Biomedical subjects

Mounir Boukadoum

Publications and source records attributed to Mounir Boukadoum.

3 recordsLinked to original sources

PubMed2006

A sequential dynamic heteroassociative memory for multistep pattern recognition and one-to-many association.

Bidirectional associative memories (BAMs) have been widely used for auto and heteroassociative learning. However, few research efforts have addressed the issue of multistep vector pattern recognition. We propose a model that can perform multi step pattern recognition without the need for a special learning algorithm, and with the capacity to learn more than two pattern series in the training set. The model can also learn pattern series of different lengths and, contrarily to previous models, the stimuli can be composed of gray-level images. The paper also shows that by adding an extra autoassociative layer, the model can accomplish one-to-many association, a task that was exclusive to feedforward networks with context units and error backpropagation learning.

Algorithms↗

PubMed2006

A bidirectional heteroassociative memory for binary and grey-level patterns.

Typical bidirectional associative memories (BAM) use an offline, one-shot learning rule, have poor memory storage capacity, are sensitive to noise, and are subject to spurious steady states during recall. Recent work on BAM has improved network performance in relation to noisy recall and the number of spurious attractors, but at the cost of an increase in BAM complexity. In all cases, the networks can only recall bipolar stimuli and, thus, are of limited use for grey-level pattern recall. In this paper, we introduce a new bidirectional heteroassociative memory model that uses a simple self-convergent iterative learning rule and a new nonlinear output function. As a result, the model can learn online without being subject to overlearning. Our simulation results show that this new model causes fewer spurious attractors when compared to others popular BAM networks, for a comparable performance in terms of tolerance to noise and storage capacity. In addition, the novel output function enables it to learn and recall grey-level patterns in a bidirectional way.

Algorithms↗

PubMed2005

An investigation of the physical forces leading to thrombosis disruption by cavitation.

Ultrasound therapy has proven to be an efficient and safe modality for the treatment of acute arterial occlusions, and the use of therapeutic ultrasound for the treatment of thrombosis and vascular diseases holds great promise in overcoming the limitations of other available therapies. Still, there exists little published work that covers the different phenomena that take place in a thorough and comprehensive way. In this paper, we endeavor to address the subject by reviewing work on the physical properties of ultrasound propagation in the blood arteries as it relates to the cavitation of microbubbles, and we compare the impact of the different forces at work for clot disruption. Our conclusion is that the most important effect of ultrasound in the treatment of thrombotic disorders is the liquid-jet impact forces that result from strong bubble collapses in the vicinity of solid boundaries.

Blood Coagulation↗