Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing deep learning to intelligently determine compression settings for compressing a digital image. For instance, the disclosed system utilizes a neural network to generate predicted perceptual quality values for compression settings on a compression quality scale. The disclosed system fits the predicted compression distortions to a perceptual distortion characteristic curve for interpolating predicted perceptual quality values across the compression settings on the compression quality scale. Additionally, the disclosed system then performs a search over the predicted perceptual quality values for the compression settings along the compression quality scale to select a compression setting based on a perceptual quality threshold. The disclosed system generates a compressed digital image according to compression parameters for the selected compression setting.

Abstract: In an embodiment, a system for detection of trains at railroad crossings is provided. The system comprises a field-deployed detection and reporting device comprising a microphone, a communication module, a microprocessor, and an application. When executed on the microprocessor, the application receives data describing sounds captured by the microphone and identifies frequencies of a first received sound. The application also transmits, based on the identified frequencies and a formula, a first message via the communication module. The first received sound is generated by a warning bell sounded at the railroad crossing. The first message is received by a backend server that issues a first broadcast based on receipt of the first message. The application further determines that the first received sound discontinues. Based on the determination, the application sends a second message via the module to the backend server which issues a second broadcast that the crossing is reopened.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing deep learning to intelligently determine compression settings for compressing a digital image. For instance, the disclosed system utilizes a neural network to generate predicted perceptual quality values for compression settings on a compression quality scale. The disclosed system fits the predicted compression distortions to a perceptual distortion characteristic curve for interpolating predicted perceptual quality values across the compression settings on the compression quality scale. Additionally, the disclosed system then performs a search over the predicted perceptual quality values for the compression settings along the compression quality scale to select a compression setting based on a perceptual quality threshold. The disclosed system generates a compressed digital image according to compression parameters for the selected compression setting.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing deep learning to intelligently determine compression settings for compressing a digital image. For instance, the disclosed system utilizes a neural network to generate predicted perceptual quality values for compression settings on a compression quality scale. The disclosed system fits the predicted compression distortions to a perceptual distortion characteristic curve for interpolating predicted perceptual quality values across the compression settings on the compression quality scale. Additionally, the disclosed system then performs a search over the predicted perceptual quality values for the compression settings along the compression quality scale to select a compression setting based on a perceptual quality threshold. The disclosed system generates a compressed digital image according to compression parameters for the selected compression setting.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing deep learning to intelligently determine compression settings for compressing a digital image. For instance, the disclosed system utilizes a neural network to generate predicted perceptual quality values for compression settings on a compression quality scale. The disclosed system fits the predicted compression distortions to a perceptual distortion characteristic curve for interpolating predicted perceptual quality values across the compression settings on the compression quality scale. Additionally, the disclosed system then performs a search over the predicted perceptual quality values for the compression settings along the compression quality scale to select a compression setting based on a perceptual quality threshold. The disclosed system generates a compressed digital image according to compression parameters for the selected compression setting.

Abstract: A system, computer-readable medium, and a method including receiving flight data engine measurements for at least one engine of the specific aircraft asset; receiving flight data aerodynamics measurements for the specific aircraft asset; combining a physics based parametric aerodynamic performance model tuned for the specific aircraft asset using the flight data aerodynamics measurements and a physics based engine model tuned for the specific aircraft asset using the flight data engine measurements; calculating, based on the combined tuned aerodynamic performance model and the tuned engine model, a performance model for the specific aircraft asset as a whole; and storing a record of the calculated performance model for the specific aircraft asset for a future deployment.

Abstract: A system, computer-readable medium, and a method including receiving flight data engine measurements for at least one engine of the specific aircraft asset; receiving flight data aerodynamics measurements for the specific aircraft asset; combining a physics based parametric aerodynamic performance model tuned for the specific aircraft asset using the flight data aerodynamics measurements and a physics based engine model tuned for the specific aircraft asset using the flight data engine measurements; calculating, based on the combined tuned aerodynamic performance model and the tuned engine model, a performance model for the specific aircraft asset as a whole; and storing a record of the calculated performance model for the specific aircraft asset for a future deployment.