A sonar system comprising a sonar transmitter, a very large array two dimensional sonar receiver, and a beamformer section transmits a series of sonar pings into an insonified volume of fluid at a rate greater than 5 pings per second, receives sonar signals reflected and scattered from objects in the insonified volume, and beamforms the reflected signals to provide a video presentation and/or to store the beamformed data for later use. The parameters controlling the sonar system are changed so that the beamformer section treats the data from the receiver section with more than one set of parameters. The stream of data is treated either in parallel or in series by different beamforming methods so that at least one beam from the beamformer has more than one value.

A sonar system comprising a sonar transmitter, a very large array two dimensional sonar receiver, and a beamformer section transmits a series of sonar pings into an insonified volume of fluid at a rate greater than 5 pings per second, receives sonar signals reflected and scattered from objects in the insonified volume, and beamforms the reflected signals to provide a video presentation and/or to store the beamformed data for later use. The parameters controlling the sonar system are changed so that the beamformer section treats the data from the receiver section with more than one set of parameters. The stream of data is treated either in parallel or in series by different beamforming methods so that at least one beam from the beamformer has more than one value.

Techniques enabling improved classification of touch or hover interactions of objects with a touch sensitive surface of a device are presented. A speaker of the device can emit an ultrasonic audio signal comprising a first frequency distribution. A microphone of the device can detect a reflected audio signal comprising a second frequency distribution. The audio signal can be reflected off of an object in proximity to the surface to produce the reflected audio signal. A classification component can determine movement status of the object, or classify the touch or hover interaction, in relation to the surface, based on analysis of the signals. The classification component also can classify the touch or hover interaction based on such ultrasound data and/or touch surface or other sensor data. The classification component can be trained, using machine learning, to perform classifications of touch or hover interactions of objects with the surface.

A receiving unit is disclosed, including at least three receivers, each configured to receive an ultrasonic signal with a wavelength λ from the transmitting unit. A first receiver is arranged at a distance of at most one half wavelength λ/2 of the ultrasonic signal from a second receiver and from a third receiver. The at least three receivers are arranged in one plane. A processor is configured to determine the respective time-of-flight from the ultrasonic signal received at each of the at least three receivers. The respective time-of-flight is a time that the ultrasonic signal requires from the transmitting unit at a defined start time to the respective receiver. The processor is further configured to determine the three-dimensional position and/or direction of the transmitting unit from the determined times-of-flight and the arrangement of the at least three receivers.

A receiving unit is disclosed, including at least three receivers, each configured to receive an ultrasonic signal with a wavelength λ from the transmitting unit. A first receiver is arranged at a distance of at most one half wavelength λ/2 of the ultrasonic signal from a second receiver and from a third receiver. The at least three receivers are arranged in one plane. A processor is configured to determine the respective time-of-flight from the ultrasonic signal received at each of the at least three receivers. The respective time-of-flight is a time that the ultrasonic signal requires from the transmitting unit at a defined start time to the respective receiver. The processor is further configured to determine the three-dimensional position and/or direction of the transmitting unit from the determined times-of-flight and the arrangement of the at least three receivers.

Described here are systems and methods for phase velocity imaging using an imaging system, such as an ultrasound system, an optical imaging system (e.g., an optical coherence tomography system), or a magnetic resonance imaging system. In general, systems and methods for constructing phase velocity images (e.g., 2D images, 3D images) from propagating mechanical wave motion data are described. The systems and methods described in the present disclosure operate in the frequency domain and can be implemented using a single frequency or a band of selected frequencies. If there are multiple mechanical wave sources within the field-of-view, directional filtering may be performed to separate mechanical waves propagating in different directions. The reconstructions described below can be performed for each of these directionally filtered components.

Described here are systems and methods for phase velocity imaging using an imaging system, such as an ultrasound system, an optical imaging system (e.g., an optical coherence tomography system), or a magnetic resonance imaging system. In general, systems and methods for constructing phase velocity images (e.g., 2D images, 3D images) from propagating mechanical wave motion data are described. The systems and methods described in the present disclosure operate in the frequency domain and can be implemented using a single frequency or a band of selected frequencies. If there are multiple mechanical wave sources within the field-of-view, directional filtering may be performed to separate mechanical waves propagating in different directions. The reconstructions described below can be performed for each of these directionally filtered components.

A method of real time three dimensional (3D) sonar imaging is disclosed, where large array of sonar signal detectors images an underwater object and electromagnetic measuring means fixed in a known position with respect to the large array of sonar detectors measure the position of an above water object which has a known position with respect to the underwater object. The position of the sonar detector may be corrected to give a stable image of the underwater object from ping to ping of the sonar imaging system.

A method of real time three dimensional (3D) sonar imaging is disclosed, where large array of sonar signal detectors images an underwater object and electromagnetic measuring means fixed in a known position with respect to the large array of sonar detectors measure the position of an above water object which has a known position with respect to the underwater object. The position of the sonar detector may be corrected to give a stable image of the underwater object from ping to ping of the sonar imaging system.

Conventional gesture detection approaches demand large memory and computation power to run efficiently, thus limiting their use in power and memory constrained edge devices. Present application/disclosure provides a Spiking Neural Network based system which is a robust low power edge compatible ultrasound-based gesture detection system. The system uses a plurality of speakers and microphones that mimics a Multi Input Multi Output (MIMO) setup thus providing requisite diversity to effectively address fading. The system also makes use of distinctive Channel Impulse Response (CIR) estimated by imposing sparsity prior for robust gesture detection. A multi-layer Convolutional Neural Network (CNN) has been trained on these distinctive CIR images and the trained CNN model is converted into an equivalent Spiking Neural Network (SNN) via an ANN (Artificial Neural Network)-to-SNN conversion mechanism. The SNN is further configured to detect/classify gestures performed by user(s).