A platform is positioned within an environment. The platform includes an image capture system connected to a controller implementing a neural network. The neural network is trained to associate visual features within the environment with a target object utilizing a known set of input data examples and labels. The image capture system captures input images from the environment and the neural network recognizes features of one or more of the input images that at least partially match one or more of the visual features within the environment associated with the target object. The input images that contain the visual features within the environment that at least partially match the target object are labeled, a geospatial position of the target object is determined based upon pixels within the labeled input images, and a class activation map is generated, which is then communicated to a supervisory system for action.

A platform is positioned within an environment. The platform includes an image capture system connected to a controller implementing a neural network. The neural network is trained to associate visual features within the environment with a target object utilizing a known set of input data examples and labels. The image capture system captures input images from the environment and the neural network recognizes features of one or more of the input images that at least partially match one or more of the visual features within the environment associated with the target object. The input images that contain the visual features within the environment that at least partially match the target object are labeled, a geospatial position of the target object is determined based upon pixels within the labeled input images, and a class activation map is generated, which is then communicated to a supervisory system for action.

A system, a control unit, and a method for deciding a geofence event of a vehicle is disclosed. The control unit includes (i) a first road information generation module configured to generate first road information based on received map data and vehicle location data, the first road information including at least one or more candidate lanes based on a vehicle location, (ii) a second road information generation module configured to generate second road information based on received radar data, the second road information including at least a detected lane based on the radar data, (iii) a calculation module configured to perform integrated calculation on the first road information and the second road information to obtain a confidence level of each candidate lane, and determine a lane of the vehicle based on the calculated confidence level, and (iv) a decision module configured to decide, based on the determined lane, whether to trigger a geofence event, the geofence event including an event that the vehicle enters a geofence and an event that the vehicle exits a geofence.

A continuous wave, frequency diverse array (FDA) Detector, Transmitter, Receiver and/or Method are disclosed. The frequencies can be radio waves or sonic waves. Different frequencies are applied to each transmitter element, to generate transmissions schemes with repeating patterns of constructive interference (e.g. each pattern may be a spiral). The patterns differ (e.g. opposite spiral directions to help determine azimuth, or different spiral rotation speeds to help determine range), to a sufficient extent that from the timing of signal reflected back as a result of each one, the azimuth and/or range of an object can be determined, irrespective of where the object/target is in the field of view. Use of continuous wave transmissions enables lower transmission powers and/or avoids requiring an expensive beam-steering transmitters or receivers.

This disclosure provides a system and method for producing ultrasound images based on Full Waveform Inversion (FWI). The system captures acoustic/(an)elastic waves transmitted through and reflected and/or diffracted from a medium. The system performs an FWI process in a time domain in conjunction with an accurate wave propagation solver. The system produces 3D maps of physical parameters that control wave propagation, such as shear and compressional wavespeeds, mass density, attenuation, Poisson's ratio, bulk and shear moduli, impedance, and even the fourth-order elastic tensor containing up to 21 independent parameters, which are of significant diagnostic value, e.g., for medical imaging and non-destructive testing.

This disclosure provides a system and method for producing ultrasound images based on Full Waveform Inversion (FWI). The system captures acoustic/(an)elastic waves transmitted through and reflected and/or diffracted from a medium. The system performs an FWI process in a time domain in conjunction with an accurate wave propagation solver. The system produces 3D maps of physical parameters that control wave propagation, such as shear and compressional wavespeeds, mass density, attenuation, Poisson's ratio, bulk and shear moduli, impedance, and even the fourth-order elastic tensor containing up to 21 independent parameters, which are of significant diagnostic value, e.g., for medical imaging and non-destructive testing.

Aspects of the technology described herein relate to wirelessly offloading, from a wearable ultrasound device, ultrasound data sufficient for forming one or more ultrasound images therefrom. The wearable ultrasound device may include an ultrasound patch. Indications that may be monitored with such a device, and therapeutic uses that may be provided by such a device, are also described. Methods and apparatuses are also described for compounding multilines of ultrasound data on an ultrasound device configured to collect the ultrasound data. Additionally, certain aspects of the technology relate to non-uniform grouping of ultrasound transducers that share a transmit/receive circuit in an ultrasound device.

According to one embodiment, an ultrasound diagnostic apparatus includes a transmitter/receiver and processing circuitry. The transmitter/receiver sequentially transmits a first transmission beam group and a second transmission beam group and receives at least one reception beam for each transmission beam, via an ultrasound probe having a plurality of transducers arranged along an azimuth direction and an elevation direction. The processing circuitry combines a first reception beam based on a first transmission beam included in the first transmission beam group and a second reception beam based on a second transmission beam included in the second transmission beam group. Transmission beams that are adjacent to each other in the azimuth direction or the elevation direction belong to transmission beam groups that are different from each other.

The ultrasonic diagnostic apparatus according to the present embodiment includes a frequency characteristic analysis circuit, a filter setting circuit, and a filter processing circuit. The frequency characteristic analysis circuit performs a frequency analysis on a first reception signal corresponding to a region of interest of each depth, and acquires a frequency characteristic of each depth. The filter setting circuit sets a reception filter of each depth based on the acquired frequency characteristic of each depth such that the acquired frequency characteristic of each depth shows a predetermined frequency characteristic. The filter processing circuit applies the set reception filter of each depth to a second reception signal corresponding to the region of interest of each depth, the second reception signal being after the first reception signal, and converts the second reception signal into a third reception signal corresponding to the region of interest of each depth.

An ultrasound diagnosis apparatus according to an embodiment is configured to implement an ultrasound beamforming method by which, among a plurality of reception signals output from a plurality of elements, reception signals from mutually-different elements are multiplied by each other, so that signals obtained as results of the multiplications are added together. The ultrasound diagnosis apparatus according to the embodiment includes processing circuitry. The processing circuitry is configured to calculate a weight coefficient on the basis of a correlation between the multiplied reception signals. The processing circuitry is configured to apply the weight coefficient to the signals obtained as the results of the multiplications.