Innovative Deep Learning Solutions

About WooFu Tech, LLC

WooFu   Tech,   LLC   is   devoted   to   developing   innovative   technological   solutions   using   complex mathematical   approaches.   Currently,   WooFu   Tech   is   mainly   focusing   on   the   development   of mobile   based   Apps,   state-of-the-art   algorithms   and   models   in   deep   learning,   and   deep learning    applications    in    financial    management,    virtual    reality,    big    data    analytics,    vision, speech, and multimodel processing. Highlights in Year 2014: • In   2014,   we   have   successfully   developed   the   first   iOS-based   hearing   test   App   for hearing    impaired    people    to    monitor    their    hearing    sensitivity    and    elderly    normal- hearing   people   to   check   their   hearing   periodically.   The   App   won   the   first   prize ($5000)   for   the   2014   UCLA   Code   for   the   Mission   Contest   Service   Category: Tackling the Big Health Problems   and freely available at UCLA app store . Highlights in Year 2015: • In   2015,   we   have   successfully   developed   the   second   iOS-based   App   to   faciliate   the learning   of   music   theory   for   students   as   well   as   musicians   (UCLA   Music   Theory).   The App   won   the   first   prize   ($5000)   for   the   2015   UCLA   Code   for   the   Mission Education   Track:   Games   that   Enhance   Learning!    and   freely   available   at   UCLA app store . Highlights in Year 2016: • Mobile-based   Apps:   In   2016,   under   the   generous   support   of   House   Ear   Institute,   we have   successfully   developed   the   second   generation   of   iOS-based   and   Android-based hearing   test   App   (uHearingTest)   for   hearing   impaired   people   to   monitor   their   hearing sensitivity   and   elderly   normal-hearing   people   to   check   their   hearing   periodically.   The App will be freely available at UCLA app store  and Goople Store . • Machine   Learning:      Successfully   completed   a   machine   learning   (CNN)   project   to   use training   images   of   caltech's   bird   dataset   and   the   annotations   on   those   images   to propose   regions   of   interests   where   birds   can   be,   and   recognize   the   region   that   the bird picture is in.