Journal of ICT Research and Applications
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    359 research outputs found

    Cover JICTRA Vol. 15 No. 1, 2021

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    An Adaptive Common Control Channel MAC with Transmission Opportunity in IEEE 802.11ac

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    Spectral utilization is a major challenge in wireless ad hoc networks due in part to using limited network resources. For ad hoc networks, the bandwidth is shared among stations that can transmit data at any point in time. It  is important to maximize the throughput to enhance the network service. In this paper, we propose an adaptive multi-channel access with transmission opportunity protocol for multi-channel ad hoc networks, called AMCA-TXOP. For the purpose of coordination, the proposed protocol uses an adaptive common control channel over which the stations negotiate their channel selection based on the entire available bandwidth and then switch to the negotiated channel. AMCA-TXOP requires a single radio interface so that each station can listen to the control channel, which can overhear all agreements made by the other stations. This allows parallel transmission to multiple stations over various channels, prioritizing data traffic to achieve the quality-of-service requirements. The proposed approach can work with the 802.11ac protocol, which has expanded the bandwidth to 160 MHz by channel bonding. Simulations were conducted to demonstrate the throughput gains that can be achieved using the AMCA-TXOP protocol. Moreover, we compared our protocol with  the IEEE 802.11ac standard protocols

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    Extraction of the Major Features of Brain Signals using Intelligent Networks

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    The brain-computer interface is considered one of the main tools for implementing and designing smart medical software. The analysis of brain signal data, called EEG, is one of the main tasks of smart medical diagnostic systems. While EEG signals have many components, one of the most important brain activities pursued is the P300 component. Detection of this component can help detect abnormalities and visualize the movement of organs of the body. In this research, a new method for processing EEG signals is proposed with the aim of detecting the P300 component. Major features were extracted from the BCI Competition IV EEG data set in a number of steps, i.e. normalization with the purpose of noise reduction using a median filter, feature extraction using a recurrent neural network, and classification using Twin Support Vector Machine. Then, a series of evaluation criteria were used to validate the proposed approach and compare it with similar methods. The results showed that the proposed approach has high accuracy

    Revealing the Characteristics of Balinese Dance Maestros by Analyzing Silhouette Sequence Patterns Using Bag of Visual Movement with HoG and SIFT Features

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    The aim of this research was to reveal and explore the characteristics of Balinese dance maestros by analyzing silhouette sequence patterns of Balinese dance movements. A method and complete scheme for the extraction and construction of silhouette features of Balinese dance movements are proposed to enable performing quantitative analysis of Balinese dance movement patterns. Two different feature extraction methods, namely the Histogram of Gradient (HoG) feature and the Scale Invariant Features Transform (SIFT) descriptor, were used to build the final feature, called the Bag of Visual Movement (BoVM) feature. This research also makes a technical contribution with the proposal of quantifying measures to analyze the movement patterns of Balinese dances and to create the profile and characteristics of dance maestros/creators. Eight Balinese dances from three different Balinese dance maestros were analyzed in this work. Based on the experimental results, the proposed method was able to visually detect and extract patterns from silhouette sequences of Balinese dance movements. Quantitatively, the pattern measures for profiling of Balinese dances and maestros revealed a number of significant characteristics of different dances and different maestros

    Design and Implementation of Triple Band Half Mode Substrate Integrated Waveguide (HMSIW) Antenna with Compact Size

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    This study investigated structure strategies and exploratory scenarios for a half mode substrate integrated waveguide (HMSIW) antenna. The proposed antenna consists of three Hilbert cells, which are simulated by using CST programming. The antenna was manufactured with the realities of minor imperfections and high incorporation. The proposed structure offers a suitable substrate integrated waveguide (SIW) with about a decrease in size by half. In addition, Hilbert cells were added to realize the triple-band characteristics with good impedance matching, radiation patterns, and radiation performance. The antenna was fabricated on h = 1 mm thick dielectric substrate with dielectric constant ( = 4.3). The Hilbert cells were drilled on the top plane of the antenna substrate and fed using a microstrip transmission line. The proposed antenna is small, with a slot side length of approximately half of the guided wavelength. The three developed Hilbert cell HMSIW antenna resonates at 3.25, 5.94 and 6.5 GHz with a bandwidth of 2.97, 2.25 and 2.29% within a return loss of ‑38.77, ‑35.82 -23.35 dB, respectively. The results showed enhancements in antenna gain of 3.56, 4.97 and 6.43 dBi, with a radiation efficiency of -1.253, -0.493 and -0.586 dB, respectively

    Cover JICTRA Vol. 13 No. 2, 2019

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    Cover JICTRA Vol. 14 No. 1, 2020

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    Cover JICTRA Vol. 14 No. 2, 2020

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