Systems, methods and apparatus are disclosed for automatic signal detection in an RF environment. An apparatus comprises at least one receiver and at least one processor coupled with at least one memory. The apparatus is at the edge of a communication network. The apparatus sweeps and learns the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The apparatus forms a knowledge map based on the learning data, scrubs a real-time spectral sweep against the knowledge map, and creates impressions on the RF environment based on a machine learning algorithm. The apparatus is operable to detect at least one signal in the RF environment.

Systems, methods and apparatus are disclosed for automatic signal detection in an RF environment. An apparatus comprises at least one receiver and at least one processor coupled with at least one memory. The apparatus is at the edge of a communication network. The apparatus sweeps and learns the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. The apparatus forms a knowledge map based on the learning data, scrubs a real-time spectral sweep against the knowledge map, and creates impressions on the RF environment based on a machine learning algorithm. The apparatus is operable to detect at least one signal in the RF environment.

Provided is a rapid over-the-air (OTA) production line test platform, including a device under test (DUT), an antenna array and two reflecting plates. The DUT has a beamforming function. The antenna array is arranged opposite to the DUT, and emits beams with beamforming. Two reflecting plates are disposed opposite to each other, and are arranged between the DUT and the antenna array. The beam OTA test of the DUT is carried out by propagation of the beams between the antenna array, the DUT and the two reflecting plates. Accordingly, the test time can be greatly shortened and the cost of test can be effectively reduced. In addition to the above-mentioned rapid OTA production line test platform, platforms for performing the OTA production line test by using horn antenna arrays together with bending waveguides and using a 3D elliptic curve are also provided.

An apparatus, methods and systems for data collection in an industrial environment are disclosed. A monitoring system can include a data collector coupled to a plurality of sensors to collect data, a data storage structured to store a plurality of data collection management plans, a data acquisition circuit structured to interpret a plurality of detection values from the collected data, and a data analysis circuit structured to analyze the collected data and select one of the plurality of data collection management plans, wherein the selected one of the plurality of data collection management plans is selected is at least in part based on a data analysis of received data from the plurality of sensors.

A method and arrangement for testing wireless connections is provided. The method comprises obtaining (500) a three-dimensional model of a given environment; obtaining (502) ray tracing calculations describing propagation of radio frequency signals in the given environment; locating (504) one or more devices in the given environment; determining (506) utilising ray tracing calculations the radio frequency signal properties of one or more devices communicating with the device under test; transmitting (508) control information to the radio frequency controller unit for updating the connections between one or more devices and a set of antenna elements to match with the determined properties; obtaining (510) information on the location and propagation environment of the one or more devices and updating (512) the radio frequency signal properties of the one or more devices if the location or propagation environment changes.

A method and arrangement for testing wireless connections is provided. The method comprises obtaining (500) a three-dimensional model of a given environment; obtaining (502) ray tracing calculations describing propagation of radio frequency signals in the given environment; locating (504) one or more devices in the given environment; determining (506) utilising ray tracing calculations the radio frequency signal properties of one or more devices communicating with the device under test; transmitting (508) control information to the radio frequency controller unit for updating the connections between one or more devices and a set of antenna elements to match with the determined properties; obtaining (510) information on the location and propagation environment of the one or more devices and updating (512) the radio frequency signal properties of the one or more devices if the location or propagation environment changes.

A test monitor extracts waveforms from a differential transmission line of an automobile network without disrupting the differential transmission line, and stores the data decoded from the extracted waveforms. The test monitor includes a first input configured to receive a voltage waveform from a voltage probe electrically coupled to the differential transmission line that electrically connects a first ECU device and a second device, a second input configured to receive a current waveform from a current probe electrically coupled to the differential transmission line, and one or more processors configured to receive the voltage waveform and the current waveform and determine a voltage of the first ECU device and a voltage of the second device based on the voltage waveform and the current waveform. The test monitor may be embodied in an FPGA. The test monitor enables monitoring of message transfers across a network in a non-intrusive and non-invasive manner, without the necessity of using a repeater or switch.

A test monitor extracts waveforms from a differential transmission line of an automobile network without disrupting the differential transmission line, and stores the data decoded from the extracted waveforms. The test monitor includes a first input configured to receive a voltage waveform from a voltage probe electrically coupled to the differential transmission line that electrically connects a first ECU device and a second device, a second input configured to receive a current waveform from a current probe electrically coupled to the differential transmission line, and one or more processors configured to receive the voltage waveform and the current waveform and determine a voltage of the first ECU device and a voltage of the second device based on the voltage waveform and the current waveform. The test monitor may be embodied in an FPGA. The test monitor enables monitoring of message transfers across a network in a non-intrusive and non-invasive manner, without the necessity of using a repeater or switch.

A platform for facilitating development of intelligence in an Industrial Internet of Things (IIoT) system generally includes a plurality of distinct data-handling layers comprising an industrial monitoring systems layer that collects data from or about a plurality of industrial entities in the IIoT system; an industrial entity-oriented data storage systems layer that stores the data collected by the industrial monitoring systems layer; an adaptive intelligent systems layer that provisions available computing resources within the platform; and an industrial management application platform layer that manages the platform in a common application environment.

Artificial Intelligence (AI) can rapidly evaluate a faulted message in 5G or 6G, calculate a likelihood that each message element is faulted, and optionally suggest a most probable corrected version for each of the likely faulted message elements. To do so, the AI takes in numerous factors besides the message itself, such as the modulation quality of each message element, the proximity and quality of a nearest demodulation reference, a signal-to-noise ratio of the message element, a measure of current electromagnetic noise during the message element, an expected format or expected codewords based on prior messages or convention, and other factors. The AI model can then provide guidance as to mitigation, such as choosing whether to request a retransmission or attempting to vary the likely faulted message elements. The AI model can be adapted to fixed-site computers or to the more limited computers of a mobile user device.