Papers by Aleksandar Jevtic
Microcalcification detection applying artificial neural networks and mathematical morphology in digital mammograms
Breast cancer is one of the leading causes to women mortality in the world and early detection is... more Breast cancer is one of the leading causes to women mortality in the world and early detection is an important means to reduce the mortality rate. The presence of microcalcifications clusters has been considered as a very important indicator of malignant types of breast cancer and its detection is important to prevent and treat the disease. This paper presents an alternative and effective approach in order to detect microcalcifications clusters in digitized mammograms based on the synergy of the image processing, pattern recognition and artificial intelligence. The mathematical morphology is an image processing technique used for the purpose of image enhancement. A k-means algorithm is used to cluster the data based on the features vectors and finally an artificial neural network-based classifier is applied and the classification performance is evaluated by a ROC curve. Experimental results indicate that the percentage of correct classification was 99.72%, obtaining 100% true positive (sensitivity) and 99.67% false positive (specificity), with the best classifier proposed. In case of the best classifier, we obtained a performance evaluation of classification of Az = 0.9875.
Ant Colony Optimization (ACO) is a group of algorithms inspired by the foraging behavior of ant c... more Ant Colony Optimization (ACO) is a group of algorithms inspired by the foraging behavior of ant colonies in nature. Like their biological counterparts, a colony of artificial ants is able to adapt to the changes in their environment, such as exhaustion of a food source and discovery of a new one. In this paper, one of the basic ACO algorithms, the Ant System algorithm, was applied for edge detection where the edge pixels represent food for the ants. A set of grayscale images obtained by a nonlinear contrast enhancement technique called Multiscale Adaptive Gain is used to create a variable environment. As the images change, the ant colony adapts to those changes leaving pheromone trails where the new edges appear while the pheromone trails that are not reinforced evaporate over time. Although the images were used to create an environmental setup in which the ants move, the colony's adaptive behavior could be demonstrated on any type of digital habitat.
Robotics and Autonomous Systems, 2015
Learning the Relation of Motion Control and Gestures Through Self-Exploration
Artificial Metaplasticity Improves Artificial Neural Networks Learning
Intelligent Automation and Soft Computing
Metaplasticity property of biological synapses is interpreted in this paper as the concept of pla... more Metaplasticity property of biological synapses is interpreted in this paper as the concept of placing greater emphasis on training patterns that are less frequent. A novel implementation is proposed in which, during the network learning phase, a priority is given to weight updating of less frequent activations over the more frequent ones. Modeling this interpretation in the training phase, the hypothesis of an improved training is tested on the Multilayer Perceptron type network with Backpropagation training. The results obtained for the chosen application show a much more efficient training, while at least maintaining the Multilayer Perceptron performance.
Automatic Multi-sensor Task Allocation Using Modified Distributed Bees Algorithm
2013 IEEE International Conference on Systems, Man, and Cybernetics, 2013
ABSTRACT In this paper, we propose a Modified Distributed Bees Algorithm (MDBA) for multi-sensor ... more ABSTRACT In this paper, we propose a Modified Distributed Bees Algorithm (MDBA) for multi-sensor task allocation in a supply chain security scenario. The MDBA assigns sensors to the upcoming tasks using a decentralized, probabilistic approach to maximize information gain while minimizing costs. Tasks are allocated based on sensors' performance, tasks' priorities and the mutual sensor-task distances. Simulation analysis compared different algorithms and indicated improved performance of 15% by using MDBA with respect to the second-best algorithm.
This paper presents the interactive robotics concept being developed by the INTRO research networ... more This paper presents the interactive robotics concept being developed by the INTRO research network. The aim is to create a new generation of intelligent mobile robots that operate in close interaction with humans in unstructured, dynamically changing environments. The INTRO network consists of a team of researchers, from academia and industry, which create a multidisciplinary framework that entails Cooperative Robot Learning, Cognitive Human-Robot Interaction (HRI), and Intelligent Interface Design. The robotic system being developed will be tested in two application scenarios: the Robot Waiter, and the Urban Search and Rescue (USAR). For these scenarios, two different robotic platforms are used in the implementation stage. This paper presents an overview of the obtained research objectives, and proposes a framework for the integration of work and the implementation of the expected results. Finally, the paper describes a potential impact through development and use of research results and proposes future lines of research.
In this paper, a novel edge detection method that computes image gradient using the concept of Ce... more In this paper, a novel edge detection method that computes image gradient using the concept of Center of Mass (COM) is presented. The algorithm runs with a constant number of operations per pixel independently from its scale by using integral image. Compared with the conventional convolutional edge detector such as Sobel edge detector, the proposed method performs faster when region size is larger than 9×9. The proposed method can be used as framework for multi-scale edge detectors when the goal is to achieve fast performance. Experimental results show that edge detection by COM is competent with Canny edge detection.
Learning the Relation of Motion Control and Gestures Through Self-Exploration
Robotics and Autonomous Systems, 2015
Concepts, Methodologies, Tools, and Applications, 2014
This chapter introduces a swarm intelligence-inspired approach for target allocation in large tea... more This chapter introduces a swarm intelligence-inspired approach for target allocation in large teams of autonomous robots. For this purpose, the Distributed Bees Algorithm (DBA) was proposed and developed by the authors. The algorithm allows decentralized decision-making by the robots based on the locally available information, which is an inherent feature of animal swarms in nature. The algorithm's performance was validated on physical robots. Moreover, a swarm simulator was developed to test the scalability of larger swarms in terms of number of robots and number of targets in the robot arena. Finally, improved target allocation in terms of deployment cost efficiency, measured as the average distance traveled by the robots, was achieved through optimization of the DBA's control parameters by means of a genetic algorithm. rescue, communication networks, monitoring, surveillance, cleaning, maintenance, and so forth. In order to efficiently perform their tasks, robots require high level of autonomy and cooperation. They use their sensing abilities to explore an unknown environment and deploy on the sites of interest, i.e. targets. However, the coordination of a robot swarm is not an easy problem, especially when the resources for the deployment task are limited. Such a large group of robots, if organized in a centralized manner, could experience information overflow that can lead to the overall system failure . For this reason, the communication between the robots can be realized through local interactions, either directly with one another or indirectly via environment .
This chapter introduces a swarm intelligence-inspired approach for target allocation in large tea... more This chapter introduces a swarm intelligence-inspired approach for target allocation in large teams of autonomous robots. For this purpose, the Distributed Bees Algorithm (DBA) was proposed and developed by the authors. The algorithm allows decentralized decision-making by the robots based on the locally available information, which is an inherent feature of animal swarms in nature. The algorithm's performance was validated on physical robots. Moreover, a swarm simulator was developed to test the scalability of larger swarms in terms of number of robots and number of targets in the robot arena. Finally, improved target allocation in terms of deployment cost efficiency, measured as the average distance traveled by the robots, was achieved through optimization of the DBA's control parameters by means of a genetic algorithm.
IEEE Systems Journal, 2012
In this paper, we propose the distributed bees algorithm (DBA) for task allocation in a swarm of ... more In this paper, we propose the distributed bees algorithm (DBA) for task allocation in a swarm of robots. In the proposed scenario, task allocation consists in assigning the robots to the found targets in a 2-D arena. The expected distribution is obtained from the targets' qualities that are represented as scalar values. Decision-making mechanism is distributed and robots autonomously choose their assignments taking into account targets' qualities and distances. We tested the scalability of the proposed DBA algorithm in terms of number of robots and number of targets. For that, the experiments were performed in the simulator for various sets of parameters, including number of robots, number of targets, and targets' utilities. Control parameters inherent to DBA were tuned to test how they affect the final robot distribution. The simulation results show that by increasing the robot swarm size, the distribution error decreased.
Sensors, 2011
Swarms of robots can use their sensing abilities to explore unknown environments and deploy on si... more 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. Even though cheap robot hardware has become widely accessible on the market, application of multi-robot systems in our everyday lives is limited. Nevertheless, due to the potential that this field has, great efforts have been made by various research groups to investigate the algorithms for coordination and control of multi-robot systems consisting of large number of units. In order to unify the research under a single framework, some researchers have proposed different multi-robot system taxonomies. Dudek et al.
Comparison of Interaction Modalities for Mobile Indoor Robot Guidance: Direct Physical Interaction, Person Following, and Pointing Control
IEEE Transactions on Human-Machine Systems, 2015
Three advanced natural interaction modalities for mobile robot guidance in an indoor environment ... more Three advanced natural interaction modalities for mobile robot guidance in an indoor environment were developed and compared using two tasks and quantitative metrics to measure performance and workload. The first interaction modality is based on direct physical interaction requiring the human user to push the robot in order to displace it. The second and third interaction modalities exploit a 3-D vision-based human skeleton tracking allowing the user to guide the robot by either walking in front of it or by pointing towards a desired location. In the first task, the participants were asked to guide the robot between different rooms in a simulated physical apartment requiring rough movement of the robot through designated areas. The second task evaluated robot guidance in the same environment through a set of waypoints, which required accurate movements. The three interaction modalities were implemented on a generic differential drive mobile platform equipped with a pan-tilt system and a Kinect camera. Task completion time and accuracy were used as metrics to assess the users’ performance, while the NASA-TLX questionnaire was used to evaluate the users’ workload. A study with 24 participants indicated that choice of interaction modality had significant effect on completion time (F(2,61)=84.874, p<0.001), accuracy (F(2,29)=4.937, p=0.016), and workload (F(2,68)=11.948, p<0.001). The direct physical interaction required less time, provided more accuracy and less workload than the two contactless interaction modalities. Between the two contactless interaction modalities, the person-following interaction modality was systematically better than the pointing-control one: the participants completed the tasks faster with less workload.
Spatially unconstrained, gesture-based human-robot interaction
2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2013
ABSTRACT For a human-robot interaction to take place, a robot needs to perceive humans. The space... more ABSTRACT For a human-robot interaction to take place, a robot needs to perceive humans. The space where a robot can perceive humans is restrained by the limitations of robot's sensors. These restrictions can be circumvented by the use of external sensors, like in intelligent environments; otherwise humans have to ensure that they can be perceived. With the robotic platform presented here, the roles are reversed and the robot autonomously ensures that the human is within the area perceived by the robot. This is achieved by a combination of hardware and algorithms capable of autonomously tracking the person, estimating their position and following them, while recognizing their gestures and moving through space.
Human-robot interaction through 3D vision and force control
Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction - HRI '14, 2014
ABSTRACT The video shows the interaction with a customized Kompa\"{i} robot. The robot c... more ABSTRACT The video shows the interaction with a customized Kompa\"{i} robot. The robot consists of the Robosoft's robuLAB10 platform, tablet PC, and a Microsoft Kinect camera mounted on a pan-tilt system. A visual control algorithm provides continuous person tracking. The newly developed robot features include gesture recognition, person following, navigation with pointing, and force control, which were integrated with the Robosoft's robuBOX SDK and the Karto SLAM algorithms. The video demonstrates all the features and puts the robot in use in an everyday home scenario.
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Papers by Aleksandar Jevtic