Abstract: A computing system and a non-transitory computer-readable medium with instructions for processing ultrasound images are presented. The computing system is configured to: receive an ultrasound image that includes image data representing subdermal tissue; apply a trained neural network to the ultrasound image to generate at least one of: a pliability parameter value which indicates pliability of the subdermal tissue, one or more regions of the ultrasound image that represents damaged tissue, or an indication of presence and/or location of an entrapped nerve.

Abstract: A computing system and a non-transitory computer-readable medium with instructions for processing ultrasound images are presented. The computing system is configured to: receive an ultrasound image that includes image data representing subdermal tissue; apply a trained neural network to the ultrasound image to generate at least one of: a pliability parameter value which indicates pliability of the subdermal tissue, one or more regions of the ultrasound image that represents damaged tissue, or an indication of presence and/or location of an entrapped nerve.

Abstract: A method of detecting and mitigating a denial of service attack is described. The method comprises monitoring incoming first traffic packets, building a first Benford distribution of the first traffic packets, the first Benford distribution corresponding to network behaviour associated with normal traffic, and detecting a denial of service attack associated with incoming second traffic packets. After detecting the denial of service attack, the method involves sorting the incoming second traffic packets according to a characteristic of the incoming second traffic packets to create a Zipf distribution, building a second Benford distribution of the second traffic packets using the Zipf distribution and the first Benford distribution, discarding incoming second traffic packets that are not consistent with the second Benford distribution, and allowing incoming second traffic packets that are consistent with the second Benford distribution.

Abstract: A computing system and a non-transitory computer-readable medium with instructions for processing ultrasound images are presented. The computing system is configured to: receive an ultrasound image that includes image data representing subdermal tissue; apply a trained neural network to the ultrasound image to generate at least one of: a pliability parameter value which indicates pliability of the subdermal tissue, one or more regions of the ultrasound image that represents damaged tissue, or an indication of presence and/or location of an entrapped nerve.

Abstract: A method of detecting and mitigating a denial of service attack is described. The method comprises monitoring incoming traffic packets directed to at least one destination server and/or connected devices, building a first Benford distribution of the incoming first traffic packets, the first Benford distribution corresponding to network behaviour when normal traffic is directed to the at least one destination server and/or connected devices, and detecting a denial of service attack directed at the destination server and/or connected devices, the denial of service attack associated with incoming second traffic packets.

Abstract: A method and system for detecting and mitigating a denial of service attack against a destination server (12) and/or connected devices (14). Incoming traffic packets (26) are monitored and a first distribution of the incoming traffic packets (26) is built in accordance with Benford's Law for normal traffic behaviour. A denial of service attack is detected when it occurs. Once an attack is detected, the incoming traffic packets (26/28) are sorted in accordance with Zipf's Law and a sorted distribution is created. The sorted distribution is compared with the first distribution. The incoming traffic packets (28) in the sorted distribution that are not consistent with the first distribution are discarded. A second distribution is then built in accordance with Benford's Law using the incoming traffic packets (28) in the sorted distribution excluding the discarded incoming traffic packets.

Abstract: A method and system for detecting and mitigating a denial of service attack against a destination server (12) and/or connected devices (14). Incoming traffic packets (26) are monitored and a first distribution of the incoming traffic packets (26) is built in accordance with Benford's Law for normal traffic behaviour. A denial of service attack is detected when it occurs. Once an attack is detected, the incoming traffic packets (26/28) are sorted in accordance with Zipf's Law and a sorted distribution is created. The sorted distribution is compared with the first distribution. The incoming traffic packets (28) in the sorted distribution that are not consistent with the first distribution are discarded. A second distribution is then built in accordance with Benford's Law using the incoming traffic packets (28) in the sorted distribution excluding the discarded incoming traffic packets.

Abstract: Novel neural networks and novel methods for training those networks are disclosed. The novel networks are feedforward networks having at least three layers of neurons. The training methods are easy to implement, converge rapidly, and are guaranteed to converge to a solution. A novel network structure is used, in which each corner of the input vector hypercube may be considered separately. The problem of mapping may be reduced to a sum of corner classification sub-problems. Four efficient, alternative classification methods for use with the novel neural networks are also disclosed.