Pub Date : 2019-06-01DOI: 10.1109/ECAI46879.2019.9042143
Xavier Quinn
The genesis of Bluetooth heralded a massive influx of tiny, easily produced, data transmitting devices, which due to their low power draw, could be connected to a decent sized battery and transmit data for weeks on end. Now that nearly all phones and computers have this capability built in, it has become useful for any number of reasons to know what type of devices are within an area, and their precise location. Systems have been developed which locate these devices, but only as long as they are within the range of multiple stationary beacons, which, depending on the area wishing to be monitored, may have severe limitations. This paper outlines the development of a system that uses a single sensor to determine the location of Bluetooth devices over any sized area.
{"title":"Single Sensor Bluetooth Multilateration from Arbitrary Locations","authors":"Xavier Quinn","doi":"10.1109/ECAI46879.2019.9042143","DOIUrl":"https://doi.org/10.1109/ECAI46879.2019.9042143","url":null,"abstract":"The genesis of Bluetooth heralded a massive influx of tiny, easily produced, data transmitting devices, which due to their low power draw, could be connected to a decent sized battery and transmit data for weeks on end. Now that nearly all phones and computers have this capability built in, it has become useful for any number of reasons to know what type of devices are within an area, and their precise location. Systems have been developed which locate these devices, but only as long as they are within the range of multiple stationary beacons, which, depending on the area wishing to be monitored, may have severe limitations. This paper outlines the development of a system that uses a single sensor to determine the location of Bluetooth devices over any sized area.","PeriodicalId":285780,"journal":{"name":"2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123001835","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 : 2019-06-01DOI: 10.1109/ECAI46879.2019.9042010
Elena Popescu, T. Popescu
In general, communication is the indispensable element for the optimal functioning of any human community, regardless of its nature and size. In particular, nonverbal communication is the accumulation of messages, which are not expressed by words and which can be decoded, creating meanings (kinesics). This signal can repeat, contradict, replace, complete or accentuate the message sent through words. The importance of nonverbal communication was demonstrated in 1967 by Albert Mehrabian. After a study, he concluded that only 5% of the message is transmitted verbally, while 38% are transmitted by voice and 55% through body language. Therefore, this research aims to demonstrate the importance of nonverbal communication empirically - in job interviews. The general description of this process is made with the help of a qualitative method - the research interview. The interviews have been registered with ten employers form Arges district in 2018–2019. The conclusions of this study were: employers do take in consideration the importance of how the future employee present himself, but only at instinctual level, the local firms do not have human resources trainers specialized in body language. They look for gestures and clothing stiles and the general body expression is taken into consideration. So the main demands form a future employee, in job interviewing, according to some local leaders, included in the interviews are: dynamism and initiative (J. Messinger, p.34), but in reality it is the gestures that makes the final decision if you correspond on not to what the organization wants/needs.
{"title":"Nonverbal Communication in Job Interviews. A Case Study on Local Organisations","authors":"Elena Popescu, T. Popescu","doi":"10.1109/ECAI46879.2019.9042010","DOIUrl":"https://doi.org/10.1109/ECAI46879.2019.9042010","url":null,"abstract":"In general, communication is the indispensable element for the optimal functioning of any human community, regardless of its nature and size. In particular, nonverbal communication is the accumulation of messages, which are not expressed by words and which can be decoded, creating meanings (kinesics). This signal can repeat, contradict, replace, complete or accentuate the message sent through words. The importance of nonverbal communication was demonstrated in 1967 by Albert Mehrabian. After a study, he concluded that only 5% of the message is transmitted verbally, while 38% are transmitted by voice and 55% through body language. Therefore, this research aims to demonstrate the importance of nonverbal communication empirically - in job interviews. The general description of this process is made with the help of a qualitative method - the research interview. The interviews have been registered with ten employers form Arges district in 2018–2019. The conclusions of this study were: employers do take in consideration the importance of how the future employee present himself, but only at instinctual level, the local firms do not have human resources trainers specialized in body language. They look for gestures and clothing stiles and the general body expression is taken into consideration. So the main demands form a future employee, in job interviewing, according to some local leaders, included in the interviews are: dynamism and initiative (J. Messinger, p.34), but in reality it is the gestures that makes the final decision if you correspond on not to what the organization wants/needs.","PeriodicalId":285780,"journal":{"name":"2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114183654","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 : 2019-06-01DOI: 10.1109/ECAI46879.2019.9042006
Ovidiu Plugariu, L. Petrica, Radu Pirea, R. Hobincu
This paper presents the implementation of a heterogeneous Hadoop cluster based on the Zynq ZedBoard development platform with GZIP FPGA offloading for high-speed and energy efficient computing. We have developed the first open source FPGA GZIP compressor, designed for educational and research purposes, that can reach 1 Gbps compression speed using a 125 MHz clock. The core uses only 10% of the Zynq-7020 SoC FPGA resources and is 5.7x faster than the ARM CPU which runs at 667 MHz. We implemented an eight-node Hadoop distributed cluster and performed the Wordcount and Terasort benchmarks using software and hardware GZIP compression during the Map stage. Results show an almost 2x more energy-efficient cluster when compression is done using our GZIP FPGA core than using the software compression. The performance of the Hadoop cluster is limited by the 512 MB of RAM and the low read-write speed of the SD cards which act as hard drives for each node.
{"title":"Hadoop ZedBoard cluster with GZIP compression FPGA acceleration","authors":"Ovidiu Plugariu, L. Petrica, Radu Pirea, R. Hobincu","doi":"10.1109/ECAI46879.2019.9042006","DOIUrl":"https://doi.org/10.1109/ECAI46879.2019.9042006","url":null,"abstract":"This paper presents the implementation of a heterogeneous Hadoop cluster based on the Zynq ZedBoard development platform with GZIP FPGA offloading for high-speed and energy efficient computing. We have developed the first open source FPGA GZIP compressor, designed for educational and research purposes, that can reach 1 Gbps compression speed using a 125 MHz clock. The core uses only 10% of the Zynq-7020 SoC FPGA resources and is 5.7x faster than the ARM CPU which runs at 667 MHz. We implemented an eight-node Hadoop distributed cluster and performed the Wordcount and Terasort benchmarks using software and hardware GZIP compression during the Map stage. Results show an almost 2x more energy-efficient cluster when compression is done using our GZIP FPGA core than using the software compression. The performance of the Hadoop cluster is limited by the 512 MB of RAM and the low read-write speed of the SD cards which act as hard drives for each node.","PeriodicalId":285780,"journal":{"name":"2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121215891","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 : 2019-06-01DOI: 10.1109/ECAI46879.2019.9042172
Cristian Monea, N. Bizon
A promising bulk detection technique for detection of prohibited substances is nuclear quadrupole resonance. Its advantages over other detection techniques have determined the research of excitation methods and development of laboratory and commercial systems. This paper presents a brief overview of the use of nuclear quadrupole resonance for detecting prohibited substances, by explaining the physical principle, detailing the detection methods and presenting some laboratory and commercial equipment.
{"title":"The Use of Nuclear Quadrupole Resonance Spectroscopy for Detection of Prohibited Substances: Techniques and Equipment","authors":"Cristian Monea, N. Bizon","doi":"10.1109/ECAI46879.2019.9042172","DOIUrl":"https://doi.org/10.1109/ECAI46879.2019.9042172","url":null,"abstract":"A promising bulk detection technique for detection of prohibited substances is nuclear quadrupole resonance. Its advantages over other detection techniques have determined the research of excitation methods and development of laboratory and commercial systems. This paper presents a brief overview of the use of nuclear quadrupole resonance for detecting prohibited substances, by explaining the physical principle, detailing the detection methods and presenting some laboratory and commercial equipment.","PeriodicalId":285780,"journal":{"name":"2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132910129","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 : 2019-06-01DOI: 10.1109/ECAI46879.2019.9041965
E. Kurt
This paper compares various types of electromagnetic harvesters in terms of their electrical and magnetic features. The harvesters have different flux morphologies and natural frequencies and therefore that can be a superiority for them to use in different applications from the ignition signal to the energy harvester aims for battery-free systems. It will be pointed out that the systems considered here have the power ranges of P = 14 mW and P = 32 µW for the optimal frequency regimes.
{"title":"Comparison of Electromagnetic Energy Harvesters","authors":"E. Kurt","doi":"10.1109/ECAI46879.2019.9041965","DOIUrl":"https://doi.org/10.1109/ECAI46879.2019.9041965","url":null,"abstract":"This paper compares various types of electromagnetic harvesters in terms of their electrical and magnetic features. The harvesters have different flux morphologies and natural frequencies and therefore that can be a superiority for them to use in different applications from the ignition signal to the energy harvester aims for battery-free systems. It will be pointed out that the systems considered here have the power ranges of P = 14 mW and P = 32 µW for the optimal frequency regimes.","PeriodicalId":285780,"journal":{"name":"2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134633574","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 : 2019-06-01DOI: 10.1109/ECAI46879.2019.9042091
Yashwant Singh, Jahangir Ahmad Lone, P. Singh, Z. Pólkowski, S. Tanwar, Sudhanshu Tyagi
Wireless Sensor Network (WSN) is emerging field in research, due to its applications almost in every field. Mostly WSN are used to sense a specific area or region which is used to monitor the chemical and biological changes within a region. The main component of a Wireless Sensor Network is a sensor node, which collects information and transmits it to the receiving station for further processing. Further, how to deploy these sensors in a network so that it will cover maximum area is very important. For efficient deployment in a network different strategies are used for node deployment. After deployment of nodes the question is whether they collect all the information in that region, it depends upon the coverage or how well a node senses a given region. In this paper, a brief description of deployment and coverage in WSN is presented. The various optimization algorithms used in deployment of WSN is also described. A comparison of various deployment methods based on their performance and area in which they are deployed is illustrated. Further, the various coverage methods are also compared on the basis of their efficiency and connectivity.
{"title":"Deployment and Coverage in Wireless Sensor Networks: A Perspective","authors":"Yashwant Singh, Jahangir Ahmad Lone, P. Singh, Z. Pólkowski, S. Tanwar, Sudhanshu Tyagi","doi":"10.1109/ECAI46879.2019.9042091","DOIUrl":"https://doi.org/10.1109/ECAI46879.2019.9042091","url":null,"abstract":"Wireless Sensor Network (WSN) is emerging field in research, due to its applications almost in every field. Mostly WSN are used to sense a specific area or region which is used to monitor the chemical and biological changes within a region. The main component of a Wireless Sensor Network is a sensor node, which collects information and transmits it to the receiving station for further processing. Further, how to deploy these sensors in a network so that it will cover maximum area is very important. For efficient deployment in a network different strategies are used for node deployment. After deployment of nodes the question is whether they collect all the information in that region, it depends upon the coverage or how well a node senses a given region. In this paper, a brief description of deployment and coverage in WSN is presented. The various optimization algorithms used in deployment of WSN is also described. A comparison of various deployment methods based on their performance and area in which they are deployed is illustrated. Further, the various coverage methods are also compared on the basis of their efficiency and connectivity.","PeriodicalId":285780,"journal":{"name":"2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133118019","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 : 2019-06-01DOI: 10.1109/ECAI46879.2019.9041995
M. Raducu
In this paper is presented a simple method to extract the parameters of a photovoltaic (PV) cell model using a single current-voltage curve. The experimental results for current-voltage curve are obtained from the constant illumination level of the PV cell. The used model of PV cell is the single-diode model and contained five parameters: $mathbf{I}_{mathbf{ph}}$ – the current generated by light in PV cell, $mathbf{I}_{mathbf{0}}$ – the dark saturation current of the diode, n – the ideality factor of the diode, $mathbf{R}_{mathbf{s}}$ – the series resistance, and $mathbf{R}_{mathbf{sh}}$ – the shunt resistance. The proposed method has been applied for a monocrystalline Si 6 inch PV cell. In order to validate the proposed models, the parameters were used in the Spice simulation.
{"title":"A Simple Method of Modeling the PV Cell from a Single Current-Voltage Curve","authors":"M. Raducu","doi":"10.1109/ECAI46879.2019.9041995","DOIUrl":"https://doi.org/10.1109/ECAI46879.2019.9041995","url":null,"abstract":"In this paper is presented a simple method to extract the parameters of a photovoltaic (PV) cell model using a single current-voltage curve. The experimental results for current-voltage curve are obtained from the constant illumination level of the PV cell. The used model of PV cell is the single-diode model and contained five parameters: $mathbf{I}_{mathbf{ph}}$ – the current generated by light in PV cell, $mathbf{I}_{mathbf{0}}$ – the dark saturation current of the diode, n – the ideality factor of the diode, $mathbf{R}_{mathbf{s}}$ – the series resistance, and $mathbf{R}_{mathbf{sh}}$ – the shunt resistance. The proposed method has been applied for a monocrystalline Si 6 inch PV cell. In order to validate the proposed models, the parameters were used in the Spice simulation.","PeriodicalId":285780,"journal":{"name":"2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123823700","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 : 2019-06-01DOI: 10.1109/ECAI46879.2019.9042173
C. Iorga, V. Neagoe
This paper presents a model of Deep Convolutional Neural Networks (CNN) based on transfer learning for image recognition. This means to use a Deep CNN system pretrained on the large ImageNet dataset of 14 million images and 1000 classes in order to learn feature selection. The results of the pretraining phase are transferred to the problem of classification for the images belonging to the UC Merced Land Use dataset with 21 classes. As benchmark, we have considered a Deep CNN trained with a fraction of the same UC Merced Land Use dataset containing the test images for classification. The experimental results have pointed out the obvious advantage of the Deep CNN with transfer learning (accuracy of 0.87 using pretraining over 0.46 for fully training on the same dataset).
{"title":"A Deep CNN Approach with Transfer Learning for Image Recognition","authors":"C. Iorga, V. Neagoe","doi":"10.1109/ECAI46879.2019.9042173","DOIUrl":"https://doi.org/10.1109/ECAI46879.2019.9042173","url":null,"abstract":"This paper presents a model of Deep Convolutional Neural Networks (CNN) based on transfer learning for image recognition. This means to use a Deep CNN system pretrained on the large ImageNet dataset of 14 million images and 1000 classes in order to learn feature selection. The results of the pretraining phase are transferred to the problem of classification for the images belonging to the UC Merced Land Use dataset with 21 classes. As benchmark, we have considered a Deep CNN trained with a fraction of the same UC Merced Land Use dataset containing the test images for classification. The experimental results have pointed out the obvious advantage of the Deep CNN with transfer learning (accuracy of 0.87 using pretraining over 0.46 for fully training on the same dataset).","PeriodicalId":285780,"journal":{"name":"2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124257116","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 : 2019-06-01DOI: 10.1109/ECAI46879.2019.9042146
I. Árpád
Based on the theoretical model of the Pixel Sieve cryptographic primitive a simple application was written to test in practice the proposed method. In this paper, some preliminary test results are presented regarding the cryptographic strength of the method. For the appointed weaknesses conclusions and proposals are made to enhance the method.
{"title":"Preliminary considerations regarding the cryptographic strength of the Pixel Sieve cryptographic primitive","authors":"I. Árpád","doi":"10.1109/ECAI46879.2019.9042146","DOIUrl":"https://doi.org/10.1109/ECAI46879.2019.9042146","url":null,"abstract":"Based on the theoretical model of the Pixel Sieve cryptographic primitive a simple application was written to test in practice the proposed method. In this paper, some preliminary test results are presented regarding the cryptographic strength of the method. For the appointed weaknesses conclusions and proposals are made to enhance the method.","PeriodicalId":285780,"journal":{"name":"2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125262103","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 : 2019-06-01DOI: 10.1109/ECAI46879.2019.9042001
Ufuoma Chima Apoki, G. Crişan
This paper aims at reviewing and applying constructivism, as a learning theory, in the design and development of automated technologies for Massive Open Online Courses in higher education. Although new in the field of online education, these courses hold the promise of ensuring many people get access to higher education. As it promises many advantages, its multiple challenges also lurk around. Applying the right learning theories would provide a platform that delivers a learning environment which is both automated (to account for the high number of users) and personalized. The methodology uses big data technologies and natural language processing skills to provide quality interaction between platform and student. A review of the types of MOOCs is done, and the challenges of delivery and assessment which limits its recognition as a standard means of education. Then, the use of software agents is explored to accommodate for the stream of big data generated by the high number of students. Learning theories and styles, which are relevant in the design of these courses, are also discussed. Finally, a model is proposed which employs software agents and constructivism (as a learning theory) to make massive open online courses more student-oriented.
{"title":"Employing Software Agents and Constructivism to make Massive Open Online Courses more student-oriented","authors":"Ufuoma Chima Apoki, G. Crişan","doi":"10.1109/ECAI46879.2019.9042001","DOIUrl":"https://doi.org/10.1109/ECAI46879.2019.9042001","url":null,"abstract":"This paper aims at reviewing and applying constructivism, as a learning theory, in the design and development of automated technologies for Massive Open Online Courses in higher education. Although new in the field of online education, these courses hold the promise of ensuring many people get access to higher education. As it promises many advantages, its multiple challenges also lurk around. Applying the right learning theories would provide a platform that delivers a learning environment which is both automated (to account for the high number of users) and personalized. The methodology uses big data technologies and natural language processing skills to provide quality interaction between platform and student. A review of the types of MOOCs is done, and the challenges of delivery and assessment which limits its recognition as a standard means of education. Then, the use of software agents is explored to accommodate for the stream of big data generated by the high number of students. Learning theories and styles, which are relevant in the design of these courses, are also discussed. Finally, a model is proposed which employs software agents and constructivism (as a learning theory) to make massive open online courses more student-oriented.","PeriodicalId":285780,"journal":{"name":"2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125929414","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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