Abstract: A system including one or more processors and one or more non-transitory computer-readable storage media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: training a neural network detection model with a training dataset comprising synthetic training images by: using a transformation algorithm to create the synthetic training images by appending edge case training images to one or more compliant images; receiving, at the neural network detection model, as trained, at least one image; and determining, using the neural network detection model, as trained, whether the at least one image comprises non-compliant content. Other embodiments are disclosed herein.

Abstract: A system including one or more processors and one or more non-transitory computer-readable storage media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: training a neural network detection model with a training dataset comprising synthetic training images by: using a transformation algorithm to create the synthetic training images by appending edge case training images to one or more compliant images; receiving, at the neural network detection model, as trained, at least one image; and determining, using the neural network detection model, as trained, whether the at least one image comprises non-compliant content. Other embodiments are disclosed herein.

Abstract: A system can include one or more processors and one or more non-transitory computer-readable storage media storing computing instructions configured to run on the one or more processors and perform: generating a training dataset for training a neural network detection model; identifying, using the neural network detection model, as trained, the non-compliant content in the synthetic training images; receiving, at the neural network detection model, at least one image; and utilizing the neural network detection model to determine whether the at least one image comprises the non-compliant content. Other embodiments are disclosed herein.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform facilitating display of instructions to a user interface of a mobile device of a user on how to rotate a medication package in front of a camera of the mobile device to capture a video of a rotated view of a non-planar surface of the medication package; capturing, using the camera, the video of the rotated view of the non-planar surface; generating, using an input pre-processing algorithm, a series of images from the video; recognizing, using an output post-processing algorithm, respective words from the respective machine-readable text data; merging, using a merging algorithm, the respective words from the images to create lines of text; and extracting, using a machine learning algorithm, prescription data associated with prescription data fields from the lines of text. Other embodiments are described.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform facilitating display of instructions to a user interface of a mobile device of a user on how to rotate a medication package in front of a camera of the mobile device to capture a video of a rotated view of a non-planar surface of the medication package; capturing, using the camera, the video of the rotated view of the non-planar surface; generating, using an input pre-processing algorithm, a series of images from the video; recognizing, using an output post-processing algorithm, respective words from the respective machine-readable text data; merging, using a merging algorithm, the respective words from the images to create lines of text; and extracting, using a machine learning algorithm, prescription data associated with prescription data fields from the lines of text. Other embodiments are described.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of receiving a digital image comprising multiple items; determining an embedding for the digital image using a machine learning algorithm trained on one or more combined digital images, the combined digital image comprising one or more annotated digital images; identifying an item of the multiple items in the digital image; and facilitating an alteration of a GUI on an electronic device in response to identifying the item in the digital image.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of receiving one or more digital images from a repository of digital images; annotating the one or more digital images from the repository of digital images; digitally altering the one or more digital images, as annotated, from the repository of digital images; digitally combining the one or more digital images, as annotated and digitally altered, with at least one or more portions of one or more other digital images of the repository of digital images to create one or more combined digital images; training a machine learning algorithm on the one or more combined digital images; and storing the machine learning algorithm, as trained, in the one or more non-transitory computer readable storage devices.

Abstract: A system can include one or more processors and one or more non-transitory computer-readable storage media storing computing instructions configured to run on the one or more processors and perform: generating a training dataset for training a neural network detection model; identifying, using the neural network detection model, as trained, the non-compliant content in the synthetic training images; receiving, at the neural network detection model, at least one image; and utilizing the neural network detection model to determine whether the at least one image comprises the non-compliant content. Other embodiments are disclosed herein.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of: generating a training dataset comprising synthetic training images for training a neural network detection model to identify non-compliant content in images; executing a training procedure that utilizes the synthetic training images to train the neural network detection model to identify the non-compliant content; receiving, at the neural network detection model, at least one image; and utilizing the neural network detection model to determine whether the at least one image includes the non-compliant content. Other embodiments are disclosed herein.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of receiving a digital image comprising multiple items; determining an embedding for the digital image using a machine learning algorithm trained on one or more combined digital images, the combined digital image comprising one or more annotated digital images; identifying an item of the multiple items in the digital image; and facilitating an alteration of a GUI on an electronic device in response to identifying the item in the digital image.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of receiving one or more digital images from a repository of digital images; annotating the one or more digital images from the repository of digital images; digitally altering the one or more digital images, as annotated, from the repository of digital images; digitally combining the one or more digital images, as annotated and digitally altered, with at least one or more portions of one or more other digital images of the repository of digital images to create one or more combined digital images; training a machine learning algorithm on the one or more combined digital images; and storing the machine learning algorithm, as trained, in the one or more non-transitory computer readable storage devices.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of: receiving an image at a first-level analysis component comprising a first neural network structure; analyzing, using the first neural network structure of the first-level analysis component, the image to determine an image category associated with the image; selecting at least one second-level analysis component that is associated with the image category to analyze the image; analyzing, using a second neural network structure associated with the at least one second-level analysis component that was selected, the image to determine whether the image includes non-compliant content; and in response to determining that the image includes non-compliant content, executing a corrective measure. Other embodiments are disclosed herein.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of: receiving an image at a first-level analysis component comprising a first neural network structure; analyzing, using the first neural network structure of the first-level analysis component, the image to determine an image category associated with the image; selecting at least one second-level analysis component that is associated with the image category to analyze the image; analyzing, using a second neural network structure associated with the at least one second-level analysis component that was selected, the image to determine whether the image includes non-compliant content; and in response to determining that the image includes non-compliant content, executing a corrective measure. Other embodiments are disclosed herein.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of: generating a training dataset comprising synthetic training images for training a neural network detection model to identify non-compliant content in images; executing a training procedure that utilizes the synthetic training images to train the neural network detection model to identify the non-compliant content; receiving, at the neural network detection model, at least one image; and utilizing the neural network detection model to determine whether the at least one image includes the non-compliant content. Other embodiments are disclosed herein.

Abstract: Some embodiments can comprise a system comprising one or more computer processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more computer processing modules a perform acts of: receiving, at the one or more computer processing modules and from a third-party electronic device, a title for a product; dividing, at the one or more computer processing modules, the title into a sequence of tokens; storing, by the one or more computer processing modules onto the one or more non-transitory storage modules, the sequence of tokens; determining, at the one or more computer processing modules and using a sequence labeling model, a type of each token of the sequence of tokens; storing, by the one or more computer processing modules onto the one or more non-transitory storage modules, the type of each token of the sequence of tokens; encoding, at the one or more computer processing modules, each token of the sequence of tokens to indicate the type o

Abstract: Some embodiments can comprise a system comprising one or more computer processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more computer processing modules a perform acts of: receiving, at the one or more computer processing modules and from a third-party electronic device, a title for a product; dividing, at the one or more computer processing modules, the title into a sequence of tokens; storing, by the one or more computer processing modules onto the one or more non-transitory storage modules, the sequence of tokens; determining, at the one or more computer processing modules and using a sequence labeling model, a type of each token of the sequence of tokens; storing, by the one or more computer processing modules onto the one or more non-transitory storage modules, the type of each token of the sequence of tokens; encoding, at the one or more computer processing modules, each token of the sequence of tokens to indicate the type o

Abstract: A system and method for reducing peak to average power ratio in a wireless communication system. A wireless communication system includes a radio frequency wireless transmitter that includes signal peak reduction circuitry configured to reduce peak to average power ratio of a signal to be transmitted by reducing amplitude of the signal to be transmitted that is greater than a predetermined amplitude. The signal peak reduction circuitry includes a bit inverter configured to invert a bit of a symbol identified as causing the amplitude of the signal to exceed the predetermined amplitude. The bit inverter is also configured to select the bit to invert such that inversion of the bit reduces the amplitude of the signal, and such that forward error correction in a receiver wirelessly coupled to the transmitter restores the bit to a pre-inversion value.

Abstract: A method of extracting sweet compounds from stevia plant powder includes mixing stevia powder and deionized water to create a stevia powder slurry, filtering the slurry and adding it to an extraction column, adding an ethanol solution to create an elute, mixing the elute with activated charcoal and filtering the elute, removing the ethanol and water from the elute, and spraying the elute to produce the sweet compounds.

Abstract: A method of extracting sweet compounds from stevia plant powder includes mixing stevia powder and deionized water to create a stevia powder slurry, filtering the slurry and adding it to an extraction column, adding an ethanol solution to create an elute, mixing the elute with activated charcoal and filtering the elute, removing the ethanol and water from the elute, and spraying the elute to produce the sweet compounds.

Abstract: A digital pre-distortion component includes: a first capturing component that captures a first sample set of data; a first generating component that generates a first change matrix associated with a portion of the first sample set of data; a first memory component that stores the first change matrix; a second capturing component that captures a second sample set of data; a second generating component that generates a second change matrix associated with a portion of the second sample set of data; a second memory component that stores the second change matrix; a third capturing component that captures a third sample set of data; a third generating component that generates a third change matrix associated with a portion of the third sample set of data; a comparing component that compares the third change matrix with the first change matrix to obtain a first comparison, and compares the third change matrix with the second change matrix to obtain a second comparison; and an adapting component that adapts the digi