Pub Date : 2022-10-14DOI: 10.1109/IET-ICETA56553.2022.9971596
Paolo Joshua R. Billones, Dailyne D. Macasaet, Shearyl U. Arenas
This study aims to mitigate the absorption of fraudulent news by exploring the feasibility of using Naive Bayes and SGD classifier models in predicting whether the English or Filipino article is real or fake. This is accomplished by training the models through large pre-processed datasets. After evaluation, both models have achieved an accuracy of 93% and 95% accuracy respectively.
{"title":"Bilingual Fake News Detection Algorithm Using Naïve Bayes and Support Vector Machine Models","authors":"Paolo Joshua R. Billones, Dailyne D. Macasaet, Shearyl U. Arenas","doi":"10.1109/IET-ICETA56553.2022.9971596","DOIUrl":"https://doi.org/10.1109/IET-ICETA56553.2022.9971596","url":null,"abstract":"This study aims to mitigate the absorption of fraudulent news by exploring the feasibility of using Naive Bayes and SGD classifier models in predicting whether the English or Filipino article is real or fake. This is accomplished by training the models through large pre-processed datasets. After evaluation, both models have achieved an accuracy of 93% and 95% accuracy respectively.","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":"13 1","pages":"1-2"},"PeriodicalIF":1.4,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73372842","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}
RF-DC converter integrated circuits (ICs) are presented for RF energy harvesting and power charging. To achieve wide incident RF signal variations that sketch the directing frequency band, an adaptive impedance matching network is used. The proposed RF signal to DC converter is fabricated in 0.18-um CMOS process. The simulated performances present the proposed circuit achieves Peak Power Converting Efficiency (PPCE) of 27% at 0 dBm input power, across 50 k$Omega$ load resistance and 1 pF load capacitance and can provide an output voltage of higher than 2 V.
提出了一种用于射频能量采集和功率充电的RF- dc变换器集成电路。为了实现宽入射射频信号的变化，勾画了指导频带，自适应阻抗匹配网络被使用。所提出的射频信号到直流转换器采用0.18 μ m CMOS工艺制作。仿真结果表明，该电路在0 dBm输入功率下，负载电阻为50 k$Omega$，负载电容为1 pF，峰值功率转换效率(PPCE)为27%，输出电压高于2 V。
{"title":"An RF-DC Converter IC for Power Charging Application","authors":"Pin-You Chen, Bo-Yuan Chen, Chia-Hung Chang, Jheng-Yu Cheng, Syuan-Sou Chen, Meng-Man Yang, Wei-Wen Hu","doi":"10.1109/IET-ICETA56553.2022.9971475","DOIUrl":"https://doi.org/10.1109/IET-ICETA56553.2022.9971475","url":null,"abstract":"RF-DC converter integrated circuits (ICs) are presented for RF energy harvesting and power charging. To achieve wide incident RF signal variations that sketch the directing frequency band, an adaptive impedance matching network is used. The proposed RF signal to DC converter is fabricated in 0.18-um CMOS process. The simulated performances present the proposed circuit achieves Peak Power Converting Efficiency (PPCE) of 27% at 0 dBm input power, across 50 k$Omega$ load resistance and 1 pF load capacitance and can provide an output voltage of higher than 2 V.","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":"195 12","pages":"1-2"},"PeriodicalIF":1.4,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72438022","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 : 2022-10-14DOI: 10.1109/IET-ICETA56553.2022.9971683
Yu-sheng Tsao, Berlin Chen, J. Hung
The highly effective deep learning-based technique FullSubNet+ employs a full-band and sub-band fusion model to fulfill the speech enhancement task. FullSubNet+ exploits the short-time magnitude spectrogram, real-and imaginary parts of the complex-valued spectrogram to learn the deep neural network that mainly comprises multi-scale time-sensitive channel attention (MulCA) modules and stacked temporal convolution network (TCN) blocks. To capture the phase information of input time-domain signals more simply, we propose using the short-time DCT-based spectrogram as an alternative for the real and imaginary spectrograms to be an input source to learn the FullSubNet+ framework. The preliminary experiments conducted with the VoiceBank-DEMAND task indicate that exploiting STDCT spectrograms in FullSubNet+ achieves higher objective speech quality and intelligibility in terms of PESQ and STOI metric scores, respectively, for the test set compared with the original FullSubNet+ arrangement. In addition, the STDCT-wise FullSubNet+ obtains a real-time factor (RTF) of 0.229, lower than 0.260, the RTF for the original FullSubNet+.
{"title":"Exploiting Discrete Cosine Transform Features in Speech Enhancement Technique FullSubNet+","authors":"Yu-sheng Tsao, Berlin Chen, J. Hung","doi":"10.1109/IET-ICETA56553.2022.9971683","DOIUrl":"https://doi.org/10.1109/IET-ICETA56553.2022.9971683","url":null,"abstract":"The highly effective deep learning-based technique FullSubNet+ employs a full-band and sub-band fusion model to fulfill the speech enhancement task. FullSubNet+ exploits the short-time magnitude spectrogram, real-and imaginary parts of the complex-valued spectrogram to learn the deep neural network that mainly comprises multi-scale time-sensitive channel attention (MulCA) modules and stacked temporal convolution network (TCN) blocks. To capture the phase information of input time-domain signals more simply, we propose using the short-time DCT-based spectrogram as an alternative for the real and imaginary spectrograms to be an input source to learn the FullSubNet+ framework. The preliminary experiments conducted with the VoiceBank-DEMAND task indicate that exploiting STDCT spectrograms in FullSubNet+ achieves higher objective speech quality and intelligibility in terms of PESQ and STOI metric scores, respectively, for the test set compared with the original FullSubNet+ arrangement. In addition, the STDCT-wise FullSubNet+ obtains a real-time factor (RTF) of 0.229, lower than 0.260, the RTF for the original FullSubNet+.","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":"29 1","pages":"1-2"},"PeriodicalIF":1.4,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73174333","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 : 2022-10-14DOI: 10.1109/IET-ICETA56553.2022.9971575
Jia-Min Chiau, Min‐Hua Ho, W. Lai
This letter presents a 5.2 GHz differential mixer design that uses MOS switch and differential CS amplifier. The fully integrated mixer is fabricated by the tsmc 0. 1S$mu$m BiCMOS process with its IIP3 of -13dBm, conversion gain of 15 dB, and the radio frequency (RF) and local oscillator (LO) to an intermediate frequency (IF) isolation of 15S and 139 dB, respectively. Overall chipset consumes 30. SmW with a supply voltage of 1.SV.
{"title":"A 5.2 GHz Differential Down Conversion Mixer Design","authors":"Jia-Min Chiau, Min‐Hua Ho, W. Lai","doi":"10.1109/IET-ICETA56553.2022.9971575","DOIUrl":"https://doi.org/10.1109/IET-ICETA56553.2022.9971575","url":null,"abstract":"This letter presents a 5.2 GHz differential mixer design that uses MOS switch and differential CS amplifier. The fully integrated mixer is fabricated by the tsmc 0. 1S$mu$m BiCMOS process with its IIP3 of -13dBm, conversion gain of 15 dB, and the radio frequency (RF) and local oscillator (LO) to an intermediate frequency (IF) isolation of 15S and 139 dB, respectively. Overall chipset consumes 30. SmW with a supply voltage of 1.SV.","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":"22 1","pages":"1-2"},"PeriodicalIF":1.4,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84477170","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 : 2022-10-14DOI: 10.1109/IET-ICETA56553.2022.9971646
Hsin-Liang Chen, Yin-Qin Ye, Jen-Shiun Chiang
A magnitude to digital converter is proposed using a latch-type comparator to replace the conventional opamp-based comparator. The PVT-dependent timing error can be relieved by employing the latch-type comparator and rearranging the decision control circuits. Besides, the power efficiency can be improved within the low and high speed operations. For increasing the linearity of the converting process, a dynamic current source is also developed to obtain the best coefficient of determination. A prototype of 10-bit converter was designed to operate at 40-kS/s with only 56.S-$mu$W of power dissipations, respectively.
{"title":"Magnitude to Digital Converter with Latch-Type Comparator and Dynamic Switching Current Scheme","authors":"Hsin-Liang Chen, Yin-Qin Ye, Jen-Shiun Chiang","doi":"10.1109/IET-ICETA56553.2022.9971646","DOIUrl":"https://doi.org/10.1109/IET-ICETA56553.2022.9971646","url":null,"abstract":"A magnitude to digital converter is proposed using a latch-type comparator to replace the conventional opamp-based comparator. The PVT-dependent timing error can be relieved by employing the latch-type comparator and rearranging the decision control circuits. Besides, the power efficiency can be improved within the low and high speed operations. For increasing the linearity of the converting process, a dynamic current source is also developed to obtain the best coefficient of determination. A prototype of 10-bit converter was designed to operate at 40-kS/s with only 56.S-$mu$W of power dissipations, respectively.","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":"206 1","pages":"1-2"},"PeriodicalIF":1.4,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76659624","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 : 2022-10-14DOI: 10.1109/IET-ICETA56553.2022.9971631
Xin-Yu Shih, Yao Lu
In this paper, we propose a systematic genetic-algorithm-based feature reduction method. It has a high design flexibility based on 5-tuple parameter adjustment. The users can decide these 5 parameters to satisfy the demands of making the focus on accuracy or reduced feature amount. The proposed algorithm is verified by decision-tree models with different data sets. As for the data set, ala, the number of features is reduced from 123 to 53 while the accuracy performance has an increase of 4.2%. In addition, for other data sets, the maximum accuracy loss is no more than 3.1% while the feature reduction ratio achieves 41.9%. Its advantage is to provide a design trade-off between accuracy and reduced feature amount.
{"title":"Systematic and Flexible Genetic-Algorithm-Based Feature Reduction for Decision Tree ML-Validation","authors":"Xin-Yu Shih, Yao Lu","doi":"10.1109/IET-ICETA56553.2022.9971631","DOIUrl":"https://doi.org/10.1109/IET-ICETA56553.2022.9971631","url":null,"abstract":"In this paper, we propose a systematic genetic-algorithm-based feature reduction method. It has a high design flexibility based on 5-tuple parameter adjustment. The users can decide these 5 parameters to satisfy the demands of making the focus on accuracy or reduced feature amount. The proposed algorithm is verified by decision-tree models with different data sets. As for the data set, ala, the number of features is reduced from 123 to 53 while the accuracy performance has an increase of 4.2%. In addition, for other data sets, the maximum accuracy loss is no more than 3.1% while the feature reduction ratio achieves 41.9%. Its advantage is to provide a design trade-off between accuracy and reduced feature amount.","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":"31 2","pages":"1-2"},"PeriodicalIF":1.4,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72614434","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 : 2022-10-14DOI: 10.1109/IET-ICETA56553.2022.9971676
Mu-Yen Chen, Hsiu-Sen Chiang, Chih-Yung Chang
In recent years, renewable energy power generation has received more and more attention. Since the forecast of electricity generation is helpful for properly using and managing electricity. Therefore, this study uses time series analysis and deep learning methods, Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Gated Recurrent Unit (GRU), to forecast solar power generation. Furthermore, this study also uses different time intervals (every ten minutes, every thirty minutes, hourly, daily) to forecast the power generation and evaluate their performances. In comparing the four deep learning models, the prediction performance of LSTM is the best, while the performance of the TCN model is poor. In addition, the time interval length greatly influences the prediction performance. The time interval is divided into smaller, and the performance of various deep learning models is relatively good and stable; otherwise, the performance of the models is poor.
{"title":"Solar Photovoltaic Power Generation Prediction based on Deep Learning Methods","authors":"Mu-Yen Chen, Hsiu-Sen Chiang, Chih-Yung Chang","doi":"10.1109/IET-ICETA56553.2022.9971676","DOIUrl":"https://doi.org/10.1109/IET-ICETA56553.2022.9971676","url":null,"abstract":"In recent years, renewable energy power generation has received more and more attention. Since the forecast of electricity generation is helpful for properly using and managing electricity. Therefore, this study uses time series analysis and deep learning methods, Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Gated Recurrent Unit (GRU), to forecast solar power generation. Furthermore, this study also uses different time intervals (every ten minutes, every thirty minutes, hourly, daily) to forecast the power generation and evaluate their performances. In comparing the four deep learning models, the prediction performance of LSTM is the best, while the performance of the TCN model is poor. In addition, the time interval length greatly influences the prediction performance. The time interval is divided into smaller, and the performance of various deep learning models is relatively good and stable; otherwise, the performance of the models is poor.","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":"5 1","pages":"1-2"},"PeriodicalIF":1.4,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72703858","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}
In recent years, electrocardiogram measuring instruments have been developing towards miniaturization, which has also promoted the development of miniaturization of circuits and low power consumption. FIR filters are suitable for filtering out noise and revealing the obvious characteristic waveforms in an electrocardiogram, but they have high power consumption. Therefore, under the premise of maintaining stable performance and the advantages of low power consumption, this paper proposed a new architecture named the compass form FIR filter. When the high order number M = 256, the power consumption of the linear phase form was 28.2112 mW, while the power consumption of the compass form FIR was 24.0749 mW. The power consumption of the compass FIR was lower than that of the linear phase form FIR, with a difference of 4.1363 mW. Therefore, the compass form FIR filter designed in this paper had the advantages of low power consumption and high filtering performance under set conditions and high order specifications.
{"title":"Design of a Low-power Compass FIR Filter for Electrocardiogram Signals Noise Removal","authors":"Kuang-Hao Lin, Guan-Zhou Lai, Bowen Peng, Ping-Seng Tseng","doi":"10.1109/IET-ICETA56553.2022.9971525","DOIUrl":"https://doi.org/10.1109/IET-ICETA56553.2022.9971525","url":null,"abstract":"In recent years, electrocardiogram measuring instruments have been developing towards miniaturization, which has also promoted the development of miniaturization of circuits and low power consumption. FIR filters are suitable for filtering out noise and revealing the obvious characteristic waveforms in an electrocardiogram, but they have high power consumption. Therefore, under the premise of maintaining stable performance and the advantages of low power consumption, this paper proposed a new architecture named the compass form FIR filter. When the high order number M = 256, the power consumption of the linear phase form was 28.2112 mW, while the power consumption of the compass form FIR was 24.0749 mW. The power consumption of the compass FIR was lower than that of the linear phase form FIR, with a difference of 4.1363 mW. Therefore, the compass form FIR filter designed in this paper had the advantages of low power consumption and high filtering performance under set conditions and high order specifications.","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":"18 1","pages":"1-2"},"PeriodicalIF":1.4,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74974367","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 : 2022-10-14DOI: 10.1109/IET-ICETA56553.2022.9971539
S. Young, Yu-Jhih Chu
In this research, Zinc Oxide nanorod (ZnO NRs) ultraviolet photodetectors (UV PDs) were synthesized through hydrothermal method and fabricated on glass substrate. The material analysis revealed the surface morpholofy and optical properties of the ZnO nanorod. Results from I-V characteristics displayed that ZnO nanorod UV PDs have great sensitivity and stability. This research approve that ZnO NRs could be a good option for develop UV PDs.
{"title":"Ultraviolet photodetector based on ZnO nanorod by hydrothermal method","authors":"S. Young, Yu-Jhih Chu","doi":"10.1109/IET-ICETA56553.2022.9971539","DOIUrl":"https://doi.org/10.1109/IET-ICETA56553.2022.9971539","url":null,"abstract":"In this research, Zinc Oxide nanorod (ZnO NRs) ultraviolet photodetectors (UV PDs) were synthesized through hydrothermal method and fabricated on glass substrate. The material analysis revealed the surface morpholofy and optical properties of the ZnO nanorod. Results from I-V characteristics displayed that ZnO nanorod UV PDs have great sensitivity and stability. This research approve that ZnO NRs could be a good option for develop UV PDs.","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":"6 1","pages":"1-2"},"PeriodicalIF":1.4,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75852325","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}
To deal with the problems in the nonlinear system, the kernel adaptive filter (KAF) was proposed by processing data in the reproducing kernel Hilbert space (RKHS). However, the kernel method dramatically improves the amount of calculation of the filter, which limits its application in practical problems. Furthermore, a critical factor in a large amount of KAF computation is due to its slow convergence speed, which requires a large amount of training data to participate in the calculation. If we can accelerate the convergence speed of KAF, the amount of training data can be reduced, thereby reducing the amount of KAF computation. This paper proposes a fast kernel least mean square algorithm (FAST-KLMS) by adaptively updating step size to address this issue. To verify the superiority of FAST-KLMS, we have applied it to the simulations of nonlinear channel equalization. The simulation results show that FAST-KLMS needs less training data to complete the convergence, which has improved the filtering performance of KAF.
{"title":"A Fast Kernel Least Mean Square Algorithm","authors":"Yijie Tang, Hailong Yan, Jialong Tang, Ying-Ren Chien","doi":"10.1109/IET-ICETA56553.2022.9971688","DOIUrl":"https://doi.org/10.1109/IET-ICETA56553.2022.9971688","url":null,"abstract":"To deal with the problems in the nonlinear system, the kernel adaptive filter (KAF) was proposed by processing data in the reproducing kernel Hilbert space (RKHS). However, the kernel method dramatically improves the amount of calculation of the filter, which limits its application in practical problems. Furthermore, a critical factor in a large amount of KAF computation is due to its slow convergence speed, which requires a large amount of training data to participate in the calculation. If we can accelerate the convergence speed of KAF, the amount of training data can be reduced, thereby reducing the amount of KAF computation. This paper proposes a fast kernel least mean square algorithm (FAST-KLMS) by adaptively updating step size to address this issue. To verify the superiority of FAST-KLMS, we have applied it to the simulations of nonlinear channel equalization. The simulation results show that FAST-KLMS needs less training data to complete the convergence, which has improved the filtering performance of KAF.","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":"3 1","pages":"1-2"},"PeriodicalIF":1.4,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79087442","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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