This work relates to the study of periodic events such as the ones that can be observed in biomedicine. Currently, biological processes exhibiting a periodic behaviour can be observed through the continuous recording of signals or images. Due to various reasons, cycle duration may slightly vary over time. For further analysis, it is important to be able to extract meaningful information from the mass of acquired data. This paper presents a new neural network based method for the extraction of a summarized cycle from long and massive cycle recordings. Its concept is simple and it could be naturally implemented on a hardware architecture to speed up the process. The proposed method is demonstrated on synthetic image sequences of the beating heart, and exploited as a prior in a new approach for the fast reconstruction of Magnetic Resonance Image sequences.
This paper evaluates the feasibility of using an Artificial Neural Network (ANN) model for estimating the nominal shear capacity of Reinforced Concrete (RC) beams against diagonal shear failure subjected to shear and flexure. A feedforward back-propagation ANN model was developed utilizing 622 experimental data points of RC beams, which include 111 deep beams data and 20 beams tested for low longitudinal steel ratios. The ANN model was trained on 70% of the data and then it was validated using the remaining 30% data (new data were not used for training). The trained ANN model was compared with three existing approaches, including the American Concrete Institute (ACI) code. The ANN model predictions when compared to the experimental data were very favorable, regarding also the other approaches. The prediction of ANN model was also checked for size effect and deep beams separately. The ANN model was found to be very robust in all situations. The safe form of ANN model was also derived and compared with the design equations of the three methods.
An image recognition system can be based on a single-layer neural network composed of Min/Max nodes. This principle is easy to use for greyscale images. However, this article deals with the possibilities of utilising neural nets for colour image recognition. Several principles are demonstrated and tested by recently developed software. A new modified Min/Max node Single Layer Net, suitable for recognition in HSV (Hue Saturation Value) colour space, is presented in this paper.
This article deals with a neural network based on Min/Max nodes and its utilisation for image recognition purposes. The general concepts of the Min/Max nodes and the single-layer neural networks are outlined. The developed software systems for simulation are briefly introduced and the results of simulations with the various settings of a neural net are presented. The subject of simulations was the recognition of human faces. Finally, the hardware design of the neural network in VHDL is shown. The design demonstrates the ease of systems realisation and the achieving of high performance.