Automatic lateral control for unmanned vehicles via genetic algorithms

It is known that the techniques under the topic of Soft Computing have a strong capability of learning and cognition, as well as a good tolerance to uncertainty and imprecision. Due to these properties they can be applied successfully to Intelligent Vehicle Systems; ITS is a broad range of technologies and techniques that hold answers to many transportation problems. The unmannedcontrol of the steering wheel of a vehicle is one of the most important challenges facing researchers in this area. This paper presents a method to adjust automatically a fuzzy controller to manage the steering wheel of a mass-produced vehicle; to reach it, information about the car state while a human driver is handling the car is taken and used to adjust, via iterative geneticalgorithms an appropriated fuzzy controller. To evaluate the obtained controllers, it will be considered the performance obtained in the track following task, as well as the smoothness of the driving carried out.

Adapting autonomously classification data mining algorithms for ubiquitous devices

Abstract Due to recent scientific and technological advances in information sys¬tems, it is now possible to perform almost every application on a mobile device. The need to make sense of such devices more intelligent opens an opportunity to design data mining algorithm that are able to autonomous execute in local devices to provide the device with knowledge. The problem behind autonomous mining deals with the proper configuration of the algorithm to produce the most appropriate results. Contextual information together with resource information of the device have a strong impact on both the feasibility of a particu¬lar execution and on the production of the proper patterns. On the other hand, performance of the algorithm expressed in terms of efficacy and efficiency highly depends on the features of the dataset to be analyzed together with values of the parameters of a particular implementation of an algorithm. However, few existing approaches deal with autonomous configuration of data mining algorithms and in any case they do not deal with contextual or resources information. Both issues are of particular significance, in particular for social net¬works application. In fact, the widespread use of social networks and consequently the amount of information shared have made the need of modeling context in social application a priority. Also the resource consumption has a crucial role in such platforms as the users are using social networks mainly on their mobile devices. This PhD thesis addresses the aforementioned open issues, focusing on i) Analyzing the behavior of algorithms, ii) mapping contextual and resources information to find the most appropriate configuration iii) applying the model for the case of a social recommender. Four main contributions are presented: - The EE-Model: is able to predict the behavior of a data mining algorithm in terms of resource consumed and accuracy of the mining model it will obtain. - The SC-Mapper: maps a situation defined by the context and resource state to a data mining configuration. - SOMAR: is a social activity (event and informal ongoings) recommender for mobile devices. - D-SOMAR: is an evolution of SOMAR which incorporates the configurator in order to provide updated recommendations. Finally, the experimental validation of the proposed contributions using synthetic and real datasets allows us to achieve the objectives and answer the research questions proposed for this dissertation.

Response threshold models, stochastic learning automata and ant colony optimization-based decentralized self-coordination algorithms for heterogeneous multi-tasks distribution in multi-robot systems

In recent decades, there has been an increasing interest in systems comprised of several autonomous mobile robots, and as a result, there has been a substantial amount of development in the eld of Articial Intelligence, especially in Robotics. There are several studies in the literature by some researchers from the scientic community that focus on the creation of intelligent machines and devices capable to imitate the functions and movements of living beings. Multi-Robot Systems (MRS) can often deal with tasks that are dicult, if not impossible, to be accomplished by a single robot. In the context of MRS, one of the main challenges is the need to control, coordinate and synchronize the operation of multiple robots to perform a specic task. This requires the development of new strategies and methods which allow us to obtain the desired system behavior in a formal and concise way. This PhD thesis aims to study the coordination of multi-robot systems, in particular, addresses the problem of the distribution of heterogeneous multi-tasks. The main interest in these systems is to understand how from simple rules inspired by the division of labor in social insects, a group of robots can perform tasks in an organized and coordinated way. We are mainly interested on truly distributed or decentralized solutions in which the robots themselves, autonomously and in an individual manner, select a particular task so that all tasks are optimally distributed. In general, to perform the multi-tasks distribution among a team of robots, they have to synchronize their actions and exchange information. Under this approach we can speak of multi-tasks selection instead of multi-tasks assignment, which means, that the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation ix of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. In addition, it is very interesting the evaluation of the results in function in each approach, comparing the results obtained by the introducing noise in the number of pending loads, with the purpose of simulate the robot's error in estimating the real number of pending tasks. The main contribution of this thesis can be found in the approach based on self-organization and division of labor in social insects. An experimental scenario for the coordination problem among multiple robots, the robustness of the approaches and the generation of dynamic tasks have been presented and discussed. The particular issues studied are: Threshold models: It presents the experiments conducted to test the response threshold model with the objective to analyze the system performance index, for the problem of the distribution of heterogeneous multitasks in multi-robot systems; also has been introduced additive noise in the number of pending loads and has been generated dynamic tasks over time. Learning automata methods: It describes the experiments to test the learning automata-based probabilistic algorithms. The approach was tested to evaluate the system performance index with additive noise and with dynamic tasks generation for the same problem of the distribution of heterogeneous multi-tasks in multi-robot systems. Ant colony optimization: The goal of the experiments presented is to test the ant colony optimization-based deterministic algorithms, to achieve the distribution of heterogeneous multi-tasks in multi-robot systems. In the experiments performed, the system performance index is evaluated by introducing additive noise and dynamic tasks generation over time.

Using clustering techniques for intelligent camera-based user interfaces

The area of Human-Machine Interface is growing fast due to its high importance in all technological systems. The basic idea behind designing human-machine interfaces is to enrich the communication with the technology in a natural and easy way. Gesture interfaces are a good example of transparent interfaces. Such interfaces must identify properly the action the user wants to perform, so the proper gesture recognition is of the highest importance. However, most of the systems based on gesture recognition use complex methods requiring high-resource devices. In this work, we propose to model gestures capturing their temporal properties, which significantly reduce storage requirements, and use clustering techniques, namely self-organizing maps and unsupervised genetic algorithm, for their classification. We further propose to train a certain number of algorithms with different parameters and combine their decision using majority voting in order to decrease the false positive rate. The main advantage of the approach is its simplicity, which enables the implementation using devices with limited resources, and therefore low cost. The testing results demonstrate its high potential.

Bio-inspired enhancement of reputation systems for intelligent environments

Providing security to the emerging field of ambient intelligence will be difficult if we rely only on existing techniques, given their dynamic and heterogeneous nature. Moreover, security demands of these systems are expected to grow, as many applications will require accurate context modeling. In this work we propose an enhancement to the reputation systems traditionally deployed for securing these systems. Different anomaly detectors are combined using the immunological paradigm to optimize reputation system performance in response to evolving security requirements. As an example, the experiments show how a combination of detectors based on unsupervised techniques (self-organizing maps and genetic algorithms) can help to significantly reduce the global response time of the reputation system. The proposed solution offers many benefits: scalability, fast response to adversarial activities, ability to detect unknown attacks, high adaptability, and high ability in detecting and confining attacks. For these reasons, we believe that our solution is capable of coping with the dynamism of ambient intelligence systems and the growing requirements of security demands.

Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors

The emergence of new horizons in the field of travel assistant management leads to the development of cutting-edge systems focused on improving the existing ones. Moreover, new opportunities are being also presented since systems trend to be more reliable and autonomous. In this paper, a self-learning embedded system for object identification based on adaptive-cooperative dynamic approaches is presented for intelligent sensor’s infrastructures. The proposed system is able to detect and identify moving objects using a dynamic decision tree. Consequently, it combines machine learning algorithms and cooperative strategies in order to make the system more adaptive to changing environments. Therefore, the proposed system may be very useful for many applications like shadow tolls since several types of vehicles may be distinguished, parking optimization systems, improved traffic conditions systems, etc.