Custom alerts may be generated based on sound type indicators determined using a machine learning classification model trained on user-provided sound recordings and user-defined sound type indicators. A device may provide a sound recording and a type indicator identifying an entity that made a sound in the recording for storage in a database that includes a plurality sound recordings associated with a plurality of type indicators. A machine learning classification model may be trained based on the stored recordings, including the user-defined recordings. The model may be used to classify sounds recorded by other devices and generate alerts identifying the type of sound. Thus, multiple users may contribute data to customize machine learning models that recognize sounds and generate alerts based on user-defined identifiers.

Systems and methods are disclosed for preventing tampering of a programmable integrated circuit device. Generally, programmable devices, such as FPGAs, have two stages of operation; a configuration stage and a user mode stage. To prevent tampering and/or reverse engineering of a programmable device, various anti-tampering techniques may be employed during either stage of operation to disable the device and/or erase sensitive information stored on the device once tampering is suspected. One type of tampering involves bombarding the device with a number of false configuration attempts in order to decipher encrypted data. By utilizing a dirty bit and a sticky error counter, the device can keep track of the number of failed configuration attempts that have occurred and initiate anti-tampering operations when tampering is suspected while the device is still in the configuration stage of operation.

A system (e.g., a monitoring device) for monitoring a first battery (e.g., a vehicle battery) provided in a vehicle is provided with a monitoring unit for monitoring the electric state of the first battery (e.g., the vehicle battery) capable of starting a motor of the vehicle; and a power supply unit that is a second battery for supplying electric power to the monitoring unit. The monitoring unit may determine whether or not the motor is stopped, and when the motor is stopped, the monitoring unit may monitor, as the electric state of the first battery, e.g., the voltage of the first battery.

A method uses telemetry data based on user habit information or user monitoring. User information is acquired from one or more sensors of a monitoring device. The user information is selected from of at least one of, a user's activities, behaviors and habit information. Signals are routed through ID circuitry at the user monitoring device. User information is communicated between the monitoring device and a telemetry system. A database of user ID's is accessed at the telemetry system. The telemetry system analyzes telemetry data based on at least one of, user's activities, behaviors and habit information, user condition, and user parameter, to create personalized information about the user. One or more contexts of a user activity are associated with an activity manager. The activity manager is a standalone device, included with the telemetry system or included with the monitoring device.

A charge amount display apparatus of an electric vehicle, which enables a driver to be easily aware that a charge amount has been set at a value less than a full charge, is provided. The charge amount display apparatus comprises a control unit 11 for setting a charge limit value which serves as the upper limit of the charge amount of a battery 2; and a display unit 13 for displaying a maximum charge amount, to which the battery 2 can be charged, and the charge limit value. The maximum charge amount is displayed by a segment group 30 composed of a plurality of segments 31 arranged side by side in one direction for representing the charge amount of the battery 2. The segments 31a for a value equal to or lower than the charge limit value, and the segments 31b for a value higher than the charge limit value are displayed in different colors. The segments for a charge amount equal to or lower than the charge limit value are displayed in a first region 21a of a first display region 21 of the display unit 13. A mark 32 indicating that the charge limit value has been set is displayed between the first region 21a and a second region 21b.

Some embodiments are directed to a lockdown apparatus for facilitating initiation of lockdown procedures at a facility that includes multiple doors and is configured for occupation by facility occupants. A lockdown actuator is disposed at the facility and configured to initiate transmission of a lockdown initiation signal upon being actuated, the lockdown actuator being configured to be recognizably distinguishable from the fire alarm actuator. A lockdown communicator is configured to produce a lockdown initiation communication for communicating initiation of lockdown procedures to the facility occupants and individuals not disposed proximate the facility upon transmission of the lockdown initiation signal, the lockdown initiation communication being recognizably distinguishable from the fire alarm communication. A display is configured to indicate status of each of the multiple doors of the facility upon transmission of the lockdown initiation signal, the status including at least one of open versus closed, and locked versus unlocked.

A method of detecting when a first mobile device is left in a building including: determining that an alarm system of a building has been armed; detecting positional data of a first mobile device for a selected period of time after the alarm system has been activated; determining a location of the first mobile device after the selected period of time in response to the positional data, the location of the first mobile device being within the building; and activating an alarm in response to the location of the first mobile device being within the building.

A system and method for patient movement detection and fall monitoring to address the need to proactively monitor patients to detect abnormal movements, in-room activity, and other movements associated with providing in-room care. The system comprises an environmental model which can be used to track the position and movement of a patient and a classifier network configured to receive movement data and classify a patient's movement as normal or abnormal movement. In addition to monitoring in-room activity, the system and method create safe zones within the room to ensure patients are proactively monitor in the event of a seizure, fall, or other unintended activity. The system will record and store in-room video in a secure environment. Videos and notifications are automatically sent to designated staff as events occur.

A method and device for leak detection and localization in at least a portion of a of fluid distribution system. At each of two or more locations, a position locator determines the location of the device and a vibration sensor generates a signal indicative of vibrations detected by the vibration sensor at the location. A processor calculates a parameter of the signal indicative of an average power of the signal at the location over a predetermined time period. For each location, the processor stores in a memory the location of the device and the value of calculated parameter, and then determines a location in the fluid distribution system where the calculated parameter has a maximum value satisfying a predetermined criterion.

Disclosed is a watch-type mobile terminal that a user can wear on the user's wrist. The mobile terminal according to the present invention may comprise: a display unit configured to display information; a sensing unit configured to detect movement of the mobile terminal; a memory configured to store behavior pattern data of the user for each time slot; and a controller configured to generate the behavior pattern data on the basis of detection signals of the sensing unit, and recommend, on the basis of the behavior pattern data, a behavior that needs to be performed by the user if the time when a predetermined behavior is expected to occur is reached, or the occurrence of the predetermined behavior is not detected although the time when the predetermined behavior is expected to occur has passed.