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Convolutional neural networks (CNNs) represent an interesting method for adaptive image processing, and form a link between general feedforward neural networks and adaptive filters. Two dimensional CNNs are formed by one or more layers of two dimensional filters, with possible non-linear activation functions and/or down-sampling. Conventional neural network error minimization methods may be used to optimize convolutional networks in order to implement quite powerful image transformations. CNNs possess key properties of translation invariance and spatially local connections (receptive fields). CNNs are an interesting alternative when the the input is spatially or temporally distributed, and the desired output of a system may be specified. The present paper presents a description of the convolutional network architecture, and an application to a practical image processing application on a mobile robot. As a formal CNN framework has not yet been specified in the literature, we describe CNNs in some detail, conceptually and formally. A CNN is used to detect and characterize cracks on an autonomous sewer inspection robot. Although cracks are relatively easy to detect by a human operator, autonomous sewer inspection necessitates the detection of pipe damage using computer vision methods. This is an appropriate application for trainable data-based computer vision methods, since prior specification of appropriate of the filtering / detection method is quite difficult. The The CNN architecture used involved a total of five layers: a single input and output map, and three hidden layers. The filter sizes used in all cases were 5x5, and the common activation function used was a log-sigmoid. The number

Abstract-For a mobile robot to move in a known environment and operate successfully, first it needs to robustly determine its initial position and orientation relative to the map, and then update its position while moving in the environment. Thus determining robot's position is one of the most important tasks in mobile robotics. This task consists of "global localization" and "robot's pose tracking". In this paper two recent sample-based evolutionary methods for globally localizing the position of a mobile robot are proposed. The first method is a modified version of genetic algorithm called Differential Evolution (DE) which is based on natural selection. The second one is Particle Swarm Optimization (PSO) which is based on bird flocking. DE evaluates initial population using the probabilistic motion and observation models and the evolution of the individuals is performed by evolutionary operators. PSO adjusts the velocity and location of particles towards target (robot's pose) through a problem space on the basis of information about each particle's previous best location and the best previous location of its neighbors. Our results illustrate the excellence of these two methods over standard Monte Carlo localization algorithm with regard to convergence rate, speed and computational cost.

In this paper, we propose a nonlinear dimensionality reduction algorithm for the manifold of Symmetric Positive Definite (SPD) matrices that considers the geometry of SPD matrices and provides a low dimensional representation of the manifold with high class discrimination. The proposed algorithm, tries to preserve the local structure of the data by preserving distance to local mean (DPLM) and also provides an implicit projection matrix. DPLM is linear in terms of the number of training samples and may use the label information when they're available in order to performance improvement in classification tasks. We performed several experiments on the multi-class dataset IIa from BCI competition IV. The results show that our approach as dimensionality reduction technique -leads to superior results in comparison with other competitor in the related literature because of its robustness against outliers. The experiments confirm that the combination of DPLM with FGMDM as the classifier leads to the state of the art performance on this dataset.

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Background Intracytoplasmic sperm injection (ICSI) requires long training and has low success rates, primarily due to poor control over the injection force. Making force feedback available to the operator will improve the success rate of the injection task. A macro-micro-teleoperation system bridges the gap between the task performed at the micro-level and the macroscopic movements of the operator. The teleoperation slave manipulator should accurately position a needle to precisely penetrate a cell membrane. Piezoelectric actuators are widely used in micromanipulation applications; however, hysteresis non-linearity limits the accuracy of these actuators.

In this paper, we propose a kernel for nonlinear dimensionality reduction over the manifold of Symmetric Positive Definite (SPD) matrices in a Motor Imagery (MI)-based Brain Computer Interface (BCI) application. The proposed kernel, which is based on Riemannian geometry, tries to preserve the topology of data points in the feature space. Topology preservation is the main challenge in nonlinear dimensionality reduction (NLDR). Our main idea is to decrease the non-Euclidean characteristics of the manifold by modifying the volume elements. We apply a conformal transform over data-dependent isometric mapping to reduce the negative eigen fraction to learn a data dependent kernel over the Riemannian manifolds. Multiple experiments were carried out using the proposed kernel for a dimensionality reduction of SPD matrices that describe the EEG signals of dataset IIa from BCI competition IV. The experiments show that this kernel adapts to the input data and leads to promising results in comparison with the most popular manifold learning methods and the Common Spatial Pattern (CSP) technique as a reference algorithm in BCI competitions. The proposed kernel is strong, particularly in the cases where data points have a complex and nonlinear separable distribution.

Lameness scoring is a routine procedure in dairy industry to screen the herds for new cases of lameness. Subjective lameness scoring, which is the most popular lameness detection and screening method in dairy herds, has several limitations. They include low intra-observer and inter-observer agreement and the discrete nature of the scores which limits its usage in monitoring the lameness. The aim of this study is to develop an automated lameness scoring system comparable with conventional subjective lameness scoring by means of artificial neural networks. The system is composed of four balanced force plates installed in a hoof-trimming box. A group of 105 dairy cows was used for the study. Twenty-three features extracted from ground reaction force (GRF) data were used in a computer training process which was performed on 60 per cent of the data. The remaining 40 per cent of the data were used to test the trained system. Repeatability of the lameness scoring system was determined by GRF samples from 25 cows, captured at two different times from the same animals. The mean sd was 0.31 and the mean coefficient of variation was 14.55 per cent, which represents a high repeatability in comparison with subjective vision-based scoring methods. Although the highest sensitivity and specificity values were seen in locomotion score groups 1 and 4, the automatic lameness system was both sensitive and specific in all groups. The sensitivity and specificity were higher than 72 per cent in locomotion score groups 1 to 4, and it was 100 per cent specific and 50 per cent sensitive for group 5.