The invention relates to a method for asseverating video images, photos, audio-video and/or data in general, acquired from different sources (10, 11, 12, 13), wherein the images are first validated to verify the absence of tampering and then subjected to a step of certifying, in which they are encrypted.

The encrypted images are accessible only to authorised and identified users, who are provided with the decryption code. The invention also comprises a system for implementing the method.

A vehicle includes receiving signals from a plurality of satellites; obtaining position information based on the received signal; detecting a driving speed and yaw rate; obtaining dead reckoning information based on position information about a position of a vehicle recognized in a previous cycle and the received detection information; predicting the position information based on the obtained dead reckoning information; obtaining a value of Euclidean distance based on the position information about the position of the vehicle recognized in the previous cycle and the obtained position information; generating a first outlier filter based on the value of the Euclidean distance; obtaining a value of Mahalanobis distance based on the obtained position information and the predicted position information; generating a second outlier filter based on the value of the Mahalanobis distance; recognizing a current position of the vehicle by fusing information passing through the first outlier filter and information passing through the second outlier filter; and outputting information about the current position of the recognized vehicle as an image or a sound.

A method of optimizing rider satisfaction includes classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as indicative of an effect on a transportation system. The method further includes predicting, using a second neural network of the hybrid neural network, at least one aspect of rider satisfaction affected by an effect on the transportation system derived from the social media data classified as indicative of an effect on the transportation system. The method still further includes optimizing, using a third neural network of the hybrid neural network, the at least one aspect of rider satisfaction for at least one rider occupying a vehicle in the transportation system.

A method for generating a message including vehicle operating parameters. The method includes the following: measuring operating parameters of an unequipped vehicle using monitoring equipment present on an equipped vehicle that is proximate to the unequipped vehicle; generating at the equipped vehicle a message including the measured operating parameters of the unequipped vehicle; and transmitting from the equipped vehicle to surrounding vehicles the message including the measured operating parameters of the unequipped vehicle to inform surrounding vehicles of the operating parameters of the unequipped vehicle.

Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.

A medical analyzer and coagulation profiler performs various interrogations on specimens. A motor with reduction gearing moves and a video camera observes the samples, the cartridges or parts thereof. Changes in images are compared and recorded with a central processor that controls a display. Power supply, temperature controller, motor and gearing are mounted in a box which attaches to a smartphone. The smartphone provides the video camera, illumination and central processor that control the movement, temperature and display. The device makes testing simpler for small hospitals, clinics, ambulances, remote locations and individuals and controls a number of parallel or serial devices operating simultaneously or sequentially. A cartridge insertion actuates a circular motion to generate a blood profile based on changes. Change is analyzed with a video camera and processor such as in a smartphone and is plotted to show an amplitude and time. A smartphone provides a specific movement pattern.

Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.

An example server device of this disclosure includes a communication unit and processing circuitry. The processing circuitry is configured to receive, via the communication unit, from one or more supplier-side modalities, one or more vehicle resource availability indicators, to receive, from one or more client devices, one or more vehicle resource usage requests, to match, each of the one or more vehicle resource usage requests to a subset of the vehicle resource indicators, and to send, via the communication unit, one or more match communications to the respective client device(s) and the supplier-side modalities from which the vehicle resource indicators are received.

A maneuver classification platform may obtain video data and telematic data that are associated with a driving event involving a vehicle. The maneuver classification platform may analyze the video data to identify a path of the vehicle along a roadway during the driving event, and based on markings of a plurality of roadways. The maneuver classification platform may analyze the video data to determine a type of a vehicle maneuver performed by the vehicle during the driving event. The maneuver classification platform may determine a maneuver score of the driving event based on the type of the vehicle maneuver, the path of the vehicle, and the telematic data. The maneuver classification platform may send a message associated with the maneuver score and a vehicle identifier of the vehicle to a client device to permit the client device to use the maneuver score.

An video analysis computing system driving analysis may include a camera associated with a first vehicle and having a viewing angle capable of capturing a video of one or more other vehicles in proximity to the first vehicle, a first memory location communicatively coupled to the camera, wherein the first memory location stores video data captured by the camera, and an evaluation module including a processor executing instructions that cause the processor to: evaluate the captured video stored in the first memory location to determine whether a driving event performed by a second vehicle has occurred, assign, in response to an identified driving event performed by the second vehicle, a driving event rating to a video showing the identified driving event, wherein the driving event rating may be calculated, at least in part, using a crowd-sourced driving event rating obtained after posting the video to a social network.