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ComEngApp : Computer Engineering and Applications Journal
Published by Universitas Sriwijaya
ISSN : 22524274     EISSN : 22525459     DOI : 10.18495
Core Subject : Science, Engineering,
Computer Science & IT Engineering
ComEngApp-Journal (Collaboration between University of Sriwijaya, Kirklareli University and IAES) is an international forum for scientists and engineers involved in all aspects of computer engineering and technology to publish high quality and refereed papers. This Journal is an open access journal that provides online publication (three times a year) of articles in all areas of the subject in computer engineering and application. ComEngApp-Journal wishes to provide good chances for academic and industry professionals to discuss recent progress in various areas of computer science and computer engineering.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 6 Documents
 1 
Search results for , issue "Vol 9 No 3 (2020)" : 6 Documents clear
A Smart Real-Time Standalone Route Recognition System for Visually Impaired Persons Ibrahim Mohammed Abdullahi; Rabiu Omeiza Isah; Daniel Aliu; Muyideen Omuya Momoh
Computer Engineering and Applications Journal Vol 9 No 3 (2020)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (294.199 KB) | DOI: 10.18495/comengapp.v9i3.342

Abstract

Visual Impairment is a common disability that results in poor or no eyesight, whose victims suffer inconveniences in performing their daily tasks. Visually impaired persons require some aids to interact with their environment safely. Existing navigation systems like electronic travel aids (ETAs) are mostly cloud-based and rely heavily on the internet and google map. This implies that systems deployment in locations with poor internet facilities and poorly structured environments is not feasible. This paper proposed a smart real-time standalone route recognition system for visually impaired persons. The proposed system makes use of a pedestrian route network, an interconnection of paths and their associated route tables, for providing directions of known locations in real-time for the user. Federal University of Technology (FUT), Minna, Gidan Kwanu campus was used as the case study. The result obtained from testing of the device search strategy on the field showed that the complexity of the algorithm used in searching for paths in the pedestrian network is , at worst-case scenario, where N is the number of paths available in the network. The accuracy of path recognition is 100%. This implies that the developed system is reliable and can be used in recognizing and navigating routes by the visual impaired in real-time.
Gas Source Localization Using Bio-inspired Algorithm for Mini Flying Sniffer Robot: Development and Experimental Investigation Muhamad Rausyan Fikri
Computer Engineering and Applications Journal Vol 9 No 3 (2020)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (479.652 KB) | DOI: 10.18495/comengapp.v9i3.343

Abstract

In this paper, we demonstrated a gas source localization (GSL) using a mini quadrotor as a mini flying sniffer robot. The algorithm employed is based on a bioinspired algorithm from insect behavioral searching and it is constrained to perform only in 2D dimension open space area. In this study, we deliver some information such as system development, and algorithm flowchart to highlight how this study can achieve the target goal. The performance of insect behavioral based for searching the source location shows an interesting result. Where we can achieve a satisfactory result to find the source position using a bioinspired algorithm. The experimental results are provided to evaluate the performance of the searching algorithm.
An Improved Throughput for Non-Binary Low-Density-Parity-Check Decoder Omowuyi Olajide; Bashir Abdulrazaq; Adewale Adedokun; Ime Umoh; Akan Bello
Computer Engineering and Applications Journal Vol 9 No 3 (2020)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (250.069 KB) | DOI: 10.18495/comengapp.v9i3.345

Abstract

Low-Density-Parity-Check (LDPC) based error control decoders find wide range of application in both storage and communication systems, because of the merits they possess which include high appropriateness towards parallelization and excellent performance in error correction. Field-Programmable Gate Array (FPGA) has provided a robust platform in terms of parallelism, resource allocation and excellent performing speed for implementing non-binary LDPC decoder architectures. This paper proposes, a high throughput LDPC decoder through the implementation of fully parallel architecture and a reduction in the maximum iteration limit, needed for complete error correction. A Galois field of eight was utilized alongside a non-uniform quantization scheme, resulting in fewer bits per Log Likelihood Ratio (LLR) for the implementation. Verilog Hardware Description Language (HDL) was used in the description of the non-binary error control decoder. The propose decoder attained a throughput of 10Gbps at 400-MHz clock frequency when synthesized on a ZYNQ 7000 Series FPGA.
Deep Convolutional Neural Networks-Based Plants Diseases Detection Using Hybrid Features Budiarianto Suryo Kusumo; Ana Heryana; Dikdik Krisnandi; Sandra Yuwana; Vicky Zilvan; Hilman F Pardede
Computer Engineering and Applications Journal Vol 9 No 3 (2020)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (303.341 KB) | DOI: 10.18495/comengapp.v9i3.346

Abstract

With advances in information technology, various ways have been developed to detect diseases in plants, one of which is by using Machine Learning. In machine learning, the choice of features affect the performance significantly. However, most features have limitations for plant diseases detection. For that reason, we propose the use of hybrid features for plant diseases detection in this paper. We append local descriptor and texture features, i.e. linear binary pattern (LBP) to color features. The hybrid features are then used as inputs for deep convolutional neural networks (DCNN) Support and VGG16 classifiers. Our evaluation on Based on our experiments, our proposed features achieved better performances than those of using color features only. Our results also suggest fast convergence of the proposed features as the good performance is achieved at low number of epoch.
Utilization of Support Vector Machine and Speeded up Robust Features Extraction in Classifying Fruit Imagery Muhathir Muhathir; Wahyu Hidayah; Dian Ifantiska
Computer Engineering and Applications Journal Vol 9 No 3 (2020)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (228.936 KB) | DOI: 10.18495/comengapp.v9i3.347

Abstract

Indonesia's various types of fruits can be met by the community. Many fruits that contain a source of vitamins are very beneficial to the body, or as an economic source for farmers. It's no wonder that many experts submit discoveries to increase the amount of productivity or just want to experiment with intelligent systems. Intelligent systems are specially designed machines in certain areas to adjust the capabilities made by the creators. This article provides the latest texture classification technique called Speeded up Robust Features (SURF) with the SVM (Support Vector Machine) method. In this concept, the representation of the image data is done by capturing features in the form of keys. SURF uses the determinant of the Hessian matrix to reach the point of interest in which descriptions and classifications are performed. This method delivers superior performance compared to existing methods in terms of processing time, accuracy, and durability. The results showed that the fruit classification by using the extraction of Speeded up Robust Features (SURF) feature and SVM (Support Vector Machine) Classification method is quite maximal and accurate. Result of 3 kinds of classification with SVM kernel function, SVM Gaussian with 72% accuracy, Polynomial SVM with 69.75% accuracy, and Linear SVM with 70.25% accuracy.
Reducing Generalization Error Using Autoencoders for The Detection of Computer Worms Nelson Ochieng Odunga; Ronald Waweru Mwangi; Ismail Ateya Lukandu
Computer Engineering and Applications Journal Vol 9 No 3 (2020)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (161.463 KB) | DOI: 10.18495/comengapp.v9i3.348

Abstract

This paper discusses computer worm detection using machine learning. More specifically, the generalization capability of autoencoders is investigated and improved using regularization and deep autoencoders. Models are constructed first without autoencoders and thereafter with autoencoders. The models with autoencoders are further improved using regularization and deep autoencoders. Results show an improved in the capability of models to generalize well to new examples.

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