Marco Antonio Aceves-Fernández

Modeling Key Parameters for Greenhouse Using Fuzzy Clustering Techniques

The clustering techniques are usually used in classification and pattern recognition. Moreover, fuzzy logic is used for system modeling when the information is scarce, inaccurate or its behavior is described using a complex mathematical model. As example of this type of system, a greenhouse is considered, where the variables are: in-house and out-house temperature, humidity for both inside and outside the greenhouse and wind direction. These variables show a dynamic and non-linear behavior; being the in-house temperature and internal humidity the variables of concern for the greenhouse control and modeling. In this project, the development and implementation of three clustering algorithms, being fuzzy K-means, Fuzzy C-means and fuzzy clustering subtractive, is presented. This project is used as the foundation for the design of fuzzy systems and its application in temperature and humidity modeling of a greenhouse used as a laboratory of biotronics at the Universidad Autonoma de Queretaro.

Comparison between Fuzzy C-means clustering and Fuzzy Clustering Subtractive in urban air pollution

Clustering is generally associated with classification problem. In this contribution, the implementation of cluster estimation method as a basis of a fuzzy model identification algorithm has been developed. A comparison between two different clustering techniques is presented, Fuzzy C-means clustering and Fuzzy Clustering Subtractive. Also, an application in modeling the relationship between temperature, humidity and PM10 concentration in urban air pollution in Liverpool at northwest of England is presented.

A Profilometric Approach for 3D Reconstruction Using Fourier and Wavelet Transforms

In this research, an improved method for three-dimensional shape measurement by fringe projection is presented. The use of Fourier and Wavelet transform based analysis to extract the 3D information from the objects is proposed. The method requires a single image which contains a sinusoidal white light fringe pattern projected on it. This fringe pattern has a known spatial frequency and this information is used to avoid the discontinuities in the fringes with high frequency. Several computer simulations and experiments have been carried out to verify the analysis. The comparison between numerical simulations and experiments has proved the validity of this proposed method.

Simulation Methodology for Mechatronic Applications using Multilanguage Techniques

Abstract This paper presents a simulation methodology proposal, applying a multilanguage technique to simulate a mechatronic system. Firstly, a way to interact between two programming languages is presented, by taking advantage of each language to facilitate the development of a complex simulation tool. Later, the mechanical structure is obtained in VRML format. Next, special software is used to compute the mathematical model for kinematics and dynamic behavior. The results are showed by graphical simulation with Graphic Libraries support. Finally, the methodology is applied to flexible manipulator with one degree of freedom as study case, and the graphical simulation is presented.

Location of mammograms ROI's and reduction of false-positive

BACKGROUND AND OBJECTIVE There are many work related with segmentation techniques, including nearest neighbor algorithm, fuzzy rules, morphological filters, image entropy, thresholding, machine learning, wavelet analysis, and so on. Such methods carry out the segmentation, but take a lot of processing time by modifying the content of the image or showing discern problems in homogeneous areas, and the segmentation technique is designed to work efficiently only with the techniques used. In this paper a method to segment mammograms in order to separate breast area from pectoral-muscle avoiding bright areas that produce noise and therefore reducing false-positives is presented. METHODS The proposed methodology is divided into four sections: 1) Pre-processing to acquire image and decreasing its size. 2) Improving the image quality through image thresholding and histogram equalization. 3) Localization of regions of interest (ROI) applying Scale-Invariant Feature Transform to find images descriptors. Clustering methods were implemented to determine the best number of clusters and which of these represent the most significant breast area. Then found ROIs coordinates are compared with the position of abnormalities diagnosed by the Mammographic Image Analysis Society. 4) Microcalcifications (mcc) detection; wavelet transform is used, and to enhance its performance different high-pass filters and high-frequency emphasis filters are evaluated. Symlet wavelets: Sym8 and Sym16 were used with different decomposition level; images results from both processes are compared and only those elements in common are detected as microcalcifications. RESULTS Moreover, muscles remnants in the corners of the regions of interest were removed using fuzzy c-means clustering. The best results in terms of sensitivity (91.27), false-positives per image (80.25), and precision (74.38) are compared with previous work. CONCLUSIONS Results shows that the breast area can be discriminated from the pectoral-muscle by avoiding to work with brightness areas that produces false positives. Moreover, because the image size is reduced the computer processing time will be decreased. This segmentation stage can be an addition to mammograms analysis broadly, not only to find mcc but abnormalities such as circumscribed masses, speculated masses and architectural distortion. Also is useful to create automatically an unsupervised segmentation in mammograms without stage of training.

Risk map generation for keyhole neurosurgery using fuzzy logic for trajectory evaluation

Planning safe trajectories in keyhole neurosurgery requires a high level of accuracy in order to reach small anatomical structures in operations such as biopsies, deep brain stimulation and other therapy/diagnostic procedures. In this study, we propose a user interface that assists the neurosurgeon to select the entry point of the procedure using an atlas which displays the risk of the different linear trajectories from the head surface skin to the target. The risk assignment is carried out in two main steps, a weighted sum which depends on the distance of a single voxel to other risk structures and a second criteria using fuzzy rules to take into account other qualities such as length, which is the main contribution of this work. As a result, we present risk maps of two targets and visualize the less risky trajectories according to the system.

Trajectory planning for keyhole neurosurgery using fuzzy logic for risk evaluation

Planning safe trajectories in keyhole neurosurgery requires a high level of accuracy in order to access to small structures either by biopsies, stimulating deep brain and others. We propose a computer system that carries out decision making based on rules using fuzzy logic to plan safe trajectories for preoperative neurosurgery. The processes to generate input values of membership functions, and implementation of the system for decision function will be explained. The results of risk weights for each candidate trajectory are evaluated and the safest calculated trajectories taking into account the risk structures that there are in the brain from the insertion points to the target point are visualized.

Analysis of Key Features of Non-Linear Behaviour Using Recurrence Quantification. Case Study: Urban Airborne Pollution at Mexico City

The use of recurrence plots have been extensively used in various fields. In this work, recurrence plots investigate the changes in the non-linear behaviour of urban air pollution using large datasets of raw data (hourly). This analysis has not been used before to extract information from large datasets for this type of non-linear problem. Two different approaches have been used to tackle this problem. The first approach is to show results according to monitoring network. The second approach is to show the results by particle type. This analysis shows the feasibility of using recurrence analysis for pollution monitoring and control.