5G F-OFDM Waveform Based Software-Defined Radio Technology

Filtered-orthogonal frequency division multiplexing (F-OFDM) is a quasi-orthogonal waveform candidate for the applications of the fifth generation (5G) communication system. In this study, an F-OFDM waveform with unequal sub-band sizes is proposed to improve the spectrum efficiency (SE) of the 5G system. The proposed waveform is modeled with the Blackman window-sinc filter and is developed based on the software-defined radio (SDR) technology for practical implementation. The result shows that the F-OFDM performance of the simulation and hardware implementation is approximately the same. The SE using the proposed F-OFDM waveform is 6% and 5.8% higher than the SE using the conventional OFDM waveform under the simulation in the LabVIEW NXG simulator and under the practical use in the universal software radio peripheral (USRP) platform, respectively.

A Method Based on Only Currents for Determining Fault Direction in Radial Distribution Networks Integrated with Distributed Generations

Nowadays, more distributed generations (DGs) are connected to a radial distribution network, so conventional overcurrent relays cannot operate correctly when a fault occurs in the network. This study proposes a method to determine the fault direction in a three-phase distribution network integrated with DGs. The obtained pre-fault and fault currents are utilized to extract their phasors by the fast Fourier transform, and the phase angle difference between the positive-sequence components of the pre-fault and fault currents is used. Moreover, the method only uses the local current measurement to calculate and identify the phase angle change of the fault current without using the voltage measurement. Matlab/Simulink software is used to simulate the three-phase distribution network integrated with DGs. The faults with different resistances are assumed to occur at backward and forward fault locations. The simulation results show that the proposed method correctly determines the fault direction.

Dynamic Power Management Model for a Wireless Sensor Node

Dynamic power management (DPM) is an efficient technique to design low-power and energy-efficient nodes for wireless sensor networks. This article demonstrates the stochastic behaviour of an input event arrival which is modelled with first-in first-out (FIFO) queue and a single server. An event-driven sensor node is developed based on semi-Markov model. The article investigates the factors affecting the performance of the individual sensor node with detailed analysis considering power consumption and lifetime to be the performance metrics under study. The results demonstrate the impact of the change in event arrival and the probability of change detection on the performance of the node. It is observed that (i) the number of generated events increases with the change in the average value of the distribution which affects the service time in turn resulting in a variation of the server utilization, and that (ii) the increase in the detection probability increases the power consumption decreasing the lifetime of the node.

Closed Loop V/f Speed Control of Multilevel Inverter Fed Induction Motors Using PID and Fuzzy Logic Controllers

The Volts per Hertz (V/f) speed control method makes induction motors available to be used in variable speed applications. However, the great harmonic distortion in supply voltage is a major issue of this method. Therefore, this study focuses on decreasing the harmonic distortion in the supply to induction motors. For that, the use of thirty-one-level multilevel inverters (MLIs) is investigated. Another drawback of the V/f speed control method is the use of proportional-integral-derivative (PID) controllers as speed controllers, which are difficult to be tuned precisely. To overcome this, the use of fuzzy logic controllers is investigated. The whole model is developed and simulated in MATLAB/SIMULINK by varying two variables, i.e., reference speed and load torque, one at a time using each controller. The observations for the changes in speed, torque, stator current, and supply voltage to the induction motors are made. The system’s performance using fuzzy logic controllers is found superior.

Supervised Learning Based Classification of Cardiovascular Diseases

Detecting cardiovascular disease (CVD) in the early stage is a difficult and crucial process. The objective of this study is to test the capability of machine learning (ML) methods for accurately diagnosing the CVD outcomes. For this study, the efficiency and effectiveness of four well renowned ML classifiers, i.e., support vector machine (SVM), logistics regression (LR), naive Bayes (NB), and decision tree (J48), are measured in terms of precision, sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), correctly and incorrectly classified instances, and model building time. These ML classifiers are applied on publically available CVD dataset. In accordance with the measured result, J48 performs better than its competitor classifiers, providing significant assistance to the cardiologists.

Artifact Removal Methods in EEG Recordings: A Review

To obtain the correct analysis of electroencephalogram (EEG) signals, non-physiological and physiological artifacts should be removed from EEG signals. This study aims to give an overview on the existing methodology for removing physiological artifacts, e.g., ocular, cardiac, and muscle artifacts. The datasets, simulation platforms, and performance measures of artifact removal methods in previous related research are summarized. The advantages and disadvantages of each technique are discussed, including regression method, filtering method, blind source separation (BSS), wavelet transform (WT), empirical mode decomposition (EMD), singular spectrum analysis (SSA), and independent vector analysis (IVA). Also, the applications of hybrid approaches are presented, including discrete wavelet transform - adaptive filtering method (DWT-AFM), DWT-BSS, EMD-BSS, singular spectrum analysis - adaptive noise canceler (SSA-ANC), SSA-BSS, and EMD-IVA. Finally, a comparative analysis for these existing methods is provided based on their performance and merits. The result shows that hybrid methods can remove the artifacts more effectively than individual methods.

Fish Species Detection Application (FiSDA) in Leyte Gulf Using Convolutional Neural Network

This study presents an application that employs a machine-learning algorithm to identify fish species found in Leyte Gulf. It aims to help students and marine scientists with their identification and data collection. The application supports 467 fish species in which 6,918 fish images are used for training, validating, and testing the generated model. The model is trained for a total of 4,000 epochs. Using convolutional neural network (CNN) algorithm, the best model during training is observed at epoch 3,661 with an accuracy rate of 96.49% and a loss value of 0.1359. It obtains 82.81% with a loss value of 1.868 during validation and 80.58% precision during testing. The result shows that the model performs well in predicting Malatindok and Sapsap species, after obtaining the highest precision of 100%. However, Hangit is sometimes misclassified by the model after attaining 55% accuracy rate from the testing results because of its feature similarity to other species.

The Effect of Band-Gap on TiO2 Thin Film Considering Various Parameters

The aim of this work is to measure the effect of band-gap on TiO2 thin films by changing tetrabutylorthotitanate (TBOT), diethanolamine (DEA), and temperature. The sol-gel method is experimentally introduced to find out the better band-gap of TiO2 thin films by varying the concentration of TBOT (4 ml to 10 ml), DEA (2 ml to 5 ml), and temperature (350°C to 650°C). With the help of an ultraviolet-visible spectrophotometer for the wavelength of 300-900 nm, these thin films are characterized concerning optical properties (transmittance spectra, absorbance spectra, direct band-gap, and indirect band-gap). The direct and indirect band-gaps are found 3.38 eV and 3.25 eV respectively, which are close to or within the standard band-gap range of TiO2 (3.2 eV to 3.35 eV) and are found at 8 ml TBOT, 3 ml DEA, and a temperature of 550°C.

Hybrid Speed Controller Design Based on Sliding Mode Controller Performance Study for Vector Controlled Induction Motor Drives

The discontinuous control of the sliding mode control (SMC) law causes chattering phenomenon in system trajectories (the oscillation around the desired value), which results in various unwanted effects such as current harmonics and torque ripples. Therefore, this study aims to investigate the performance of a sliding mode speed controller for a three-phase induction motor (IM) controlled by a rotor flux orientation technique to obtain optimum performance. The study results show that the experimental control gains found in the control law have a clear effect on limiting chattering and the system response speed. According to the study results, a hybrid controller is designed based on the fuzzy logic control (FLC) approach to optimally tune these gains. The designed hybrid controller is verified by experimental approximation of simulations using Matlab/Simulink. The simulation results show that the hybrid controller reduces the chattering phenomenon and improves the system’s dynamic performance.

Low Profile Wideband Dual-Ring Slot Antenna for Biomedical Applications

In this study, a low-profile, co-planar waveguide (CPW) fed, wideband, and dual-ring slot antenna design for biomedical applications is proposed. The proposed antenna has a total area of 10 mm × 10 mm and a height of 0.4 mm, and is designed by using a thin and biocompatible FR4 epoxy (εr = 4.4) substrate to accomplish human body isolation and great flexibility obtained by implantation. This wideband antenna covers a large bandwidth of industrial scientific and medical (ISM) frequency band, including 902.8 MHz to 928 MHz, 1.395 GHz to 1.4 GHz, 1.427 GHz to 1.432 GHz, 2.4 GHz to 2.485 GHz, and above. The simulation results of return loss, voltage standing wave ratio (VSWR), impedance matching, gain, and radiation pattern of the proposed antenna are obtained through High Frequency Structure Simulator (HFSS) 14 software.