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Industry analysts say that Google, which has little experience with complex hardware, faces significant challenges.
However, implementation of these counters in hardware faces critical challenges: high memory consumption, control inflexibility, and low statistical accuracy.
The problem is that Silent Circle, and its newly announced Blackphone hardware faces the same modern difficulties as Blackberry (and to some extent Microsoft) has.
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Fuji FinePix F31FD w/ Hardware Face Detection [i4u].
There are still two armies, backed by an awful lot of military hardware, facing each other across a disputed and volatile border.
There have been a few researches for hardware face detector implementations on FPGAs [11, 12], but most of the proposed solutions are not very compact and the implementations are not purely on hardware.
The point-and-shoot has a hardware face detector that identifies and optimizes the shot within 0.05 seconds, much faster than traditional cameras.
(As an aside, huge high five to whoever it was on the user experience team that though of rounding the corners of the entire OS. It allows the OS to fade into the black hardware face in a way that is simply stunning, especially when screen elements are sliding around and resizing).
Algorithms in small mobile hardware face constraints, such as the need to use little energy (hence little random access memory), while maintaining prescribed time constraints.
Delivering value at scale may be the biggest challenge early VR hardware may face.
Crowdfunded hardware campaigns face a large number of pitfalls that can trip them up.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com