According to one embodiment, a condition monitoring device includes a processor. The processor is configured to acquire a time-series signal about a condition of a monitor target from a first sensor, acquire operation timing information indicating start of operation of the monitor target, detect a first operation segment signal from the time-series signal based on the operation timing information, detect a second operation segment signal from the first operation segment signal based on a waveform feature of the first operation segment signal, and determine the condition of the monitor target based on the second operation segment signal.

According to one embodiment, a condition monitoring device includes a processor. The processor is configured to acquire a time-series signal about a condition of a monitor target from a first sensor, acquire operation timing information indicating start of operation of the monitor target, detect a first operation segment signal from the time-series signal based on the operation timing information, detect a second operation segment signal from the first operation segment signal based on a waveform feature of the first operation segment signal, and determine the condition of the monitor target based on the second operation segment signal.

A cloud server including a communication interface; and at least one processor configured to: detect a plurality of IoT devices connected to the cloud server from the communication interface, based on detecting the plurality of IoT devices connected to the cloud server, determine that the plurality of IoT devices are equipped with a corresponding plurality of intelligence engines, read and store a plurality of intelligence engine types of the plurality of intelligence engines, obtain a plurality of online states of the plurality of IoT devices, receive a search instruction sent from a receiving IoT device of the plurality of IoT devices, identify an instruction type of the search instruction, and store a correspondence between the instruction type and a preferred intelligence engine type, and select an IoT device based on the correspondence.

A cloud server including a communication interface; and at least one processor configured to: detect a plurality of IoT devices connected to the cloud server from the communication interface, based on detecting the plurality of IoT devices connected to the cloud server, determine that the plurality of IoT devices are equipped with a corresponding plurality of intelligence engines, read and store a plurality of intelligence engine types of the plurality of intelligence engines, obtain a plurality of online states of the plurality of IoT devices, receive a search instruction sent from a receiving IoT device of the plurality of IoT devices, identify an instruction type of the search instruction, and store a correspondence between the instruction type and a preferred intelligence engine type, and select an IoT device based on the correspondence.

An embedded sensor can include an audio detector, a digital signal processor, a library, and a rules engine. The digital signal processor can be configured to receive signals from the audio detector and to identify the environment in which the embedded sensor is located. The library can store statistical models associated with specific environments, and the digital signal processor can be configured identify specific events based on detected sounds within the particular environment by utilizing the statistical model associated with the particular environment. The DSP can associate a probability of accuracy for the identified audible event. A rules engine can be configured to receive the probability and transmit a report of the detected audible event.

An embedded sensor can include an audio detector, a digital signal processor, a library, and a rules engine. The digital signal processor can be configured to receive signals from the audio detector and to identify the environment in which the embedded sensor is located. The library can store statistical models associated with specific environments, and the digital signal processor can be configured identify specific events based on detected sounds within the particular environment by utilizing the statistical model associated with the particular environment. The DSP can associate a probability of accuracy for the identified audible event. A rules engine can be configured to receive the probability and transmit a report of the detected audible event.

A trans-boundary water resource usage detection method, system, and computer program product, including predicting an estimated water usage by an upstream administrative region using a machine learning model and a tracked water usage, detecting an excess water usage when the estimated water usage exceeds a contracted usage value by more than a threshold value by executing a smart contract on blockchain, and penalizing, via a token exchange in blockchain, the upstream administrative region for the excess water usage by executing the smart contract on blockchain.

An example system may include a processor and a non-transitory machine-readable storage medium storing instructions executable by the processor to generate, from data collected by a sensor, a model of an area within which a mmWave sensor is to be N utilized for presence detection; shape, based on the model, a detection field of view of the mmWave sensor to be contained within the N area; and perform the presence detection within the area utilizing the shaped detection field of view of the mmWave sensor.

A method of predicting evolution of simulation results for an Internet of Things (IoT) network comprising creating a source digital twin outputting a state of object(s). A main digital twin sequence is formed by creating clone digital twin(s), connecting an input of one clone digital twin with an output of the source digital twin where a time increment is added to the output of the source digital twin and connecting an input of any further clone digital twin with an output of a preceding clone digital twin where a further time increment is added to the output of the preceding clone digital twin. An evolved modified state of the object(s) is provided at additionally incremented time as an output of an exploratory digital twin which has an input connected with an output of one of the source digital twin, the one clone digital twin, and any further clone digital twin.

A networking device, such as an Internet of Things (IoT) device, implements an operative neural network (ONN) to optimize an internal wireless transceiver based on detected radio frequency (RF) spectrum conditions. The wireless transceiver detects the RF spectrum conditions local to the networking device and generates a representation of the RF spectrum conditions. The ONN determines transceiver parameters based on the RF spectrum conditions. A controller causes the representation of the RF spectrum conditions to be transmitted to a network node. Independent of the networking device, a training neural network (TNN) is trained based on the representation of the RF spectrum conditions, and neural network (NN) parameters are generated via the training a function of the representation of the RF spectrum conditions. The controller then reconfigures the ONN based on the NN parameters.