2014 International Conference on Computer, Control, Informatics and Its Applications (IC3INA)最新文献

Simple additive weighting method on intelligent agent for urban forest health monitoring 城市森林健康监测智能体的简单加性加权法

2014 International Conference on Computer, Control, Informatics and Its Applications (IC3INA) Pub Date : 2014-10-01 DOI: 10.1109/IC3INA.2014.7042614

A. Pranolo, S. M. Widyastuti

引用次数: 27

Characterization of thin slab artificial dielectric material using rectangular waveguide 用矩形波导表征薄板人工介电材料

2014 International Conference on Computer, Control, Informatics and Its Applications (IC3INA) Pub Date : 2014-10-01 DOI: 10.1109/IC3INA.2014.7042616

Zaki Abdurrasyid, A. Munir

引用次数: 4

A register-free and homogenous architecture for square root algorithm 平方根算法的无寄存器同质结构

2014 International Conference on Computer, Control, Informatics and Its Applications (IC3INA) Pub Date : 2014-10-01 DOI: 10.1109/IC3INA.2014.7042602

Rachmad Vidya Wicaksana Putra, T. Adiono

引用次数: 2

Semantic mapping relational to graph model 语义映射关系到图形模型

2014 International Conference on Computer, Control, Informatics and Its Applications (IC3INA) Pub Date : 2014-10-01 DOI: 10.1109/IC3INA.2014.7042620

D. Wardani, Josef Kiing

引用次数: 19

Identification of alertness state based on EEG signal using wavelet extraction and neural networks 基于小波提取和神经网络的脑电信号警觉性状态识别

2014 International Conference on Computer, Control, Informatics and Its Applications (IC3INA) Pub Date : 2014-10-01 DOI: 10.1109/IC3INA.2014.7042623

E. C. Djamal, Suprijanto, A. Arif

引用次数: 12

Design of cost-effective range meter for robotic applications 为机器人应用设计具有成本效益的测距仪

2014 International Conference on Computer, Control, Informatics and Its Applications (IC3INA) Pub Date : 2014-10-01 DOI: 10.1109/IC3INA.2014.7042598

Georges Freiha, Mohammad Mkhadder, M. Owayjan

引用次数: 0

Accuracy analysis of FM chirp in GNU radio-based FMCW radar for multiple target detection 基于GNU无线电的FMCW雷达多目标检测调频啁啾精度分析

2014 International Conference on Computer, Control, Informatics and Its Applications (IC3INA) Pub Date : 2014-10-01 DOI: 10.1109/IC3INA.2014.7042611

Ershad Junus Amin, A. B. Suksmono, A. Munir

{"title":"Accuracy analysis of FM chirp in GNU radio-based FMCW radar for multiple target detection","authors":"Ershad Junus Amin, A. B. Suksmono, A. Munir","doi":"10.1109/IC3INA.2014.7042611","DOIUrl":"https://doi.org/10.1109/IC3INA.2014.7042611","url":null,"abstract":"In this paper, different waveforms of frequency modulation (FM) chirp are investigated to analyze the accuracy of GNU radio-based frequency-modulated continuous wave (FMCW) radar for multiple target detection. The 3 waveforms used for the investigation as FM chirp are sinusoidal, triangular, and sawtooth waveforms. The analysis is performed by use of GNU radio referred as an open source software-define-radio project. There are 2 methods employed for the detection process; the first is real-condition simulation method and the second is USRP-based implementation method. In the analysis, some targets in different ranges are characterized using both methods to determine the accuracy of target range. By using FFT (Fast Fourier Transform) function from Matlab® to obtain the result in frequency domain, both methods show that the triangular waveform has the highest average accuracy, i.e. 95.73% for the 1st method and 99.75% for the 2nd method. The sawtooth waveform has the lower average accuracy than the triangular, i.e. 94.93 for the 1st method and 98.33% for the 2nd method, whilst the sinusoidal waveform has the lowest average accuracy, i.e. 92.60% for the 1st method and 98.59% for the 2nd method. From the result, it shows that the USRP-based implementation method has better average accuracy than the real-condition simulation method.","PeriodicalId":120043,"journal":{"name":"2014 International Conference on Computer, Control, Informatics and Its Applications (IC3INA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125122090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

DOM: A big data analytics framework for mining Thai public opinions DOM:一个挖掘泰国公众意见的大数据分析框架

2014 International Conference on Computer, Control, Informatics and Its Applications (IC3INA) Pub Date : 2014-10-01 DOI: 10.1109/IC3INA.2014.7042591

S. Prom-on, Sirapop Na Ranong, Patcharaporn Jenviriyakul, Thepparit Wongkaew, Nareerat Saetiew, T. Achalakul

引用次数: 8

Signal processing of range detection for SFCW radars using Matlab and GNU radio 利用Matlab和GNU无线电对SFCW雷达距离检测信号进行处理

2014 International Conference on Computer, Control, Informatics and Its Applications (IC3INA) Pub Date : 2014-10-01 DOI: 10.1109/IC3INA.2014.7042617

Azizah, A. B. Suksmono, A. Munir

引用次数: 16

Flower image classification modeling using neural network 基于神经网络的花卉图像分类建模

2014 International Conference on Computer, Control, Informatics and Its Applications (IC3INA) Pub Date : 2014-10-01 DOI: 10.1109/IC3INA.2014.7042605

Fadzilah Siraj, H. M. Ekhsan, A. Zulkifli

{"title":"Flower image classification modeling using neural network","authors":"Fadzilah Siraj, H. M. Ekhsan, A. Zulkifli","doi":"10.1109/IC3INA.2014.7042605","DOIUrl":"https://doi.org/10.1109/IC3INA.2014.7042605","url":null,"abstract":"Image processing plays an important role in extracting useful information from images. However, the image processing and the process of translating an image into a statistical distribution of low-level features is not an easy task. These tasks are complicated since the acquired image data often noisy, and target objects are influenced by lighting, intensity or illumination. In the case of flower classification, image processing is a crucial step for computer-aided plant species identification. Flower image classification is based on the low-level features such as colour and texture to define and describe the image content. Colour features are extracted using normalized colour histogram and texture features are extracted using gray-level co-occurrence matrix. In this study, a dataset consists of 180 patterns with 7 attributes for each type of flower has been gathered. The finding from the study reveals that the number of images generated to represent each type of flower influences the classification accuracy. One interesting observation is that duplication of very hard to learn images assist Neural Network to improve its classification accuracy. This is also another area that could lead to better understanding towards the behaviour of images when applied to Neural Network classification.","PeriodicalId":120043,"journal":{"name":"2014 International Conference on Computer, Control, Informatics and Its Applications (IC3INA)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122310007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18