University of Southern California

Other

John Stang

Mahta Moghaddam

Guanbo Chen

Mark S Haynes

Wei Guang Wu

Yifei Wang

Samuel M Prager

Pratik Shah

Systems and methods for software defined radar are disclosed. Exemplary systems utilize a frequency stacking bandwidth reconstruction technique for a stepped frequency signal to create a synthetic wideband waveform. The methods enable low-cost, reconfigurable applications such as ground penetrating radar or small unmanned aerial vehicle synthetic aperture radar platforms.

Software defined radar

Methods and systems of monitoring and controlling thermal therapy treatments. One method includes determining, with an electronic processor, a propagation constant for a plurality of waveports. The method also includes modeling an object within an imaging cavity using a sparse mesh model. The method also includes determining a simulated impedance of the object based on the model of the object. The method also includes calibrating the simulated impedance with a theoretical impedance numerically calculated for the object. The method also includes determining a distribution of an electric field and a distribution of a magnetic field for a mode in a conformal modeled waveport based on the calibrated impedance and the model of the object. The method also includes exciting the plurality of waveports to generate the determined distribution of the electric field and the distribution of the magnetic field.

Real-time imaging system for monitoring and control of thermal therapy treatments

A method for determining a change of temperature of an object. The method may include heating an object and measuring scattering parameters (S-parameters) of scattered microwave electric fields from the object. A distorted Born iterative method may be used to determine a change of a dielectric property of the object based on the measured S-parameters. A change of temperature of the object may be determined based on the change of the dielectric property of the object.

Real-time 3D microwave monitoring of thermal therapy

A Convolutional Neural Network (CNN) assisted dielectric imaging method is provided. The method used CNN to incorporate the abundant image information from Magnetic Resonance (MR) images into the inverse scattering model-based microwave imaging process and generate high-fidelity dielectric images. A CNN is designed and trained to learn the complex mapping function from MR T1 images to dielectric images. Once trained, the new patients' MR T1 images are fed into the CNN to generate predicted dielectric images, which are used as the starting image for the microwave inverse scattering imaging. The CNN-predicted dielectric image significantly reduces the non-linearity and ill-posedness of the inverse scattering problem. The application of the proposed method to recover human brain dielectric images at 4 mm and 2 mm resolution with single-frequency and multi-frequency microwave measurements is provided.

Learning-assisted multi-modality dielectric imaging

A group of Hyperthermia micro/nano particles are prepared. Each nanoparticle has a first diameter between 1 micron to 50 micron and a first thickness between 100 nm to 5 micron, in a disk-like shape. The hyperthermia micro/nano particles in the present show enhanced heat properties under microwave radiation which can be used for diagnosis and therapeutic purpose in cancer treatment.

Growing community of inventors

South Pasadena, CA, United States of America

John Stang