BUILDING BT RECEIVERTO BUILD BT RECEIVER YOU WILL NEED:– An Arduino Board like that: Arduino Pro Mini 328 – 3.3V/8MHz– A USB Host Shield from Oleg Mazurov’s Circuitathome.com site– An compatible bluetooth module like that: Bluetooth SMD Module – RN-42for Bluetooth Module HC-06 check out eBay– Pololu 5V Step-Up Voltage Regulator NCP1402

Source: Building BT Receiver | DslrSystems

Source: Service Mode for C-Series – SamyGO | Evernote Web

Source: SamyGO

Thunderbird directory setupDavMail Directory support is now available to access Exchange address book through LDAP.

Source: DavMail POP/IMAP/SMTP/Caldav/Carddav/LDAP Exchange Gateway – Thunderbird directory setup

Source: EmerytHacks: Connecting an iPad retina LCD to a PC

Arduino and Netduino are kinda of like small computers that have allowed us to do marvelous things, like controlling our environment and more they are not really that expensive Arduino mega 2560 is…

Source: Arduino vs Netduino : the more things change, more they remain same. | ramblings of a mad man

Source: Netduino :: home | Evernote Web

Source: What is the difference betweet Netduino and .NET Gadgeteer? – Stack Overflow

Microsoft .NET Gadgeteer is an open-source toolkit for building small electronic devices using the .NET Micro Framework. It combines the advantages of object-oriented programming, solderless assembly of electronics, and support for customizable physical design.

Source: Gadgeteer | .NET Micro Framework

The angular diameter or apparent size is an angular measurement describing how large a sphere or circle appears from a given point of view. In the vision sciences it is called the visual angle and in optics it is the angular aperture (of a lens). The angular diameter can alternatively be thought of as the angle through which an eye or camera must rotate to look from one side of an apparent circle to the opposite side. Angular radius equals half the angular diameter.

Source: Angular diameter – Wikipedia

​With 5G, carriers hope  to deliver data to smartphone users at speeds 10 times as fast as on  today’s 4G networks, and with only 1 millisecond of delay.

Both Verizon and AT&T say their 2017 fixed wireless networks will rely on millimeter waves, which are arguably the hottest new 5G technology. Millimeter waves are officially defined as waves transmitted at frequencies between 30 and 300 gigahertz, and they are between 1 and 10 millimeters in length.

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Tensor processing unit
Tensor processing units (or TPUs) are application-specific integrated circuits (ASIC) developed specifically for machine learning. Compared to graphics processing units (which as of 2016 are frequently used for the same tasks), they are designed explicitly for a higher volume of reduced precision computation with higher IOPS per watt (e.g. as little as 8-bit precision[1]), and lack hardware for rasterisation/texture mapping.[2] The chip has been specifically designed for Google’s TensorFlow framework, however Google still uses CPUs and GPUs for other machine learning.[3] Other AI accelerator designs are appearing from other vendors also and are aimed at embedded and robotics markets.

Google has stated that its proprietary tensor processing units were used in the AlphaGo versus Lee Sedol series of man-machine Go games.[2] Google has also used TPUs for Google Street View text processing, and was able to find all the text in the Street View database in less than five days. In Google Photos, an individual TPU can process over 100 million photos a day. It is also used in RankBrain which Google uses to provide search results.[4] The tensor processing unit was announced in 2016 at Google I/O, although the company stated that the TPU had been used inside their datacenter for over a year prior.[3][2]

The chip size can fit in a hard drive slot within a data center rack according to Google Distinguished Hardware Engineer Norm Jouppi.[3]

See also Edit

Vision processing unit a similar device specialised for vision processing.
TrueNorth a similar device simulating spiking neurons instead of low precision tensors.
Neural processing unit
References Edit

^ Armasu, Lucian (2016-05-19). “Google’s Big Chip Unveil For Machine Learning: Tensor Processing Unit With 10x Better Efficiency (Updated)”. Tom’s Hardware. Retrieved 2016-06-26.
^ a b c Jouppi, Norm (May 18, 2016). “Google supercharges machine learning tasks with TPU custom chip”. Google Cloud Platform Blog. Google. Retrieved 2017-01-22.
^ a b c “Google’s Tensor Processing Unit explained: this is what the future of computing looks like”. TechRadar. Retrieved 2017-01-19.
^ “Google’s Tensor Processing Unit could advance Moore’s Law 7 years into the future”. PCWorld. Retrieved 2017-01-19.

Last edited 12 days ago by PirateImpulse

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