Abstract: Systems and methods provide a HybridOps model for the identification, capture, isolation, feature engineering and adjudication of source signal data signatures for inclusion in calibration quality standard reference signal data signature libraries that improve machine learning and validation, reduces model bias and reduces model drift. The HybridOps model may include an “unlocked” AI/ML (machine learning enabled) public facing deployment pipeline in parallel with a clone AI/ML deployed in an internal development environment using a ML-Ops pipeline and in parallel with a clone “locked” AI/ML (machine learning disabled) as a standard reference. The three deployed models enables monitoring and measuring model drift, context drift and product progression for improved verification and validation of model reliability.

Abstract: Systems and methods of the present disclosure enable authentication and/or anomaly detection using machine learning-based modelling. Audio recordings that represent audio from a forced cough vocalizations are received from a user device. One or more audio filters extract forced cough vocalization recordings from the audio recordings and signal data signatures representative of the forced cough vocalization recordings are generated. Gaussian mixture models are produced for each unique combination of the signal data signatures, where each unique combination include a group of model baselines and a test match baseline. Each Gaussian mixture model is used to produce a match value for the associated test match baseline based on the associated model baselines, and a statistical score is determined for each match value. One or more baseline Gaussian mixture models are determined based on the statistical score and stored in a user profile.

Abstract: Systems and methods of the present disclosure enable signal data signature detection using a memory unit and processor, where the memory using stores a computer program or computer programs created by the physical interface on a temporary basis. The computer program, when executed, cause the processor to perform steps to receive a signal data signature recording from at least one data source, receive a dataset of labeled signal data signature recordings including signal data signature recording labels, identify, using at least one machine learning model, boundaries within the dataset of labeled signal data signature recordings, classify the signal data signature recording to produce an output label using a compendium of signal data signature classifiers based on the boundaries within the dataset of labeled signal data signature recordings, determine an output type of the signal data signature recording, and display the output label on a display media.

Abstract: Examples herein describe systems and methods for dynamic building ventilation control. A control system can include a controller that receives information from the indoor and outdoor sensor packages. The control system can compare indoor and outdoor air quality using information from the indoor and outdoor sensor packages. The controller can also determine a current operational state of a heating, ventilation, and air conditioning (“HVAC”) system, such as heating, cooling, and fan status. Based on the air quality comparison and current operational state, the controller can open a first damper and closing a second damper to change ventilation of a building.

Abstract: A dynamic misting control system can supply water to a condenser coil in varying degrees. The control system can include a controller, flow regulator (e.g., pump or separate), and mist generator (e.g., spray nozzles or otherwise). The controller can read outdoor humidity and temperature and vary power to the pump accordingly. The controller can also compare temperatures of the condenser coil to the outdoor humidity and temperature in adjusting the voltage. This can include calculating a voltage based on a first temperature of a coolant supply line and a second temperature in a return line and comparing those temperatures to the outside temperature and humidity.