A method of confirming the identity of a person issued a token and to track that person Possession of the token by the same person is confirmed by using sensors in the token which track the movements of the person. A machine learning system is trained to evaluate the sensor data detecting transfer of possession of the token. The state of continuous possession since the token was issued or set to an enabled state is confirmed and the token is tracked. The method of identity confirmation is used in various contexts such as for to control entry to a location, use of a facility or service. It is also useful to determine continuous possession of a weapon to prevent misuse after the weapon is stolen, dropped or lost. Servers, beacons and outside sources of data or inputs to be measured by the sensor can also be used.

The described payment device includes an integrated power source including a piezoelectric accelerometer. Upon motion of the payment device, kinetic energy may be translated to a mass of the piezoelectric accelerometer to generate electrical energy to power other functions of the payment device. Motion translated to the piezoelectric accelerometer may also be used in combination with other inputs to implement security functions of the payment device.

A system and method for counting persons using passages entering or exiting an area by analyzing vibrations in the floor with sensors and a machine learning system. The machine learning system uses a model, usually implemented as a neural network on a processor. The network is trained in levels and implemented in layers. Different levels classify and analyze vibrations by timing and frequency, by movements of persons, and by identity of persons The same person is identified by patterns in the vibrations and the vibrations are correlated to determine when a person uses a combination of passages and is thereby counted. Location information for the person is used to identify persons in places and doing activities of interest. The model may be trained on one processor and then downloaded to another processor for evaluation. Additional sensors and levels of training may be implemented on the latter processor.

A tracking system comprising a tag reader comprising an interrogating antenna. One or more tags comprising an electrical energy generator configured to convert environmental energy to electrical energy. A radio frequency, RF, communication circuit. A controller configured to use the electrical energy generated by the electrical energy generator to transmit a data signal to the tag reader using the RF communication circuit.

A method of confirming the identity of a person who issued a token to signify eligibility for a privilege. Possession token is confirmed to be by the same person by using sensors in the token which track the movements of the person. A machine learning system is trained to evaluate the sensor data detecting transfer of possession of the token. The state of continuous possession since the token was issued or set to an enabled state is confirmed and the privilege is granted. The method of identity confirmation is used in various contexts such as for to control entry to a location, use of a facility or service. It is also useful to determine continuous possession of a weapon to prevent misuse after the weapon is stolen, dropped or lost. Servers, beacons and outside sources of data or inputs to be measured by the sensor can also be used.

A system and method for counting persons using passages entering or exiting an area by analyzing vibrations in the floor with sensors and a machine learning system. The machine learning system uses a model, usually implemented as a neural network on a processor. The network is trained in levels and implemented in layers. Different levels classify and analyze vibrations by timing and frequency, by movements of persons, and by identity of persons The same person is identified by patterns in the vibrations and the vibrations are correlated to determine when a person uses a combination of passages and is thereby counted. Location information for the person is used to identify persons in places and doing activities of interest. The model may be trained on one processor and then downloaded to another processor for evaluation. Additional sensors and levels of training may be implemented on the latter processor.

A hybrid document includes a document having visible markings, one or more light-controlling elements embedded in or on the document, and a controller including a circuit having a non-volatile memory. The controller is embedded in or on the document and electrically connected to the one or more light-controlling elements for controlling the one or more light-controlling elements. A power input connection is electrically connected to any one or all of the controller, the circuit, the memory, or the one or more light-emitting elements. The memory stores a state and the circuit causes the one or more light-controlling elements to indicate the state. A hybrid document validation machine is adapted to accept one or more of the hybrid documents, change the state of the hybrid documents, and optionally display the state on a display.

A system and method for counting persons using passages entering or exiting an area by analyzing vibrations in the floor with sensors and a machine learning system. The machine learning system uses a model, usually implemented as a neural network on a processor. The network is trained in levels and implemented in layers. Different levels classify and analyze vibrations by timing and frequency, by movements of persons, and by identity of persons The same person is identified by patterns in the vibrations and the vibrations are correlated to determine when a person uses a combination of passages and is thereby counted. Location information for the person is used to identify persons in places and doing activities of interest. The model may be trained on one processor and then downloaded to another processor for evaluation. Additional sensors and levels of training may be implemented on the latter processor.

A system and method for counting persons using passages entering or exiting an area by analyzing vibrations in the floor with sensors and a machine learning system. The machine learning system uses a model, usually implemented as a neural network on a processor. The network is trained in levels and implemented in layers. Different levels classify and analyze vibrations by timing and frequency, by movements of persons, and by identity of persons The same person is identified by patterns in the vibrations and the vibrations are correlated to determine when a person uses a combination of passages and is thereby counted. Location information for the person is used to identify persons in places and doing activities of interest. The model may be trained on one processor and then downloaded to another processor for evaluation. Additional sensors and levels of training may be implemented on the latter processor.

A method of confirming the identity of a person who issued a token to signify eligibility for a privilege. Possession token is confirmed to be by the same person by using sensors in the token which track the movements of the person. A machine learning system is trained to evaluate the sensor data detecting transfer of possession of the token. The state of continuous possession since the token was issued or set to an enabled state is confirmed and the privilege is granted. The method of identity confirmation is used in various contexts such as for to control entry to a location, use of a facility or service. It is also useful to determine continuous possession of a weapon to prevent misuse after the weapon is stolen, dropped or lost. Servers, beacons and outside sources of data or inputs to be measured by the sensor can also be used.