Abstract: In some embodiments, apparatuses and methods are provided herein useful to the sortation of products using a conveyor assembly.

Abstract: A system and method for implementing a distributed source coding quantization scheme is provided. In one example, two independent but statistically correlated data sources can be asymmetrically compressed so that one source is compressed at a higher ratio than the other. The resulting signals are transmitted and decoded by a receiver. The highly compressed source can utilize the non-highly compressed source as side information so as to minimize the compression loss associated with the higher compression ratio. A conditional codebook can be created that not only depends on the highly compressed quantizer, but also depends on the quantized symbol received from the non-highly compressed data source.

Abstract: A system for machine learning model parameters for image compression, including partitioning image files into a first set of regions, determining a first set of machine learned model parameters based on the regions, the first set of machine learned model parameters representing a first level of patterns in the image files, constructing a representation of each of the regions based on the first set of machine learned model parameters, constructing representations of the image files by combining the representations of the regions in the first set of regions, partitioning the representations of the image files into a second set of regions, and determining a second set of machine learned model parameters based on the second set of regions, the second set of machine learned model parameters representing a second level of patterns in the image files.

Abstract: A system and method for implementing a distributed source coding quantization scheme is provided. In one example, two independent but statistically correlated data sources can be asymmetrically compressed so that one source is compressed at a higher ratio than the other. The resulting signals are transmitted and decoded by a receiver. The highly compressed source can utilize the non-highly compressed source as side information so as to minimize the compression loss associated with the higher compression ratio. A conditional codebook can be created that not only depends on the highly compressed quantizer, but also depends on the quantized symbol received from the non-highly compressed data source.

Abstract: A system and method for implementing a distributed source coding quantization scheme is provided. In one example, two independent but statistically correlated data sources can be asymmetrically compressed so that one source is compressed at a higher ratio than the other. The resulting signals are transmitted and decoded by a receiver. The highly compressed source can utilize the non-highly compressed source as side information so as to minimize the compression loss associated with the higher compression ratio. A conditional codebook can be created that not only depends on the highly compressed quantizer, but also depends on the quantized symbol received from the non-highly compressed data source.

Abstract: A system and method for implementing a distributed source coding quantization scheme is provided. In one example, two independent but statistically correlated data sources can be asymmetrically compressed so that one source is compressed at a higher ratio than the other. The resulting signals are transmitted and decoded by a receiver. The highly compressed source can utilize the non-highly compressed source as side information so as to minimize the compression loss associated with the higher compression ratio. A conditional codebook can be created that not only depends on the highly compressed quantizer, but also depends on the quantized symbol received from the non-highly compressed data source.

Abstract: A system and method for implementing a distributed source coding quantization scheme is provided. In one example, two independent but statistically correlated data sources can be asymmetrically compressed so that one source is compressed at a higher ratio than the other. The resulting signals are transmitted and decoded by a receiver. The highly compressed source can utilize the non-highly compressed source as side information so as to minimize the compression loss associated with the higher compression ratio. A conditional codebook can be created that not only depends on the highly compressed quantizer, but also depends on the quantized symbol received from the non-highly compressed data source.

Abstract: A system and method for implementing a distributed source coding quantization scheme is provided. In one example, two independent but statistically correlated data sources can be asymmetrically compressed so that one source is compressed at a higher ratio than the other. The resulting signals are transmitted and decoded by a receiver. The highly compressed source can utilize the non-highly compressed source as side information so as to minimize the compression loss associated with the higher compression ratio. A conditional codebook can be created that not only depends on the highly compressed quantizer, but also depends on the quantized symbol received from the non-highly compressed data source.

Abstract: A system for machine learning model parameters for image compression, including partitioning image files into a first set of regions, determining a first set of machine learned model parameters based on the regions, the first set of machine learned model parameters representing a first level of patterns in the image files, constructing a representation of each of the regions based on the first set of machine learned model parameters, constructing representations of the image files by combining the representations of the regions in the first set of regions, partitioning the representations of the image files into a second set of regions, and determining a second set of machine learned model parameters based on the second set of regions, the second set of machine learned model parameters representing a second level of patterns in the image files.