Pub Date : 2023-03-17DOI: 10.1109/iCoMET57998.2023.10099190
Shamsa Waheed, Rashid Amin, J. Iqbal, Mudassar Hussain, Muhammad Adeel Bashir
Human activity recognition has captivated the interest of researchers due to its significant applications such as smart home health care systems in which this technology can be applied to enhance the patients' rehabilitation. Different sensors can be used in a variety of ways to recognize human activity in a smartly managed environment. It is also used in pedestrian detection, robotics and human-computer interface, etc. Hence, with the development of Artificial Intelligence, researchers are keen to solve problems related to human action recognition and classification. We propose a novel method using Deep Learning (DL) algorithm for the task of human action recognition. The suggested framework is trained and evaluated on a publically available database containing recorded movements performed by both male and female participants. We tested various DL architectures and their parameters by changing epochs, learning rates, batch size, and optimizers before reaching the final architecture. The optimal architecture consists is trained on 6 epochs, a mini-batch of 128 on an adam optimizer, and a 0.001 learning rate. The system attained the highest accuracy of 98% on unseen test samples. The results prove the method's robustness and can be deployed for real-time human activity recognition and classification.
{"title":"An Automated Human Action Recognition and Classification Framework Using Deep Learning","authors":"Shamsa Waheed, Rashid Amin, J. Iqbal, Mudassar Hussain, Muhammad Adeel Bashir","doi":"10.1109/iCoMET57998.2023.10099190","DOIUrl":"https://doi.org/10.1109/iCoMET57998.2023.10099190","url":null,"abstract":"Human activity recognition has captivated the interest of researchers due to its significant applications such as smart home health care systems in which this technology can be applied to enhance the patients' rehabilitation. Different sensors can be used in a variety of ways to recognize human activity in a smartly managed environment. It is also used in pedestrian detection, robotics and human-computer interface, etc. Hence, with the development of Artificial Intelligence, researchers are keen to solve problems related to human action recognition and classification. We propose a novel method using Deep Learning (DL) algorithm for the task of human action recognition. The suggested framework is trained and evaluated on a publically available database containing recorded movements performed by both male and female participants. We tested various DL architectures and their parameters by changing epochs, learning rates, batch size, and optimizers before reaching the final architecture. The optimal architecture consists is trained on 6 epochs, a mini-batch of 128 on an adam optimizer, and a 0.001 learning rate. The system attained the highest accuracy of 98% on unseen test samples. The results prove the method's robustness and can be deployed for real-time human activity recognition and classification.","PeriodicalId":369792,"journal":{"name":"2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128597156","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 : 2023-03-17DOI: 10.1109/iCoMET57998.2023.10099226
Sanan Ahmed, Samreen Bano, Bakhtawar Iftikhar, S. Naqvi, Y. Amin
This work proposes a square shaped compact patch antenna with microstrip feed line, supporting the terahertz (THz) communication applications. The geometry proposed in this work is realized using polyimide substrate, whereas the conducting material used for the patch is Gold metal. The presented antenna with overall substrate dimensions of 300 x 300 µm2 offers wide bandwidth ranging from 580 - 660 GHz and high gain of 7.155 dB. The simulation results demonstrates good radiation behavior of the antenna. Thus, it is ascertained that the suggested antenna is a promising contender for THz wireless communication systems.
本文提出了一种支持太赫兹(THz)通信应用的带微带馈线的方形紧凑型贴片天线。在这项工作中提出的几何结构是用聚酰亚胺衬底实现的，而用于贴片的导电材料是金金属。该天线的总基片尺寸为300 x 300µm2，具有580 - 660 GHz的宽带宽和7.155 dB的高增益。仿真结果表明该天线具有良好的辐射性能。因此，确定了所建议的天线是太赫兹无线通信系统的一个有前途的竞争者。
{"title":"Polyimide Substrate based Compact Antenna for Terahertz Wireless Communication Applications","authors":"Sanan Ahmed, Samreen Bano, Bakhtawar Iftikhar, S. Naqvi, Y. Amin","doi":"10.1109/iCoMET57998.2023.10099226","DOIUrl":"https://doi.org/10.1109/iCoMET57998.2023.10099226","url":null,"abstract":"This work proposes a square shaped compact patch antenna with microstrip feed line, supporting the terahertz (THz) communication applications. The geometry proposed in this work is realized using polyimide substrate, whereas the conducting material used for the patch is Gold metal. The presented antenna with overall substrate dimensions of 300 x 300 µm2 offers wide bandwidth ranging from 580 - 660 GHz and high gain of 7.155 dB. The simulation results demonstrates good radiation behavior of the antenna. Thus, it is ascertained that the suggested antenna is a promising contender for THz wireless communication systems.","PeriodicalId":369792,"journal":{"name":"2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115200972","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 : 2021-11-10DOI: 10.1109/iCoMET57998.2023.10099156
Subhan Khan, A. Batool
In this paper, a unique and efficient method for learning the complex receiver structure of Galileo E5 AltBOC (Alternative Binary Offset Carrier) is presented. The methodology involves a software application designed in Matlab, which demonstrates the steps required to design a software receiver for Galileo E5 signal. The software application covers key concepts related to the Galileo E5 signal, such as signal acquisition, signal tracking, navigation data extraction, power spectral density (PSD) of the AltBOC (15,10), Fast Fourier Transform (FFT) of the E5a and E5b signals, and implementation of the subcarrier used for the AltBOC (15,10). Additionally, this paper presents a novel approach for extracting navigation data using the prompt channel of carrier tracking from the code loop discriminator.
{"title":"Efficient and Unique Learning of The Complex Receiver Structure of Galileo E5 AltBOC Using an Educational Software in Matlab","authors":"Subhan Khan, A. Batool","doi":"10.1109/iCoMET57998.2023.10099156","DOIUrl":"https://doi.org/10.1109/iCoMET57998.2023.10099156","url":null,"abstract":"In this paper, a unique and efficient method for learning the complex receiver structure of Galileo E5 AltBOC (Alternative Binary Offset Carrier) is presented. The methodology involves a software application designed in Matlab, which demonstrates the steps required to design a software receiver for Galileo E5 signal. The software application covers key concepts related to the Galileo E5 signal, such as signal acquisition, signal tracking, navigation data extraction, power spectral density (PSD) of the AltBOC (15,10), Fast Fourier Transform (FFT) of the E5a and E5b signals, and implementation of the subcarrier used for the AltBOC (15,10). Additionally, this paper presents a novel approach for extracting navigation data using the prompt channel of carrier tracking from the code loop discriminator.","PeriodicalId":369792,"journal":{"name":"2023 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124654793","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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