Area-Efficient Linear Regression Architecture for Real-Time Signal Processing on FPGAs

Linear regression is a technique widely used in digital signal processing. It consists on finding the linear function that better fits a given set of samples. This paper proposes different hardware architectures for the implementation of the linear regression method on FPGAs, specially targeting area restrictive systems. It saves area at the cost of constraining the lengths of the input signal to some fixed values. We have implemented the proposed scheme in an Automatic Modulation Classifier, meeting the hard real-time constraints this kind of systems have.

Preliminary design of symmetric arch-gravity dams

In this paper, a set of design parameters, such as the slopes of upstream and downstream faces of the dam, radius of the upper arch, width of the dam at the top level and height of the vertical upper part of the dam, are given as function of the valley characteristics when the dam is situated, such as its geometry and its geotechnical properties. These tables have been obtained using a regression of the design parameters of an arch-gravity dam with a minimum concrete volume, placed in a large number of valleys with different characteristics and properties. Elasticites for these design parameters are also discussed.

Polynomial regression using a perceptron with axo-axonic connections

Social behavior is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuals to get optimal solutions. Such co-operative model differs from competitive models in the way that individuals die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these networks