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Proceeding of the Electrical Engineering Computer Science and Informatics
Published by Institute of Advanced Engineering and Science
ISSN : 2407439X     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Proceeding of the Electrical Engineering Computer Science and Informatics publishes papers of the "International Conference on Electrical Engineering Computer Science and Informatics (EECSI)" Series in high technical standard. The Proceeding is aimed to bring researchers, academicians, scientists, students, engineers and practitioners together to participate and present their latest research finding, developments and applications related to the various aspects of electrical, electronics, power electronics, instrumentation, control, computer & telecommunication engineering, signal processing, soft computing, computer science and informatics.
Arjuna Subject : -
Articles 53 Documents
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Search results for , issue "Vol 7, No 1: EECSI 2020" : 53 Documents clear
Design of Integrated Bioimpedance Analysis and Body Mass Index for Users with Special Needs Ganjar Winasis; Munawar Riyadi; Teguh Prakoso
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2057

Abstract

This research was conducted with the aim to build integration between Bioimpedance Analysis (BIA) and Body Mass Index (BMI) for users with special needs. The proposed system can measure height, weight, BMI and body composition simultaneously to be used by the elderly population and handicapped users. The proposed system is developed as a chair equipped with several system blocks, namely BIA block, BMI block, power supply block, and microcontroller block. Before starting the measurement, users only need to enter their age and gender data. The whole system is controlled by using Arduino Mega 2560 on the microcontroller block equipped with keypad for data input and an LCD to display measurement results. System testing is performed by comparing the measurement results with Omron HBF-375. The test involved 8 volunteers (4 males and 4 females). The test results show that the integrated BIA-BMI works well with an average error of 1.5%.
Investigation of Structural Parameter Variation on Extended Gate TFET for Bio-Sensor Applications Sudipta Mukherjee; Somnath Chakraborty; Deven Diwakar; Apurba Laha; Udayan Ganguly; Swaroop Ganguly
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2058

Abstract

Traditional Gate engineered Metal Oxide Semiconductor (MOS) technology faced serious challenges in terms of greater sensitivity for target biomolecules and to be utilized as the state-of-the-art Nano-recognition tool. Research on a tunnel field-effect transistor (TFET) started with the aim to achieve fast detection, low power consumption, and its potential for on-chip integration capability. Dielectric Modulated TFET (DMTFET) has established itself to be a primary candidate for sensing both charged and charge-neutral species with volumetric sensitivity. As extended gate DMTFET happens to be inferior to its short gate counterpart, we have devised ways to achieve superior performance only by making variations over structural electrostatics. With the incorporation of most possible ways of modulation, we present two orders of magnitude on-current increment and a considerable percentage of sensitivity improvement over the conventional one. Future scopes having noteworthy diversifications have also been analyzed with proper justification.
Spoken Word and Speaker Recognition Using MFCC and Multiple Recurrent Neural Networks Yoga Utomo; Esmeralda Contessa Djamal; Fikri Nugraha; Faiza Renaldi
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2059

Abstract

Identification of spoken word and speaker has been featured in many kinds of research. The problem or obstacle that persists is in the pronunciation of a particular word. So it is the noise that causes the difficulty of words to be identified. Furthermore, every human has different pronunciation habits and is influenced by several variables, such as amplitude, frequency, tempo, and rhythmic. This study proposed the identification of spoken sounds by using specific word input to determine the patterns of the speaker and spoken using Mel-frequency Cepstrum Coefficients (MFCC) and Multiple Recurrent Neural Networks (RNN). The Mel coefficient of MFCC is used as an input feature for identifying spoken words and speakers using RNN and Long Short Term Memory (LSTM). Multiple RNN works spoken word and speaker in parallel. The results obtained by multiple RNN have an accuracy of 87.74%, while single RNNs have 80.58% using Adam of new data. In order to test our model computational regularly, the experiment tested K-fold Cross-Validation of datasets for spoken and speakers with an average accuracy of 86.07%, which means the model to be able to learn on the dataset without being affected by the order or selection of test data.
IoT-Enabled Community Care for Ageing-in-Place: The Singapore Experience Hwee Pink Tan
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2060

Abstract

The paradigm of ageing-in-place - where the elderly live and age in their own homes, independently and safely, with care provided by the community - is compelling, especially in societies that face both shortages in institutionalized eldercare resources, and rapidly ageing populations. When the number of elderly who live alone rises rapidly, support and care from their communities become increasing crucial. Internet of Things (IoT) technologies. They can become the fundamental enabler for smart community eldercare. In this presentation, I would like to share our experiences and learnings gathered from large-scale deployments of IoT systems in in-home and community spaces that elderly living alone interact with, focusing on the key insights as well as operational and usability aspects of such systems.
Smart Navigation Equipment Monitoring System Muhammad Arif Sulaiman; Trio Adiono
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2061

Abstract

Digital image processing is a processing of digital frames using digital computation. Image processing has been used in many sectors such as military, biomedics, and in this paper, the authors will implement it in the civil aviation sector by introducing a new method to monitor an aviation navigation equipment. It can be used on all LED-based Built-in Monitor navigation equipment, despite it is a low-cost system. The image processing of this research is done by doing perspective correction and then continue with BLOB detection in a segmentation stage. The final result will be displayed on a web page. Compared to its predecessor, this method gives better flexibility which does not need to be electrically connected with monitored equipment and not limited to certain brands.
Security and Privacy for the Internet of Things Biplab Sikdar
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2062

Abstract

The Internet of Things (IoT) represents a great opportunity to connect people, information, and things, which will in turn cause a paradigm shift in the way we work, interact, and think. The IoT is envisioned as the enabling technology for smart cities, power grids, health care, and control systems for critical installments and public infrastructure. This diversity, increased control and interaction of devices, and the fact that IoT systems use public networks to transfer large amounts of data make them a prime target for cyber attacks. In addition, IoT devices are usually small, low cost and have limited resources. Therefore, any protocol designed for IoT systems should not only be secure but also efficient in terms of usage of chip area, energy, storage, and processing. This presentation will start by highlighting the unique security requirements of IoT devices and the inadequacy of existing security protocols and techniques of the Internet in the context to IoT systems. Next, we will focus on security solutions for the IoT, with special focus on protection against physical and side channel attacks. In particular, we will focus on mutual authentication protocols for IoT devices based on security primitives that exploit hardware level characteristics of IoT devices.
Ball and Beam Control using Adaptive PID based on Q-Learning Brilian Putra Amiruddin; Rusdhianto Effendi Abdul Kadir
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2063

Abstract

The ball and beam system is one of the most used systems for benchmarking the controller response because it has nonlinear and unstable characteristics. Furthermore, in line with the increasing of computation power availability and artificial intelligence research intensity, especially the reinforcement learning field, nowadays plenty of researchers are working on a learning control approach for controlling systems. Due to that, in this paper, the adaptive PID controller based on Q-Learning (Q-PID) was used to control the ball position on the ball and beam system. From the simulation result, Q-PID outperforms the conventional PID and heuristic PID controller technique with the swifter settling time and lower overshoot percentage.
Framework Design for the Retrieval of Instant Messaging in Social Media as Electronic Evidence Linda Rosselina; Yohan Suryanto; Tofan Hermawan; Fahdiaz Alief
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2064

Abstract

The rapid growth of social media features not only brings many advantages but also causes problems. Mainly related to digital evidence when cybercrime occurs. One of the social media features that are currently popular is the unsend message feature in instant messaging applications such as Instagram, Whatsapp, Facebook Messenger, Skype, Viber, and Telegram. In the case of cybercrime, the perpetrator can delete the messages and erase digital evidence, making it difficult to trace. Those artifact messages might be useful for law enforcement or forensic investigators to be used as digital evidence in court. Therefore, an effective and efficient framework is needed in the mobile forensic investigation process to guarantee the integrity of the data obtained. This paper will discuss the review of several international standards on mobile forensics, namely NIST SP 800-101, ISO/ IEC, and SWGDE. This paper also proposes a framework design to retrieve unsend data artifacts on social media according to official and widely used international mobile forensic standards.
Memory Prediction on Real-Time User Behavior Traffic Detection Rahmat Budiarto
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2065

Abstract

Human brain is a learning system. Human have to learn by getting exposed to something. This capability of learning system to recognize new patterns is called generalization. The abilities of human brain to perform generalization are yet to be matched by neural network or even by any of artificial intelligence algorithm in general. Thus, the need for new machine intelligence approach is imperative. Neural network is designed to take advantages of the speed of computers to solve engineering and computational complex problems intelligently. On the other hand, human brain is somewhat not computationally powerful. Human brain is not even able to calculate quadratic problems within milliseconds. Instead, it uses its vast amounts of memory to store everything human know and have learned. According to a modern neuroscience theory named memory-prediction framework, introduced by Hawkins and Blakeslee in 2005, human brain uses this memory-based model to make continuous predictions of future events. Therefore, a hybrid approach that possesses the ability to compute like neural network and at the same time think like human brain will shed some light in the advancement of machine learning research as well as the development of a truly intelligent machine. This talk discusses the memory-prediction framework and proposes simplified single cell assembled sequential hierarchical memory (s-SCASHM) model instead of hierarchical temporal memory (HTM) in order to speed up the learning convergence. s-SCASHM consists of single neuronal cell (SNC) model and simplified sequential hierarchical superset (SHS) platform. The SHS platform is designed by simplifying to have a region with four rows columnar architecture instead of having six rows per region as in human neocortex. Then, the s-SCASHM is implemented as the prediction engine of user behavior analysis tool to detect insider attacks/anomalies. As nearly half of incidents in enterprise security triggered by the Insider, it is important to deploy more intelligent defense system to assist the enterprise be able to pinpoint and resolve any incidents caused by the Insider or malicious software (malware). The attacks evolve; however, current detection systems that use the deep learning techniques cannot perform online (on-the-fly) learning. Thus, an intelligent detection system with on-the-fly learning capability is required. Experimental results show that the proposed memory model is able to predict user behavior traffic with significant level of accuracy and performs on-the-fly learning.
Experimental Investigation of Algorithms for Simultaneous Localization and Mapping Tamara Zhukabayeva; Aigul Adamova; Laula Zhumabayeva
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2066

Abstract

This paper describes a mobile robot system designed for simultaneous localization and mapping. The architecture of a robotic mobile system based on the mini-tractor chassis is considered. The existing and modern methods and approaches to solving the SLAM problem are described, as well as the results of experimental studies of the work of methods on a mobile robot. A description of the developed robotic system for solving the navigation problem and constructing a route map is given. The issues addressed in this paper include the design, development and experimental testing of the mobile robot. The advantages, disadvantages of the algorithm, as well as the direction of further research are described in this work.

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