Abstract: A system and method for characterising tissues are provided. The system comprises a point-of-care ultrasound device for obtaining ultrasound images of tissues within a system of interest, a processor, and a memory comprising instructions which when executed by the processor configure the processor to perform the method. The method comprises obtaining an ultrasound image of a tissue types within a system of interest; identifying features of the tissue on the ultrasound image, feeding said identified features to a trained model, and identifying a tissue pathology based on the identified features.

Abstract: A system and method for characterising tissues are provided. The system comprises a processor, and a memory comprising instructions which when executed by the processor, configure the processor to perform the method. The method comprises receiving raw data corresponding to a dense two-dimensional (2D) image or signals arising from a scan of tissues within a system of interest, generating a three-dimensional (3D) data set from the dense 2D image, and inputting the 3D data set into a convolutional network having a plurality of filters. The convolutional network converting the 3D data set into a 1D array corresponding to the frequency domain of the 3D data set, and extracting features form the 1D array and classify the 1D array into a tissue pathology classification.

Abstract: A digital pre-distortion component includes: a first capturing component that captures a first sample set of data; a first generating component that generates a first change matrix associated with a portion of the first sample set of data; a first memory component that stores the first change matrix; a second capturing component that captures a second sample set of data; a second generating component that generates a second change matrix associated with a portion of the second sample set of data; a second memory component that stores the second change matrix; a third capturing component that captures a third sample set of data; a third generating component that generates a third change matrix associated with a portion of the third sample set of data; a comparing component that compares the third change matrix with the first change matrix to obtain a first comparison, and compares the third change matrix with the second change matrix to obtain a second comparison; and an adapting component that adapts the digi

Abstract: A digital pre-distortion component includes: a first capturing component that captures a first sample set of data; a first generating component that generates a first change matrix associated with a portion of the first sample set of data; a first memory component that stores the first change matrix; a second capturing component that captures a second sample set of data; a second generating component that generates a second change matrix associated with a portion of the second sample set of data; a second memory component that stores the second change matrix; a third capturing component that captures a third sample set of data; a third generating component that generates a third change matrix associated with a portion of the third sample set of data; a comparing component that compares the third change matrix with the first change matrix to obtain a first comparison, and compares the third change matrix with the second change matrix to obtain a second comparison; and an adapting component that adapts the digi

Abstract: A method of predistorting an input signal (902) for an amplifier is disclosed (FIG. 9). The method includes predistorting the input signal with a first set of parameters (FDPD) and a second set of parameters (CDPD) at a first time (904). The first set of parameters is updated at a second time (914). The second set of parameters is updated separately from the first set of parameters at a third time (920).

Abstract: A method of predistorting an input signal (902) for an amplifier is disclosed (FIG. 9). The method includes predistorting the input signal with a first set of parameters (FDPD) and a second set of parameters (CDPD) at a first time (904). The first set of parameters is updated at a second time (914). The second set of parameters is updated separately from the first set of parameters at a third time (920).

Abstract: A method of predistorting an input signal (902) for an amplifier is disclosed (FIG. 9). The method includes predistorting the input signal with a first set of parameters (FDPD) and a second set of parameters (CDPD) at a first time (904). The first set of parameters is updated at a second time (914). The second set of parameters is updated separately from the first set of parameters at a third time (920).

Abstract: A method of predistorting an input signal (902) for an amplifier is disclosed (FIG. 9). The method includes predistorting the input signal with a first set of parameters (FDPD) and a second set of parameters (CDPD) at a first time (904). The first set of parameters is updated at a second time (914). The second set of parameters is updated separately from the first set of parameters at a third time (920).