Pub Date : 2019-03-01DOI: 10.1109/PICECE.2019.8747178
Jawdat Y. Abu-Taha
This research paper details the DTMOS transistor method to enhance the bandwidth of the transimpedance amplifier. The proposed TIA is based on boosting the trans conductance of a MOS transistor using a composite transistor configuration with a higher significance of transconductance than the regular DTMOS. This methodology is anchored by a design in a $0.18mu mathrm{m}$ CMOS innovation. The photodiode has a capacitance of 200fF, which permits the TIA to achieve a wide bandwidth of 2.6GHz. It is seen that the proposed TIA provides transimpedance gain of 56. 5 $mathrm{d}mathrm{B}Omega$ and input inferred noise-Current Spectral Density of $8mathrm{p}mathrm{A}/sqrt{mathrm{H}mathrm{z}}$ and the mean group-delay fluctuation is 4ps through the 3-dB bandwidth. The power consumption is recorded at 1.1mW from a 1.8V supply.
{"title":"High Gain, Widebandwidth and Low PowerTransimpedance Amplifier Using DTMOS Transistor","authors":"Jawdat Y. Abu-Taha","doi":"10.1109/PICECE.2019.8747178","DOIUrl":"https://doi.org/10.1109/PICECE.2019.8747178","url":null,"abstract":"This research paper details the DTMOS transistor method to enhance the bandwidth of the transimpedance amplifier. The proposed TIA is based on boosting the trans conductance of a MOS transistor using a composite transistor configuration with a higher significance of transconductance than the regular DTMOS. This methodology is anchored by a design in a $0.18mu mathrm{m}$ CMOS innovation. The photodiode has a capacitance of 200fF, which permits the TIA to achieve a wide bandwidth of 2.6GHz. It is seen that the proposed TIA provides transimpedance gain of 56. 5 $mathrm{d}mathrm{B}Omega$ and input inferred noise-Current Spectral Density of $8mathrm{p}mathrm{A}/sqrt{mathrm{H}mathrm{z}}$ and the mean group-delay fluctuation is 4ps through the 3-dB bandwidth. The power consumption is recorded at 1.1mW from a 1.8V supply.","PeriodicalId":375980,"journal":{"name":"2019 IEEE 7th Palestinian International Conference on Electrical and Computer Engineering (PICECE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122949162","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 : 2019-03-01DOI: 10.1109/PICECE.2019.8747236
Mahmoud R. Alfarra, Abdalfattah M. Alfarra, Ahmed Salahedden
The huge collection of text available represents a remarkable challenge to process and exploit it in many fields. Therefore, there is a multitude of articles that are being proposed to summarize text automatically. More accurate and higher performing models are still required for text summarization. It is one of the most common tasks of text mining. In this paper, a novel Graph-based Growing self-organizing map for Single Document Summarization (GGSDS). GGSDS is an unsupervised extractive summarization approach composed mainly of five tasks: text pre-processing, document representation, sub-topics identification, sentence ranking and finally summary generation. The entire text of a document is represented in GGSDS by one accumulative graph. The choice of this representation model supports the extraction of all required features as to achieve the most suitable summary of text, especially the shared phrases between sentences. The impact of the sub-topics on the accuracy and comprehensiveness of the generated summary is taken into account in the design of GGSDS model. For this purpose, G-GSOM is employed to cluster sentences into clusters to represent the sub-topics of text. Next, sentences are scored using TextRank algorithm under the assumption that when a sentence has more relation with others, it is considered as more important and more representative to a sub-topic. Finally, the sentences with the highest score in each cluster are selected for generating the summary. Experimental results showed that GGSDS generated summaries of single documents with more than 80% accuracy of two datasets. Furthermore, these summaries covered most of the sub-topics of the documents.
{"title":"Graph-based Growing self-organizing map for Single Document Summarization (GGSDS)","authors":"Mahmoud R. Alfarra, Abdalfattah M. Alfarra, Ahmed Salahedden","doi":"10.1109/PICECE.2019.8747236","DOIUrl":"https://doi.org/10.1109/PICECE.2019.8747236","url":null,"abstract":"The huge collection of text available represents a remarkable challenge to process and exploit it in many fields. Therefore, there is a multitude of articles that are being proposed to summarize text automatically. More accurate and higher performing models are still required for text summarization. It is one of the most common tasks of text mining. In this paper, a novel Graph-based Growing self-organizing map for Single Document Summarization (GGSDS). GGSDS is an unsupervised extractive summarization approach composed mainly of five tasks: text pre-processing, document representation, sub-topics identification, sentence ranking and finally summary generation. The entire text of a document is represented in GGSDS by one accumulative graph. The choice of this representation model supports the extraction of all required features as to achieve the most suitable summary of text, especially the shared phrases between sentences. The impact of the sub-topics on the accuracy and comprehensiveness of the generated summary is taken into account in the design of GGSDS model. For this purpose, G-GSOM is employed to cluster sentences into clusters to represent the sub-topics of text. Next, sentences are scored using TextRank algorithm under the assumption that when a sentence has more relation with others, it is considered as more important and more representative to a sub-topic. Finally, the sentences with the highest score in each cluster are selected for generating the summary. Experimental results showed that GGSDS generated summaries of single documents with more than 80% accuracy of two datasets. Furthermore, these summaries covered most of the sub-topics of the documents.","PeriodicalId":375980,"journal":{"name":"2019 IEEE 7th Palestinian International Conference on Electrical and Computer Engineering (PICECE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132743430","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 : 2019-03-01DOI: 10.1109/PICECE.2019.8747209
Ahmed Nassar, E. Sezer
In this study, we aimed to improve the performance results of Arabic sentiment analysis. This can be achieved by investigating the most successful machine learning method and the most useful feature vector to classify sentiments in both term and document levels into two (positive or negative) categories. Moreover, specification of one polarity degree for the term that has more than one is investigated. Also to handle the negations and intensifications, some rules are developed. According to the obtained results, Artificial Neural Network classifier is nominated as the best classifier in both term and document level sentiment analysis (SA) for Arabic Language. Furthermore, the average F-score achieved in the term level SA for both positive and negative testing classes is 0.92. In the document level SA, the average F-score for positive testing classes is 0.94, while for negative classes is 0.93.
{"title":"Multilevel Sentiment Analysis In Arabic","authors":"Ahmed Nassar, E. Sezer","doi":"10.1109/PICECE.2019.8747209","DOIUrl":"https://doi.org/10.1109/PICECE.2019.8747209","url":null,"abstract":"In this study, we aimed to improve the performance results of Arabic sentiment analysis. This can be achieved by investigating the most successful machine learning method and the most useful feature vector to classify sentiments in both term and document levels into two (positive or negative) categories. Moreover, specification of one polarity degree for the term that has more than one is investigated. Also to handle the negations and intensifications, some rules are developed. According to the obtained results, Artificial Neural Network classifier is nominated as the best classifier in both term and document level sentiment analysis (SA) for Arabic Language. Furthermore, the average F-score achieved in the term level SA for both positive and negative testing classes is 0.92. In the document level SA, the average F-score for positive testing classes is 0.94, while for negative classes is 0.93.","PeriodicalId":375980,"journal":{"name":"2019 IEEE 7th Palestinian International Conference on Electrical and Computer Engineering (PICECE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116659308","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 : 2019-03-01DOI: 10.1109/PICECE.2019.8747174
A. A. Abu Tair, M. El Astal, M. Shaat, F. El-Nahal
In Cognitive Radio (CR) systems, the spectrum handoff is a key-factor as it could affect propagation path nature. Accordingly, it may affect the overall system coverage and performance. In this article, a new spectrum-handoff strategy is proposed in order to minimize the occurrence of spectrum handoff and hence to achieve a better transmission performance. Specifically, it provide a better channel-selection strategy combined with a power adaptation scheme. This is to reduce/avoid interference and also to conserve power consumption in the system and hence get a greener communication system. The simulation results show an enhancement of up to 64%in switching requests. Also, the system is analyzed in terms of BER and compared to others to show the performance maintained.
{"title":"On Developing SeamlessSpectrum Handoff Strategyfor Cognitive Radio Networks","authors":"A. A. Abu Tair, M. El Astal, M. Shaat, F. El-Nahal","doi":"10.1109/PICECE.2019.8747174","DOIUrl":"https://doi.org/10.1109/PICECE.2019.8747174","url":null,"abstract":"In Cognitive Radio (CR) systems, the spectrum handoff is a key-factor as it could affect propagation path nature. Accordingly, it may affect the overall system coverage and performance. In this article, a new spectrum-handoff strategy is proposed in order to minimize the occurrence of spectrum handoff and hence to achieve a better transmission performance. Specifically, it provide a better channel-selection strategy combined with a power adaptation scheme. This is to reduce/avoid interference and also to conserve power consumption in the system and hence get a greener communication system. The simulation results show an enhancement of up to 64%in switching requests. Also, the system is analyzed in terms of BER and compared to others to show the performance maintained.","PeriodicalId":375980,"journal":{"name":"2019 IEEE 7th Palestinian International Conference on Electrical and Computer Engineering (PICECE)","volume":"30 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117048919","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 : 2019-03-01DOI: 10.1109/PICECE.2019.8747189
Moayed Almobaied, I. Eksin, M. Guzelkaya
This paper proposes an inverse optimal controller design method for discrete-time affine nonlinear systems that relies on a multi-objective optimization criterion. Inverse optimal control approach circumvents the tedious task of solving the Hamilton-Jacobi-Bellman equation (HJB) that results from the classical solution of the nonlinear optimal control problem. Here, the inverse optimal controller is based on defining an appropriate quadratic control Lyapunov function (CLF) where the parameters of this candidate CLF were optimized in an off-line manner by using Big Bang-Big Crunch algorithm. The root-mean-square-error (RMSE) of system states with respect to a reference trajectory and the sum-of-squares of control effort are utilized as the multi-objective optimization criterion in the Big Bang-Big Crunch optimizing algorithm. In order to test the performance of the proposed method, a nonlinear example from the literature of inverse optimal control is taken into consideration. The simulation results enlighten the designer in making a choice between the classical inverse optimal control solution and the multi-objective function included case.
{"title":"Inverse Optimal Controller Design Based on Multi-Objective Optimization Criteria for Discrete-Time Nonlinear Systems","authors":"Moayed Almobaied, I. Eksin, M. Guzelkaya","doi":"10.1109/PICECE.2019.8747189","DOIUrl":"https://doi.org/10.1109/PICECE.2019.8747189","url":null,"abstract":"This paper proposes an inverse optimal controller design method for discrete-time affine nonlinear systems that relies on a multi-objective optimization criterion. Inverse optimal control approach circumvents the tedious task of solving the Hamilton-Jacobi-Bellman equation (HJB) that results from the classical solution of the nonlinear optimal control problem. Here, the inverse optimal controller is based on defining an appropriate quadratic control Lyapunov function (CLF) where the parameters of this candidate CLF were optimized in an off-line manner by using Big Bang-Big Crunch algorithm. The root-mean-square-error (RMSE) of system states with respect to a reference trajectory and the sum-of-squares of control effort are utilized as the multi-objective optimization criterion in the Big Bang-Big Crunch optimizing algorithm. In order to test the performance of the proposed method, a nonlinear example from the literature of inverse optimal control is taken into consideration. The simulation results enlighten the designer in making a choice between the classical inverse optimal control solution and the multi-objective function included case.","PeriodicalId":375980,"journal":{"name":"2019 IEEE 7th Palestinian International Conference on Electrical and Computer Engineering (PICECE)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115255820","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 : 2019-03-01DOI: 10.1109/PICECE.2019.8747173
H. J. Khozondar, Lee Jun yong, Dr.-Ing. Alexander W. Koch
In this study, a review on several maximum power point tracking (MPPT) algorithms is given. MPPT algorithms have three major categories: Conventional methods, Advanced Soft Computing methods and Hybrid algorithms. MPPT algorithms are significant to operate the photovoltaic energy conversion systems as close as possible to the MPP resulting in photovoltaic (PV) arrays with high efficiency. In this review, the MPPT algorithms implementation complexity, the complete procedure and its effects in the PV output were given with special attention to the sensors used in the system in the first portion. Further, different MPPT algorithms for PV systems are highlighted with examples, it’s parameters like complexity, and sensors used are described. In the end, the MPPT algorithms for PV systems were compared, reviewed and reported. The ultimatum of this work is to provide a survey reference for users of PV based power generation and valued information for researchers of this particular field.
{"title":"Recapitulation and comparative study for Photovoltaic Maximum Power Point Tracking techniques in particular sensor quality","authors":"H. J. Khozondar, Lee Jun yong, Dr.-Ing. Alexander W. Koch","doi":"10.1109/PICECE.2019.8747173","DOIUrl":"https://doi.org/10.1109/PICECE.2019.8747173","url":null,"abstract":"In this study, a review on several maximum power point tracking (MPPT) algorithms is given. MPPT algorithms have three major categories: Conventional methods, Advanced Soft Computing methods and Hybrid algorithms. MPPT algorithms are significant to operate the photovoltaic energy conversion systems as close as possible to the MPP resulting in photovoltaic (PV) arrays with high efficiency. In this review, the MPPT algorithms implementation complexity, the complete procedure and its effects in the PV output were given with special attention to the sensors used in the system in the first portion. Further, different MPPT algorithms for PV systems are highlighted with examples, it’s parameters like complexity, and sensors used are described. In the end, the MPPT algorithms for PV systems were compared, reviewed and reported. The ultimatum of this work is to provide a survey reference for users of PV based power generation and valued information for researchers of this particular field.","PeriodicalId":375980,"journal":{"name":"2019 IEEE 7th Palestinian International Conference on Electrical and Computer Engineering (PICECE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129674280","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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