Systems and methods for detecting and grouping anomalies in data

    公开(公告)号:US12112241B2

    公开(公告)日:2024-10-08

    申请号:US16577699

    申请日:2019-09-20

    CPC classification number: G06N20/00 G06F17/18

    Abstract: The present disclosure generally relates to apparatus, software and methods for detecting anomalous elements in data. For example, the data can be any time series, such as but not limited to radio frequency data, temperature data, stock data, or production data. Each type of data may be susceptible to repeating phenomena that produce recognizable features of anomalous elements. In some embodiments, the features can be characterized as known patterns and used to train a machine learning model via supervised learning to recognize those features in a new data series.

    Systems and methods for selecting a spectral segment for a downlink transmission

    公开(公告)号:US11082171B2

    公开(公告)日:2021-08-03

    申请号:US16593767

    申请日:2019-10-04

    Abstract: The present disclosure generally relates to systems, methods and software for selecting a spectral segment for important downlink and/or uplink transmissions. Particularly, the spectral segment may be a set of contiguous subcarriers within a plurality of subcarriers transmitting over a channel to a population of cable modems. In an embodiment, the systems, methods and software disclosed herein optimize placement of a physical link channel (PLC) within an OFDM channel. In an embodiment, the system, methods and software disclosed herein optimize placement of a ranging region within an OFDMA channel.

    SYSTEMS AND METHODS FOR DOCSIS PROFILE MANAGEMENT

    公开(公告)号:US20240235878A1

    公开(公告)日:2024-07-11

    申请号:US18092380

    申请日:2023-01-02

    CPC classification number: H04L12/2801 H04L41/0893

    Abstract: A profile optimizing method is provided for a downstream channel transmission of active subcarriers to user devices. The method includes steps of receiving channel measurement data from each user device for each subcarrier, calculating a maximum bit-loading value for each user device per subcarrier, grouping the user devices into a plurality of clusters based on a proximity of the maximum bit-loading values of a first user device to those of a second user device within the particular cluster, assigning each user device within the particular cluster to a single cluster profile. A plurality of single cluster profiles for the plurality of clusters forms a set of cluster profiles. The method further includes steps of determining a channel capacity ratio for the set of cluster profiles, and combining at least two single profiles of the set of cluster profiles into a coalesced profile pair.

    Systems and methods for DOCSIS profile management

    公开(公告)号:US11546080B2

    公开(公告)日:2023-01-03

    申请号:US16377030

    申请日:2019-04-05

    Abstract: A profile optimizing method is provided for a downstream channel transmission of active subcarriers to user devices. The method includes steps of receiving channel measurement data from each user device for each subcarrier, calculating a maximum bit-loading value for each user device per subcarrier, grouping the user devices into a plurality of clusters based on a proximity of the maximum bit-loading values of a first user device to those of a second user device within the particular cluster, assigning each user device within the particular cluster to a single cluster profile. A plurality of single cluster profiles for the plurality of clusters forms a set of cluster profiles. The method further includes steps of determining a channel capacity ratio for the set of cluster profiles, and combining at least two single profiles of the set of cluster profiles into a coalesced profile pair.

    Systems and methods for detecting and classifying anomalous features in one-dimensional data

    公开(公告)号:US11468273B2

    公开(公告)日:2022-10-11

    申请号:US16577304

    申请日:2019-09-20

    Abstract: The present disclosure generally relates to apparatus, software and methods for detecting and classifying anomalous features in one-dimensional data. The apparatus, software and methods disclosed herein use a YOLO-type algorithm on one-dimensional data. For example, the data can be any one-dimensional data or time series, such as but not limited to be power over time data, signal to noise ratio (SNR) over time data, modulation error ratio (MER) data, full band capture data, radio frequency data, temperature data, stock data, or production data. Each type of data may be susceptible to repeating phenomena that produce recognizable anomalous features. In some embodiments, the features can be characterized or labeled as known phenomena and used to train a machine learning model via supervised learning to recognize those features in a new data series.

    Systems and methods for transmitting data via an electrical cable

    公开(公告)号:US11057261B2

    公开(公告)日:2021-07-06

    申请号:US16727558

    申请日:2019-12-26

    Abstract: A method for transmitting data via a coaxial electrical cable includes (a) converting symbols of each input data stream of a plurality of parallel input data streams from digital form to analog form, (b) individually filtering symbols of each input data stream, (c) transforming symbols of each input data stream from a first frequency-domain to a first time-domain, to generate parallel first time-domain samples, (d) converting the first time-domain samples to a serial multi-carrier signal, and (e) injecting the multi-carrier signal onto the coaxial electrical cable.

    Bandwidth allocation method and associated optical line terminal

    公开(公告)号:US12095512B1

    公开(公告)日:2024-09-17

    申请号:US18211482

    申请日:2023-06-19

    CPC classification number: H04B10/6932 H04B10/5161

    Abstract: A method for allocating bandwidth to a first ONU, a second ONU, M1 ONUs, and M2 ONUs includes, during an allocation cycle, (i) granting a respective upstream time slot to, of a plurality of N ONUs, only each of the M1 ONUs, and (ii) granting a first upstream time slot to the first ONU. Each of the M1 ONUs and M2 ONUs is one of the plurality of N ONUs. The method also includes, during a subsequent cycle, (i) granting a respective upstream time slot to, of the plurality of N ONUs, only each of the M2 ONUs. The N ONUs includes a skipped-ONU that is one of either, and not both, the M1 ONUs and the M2 ONUs. The method includes, during the subsequent allocation cycle, granting a second upstream time slot to a second ONU, which is not one of the plurality of N ONUs.

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