Aleksandar Jevtić

Ant based edge linking algorithm

Conventional image edge detectors always result in missing parts of the edges. Broken edge linking is an image improvement technique that is complementary to edge detection, where the broken edges are connected to form closed contours in order to separate the regions in the image. In this paper, Ant System (AS) algorithm is modified for edge linking problem. As input, a binary image obtained after applying the Sobel edge operator is used. The proposed method defines a novel fitness function dependent on two variables: the grayscale visibility of the pixels and the length of the connecting edge, in order to obtain effective solution evaluation. Another novelty is of applying the grayscale visibility matrix as the initial pheromone trails matrix so that the pixels belonging to true edges have a higher probability of being chosen by ants on their initial routes, which reduces computational load. The results of the experiments are presented to confirm the effectiveness of the proposed method.

Edge detection using ant colony search algorithm and multiscale contrast enhancement

In this paper, Ant Colony System (ACS) algorithm is applied for edge detection in grayscale images. The novelty of the proposed method is to extract a set of images from the original grayscale image using Multiscale Adaptive Gain for image contrast enhancement and then apply the ACS algorithm to detect the edges on each of the extracted images. The resulting set of images represents the pheromone trails matrices which are summed to produce the output image. The image contrast enhancement makes ACS algorithm more effective when accumulating pheromone trails on the true edge pixels. The results of the experiments are presented to confirm the effectiveness of the proposed method.

Error Weighting in Artificial Neural Networks Learning Interpreted as a Metaplasticity Model

Many Artificial Neural Networks design algorithms or learning methods imply the minimization of an error objective function. During learning, weight values are updated following a strategy that tends to minimize the final mean error in the Network performance. Weight values are classically seen as a representation of the synaptic weights in biological neurons and their ability to change its value could be interpreted as artificial plasticity inspired by this biological property of neurons. In such a way, metaplasticity is interpreted in this paper as the ability to change the efficiency of artificial plasticity giving more relevance to weight updating of less frequent activations and resting relevance to frequent ones. Modeling this interpretation in the training phase, the hypothesis of an improved training is tested in the Multilayer Perceptron with Backpropagation case. The results show a much more efficient training maintaining the Artificial Neural Network performance.

Distributed Bees Algorithm Parameters Optimization for a Cost Efficient Target Allocation in Swarms of Robots

Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the Distributed Bees Algorithm (DBA), previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBA’s control parameters by means of a Genetic Algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robots’ distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce.

Image Segmentation Using Ant System-Based Clustering Algorithm

Industrial applications of computer vision sometimes require detection of atypical objects that occur as small groups of pixels in digital images. These objects are difficult to single out because they are small and randomly distributed. In this work we propose an image segmentation method using the novel Ant System-based Clustering Algorithm (ASCA). ASCA models the foraging behaviour of ants, which move through the data space searching for high data-density regions, and leave pheromone trails on their path. The pheromone map is used to identify the exact number of clusters, and assign the pixels to these clusters using the pheromone gradient. We applied ASCA to detection of microcalcifications in digital mammograms and compared its performance with state-of-the-art clustering algorithms such as 1D Self-Organizing Map, k-Means, Fuzzy c-Means and Possibilistic Fuzzy c-Means. The main advantage of ASCA is that the number of clusters needs not to be known a priori. The experimental results show that ASCA is more efficient than the other algorithms in detecting small clusters of atypical data.

Combination of nonlinear filters and ANN for detection of microcalcifications in digitized mammography

Breast cancer is one of the leading causes to women mortality in the world. Cluster of Microcalcifications (MCCs) in mammograms can be an important early sign of breast cancer, the detection is important to prevent and treat the disease. In this paper, we present a novel method for the detection of MCCs in mammograms which consists of image enhancement by histogram adaptive equalization technique, MCCs edge detection by coordinate logic filters (CLF), generation, clustering and labelling of suboptimal features vectors by self organizing map (SOM) neural network. The experiment results show that the proposed method can locate MCCs in an efficient way.

Swarm architectures for Ground-based Air Defense Systems of Systems

New generations of Ground based Air Defense Systems of Systems used in modernized Armed Forces maintain the architectures used in their previous versions, constrained by hierarchical radio communications and centralized Command and Control restrictions. Current state-of-the-art technology in these areas allows a refocusing of these architectures towards a swarm approach. Advantages in terms of effectiveness, scalability and survivability are identified, and a preliminary set of swarm algorithms are proposed and analyzed. Upgrades of these preliminary algorithms are proposed as future research areas.

Sustainable agriculture using an intelligent mechatronic system

The goal of the Project group created by U.P.M. in collaboration with foreign universities, research institutions and companies is the development of an intelligent mechatronic system for the use of precision and sustainable agriculture. The project as a whole includes the following components: photographing and decoding of the soil surface; fertility determination and formation of the fertility map; generation of the controlling signal for mechatronic dosing device; intelligent dosing of fertilizers; simulation, prototype and testing; human-machine interaction and training preparation.

Improved Multilayer Perceptron Design by Weighted Learning

This paper presents new relevant results on the application of the optimization of backpropagation algorithm by a weighting operation on an artificial neural network weights actualization during the learning phase. This modified backpropagation technique has been recently proposed by the author, and it is applied to a multilayer perceptron artificial neural network training in order to drastically improve the efficiency of the given training patterns. The purpose is to modify the mean square error (MSE) objective function in order to improve the training efficiency. We show how the application of the weighting function drastically accelerates training convergence whereas it maintains neural networks (NN) performance.

A mechatronic system design case study: Control of a robotic swarm using networked control algorithms

This paper describes the use of networked control algorithms in designing a robotic swarm. The main goal of a robotic swarm is to divide one task into multiple simpler tasks. Have we designed a swarm this way, the main challenge would be the problem of delay in communication between individual robots. This paper also goes through the Swarm Intelligence concept and proposes the Network Formation Control algorithms to control a group of robots.