A method and beverage container assembly for determining a level of liquid in a beverage container having a lid removably attached thereto. The beverage container assembly including a sensor configured to measure sound waves in the beverage container assembly. A processor is electronically coupled to the sensor, and to memory provided with executable instructions for causing the processor to determine the level of liquid. The executable instructions, when executed by the processor, cause the processor to receive a sound wave measurement from the sensor, obtain a frequency characteristic of the sound wave measurement, and obtain reference information regarding a set of characteristics of the beverage container assembly. The processor may determine the level of liquid in the beverage container assembly based on the frequency characteristic and the reference information.

A device and method for reducing noise of an external measurement sensor of mobility, including a storage unit that stores measurement values of an external measurement sensor of mobility including an ultrasonic sensor measuring distance values between the mobility and an object outside the mobility; and an operation unit that generates a plurality of functions capable of being derived from the measurement values stored in the storage unit, derives a final function having the most measurement values within a first range, among the plurality of functions, derives an estimated value that is an expected value of the measurement value through the final function, and selects a candidate final value that is the closest to the estimated value within a second range, among actual measurement values at a time point corresponding to the estimated value, as a final value.

A device and method for reducing noise of an external measurement sensor of mobility, including a storage unit that stores measurement values of an external measurement sensor of mobility including an ultrasonic sensor measuring distance values between the mobility and an object outside the mobility; and an operation unit that generates a plurality of functions capable of being derived from the measurement values stored in the storage unit, derives a final function having the most measurement values within a first range, among the plurality of functions, derives an estimated value that is an expected value of the measurement value through the final function, and selects a candidate final value that is the closest to the estimated value within a second range, among actual measurement values at a time point corresponding to the estimated value, as a final value.

Gesture recognition is a key requirement for Human Computer Interaction (HCI) and multiple modalities are explored in literature. Conventionally, channel taps are estimated using least square based estimation and tap corresponding to finger motion is tracked. These assume that noise component is negligible and can reduce the tracking accuracy for low SNR. Thus, to mitigate the above-mentioned limitation, the system and method of the present disclosure explore the feasibility of using speaker and microphone setup available in most of smart devices and transmit inaudible frequencies (acoustic) for detecting the human finger level gestures accurately. More specifically, System implements the method for millimeter level finger tracking and low power gesture detection on this tracked gesture. The system uses a subspace based high resolution technique for delay estimation and use microphone pairs to jointly estimate the multi-coordinates of finger movement.

A system and method for qualitative analysis of time progressive signals, comprising: a qualitative signal analysis module, comprising at least a processor, a memory, and a long term storage device; and an output processor module comprising at least a processor a memory and a network interface has been devised. The qualitative signal analysis module retrieves signal data over time and applies pre-programmed protocols to compare multiple aspects of the signal data to derive meaningful data. The output processor module encodes data generated by the qualitative signal analysis module for use in subsequent analytical steps such as further manipulation, classification or long term storage.

A system and method for qualitative analysis of time progressive signals, comprising: a qualitative signal analysis module, comprising at least a processor, a memory, and a long term storage device; and an output processor module comprising at least a processor a memory and a network interface has been devised. The qualitative signal analysis module retrieves signal data over time and applies pre-programmed protocols to compare multiple aspects of the signal data to derive meaningful data. The output processor module encodes data generated by the qualitative signal analysis module for use in subsequent analytical steps such as further manipulation, classification or long term storage.

A Continuous Transmission Frequency Modulated (CTFM) detection apparatus includes a projector, a sensor, and a hardware processor. The projector is configured to transmit underwater a frequency modulated transmission wave based on a transmission signal. The sensor is configured to form a reception beam directed downward and laterally outward to a side of the boat or the ship and receive a reflected wave, the reflected wave comprising a reflection of the transmission wave on a target object. The hardware processor is programmed to at least generate a beat signal based at least in part on the transmission signal and the reflected wave, extract a processing signal from the beat signal, and generate an image information related to the target object based on the extracted processing signal.

A Continuous Transmission Frequency Modulated (CTFM) detection apparatus includes a projector, a sensor, and a hardware processor. The projector is configured to transmit underwater a frequency modulated transmission wave based on a transmission signal. The sensor is configured to form a reception beam directed downward and laterally outward to a side of the boat or the ship and receive a reflected wave, the reflected wave comprising a reflection of the transmission wave on a target object. The hardware processor is programmed to at least generate a beat signal based at least in part on the transmission signal and the reflected wave, extract a processing signal from the beat signal, and generate an image information related to the target object based on the extracted processing signal.

An obstacle detection apparatus includes: a transceiver transmitting a transmission wave and receiving an ultrasonic wave; a transmission controller; a receiver circuit detecting a signal level of a receiving wave; a distance calculator sequentially calculating a distance to an object reflecting the transmission wave; a memory storing the distance to the object; an obstacle determinator determining whether the object is an obstacle; and a reception level monitoring device monitoring the signal level of the receiving wave before the transmission wave being transmitted. When the signal level exceeds a predetermined threshold, the obstacle determinator sets a first number of determination data elements to an increased number of determinations for a predetermined period to be used for determining whether the object is the obstacle, as being larger than a second number of determination data elements used when the signal level does not exceed the predetermined threshold.

An obstacle detection apparatus includes: a transceiver transmitting a transmission wave and receiving an ultrasonic wave; a transmission controller; a receiver circuit detecting a signal level of a receiving wave; a distance calculator sequentially calculating a distance to an object reflecting the transmission wave; a memory storing the distance to the object; an obstacle determinator determining whether the object is an obstacle; and a reception level monitoring device monitoring the signal level of the receiving wave before the transmission wave being transmitted. When the signal level exceeds a predetermined threshold, the obstacle determinator sets a first number of determination data elements to an increased number of determinations for a predetermined period to be used for determining whether the object is the obstacle, as being larger than a second number of determination data elements used when the signal level does not exceed the predetermined threshold.