Abstract: A web-based virtual meeting platform with echo suppression functionality that is hardware agnostic. The platform is configured in a client-server distributed architecture, wherein a plurality of user devices are connected to a server. A baseline for audio generated by a user device on the client-side of the client-server platform is established. Upon detection of a deviation from the baseline, including electro acoustic feedback and ambient noise, suppressive gain is applied to the audio input at the client-side of the client-server architecture. The suppressed audio stream is then transmitted to a cloud server and distributed to a plurality of user devices connected to the cloud service. The platform is accessible via an internet browser and is non-discriminatory as to the hardware of the device used to access the platform. Further, the platform does not distribute data in a peer-to-peer architecture.

Abstract: Systems, methods, and computer-program products for fluid analysis and monitoring are disclosed. Embodiments include a sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and data may be collected from the fluid via the sampling system. The sampling system may process and transmit the data to the analytical system. The analytical system may include a command and control system to receive and store the data in a database and compare the data to existing data for the fluid in the database to identify conditions in the fluid. The system may further include a cooling system configured to enclose at least one member of the fluid analysis system. The cooling system encloses at least one member of the fluid analysis system including the excitation system, the detection system, the fluid inlet, the sample chamber, the Raman probe, and combinations thereof.

Abstract: Methods and apparatus for monitoring fluid-dynamic drag on an object, such as a bicycle, ground vehicle, watercraft, aircraft, or portion of a wind turbine are provided. An array of sensors obtain sensor readings for example indicating: power input for propelling the object; air speed and direction relative to motion of the object; and ground speed of the object. Sensor readings may also indicate: temperature; elevation and humidity for providing a measurement of air density. Sensor readings may also indicate inclination angle and forward acceleration. Processing circuitry determines a coefficient of drag area based on the sensor readings and optionally one or more stored parameters, according to a predetermined relationship. A pitot tube based apparatus for measuring fluid speed and direction is also provided. Methods for dynamic in situ calibration of the pitot tube apparatus, and of adjusting correction factors applied to correct measurement errors of this apparatus are also provided.

Abstract: A soil sampler including an elongated barrel having an inner surface defining a piston chamber with open top and bottom ends; a plunger shaped to be slidably received in the chamber and including a piston shaped to frictionally engage the inner surface of the chamber and a plunger cap joined to the piston by a shaft; and at least one longitudinally extending rib projecting inwardly from the inner surface of the chamber and having a bottom surface spaced from the open bottom end to block the piston from moving further towards the open top end, and wherein the plunger cap has at least one notch shaped to receive the at least one rib. A method of assembling a soil sampler includes aligning a notch of a plunger cap of a plunger with a rib in a piston chamber and passing the plunger cap through the chamber.

Abstract: A soil sampler including an elongated barrel having an inner surface defining a piston chamber with open top and bottom ends; a plunger shaped to be slidably received in the chamber and including a piston shaped to frictionally engage the inner surface of the chamber and a plunger cap joined to the piston by a shaft; and at least one longitudinally extending rib projecting inwardly from the inner surface of the chamber and having a bottom surface spaced from the open bottom end to block the piston from moving further towards the open top end, and wherein the plunger cap has at least one notch shaped to receive the at least one rib. A method of assembling a soil sampler includes aligning a notch of a plunger cap of a plunger with a rib in a piston chamber and passing the plunger cap through the chamber.

Abstract: Methods and apparatus for monitoring fluid-dynamic drag on an object, such as a bicycle, ground vehicle, watercraft, aircraft, or portion of a wind turbine are provided. An array of sensors obtain sensor readings for example indicating: power input for propelling the object; air speed and direction relative to motion of the object; and ground speed of the object. Sensor readings may also indicate: temperature; elevation and humidity for providing a measurement of air density. Sensor readings may also indicate inclination angle and forward acceleration. Processing circuitry determines a coefficient of drag area based on the sensor readings and optionally one or more stored parameters, according to a predetermined relationship. A pitot tube based apparatus for measuring fluid speed and direction is also provided. Methods for dynamic in situ calibration of the pitot tube apparatus, and of adjusting correction factors applied to correct measurement errors of this apparatus are also provided.

Abstract: Systems, methods, and computer-program products for fluid analysis and monitoring are disclosed. Embodiments include a removable and replaceable sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and data may be collected from the fluid via the sampling system. The sampling system may process and transmit the data to the analytical system. The analytical system may include a command and control system to receive and store the data in a database and compare the data to existing data for the fluid in the database to identify conditions in the fluid. Fluid conditions may be determined using machine learning models that are generated from well-characterized known training data. Predicted fluid conditions may then be used to automatically implement control processes for an operating machine containing the fluid.

Abstract: Methods and apparatus for monitoring fluid-dynamic drag on an object, such as a bicycle, ground vehicle, watercraft, aircraft, or portion of a wind turbine are provided. An array of sensors obtain sensor readings for example indicating: power input for propelling the object; air speed and direction relative to motion of the object; and ground speed of the object. Sensor readings may also indicate: temperature; elevation and humidity for providing a measurement of air density. Sensor readings may also indicate inclination angle and forward acceleration. Processing circuitry determines a coefficient of drag area based on the sensor readings and optionally one or more stored parameters, according to a predetermined relationship. A pitot tube based apparatus for measuring fluid speed and direction is also provided. Methods for dynamic in situ calibration of the pitot tube apparatus, and of adjusting correction factors applied to correct measurement errors of this apparatus are also provided.

Abstract: Systems, methods, and computer-program products for fluid analysis and monitoring are disclosed. Embodiments include a removable and replaceable sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and data may be collected from the fluid via the sampling system. The sampling system may process and transmit the data to the analytical system. The analytical system may include a command and control system to receive and store the data in a database and compare the data to existing data for the fluid in the database to identify conditions in the fluid. Fluid conditions may be determined using machine learning models that are generated from well-characterized known training data. Predicted fluid conditions may then be used to automatically implement control processes for an operating machine containing the fluid.

Abstract: Systems, methods, and computer-program products for fluid analysis and monitoring are disclosed. Embodiments include a removable and replaceable sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and data may be collected from the fluid via the sampling system. The sampling system may process and transmit the data to the analytical system. The analytical system may include a command and control system to receive and store the data in a database and compare the data to existing data for the fluid in the database to identify conditions in the fluid. Fluid conditions may be determined using machine learning models that are generated from well-characterized known training data. Predicted fluid conditions may then be used to automatically implement control processes for an operating machine containing the fluid.

Abstract: Embodiments of the present disclosure provide for systems, apparatuses, and methods for real-time fluid analysis. Embodiments include a removable and replaceable sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and real-time data may be collected from the fluid via the sampling system. The sampling system may process and transmit the real-time data to the analytical system. The analytical system may include a command and control system that may receive and store the real-time data in a database and compare the real-time data to existing data for the fluid in the database to identify conditions in the fluid.

Abstract: Systems, methods, and computer-program products for fluid analysis and monitoring are disclosed. Embodiments include a removable and replaceable sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and data may be collected from the fluid via the sampling system. The sampling system may process and transmit the data to the analytical system. The analytical system may include a command and control system to receive and store the data in a database and compare the data to existing data for the fluid in the database to identify conditions in the fluid. Fluid conditions may be determined using machine learning models that are generated from well-characterized known training data. Predicted fluid conditions may then be used to automatically implement control processes for an operating machine containing the fluid.

Abstract: Systems, methods, and computer-program products for fluid analysis and monitoring are disclosed. Embodiments include a sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and data may be collected from the fluid via the sampling system. The sampling system may process and transmit the data to the analytical system. The analytical system may include a command and control system to receive and store the data in a database and compare the data to existing data for the fluid in the database to identify conditions in the fluid. The system may further include a cooling system configured to enclose at least one member of the fluid analysis system. The cooling system encloses at least one member of the fluid analysis system including the excitation system, the detection system, the fluid inlet, the sample chamber, the Raman probe, and combinations thereof.

Abstract: Methods and apparatus for monitoring fluid-dynamic drag on an object, such as a bicycle, ground vehicle, watercraft, aircraft, or portion of a wind turbine are provided. An array of sensors obtain sensor readings for example indicating: power input for propelling the object; air speed and direction relative to motion of the object; and ground speed of the object. Sensor readings may also indicate: temperature; elevation and humidity for providing a measurement of air density. Sensor readings may also indicate inclination angle and forward acceleration. Processing circuitry determines a coefficient of drag area based on the sensor readings and optionally one or more stored parameters, according to a predetermined relationship. A pitot tube based apparatus for measuring fluid speed and direction is also provided. Methods for dynamic in situ calibration of the pitot tube apparatus, and of adjusting correction factors applied to correct measurement errors of this apparatus are also provided.

Abstract: Systems, methods, and computer-program products for fluid analysis and monitoring are disclosed. Embodiments include a removable and replaceable sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and data may be collected from the fluid via the sampling system. The sampling system may process and transmit the data to the analytical system. The analytical system may include a command and control system to receive and store the data in a database and compare the data to existing data for the fluid in the database to identify conditions in the fluid. Fluid conditions may be determined using machine learning models that are generated from well-characterized known training data. Predicted fluid conditions may then be used to automatically implement control processes for an operating machine containing the fluid.

Abstract: Systems, methods, and computer-program products for fluid analysis and monitoring are disclosed. Embodiments include a removable and replaceable sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and data may be collected from the fluid via the sampling system. The sampling system may process and transmit the data to the analytical system. The analytical system may include a command and control system to receive and store the data in a database and compare the data to existing data for the fluid in the database to identify conditions in the fluid. Fluid conditions may be determined using machine learning models that are generated from well-characterized known training data. Predicted fluid conditions may then be used to automatically implement control processes for an operating machine containing the fluid.

Abstract: Embodiments of the present disclosure provide for systems, apparatuses, and methods for real-time fluid analysis. Embodiments include a removable and replaceable sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and real-time data may be collected from the fluid via the sampling system. The sampling system may process and transmit the real-time data to the analytical system. The analytical system may include a command and control system that may receive and store the real-time data in a database and compare the real-time data to existing data for the fluid in the database to identify conditions in the fluid.

Abstract: The present disclosure provides systems, apparatuses, and methods for fluid analysis. Embodiments include a removable and replaceable sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and real-time data may be collected from the fluid via the sampling system. The sampling system may process and transmit the real-time data to the analytical system. The analytical system may include a command and control system that may receive and store the real-time data in a database and compare the real-time data to existing data for the fluid in the database to identify conditions in the fluid.

Abstract: Systems, methods, and computer-program products for fluid analysis and monitoring are disclosed. Embodiments include a removable and replaceable sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and data may be collected from the fluid via the sampling system. The sampling system may process and transmit the data to the analytical system. The analytical system may include a command and control system to receive and store the data in a database and compare the data to existing data for the fluid in the database to identify conditions in the fluid. Fluid conditions may be determined using machine learning models that are generated from well-characterized known training data. Predicted fluid conditions may then be used to automatically implement control processes for an operating machine containing the fluid.

Abstract: Systems, methods, and computer-program products for fluid analysis and monitoring are disclosed. Embodiments include a removable and replaceable sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and data may be collected from the fluid via the sampling system. The sampling system may process and transmit the data to the analytical system. The analytical system may include a command and control system to receive and store the data in a database and compare the data to existing data for the fluid in the database to identify conditions in the fluid. Fluid conditions may be determined using machine learning models that are generated from well-characterized known training data. Predicted fluid conditions may then be used to automatically implement control processes for an operating machine containing the fluid.