Abstract: Examples of biomimetic codecs and biomimetic coding techniques are described herein. Morphologically-adaptive coding networks can be developed in accordance with energy dissipation driven “heat” generated by application of training data. The morphologically-adaptive coding networks may be representative of common features expected in an input signal or data stream. Decoding may proceed using the morphologically-adaptive coding network. Morphologically-adaptive coding networks may be used as a cortex that can be shared for boosting multimedia data compression rates and/or increasing the encode-decode fidelity of information content while the features remain queryable in encoded form. Examples of the biomimetic codecs and biomimetic coding techniques provide a broad-based technology platform that can be used in context-IDed multimedia storage, pattern recognition, and high-performance computing/big data management, the hallmarks of web- and cloud-based systems.

Abstract: Disclosed is an air cleaner filter containing an improved organic gel, wherein an ultra-high efficiency organic gel microbial air (UGMA) filter cartridge has a nano-porous texture on organic gel in spongy carcass diameters of which are randomly distributed, randomly distributed organic gel in spongy carcass as microbial retainer generated by a layer covering inner surfaces of a spongy carcass having randomly distributed pores at the grade selected in the range of 0.5 mm to 2.5 mm according to need, air inlet channels that allow air to be passed into sandwich structure UGMA filter cartridge, air flow channels such that microbial loads smaller than 0.1 pm are retained by pores and the air wipes the nano-pores by generating micro turbulence therein. Also disclosed is a production method for manufacturing the UGMA filter.

Abstract: Examples of biomimetic codecs and biomimetic coding techniques are described herein. Morphologically-adaptive coding networks can be developed in accordance with energy dissipation driven “heat” generated by application of training data. The morphologically-adaptive coding networks may be representative of common features expected in an input signal or data stream. Decoding may proceed using the morphologically-adaptive coding network. Morphologically-adaptive coding networks may be used as a cortex that can be shared for boosting multimedia data compression rates and/or increasing the encode-decode fidelity of information content while the features remain queryable in encoded form. Examples of the biomimetic codecs and biomimetic coding techniques provide a broad-based technology platform that can be used in context-IDed multimedia storage, pattern recognition, and high-performance computing/big data management, the hallmarks of web- and cloud-based systems.