A radio for determining the occupancy of a variable width channel for secondary use, the variable width channel with a bandwidth equal to an integer multiple of the bandwidth of a plurality of narrowband channels in an RF frequency band. The radio decides whether the variable width channel in the RF band is occupied by whether classifying each of the narrowband channels corresponding to the variable width channel and each of a plurality of sub-bands with bandwidths of narrowband channels but center frequencies shifted by one-half of the bandwidth of the narrowband channel and overlap with the bandwidth of the variable width channel as containing signal or noise based on measured energy levels.

One or more computing devices, systems, and/or methods for system parameter identification and network component configuration are provided. A state comprising a system parameter combination, a traffic model, and a channel assignment may be generated. A network traffic scenario is executed through a virtualized testbed using the state. A reward for the system parameter combination may be generated based upon key performance indicators output by the network traffic scenario. A reward policy and rewards generated for system parameter combinations are used to select a system parameter combination that is used to configure a network component of a communication network.

One or more computing devices, systems, and/or methods for system parameter identification and network component configuration are provided. A state comprising a system parameter combination, a traffic model, and a channel assignment may be generated. A network traffic scenario is executed through a virtualized testbed using the state. A reward for the system parameter combination may be generated based upon key performance indicators output by the network traffic scenario. A reward policy and rewards generated for system parameter combinations are used to select a system parameter combination that is used to configure a network component of a communication network.

Certain aspects of the present disclosure provide techniques for adaptively tuning a wireless data transmission system in an electronic device, including: generating a plurality of measurements of an element of the wireless data transmission system in the electronic device; generating a determined use case for the electronic device based on a use case determination model and the plurality of measurements; determining one or more antenna settings associated with the determined use case; tuning the wireless data transmission system based on the one or more antenna settings; and transmitting data via the wireless data transmission system using the one or more antenna settings.

Certain aspects of the present disclosure provide techniques for adaptively tuning a wireless data transmission system in an electronic device, including: generating a plurality of measurements of an element of the wireless data transmission system in the electronic device; generating a determined use case for the electronic device based on a use case determination model and the plurality of measurements; determining one or more antenna settings associated with the determined use case; tuning the wireless data transmission system based on the one or more antenna settings; and transmitting data via the wireless data transmission system using the one or more antenna settings.

A method and apparatus for monitoring network performance in near real-time by making measurements on packets received at an intermediate node in a wireless communication network. The solution is useful for any packetized communication network that connects a client and application server, and particularly for any application running over TCP/IP protocol. A method is disclosed for measuring end-to-end qualities of a packet-based communication session between a data sender (DS) and a data receiver (DR) at an intermediate node. The measured end-to-end communication qualities may include latency and packet delay variation.

A computer-implemented method and apparatus for monitoring the performance of a plurality of network nodes interconnected in a multi-hop arrangement using at least one node performance assessment threshold is described. A plurality of data sets is obtained. A data set comprises a respective value of a performance metric for each of the plurality of network nodes. Each of the plurality of data sets is classified as normal or abnormal by comparing the respective values of the performance metric of each of the plurality of network nodes to a corresponding normality threshold, thus providing a plurality of classified data sets. The plurality of classified data sets is processed using a machine-learning algorithm in order to derive, for at least one network node of the plurality of network nodes, a node performance assessment threshold indicative of a value of the performance metric of the at least one node at which the plurality of network nodes has a predetermined likelihood of being classified as normal.

Implementations relate to selection of a physics-specific model for determination of characteristics of radio frequency signal propagation. In some implementations, a method includes receiving a plurality of first propagation characteristics of a radio frequency (RF) signal, determining a feature vector based on the first propagation characteristics, inputting the feature vector to a machine-learning meta-model, and executing the machine learning meta-model to select a particular physics-specific model from multiple physics-specific models, where each of the physics-specific models is for a different RF signal propagation environment. The feature vector is input to the particular physics-specific model, and the particular physics-specific model is executed to output an estimate of one or more second propagation characteristics of the RF signal based on the feature vector.

Implementations relate to selection of a physics-specific model for determination of characteristics of radio frequency signal propagation. In some implementations, a method includes receiving a plurality of first propagation characteristics of a radio frequency (RF) signal, determining a feature vector based on the first propagation characteristics, inputting the feature vector to a machine-learning meta-model, and executing the machine learning meta-model to select a particular physics-specific model from multiple physics-specific models, where each of the physics-specific models is for a different RF signal propagation environment. The feature vector is input to the particular physics-specific model, and the particular physics-specific model is executed to output an estimate of one or more second propagation characteristics of the RF signal based on the feature vector.

A non-destructive testing and inspection (NDT/NDI) system having a communication gateway removably secured to an existing NDT/NDI instrument by a clip-on clasp. The gateway comprises a processor, a network interface, a housing configured to be removably secured onto the NDT/NDI instrument, an interconnect configured to be communicatively coupled with and to carry data to or from the NDT/NDI instrument, and memory storing computer readable code which, when executed on the processor, causes the processor to communicate data with an external network over the network interface. The data includes inspection data which can be any one of the following: ultrasonic scans of A, B, C or S, eddy current strip charts.