59 results on '"Mario Alberto Ibarra-Manzano"'
Search Results
2. Emotion recognition in EEG signals using the continuous wavelet transform and CNNs
- Author
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Oscar Almanza-Conejo, Dora Luz Almanza-Ojeda, Jose Luis Contreras-Hernandez, and Mario Alberto Ibarra-Manzano
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Artificial Intelligence ,Software - Published
- 2022
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Catalog
3. Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms
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Jesus A. Basurto-Hurtado, Irving A. Cruz-Albarran, Manuel Toledano-Ayala, Mario Alberto Ibarra-Manzano, Luis A. Morales-Hernandez, and Carlos A. Perez-Ramirez
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Cancer Research ,Oncology - Abstract
Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications. more...
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- 2022
4. Kinect Validation of Ergonomics in Human Pick and Place Activities Through Lateral Automatic Posture Detection
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Juan Carlos Gomez, Dora-Luz Almanza-Ojeda, Ernesto Rocha-Ibarra, Mario Alberto Ibarra-Manzano, Andres Rosales-Castellanos, Gabriel-Armando Lugo-Bustillo, and Marvella-Izamar Oros-Flores
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workplace MSDs ,Kinect ,General Computer Science ,Computer science ,business.industry ,media_common.quotation_subject ,Work (physics) ,General Engineering ,Human factors and ergonomics ,Task (project management) ,TK1-9971 ,Margin (machine learning) ,Perception ,Task analysis ,SMT placement equipment ,General Materials Science ,Computer vision ,RULA ,False positive rate ,Artificial intelligence ,Ergonomics ,Electrical engineering. Electronics. Nuclear engineering ,business ,media_common - Abstract
In this paper we evaluate a system based on the Microsoft Kinect™ sensor, aimed at the automatic detection of risk postures during human work activities. We first introduce a pick and place task, where three different lateral standing subjects move light cardboard boxes from the various levels of a bookcase to its top, and then putting them back to their original places. They repeat the task over several work cycles and we capture all their natural movements in a continuous way using Kinect, storing the joint positions and the color images. Secondly, from the joint positions, our system detects specific risk postures following the definitions of the Rapid Upper Limb Assessment (RULA) method. Finally, we compare the posture detections by our system with the baseline detections made by a panel of five experts who used the captured color images. In our study we find that the experts have problems to distinguish among some RULA postures during a work cycle because of the narrow detection margin and the difficulty to perceive if a limb reached a certain position; which is particularly true for the cases of wrist and neck. This leads to a larger false positive rate and to a lower general accuracy, with our system detecting postures that experts do not. After applying a ±1° of relaxation to our system, which in negligible for human perception, we are able to reach an accuracy of 0.93 in the comparison with the baseline. Our results show the suitability of Kinect for lateral risk posture detection in pick and place activities. more...
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- 2021
5. Digital Pole Control for Speed and Torque Variation in an Axial Flux Motor with Permanent Magnets
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Adrián González-Parada, José Merced Lozano-García, and Mario Alberto Ibarra-Manzano
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TK7800-8360 ,Computer Networks and Communications ,Hardware and Architecture ,Control and Systems Engineering ,digital control ,pole commutation ,axial flux motor ,Signal Processing ,Electrical and Electronic Engineering ,Electronics - Abstract
The use of renewable energies in the transportation industry has prompted the development of higher power electric motors and intelligent electronic traction systems. However, the typical coupling between the two continues to be mechanical, which reduces its efficiency and useful life. On the other hand, permanent magnet axial flux motor configurations make it possible to dispense with mechanical couplings, due to their high torque at low speeds due to their direct application on the wheels of vehicles. In this work, the design of a digital pole commutation system is presented, applied to an axial flux motor with permanent magnets for speed and torque control at a constant speed. The performance of the system is evaluated with experimental measurements; proving the effectiveness of the design, obtaining torques of up to 1784 Nm without extra mechanical couplings and maximum speed regulation errors of 8.43%. more...
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- 2022
6. Stokes Dynamic Polarimeter for Non-Organic and Organic Samples Characterization
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Dora-Luz Almanza-Ojeda, Daniela Rodriguez-Sotelo, Rogelio Castro-Sanchez, Rene Martinez-Celorio, and Mario-Alberto Ibarra-Manzano
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Light ,Image Processing, Computer-Assisted ,Physics::Optics ,light polarization ,Mueller matrix ,photoelastic modulator ,synchronization ,surface fruit ,Electrical and Electronic Engineering ,Biochemistry ,Instrumentation ,Atomic and Molecular Physics, and Optics ,Analytical Chemistry - Abstract
The light polarization properties provide relevant information about linear–optical media quality and condition. The Stokes–Mueller formalism is commonly used to represent the polarization properties of the incident light over sample tests. Currently, different Stokes Polarimeters are mainly defined by resolution, acquisition rate, and light to carry out accurate and fast measurements. This work presents the implementation of an automatic Stokes dynamic polarimeter to characterize non-biological and biological material samples. The proposed system is configured to work in the He-Ne laser beam’s reflection or transmission mode to calculate the Mueller matrix. The instrumentation stage includes two asynchronous photoelastic modulators, two nano-stepper motors, and an acquisition data card at 2% of accuracy. The Mueller matrix is numerically calculated by software using the 36 measures method without requiring image processing. Experiments show the efficiency of the proposed optical array to calculate the Mueller matrix in reflection and transmission mode for different samples. The mean squared error is calculated for each element of the obtained matrix using referenced values of the air and a mirror. A comparison with similar works in the literature validates the proposed optical array. more...
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- 2022
7. Emotion Recognition Using Time-Frequency Distribution and GLCM Features from EEG Signals
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Jose Luis Contreras-Hernandez, Dora Luz Almanza-Ojeda, Oscar Almanza-Conejo, and Mario-Alberto Ibarra-Manzano
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- 2022
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8. On removing conflicts for machine learning
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Dora Luz Almanza-Ojeda, Mario-Alberto Ibarra-Manzano, Juan Gabriel Avina-Cervantes, Sergio Ledesma, and Eduardo Cabal-Yepez
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Artificial Intelligence ,General Engineering ,Computer Science Applications - Published
- 2022
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9. Artificial Intelligence to Analyze the Cortical Thickness Through Age
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Sergio Ledesma, Mario-Alberto Ibarra-Manzano, Dora-Luz Almanza-Ojeda, Pascal Fallavollita, and Jason Steffener
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neuroimaging ,adaptive models ,Artificial neural network ,Computer science ,business.industry ,changes with age ,modeling ,QA75.5-76.95 ,cortical thickness ,Lateralization of brain function ,Reduction (complexity) ,Noise ,Neuroimaging ,Artificial Intelligence ,Electronic computers. Computer science ,Healthy individuals ,derivative ,Hidden layer ,Artificial intelligence ,Right hemisphere ,business ,artificial neural network ,Original Research - Abstract
In this study, Artificial Intelligence was used to analyze a dataset containing the cortical thickness from 1,100 healthy individuals. This dataset had the cortical thickness from 31 regions in the left hemisphere of the brain as well as from 31 regions in the right hemisphere. Then, 62 artificial neural networks were trained and validated to estimate the number of neurons in the hidden layer. These neural networks were used to create a model for the cortical thickness through age for each region in the brain. Using the artificial neural networks and kernels with seven points, numerical differentiation was used to compute the derivative of the cortical thickness with respect to age. The derivative was computed to estimate the cortical thickness speed. Finally, color bands were created for each region in the brain to identify a positive derivative, that is, a part of life with an increase in cortical thickness. Likewise, the color bands were used to identify a negative derivative, that is, a lifetime period with a cortical thickness reduction. Regions of the brain with similar derivatives were organized and displayed in clusters. Computer simulations showed that some regions exhibit abrupt changes in cortical thickness at specific periods of life. The simulations also illustrated that some regions in the left hemisphere do not follow the pattern of the same region in the right hemisphere. Finally, it was concluded that each region in the brain must be dynamically modeled. One advantage of using artificial neural networks is that they can learn and model non-linear and complex relationships. Also, artificial neural networks are immune to noise in the samples and can handle unseen data. That is, the models based on artificial neural networks can predict the behavior of samples that were not used for training. Furthermore, several studies have shown that artificial neural networks are capable of deriving information from imprecise data. Because of these advantages, the results obtained in this study by the artificial neural networks provide valuable information to analyze and model the cortical thickness. more...
- Published
- 2021
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10. Differential Neural Networks (DNN)
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Juan Gabriel Avina-Cervantes, Pascal Fallavollita, Mario Alberto Ibarra-Manzano, Eduardo Cabal Yépez, Dora-Luz Almanza-Ojeda, and Sergio Ledesma
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Differential neural network ,neural network structure ,Quantitative Biology::Neurons and Cognition ,General Computer Science ,Artificial neural network ,business.industry ,Computer science ,Computer Science::Neural and Evolutionary Computation ,Activation function ,General Engineering ,Pattern recognition ,Topology (electrical circuits) ,Derivative ,artificial intelligence ,derivative estimation ,Set (abstract data type) ,Error function ,General Materials Science ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,Artificial intelligence ,Differential (infinitesimal) ,business ,lcsh:TK1-9971 ,multilayer network - Abstract
In this work, we propose an artificial neural network topology to estimate the derivative of a function. This topology is called a differential neural network because it allows the estimation of the derivative of any of the network outputs with respect to any of its inputs. The main advantage of a differential neural network is that it uses some of the weights of a multilayer neural network. Therefore, a differential neural network does not need to be trained. First, a multilayer neural network is trained to find the best set of weights that minimize an error function. Second, the weights of the trained network and its neuron activations are used to build a differential neural network. Consequently, a multilayer artificial neural can produce a specific output, and simultaneously, estimate the derivative of any of its outputs with respect to any of its inputs. Several computer simulations were carried out to validate the performance of the proposed method. The computer simulation results showed that differential neural networks are capable of estimating with good accuracy the derivative of a function. The method was developed for an artificial neural network with two layers; however, the method can be extended to more than two layers. Similarly, the analysis in this study is presented for two common activation functions. Nonetheless, other activation functions can be used as long as the derivative of the activation function can be computed. more...
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- 2020
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11. Complex Color Space Segmentation to Classify Objects in Urban Environments
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Juan-Jose Cardenas-Cornejo, Mario-Alberto Ibarra-Manzano, Daniel-Alberto Razo-Medina, and Dora-Luz Almanza-Ojeda
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image segmentation ,complex numbers ,CNN classifier ,outdoor environments ,General Mathematics ,Computer Science (miscellaneous) ,Engineering (miscellaneous) - Abstract
Color image segmentation divides the image into areas that represent different objects and focus points. One of the biggest problems in color image segmentation is the lack of homogeneity in the color of real urban images, which generates areas of over-segmentation when traditional color segmentation techniques are used. This article describes an approach to detecting and classifying objects in urban environments based on a new chromatic segmentation to locate focus points. Based on components a and b on the CIELab space, we define a chromatic map on the complex space to determine the highest threshold values by comparing neighboring blocks and thus divide various areas of the image automatically. Even though thresholds can result in broad segmentation areas, they suffice to locate centroids of patches on the color image that are then classified using a convolutional neural network (CNN). Thus, this broadly segmented image helps to crop only outlying areas instead of classifying the entire image. The CNN is trained to use six classes based on the patches drawn from the database of reference images from urban environments. Experimental results show a high score for classification accuracy that confirms the contribution of this segmentation approach. more...
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- 2022
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12. Motor Imagery Classification Based on a Recurrent-Convolutional Architecture to Control a Hexapod Robot
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Tat’y Mwata-Velu, Juan Gabriel Avina-Cervantes, Horacio Rostro-Gonzalez, Mario Alberto Ibarra-Manzano, Jose Ruiz-Pinales, and Jorge M. Cruz-Duarte
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Computer science ,General Mathematics ,Convolutional neural network ,03 medical and health sciences ,0302 clinical medicine ,Motor imagery ,Computer Science (miscellaneous) ,Computer vision ,CNN-LSTM architectures ,Engineering (miscellaneous) ,030304 developmental biology ,Robot locomotion ,Brain–computer interface ,0303 health sciences ,Hexapod ,business.industry ,Deep learning ,lcsh:Mathematics ,brain-computer interface ,Mobile robot ,lcsh:QA1-939 ,EEG motor imagery ,Pattern recognition (psychology) ,Artificial intelligence ,business ,030217 neurology & neurosurgery ,real-time motion imagery recognition - Abstract
Advances in the field of Brain-Computer Interfaces (BCIs) aim, among other applications, to improve the movement capacities of people suffering from the loss of motor skills. The main challenge in this area is to achieve real-time and accurate bio-signal processing for pattern recognition, especially in Motor Imagery (MI). The significant interaction between brain signals and controllable machines requires instantaneous brain data decoding. In this study, an embedded BCI system based on fist MI signals is developed. It uses an Emotiv EPOC+ Brainwear®, an Altera SoCKit® development board, and a hexapod robot for testing locomotion imagery commands. The system is tested to detect the imagined movements of closing and opening the left and right hand to control the robot locomotion. Electroencephalogram (EEG) signals associated with the motion tasks are sensed on the human sensorimotor cortex. Next, the SoCKit processes the data to identify the commands allowing the controlled robot locomotion. The classification of MI-EEG signals from the F3, F4, FC5, and FC6 sensors is performed using a hybrid architecture of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. This method takes advantage of the deep learning recognition model to develop a real-time embedded BCI system, where signal processing must be seamless and precise. The proposed method is evaluated using k-fold cross-validation on both created and public Scientific-Data datasets. Our dataset is comprised of 2400 trials obtained from four test subjects, lasting three seconds of closing and opening fist movement imagination. The recognition tasks reach 84.69% and 79.2% accuracy using our data and a state-of-the-art dataset, respectively. Numerical results support that the motor imagery EEG signals can be successfully applied in BCI systems to control mobile robots and related applications such as intelligent vehicles. more...
- Published
- 2021
13. Trend-Based Categories Recommendations and Age-Gender Prediction for Pinterest and Twitter Users
- Author
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Yair A. Andrade-Ambriz, Sergio Ledesma, Mario Alberto Ibarra-Manzano, Dora-Luz Almanza-Ojeda, Juan Carlos Gomez, and Roberto Garcia-Guzman
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social networks ,Association rule learning ,Computer science ,media_common.quotation_subject ,02 engineering and technology ,lcsh:Technology ,Convolutional neural network ,lcsh:Chemistry ,020204 information systems ,convolutional neural networks ,0202 electrical engineering, electronic engineering, information engineering ,Feature (machine learning) ,General Materials Science ,lcsh:QH301-705.5 ,Instrumentation ,media_common ,Fluid Flow and Transfer Processes ,Information retrieval ,User profile ,lcsh:T ,Heuristic ,Process Chemistry and Technology ,General Engineering ,Certainty ,gender and age prediction ,lcsh:QC1-999 ,Computer Science Applications ,lcsh:Biology (General) ,lcsh:QD1-999 ,lcsh:TA1-2040 ,Order (business) ,category suggestion ,020201 artificial intelligence & image processing ,lcsh:Engineering (General). Civil engineering (General) ,lcsh:Physics ,Meaning (linguistics) - Abstract
Category suggestions or recommendations for customers or users have become an essential feature for commerce or leisure websites. This is a growing topic that follows users&rsquo, activity in social networks generating a huge quantity of information about their interests, contacts, among many others. These data are usually collected to analyze people&rsquo, s behavior, trends, and integrate a complete user profile. In this sense, we analyze a dataset collected from Pinterest to predict the gender and age by processing input images using a Convolutional Neural Network. Our method is based on the meaning of the image rather than the visual content. Additionally, we propose a heuristic-based approach for text analysis to predict users&rsquo, age and gender from Twitter. Both of the classifiers are based on text and images and they are compared with various similar approaches in the state of the art. Suggested categories are based on association rules conformed by the activity of thousands of users in order to estimate trends. Computer simulations showed that our approach can recommend interesting categories for a user analyzing his current interest and comparing this interest with similar users&rsquo, profiles or trends and, therefore, achieve an improved user profile. The proposed method is capable of predicting the user&rsquo, s age with high accuracy, and at the same time, it is able to predict gender and category information from the user. The certainty that one or more suggested categories be interesting to people is higher for those users with a large number of publications. more...
- Published
- 2020
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14. Hybrid LQR-PI Control for Microgrids under Unbalanced Linear and Nonlinear Loads
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Luis Ramon Merchan-Villalba, Jose M. Lozano-Garcia, Guillermo Tapia-Tinoco, Mario Alberto Ibarra-Manzano, Gerardo Humberto Valencia-Rivera, and Juan Gabriel Avina-Cervantes
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grid-tied mode ,Fitness function ,Settling time ,020209 energy ,General Mathematics ,lcsh:Mathematics ,020208 electrical & electronic engineering ,PID controller ,Root locus ,02 engineering and technology ,Linear-quadratic regulator ,power quality ,lcsh:QA1-939 ,genetic algorithms ,Nonlinear system ,microgrid ,LQR-PI control ,Control theory ,Harmonics ,0202 electrical engineering, electronic engineering, information engineering ,Computer Science (miscellaneous) ,Microgrid ,current imbalance ,Engineering (miscellaneous) ,Mathematics - Abstract
A hybrid Linear Quadratic Regulator (LQR) and Proportional-Integral (PI) control for a MicroGrid (MG) under unbalanced linear and nonlinear loads was presented and evaluated in this paper. The designed control strategy incorporates the microgrid behavior, low-cost LQR, and error reduction in the stationary state by the PI control, to reduce the overall energetic cost of the classical PI control applied to MGs. A Genetic Algorithm (GA) calculates the parameters of LQR with high-accuracy fitness function to obtain the optimal controller parameters as settling time and overshoot. The gain values of the classical PI controller were determined through the improved LQR values and geometrical root locus. When MG operates in the grid-tied mode under unbalanced conditions, the controller performance of the Current Source Inverter (CSI) of the MG is considerably affected. Consequently, the CSI operates in a negative-sequence mode to compensate for unbalanced current at the Point of Common Coupling (PCC) between the MG and the utility grid. The study cases involved the reduction of the negative-sequence percentage in the current at the PCC, mitigation of harmonics in the current signal injected by the MG, and close related power quality issues. All these cases have been analyzed by implementing an MG connected at the PCC of a low-voltage distribution network. A numerical model of the MG in Matlab/Simulink was implemented to verify the performance of the designed LQR-PI control to mitigate or overcome the power quality concerns. The extensive simulations have permitted verifying the robustness and effectiveness of the proposed strategy. more...
- Published
- 2020
15. Artificial Intelligence to Design a Mask Insensible to the Distance From the Camera to the Scene Objects
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Sergio Ledesma, Eduardo Cabal-Yepez, Dora-Luz Almanza-Ojeda, Mario Alberto Ibarra-Manzano, and Pascal Fallavollita
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Chebyshev polynomials ,Artificial intelligence ,General Computer Science ,business.industry ,Computer science ,General Engineering ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,high-resolution imaging ,Field (computer science) ,blurring in an image ,image processing ,Cardinal point ,General Materials Science ,Augmented reality ,multi-focus image ,Depth of field ,simulated annealing ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,Set (psychology) ,Optical resolution ,business ,Focus (optics) ,lcsh:TK1-9971 - Abstract
The sharpness of an image depends on the spatial frequency response of the photographic imaging system and the sensor characteristics. In conventional digital cameras, only those objects within a distance range are in focus, while other objects are captured with different amounts of blurring depending on their distance to the focal plane. This can be desired for some applications; however, this can also be undesired because some objects in the scene may be blurred and impossible to recover. In the field of augmented reality, simulating this natural effect of showing sharp objects in combination with blurred objects increases the visual realism of augmented video. In order to simulate this effect, it is very important to capture all objects in the scene with high quality so that it could be possible to dynamically blur different objects in the scene at runtime. In this paper, we present an algorithm to find a set of possible complex-amplitude transmittance masks capable of considerably reducing the impact of focus errors in the scene objects. Computer simulations are used to compare the masks found in this paper with a classic mask in the state of the art. The main contribution of this paper is the use of Chebyshev polynomials to model an optical mask, and then, use artificial intelligence to establish some properties of this mask, such as the depth of field, the resolution, and the amount of gathered light. more...
- Published
- 2019
16. Human activity recognition using temporal convolutional neural network architecture
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Yair A. Andrade-Ambriz, Sergio Ledesma, Mario-Alberto Ibarra-Manzano, Marvella I. Oros-Flores, and Dora-Luz Almanza-Ojeda
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Artificial Intelligence ,General Engineering ,Computer Science Applications - Published
- 2022
- Full Text
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17. Object Detection from a Range Image Using Sparse Keypoint Detector Technique
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Dora Luz Almanza Ojeda, Alejandra Cruz Bernal, and Mario Alberto Ibarra Manzano
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Surface (mathematics) ,General Computer Science ,Computer science ,business.industry ,Feature extraction ,Detector ,020207 software engineering ,Pattern recognition ,02 engineering and technology ,Object (computer science) ,Object detection ,Region of interest ,0202 electrical engineering, electronic engineering, information engineering ,Probability mass function ,Range (statistics) ,020201 artificial intelligence & image processing ,Artificial intelligence ,Electrical and Electronic Engineering ,business - Abstract
This proposal presents the object detection using Sparse Keypoint Detector technique, in which is computed the interesting points from directional surface. This surface is built with the normal vectors corresponding to the homogeneous surface, and this in turn, is obtained from range image. A probability dense function (pdf) analysis applied to the normal vectors contained in the directional surface, allows us to select the highlight points to obtain the contour of the scene. Furthermore, a probability mass function (pmf) analysis applied to the information contained in the surrounding of these points, aim to select a region of interest, in which is found the object 3D to be detected. Finally, it is applied a Chess distance to the interesting points contained in the region of interest to detect the object. The presented experimental tests involve a qualitative and quantitative analysis using the Middlebury and DSPLab dataset. more...
- Published
- 2018
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18. Analysis of Data Sets With Learning Conflicts for Machine Learning
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Juan Gabriel Avina-Cervantes, Eduardo Cabal-Yepez, Dora-Luz Almanza-Ojeda, Sergio Ledesma, and Mario Alberto Ibarra-Manzano
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Data set ,General Computer Science ,Computer science ,020209 energy ,02 engineering and technology ,target value ,Machine learning ,computer.software_genre ,Field (computer science) ,03 medical and health sciences ,0302 clinical medicine ,0202 electrical engineering, electronic engineering, information engineering ,General Materials Science ,conflict removal ,Artificial neural network ,business.industry ,Supervised learning ,General Engineering ,Test (assessment) ,Euclidean distance ,machine learning ,conflict level ,Data analysis ,Unsupervised learning ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,Artificial intelligence ,business ,lcsh:TK1-9971 ,computer ,030217 neurology & neurosurgery - Abstract
In supervised learning, a machine learning system requires a data set. In occasions, however, the data set may have learning conflicts that may drastically affect the performance of the learning system. This paper presents a method to analyze the learning conflicts in a data set. Several computer simulations to test and validate our method are performed. Two common functions in the field of optimization are used to create clean data sets. The data sets are, then, contaminated with random data, and the total learning conflict level for each case is computed. The proposed algorithm is used to identify the learning conflicts that are intentionally inserted. Next, an artificial neural network is trained and evaluated using the contaminated data set. The algorithm proposed in this paper is used in a real-world application to detect problems in a data set for a refrigeration system. It is concluded that the algorithm can be used to improve the performance of machine learning systems. more...
- Published
- 2018
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19. Mathematical Models to Predict and Analyze the Energy Consumption of a Domestic Refrigerator for Different Position of the Shelves
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Sergio Ledesma, Eduardo Cabal-Yepez, J.A. Alfaro-Ayala, Juan M. Belman-Flores, A. Morales-Fuentes, and Mario Alberto Ibarra-Manzano
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Mathematical optimization ,General Computer Science ,Artificial neural network ,Mathematical model ,Computer science ,020209 energy ,General Engineering ,Refrigerator car ,02 engineering and technology ,Energy consumption ,moving average ,Domestic refrigerator ,Position (vector) ,energy consumption ,prediction methods ,0202 electrical engineering, electronic engineering, information engineering ,General Materials Science ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,lcsh:TK1-9971 ,artificial neural network - Abstract
In this paper, a domestic refrigerator was tested to determine the power consumption for different positions of the shelves. The main contribution of this paper is the application of several strategies to analyze and model the power consumption of a domestic refrigerator when the fresh food shelves are changed of position. First, computer simulations were performed to analyze the power consumption using the moving average, the numerical derivative, and the Fourier transform. These simulations were used to study changes, periodic behaviors, minimum, maximum, and average values for the power consumption. Second, prediction methods were used to model the power consumption, these methods include cubic splines, the bilinear method, and artificial neural networks. With these methods, 2-D color graphs were built to predict the power consumption for any shelves positions. The validation results revealed that the cubic spline method provides the best results. Finally, it can be concluded that the models proposed in this paper provides new insights that can be used to design the internal compartments of the fresh food in order to try reducing the energy consumption. more...
- Published
- 2018
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20. User Identification in Pinterest Through the Refinement of Cascade Fusion of Text and Images
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Juan Carlos Gomez, Dora-Luz Almanza-Ojeda, and Mario Alberto Ibarra-Manzano
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0209 industrial biotechnology ,Fusion ,Computer science ,business.industry ,Pattern recognition ,02 engineering and technology ,General Medicine ,Identification (information) ,020901 industrial engineering & automation ,Cascade ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Artificial intelligence ,business - Published
- 2017
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21. Computationally-efficient algorithm for applying motion-controlled kinematics in stepper motors
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Hector Roman Frias-Fonseca, Rigoberto Rafyani Torres-Camacho, Jesus Rooney Rivera-Guillen, and Mario Alberto Ibarra Manzano
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Computer science ,020208 electrical & electronic engineering ,Control engineering ,010103 numerical & computational mathematics ,02 engineering and technology ,Kinematics ,Mechatronics ,Motion control ,01 natural sciences ,Computer Science::Robotics ,Acceleration ,Position (vector) ,0202 electrical engineering, electronic engineering, information engineering ,Numerical control ,Robot ,0101 mathematics ,Stepper - Abstract
The motion control of most industrial systems (such as Computer Numerical Control (CNC) machines and robots) require to consider the physical limitations in position, speed and acceleration. A kinematic motion profile describes the desired movement considering such constraints. The profile generator is in charge of generating the motion controlled kinematics for being applied to the mechatronic system. Several works have been proposed for achieving this task; however, most of them use either complex algorithms or pre-calculated profiles. In this work a low-computational-cost algorithm for the generation of motion-controlled kinematics that can be implemented in low-cost microcontrollers is proposed. The effectiveness of the proposed algorithm is validates through simulation and experimentation more...
- Published
- 2019
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22. Contrast and Homogeneity Feature Analysis for Classifying Tremor Levels in Parkinson’s Disease Patients
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Mario Alberto Ibarra-Manzano, Dora-Luz Almanza-Ojeda, Guillermina Vivar, Irene Cheng, Juan Carlos Gomez, and Jose A. Andrade-Lucio
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Male ,Parkinson's disease ,Computer science ,Early detection ,SDH method ,02 engineering and technology ,lcsh:Chemical technology ,Biochemistry ,Severity of Illness Index ,Article ,Analytical Chemistry ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Humans ,lcsh:TP1-1185 ,Electrical and Electronic Engineering ,Instrumentation ,Monitoring, Physiologic ,business.industry ,Homogeneity (statistics) ,Pattern recognition ,Parkinson Disease ,medicine.disease ,tremor ,Atomic and Molecular Physics, and Optics ,nervous system diseases ,classification ,Quality of Life ,Parkinson’s disease ,020201 artificial intelligence & image processing ,Female ,Artificial intelligence ,business ,030217 neurology & neurosurgery - Abstract
Early detection of different levels of tremors helps to obtain a more accurate diagnosis of Parkinson&rsquo, s disease and to increase the therapy options for a better quality of life for patients. This work proposes a non-invasive strategy to measure the severity of tremors with the aim of diagnosing one of the first three levels of Parkinson&rsquo, s disease by the Unified Parkinson&rsquo, s Disease Rating Scale (UPDRS). A tremor being an involuntary motion that mainly appears in the hands, the dataset is acquired using a leap motion controller that measures 3D coordinates of each finger and the palmar region. Texture features are computed using sum and difference of histograms (SDH) to characterize the dataset, varying the window size, however, only the most fundamental elements are used in the classification stage. A machine learning classifier provides the final classification results of the tremor level. The effectiveness of our approach is obtained by a set of performance metrics, which are also used to show a comparison between different proposed designs. more...
- Published
- 2019
23. Prediction of Personality Traits in Twitter Users with Latent Features
- Author
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Mario Alberto Ibarra-Manzano, Daniel Ricardo Jaimes Moreno, Dora-Luz Almanza-Ojeda, and Juan Carlos Gomez
- Subjects
Computer science ,Dimensionality reduction ,media_common.quotation_subject ,Context (language use) ,02 engineering and technology ,Data science ,Clef ,Task (project management) ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,Task analysis ,Personality ,020201 artificial intelligence & image processing ,Big Five personality traits ,media_common ,Diversity (politics) - Abstract
The globalization of Economy has forced the society to maintain a constant evolution in marketing techniques. It is thus very important to design tools and methods that allow knowing and characterize individuals in groups to develop effective marketing strategies. In this context, any company would be interested in finding the tastes and preferences of people regarding the products and services offered in the global market. One technique that could help in this, is the analysis of the personality of each individual to identify their tastes and preferences. In this way we can offer products and services that meet their needs through appropriate advertising for each type of personality. In this work, we propose the use of latent features, extracted with a diversity of dimensionality reduction methods, to infer the personality of Twitter users using textual content-based features, and we compare the performance of the different techniques. For conducting our experiments, we use the PAN CLEF 2015 dataset consisting of 14,166 tweets in English of 152 different users, and a diversity of classification methods. Our results shows interesting insight about the personality prediction task. more...
- Published
- 2019
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24. Rotor unbalance and broken rotor bar detection in inverter-fed induction motors at start-up and steady-state regimes by high-resolution spectral analysis
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Rene de Jesus Romero-Troncoso, Arturo Garcia-Perez, Oscar Duque-Perez, Daniel Morinigo-Sotelo, Mario Alberto Ibarra-Manzano, and Roque Alfredo Osornio-Rios
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Engineering ,Steady state (electronics) ,Rotor (electric) ,business.industry ,Bar (music) ,020209 energy ,020208 electrical & electronic engineering ,Energy Engineering and Power Technology ,02 engineering and technology ,Fault (power engineering) ,Fault detection and isolation ,law.invention ,law ,Control theory ,Harmonics ,0202 electrical engineering, electronic engineering, information engineering ,Inverter ,Electrical and Electronic Engineering ,business ,Induction motor - Abstract
Fault detection in induction motors operated by inverters is an actual industrial need. Most line-fed machines are being replaced by inverter-fed drives, due to their improved speed regulation, and fast dynamic response, despite the insertion of undesired harmonics. Under this particular operating condition, most of the detection techniques so far developed are unable to distinguish induction motor faults. This paper presents a detection methodology based on the combined use of two techniques: Complete Ensemble Empirical Mode Decomposition and the Multiple Signal Classification. The proposed methodology is applied to an inverter-fed induction motor during a start-up followed by a steady-state regime, where it is verified its capability to identify a single broken rotor bar, mixed eccentricity in the form of motor-load misalignment, and the combination of both faults. From the experimental results, the proposed methodology is proven to be sensitive enough to detect the fault evolution in the time-frequency plane of single and combined faults under different operating regimes (start-up and steady-state) in inverter-fed induction motor. more...
- Published
- 2016
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25. Identification of Age and Gender in Pinterest by Combining Textual and Deep Visual Features
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Mario Alberto Ibarra-Manzano, Dora-Luz Almanza-Ojeda, Sandra-Pamela Bravo-Marmolejo, Juan Carlos Gomez, Claudia Pérez-Martínez, and Jorge Moreno
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050101 languages & linguistics ,Information retrieval ,Exploit ,business.industry ,Computer science ,05 social sciences ,Big data ,02 engineering and technology ,Convolutional neural network ,Writing style ,Identification (information) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,0501 psychology and cognitive sciences ,Social media ,Representation (mathematics) ,business ,Publication - Abstract
In social media users share a lot of content, such as comments, news, photos, videos, etc. This information can be used by automated systems to segment the users to provide them with specific recommendations or focused content. One of the most popular way to segment the users is by age and gender. Nevertheless, such demographic variables are frequently hidden, and thus becomes useful to indirectly infer them. Commonly, these variables are learned using the text comments the users publish, analyzing the style of writing or frequency of words. In this paper, we present a study of several machine learning models that employ user generated images and text trying to exploit both types of information to infer the age and gender for Pinterest users. We experiment with the models using a dataset composed of 548,761 pins, posted by 264 users. Each pin is a combination of an image and a short comment. We transformed the images to a deep visual representation using the pretrained convolutional neural network ResNet-50, and transformed the comments using the tf-idf method. We compare the models among them and between the types of information using different performance metrics. Our experiments show interesting results and the viability of employing the user generated image and text content to characterize users. more...
- Published
- 2019
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26. Indoor Positioning System Based on Visible Light Communications
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Dora-Luz Almanza-Ojeda, Jose de Jesus Rangel Magdaleno, Jesus Rooney Rivera Guillen, Juan-Carlos Gomez-Carranza, Carlos Miguel Avendano Lopez, Juan Pablo Amezquita Sanchez, R. Castro-Sánchez, and Mario Alberto Ibarra-Manzano more...
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Computational model ,Artificial neural network ,business.industry ,Computer science ,Estimator ,Visible light communication ,020206 networking & telecommunications ,02 engineering and technology ,Least squares ,law.invention ,LED lamp ,020210 optoelectronics & photonics ,Indoor positioning system ,law ,0202 electrical engineering, electronic engineering, information engineering ,Computer vision ,Artificial intelligence ,business ,Trilateration - Abstract
In this work is proposed an implementation of a bidimensional indoor positioning system based on visible light communications. The system needs at least three led lamps forming a triangle to localize objects. The trilateration is used to calculate the position of the object, and the RSS to estimate the distance between the object and the lights. The estimators used are deterministic and computational models. The deterministic estimator used is a quadratic regression model by least squares. Computational models used are artificial neural networks: one first option includes an ANN for each lamp and a second one a general ANN for the three lamps system. Best results achieved with this system were about 5 cm of accuracy and 2.5 cm of precision, and this can be intended for the positioning of small targets moving in a room. more...
- Published
- 2018
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27. Design and Implementation of a Demonstrative Palletizer Robot with Navigation for Educational Purposes
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Perla-Lizeth Garza-Barron, Carlos RubinMontoro-Sanjose, Dora-Luz Almanza-Ojeda, and Mario Alberto Ibarra-Manzano
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Demonstrative ,Palletizer ,Engineering drawing ,Computer science ,Robot - Published
- 2018
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28. Body Movement Monitoring for Parkinson’s Disease Patients Using A Smart Sensor Based Non-Invasive Technique
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Sara Soltaninejad, Fang Ba, Andres Rosales-Castellanos, Mario Alberto Ibarra-Manzano, and Irene Cheng
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Motion analysis ,Ground truth ,Rehabilitation ,Computer science ,business.industry ,medicine.medical_treatment ,Feature extraction ,020206 networking & telecommunications ,Body movement ,02 engineering and technology ,Kinematics ,Motion (physics) ,Random forest ,03 medical and health sciences ,0302 clinical medicine ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Computer vision ,Artificial intelligence ,business ,030217 neurology & neurosurgery - Abstract
There have been increasing interests in recent years on using smart sensor technology, e.g., Kinect and Leap Motion, to capture and analyze human body movements, with the goal to benefit not only games, but also health care and rehab applications. We propose a non-invasive approach using movement data captured from Kinect to monitor motor deficits of Parkinson’s disease (PD) patients. We captured and evaluated simple exercises, normally performed in rehabilitation sessions by physical therapist: Stride Length, Tremor and Timed Up & Go (TUG). The standard medical UPDRS scale is used by a physical therapist to determine the level of severity as the ground truth. The general framework after getting the motion data includes two steps feature extraction from the kinematic motion data, and classification using random forest (RF) (for the stride length and tremor data) and K-means (for the TUG data). Our technique was validated by inviting a group of subjects whose kinematic data are used for PD motion analysis. The experimental results demonstrate the high accuracy of our approach in the assessment of PD using kinematic motion data. Our technique is also suitable in a remote monitoring environment, where data collected can be transmitted to experts for assessment. more...
- Published
- 2018
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29. Analysis of the efficiency of the census transform algorithm implemented on FPGA
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C.-A. Tavera-Vaca, Dora-Luz Almanza-Ojeda, and Mario Alberto Ibarra-Manzano
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Hardware architecture ,Similarity (geometry) ,Stereopsis ,Artificial Intelligence ,Computer Networks and Communications ,Hardware and Architecture ,Computer science ,Hamming distance ,Field-programmable gate array ,Algorithm ,Software ,Field (computer science) - Abstract
Over the course of the last two decades, continuous advances in the stereo vision field have been documented. In this paper we present an analysis of the efficiency for the stereo vision algorithm of the Census Transform algorithm. In addition to the conventional correlation method based on Hamming distance minimization, we use two similarity measures: the Tanimoto and the Dixon-Koehler distances. Then, we compare its performance in terms of accuracy and hardware resources needed for implementation. These comparisons are performed by introducing a generalized model for each hardware architecture, scalable depending on design parameters such as Census Transform window size and maximum disparity range. more...
- Published
- 2015
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30. Tremor Signal Analysis for Parkinson’s Disease Detection Using Leap Motion Device
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Dora-Luz Almanza-Ojeda, Mario Alberto Ibarra-Manzano, and Guillermina Vivar-Estudillo
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Signal processing ,Parkinson's disease ,Computer science ,business.industry ,020206 networking & telecommunications ,Pattern recognition ,02 engineering and technology ,medicine.disease ,nervous system diseases ,03 medical and health sciences ,0302 clinical medicine ,Leap motion ,Histogram ,Hand tremor ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Statistical analysis ,Artificial intelligence ,business ,030217 neurology & neurosurgery - Abstract
Tremor is an involuntary rhythmic movement observed in people with Parkinson’s disease (PD), specifically, hand tremor is a measurement for diagnosing this disease. In this paper, we use hand positions acquired by Leap Motion device for statistical analysis of hand tremor based on the sum and difference of histograms (SDH). Tremor is measured using only one coordinate of the center palm during predefined exercises performed by volunteers at Hospital. In addition, the statistical features obtained with SDH are used to classify tremor signal as with PD or not. Experimental results show that the classification is independent of the hand used during tests, achieving \(98\%\) of accuracy for our proposed approach using different supervised machine learning classifiers. Additionally, we compare our result with others classifiers proposed in the literature. more...
- Published
- 2018
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31. Obstacle Detecction and Avoidance by a Mobile Robot Using Probabilistic Models
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Yazmin Gomar Vera, Mario Alberto Ibarra Manzano, and Dora Luz Almanza Ojeda
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General Computer Science ,Computer science ,business.industry ,Probabilistic logic ,Mobile robot ,Mobile robot navigation ,Robot control ,Obstacle ,Obstacle avoidance ,Robot ,Computer vision ,Artificial intelligence ,Motion planning ,Electrical and Electronic Engineering ,business - Abstract
This article presents the implementation of an strategy for detecting, mapping and avoiding obstacles using the mobile robotic platform DaNI 2.0. This mobile robot includes an ultrasonic sensor, which is modeled based on a Gaussian function with the aim of reaching higher precision of the distance position measured for each obstacle in the scene. An avoidance obstacle algorithm, based on reactive path planning, allows the mobile robot navigates coherently around the environment. The path planning method analyses the change in potential field around the environment (the goal provides attraction forces and obstacles repulsion forces) and it is implemented on the robot DaNI using the VxWorks system. Furthermore, the implementation of an occupation grid based on the log-odds algorithm for mapping the environment is described. The robot navigation in real indoor environments with obstacles are analyzed and discussed at the end of this document. more...
- Published
- 2015
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32. Neural lab a simulator for artificial neural networks
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Ma-Guadalupe Garcia-Hernandez, Sergio Ledesma, Dora-Luz Almanza-Ojeda, and Mario Alberto Ibarra-Manzano
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Self-organizing map ,Quantitative Biology::Neurons and Cognition ,Artificial neural network ,Computer science ,business.industry ,Computer Science::Neural and Evolutionary Computation ,Feed forward ,Probabilistic logic ,Computer experiment ,Set (abstract data type) ,Software ,Neural Lab ,business ,Simulation - Abstract
Artificial neural networks are inspired by biologic processes. Artificial neural networks are important because they can be used to deduct a function from observations, in other words artificial neural networks can learn from experience. This paper introduces a simulator called Neural Lab for artificial neural networks. Neural Lab is implemented using object-oriented programming by a set of C++ classes and these classes can be used to create a standalone application with artificial neural networks. Several optimization techniques were applied in the design and implementation of this simulator. This simulator provides support for multi-layer feed forward networks, probabilistic neural networks and Kohonen networks. Finally, in order to test the proposed simulator, several computer experiments were performed. These tests included mapping problems and auto-associate problems. more...
- Published
- 2017
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33. Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation
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Claire Chalopin, Elisee Ilunga-Mbuyamba, Mario Alberto Ibarra-Manzano, Juan Gabriel Avina-Cervantes, and Jonathan Cepeda-Negrete
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Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Scale-space segmentation ,Health Informatics ,02 engineering and technology ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Image Interpretation, Computer-Assisted ,0202 electrical engineering, electronic engineering, information engineering ,Humans ,Computer vision ,Selection (genetic algorithm) ,Active contour model ,business.industry ,Segmentation-based object categorization ,Brain Neoplasms ,Process (computing) ,Brain ,Pattern recognition ,Image segmentation ,Distribution fitting ,Magnetic Resonance Imaging ,Computer Science Applications ,Region growing ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,Algorithms - Abstract
Brain tumor segmentation is a routine process in a clinical setting and provides useful information for diagnosis and treatment planning. Manual segmentation, performed by physicians or radiologists, is a time-consuming task due to the large quantity of medical data generated presently. Hence, automatic segmentation methods are needed, and several approaches have been introduced in recent years including the Localized Region-based Active Contour Model (LRACM). There are many popular LRACM, but each of them presents strong and weak points. In this paper, the automatic selection of LRACM based on image content and its application on brain tumor segmentation is presented. Thereby, a framework to select one of three LRACM, i.e., Local Gaussian Distribution Fitting (LGDF), localized Chan-Vese (C-V) and Localized Active Contour Model with Background Intensity Compensation (LACM-BIC), is proposed. Twelve visual features are extracted to properly select the method that may process a given input image. The system is based on a supervised approach. Applied specifically to Magnetic Resonance Imaging (MRI) images, the experiments showed that the proposed system is able to correctly select the suitable LRACM to handle a specific image. Consequently, the selection framework achieves better accuracy performance than the three LRACM separately. more...
- Published
- 2017
34. An Architecture for Measuring Joint Angles Using a Long Period Fiber Grating-Based Sensor
- Author
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Julian M. Estudillo-Ayala, Francisco J. Mendoza-Galindo, Dora-Luz Almanza-Ojeda, Carlos A. Perez-Ramirez, Mario Alberto Ibarra-Manzano, and Jesus N. Guerrero-Tavares
- Subjects
Digital signal processor ,business.industry ,Computer science ,fiber-optics sensor ,Filter (signal processing) ,Long-period fiber grating ,lcsh:Chemical technology ,long-period fiber grating ,Biochemistry ,Signal ,Atomic and Molecular Physics, and Optics ,Article ,Analytical Chemistry ,Gaussian mixture model ,finger movement ,Electronic engineering ,real-time system ,lcsh:TP1-1185 ,recursive least-square estimator ,Electrical and Electronic Engineering ,business ,Field-programmable gate array ,Instrumentation ,Digital signal processing ,Simulation - Abstract
The implementation of signal filters in a real-time form requires a tradeoff between computation resources and the system performance. Therefore, taking advantage of low lag response and the reduced consumption of resources, in this article, the Recursive Least Square (RLS) algorithm is used to filter a signal acquired from a fiber-optics-based sensor. In particular, a Long-Period Fiber Grating (LPFG) sensor is used to measure the bending movement of a finger. After that, the Gaussian Mixture Model (GMM) technique allows us to classify the corresponding finger position along the motion range. For these measures to help in the development of an autonomous robotic hand, the proposed technique can be straightforwardly implemented on real time platforms such as Field Programmable Gate Array (FPGA) or Digital Signal Processors (DSP). Different angle measurements of the finger’s motion are carried out by the prototype and a detailed analysis of the system performance is presented. more...
- Published
- 2014
35. Fast and Accurate Cell Tracking by a Novel Optical-Digital Hybrid Method
- Author
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Miguel Torres-Cisneros, F. Ambriz-Colin, H. Plascencia-Mora, Oscar Ibarra-Manzano, Olivier Debeir, Eduardo Aguilera-Gómez, J. J. Sánchez-Mondragón, Rafael Guzmán-Cabrera, Mario Alberto Ibarra-Manzano, Juan Gabriel Avina-Cervantes, Verónica Tinoco, and Eduardo Perez-Careta more...
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Microscope ,Optics ,law ,Robustness (computer science) ,business.industry ,Computer science ,Computer vision ,Cell tracking ,Artificial intelligence ,Condensed Matter Physics ,business ,Morphological operators ,law.invention - Abstract
An innovative methodology to detect and track cells using microscope images enhanced by optical cross-correlation techniques is proposed in this paper. In order to increase the tracking sensibility, image pre-processing has been implemented as a morphological operator on the microscope image. Results show that the pre-processing process allows for additional frames of cell tracking, therefore increasing its robustness. The proposed methodology can be used in analyzing different problems such as mitosis, cell collisions, and cell overlapping, ultimately designed to identify and treat illnesses and malignancies. more...
- Published
- 2013
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36. Improving EEG-Based Motor Imagery Classification for Real-Time Applications Using the QSA Method
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Erick Israel Guerra-Hernandez, Patricia Batres-Mendoza, Carlos R. Montoro-Sanjose, Dora-Luz Almanza-Ojeda, Rene de Jesus Romero-Troncoso, Mario Alberto Ibarra-Manzano, and Horacio Rostro-Gonzalez
- Subjects
Male ,General Computer Science ,Article Subject ,Computer science ,Movement ,General Mathematics ,0206 medical engineering ,Decision tree ,Sample (statistics) ,02 engineering and technology ,Electroencephalography ,lcsh:Computer applications to medicine. Medical informatics ,Signal ,Functional Laterality ,lcsh:RC321-571 ,03 medical and health sciences ,0302 clinical medicine ,Motor imagery ,Sampling (signal processing) ,medicine ,Humans ,lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry ,Brain Mapping ,Signal processing ,medicine.diagnostic_test ,business.industry ,General Neuroscience ,Brain ,Contrast (statistics) ,Signal Processing, Computer-Assisted ,Pattern recognition ,General Medicine ,Brain Waves ,020601 biomedical engineering ,Brain-Computer Interfaces ,Imagination ,lcsh:R858-859.7 ,Female ,Artificial intelligence ,business ,Algorithms ,Photic Stimulation ,030217 neurology & neurosurgery ,Research Article - Abstract
We present an improvement to the quaternion-based signal analysis (QSA) technique to extract electroencephalography (EEG) signal features with a view to developing real-time applications, particularly in motor imagery (IM) cognitive processes. The proposed methodology (iQSA, improved QSA) extracts features such as the average, variance, homogeneity, and contrast of EEG signals related to motor imagery in a more efficient manner (i.e., by reducing the number of samples needed to classify the signal and improving the classification percentage) compared to the original QSA technique. Specifically, we can sample the signal in variable time periods (from 0.5 s to 3 s, in half-a-second intervals) to determine the relationship between the number of samples and their effectiveness in classifying signals. In addition, to strengthen the classification process a number of boosting-technique-based decision trees were implemented. The results show an 82.30% accuracy rate for 0.5 s samples and 73.16% for 3 s samples. This is a significant improvement compared to the original QSA technique that offered results from 33.31% to 40.82% without sampling window and from 33.44% to 41.07% with sampling window, respectively. We can thus conclude that iQSA is better suited to develop real-time applications. more...
- Published
- 2017
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37. Color-Texture Image Analysis for Automatic Failure Detection in Tiles
- Author
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Miyuki-Teri Villalon-Hernandez, Dora-Luz Almanza-Ojeda, and Mario Alberto Ibarra-Manzano
- Subjects
0209 industrial biotechnology ,Artificial neural network ,business.industry ,Computer science ,020208 electrical & electronic engineering ,Pattern recognition ,02 engineering and technology ,Real image ,Automation ,Visual inspection ,Color texture ,020901 industrial engineering & automation ,Computer Science::Computer Vision and Pattern Recognition ,visual_art ,0202 electrical engineering, electronic engineering, information engineering ,visual_art.visual_art_medium ,Artificial intelligence ,Tile ,business ,Classifier (UML) ,Vision algorithms - Abstract
The defects in tiles are directly related with changes in the structure or color components producing spots or stains in the final product. Usually, a visual inspection is carried out in order to detect one of such common defects in tiles; however this process depends on the expertise and abilities of the operator on duty. In this paper, we present the automation of defect detection in tiles using vision algorithms and Artificial Neural Networks (ANN). Color and texture information extracted from real tile images are used as input to a classifier based on neural networks. Setting parameters for extracting the texture attributes are obtained performing detailed tests of different distances, orientations and window sizes. An initial architecture of the ANN is obtained using texture features extracted from Brodatz images. Next, the neural network parameters are computed using real images from the tile database. The experimental tests validate the global performance, accuracy and feasibility of our approach. more...
- Published
- 2017
- Full Text
- View/download PDF
38. Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals
- Author
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Carlos R. Montoro-Sanjose, Mario Alberto Ibarra-Manzano, Horacio Rostro-Gonzalez, Rene de Jesus Romero-Troncoso, Dora-Luz Almanza-Ojeda, Patricia Batres-Mendoza, and Erick Israel Guerra-Hernandez
- Subjects
Support Vector Machine ,Computer science ,Movement ,Speech recognition ,02 engineering and technology ,Electroencephalography ,lcsh:Chemical technology ,Biochemistry ,Article ,Analytical Chemistry ,Cognition ,Motor imagery ,motor imagery ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Humans ,lcsh:TP1-1185 ,Time domain ,electroencephalography (EEG) ,Electrical and Electronic Engineering ,Quaternion ,Instrumentation ,Brain Mapping ,Signal processing ,quaternion-based signal analysis (QSA) ,medicine.diagnostic_test ,business.industry ,020208 electrical & electronic engineering ,Brain ,brain-computer interface (BCI) ,Pattern recognition ,Atomic and Molecular Physics, and Optics ,Support vector machine ,Brain-Computer Interfaces ,020201 artificial intelligence & image processing ,Artificial intelligence ,business - Abstract
Quaternions can be used as an alternative to model the fundamental patterns of electroencephalographic (EEG) signals in the time domain. Thus, this article presents a new quaternion-based technique known as quaternion-based signal analysis (QSA) to represent EEG signals obtained using a brain-computer interface (BCI) device to detect and interpret cognitive activity. This quaternion-based signal analysis technique can extract features to represent brain activity related to motor imagery accurately in various mental states. Experimental tests in which users where shown visual graphical cues related to left and right movements were used to collect BCI-recorded signals. These signals were then classified using decision trees (DT), support vector machine (SVM) and k-nearest neighbor (KNN) techniques. The quantitative analysis of the classifiers demonstrates that this technique can be used as an alternative in the EEG-signal modeling phase to identify mental states. more...
- Published
- 2016
39. Detecting objects using color and depth segmentation with Kinect sensor
- Author
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Mario Alberto Ibarra-Manzano, Jose-Juan Hernandez-Lopez, Dora-Luz Almanza-Ojeda, Ana-Linnet Quintanilla-Olvera, José-Luis López-Ramírez, and Francisco-Javier Rangel-Butanda
- Subjects
Kinect ,Object detection ,Segmentation-based object categorization ,business.industry ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Scale-space segmentation ,Image segmentation ,Color space ,Mobile robotics ,Object (computer science) ,Geography ,Color segmentation ,General Earth and Planetary Sciences ,Robot ,Computer vision ,Segmentation ,Depth segmentation ,Artificial intelligence ,business ,General Environmental Science - Abstract
In order to optimize the movements of a robot, every object found in the work environment must not just be identified, but located in reference to the robot itself. Usually, object segmentation from an image is achieved using color segmentation. This segmentation can be achieved by processing the R, G and B chromatic components. However, this method has the disadvantage of been very sensitive to the changes on lighting. Converting the RGB image to the CIE-Lab color space avoids the lack of sensitivity by increasing the accuracy of the color segmentation. Unfortunately, if multiple objects of the same color are presented in the scene, is not possible to identify one of these objects using only this color space. Therefore, we need to consider an additional data source, in this case the depth, in order to discriminate objects that are not in the same plane as the object of interest. In this paper, we introduce an algorithm to detect objects, essentially on indoor environments, using CIE-Lab and depth segmentation techniques. We process the color and depth images provided by the Kinect sensor for proposing a visual strategy with real-time performance more...
- Published
- 2012
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40. Contour Detection at Range Images Using Sparse Normal Detector
- Author
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Dora-Luz Alamanza-Ojeda, Alejandra Cruz-Bernal, and Mario Alberto Ibarra-Manzano
- Subjects
Surface (mathematics) ,Computer science ,business.industry ,Homogeneity (statistics) ,010401 analytical chemistry ,Detector ,Pattern recognition ,Probability density function ,02 engineering and technology ,01 natural sciences ,0104 chemical sciences ,Feature (computer vision) ,Computer Science::Computer Vision and Pattern Recognition ,Histogram ,0202 electrical engineering, electronic engineering, information engineering ,Range (statistics) ,020201 artificial intelligence & image processing ,Point (geometry) ,Computer vision ,Artificial intelligence ,business - Abstract
The object surfaces on the Range images can be easily treated as elevations, at each point of these surfaces. The Sparse Normal Detector technique focuses in the extraction of keypoints, from homogeneous surface of Range images. Additionally, the contour of the objects in the scene can be represented through these points. First, the homogeneity feature is computed by means of the Sum and Difference Histogram technique, producing the Homogeneity image. Then, the corresponding dense normal vectors of the surface formed by this image are computed. A normal probability density function is used to select the most outstanding dense vectors, yielding the Sparse Normal descriptor. These vectors form new flat directional surfaces. The final detection of interesting point is performed using the Sparse Keypoints Detector technique. The experimental test involves a qualitative analysis, using the Middlebury and DSPLab dataset, and a quantitative evaluation of repeatability. more...
- Published
- 2016
- Full Text
- View/download PDF
41. Higher Order Polynomial Demosaicing
- Author
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M. Magali Flores-Barranco, Dora-Luz Almanza-Ojeda, Jose-Juan Hernandez-Lopez, Mario Alberto Ibarra-Manzano, and Sergio Eduardo Ledesma-Orozco
- Subjects
Color histogram ,Demosaicing ,Bayer filter ,Pixel ,Color image ,business.industry ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Color co-site sampling ,Computer Science::Computer Vision and Pattern Recognition ,RGB color model ,Color filter array ,Computer vision ,Artificial intelligence ,business ,ComputingMethodologies_COMPUTERGRAPHICS ,Mathematics - Abstract
Traditional digital cameras use a single charged coupled device (CCD) to capture images. In order to capture all color components (RGB), a light filter is located between the lens and the sensor, to separate the light and capture only one of these colors per pixel, this filter is known as Bayer filter. The resultant image contains the coded color components in two green pixels, one red pixel and one blue pixel per quartet, and a demosaicing process is needed to recover the coded color components. In this paper, we propose a novel methodology to perform this demosaicing on Bayer images, utilizing a higher order polynomial interpolation. We evaluate the second and third order polynomial, but, any order polynomial is easily implementable. Finally, we compare our method against well known methods such as simple interpolation demosiacing and nearest neighbor. more...
- Published
- 2014
- Full Text
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42. Broken rotor bar detection in VSD-fed induction motors at startup by high-resolution spectral analysis
- Author
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Roque Alfredo Osornio-Rios, Daniel Morinigo-Sotelo, Arturo Garcia-Perez, Rene de Jesus Romero-Troncoso, Oscar Duque-Perez, and Mario Alberto Ibarra-Manzano
- Subjects
Engineering ,Rotor (electric) ,business.industry ,Bar (music) ,High resolution ,Fault detection and isolation ,law.invention ,law ,Control theory ,Harmonics ,Electronic engineering ,Spectral analysis ,Transient (oscillation) ,business ,Induction motor - Abstract
Φ Abstract -- The fault detection in an induction motor (IM) operated by a variable speed drive (VSD) is an actual industrial need as most of the line-fed machines are replaced by a VSD, due to their improved speed regulation and fast dynamic response. However, undesired harmonics are always present when the IM is fed through a VSD. Under this operating condition, most developed techniques are unable to detect faults in the IM. This paper presents a technique based on the multiple signal classification (MUSIC) method, and it is applied to a VSD-fed IM during the startup transient; in order to verify the capability of the method to identify one broken rotor bar. From the experimental results, the proposed method is proven to be sensitive enough to detect one broken rotor bar, enabling a reliable diagnosis under different fundamental supply frequencies and load conditions. more...
- Published
- 2014
- Full Text
- View/download PDF
43. Enhancing Biomedical Images Using the UFIR Filters with Recursive Responses
- Author
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Mario Alberto Ibarra-Manzano, Yuriy S. Shmaliy, R.F. Vazquez-Bautista, Mario Gonzalez-Lee, Jaime Martinez-Castillo, and Luis J. Morales-Mendoza
- Subjects
Matrix (mathematics) ,Mean squared error ,Computer science ,Discrete orthogonal polynomials ,Quantitative Evaluations ,Recursive form ,Algorithm ,Impulse response ,Image (mathematics) - Abstract
In this paper we present a novel computational scheme to determine the impulse response of the UFIR filters. A recursive form of impulse response is developed using the theory of the discrete orthogonal polynomials. An example of an enhanced of medical image is considered to compare its performance versus the matrix formulation of the impulse response UFIR filters. Finally, some quantitative and qualitative evaluations are carried out to verify its efficiency based on RMSE analysis. more...
- Published
- 2013
- Full Text
- View/download PDF
44. Control de acceso usando FPGA y RFID
- Author
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Emmanuel Ortiz López, Mario Alberto Ibarra-Manzano, Jose A. Andrade-Lucio, and Dora Luz Almanza Ojeda
- Subjects
RFID ,Engineering ,control de acceso ,business.industry ,Control (management) ,Access control ,lcsh:Social Sciences ,lcsh:H ,Set (abstract data type) ,Identification (information) ,Embedded system ,Multidisciplinarias (Ciencias Sociales) ,LabVIEW ,General Earth and Planetary Sciences ,Radio-frequency identification ,Radio frequency ,lcsh:Science (General) ,business ,Field-programmable gate array ,FPGA ,lcsh:Q1-390 ,General Environmental Science ,Graphical user interface - Abstract
Este trabajo presenta el diseño e implementación de un sistema de control de acceso mediante Identificación por Radiofrecuencia (RFID, Radio Frequency Identification) controlado por una Matriz de compuertas programables (FPGA, Field Programmable Gate Array). El sistema está constituido por un par de dispositivos de adquisición de radiofrecuencia, una FPGA, un juego de etiquetas y tarjetas pasivas de identificación. Mediante una interfaz gráfica de usuario es posible controlar todo movimiento dentro de una zona determinada, desde los accesos hasta la disponibilidad de equipo; utilizando los dispositivos de adquisición de radiofrecuencia se puede acceder a la información de los usuarios autorizados, así como al control del equipo. Con este sistema es posible monitorear, administrar y reportar todo acceso de personal, movimiento de equipo o plagio de manera eficiente y evitando un gran número de errores humanos. more...
- Published
- 2012
45. 3D Visual Information for Dynamic Objects Detection and Tracking During Mobile Robot Navigation
- Author
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Dora-Luz Almanza-Ojeda and Mario Alberto Ibarra-Manzano
- Subjects
Active contour model ,Stereopsis ,Salience (neuroscience) ,Computer science ,business.industry ,Feature vector ,Robot ,Mobile robot ,Computer vision ,Artificial intelligence ,business ,Mobile robot navigation ,Silhouette - Abstract
An autonomous mobile robot that navigates in outdoor environments requires functional and decisional routines enabling it to supervise the estimation and the performance of all its movements for carrying out an envisaged trajectory. At this end, a robot is usually equipped with several high-performance sensors. However, we are often interested in less complex and low-cost sensors that could provide enough information to detect in real-time when the trajectory is free of dynamic obstacles. In this context, our strategy was focused on visual sensors, particulary on stereo vision since this provides the depth coordinate for allowing a better perception of the environment. Visual perception for robot mobile navigation is a complex function that requires the presence of "salience" or "evident" patrons to identify something that "breaks" the continuous tendency of data. Usually, interesting points or segments are used for evaluating patrons in position, velocity, appearance or other characteristics that allows us forming groups (Lookingbill et al., 2007), (Talukder & Matthies, 2004). Whereas complete feature vectors are more expressive for explaining objects, here we use 3D feature points for proposing a strategy computationally less demanding conserving the main objective of the work: detect and track moving objects in real time. This chapter presents a strategy for detecting and tracking dynamic objects using a stereo-vision systemmounted on a mobile robot. First, a set of interesting points are extracted from the left image. A disparity map, provided by a real-time stereo vision algorithm implemented on FPGA, gives the 3D position of each point. In addition, velocity magnitude and orientation are obtained to characterize the set of points on the space R6. Groups of dynamic 2D points are formed using the a contrario clustering technique in the 4D space and then evaluated on their depth value yielding groups of dynamic 3D-points. Each one of these groups is initialized by a convex contour with the velocity and orientation of the points given a first estimation of the dynamic object position and velocity. Then an active contour defines a more detailed silhouette of the object based on the intensity and depth value inside of the contour. It is well known that active contour techniques require a highly dense computations. Therefore, in order to reduce the time of processing a fixed number of iterations is used at each frame, so the convergence of the object real limits will be incrementally achieved along more...
- Published
- 2011
- Full Text
- View/download PDF
46. Implementation and Test of Appearance-Based Vision Algorithms Using High-Level Synthesis in FPGA
- Author
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Jose-Amparo Andrade-Lucio, Mario Alberto Ibarra-Manzano, Emmanuel Ortiz-Lopez, and Oscar-Gerardo Ibarra-Manzano
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Rapid prototyping ,Hardware architecture ,business.industry ,Computer science ,Hardware description language ,Software prototyping ,Object detection ,Embedded system ,High-level synthesis ,Field-programmable gate array ,business ,computer ,computer.programming_language ,FPGA prototype - Abstract
This article presents an architecture to detect objects from images based on color and texture features. This architecture is simplified and efficient as a result of the optimization of Adequacy of Sum and Diference of Histograms(ASDH) for embebed systems. Our architecture was prototyped using LabVIEW FPGA which is a practical tool to develop high-level synthesis. We take advantage of LabVIEW FPGA to do rapid prototyping and implement the architecture and to make a general comparison among this architecture implemented with Hardware Description Language (HDL) and LabVIEW FPGA, this lets us analyze if the use of high level synthesis improve the systems performance. The use of high level synthesis give us an interesting option to improve in digital design made it the time of prototyping more shortly, efficiently and flexible for example in applications for vision systems. more...
- Published
- 2011
- Full Text
- View/download PDF
47. Design and Optimization of Real-Time Boosting for Image Interpretation Based on FPGA Architecture
- Author
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Mario Alberto Ibarra-Manzano and Dora-Luz Almanza-Ojeda
- Subjects
Boosting (machine learning) ,Contextual image classification ,Pixel ,business.industry ,Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Decision tree ,Object detection ,ComputingMethodologies_PATTERNRECOGNITION ,Image texture ,Computer vision ,Artificial intelligence ,Architecture ,business ,Field-programmable gate array - Abstract
This paper presents a reconfigurable architecture of a classification module based on the Adaboost algorithm. This architecture is used for object detection based on the attributes of color and texture. The Adaboost algorithm module uses the technique of decision trees as weak classifiers. This high-performance architecture processes up to 325 dense images of size 640 × 480 pixels, classifying all the structured objects contained on the image. Classification results are provided on an image with the same size. Both architectures, Adaboost algorithm and decision trees, are discussed and compared with several studies found in the literature. The conclusions and perspectives of the project are provided at the end of this document. more...
- Published
- 2011
- Full Text
- View/download PDF
48. High-Speed Architecure Based on FPGA for a Stereo-vision Algorithm
- Author
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Mario Alberto Ibarra-Manzano and Dora-Luz Almanza-Ojeda
- Subjects
Stereopsis ,business.industry ,Computer science ,Computer vision ,Artificial intelligence ,Field-programmable gate array ,business - Published
- 2011
49. An FPGA Implementation for Texture Analysis Considering the Real-Time Requirements of Vision-Based Systems
- Author
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Mario Alberto Ibarra-Manzano and Dora-Luz Almanza-Ojeda
- Subjects
Service (systems architecture) ,Identification (information) ,Process (engineering) ,Computer science ,Real-time computing ,Reference architecture ,Reuse ,Architecture ,Autonomous robot ,Field-programmable gate array - Abstract
This article presents an architecture based on FPGA for the calculation of texture attributes using an adequacy of the technique of sum and differences of histograms. The attributes calculated by this architecture will be used in a process of classification for identification of objects during the navigation of an autonomous robot of service. Because of that, the constraint of real-time execution plays an essential role during the architecture design. So, the architecture is designed to calculate 30 dense images with 6 different attributes of texture for 10 different displacements. Exploiting the reuse of operations in parallel on the FPGA and taking into account the requisites in the time of calculation, it is possible to use the resources in an efficient and optimised way in order to obtain an architecture with the best trade off between resources and the time of calculation. Thanks to the high performance of this architecture, it can be used in applications like medical diagnosis or target detection. more...
- Published
- 2011
- Full Text
- View/download PDF
50. Real-time classification based on color and texture attributes on an FPGA-based architecture
- Author
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Michel Devy, Jean-Louis Boizard, and Mario Alberto Ibarra-Manzano
- Subjects
Pixel ,Contextual image classification ,Computer science ,business.industry ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Image processing ,Color space ,Statistical classification ,Image texture ,Computer Science::Computer Vision and Pattern Recognition ,Color depth ,Chrominance ,Computer vision ,Artificial intelligence ,business - Abstract
The design and the implementation of algorithms on FPGA-based architectures, is a complex task, above all for image processing. Many vision applications (video monitoring, obstacle detection from a vehicle) require real time performance. This paper analyzes only a classical function involved in these applications: pixel characterization by an attribute vector, and pixel classification as belonging or not to an interest class. Typical attributes are color and texture. Color is described by the chrominance given by the a and b coordinates in the CIE-Lab color space. Texture is only computed from the L* coordinate, describing the local intensity variations in a neighborhood of every pixel. AdaBoost has been selected in order to learn how to classify every pixel from its attribute vector. From a learning data base, it is learnt off line how to select and combine a given number of weak classifiers; then, the classifier parameters are loaded on an FPGA-based kit. This paper proposes different architectures and presents some results obtained from images acquired from a robot, in order to classify a pixel as Ground or Obstacle. more...
- Published
- 2010
- Full Text
- View/download PDF
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