New CMOS realization of Current Differencing Current Conveyor (CDCC) with biquad filter application

Feb 28, 2014

publisher IEEE (Institute of Electrical and Electronics Engineers)

DOI 10.1109/LASCAS.2014.6820248

In this paper, a new CMOS realization of Current Differencing Current Conveyor (CDCC) is proposed. Furthermore, to demonstrate the performance of the CMOS circuit, a new biquad filter configuration is introduced employing a single CDCC and four passive components. The proposed filter has three-inputs, a singleoutput and realizes current-mode low-pass, band-pass, high-pass, and band-stop filter functions. The biquad filters realizing these filter functions are simulated using TSMC CMOS 0.35 μm technology. PSPICE simulation results agree well with the theoretical analysis.

Study of Substrate-Materials Impact on Compact Antenna Performance (Conference Paper)

Oct 22, 2022

DOI 10.1109/ISMSIT56059.2022.9932836

Improving bandwidth and reducing size are significant design aspects for practical compact antenna applications. To reach wideband and decrease the scale of microstrip antennas, numerous strategies were brought into practical action. This article demonstrates a survey of various miniaturization strategies. It analyzes the effectiveness of the compact and miniaturized multi-use antennas in terms of their size, forms of substrate material, conductivity, loss tangent, and thickness that can influence the bandwidth, gain, radiation efficiency, Specific absorption rate (SAR) value antenna type, feeding techniques, and further significant parameters. Noteworthy, the most popular technique is to use a high dielectric constant for its substratum content to reduce the size of a printed antenna. Thus, the directed wavelength under the patch and the resonant patch size can be decreased correspondingly.

Detecting fake news spreaders on twitter

Dec 2, 2023

Journal Indonesian Journal of Electrical Engineering and Computer Science

DOI 10.11591/IJEECS.V32.I3.PP1648-1654

Volume 32

Nowadays, fake news is prevalent and too simple to propagate through social media, particularly during elections and pandemics like COVID-19. Several fake news stories have appeared on social media sites like LINE, Facebook, and Twitter after the COVID-19 epidemic throughout the world. Also, a lot of older individuals simply forward these communications without checking their veracity, which speeds up the dissemination of fake information. So, our goal is to identify fake news using machine learning. In this paper, we describe a supervised method that automatically gathers a sizable but noisy training dataset made up of a significant number of tweets. We will categorize tweets during collection into trustworthy and untrustworthy sources, then using the dataset to train a classifier. The categorization of fake and real tweets is the next classification objective for which we apply that classifier. We first demonstrate that real news is larger in size, shared on Twitter for a longer length of time, and shared by people with more followers than following. Second, we employed machine learning models like support vector machine (SVM), random forest (RF), and decision tree (DT), and we found out that the SVM is the best of all the models due to its best results and 99% accuracy.