Abstract: A computer system may train and use a machine-learning model to quantitatively analyze an image. In particular, the computer system may generate the machine-learning model based on a set of reference images that include content with instances of a quantitative feature attribute and one or more feedback metrics that specify locations of the instances of the quantitative feature attribute in the reference images and numerical values associated with the instances of the quantitative feature attribute. Then, after receiving the image from an electronic device, the computer system may analyze the image using the machine-learning model to perform measurements of one or more additional instances of the quantitative feature attribute in the image. Moreover, the computer system may provide a measurement result for the image, the measurement result including a second numerical value associated with the one or more additional instances of the quantitative feature attribute in the image.

Abstract: In order to determine a sounding interval, an electronic device iteratively revises a set of potential sounding intervals based on transmission statistics associated with communication of some packets with and other packets without transmission beamforming for the set of potential sounding intervals. In particular, the electronic device calculates rank positions for the set of potential sounding intervals based on an estimated throughput and numbers of packets transmitted with transmission beamforming for the set of potential sounding intervals out of a total number of packets transmitted. Then, the electronic device may determine the output sounding interval based on the ranking. When the convergence criterion is achieved, the electronic device may determine a moment based on calculated frequencies of the rank positions over multiple iterations, which is used to revise the set of potential sounding intervals in the next iteration.

Abstract: A computer system may train and use a machine-learning model to quantitatively analyze an image. In particular, the computer system may generate the machine-learning model based on a set of reference images that include content with instances of a quantitative feature attribute and one or more feedback metrics that specify locations of the instances of the quantitative feature attribute in the reference images and numerical values associated with the instances of the quantitative feature attribute. Then, after receiving the image from an electronic device, the computer system may analyze the image using the machine-learning model to perform measurements of one or more additional instances of the quantitative feature attribute in the image. Moreover, the computer system may provide a measurement result for the image, the measurement result including a second numerical value associated with the one or more additional instances of the quantitative feature attribute in the image.

Abstract: In order to determine a sounding interval, an electronic device iteratively revises a set of potential sounding intervals based on transmission statistics associated with communication of some packets with and other packets without transmission beamforming for the set of potential sounding intervals. In particular, the electronic device calculates rank positions for the set of potential sounding intervals based on an estimated throughput and numbers of packets transmitted with transmission beamforming for the set of potential sounding intervals out of a total number of packets transmitted. Then, the electronic device may determine the output sounding interval based on the ranking. When the convergence criterion is achieved, the electronic device may determine a moment based on calculated frequencies of the rank positions over multiple iterations, which is used to revise the set of potential sounding intervals in the next iteration.

Abstract: A computer system may train and use a machine-learning model to quantitatively analyze an image. In particular, the computer system may generate the machine-learning model based on a set of reference images that include content with instances of a quantitative feature attribute and one or more feedback metrics that specify locations of the instances of the quantitative feature attribute in the reference images and numerical values associated with the instances of the quantitative feature attribute. Then, after receiving the image from an electronic device, the computer system may analyze the image using the machine-learning model to perform measurements of one or more additional instances of the quantitative feature attribute in the image. Moreover, the computer system may provide a measurement result for the image, the measurement result including a second numerical value associated with the one or more additional instances of the quantitative feature attribute in the image.

Abstract: A routing method for communication from a source node to a destination node in a distributed wireless network which comprises a plurality of wireless mobile nodes.

Abstract: System and methodologies for timing synchronization in a wireless communication system are provided herein. The provided systems and methodologies can utilize various timing synchronization algorithms and an associated state machine to reduce the down time of a wireless communication system due to the presence of simultaneously operating piconets (SOP) and/or other factors. Frequency band finger pattern detection techniques are additionally described that can reduce boundary mismatch rates for wireless receivers. In one example, by making use of the fact that time frequency codes (TFCs) possess unique frequency hopping patterns, system down time can be reduced and OFDM boundary matching can be enhanced to allow a receiver to obtain correct timing information even for communication channels having a very low SINR.