Odometry-Based Viterbi Localization with Artificial Neural Networks and Laser Range Finders for Mobile Robots

This paper proposes an approach that solves the Robot Localization problem by using a conditional state-transition Hidden Markov Model (HMM). Through the use of Self Organized Maps (SOMs) a Tolerant Observation Model (TOM) is built, while odometer-dependent transition probabilities are used for building an Odometer-Dependent Motion Model (ODMM). By using the Viterbi Algorithm and establishing a trigger value when evaluating the state-transition updates, the presented approach can easily take care of Position Tracking (PT), Global Localization (GL) and Robot Kidnapping (RK) with an ease of implementation difficult to achieve in most of the state-of-the-art localization algorithms. Also, an optimization is presented to allow the algorithm to run in standard microprocessors in real time, without the need of huge probability gridmaps.     

The Kosko Subsethood Fuzzy Associative Memory (KS-FAM): Mathematical Background and Applications in Computer Vision

Many well-known fuzzy associative memory (FAM) models can be viewed as (fuzzy) morphological neural networks (MNNs) because they perform an operation of (fuzzy) mathematical morphology at every node, possibly followed by the application of an activation function. The vast majority of these FAMs represent distributive models given by single-layer matrix memories. Although the Kosko subsethood FAM (KS-FAM) can also be classified as a fuzzy morphological associative memory (FMAM), the KS-FAM constitutes a two-layer non-distributive model. In this paper, we prove several theorems concerning the conditions of perfect recall, the absolute storage capacity, and the output patterns produced by the KS-FAM. In addition, we propose a normalization strategy for the training and recall phases of the KS-FAM. We employ this strategy to compare the error correction capabilities of the KS-FAM and other fuzzy and gray-scale associative memories in terms of some experimental results concerning gray-scale image reconstruction. Finally, we apply the KS-FAM to the task of vision-based self-localization in robotics.     

Distance maps from unthresholded magnitudes

A straightforward algorithm that computes distance maps from unthresholded magnitudes is presented, suitable for still images and video sequences. While results on binary images are similar to classic Euclidean Distance Transforms, the proposed approach does not require a binarization step. Thus, no thresholds are needed and no information is lost in intermediate classification stages. Experiments include the evaluation of spatial and temporal coherence of distance map values, showing better results in both measurements than those obtained with Sobel or Deriche gradients and classic chessboard distance transforms.     

A new robust algorithmic for multi-camera calibration with a 1D object under general motions without prior knowledge of any camera intrinsic parameter

In computer vision, camera calibration is a necessary process when the retrieval of information such as angles and distances is required. This paper addresses the multi-camera calibration problem with a single dimension calibration pattern under general motions. Currently, the known algorithms for solving this problem are based on the estimation of vanishing points. However, this estimate is very susceptible to noise, making the methods unsuitable for practical applications. Instead, this paper presents a new calibration algorithm, where the cameras are divided into binocular sets. The fundamental matrix of each binocular set is then estimated, allowing to perform a projective calibration of each camera. Then, the calibration is updated for the Euclidean space, ending the process. The calibration is possible without imposing any restrictions on the movement of the pattern and without any prior information about the cameras or motion. Experiments on synthetic and real images validate the new method and show that its accuracy makes it suitable also for practical applications.     

A case study on business process recovery using an e‐government system

Business processes have become one of the key assets of organization, since these processes allow them to discover and control what occurs in their environments, with information systems automating most of an organization's processes. Unfortunately, and as a result of uncontrolled maintenance, information systems age over time until it is necessary to replace them with new and modernized systems. However, while systems are aging, meaningful business knowledge that is not present in any of the organization's other assets gradually becomes embedded in them. The preservation of this knowledge through the recovery of the underlying business processes is, therefore, a critical problem. This paper provides, as a solution to the aforementioned problem, a model‐driven procedure for recovering business processes from legacy information systems. The procedure proposes a set of models at different abstraction levels, along with the model transformations between them. The paper also provides a supporting tool, which facilitates its adoption. Moreover, a real‐life case study concerning an e‐government system applies the proposed recovery procedure to validate its effectiveness and efficiency. The case study was carried out by following a formal protocol to improve its rigor and replicability. Copyright © 2011 John Wiley & Sons, Ltd.     

Capacity estimation algorithm for simultaneous support of multi-class traffic services in Mobile WiMAX

Mobile profile of WiMAX technology, based on IEEE 802.16e standard, is due to support multi-application services, while benefiting a wide range of implementation flexibilities. The current document presents a simple capacity estimation methodology for WiMAX systems. In this paper, the term capacity concerns with the maximum number of simultaneous multi-service users that each specific Mobile WiMAX access point can jointly support. A dynamic PHY+MAC overhead removal method is formulated to achieve an improved system goodput with respect to users distribution and multi-burst construction strategy. A service delivery model is presented that investigates the QoS requirements of the services to be supported, along with elaborating an application profile as a consistent input for capacitating and dimensioning studies. The minimum resource consumption calculation for the joint-application users is derived at each given time. An incremental algorithm compares the optimal available resources with minimum service demand for each number of users to arrive at maximum system capacity. Using the proposed algorithm, different simulation scenarios are studied based on the most used WiMAX implementation parameters in practice. The simulation results prove the significant roll of overhead calculation in performance evaluation studies. Furthermore, these results can be used for network planning and dimensioning purposes, as well as providing reference measures for scheduling performance analysis and detailed simulations.     

Integration of collaboration and interaction analysis mechanisms in a concern-based architecture for groupware systems

Collaboration and interaction analysis allows for the characterization and study of the collaborative work performed by the users of a groupware system. The results of the analyzed processes allow problems in users’ collaborative work and shortcomings in the functionalities of the groupware system to be identified. Therefore, automating collaboration and interaction analysis enables users’ work to be assessed and groupware system support and behavior to be improved. This article proposes a concern-based architecture to be used by groupware developers as a guide to the integration of analysis subsystems into groupware systems. This architecture was followed to design the COLLECE groupware system, which supports collaborative programming practices and integrates an analysis subsystem that assesses different aspects of the work carried out by the programmers and adapts the functionality of the system under specific conditions.