Pub Date : 2023-06-06DOI: 10.1109/MECO58584.2023.10155097
Zichao Shen, J. Núñez-Yáñez, N. Dahnoun
This paper investigates an indoor multiple human tracking and fall detection system based on the usage of multiple Millimeter-Wave radars from Texas Instruments. We propose a real-time system framework to merge the signals received from radars and track the position and body status of human objects. In order to guarantee the overall accuracy of our system, we develop novel strategies such as dynamic DBSCAN clustering based on signal energy levels and a possibility matrix for multiple object tracking. Our prototype system, which employs three radars placed on x-y-z surfaces, demonstrates higher accuracy than the solution in [1] (90%), with 98.5% and 98.2% accuracy in multiple human tracking and fall detection respectively. The accuracy reaches 99.7% for single human tracking.
{"title":"Multiple Human Tracking and Fall Detection Real-Time System Using Millimeter-Wave Radar and Data Fusion","authors":"Zichao Shen, J. Núñez-Yáñez, N. Dahnoun","doi":"10.1109/MECO58584.2023.10155097","DOIUrl":"https://doi.org/10.1109/MECO58584.2023.10155097","url":null,"abstract":"This paper investigates an indoor multiple human tracking and fall detection system based on the usage of multiple Millimeter-Wave radars from Texas Instruments. We propose a real-time system framework to merge the signals received from radars and track the position and body status of human objects. In order to guarantee the overall accuracy of our system, we develop novel strategies such as dynamic DBSCAN clustering based on signal energy levels and a possibility matrix for multiple object tracking. Our prototype system, which employs three radars placed on x-y-z surfaces, demonstrates higher accuracy than the solution in [1] (90%), with 98.5% and 98.2% accuracy in multiple human tracking and fall detection respectively. The accuracy reaches 99.7% for single human tracking.","PeriodicalId":187825,"journal":{"name":"2023 12th Mediterranean Conference on Embedded Computing (MECO)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131986846","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 : 2023-06-06DOI: 10.1109/MECO58584.2023.10154952
Hossein Yarahmadi, M. Shiri, Moharram Challenger, H. Navidi, Arash Sharifi
In this paper, we provide a review of cyber-physical systems (CPSs) and explore the applications of Multi-Agent Systems (MAS), Multi-Agent Reinforcement Learning (MARL), and Multi-Agent Credit Assignment Problem (MCA) in CPSs. Our primary focus is on mapping specific domains, including job scheduling, energy management, and smart transport systems, to MAS and applying MARL and MCA techniques to solve the problems. To evaluate the effectiveness of our proposed method, we applied it to the job scheduling problem, using two parameters, CPU and bandwidth, and tested its performance for four different tasks: Face Detection and Window Blind Control (FDWC), Finger Touch and Gate Control (FTGC), Weather Check and Thermostat Control (WCTC), and Temperature Check and Start Fan (TCSF). The results indicate that prioritizing tasks significantly improves the performance of the proposed method. We conclude that MAS, MARL, and MCA are powerful tools for solving problems in CPSs and IoT. Mapping these problems to MAS can help overcome the challenges associated with CPSs and IoT, and improve system performance.
{"title":"On the Use of Multi-agent Reinforcement Learning in Cyber-physical and Internet of Thing Systems","authors":"Hossein Yarahmadi, M. Shiri, Moharram Challenger, H. Navidi, Arash Sharifi","doi":"10.1109/MECO58584.2023.10154952","DOIUrl":"https://doi.org/10.1109/MECO58584.2023.10154952","url":null,"abstract":"In this paper, we provide a review of cyber-physical systems (CPSs) and explore the applications of Multi-Agent Systems (MAS), Multi-Agent Reinforcement Learning (MARL), and Multi-Agent Credit Assignment Problem (MCA) in CPSs. Our primary focus is on mapping specific domains, including job scheduling, energy management, and smart transport systems, to MAS and applying MARL and MCA techniques to solve the problems. To evaluate the effectiveness of our proposed method, we applied it to the job scheduling problem, using two parameters, CPU and bandwidth, and tested its performance for four different tasks: Face Detection and Window Blind Control (FDWC), Finger Touch and Gate Control (FTGC), Weather Check and Thermostat Control (WCTC), and Temperature Check and Start Fan (TCSF). The results indicate that prioritizing tasks significantly improves the performance of the proposed method. We conclude that MAS, MARL, and MCA are powerful tools for solving problems in CPSs and IoT. Mapping these problems to MAS can help overcome the challenges associated with CPSs and IoT, and improve system performance.","PeriodicalId":187825,"journal":{"name":"2023 12th Mediterranean Conference on Embedded Computing (MECO)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132234850","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 : 2023-06-06DOI: 10.1109/MECO58584.2023.10155016
Mira Šorović, N. Kapidani, Zarko Luksic, Toni Maričević, Šime Marušić, V. Frančić, David Brčić, Marko Strabic, Zorica Ðurović
Through the theoretical background of Sea Traffic Management, this paper briefly describes this digitalization concept in the maritime sector, aiming to contribute to a safer, more environmentally sustainable, and operationally efficient sea transport. It will be followed by an overall presentation of the STM components, in terms of its basic principles, objectives, and operational concepts, and in accordance with the main EUREKA Project objectives related to the maritime area of the Adriatic Sea.
{"title":"Towards the Introduction of the Sea Traffic Management System in the Adriatic Sea","authors":"Mira Šorović, N. Kapidani, Zarko Luksic, Toni Maričević, Šime Marušić, V. Frančić, David Brčić, Marko Strabic, Zorica Ðurović","doi":"10.1109/MECO58584.2023.10155016","DOIUrl":"https://doi.org/10.1109/MECO58584.2023.10155016","url":null,"abstract":"Through the theoretical background of Sea Traffic Management, this paper briefly describes this digitalization concept in the maritime sector, aiming to contribute to a safer, more environmentally sustainable, and operationally efficient sea transport. It will be followed by an overall presentation of the STM components, in terms of its basic principles, objectives, and operational concepts, and in accordance with the main EUREKA Project objectives related to the maritime area of the Adriatic Sea.","PeriodicalId":187825,"journal":{"name":"2023 12th Mediterranean Conference on Embedded Computing (MECO)","volume":"1 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127980377","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 : 2023-06-06DOI: 10.1109/MECO58584.2023.10154993
Ravishankar Mehta, Akbar Sheikh-Akbari, K. K. Singh
Convolutional Neural Networks (CNNs) have emerged as a popular choice of researchers for their robust feature extraction and information mining capability. In the last decades, CNNs have depicted impressive performance on various applications of computer vision tasks like object detection, image segmentation, and image classification. As a consequence, the ear-based recognition system has not gained many benefits from deep learning and CNN-based applications and is still lacking behind due to the availability of sufficient data and varying conditions of captured sample images. In this paper, transfer learning techniques have been applied to the well-known convolutional neural network model VGG16 integrated with the support vector machine(SVM) that acts as a hybrid algorithm for recognizing the person using their ear images. The proposed model is validated on an ear dataset containing a total of 2600 images with variability in terms of pose, rotation, and illumination changes. The proposed model is able to classify the ear images with the highest recognition accuracy of 98.72%. To show the effectiveness of the proposed model, comparative studies of the proposed model with other existing methods have been reported in the literature.
{"title":"A Noble Approach to 2D Ear Recognition System using Hybrid Transfer Learning","authors":"Ravishankar Mehta, Akbar Sheikh-Akbari, K. K. Singh","doi":"10.1109/MECO58584.2023.10154993","DOIUrl":"https://doi.org/10.1109/MECO58584.2023.10154993","url":null,"abstract":"Convolutional Neural Networks (CNNs) have emerged as a popular choice of researchers for their robust feature extraction and information mining capability. In the last decades, CNNs have depicted impressive performance on various applications of computer vision tasks like object detection, image segmentation, and image classification. As a consequence, the ear-based recognition system has not gained many benefits from deep learning and CNN-based applications and is still lacking behind due to the availability of sufficient data and varying conditions of captured sample images. In this paper, transfer learning techniques have been applied to the well-known convolutional neural network model VGG16 integrated with the support vector machine(SVM) that acts as a hybrid algorithm for recognizing the person using their ear images. The proposed model is validated on an ear dataset containing a total of 2600 images with variability in terms of pose, rotation, and illumination changes. The proposed model is able to classify the ear images with the highest recognition accuracy of 98.72%. To show the effectiveness of the proposed model, comparative studies of the proposed model with other existing methods have been reported in the literature.","PeriodicalId":187825,"journal":{"name":"2023 12th Mediterranean Conference on Embedded Computing (MECO)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115404537","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 : 2023-06-06DOI: 10.1109/MECO58584.2023.10154991
Aleksandar Petkovski, Visar Shehu
Aquaculture has a great importance in economic development and food production. Maintaining an ecological environment with good water quality is the most critical link to ensure the efficient and qualitative of aquaculture. Good management of the water quality can avoid occurrence of abnormal conditions and significantly contribute to secure food in the future. Detection of anomalies ensures that the aquaculture environment is maintained properly to meet healthy and proper requirements for fish farming. The main focus of this paper is the use of machine learning approaches to detect anomalies for water quality data in aquaculture environment. It presents an analysis of three machine learning anomaly detection techniques: the K-Means clustering, the Local Outlier Factor, and the Isolation Forest. Extensive analysis of the mentioned techniques was conducted using several sensor datasets obtained from a real-world IoT aquaculture system, specifically for the parameters of temperature, dissolved oxygen, and pH. The evaluation analysis reveals that K-Means and Isolation Forest anomaly detection methods show promising results in detecting anomalies for the three aquaculture parameters.
{"title":"Anomaly Detection on Univariate Sensing Time Series Data for Smart Aquaculture Using K-Means, Isolation Forest, and Local Outlier Factor","authors":"Aleksandar Petkovski, Visar Shehu","doi":"10.1109/MECO58584.2023.10154991","DOIUrl":"https://doi.org/10.1109/MECO58584.2023.10154991","url":null,"abstract":"Aquaculture has a great importance in economic development and food production. Maintaining an ecological environment with good water quality is the most critical link to ensure the efficient and qualitative of aquaculture. Good management of the water quality can avoid occurrence of abnormal conditions and significantly contribute to secure food in the future. Detection of anomalies ensures that the aquaculture environment is maintained properly to meet healthy and proper requirements for fish farming. The main focus of this paper is the use of machine learning approaches to detect anomalies for water quality data in aquaculture environment. It presents an analysis of three machine learning anomaly detection techniques: the K-Means clustering, the Local Outlier Factor, and the Isolation Forest. Extensive analysis of the mentioned techniques was conducted using several sensor datasets obtained from a real-world IoT aquaculture system, specifically for the parameters of temperature, dissolved oxygen, and pH. The evaluation analysis reveals that K-Means and Isolation Forest anomaly detection methods show promising results in detecting anomalies for the three aquaculture parameters.","PeriodicalId":187825,"journal":{"name":"2023 12th Mediterranean Conference on Embedded Computing (MECO)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115702395","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 : 2023-06-06DOI: 10.1109/MECO58584.2023.10155054
Andrej Flogie, Maja Vičič Krabonja
Implementing digital technology, and especially artificial intelligence, in schools is becoming an increasingly significant challenge for society. Digital services supported by artificial intelligence are becoming more prevalent in all aspects of social life, including schools. The project “Innovative Learning Environments Supported by Digital Technologies” aims to introduce artificial intelligence as a support for transforming teaching in a way that prepares learning opportunities for students, where they can acquire knowledge and develop digital competencies. Teachers need knowledge and tools to assess whether AI-supported activities are appropriate for achieving their goals and enabling the transformation of teaching. Within the project, we have tested three models to assist teachers in implementing digital technology and AI. Based on the analysis of submitted good practice cases, we found that the most suitable scale for teachers in the project “Innovative Learning Environments Supported by ICT” is the RAT scale.
{"title":"Artificial Intelligence in Education: Developing Competencies and Supporting Teachers in Implementing AI in School Learning Environments","authors":"Andrej Flogie, Maja Vičič Krabonja","doi":"10.1109/MECO58584.2023.10155054","DOIUrl":"https://doi.org/10.1109/MECO58584.2023.10155054","url":null,"abstract":"Implementing digital technology, and especially artificial intelligence, in schools is becoming an increasingly significant challenge for society. Digital services supported by artificial intelligence are becoming more prevalent in all aspects of social life, including schools. The project “Innovative Learning Environments Supported by Digital Technologies” aims to introduce artificial intelligence as a support for transforming teaching in a way that prepares learning opportunities for students, where they can acquire knowledge and develop digital competencies. Teachers need knowledge and tools to assess whether AI-supported activities are appropriate for achieving their goals and enabling the transformation of teaching. Within the project, we have tested three models to assist teachers in implementing digital technology and AI. Based on the analysis of submitted good practice cases, we found that the most suitable scale for teachers in the project “Innovative Learning Environments Supported by ICT” is the RAT scale.","PeriodicalId":187825,"journal":{"name":"2023 12th Mediterranean Conference on Embedded Computing (MECO)","volume":"488 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116323871","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 : 2023-06-06DOI: 10.1109/MECO58584.2023.10155048
Po-Hsuan Chou, Chao Wang, Chih-Shuo Mei
The wide applications of deep learning techniques have motivated the inclusion of both embedded GPU devices and workstation GPU cards into contemporary Industrial Internet-of-Things (IIoT) systems. Due to substantial differences between the two types of GPUs, deep-learning model training in its current practice is run on GPU cards, and embedded GPU devices are used for inferences or partial model training at best. To supply with empirical evidence and aid the decision of deep learning workload placement, this paper reports a set of experiments on the timeliness and energy efficiency of each GPU type, running both Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model training. The results suggest that embedded GPUs did save the total energy cost despite the longer response time, but the amount of energy saving might not be significant in a practical sense. Further in this paper we report a case study for prognostics applications using LSTM. The results suggest that, by comparison, an embedded GPU may save about 90 percent of energy consumption at the cost of doubling the application response time. But neither the save in energy cost nor the increase in response time is significant enough to impact the application. These findings suggest that it may be feasible to place model training workload on either workstation GPU or embedded GPU.
{"title":"Applicability of Deep Learning Model Trainings on Embedded GPU Devices: An Empirical Study","authors":"Po-Hsuan Chou, Chao Wang, Chih-Shuo Mei","doi":"10.1109/MECO58584.2023.10155048","DOIUrl":"https://doi.org/10.1109/MECO58584.2023.10155048","url":null,"abstract":"The wide applications of deep learning techniques have motivated the inclusion of both embedded GPU devices and workstation GPU cards into contemporary Industrial Internet-of-Things (IIoT) systems. Due to substantial differences between the two types of GPUs, deep-learning model training in its current practice is run on GPU cards, and embedded GPU devices are used for inferences or partial model training at best. To supply with empirical evidence and aid the decision of deep learning workload placement, this paper reports a set of experiments on the timeliness and energy efficiency of each GPU type, running both Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model training. The results suggest that embedded GPUs did save the total energy cost despite the longer response time, but the amount of energy saving might not be significant in a practical sense. Further in this paper we report a case study for prognostics applications using LSTM. The results suggest that, by comparison, an embedded GPU may save about 90 percent of energy consumption at the cost of doubling the application response time. But neither the save in energy cost nor the increase in response time is significant enough to impact the application. These findings suggest that it may be feasible to place model training workload on either workstation GPU or embedded GPU.","PeriodicalId":187825,"journal":{"name":"2023 12th Mediterranean Conference on Embedded Computing (MECO)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123835989","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 : 2023-06-06DOI: 10.1109/MECO58584.2023.10155060
Ercan Canhasi, Dhuratë Hyseni
The heightened transmissibility of the coronavirus presents significant hazards for medical professionals conducting face-to-face COVID-19 swab tests. To address this concern, we recommend employing a 4-axis instructional robotic arm, outfitted with a variety of control mechanisms for swab collection. The robot is designed with a versatile manipulator, an endoscope paired with a display, and a master apparatus. The adaptable manipulator enhances testee safety, while the master apparatus's analogous structure simplifies operation for medical professionals. By leveraging the endoscope's visual output, the practitioner can manage the movement of the swab affixed to the manipulator during the collection process. This paper introduces the preliminary robotic system, encompassing its functional space and procedural steps. We also propose future experimental endeavors, such as 1) assessing the manipulator's precision under visual supervision, and 2) conducting a human phantom experiment to validate the robot's practicality.
{"title":"A Development of a Prototype COVID-19 Swab Sampling using Educational 4-axis Robotic Arm","authors":"Ercan Canhasi, Dhuratë Hyseni","doi":"10.1109/MECO58584.2023.10155060","DOIUrl":"https://doi.org/10.1109/MECO58584.2023.10155060","url":null,"abstract":"The heightened transmissibility of the coronavirus presents significant hazards for medical professionals conducting face-to-face COVID-19 swab tests. To address this concern, we recommend employing a 4-axis instructional robotic arm, outfitted with a variety of control mechanisms for swab collection. The robot is designed with a versatile manipulator, an endoscope paired with a display, and a master apparatus. The adaptable manipulator enhances testee safety, while the master apparatus's analogous structure simplifies operation for medical professionals. By leveraging the endoscope's visual output, the practitioner can manage the movement of the swab affixed to the manipulator during the collection process. This paper introduces the preliminary robotic system, encompassing its functional space and procedural steps. We also propose future experimental endeavors, such as 1) assessing the manipulator's precision under visual supervision, and 2) conducting a human phantom experiment to validate the robot's practicality.","PeriodicalId":187825,"journal":{"name":"2023 12th Mediterranean Conference on Embedded Computing (MECO)","volume":"49 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121007006","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 : 2023-06-06DOI: 10.1109/MECO58584.2023.10154966
R. Stojanovic, Jovan Djurkovic, Slaviša Mijušković, B. Lutovac, A. Škraba
Healthcare wearables have become very powerful and useful devices that are able to detect and monitor vital signs. Through recent applications and products, they have proven to be particularly effective in detecting symptoms of COVID-19. In this paper, we present an optimized design of a device, named SYNTROFOS, capable of detecting heart rate, respiration rate, and temperature. The analog and digital hardware employs off-the-shelf components, while the signal processing algorithms are optimized for implementation on low-power, low-cost, and small-sized memory microcontrollers. The decision-making and visualization interface is extremely simplified, indicating only good and bad states. Via the attached BLE Beacon the system sends the altering messages to close environment or remote medical staff. During the testing, significant noise immunity and satisfactory accuracy, less than 1 beat (breaths) per minute, are achieved. Although the presentation includes the overall system architecture, the focus is on hardware design challenges and optimized signal processing algorithms.
{"title":"SYNTROFOS: A Wearable Device for Vital Sign Monitoring, Hardware and Signal Processing Aspects","authors":"R. Stojanovic, Jovan Djurkovic, Slaviša Mijušković, B. Lutovac, A. Škraba","doi":"10.1109/MECO58584.2023.10154966","DOIUrl":"https://doi.org/10.1109/MECO58584.2023.10154966","url":null,"abstract":"Healthcare wearables have become very powerful and useful devices that are able to detect and monitor vital signs. Through recent applications and products, they have proven to be particularly effective in detecting symptoms of COVID-19. In this paper, we present an optimized design of a device, named SYNTROFOS, capable of detecting heart rate, respiration rate, and temperature. The analog and digital hardware employs off-the-shelf components, while the signal processing algorithms are optimized for implementation on low-power, low-cost, and small-sized memory microcontrollers. The decision-making and visualization interface is extremely simplified, indicating only good and bad states. Via the attached BLE Beacon the system sends the altering messages to close environment or remote medical staff. During the testing, significant noise immunity and satisfactory accuracy, less than 1 beat (breaths) per minute, are achieved. Although the presentation includes the overall system architecture, the focus is on hardware design challenges and optimized signal processing algorithms.","PeriodicalId":187825,"journal":{"name":"2023 12th Mediterranean Conference on Embedded Computing (MECO)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122585013","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 : 2023-06-06DOI: 10.1109/MECO58584.2023.10155005
John W. Handler, M. Harker, G. Rath
This paper presents a new approach to the task of time-domain model matching for state-space systems. The traditional problem formulation of designing a controller to match a reference model is relaxed to matching only a desired reference response. The presented algorithm then computes the feedback gain that delivers the best fit solution to the reference response under general norms. Additionally, the proposed discretization approach enables the employment of sparse matrix methods which enables a numerically efficient implementation. The new method is successfully verified using a random system. Additionally, an application example involving a simplified gantry crane system is presented, showcasing the practicality of the approach. Overall, the new method provides an intuitive and numerically efficient solution to the problem of time-domain model matching for state-space systems.
{"title":"Time-Domain Model Matching Under General Norms via Sparse Matrix Methods","authors":"John W. Handler, M. Harker, G. Rath","doi":"10.1109/MECO58584.2023.10155005","DOIUrl":"https://doi.org/10.1109/MECO58584.2023.10155005","url":null,"abstract":"This paper presents a new approach to the task of time-domain model matching for state-space systems. The traditional problem formulation of designing a controller to match a reference model is relaxed to matching only a desired reference response. The presented algorithm then computes the feedback gain that delivers the best fit solution to the reference response under general norms. Additionally, the proposed discretization approach enables the employment of sparse matrix methods which enables a numerically efficient implementation. The new method is successfully verified using a random system. Additionally, an application example involving a simplified gantry crane system is presented, showcasing the practicality of the approach. Overall, the new method provides an intuitive and numerically efficient solution to the problem of time-domain model matching for state-space systems.","PeriodicalId":187825,"journal":{"name":"2023 12th Mediterranean Conference on Embedded Computing (MECO)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125025936","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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