Detection and recognition of traffic signs in a video streams consist of two steps: the detection of signs in the road scene and the recognition of their type. We usually evaluate globally this process. This evaluated approach unfortunately does not allow to finely analyze the performance of each step. It is difficult to know what step needs to be improved to obtain a more efficient system. Our...

BACKGROUND AND AIM In this study, performances of classification techniques were compared in order to predict type of pain in patients with spinal cord injury. Pain is one of the main problems in people with spinal cord injury. Identifying the optimal classification technique will help improve decision support systems in clinical settings. METHODS A descriptive retrospective analysis was perf...

This paper presents a (semi-)automatic processing technique for GPR data analysis. Exploiting the generalization capabilities of artificial neural networks (ANN), it will be shown that it is possible to feed a Multi-Layer Perceptron (MLP) with a suitable set of input features in order to determine the permittivity of a ground layer. A detailed performance assessment have proven that the algorit...

Meta-learning consists in learning learning algorithms. We use a Long Short Term Memory (LSTM) based network to learn to compute on-line updates of the parameters of another neural network. These parameters are stored in the cell state of the LSTM. Our framework allows to compare learned algorithms to hand-made algorithms within the traditional train and test methodology. In an experiment, we l...

A new method for feature extraction based on FPGA (Field Programmable Gate Arrays) implementation is proposed in this paper. The specific application is offline Farsi handwritten digit recognition. The classification is based on a simple two layer MLP (Multi Layer Perceptron). This method of feature extraction is appropriate for FPGA implementation as it can be implemented only with addition an...

In this paper we present an adaptive appoach to observe the joint velocity for rigid robot manipulators. This approach is based on the neural network and sliding mode technique. The adopted neural network is of the MLP (Multi Layer Perceptron) type with one hidden layer. The functions of the dynamic model of the robot manipulator are assumed unknown. The neural network parameters are adapted on...

An iris pattern recognition system is developed for CASIA database using Singular Value Decomposition transform as a tool for feature extraction. Experimental prototype pattern recognition (PR) system is designed in which iris images of ten different persons are given as input to the system. After localizing region of interest (ROI), features are extracted with respect to image statistics, text...

The nearest-neighbor multilayer perceptron (NN-MLP) is a single-hidden-layer network suitable for pattern recognition. To design an NN-MLP efficiently, this paper proposes a new evolutionary algorithm consisting of four basic operations: recognition, remembrance, reduction, and review. Experimental results show that this algorithm can produce the smallest or nearly smallest networks from random...

Artificial neural networks (ANNs, or simply NNs) are inspired by biological nervous systems and consist of simple processing units (artificial neurons) that are interconnected by weighted connections. Neural networks can be ”trained” to solve problems that are difficult to solve by conventional computer algorithms. This paper presents the development and implementation of a generalized back-pro...

In this paper, we present a non linear prediction scheme based on a Multi-Layer Perceptron for Predictive Vector Quantization (PVQ-MLP) of MFCC for very low bit-rate coding of acoustic features in distributed speech recognition (DSR). Certain applications like voice enabled web-browsing or speech controlled processes in large industrial plants, where hundreds of users access simultaneously to t...