Accelerate the data: Fast data = fast insight = fast outcomes.
In recent years, the wavelet transform has been shown to be a successful tool in signal processing applications such as data compression and fast computations.
This makes neural nets far more powerful, and allows for complex, domain-specific compression; anything from storing a gazillion selfies on Facebook, to faster YouTube video streaming, to scientific data compression, to reducing the space needed for your personal iTunes library.
The readout chips must be able to operate synchronously with the CPCCDs and ensure fast signal processing with low noise and data compression.
Last year, Facebook founder Mark Zuckerberg said his company, too, is working fast with telecoms firms to build new data compression and infrastructure technology in a major scheme to offer global internet connectivity.
These offer users a web experience that Opera says is faster than other browsers, and — because of data compression algorithms — use less data.
Baidu won because it offered the fastest browsing with the lowest data consumption, with data compression up to 90percentt in some cases — important in a market where users are especially cost-sensitive and most of the data traffic will happen over metered 3G rather than Wi-Fi networks.
At 9600 bps (with the potential to run at 19,200 bps), with data compression, the interfaces can deliver data faster than the respective applications can consume, massage it and disgorge responses.
Thus, it is unlikely that CS would find widespread application in data compression, as there are methods with faster reconstruction, e.g., vector quantization (VQ) [3] and floating-point array compression [4].
