Pub Date : 2018-04-02DOI: 10.1109/ISACV.2018.8354033
Salma Bouaich, Mohamed Adnane Mahraz, Jamal Rifïi, H. Tairi
To address the challenge of congestion, we propose a system that estimates the state of the road. To do that, we must be gone through several steps. In this work, we will present the first and the important step to estimate the vehicle flow; this later helps us to count the vehicles using the virtual line. Generally, we start with the background subtraction to isolate moving objects. To facilitate crossing of vehicles with the line, we apply the detection of objects. Our system uses the K-nearest neighbor (KNN) as a method to subtract the background, in order to apply our counting algorithm.
{"title":"Vehicle counting system in real-time","authors":"Salma Bouaich, Mohamed Adnane Mahraz, Jamal Rifïi, H. Tairi","doi":"10.1109/ISACV.2018.8354033","DOIUrl":"https://doi.org/10.1109/ISACV.2018.8354033","url":null,"abstract":"To address the challenge of congestion, we propose a system that estimates the state of the road. To do that, we must be gone through several steps. In this work, we will present the first and the important step to estimate the vehicle flow; this later helps us to count the vehicles using the virtual line. Generally, we start with the background subtraction to isolate moving objects. To facilitate crossing of vehicles with the line, we apply the detection of objects. Our system uses the K-nearest neighbor (KNN) as a method to subtract the background, in order to apply our counting algorithm.","PeriodicalId":184662,"journal":{"name":"2018 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123409595","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}
Pub Date : 2018-04-02DOI: 10.1109/ISACV.2018.8354054
Youssef Lamrani Alaoui, M. Tkiouat
The failure of the microfinance institution (MFIs) to respond to their customers' needs and expectations is one of the main reasons they lose customers. Nowadays, customer satisfaction becomes a central concern for MFIs in order to improve the quality of their products and services. The aim of this study is to develop a decision making support tool that can help managers in microfinance industry perform scenarios analysis and then plan customer satisfaction strategies. We managed to build a Bayesian networks model; such approach is widely required for modeling complex systems characterized by scarce or uncertain information. It has also the ability to take into account several factors and the interactions among them, which can successfully meet the current study requirements.
{"title":"Developing a decision making support tool for planning customer satisfaction strategies in microfinance industry","authors":"Youssef Lamrani Alaoui, M. Tkiouat","doi":"10.1109/ISACV.2018.8354054","DOIUrl":"https://doi.org/10.1109/ISACV.2018.8354054","url":null,"abstract":"The failure of the microfinance institution (MFIs) to respond to their customers' needs and expectations is one of the main reasons they lose customers. Nowadays, customer satisfaction becomes a central concern for MFIs in order to improve the quality of their products and services. The aim of this study is to develop a decision making support tool that can help managers in microfinance industry perform scenarios analysis and then plan customer satisfaction strategies. We managed to build a Bayesian networks model; such approach is widely required for modeling complex systems characterized by scarce or uncertain information. It has also the ability to take into account several factors and the interactions among them, which can successfully meet the current study requirements.","PeriodicalId":184662,"journal":{"name":"2018 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122879899","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}
Pub Date : 2018-04-02DOI: 10.1109/ISACV.2018.8354026
Amir Reza Rezvan Talab, M. H. Shakoor
This research proposes a new mapping technique of Local Binary Patterns (LBPs) for texture classification. Mapping is an approach for producing a vector of features from the features that are extracted. This mapping method is based on extending nonuniform patterns for better classification of defects in patterned fabrics. By extending nonuniform patterns, a new mapping technique is suggested that extracts more discriminative features from textures. This new mapping can be used for various types of LBP and is tested for CLBP operator to show the improvement on the accuracy of the classification. The developed mapping technique is rotation invariant and has all the positive points of previous approaches. The proposed approach can code nonuniform patterns into more than one feature for producing distinctive features and better classification rate. Implementation of the proposed mapping on our patterned dataset shows that proposed method can improve the classification accuracy. Besides, the suggested approach improves the classification rate for all types of LBPs, particularly those with large neighborhoods.
{"title":"Fabric classification using new mapping of local binary pattern","authors":"Amir Reza Rezvan Talab, M. H. Shakoor","doi":"10.1109/ISACV.2018.8354026","DOIUrl":"https://doi.org/10.1109/ISACV.2018.8354026","url":null,"abstract":"This research proposes a new mapping technique of Local Binary Patterns (LBPs) for texture classification. Mapping is an approach for producing a vector of features from the features that are extracted. This mapping method is based on extending nonuniform patterns for better classification of defects in patterned fabrics. By extending nonuniform patterns, a new mapping technique is suggested that extracts more discriminative features from textures. This new mapping can be used for various types of LBP and is tested for CLBP operator to show the improvement on the accuracy of the classification. The developed mapping technique is rotation invariant and has all the positive points of previous approaches. The proposed approach can code nonuniform patterns into more than one feature for producing distinctive features and better classification rate. Implementation of the proposed mapping on our patterned dataset shows that proposed method can improve the classification accuracy. Besides, the suggested approach improves the classification rate for all types of LBPs, particularly those with large neighborhoods.","PeriodicalId":184662,"journal":{"name":"2018 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124664160","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}
Pub Date : 2018-04-02DOI: 10.1109/ISACV.2018.8354011
Abdelali El Boutahiri, Karim El khadiri, H. Qjidaa, A. Aarab, R. El Alami, L. Zenkouar
This paper presents the simulation results of an Amplitude-shift keying (ASK) modulator in 180nm CMOS technology for an radio frequency identification (RFID) tag. It can operates even at low voltage VDD=1V. This modulator based on the voltage multiplier. The proposed circuit can made the multiplication of two signals, a carrier signal (high frequency sinusoidal signal) and a modulating signal (low frequency signal), we chose a square signal that has a binary sequence of data to transmit, the output voltage presents a variation of amplitude of the carrier and is called the modulated signal. We designed two circuits do the same function and can be used for RFID tags.
{"title":"ASK-modulator design of RFID tag in 180nm CMOS technology","authors":"Abdelali El Boutahiri, Karim El khadiri, H. Qjidaa, A. Aarab, R. El Alami, L. Zenkouar","doi":"10.1109/ISACV.2018.8354011","DOIUrl":"https://doi.org/10.1109/ISACV.2018.8354011","url":null,"abstract":"This paper presents the simulation results of an Amplitude-shift keying (ASK) modulator in 180nm CMOS technology for an radio frequency identification (RFID) tag. It can operates even at low voltage VDD=1V. This modulator based on the voltage multiplier. The proposed circuit can made the multiplication of two signals, a carrier signal (high frequency sinusoidal signal) and a modulating signal (low frequency signal), we chose a square signal that has a binary sequence of data to transmit, the output voltage presents a variation of amplitude of the carrier and is called the modulated signal. We designed two circuits do the same function and can be used for RFID tags.","PeriodicalId":184662,"journal":{"name":"2018 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133926902","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}
Pub Date : 2018-04-02DOI: 10.1109/ISACV.2018.8354013
Christophe Ishimwe Ngabo, Omar El Beqqali
Real-time tilt sensing in urban areas can provide crucial information regarding the instant evolution of sensitive assets of the city to better safeguard the sites sustainability and security of people. In this paper, we proposed a novel approach based on the accelerometer sensor to recognize tilt angle between the vertical plan (the light poles along the road, the transmission tower, crossing bridges, etc.) and the ground. Indeed, we used the tri-axial accelerometer embedded in a Wireless Sensor Network (WSN) platform manufactured by Libellium, named “Waspmote Starter Kit”. The collected data are sent from one sensor node to another until the gateway and then to the processing center. Our recognition method uses the calculation and mapping of the values sensed by an accelerometer to a corresponding angle. The z-component corresponding to the gravity allows recognizing the real-time tilt of the horizontal plane while the x and y-components indicate in which side the plan is bent. This approach is applied to the smart city to provide an effective solution for real-time electric transmission pole monitoring. A computer program processes the angle results of the mapping and plots the 3D scene on the monitoring screen reproducing the behaviour of electric pole remotely located. The experimental results of our approach are satisfactory because the average error that can be committed by comparing the calculated and measured angle is about 1.0.
{"title":"3D tilt sensing by using accelerometer-based wireless sensor networks: Real case study: Application in the smart cities","authors":"Christophe Ishimwe Ngabo, Omar El Beqqali","doi":"10.1109/ISACV.2018.8354013","DOIUrl":"https://doi.org/10.1109/ISACV.2018.8354013","url":null,"abstract":"Real-time tilt sensing in urban areas can provide crucial information regarding the instant evolution of sensitive assets of the city to better safeguard the sites sustainability and security of people. In this paper, we proposed a novel approach based on the accelerometer sensor to recognize tilt angle between the vertical plan (the light poles along the road, the transmission tower, crossing bridges, etc.) and the ground. Indeed, we used the tri-axial accelerometer embedded in a Wireless Sensor Network (WSN) platform manufactured by Libellium, named “Waspmote Starter Kit”. The collected data are sent from one sensor node to another until the gateway and then to the processing center. Our recognition method uses the calculation and mapping of the values sensed by an accelerometer to a corresponding angle. The z-component corresponding to the gravity allows recognizing the real-time tilt of the horizontal plane while the x and y-components indicate in which side the plan is bent. This approach is applied to the smart city to provide an effective solution for real-time electric transmission pole monitoring. A computer program processes the angle results of the mapping and plots the 3D scene on the monitoring screen reproducing the behaviour of electric pole remotely located. The experimental results of our approach are satisfactory because the average error that can be committed by comparing the calculated and measured angle is about 1.0.","PeriodicalId":184662,"journal":{"name":"2018 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128325732","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}
Pub Date : 2018-04-02DOI: 10.1109/ISACV.2018.8354083
Abdelmajid Bousselham, O. Bouattane, M. Youssfi, A. Raihani
This study analyzed the thermal effect of brain tumors on computer simulated MRI images. Magnetic Resonance Imaging (MRI) is an imaging technique that gives a large number of information on the observed tissues and tumors according to the nuclear magnetic resonance parameters. However, some of these parameters may vary depending on temperature. The temperature distribution is increased in the tumorous region compared to the surrounding normal tissues, which causes a significant change on MR parameters, the studied MR parameter in this work is spin lattice relaxation time T1. The problem is expressed mathematically and computer simulated T1-weighted images of realistic geometry of brain tissues containing a circular tumor are obtained using spin echo pulse sequence. The temperature distribution is calculated using Pennes bioheat transfer equation and implemented numerically by Finite Difference Method. Results show that heat generation by tumor has a significant impact on T1-weighted signal intensity.
{"title":"Thermal effect analysis of brain tumor on simulated T1-weighted MRI images","authors":"Abdelmajid Bousselham, O. Bouattane, M. Youssfi, A. Raihani","doi":"10.1109/ISACV.2018.8354083","DOIUrl":"https://doi.org/10.1109/ISACV.2018.8354083","url":null,"abstract":"This study analyzed the thermal effect of brain tumors on computer simulated MRI images. Magnetic Resonance Imaging (MRI) is an imaging technique that gives a large number of information on the observed tissues and tumors according to the nuclear magnetic resonance parameters. However, some of these parameters may vary depending on temperature. The temperature distribution is increased in the tumorous region compared to the surrounding normal tissues, which causes a significant change on MR parameters, the studied MR parameter in this work is spin lattice relaxation time T1. The problem is expressed mathematically and computer simulated T1-weighted images of realistic geometry of brain tissues containing a circular tumor are obtained using spin echo pulse sequence. The temperature distribution is calculated using Pennes bioheat transfer equation and implemented numerically by Finite Difference Method. Results show that heat generation by tumor has a significant impact on T1-weighted signal intensity.","PeriodicalId":184662,"journal":{"name":"2018 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128077423","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}
Pub Date : 2018-04-02DOI: 10.1109/ISACV.2018.8354021
Ouafae El Aissaoui, Yasser El Madani El Alami, L. Oughdir, Youssouf El Allioui
With the technological revolution of Internet and the information overload, adaptive E-learning has become the promising solution for educational institutions since it enhances students' learning process according to many factors such as their learning styles. Learning styles are a criteria of great import in E-learning environment because they can help the system to effectively personalize students' learning process. Generally, the traditional way of detecting students' learning style is based on asking students to fill out a questionnaire. However, using this static technique presents many problems. Some of these problems include the lack of self-awareness of students of their learning preferences. In addition, almost all students are bored when they are asked to fill out a questionnaire. Thus, in this work, we present an automatic approach for detecting students' learning style based on web usage mining. It consists in classifying students' log files according to a specific learning style model (Felder and Silverman model) using clustering algorithms (K-means algorithm). In order to test the efficiency of our work, we use a real-world dataset gathered from an E-learning system. Experimental results show that our approach provide promising results.
{"title":"Integrating web usage mining for an automatic learner profile detection: A learning styles-based approach","authors":"Ouafae El Aissaoui, Yasser El Madani El Alami, L. Oughdir, Youssouf El Allioui","doi":"10.1109/ISACV.2018.8354021","DOIUrl":"https://doi.org/10.1109/ISACV.2018.8354021","url":null,"abstract":"With the technological revolution of Internet and the information overload, adaptive E-learning has become the promising solution for educational institutions since it enhances students' learning process according to many factors such as their learning styles. Learning styles are a criteria of great import in E-learning environment because they can help the system to effectively personalize students' learning process. Generally, the traditional way of detecting students' learning style is based on asking students to fill out a questionnaire. However, using this static technique presents many problems. Some of these problems include the lack of self-awareness of students of their learning preferences. In addition, almost all students are bored when they are asked to fill out a questionnaire. Thus, in this work, we present an automatic approach for detecting students' learning style based on web usage mining. It consists in classifying students' log files according to a specific learning style model (Felder and Silverman model) using clustering algorithms (K-means algorithm). In order to test the efficiency of our work, we use a real-world dataset gathered from an E-learning system. Experimental results show that our approach provide promising results.","PeriodicalId":184662,"journal":{"name":"2018 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115052216","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}
Pub Date : 2018-04-01DOI: 10.1109/ISACV.2018.8354016
Khadija El Hachami, M. Tkiouat
As our aim is to explore the future and anticipate economic changes, we have tried to analyze the economic system by performing an empirical study of the interactions between different economic agents. Therefore, the purpose of our work is to propose an agent-based model, in which interactions between different types of agents are modeled in an economic context; and that by introducing different economic activities; including hiring, firing, production and consumption. Furthermore, what can make this paper special is that the consumption of a group of agents will depend on their wealth and also on the consumption of their neighbors. However, to highlight this work, it will be edited later by simulating it using the Netlogo platform; in order to discover the effect and the outcome of some economic policies.
{"title":"An approach for modeling the economy as a complex system using agent-based theory","authors":"Khadija El Hachami, M. Tkiouat","doi":"10.1109/ISACV.2018.8354016","DOIUrl":"https://doi.org/10.1109/ISACV.2018.8354016","url":null,"abstract":"As our aim is to explore the future and anticipate economic changes, we have tried to analyze the economic system by performing an empirical study of the interactions between different economic agents. Therefore, the purpose of our work is to propose an agent-based model, in which interactions between different types of agents are modeled in an economic context; and that by introducing different economic activities; including hiring, firing, production and consumption. Furthermore, what can make this paper special is that the consumption of a group of agents will depend on their wealth and also on the consumption of their neighbors. However, to highlight this work, it will be edited later by simulating it using the Netlogo platform; in order to discover the effect and the outcome of some economic policies.","PeriodicalId":184662,"journal":{"name":"2018 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120824187","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}
Pub Date : 2018-04-01DOI: 10.1109/ISACV.2018.8354039
Ismaël Koné, L. Boulmane
Machine learning models produce state-of-the-art results in many MRI images segmentation. However, most of these models are trained on very large datasets which come from experts manual labeling. This labeling process is very time consuming and costs experts work. Therefore finding a way to reduce this cost is on high demand. In this paper, we propose a segmentation method which exploits MRI images sequential structure to nearly drop out this labeling task. Only the first slice needs to be manually labeled to train the model which then infers the next slice's segmentation. Inference result is another datum used to train the model again. The updated model then infers the third slice and the same process is carried out until the last slice. The proposed model is an combination of two Random Forest algorithms: the classical one and a recent one namely Mondrian Forests. We applied our method on human left ventricle segmentation and results are very promising. This method can also be used to generate labels.
{"title":"Hybrid forests for left ventricle segmentation using only the first slice label","authors":"Ismaël Koné, L. Boulmane","doi":"10.1109/ISACV.2018.8354039","DOIUrl":"https://doi.org/10.1109/ISACV.2018.8354039","url":null,"abstract":"Machine learning models produce state-of-the-art results in many MRI images segmentation. However, most of these models are trained on very large datasets which come from experts manual labeling. This labeling process is very time consuming and costs experts work. Therefore finding a way to reduce this cost is on high demand. In this paper, we propose a segmentation method which exploits MRI images sequential structure to nearly drop out this labeling task. Only the first slice needs to be manually labeled to train the model which then infers the next slice's segmentation. Inference result is another datum used to train the model again. The updated model then infers the third slice and the same process is carried out until the last slice. The proposed model is an combination of two Random Forest algorithms: the classical one and a recent one namely Mondrian Forests. We applied our method on human left ventricle segmentation and results are very promising. This method can also be used to generate labels.","PeriodicalId":184662,"journal":{"name":"2018 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126151902","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}
Pub Date : 2018-04-01DOI: 10.1109/ISACV.2018.8354018
Youssef Bikrat, D. Moussaid, Abdelhamid Benali, Ahmad Benlghazi
In this article, we propose an electronic system which allows the acquisition, the processing and the transfer of the data of a photovoltaic station, due to a wireless link. The system is designed around a Raspberry PI3 card, with Bluetooth and WiFi as wireless protocols. The objective of our work is the long-distance supervision of an example installation: a photovoltaic station. Such a system can greatly reduce the movement of an operator on the site of the installation before any anomalies or interventions. It allows the status of the installation to be transferred to the operator in real time, and has cost, reliability and performance advantages. The proposed electronic system consisting of a hardware part and a software part, we will insist, in this article, on both parties.
{"title":"Electronic and computer system for monitoring a photovoltaic station","authors":"Youssef Bikrat, D. Moussaid, Abdelhamid Benali, Ahmad Benlghazi","doi":"10.1109/ISACV.2018.8354018","DOIUrl":"https://doi.org/10.1109/ISACV.2018.8354018","url":null,"abstract":"In this article, we propose an electronic system which allows the acquisition, the processing and the transfer of the data of a photovoltaic station, due to a wireless link. The system is designed around a Raspberry PI3 card, with Bluetooth and WiFi as wireless protocols. The objective of our work is the long-distance supervision of an example installation: a photovoltaic station. Such a system can greatly reduce the movement of an operator on the site of the installation before any anomalies or interventions. It allows the status of the installation to be transferred to the operator in real time, and has cost, reliability and performance advantages. The proposed electronic system consisting of a hardware part and a software part, we will insist, in this article, on both parties.","PeriodicalId":184662,"journal":{"name":"2018 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114450122","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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