Pub Date : 2019-10-01DOI: 10.1109/TENCON.2019.8929601
Subhabrata Roy, A. Chandra
Orthogonal frequency division multiplexing (OFDM), being a significant aspect of multicarrier modulation (MCM), is accomplished to encounter the effect of multipath reception by splitting the entire allotted bandwidth into several narrow subbands which leads to an advancement in spectral efficiency and diminishes the effect of intersymbol interference (ISI). However, OFDM fails to meet the requirements of high data rate communication networks such as 5G. To achieve the ever increasing demand of 5G cellular networks, this paper presents an innovative filtered-OFDM (F-OFDM) technique based on narrow-band finite impulse response filter which is operated on interpolated band-pass method (IBM). Simulation results show that the F-OFDM with IBM based narrow-band finite impulse response filter (FIR) accomplishes shorter out-of-band emission (OOBE) compared to the F-OFDM designed with some state-of-the-art narrow transition band filtering techniques.
{"title":"Interpolated Band-pass Method Based Narrow-band FIR Filter : A Prospective Candidate in Filtered-OFDM Technique for the 5G Cellular Network","authors":"Subhabrata Roy, A. Chandra","doi":"10.1109/TENCON.2019.8929601","DOIUrl":"https://doi.org/10.1109/TENCON.2019.8929601","url":null,"abstract":"Orthogonal frequency division multiplexing (OFDM), being a significant aspect of multicarrier modulation (MCM), is accomplished to encounter the effect of multipath reception by splitting the entire allotted bandwidth into several narrow subbands which leads to an advancement in spectral efficiency and diminishes the effect of intersymbol interference (ISI). However, OFDM fails to meet the requirements of high data rate communication networks such as 5G. To achieve the ever increasing demand of 5G cellular networks, this paper presents an innovative filtered-OFDM (F-OFDM) technique based on narrow-band finite impulse response filter which is operated on interpolated band-pass method (IBM). Simulation results show that the F-OFDM with IBM based narrow-band finite impulse response filter (FIR) accomplishes shorter out-of-band emission (OOBE) compared to the F-OFDM designed with some state-of-the-art narrow transition band filtering techniques.","PeriodicalId":36690,"journal":{"name":"Platonic Investigations","volume":"32 1","pages":"311-315"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81098497","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 : 2019-10-01DOI: 10.1109/TENCON.2019.8929619
M. A. Raushan, N. Alam, M. J. Siddiqui
In this paper, we propose the use of pockets to improve the performance of an electrostatically doped double-gate tunnel field effect transistor. The required n-i-p+structure for tunneling is formed on a thin intrinsic silicon film by electrostatic doping based on charge plasma concept. The source, drain and gate electrodes of different workfunctions are employed to form the p-type, n-type and intrinsic regions. Analysis using 2D TCAD simulations suggest that the proposed structure can significantly overcome the low ION and ambipolarity problem in conventional dopingless tunnel field effect transistor (DLTFET). The n-type pocket in channel and p-type pocket in source provides noteworthy improvement in the transfer characteristics of pocket engineered (PE) DLTFET. The proposed PE-DLTFET offers higher ION(2.4x10−5A), high ION/IOFF ratio ∼109and lower subthreshold slope (SS) of 42 mV/dec. We also propose an n-type pocket in the channel adjacent to the drain region to reduce the inherent ambipolarity in DLTFET. The results show that PE-DLTFET effectively suppresses ambipolarity and offers 25x higher ION as compared to DLTFET, making it a viable candidate for switching applications.
{"title":"Pocket engineered electrostatically doped tunnel field effect transistor","authors":"M. A. Raushan, N. Alam, M. J. Siddiqui","doi":"10.1109/TENCON.2019.8929619","DOIUrl":"https://doi.org/10.1109/TENCON.2019.8929619","url":null,"abstract":"In this paper, we propose the use of pockets to improve the performance of an electrostatically doped double-gate tunnel field effect transistor. The required n-i-p+structure for tunneling is formed on a thin intrinsic silicon film by electrostatic doping based on charge plasma concept. The source, drain and gate electrodes of different workfunctions are employed to form the p-type, n-type and intrinsic regions. Analysis using 2D TCAD simulations suggest that the proposed structure can significantly overcome the low ION and ambipolarity problem in conventional dopingless tunnel field effect transistor (DLTFET). The n-type pocket in channel and p-type pocket in source provides noteworthy improvement in the transfer characteristics of pocket engineered (PE) DLTFET. The proposed PE-DLTFET offers higher ION(2.4x10−5A), high ION/IOFF ratio ∼109and lower subthreshold slope (SS) of 42 mV/dec. We also propose an n-type pocket in the channel adjacent to the drain region to reduce the inherent ambipolarity in DLTFET. The results show that PE-DLTFET effectively suppresses ambipolarity and offers 25x higher ION as compared to DLTFET, making it a viable candidate for switching applications.","PeriodicalId":36690,"journal":{"name":"Platonic Investigations","volume":"17 1","pages":"1842-1845"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81112555","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 : 2019-10-01DOI: 10.1109/TENCON.2019.8929465
Purnendu Mishra, K. Sarawadekar
Learning rate (LR) is one of the most important hyper-parameters in any deep neural network (DNN) optimization process. It controls the speed of network convergence to the point of global minima by navigation through non-convex loss surface. The performance of a DNN is affected by presence of local minima, saddle points, etc. in the loss surface. Decaying the learning rate by a factor at fixed number of epochs or exponentially is the conventional way of varying the LR. Recently, two new approaches for setting learning rate have been introduced namely cyclical learning rate and stochastic gradient descent with warm restarts. In both of these approaches, the learning rate value is varied in a cyclic pattern between two boundary values. This paper introduces another warm restart technique which is inspired by these two approaches and it uses “poly” LR policy. The proposed technique is called as polynomial learning rate with warm restart and it requires only a single warm restart. The proposed LR policy helps in faster convergence of the DNN and it has slightly higher classification accuracy. The performance of the proposed LR policy is demonstrated on CIFAR-10, CIFAR-100 and tiny ImageNet dataset with CNN, ResNets and Wide Residual Networks (WRN) architectures.
{"title":"Polynomial Learning Rate Policy with Warm Restart for Deep Neural Network","authors":"Purnendu Mishra, K. Sarawadekar","doi":"10.1109/TENCON.2019.8929465","DOIUrl":"https://doi.org/10.1109/TENCON.2019.8929465","url":null,"abstract":"Learning rate (LR) is one of the most important hyper-parameters in any deep neural network (DNN) optimization process. It controls the speed of network convergence to the point of global minima by navigation through non-convex loss surface. The performance of a DNN is affected by presence of local minima, saddle points, etc. in the loss surface. Decaying the learning rate by a factor at fixed number of epochs or exponentially is the conventional way of varying the LR. Recently, two new approaches for setting learning rate have been introduced namely cyclical learning rate and stochastic gradient descent with warm restarts. In both of these approaches, the learning rate value is varied in a cyclic pattern between two boundary values. This paper introduces another warm restart technique which is inspired by these two approaches and it uses “poly” LR policy. The proposed technique is called as polynomial learning rate with warm restart and it requires only a single warm restart. The proposed LR policy helps in faster convergence of the DNN and it has slightly higher classification accuracy. The performance of the proposed LR policy is demonstrated on CIFAR-10, CIFAR-100 and tiny ImageNet dataset with CNN, ResNets and Wide Residual Networks (WRN) architectures.","PeriodicalId":36690,"journal":{"name":"Platonic Investigations","volume":"21 1","pages":"2087-2092"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78570514","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 : 2019-10-01DOI: 10.1109/TENCON.2019.8929243
P. Kumar, Sayali Chande, S. Sinha
Face recognition is the process of assignment of correct label to the face under consideration. Process of face recognition comprises of extraction of features from underlying face and feeding the extracted features to classifier to identify the corresponding individual. The Accuracy of classifier is greatly affected by the nature of features extracted. Conventional face recognition system relies on manually engineered, handcrafted features. Convolutional neural network is a deep learning model which automatically extract features from the raw data in the process of end to end classification. The automated feature extraction property of convolutional neural network not only saves the effort in manually extracting features but also solve the dilemma of set of features to be used for classification. In this work, we present a face recognition system based on convolutional neural network. We create our own dataset to test the efficiency of the proposed system. The accuracy of around 96% is achieved on the test dataset consisting of around 1900 images with 10 different classes.
{"title":"Convolutional neural network based face recognition approach","authors":"P. Kumar, Sayali Chande, S. Sinha","doi":"10.1109/TENCON.2019.8929243","DOIUrl":"https://doi.org/10.1109/TENCON.2019.8929243","url":null,"abstract":"Face recognition is the process of assignment of correct label to the face under consideration. Process of face recognition comprises of extraction of features from underlying face and feeding the extracted features to classifier to identify the corresponding individual. The Accuracy of classifier is greatly affected by the nature of features extracted. Conventional face recognition system relies on manually engineered, handcrafted features. Convolutional neural network is a deep learning model which automatically extract features from the raw data in the process of end to end classification. The automated feature extraction property of convolutional neural network not only saves the effort in manually extracting features but also solve the dilemma of set of features to be used for classification. In this work, we present a face recognition system based on convolutional neural network. We create our own dataset to test the efficiency of the proposed system. The accuracy of around 96% is achieved on the test dataset consisting of around 1900 images with 10 different classes.","PeriodicalId":36690,"journal":{"name":"Platonic Investigations","volume":"122 1","pages":"2525-2528"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85694322","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 : 2019-10-01DOI: 10.1109/TENCON.2019.8929247
U. Dorji, Chaiyaphum Siripanpornchana, Navaporn Surasvadi, Anon Plangprasopchok, S. Thajchayapong
This paper explores the feasibility in using Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) night light data as a proxy for approximation of socioeconomic indicators like poverty rate and income inequality. The feasibility is assessed by testing if features of night light data correlate to: 1) the rate of poverty and 2) income inequality. The poverty rate is derived from Thai People Map and Analytics Platform (TPMAP), a project initiated by the Thai government with the goal of developing a data analytics platform for precision poverty alleviation by enabling policymakers to identify the poor, locate them, and understand their basic needs. The poverty rate from TPMAP is computed using Multidimensional Poverty Index and income inequality statistics are calculated using data from three disparate sources. These two measures are used as the target of this study. We show night light data features that have high correlations with monetary based income features and moderate correlations with poverty rate at the province level of Thailand.
{"title":"Exploring Night Light as Proxy for Poverty and Income Inequality Approximation in Thailand","authors":"U. Dorji, Chaiyaphum Siripanpornchana, Navaporn Surasvadi, Anon Plangprasopchok, S. Thajchayapong","doi":"10.1109/TENCON.2019.8929247","DOIUrl":"https://doi.org/10.1109/TENCON.2019.8929247","url":null,"abstract":"This paper explores the feasibility in using Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) night light data as a proxy for approximation of socioeconomic indicators like poverty rate and income inequality. The feasibility is assessed by testing if features of night light data correlate to: 1) the rate of poverty and 2) income inequality. The poverty rate is derived from Thai People Map and Analytics Platform (TPMAP), a project initiated by the Thai government with the goal of developing a data analytics platform for precision poverty alleviation by enabling policymakers to identify the poor, locate them, and understand their basic needs. The poverty rate from TPMAP is computed using Multidimensional Poverty Index and income inequality statistics are calculated using data from three disparate sources. These two measures are used as the target of this study. We show night light data features that have high correlations with monetary based income features and moderate correlations with poverty rate at the province level of Thailand.","PeriodicalId":36690,"journal":{"name":"Platonic Investigations","volume":"45 1","pages":"1082-1087"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84061853","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 : 2019-10-01DOI: 10.1109/TENCON.2019.8929556
Siva Ramakrishna Pillutla, Lakshmi Boppana
The performance of many data security and reliability applications depends on computations in finite fields $text{GF} (2^{m})$. In finite field arithmetic, field multiplication is a complex operation and is also used in other operations such as inversion and exponentiation. By considering the application domain needs, a variety of efficient algorithms and architectures are proposed in the literature for field $text{GF} (2^{m})$ multiplier. With the rapid emergence of Internet of Things (IoT) and Wireless Sensor Networks (WSN), many resource-constrained devices such as IoT edge devices and WSN end nodes came into existence. The data bus width of these constrained devices is typically smaller. Digit-level architectures which can make use of the full data bus are suitable for these devices. In this paper, we propose a new fully digit-serial polynomial basis finite field $text{GF} (2^{m})$ multiplier where both the operands enter the architecture concurrently at digit-level. Though there are many digit-level multipliers available for polynomial basis multiplication in the literature, it is for the first time to propose a fully digit-serial polynomial basis multiplier. The proposed multiplication scheme is based on the multiplication scheme presented in the literature for a redundant basis multiplication. The proposed polynomial basis multiplication results in a high-throughput architecture. This multiplier is applicable for a class of trinomials, and this class of irreducible polynomials is highly desirable for IoT edge devices since it allows the least area and time complexities. The proposed multiplier achieves better throughput when compared with previous digit-level architectures.
{"title":"A high-throughput fully digit-serial polynomial basis finite field $text{GF}(2^{m})$ multiplier for IoT applications","authors":"Siva Ramakrishna Pillutla, Lakshmi Boppana","doi":"10.1109/TENCON.2019.8929556","DOIUrl":"https://doi.org/10.1109/TENCON.2019.8929556","url":null,"abstract":"The performance of many data security and reliability applications depends on computations in finite fields $text{GF} (2^{m})$. In finite field arithmetic, field multiplication is a complex operation and is also used in other operations such as inversion and exponentiation. By considering the application domain needs, a variety of efficient algorithms and architectures are proposed in the literature for field $text{GF} (2^{m})$ multiplier. With the rapid emergence of Internet of Things (IoT) and Wireless Sensor Networks (WSN), many resource-constrained devices such as IoT edge devices and WSN end nodes came into existence. The data bus width of these constrained devices is typically smaller. Digit-level architectures which can make use of the full data bus are suitable for these devices. In this paper, we propose a new fully digit-serial polynomial basis finite field $text{GF} (2^{m})$ multiplier where both the operands enter the architecture concurrently at digit-level. Though there are many digit-level multipliers available for polynomial basis multiplication in the literature, it is for the first time to propose a fully digit-serial polynomial basis multiplier. The proposed multiplication scheme is based on the multiplication scheme presented in the literature for a redundant basis multiplication. The proposed polynomial basis multiplication results in a high-throughput architecture. This multiplier is applicable for a class of trinomials, and this class of irreducible polynomials is highly desirable for IoT edge devices since it allows the least area and time complexities. The proposed multiplier achieves better throughput when compared with previous digit-level architectures.","PeriodicalId":36690,"journal":{"name":"Platonic Investigations","volume":"3 1","pages":"920-924"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84451980","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 : 2019-10-01DOI: 10.1109/TENCON.2019.8929342
Deepankar Nankani, R. Baruah
Electrocardiogram (ECG) inspection is performed by expert cardiologists for diagnosing cardiac diseases such as atrial fibrillation that is ubiquitous in 1–2% of the population worldwide. Prolonged presence of atrial fibrillation tends to form blood clots that travel to the brain through blood stream and cause stroke that inevitably leads to death, making its detection of utmost priority. In the past, people have developed temporal and morphological features to tackle this problem but these features are prone to rhythm changes. Very recently, deep learning methods have shown remarkable performance for better ECG classification. Hence, we aim to develop an end-to-end framework for classifying different length ECG segments into four classes namely, atrial fibrillation, normal, other and noisy rhythms using a deep residual neural network thereby eliminating the need of handcrafted features. To make the model more robust towards noise, a data augmentation technique is employed. The proposed method produced an $F_{1}$ score of $0.88 pm 0.02$ on PhysioNet/Computing in Cardiology Challenge 2017 database, which is better than existing methods in the literature.
心电图(ECG)检查由心脏病专家执行，用于诊断心脏疾病，如房颤，在全球1-2%的人口中普遍存在。房颤的长期存在往往会形成血凝块，通过血流进入大脑，导致中风，不可避免地导致死亡，因此对房颤的检测是重中之重。过去，人们开发了时间和形态特征来解决这个问题，但这些特征容易发生节奏变化。最近，深度学习方法在更好的心电分类方面表现出了显著的性能。因此，我们的目标是开发一个端到端框架，用于使用深度残差神经网络将不同长度的ECG段分为四类，即心房颤动，正常，其他和嘈杂的节律，从而消除了手工制作特征的需要。为了提高模型对噪声的鲁棒性，采用了数据增强技术。该方法在PhysioNet/Computing in Cardiology Challenge 2017数据库上产生$F_{1}$分数为$0.88 pm 0.02$，优于现有文献中的方法。
{"title":"An End-to-End framework for automatic detection of Atrial Fibrillation using Deep Residual Learning","authors":"Deepankar Nankani, R. Baruah","doi":"10.1109/TENCON.2019.8929342","DOIUrl":"https://doi.org/10.1109/TENCON.2019.8929342","url":null,"abstract":"Electrocardiogram (ECG) inspection is performed by expert cardiologists for diagnosing cardiac diseases such as atrial fibrillation that is ubiquitous in 1–2% of the population worldwide. Prolonged presence of atrial fibrillation tends to form blood clots that travel to the brain through blood stream and cause stroke that inevitably leads to death, making its detection of utmost priority. In the past, people have developed temporal and morphological features to tackle this problem but these features are prone to rhythm changes. Very recently, deep learning methods have shown remarkable performance for better ECG classification. Hence, we aim to develop an end-to-end framework for classifying different length ECG segments into four classes namely, atrial fibrillation, normal, other and noisy rhythms using a deep residual neural network thereby eliminating the need of handcrafted features. To make the model more robust towards noise, a data augmentation technique is employed. The proposed method produced an $F_{1}$ score of $0.88 pm 0.02$ on PhysioNet/Computing in Cardiology Challenge 2017 database, which is better than existing methods in the literature.","PeriodicalId":36690,"journal":{"name":"Platonic Investigations","volume":"51 1","pages":"690-695"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78289980","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 : 2019-10-01DOI: 10.1109/TENCON.2019.8929326
W. Mahdi, M. M. Shaky, N. Ashraf, Fatema Fairooz, M. Chowdhury
This study provides the response of placing a graphene layer along with plasmonic nanoparticles on top of Si substrate, which significantly increases the efficiency of the photo-voltaic (PV) cells. The advantage of using plasmonic nanoparticles has already been studied in previous studies. However, not much study has been done by using graphene layers in conjunction with plasmonic nanoparticles to improve the efficiency of solar cells. The reason behind choosing graphene is because of its high electrical and thermal conductivity, transparency, excellent flexibility, bending stability, and most importantly, thickness of one atom. Another big advantage is that graphene is inexpensive and widely available commercially. Therefore, in this study, the task was to place a layer of graphene on top of the Si substrate and at the top of this layer a single Ag nanoparticle was placed. Then, the optical absorption, short circuit current, open circuit voltage, output power, fill factor and near-field enhancementswere computed. These results due to the presence of a graphene layer below the Ag nanoparticle were compared with the results of placing Ag nanoparticle on top of the Si substrate without the presence of graphene layer. For both the cases, the diameter for Ag nanoparticle was kept at 20nm, 50nm and 100nm, respectively. After comparing these results, it was found that the use of graphene in addition to the plasmonic nanoparticles, as a layer on the Si substrate, significantly improves the efficiency of the PV cells. Further analysis was done by varying the thickness of graphene layer and it was found that the thickness of 2 nm graphene layer yields the optimum efficiency.
{"title":"Use of graphene in combination with plasmonic metal nanoparticles to enhance the opto-electronic efficiency of thin-film solar cells","authors":"W. Mahdi, M. M. Shaky, N. Ashraf, Fatema Fairooz, M. Chowdhury","doi":"10.1109/TENCON.2019.8929326","DOIUrl":"https://doi.org/10.1109/TENCON.2019.8929326","url":null,"abstract":"This study provides the response of placing a graphene layer along with plasmonic nanoparticles on top of Si substrate, which significantly increases the efficiency of the photo-voltaic (PV) cells. The advantage of using plasmonic nanoparticles has already been studied in previous studies. However, not much study has been done by using graphene layers in conjunction with plasmonic nanoparticles to improve the efficiency of solar cells. The reason behind choosing graphene is because of its high electrical and thermal conductivity, transparency, excellent flexibility, bending stability, and most importantly, thickness of one atom. Another big advantage is that graphene is inexpensive and widely available commercially. Therefore, in this study, the task was to place a layer of graphene on top of the Si substrate and at the top of this layer a single Ag nanoparticle was placed. Then, the optical absorption, short circuit current, open circuit voltage, output power, fill factor and near-field enhancementswere computed. These results due to the presence of a graphene layer below the Ag nanoparticle were compared with the results of placing Ag nanoparticle on top of the Si substrate without the presence of graphene layer. For both the cases, the diameter for Ag nanoparticle was kept at 20nm, 50nm and 100nm, respectively. After comparing these results, it was found that the use of graphene in addition to the plasmonic nanoparticles, as a layer on the Si substrate, significantly improves the efficiency of the PV cells. Further analysis was done by varying the thickness of graphene layer and it was found that the thickness of 2 nm graphene layer yields the optimum efficiency.","PeriodicalId":36690,"journal":{"name":"Platonic Investigations","volume":"51 1","pages":"197-202"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72896191","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 : 2019-10-01DOI: 10.1109/TENCON.2019.8929320
K. Kaur, T. Upadhyaya
Design and analysis of ultrathin wideband nontransparent, semi-transparent and optically transparent microwave metamaterial absorbers have been reported in this article. Non-transparent absorber composed of FR4 dielectric substrate has been fabricated and experimentally verified. Transparent and semi-transparent absorbers are developed by utilizing AgHT-8 conducting sheet and copper sheet, respectively. Both the absorbers are analyzed and compared for different substrates, viz. glass and PET. The FWHM bandwidth of about 25.8% has been observed for nontransparent absorber which is ranging from 2.14 GHz to 2.77 GHz. Under normal incidence, simulated −10dB absorption bandwidth of about 17.3% has been attained from 2.223 GHz to 2.645 GHz covering S-band frequencies. Standard printed circuit board technique is utilized for fabrication and the absorber prototype is verified by waveguide measurement method. The measured and simulated results are in agreement. The proposed non-transparent absorber has ultrathin thickness of $boldsymbol{lambda}_{0}/40$ and is compact with cell size of $0.21boldsymbol{lambda}_{0}$ at the lower absorption frequency of 2.28 GHz.
{"title":"Design and Analysis of Wideband Non-Transparent and Optically Transparent Microwave Metamaterial Absorbers","authors":"K. Kaur, T. Upadhyaya","doi":"10.1109/TENCON.2019.8929320","DOIUrl":"https://doi.org/10.1109/TENCON.2019.8929320","url":null,"abstract":"Design and analysis of ultrathin wideband nontransparent, semi-transparent and optically transparent microwave metamaterial absorbers have been reported in this article. Non-transparent absorber composed of FR4 dielectric substrate has been fabricated and experimentally verified. Transparent and semi-transparent absorbers are developed by utilizing AgHT-8 conducting sheet and copper sheet, respectively. Both the absorbers are analyzed and compared for different substrates, viz. glass and PET. The FWHM bandwidth of about 25.8% has been observed for nontransparent absorber which is ranging from 2.14 GHz to 2.77 GHz. Under normal incidence, simulated −10dB absorption bandwidth of about 17.3% has been attained from 2.223 GHz to 2.645 GHz covering S-band frequencies. Standard printed circuit board technique is utilized for fabrication and the absorber prototype is verified by waveguide measurement method. The measured and simulated results are in agreement. The proposed non-transparent absorber has ultrathin thickness of $boldsymbol{lambda}_{0}/40$ and is compact with cell size of $0.21boldsymbol{lambda}_{0}$ at the lower absorption frequency of 2.28 GHz.","PeriodicalId":36690,"journal":{"name":"Platonic Investigations","volume":"76 1","pages":"1892-1897"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79962939","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 : 2019-10-01DOI: 10.1109/TENCON.2019.8929309
A. Salam, M. Mahadevappa
A single image super-resolution technique is used to generate a high-resolution image from a low-resolution image by combining Non-subsampled contourlet transform and example based learning method. Sets of filters are learnt at coarse and fine scale from training dataset to produce an image of higher quality. The proposed method shows improvement both quantitatively and qualitatively in generating images of higher quality. The PSNR values have improved by a mean value of 3.64 dB when compared with bicubic interpolation and a mean value of 1.42 dB when compared with a state of the art method.
{"title":"Single Image Super-Resolution Technique using Precision learning of Low Resolution Images","authors":"A. Salam, M. Mahadevappa","doi":"10.1109/TENCON.2019.8929309","DOIUrl":"https://doi.org/10.1109/TENCON.2019.8929309","url":null,"abstract":"A single image super-resolution technique is used to generate a high-resolution image from a low-resolution image by combining Non-subsampled contourlet transform and example based learning method. Sets of filters are learnt at coarse and fine scale from training dataset to produce an image of higher quality. The proposed method shows improvement both quantitatively and qualitatively in generating images of higher quality. The PSNR values have improved by a mean value of 3.64 dB when compared with bicubic interpolation and a mean value of 1.42 dB when compared with a state of the art method.","PeriodicalId":36690,"journal":{"name":"Platonic Investigations","volume":"200 1","pages":"306-310"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76024387","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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