Abstract: A hub device or edge device implements adaptive data compression. The model training service analyzes the received data. A machine learning model of the hub device receives time-series data from one or more data sources and classifies respective portions of the time-series data as respective patterns. A data compressor at the hub device generates compressed data by applying different compression techniques to the respective portions of the time-series data according to a mapping of the compression techniques to the respective patterns. The hub device then transmits the compressed data to an endpoint for processing (e.g., another device that uses the compressed data). The hub device receives feedback for the compressed data. In response to the feedback, the hub device changes one or more of the compression techniques that are mapped to the respective patterns.

Abstract: A hub device or edge device implements adaptive data compression. The model training service analyzes the received data. A machine learning model of the hub device receives time-series data from one or more data sources and classifies respective portions of the time-series data as respective patterns. A data compressor at the hub device generates compressed data by applying different compression techniques to the respective portions of the time-series data according to a mapping of the compression techniques to the respective patterns. The hub device then transmits the compressed data to an endpoint for processing (e.g., another device that uses the compressed data). The hub device receives feedback for the compressed data. In response to the feedback, the hub device changes one or more of the compression techniques that are mapped to the respective patterns.

Abstract: Methods of data encoding using trees formed with logic gates are described which lead to spatial compression of image data. Data encoding is achieved using a five-level wavelet transform, such as the Haar or the 2/10 transform. A dual transform engine is used, the first and engine being used for the first part of the first-level transform, the second part of the first-level transform and the subsequent-level transforms being performed by the second transform engine within a time interval which is less than or equal to the time taken by the first transform engine to effect the part-transform. Each bit plane of the resulting coefficients is then encoded by forming a tree structure from the bits and OR logical combinations thereof. Redundant data are removed from the resulting tree structure, and further data can be removed by using a predetermined compression profile.

Abstract: Provided is a wireless communication device which can improve the notification accuracy of the channel state information (CSI) without straining the feedback circuit. In this device, a channel estimator (105) uses a pilot signal input from a wireless receiver (102) to estimate the channel and obtain a plurality of path gains in each of a plurality of delay periods. Then, the channel estimator (105) outputs the plurality of path gains to a quantizer (107). The quantizer (107) quantizes the plurality of path gains in the number of notification bits corresponding to each of the plurality of delay periods based on the correspondence of the delay period and the number of notification bits input from a setting unit (106).