Pub Date : 2022-09-23DOI: 10.1109/SEEDA-CECNSM57760.2022.9932933
G. Doumenis, Ioannis Masklavanos, K. Tsiapali
Perpetual operation is a potential necessity in self-powered IoT devices. System level low power optimization can significantly decrease the power consumption and increase system lifetime. Further, energy scavenging technologies have been proposed to improve the lifetime of such a system, up to perpetual operation. However, fixed duty cycle (active/sleep) operation can cause inefficiencies in the presence of variable input power; further, in cases of limited storage capacity and intermittent input power, the system’s perpetual operation is not guaranteed. Energy forecasting algorithms can propose adaptable duty cycles, at the expense of heavy computing workloads. In this paper, we propose adaptable duty cycle operation for energy efficient IoT sensor nodes, utilizing a very simple -and very lightweight - scheduling algorithm that depends only on the periodical provision of the accumulator’s state-of-charge. The proposed algorithm achieves energy balance in the presence of harvested energy and guarantees a minimum operation time in the absence of harvested energy.
{"title":"Lightweight operation scheduling for self-powered IoT devices","authors":"G. Doumenis, Ioannis Masklavanos, K. Tsiapali","doi":"10.1109/SEEDA-CECNSM57760.2022.9932933","DOIUrl":"https://doi.org/10.1109/SEEDA-CECNSM57760.2022.9932933","url":null,"abstract":"Perpetual operation is a potential necessity in self-powered IoT devices. System level low power optimization can significantly decrease the power consumption and increase system lifetime. Further, energy scavenging technologies have been proposed to improve the lifetime of such a system, up to perpetual operation. However, fixed duty cycle (active/sleep) operation can cause inefficiencies in the presence of variable input power; further, in cases of limited storage capacity and intermittent input power, the system’s perpetual operation is not guaranteed. Energy forecasting algorithms can propose adaptable duty cycles, at the expense of heavy computing workloads. In this paper, we propose adaptable duty cycle operation for energy efficient IoT sensor nodes, utilizing a very simple -and very lightweight - scheduling algorithm that depends only on the periodical provision of the accumulator’s state-of-charge. The proposed algorithm achieves energy balance in the presence of harvested energy and guarantees a minimum operation time in the absence of harvested energy.","PeriodicalId":68279,"journal":{"name":"计算机工程与设计","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89777094","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 : 2022-09-23DOI: 10.1109/SEEDA-CECNSM57760.2022.9932906
E. Spyrou, Christoforos Nestoris, C. Stylios
Coprolalia is a symptom of Tourette’s syndrome, which is essentially involuntary cursing. The obscene words usually have a meaning, which is associated with offending social acquaintances often based on locality or sexuality. This results in a patient’s social isolation and increased anxiety. The patient finds herself stopping things that she would do in her everyday life. In this work, we aim to explain the Coprolalia symptom with the means of bipartite graph matching. We assume that the brain network performs some kind of rerouting of thoughts, which results in the association of normal thoughts with their matched obscene ones. With this we aim to decode the operation that results in Coprolalia, in order for the patient to be informed of this behaviour. Furthermore, we propose a game-theoretic model based on the Battle of the Sexes, which has two equilibria in the pure strategies, namely to jam the intrusive coprolalic thoughts or to pass through the brain. In this way, we provide a mechanism for the patient to stand in social activities, in which the symptoms are severe. Moreover, we align with Cognitive Behavioural Therapy (CBT), with which intrusive thoughts pass through the brain with the aim of deteriorating.
{"title":"Tourette Syndrome’s Coprolalia Explanation using Bipartite Graph Matching of Thoughts and Game Theoretic Model for Symptoms Minimisation","authors":"E. Spyrou, Christoforos Nestoris, C. Stylios","doi":"10.1109/SEEDA-CECNSM57760.2022.9932906","DOIUrl":"https://doi.org/10.1109/SEEDA-CECNSM57760.2022.9932906","url":null,"abstract":"Coprolalia is a symptom of Tourette’s syndrome, which is essentially involuntary cursing. The obscene words usually have a meaning, which is associated with offending social acquaintances often based on locality or sexuality. This results in a patient’s social isolation and increased anxiety. The patient finds herself stopping things that she would do in her everyday life. In this work, we aim to explain the Coprolalia symptom with the means of bipartite graph matching. We assume that the brain network performs some kind of rerouting of thoughts, which results in the association of normal thoughts with their matched obscene ones. With this we aim to decode the operation that results in Coprolalia, in order for the patient to be informed of this behaviour. Furthermore, we propose a game-theoretic model based on the Battle of the Sexes, which has two equilibria in the pure strategies, namely to jam the intrusive coprolalic thoughts or to pass through the brain. In this way, we provide a mechanism for the patient to stand in social activities, in which the symptoms are severe. Moreover, we align with Cognitive Behavioural Therapy (CBT), with which intrusive thoughts pass through the brain with the aim of deteriorating.","PeriodicalId":68279,"journal":{"name":"计算机工程与设计","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91305252","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 : 2022-09-23DOI: 10.1109/SEEDA-CECNSM57760.2022.9932895
D. Amanatidis, K. Vaitsi, Michael F. Dossis
As Deep Learning and Bioinformatics are constantly evolving fields, this review focuses on four types of Deep Neural Networks; Feedforward, Recurrent, Convolutional and Generative Adversarial Networks and their respective latest applications in seven important fields of Bioinformatics; Systems Biology, Sequence Analysis, Structure Prediction and Representation, Biomolecular Property and Function Prediction, Biomedical Image Processing and Diagnosis, Biomolecular Interaction Prediction and Protein Engineering. This two-level hierarchy is retained throughout the paper, enabling a clear and comprehensive presentation.
{"title":"Deep Neural Network Applications for Bioinformatics","authors":"D. Amanatidis, K. Vaitsi, Michael F. Dossis","doi":"10.1109/SEEDA-CECNSM57760.2022.9932895","DOIUrl":"https://doi.org/10.1109/SEEDA-CECNSM57760.2022.9932895","url":null,"abstract":"As Deep Learning and Bioinformatics are constantly evolving fields, this review focuses on four types of Deep Neural Networks; Feedforward, Recurrent, Convolutional and Generative Adversarial Networks and their respective latest applications in seven important fields of Bioinformatics; Systems Biology, Sequence Analysis, Structure Prediction and Representation, Biomolecular Property and Function Prediction, Biomedical Image Processing and Diagnosis, Biomolecular Interaction Prediction and Protein Engineering. This two-level hierarchy is retained throughout the paper, enabling a clear and comprehensive presentation.","PeriodicalId":68279,"journal":{"name":"计算机工程与设计","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88784192","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 : 2022-09-23DOI: 10.1109/SEEDA-CECNSM57760.2022.9932976
Konstantinos Sakkas, Alexandra Tsogka, A. Gkimitzoudis, N. Giannakeas, Katerina D. Tzimourta, M. Tsipouras, E. Glavas, A. Tzallas
Emotion is a psychosomatic process that is caused either by a conscious or non-conscious perception of an object or a situation whereas, the result of that process is depicted either by the expression of the face, or physically, or with a combination of the above. An important mechanism playing a significant role in the recognition of emotions is the encephalogram, which enables us to detect significant signals of the brain. The methodology developed aims to the identification of basic emotions by using an electroencephalogram through an experimental process where participants were asked to evaluate the experiment according to the valence, arousal and dominance of each emotion inflicted on them by videos they were subjected to watching. Through the experimental process, a vector of characteristics is analyzed for each of the electrodes of the electroencephalogram and for a series of videos. The training of a model and classification of algorithms were the key to finding the best methods in terms of sensitivity, accuracy, and specification of our data.
{"title":"Analysis of Emotions through the Use of Physiological Signals","authors":"Konstantinos Sakkas, Alexandra Tsogka, A. Gkimitzoudis, N. Giannakeas, Katerina D. Tzimourta, M. Tsipouras, E. Glavas, A. Tzallas","doi":"10.1109/SEEDA-CECNSM57760.2022.9932976","DOIUrl":"https://doi.org/10.1109/SEEDA-CECNSM57760.2022.9932976","url":null,"abstract":"Emotion is a psychosomatic process that is caused either by a conscious or non-conscious perception of an object or a situation whereas, the result of that process is depicted either by the expression of the face, or physically, or with a combination of the above. An important mechanism playing a significant role in the recognition of emotions is the encephalogram, which enables us to detect significant signals of the brain. The methodology developed aims to the identification of basic emotions by using an electroencephalogram through an experimental process where participants were asked to evaluate the experiment according to the valence, arousal and dominance of each emotion inflicted on them by videos they were subjected to watching. Through the experimental process, a vector of characteristics is analyzed for each of the electrodes of the electroencephalogram and for a series of videos. The training of a model and classification of algorithms were the key to finding the best methods in terms of sensitivity, accuracy, and specification of our data.","PeriodicalId":68279,"journal":{"name":"计算机工程与设计","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85029749","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 : 2022-09-23DOI: 10.1109/SEEDA-CECNSM57760.2022.9932966
Oceane Peretti, Yannis Spyridis, Achilleas Sesis, G. Efstathopoulos, A. Lytos, T. Lagkas, P. Sarigiannidis
This paper presents an augmented reality (AR) training, command and control framework targeting the preparation of first responders for emergency situations. Utilising novel AR and virtual reality (VR) technologies, the framework aims to offer efficient training sessions, while minimising the cost and complexity associated with real-world exercises. An assortment of training examples and task execution sequences are available, while particular focus is given in the ability to personalise each training session according to specific emergencies or first responder needs. The control aspect of the framework offers insight about the deployment of each first responder on the field, allowing real-time visualisation based on their current location, while displaying useful information regarding health status and mission data.
{"title":"Augmented reality training, command and control framework for first responders","authors":"Oceane Peretti, Yannis Spyridis, Achilleas Sesis, G. Efstathopoulos, A. Lytos, T. Lagkas, P. Sarigiannidis","doi":"10.1109/SEEDA-CECNSM57760.2022.9932966","DOIUrl":"https://doi.org/10.1109/SEEDA-CECNSM57760.2022.9932966","url":null,"abstract":"This paper presents an augmented reality (AR) training, command and control framework targeting the preparation of first responders for emergency situations. Utilising novel AR and virtual reality (VR) technologies, the framework aims to offer efficient training sessions, while minimising the cost and complexity associated with real-world exercises. An assortment of training examples and task execution sequences are available, while particular focus is given in the ability to personalise each training session according to specific emergencies or first responder needs. The control aspect of the framework offers insight about the deployment of each first responder on the field, allowing real-time visualisation based on their current location, while displaying useful information regarding health status and mission data.","PeriodicalId":68279,"journal":{"name":"计算机工程与设计","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89395843","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 : 2022-09-23DOI: 10.1109/SEEDA-CECNSM57760.2022.9932972
Emmanouil Karantoumanis, Vasileios Balafas, M. Louta, N. Ploskas
There is an abundance of deep neural network models for plant disease detection. Prior to applying these models, image preprocessing techniques are applied in order to improve the detection results. However, there is a lack of computational comparisons on the application of different image preprocessing techniques before applying object detection algorithms for plant disease detection. This paper aims to fill this gap by presenting a computational comparison of seven different image preprocessing techniques (auto-orientation, object isolation, resizing, grayscale conversion, static crop, contrast adjustment, tiling) applied prior to the execution of two state-of-the-art object detection algorithms, one single-stage detector, YOLOV5, and one two-stage detector, Faster-RCNN. We investigate whether or not these preprocessing techniques improve the accuracy, training time, and inference time, of plant disease detection. Apart from comparing these techniques solely, we also perform combinations of the preprocessing techniques. The PlantDoc dataset was used for this experimental study. Computational results show that the best method improves the mean average precision by 9% and 3% for YOLOv5 and Faster-RCNN, respectively. Finally, the combination of all seven preprocessing techniques yields an improvement of about 13% in the mean average precision of both object detectors.
{"title":"Computational comparison of image preprocessing techniques for plant diseases detection","authors":"Emmanouil Karantoumanis, Vasileios Balafas, M. Louta, N. Ploskas","doi":"10.1109/SEEDA-CECNSM57760.2022.9932972","DOIUrl":"https://doi.org/10.1109/SEEDA-CECNSM57760.2022.9932972","url":null,"abstract":"There is an abundance of deep neural network models for plant disease detection. Prior to applying these models, image preprocessing techniques are applied in order to improve the detection results. However, there is a lack of computational comparisons on the application of different image preprocessing techniques before applying object detection algorithms for plant disease detection. This paper aims to fill this gap by presenting a computational comparison of seven different image preprocessing techniques (auto-orientation, object isolation, resizing, grayscale conversion, static crop, contrast adjustment, tiling) applied prior to the execution of two state-of-the-art object detection algorithms, one single-stage detector, YOLOV5, and one two-stage detector, Faster-RCNN. We investigate whether or not these preprocessing techniques improve the accuracy, training time, and inference time, of plant disease detection. Apart from comparing these techniques solely, we also perform combinations of the preprocessing techniques. The PlantDoc dataset was used for this experimental study. Computational results show that the best method improves the mean average precision by 9% and 3% for YOLOv5 and Faster-RCNN, respectively. Finally, the combination of all seven preprocessing techniques yields an improvement of about 13% in the mean average precision of both object detectors.","PeriodicalId":68279,"journal":{"name":"计算机工程与设计","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86872451","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}
Gamification has gained significant attention in the last decade, since nowadays developments and modern applications need to motivate and engage users. Gamification is the attempt to build systems and applications by adding game like elements and game principles. The concept of gamification has become very popular in educational contexts, since many applications and systems have been designed and developed in the educational area where supporting and retaining engagement is of great importance. The proposed mobile application is a game that tries to educate the user in safe driving concepts. Educating people on good driving practices is a necessity since, despite we drive safer cars on safer roads, the number of accidents and fatalities is still high. The application implements the most significant scenarios of road traffic accidents according to the Hellenic Statistical Authority and provides rewards to users who follow and apply good safe driving practices. The application is realized as a game of emergence where a small number of safe driving rules are combined and produce large number of variation while the content generation is implemented according to procedural content generation technique.
{"title":"Safe Driving Mobile Application using Gamification","authors":"Platon Dimitriadis, Nikos Dimokas, Petros Karvelis","doi":"10.1109/SEEDA-CECNSM57760.2022.9932988","DOIUrl":"https://doi.org/10.1109/SEEDA-CECNSM57760.2022.9932988","url":null,"abstract":"Gamification has gained significant attention in the last decade, since nowadays developments and modern applications need to motivate and engage users. Gamification is the attempt to build systems and applications by adding game like elements and game principles. The concept of gamification has become very popular in educational contexts, since many applications and systems have been designed and developed in the educational area where supporting and retaining engagement is of great importance. The proposed mobile application is a game that tries to educate the user in safe driving concepts. Educating people on good driving practices is a necessity since, despite we drive safer cars on safer roads, the number of accidents and fatalities is still high. The application implements the most significant scenarios of road traffic accidents according to the Hellenic Statistical Authority and provides rewards to users who follow and apply good safe driving practices. The application is realized as a game of emergence where a small number of safe driving rules are combined and produce large number of variation while the content generation is implemented according to procedural content generation technique.","PeriodicalId":68279,"journal":{"name":"计算机工程与设计","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79723114","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 : 2022-09-23DOI: 10.1109/SEEDA-CECNSM57760.2022.9932899
Vasiliki Naskari, G. Doumenis, Christos Koutsos, Ioannis Masklavanos
PV cells are a technology of choice for powering outdoor energy autonomous IoT systems. Despite an early start, indoor PV cells are not so prominent in powering indoor IoT nodes. Microelectronic technology and power management advancements have decreased the power consumption of modern IoT devices down to the microwatts scale. Such low power requirements can be fulfilled in indoor environments -even with low light irradiance-by utilizing specialized PV cells. This has created the need for rigid PV cell validation in representative low light conditions. In this paper we investigate the design of a light simulator using low-cost LED components. We propose a process to determine the number and positions of the LEDs in order to fulfill intensity and uniformity requirements. We propose a simple simulation model that allows the three-dimensional representation of the angular intensity distribution of an LED. Based on the study and the conclusions drawn, a low-cost simulator is built and demonstrated.
{"title":"Design and implementation of an indoors light simulator","authors":"Vasiliki Naskari, G. Doumenis, Christos Koutsos, Ioannis Masklavanos","doi":"10.1109/SEEDA-CECNSM57760.2022.9932899","DOIUrl":"https://doi.org/10.1109/SEEDA-CECNSM57760.2022.9932899","url":null,"abstract":"PV cells are a technology of choice for powering outdoor energy autonomous IoT systems. Despite an early start, indoor PV cells are not so prominent in powering indoor IoT nodes. Microelectronic technology and power management advancements have decreased the power consumption of modern IoT devices down to the microwatts scale. Such low power requirements can be fulfilled in indoor environments -even with low light irradiance-by utilizing specialized PV cells. This has created the need for rigid PV cell validation in representative low light conditions. In this paper we investigate the design of a light simulator using low-cost LED components. We propose a process to determine the number and positions of the LEDs in order to fulfill intensity and uniformity requirements. We propose a simple simulation model that allows the three-dimensional representation of the angular intensity distribution of an LED. Based on the study and the conclusions drawn, a low-cost simulator is built and demonstrated.","PeriodicalId":68279,"journal":{"name":"计算机工程与设计","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82869748","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 : 2022-09-23DOI: 10.1109/SEEDA-CECNSM57760.2022.9932978
Theodora Sanida, Argyrios Sideris, Antonios Chatzisavvas, Michael F. Dossis, M. Dasygenis
A serious public health concern is Novel Coronavirus Disease (COVID-19), which spread quickly over the globe at the end of 2019. This coronavirus is still able to propagate rapidly even after two years. Chest X-rays are crucial for diagnosing infected individuals in the worldwide battle against this illness. Therefore, various COVID-19 quick classification technologies can provide excellent classification accuracy to help medical professionals make the best choices. Here, we propose a trustworthy, compact network that, with the aid of encouraging classification results, can correctly identify COVID-19 from chest X-rays. The experimental findings demonstrated that, in a low-power embedded system, the modified architecture of the proposed model produced excellent performance metrics for four classes. The suggested classification architecture had an overall accuracy speed of 97.67% and an f1-score of 97.64%. This classification model is better than the other classification models used to classify patients with COVID-19 infection.
{"title":"Radiography Images with Transfer Learning on Embedded System","authors":"Theodora Sanida, Argyrios Sideris, Antonios Chatzisavvas, Michael F. Dossis, M. Dasygenis","doi":"10.1109/SEEDA-CECNSM57760.2022.9932978","DOIUrl":"https://doi.org/10.1109/SEEDA-CECNSM57760.2022.9932978","url":null,"abstract":"A serious public health concern is Novel Coronavirus Disease (COVID-19), which spread quickly over the globe at the end of 2019. This coronavirus is still able to propagate rapidly even after two years. Chest X-rays are crucial for diagnosing infected individuals in the worldwide battle against this illness. Therefore, various COVID-19 quick classification technologies can provide excellent classification accuracy to help medical professionals make the best choices. Here, we propose a trustworthy, compact network that, with the aid of encouraging classification results, can correctly identify COVID-19 from chest X-rays. The experimental findings demonstrated that, in a low-power embedded system, the modified architecture of the proposed model produced excellent performance metrics for four classes. The suggested classification architecture had an overall accuracy speed of 97.67% and an f1-score of 97.64%. This classification model is better than the other classification models used to classify patients with COVID-19 infection.","PeriodicalId":68279,"journal":{"name":"计算机工程与设计","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81099980","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 : 2022-09-23DOI: 10.1109/SEEDA-CECNSM57760.2022.9932923
Fotios Bosmos, A. Tzallas, M. Tsipouras, N. Giannakeas
The objective of this paper is the development and evaluation of mobile applications for learning tours in cultural and archaeological sites, using virtual and augmented reality. Firstly, the theoretical framework of virtual and augmented reality techniques is presented. Then the virtual model of the bridge of Arta is constructed. The model is to be integrated into the unity gaming machine to accompany a virtual navigation application. The application is enriched with multimedia elements and is connected to a corresponding augmented reality application and is evaluated by secondary school students.
{"title":"Virtual and augmented experience in cultural places: The perspective of integration in the learning process","authors":"Fotios Bosmos, A. Tzallas, M. Tsipouras, N. Giannakeas","doi":"10.1109/SEEDA-CECNSM57760.2022.9932923","DOIUrl":"https://doi.org/10.1109/SEEDA-CECNSM57760.2022.9932923","url":null,"abstract":"The objective of this paper is the development and evaluation of mobile applications for learning tours in cultural and archaeological sites, using virtual and augmented reality. Firstly, the theoretical framework of virtual and augmented reality techniques is presented. Then the virtual model of the bridge of Arta is constructed. The model is to be integrated into the unity gaming machine to accompany a virtual navigation application. The application is enriched with multimedia elements and is connected to a corresponding augmented reality application and is evaluated by secondary school students.","PeriodicalId":68279,"journal":{"name":"计算机工程与设计","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86550578","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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