Papers by Jose Rangel-Magdaleno
2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
This work presents an automated acquisition system for microbolometer array. The solution is base... more This work presents an automated acquisition system for microbolometer array. The solution is based on the case scenario where microbolometer array parameters has to be determined automatically (from an unknown device), avoiding the detector damage hazard. For this reason, a data management system is considered in the design presented. The automated acquisition system designed is driven by FPGA-based digital algorithms; these algorithms command the microbolometer power, and the signal readout from a digital to analog converter, and analog to digital converter, respectively. The data obtained, from the proposed system, are sent to a software-based application programmed in MATLAB. Considering that detector structure has a non-lineal behavior at self-heating operating condition, which leads on a device damage, the data are processed by the high level application; thereby, the FPGA-based digital algorithms are reconfigured to keep the detector at save operating conditions either forward or reverse polarization. The proposed system is a modular and flexible solution that is capable of reaching several microbolometer array configurations. The main contribution of this work yields on the automated acquisition application, where microbolometer array data can be readout and stored, preventing the detector damage caused by the self-heating condition; the data obtained can be used to determine the microbolometer array parameters, and the optimal settings to drive each detector. The system effectiveness is experimentally verified on a $19\ x\ 20$ microbolometer array.
2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
Induction Motors (IMs) have been extensively used in the industry due to their low-cost, fast ins... more Induction Motors (IMs) have been extensively used in the industry due to their low-cost, fast installation, and easy operation. However, it is necessary to have adequate maintenance programs because sudden faults, in the IM, could lead in important financial losses. Several methods have been proposed in the literature, where the thermography images analysis is an excellent candidate due to it is a non-invasive and non-contact technique, avoiding problems that exist in traditional techniques as indirect and unnoticed interference from other equipment in the signal to analyze, e.g. vibration or current. In this paper, a methodology based on the analysis of the specific region in thermography images to detect bearing damage is developed. In this manner, a study of two common damages is performed. The analysis is performed in three regions in the IM, with mechanical load, and with different supply frequencies. The difference of temperature among the regions are studied and the results are shown a difference in about 1.8°C between healthy and damaged condition, enough difference to detect the damage in the bearing taking into account that the IM has a cooling fan in the back.
2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), 2017
Laser speckle contrast imaging (LSCI) is an optical technique capable for blood flow analyzing ba... more Laser speckle contrast imaging (LSCI) is an optical technique capable for blood flow analyzing based on local speckle contrast. In general, a CCD camera combined with a simple optical arrangement allows to obtain blood flow information. Unfortunately, there are important limitations in the case of deep blood vessels (about 300μm). In this case, it is difficult to obtain blood vessel information since speckle patterns inside the images are sparse. In this work, we propose an iterative spatiotemporal anisotropic filtering approach which, aims to improve the performance under deep vessels images. This approach consists in two different filters combined with an iterative behavior and a post processing step. First, a temporal filter computes the contrast gradient (K) for all pixels within the input image. Then, contrast directions are computed. Finally, a spatiotemporal filter analyzes the blood flow behavior based on the contrast directions. Experimental results are encouraging, the proposed approach improves the performance under deep vessels, compared with previous work. Furthermore, these results (which were obtained at different samples) demonstrated that it is possible to improve the global performance by using an iterative behavior.
2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2017
In this paper, the classification of three Induction Motor conditions using the Orthogonal Matchi... more In this paper, the classification of three Induction Motor conditions using the Orthogonal Matching Pursuit algorithm is presented. The OMP algorithm was implemented into a Field Programmable Gate Array to obtain the Sparse representation of the signal given a Dictionary. Then, the signal information obtained from the Sparse representation is evaluated and classified. The dictionaries were obtained by K-Singular Value Decomposition (K-SVD) algorithm developed in Matlab software. The FPGA implementation is low-complexity, cheap on hardware, compact, and works at 100MHz.
2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2020
Nowadays, the monitoring of rotatory machinery is an essential task in the industry, due to its h... more Nowadays, the monitoring of rotatory machinery is an essential task in the industry, due to its help in specific maintenance programs, increasing the life of the machine, reducing interventions, and getting off costs in spare parts. The bearings are essential pieces of rotatory machinery; thus, monitoring and fault detection are important tasks to maintain their proper functioning. In this paper, a methodology for bearing fault detection on induction motors based on Motor Current Signature Analysis, the Sparse Representation, and Dictionary Learning techniques are presented. The fault detection is possible due to the Sparse Representation is developed with dictionaries trained with specific faults each one, obtaining a minor error with the dictionary related to the same fault of the analyzed signal. In this way, the detection of related to localized defects and distributed defects are possible. Bearings of three-phase and 1-HP induction motors with two load conditions were used to testing. The proposed methodology presents an accuracy greater than 96%.
2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS), 2017
The use of Drones in areas such as cinema, sports, social events, and even video selfies has been... more The use of Drones in areas such as cinema, sports, social events, and even video selfies has been increasing due to their flexibility to capture video in scenarios where there is an interest to keep a target within the field of view of the drone's onboard camera. In order to remove the dependence of the pilot that controls the drone, in this work we present a system for an autonomous flight control of the drone with the goal of keeping a target within the field of view of its onboard camera. For the latter, the images were captured by a camera onboard the drone, whose output was combined with a stochastic estimator of the target states, based on the Unscented Kalman Filter, to generate control commands in order that the drone which performs such recording autonomously. The system was validated with real-time tests involving different targets moving with different trajectories and it was compared against human pilots. Our approach kept the target within the field of view with a 96.6% of success compared to 83.3% of success obtained by human pilots. The latter indicates that our approach has the potential to be used in applications where autonomous drones could be used for aerial video recording, with a special interest in keeping a target within the field of view of the drone's camera.
2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2018
Parameter measurement of liquids in containers such as level, density and viscosity is a widely i... more Parameter measurement of liquids in containers such as level, density and viscosity is a widely investigated field due to its relevance in a variety of industrial fields. In this work an analysis of vibration modes related to liquid parameters for a vertical immersed beam based on a general Bernoulli-Euler beam model for different liquids is presented. Experimental and theoretical results are compared to a Finite Element Model (FEM) simulation implemented in COMSOL, using a distributed mass approach. This analysis aims to characterization of axisymmetric containers using an immersed beam model where the vibration modes are linked to surrounding liquid properties. Results showed a good approximation with an error of 6.5 % in average of the full scale range when the first and second vibration mode are considered.
2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence (ColCACI), 2018
Many computer algorithms have been developed, providing an initial aided diagnosis to the medical... more Many computer algorithms have been developed, providing an initial aided diagnosis to the medical expertise. Most important previous stage in the automatic classificatión to grading diseases using images is to obtain a well-segmented región of interest from. Several related research in image classificatión uses a great number of image processing techniques previous to the classificatión stage. In this paper, we compare the automatic segmentatión based on two leading machine learning techniques: Differential Evolutión (DE) and the Self-Organizing Multilayer (SOM) Neural Network (NN) methods. The results are also compared with K-means algorithm for multi-level segmentatión from slit-lamp images. Segmented images were obtained relying on a thresholding approach based on fuzzy partitións of the image histogram and a fuzzy entropy measure optimized via a neural process and by the evolutive technique. The resulting approaches were also compared with the classical Shannon entropy.
2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2018
Squirrel-cage induction motors are among most used rotary machinery in many industrial fields. Fa... more Squirrel-cage induction motors are among most used rotary machinery in many industrial fields. Fault detection in early stages is in high relevance due to technical and economic issues, and broken bars are among the most common faults in induction motors. This paper presents an approach to carry out detection of this failure using as input the current signal measured from one of the three motors phases. Independent Component Analysis (ICA) is used over the Fourier-domain spectral signals obtained from the input and its autocorrelation function. A notable difference on the standard deviation over a region of interest in one output can be distinguished in the current signals obtained from damaged and healthy motors. Obtained results show a correct classification percentage of 95.3% in average.
2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), 2017
Currently, the Induction Motor is widely used in industry, due to its easy installation and opera... more Currently, the Induction Motor is widely used in industry, due to its easy installation and operation. Induction motors require a more reliable monitoring due to constant operation increases the possibility of faults, for example, a broken rotor bar fault. Early stage, broken bar is not easy to detect, and its evolves is slow and quiet. In the most of cases, it is detected when the fault is critical and other faults have appeared. Many techniques have been proposed in the literature, but majority of these performs analysis in frequency domain, applying additional transformation or preprocessing methods. In this paper, a novel methodology to detect a half-broken bar fault is proposed, making use of the vibration signal from induction motor under two fault conditions: healthy and half-broken bar; and three load conditions: unloaded, half-loaded and three-fourths loaded. The detection is possible due to the sparse representation of the raw signal which is obtained and then evaluated by minimal decomposition error criterion. In this way, preprocessing methods are not needed, and the fault is detected early and directly. These tests were developed in Matlab software, with vibration signals from induction motors in steady state.
2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2018
Broken bars are one of the most common faults in induction motors. Current and vibration signals ... more Broken bars are one of the most common faults in induction motors. Current and vibration signals have been studied and analyzed with diverse mathematical tool, such as the Fast Fourier Transform (FFT) that allows the analysis of signals in the frequency domain, or the Wavelet Transform (WT) that performs the analysis, maintaining the characteristics of frequency and time in the transient state of the signals. In this paper we propose to use the Maximal Overlap Discrete Wavelet Transform (MODWT) wavelet coefficients and statistical parameters such as entropy and standard deviation (STD) as classifiers to determine the half and one broken bar in the Induction Motor (IM). Following this proposal, classification percentages higher than 95% were achieved to detect one broken bar with vibration signals. Also the classification percentages for half broken bar with respect to the Discrete Wavelet Transform (DWT) are exceeded.
2018 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), 2018
Noise is present in everyday life, that it is a fact to find it in everything. Depending on the a... more Noise is present in everyday life, that it is a fact to find it in everything. Depending on the applications, the noise can present or not issues. In sensing process, the noise would be a huge issue when the accuracy of the system is high, when the resolution of the sensor is better than, e.g., 40 mV/A. In this case, the outputs less than 3A have a high SNR. If these signals are used in signals processing, depending on its focus, they can provoke malfunctions and false positives. This paper presents, a methodology for noise reduction based on the sparse representation of a signal using a dictionary based on Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST). The proposed method is tested with synthetic signals to research its effectiveness, and then it is tested with real signals taken from the current that supply Induction Motors. After the methodology is applied, signals are analyzed to prove that the information contained is preserved. The noise reduction of the cu...
2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), 2017
A BCI system (Brain-Computer Interface) aims to the interpretation of brain signals perceived thr... more A BCI system (Brain-Computer Interface) aims to the interpretation of brain signals perceived through electroencephalography (EEG) sensors in order to allow the user interaction with the environment through specific actions. In this paper we present an experiment of EEG signal classification under the motor imagery paradigm using two feature extraction methods for comparison purposes: discrete wavelet transform (DWT) and maximum overlap discrete wavelet transform (MODWT). The feature vectors are fed into a support vector machine (SVM) classification system. The results obtained show an accuracy of 98.81% in average.
Sensors, 2021
Sudden Cardiac Death (SCD) is an unexpected sudden death due to a loss of heart function and repr... more Sudden Cardiac Death (SCD) is an unexpected sudden death due to a loss of heart function and represents more than 50% of the deaths from cardiovascular diseases. Since cardiovascular problems change the features in the electrical signal of the heart, if significant changes are found with respect to a reference signal (healthy), then it is possible to indicate in advance a possible SCD occurrence. This work proposes SCD identification using Electrocardiogram (ECG) signals and a sparse representation technique. Moreover, the use of fixed feature ranking is avoided by considering a dictionary as a flexible set of features where each sparse representation could be seen as a dynamic feature extraction process. In this way, the involved features may differ within the dictionary’s margin of similarity, which is better-suited to the large number of variations that an ECG signal contains. The experiments were carried out using the ECG signals from the MIT/BIH-SCDH and the MIT/BIH-NSR databas...
2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), 2017
Induction Motor is the most popular rotatory machine used in the industry. Thus, it requires a ma... more Induction Motor is the most popular rotatory machine used in the industry. Thus, it requires a major periodical revision, because any fault during the operation can be catastrophic. A critical fault is the Broken Rotor Bar, which starts very small and increases until breaks the bar. Thereby, the machine, which has a defect of this type, will increase the possibility to develop others faults that contribute to a rapid and dramatic decrease in its structural integrity. In this paper, a novelty technique to detect Half-Broken Rotor Bar fault is presented, it is based on the Kolmogorov-Smirnov Test, where the differentiation between signals without fault and signals with fault is possible.
Review of Scientific Instruments, 2021
Paper published as part of the special topic on Advances in Measurements and Instrumentation Leve... more Paper published as part of the special topic on Advances in Measurements and Instrumentation Leveraging Embedded Systems Review of Scientific Instruments REVIEW scitation.org/journal/rsi
Three-Dimensional Imaging, Visualization, and Display 2020, 2020
We present a case study for a time-of-flight (ToF) 3D imaging system using single-pixel imaging (... more We present a case study for a time-of-flight (ToF) 3D imaging system using single-pixel imaging (SPI) approach based on compressive sensing (CS), accompanied by the Time-of-flight (ToF) principle applied to four reference points of the 2D image created and then mapped to the rest of the SPI generated virtual pixels. In this analysis we have developed a mathematical model of the system and evaluated three different scenarios considering different performance issues based on signal-to-noise ratio, different levels of background illumination, distance, spatial resolution, and material reflectivity presented by the objects in the scene. To be able to reduce the background photon shot noise and enable the correct functionality of the system also in harsh environments (in presence of micrometer size particles such as rain, snow, fog or smoke) we propose using near infra-red (NIR) active illumination with a peak wavelength of 1550 nm. The SPI principle is based here on an array of NIR emitting LEDs and the Thorlab FGA015 InGaAs single photodiode. For the system modelling and analysis, we considered the maximum background illumination intensity of up to 100 klux, different reflection coefficients of the target material to be detected, and measurement distances between 1 and 10 m. Using the ToF principle, we evaluated the direct ToF using both, pulsed laser NIR source as well as an array of NIR emitting LEDs combined with a single InGaAs photodiode on the one side, and an InGaAs single-photon avalanche diode (SPAD) on the other. Using the model developed, we estimated the spatial resolution (standard deviation from the distance measured) the proposed system might reach for each of the ToF methods analyzed and combining different system elements. Finally, we propose a SPI-ToF 3D imaging and ranging system for drone outdoors applications.
2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2019
Please refer to published version for the most recent bibliographic citation information. If a pu... more Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.
2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2018
Due to its straightforward installation, operation and low cost, the Induction Motor (IM) is wide... more Due to its straightforward installation, operation and low cost, the Induction Motor (IM) is widely used in industry. Because of its constant service, there is a growing necessity of fault detection techniques for IM. Bearings fault (BF) is a common fault that may appear in an induction motor, and it may cause severe damage to the device. Thus, early detection is necessary to program the corresponding maintenance and extend the service of the machine and to reduce expenses for maintenance service or replacement. In literature, different techniques have been proposed to detect this fault involving a signal preprocessing step and applying time or frequency domain approaches, which require time and resource consumption. In this paper, a technique to detect BF based on Motor Current Signal Analysis (MCSA) via a statistical analysis named Kolmogorov-Smirnov Test (K-S test), is presented. K-S test determines if two samples come from the same distribution by measuring the maximum distance between them. We compared signals of two motor conditions, no damage (ND) and BF. We applied the test to the raw current signal.
2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2021
This paper presents a biometric system on dorsal hand vein images in the near infrared (NIR), wit... more This paper presents a biometric system on dorsal hand vein images in the near infrared (NIR), with an approach based on fusion of classifiers at score level. Fiducial features containing information on texture and shape are used with two classifiers based on Chi-square distance and Dynamic Time Warping (DTW), respectively, and further fused at score level. A collection of experiments using a publicly available dataset obtained from Universidad de Las Palmas de Gran Canaria was carried out. The obtained results indicate an Equal Error Rate of EER=0.0486 and EER=0.0274 and in average with classifiers fusion using sum and multiplication of scores in verification mode, and recognition rate of RR=95.80% and RR=97.30% in identification mode, respectively. These results represent an improvement with respect to results obtained when both classifiers and features are used individually.
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Papers by Jose Rangel-Magdaleno