Abstract: A distributed acoustic sensing (DAS) system may include an optical fiber, a phase-sensitive OTDR (?-OTDR) coupled to the optical fiber, and a processor cooperating with the ?-OTDR. The processor may be configured to generate a series of covariance matrices for DAS data from the ?-OTDR, and determine an acoustic event based upon comparing the series of covariance matrices with a corresponding Toeplitz matrix.

Abstract: A distributed acoustic sensing (DAS) system may include an optical fiber, a phase-sensitive OTDR (?-OTDR) coupled to the optical fiber, and a processor cooperating with the ?-OTDR. The processor may be configured to train a plurality of machine learning networks with DAS data from the ?-OTDR based upon different respective optimizers, select a trained machine learning network from among the plurality thereof based upon a game theoretic model, and generate an acoustic event report from the DAS data using the selected trained machine learning network.

Abstract: A distributed acoustic sensing (DAS) system may include an optical fiber, a phase-sensitive OTDR (?-OTDR) coupled to the optical fiber, and a processor cooperating with the ?-OTDR. The processor may be configured to train a plurality of machine learning networks with DAS data from the ?-OTDR based upon different respective optimizers, select a trained machine learning network from among the plurality thereof based upon a game theoretic model, and generate an acoustic event report from the DAS data using the selected trained machine learning network.

Abstract: A distributed acoustic sensing (DAS) system may include an optical fiber, a phase-sensitive OTDR (?-OTDR) coupled to the optical fiber, and a processor cooperating with the ?-OTDR. The processor may be configured to generate a series of covariance matrices for DAS data from the ?-OTDR, and determine an acoustic event based upon comparing the series of covariance matrices with a corresponding Toeplitz matrix.

Abstract: A distributed acoustic sensing (DAS) system may include an optical fiber, a phase-sensitive OTDR (?-OTDR) coupled to the optical fiber, and a processor cooperating with the ?-OTDR. The processor may be configured to generate a series of covariance matrices for DAS data from the ?-OTDR, determine acoustic events based upon the covariance matrices and a machine learning network, and generate an acoustic event report from the acoustic events.

Abstract: A fish hook remover, designed to minimize damage to an artificial fly and to minimize and reduce human contact with a fish during fish hook removal, comprises an elongated body, designed of wire which is controlled by a handle of cork or other floating material, which allows the angler to hold and control the wire or tubular stem, which terminates with a hook removing extraction loop. The extraction loop terminates in the form of a guiding flange. This fish hook remover is designed to allow the angler to slide the fish line, which has a fish attached to a hook on the end of the line, into a loop which can be guided down the line to the shank of the hook. Once the hook shank is engaged the angler can lift the fish hook remover and the fish is released.

Abstract: An optical channel switching element routes optical signals between optical waveguides or optical fibers by creating a transmit or reflect state in a polymer waveguide crossing junction by utilizing Total Internal Reflection (TIR) off a trench placed between the waveguide paths. The presence of a fluid or gel (usually selected to match the waveguide refractive index) within the trench creates a transmit state (no light is reflected) allowing light to continue along the initial waveguide path. The lack of index matching fluid at the waveguide junction, or presence of an air bubble, creates a TIR state (all light is reflected and none is transmitted) causing the light to reflect off of the trench and continue along the path of the crossing guide.