A communication unit includes: a plurality of wireless communication units configured to perform wireless communication with another communication device; and a control unit configured to estimate a relative position of the other communication device relative to the communication unit, wherein the control unit estimates the relative position of the other communication device on the basis of a plurality of distance measurement values indicating a distance between each of the plurality of wireless communication units and the other communication device obtained by each of the plurality of wireless communication units performing wireless communication with the other communication device, and a position characteristic including a position of each of the plurality of wireless communication units in the communication unit.

A method for predicting a structure of an indoor space using radio propagation channel analysis through deep-learning is disclosed. Channel data of radio signals are collected for various indoor spaces, and radio channel parameter data such as PDP, AoA, and AoD are extracted therefrom. A large amount of propagation channel parameter data is input to an artificial neural network together with vertex coordinate data of the corresponding indoor space and deep-learning is performed in advance. The propagation channel parameter data are extracted from the indoor space to be predicted, the best matching indoor space is detected based on the trained artificial neural network. The best matching indoor space is predicted as the structure of the indoor space.

A method comprises emitting detection radiation into a container; receiving a reflection of the emitted radiation from contents of the container; interpreting the received reflection to determine the contents of the container.

The disclosure provides a multimodal speech recognition method and system, and a computer-readable storage medium. The method includes calculating a first logarithmic mel-frequency spectral coefficient and a second logarithmic mel-frequency spectral coefficient when a target millimeter-wave signal and a target audio signal both contain speech information corresponding to a target user; inputting the first and the second logarithmic mel-frequency spectral coefficient into a fusion network to determine a target fusion feature, where the fusion network includes at least a calibration module and a mapping module, the calibration module is configured to perform mutual feature calibration on the target audio/millimeter-wave signals, and the mapping module is configured to fuse a calibrated millimeter-wave feature and a calibrated audio feature; and inputting the target fusion feature into a semantic feature network to determine a speech recognition result corresponding to the target user. The disclosure can implement high-accuracy speech recognition.

The disclosure provides a multimodal speech recognition method and system, and a computer-readable storage medium. The method includes calculating a first logarithmic mel-frequency spectral coefficient and a second logarithmic mel-frequency spectral coefficient when a target millimeter-wave signal and a target audio signal both contain speech information corresponding to a target user; inputting the first and the second logarithmic mel-frequency spectral coefficient into a fusion network to determine a target fusion feature, where the fusion network includes at least a calibration module and a mapping module, the calibration module is configured to perform mutual feature calibration on the target audio/millimeter-wave signals, and the mapping module is configured to fuse a calibrated millimeter-wave feature and a calibrated audio feature; and inputting the target fusion feature into a semantic feature network to determine a speech recognition result corresponding to the target user. The disclosure can implement high-accuracy speech recognition.

Aspects of the present disclosure provide a simplified solution for proximity detection of an object in a wireless communication that does not require complex hardware to maintain mutual coupling reference signal. Specifically, in accordance with aspects of the present disclosure, the received signal that may include the mutual coupling signal and target signal may be multiplied by itself to extract the delay information associated with the target signal. The techniques outlined here may provide a greater robustness to variations of mutual coupling induced by phone covers, for example, being added by the user.

A subsurface imaging technique using distributed sensors is introduced. Instead of monostatic transceivers employed in conventional ground penetrating radars, the proposed technique utilizes bi-static transceivers to sample the reflected signals from the ground at different positions and create a large two-dimensional aperture for high resolution subsurface imaging. The coherent processing of the samples in the proposed imaging method eliminates the need for large antenna arrays for obtaining high lateral resolution images. In addition, it eliminates the need for sampling on a grid which is a time-consuming task in imaging using ground penetration radar. Imaging results show that the method can provide high-resolution images of the buried targets using only samples of the reflected signals on a circle with the center at the transmitter location.

Deep learning to improve or gauge the performance of a surface-penetrating radar (SPR) system for localization or navigation. A vehicle may employ a terrain monitoring system including SPR for obtaining SPR signals as the vehicle travels along a route. An on-board computer including a processor and electronically stored instructions, executable by the processor, may analyze the acquired SPR images and computationally identify subsurface structures therein by using the acquired image as input to a predictor that has been computationally trained to identify subsurface structures in SPR images.

A system for estimating a substance level in a storage unit is disclosed. In one embodiment, the system includes a cable and a control device. The control device sense pulses down the cable and based on the time of reflected pulses determines the level of substance in the storage unit.

A system for estimating a substance level in a storage unit is disclosed. In one embodiment, the system includes a cable and a control device. The control device sense pulses down the cable and based on the time of reflected pulses determines the level of substance in the storage unit.