Big Data To Benefit from new Compression Technique


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Anamorphic Stretch Transform (AST) is a new technique which reshapes signals for transmission over the internet. The process has been inspired by the operation of Forvea centralis in the human eye. AST causes feature-selective stretching of data, which allows conventional digitizers to capture fast temporal features.

A team of UCLA researchers led by Bahram Jalali carried out research on AST deriving from work done on time stretch dispersive Fourier transform – a method which amplifies fast signal detection in real time.

As part AST’s operation, a portion of information contained in the signal is transferred into the phase of the carrier resulting in lossless compression. Big data applications are set to benefit from AST compression algorithms as the volume of generated data is minimized. With this comes efficient use of resources namely, storage and transmission.

More information on AST can be found here.

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