The present application relates to the field of logistics, discloses an anti-disassembly logistics appliance and anti-disassembly method therefor, realizing the anti-disassembly alarm of a designated module under the premise of simple structure and low power consumption. An anti-disassembly logistics appliance, comprising: a confined space and an anti-disassembly module disposed in the confined space; the anti-disassembly module comprises: one or more photoelectric devices, a wireless communication unit and a determining unit; the determining unit is configured to determine whether an electrical signal output by the photoelectric device is greater than a preset threshold, and if so, triggers the wireless communication unit to transmit an alarm signal; the structure of the confined space satisfies following requirements: the confined space is an light-tight dark space after the anti-disassembly module is installed; during the process of disassembling the anti-disassembly module from the logistics appliance, the light-tightness of the confined space is at least temporarily destroyed to enable the optoelectronic device to detect light.

According to one configuration, an example surveillance system includes a sensor device, analyzer hardware, and processing hardware. During operation, the sensor device scans a monitored location and generates scan data. In one embodiment, the scan data (such as distance-based data) indicates (defines) surface textures of one or more objects present at the monitored location (such as a location of interest) based on distance measurements. The analyzer hardware analyzes the scan data and change in surface textures. The controller hardware: i) generates a communication based on the detected surface textures, and ii) transmits the communication to a remote station.

A system and method is disclosed where an operating state may be determined by selecting one or more transmitting nodes for transmitting one or more radio-frequency (RF) signals. One or more receiving nodes may receive the one or more RF signals that may include one or more channel state data. The operating state may be determined based on one or more features extracted from the one or more channel state data. Another system and method is disclosed where a range compensation value may be determined by transmitting a radio-frequency (RF) signal from at least one transmitting node. The one or more receiving nodes may receive the RF signal and a channel state data may be estimated using the signal. The range compensation value may be determined using the channel state data and a position value indicating a location of the at least one transmitting node.

A system is provided, including: a radar sensor configured to transmit and receive a radar signal from a person; a depth camera configured to receive a depth image of the person; one or more processors communicative with memory having stored thereon computer program code configured when executed by the one or more processors to cause the one or more processors to perform a method comprising: detect the person; determine depth information relating to the person using the depth image; determine a correlation between the depth information of the person and the radar signal received from the person; and in response to the correlation not within a range of expected values, generating an alert. The depth information may be a volume or surface area of the person.

A process for dynamic vehicular threat detection perimeter modification for an exited vehicular occupant includes prior to detecting a vehicular occupant exiting the vehicle, establishing a first sized vehicular geofence surrounding the vehicle as a function of one or more stored vehicular perimeter distances. The first sized vehicular geofence is monitored for a first breach via one of a 360 degree vehicular light imaging and radio wave distancing system. In response to detecting that the vehicular occupant previously inside the vehicle has exited the vehicle, the one or more stored vehicular perimeter distances is modified as a function of a detected location of the exited vehicular occupant to establish a second sized vehicular geofence surrounding the vehicle different than the first sized vehicular geofence. The second sized vehicular geofence is monitored for a second breach via one of the 360 degree vehicular light imaging and radio wave distancing system

The present disclosure provides microwave intrusion detecting device with multi-direction and multi-range fence which detects human or vehicle intrusions within altering range by transmitting microwave signals and receiving echo signals. It includes pair of detecting units, a signal unit, a processor, a control interface, an alarm unit, a storage unit, a communication unit, and a power supply unit. The pair of detection units are physically composed back-to-back which can form a long-distance electronic fence. The second detection unit can be separated from a main body to adjust the antenna coverage angle to adapt to environment or obstacles. The signal unit generates the frequency modulated continuous wave signal to the two detecting units respectively. The processor can set thresholds segments to from passing gates in microwave intrusion detecting device and can generate an alarm unit message.

Apparatuses, methods, and computer program products disclosed herein provide improved unmanned aerial vehicle (UAV) detection. A method may include receiving data including wireless signal strength collected by one or more devices, monitoring the data including the wireless signal strength to determine if the wireless signal strength of a wireless signal source increases over time in a manner to satisfy a predefined threshold in order to be indicative of a UAV, determining a trajectory of the UAV based upon the data including the wireless signal strength, and generating an alert based on, at least, the trajectory of the UAV as indicated by the data including the wireless signal strength.

A system and method for an optical displacement detector system with adjustable pattern direction are disclosed. In an embodiment, the detector system includes a mounting bracket and a sensor assembly. The sensor assembly includes a housing, and a sensor module in the housing. The sensor assembly can be rotated with respect to the mounting bracket to adjust an optical axis of the sensor module. In another embodiment, the detector system includes a turntable that enables additional adjustment of the optical axis. Such a system significantly improves installation of these detector systems as compared to existing optical displacement detector systems, which have fixed optical axes. In one application, the detector system is installed in a door system having a door and door frame, and detects displacement of the door.

According to one configuration, an example surveillance system includes a sensor device, analyzer hardware, and processing hardware. During operation, the sensor device scans a monitored location and generates scan data. In one embodiment, the scan data (such as distance-based data) indicates (defines) surface textures of one or more objects present at the monitored location (such as a location of interest) based on distance measurements. The analyzer hardware analyzes the scan data and change in surface textures. The controller hardware: i) generates a communication based on the detected surface textures, and ii) transmits the communication to a remote station.

A surveillance system for detecting an object within a monitored infrastructure and to a hybrid 3D surveying device, wherein a LiDAR device is configured that scanning is carried out with respect to two essentially orthogonal axes and wherein the LiDAR device comprises a cover mounted on the base, such that the base and the cover form an enclosure that encloses all moving parts of the LiDAR device, wherein the cover is configured to be opaque for visible light and translucent for the wavelength range of the LiDAR transmission radiation. The system further comprises a computing unit configured for processing the LiDAR measurement data to generate a 3D point cloud of the monitored infrastructure, and an object detector configured for classification of the object based on the 3D point cloud.