Patents by Inventor Charles C. HILLYER

Charles C. HILLYER has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20230100511
    Abstract: Disclosed are various embodiments for optimized sensor deployment and fault detection in the context of agricultural irrigation and similar applications. For instance, a computing device may execute a genetic algorithm (GA) routine to determine an optimal sensor deployment scheme such that a mean-time-to-failure (MTTF) for the system is maximized, thereby improving communication of sensor measurements. Moreover, in various embodiments, a centralized fault detection scheme may be employed and a soil moisture of a field can be determined by statistically inferring soil moistures at locations of faulty nodes using spatial and temporal correlations.
    Type: Application
    Filed: October 19, 2022
    Publication date: March 30, 2023
    Applicant: The Texas A&M University System
    Inventors: Yanxiang YANG, Jiang HU, Dana O. PORTER, Thomas H. MAREK, Charles C. HILLYER, Lijia SUN
  • Patent number: 11520647
    Abstract: Disclosed are various embodiments for optimized sensor deployment and fault detection in the context of agricultural irrigation and similar applications. For instance, a computing device may execute a genetic algorithm (GA) routine to determine an optimal sensor deployment scheme such that a mean-time-to-failure (MTTF) for the system is maximized, thereby improving communication of sensor measurements. Moreover, in various embodiments, a centralized fault detection scheme may be employed and a soil moisture of a field can be determined by statistically inferring soil moistures at locations of faulty nodes using spatial and temporal correlations.
    Type: Grant
    Filed: December 11, 2018
    Date of Patent: December 6, 2022
    Assignee: THE TEXAS A&M UNIVERSITY SYSTEM
    Inventors: Yanxiang Yang, Jiang Hu, Dana O. Porter, Thomas H. Marek, Charles C. Hillyer, Lijia Sun
  • Patent number: 11516976
    Abstract: Disclosed are various embodiments for reinforcement learning-based irrigation control to maintain or increase a crop yield or reduce water use. A computing device may be configured to determine an optimal irrigation schedule for a crop planted in a field by applying reinforcement learning (RL), where, for a given state of a total soil moisture, the computing device performs an action, the action comprising waiting or irrigating crop. An immediate reward may be assigned to a state-action pair, the state-action pair comprising the given state of the total soil moisture and the action performed. The computing device may instruct an irrigation system to apply irrigation to at least one crop in accordance with the optimal irrigation schedule determined, where the optimal irrigation schedule includes an amount of water to be applied at a predetermined time.
    Type: Grant
    Filed: December 11, 2018
    Date of Patent: December 6, 2022
    Assignee: The Texas A&M University System
    Inventors: Lijia Sun, Jiang Hu, Dana O. Porter, Thomas H. Marek, Charles C. Hillyer, Yanxiang Yang
  • Publication number: 20200387419
    Abstract: Disclosed are various embodiments for optimized sensor deployment and fault detection in the context of agricultural irrigation and similar applications. For instance, a computing device may execute a genetic algorithm (GA) routine to determine an optimal sensor deployment scheme such that a mean-time-to-failure (MTTF) for the system is maximized, thereby improving communication of sensor measurements. Moreover, in various embodiments, a centralized fault detection scheme may be employed and a soil moisture of a field can be determined by statistically inferring soil moistures at locations of faulty nodes using spatial and temporal correlations.
    Type: Application
    Filed: December 11, 2018
    Publication date: December 10, 2020
    Applicant: The Texas A&M University System
    Inventors: Yanxiang YANG, Jiang HU, Dana O. PORTER, Thomas H. MAREK, Charles C. HILLYER, Lijia SUN
  • Publication number: 20200296906
    Abstract: Disclosed are various embodiments for reinforcement learning-based irrigation control to maintain or increase a crop yield or reduce water use. A computing device may be configured to determine an optimal irrigation schedule for a crop planted in a field by applying reinforcement learning (RL), where, for a given state of a total soil moisture, the computing device performs an action, the action comprising waiting or irrigating crop. An immediate reward may be assigned to a state-action pair, the state-action pair comprising the given state of the total soil moisture and the action performed. The computing device may instruct an irrigation system to apply irrigation to at least one crop in accordance with the optimal irrigation schedule determined, where the optimal irrigation schedule includes an amount of water to be applied at a predetermined time.
    Type: Application
    Filed: December 11, 2018
    Publication date: September 24, 2020
    Applicant: The Texas A&M University System
    Inventors: Lijia SUN, Jiang HU, Dana O. PORTER, Thomas H. MAREK, Charles C. HILLYER, Yanxiang YANG