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  2. Convolutional code - Wikipedia

    en.wikipedia.org/wiki/Convolutional_code

    Convolutional code. In telecommunication, a convolutional code is a type of error-correcting code that generates parity symbols via the sliding application of a boolean polynomial function to a data stream. The sliding application represents the 'convolution' of the encoder over the data, which gives rise to the term 'convolutional coding'.

  3. Autoencoder - Wikipedia

    en.wikipedia.org/wiki/Autoencoder

    e. An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data ( unsupervised learning ). [ 1][ 2] An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.

  4. Rotary encoder - Wikipedia

    en.wikipedia.org/wiki/Rotary_encoder

    Rotary encoder. A Gray code absolute rotary encoder with 13 tracks. At the top, the housing, interrupter disk, and light source can be seen; at the bottom the sensing element and support components. A rotary encoder, also called a shaft encoder, is an electro-mechanical device that converts the angular position or motion of a shaft or axle to ...

  5. Text-to-image model - Wikipedia

    en.wikipedia.org/wiki/Text-to-image_model

    Text-to-image model. An image conditioned on the prompt "an astronaut riding a horse, by Hiroshige ", generated by Stable Diffusion, a large-scale text-to-image model released in 2022. A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description.

  6. Reed–Solomon error correction - Wikipedia

    en.wikipedia.org/wiki/Reed–Solomon_error...

    The first element of a CIRC decoder is a relatively weak inner (32,28) Reed–Solomon code, shortened from a (255,251) code with 8-bit symbols. This code can correct up to 2 byte errors per 32-byte block. More importantly, it flags as erasures any uncorrectable blocks, i.e., blocks with more than 2 byte errors.

  7. Generative pre-trained transformer - Wikipedia

    en.wikipedia.org/wiki/Generative_pre-trained...

    Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] [18] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.

  8. Specific Area Message Encoding - Wikipedia

    en.wikipedia.org/wiki/Specific_Area_Message_Encoding

    Specific Area Message Encoding (SAME) is a protocol used for framing and classification of broadcasting emergency warning messages. It was developed by the United States National Weather Service for use on its NOAA Weather Radio (NWR) network, and was later adopted by the Federal Communications Commission for the Emergency Alert System, then subsequently by Environment Canada for use on its ...

  9. Stable Diffusion - Wikipedia

    en.wikipedia.org/wiki/Stable_Diffusion

    Stable Diffusion. Stable Diffusion is a deep learning, text-to-image model released in 2022 based on diffusion techniques. The generative artificial intelligence technology is the premier product of Stability AI and is considered to be a part of the ongoing artificial intelligence boom .