Abstract: Systems and methods are provided for an improved machine learning (ML) model system. The improved ML system can be configured to (1) initially classify the types of images and videos received by the various devices and provide the classified input to different ML models based on the classification (e.g., of the distortion level, etc.), and/or (2) reuse portions (referred to as base components) of each ML model where parameters of the base components are unchanged across the various ML models, while replacing other portions (referred to as adapted components) of the ML model where the parameters of the adapted components may change greatly.

Abstract: Example method includes: transmitting a plurality of probe images from an IoT device at an edge network to a server hosting a target DNN, wherein the plurality of images are injected with a limited amount of noise to probe sensitivities of the target DNN to the red, green, and blue colors; receiving a feedback comprising a plurality of DCT coefficients unique to target DNN from the server hosting the target DNN; computing a plurality of color conversion weights based on the feedback received from the server; converting a set of real-time images from RGB color space to YUV color space using the plurality of color conversion weights unique to the target DNN; compressing the set of real-time images using a quantization table unique to the target DNN by the IoT device; and transmitting the compressed set of real-time images to the server hosting the target DNN for DNN inferences.

Abstract: Example method includes: transmit a plurality of probe images from an Internet of Things (IoT) device at an edge network to a server hosting a target deep neural network (DNN), wherein the plurality of images are injected with a limited amount of noise; receive a feedback comprising a plurality of discrete cosine transform (DCT) coefficients from the server hosting the target DNN, wherein the plurality of DCT coefficients are unique to the target DNN; generate a quantization table based on the feedback received from the server hosting the target DNN; compress a set of real-time images using the generated quantization table by the IoT device at the edge network; and transmit the compressed set of real-time images to the server hosting the target DNN for DNN inferences.

Abstract: Example method includes: transmit a plurality of probe images from an Internet of Things (IoT) device at an edge network to a server hosting a target deep neural network (DNN), wherein the plurality of images are injected with a limited amount of noise; receive a feedback comprising a plurality of discrete cosine transform (DCT) coefficients from the server hosting the target DNN, wherein the plurality of DCT coefficients are unique to the target DNN; generate a quantization table based on the feedback received from the server hosting the target DNN; compress a set of real-time images using the generated quantization table by the IoT device at the edge network; and transmit the compressed set of real-time images to the server hosting the target DNN for DNN inferences.

Abstract: Example method includes: transmitting a plurality of probe images from an IoT device at an edge network to a server hosting a target DNN, wherein the plurality of images are injected with a limited amount of noise to probe sensitivities of the target DNN to the red, green, and blue colors; receiving a feedback comprising a plurality of DCT coefficients unique to target DNN from the server hosting the target DNN; computing a plurality of color conversion weights based on the feedback received from the server; converting a set of real-time images from RGB color space to YUV color space using the plurality of color conversion weights unique to the target DNN; compressing the set of real-time images using a quantization table unique to the target DNN by the IoT device; and transmitting the compressed set of real-time images to the server hosting the target DNN for DNN inferences.

Abstract: Methods and systems for beam forming, implemented in a base station used in a communication system, include measuring channel state information (CSI) for a number of active phased-array antennas less than a full number of phased-array antennas. Analog beam forming weights are determined using the measured CSI. An optimal digital precoder is determined from the analog beam forming weights. The analog beam forming weights and optimal digital precoder are applied to one phased-array antenna.

Abstract: Methods and systems for beam forming include measuring channel state information for a set of different codebook entries. An angle of arrival (AoA) distribution is determined with a processor using compressive testing based on the measured channel state information. A set of phase shift values is determined based on the determined AoA to perform phased array beamforming.

Abstract: Methods and systems for beam forming include measuring channel state information for a set of different codebook entries. An angle of arrival (AoA) distribution is determined with a processor using compressive testing based on the measured channel state information. A set of phase shift values is determined based on the determined AoA to perform phased array beamforming.

Abstract: Methods and systems for beam forming, implemented in a base station used in a communication system, include measuring channel state information (CSI) for a number of active phased-array antennas less than a full number of phased-array antennas. Analog beam forming weights are determined using the measured CSI. An optimal digital precoder is determined from the analog beam forming weights. The analog beam forming weights and optimal digital precoder are applied to one phased-array antenna.