Timed mind maps using mindmanager & msproject for educational innovation

The adaptation to the European Higher Education Area (EHEA) is becoming a great challenge for the University Community, especially for its teaching and research staff, which is involved actively in the teaching-learning process. It is also inducing a paradigm change for lecturers and students. Among the methodologies used for processes of teaching innovation, system thinking plays an important role when working mainly with mind maps, and is focused to highlighting the essence of the knowledge, allowing its visual representation. In this paper, a method for using these mind maps for organizing a particular subject is explained. This organization is completed with the definition of duration, precedence relationships and resources for each of these activities, as well as with their corresponding monitoring. Mind maps are generated by means of the MINDMANAGER package whilst Ms-PROJECT is used for establishing tasks relationships, durations, resources, and monitoring. Summarizing, a procedure and the necessary set of applications for self organizing and managing (timed) scheduled teaching tasks has been described in this paper

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.

Cryptographic properties of Boolean functions defining elementary cellular automata

In this work, the algebraic properties of the local transition functions of elementary cellular automata (ECA) were analysed. Specifically, a classification of such cellular automata was done according to their algebraic degree, the balancedness, the resiliency, nonlinearity, the propagation criterion and the existence of non-zero linear structures. It is shown that there is not any ECA satisfying all properties at the same time.

Fighting tax evasion: a cellular automata approach

Estudio de la dinámica de una población donde los individuos son contribuyentes (pagadores de impuestos) o no mediante un autómata celular 2D

Uniform Distributed Pushdown Automata Systems.

We consider here uniform distributed pushdown automata systems (UDPAS), namely distributed pushdown automata systems having all components identical pushdown automata. We consider here just a single protocol for activating/deactivating components, namely a component stays active as long as it can perform moves, as well as two ways of accepting the input word: by empty stacks (all components have empty stacks) or by final states (all components are in final states), when the input word is completely read. We mainly investigate the computational power of UDPAS accepting by empty stacks and a few decidability and closure properties of the families of languages they define. Some directions for further work and open problems are also discussed.

Decentralized multi-tasks distribution in heterogeneous robot teams by means of ant colony optimization and learning automata

This paper focuses on the general problem of coordinating multiple robots. More specifically, it addresses the self-election of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly interested on decentralized solution where the robots themselves autonomously and in an individual manner, are responsible of selecting a particular task so that all the existing tasks are optimally distributed and executed. In this regard, we have established an experimental scenario to solve the corresponding multi-tasks distribution problem and we propose a solution using two different approaches by applying Ant Colony Optimization-based deterministic algorithms as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithm, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

Response threshold models and stochastic learning automata for self-coordination of heterogeneous multi-task distribution in multi-robot systems.

This paper focuses on the general problem of coordinating multiple robots. More specifically, it addresses the self-selection of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly interested in a decentralized solution where the robots themselves autonomously and in an individual manner, are responsible for selecting a particular task so that all the existing tasks are optimally distributed and executed. In this regard, we have established an experimental scenario to solve the corresponding multi-task distribution problem and we propose a solution using two different approaches by applying Response Threshold Models as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithms, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

Fusion of probabilistic knowledge-based classification rules and learning automata for automatic recognition of digital images

In this paper, the fusion of probabilistic knowledge-based classification rules and learning automata theory is proposed and as a result we present a set of probabilistic classification rules with self-learning capability. The probabilities of the classification rules change dynamically guided by a supervised reinforcement process aimed at obtaining an optimum classification accuracy. This novel classifier is applied to the automatic recognition of digital images corresponding to visual landmarks for the autonomous navigation of an unmanned aerial vehicle (UAV) developed by the authors. The classification accuracy of the proposed classifier and its comparison with well-established pattern recognition methods is finally reported.

A self-timed multipurpose delay sensor for field programmable gate arrays (FPGAs)

This paper presents a novel self-timed multi-purpose sensor especially conceived for Field Programmable Gate Arrays (FPGAs). The aim of the sensor is to measure performance variations during the life-cycle of the device, such as process variability, critical path timing and temperature variations. The proposed topology, through the use of both combinational and sequential FPGA elements, amplifies the time of a signal traversing a delay chain to produce a pulse whose width is the sensor’s measurement. The sensor is fully self-timed, avoiding the need for clock distribution networks and eliminating the limitations imposed by the system clock. One single off- or on-chip time-to-digital converter is able to perform digitization of several sensors in a single operation. These features allow for a simplified approach for designers wanting to intertwine a multi-purpose sensor network with their application logic. Employed as a temperature sensor, it has been measured to have an error of ±0.67 °C, over the range of 20–100 °C, employing 20 logic elements with a 2-point calibration.

A remarkable, precisely timed release of hyperglycemic hormone from endocrine cells in the gut is associated with ecdysis in the crab Carcinus maenas

Molting or ecdysis is the most fundamentally important process in arthropod life history, because shedding of the exoskeleton is an absolute prerequisite for growth and metamorphosis. Although the hormonal mechanisms driving ecdysis in insects have been studied extensively, nothing is known about these processes in crustaceans. During late premolt and during ecdysis in the crab Carcinus maenas, we observed a precise and reproducible surge in hemolymph hyperglycemic hormone (CHH) levels, which was over 100-fold greater than levels seen in intermolt animals. The source of this hormone surge was not from the eyestalk neurosecretory tissues but from previously undescribed endocrine cells (paraneurons), in defined areas of the foregut and hindgut. During premolt (the only time when CHH is expressed by these tissues), the gut is the largest endocrine tissue in the crab. The CHH surge, which is a result of an unusual, almost complete discharge of the contents of the gut endocrine cell, regulates water and ion uptake during molting, thus allowing the swelling necessary for successful ecdysis and the subsequent increase in size during postmolt. This study defines an endocrine brain/gut axis in the arthropods. We propose that the ionoregulatory process controlled by CHH may be common to arthropods, in that, for insects, a similar mechanism seems to be involved in antidiuresis. It also seems likely that a cascade of very precisely coordinated release of (neuro) hormones controls ecdysis.

The treatise of al-Jazarī on automata; leaves from a manuscript of the Kitāb fī maʻarifat al-hiyal al handasiya in the Museum of fine arts. Boston, and elsewhere,

Plates, accompanied by unnumbered leaf, laid in.

Some theorems and algorithms on a new form of decomposition for automata,

Bibliography: p. 28.

Partially ordered classes of finite automata /

Vita.

Probabilistic languages and automata.

Vita.

The automorphism group of a strongly connected automaton and its quotient automata /

"February 14, 1966."