Development and in vivo response of hydroxyapatite/whitlockite from chicken bones as bone substitute using a chitosan membrane for guided bone regeneration

Biomaterials that meet the requirements to stimulate bone tissue formation play a vital role in orthopedics and dentistry. In this work, chitosan and a biphasic, non-cytotoxic material hydroxyapatite/whitlockite were obtained from natural sources, which are available as organic waste. The osteogenic activity was assessed using a rabbit model animal with a chitosan barrier membrane in combination with a bone-filling graft substitute composed of hydroxyapatite/whitlockite. FT-IR results showed the typical absorption bands of the chitosan and hydroxyapatite. Moreover, the X-ray diffraction pattern revealed a typical hexagonal phase of hydroxyapatite and rhombohedral structures related to whitlockite. Masson's trichrome stain showed an early formation of extracellular matrix mineralized, in accord with the surface morphology of a cortical mature bond observed by Scanning Electron Microscopy. The immunocytochemistry results showed a significant increase of positive immunoreactive cells to osteonectin in the treated defects in comparison with the control defects 6 and 8 weeks postoperatively. Overall, the results confirm that the use of this low-cost and versatile biomaterial as a barrier membrane and a bone substitute graft are useful for bone tissue engineering.     

A framework for defining and learning fuzzy behaviors for autonomous mobile robots

In this paper we show our work on the use of fuzzy behaviors in the field of autonomous mobile robots. We address here how we use learning techniques to efficiently coordinate the conflicts between the different behaviors that compete with each other to take control of the robot. We use fuzzy rules to perform such fusion. These rules can be set using expert knowledge, but as this can be a complex task, we show how to automatically define them using genetic algorithms. We also describe the working environment, which includes a custom programming language (named BG) based on the multi‐agent paradigm. Finally, some results related to simple goods‐delivery tasks in an unknown environment are presented. © 2002 John Wiley & Sons, Inc.     

A diagnoser algorithm for anomaly detection in DEDS under partial and unreliable observations: characterization and inclusion in sensor configuration optimization

Complex engineering systems have to be carefully monitored to meet demanding performance requirements, including detecting anomalies in their operations. There are two major monitoring challenges for these systems. The first challenge is that information collected from the monitored system is often partial and/or unreliable, in the sense that some occurred events may not be reported and/or may be reported incorrectly (e.g., reported as another event). The second is that anomalies often consist of sequences of event patterns separated in space and time. This paper introduces and analyzes a diagnoser algorithm that meets these challenges for detecting and counting occurrences of anomalies in engineering systems. The proposed diagnoser algorithm assumes that models are available for characterizing plant operations (via stochastic automata) and sensors (via probabilistic mappings) used for reporting partial and unreliable information. Methods for analyzing the effects of model uncertainties on the diagnoser performance are also discussed. In order to select configurations that reduce sensor costs, while satisfying diagnoser performance requirements, a sensor configuration selection algorithm developed in previous work is then extended for the proposed diagnoser algorithm. The proposed algorithms and methods are then applied to a multi-unit-operation system, which is derived from an actual facility application. Results show that the proposed diagnoser algorithm is able to detect and count occurrences of anomalies accurately and that its performance is robust to model uncertainties. Furthermore, the sensor configuration selection algorithm is able to suggest optimal sensor configurations with significantly reduced costs, while still yielding acceptable performance for counting the occurrences of anomalies.     

Dynamic classifier selection for One-vs-One strategy: Avoiding non-competent classifiers

The One-vs-One strategy is one of the most commonly used decomposition technique to overcome multi-class classification problems; this way, multi-class problems are divided into easier-to-solve binary classification problems considering pairs of classes from the original problem, which are then learned by independent base classifiers.The way of performing the division produces the so-called non-competence. This problem occurs whenever an instance is classified, since it is submitted to all the base classifiers although the outputs of some of them are not meaningful (they were not trained using the instances from the class of the instance to be classified). This issue may lead to erroneous classifications, because in spite of their incompetence, all classifiers' decisions are usually considered in the aggregation phase.In this paper, we propose a dynamic classifier selection strategy for One-vs-One scheme that tries to avoid the non-competent classifiers when their output is probably not of interest. We consider the neighborhood of each instance to decide whether a classifier may be competent or not. In order to verify the validity of the proposed method, we will carry out a thorough experimental study considering different base classifiers and comparing our proposal with the best performer state-of-the-art aggregation within each base classifier from the five Machine Learning paradigms selected. The findings drawn from the empirical analysis are supported by the appropriate statistical analysis.     

Associative Gray Level Pattern Processing using Binary Decomposition and α β Memories

In this note we show how a binary memory can be used to recall gray-level patterns. We take as example the α β associative memories recently proposed in Yáñez, Associative Memories based on order Relations and Binary Operators(In Spanish), PhD Thesis, Center for computing Research, February of 2002, only useful in the binary case. Basically, the idea consists on that given a set of gray-level patterns to be first memorized: (1) Decompose them into their corresponding binary patterns, and (2) Build the corresponding binary associative memory (one memory for each binary layer) with each training pattern set (by layers). A given pattern or a distorted version of it, it is recalled in three steps: (1) Decomposition of the pattern by layers into its binary patterns, (2) Recalling of each one of its binary components, layer by layer also, and (3) Reconstruction of the pattern from the binary patterns already recalled in step 2. The proposed methodology operates at two phases: training and recalling. Conditions for perfect recall of a pattern either from the fundamental set or from a distorted version of one them are also given. Experiments where the efficiency of the proposal is tested are also given.     

Modularizing application and database evolution – an aspect‐oriented framework for orthogonal persistence

In the maintenance of software applications, database evolution is one common difficulty. In object‐oriented databases, this process comprises schema evolution and instance adaptation. Both tasks usually require significant effort from programmers and database administrators. In this paper, we propose orthogonal persistence and aspect‐oriented programming to support semi‐transparent database evolution. A default mechanism for instance evolution is defined, but the user may provide modularized solutions using the aspect‐oriented paradigm. We present our framework AOF4OOP to test the feasibility of our proposed approach. This prototype allows programmes to transparently access data in other versions of the database schema. We evaluate our framework, comparing it to related approaches using two real applications and measuring the improvement of the productivity of the programmer. Copyright © 2016 John Wiley & Sons, Ltd.     

PD+SMC Quadrotor Control for Altitude and Crack Recognition Using Deep Learning

International Journal of Control, Automation and Systems - Building inspection is a vital task because infrastructure damage puts people at risk or causes economic losses. Thanks to the...     

Immersive and interactive virtual reality applications based on 3D web browsers

This study explores the possibility of effectively using web-standard technologies (i.e., HTML5, JavaScript, WebGL, and WebRTC) to deploy full immersive and interactive virtual reality applications. These applications are based on computer clusters and run in multi-projection environments, such as CAVEs, Panoramas and Power Walls. Until recently, these applications were build using tailored solutions, such low-level libraries to distribute data. We discuss the modern-day web technology which allows these applications meet a multi-platform requirement. This paper also evaluates the communication and synchronization requirements of these applications that are traditionally the main bottleneck. As a proof-of-concept to show the feasibility of our study, we devised and implemented an immersive and interactive virtual reality application employing only web technologies. Our proof-of-concept runs on a miniCAVE environment with three displays and a 6-node cluster.