A system for object detection within a monitored zone around a vehicle includes: at least one processor; and memory encoding instructions which, when executed by the processor, cause the system to: detect an object; and suppress an alert associated with detection of the object when a size of the detected object is below a lower size threshold value or above an upper size threshold value.

In a general aspect, motion is detected using wireless signals. In an example, a method includes receiving first motion detection output data representing a degree of motion detected by a wireless sensing system based on wireless signals exchanged in a wireless communication network. The wireless communication network includes a first access point device and a first client device communicatively coupled by a first wireless communication link. The method also includes receiving network information indicative of a network topology of the wireless communication network, and generating a first control signal configured to change a characteristic of the first wireless communication link based on the first motion detection output data and network information. The method further includes receiving second motion detection output data representing a degree of motion detected by the wireless sensing system after the characteristic of the first wireless communication link has changed in response to the first control signal.

A unified presence detection and prediction platform that is privacy aware is described. The platform is receives signals from plural sensor devices that are disposed within a premises. The platform produces profiles of entities based on detected characteristics developed from relatively inexpensive and privacy-aware sensors, i.e., non-video and non-audio sensor devices. The platform using these profiles and sensor signals from relatively inexpensive and privacy-aware sensors determines specific identification and produces historical patterns. Also described are techniques that allow users (persons), when authorized, to control remote devices/systems generally without direct interaction with such systems merely by the systems detecting and in instances predicting the specific presence of an identified individual in a location within the premises.

A system, including: a radar including a transmitter and a receiver, the transmitter configured to transmit an electromagnetic wave; a reflector placed a predetermined distance away from the radar, the reflector configured to reflect the transmitted electromagnetic wave back to the receiver; and a control unit coupled to the radar and configured to measure a magnitude of the reflected electromagnetic wave received at the receiver from the reflector by measuring Doppler signal corresponding to the predetermined distance, wherein the control unit is configured to measure a reference value of the magnitude of the reflected electromagnetic wave when no intrusions are occurring, and wherein the control unit is configured to trigger an alarm when the magnitude of the reflected electromagnetic wave changes from the reference value.

A vehicle (1) security system and method that comprises at least one controlled magnetic field sensor (10) connected to at least one antenna (11); where this antenna (11) is configured like a single electrode, in such a way that the controlled field sensor (10) measures the disturbances of the electrostatic field around the antenna (11); and where the controlled electrostatic fields (10) is configured to detect a disturbance in the electrostatic field (3), created by its own antenna (11); and where at least one antenna (11) is in a position between an area external to the vehicle (1); an area internal to the vehicle (1); and a parking area and where each sensor (10) is connected to a control device (100).

A magnetometer sensor that detects tampering is described. The sensor samples magnetometer data and computes a set of vectors based on the magnetometer data. The sensor detects a tampering of the magnetic sensor based on determining that the set of vectors is outside the reference plane for the magnetometer sensor. The sensor generates a notification to a security system coupled to the magnetometer sensor. The notification indicating the tampering of the magnetometer sensor.

In a general aspect, motion is detected using wireless signals. In one example, beamforming dynamic information is obtained based on a set of wireless signals transmitted through a space from a first wireless communication device to a second wireless communication device. Motion of an object in the space is detected based on the beamforming dynamic information.

The present disclosure provides an early warning method and an early warning device based on WIFI signal strength disturbance, and a storage medium. The early warning method includes receiving and analyzing WIFI signals in a monitoring area in real time to obtain amplitude and phase of a current WIFI signal in the monitoring area; determining whether the amplitude and the phase of the current WIFI signal are disturbed according to a difference between the amplitude and the phase of the current WIFI signal and those of a previous WIFI signal; and sending out an early warning signal when it is determined that the amplitude and the phase of the current WIFI signal are disturbed.

The present invention relates the operation and handling of incidents, for example in relation to alarms and events generated in relation to a security system by a plurality of surveillance devices. The invention also relates to a corresponding incident handling method and a computer program product.

Described are techniques for determining a quotation score that would be applicable across lines of insurance and/or carriers, and which involves the collection in real time of sensor information from plural groups of sensors deployed in a specific premises and associated with intrusion detection, access control, burglar, fire alarm systems and surveillance systems and/or other systems that monitor for physical/chemical/biological conditions. The techniques execute unsupervised learning models to continually analyze the collected sensor information to produce sequences of state transitions that are assign scores and from which a quotation score is produced.