Abstract: A multiple sensor-based abnormal condition monitoring method includes determining an operation mode based on condition information in a target area in which an abnormal condition is monitored, receiving sensing signals from a plurality of sensors installed in the target area in response to a first mode in which different objects are simultaneously detected being determined as the operation mode, processing the sensing signals and outputting first sensing data obtained by converting the sensing signals into digital signals, generating first input data to be input to a first neural network model by variably adjusting a size of the first sensing data, performing an operation in an analog domain on the first input data using the first neural network mode and outputting first operation data obtained by converting an operation result into digital data, and outputting a first monitoring result obtained by classifying an abnormal condition based on the first operation data.

Abstract: A method of monitoring an abnormal condition based on multiple sensors includes processing sensing signals received from a plurality of sensors configured to detect different objects and outputting sensing data obtained by converting the sensing signals into digital signals, generating input data to be input to a neural network model by variably adjusting a size of the sensing data, performing an operation in an analog domain on the input data using the neural network mode and outputting operation data obtained by converting an operation result into digital data, and outputting a monitoring result obtained by classifying an abnormal condition based on the operation data.

Abstract: Provided are a scalable analog passive intermodulation (PIM) module, a method of controlling analog PIM, a signal processing circuit, and a sensor device. The scalable analog PIM module includes a first plural number of digital-to-analog converters (DACs), a first plural number of static random access memory (SRAM) calculators connected to the first plural number of DACs, at least one analog-to-digital converter (ADC) connected to the first plural number of SRAM calculators and configured to convert an analog convolution result signal into digital convolution data, and an analog PIM controller configured to output an enable control signal for enabling a second number, which is equal to or less than first plural number, of SRAM calculators among the first plural number of SRAM calculators to the first plural number of SRAM calculators on the basis of the convolution data output from the ADC.

Abstract: Provided are a scalable analog passive intermodulation (PIM) module, a method of controlling analog PIM, a signal processing circuit, and a sensor device. The scalable analog PIM module includes a first plural number of digital-to-analog converters (DACs), a first plural number of static random access memory (SRAM) calculators connected to the first plural number of DACs, at least one analog-to-digital converter (ADC) connected to the first plural number of SRAM calculators and configured to convert an analog convolution result signal into digital convolution data, and an analog PIM controller configured to output an enable control signal for enabling a second number, which is equal to or less than first plural number, of SRAM calculators among the first plural number of SRAM calculators to the first plural number of SRAM calculators on the basis of the convolution data output from the ADC.

Abstract: The present invention relates to an integrated circuit for processing biosignals, a biosignal processing apparatus, and a biosignal processing system, and the integrated circuit includes: a digital conversion unit for converting an analog biosignal input through a biosignal input terminal into digital biodata; and an AI block for processing a plurality of biodatas converted through the digital conversion unit according to an artificial intelligence processing flow, and outputting a result data according to processing of the plurality of biodatas.

Abstract: Provided is a device-to-device (D2D)-based wireless power receiving device including an observation part configured to monitor a battery, an estimator configured to estimate usage and a workload of the battery on the basis of a result of monitoring the battery, a mode determiner configured to determine required power of the battery on the basis of the usage and the workload, and determine a reception mode according to the required power, and a power receiver configured to wirelessly receive power from another device according to the reception mode. Accordingly, wireless power charging efficiency can be maximized.

Abstract: Provided are a scalable analog passive intermodulation (PIM) module, a method of controlling analog PIM, a signal processing circuit, and a sensor device. The scalable analog PIM module includes a first plural number of digital-to-analog converters (DACs), a first plural number of static random access memory (SRAM) calculators connected to the first plural number of DACs, at least one analog-to-digital converter (ADC) connected to the first plural number of SRAM calculators and configured to convert an analog convolution result signal into digital convolution data, and an analog PIM controller configured to output an enable control signal for enabling a second number, which is equal to or less than first plural number, of SRAM calculators among the first plural number of SRAM calculators to the first plural number of SRAM calculators on the basis of the convolution data output from the ADC.

Abstract: The present invention relates to an integrated circuit for processing biosignals, a biosignal processing apparatus, and a biosignal processing system, and the integrated circuit includes: a digital conversion unit for converting an analog biosignal input through a biosignal input terminal into a digital biodata; and an AI block for processing a plurality of biodata converted through the digital conversion unit according to an artificial intelligence processing flow, and outputting a result data according to processing of the plurality of biodata.