Pub Date : 2021-07-07DOI: 10.1201/9781003191148-20
José Luis Peinado Portillo, Carlos A. Ochoa-Ortíz, S. Paiva, Darwin Young
. In our days, we are living the epitome of Industry 4.0, where each component is intelligent and suitable for Smart Manufacturing users, which is why the specific use of Big Data is proposed to determine the continuous improvement of the competitiveness of a car assembling industry. The Boston Consulting Group [1] has identified nine pillars of I4.0, which are: (i) Big Data and Analytics, (ii) Autonomous Robots, (iii) Simulation, (iv) Vertical and Horizontal Integration of Systems, (v) Industrial Internet of Things (IoT for its acronym in English), (vi) Cybersecurity, (vii) Cloud or Cloud, (viii) Additive Manufacturing including 3D printing, and (ix) Augmented Reality. These pillars can all be implemented in factories or take some depending on the case you want to improve. In Industry 4.0, the Industrial IoT is a fundamental component and its penetration in the market is growing. Car manufacturers such as General Motors or Ford expect that by 2020 there will be 50 billion (trillion in English) of connected devices and Ericsson Inc. estimates 18 billion. These estimated quantities of connected devices will be due to the increase in technological development, development in telecommunications and adoption of digital devices, and this will invariably lead to the increase in the generation of data and digital transactions, which leads to the mandatory increase in regulations, for security, privacy and informed consent in the integration of these diverse entities that will be connected and interacting among themselves and with the users. Finally, the use of Fuzzy Logic type 2 is proposed to adapt the correct decision making and achieve the reduction of uncertainty in the car assembly industry in the Northeast of Mexico. fuzzy logic type 2 for decision makings.
{"title":"Implementation of an Intelligent Model based on Big Data and Decision Making using Fuzzy Logic Type-2 for the Car Assembly Industry in an Industrial Estate in Northern Mexico","authors":"José Luis Peinado Portillo, Carlos A. Ochoa-Ortíz, S. Paiva, Darwin Young","doi":"10.1201/9781003191148-20","DOIUrl":"https://doi.org/10.1201/9781003191148-20","url":null,"abstract":". In our days, we are living the epitome of Industry 4.0, where each component is intelligent and suitable for Smart Manufacturing users, which is why the specific use of Big Data is proposed to determine the continuous improvement of the competitiveness of a car assembling industry. The Boston Consulting Group [1] has identified nine pillars of I4.0, which are: (i) Big Data and Analytics, (ii) Autonomous Robots, (iii) Simulation, (iv) Vertical and Horizontal Integration of Systems, (v) Industrial Internet of Things (IoT for its acronym in English), (vi) Cybersecurity, (vii) Cloud or Cloud, (viii) Additive Manufacturing including 3D printing, and (ix) Augmented Reality. These pillars can all be implemented in factories or take some depending on the case you want to improve. In Industry 4.0, the Industrial IoT is a fundamental component and its penetration in the market is growing. Car manufacturers such as General Motors or Ford expect that by 2020 there will be 50 billion (trillion in English) of connected devices and Ericsson Inc. estimates 18 billion. These estimated quantities of connected devices will be due to the increase in technological development, development in telecommunications and adoption of digital devices, and this will invariably lead to the increase in the generation of data and digital transactions, which leads to the mandatory increase in regulations, for security, privacy and informed consent in the integration of these diverse entities that will be connected and interacting among themselves and with the users. Finally, the use of Fuzzy Logic type 2 is proposed to adapt the correct decision making and achieve the reduction of uncertainty in the car assembly industry in the Northeast of Mexico. fuzzy logic type 2 for decision makings.","PeriodicalId":220522,"journal":{"name":"Res. Comput. Sci.","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121715184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The work is supported by KAUST Catalysis Center. I want to thank the KAUST Supercomputing Laboratory (KSL) for allowing me to use the resources available, especially the Shaheen and Ibex supercomputers.
{"title":"Random Forest and Deep Learning Performance on the Malaria DREAM Sub Challenge One","authors":"Didier Barradas-Bautista","doi":"10.5281/ZENODO.4018883","DOIUrl":"https://doi.org/10.5281/ZENODO.4018883","url":null,"abstract":"The work is supported by KAUST Catalysis Center. I want to thank the KAUST Supercomputing Laboratory (KSL) for allowing me to use \u0000the resources available, especially the Shaheen and Ibex supercomputers.","PeriodicalId":220522,"journal":{"name":"Res. Comput. Sci.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125587865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper I present our ongoing work on developing a Learning Ecosystem for Training Linemen in Maintenance Maneuvers. First, challenges involved in training Linemen are introduced. Then, I discuss opportunities for creating a Learning Ecosystem for Linemen training using the Experience API standard and Learning Analytics. Although presented experimental results are reduced, these already show the value of Learning Analytics in exploiting data from already adopted technologies and new educational data sources for enhancing Linemen training.
{"title":"Towards a Learning Ecosystem for Linemen Training","authors":"G. Bonfil","doi":"10.13053/rcs-148-5-14","DOIUrl":"https://doi.org/10.13053/rcs-148-5-14","url":null,"abstract":"In this paper I present our ongoing work on developing a Learning Ecosystem for Training Linemen in Maintenance Maneuvers. First, challenges involved in training Linemen are introduced. Then, I discuss opportunities for creating a Learning Ecosystem for Linemen training using the Experience API standard and Learning Analytics. Although presented experimental results are reduced, these already show the value of Learning Analytics in exploiting data from already adopted technologies and new educational data sources for enhancing Linemen training.","PeriodicalId":220522,"journal":{"name":"Res. Comput. Sci.","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127340037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. LuisA.Santamaría, H. SarahíZuñiga, I. H. P. Torres, M. J. S. García, Mario Rossainz López
In recent years, the field of machine learning has progressed enormously in addressing difficult classification problems. The problem raised in this article is to recognize DNA sequences, recognize the boundaries between exons and introns using a graphic representation of DNA sequences and recent methods of deep learning. The objective of this work is to classify DNA sequences using a convolutional neuronal network (CNN). The set of DNA sequences used for the recognition were 1847 sequences from a database with 4 types of hepatitis C virus (type 1, 2, 3 and 6) taken from the repository available on the ViPR page. The other set of sequences used to recognize limits between exons and introns were sequences from the Molecular database (Splice-junction Gene Sequences) Data Set that has 3190 sequences, available on the ICU page, with three classes of sequences: limit exon-intron, limit intron-exon and none. For the processing of the DNA sequences, a representation method was designed where each nitrogenous base is represented in gray scale to form an image. The generated images were used to train the convolutional neuronal network. The results obtained from the CNN trained with the Hepatitis C virus database suggest that the CNNs are suitable for the classification of the images generated from the DNA sequences. This result led us to perform the experiments for the recognition of exons and introns with the UCI database for the recognition of limits between exons and introns. The results obtained were a training precision of 82%, a validation accuracy of 75% and an evaluation accuracy of 80.8%. It is concluded that it is possible to classify the images of DNA sequences of the databases used.
{"title":"DNA Sequence Recognition using Image Representation","authors":"C. LuisA.Santamaría, H. SarahíZuñiga, I. H. P. Torres, M. J. S. García, Mario Rossainz López","doi":"10.13053/rcs-148-3-9","DOIUrl":"https://doi.org/10.13053/rcs-148-3-9","url":null,"abstract":"In recent years, the field of machine learning has progressed enormously in addressing difficult classification problems. The problem raised in this article is to recognize DNA sequences, recognize the boundaries between exons and introns using a graphic representation of DNA sequences and recent methods of deep learning. The objective of this work is to classify DNA sequences using a convolutional neuronal network (CNN). The set of DNA sequences used for the recognition were 1847 sequences from a database with 4 types of hepatitis C virus (type 1, 2, 3 and 6) taken from the repository available on the ViPR page. The other set of sequences used to recognize limits between exons and introns were sequences from the Molecular database (Splice-junction Gene Sequences) Data Set that has 3190 sequences, available on the ICU page, with three classes of sequences: limit exon-intron, limit intron-exon and none. For the processing of the DNA sequences, a representation method was designed where each nitrogenous base is represented in gray scale to form an image. The generated images were used to train the convolutional neuronal network. The results obtained from the CNN trained with the Hepatitis C virus database suggest that the CNNs are suitable for the classification of the images generated from the DNA sequences. This result led us to perform the experiments for the recognition of exons and introns with the UCI database for the recognition of limits between exons and introns. The results obtained were a training precision of 82%, a validation accuracy of 75% and an evaluation accuracy of 80.8%. It is concluded that it is possible to classify the images of DNA sequences of the databases used.","PeriodicalId":220522,"journal":{"name":"Res. Comput. Sci.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125054259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Claudia Angélica Márquez-Mata, H. R. Vega-Carrillo, María De Jesús Mata Chávez, José de Jesús Araiza-Ibarra
{"title":"Caracterización óptica, química y nuclear del ónix mexicano (CaCO3), correspondiente a la zona del semidesierto Zacatecano","authors":"Claudia Angélica Márquez-Mata, H. R. Vega-Carrillo, María De Jesús Mata Chávez, José de Jesús Araiza-Ibarra","doi":"10.13053/rcs-148-1-4","DOIUrl":"https://doi.org/10.13053/rcs-148-1-4","url":null,"abstract":"","PeriodicalId":220522,"journal":{"name":"Res. Comput. Sci.","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122663856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hill coding, based on linear algebra, by the American mathematician Lester S. Hill in 1929 in this method we use a square matrix A of integers as a key, which determines the linear transformation Y = A * X where Y, X they are the column vectors. Using this encryption method, a text was encrypted to later decrypt it with the use of brute force, that is, to test each of the possible combinations of keys to find the original text in this article. A 2x2 key was used to encrypt the text with a limit from 1 to 256 for each element in the matrix 256 x 256 x 256 x 256 permutations were found that is 4,294,967,296 possible keys for this decipher this text as it can be clearly seen there are too many operations to perform that can consume a considerable time for the CPU since he must decipher the text for each of these combinations and find the correct one, that is why to do this arduous task, parallel programming was used to generate each of the keys and work with each one of them.
希尔编码，基于线性代数，由美国数学家莱斯特·s·希尔在1929年提出的这种方法中我们用一个整数的方阵a作为键，它确定了线性变换Y = a * X其中Y、X它们是列向量。使用这种加密方法，对文本进行加密，以便稍后使用蛮力对其进行解密，也就是说，测试每个可能的密钥组合，以找到本文中的原始文本。2 x2密钥用于加密的文本限制从1到256矩阵中的每个元素256 x 256 x 256 x 256排列被发现的4294967296种可能的钥匙这个解读文本,因为它可以清楚地看到有太多的操作来执行对CPU的消耗相当大的时间因为他必须为每个这些组合和解读文本找到正确的一个,这就是为什么做这个艰巨的任务,并行编程用于生成每个键并处理每个键。
{"title":"Hill Algorithm Decryption using Parallel Calculations by Brute Force","authors":"B. Sánchez-Rinza, Juan Carlos García Lezama","doi":"10.13053/rcs-148-3-7","DOIUrl":"https://doi.org/10.13053/rcs-148-3-7","url":null,"abstract":"Hill coding, based on linear algebra, by the American mathematician Lester S. Hill in 1929 in this method we use a square matrix A of integers as a key, which determines the linear transformation Y = A * X where Y, X they are the column vectors. Using this encryption method, a text was encrypted to later decrypt it with the use of brute force, that is, to test each of the possible combinations of keys to find the original text in this article. A 2x2 key was used to encrypt the text with a limit from 1 to 256 for each element in the matrix 256 x 256 x 256 x 256 permutations were found that is 4,294,967,296 possible keys for this decipher this text as it can be clearly seen there are too many operations to perform that can consume a considerable time for the CPU since he must decipher the text for each of these combinations and find the correct one, that is why to do this arduous task, parallel programming was used to generate each of the keys and work with each one of them.","PeriodicalId":220522,"journal":{"name":"Res. Comput. Sci.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128413180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Susana L. Pacheco-González, L. A. Zanella-Calzada, C. Galván-Tejada, Nubia M. Chávez-Lamas, J. F. Rivera-Gómez, J. Galván-Tejada
. Depression is a mental disorder manifested through a set of psychological and physical symptoms, such as the presence of sad-ness, apathy, hopelessness and irritability, among others. According to the World Health Organization (WHO), depression is affecting more than 300 million people worldwide, presenting a prevalence between 3 and 21%. One of the main problems of this high prevalence is the incorrect classification of patients, since many cases are false positive and false negative diagnoses. In this work it is proposed the study of the behavior of five different classification techniques, random forest (RF), conditional inference trees (cTree), K-nearest neighbor (K-NN), support vector machine (SVM) and Na¨ıve Bayes, to identify depressive states through the motor activity of patients contained in the Depresjon dataset. The activity of this dataset is acquired through the smart watch “Actigraph”, based on actigraphy. The evaluation of these classification techniques is finally performed in terms of sensitivity, specificity, the receiver operating characteristic (ROC) curve and area under the curve (AUC), to know their performance to automatically detect depressive patients. The results shown values of sensitivity, specificity and AUC, statistically significant, specially for the RF method, which presents sensitivity = 0.8148, specificity = 0.8158 and AUC = 0.8314. Therefore, it is concluded that these classifiers are able to distinguish patients with depression from controls, based on their motor activity, allowing the development of a non-invasive diagnosis tool to support specialists in the correct diagnosis of depression.
。抑郁症是一种精神障碍，表现为一系列心理和身体症状，如悲伤、冷漠、绝望和易怒等。根据世界卫生组织(世卫组织)的数据，全世界有3亿多人患有抑郁症，患病率在3%至21%之间。这种高流行率的主要问题之一是对患者的不正确分类，因为许多病例是假阳性和假阴性诊断。在这项工作中，提出了五种不同分类技术的行为研究，随机森林(RF)，条件推理树(cTree)， k -最近邻(K-NN)，支持向量机(SVM)和纳伊ıve贝叶斯，通过抑郁症数据集中包含的患者的运动活动来识别抑郁状态。该数据集的活动是通过智能手表“Actigraph”获取的，基于Actigraph。最后从灵敏度、特异度、受试者工作特征(ROC)曲线和曲线下面积(AUC)等方面对这些分类技术进行评价，了解其在自动检测抑郁症患者中的表现。结果显示，灵敏度、特异度和AUC值均具有统计学意义，其中射频法的灵敏度= 0.8148，特异度= 0.8158,AUC = 0.8314。因此，我们得出结论，这些分类器能够根据患者的运动活动区分抑郁症患者和对照组，从而允许开发一种非侵入性诊断工具，以支持专家正确诊断抑郁症。
{"title":"Evaluation of Five Classifiers for Depression Episodes Detection","authors":"Susana L. Pacheco-González, L. A. Zanella-Calzada, C. Galván-Tejada, Nubia M. Chávez-Lamas, J. F. Rivera-Gómez, J. Galván-Tejada","doi":"10.13053/rcs-148-10-11","DOIUrl":"https://doi.org/10.13053/rcs-148-10-11","url":null,"abstract":". Depression is a mental disorder manifested through a set of psychological and physical symptoms, such as the presence of sad-ness, apathy, hopelessness and irritability, among others. According to the World Health Organization (WHO), depression is affecting more than 300 million people worldwide, presenting a prevalence between 3 and 21%. One of the main problems of this high prevalence is the incorrect classification of patients, since many cases are false positive and false negative diagnoses. In this work it is proposed the study of the behavior of five different classification techniques, random forest (RF), conditional inference trees (cTree), K-nearest neighbor (K-NN), support vector machine (SVM) and Na¨ıve Bayes, to identify depressive states through the motor activity of patients contained in the Depresjon dataset. The activity of this dataset is acquired through the smart watch “Actigraph”, based on actigraphy. The evaluation of these classification techniques is finally performed in terms of sensitivity, specificity, the receiver operating characteristic (ROC) curve and area under the curve (AUC), to know their performance to automatically detect depressive patients. The results shown values of sensitivity, specificity and AUC, statistically significant, specially for the RF method, which presents sensitivity = 0.8148, specificity = 0.8158 and AUC = 0.8314. Therefore, it is concluded that these classifiers are able to distinguish patients with depression from controls, based on their motor activity, allowing the development of a non-invasive diagnosis tool to support specialists in the correct diagnosis of depression.","PeriodicalId":220522,"journal":{"name":"Res. Comput. Sci.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129081152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lauro Reyes Cocoletzi, Iván Olmos, J. A. Olvera-López
. Obstacle detection and trajectory estimation in vehicular environments is an open problem in autonomous vehicles development. The automotive industry has made significant progress in research and development of tools; however, there are still challenges to overcome and opportunity areas to be exploited in order to achieve full autonomy in vehicles. This paper presents an analysis of different methods proposed for obstacle detection and trajectory estimation, leading into a proposal of solution for solving the problem of trajectory estimation based on computer vision techniques. This proposal covers the context of traffic environments in Latin America, where basic signage (such as the dividing lines of the road) is absent or not easily visible, among other typical characteristics of countries (like Mexico and others) where the infrastructure for maintaining safe road conditions (speedbumps, potholes) is limited.
{"title":"Obstacle Detection and Trajectory Estimation in Vehicular Displacements based on Computational Vision","authors":"Lauro Reyes Cocoletzi, Iván Olmos, J. A. Olvera-López","doi":"10.13053/rcs-148-9-5","DOIUrl":"https://doi.org/10.13053/rcs-148-9-5","url":null,"abstract":". Obstacle detection and trajectory estimation in vehicular environments is an open problem in autonomous vehicles development. The automotive industry has made significant progress in research and development of tools; however, there are still challenges to overcome and opportunity areas to be exploited in order to achieve full autonomy in vehicles. This paper presents an analysis of different methods proposed for obstacle detection and trajectory estimation, leading into a proposal of solution for solving the problem of trajectory estimation based on computer vision techniques. This proposal covers the context of traffic environments in Latin America, where basic signage (such as the dividing lines of the road) is absent or not easily visible, among other typical characteristics of countries (like Mexico and others) where the infrastructure for maintaining safe road conditions (speedbumps, potholes) is limited.","PeriodicalId":220522,"journal":{"name":"Res. Comput. Sci.","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123825336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Barrera, J. Soto, Adrian Vázquez Osorio, Elvira Rolón Aguilar, Julio C. Rolón
Nowadays, information and communication technology has become a necessary component in the planning, design and management of the different processes in the industry sector. To manufacturing companies, the use of multiagent systems aiming to develop hazardous waste monitoring systems facilitates the planning, monitoring, collection, and management of hazardous waste. Intelligent agents have proven to be an efficient solution, since they can do tasks on behalf of the users. Moreover, these agents can use different intelligent techniques and communicate among themselves. For this reason, this work proposes the use of software agents for hazardous waste monitoring in manufacturing companies. This article will describe the analysis and design of our proposal using the INGENIAS methodology.
{"title":"Modelado de un sistema multi-agente para el monitoreo de residuos peligrosos en la industria manufacturera","authors":"C. Barrera, J. Soto, Adrian Vázquez Osorio, Elvira Rolón Aguilar, Julio C. Rolón","doi":"10.13053/rcs-148-8-36","DOIUrl":"https://doi.org/10.13053/rcs-148-8-36","url":null,"abstract":"Nowadays, information and communication technology has become a necessary component in the planning, design and management of the different processes in the industry sector. To manufacturing companies, the use of multiagent systems aiming to develop hazardous waste monitoring systems facilitates the planning, monitoring, collection, and management of hazardous waste. Intelligent agents have proven to be an efficient solution, since they can do tasks on behalf of the users. Moreover, these agents can use different intelligent techniques and communicate among themselves. For this reason, this work proposes the use of software agents for hazardous waste monitoring in manufacturing companies. This article will describe the analysis and design of our proposal using the INGENIAS methodology.","PeriodicalId":220522,"journal":{"name":"Res. Comput. Sci.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123832814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Luis Daniel Reyes Crusaley, J. R. Cárdenas-Valdez, G. Vázquez, Manuel Ortega, A. Calvillo-Téllez
The present work presents the design of a wireless electromyographic biomedical signal acquisition system, which records the muscle signals in the EKG / EMG development card, the signals are transmitted through the ZigBee protocol in point-to-point or multipoint link, so it is scalable for more than one patient in parallel. The transmission of the data is received in the Raspberry Pi3 development card which truncates the received signal and is sent to the cloud for a classification process. The developed system is a precise proposal of low cost for the analysis of several patients, the proposed technique represents the stage of acquisition, analysis and truncation of data for a signal classification process based on support of vector machines (SVM) with the In order to predict the best type of therapy for a given patient. Experimental and simulation tests developed in hardware and classified in software through SVM show that the complete
{"title":"Sistema de clasificación SVM de señales electromiográficas extraídas en un sistema embebido","authors":"Luis Daniel Reyes Crusaley, J. R. Cárdenas-Valdez, G. Vázquez, Manuel Ortega, A. Calvillo-Téllez","doi":"10.13053/rcs-148-2-11","DOIUrl":"https://doi.org/10.13053/rcs-148-2-11","url":null,"abstract":"The present work presents the design of a wireless electromyographic biomedical signal acquisition system, which records the muscle signals in the EKG / EMG development card, the signals are transmitted through the ZigBee protocol in point-to-point or multipoint link, so it is scalable for more than one patient in parallel. The transmission of the data is received in the Raspberry Pi3 development card which truncates the received signal and is sent to the cloud for a classification process. The developed system is a precise proposal of low cost for the analysis of several patients, the proposed technique represents the stage of acquisition, analysis and truncation of data for a signal classification process based on support of vector machines (SVM) with the In order to predict the best type of therapy for a given patient. Experimental and simulation tests developed in hardware and classified in software through SVM show that the complete","PeriodicalId":220522,"journal":{"name":"Res. Comput. Sci.","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114201184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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