Pub Date : 2022-01-01DOI: 10.5220/0011357400003271
Conceição Silva, Neandra Ferreira, Sharlene Meireles, Mario Otani, V. Silva, C. Freitas, Felipe G. Oliveira
: The growing demand for increasing memory storage capacity has required a high density of integration within the semiconductor encapsulation and, consequently, has made this process more complex and susceptible to failures during the production stage. In the semiconductor encapsulation area, the costs of materials and equipment are high and the profit margin is narrow, making it necessary to rigorously inspect the process steps to keep the productive activity viable. This work addresses the problem of quality control in silicon wafers soldering procedure, allowing error detection before the epoxy resin molding process, generating useful information for correcting equipment configurations and predicting failures from the raw materials and inputs used in the process. We propose an approach to classify solder balls, in the soldering process of silicon wafers on Ball Grid Array (BGA), contained in the Printed Circuit Board (PCB) substrates. The proposed methodology is composed of two main steps: i ) Solder ball segmentation; and ii ) Solder ball classification through deep learning. The proposed predictive model learns the relation between visual features and the different soldering conditions. Real and simulated experiments were carried out to validate the proposed approach. Results show the obtained accuracy of 99.4%, using Convolutional Neural Network (CNN) classification model. Furthermore, the proposed approach presents high accuracy even regarding noisy images, resulting in accuracy of 92.8% and 75.7% for a Salt and Pepper and Gaussian noise, respectively, in the worst scenario. Experiments demonstrate reliability and robustness, optimizing the manufacturing.
{"title":"The Visual Inspection of Solder Balls in Semiconductor Encapsulation","authors":"Conceição Silva, Neandra Ferreira, Sharlene Meireles, Mario Otani, V. Silva, C. Freitas, Felipe G. Oliveira","doi":"10.5220/0011357400003271","DOIUrl":"https://doi.org/10.5220/0011357400003271","url":null,"abstract":": The growing demand for increasing memory storage capacity has required a high density of integration within the semiconductor encapsulation and, consequently, has made this process more complex and susceptible to failures during the production stage. In the semiconductor encapsulation area, the costs of materials and equipment are high and the profit margin is narrow, making it necessary to rigorously inspect the process steps to keep the productive activity viable. This work addresses the problem of quality control in silicon wafers soldering procedure, allowing error detection before the epoxy resin molding process, generating useful information for correcting equipment configurations and predicting failures from the raw materials and inputs used in the process. We propose an approach to classify solder balls, in the soldering process of silicon wafers on Ball Grid Array (BGA), contained in the Printed Circuit Board (PCB) substrates. The proposed methodology is composed of two main steps: i ) Solder ball segmentation; and ii ) Solder ball classification through deep learning. The proposed predictive model learns the relation between visual features and the different soldering conditions. Real and simulated experiments were carried out to validate the proposed approach. Results show the obtained accuracy of 99.4%, using Convolutional Neural Network (CNN) classification model. Furthermore, the proposed approach presents high accuracy even regarding noisy images, resulting in accuracy of 92.8% and 75.7% for a Salt and Pepper and Gaussian noise, respectively, in the worst scenario. Experiments demonstrate reliability and robustness, optimizing the manufacturing.","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"43 1","pages":"750-757"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85263164","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 : 2022-01-01DOI: 10.5220/0011274100003271
Moritz Fehsenfeld, Johannes Kühn, Zygimantas Ziaukas, Hans-Georg Jacob
{"title":"Comparison of Different Excitation Strategies for Fault Diagnosis of Belt Drives: Industrial Application Scenarios","authors":"Moritz Fehsenfeld, Johannes Kühn, Zygimantas Ziaukas, Hans-Georg Jacob","doi":"10.5220/0011274100003271","DOIUrl":"https://doi.org/10.5220/0011274100003271","url":null,"abstract":"","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"104 1","pages":"177-184"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83413315","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 : 2022-01-01DOI: 10.5220/0011320400003271
Pedro Mariano, S. Almeida, Alexandre P. Almeida, C. Correia, Vânia Martins, José M. F. Moura, T. Brandão, Pedro Santana
: Engineering the information system that runs a heterogeneous mobile sensor network is a complex task. In this paper we present the solution that was developed in the context of the ExpoLIS project. The goal of this project is to deploy a network of mobile (low-cost) sensors in city buses. Besides the software that needs to transfer, process, and store sensor data, we also developed a mobile application to increase awareness on air pollution, and a tool that allows scientists to subscribe to sensor data. We present the engineering solutions that form the backbone of the information system, and the structure and design of developing supporting tools. We discuss our choices regarding how sensor data are processed in order to make these data available for the common citizen. We mention possible future directions for the software that we have developed.
{"title":"An Information System for Air Quality Monitoring using Mobile Sensor Networks","authors":"Pedro Mariano, S. Almeida, Alexandre P. Almeida, C. Correia, Vânia Martins, José M. F. Moura, T. Brandão, Pedro Santana","doi":"10.5220/0011320400003271","DOIUrl":"https://doi.org/10.5220/0011320400003271","url":null,"abstract":": Engineering the information system that runs a heterogeneous mobile sensor network is a complex task. In this paper we present the solution that was developed in the context of the ExpoLIS project. The goal of this project is to deploy a network of mobile (low-cost) sensors in city buses. Besides the software that needs to transfer, process, and store sensor data, we also developed a mobile application to increase awareness on air pollution, and a tool that allows scientists to subscribe to sensor data. We present the engineering solutions that form the backbone of the information system, and the structure and design of developing supporting tools. We discuss our choices regarding how sensor data are processed in order to make these data available for the common citizen. We mention possible future directions for the software that we have developed.","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"94 1","pages":"238-246"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90725971","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 : 2022-01-01DOI: 10.5220/0011268200003271
Zunaed Kibria, S. Commuri
: The control of a prosthetic leg for above-knee amputees is fraught with several challenges. While the dynamics of the knee-ankle system are complex and unknown, the control problem is exacerbated by the lack of desired joint trajectories as they are dictated by the locomotion needs of the individual. Improper movement of the knee and ankle joints can have serious implications for the safety of the user. Further, dissimilarities in the gait of the amputated side and the intact side can result in gait abnormalities that result in increased metabolic energy consumption and musculo-skeletal pains in the short term, and cardiovascular and other health complications in the long term. In this paper, we propose a novel neuro-dynamic control strategy that can guarantee stable control of the prosthetic limb while minimizing the gait asymmetry between the intact and prosthetic limb. Further, the algorithm learns the unknown elements of the dynamics and adapts to the changing locomotion needs of the individual. The efficacy of the proposed approach is demonstrated through numerical simulations.
{"title":"Neuro-dynamic Control of an above Knee Prosthetic Leg","authors":"Zunaed Kibria, S. Commuri","doi":"10.5220/0011268200003271","DOIUrl":"https://doi.org/10.5220/0011268200003271","url":null,"abstract":": The control of a prosthetic leg for above-knee amputees is fraught with several challenges. While the dynamics of the knee-ankle system are complex and unknown, the control problem is exacerbated by the lack of desired joint trajectories as they are dictated by the locomotion needs of the individual. Improper movement of the knee and ankle joints can have serious implications for the safety of the user. Further, dissimilarities in the gait of the amputated side and the intact side can result in gait abnormalities that result in increased metabolic energy consumption and musculo-skeletal pains in the short term, and cardiovascular and other health complications in the long term. In this paper, we propose a novel neuro-dynamic control strategy that can guarantee stable control of the prosthetic limb while minimizing the gait asymmetry between the intact and prosthetic limb. Further, the algorithm learns the unknown elements of the dynamics and adapts to the changing locomotion needs of the individual. The efficacy of the proposed approach is demonstrated through numerical simulations.","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"36 1","pages":"29-37"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85629777","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 : 2022-01-01DOI: 10.5220/0011289800003271
Andrés Guatibonza, Carlos Zabala, L. Solaque, Alexandra Velasco, Lina Peñuela
: Diseases related to upper limb mobility are increasingly common among the actual population. For this reason, robotic physical assistive systems have been proposed to support therapy processes and improve the functional capabilities of people. However, there are still open issues related to mechanical design, such as joint coupling and bidirectional configurations. In this work, we present a novel design of a 7 DoF robotic assistive system with anthropometric adjustment, arm change configuration for elbow tendinopathies rehabilitation to use it in both arms. The design is supported by the analysis of the upper limb pathophysiology and the exercises required to treat elbow tendinopathies.
{"title":"Mechanical Design of an Assistive Robotic System for Bilateral Elbow Tendinopathy Rehabilitation","authors":"Andrés Guatibonza, Carlos Zabala, L. Solaque, Alexandra Velasco, Lina Peñuela","doi":"10.5220/0011289800003271","DOIUrl":"https://doi.org/10.5220/0011289800003271","url":null,"abstract":": Diseases related to upper limb mobility are increasingly common among the actual population. For this reason, robotic physical assistive systems have been proposed to support therapy processes and improve the functional capabilities of people. However, there are still open issues related to mechanical design, such as joint coupling and bidirectional configurations. In this work, we present a novel design of a 7 DoF robotic assistive system with anthropometric adjustment, arm change configuration for elbow tendinopathies rehabilitation to use it in both arms. The design is supported by the analysis of the upper limb pathophysiology and the exercises required to treat elbow tendinopathies.","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"24 1","pages":"320-329"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84354922","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 : 2022-01-01DOI: 10.5220/0011327100003271
Yousif Ahmed Al-Wajih, W. Hamanah, M. Abido, Fouad Al-Sunni, Fakhraddin Alwajih
: The Automated Fingerprint Identification System (AFIS) is a biometric identification methodology that uses digital imaging technology to obtain, store, and analyse fingerprint information. There has been an increased interest in fingerprint-based security systems with the rise in demand for collecting demographic data through security applications. Reliable and highly secure, these systems are used to identify people using the unique biometric information of fingerprints. In this work, a learning-based method of identifying fingerprints was investigated. Using deep learning tools, the performance of the AFIS in terms of search time and speed of matching between fingerprint databases was successfully enhanced. A convolutional neural network (CNN) model was proposed and developed to classify fingerprints and predict fingerprint types. The proposed classification system is a novel approach that classifies fingerprints based on figure type. Two public datasets were used to train and evaluate the proposed CNN model. The proposed model achieved high validation accuracy with both databases, with an overall accuracy in predicting fingerprint types at around 94%.
{"title":"Finger Type Classification with Deep Convolution Neural Networks","authors":"Yousif Ahmed Al-Wajih, W. Hamanah, M. Abido, Fouad Al-Sunni, Fakhraddin Alwajih","doi":"10.5220/0011327100003271","DOIUrl":"https://doi.org/10.5220/0011327100003271","url":null,"abstract":": The Automated Fingerprint Identification System (AFIS) is a biometric identification methodology that uses digital imaging technology to obtain, store, and analyse fingerprint information. There has been an increased interest in fingerprint-based security systems with the rise in demand for collecting demographic data through security applications. Reliable and highly secure, these systems are used to identify people using the unique biometric information of fingerprints. In this work, a learning-based method of identifying fingerprints was investigated. Using deep learning tools, the performance of the AFIS in terms of search time and speed of matching between fingerprint databases was successfully enhanced. A convolutional neural network (CNN) model was proposed and developed to classify fingerprints and predict fingerprint types. The proposed classification system is a novel approach that classifies fingerprints based on figure type. Two public datasets were used to train and evaluate the proposed CNN model. The proposed model achieved high validation accuracy with both databases, with an overall accuracy in predicting fingerprint types at around 94%.","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"91 1","pages":"247-254"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79151360","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 : 2022-01-01DOI: 10.5220/0011265600003271
L. Emmi, Jesus Herrera-Diaz, P. Santos
: This paper presents a general procedure for enabling autonomous row following in crops during early-stage growth, without relying on absolute localization systems. A model based on deep learning techniques (object detection for wide-row crops and segmentation for narrow-row crops) was applied to accurately detect both types of crops. Tests were performed using a manually operated mobile platform equipped with an RGB and a time-of-flight (ToF) cameras. Data were acquired during different time periods and weather conditions, in maize and wheat fields. The results showed the success on crop detection and enables the future development of a fully autonomous navigation system in cultivated fields during early stage of crop growth.
{"title":"Toward Autonomous Mobile Robot Navigation in Early-Stage Crop Growth","authors":"L. Emmi, Jesus Herrera-Diaz, P. Santos","doi":"10.5220/0011265600003271","DOIUrl":"https://doi.org/10.5220/0011265600003271","url":null,"abstract":": This paper presents a general procedure for enabling autonomous row following in crops during early-stage growth, without relying on absolute localization systems. A model based on deep learning techniques (object detection for wide-row crops and segmentation for narrow-row crops) was applied to accurately detect both types of crops. Tests were performed using a manually operated mobile platform equipped with an RGB and a time-of-flight (ToF) cameras. Data were acquired during different time periods and weather conditions, in maize and wheat fields. The results showed the success on crop detection and enables the future development of a fully autonomous navigation system in cultivated fields during early stage of crop growth.","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"35 1","pages":"411-418"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76232916","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 : 2022-01-01DOI: 10.5220/0011276200003271
Hassan Shehawy, A. Zanchettin, P. Rocco
{"title":"Autonomous Loading of a Washing Machine with a Single-arm Robot","authors":"Hassan Shehawy, A. Zanchettin, P. Rocco","doi":"10.5220/0011276200003271","DOIUrl":"https://doi.org/10.5220/0011276200003271","url":null,"abstract":"","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"187 1","pages":"443-450"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74170187","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 : 2022-01-01DOI: 10.5220/0011353500003271
Hamza Bouzerzour, M. Guiatni, M. Hamerlain, Ahmed Allam
: This paper propose a robust approach based on vision and sliding mode controller for searching and tracking an uncooperative and unidentified mobile ground target using a quadcopter UAV (QUAV). The proposed strategy is an Image-Based Visual Servoing (IBVS) approach using target’s visual data projected in a virtual camera combined with the information provided by the QUAV’s internal sensors. For an effective visual target searching, a circular search trajectory is followed, with a high altitude using the Camera Coverage Area (CCA). A Sliding Mode Controller (SMC) based on Exponential Reaching Law (ERL) is used to ensure the QUAV control in the presence of external disturbances and measurement uncertainties. Simulation results are presented to assess the proposer strategy considering different scenarios.
{"title":"Vision-based Sliding Mode Control with Exponential Reaching Law for Uncooperative Ground Target Searching and Tracking by Quadcopter","authors":"Hamza Bouzerzour, M. Guiatni, M. Hamerlain, Ahmed Allam","doi":"10.5220/0011353500003271","DOIUrl":"https://doi.org/10.5220/0011353500003271","url":null,"abstract":": This paper propose a robust approach based on vision and sliding mode controller for searching and tracking an uncooperative and unidentified mobile ground target using a quadcopter UAV (QUAV). The proposed strategy is an Image-Based Visual Servoing (IBVS) approach using target’s visual data projected in a virtual camera combined with the information provided by the QUAV’s internal sensors. For an effective visual target searching, a circular search trajectory is followed, with a high altitude using the Camera Coverage Area (CCA). A Sliding Mode Controller (SMC) based on Exponential Reaching Law (ERL) is used to ensure the QUAV control in the presence of external disturbances and measurement uncertainties. Simulation results are presented to assess the proposer strategy considering different scenarios.","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"21 1","pages":"555-564"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80032937","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 : 2022-01-01DOI: 10.5220/0011274300003271
Daniel Ibáñez Bordallo, E. García, J. Martos, J. Soret
{"title":"A Novel Real-Time Wear Detection System for the Secondary Circuit of Resistance Welding Guns","authors":"Daniel Ibáñez Bordallo, E. García, J. Martos, J. Soret","doi":"10.5220/0011274300003271","DOIUrl":"https://doi.org/10.5220/0011274300003271","url":null,"abstract":"","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"33 1","pages":"185-192"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73417039","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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