waveguide

In the age of digital transformation of production processes in industry and science the development and design of intelligent flow sensors for granular and liquid substances transferring through pipelines becomes more important. With this in view new approaches for improving the accuracy of microwave flowmeters are proposed. Taking into account the characteristics ofelectromagnetic waves propagating through a pipeline, a wave scattered by inhomogeneities of the controlled medium is analyzed. Features of the transformation of the polarized scattered wave limiting the geometric dimensions of the pipeline and optimizing the values of the useful scattered signal are revealed. Expediency of collection of the information signal with orthogonal polarization of the scattered wave and through a directional coupler is substantiated. The method of estimating the measurement accuracy with reference to the signal-to-noise ratio at the input of the processing device is given. The research results can be used in cryogenic machine engineering to measure volume and mass flows of liquid cryogenic products.

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Online Feature Extraction Algorithms for Data Streams

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.132.6 ◽

2012 ◽

Vol 132 (1) ◽

pp. 6-13

Author(s):

Seiichi Ozawa

Keyword(s):

Feature Extraction ◽

Data Streams

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Improved Macro-clusters generation using Top-k shared Micro-clusters in Data Streams

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i10.400 ◽

2017 ◽

Vol 7 (10) ◽

pp. 52

Author(s):

LAKSHMI PRANEETHA

Keyword(s):

Real Time ◽

Data Streams ◽

Bloom Filter ◽

Scientific Applications ◽

Pruning Algorithm ◽

Density Data ◽

Data Points ◽

Short Time ◽

Information Streams

Now-a-days data streams or information streams are gigantic and quick changing. The usage of information streams can fluctuate from basic logical, scientific applications to vital business and money related ones. The useful information is abstracted from the stream and represented in the form of micro-clusters in the online phase. In offline phase micro-clusters are merged to form the macro clusters. DBSTREAM technique captures the density between micro-clusters by means of a shared density graph in the online phase. The density data in this graph is then used in reclustering for improving the formation of clusters but DBSTREAM takes more time in handling the corrupted data points In this paper an early pruning algorithm is used before pre-processing of information and a bloom filter is used for recognizing the corrupted information. Our experiments on real time datasets shows that using this approach improves the efficiency of macro-clusters by 90% and increases the generation of more number of micro-clusters within in a short time.

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